diff --git a/.file-header b/.file-header deleted file mode 100644 index 83eef0801ee..00000000000 --- a/.file-header +++ /dev/null @@ -1,10 +0,0 @@ -# Copyright \(c\) (\d{4}|\d{4}-\d{4}) Intel Corporation -# Licensed under the Apache License, Version 2.0 \(the "License"\); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# http://www.apache.org/licenses/LICENSE-2.0 -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/.github/workflows/mypy.yml b/.github/workflows/mypy.yml new file mode 100644 index 00000000000..f4e8bb6c774 --- /dev/null +++ b/.github/workflows/mypy.yml @@ -0,0 +1,23 @@ +name: mypy + +on: + pull_request: + types: + - opened + - reopened + - synchronize + +jobs: + mypy: + runs-on: ubuntu-20.04 + steps: + - uses: actions/checkout@v3 + - uses: actions/setup-python@v3 + with: + python-version: 3.8.10 + - name: Install NNCF + run: pip install -e . + - name: Install mypy + run: pip install mypy==1.8.0 + - name: Run mypy + run: mypy --install-types --config-file=.mypy.ini --non-interactive diff --git a/.mypy.ini b/.mypy.ini new file mode 100644 index 00000000000..42b6329f72c --- /dev/null +++ b/.mypy.ini @@ -0,0 +1,9 @@ +[mypy] +files = nncf/common/sparsity +follow_imports = silent +strict = True + +# should be removed later +# mypy recommends the following tool as an autofix: +# https://github.com/hauntsaninja/no_implicit_optional +implicit_optional = True diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 5066e543001..11dd747ce2e 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -26,3 +26,4 @@ repos: hooks: - id: markdownlint args: [--config=.markdownlint.yaml] + diff --git a/Makefile b/Makefile index 205c19101da..3a39add44f8 100644 --- a/Makefile +++ b/Makefile @@ -23,6 +23,8 @@ install-pre-commit: install-onnx-test: pip install -U pip pip install -e .[onnx] + pip install "git+https://github.com/openvinotoolkit/open_model_zoo.git@37f60eb#egg=omz-tools&subdirectory=tools/model_tools" + pip install "git+https://github.com/openvinotoolkit/open_model_zoo.git@37f60eb#egg=accuracy_checker&subdirectory=tools/accuracy_checker" pip install -r tests/onnx/requirements.txt pip install -r tests/cross_fw/install/requirements.txt pip install -r tests/cross_fw/examples/requirements.txt @@ -50,14 +52,14 @@ test-examples-onnx: install-openvino-test: pip install -U pip pip install -e .[openvino] + pip install "git+https://github.com/openvinotoolkit/open_model_zoo.git@37f60eb#egg=omz-tools&subdirectory=tools/model_tools" + pip install "git+https://github.com/openvinotoolkit/open_model_zoo.git@37f60eb#egg=accuracy_checker&subdirectory=tools/accuracy_checker" pip install tensorflow==2.12.0 pip install -r tests/openvino/requirements.txt pip install -r tests/cross_fw/install/requirements.txt pip install -r tests/cross_fw/examples/requirements.txt install-openvino-dev: install-openvino-test install-pre-commit - pip install -r examples/experimental/openvino/bert/requirements.txt - pip install -r examples/experimental/openvino/yolo_v5/requirements.txt pip install -r examples/post_training_quantization/openvino/mobilenet_v2/requirements.txt pip install -r examples/post_training_quantization/openvino/anomaly_stfpm_quantize_with_accuracy_control/requirements.txt pip install -r examples/post_training_quantization/openvino/yolov8/requirements.txt @@ -81,6 +83,8 @@ test-examples-openvino: install-tensorflow-test: pip install -U pip pip install -e .[tf] + pip install "git+https://github.com/openvinotoolkit/open_model_zoo.git@37f60eb#egg=omz-tools&subdirectory=tools/model_tools" + pip install "git+https://github.com/openvinotoolkit/open_model_zoo.git@37f60eb#egg=accuracy_checker&subdirectory=tools/accuracy_checker" pip install -r tests/tensorflow/requirements.txt pip install -r tests/cross_fw/install/requirements.txt pip install -r tests/cross_fw/examples/requirements.txt @@ -105,6 +109,8 @@ test-examples-tensorflow: install-torch-test: pip install -U pip pip install -e .[torch] --index-url https://download.pytorch.org/whl/cu118 --extra-index-url=https://pypi.org/simple # ticket 119128 + pip install "git+https://github.com/openvinotoolkit/open_model_zoo.git@37f60eb#egg=omz-tools&subdirectory=tools/model_tools" + pip install "git+https://github.com/openvinotoolkit/open_model_zoo.git@37f60eb#egg=accuracy_checker&subdirectory=tools/accuracy_checker" pip install -r tests/torch/requirements.txt --index-url https://download.pytorch.org/whl/cu118 --extra-index-url=https://pypi.org/simple pip install -r tests/cross_fw/install/requirements.txt pip install -r tests/cross_fw/examples/requirements.txt @@ -170,3 +176,12 @@ test-examples: # Pre commit check pre-commit: pre-commit run -a + + +############################################################################### +# Fuzzing tests +install-fuzz-test: install-common-test + pip install -r tests/cross_fw/sdl/fuzz/requirements.txt + +test-fuzz: + python tests/cross_fw/sdl/fuzz/quantize_api.py diff --git a/README.md b/README.md index 88765c60477..cc201f0a4ff 100644 --- a/README.md +++ b/README.md @@ -274,41 +274,45 @@ For a quicker start with NNCF-powered compression, try sample notebooks and scri A collection of ready-to-run Jupyter* notebooks tutorials and demos are available to explain and display NNCF compression algorithms for optimizing models for inference with the OpenVINO Toolkit. -| Notebook Tutorial Name | Compression Algorithm | Backend | Domain | -|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------:|:----------:|:-----------------------------------:| -| [BERT Quantization](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/105-language-quantize-bert)
[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/105-language-quantize-bert/105-language-quantize-bert.ipynb) | Post-Training Quantization | OpenVINO | NLP | -| [MONAI Segmentation Model Quantization](https://github.com/openvinotoolkit/openvino_notebooks/blob/main/notebooks/110-ct-segmentation-quantize)
[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?filepath=notebooks%2F110-ct-segmentation-quantize%2F110-ct-scan-live-inference.ipynb) | Post-Training Quantization | OpenVINO | Segmentation | -| [PyTorch Model Quantization](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/112-pytorch-post-training-quantization-nncf) | Post-Training Quantization | PyTorch | Image Classification | -| [TensorFlow Model Quantization](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/301-tensorflow-training-openvino) | Post-Training Quantization | Tensorflow | Image Classification | -| [Migrating from POT to NNCF](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/111-yolov5-quantization-migration)
[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/111-yolov5-quantization-migration/111-yolov5-quantization-migration.ipynb) | Post-Training Quantization | OpenVINO | Object detection | -| [Quantization with Accuracy Control](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/122-quantizing-model-with-accuracy-control) | Post-Training Quantization with Accuracy Control | OpenVINO | Speech-to-Text,
Object Detection | -| [TensorFlow Training-Time Compression](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/301-tensorflow-training-openvino) | Training-Time Compression | Tensorflow | Image Classification | -| [Joint Pruning, Quantization and Distillation for BERT](https://github.com/openvinotoolkit/openvino_notebooks/blob/main/notebooks/116-sparsity-optimization) | Joint Pruning, Quantization and Distillation | OpenVINO | NLP | +| Notebook Tutorial Name | Compression Algorithm | Backend | Domain | +|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------:|:----------:|:-----------------------------------:| +| [BERT Quantization](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/105-language-quantize-bert)
[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/105-language-quantize-bert/105-language-quantize-bert.ipynb) | Post-Training Quantization | OpenVINO | NLP | +| [MONAI Segmentation Model Quantization](https://github.com/openvinotoolkit/openvino_notebooks/blob/main/notebooks/110-ct-segmentation-quantize)
[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?filepath=notebooks%2F110-ct-segmentation-quantize%2F110-ct-scan-live-inference.ipynb) | Post-Training Quantization | OpenVINO | Segmentation | +| [PyTorch Model Quantization](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/112-pytorch-post-training-quantization-nncf) | Post-Training Quantization | PyTorch | Image Classification | +| [TensorFlow Model Quantization](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/301-tensorflow-training-openvino) | Post-Training Quantization | Tensorflow | Image Classification | +| [Migrating from POT to NNCF](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/111-yolov5-quantization-migration)
[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/111-yolov5-quantization-migration/111-yolov5-quantization-migration.ipynb) | Post-Training Quantization | OpenVINO | Object detection | +| [Quantization with Accuracy Control](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/122-quantizing-model-with-accuracy-control) | Post-Training Quantization with Accuracy Control | OpenVINO | Speech-to-Text,
Object Detection | +| [PyTorch Training-Time Compression](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/302-pytorch-quantization-aware-training) | Training-Time Compression | PyTorch | Image Classification | +| [TensorFlow Training-Time Compression](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/301-tensorflow-training-openvino) | Training-Time Compression | Tensorflow | Image Classification | +| [Joint Pruning, Quantization and Distillation for BERT](https://github.com/openvinotoolkit/openvino_notebooks/blob/main/notebooks/116-sparsity-optimization) | Joint Pruning, Quantization and Distillation | OpenVINO | NLP | Below is a list of notebooks demonstrating OpenVINO conversion and inference together with NNCF compression for models from various domains. -| Demo Model | Compression Algorithm | Backend | Domain | -|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------:|:-----------------------------------------------------------------:| -| [YOLOv8](https://github.com/openvinotoolkit/openvino_notebooks/blob/main/notebooks/230-yolov8-optimization)
[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/230-yolov8-optimization/230-yolov8-object-detection.ipynb) | Post-Training Quantization | OpenVINO | Object Detection,
KeyPoint Detection,
Instance Segmentation | -| [YOLOv7](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/226-yolov7-optimization) | Post-Training Quantization | OpenVINO | Object Detection | -| [Segment Anything Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/237-segment-anything) | Post-Training Quantization | OpenVINO | Panoptic Segmentation | -| [OneFormer](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/249-oneformer-segmentation) | Post-Training Quantization | OpenVINO | Panoptic Segmentation | -| [InstructPix2Pix](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/231-instruct-pix2pix-image-editing) | Post-Training Quantization | OpenVINO | Image-to-Image | -| [CLIP](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/228-clip-zero-shot-image-classification) | Post-Training Quantization | OpenVINO | Image-to-Text | -| [BLIP](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/233-blip-visual-language-processing) | Post-Training Quantization | OpenVINO | Image-to-Text | -| [Latent Consistency Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/263-latent-consistency-models-image-generation) | Post-Training Quantization | OpenVINO | Text-to-Image | -| [Würstchen](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/265-wuerstchen-image-generation) | Post-Training Quantization | OpenVINO | Text-to-Image | -| [ControlNet QR Code Monster](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/264-qrcode-monster) | Post-Training Quantization | OpenVINO | Text-to-Image | -| [SDXL-turbo](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/271-sdxl-turbo) | Post-Training Quantization | OpenVINO | Text-to-Image,
Image-to-Image | -| [DeepFloyd IF](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/238-deepfloyd-if) | Post-Training Quantization,
Weight Compression | OpenVINO | Text-to-Image,
Image-to-Image | -| [ImageBind](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/239-image-bind) | Post-Training Quantization | OpenVINO | Multi-Modal Retrieval | -| [Distil-Whisper](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/267-distil-whisper-asr) | Post-Training Quantization | OpenVINO | Speech-to-Text | -| [Whisper](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/227-whisper-subtitles-generation)
[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/227-whisper-subtitles-generation/227-whisper-convert.ipynb) | Post-Training Quantization | OpenVINO | Speech-to-Text | -| [MMS Speech Recognition](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/255-mms-massively-multilingual-speech) | Post-Training Quantization | OpenVINO | Speech-to-Text | -| [Grammar Error Correction](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/214-grammar-correction) | Post-Training Quantization | OpenVINO | NLP, Grammar Correction | -| [LLM Instruction Following](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/275-llm-question-answering) | Weight Compression | OpenVINO | NLP, Instruction Following | -| [Dolly 2.0](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/240-dolly-2-instruction-following) | Weight Compression | OpenVINO | NLP, Instruction Following | -| [LLM Chat Bots](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/254-llm-chatbot) | Weight Compression | OpenVINO | NLP, Chat Bot | +| Demo Model | Compression Algorithm | Backend | Domain | +|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------:|:---------:|:--------------------------------------------------------------------:| +| [YOLOv8](https://github.com/openvinotoolkit/openvino_notebooks/blob/main/notebooks/230-yolov8-optimization)
[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/230-yolov8-optimization/230-yolov8-object-detection.ipynb) | Post-Training Quantization | OpenVINO | Object Detection,
KeyPoint Detection,
Instance Segmentation | +| [YOLOv7](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/226-yolov7-optimization) | Post-Training Quantization | OpenVINO | Object Detection | +| [EfficientSAM](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/274-efficient-sam) | Post-Training Quantization | OpenVINO | Image Segmentation | +| [Segment Anything Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/237-segment-anything) | Post-Training Quantization | OpenVINO | Image Segmentation | +| [OneFormer](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/249-oneformer-segmentation) | Post-Training Quantization | OpenVINO | Image Segmentation | +| [InstructPix2Pix](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/231-instruct-pix2pix-image-editing) | Post-Training Quantization | OpenVINO | Image-to-Image | +| [CLIP](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/228-clip-zero-shot-image-classification) | Post-Training Quantization | OpenVINO | Image-to-Text | +| [BLIP](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/233-blip-visual-language-processing) | Post-Training Quantization | OpenVINO | Image-to-Text | +| [Segmind-VegaRT](https://github.com/openvinotoolkit/openvino_notebooks/blob/main/notebooks/248-stable-diffusion-xl/248-segmind-vegart.ipynb) | Post-Training Quantization | OpenVINO | Text-to-Image | +| [Latent Consistency Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/263-latent-consistency-models-image-generation) | Post-Training Quantization | OpenVINO | Text-to-Image | +| [Würstchen](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/265-wuerstchen-image-generation) | Post-Training Quantization | OpenVINO | Text-to-Image | +| [ControlNet QR Code Monster](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/264-qrcode-monster) | Post-Training Quantization | OpenVINO | Text-to-Image | +| [SDXL-turbo](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/271-sdxl-turbo) | Post-Training Quantization | OpenVINO | Text-to-Image,
Image-to-Image | +| [DeepFloyd IF](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/238-deepfloyd-if) | Post-Training Quantization,
Weight Compression | OpenVINO | Text-to-Image,
Image-to-Image | +| [ImageBind](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/239-image-bind) | Post-Training Quantization | OpenVINO | Multi-Modal Retrieval | +| [Distil-Whisper](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/267-distil-whisper-asr) | Post-Training Quantization | OpenVINO | Speech-to-Text | +| [Whisper](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/227-whisper-subtitles-generation)
[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/227-whisper-subtitles-generation/227-whisper-convert.ipynb) | Post-Training Quantization | OpenVINO | Speech-to-Text | +| [MMS Speech Recognition](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/255-mms-massively-multilingual-speech) | Post-Training Quantization | OpenVINO | Speech-to-Text | +| [Grammar Error Correction](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/214-grammar-correction) | Post-Training Quantization | OpenVINO | NLP, Grammar Correction | +| [LLM Instruction Following](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/275-llm-question-answering) | Weight Compression | OpenVINO | NLP, Instruction Following | +| [Dolly 2.0](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/240-dolly-2-instruction-following) | Weight Compression | OpenVINO | NLP, Instruction Following | +| [Stable-Zephyr-3b](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/273-stable-zephyr-3b-chatbot) | Weight Compression | OpenVINO | NLP, Chat Bot | +| [LLM Chat Bots](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/254-llm-chatbot) | Weight Compression | OpenVINO | NLP, Chat Bot | ### Post-Training Quantization Examples @@ -352,13 +356,6 @@ NNCF may be straightforwardly integrated into training/evaluation pipelines of t NNCF is used as a compression backend within the renowned `transformers` repository in HuggingFace Optimum Intel. -### Git patches for third-party repository - -See [third_party_integration](./third_party_integration) for examples of code modifications (Git patches and base commit IDs are provided) that are necessary to integrate NNCF into the following repositories: - -- [huggingface-transformers](third_party_integration/huggingface_transformers/README.md) -**NOTE**: this patch is deprecated and will be removed from NNCF repository in future releases. - ## Installation Guide For detailed installation instructions please refer to the [Installation](./docs/Installation.md) page. @@ -383,8 +380,6 @@ NNCF is also available via [conda](https://anaconda.org/conda-forge/nncf): conda install -c conda-forge nncf ``` -You may also use one of the Dockerfiles in the [docker](./docker) directory to build an image with an environment already set up and ready for running NNCF [sample scripts](#demos-tutorials-and-samples). - ### System requirements - Ubuntu\* 18.04 or later (64-bit) diff --git a/ReleaseNotes.md b/ReleaseNotes.md index 86b10fe2a43..58b37cd0a1a 100644 --- a/ReleaseNotes.md +++ b/ReleaseNotes.md @@ -1,5 +1,42 @@ # Release Notes +## New in Release 2.9.0 + +Post-training Quantization: + +- Features: + - (OpenVINO) Added modified AWQ algorithm for 4-bit data-aware weights compression. This algorithm applied only for patterns `MatMul->Multiply->Matmul`. For that `awq` optional parameter has been added to `nncf.compress_weights()` and can be used to minimize accuracy degradation of compressed models (note that this option increases the compression time). + - (ONNX) Introduced support for the ONNX backend in the `nncf.quantize_with_accuracy_control()` method. Users can now perform quantization with accuracy control for `onnx.ModelProto`. By leveraging this feature, users can enhance the accuracy of quantized models while minimizing performance impact. + - (ONNX) Added an example based on the YOLOv8n-seg model for demonstrating the usage of quantization with accuracy control for the ONNX backend. + - (PT) Added SmoothQuant algorithm for PyTorch backend in `nncf.quantize()`. + - (OpenVINO) Added [an example](examples/llm_compression/openvino/tiny_llama_find_hyperparams) with the hyperparameters tuning for the TinyLLama model. + - Introduced the `nncf.AdvancedAccuracyRestorerParameters`. + - Introduced the `subset_size` option for the `nncf.compress_weights()`. + - Introduced `TargetDevice.NPU` as the replacement for `TargetDevice.VPU`. +- Fixes: + - Fixed API Enums serialization/deserialization issue. + - Fixed issue with required arguments for `revert_operations_to_floating_point_precision` method. +- Improvements: + - (ONNX) Aligned statistics collection with OpenVINO and PyTorch backends. + - Extended `nncf.compress_weights()` with Convolution & Embeddings compression in order to reduce memory footprint. +- Deprecations/Removals: + - (OpenVINO) Removed outdated examples with `nncf.quantize()` for BERT and YOLOv5 models. + - (OpenVINO) Removed outdated example with `nncf.quantize_with_accuracy_control()` for SSD MobileNetV1 FPN model. + - (PyTorch) Deprecated the `binarization` algorithm. + - Removed Post-training Optimization Tool as OpenVINO backend. + - Removed Dockerfiles. + - `TargetDevice.VPU` was replaced by `TargetDevice.NPU`. +- Tutorials: + - [Post-Training Optimization of Stable Diffusion v2 Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/236-stable-diffusion-v2/236-stable-diffusion-v2-text-to-image.ipynb) + - [Post-Training Optimization of DeciDiffusion Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/259-decidiffusion-image-generation/259-decidiffusion-image-generation.ipynb) + - [Post-Training Optimization of DepthAnything Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/280-depth-anything/280-depth-anything.ipynb) + - [Post-Training Optimization of Stable Diffusion ControlNet Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/235-controlnet-stable-diffusion/235-controlnet-stable-diffusion.ipynb) + +Compression-aware training: + +- Fixes + - (PyTorch) Fixed issue with `NNCFNetworkInterface.get_clean_shallow_copy` missed arguments. + ## New in Release 2.8.1 Post-training Quantization: @@ -22,7 +59,7 @@ Post-training Quantization: - (OpenVINO) Changed default OpenVINO opset from 9 to 13. - Features: - (OpenVINO) Added 4-bit data-aware weights compression. For that `dataset` optional parameter has been added to `nncf.compress_weights()` and can be used to minimize accuracy degradation of compressed models (note that this option increases the compression time). - - (PyTorch) Added support for PyTorch models with shared weights and custom PyTorch modules in `nncf.compress_weights()`. The weights compression algorithm for PyTorch models is now based on tracing the model graph. The `dataset` parameter is now required in `nncf.compress_weights()` for the compression of PyTorch models. + - (PyTorch) Added support for PyTorch models with shared weights and custom PyTorch modules in nncf.compress_weights(). The weights compression algorithm for PyTorch models is now based on tracing the model graph. The dataset parameter is now required in nncf.compress_weights() for the compression of PyTorch models. - (Common) Renamed the `nncf.CompressWeightsMode.INT8` to `nncf.CompressWeightsMode.INT8_ASYM` and introduce `nncf.CompressWeightsMode.INT8_SYM` that can be efficiently used with dynamic 8-bit quantization of activations. The original `nncf.CompressWeightsMode.INT8` enum value is now deprecated. - (OpenVINO) Added support for quantizing the ScaledDotProductAttention operation from OpenVINO opset 13. diff --git a/docker/README.md b/docker/README.md deleted file mode 100644 index 7a1f4d37465..00000000000 --- a/docker/README.md +++ /dev/null @@ -1,45 +0,0 @@ -# Using docker - -## Step 1. Install docker - -Review the instructions for installation docker [here](https://docs.docker.com/engine/install/ubuntu/) and configure Docker -to use a proxy server as [here](https://docs.docker.com/network/proxy/#configure-the-docker-client). - -## Step 2. Install nvidia-docker - -*Skip this step if you don't have GPU.* - -Review the instructions for installation docker [here](https://github.com/NVIDIA/nvidia-docker). - -## Step 3. Build image - -In the project folder run in terminal: - -```bash -sudo docker image build --network=host -``` - -Use `--network` to duplicate the network settings of your localhost into context build. - -## Step 4. Run container - -Run in terminal: - -```bash -sudo docker run \ --it \ ---name= \ ---runtime=nvidia \ ---network=host \ ---shm-size=1g \ ---ulimit memlock=-1 \ ---mount type=bind,source=,target= \ ---mount type=bind,source=,target=/home/nncf \ - - ``` - -You should not use `--runtime=nvidia` if you want to use `--cpu-only` mode. - -Use `--shm-size` to increase the size of the shared memory directory. - -Now you have a working container and you can run examples. diff --git a/docker/onnx/openvinoep/Dockerfile b/docker/onnx/openvinoep/Dockerfile deleted file mode 100644 index fd5a5d6a3e6..00000000000 --- a/docker/onnx/openvinoep/Dockerfile +++ /dev/null @@ -1,57 +0,0 @@ -FROM ubuntu:20.04 - -ARG PIP_EXTRA_INDEX_URL -ARG PIP_TRUSTED_HOST -ARG http_proxy -ARG https_proxy -ARG no_proxy - -RUN echo "PIP_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL}" && \ - echo "PIP_TRUSTED_HOST=${PIP_TRUSTED_HOST}" && \ - echo "http_proxy=${http_proxy}" && \ - echo "https_proxy=${https_proxy}" && \ - echo "no_proxy=${no_proxy}" && \ - -ARG DEBIAN_FRONTEND=noninteractive -ARG TZ=Etc/UTC - -RUN apt-get update && apt-get -y install --no-install-recommends \ - git=2.25 \ - build-essential=12.8 \ - python3.8-dev=3.8 \ - python3.8-venv=3.8 \ - python3-opencv=4.2 \ - && rm -rf /var/lib/apt/lists/* - -# Add user -ARG BUILD_UID=1001 -ARG BUILD_USER=onnxruntimedev - -RUN adduser --uid $BUILD_UID $BUILD_USER && \ - usermod -a -G video,users ${BUILD_USER} -ENV WORKDIR_PATH /home/${BUILD_USER} - -# Copy nncf -WORKDIR ${WORKDIR_PATH}/nncf -COPY nncf nncf -COPY examples examples -COPY tests tests -COPY setup.py ./ -COPY README.md ./ -COPY Makefile ./ - -WORKDIR ${WORKDIR_PATH} -RUN chown -R ${BUILD_USER}:${BUILD_USER} nncf - -USER ${BUILD_USER} - -# Create & activate venv -ENV VIRTUAL_ENV=${WORKDIR_PATH}/venv -RUN python3 -m venv $VIRTUAL_ENV -ENV PATH="$VIRTUAL_ENV/bin:$PATH" - -WORKDIR ${WORKDIR_PATH}/nncf -ENV PYTHONPATH=$PYTHONPATH:${WORKDIR_PATH}/nncf -RUN make install-onnx-dev - -WORKDIR ${WORKDIR_PATH} diff --git a/docker/onnx/openvinoep/build.sh b/docker/onnx/openvinoep/build.sh deleted file mode 100755 index b49c484f9a3..00000000000 --- a/docker/onnx/openvinoep/build.sh +++ /dev/null @@ -1,15 +0,0 @@ -#!/usr/bin/env bash - -SCRIPT_DIR=$(dirname "$0") -WORK_DIR="${SCRIPT_DIR}/../../../" - -cd $WORK_DIR && echo "WORK_DIR=$PWD" - -docker build -t onnx_ptq_experimental:dev \ - --build-arg http_proxy=$http_proxy \ - --build-arg https_proxy=$https_proxy \ - --build-arg no_proxy=$no_proxy \ - --build-arg PIP_EXTRA_INDEX_URL=$PIP_EXTRA_INDEX_URL \ - --build-arg PIP_TRUSTED_HOST=$PIP_TRUSTED_HOST \ - -f docker/onnx/openvinoep/Dockerfile \ - . diff --git a/docker/tensorflow/gpu/Dockerfile b/docker/tensorflow/gpu/Dockerfile deleted file mode 100644 index 8f44ab59b0c..00000000000 --- a/docker/tensorflow/gpu/Dockerfile +++ /dev/null @@ -1,44 +0,0 @@ -FROM nvidia/cuda:11.0.3-cudnn8-runtime-ubuntu20.04 - -RUN apt-get update \ - && apt-get install -y --no-install-recommends \ - apt-transport-https=2.0 \ - git=2.25 && \ - rm -rf /var/lib/apt/lists/* - -RUN apt-get update \ - && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \ - build-essential=12.8 \ - libgl1-mesa-glx=21.2 \ - libglib2.0-dev=2.64 \ - wget=1.20 \ - curl=7.68 \ - zip=3.0 \ - unzip=6.0 \ - nano=4.8 \ - openssh-server=1:8.2 \ - openssh-client=1:8.2 \ - sudo=1.8 \ - python3=3.8 \ - python3-dev=3.8 \ - python3-pip=20.0 \ - && apt-get clean \ - && rm -rf /var/lib/apt/lists/* - -WORKDIR /usr/bin -RUN ln -s python3.8 python - -RUN pip3 install --no-cache-dir --upgrade pip==23.3 \ - && pip3 install --no-cache-dir setuptools==69.0 - -ENV PATH /usr/local/nvidia/bin:/usr/local/cuda/bin:${PATH} -ENV LD_LIBRARY_PATH /usr/local/nvidia/lib:/usr/local/nvidia/lib64 - -# nvidia-container-runtime -ENV NVIDIA_VISIBLE_DEVICES all -ENV NVIDIA_DRIVER_CAPABILITIES compute,utility - -ENTRYPOINT ["cd /home/nncf \ - && python setup.py install --tf \ - && pip3 install -r examples/tensorflow/requirements.txt \ - && bash"] diff --git a/docker/torch/cpu/Dockerfile b/docker/torch/cpu/Dockerfile deleted file mode 100755 index eea028ef8df..00000000000 --- a/docker/torch/cpu/Dockerfile +++ /dev/null @@ -1,28 +0,0 @@ -FROM ubuntu:20.04 - -RUN apt-get update && apt-get install -y --no-install-recommends \ - apt-transport-https=2.0 \ - git=2.25 && \ - rm -rf /var/lib/apt/lists/* - -RUN apt-get update && \ - DEBIAN_FRONTEND=noninteractive \ - apt-get install -y --no-install-recommends \ - wget=1.20 \ - curl=7.68 \ - zip=3.0 \ - unzip=6.0 \ - nano=4.8 \ - openssh-server=1:8.2 \ - openssh-client=1:8.2 \ - sudo=1.8 \ - python3=3.8 \ - python3-dev=3.8 \ - python3-pip=20.0 \ - && apt-get clean \ - && rm -rf /var/lib/apt/lists/* - -ENTRYPOINT ["cd /home/nncf \ - && python setup.py install --torch \ - && pip3 install -r examples/torch/requirements.txt \ - && bash"] diff --git a/docker/torch/gpu/Dockerfile b/docker/torch/gpu/Dockerfile deleted file mode 100755 index 26e902f3c89..00000000000 --- a/docker/torch/gpu/Dockerfile +++ /dev/null @@ -1,44 +0,0 @@ -FROM nvidia/cuda:11.1.1-cudnn8-devel-ubuntu20.04 - -RUN apt-get update && apt-get install -y --no-install-recommends \ - apt-transport-https=2.0 \ - git=2.25 && \ - rm -rf /var/lib/apt/lists/* - -# Required for nvidia-docker v1 -RUN echo "/usr/local/nvidia/lib" >> /etc/ld.so.conf.d/nvidia.conf && \ - echo "/usr/local/nvidia/lib64" >> /etc/ld.so.conf.d/nvidia.conf - - -RUN apt-get update && \ - DEBIAN_FRONTEND=noninteractive \ - apt-get install -y --no-install-recommends \ - wget=1.20 \ - curl=7.68 \ - zip=3.0 \ - unzip=6.0 \ - nano=4.8 \ - openssh-server=1:8.2 \ - openssh-client=1:8.2 \ - sudo=1.8 \ - python3=3.8 \ - python3-dev=3.8 \ - python3-pip=20.0 \ - && apt-get clean \ - && rm -rf /var/lib/apt/lists/* - -WORKDIR /usr/bin -RUN ln -s python3.8 python - - -ENV PATH /usr/local/nvidia/bin:/usr/local/cuda/bin:${PATH} -ENV LD_LIBRARY_PATH /usr/local/nvidia/lib:/usr/local/nvidia/lib64 - -# nvidia-container-runtime -ENV NVIDIA_VISIBLE_DEVICES all -ENV NVIDIA_DRIVER_CAPABILITIES compute,utility - -ENTRYPOINT ["cd /home/nncf \ - && python setup.py install --torch \ - && pip3 install -r examples/torch/requirements.txt \ - && bash"] diff --git a/docs/ConfigFile.md b/docs/ConfigFile.md index ed131bf495b..2573509408d 100644 --- a/docs/ConfigFile.md +++ b/docs/ConfigFile.md @@ -27,7 +27,7 @@ Below is an example of the NNCF configuration file: }, ] }, - "target_device": "VPU", // The target device, the specificity of which will be taken into account while compressing in order to obtain the best performance for this type of device. The default "ANY" means compatible quantization supported by any HW. The parameter takes values from the set ('CPU', 'GPU', 'VPU', 'ANY', 'TRIAL', 'CPU_SPR'). Set this value to 'TRIAL' if you are going to use a custom quantization schema.. Optional. + "target_device": "NPU", // The target device, the specificity of which will be taken into account while compressing in order to obtain the best performance for this type of device. The default "ANY" means compatible quantization supported by any HW. The parameter takes values from the set ('CPU', 'GPU', 'NPU', 'ANY', 'TRIAL', 'CPU_SPR'). Set this value to 'TRIAL' if you are going to use a custom quantization schema.. Optional. "compression": [ // One or more definitions for the compression algorithms to be applied to the model; either a single JSON object or an array of JSON objects. See README for each compression algorithm for a description of the available config parameters. { "algorithm": quantization, diff --git a/docs/FAQ.md b/docs/FAQ.md index 39d8a6e0bc3..21ccaa74144 100644 --- a/docs/FAQ.md +++ b/docs/FAQ.md @@ -151,7 +151,7 @@ class MyInitializingDataLoader(PTInitializingDataLoader): # in this function, for instance, if your dataloader returns dictionaries where # the input image is under key `"img"`, and your YOLOv8 model accepts the input # images as 0-th `forward` positional arg, you would do: - return dataloader_output["img"], {} + return (dataloader_output["img"],), {} def get_target(self, dataloader_output: Any) -> Any: # and in this function you should extract the "ground truth" value from your diff --git a/docs/Installation.md b/docs/Installation.md index 9f0dbce77f9..3aec19e8e0f 100644 --- a/docs/Installation.md +++ b/docs/Installation.md @@ -58,10 +58,6 @@ pip install git+https://github.com/openvinotoolkit/nncf@bd189e2#egg=nncf Note that in order for this to work for pip versions >= 21.3, your Git version must be at least 2.22. -## As a Docker image - -Use one of the Dockerfiles in the [docker](../docker) directory to build an image with an environment already set up and ready for running NNCF [sample scripts](../README.md#model-compression-samples). - ## Corresponding versions The following table lists the recommended corresponding versions of backend packages @@ -69,7 +65,8 @@ as well as the supported versions of Python: | NNCF | OpenVINO | PyTorch | ONNX | TensorFlow | Python | |-----------|------------|----------|----------|------------|--------| -| `develop` | `2023.3.0` | `2.1.2` | `1.13.1` | `2.12.0` | `3.8` | +| `develop` | `2024.4.0` | `2.1.2` | `1.13.1` | `2.12.0` | `3.8` | +| `2.9.0` | `2024.4.0` | `2.1.2` | `1.13.1` | `2.12.0` | `3.8` | | `2.8.1` | `2023.3.0` | `2.1.2` | `1.13.1` | `2.12.0` | `3.8` | | `2.8.0` | `2023.3.0` | `2.1.2` | `1.13.1` | `2.12.0` | `3.8` | | `2.7.0` | `2023.2.0` | `2.1` | `1.13.1` | `2.12.0` | `3.8` | diff --git a/docs/api/source/conf.py b/docs/api/source/conf.py index bb529ea22c5..81ef16c0696 100644 --- a/docs/api/source/conf.py +++ b/docs/api/source/conf.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -22,7 +22,7 @@ project = "NNCF" html_title = "NNCF" -copyright_ = "2023, Intel Corporation" +copyright_ = "2024, Intel Corporation" author = "Intel Corporation" extensions = ["autoapi.extension", "sphinx.ext.autodoc", "sphinx.ext.linkcode"] @@ -83,28 +83,30 @@ def collect_api_entities() -> APIInfo: print(f"{modname}") for obj_name, obj in inspect.getmembers(module): objects_module = getattr(obj, "__module__", None) - if objects_module == modname: - if inspect.isclass(obj) or inspect.isfunction(obj): - if hasattr(obj, api.API_MARKER_ATTR): - marked_object_name = obj._nncf_api_marker - # Check the actual name of the originally marked object - # so that the classes derived from base API classes don't - # all automatically end up in API - if marked_object_name != obj.__name__: - continue - fqn = f"{modname}.{obj_name}" - if hasattr(obj, api.CANONICAL_ALIAS_ATTR): - canonical_import_name = getattr(obj, api.CANONICAL_ALIAS_ATTR) - if canonical_import_name in canonical_imports_seen: - assert False, f"Duplicate canonical_alias detected: {canonical_import_name}" - retval.fqn_vs_canonical_name[fqn] = canonical_import_name - retval.canonical_name_vs_fqn[canonical_import_name] = fqn - canonical_imports_seen.add(canonical_import_name) - if canonical_import_name == fqn: - print(f"\t{obj_name}") - else: - print(f"\t{obj_name} -> {canonical_import_name}") - retval.api_names_vs_obj_dict[fqn] = obj + if ( + objects_module == modname + and (inspect.isclass(obj) or inspect.isfunction(obj)) + and hasattr(obj, api.API_MARKER_ATTR) + ): + marked_object_name = obj._nncf_api_marker + # Check the actual name of the originally marked object + # so that the classes derived from base API classes don't + # all automatically end up in API + if marked_object_name != obj.__name__: + continue + fqn = f"{modname}.{obj_name}" + if hasattr(obj, api.CANONICAL_ALIAS_ATTR): + canonical_import_name = getattr(obj, api.CANONICAL_ALIAS_ATTR) + if canonical_import_name in canonical_imports_seen: + assert False, f"Duplicate canonical_alias detected: {canonical_import_name}" + retval.fqn_vs_canonical_name[fqn] = canonical_import_name + retval.canonical_name_vs_fqn[canonical_import_name] = fqn + canonical_imports_seen.add(canonical_import_name) + if canonical_import_name == fqn: + print(f"\t{obj_name}") + else: + print(f"\t{obj_name} -> {canonical_import_name}") + retval.api_names_vs_obj_dict[fqn] = obj print() skipped_str = "\n".join([f"{k}: {v}" for k, v in skipped_modules.items()]) diff --git a/docs/compression_algorithms/CompressWeights.md b/docs/compression_algorithms/CompressWeights.md index 83b38785703..927c3671bb3 100644 --- a/docs/compression_algorithms/CompressWeights.md +++ b/docs/compression_algorithms/CompressWeights.md @@ -4,14 +4,14 @@ #### The algorithm description -The Weights Compression algorithm is aimed at compressing the weights of the models and can be used to optimize the model footprint and performance of large models where the size of weights is relatively larger than the size of activations, for example, Large Language Models (LLM). The algorithm compresses weights only for Linear and Embedding layers. +The Weights Compression algorithm is aimed at compressing the weights of the models and can be used to optimize the model footprint and performance of large models where the size of weights is relatively larger than the size of activations, for example, Large Language Models (LLM). The algorithm compresses weights for Linear, Convolution and Embedding layers. #### Supported modes By default, weights are compressed asymmetrically to 8-bit integer data type - "INT8_ASYM" mode. OpenVINO backend also supports 3 modes of mixed precision weight quantization with a 4-bit data type as a primary precision - INT4_SYM, INT4_ASYM and NF4. The primary precision in case of INT4_SYM mode is unsigned 4-bit integer and weights are quantized to it [symmetrically](https://github.com/openvinotoolkit/nncf/blob/develop/docs/compression_algorithms/Quantization.md#symmetric-quantization) with a fixed zero point equals to 8. In case of INT4_ASYM mode - also unsigned 4-bit integer, but weight are quantized to it [asymmetrically](https://github.com/openvinotoolkit/nncf/blob/develop/docs/compression_algorithms/Quantization.md#asymmetric-quantization) with a typical non-fixed zero point. In case of NF4 mode - [nf4](https://arxiv.org/pdf/2305.14314v1.pdf) data type without zero point. All 4-bit modes have a grouped quantization support, when small group of weights (e.g. 128) in the channel dimension share quantization parameters (scale). -All embeddings and last linear layers are always compressed to 8-bit integer data type. +All embeddings, convolutions and last linear layers are always compressed to 8-bit integer data type. To quantize embeddings and last linear layers to 4-bit, use `all_layers=True`. Percent of the rest layers compressed to 4-bit can be configured by "ratio" parameter. E.g. ratio=0.9 means 90% of layers compressed to the corresponding 4-bit data type and the rest to 8-bit asymmetric integer data type. #### User guide @@ -30,7 +30,7 @@ from nncf import compress_weights, CompressWeightsMode compressed_model = compress_weights(model, mode=CompressWeightsMode.INT8_SYM) # model is openvino.Model object ``` -- Compress weights symmetrically to 4-bit integer data type with group size = 128, except embeddings and last linear layers - they are compressed asymmetrically to 8-bit integer data type. +- Compress weights symmetrically to 4-bit integer data type with group size = 128, except embeddings, convolutions and last linear layers - they are compressed asymmetrically to 8-bit integer data type. ```python from nncf import compress_weights, CompressWeightsMode @@ -39,7 +39,7 @@ compressed_model = compress_weights(model, mode=CompressWeightsMode.INT4_SYM) # - Generally, `INT4_SYM` mode is the fastest mixed-precision mode, but it may lead to a significant accuracy degradation or perplexity increase. Compressing weights asymmetrically (`INT4_ASYM` mode) is the way to increase accuracy, however in turns it slows down inference a bit. - If the accuracy or perplexity is still not satisfying, there are 2 more hyper-parameters to tune: `group_size` and `ratio`. + If the accuracy or perplexity is still not satisfying, there are 2 more hyper-parameters to tune: `group_size` and `ratio`. Please refer to the [example](https://github.com/openvinotoolkit/nncf/blob/develop/examples/llm_compression/openvino/tiny_llama_find_hyperparams) how to automatically tune these parameters. Lower group size and less ratio of 4-bit layers usually improve accuracy at the sacrifice of inference speed. Below is the example how to compress weights of 90% of layers to 4-bit integer asymmetrically with the group size 64, and the rest of layers to 8-bit asymmetric integer data type. The same parametrization is applicable for `INT4_SYM` mode. @@ -61,6 +61,64 @@ nncf_dataset = nncf.Dataset(data_source, transform_fn) compressed_model = compress_weights(model, mode=CompressWeightsMode.INT4_SYM, ratio=0.8, dataset=nncf_dataset) # model is openvino.Model object ``` +- Accuracy of the 4-bit compressed models also can be improved by using AWQ algorithm over data-based mixed-precision algorithm. It is capable to equalize some subset of weights to minimize difference between +original precision and 4-bit. +Below is the example how to compress 80% of layers to 4-bit integer with a default data-based mixed precision algorithm and AWQ. +It requires to set `awq` to `True` additionally to data-based mixed-precision algorithm. + +```python +from datasets import load_dataset +from functools import partial +from nncf import compress_weights, CompressWeightsMode, Dataset +from optimum.intel.openvino import OVModelForCausalLM +from transformers import AutoTokenizer + +def transform_func(item, tokenizer, input_shapes): + text = item['text'] + tokens = tokenizer(text) + + res = {'input_ids': np.expand_dims(np.array(tokens['input_ids']), 0), + 'attention_mask': np.expand_dims(np.array(tokens['attention_mask']), 0)} + + if 'position_ids' in input_shapes: + position_ids = np.cumsum(res['attention_mask'], axis=1) - 1 + position_ids[res['attention_mask'] == 0] = 1 + res['position_ids'] = position_ids + + for name, shape in input_shapes.items(): + if name in res: + continue + res[name] = np.zeros(shape) + + return res + +def get_input_shapes(model, batch_size = 1): + inputs = {} + + for val in model.model.inputs: + name = val.any_name + shape = list(val.partial_shape.get_min_shape()) + shape[0] = batch_size + inputs[name] = shape + + return inputs + +# load your model and tokenizer +model = OVModelForCausalLM.from_pretrained(...) +tokenizer = AutoTokenizer.from_pretrained(...) + +# prepare dataset for compression +dataset = load_dataset('wikitext', 'wikitext-2-v1', split='train') +dataset = dataset.filter(lambda example: len(example["text"]) > 80) +input_shapes = get_input_shapes(model) +nncf_dataset = Dataset(dataset, partial(transform_func, tokenizer=tokenizer, + input_shapes=input_shapes)) + +model.model = compress_weights(model.model, mode=CompressWeightsMode.INT4_SYM, ratio=0.8, dataset=nncf_dataset, awq=True) + +model.save_pretrained(...) +``` + - `NF4` mode can be considered for improving accuracy, but currently models quantized to nf4 should not be faster models quantized to 8-bit asymmetric integer. Here's the example how to compress weights to nf4 data type with group size = 128. Different `group_size` and `ratio` are also supported. @@ -258,6 +316,11 @@ Here is the word perplexity with data-free and data-aware mixed-precision INT4-I int4_sym_g64_r80 10.83 + + stable-zephyr-3b-dpo + int4_sym_g64_r80_data_awq + 21.62 + stable-zephyr-3b-dpo int4_sym_g64_r80_data @@ -293,6 +356,8 @@ Here is the word perplexity with data-free and data-aware mixed-precision INT4-I - [LLM Weight Compression](https://docs.openvino.ai/nightly/weight_compression.html) - [Optimize and Deploy Generative AI Models using Hugging Face Optimum Intel](https://docs.openvino.ai/nightly/gen_ai_guide.html) - [Optimum Intel documentation](https://huggingface.co/docs/optimum/intel/inference) +- [Large Language Models Weight Compression Example](https://github.com/openvinotoolkit/nncf/blob/develop/examples/llm_compression/openvino/tiny_llama) +- [Tuning Ratio and Group Size Example](https://github.com/openvinotoolkit/nncf/blob/develop/examples/llm_compression/openvino/tiny_llama_find_hyperparams) List of notebooks demonstrating OpenVINO conversion and inference together with NNCF weight compression for models from various domains: diff --git a/docs/compression_algorithms/Quantization.md b/docs/compression_algorithms/Quantization.md index 977d77483eb..b46ddf6b845 100644 --- a/docs/compression_algorithms/Quantization.md +++ b/docs/compression_algorithms/Quantization.md @@ -282,7 +282,7 @@ To evaluate the goodness of a policy, NNCF backend quantizes the workload accord ```json5 { - "target_device": "VPU", + "target_device": "NPU", "compression": { "algorithm": "quantization", "initializer": { diff --git a/examples/__init__.py b/examples/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/examples/__init__.py +++ b/examples/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/common/__init__.py b/examples/common/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/examples/common/__init__.py +++ b/examples/common/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/common/paths.py b/examples/common/paths.py index 93053df3429..8dad01e2f8e 100644 --- a/examples/common/paths.py +++ b/examples/common/paths.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/common/sample_config.py b/examples/common/sample_config.py index bd23ffb3dc7..bec980c9699 100644 --- a/examples/common/sample_config.py +++ b/examples/common/sample_config.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -15,6 +15,7 @@ import jstyleson as json from addict import Dict +import nncf from nncf import NNCFConfig from nncf.common.utils.os import safe_open @@ -136,7 +137,7 @@ def _embed_nncf_config(args, sample_config: SampleConfig) -> SampleConfig: def _fail_if_training_with_eval_only_config(sample_config: SampleConfig): if sample_config.eval_only and "train" in sample_config.mode: - raise RuntimeError(EVAL_ONLY_ERROR_TEXT) + raise nncf.ValidationError(EVAL_ONLY_ERROR_TEXT) def create_sample_config(args, parser, **kwargs) -> SampleConfig: diff --git a/examples/experimental/__init__.py b/examples/experimental/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/examples/experimental/__init__.py +++ b/examples/experimental/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/experimental/openvino/bert/main.py b/examples/experimental/openvino/bert/main.py deleted file mode 100644 index ea7a3772430..00000000000 --- a/examples/experimental/openvino/bert/main.py +++ /dev/null @@ -1,161 +0,0 @@ -# Copyright (c) 2023 Intel Corporation -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# http://www.apache.org/licenses/LICENSE-2.0 -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import subprocess -from pathlib import Path -from typing import Any, Iterable - -import datasets -import evaluate -import numpy as np -import openvino.runtime as ov -import torch -import transformers - -import nncf -from nncf.parameters import ModelType - -# Path to the `bert` directory. -ROOT = Path(__file__).parent.resolve() -# Path to the directory where the original and quantized IR will be saved. -MODEL_DIR = ROOT / "bert_quantization" -# Path to the pre-trained model directory. -PRETRAINED_MODEL_DIR = ROOT / "MRPC" - -TASK_NAME = "mrpc" -MAX_SEQ_LENGTH = 128 - - -ie = ov.Core() - - -def run_example(): - """ - Runs the BERT quantization example. - """ - # Step 1: Load pre-trained model. - model = transformers.BertForSequenceClassification.from_pretrained(PRETRAINED_MODEL_DIR) - model.eval() - - # Step 2: Converts PyTorch model to the OpenVINO model. - ov_model = convert_torch_to_openvino(model) - - # Step 3: Create calibration dataset. - data_source = create_data_source() - - # Step 4: Apply quantization algorithm. - - # Define the transformation method. This method should take a data item returned - # per iteration through the `data_source` object and transform it into the model's - # expected input that can be used for the model inference. - INPUT_NAMES = [x.any_name for x in ov_model.inputs] - - def transform_fn(data_item): - inputs = {name: np.asarray(data_item[name], dtype=np.int64) for name in INPUT_NAMES} - return [inputs] - - # Wrap framework-specific data source into the `nncf.Dataset` object. - calibration_dataset = nncf.Dataset(data_source, transform_fn) - quantized_model = nncf.quantize(ov_model, calibration_dataset, model_type=ModelType.TRANSFORMER) - - # Step 5: Save quantized model. - ir_qmodel_xml = MODEL_DIR / "bert_base_mrpc_quantized.xml" - ir_qmodel_bin = MODEL_DIR / "bert_base_mrpc_quantized.bin" - ov.serialize(quantized_model, str(ir_qmodel_xml), str(ir_qmodel_bin)) - - # Step 6: Compare the accuracy of the original and quantized models. - print("Checking the accuracy of the original model:") - metric = validation_fn(ov_model, data_source) - print(f"F1 score: {metric}") - - print("Checking the accuracy of the quantized model:") - metric = validation_fn(quantized_model, data_source) - print(f"F1 score: {metric}") - - # Step 7: Compare Performance of the original and quantized models. - # benchmark_app -m bert_quantization/bert_base_mrpc.xml -d CPU -api async - # benchmark_app -m bert_quantization/bert_base_mrpc_quantized.xml -d CPU -api async - - -def convert_torch_to_openvino(model: torch.nn.Module) -> ov.Model: - """ - Converts the fine-tuned BERT model for the MRPC task to the OpenVINO IR format. - """ - if not MODEL_DIR.exists(): - MODEL_DIR.mkdir() - - # Export PyTorch model to the ONNX format. - onnx_model_path = MODEL_DIR / "bert_base_mrpc.onnx" - dummy_input = ( - torch.ones(1, MAX_SEQ_LENGTH, dtype=torch.int64), # input_ids - torch.ones(1, MAX_SEQ_LENGTH, dtype=torch.int64), # attention_mask - torch.ones(1, MAX_SEQ_LENGTH, dtype=torch.int64), # token_type_ids - ) - torch.onnx.export( - model, - dummy_input, - onnx_model_path, - verbose=False, - opset_version=11, - input_names=["input_ids", "attention_mask", "token_type_ids"], - output_names=["output"], - ) - - # Run Model Optimizer to convert ONNX model to OpenVINO IR. - mo_command = f"mo --framework onnx -m {onnx_model_path} --output_dir {MODEL_DIR}" - subprocess.call(mo_command, shell=True) # nosec - - ir_model_xml = MODEL_DIR / "bert_base_mrpc.xml" - ir_model_bin = MODEL_DIR / "bert_base_mrpc.bin" - ov_model = ie.read_model(model=ir_model_xml, weights=ir_model_bin) - - return ov_model - - -def create_data_source() -> datasets.Dataset: - """ - Creates validation MRPC dataset. - - :return: The `datasets.Dataset` object. - """ - raw_dataset = datasets.load_dataset("glue", TASK_NAME, split="validation") - tokenizer = transformers.BertTokenizer.from_pretrained(PRETRAINED_MODEL_DIR) - - def _preprocess_fn(examples): - texts = (examples["sentence1"], examples["sentence2"]) - result = tokenizer(*texts, padding="max_length", max_length=MAX_SEQ_LENGTH, truncation=True) - result["labels"] = examples["label"] - return result - - processed_dataset = raw_dataset.map(_preprocess_fn, batched=True, batch_size=1) - - return processed_dataset - - -def validation_fn(model: ov.Model, validation_dataset: Iterable[Any]) -> float: - compiled_model = ie.compile_model(model, device_name="CPU") - output_layer = compiled_model.output(0) - - metric = evaluate.load("glue", TASK_NAME) - INPUT_NAMES = [x.any_name for x in compiled_model.inputs] - for batch in validation_dataset: - inputs = [np.expand_dims(np.asarray(batch[key], dtype=np.int64), 0) for key in INPUT_NAMES] - outputs = compiled_model(inputs)[output_layer] - predictions = outputs[0].argmax(axis=-1) - metric.add_batch(predictions=[predictions], references=[batch["labels"]]) - metrics = metric.compute() - f1_score = metrics["f1"] - - return f1_score - - -if __name__ == "__main__": - run_example() diff --git a/examples/experimental/openvino/bert/requirements.txt b/examples/experimental/openvino/bert/requirements.txt deleted file mode 100644 index 716d041f0d8..00000000000 --- a/examples/experimental/openvino/bert/requirements.txt +++ /dev/null @@ -1,6 +0,0 @@ -datasets -evaluate -transformers -torch -openvino-dev -numpy diff --git a/examples/experimental/openvino/ssd_mobilenet_v1_fpn/main.py b/examples/experimental/openvino/ssd_mobilenet_v1_fpn/main.py deleted file mode 100644 index 5daf72f76d5..00000000000 --- a/examples/experimental/openvino/ssd_mobilenet_v1_fpn/main.py +++ /dev/null @@ -1,108 +0,0 @@ -# Copyright (c) 2023 Intel Corporation -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# http://www.apache.org/licenses/LICENSE-2.0 -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from functools import partial -from pathlib import Path -from typing import Any, Iterable - -import numpy as np -import openvino.runtime as ov -from openvino.tools.pot.api.samples.object_detection.data_loader import COCOLoader -from openvino.tools.pot.api.samples.object_detection.metric import MAP - -import nncf - -FILE = Path(__file__).resolve() -# Relative path to the `ssd_mobilenet_v1_fpn` directory. -ROOT = FILE.parent.relative_to(Path.cwd()) -# Path to the directory where the original and quantized IR will be saved. -MODEL_DIR = ROOT / "ssd_mobilenet_v1_fpn_quantization" -# Path to COCO validation dataset. -DATASET_DIR = ROOT / "coco2017" / "val2017" -ANNOTATION_FILE = ROOT / "coco2017" / "instances_val2017.json" - - -ie = ov.Core() - - -def run_example(): - """ - Runs the SSD MobileNetV1 FPN quantize with accuracy control example. - """ - # Step 1: Load the OpenVINO model. - ir_model_xml = ROOT / "public" / "ssd_mobilenet_v1_fpn_coco" / "FP32" / "ssd_mobilenet_v1_fpn_coco.xml" - ov_model = ie.read_model(ir_model_xml) - - # Step 2: Create data source. - dataset_config = { - "images_path": f"{DATASET_DIR}/", - "annotation_path": ANNOTATION_FILE, - } - data_source = COCOLoader(dataset_config) - - # Step 4: Apply quantization algorithm. - - # Define the transformation method. This method should take a data item returned - # per iteration through the `data_source` object and transform it into the model's - # expected input that can be used for the model inference. - def transform_fn(data): - images, _ = data - return np.expand_dims(images, 0) - - metric = MAP(91, data_source.labels) - # Wrap framework-specific data source into the `nncf.Dataset` object. - validation_dataset = nncf.Dataset(data_source, transform_fn) - quantized_model = nncf.quantize_with_accuracy_control( - ov_model, - validation_dataset, - validation_dataset, - validation_fn=partial(validation_fn, metric=metric), - max_drop=0.004, - preset=nncf.QuantizationPreset.MIXED, - ) - - # Step 4: Save the quantized model. - if not MODEL_DIR.exists(): - MODEL_DIR.mkdir() - - ir_qmodel_xml = MODEL_DIR / "ssd_mobilenet_v1_fpn_quantized.xml" - ir_qmodel_bin = MODEL_DIR / "ssd_mobilenet_v1_fpn_quantized.bin" - ov.serialize(quantized_model, str(ir_qmodel_xml), str(ir_qmodel_bin)) - - # Step 6: Compare the accuracy of the original and quantized models. - print("Checking the accuracy of the original model:") - compiled_model = ie.compile_model(ov_model, device_name="CPU") - metric = validation_fn(compiled_model, data_source, data_source.labels) - print(f"mAP: {metric}") - - print("Checking the accuracy of the quantized model:") - compiled_model = ie.compile_model(quantized_model, device_name="CPU") - metric = validation_fn(compiled_model, data_source, data_source.labels) - print(f"mAP: {metric}") - - # Step 7: Compare Performance of the original and quantized models. - # benchmark_app -m ssd_mobilenet_v1_fpn_quantization/ssd_mobilenet_v1_fpn.xml -d CPU -api async - # benchmark_app -m ssd_mobilenet_v1_fpn_quantization/ssd_mobilenet_v1_fpn_quantized.xml -d CPU -api async - - -def validation_fn(compiled_model: ov.CompiledModel, validation_dataset: Iterable[Any], metric) -> float: - output_layer = compiled_model.output(0) - - for images, labels in validation_dataset: - input_data = np.expand_dims(images, 0) - output = compiled_model([input_data])[output_layer] - metric.update(output, [labels]) - - return metric.avg_value["MAP"].item() - - -if __name__ == "__main__": - run_example() diff --git a/examples/experimental/openvino/yolo_v5/main.py b/examples/experimental/openvino/yolo_v5/main.py deleted file mode 100644 index fea4c7c305d..00000000000 --- a/examples/experimental/openvino/yolo_v5/main.py +++ /dev/null @@ -1,145 +0,0 @@ -# Copyright (c) 2023 Intel Corporation -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# http://www.apache.org/licenses/LICENSE-2.0 -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import subprocess -from pathlib import Path - -import openvino.runtime as ov -import torch -from yolov5.utils.dataloaders import create_dataloader -from yolov5.utils.general import check_dataset -from yolov5.utils.general import download -from yolov5.val import run as validation_fn - -import nncf - -# Path to the `yolo_v5` directory. -ROOT = Path(__file__).parent.resolve() -# Path to the directory where the original and quantized IR will be saved. -MODEL_DIR = ROOT / "yolov5m_quantization" -# Path to the dataset config from the `ultralytics/yolov5` repository. -DATASET_CONFIG = ROOT / "yolov5" / "data" / "coco.yaml" - - -ie = ov.Core() - - -def run_example(): - """ - Runs the YOLOv5 quantization example. - """ - # Step 1: Initialize model from the PyTorch Hub. - repo_path = str(ROOT.joinpath("yolov5")) - model = torch.hub.load(repo_path, "yolov5m", pretrained=True, source="local") - model.eval() - - # Step 2: Converts PyTorch model to the OpenVINO model. - ov_model = convert_torch_to_openvino(model) - - # Step 3: Create calibration dataset. - data_source = create_data_source() - - # Step 4: Apply quantization algorithm. - - # Define the transformation method. This method should take a data item returned - # per iteration through the `data_source` object and transform it into the model's - # expected input that can be used for the model inference. - def transform_fn(data_item): - images, *_ = data_item - images = images.float() - images = images / 255 - images = images.cpu().detach().numpy() - return images - - # Wrap framework-specific data source into the `nncf.Dataset` object. - calibration_dataset = nncf.Dataset(data_source, transform_fn) - quantized_model = nncf.quantize(ov_model, calibration_dataset, preset=nncf.QuantizationPreset.MIXED) - - # Step 5: Save the quantized model. - ir_qmodel_xml = MODEL_DIR / "yolov5m_quantized.xml" - ir_qmodel_bin = MODEL_DIR / "yolov5m_quantized.bin" - ov.serialize(quantized_model, str(ir_qmodel_xml), str(ir_qmodel_bin)) - - # Step 6: Compare the accuracy of the original and quantized models. - print("Checking the accuracy of the original model:") - metrics = validation_fn( - data=DATASET_CONFIG, - weights=MODEL_DIR.joinpath("yolov5m.xml"), # Already supports. - batch_size=1, - workers=1, - plots=False, - device="cpu", - iou_thres=0.65, - ) - print(f"mAP@.5 = {metrics[0][2]}") - print(f"mAP@.5:.95 = {metrics[0][3]}") - - print("Checking the accuracy of the quantized model:") - metrics = validation_fn( - data=DATASET_CONFIG, - weights=ir_qmodel_xml, # Already supports. - batch_size=1, - workers=1, - plots=False, - device="cpu", - iou_thres=0.65, - ) - print(f"mAP@.5 = {metrics[0][2]}") - print(f"mAP@.5:.95 = {metrics[0][3]}") - - # Step 7: Compare Performance of the original and quantized models - # benchmark_app -m yolov5m_quantization/yolov5m.xml -d CPU -api async - # benchmark_app -m yolov5m_quantization/yolov5m_quantized.xml -d CPU -api async - - -def convert_torch_to_openvino(model: torch.nn.Module) -> ov.Model: - """ - Converts PyTorch YOLOv5 model to the OpenVINO IR format. - """ - if not MODEL_DIR.exists(): - MODEL_DIR.mkdir() - - # Export PyTorch model to the ONNX format. - onnx_model_path = MODEL_DIR / "yolov5m.onnx" - dummy_input = torch.randn(1, 3, 640, 640) - torch.onnx.export(model, dummy_input, onnx_model_path, verbose=False) - - # Run Model Optimizer to convert ONNX model to OpenVINO IR. - mo_command = f"mo --framework onnx -m {onnx_model_path} --output_dir {MODEL_DIR}" - subprocess.call(mo_command, shell=True) # nosec - - ir_model_xml = MODEL_DIR / "yolov5m.xml" - ir_model_bin = MODEL_DIR / "yolov5m.bin" - ov_model = ie.read_model(model=ir_model_xml, weights=ir_model_bin) - - return ov_model - - -def create_data_source() -> torch.utils.data.DataLoader: - """ - Creates COCO 2017 validation data loader. The method downloads COCO 2017 - dataset if it does not exist. - """ - if not ROOT.joinpath("datasets", "coco").exists(): - urls = ["https://github.com/ultralytics/yolov5/releases/download/v1.0/coco2017labels.zip"] - download(urls, dir=ROOT.joinpath("datasets")) - - urls = ["http://images.cocodataset.org/zips/val2017.zip"] - download(urls, dir=ROOT.joinpath("datasets", "coco", "images")) - - data = check_dataset(DATASET_CONFIG) - val_dataloader = create_dataloader(data["val"], imgsz=640, batch_size=1, stride=32, pad=0.5, workers=1)[0] - - return val_dataloader - - -if __name__ == "__main__": - run_example() diff --git a/examples/experimental/openvino/yolo_v5/requirements.txt b/examples/experimental/openvino/yolo_v5/requirements.txt deleted file mode 100644 index fae87a4c997..00000000000 --- a/examples/experimental/openvino/yolo_v5/requirements.txt +++ /dev/null @@ -1,3 +0,0 @@ -yolov5 -openvino-dev -torch diff --git a/examples/experimental/torch/__init__.py b/examples/experimental/torch/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/examples/experimental/torch/__init__.py +++ b/examples/experimental/torch/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/experimental/torch/classification/Quickstart.md b/examples/experimental/torch/classification/Quickstart.md index 823abaa0a4d..a192c61501a 100644 --- a/examples/experimental/torch/classification/Quickstart.md +++ b/examples/experimental/torch/classification/Quickstart.md @@ -1,32 +1,8 @@ # Setup -## PyTorch +## NNCF and PyTorch -Install PyTorch and Torchvision using the [PyTorch installation guide](https://pytorch.org/get-started/locally/#start-locally). NNCF currently supports PyTorch 1.12.1. For this quickstart, PyTorch 1.12.1 and Torchvision 0.13.1 with CUDA 11.3 was installed using: - -```bash -pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113 -``` - -## NNCF - -There are two options for installing [***NNCF***](https://github.com/openvinotoolkit/nncf#installation): - -- Package built from NNCF repository or -- PyPI package. - -To install NNCF and dependencies from the NNCF repository, install by running the following in the repository root directory and also set `PYTHONPATH` variable to include the root directory: - -```bash -python setup.py develop -export PYTHONPATH="${PYTHONPATH}:/nncf" -``` - -To install NNCF and dependencies as a PyPI package, use the following: - -```bash -pip install nncf -``` +Install NNCF and PyTorch using the latest instructions in https://github.com/openvinotoolkit/nncf/blob/develop/docs/Installation.md. The ```examples``` folder from the NNCF repository ***is not*** included when you install NNCF using a package manager. To run the BootstrapNAS examples, you will need to obtain this folder from the repository and add it to your path. diff --git a/examples/experimental/torch/classification/__init__.py b/examples/experimental/torch/classification/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/examples/experimental/torch/classification/__init__.py +++ b/examples/experimental/torch/classification/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/experimental/torch/classification/bootstrap_nas.py b/examples/experimental/torch/classification/bootstrap_nas.py index 55b18b12e8d..f35a19fd16d 100644 --- a/examples/experimental/torch/classification/bootstrap_nas.py +++ b/examples/experimental/torch/classification/bootstrap_nas.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -34,7 +34,6 @@ from examples.torch.common.model_loader import load_model from examples.torch.common.optimizer import get_parameter_groups from examples.torch.common.optimizer import make_optimizer -from examples.torch.common.utils import SafeMLFLow from examples.torch.common.utils import configure_device from examples.torch.common.utils import configure_logging from examples.torch.common.utils import create_code_snapshot @@ -106,7 +105,6 @@ def main(argv): def main_worker(current_gpu, config: SampleConfig): configure_device(current_gpu, config) - config.mlflow = SafeMLFLow(config) if is_main_process(): configure_logging(logger, config) print_args(config) diff --git a/examples/experimental/torch/classification/bootstrap_nas_search.py b/examples/experimental/torch/classification/bootstrap_nas_search.py index 604bd0d8ce1..85eff995362 100644 --- a/examples/experimental/torch/classification/bootstrap_nas_search.py +++ b/examples/experimental/torch/classification/bootstrap_nas_search.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -30,7 +30,6 @@ from examples.torch.common.execution import set_seed from examples.torch.common.execution import start_worker from examples.torch.common.model_loader import load_model -from examples.torch.common.utils import SafeMLFLow from examples.torch.common.utils import configure_device from examples.torch.common.utils import configure_logging from examples.torch.common.utils import create_code_snapshot @@ -93,7 +92,6 @@ def main(argv): def main_worker(current_gpu, config: SampleConfig): configure_device(current_gpu, config) - config.mlflow = SafeMLFLow(config) if is_main_process(): configure_logging(logger, config) print_args(config) diff --git a/examples/llm_compression/openvino/tiny_llama/README.md b/examples/llm_compression/openvino/tiny_llama/README.md new file mode 100644 index 00000000000..ef96e450b9a --- /dev/null +++ b/examples/llm_compression/openvino/tiny_llama/README.md @@ -0,0 +1,26 @@ +# Large Language Models Weight Compression Example + +This example demonstrates how to optimize Large Language Models (LLMs) using NNCF weight compression API. The example applies 4/8-bit mixed-precision quantization to weights of Linear (Fully-connected) layers of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) model. This leads to a significant decrease in model footprint and performance improvement with OpenVINO. + +## Prerequisites + +To use this example: + +- Create a separate Python* environment and activate it: `python3 -m venv nncf_env && source nncf_env/bin/activate` +- Install dependencies: + +```bash +pip install -U pip +pip install -r requirements.txt +pip install ../../../../ +``` + +## Run Example + +To run example: + +```bash +python main.py +``` + +It will automatically download the dataset and baseline model and save the resulting model. diff --git a/examples/llm_compression/openvino/tiny_llama/main.py b/examples/llm_compression/openvino/tiny_llama/main.py new file mode 100644 index 00000000000..f2be54ce1aa --- /dev/null +++ b/examples/llm_compression/openvino/tiny_llama/main.py @@ -0,0 +1,83 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import time +from functools import partial + +import datasets +import numpy as np +import openvino as ov +from optimum.intel.openvino import OVModelForCausalLM +from transformers import AutoTokenizer + +import nncf + + +def main(): + MODEL_ID = "PY007/TinyLlama-1.1B-Chat-v0.3" + OUTPUT_DIR = "tinyllama_compressed" + + dataset = datasets.load_dataset("wikitext", "wikitext-2-raw-v1", split="test") + + tokenizer = AutoTokenizer.from_pretrained(MODEL_ID) + model = OVModelForCausalLM.from_pretrained(MODEL_ID, export=True, load_in_8bit=False, compile=False, stateful=False) + + def transform_fn(data, model, tokenizer): + tokenized_text = tokenizer(data["text"], return_tensors="np") + input_ids = tokenized_text["input_ids"] + attention_mask = tokenized_text["attention_mask"] + + inputs = {} + inputs["input_ids"] = input_ids + inputs["attention_mask"] = tokenized_text["attention_mask"] + position_ids = np.cumsum(attention_mask, axis=1) - 1 + position_ids[attention_mask == 0] = 1 + + # The magic forms KV cache as model inputs + batch_size = input_ids.shape[0] + for input_name in model.key_value_input_names: + model_inputs = model.model.input(input_name) + shape = model_inputs.get_partial_shape() + shape[0] = batch_size + if shape[2].is_dynamic: + shape[2] = 0 + else: + shape[1] = 0 + inputs[input_name] = ov.Tensor(model_inputs.get_element_type(), shape.get_shape()) + + inputs["position_ids"] = position_ids + return inputs + + quantization_dataset = nncf.Dataset(dataset, partial(transform_fn, model=model, tokenizer=tokenizer)) + + # Comment this text to turn off model optimization and measure performance of baseline model + model.model = nncf.compress_weights( + model.model, + dataset=quantization_dataset, + mode=nncf.CompressWeightsMode.INT4_SYM, + ratio=0.8, + sensitivity_metric=nncf.SensitivityMetric.HESSIAN_INPUT_ACTIVATION, + ) + model.save_pretrained(OUTPUT_DIR) + + model = OVModelForCausalLM.from_pretrained(OUTPUT_DIR) + input_ids = tokenizer("What is PyTorch?", return_tensors="pt").to(device=model.device) + + start_t = time.time() + output = model.generate(**input_ids, max_new_tokens=100) + print("Elapsed time: ", time.time() - start_t) + + output_text = tokenizer.decode(output[0]) + print(output_text) + return output_text + + +if __name__ == "__main__": + main() diff --git a/examples/llm_compression/openvino/tiny_llama/requirements.txt b/examples/llm_compression/openvino/tiny_llama/requirements.txt new file mode 100644 index 00000000000..390b0576817 --- /dev/null +++ b/examples/llm_compression/openvino/tiny_llama/requirements.txt @@ -0,0 +1,3 @@ +transformers +datasets==2.14.7 +optimum-intel[openvino] diff --git a/examples/llm_compression/openvino/tiny_llama_find_hyperparams/README.md b/examples/llm_compression/openvino/tiny_llama_find_hyperparams/README.md new file mode 100644 index 00000000000..622db69aeae --- /dev/null +++ b/examples/llm_compression/openvino/tiny_llama_find_hyperparams/README.md @@ -0,0 +1,40 @@ +# Find the appropriate hyperparameters to compress the TinyLLama model + +This example demonstrates how to find the appropriate `awq`, `ratio` and `group_size` parameters to compress the weights of the TinyLLama model from the HuggingFace Transformers. OpenVINO backend supports inference of mixed-precision models with weights compressed to a 4-bit data type as a primary precision. The fastest mixed-precision mode is `INT4_SYM`, but it may lead to a significant accuracy degradation, especially for models of moderate size. In this example, the allowed maximum deviation from the original model is `0.2` points of the similarity metric. If the similarity of the compressed model is not satisfying, there are 3 hyper-parameters to tune: `awq`, `group_size` and `ratio`. Smaller `group_size` and `ratio` of 4-bit layers usually improve accuracy at the sacrifice of model size and inference latency. Generally, the accuracy of the 4-bit compressed models also can be improved by using AWQ algorithm over data-based mixed-precision algorithm. +To evaluate the accuracy of the compressed model we measure similarity between two texts generated by the baseline and compressed models using [WhoWhatBench](https://github.com/openvinotoolkit/openvino.genai/tree/master/llm_bench/python/who_what_benchmark) library. + +The example includes the following steps: + +- Prepare `wikitext` dataset. +- Prepare `TinyLlama/TinyLlama-1.1B-step-50K-105b` text-generation model in OpenVINO representation using [Optimum-Intel](https://huggingface.co/docs/optimum/intel/inference). +- Compress weights of the model with NNCF Weight compression algorithm. +- Find appropriate `awq`, `ratio` and `group_size` if acceptable similarity is not achieved. +- Measure the similarity and footprint of the final model. + +## Install requirements + +To run the example you should install the corresponding Python dependencies: + +- Create a separate Python* environment and activate it: `python3 -m venv nncf_env && source nncf_env/bin/activate` +- Install dependencies: + +```bash +pip install -U pip +pip install ../../../../ +pip install -r requirements.txt +``` + +## Run Example + +The example is fully automated. Just run the following command in the prepared Python environment: + +```bash +python main.py +``` + +To find the appropriate `awq`, `ratio` and `group_size` parameters for your HF model, change the `model_id`, `dataset` and `transform_func`. +Please refer to the `` comments in `main.py` to find the exact places that should be modified. + +## See also + +- [Weight compression](../../../../docs/compression_algorithms/CompressWeights.md) diff --git a/examples/llm_compression/openvino/tiny_llama_find_hyperparams/main.py b/examples/llm_compression/openvino/tiny_llama_find_hyperparams/main.py new file mode 100644 index 00000000000..dbe9f181850 --- /dev/null +++ b/examples/llm_compression/openvino/tiny_llama_find_hyperparams/main.py @@ -0,0 +1,271 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import datetime +import itertools +from functools import partial +from pathlib import Path +from typing import Callable, Iterable, Optional, Tuple, TypeVar + +import numpy as np +import openvino as ov +from datasets import load_dataset +from optimum.intel import OVModelForCausalLM +from transformers import AutoTokenizer +from whowhatbench import Evaluator + +import nncf +from nncf.common.logging import nncf_logger + +DataItem = TypeVar("DataItem") +ModelInput = TypeVar("ModelInput") + +ROOT = Path(__file__).parent.resolve() +MODEL_PATH = ROOT / "compressed_model.xml" + +COMPRESSION_MODE = nncf.parameters.CompressWeightsMode.INT4_SYM +MAX_DROP = 0.2 +# We consider the following range of parameters: group_size - [64, 128], ratio - [0.5,...,1.0] +MIN_GROUP_SIZE = 64 +MAX_GROUP_SIZE = 128 +MIN_RATIO = 0.5 +MAX_RATIO = 1.0 +RATIO_STEP = 0.1 + + +def compress_model( + ov_model: ov.Model, nncf_dataset: nncf.Dataset, ratio: float, group_size: int, awq: bool +) -> ov.Model: + """ + Compress the given OpenVINO model using NNCF weight compression. + + :param ov_model: The original OpenVINO model to be compressed. + :param nncf_dataset: A representative dataset for the weight compression algorithm. + :param ratio: The ratio between baseline and backup precisions + :param group_size: Number of weights (e.g. 128) in the channel dimension + that share quantization parameters (scale). + :param awq: Indicates whether use AWQ weights correction. + :return: The OpenVINO model with compressed weights. + """ + optimized_ov_model = nncf.compress_weights( + ov_model.clone(), # we should clone the model because `compress_weights` works in place + dataset=nncf_dataset, + mode=COMPRESSION_MODE, + ratio=ratio, + group_size=group_size, + awq=awq, + sensitivity_metric=nncf.parameters.SensitivityMetric.MAX_ACTIVATION_VARIANCE, + ) + return optimized_ov_model + + +def evaluate_model( + hf_model: OVModelForCausalLM, original_ov_model: ov.Model, optimized_model: ov.Model, evaluator: Evaluator +) -> float: + """ + Get similarity of text generation between original and optimized models. + + :param hf_model: The OpenVINO model for causal language modeling. + :param original_ov_model: The original OpenVINO model + :param optimized_model: The OpenVINO model with compressed weights. + :param evaluator: The evaluator object from whowhatbench Benchmark. + :return: The similarity score between the original and optimized models. + """ + hf_model.model = optimized_model + hf_model.request = None + _, all_metrics = evaluator.score(hf_model) + hf_model.model = original_ov_model.clone() + hf_model.request = None + similarity = all_metrics["similarity"][0] + group_size = optimized_model.get_rt_info()["nncf"]["weight_compression"]["group_size"].value + ratio = float(optimized_model.get_rt_info()["nncf"]["weight_compression"]["ratio"].value) + awq = optimized_model.get_rt_info()["nncf"]["weight_compression"]["awq"].value + all_layers = optimized_model.get_rt_info()["nncf"]["weight_compression"]["all_layers"].value + params_info = f"The similarity of model compressed with group_size={group_size}, ratio={ratio:.1f}, awq={awq}" + if all_layers == "True": + params_info = params_info + ", all_layers=True" + nncf_logger.info(params_info + f" is {similarity:.3f}") + return similarity + + +def get_nncf_dataset( + data_source: Iterable[DataItem], transform_func: Optional[Callable[[DataItem], ModelInput]] = None +) -> nncf.Dataset: + """ + Create an NNCF dataset for the weight compression algorithm. + + :param data_source: The iterable object serving as the source of data items. + :param transform_func: The transformation function applied to the data_source. + :return: nncf_dataset: NNCF Dataset for the weight compression algorithm. + """ + if data_source is None: + return None + if transform_func: + return nncf.Dataset(data_source, transform_func) + return nncf.Dataset(data_source) + + +def print_results(optimized_model: ov.Model, similarity: float) -> None: + """ + Print report with optimization details, memory footprint, and similarity score. + + :param optimized_model: The OpenVINO model with compressed weights. + :param similarity: The similarity score between the original and optimized models. + """ + ov.save_model(optimized_model, MODEL_PATH) + print(f"Compressed model was saved to: {MODEL_PATH}") + group_size = optimized_model.get_rt_info()["nncf"]["weight_compression"]["group_size"].value + ratio = float(optimized_model.get_rt_info()["nncf"]["weight_compression"]["ratio"].value) + awq = optimized_model.get_rt_info()["nncf"]["weight_compression"]["awq"].value + all_layers = optimized_model.get_rt_info()["nncf"]["weight_compression"]["all_layers"].value + best_params_info = f"Best parameters: group_size={group_size}, ratio={ratio:.1f}, awq={awq}" + if all_layers == "True": + print(best_params_info + ", all_layers=True") + else: + print(best_params_info) + footprint = Path(MODEL_PATH).with_suffix(".bin").stat().st_size + print(f"Memory footprint: {footprint / 2**20 :.2f} MB") + print(f"Similarity: {similarity:.2f}") + + +def find_parameters( + evaluator: Evaluator, model: OVModelForCausalLM, nncf_dataset: nncf.Dataset +) -> Tuple[bool, float, int]: + """ + Find the optimal `awq`, `ratio` and `group_size` for weight compression algorithm. + + :param evaluator: The evaluator object from whowhatbench Benchmark. + :param model: The OpenVINO model for causal language modeling. + :param nncf_dataset: A representative dataset for the weight compression algorithm. + :return: The optimal awq, ratio and group_size. + """ + original_ov_model = model.model + evaluate_fn = partial(evaluate_model, hf_model=model, original_ov_model=original_ov_model, evaluator=evaluator) + + # Generating a grid of hyperparameter values for tuning, combining ratios and group sizes. + ratio_grid = np.arange(MAX_RATIO, MIN_RATIO - RATIO_STEP, -RATIO_STEP) + param_grid = list(itertools.product(ratio_grid, [MAX_GROUP_SIZE, MIN_GROUP_SIZE])) + + # First, we try to use the maximum ratio and group_size to get the most efficient model + ratio, group_size = param_grid[0] # (MAX_GROUP_SIZE, MAX_RATIO) + use_awq = False + optimized_model = compress_model(original_ov_model, nncf_dataset, ratio, group_size, awq=use_awq) + similarity = evaluate_fn(optimized_model=optimized_model) + if similarity >= 1 - MAX_DROP: + # If model with the maximum ratio and group_size is acceptable, + # we try to compress embeddings and last MatMul layers to a primary precision + full_optimized_model = nncf.compress_weights( + original_ov_model.clone(), + mode=COMPRESSION_MODE, + ratio=ratio, + group_size=group_size, + all_layers=True, + ) + all_layers_similarity = evaluate_fn(optimized_model=full_optimized_model) + if all_layers_similarity >= 1 - MAX_DROP: + print_results(full_optimized_model, all_layers_similarity) + else: + print_results(optimized_model, similarity) + return use_awq, ratio, group_size + + # If the best performing model is not acceptable, we try to use AWQ weights correction and compare similarity + use_awq = True + optimized_model = compress_model(original_ov_model, nncf_dataset, ratio, group_size, awq=use_awq) + awq_similarity = evaluate_fn(optimized_model=optimized_model) + if awq_similarity >= 1 - MAX_DROP: + print_results(optimized_model, awq_similarity) + return use_awq, ratio, group_size + use_awq = awq_similarity > similarity + + # If the best performing model is not acceptable, we try to use the smallest ratio and group_size + # to check the reachability of the max drop criterion + ratio, group_size = param_grid[-1] # (MIN_GROUP_SIZE, MIN_RATIO) + optimized_model = compress_model(original_ov_model, nncf_dataset, ratio, group_size, awq=use_awq) + similarity = evaluate_fn(optimized_model=optimized_model) + if similarity < 1 - MAX_DROP: + nncf_logger.info( + "The model was compressed with the minimum ratio and group_size, " + "but it could not achieve the required accuracy drop. " + "We recommend choosing a different mode for weight compression." + ) + print_results(optimized_model, similarity) + return use_awq, ratio, group_size + + # If max drop criterion is achivable, we run a grid-search to find the best parameters + for ratio, group_size in param_grid[1:-1]: + optimized_model = compress_model(original_ov_model, nncf_dataset, ratio, group_size, awq=use_awq) + similarity = evaluate_fn(optimized_model=optimized_model) + if similarity >= 1 - MAX_DROP: + print_results(optimized_model, similarity) + return use_awq, ratio, group_size + + optimized_model = compress_model(original_ov_model, nncf_dataset, MIN_RATIO, MIN_GROUP_SIZE, awq=use_awq) + print_results(optimized_model, similarity) + return use_awq, MIN_RATIO, MIN_GROUP_SIZE + + +def tiny_llama_transform_func(item, tokenizer, ov_model): # + input_dtypes = {inp.get_any_name(): inp.get_element_type() for inp in ov_model.inputs} + tokens = tokenizer(item["text"]) + input_ids = np.expand_dims(np.array(tokens["input_ids"]), 0) + attention_mask = np.expand_dims(np.array(tokens["attention_mask"]), 0) + position_ids = np.cumsum(attention_mask, axis=1) - 1 + position_ids[attention_mask == 0] = 1 + res = { + "input_ids": ov.Tensor(input_ids, input_ids.shape, input_dtypes["input_ids"]), + "attention_mask": ov.Tensor(attention_mask, attention_mask.shape, input_dtypes["attention_mask"]), + "position_ids": position_ids.reshape(*attention_mask.shape), + } + + def gen_pkv(num_heads, head_dim, num_layers): + res = {} + shape = (1, num_heads, 0, head_dim) + for i in range(num_layers): + key_name = f"past_key_values.{i}.key" + val_name = f"past_key_values.{i}.value" + res[key_name] = ov.Tensor(shape=shape, type=input_dtypes[key_name]) + res[val_name] = ov.Tensor(shape=shape, type=input_dtypes[val_name]) + return res + + res.update(gen_pkv(4, 64, 22)) + return res + + +def main(): + model_id = "TinyLlama/TinyLlama-1.1B-step-50K-105b" # + ov_config = {"PERFORMANCE_HINT": "LATENCY", "NUM_STREAMS": "1", "CACHE_DIR": ""} + model = OVModelForCausalLM.from_pretrained( + model_id, + export=True, + trust_remote_code=True, + use_cache=True, + ov_config=ov_config, + stateful=False, + load_in_8bit=False, + ) + tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) + dataset = load_dataset("wikitext", "wikitext-2-v1", split="train[:1000]") # + dataset = dataset.filter(lambda example: len(example["text"]) > 128) + transform_func = partial(tiny_llama_transform_func, tokenizer=tokenizer, ov_model=model.model) + + start = datetime.datetime.now() + evaluator = Evaluator(model, tokenizer=tokenizer, metrics=("similarity",)) + nncf_dataset = get_nncf_dataset(dataset, transform_func) + awq, ratio, group_size = find_parameters(evaluator, model, nncf_dataset) + end = datetime.datetime.now() + delta = end - start + delta -= datetime.timedelta(microseconds=delta.microseconds) + print(f"Elapsed time: {delta}") + return awq, ratio, group_size + + +if __name__ == "__main__": + main() diff --git a/examples/llm_compression/openvino/tiny_llama_find_hyperparams/requirements.txt b/examples/llm_compression/openvino/tiny_llama_find_hyperparams/requirements.txt new file mode 100644 index 00000000000..89d911a3aa6 --- /dev/null +++ b/examples/llm_compression/openvino/tiny_llama_find_hyperparams/requirements.txt @@ -0,0 +1,6 @@ +datasets +whowhatbench @ git+https://github.com/andreyanufr/who_what_benchmark.git +numpy>=1.23.5 +openvino==2024.0.0 +optimum-intel[openvino]>=1.13.0 +transformers>=4.35.2 diff --git a/examples/post_training_quantization/onnx/mobilenet_v2/main.py b/examples/post_training_quantization/onnx/mobilenet_v2/main.py index 2d9f94d64be..e35a3cc61bd 100755 --- a/examples/post_training_quantization/onnx/mobilenet_v2/main.py +++ b/examples/post_training_quantization/onnx/mobilenet_v2/main.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -44,11 +44,11 @@ def download_model() -> Path: return download_url(MODEL_URL, Path(MODEL_PATH).resolve()) -def validate(onnx_model: onnx.ModelProto, validation_loader: torch.utils.data.DataLoader) -> float: +def validate(path_to_model: str, validation_loader: torch.utils.data.DataLoader) -> float: predictions = [] references = [] - compiled_model = ov.compile_model(onnx_model) + compiled_model = ov.compile_model(path_to_model) output = compiled_model.outputs[0] for images, target in tqdm(validation_loader): diff --git a/examples/post_training_quantization/onnx/mobilenet_v2/requirements.txt b/examples/post_training_quantization/onnx/mobilenet_v2/requirements.txt index 525c424e2be..ce33dbb17b1 100644 --- a/examples/post_training_quantization/onnx/mobilenet_v2/requirements.txt +++ b/examples/post_training_quantization/onnx/mobilenet_v2/requirements.txt @@ -4,4 +4,4 @@ scikit-learn fastdownload onnx~=1.13.1 onnxruntime~=1.14.1 -openvino-dev==2023.3 +openvino-dev==2024.0 diff --git a/examples/post_training_quantization/onnx/yolov8_quantize_with_accuracy_control/README.md b/examples/post_training_quantization/onnx/yolov8_quantize_with_accuracy_control/README.md new file mode 100644 index 00000000000..906c9ae483f --- /dev/null +++ b/examples/post_training_quantization/onnx/yolov8_quantize_with_accuracy_control/README.md @@ -0,0 +1,47 @@ +# Post-Training Quantization of YOLOv8 ONNX Model + +This example demonstrates how to use Post-Training Quantization API from Neural Network Compression Framework (NNCF) to quantize YOLOv8n ONNX model +with accuracy control. + +The example includes the following steps: + +- Download and prepare COCO-128-seg dataset. +- Quantize the model with accuracy control. +- Measure accuracy and performance of the floating-point and quantized models. +- Convert the resulted INT8 model to the OpenVINO format. + +## Install requirements + +To run the example you should install the corresponding Python dependencies: + +- Install NNCF from source: + + ```bash + git clone https://github.com/openvinotoolkit/nncf.git + cd nncf + pip install . + ``` + +- Install 3rd party dependencies of this example: + + ```bash + pip install -r requirements.txt + ``` + +## Run Example + +The example is fully automated. Just run the following command in the prepared Python environment: + +```bash +python main.py +``` + +Run the following command to convert resulted INT8 model to the OpenVINO format: + +```bash +python deploy.py +``` + +## See also + +- [YOLOv8 Jupyter notebook](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/230-yolov8-optimization) diff --git a/examples/post_training_quantization/onnx/yolov8_quantize_with_accuracy_control/deploy.py b/examples/post_training_quantization/onnx/yolov8_quantize_with_accuracy_control/deploy.py new file mode 100644 index 00000000000..0e8e349bb29 --- /dev/null +++ b/examples/post_training_quantization/onnx/yolov8_quantize_with_accuracy_control/deploy.py @@ -0,0 +1,111 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import re +import subprocess +from pathlib import Path +from typing import Dict, Optional, Tuple + +import openvino as ov +import torch +from tqdm import tqdm +from ultralytics.cfg import get_cfg +from ultralytics.engine.validator import BaseValidator as Validator +from ultralytics.models.yolo import YOLO +from ultralytics.utils import DEFAULT_CFG +from ultralytics.utils.metrics import ConfusionMatrix + +from examples.post_training_quantization.onnx.yolov8_quantize_with_accuracy_control.main import prepare_validation +from examples.post_training_quantization.onnx.yolov8_quantize_with_accuracy_control.main import print_statistics + +ROOT = Path(__file__).parent.resolve() +MODEL_NAME = "yolov8n-seg" +FP32_ONNX_MODEL_PATH = ROOT / f"{MODEL_NAME}.onnx" +INT8_ONNX_MODEL_PATH = ROOT / f"{MODEL_NAME}_int8.onnx" +FP32_OV_MODEL_PATH = ROOT / f"{MODEL_NAME}.xml" +INT8_OV_MODEL_PATH = ROOT / f"{MODEL_NAME}_int8.xml" + + +def validate_ov_model( + ov_model: ov.Model, + data_loader: torch.utils.data.DataLoader, + validator: Validator, + num_samples: Optional[int] = None, +) -> Tuple[Dict, int, int]: + validator.seen = 0 + validator.jdict = [] + validator.stats = [] + validator.batch_i = 1 + validator.confusion_matrix = ConfusionMatrix(nc=validator.nc) + compiled_model = ov.compile_model(ov_model) + num_outputs = len(compiled_model.outputs) + for batch_i, batch in enumerate(data_loader): + if num_samples is not None and batch_i == num_samples: + break + batch = validator.preprocess(batch) + results = compiled_model(batch["img"]) + if num_outputs == 1: + preds = torch.from_numpy(results[compiled_model.output(0)]) + else: + preds = [ + torch.from_numpy(results[compiled_model.output(0)]), + torch.from_numpy(results[compiled_model.output(1)]), + ] + preds = validator.postprocess(preds) + validator.update_metrics(preds, batch) + stats = validator.get_stats() + return stats, validator.seen, validator.nt_per_class.sum() + + +def run_benchmark(model_path: str, config) -> float: + command = f"benchmark_app -m {model_path} -d CPU -api async -t 30" + command += f' -shape "[1,3,{config.imgsz},{config.imgsz}]"' + cmd_output = subprocess.check_output(command, shell=True) # nosec + + match = re.search(r"Throughput\: (.+?) FPS", str(cmd_output)) + return float(match.group(1)) + + +args = get_cfg(cfg=DEFAULT_CFG) +args.data = "coco128-seg.yaml" + +print(f"[1/7] Save FP32 OpenVINO model: {FP32_OV_MODEL_PATH}") +fp32_ov_model = ov.convert_model(FP32_ONNX_MODEL_PATH) +ov.save_model(fp32_ov_model, FP32_OV_MODEL_PATH, compress_to_fp16=False) + +print(f"[2/7] Save INT8 OpenVINO model: {INT8_OV_MODEL_PATH}") +int8_ov_model = ov.convert_model(INT8_ONNX_MODEL_PATH) +ov.save_model(int8_ov_model, INT8_OV_MODEL_PATH, compress_to_fp16=False) + +print("[3/7] Benchmark FP32 OpenVINO model:", end=" ") +fp32_fps = run_benchmark(FP32_OV_MODEL_PATH, args) +print(f"{fp32_fps} FPS") + +print("[4/7] Benchmark INT8 OpenVINO model:", end=" ") +int8_fps = run_benchmark(INT8_OV_MODEL_PATH, args) +print(f"{int8_fps} FPS") + +validator, data_loader = prepare_validation(YOLO(ROOT / f"{MODEL_NAME}.pt"), args) + +print("[5/7] Validate OpenVINO FP32 model:") +fp32_stats, total_images, total_objects = validate_ov_model(fp32_ov_model, tqdm(data_loader), validator) +print_statistics(fp32_stats, total_images, total_objects) + +print("[6/7] Validate OpenVINO INT8 model:") +int8_stats, total_images, total_objects = validate_ov_model(int8_ov_model, tqdm(data_loader), validator) +print_statistics(int8_stats, total_images, total_objects) + +print("[7/7] Report:") +box_metric_drop = fp32_stats["metrics/mAP50-95(B)"] - int8_stats["metrics/mAP50-95(B)"] +mask_metric_drop = fp32_stats["metrics/mAP50-95(M)"] - int8_stats["metrics/mAP50-95(M)"] +print(f"\tMetric drop mAP50-95(B): {box_metric_drop:.6f}") +print(f"\tMetric drop mAP50-95(M): {mask_metric_drop:.6f}") +print(f"\tPerformance speed up (throughput mode): {int8_fps / fp32_fps:.3f}") diff --git a/examples/post_training_quantization/onnx/yolov8_quantize_with_accuracy_control/main.py b/examples/post_training_quantization/onnx/yolov8_quantize_with_accuracy_control/main.py new file mode 100644 index 00000000000..16d7bf53a1f --- /dev/null +++ b/examples/post_training_quantization/onnx/yolov8_quantize_with_accuracy_control/main.py @@ -0,0 +1,258 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from functools import partial +from pathlib import Path +from typing import Any, Dict, Tuple + +import numpy as np +import onnx +import onnxruntime +import torch +from tqdm import tqdm +from ultralytics.cfg import get_cfg +from ultralytics.data.converter import coco80_to_coco91_class +from ultralytics.data.utils import check_det_dataset +from ultralytics.engine.validator import BaseValidator as Validator +from ultralytics.models.yolo import YOLO +from ultralytics.utils import DATASETS_DIR +from ultralytics.utils import DEFAULT_CFG +from ultralytics.utils import ops +from ultralytics.utils.metrics import ConfusionMatrix + +import nncf + +ROOT = Path(__file__).parent.resolve() + + +def validate( + model: onnx.ModelProto, data_loader: torch.utils.data.DataLoader, validator: Validator, num_samples: int = None +) -> Tuple[Dict, int, int]: + validator.seen = 0 + validator.jdict = [] + validator.stats = [] + validator.batch_i = 1 + validator.confusion_matrix = ConfusionMatrix(nc=validator.nc) + + input_name = model.graph.input[0].name + serialized_model = model.SerializeToString() + + session = onnxruntime.InferenceSession(serialized_model, providers=["CPUExecutionProvider"]) + output_names = [output.name for output in session.get_outputs()] + num_outputs = len(output_names) + + for batch_i, batch in enumerate(data_loader): + if num_samples is not None and batch_i == num_samples: + break + batch = validator.preprocess(batch) + + input_feed = {input_name: batch["img"].numpy()} + results = session.run(output_names, input_feed=input_feed) + + if num_outputs == 1: + preds = torch.from_numpy(results[0]) + else: + preds = [ + torch.from_numpy(results[0]), + torch.from_numpy(results[1]), + ] + preds = validator.postprocess(preds) + validator.update_metrics(preds, batch) + stats = validator.get_stats() + + return stats, validator.seen, validator.nt_per_class.sum() + + +def print_statistics(stats: np.ndarray, total_images: int, total_objects: int) -> None: + print("Metrics(Box):") + mp, mr, map50, mean_ap = ( + stats["metrics/precision(B)"], + stats["metrics/recall(B)"], + stats["metrics/mAP50(B)"], + stats["metrics/mAP50-95(B)"], + ) + s = ("%20s" + "%12s" * 6) % ("Class", "Images", "Labels", "Precision", "Recall", "mAP@.5", "mAP@.5:.95") + print(s) + pf = "%20s" + "%12i" * 2 + "%12.3g" * 4 # print format + print(pf % ("all", total_images, total_objects, mp, mr, map50, mean_ap)) + + # print the mask metrics for segmentation + if "metrics/precision(M)" in stats: + print("Metrics(Mask):") + s_mp, s_mr, s_map50, s_mean_ap = ( + stats["metrics/precision(M)"], + stats["metrics/recall(M)"], + stats["metrics/mAP50(M)"], + stats["metrics/mAP50-95(M)"], + ) + # Print results + s = ("%20s" + "%12s" * 6) % ("Class", "Images", "Labels", "Precision", "Recall", "mAP@.5", "mAP@.5:.95") + print(s) + pf = "%20s" + "%12i" * 2 + "%12.3g" * 4 # print format + print(pf % ("all", total_images, total_objects, s_mp, s_mr, s_map50, s_mean_ap)) + + +def prepare_validation(model: YOLO, args: Any) -> Tuple[Validator, torch.utils.data.DataLoader]: + validator = model.smart_load("validator")(args) + validator.data = check_det_dataset(args.data) + + data_loader = validator.get_dataloader(f"{DATASETS_DIR}/coco128-seg", 1) + + validator = model.smart_load("validator")(args) + + validator.is_coco = True + validator.class_map = coco80_to_coco91_class() + validator.names = model.model.names + validator.metrics.names = validator.names + validator.nc = model.model.model[-1].nc + validator.nm = 32 + validator.process = ops.process_mask + validator.plot_masks = [] + + return validator, data_loader + + +def prepare_onnx_model(model: YOLO, model_name: str) -> Tuple[onnx.ModelProto, Path]: + model_path = ROOT / f"{model_name}.onnx" + if not model_path.exists(): + model.export(format="onnx", dynamic=True, half=False) + + model = onnx.load(model_path) + return model, model_path + + +def quantize_ac( + model: onnx.ModelProto, data_loader: torch.utils.data.DataLoader, validator_ac: Validator +) -> onnx.ModelProto: + input_name = model.graph.input[0].name + + def transform_fn(data_item: Dict): + input_tensor = validator_ac.preprocess(data_item)["img"].numpy() + return {input_name: input_tensor} + + def validation_ac( + val_model: onnx.ModelProto, + validation_loader: torch.utils.data.DataLoader, + validator: Validator, + num_samples: int = None, + ) -> float: + validator.seen = 0 + validator.jdict = [] + validator.stats = [] + validator.batch_i = 1 + validator.confusion_matrix = ConfusionMatrix(nc=validator.nc) + + rt_session_options = {"providers": ["CPUExecutionProvider"]} + serialized_model = val_model.SerializeToString() + session = onnxruntime.InferenceSession(serialized_model, **rt_session_options) + output_names = [output.name for output in session.get_outputs()] + num_outputs = len(output_names) + + counter = 0 + for batch_i, batch in enumerate(validation_loader): + if num_samples is not None and batch_i == num_samples: + break + batch = validator.preprocess(batch) + input_feed = {input_name: batch["img"].numpy()} + results = session.run(output_names, input_feed=input_feed) + + if num_outputs == 1: + preds = torch.from_numpy(results[0]) + else: + preds = [ + torch.from_numpy(results[0]), + torch.from_numpy(results[1]), + ] + preds = validator.postprocess(preds) + validator.update_metrics(preds, batch) + counter += 1 + stats = validator.get_stats() + if num_outputs == 1: + stats_metrics = stats["metrics/mAP50-95(B)"] + else: + stats_metrics = stats["metrics/mAP50-95(M)"] + print(f"Validate: dataset length = {counter}, metric value = {stats_metrics:.3f}") + return stats_metrics, None + + quantization_dataset = nncf.Dataset(data_loader, transform_fn) + + validation_fn = partial(validation_ac, validator=validator_ac) + + quantized_model_ac = nncf.quantize_with_accuracy_control( + model, + quantization_dataset, + quantization_dataset, + validation_fn=validation_fn, + max_drop=0.003, + preset=nncf.QuantizationPreset.MIXED, + ignored_scope=nncf.IgnoredScope( + types=["Mul", "Sub", "Sigmoid"], # ignore operations + names=[ + "/model.22/dfl/conv/Conv", # in the post-processing subgraph + "/model.22/Add", + "/model.22/Add_1", + "/model.22/Add_2", + "/model.22/Add_3", + "/model.22/Add_4", + "/model.22/Add_5", + "/model.22/Add_6", + "/model.22/Add_7", + "/model.22/Add_8", + "/model.22/Add_9", + "/model.22/Add_10", + "/model.22/Add_11", + ], + ), + ) + return quantized_model_ac + + +def run_example(): + MODEL_NAME = "yolov8n-seg" + + model = YOLO(ROOT / f"{MODEL_NAME}.pt") + args = get_cfg(cfg=DEFAULT_CFG) + args.data = "coco128-seg.yaml" + + validator, data_loader = prepare_validation(model, args) + + fp32_model, fp32_model_path = prepare_onnx_model(model, MODEL_NAME) + print(f"[1/5] Save FP32 model: {fp32_model_path}") + + int8_model = quantize_ac(fp32_model, data_loader, validator) + + int8_model_path = ROOT / f"{MODEL_NAME}_int8.onnx" + onnx.save(int8_model, int8_model_path) + print(f"[2/5] Save INT8 model: {int8_model_path}") + + print("[3/5] Validate ONNX FP32 model:") + fp_stats, total_images, total_objects = validate(fp32_model, tqdm(data_loader), validator) + print_statistics(fp_stats, total_images, total_objects) + + print("[4/5] Validate ONNX INT8 model:") + q_stats, total_images, total_objects = validate(int8_model, tqdm(data_loader), validator) + print_statistics(q_stats, total_images, total_objects) + + print("[5/5] Report:") + metric_drop = fp_stats["metrics/mAP50-95(B)"] - q_stats["metrics/mAP50-95(B)"] + print(f"Metric drop: {metric_drop}") + + # fp32_box_mAP, fp32_mask_mAP, int8_box_mAP, int8_mask_mAP + return ( + fp_stats["metrics/mAP50-95(B)"], + fp_stats["metrics/mAP50-95(M)"], + q_stats["metrics/mAP50-95(B)"], + q_stats["metrics/mAP50-95(M)"], + ) + + +if __name__ == "__main__": + run_example() diff --git a/examples/post_training_quantization/onnx/yolov8_quantize_with_accuracy_control/requirements.txt b/examples/post_training_quantization/onnx/yolov8_quantize_with_accuracy_control/requirements.txt new file mode 100644 index 00000000000..d61d43d3e8b --- /dev/null +++ b/examples/post_training_quantization/onnx/yolov8_quantize_with_accuracy_control/requirements.txt @@ -0,0 +1,3 @@ +ultralytics==8.0.170 +onnx>=1.12.0 +openvino-dev==2024.0 \ No newline at end of file diff --git a/examples/post_training_quantization/openvino/anomaly_stfpm_quantize_with_accuracy_control/README.md b/examples/post_training_quantization/openvino/anomaly_stfpm_quantize_with_accuracy_control/README.md index c425ecbdef0..e37071a366e 100644 --- a/examples/post_training_quantization/openvino/anomaly_stfpm_quantize_with_accuracy_control/README.md +++ b/examples/post_training_quantization/openvino/anomaly_stfpm_quantize_with_accuracy_control/README.md @@ -29,8 +29,8 @@ pip install -r requirements.txt It's pretty simple. The example does not require additional preparation. It will do the preparation itself, such as loading the dataset and model, etc. -The maximum accuracy drop you can pass as a command line argument. F1 score is calculted in range [0,1] for STFPM. Thus if you want to specify the maximum accuracy drop between the quantized and pre-trained model of 0.1% you must specify 0.001 as a command line argument: +The maximum accuracy drop you can pass as a command line argument. F1 score is calculted in range [0,1] for STFPM. Thus if you want to specify the maximum accuracy drop between the quantized and pre-trained model of 0.5% you must specify 0.005 as a command line argument: ```bash -python main.py 0.001 +python main.py 0.005 ``` diff --git a/examples/post_training_quantization/openvino/anomaly_stfpm_quantize_with_accuracy_control/main.py b/examples/post_training_quantization/openvino/anomaly_stfpm_quantize_with_accuracy_control/main.py index d915fa73371..cd37262a559 100644 --- a/examples/post_training_quantization/openvino/anomaly_stfpm_quantize_with_accuracy_control/main.py +++ b/examples/post_training_quantization/openvino/anomaly_stfpm_quantize_with_accuracy_control/main.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -44,7 +44,7 @@ ) DATASET_PATH = HOME_PATH / ".cache/nncf/datasets/mvtec_capsule" -max_accuracy_drop = 0.001 if len(sys.argv) < 2 else float(sys.argv[1]) +max_accuracy_drop = 0.005 if len(sys.argv) < 2 else float(sys.argv[1]) def download_and_extract(root: Path, info: download.DownloadInfo) -> None: diff --git a/examples/post_training_quantization/openvino/anomaly_stfpm_quantize_with_accuracy_control/requirements.txt b/examples/post_training_quantization/openvino/anomaly_stfpm_quantize_with_accuracy_control/requirements.txt index 3cbbd4122ff..d769dcc4d85 100644 --- a/examples/post_training_quantization/openvino/anomaly_stfpm_quantize_with_accuracy_control/requirements.txt +++ b/examples/post_training_quantization/openvino/anomaly_stfpm_quantize_with_accuracy_control/requirements.txt @@ -1,2 +1,2 @@ anomalib==0.6.0 -openvino-dev==2023.3 +openvino-dev==2024.0 diff --git a/examples/post_training_quantization/openvino/mobilenet_v2/main.py b/examples/post_training_quantization/openvino/mobilenet_v2/main.py index d6533bd61d8..f2a4a136754 100644 --- a/examples/post_training_quantization/openvino/mobilenet_v2/main.py +++ b/examples/post_training_quantization/openvino/mobilenet_v2/main.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/post_training_quantization/openvino/mobilenet_v2/requirements.txt b/examples/post_training_quantization/openvino/mobilenet_v2/requirements.txt index 91d49549fe7..1ddc8768407 100644 --- a/examples/post_training_quantization/openvino/mobilenet_v2/requirements.txt +++ b/examples/post_training_quantization/openvino/mobilenet_v2/requirements.txt @@ -2,4 +2,4 @@ torchvision tqdm scikit-learn fastdownload -openvino-dev==2023.3 \ No newline at end of file +openvino-dev==2024.0 \ No newline at end of file diff --git a/examples/post_training_quantization/openvino/yolov8/main.py b/examples/post_training_quantization/openvino/yolov8/main.py index 8280d9391b4..d8513400e4d 100644 --- a/examples/post_training_quantization/openvino/yolov8/main.py +++ b/examples/post_training_quantization/openvino/yolov8/main.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/post_training_quantization/openvino/yolov8/requirements.txt b/examples/post_training_quantization/openvino/yolov8/requirements.txt index 8bbd9338b72..d61d43d3e8b 100644 --- a/examples/post_training_quantization/openvino/yolov8/requirements.txt +++ b/examples/post_training_quantization/openvino/yolov8/requirements.txt @@ -1,3 +1,3 @@ ultralytics==8.0.170 onnx>=1.12.0 -openvino-dev==2023.3 \ No newline at end of file +openvino-dev==2024.0 \ No newline at end of file diff --git a/examples/post_training_quantization/openvino/yolov8_quantize_with_accuracy_control/main.py b/examples/post_training_quantization/openvino/yolov8_quantize_with_accuracy_control/main.py index f56bbed26bf..79722754ced 100644 --- a/examples/post_training_quantization/openvino/yolov8_quantize_with_accuracy_control/main.py +++ b/examples/post_training_quantization/openvino/yolov8_quantize_with_accuracy_control/main.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -182,7 +182,7 @@ def validation_ac( quantization_dataset, quantization_dataset, validation_fn=validation_fn, - max_drop=0.005, + max_drop=0.003, preset=nncf.QuantizationPreset.MIXED, ignored_scope=nncf.IgnoredScope( types=["Multiply", "Subtract", "Sigmoid"], # ignore operations @@ -199,6 +199,7 @@ def validation_ac( "/model.22/Add_8", "/model.22/Add_9", "/model.22/Add_10", + "/model.22/Add_11", ], ), ) diff --git a/examples/post_training_quantization/openvino/yolov8_quantize_with_accuracy_control/requirements.txt b/examples/post_training_quantization/openvino/yolov8_quantize_with_accuracy_control/requirements.txt index 8bbd9338b72..d61d43d3e8b 100644 --- a/examples/post_training_quantization/openvino/yolov8_quantize_with_accuracy_control/requirements.txt +++ b/examples/post_training_quantization/openvino/yolov8_quantize_with_accuracy_control/requirements.txt @@ -1,3 +1,3 @@ ultralytics==8.0.170 onnx>=1.12.0 -openvino-dev==2023.3 \ No newline at end of file +openvino-dev==2024.0 \ No newline at end of file diff --git a/examples/post_training_quantization/tensorflow/mobilenet_v2/main.py b/examples/post_training_quantization/tensorflow/mobilenet_v2/main.py index 1dce1f98f35..44ef09d4b60 100644 --- a/examples/post_training_quantization/tensorflow/mobilenet_v2/main.py +++ b/examples/post_training_quantization/tensorflow/mobilenet_v2/main.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/post_training_quantization/tensorflow/mobilenet_v2/requirements.txt b/examples/post_training_quantization/tensorflow/mobilenet_v2/requirements.txt index ba7f0ba3922..a9347d4c80c 100644 --- a/examples/post_training_quantization/tensorflow/mobilenet_v2/requirements.txt +++ b/examples/post_training_quantization/tensorflow/mobilenet_v2/requirements.txt @@ -1,4 +1,4 @@ tensorflow~=2.12.0 tensorflow-datasets tqdm -openvino-dev==2023.3 +openvino-dev==2024.0 diff --git a/examples/post_training_quantization/torch/mobilenet_v2/main.py b/examples/post_training_quantization/torch/mobilenet_v2/main.py index e2169022e3b..b053ad43499 100644 --- a/examples/post_training_quantization/torch/mobilenet_v2/main.py +++ b/examples/post_training_quantization/torch/mobilenet_v2/main.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/post_training_quantization/torch/mobilenet_v2/requirements.txt b/examples/post_training_quantization/torch/mobilenet_v2/requirements.txt index fdbf288a718..60a0bd77630 100644 --- a/examples/post_training_quantization/torch/mobilenet_v2/requirements.txt +++ b/examples/post_training_quantization/torch/mobilenet_v2/requirements.txt @@ -1,5 +1,5 @@ fastdownload==0.0.7 -openvino-dev==2023.3 +openvino-dev==2024.0 scikit-learn torch==2.1.0 torchvision==0.16.0 diff --git a/examples/post_training_quantization/torch/ssd300_vgg16/main.py b/examples/post_training_quantization/torch/ssd300_vgg16/main.py index 35324447301..fc359e750b3 100644 --- a/examples/post_training_quantization/torch/ssd300_vgg16/main.py +++ b/examples/post_training_quantization/torch/ssd300_vgg16/main.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -119,7 +119,7 @@ def validate(model: torch.nn.Module, dataset: COCO128Dataset, device: torch.devi with torch.no_grad(): for img, target in track(dataset, description="Validating"): prediction = model(img.to(device)[None])[0] - for k in prediction.keys(): + for k in prediction: prediction[k] = prediction[k].to(torch.device("cpu")) metric.update([prediction], [target]) computed_metrics = metric.compute() diff --git a/examples/post_training_quantization/torch/ssd300_vgg16/requirements.txt b/examples/post_training_quantization/torch/ssd300_vgg16/requirements.txt index e530c41a4b0..915d5238e29 100644 --- a/examples/post_training_quantization/torch/ssd300_vgg16/requirements.txt +++ b/examples/post_training_quantization/torch/ssd300_vgg16/requirements.txt @@ -1,6 +1,6 @@ fastdownload==0.0.7 onnx==1.13.1 -openvino-dev==2023.3 +openvino-dev==2024.0 pycocotools==2.0.7 torch==2.0.1 # ssd300_vgg16 can not be exported with 2.1.0, reference: https://github.com/pytorch/pytorch/issues/113155 torchmetrics==1.0.1 diff --git a/examples/tensorflow/__init__.py b/examples/tensorflow/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/examples/tensorflow/__init__.py +++ b/examples/tensorflow/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/classification/datasets/builder.py b/examples/tensorflow/classification/datasets/builder.py index 136fd374c5c..6173b1ab964 100644 --- a/examples/tensorflow/classification/datasets/builder.py +++ b/examples/tensorflow/classification/datasets/builder.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -11,6 +11,7 @@ import tensorflow as tf +import nncf from examples.tensorflow.classification.datasets import tfrecords as records_dataset from examples.tensorflow.classification.datasets.preprocessing_selector import get_label_preprocessing_fn from examples.tensorflow.classification.datasets.preprocessing_selector import get_preprocessing @@ -64,7 +65,7 @@ def dtype(self): } dtype = dtype_map.get(self._dtype, None) if dtype is None: - raise ValueError("Invalid DType provided. Supported types: {}".format(dtype_map.keys())) + raise nncf.ValidationError("Invalid DType provided. Supported types: {}".format(dtype_map.keys())) return dtype diff --git a/examples/tensorflow/classification/datasets/preprocessing/cifar.py b/examples/tensorflow/classification/datasets/preprocessing/cifar.py index 94a1d7fd817..f7e58422b50 100644 --- a/examples/tensorflow/classification/datasets/preprocessing/cifar.py +++ b/examples/tensorflow/classification/datasets/preprocessing/cifar.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/classification/datasets/preprocessing/imagenet.py b/examples/tensorflow/classification/datasets/preprocessing/imagenet.py index fb342b0f508..93efc55a38d 100644 --- a/examples/tensorflow/classification/datasets/preprocessing/imagenet.py +++ b/examples/tensorflow/classification/datasets/preprocessing/imagenet.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/classification/datasets/preprocessing/utils.py b/examples/tensorflow/classification/datasets/preprocessing/utils.py index 9b0221b3f51..195d74a1704 100644 --- a/examples/tensorflow/classification/datasets/preprocessing/utils.py +++ b/examples/tensorflow/classification/datasets/preprocessing/utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -13,6 +13,8 @@ import tensorflow as tf +import nncf + def resize_image(image: tf.Tensor, height: int, width: int) -> tf.Tensor: """ @@ -39,10 +41,10 @@ def mean_image_subtraction( :return: the centered image. """ if image.get_shape().ndims != 3: - raise ValueError("Input must be of size [height, width, C>0]") + raise nncf.ValidationError("Input must be of size [height, width, C>0]") if len(means) != num_channels: - raise ValueError("len(means) must match the number of channels") + raise nncf.ValidationError("len(means) must match the number of channels") means = tf.broadcast_to(means, tf.shape(image)) if dtype is not None: @@ -64,10 +66,10 @@ def standardize_image( :return: the centered image. """ if image.get_shape().ndims != 3: - raise ValueError("Input must be of size [height, width, C>0]") + raise nncf.ValidationError("Input must be of size [height, width, C>0]") if len(stddev) != num_channels: - raise ValueError("len(stddev) must match the number of channels") + raise nncf.ValidationError("len(stddev) must match the number of channels") stddev = tf.broadcast_to(stddev, tf.shape(image)) if dtype is not None: diff --git a/examples/tensorflow/classification/datasets/preprocessing_selector.py b/examples/tensorflow/classification/datasets/preprocessing_selector.py index 1ed7530682a..662374ff4f0 100644 --- a/examples/tensorflow/classification/datasets/preprocessing_selector.py +++ b/examples/tensorflow/classification/datasets/preprocessing_selector.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -9,6 +9,7 @@ # See the License for the specific language governing permissions and # limitations under the License. +import nncf from examples.tensorflow.classification.datasets.preprocessing.cifar import cifar10_preprocess_image from examples.tensorflow.classification.datasets.preprocessing.cifar import cifar100_preprocess_image from examples.tensorflow.classification.datasets.preprocessing.imagenet import imagenet_1000_to_1001_classes @@ -29,7 +30,7 @@ def get_preprocessing(dataset_name, model_name, preset=None): if not preset: preset = dataset_name if preset not in PREPROCESSING_FN_MAP: - raise ValueError( + raise nncf.ValidationError( "Preprocessing for dataset {} and model {} was not recognized".format(dataset_name, model_name) ) diff --git a/examples/tensorflow/classification/datasets/tfrecords/__init__.py b/examples/tensorflow/classification/datasets/tfrecords/__init__.py index 3457f2e45d5..bbd9f0f8c70 100644 --- a/examples/tensorflow/classification/datasets/tfrecords/__init__.py +++ b/examples/tensorflow/classification/datasets/tfrecords/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/classification/datasets/tfrecords/cifar10.py b/examples/tensorflow/classification/datasets/tfrecords/cifar10.py index 8132bca3ac2..a26fdf6fa80 100644 --- a/examples/tensorflow/classification/datasets/tfrecords/cifar10.py +++ b/examples/tensorflow/classification/datasets/tfrecords/cifar10.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/classification/datasets/tfrecords/cifar100.py b/examples/tensorflow/classification/datasets/tfrecords/cifar100.py index 23ae433515d..8715338cfbf 100644 --- a/examples/tensorflow/classification/datasets/tfrecords/cifar100.py +++ b/examples/tensorflow/classification/datasets/tfrecords/cifar100.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/classification/datasets/tfrecords/imagenet2012.py b/examples/tensorflow/classification/datasets/tfrecords/imagenet2012.py index b0eea9483d4..52f8d6457a3 100644 --- a/examples/tensorflow/classification/datasets/tfrecords/imagenet2012.py +++ b/examples/tensorflow/classification/datasets/tfrecords/imagenet2012.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/classification/main.py b/examples/tensorflow/classification/main.py index b6e63e81fee..1f977068b56 100644 --- a/examples/tensorflow/classification/main.py +++ b/examples/tensorflow/classification/main.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -16,6 +16,7 @@ import tensorflow as tf import tensorflow_addons as tfa +import nncf from examples.common.paths import configure_paths from examples.common.sample_config import create_sample_config from examples.tensorflow.classification.datasets.builder import DatasetBuilder @@ -112,7 +113,7 @@ def load_checkpoint(checkpoint, ckpt_path): if not path_to_checkpoint: logger.info("No checkpoint detected.") if ckpt_path: - raise RuntimeError(f"ckpt_path was given, but no checkpoint detected in path: {ckpt_path}") + raise nncf.ValidationError(f"ckpt_path was given, but no checkpoint detected in path: {ckpt_path}") logger.info("Checkpoint file {} found and restoring from checkpoint".format(path_to_checkpoint)) diff --git a/examples/tensorflow/common/argparser.py b/examples/tensorflow/common/argparser.py index 0f8d3c14325..8ec03edd5be 100644 --- a/examples/tensorflow/common/argparser.py +++ b/examples/tensorflow/common/argparser.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/common/callbacks.py b/examples/tensorflow/common/callbacks.py index 071d97b7c64..265ce3ac05b 100644 --- a/examples/tensorflow/common/callbacks.py +++ b/examples/tensorflow/common/callbacks.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/common/dataset_builder.py b/examples/tensorflow/common/dataset_builder.py index 64d81a6f9c2..e81c4587bc8 100644 --- a/examples/tensorflow/common/dataset_builder.py +++ b/examples/tensorflow/common/dataset_builder.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -14,6 +14,7 @@ import tensorflow_datasets as tfds +import nncf from examples.tensorflow.common.logger import logger from examples.tensorflow.common.utils import set_hard_limit_num_open_files @@ -87,7 +88,7 @@ def build(self): builder = dataset_builders.get(self._dataset_type, None) if builder is None: - raise ValueError("Unknown dataset type {}".format(self._dataset_type)) + raise nncf.UnknownDatasetError("Unknown dataset type {}".format(self._dataset_type)) dataset = builder() dataset = self._pipeline(dataset) @@ -124,7 +125,7 @@ def _load_tfrecords(self): if dataset_key in self._tfrecord_datasets: self._dataset_loader = self._tfrecord_datasets[dataset_key](config=self._config, is_train=self._is_train) else: - raise ValueError("Unknown dataset name: {}".format(self._dataset_name)) + raise nncf.UnknownDatasetError("Unknown dataset name: {}".format(self._dataset_name)) dataset = self._dataset_loader.as_dataset() diff --git a/examples/tensorflow/common/distributed.py b/examples/tensorflow/common/distributed.py index dd61ab8d2d2..b1270e189cd 100644 --- a/examples/tensorflow/common/distributed.py +++ b/examples/tensorflow/common/distributed.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -13,6 +13,7 @@ import tensorflow as tf +import nncf from examples.tensorflow.common.utils import set_memory_growth @@ -27,7 +28,7 @@ def get_distribution_strategy(config): if "CUDA_VISIBLE_DEVICES" not in os.environ or _gpu_id in os.environ["CUDA_VISIBLE_DEVICES"].split(","): os.environ["CUDA_VISIBLE_DEVICES"] = _gpu_id else: - raise RuntimeError( + raise nncf.ValidationError( "GPU with id = {id} was not found in the specified " "CUDA_VISIBLE_DEVICES environment variable. " "Please do not export the CUDA_VISIBLE_DEVICES environment variable " diff --git a/examples/tensorflow/common/experimental_patcher.py b/examples/tensorflow/common/experimental_patcher.py index 1e9a45563e9..144e45ccdcb 100644 --- a/examples/tensorflow/common/experimental_patcher.py +++ b/examples/tensorflow/common/experimental_patcher.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/common/export.py b/examples/tensorflow/common/export.py index 3f615c0b7e2..29238dd4f54 100644 --- a/examples/tensorflow/common/export.py +++ b/examples/tensorflow/common/export.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/common/logger.py b/examples/tensorflow/common/logger.py index d48dc3c02fd..d1ab86f5e98 100644 --- a/examples/tensorflow/common/logger.py +++ b/examples/tensorflow/common/logger.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/common/model_loader.py b/examples/tensorflow/common/model_loader.py index 373d82fa876..3e5b7e75751 100644 --- a/examples/tensorflow/common/model_loader.py +++ b/examples/tensorflow/common/model_loader.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/common/models.py b/examples/tensorflow/common/models.py index 6f1c7e26d3a..6cd3b8df941 100644 --- a/examples/tensorflow/common/models.py +++ b/examples/tensorflow/common/models.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -12,6 +12,7 @@ import tensorflow as tf import tensorflow_hub as hub +import nncf from nncf.tensorflow.tf_internals import Rescaling from nncf.tensorflow.tf_internals import imagenet_utils @@ -42,7 +43,7 @@ def MobileNetV3(stack_fn, last_point_ch, input_shape=None, model_type="large", * rows = input_shape[row_axis] cols = input_shape[col_axis] if rows and cols and (rows < 32 or cols < 32): - raise ValueError("Input size must be at least 32x32; got `input_shape=" + str(input_shape) + "`") + raise nncf.ValidationError("Input size must be at least 32x32; got `input_shape=" + str(input_shape) + "`") img_input = tf.keras.layers.Input(shape=input_shape) channel_axis = 1 if tf.keras.backend.image_data_format() == "channels_first" else -1 diff --git a/examples/tensorflow/common/object_detection/architecture/darknet.py b/examples/tensorflow/common/object_detection/architecture/darknet.py index 792d7fd0b63..94678c3bda6 100644 --- a/examples/tensorflow/common/object_detection/architecture/darknet.py +++ b/examples/tensorflow/common/object_detection/architecture/darknet.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/common/object_detection/architecture/factory.py b/examples/tensorflow/common/object_detection/architecture/factory.py index a5a9553a1db..e9cdbd4e513 100644 --- a/examples/tensorflow/common/object_detection/architecture/factory.py +++ b/examples/tensorflow/common/object_detection/architecture/factory.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/common/object_detection/architecture/fpn.py b/examples/tensorflow/common/object_detection/architecture/fpn.py index 3449b789c0b..f1e4db2eaa6 100644 --- a/examples/tensorflow/common/object_detection/architecture/fpn.py +++ b/examples/tensorflow/common/object_detection/architecture/fpn.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/common/object_detection/architecture/heads.py b/examples/tensorflow/common/object_detection/architecture/heads.py index 71fd9a89bbe..2f22994e963 100644 --- a/examples/tensorflow/common/object_detection/architecture/heads.py +++ b/examples/tensorflow/common/object_detection/architecture/heads.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/common/object_detection/architecture/nn_ops.py b/examples/tensorflow/common/object_detection/architecture/nn_ops.py index 9d90f5fe8f5..cc4d82163be 100644 --- a/examples/tensorflow/common/object_detection/architecture/nn_ops.py +++ b/examples/tensorflow/common/object_detection/architecture/nn_ops.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/common/object_detection/architecture/resnet.py b/examples/tensorflow/common/object_detection/architecture/resnet.py index e30a7c71948..d3aae386cd0 100644 --- a/examples/tensorflow/common/object_detection/architecture/resnet.py +++ b/examples/tensorflow/common/object_detection/architecture/resnet.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/common/object_detection/base_model.py b/examples/tensorflow/common/object_detection/base_model.py index 37023eeab80..0d0826cf670 100644 --- a/examples/tensorflow/common/object_detection/base_model.py +++ b/examples/tensorflow/common/object_detection/base_model.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/common/object_detection/checkpoint_utils.py b/examples/tensorflow/common/object_detection/checkpoint_utils.py index 3489b395aaa..e4183b82455 100644 --- a/examples/tensorflow/common/object_detection/checkpoint_utils.py +++ b/examples/tensorflow/common/object_detection/checkpoint_utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/common/object_detection/datasets/builder.py b/examples/tensorflow/common/object_detection/datasets/builder.py index c958cfea9e5..d6b7fa8da85 100644 --- a/examples/tensorflow/common/object_detection/datasets/builder.py +++ b/examples/tensorflow/common/object_detection/datasets/builder.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/common/object_detection/datasets/preprocessing_selector.py b/examples/tensorflow/common/object_detection/datasets/preprocessing_selector.py index a3d1e2f7dbe..43eb290997f 100644 --- a/examples/tensorflow/common/object_detection/datasets/preprocessing_selector.py +++ b/examples/tensorflow/common/object_detection/datasets/preprocessing_selector.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/common/object_detection/datasets/tfrecords/__init__.py b/examples/tensorflow/common/object_detection/datasets/tfrecords/__init__.py index f867dc3be79..d86ba32d10c 100644 --- a/examples/tensorflow/common/object_detection/datasets/tfrecords/__init__.py +++ b/examples/tensorflow/common/object_detection/datasets/tfrecords/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/common/object_detection/datasets/tfrecords/coco2017.py b/examples/tensorflow/common/object_detection/datasets/tfrecords/coco2017.py index 2ebb3cfea95..2a11b2ff0c9 100644 --- a/examples/tensorflow/common/object_detection/datasets/tfrecords/coco2017.py +++ b/examples/tensorflow/common/object_detection/datasets/tfrecords/coco2017.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/common/object_detection/evaluation/coco_evaluator.py b/examples/tensorflow/common/object_detection/evaluation/coco_evaluator.py index ed8c0e219b3..605f1f090c9 100644 --- a/examples/tensorflow/common/object_detection/evaluation/coco_evaluator.py +++ b/examples/tensorflow/common/object_detection/evaluation/coco_evaluator.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/common/object_detection/evaluation/coco_utils.py b/examples/tensorflow/common/object_detection/evaluation/coco_utils.py index 89f55529fda..f87561fb450 100644 --- a/examples/tensorflow/common/object_detection/evaluation/coco_utils.py +++ b/examples/tensorflow/common/object_detection/evaluation/coco_utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/common/object_detection/losses.py b/examples/tensorflow/common/object_detection/losses.py index ede14814f25..5b9aaed7aca 100644 --- a/examples/tensorflow/common/object_detection/losses.py +++ b/examples/tensorflow/common/object_detection/losses.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -379,7 +379,7 @@ class groundturth targets. num_positives_sum = tf.reduce_sum(input_tensor=num_positives) + 1.0 cls_losses = [] - for level in cls_outputs.keys(): + for level in cls_outputs: cls_losses.append(self.class_loss(cls_outputs[level], labels[int(level)], num_positives_sum)) # Sums per level losses to total loss. @@ -440,7 +440,7 @@ def __call__(self, box_outputs, labels, num_positives): num_positives_sum = tf.reduce_sum(input_tensor=num_positives) + 1.0 box_losses = [] - for level in box_outputs.keys(): + for level in box_outputs: box_targets_l = labels[int(level)] box_losses.append(self.box_loss(box_outputs[level], box_targets_l, num_positives_sum)) diff --git a/examples/tensorflow/common/object_detection/ops/nms.py b/examples/tensorflow/common/object_detection/ops/nms.py index d50816ea57c..08c59d80588 100644 --- a/examples/tensorflow/common/object_detection/ops/nms.py +++ b/examples/tensorflow/common/object_detection/ops/nms.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/common/object_detection/ops/postprocess_ops.py b/examples/tensorflow/common/object_detection/ops/postprocess_ops.py index 446a4e89acb..d8790ab93a7 100644 --- a/examples/tensorflow/common/object_detection/ops/postprocess_ops.py +++ b/examples/tensorflow/common/object_detection/ops/postprocess_ops.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/common/object_detection/ops/roi_ops.py b/examples/tensorflow/common/object_detection/ops/roi_ops.py index 8451bfcc561..b08f3986881 100644 --- a/examples/tensorflow/common/object_detection/ops/roi_ops.py +++ b/examples/tensorflow/common/object_detection/ops/roi_ops.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/common/object_detection/ops/spatial_transform_ops.py b/examples/tensorflow/common/object_detection/ops/spatial_transform_ops.py index 1d404558bec..3a53bd541ce 100644 --- a/examples/tensorflow/common/object_detection/ops/spatial_transform_ops.py +++ b/examples/tensorflow/common/object_detection/ops/spatial_transform_ops.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/common/object_detection/ops/target_ops.py b/examples/tensorflow/common/object_detection/ops/target_ops.py index ab7c8c8a180..584b7a2cd21 100644 --- a/examples/tensorflow/common/object_detection/ops/target_ops.py +++ b/examples/tensorflow/common/object_detection/ops/target_ops.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/common/object_detection/utils/anchor.py b/examples/tensorflow/common/object_detection/utils/anchor.py index 98acd24cd0a..afe3e7e3164 100644 --- a/examples/tensorflow/common/object_detection/utils/anchor.py +++ b/examples/tensorflow/common/object_detection/utils/anchor.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/common/object_detection/utils/argmax_matcher.py b/examples/tensorflow/common/object_detection/utils/argmax_matcher.py index 6ddba6df496..9c7674dfd26 100644 --- a/examples/tensorflow/common/object_detection/utils/argmax_matcher.py +++ b/examples/tensorflow/common/object_detection/utils/argmax_matcher.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -75,13 +75,12 @@ def __init__( raise ValueError("unmatched_threshold needs to be smaller or equal to matched_threshold") self._unmatched_threshold = unmatched_threshold - if not negatives_lower_than_unmatched: - if self._unmatched_threshold == self._matched_threshold: - raise ValueError( - "When negatives are in between matched and " - "unmatched thresholds, these cannot be of equal " - "value. matched: {}, unmatched: {}".format(self._matched_threshold, self._unmatched_threshold) - ) + if not negatives_lower_than_unmatched and self._unmatched_threshold == self._matched_threshold: + raise ValueError( + "When negatives are in between matched and " + "unmatched thresholds, these cannot be of equal " + "value. matched: {}, unmatched: {}".format(self._matched_threshold, self._unmatched_threshold) + ) self._force_match_for_each_row = force_match_for_each_row self._negatives_lower_than_unmatched = negatives_lower_than_unmatched diff --git a/examples/tensorflow/common/object_detection/utils/balanced_positive_negative_sampler.py b/examples/tensorflow/common/object_detection/utils/balanced_positive_negative_sampler.py index 1645ba9f685..bf948aa20aa 100644 --- a/examples/tensorflow/common/object_detection/utils/balanced_positive_negative_sampler.py +++ b/examples/tensorflow/common/object_detection/utils/balanced_positive_negative_sampler.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/common/object_detection/utils/box_coder.py b/examples/tensorflow/common/object_detection/utils/box_coder.py index 36548ff3e22..341ae3baa8d 100644 --- a/examples/tensorflow/common/object_detection/utils/box_coder.py +++ b/examples/tensorflow/common/object_detection/utils/box_coder.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/common/object_detection/utils/box_list.py b/examples/tensorflow/common/object_detection/utils/box_list.py index 7900548a169..c25366340e4 100644 --- a/examples/tensorflow/common/object_detection/utils/box_list.py +++ b/examples/tensorflow/common/object_detection/utils/box_list.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/common/object_detection/utils/box_utils.py b/examples/tensorflow/common/object_detection/utils/box_utils.py index e07efd80a1b..0d6658a80c1 100644 --- a/examples/tensorflow/common/object_detection/utils/box_utils.py +++ b/examples/tensorflow/common/object_detection/utils/box_utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/common/object_detection/utils/dataloader_utils.py b/examples/tensorflow/common/object_detection/utils/dataloader_utils.py index 80529bca94b..b1b308d89d6 100644 --- a/examples/tensorflow/common/object_detection/utils/dataloader_utils.py +++ b/examples/tensorflow/common/object_detection/utils/dataloader_utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/common/object_detection/utils/faster_rcnn_box_coder.py b/examples/tensorflow/common/object_detection/utils/faster_rcnn_box_coder.py index 78bb3602a7c..960c1949cc0 100644 --- a/examples/tensorflow/common/object_detection/utils/faster_rcnn_box_coder.py +++ b/examples/tensorflow/common/object_detection/utils/faster_rcnn_box_coder.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/common/object_detection/utils/input_utils.py b/examples/tensorflow/common/object_detection/utils/input_utils.py index 79077497367..b337da69ac7 100644 --- a/examples/tensorflow/common/object_detection/utils/input_utils.py +++ b/examples/tensorflow/common/object_detection/utils/input_utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/common/object_detection/utils/mask_utils.py b/examples/tensorflow/common/object_detection/utils/mask_utils.py index 569f2156074..6faaa539058 100644 --- a/examples/tensorflow/common/object_detection/utils/mask_utils.py +++ b/examples/tensorflow/common/object_detection/utils/mask_utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/common/object_detection/utils/matcher.py b/examples/tensorflow/common/object_detection/utils/matcher.py index 229045aad8b..849372349ba 100644 --- a/examples/tensorflow/common/object_detection/utils/matcher.py +++ b/examples/tensorflow/common/object_detection/utils/matcher.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/common/object_detection/utils/minibatch_sampler.py b/examples/tensorflow/common/object_detection/utils/minibatch_sampler.py index f314a7949c3..8847763d599 100644 --- a/examples/tensorflow/common/object_detection/utils/minibatch_sampler.py +++ b/examples/tensorflow/common/object_detection/utils/minibatch_sampler.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/common/object_detection/utils/ops.py b/examples/tensorflow/common/object_detection/utils/ops.py index b03f921be60..2e65d166e38 100644 --- a/examples/tensorflow/common/object_detection/utils/ops.py +++ b/examples/tensorflow/common/object_detection/utils/ops.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/common/object_detection/utils/region_similarity_calculator.py b/examples/tensorflow/common/object_detection/utils/region_similarity_calculator.py index 467e4c756ea..60d2c3d5dad 100644 --- a/examples/tensorflow/common/object_detection/utils/region_similarity_calculator.py +++ b/examples/tensorflow/common/object_detection/utils/region_similarity_calculator.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/common/object_detection/utils/shape_utils.py b/examples/tensorflow/common/object_detection/utils/shape_utils.py index c65c6ba1fcc..5f25c7f08f8 100644 --- a/examples/tensorflow/common/object_detection/utils/shape_utils.py +++ b/examples/tensorflow/common/object_detection/utils/shape_utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/common/object_detection/utils/target_assigner.py b/examples/tensorflow/common/object_detection/utils/target_assigner.py index fa85a92f83d..aa014afbd7e 100644 --- a/examples/tensorflow/common/object_detection/utils/target_assigner.py +++ b/examples/tensorflow/common/object_detection/utils/target_assigner.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/common/object_detection/utils/yolo_v4_utils.py b/examples/tensorflow/common/object_detection/utils/yolo_v4_utils.py index 52c62db617a..0ce629f3ded 100644 --- a/examples/tensorflow/common/object_detection/utils/yolo_v4_utils.py +++ b/examples/tensorflow/common/object_detection/utils/yolo_v4_utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/common/optimizer.py b/examples/tensorflow/common/optimizer.py index a3267c4f96c..78fead279ec 100644 --- a/examples/tensorflow/common/optimizer.py +++ b/examples/tensorflow/common/optimizer.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/common/prepare_checkpoint.py b/examples/tensorflow/common/prepare_checkpoint.py index fb272feb929..03e1710369b 100644 --- a/examples/tensorflow/common/prepare_checkpoint.py +++ b/examples/tensorflow/common/prepare_checkpoint.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -13,6 +13,7 @@ import tensorflow as tf +import nncf from examples.common.sample_config import create_sample_config from examples.tensorflow.common.argparser import get_common_argument_parser from examples.tensorflow.common.logger import logger @@ -40,7 +41,7 @@ def get_config_and_model_type_from_argv(argv, parser): elif args.model_type == ModelType.segmentation: predefined_config = get_predefined_seg_config(config_from_json.model) else: - raise RuntimeError("Wrong model type specified") + raise nncf.ValidationError("Wrong model type specified") predefined_config.update(config_from_json) return predefined_config, args.model_type diff --git a/examples/tensorflow/common/scheduler.py b/examples/tensorflow/common/scheduler.py index 246e8a024d7..8521f5ae13c 100644 --- a/examples/tensorflow/common/scheduler.py +++ b/examples/tensorflow/common/scheduler.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -74,9 +74,8 @@ def get_config(self): def schedule_base_lr_check(schedule_type, base_lr): schedules_with_base_lr = ["exponential", "multistep", "step", "cosine"] - if schedule_type in schedules_with_base_lr: - if base_lr is None: - raise ValueError("`base_lr` parameter must be specified for the %s scheduler" % schedule_type) + if schedule_type in schedules_with_base_lr and base_lr is None: + raise ValueError("`base_lr` parameter must be specified for the %s scheduler" % schedule_type) def build_scheduler(config, steps_per_epoch): diff --git a/examples/tensorflow/common/tfrecords_dataset.py b/examples/tensorflow/common/tfrecords_dataset.py index 6064129d02a..35c9415b923 100644 --- a/examples/tensorflow/common/tfrecords_dataset.py +++ b/examples/tensorflow/common/tfrecords_dataset.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/common/utils.py b/examples/tensorflow/common/utils.py index d1a8cccf379..2108cfe4582 100644 --- a/examples/tensorflow/common/utils.py +++ b/examples/tensorflow/common/utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/object_detection/main.py b/examples/tensorflow/object_detection/main.py index 93b73abe829..829e8858a7f 100644 --- a/examples/tensorflow/object_detection/main.py +++ b/examples/tensorflow/object_detection/main.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -16,6 +16,7 @@ import numpy as np import tensorflow as tf +import nncf from examples.common.paths import configure_paths from examples.common.sample_config import create_sample_config from examples.tensorflow.common.argparser import get_common_argument_parser @@ -90,7 +91,7 @@ def load_checkpoint(checkpoint, ckpt_path): if not path_to_checkpoint: logger.info("No checkpoint detected.") if ckpt_path: - raise RuntimeError(f"ckpt_path was given, but no checkpoint detected in path: {ckpt_path}") + raise nncf.ValidationError(f"ckpt_path was given, but no checkpoint detected in path: {ckpt_path}") logger.info("Checkpoint file {} found and restoring from checkpoint".format(path_to_checkpoint)) status = checkpoint.restore(path_to_checkpoint) diff --git a/examples/tensorflow/object_detection/models/model_selector.py b/examples/tensorflow/object_detection/models/model_selector.py index 861b8262260..af38ba2e07c 100644 --- a/examples/tensorflow/object_detection/models/model_selector.py +++ b/examples/tensorflow/object_detection/models/model_selector.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/object_detection/models/retinanet_config.py b/examples/tensorflow/object_detection/models/retinanet_config.py index b05d379cb8b..aa837b865cf 100644 --- a/examples/tensorflow/object_detection/models/retinanet_config.py +++ b/examples/tensorflow/object_detection/models/retinanet_config.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/object_detection/models/retinanet_model.py b/examples/tensorflow/object_detection/models/retinanet_model.py index b089eb76898..9eefe935469 100644 --- a/examples/tensorflow/object_detection/models/retinanet_model.py +++ b/examples/tensorflow/object_detection/models/retinanet_model.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/object_detection/models/yolo_v4_config.py b/examples/tensorflow/object_detection/models/yolo_v4_config.py index 7719747296e..5ad1a6d8426 100644 --- a/examples/tensorflow/object_detection/models/yolo_v4_config.py +++ b/examples/tensorflow/object_detection/models/yolo_v4_config.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/object_detection/models/yolo_v4_model.py b/examples/tensorflow/object_detection/models/yolo_v4_model.py index b6b8a090b5b..b0dfc370b3b 100644 --- a/examples/tensorflow/object_detection/models/yolo_v4_model.py +++ b/examples/tensorflow/object_detection/models/yolo_v4_model.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/object_detection/postprocessing/yolo_v4_postprocessing.py b/examples/tensorflow/object_detection/postprocessing/yolo_v4_postprocessing.py index 282a75ae498..12a9f616c91 100644 --- a/examples/tensorflow/object_detection/postprocessing/yolo_v4_postprocessing.py +++ b/examples/tensorflow/object_detection/postprocessing/yolo_v4_postprocessing.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/object_detection/preprocessing/retinanet_preprocessing.py b/examples/tensorflow/object_detection/preprocessing/retinanet_preprocessing.py index 61f7d4b7c30..c047176ea43 100644 --- a/examples/tensorflow/object_detection/preprocessing/retinanet_preprocessing.py +++ b/examples/tensorflow/object_detection/preprocessing/retinanet_preprocessing.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/object_detection/preprocessing/yolo_v4_preprocessing.py b/examples/tensorflow/object_detection/preprocessing/yolo_v4_preprocessing.py index 20f3563681a..a1948097910 100644 --- a/examples/tensorflow/object_detection/preprocessing/yolo_v4_preprocessing.py +++ b/examples/tensorflow/object_detection/preprocessing/yolo_v4_preprocessing.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -325,89 +325,14 @@ def _convert_labels_to_91_classes(features): # 0..79 --> 0..90 match = tf.constant( [ - 1, - 2, - 3, - 4, - 5, - 6, - 7, - 8, - 9, - 10, - 11, - 13, - 14, - 15, - 16, - 17, - 18, - 19, - 20, - 21, - 22, - 23, - 24, - 25, - 27, - 28, - 31, - 32, - 33, - 34, - 35, - 36, - 37, - 38, - 39, - 40, - 41, - 42, - 43, - 44, - 46, - 47, - 48, - 49, - 50, - 51, - 52, - 53, - 54, - 55, - 56, - 57, - 58, - 59, - 60, - 61, - 62, - 63, - 64, - 65, - 67, - 70, - 72, - 73, - 74, - 75, - 76, - 77, - 78, - 79, - 80, - 81, - 82, - 84, - 85, - 86, - 87, - 88, - 89, - 90, + 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, + 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, + 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, + 59, 60, 61, 62, 63, 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, + 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, ], dtype=tf.int64, - ) + ) # fmt: skip labels = features["objects"]["label"] labels = tf.gather(match, labels, axis=None) diff --git a/examples/tensorflow/segmentation/evaluation.py b/examples/tensorflow/segmentation/evaluation.py index fec08a2434b..40ce095a405 100644 --- a/examples/tensorflow/segmentation/evaluation.py +++ b/examples/tensorflow/segmentation/evaluation.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -12,6 +12,7 @@ import tensorflow as tf +import nncf from examples.common.paths import configure_paths from examples.common.sample_config import SampleConfig from examples.common.sample_config import create_sample_config @@ -108,7 +109,7 @@ def load_checkpoint(checkpoint, ckpt_path): if not path_to_checkpoint: logger.info("No checkpoint detected.") if ckpt_path: - raise RuntimeError(f"ckpt_path was given, but no checkpoint detected in path: {ckpt_path}") + raise nncf.ValidationError(f"ckpt_path was given, but no checkpoint detected in path: {ckpt_path}") logger.info("Checkpoint file {} found and restoring from checkpoint".format(path_to_checkpoint)) status = checkpoint.restore(path_to_checkpoint) @@ -284,7 +285,7 @@ def main(argv): patch_if_experimental_quantization(config.nncf_config) if config.dataset_type != "tfrecords": - raise RuntimeError("The train.py does not support TensorFlow Datasets (TFDS). Please use TFRecords.") + raise nncf.ValidationError("The train.py does not support TensorFlow Datasets (TFDS). Please use TFRecords.") if "train" in config.mode or "test" in config.mode: run_evaluation(config) diff --git a/examples/tensorflow/segmentation/models/maskrcnn_config.py b/examples/tensorflow/segmentation/models/maskrcnn_config.py index 3faefbf811b..63980b8142d 100644 --- a/examples/tensorflow/segmentation/models/maskrcnn_config.py +++ b/examples/tensorflow/segmentation/models/maskrcnn_config.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/segmentation/models/maskrcnn_model.py b/examples/tensorflow/segmentation/models/maskrcnn_model.py index 4ab689c7597..d8d2de223ce 100644 --- a/examples/tensorflow/segmentation/models/maskrcnn_model.py +++ b/examples/tensorflow/segmentation/models/maskrcnn_model.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/segmentation/models/model_selector.py b/examples/tensorflow/segmentation/models/model_selector.py index cd83ade29a1..8890204d9c4 100644 --- a/examples/tensorflow/segmentation/models/model_selector.py +++ b/examples/tensorflow/segmentation/models/model_selector.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/segmentation/preprocessing/maskrcnn_preprocessing.py b/examples/tensorflow/segmentation/preprocessing/maskrcnn_preprocessing.py index 9d70a92bcf4..32e1f2fdc22 100644 --- a/examples/tensorflow/segmentation/preprocessing/maskrcnn_preprocessing.py +++ b/examples/tensorflow/segmentation/preprocessing/maskrcnn_preprocessing.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/tensorflow/segmentation/train.py b/examples/tensorflow/segmentation/train.py index 6697875ce93..b87c0fcde87 100644 --- a/examples/tensorflow/segmentation/train.py +++ b/examples/tensorflow/segmentation/train.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -16,6 +16,7 @@ import numpy as np import tensorflow as tf +import nncf from examples.common.paths import configure_paths from examples.common.sample_config import EVAL_ONLY_ERROR_TEXT from examples.common.sample_config import SampleConfig @@ -72,7 +73,7 @@ def get_config_from_argv(argv, parser): config_from_json = create_sample_config(args, parser, mode="train") if config_from_json.eval_only: - raise RuntimeError(EVAL_ONLY_ERROR_TEXT) + raise nncf.ValidationError(EVAL_ONLY_ERROR_TEXT) predefined_config = get_predefined_config(config_from_json.model) @@ -324,7 +325,7 @@ def main(argv): create_code_snapshot(nncf_root, os.path.join(config.log_dir, "snapshot.tar.gz")) if config.dataset_type != "tfrecords": - raise RuntimeError("The train.py does not support TensorFlow Datasets (TFDS). Please use TFRecords.") + raise nncf.ValidationError("The train.py does not support TensorFlow Datasets (TFDS). Please use TFRecords.") run_train(config) diff --git a/examples/torch/README.md b/examples/torch/README.md index be3c1321a12..894bc9d0e58 100644 --- a/examples/torch/README.md +++ b/examples/torch/README.md @@ -24,4 +24,4 @@ pip install -r examples/torch/requirements.txt ## Results -Please see compression results for PyTorch models at our [Model Zoo page](../../../docs/ModelZoo.md#pytorch). +Please see compression results for PyTorch models at our [Model Zoo page](../../docs/ModelZoo.md#pytorch). diff --git a/examples/torch/__init__.py b/examples/torch/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/examples/torch/__init__.py +++ b/examples/torch/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/classification/__init__.py b/examples/torch/classification/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/examples/torch/classification/__init__.py +++ b/examples/torch/classification/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/classification/configs/mixed_precision/mobilenet_v2_imagenet_mixed_int_autoq_staged.json b/examples/torch/classification/configs/mixed_precision/mobilenet_v2_imagenet_mixed_int_autoq_staged.json index 5e0b82e52f5..290e8b82d39 100644 --- a/examples/torch/classification/configs/mixed_precision/mobilenet_v2_imagenet_mixed_int_autoq_staged.json +++ b/examples/torch/classification/configs/mixed_precision/mobilenet_v2_imagenet_mixed_int_autoq_staged.json @@ -9,7 +9,7 @@ "epochs": 10, "workers": 6, "multiprocessing_distributed": true, - "target_device": "VPU", + "target_device": "NPU", "compression": { "algorithm": "quantization", "initializer": { diff --git a/examples/torch/classification/configs/mixed_precision/mobilenet_v2_imagenet_mixed_int_hawq.json b/examples/torch/classification/configs/mixed_precision/mobilenet_v2_imagenet_mixed_int_hawq.json index ab340db9e53..41e6d221349 100644 --- a/examples/torch/classification/configs/mixed_precision/mobilenet_v2_imagenet_mixed_int_hawq.json +++ b/examples/torch/classification/configs/mixed_precision/mobilenet_v2_imagenet_mixed_int_hawq.json @@ -23,7 +23,7 @@ }, "weight_decay": 1e-05 }, - "target_device": "VPU", + "target_device": "NPU", "compression": { "algorithm": "quantization", "quantize_inputs": true, diff --git a/examples/torch/classification/configs/mixed_precision/resnet50_imagenet_mixed_int_autoq_staged.json b/examples/torch/classification/configs/mixed_precision/resnet50_imagenet_mixed_int_autoq_staged.json index aeed86e7816..706ed494fea 100644 --- a/examples/torch/classification/configs/mixed_precision/resnet50_imagenet_mixed_int_autoq_staged.json +++ b/examples/torch/classification/configs/mixed_precision/resnet50_imagenet_mixed_int_autoq_staged.json @@ -14,7 +14,7 @@ "epochs": 8, "workers": 6, "multiprocessing_distributed": true, - "target_device": "VPU", + "target_device": "NPU", "compression": { "algorithm": "quantization", "initializer": { diff --git a/examples/torch/classification/configs/mixed_precision/resnet50_imagenet_mixed_int_hawq.json b/examples/torch/classification/configs/mixed_precision/resnet50_imagenet_mixed_int_hawq.json index 8185a4e48fa..fe10ff36ff5 100644 --- a/examples/torch/classification/configs/mixed_precision/resnet50_imagenet_mixed_int_hawq.json +++ b/examples/torch/classification/configs/mixed_precision/resnet50_imagenet_mixed_int_hawq.json @@ -14,7 +14,7 @@ "workers": 6, "multiprocessing_distributed": true, "epochs": 30, - "target_device": "VPU", + "target_device": "NPU", "compression": { "algorithm": "quantization", "initializer": { diff --git a/examples/torch/classification/configs/mixed_precision/squeezenet1_1_imagenet_mixed_int_hawq.json b/examples/torch/classification/configs/mixed_precision/squeezenet1_1_imagenet_mixed_int_hawq.json index 4dfe3248a59..cfb6b44abe9 100644 --- a/examples/torch/classification/configs/mixed_precision/squeezenet1_1_imagenet_mixed_int_hawq.json +++ b/examples/torch/classification/configs/mixed_precision/squeezenet1_1_imagenet_mixed_int_hawq.json @@ -21,7 +21,7 @@ }, "weight_decay": 1e-05 }, - "target_device": "VPU", + "target_device": "NPU", "compression": { "algorithm": "quantization", "initializer": { diff --git a/examples/torch/classification/configs/mixed_precision/squeezenet1_1_imagenet_mixed_int_hawq_old_eval.json b/examples/torch/classification/configs/mixed_precision/squeezenet1_1_imagenet_mixed_int_hawq_old_eval.json index d161e8f3ac7..e47434b34b6 100644 --- a/examples/torch/classification/configs/mixed_precision/squeezenet1_1_imagenet_mixed_int_hawq_old_eval.json +++ b/examples/torch/classification/configs/mixed_precision/squeezenet1_1_imagenet_mixed_int_hawq_old_eval.json @@ -5,7 +5,7 @@ }, "num_classes": 1000, "batch_size": 256, - // to resume from state created with obsolete VPU Config + // to resume from state created with obsolete NPU Config "target_device": "TRIAL", "compression": { "algorithm": "quantization" diff --git a/examples/torch/classification/main.py b/examples/torch/classification/main.py index 8b4bd35793f..0b6d2a91492 100644 --- a/examples/torch/classification/main.py +++ b/examples/torch/classification/main.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -56,14 +56,12 @@ from examples.torch.common.optimizer import make_optimizer from examples.torch.common.utils import MockDataset from examples.torch.common.utils import NullContextManager -from examples.torch.common.utils import SafeMLFLow from examples.torch.common.utils import configure_device from examples.torch.common.utils import configure_logging from examples.torch.common.utils import create_code_snapshot from examples.torch.common.utils import get_run_name from examples.torch.common.utils import is_pretrained_model_requested from examples.torch.common.utils import is_staged_quantization -from examples.torch.common.utils import log_common_mlflow_params from examples.torch.common.utils import make_additional_checkpoints from examples.torch.common.utils import print_args from examples.torch.common.utils import write_metrics @@ -145,7 +143,6 @@ def inception_criterion_fn(model_outputs: Any, target: Any, criterion: _Loss) -> def main_worker(current_gpu, config: SampleConfig): configure_device(current_gpu, config) - config.mlflow = SafeMLFLow(config) if is_main_process(): configure_logging(logger, config) print_args(config) @@ -261,8 +258,6 @@ def model_eval_fn(model): else: logger.info("=> loaded checkpoint '{}'".format(resuming_checkpoint_path)) - log_common_mlflow_params(config) - if config.execution_mode != ExecutionMode.CPU_ONLY: cudnn.benchmark = True @@ -324,8 +319,6 @@ def configure_optimizers_fn(): val_model = model validate(val_loader, val_model, criterion, config) - config.mlflow.end_run() - if "export" in config.mode: export_model(compression_ctrl, config) @@ -382,7 +375,6 @@ def train( if config.metrics_dump is not None: acc = best_acc1 / 100 write_metrics(acc, config.metrics_dump) - config.mlflow.safe_call("log_metric", "best_acc1", best_acc1) checkpoint_path = osp.join(config.checkpoint_save_dir, get_run_name(config) + "_last.pth") checkpoint = { @@ -399,7 +391,6 @@ def train( make_additional_checkpoints(checkpoint_path, is_best, epoch + 1, config) for key, value in prepare_for_tensorboard(statistics).items(): - config.mlflow.safe_call("log_metric", "compression/statistics/{0}".format(key), value, epoch) config.tb.add_scalar("compression/statistics/{0}".format(key), value, len(train_loader) * epoch) @@ -727,9 +718,6 @@ def validate(val_loader, model, criterion, config, epoch=0, log_validation_info= config.tb.add_scalar("val/loss", losses.avg, len(val_loader) * epoch) config.tb.add_scalar("val/top1", top1.avg, len(val_loader) * epoch) config.tb.add_scalar("val/top5", top5.avg, len(val_loader) * epoch) - config.mlflow.safe_call("log_metric", "val/loss", float(losses.avg), epoch) - config.mlflow.safe_call("log_metric", "val/top1", float(top1.avg), epoch) - config.mlflow.safe_call("log_metric", "val/top5", float(top5.avg), epoch) logger.info(" * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}\n".format(top1=top1, top5=top5)) diff --git a/examples/torch/classification/models/__init__.py b/examples/torch/classification/models/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/examples/torch/classification/models/__init__.py +++ b/examples/torch/classification/models/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/classification/models/mobilenet_v2_32x32.py b/examples/torch/classification/models/mobilenet_v2_32x32.py index e70a22c6b0d..11a2798ca8a 100644 --- a/examples/torch/classification/models/mobilenet_v2_32x32.py +++ b/examples/torch/classification/models/mobilenet_v2_32x32.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/classification/staged_quantization_worker.py b/examples/torch/classification/staged_quantization_worker.py index ad6ad5d1312..5258e7e5b5a 100644 --- a/examples/torch/classification/staged_quantization_worker.py +++ b/examples/torch/classification/staged_quantization_worker.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -22,6 +22,7 @@ from torch.backends import cudnn from torchvision.models import InceptionOutputs +import nncf from examples.torch.classification.main import AverageMeter from examples.torch.classification.main import accuracy from examples.torch.classification.main import create_data_loaders @@ -39,12 +40,10 @@ from examples.torch.common.model_loader import extract_model_and_compression_states from examples.torch.common.model_loader import load_model from examples.torch.common.model_loader import load_resuming_checkpoint -from examples.torch.common.utils import SafeMLFLow from examples.torch.common.utils import configure_device from examples.torch.common.utils import configure_logging from examples.torch.common.utils import get_run_name from examples.torch.common.utils import is_pretrained_model_requested -from examples.torch.common.utils import log_common_mlflow_params from examples.torch.common.utils import make_additional_checkpoints from examples.torch.common.utils import print_args from nncf.api.compression import CompressionStage @@ -105,10 +104,9 @@ def step(self, epoch_fraction): for group in self.optimizer.param_groups: group["lr"] = lr - if self.disable_wd_start_epoch is not None: - if epoch_float > self.disable_wd_start_epoch: - for group in self.optimizer.param_groups: - group["weight_decay"] = 0.0 + if self.disable_wd_start_epoch is not None and epoch_float > self.disable_wd_start_epoch: + for group in self.optimizer.param_groups: + group["weight_decay"] = 0.0 def epoch_step(self, epoch=None): if epoch is not None: @@ -124,7 +122,6 @@ def state_dict(self): def staged_quantization_main_worker(current_gpu, config): configure_device(current_gpu, config) - config.mlflow = SafeMLFLow(config) if is_main_process(): configure_logging(logger, config) @@ -190,7 +187,7 @@ def autoq_eval_fn(model, eval_loader): load_state(model, model_state_dict, is_resume=True) if not isinstance(compression_ctrl, (BinarizationController, QuantizationController)): - raise RuntimeError( + raise nncf.InternalError( "The stage quantization sample worker may only be run with the binarization and quantization algorithms!" ) @@ -225,8 +222,6 @@ def autoq_eval_fn(model, eval_loader): else: logger.info("=> loaded checkpoint '{}'".format(resuming_checkpoint_path)) - log_common_mlflow_params(config) - if is_export_only: export_model(compression_ctrl, config) return @@ -346,7 +341,6 @@ def train_staged( make_additional_checkpoints(checkpoint_path, is_best, epoch + 1, config) for key, value in prepare_for_tensorboard(statistics).items(): - config.mlflow.safe_call("log_metric", "compression/statistics/{0}".format(key), value, epoch) config.tb.add_scalar("compression/statistics/{0}".format(key), value, len(train_loader) * epoch) diff --git a/examples/torch/common/__init__.py b/examples/torch/common/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/examples/torch/common/__init__.py +++ b/examples/torch/common/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/common/argparser.py b/examples/torch/common/argparser.py index 62c67625125..82e1d64dba1 100644 --- a/examples/torch/common/argparser.py +++ b/examples/torch/common/argparser.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -9,6 +9,7 @@ # See the License for the specific language governing permissions and # limitations under the License. +import nncf from examples.common.sample_config import CustomArgumentParser from nncf.common.hardware.config import HWConfigType @@ -204,5 +205,5 @@ def get_common_argument_parser(): def parse_args(parser, argv): args = parser.parse_args(argv) if "export" in args.mode and args.export_model_path is None: - raise RuntimeError("--mode export requires --export-model-path argument to be set") + raise nncf.ValidationError("--mode export requires --export-model-path argument to be set") return args diff --git a/examples/torch/common/distributed.py b/examples/torch/common/distributed.py index 78a9fa900dc..e57896df607 100644 --- a/examples/torch/common/distributed.py +++ b/examples/torch/common/distributed.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -17,6 +17,7 @@ from torch import distributed as dist from torch.utils.data import Sampler +import nncf from examples.torch.common.example_logger import logger @@ -44,11 +45,11 @@ def __init__(self, dataset, rank=None, world_size=None): super().__init__(dataset) if world_size is None: if not dist.is_available(): - raise RuntimeError("Requires distributed package to be available") + raise nncf.ValidationError("Requires distributed package to be available") world_size = dist.get_world_size() if rank is None: if not dist.is_available(): - raise RuntimeError("Requires distributed package to be available") + raise nncf.ValidationError("Requires distributed package to be available") rank = dist.get_rank() self.world_size = world_size self.rank = rank diff --git a/examples/torch/common/example_logger.py b/examples/torch/common/example_logger.py index fb07f54ca5d..9a645173ca9 100644 --- a/examples/torch/common/example_logger.py +++ b/examples/torch/common/example_logger.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/common/execution.py b/examples/torch/common/execution.py index 26a3275d935..fb20962c01b 100644 --- a/examples/torch/common/execution.py +++ b/examples/torch/common/execution.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/common/export.py b/examples/torch/common/export.py index 67a8a1ac1a3..543574497f0 100644 --- a/examples/torch/common/export.py +++ b/examples/torch/common/export.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -53,15 +53,14 @@ def export_model(ctrl: CompressionAlgorithmController, config: SampleConfig) -> torch.onnx.export(model, input_tensor_list, model_path, input_names=input_names) elif extension == ".xml": import openvino as ov - from openvino.tools.mo import convert_model if config.export_to_ir_via_onnx: model_onnx_path = model_path.with_suffix(".onnx") with torch.no_grad(): torch.onnx.export(model, input_tensor_list, model_onnx_path, input_names=input_names) - ov_model = convert_model(model_onnx_path) + ov_model = ov.convert_model(model_onnx_path) else: - ov_model = convert_model(model, example_input=input_tensor_list, input_shape=input_shape_list) + ov_model = ov.convert_model(model, example_input=input_tensor_list, input=tuple(input_shape_list)) # Rename input nodes for input_node, input_name in zip(ov_model.inputs, input_names): input_node.node.set_friendly_name(input_name) diff --git a/examples/torch/common/model_loader.py b/examples/torch/common/model_loader.py index be8a55d5ae6..1fb598b35d2 100644 --- a/examples/torch/common/model_loader.py +++ b/examples/torch/common/model_loader.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/common/models/__init__.py b/examples/torch/common/models/__init__.py index 74a27ab5f05..ccda0a49039 100644 --- a/examples/torch/common/models/__init__.py +++ b/examples/torch/common/models/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/common/models/classification/__init__.py b/examples/torch/common/models/classification/__init__.py index 2a29147708b..b0a1cdd3993 100644 --- a/examples/torch/common/models/classification/__init__.py +++ b/examples/torch/common/models/classification/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/common/models/classification/efficientnet.py b/examples/torch/common/models/classification/efficientnet.py index 2828ed57395..99b55946ff8 100644 --- a/examples/torch/common/models/classification/efficientnet.py +++ b/examples/torch/common/models/classification/efficientnet.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/common/models/classification/inceptionv3_cifar100.py b/examples/torch/common/models/classification/inceptionv3_cifar100.py index 3749d22af3d..e1492cdcc04 100644 --- a/examples/torch/common/models/classification/inceptionv3_cifar100.py +++ b/examples/torch/common/models/classification/inceptionv3_cifar100.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/common/models/classification/mobilenet_v2_cifar10.py b/examples/torch/common/models/classification/mobilenet_v2_cifar10.py index ce67419ddc9..a96e1248193 100644 --- a/examples/torch/common/models/classification/mobilenet_v2_cifar10.py +++ b/examples/torch/common/models/classification/mobilenet_v2_cifar10.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/common/models/classification/mobilenet_v2_tv_092.py b/examples/torch/common/models/classification/mobilenet_v2_tv_092.py index 43d787bb3b8..d7059f87e53 100644 --- a/examples/torch/common/models/classification/mobilenet_v2_tv_092.py +++ b/examples/torch/common/models/classification/mobilenet_v2_tv_092.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/common/models/classification/mobilenet_v3_tv_092.py b/examples/torch/common/models/classification/mobilenet_v3_tv_092.py index 695622a567b..5ce32170bd2 100644 --- a/examples/torch/common/models/classification/mobilenet_v3_tv_092.py +++ b/examples/torch/common/models/classification/mobilenet_v3_tv_092.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/common/models/classification/resnet_cifar.py b/examples/torch/common/models/classification/resnet_cifar.py index 141e7b34cf2..5406bf158c3 100644 --- a/examples/torch/common/models/classification/resnet_cifar.py +++ b/examples/torch/common/models/classification/resnet_cifar.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/common/models/classification/resnet_cifar10.py b/examples/torch/common/models/classification/resnet_cifar10.py index ef4d79f02f3..c449184e06d 100644 --- a/examples/torch/common/models/classification/resnet_cifar10.py +++ b/examples/torch/common/models/classification/resnet_cifar10.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/common/models/classification/rmnet_cifar.py b/examples/torch/common/models/classification/rmnet_cifar.py index b977b02ce42..963546acb01 100644 --- a/examples/torch/common/models/classification/rmnet_cifar.py +++ b/examples/torch/common/models/classification/rmnet_cifar.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/common/models/classification/vgg11_bn_cifar10.py b/examples/torch/common/models/classification/vgg11_bn_cifar10.py index 4f1a0496d01..a545834af17 100644 --- a/examples/torch/common/models/classification/vgg11_bn_cifar10.py +++ b/examples/torch/common/models/classification/vgg11_bn_cifar10.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/common/models/segmentation/__init__.py b/examples/torch/common/models/segmentation/__init__.py index 43a20be3b34..32640609e20 100644 --- a/examples/torch/common/models/segmentation/__init__.py +++ b/examples/torch/common/models/segmentation/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/common/models/segmentation/enet.py b/examples/torch/common/models/segmentation/enet.py index a9cbbeb35f9..e64f0f56496 100644 --- a/examples/torch/common/models/segmentation/enet.py +++ b/examples/torch/common/models/segmentation/enet.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -15,6 +15,7 @@ import torch from torch import nn +import nncf from examples.torch.common.example_logger import logger @@ -134,7 +135,7 @@ def __init__( # Check in the internal_scale parameter is within the expected range # [1, channels] if internal_ratio <= 1 or internal_ratio > channels: - raise RuntimeError( + raise nncf.ValidationError( "Value out of range. Expected value in the " "interval [1, {0}], got internal_scale={1}.".format(channels, internal_ratio) ) @@ -292,7 +293,7 @@ def __init__( # Check in the internal_scale parameter is within the expected range # [1, channels] if internal_ratio <= 1 or internal_ratio > in_channels: - raise RuntimeError( + raise nncf.ValidationError( "Value out of range. Expected value in the " "interval [1, {0}], got internal_scale={1}. ".format(in_channels, internal_ratio) ) @@ -426,7 +427,7 @@ def __init__( # Check in the internal_scale parameter is within the expected range # [1, channels] if internal_ratio <= 1 or internal_ratio > in_channels: - raise RuntimeError( + raise nncf.ValidationError( "Value out of range. Expected value in the " "interval [1, {0}], got internal_scale={1}. ".format(in_channels, internal_ratio) ) diff --git a/examples/torch/common/models/segmentation/icnet.py b/examples/torch/common/models/segmentation/icnet.py index 6fafce1b532..f50e9899698 100644 --- a/examples/torch/common/models/segmentation/icnet.py +++ b/examples/torch/common/models/segmentation/icnet.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/common/models/segmentation/unet.py b/examples/torch/common/models/segmentation/unet.py index 6148294b9b0..11ea2ec50c8 100644 --- a/examples/torch/common/models/segmentation/unet.py +++ b/examples/torch/common/models/segmentation/unet.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/common/optimizer.py b/examples/torch/common/optimizer.py index 78d9d79c63c..18b31a07dad 100644 --- a/examples/torch/common/optimizer.py +++ b/examples/torch/common/optimizer.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/common/restricted_pickle_module.py b/examples/torch/common/restricted_pickle_module.py index 4ee052a109a..2589eace3d1 100644 --- a/examples/torch/common/restricted_pickle_module.py +++ b/examples/torch/common/restricted_pickle_module.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/common/utils.py b/examples/torch/common/utils.py index 8a42acd2415..a3a7de047b8 100644 --- a/examples/torch/common/utils.py +++ b/examples/torch/common/utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -19,13 +19,9 @@ from pathlib import Path from shutil import copyfile from typing import Tuple -from urllib.request import url2pathname -import mlflow import torch from PIL import Image -from returns.maybe import Maybe -from returns.maybe import Nothing from torch.utils import data from torch.utils.tensorboard import SummaryWriter @@ -34,9 +30,7 @@ from examples.torch.common.example_logger import logger as default_logger from examples.torch.common.execution import ExecutionMode from examples.torch.common.execution import get_device -from nncf.common.utils.os import is_linux from nncf.config.schemata.defaults import QUANTIZATION_BITS -from nncf.torch.utils import is_main_process GENERAL_LOG_FILE_NAME = "output.log" NNCF_LOG_FILE_NAME = "nncf_output.log" @@ -80,52 +74,6 @@ def write_metrics(acc, filename): json.dump(metrics, outfile) -class SafeMLFLow: - """Wrapper to encapsulate safe access to mlflow methods without checking for None and with automatic closing""" - - def __init__(self, config): - self.is_suitable_mode = "train" in config.mode - log_dir = Path(config.log_dir) - root_log_dir = log_dir.parent.parent - self.safe_call("set_tracking_uri", (root_log_dir / "mlruns").resolve().as_uri()) - - self.safe_call("get_experiment_by_name", config.name).or_else_call( - lambda: self.safe_call("create_experiment", config.name) - ) - - self.safe_call("set_experiment", config.name) - self.safe_call("start_run") - - def create_symlink_fn(mlflow_active_run: mlflow.ActiveRun): - if is_linux(): - artifact_path = Path(url2pathname(mlflow_active_run.info.artifact_uri.split("file:")[1])) - symlink_path = artifact_path / log_dir.name - symlink_path.symlink_to(config.log_dir, target_is_directory=True) - return Nothing - - self.safe_call("active_run").bind(create_symlink_fn) - - def __del__(self): - self.safe_call("end_run") - - def safe_call(self, func: str, *args, **kwargs) -> Maybe: - """Calls mlflow method, if it's enabled and safely does nothing in the opposite case""" - return Maybe.from_optional(self._get_mlflow()).bind( - lambda obj: Maybe.from_value(getattr(obj, func)(*args, **kwargs)) - ) - - def end_run(self): - self.safe_call("end_run") - - def _is_enabled(self): - return self.is_suitable_mode and is_main_process() - - def _get_mlflow(self): - if self._is_enabled(): - return mlflow - return None - - def configure_device(current_gpu, config: SampleConfig): config.current_gpu = current_gpu config.distributed = config.execution_mode in (ExecutionMode.DISTRIBUTED, ExecutionMode.MULTIPROCESSING_DISTRIBUTED) @@ -154,14 +102,6 @@ def configure_logging(sample_logger, config): nncf_logger.addHandler(nncf_log_file_handler) -def log_common_mlflow_params(config): - optimizer = config.nncf_config.get("optimizer", {}) - config.mlflow.safe_call("log_param", "epochs", config.get("epochs", "None")) - config.mlflow.safe_call("log_param", "schedule_type", optimizer.get("schedule_type", "None")) - config.mlflow.safe_call("log_param", "lr", optimizer.get("base_lr", "None")) - config.mlflow.safe_call("set_tag", "Log Dir Path", config.log_dir) - - def is_on_first_rank(config): return not config.multiprocessing_distributed or ( config.multiprocessing_distributed and config.rank % config.ngpus_per_node == 0 diff --git a/examples/torch/object_detection/__init__.py b/examples/torch/object_detection/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/examples/torch/object_detection/__init__.py +++ b/examples/torch/object_detection/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/object_detection/dataset.py b/examples/torch/object_detection/dataset.py index bcac99a6e2a..ea63d2403af 100644 --- a/examples/torch/object_detection/dataset.py +++ b/examples/torch/object_detection/dataset.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/object_detection/datasets/__init__.py b/examples/torch/object_detection/datasets/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/examples/torch/object_detection/datasets/__init__.py +++ b/examples/torch/object_detection/datasets/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/object_detection/datasets/coco.py b/examples/torch/object_detection/datasets/coco.py index 558836931b4..deb1d80fa64 100644 --- a/examples/torch/object_detection/datasets/coco.py +++ b/examples/torch/object_detection/datasets/coco.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -157,6 +157,6 @@ def pull_anno(self, index): def get_img_names(self): img_names = [] - for full_name in self.annotation.keys(): + for full_name in self.annotation: img_names.append(full_name[full_name.rfind("/") + 1 : full_name.rfind(".")]) return img_names diff --git a/examples/torch/object_detection/datasets/voc0712.py b/examples/torch/object_detection/datasets/voc0712.py index 745c151d450..8f87a5fc5c9 100644 --- a/examples/torch/object_detection/datasets/voc0712.py +++ b/examples/torch/object_detection/datasets/voc0712.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/object_detection/eval.py b/examples/torch/object_detection/eval.py index 1bd078578aa..e06a19cc161 100644 --- a/examples/torch/object_detection/eval.py +++ b/examples/torch/object_detection/eval.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -197,20 +197,19 @@ def extract_gt_bboxes(classname, dataset, gt, imagenames): def load_detection_annotations(cachedir, dataset): cachefile = os.path.join(cachedir, "annots_{}.json".format(dataset.name)) imagenames = dataset.get_img_names() - if is_main_process(): - if not os.path.isfile(cachefile): - # load annots - gt = {} - for i, imagename in enumerate(imagenames): - _, gt[imagename] = dataset.pull_anno(i) - - if i % 100 == 0: - logger.info("Reading annotation for {:d}/{:d}".format(i + 1, len(imagenames))) - # save - logger.info("Saving cached annotations to {:s}".format(cachefile)) - pathlib.Path(cachedir).mkdir(parents=True, exist_ok=True) - with open(cachefile, "w", encoding="utf8") as f: - json.dump(gt, f) + if is_main_process() and not os.path.isfile(cachefile): + # load annots + gt = {} + for i, imagename in enumerate(imagenames): + _, gt[imagename] = dataset.pull_anno(i) + + if i % 100 == 0: + logger.info("Reading annotation for {:d}/{:d}".format(i + 1, len(imagenames))) + # save + logger.info("Saving cached annotations to {:s}".format(cachefile)) + pathlib.Path(cachedir).mkdir(parents=True, exist_ok=True) + with open(cachefile, "w", encoding="utf8") as f: + json.dump(gt, f) if is_dist_avail_and_initialized(): dist.barrier() with open(cachefile, "r", encoding="utf8") as f: diff --git a/examples/torch/object_detection/layers/__init__.py b/examples/torch/object_detection/layers/__init__.py index 7430d153244..33aac5df4be 100644 --- a/examples/torch/object_detection/layers/__init__.py +++ b/examples/torch/object_detection/layers/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/object_detection/layers/box_utils.py b/examples/torch/object_detection/layers/box_utils.py index d39d1242362..cdbcae6671c 100644 --- a/examples/torch/object_detection/layers/box_utils.py +++ b/examples/torch/object_detection/layers/box_utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/object_detection/layers/extensions/__init__.py b/examples/torch/object_detection/layers/extensions/__init__.py index e78d45d9a95..eea5a7256cf 100644 --- a/examples/torch/object_detection/layers/extensions/__init__.py +++ b/examples/torch/object_detection/layers/extensions/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/object_detection/layers/functions/__init__.py b/examples/torch/object_detection/layers/functions/__init__.py index 996ef5af0ba..8b55c215db1 100644 --- a/examples/torch/object_detection/layers/functions/__init__.py +++ b/examples/torch/object_detection/layers/functions/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/object_detection/layers/functions/detection.py b/examples/torch/object_detection/layers/functions/detection.py index a774ecf0e45..85cdd75a473 100644 --- a/examples/torch/object_detection/layers/functions/detection.py +++ b/examples/torch/object_detection/layers/functions/detection.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/object_detection/layers/functions/prior_box.py b/examples/torch/object_detection/layers/functions/prior_box.py index 5963cb14ac1..a6c11966409 100644 --- a/examples/torch/object_detection/layers/functions/prior_box.py +++ b/examples/torch/object_detection/layers/functions/prior_box.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/object_detection/layers/modules/__init__.py b/examples/torch/object_detection/layers/modules/__init__.py index 0052de9b73e..4a9bdaf5fb4 100644 --- a/examples/torch/object_detection/layers/modules/__init__.py +++ b/examples/torch/object_detection/layers/modules/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/object_detection/layers/modules/l2norm.py b/examples/torch/object_detection/layers/modules/l2norm.py index e1d4ae26104..3f623806302 100644 --- a/examples/torch/object_detection/layers/modules/l2norm.py +++ b/examples/torch/object_detection/layers/modules/l2norm.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/object_detection/layers/modules/multibox_loss.py b/examples/torch/object_detection/layers/modules/multibox_loss.py index 24fb13c7e23..8215a1e3d80 100644 --- a/examples/torch/object_detection/layers/modules/multibox_loss.py +++ b/examples/torch/object_detection/layers/modules/multibox_loss.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/object_detection/layers/modules/ssd_head.py b/examples/torch/object_detection/layers/modules/ssd_head.py index 0ff575eaf38..2c7b184e9ad 100644 --- a/examples/torch/object_detection/layers/modules/ssd_head.py +++ b/examples/torch/object_detection/layers/modules/ssd_head.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/object_detection/main.py b/examples/torch/object_detection/main.py index 1c50a74c4fd..bd78eebf0cf 100644 --- a/examples/torch/object_detection/main.py +++ b/examples/torch/object_detection/main.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -37,14 +37,12 @@ from examples.torch.common.model_loader import load_resuming_checkpoint from examples.torch.common.optimizer import get_parameter_groups from examples.torch.common.optimizer import make_optimizer -from examples.torch.common.utils import SafeMLFLow from examples.torch.common.utils import configure_device from examples.torch.common.utils import configure_logging from examples.torch.common.utils import create_code_snapshot from examples.torch.common.utils import get_run_name from examples.torch.common.utils import is_on_first_rank from examples.torch.common.utils import is_pretrained_model_requested -from examples.torch.common.utils import log_common_mlflow_params from examples.torch.common.utils import make_additional_checkpoints from examples.torch.common.utils import print_args from examples.torch.common.utils import write_metrics @@ -110,7 +108,6 @@ def main_worker(current_gpu, config): # Setup experiment environment ################################# configure_device(current_gpu, config) - config.mlflow = SafeMLFLow(config) if is_on_first_rank(config): configure_logging(logger, config) print_args(config) @@ -222,8 +219,6 @@ def model_eval_fn(model): optimizer.load_state_dict(resuming_checkpoint.get("optimizer", optimizer.state_dict())) config.start_epoch = resuming_checkpoint.get("epoch", 0) + 1 - log_common_mlflow_params(config) - if is_export_only: export_model(compression_ctrl, config) return diff --git a/examples/torch/object_detection/model.py b/examples/torch/object_detection/model.py index 8208b72fa17..565720b5069 100644 --- a/examples/torch/object_detection/model.py +++ b/examples/torch/object_detection/model.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/object_detection/models/__init__.py b/examples/torch/object_detection/models/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/examples/torch/object_detection/models/__init__.py +++ b/examples/torch/object_detection/models/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/object_detection/models/ssd_mobilenet.py b/examples/torch/object_detection/models/ssd_mobilenet.py index 6ab00edecea..34a6392579a 100644 --- a/examples/torch/object_detection/models/ssd_mobilenet.py +++ b/examples/torch/object_detection/models/ssd_mobilenet.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/object_detection/models/ssd_vgg.py b/examples/torch/object_detection/models/ssd_vgg.py index 39d96394ab9..8dda2b67fd8 100644 --- a/examples/torch/object_detection/models/ssd_vgg.py +++ b/examples/torch/object_detection/models/ssd_vgg.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/object_detection/utils/__init__.py b/examples/torch/object_detection/utils/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/examples/torch/object_detection/utils/__init__.py +++ b/examples/torch/object_detection/utils/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/object_detection/utils/augmentations.py b/examples/torch/object_detection/utils/augmentations.py index d37fc3d5e9e..3b784475708 100644 --- a/examples/torch/object_detection/utils/augmentations.py +++ b/examples/torch/object_detection/utils/augmentations.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/requirements.txt b/examples/torch/requirements.txt index 7f4cf126685..80e383f3f9f 100644 --- a/examples/torch/requirements.txt +++ b/examples/torch/requirements.txt @@ -3,8 +3,7 @@ pillow>=8.0.1 tensorboard>=2.1 matplotlib>=3.3.3 defusedxml>=0.7.0rc1 -mlflow~=2.9 -returns>0.14 +tabulate~=0.9.0 opencv-python>=4.4.0.46 torchvision>=0.10.0,<0.17 # the minor version should always match the torch minor version that is installed via NNCF's `pip install nncf[torch]`; TV minor version is torch minor version +1 efficientnet_pytorch diff --git a/examples/torch/semantic_segmentation/datasets/__init__.py b/examples/torch/semantic_segmentation/datasets/__init__.py index e6a42186e03..654fd4439b1 100644 --- a/examples/torch/semantic_segmentation/datasets/__init__.py +++ b/examples/torch/semantic_segmentation/datasets/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/semantic_segmentation/datasets/camvid.py b/examples/torch/semantic_segmentation/datasets/camvid.py index 51c2b9b8d1a..268f6cf834a 100644 --- a/examples/torch/semantic_segmentation/datasets/camvid.py +++ b/examples/torch/semantic_segmentation/datasets/camvid.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -15,6 +15,7 @@ from torch.utils import data import examples.torch.semantic_segmentation.utils.data as data_utils +import nncf class CamVid(data.Dataset): @@ -104,7 +105,7 @@ def __init__(self, root, image_set="train", transforms=None, loader=data_utils.p os.path.join(self.root_dir, self.test_lbl_folder), extension_filter=self.img_extension ) else: - raise RuntimeError("Unexpected dataset mode. Supported modes are: train, val and test") + raise nncf.ValidationError("Unexpected dataset mode. Supported modes are: train, val and test") def __getitem__(self, index): """ @@ -123,7 +124,7 @@ def __getitem__(self, index): elif self.mode.lower() == "test": data_path, label_path = self.test_data[index], self.test_labels[index] else: - raise RuntimeError("Unexpected dataset mode. Supported modes are: train, val and test") + raise nncf.ValidationError("Unexpected dataset mode. Supported modes are: train, val and test") img, label = self.loader(data_path, label_path) @@ -141,4 +142,4 @@ def __len__(self): if self.mode.lower() == "test": return len(self.test_data) - raise RuntimeError("Unexpected dataset mode. Supported modes are: train, val and test") + raise nncf.ValidationError("Unexpected dataset mode. Supported modes are: train, val and test") diff --git a/examples/torch/semantic_segmentation/datasets/cityscapes.py b/examples/torch/semantic_segmentation/datasets/cityscapes.py index a1d82d5836d..d823576b45d 100644 --- a/examples/torch/semantic_segmentation/datasets/cityscapes.py +++ b/examples/torch/semantic_segmentation/datasets/cityscapes.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -15,6 +15,7 @@ from torch.utils import data import examples.torch.semantic_segmentation.utils.data as data_utils +import nncf class Cityscapes(data.Dataset): @@ -193,7 +194,7 @@ def __init__(self, root, image_set="train", transforms=None, loader=data_utils.p extension_filter=self.img_extension, ) else: - raise RuntimeError("Unexpected dataset mode. Supported modes are: train, val and test") + raise nncf.ValidationError("Unexpected dataset mode. Supported modes are: train, val and test") def __getitem__(self, index): """ @@ -212,7 +213,7 @@ def __getitem__(self, index): elif self.mode.lower() == "test": data_path, label_path = self.test_data[index], self.test_labels[index] else: - raise RuntimeError("Unexpected dataset mode. Supported modes are: train, val and test") + raise nncf.ValidationError("Unexpected dataset mode. Supported modes are: train, val and test") img, label = self.loader(data_path, label_path) @@ -233,4 +234,4 @@ def __len__(self): if self.mode.lower() == "test": return len(self.test_data) - raise RuntimeError("Unexpected dataset mode. Supported modes are: train, val and test") + raise nncf.ValidationError("Unexpected dataset mode. Supported modes are: train, val and test") diff --git a/examples/torch/semantic_segmentation/datasets/mapillary.py b/examples/torch/semantic_segmentation/datasets/mapillary.py index 82eb408f6d2..c74658d2f62 100644 --- a/examples/torch/semantic_segmentation/datasets/mapillary.py +++ b/examples/torch/semantic_segmentation/datasets/mapillary.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -15,6 +15,7 @@ from torch.utils import data import examples.torch.semantic_segmentation.utils.data as data_utils +import nncf class Mapillary(data.Dataset): @@ -113,7 +114,7 @@ def __init__(self, root, image_set="train", transforms=None, loader=data_utils.p os.path.join(self.root_dir, self.test_lbl_folder), extension_filter=self.label_extension ) else: - raise RuntimeError("Unexpected dataset mode. Supported modes are: train, val and test") + raise nncf.ValidationError("Unexpected dataset mode. Supported modes are: train, val and test") def __getitem__(self, index): """ @@ -132,7 +133,7 @@ def __getitem__(self, index): elif self.mode.lower() == "test": data_path, label_path = self.test_data[index], self.test_labels[index] else: - raise RuntimeError("Unexpected dataset mode. Supported modes are: train, val and test") + raise nncf.ValidationError("Unexpected dataset mode. Supported modes are: train, val and test") img, color_labels = self.loader(data_path, label_path) label = data_utils.color_to_label(color_labels, self.color_encoding) @@ -151,4 +152,4 @@ def __len__(self): if self.mode.lower() == "test": return len(self.test_data) - raise RuntimeError("Unexpected dataset mode. Supported modes are: train, val and test") + raise nncf.ValidationError("Unexpected dataset mode. Supported modes are: train, val and test") diff --git a/examples/torch/semantic_segmentation/main.py b/examples/torch/semantic_segmentation/main.py index c9c290d1a4f..3c628d2cbef 100644 --- a/examples/torch/semantic_segmentation/main.py +++ b/examples/torch/semantic_segmentation/main.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -24,6 +24,7 @@ import examples.torch.semantic_segmentation.utils.data as data_utils import examples.torch.semantic_segmentation.utils.transforms as JT +import nncf from examples.common.paths import configure_paths from examples.common.sample_config import create_sample_config from examples.torch.common.argparser import get_common_argument_parser @@ -38,12 +39,10 @@ from examples.torch.common.model_loader import load_model from examples.torch.common.model_loader import load_resuming_checkpoint from examples.torch.common.optimizer import make_optimizer -from examples.torch.common.utils import SafeMLFLow from examples.torch.common.utils import configure_device from examples.torch.common.utils import configure_logging from examples.torch.common.utils import get_run_name from examples.torch.common.utils import is_pretrained_model_requested -from examples.torch.common.utils import log_common_mlflow_params from examples.torch.common.utils import make_additional_checkpoints from examples.torch.common.utils import print_args from examples.torch.common.utils import write_metrics @@ -149,7 +148,7 @@ def get_dataset(dataset_name: str) -> torch.utils.data.Dataset: from examples.torch.semantic_segmentation.datasets import Mapillary as dataset else: # Should never happen...but just in case it does - raise RuntimeError('"{0}" is not a supported dataset.'.format(dataset_name)) + raise nncf.UnsupportedDatasetError('"{0}" is not a supported dataset.'.format(dataset_name)) return dataset @@ -471,7 +470,6 @@ def predict(model, images, class_encoding, config): def main_worker(current_gpu, config): configure_device(current_gpu, config) - config.mlflow = SafeMLFLow(config) if is_main_process(): configure_logging(logger, config) print_args(config) @@ -545,8 +543,6 @@ def autoq_test_fn(model, eval_loader): if config.distributed: compression_ctrl.distributed() - log_common_mlflow_params(config) - if is_export_only: export_model(compression_ctrl, config) return diff --git a/examples/torch/semantic_segmentation/metric/__init__.py b/examples/torch/semantic_segmentation/metric/__init__.py index a906e4bff4f..e703622fc9b 100644 --- a/examples/torch/semantic_segmentation/metric/__init__.py +++ b/examples/torch/semantic_segmentation/metric/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/semantic_segmentation/metric/confusionmatrix.py b/examples/torch/semantic_segmentation/metric/confusionmatrix.py index f0c82ef74e8..0d6f86af040 100644 --- a/examples/torch/semantic_segmentation/metric/confusionmatrix.py +++ b/examples/torch/semantic_segmentation/metric/confusionmatrix.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/semantic_segmentation/metric/iou.py b/examples/torch/semantic_segmentation/metric/iou.py index f5663ca4bb3..35074b36462 100644 --- a/examples/torch/semantic_segmentation/metric/iou.py +++ b/examples/torch/semantic_segmentation/metric/iou.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/semantic_segmentation/metric/metric.py b/examples/torch/semantic_segmentation/metric/metric.py index 343e3cde7d0..0fd55abbf83 100644 --- a/examples/torch/semantic_segmentation/metric/metric.py +++ b/examples/torch/semantic_segmentation/metric/metric.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/semantic_segmentation/test.py b/examples/torch/semantic_segmentation/test.py index 8926e840360..01332aee0bf 100644 --- a/examples/torch/semantic_segmentation/test.py +++ b/examples/torch/semantic_segmentation/test.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/semantic_segmentation/train.py b/examples/torch/semantic_segmentation/train.py index a8f11e4fc76..12e268e790f 100644 --- a/examples/torch/semantic_segmentation/train.py +++ b/examples/torch/semantic_segmentation/train.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/semantic_segmentation/utils/checkpoint.py b/examples/torch/semantic_segmentation/utils/checkpoint.py index 51575b72b62..0172994e530 100644 --- a/examples/torch/semantic_segmentation/utils/checkpoint.py +++ b/examples/torch/semantic_segmentation/utils/checkpoint.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/semantic_segmentation/utils/data.py b/examples/torch/semantic_segmentation/utils/data.py index b6dc525c504..4873dcc60f5 100644 --- a/examples/torch/semantic_segmentation/utils/data.py +++ b/examples/torch/semantic_segmentation/utils/data.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -20,6 +20,8 @@ from torchvision.transforms import ToPILImage from tqdm import tqdm +import nncf + def get_files(folder, name_filter=None, extension_filter=None): """Helper function that returns the list of files in a specified folder @@ -34,7 +36,7 @@ def get_files(folder, name_filter=None, extension_filter=None): """ if not os.path.isdir(folder): - raise RuntimeError('"{0}" is not a folder.'.format(folder)) + raise nncf.InvalidPathError('"{0}" is not a folder.'.format(folder)) # Filename filter: if not specified don't filter (condition always true); # otherwise, use a lambda expression to filter out files that do not diff --git a/examples/torch/semantic_segmentation/utils/loss_funcs.py b/examples/torch/semantic_segmentation/utils/loss_funcs.py index f25d4647e1b..9cd5b7e861c 100644 --- a/examples/torch/semantic_segmentation/utils/loss_funcs.py +++ b/examples/torch/semantic_segmentation/utils/loss_funcs.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/examples/torch/semantic_segmentation/utils/transforms.py b/examples/torch/semantic_segmentation/utils/transforms.py index fa45b72c2a2..5891f04e9de 100644 --- a/examples/torch/semantic_segmentation/utils/transforms.py +++ b/examples/torch/semantic_segmentation/utils/transforms.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/__init__.py b/nncf/__init__.py index 06e7227988b..d14ff86960e 100644 --- a/nncf/__init__.py +++ b/nncf/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -18,6 +18,20 @@ from nncf.common.strip import strip as strip from nncf.config import NNCFConfig as NNCFConfig from nncf.data import Dataset as Dataset +from nncf.errors import BufferFullError as BufferFullError +from nncf.errors import InstallationError as InstallationError +from nncf.errors import InternalError as InternalError +from nncf.errors import InvalidCollectorTypeError as InvalidCollectorTypeError +from nncf.errors import InvalidPathError as InvalidPathError +from nncf.errors import InvalidQuantizerGroupError as InvalidQuantizerGroupError +from nncf.errors import ModuleNotFoundError as ModuleNotFoundError +from nncf.errors import ParameterNotSupportedError as ParameterNotSupportedError +from nncf.errors import UnknownDatasetError as UnknownDatasetError +from nncf.errors import UnsupportedBackendError as UnsupportedBackendError +from nncf.errors import UnsupportedDatasetError as UnsupportedDatasetError +from nncf.errors import UnsupportedModelError as UnsupportedModelError +from nncf.errors import UnsupportedVersionError as UnsupportedVersionError +from nncf.errors import ValidationError as ValidationError from nncf.parameters import CompressWeightsMode as CompressWeightsMode from nncf.parameters import DropType as DropType from nncf.parameters import ModelType as ModelType @@ -28,6 +42,9 @@ from nncf.quantization import compress_weights as compress_weights from nncf.quantization import quantize as quantize from nncf.quantization import quantize_with_accuracy_control as quantize_with_accuracy_control +from nncf.quantization.advanced_parameters import ( + AdvancedAccuracyRestorerParameters as AdvancedAccuracyRestorerParameters, +) from nncf.quantization.advanced_parameters import AdvancedQuantizationParameters as AdvancedQuantizationParameters from nncf.scopes import IgnoredScope as IgnoredScope from nncf.version import __version__ as __version__ diff --git a/nncf/api/__init__.py b/nncf/api/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/api/__init__.py +++ b/nncf/api/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/api/compression.py b/nncf/api/compression.py index 028af95bb0f..a2f49bf9b54 100644 --- a/nncf/api/compression.py +++ b/nncf/api/compression.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/api/statistics.py b/nncf/api/statistics.py index 352b8fdb6ba..01b9b644ae8 100644 --- a/nncf/api/statistics.py +++ b/nncf/api/statistics.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/__init__.py b/nncf/common/__init__.py index b687a5c33f7..1d978d2710f 100644 --- a/nncf/common/__init__.py +++ b/nncf/common/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/accuracy_aware_training/__init__.py b/nncf/common/accuracy_aware_training/__init__.py index 04411f01716..35a9f765b7c 100644 --- a/nncf/common/accuracy_aware_training/__init__.py +++ b/nncf/common/accuracy_aware_training/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/accuracy_aware_training/runner.py b/nncf/common/accuracy_aware_training/runner.py index 884ca5bb92a..69ebf254ae2 100644 --- a/nncf/common/accuracy_aware_training/runner.py +++ b/nncf/common/accuracy_aware_training/runner.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/accuracy_aware_training/runner_factory.py b/nncf/common/accuracy_aware_training/runner_factory.py index 8412ed54dd4..340253981ef 100644 --- a/nncf/common/accuracy_aware_training/runner_factory.py +++ b/nncf/common/accuracy_aware_training/runner_factory.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -13,6 +13,7 @@ from abc import abstractmethod from typing import Dict +import nncf from nncf.api.compression import CompressionAlgorithmController from nncf.common.accuracy_aware_training.runner import BaseAccuracyAwareTrainingRunner from nncf.common.accuracy_aware_training.runner import BaseAdaptiveCompressionLevelTrainingRunner @@ -79,7 +80,7 @@ def create_training_loop(self) -> BaseAccuracyAwareTrainingRunner: self.dump_checkpoints, self.lr_updates_needed, ) - raise RuntimeError("Got an unsupported value of nncf_backend") + raise nncf.UnsupportedBackendError("Got an unsupported value of nncf_backend") class AdaptiveCompressionLevelTrainingRunnerCreator(TrainingRunnerCreator): @@ -139,4 +140,4 @@ def create_training_loop(self) -> BaseAdaptiveCompressionLevelTrainingRunner: self.minimal_compression_rate, self.maximal_compression_rate, ) - raise RuntimeError("Got an unsupported value of nncf_backend") + raise nncf.UnsupportedBackendError("Got an unsupported value of nncf_backend") diff --git a/nncf/common/accuracy_aware_training/statistics.py b/nncf/common/accuracy_aware_training/statistics.py index 5c2f96e8147..4a02ba9e352 100644 --- a/nncf/common/accuracy_aware_training/statistics.py +++ b/nncf/common/accuracy_aware_training/statistics.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/accuracy_aware_training/training_loop.py b/nncf/common/accuracy_aware_training/training_loop.py index 11e63db07e1..70bc4a03cfb 100644 --- a/nncf/common/accuracy_aware_training/training_loop.py +++ b/nncf/common/accuracy_aware_training/training_loop.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -20,6 +20,7 @@ import numpy as np from scipy.interpolate import interp1d +import nncf from nncf.api.compression import CompressionAlgorithmController from nncf.common.accuracy_aware_training.runner import BaseAccuracyAwareTrainingRunner from nncf.common.accuracy_aware_training.runner_factory import AdaptiveCompressionLevelTrainingRunnerCreator @@ -278,7 +279,7 @@ def __init__( super().__init__(compression_controller) self.adaptive_controller = self._get_adaptive_compression_ctrl(compression_controller) if self.adaptive_controller is None: - raise RuntimeError( + raise nncf.InternalError( "No compression algorithm supported by the accuracy-aware training " "runner was specified in the config" ) @@ -305,7 +306,7 @@ def remove_registry_prefix(algo_name): for prefix in ("pt_", "tf_"): if algo_name.startswith(prefix): return algo_name[len(prefix) :] - raise RuntimeError( + raise nncf.ValidationError( "Compression algorithm names in the adaptive controllers " 'registry should be prefixed with "pt_" or "tf_" depending on the ' "backend framework" @@ -323,11 +324,13 @@ def remove_registry_prefix(algo_name): for ctrl_type in adaptive_compression_controllers.values(): if isinstance(controller, ctrl_type): return controller - elif isinstance(compression_controller, CompressionAlgorithmController): - if compression_controller.name in adaptive_compression_controllers: - return compression_controller + elif ( + isinstance(compression_controller, CompressionAlgorithmController) + and compression_controller.name in adaptive_compression_controllers + ): + return compression_controller - raise RuntimeError( + raise nncf.InternalError( "No compression algorithm that supports adaptive compression accuracy-aware training was specified" ) @@ -570,4 +573,4 @@ def create_accuracy_aware_training_loop( return AdaptiveCompressionTrainingLoop( nncf_config, compression_ctrl, uncompressed_model_accuracy, **additional_runner_args ) - raise RuntimeError("Incorrect accuracy aware mode in the config file") + raise nncf.InternalError("Incorrect accuracy aware mode in the config file") diff --git a/nncf/common/collector.py b/nncf/common/collector.py index d037618270a..23807f1f018 100644 --- a/nncf/common/collector.py +++ b/nncf/common/collector.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/composite_compression.py b/nncf/common/composite_compression.py index b6989b4e6f0..393a4a8f9a0 100644 --- a/nncf/common/composite_compression.py +++ b/nncf/common/composite_compression.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -11,6 +11,7 @@ from typing import Any, Dict, List, Optional, Tuple +import nncf from nncf import NNCFConfig from nncf.api.compression import CompressionAlgorithmBuilder from nncf.api.compression import CompressionAlgorithmController @@ -76,7 +77,7 @@ def calculate(self, *args, **kwargs) -> Any: """ if len(self._child_losses) == 0: - raise RuntimeError("Cannot calculate the loss value because the number of child loss is 0.") + raise nncf.InternalError("Cannot calculate the loss value because the number of child loss is 0.") result_loss = 0 for loss in self._child_losses: @@ -200,7 +201,9 @@ def add(self, child_ctrl: CompressionAlgorithmController) -> None: :param child_ctrl: A `CompressionAlgorithmController` instance. """ if child_ctrl.model is not self.model: - raise RuntimeError("Cannot create a composite controller from controllers belonging to different models!") + raise nncf.InternalError( + "Cannot create a composite controller from controllers belonging to different models!" + ) self._child_ctrls.append(child_ctrl) self._loss.add(child_ctrl.loss) @@ -345,7 +348,7 @@ def disable_scheduler(self) -> None: def get_compression_state(self) -> Dict[str, Any]: if self._builder_state is None: - raise RuntimeError("Internal error: builder state is not set for the controller") + raise nncf.InternalError("Internal error: builder state is not set for the controller") return {self.BUILDER_STATE: self._builder_state, self.CONTROLLER_STATE: self.get_state()} diff --git a/nncf/common/compression.py b/nncf/common/compression.py index c665152560f..21aa9dfb9f9 100644 --- a/nncf/common/compression.py +++ b/nncf/common/compression.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -13,6 +13,7 @@ from pathlib import Path from typing import Any, Dict, List, Optional, Tuple, TypeVar +import nncf from nncf import NNCFConfig from nncf.api.compression import CompressionAlgorithmBuilder from nncf.api.compression import CompressionAlgorithmController @@ -66,7 +67,7 @@ def __init__(self, target_model: TModel): @property def name(self): if self._name is None: - raise RuntimeError("Internal error: name of the controller is not set!") + raise nncf.InternalError("Internal error: name of the controller is not set!") return self._name @property @@ -177,7 +178,7 @@ def get_compression_state(self) -> Dict[str, Any]: :return: The compression state. """ if self._builder_state is None: - raise RuntimeError("Internal error: builder state is not set for the controller") + raise nncf.InternalError("Internal error: builder state is not set for the controller") return {self.BUILDER_STATE: self._builder_state, self.CONTROLLER_STATE: self.get_state()} diff --git a/nncf/common/deprecation.py b/nncf/common/deprecation.py index 8684c2ca3b0..352725c830c 100644 --- a/nncf/common/deprecation.py +++ b/nncf/common/deprecation.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/engine.py b/nncf/common/engine.py index 3e2f1f34b5c..1ada7e7be95 100644 --- a/nncf/common/engine.py +++ b/nncf/common/engine.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/exporter.py b/nncf/common/exporter.py index 6eabac3196e..030a13aa0ac 100644 --- a/nncf/common/exporter.py +++ b/nncf/common/exporter.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/factory.py b/nncf/common/factory.py index f2d85ecefa9..6616f9dbe3a 100644 --- a/nncf/common/factory.py +++ b/nncf/common/factory.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -11,6 +11,7 @@ from typing import TypeVar +import nncf from nncf.common.engine import Engine from nncf.common.graph.graph import NNCFGraph from nncf.common.graph.model_transformer import ModelTransformer @@ -43,18 +44,19 @@ def create(model: TModel) -> NNCFGraph: return GraphConverter.create_nncf_graph(model) if model_backend == BackendType.TORCH: return model.nncf.get_graph() - raise RuntimeError( + raise nncf.UnsupportedBackendError( "Cannot create backend-specific graph because {} is not supported!".format(model_backend.value) ) class ModelTransformerFactory: @staticmethod - def create(model: TModel) -> ModelTransformer: + def create(model: TModel, inplace: bool = False) -> ModelTransformer: """ Factory method to create backend-specific ModelTransformer instance based on the input model. :param model: backend-specific model instance + :param inplace: apply transformations inplace :return: backend-specific ModelTransformer instance """ model_backend = get_backend(model) @@ -65,12 +67,12 @@ def create(model: TModel) -> ModelTransformer: if model_backend == BackendType.OPENVINO: from nncf.openvino.graph.model_transformer import OVModelTransformer - return OVModelTransformer(model) + return OVModelTransformer(model, inplace=inplace) if model_backend == BackendType.TORCH: from nncf.torch.model_transformer import PTModelTransformer return PTModelTransformer(model) - raise RuntimeError( + raise nncf.UnsupportedBackendError( "Cannot create backend-specific model transformer because {} is not supported!".format(model_backend.value) ) @@ -97,7 +99,7 @@ def create(model: TModel) -> Engine: from nncf.torch.engine import PTEngine return PTEngine(model) - raise RuntimeError( + raise nncf.UnsupportedBackendError( "Cannot create backend-specific engine because {} is not supported!".format(model_backend.value) ) @@ -116,7 +118,13 @@ def create(model: TModel) -> CommandCreator: from nncf.openvino.graph.transformations.command_creation import OVCommandCreator return OVCommandCreator() - raise RuntimeError( + + if model_backend == BackendType.ONNX: + from nncf.onnx.graph.transformations.command_creation import ONNXCommandCreator + + return ONNXCommandCreator() + + raise nncf.UnsupportedBackendError( "Cannot create backend-specific command creator because {} is not supported!".format(model_backend.value) ) @@ -143,7 +151,7 @@ def create(model: TModel, dataset: Dataset) -> aggregator.StatisticsAggregator: from nncf.torch.statistics.aggregator import PTStatisticsAggregator return PTStatisticsAggregator(dataset) - raise RuntimeError( + raise nncf.UnsupportedBackendError( "Cannot create backend-specific statistics aggregator because {} is not supported!".format( model_backend.value ) diff --git a/nncf/common/graph/__init__.py b/nncf/common/graph/__init__.py index e5320fa9b55..2358ac0f698 100644 --- a/nncf/common/graph/__init__.py +++ b/nncf/common/graph/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/graph/definitions.py b/nncf/common/graph/definitions.py index c88f077dd9b..14925b0b36c 100644 --- a/nncf/common/graph/definitions.py +++ b/nncf/common/graph/definitions.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/graph/graph.py b/nncf/common/graph/graph.py index f9c8a306a1c..1f9795fc721 100644 --- a/nncf/common/graph/graph.py +++ b/nncf/common/graph/graph.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -15,6 +15,7 @@ import networkx as nx import networkx.algorithms.isomorphism as iso +import nncf from nncf.common.graph.graph_matching import find_subgraphs_matching_pattern from nncf.common.graph.layer_attributes import BaseLayerAttributes from nncf.common.graph.layer_attributes import Dtype @@ -639,9 +640,9 @@ def _get_graph_for_visualization(self) -> nx.DiGraph: def get_node_by_name(self, name: NNCFNodeName) -> NNCFNode: node_ids = self._node_name_to_node_id_map.get(name, None) if node_ids is None: - raise RuntimeError("Could not find a node {} in NNCFGraph!".format(name)) + raise nncf.InternalError("Could not find a node {} in NNCFGraph!".format(name)) if len(node_ids) > 1: - raise RuntimeError(f"More than one node in NNCFGraph matches name {name}") + raise nncf.InternalError(f"More than one node in NNCFGraph matches name {name}") node_key = f"{node_ids[0]} {name}" return self._nodes[node_key] @@ -692,7 +693,7 @@ def get_nncf_graph_pattern_io(self, match: List[str]) -> NNCFGraphPatternIO: elif to_node_key in match: input_nncf_edges.append(nncf_edge) else: - raise RuntimeError("Invalid graph expression supplied!") + raise nncf.InternalError("Invalid graph expression supplied!") return NNCFGraphPatternIO(input_nncf_edges, output_nncf_edges) diff --git a/nncf/common/graph/graph_matching.py b/nncf/common/graph/graph_matching.py index 50784176f54..a4af34edf43 100644 --- a/nncf/common/graph/graph_matching.py +++ b/nncf/common/graph/graph_matching.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -29,10 +29,9 @@ def _are_nodes_matched(node_1, node_2) -> bool: continue # Torch and TF pattern mapping based on 'type' section, # While ONNX mapping based on metatypes - - # to support all of them, we need to check the existane of the attributes - if GraphPattern.NODE_TYPE_ATTR in node_1: - if node_1[GraphPattern.NODE_TYPE_ATTR] in node_2[attr]: - continue + # to support all of them, we need to check the existence of the attributes + if GraphPattern.NODE_TYPE_ATTR in node_1 and node_1[GraphPattern.NODE_TYPE_ATTR] in node_2[attr]: + continue if node_1[attr] not in node_2[attr]: return False return True diff --git a/nncf/common/graph/layer_attributes.py b/nncf/common/graph/layer_attributes.py index e92570e62cd..aa6358d8c0b 100644 --- a/nncf/common/graph/layer_attributes.py +++ b/nncf/common/graph/layer_attributes.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/graph/model_transformer.py b/nncf/common/graph/model_transformer.py index e7d28640fbc..18874405efd 100644 --- a/nncf/common/graph/model_transformer.py +++ b/nncf/common/graph/model_transformer.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/graph/operator_metatypes.py b/nncf/common/graph/operator_metatypes.py index d726b8e6104..ac3a3c0e9fb 100644 --- a/nncf/common/graph/operator_metatypes.py +++ b/nncf/common/graph/operator_metatypes.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -11,6 +11,7 @@ from typing import List, Optional, Type +import nncf from nncf.common.graph.definitions import NNCFGraphNodeType from nncf.common.utils.registry import Registry @@ -102,7 +103,7 @@ def wrap(obj: Type[OperatorMetatype]): op_names = obj.get_all_aliases() for name in op_names: if name in self._op_name_to_op_meta_dict and not obj.subtype_check(self._op_name_to_op_meta_dict[name]): - raise RuntimeError( + raise nncf.InternalError( "Inconsistent operator metatype registry - single patched " "op name maps to multiple metatypes!" ) diff --git a/nncf/common/graph/patterns/__init__.py b/nncf/common/graph/patterns/__init__.py index 8092ba8d1ff..9b21cf6f8d7 100644 --- a/nncf/common/graph/patterns/__init__.py +++ b/nncf/common/graph/patterns/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/graph/patterns/manager.py b/nncf/common/graph/patterns/manager.py index 0a3198852be..0139ae12e50 100644 --- a/nncf/common/graph/patterns/manager.py +++ b/nncf/common/graph/patterns/manager.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/graph/patterns/patterns.py b/nncf/common/graph/patterns/patterns.py index a5aff2990d4..98da197dda7 100644 --- a/nncf/common/graph/patterns/patterns.py +++ b/nncf/common/graph/patterns/patterns.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -18,6 +18,7 @@ import networkx as nx import networkx.algorithms.isomorphism as ism +import nncf from nncf.common.utils.dot_file_rw import write_dot_graph from nncf.parameters import ModelType from nncf.parameters import TargetDevice @@ -232,9 +233,8 @@ def join_patterns(self, other: "GraphPattern", edges: Optional[List[Tuple[Hashab self._graph.add_edges_from(remapped_edges) def add_node(self, **attrs) -> int: - if GraphPattern.METATYPE_ATTR in attrs: - if not isinstance(attrs[GraphPattern.METATYPE_ATTR], list): - attrs[GraphPattern.METATYPE_ATTR] = [attrs[GraphPattern.METATYPE_ATTR]] + if GraphPattern.METATYPE_ATTR in attrs and not isinstance(attrs[GraphPattern.METATYPE_ATTR], list): + attrs[GraphPattern.METATYPE_ATTR] = [attrs[GraphPattern.METATYPE_ATTR]] self._graph.add_node(self._node_counter, **attrs) self._node_counter += 1 return self._node_counter - 1 @@ -258,7 +258,7 @@ def merge_two_types_of_operations(first_op: Dict, second_op: Dict, label: str) - res[GraphPattern.METATYPE_ATTR].extend(second_op[GraphPattern.METATYPE_ATTR]) res[GraphPattern.LABEL_ATTR] = label return res - raise RuntimeError("Incorrect dicts of operations") + raise nncf.InternalError("Incorrect dicts of operations") @dataclass @@ -358,7 +358,7 @@ class HWFusedPatternNames(Enum): # DEVICE PATTERNS HSWISH_ACTIVATION_CLAMP_MULTIPLY = PatternDesc( "hswish_activation_clamp_multiply", - devices=[TargetDevice.ANY, TargetDevice.CPU, TargetDevice.GPU, TargetDevice.VPU], + devices=[TargetDevice.ANY, TargetDevice.CPU, TargetDevice.GPU, TargetDevice.NPU], ) LINEAR_SCALE_SHIFT = PatternDesc( "linear_scale_shift", devices=[TargetDevice.ANY, TargetDevice.CPU, TargetDevice.GPU] @@ -394,7 +394,7 @@ class IgnoredPatternNames(Enum): MULTIHEAD_ATTENTION_OUTPUT = PatternDesc( "multihead_attention_output", model_types=[ModelType.TRANSFORMER], - devices=[TargetDevice.ANY, TargetDevice.CPU, TargetDevice.GPU, TargetDevice.VPU], + devices=[TargetDevice.ANY, TargetDevice.CPU, TargetDevice.GPU, TargetDevice.NPU], ) SE_BLOCK = PatternDesc("se_block") FC_BN_HSWISH_ACTIVATION = PatternDesc("fc_bn_hswish_activation") diff --git a/nncf/common/graph/transformations/__init__.py b/nncf/common/graph/transformations/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/common/graph/transformations/__init__.py +++ b/nncf/common/graph/transformations/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/graph/transformations/command_creation.py b/nncf/common/graph/transformations/command_creation.py index 257ea79ac47..607549d94e5 100644 --- a/nncf/common/graph/transformations/command_creation.py +++ b/nncf/common/graph/transformations/command_creation.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/graph/transformations/commands.py b/nncf/common/graph/transformations/commands.py index c40183dd94e..c03b9987e7c 100644 --- a/nncf/common/graph/transformations/commands.py +++ b/nncf/common/graph/transformations/commands.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/graph/transformations/layout.py b/nncf/common/graph/transformations/layout.py index 025b5c78ccb..2698315075b 100644 --- a/nncf/common/graph/transformations/layout.py +++ b/nncf/common/graph/transformations/layout.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/graph/utils.py b/nncf/common/graph/utils.py index 0352693e6a5..a49bcf031a0 100644 --- a/nncf/common/graph/utils.py +++ b/nncf/common/graph/utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/hardware/__init__.py b/nncf/common/hardware/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/common/hardware/__init__.py +++ b/nncf/common/hardware/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/hardware/config.py b/nncf/common/hardware/config.py index b683d974ad0..4cc89ed3e7b 100644 --- a/nncf/common/hardware/config.py +++ b/nncf/common/hardware/config.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -17,6 +17,7 @@ import jstyleson as json +import nncf from nncf.common.graph.operator_metatypes import OperatorMetatype from nncf.common.logging import nncf_logger from nncf.common.quantization import quantizers as quant @@ -31,13 +32,13 @@ class HWConfigType(Enum): CPU = "CPU" GPU = "GPU" - VPU = "VPU" + NPU = "NPU" HW_CONFIG_TYPE_TARGET_DEVICE_MAP = { "ANY": HWConfigType.CPU.value, "CPU": HWConfigType.CPU.value, - "VPU": HWConfigType.VPU.value, + "NPU": HWConfigType.NPU.value, "GPU": HWConfigType.GPU.value, "CPU_SPR": HWConfigType.CPU.value, } @@ -66,7 +67,7 @@ class HWConfig(list, ABC): UNIFIED_TYPE_NAME = "unified" ADJUST_PADDING_ATTRIBUTE_NAME = "adjust_padding" - TYPE_TO_CONF_NAME_DICT = {HWConfigType.CPU: "cpu.json", HWConfigType.VPU: "vpu.json", HWConfigType.GPU: "gpu.json"} + TYPE_TO_CONF_NAME_DICT = {HWConfigType.CPU: "cpu.json", HWConfigType.NPU: "npu.json", HWConfigType.GPU: "gpu.json"} def __init__(self): super().__init__() @@ -140,7 +141,7 @@ def get_quantization_mode_from_config_value(str_val: str): return QuantizationMode.SYMMETRIC if str_val == "asymmetric": return QuantizationMode.ASYMMETRIC - raise RuntimeError("Invalid quantization type specified in HW config") + raise nncf.ValidationError("Invalid quantization type specified in HW config") @staticmethod def get_is_per_channel_from_config_value(str_val: str): @@ -148,7 +149,7 @@ def get_is_per_channel_from_config_value(str_val: str): return True if str_val == "pertensor": return False - raise RuntimeError("Invalid quantization granularity specified in HW config") + raise nncf.ValidationError("Invalid quantization granularity specified in HW config") @staticmethod def get_qconf_from_hw_config_subdict(quantization_subdict: Dict): diff --git a/nncf/common/hardware/configs/vpu.json b/nncf/common/hardware/configs/npu.json similarity index 99% rename from nncf/common/hardware/configs/vpu.json rename to nncf/common/hardware/configs/npu.json index 6510495d35c..0148a9a44ff 100644 --- a/nncf/common/hardware/configs/vpu.json +++ b/nncf/common/hardware/configs/npu.json @@ -1,5 +1,5 @@ { - "target_device": "VPU", + "target_device": "NPU", "config": { "quantization": { "q8_tn": { diff --git a/nncf/common/hardware/opset.py b/nncf/common/hardware/opset.py index 2526976d3c7..c01e950b417 100644 --- a/nncf/common/hardware/opset.py +++ b/nncf/common/hardware/opset.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/hook_handle.py b/nncf/common/hook_handle.py new file mode 100644 index 00000000000..0dde089455a --- /dev/null +++ b/nncf/common/hook_handle.py @@ -0,0 +1,65 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import weakref +from typing import Any, Dict, Union + + +class HookHandle: + """ + A handle to remove a hook. Does not guarantee that + hook_id is unique for the hooks_registry. + """ + + def __init__(self, hooks_registry: Dict[Any, Any], hook_id: str): + """ + :param hooks_registry: A dictionary of hooks, indexed by hook `id`. + :param hook_id: Hook id to use as key in the dictionary of hooks. + """ + self.hooks_registry_ref = weakref.ref(hooks_registry) + self._hook_id = hook_id + + @property + def hook_id(self) -> Union[int, str]: + """ + Key to use to retrieve handle from the registry. + """ + return self._hook_id + + def remove(self) -> None: + """ + Removes the corresponding operation from the registered hooks registry if it is possible. + """ + if self.hooks_registry_ref is None: + return + + hooks_registry = self.hooks_registry_ref() + if hooks_registry is not None and self.hook_id in hooks_registry: + del hooks_registry[self.hook_id] + self.hooks_registry_ref = None + + +def add_op_to_registry(hooks_registry: Dict[Any, Any], op: Any) -> HookHandle: + """ + Registers op into the hooks_registry and returns HookHandler instance. + + :param hooks_registry: A dictionary of hooks, indexed by hook `id`. + :param op: Operation to set to registered hooks registry. + :return: HookHandle that contains the registry of hooks and + the id of operation to remove it from the registry. + """ + if hooks_registry: + hook_id = max(map(int, hooks_registry)) + hook_id = str(hook_id + 1) + else: + hook_id = "0" + hooks_registry[hook_id] = op + return HookHandle(hooks_registry, hook_id) diff --git a/nncf/common/initialization/__init__.py b/nncf/common/initialization/__init__.py index 39cabd39176..a0f17af37fa 100644 --- a/nncf/common/initialization/__init__.py +++ b/nncf/common/initialization/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/initialization/batchnorm_adaptation.py b/nncf/common/initialization/batchnorm_adaptation.py index c8d6d996c46..8b8e2ce8d8b 100644 --- a/nncf/common/initialization/batchnorm_adaptation.py +++ b/nncf/common/initialization/batchnorm_adaptation.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/initialization/dataloader.py b/nncf/common/initialization/dataloader.py index 541dad380a2..07fa4b98ab9 100644 --- a/nncf/common/initialization/dataloader.py +++ b/nncf/common/initialization/dataloader.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/insertion_point_graph.py b/nncf/common/insertion_point_graph.py index c3fa6f52fe7..5cd1a8e4710 100644 --- a/nncf/common/insertion_point_graph.py +++ b/nncf/common/insertion_point_graph.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/logging/__init__.py b/nncf/common/logging/__init__.py index be651872924..d1f1cd1354e 100644 --- a/nncf/common/logging/__init__.py +++ b/nncf/common/logging/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/logging/logger.py b/nncf/common/logging/logger.py index 2d03e8d7207..26284dae1f3 100644 --- a/nncf/common/logging/logger.py +++ b/nncf/common/logging/logger.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/logging/progress_bar.py b/nncf/common/logging/progress_bar.py index 4dd6c504917..322f04e070c 100644 --- a/nncf/common/logging/progress_bar.py +++ b/nncf/common/logging/progress_bar.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/logging/track_progress.py b/nncf/common/logging/track_progress.py index b82a95f32bf..90455fab2ef 100644 --- a/nncf/common/logging/track_progress.py +++ b/nncf/common/logging/track_progress.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/plotting.py b/nncf/common/plotting.py index bcb189561e3..d3f97d66857 100644 --- a/nncf/common/plotting.py +++ b/nncf/common/plotting.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/pruning/__init__.py b/nncf/common/pruning/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/common/pruning/__init__.py +++ b/nncf/common/pruning/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/pruning/clusterization.py b/nncf/common/pruning/clusterization.py index 8e8459e1e62..47457d01ca7 100644 --- a/nncf/common/pruning/clusterization.py +++ b/nncf/common/pruning/clusterization.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/pruning/mask_propagation.py b/nncf/common/pruning/mask_propagation.py index 63b7da58027..dfa0c79da48 100644 --- a/nncf/common/pruning/mask_propagation.py +++ b/nncf/common/pruning/mask_propagation.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/pruning/model_analysis.py b/nncf/common/pruning/model_analysis.py index 566f578ac3c..47d7e79d454 100644 --- a/nncf/common/pruning/model_analysis.py +++ b/nncf/common/pruning/model_analysis.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/pruning/node_selector.py b/nncf/common/pruning/node_selector.py index 4fc8d286327..a58ed7e1f37 100644 --- a/nncf/common/pruning/node_selector.py +++ b/nncf/common/pruning/node_selector.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/pruning/operations.py b/nncf/common/pruning/operations.py index 905285b6396..022f4bd375e 100644 --- a/nncf/common/pruning/operations.py +++ b/nncf/common/pruning/operations.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -315,7 +315,7 @@ def generate_output_masks( # propagate as is return input_mask - if not input_mask.shape[0] == sum(output_shapes): + if input_mask.shape[0] != sum(output_shapes): return None split_masks = tensor_processor.split(input_mask, output_shapes) diff --git a/nncf/common/pruning/schedulers.py b/nncf/common/pruning/schedulers.py index 3230447f4f0..facd3795588 100644 --- a/nncf/common/pruning/schedulers.py +++ b/nncf/common/pruning/schedulers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/pruning/shape_pruning_processor.py b/nncf/common/pruning/shape_pruning_processor.py index 9bb099cb17f..6214551bf49 100644 --- a/nncf/common/pruning/shape_pruning_processor.py +++ b/nncf/common/pruning/shape_pruning_processor.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -11,6 +11,7 @@ from typing import Any, Callable, Dict, List, Tuple +import nncf from nncf.common.graph import NNCFGraph from nncf.common.graph import NNCFNode from nncf.common.graph import NNCFNodeName @@ -168,7 +169,7 @@ def _get_next_node_sparse_multiplier( if mask_producer.id in cluster_nodes_idxs: return mask_producer.sparse_multiplier - raise RuntimeError(f"Next node for cluster {cluster.elements} doesn't have closing mask") + raise nncf.ValidationError(f"Next node for cluster {cluster.elements} doesn't have closing mask") def get_next_nodes( self, graph: NNCFGraph, pruning_groups: Clusterization[PrunedLayerInfoBase] diff --git a/nncf/common/pruning/statistics.py b/nncf/common/pruning/statistics.py index 006fd4b5b0b..5310ebde754 100644 --- a/nncf/common/pruning/statistics.py +++ b/nncf/common/pruning/statistics.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/pruning/structs.py b/nncf/common/pruning/structs.py index 1111ab27d6e..72182c71ba2 100644 --- a/nncf/common/pruning/structs.py +++ b/nncf/common/pruning/structs.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/pruning/symbolic_mask.py b/nncf/common/pruning/symbolic_mask.py index 49b9f98668d..6e67e31ceba 100644 --- a/nncf/common/pruning/symbolic_mask.py +++ b/nncf/common/pruning/symbolic_mask.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -11,6 +11,7 @@ from typing import List, Union +import nncf from nncf.common.pruning.tensor_processor import NNCFPruningBaseTensorProcessor from nncf.common.tensor import NNCFTensor @@ -109,7 +110,7 @@ def concatenate(cls, tensors: List[SymbolicMask], axis: int) -> SymbolicMask: def ones(cls, shape: Union[int, List[int]], device) -> SymbolicMask: if isinstance(shape, list): if len(shape) != 1: - raise RuntimeError(f"Unexpected shape = {shape} for 1D symbolic mask") + raise nncf.ValidationError(f"Unexpected shape = {shape} for 1D symbolic mask") shape = shape[0] return SymbolicMask(shape) @@ -139,7 +140,7 @@ def elementwise_mask_propagation(cls, input_masks: List[SymbolicMask]) -> Symbol """ producers = SymbolicMaskProducer.merge_producers(input_masks) for input_mask in input_masks[1:]: - if not input_masks[0].shape == input_mask.shape: + if input_masks[0].shape != input_mask.shape: return AmbiguousSymbolicMask(producers) return SymbolicMask(input_masks[0].shape[0], producers) diff --git a/nncf/common/pruning/tensor_processor.py b/nncf/common/pruning/tensor_processor.py index e160c045120..457694874a6 100644 --- a/nncf/common/pruning/tensor_processor.py +++ b/nncf/common/pruning/tensor_processor.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/pruning/utils.py b/nncf/common/pruning/utils.py index e119d017b92..b67deff12a8 100644 --- a/nncf/common/pruning/utils.py +++ b/nncf/common/pruning/utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -16,6 +16,7 @@ import numpy as np +import nncf from nncf.common.graph import NNCFGraph from nncf.common.graph import NNCFNode from nncf.common.graph import NNCFNodeName @@ -385,7 +386,7 @@ def get_input_channels(node: NNCFNode) -> int: return layer_attrs.in_channels if isinstance(layer_attrs, LinearLayerAttributes): return layer_attrs.in_features - raise RuntimeError(f"Can't get count of input channels from node {node}") + raise nncf.InternalError(f"Can't get count of input channels from node {node}") def get_output_channels(node: NNCFNode) -> int: @@ -400,7 +401,7 @@ def get_output_channels(node: NNCFNode) -> int: return layer_attrs.out_channels if isinstance(layer_attrs, LinearLayerAttributes): return layer_attrs.out_features - raise RuntimeError(f"Can't get count of output channels from node {node}") + raise nncf.InternalError(f"Can't get count of output channels from node {node}") def identity_mask_propagation(node: NNCFNode, graph: NNCFGraph) -> None: diff --git a/nncf/common/pruning/weights_flops_calculator.py b/nncf/common/pruning/weights_flops_calculator.py index 85eb53a963f..85554ab2649 100644 --- a/nncf/common/pruning/weights_flops_calculator.py +++ b/nncf/common/pruning/weights_flops_calculator.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/quantization/__init__.py b/nncf/common/quantization/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/common/quantization/__init__.py +++ b/nncf/common/quantization/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/quantization/collectors.py b/nncf/common/quantization/collectors.py index 6c82f6f3f2e..a5da5da7f1b 100644 --- a/nncf/common/quantization/collectors.py +++ b/nncf/common/quantization/collectors.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/quantization/config_assignment.py b/nncf/common/quantization/config_assignment.py index 15a4283c1b7..2ec4e5108f1 100644 --- a/nncf/common/quantization/config_assignment.py +++ b/nncf/common/quantization/config_assignment.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -36,7 +36,7 @@ def get_scoped_quantizer_config( qconfig = deepcopy(base_config) if scope_overrides is None: scope_overrides = {} - for overridden_scope in scope_overrides.keys(): + for overridden_scope in scope_overrides: if matches_any(scope_str, overridden_scope): config_overrides = scope_overrides[overridden_scope] if config_overrides.get("bits") is not None: diff --git a/nncf/common/quantization/initialization/__init__.py b/nncf/common/quantization/initialization/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/common/quantization/initialization/__init__.py +++ b/nncf/common/quantization/initialization/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/quantization/initialization/range.py b/nncf/common/quantization/initialization/range.py index 6486c44d2c0..93a248298e4 100644 --- a/nncf/common/quantization/initialization/range.py +++ b/nncf/common/quantization/initialization/range.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -12,7 +12,7 @@ from typing import Dict, List, Optional from nncf.common.initialization.dataloader import NNCFDataLoader -from nncf.common.quantization.structs import QuantizationScheme as QuantizationMode +from nncf.common.quantization.structs import QuantizationScheme from nncf.common.quantization.structs import QuantizerGroup from nncf.config.schemata.defaults import NUM_INIT_SAMPLES @@ -148,17 +148,39 @@ class RangeInitCollectorParams: Defines low-level parameters that are used to instantiate statistic collectors. """ - def __init__(self, is_weights: bool, mode: QuantizationMode, per_channel: bool): + def __init__(self, is_weights: bool, scheme: QuantizationScheme, per_channel: bool): """ Initializes Range Initialization Collector Parameters. :param is_weights: Boolean that defines tensor type. True for Weights, False for Activations. - :param mode: Quantization mode: symmetric or asymmetric. + :param scheme: Quantization scheme: symmetric or asymmetric. :param per_channel: Quantization granularity. """ self._is_weights = is_weights - self._mode = mode - self._per_channel = per_channel + self._scheme = scheme + self._is_per_channel = per_channel + + @property + def is_weights(self) -> bool: + """ + Returns boolean that defines tensor type. + True for Weights, False for Activations. + """ + return self._is_weights + + @property + def scheme(self) -> QuantizationScheme: + """ + Returns quantization scheme: symmetric or asymmetric. + """ + return self._scheme + + @property + def is_per_channel(self) -> bool: + """ + Returns quantization granularity. + """ + return self._is_per_channel def use_per_sample_stats(self, per_sample_stats) -> bool: """ @@ -173,12 +195,12 @@ def use_per_sample_stats(self, per_sample_stats) -> bool: @property def use_abs_max(self) -> bool: """Applies abs(max) for symmetric quantization.""" - return self._mode == QuantizationMode.SYMMETRIC + return self._scheme == QuantizationScheme.SYMMETRIC @property def use_means_of_mins(self) -> bool: - return not self._is_weights and not self._per_channel and self._mode == "asymmetric" + return not self._is_weights and not self._is_per_channel and self._scheme == "asymmetric" @property def use_means_of_maxs(self) -> bool: - return not self._is_weights and not self._per_channel + return not self._is_weights and not self._is_per_channel diff --git a/nncf/common/quantization/quantizer_propagation/__init__.py b/nncf/common/quantization/quantizer_propagation/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/common/quantization/quantizer_propagation/__init__.py +++ b/nncf/common/quantization/quantizer_propagation/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/quantization/quantizer_propagation/graph.py b/nncf/common/quantization/quantizer_propagation/graph.py index fe8e37acf64..03cb4ed51dc 100644 --- a/nncf/common/quantization/quantizer_propagation/graph.py +++ b/nncf/common/quantization/quantizer_propagation/graph.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -17,6 +17,7 @@ import networkx as nx +import nncf from nncf import nncf_logger from nncf.common.graph import NNCFNode from nncf.common.graph import NNCFNodeName @@ -221,7 +222,7 @@ def ipg_node_type_to_qpsg_node_type( return QuantizerPropagationStateGraphNodeType.POST_HOOK if ipg_node_type == InsertionPointGraphNodeType.OPERATOR: return QuantizerPropagationStateGraphNodeType.OPERATOR - raise RuntimeError("Invalid insertion point graph node type.") + raise nncf.ValidationError("Invalid insertion point graph node type.") @staticmethod def get_barrier_node_key(node_key: str) -> str: @@ -253,7 +254,7 @@ def mark_act_quantizer_as_dependent_on_weights(self, pq: PropagatingQuantizer, o pq in self._pqs_after_weight_dependent_output_quantized_nodes and self._pqs_after_weight_dependent_output_quantized_nodes[pq] != operator_node_key ): - raise RuntimeError( + raise nncf.InternalError( f"Propagating quantizer {pq.id} is already marked as depending on node " f"{operator_node_key} weight quantization!" ) @@ -329,7 +330,7 @@ def merge_quantizer_into_path(self, prop_quantizer: PropagatingQuantizer, path: affecting_quantizers.append(pq) self.remove_propagating_quantizer(prop_quantizer) else: - raise RuntimeError( + raise nncf.InternalError( "Surviving_quantizers not found !" " Nodes quantized with quantizer #{} will be lost".format(prop_quantizer.id) ) @@ -370,7 +371,7 @@ def merge_quantizers_for_branching_node( pre_hook_ip = edge_from_pre_hook_ip_to_op[0] target_ip_node_keys.append(pre_hook_ip) else: - raise RuntimeError("Unsupported branching QPSG node type: {}".format(branching_node_type)) + raise nncf.InternalError("Unsupported branching QPSG node type: {}".format(branching_node_type)) if not target_ip_node_keys: return [] @@ -420,7 +421,7 @@ def merge_quantizers_for_branching_node( if merge_gid is not None: self._unified_scale_group_manager.add_to_group(merge_gid, merge_pq) else: - raise RuntimeError("Unsupported target type for merge PQ insertion: {}".format(target_type)) + raise nncf.InternalError("Unsupported target type for merge PQ insertion: {}".format(target_type)) merge_pqs.append(merge_pq) @@ -563,7 +564,7 @@ def add_propagating_quantizer( if ip_type != QuantizerPropagationStateGraphNodeType.PRE_HOOK: # The insertion point key should immediately precede a quantizable op, # otherwise it is hard to determine affected node here (although possible) - raise RuntimeError("Can only add propagating quantizers into pre-hook spots!") + raise nncf.InternalError("Can only add propagating quantizers into pre-hook spots!") prop_quantizer = PropagatingQuantizer( self._get_next_prop_quantizer_id(), qconf_list, ip_node_key, unified_scale_type @@ -603,13 +604,13 @@ def _verify_nodes_and_edges_for_pq(self, prop_quantizer: PropagatingQuantizer): for node_key in node_keys_to_verify: if node_key not in self.nodes: - raise RuntimeError( + raise nncf.InternalError( "Unknown node referenced by propagating quantizer to be registered: {}".format(node_key) ) edge_keys_to_verify = list(prop_quantizer.affected_edges) + list(prop_quantizer.propagation_path) for edge_key in edge_keys_to_verify: if edge_key not in self.edges: - raise RuntimeError( + raise nncf.InternalError( "Unknown edge referenced by propagating quantizer to be registered: {}".format(edge_key) ) @@ -619,7 +620,7 @@ def _verify_qconfig_matching( ): for existing_pq in existing_prop_quantizers: if existing_pq.potential_quant_configs != prop_quantizer.potential_quant_configs: - raise RuntimeError( + raise nncf.InternalError( "Configurations of the quantizer to be registered are conflicting with " "existing quantizer {}".format(existing_pq.id) ) @@ -629,14 +630,14 @@ def register_propagating_quantizer(self, prop_quantizer: PropagatingQuantizer): all_pqs = self.collect_all_propagating_quantizers() for existing_pq_id in all_pqs: if prop_quantizer.id == existing_pq_id: - raise RuntimeError( + raise nncf.InternalError( "The propagating quantizer to be registered has an ID that is already assigned to " "an existing propagating quantizer!" ) target_node = self.nodes[prop_quantizer.current_location_node_key] pq_in_target_node = target_node[QuantizerPropagationStateGraph.PROPAGATING_QUANTIZER_NODE_ATTR] if pq_in_target_node is not None: - raise RuntimeError( + raise nncf.InternalError( "The propagating quantizer to be registered is occupying the same position " "as an existing propagating quantizer {}!".format(pq_in_target_node.id) ) @@ -644,7 +645,7 @@ def register_propagating_quantizer(self, prop_quantizer: PropagatingQuantizer): QuantizerPropagationStateGraph.AFFECTING_PROPAGATING_QUANTIZERS_ATTR ] if target_node_affecting_quantizers: - raise RuntimeError( + raise nncf.InternalError( "Cannot register a propagating quantizer into a node that is already " "affected by existing propagating quantizers (ids: {})!".format( [pq.id for pq in target_node_affecting_quantizers] @@ -761,7 +762,7 @@ def recursive_helper(curr_node_key: str, target_node_list: List[str]): successor_node_type = successor[QuantizerPropagationStateGraph.NODE_TYPE_NODE_ATTR] if successor_node_type == QuantizerPropagationStateGraphNodeType.OPERATOR: trait = successor[QuantizerPropagationStateGraph.QUANTIZATION_TRAIT_NODE_ATTR] - if not trait == QuantizationTrait.QUANTIZATION_AGNOSTIC: + if trait != QuantizationTrait.QUANTIZATION_AGNOSTIC: target_node_list.append(successor_key) return recursive_helper(successor_key, target_node_list) @@ -961,7 +962,7 @@ def get_visualized_graph(self): elif node_type == QuantizerPropagationStateGraphNodeType.AUXILIARY_BARRIER: out_graph.add_node(node_key, color="green", label=node["label"]) else: - raise RuntimeError("Invalid QuantizerPropagationStateGraph node!") + raise nncf.InternalError("Invalid QuantizerPropagationStateGraph node!") for u, v in self.edges: edge = self.edges[u, v] attrs = {} @@ -1076,7 +1077,7 @@ def recursive_helper(curr_node_key: str, curr_input_quantizer_ids_list: List[int return elif curr_node_type == QuantizerPropagationStateGraphNodeType.OPERATOR: trait = curr_node[QuantizerPropagationStateGraph.QUANTIZATION_TRAIT_NODE_ATTR] - if not trait == QuantizationTrait.QUANTIZATION_AGNOSTIC: + if trait != QuantizationTrait.QUANTIZATION_AGNOSTIC: return elif curr_node_type == QuantizerPropagationStateGraphNodeType.AUXILIARY_BARRIER: return @@ -1285,7 +1286,7 @@ def create_quantizer_setup( grouped_ids.add(qp_id) gid = setup.register_shared_inputs_group(list(grouped_ids)) - for weighted_node_name in weight_quantizable_node_names_vs_configs.keys(): + for weighted_node_name in weight_quantizable_node_names_vs_configs: for affected_node_key in group.affected_op_node_keys: underlying_node_names = [ n.node_name for n in self.op_node_keys_to_underlying_nodes_mapping[affected_node_key] diff --git a/nncf/common/quantization/quantizer_propagation/grouping.py b/nncf/common/quantization/quantizer_propagation/grouping.py index 2d58238c6d1..70c6933e3a0 100644 --- a/nncf/common/quantization/quantizer_propagation/grouping.py +++ b/nncf/common/quantization/quantizer_propagation/grouping.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/quantization/quantizer_propagation/solver.py b/nncf/common/quantization/quantizer_propagation/solver.py index 4771f41d685..7d0314bfc8b 100644 --- a/nncf/common/quantization/quantizer_propagation/solver.py +++ b/nncf/common/quantization/quantizer_propagation/solver.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -19,6 +19,7 @@ import networkx as nx +import nncf from nncf.common.graph import NNCFNodeName from nncf.common.graph.operator_metatypes import INPUT_NOOP_METATYPES from nncf.common.graph.operator_metatypes import OUTPUT_NOOP_METATYPES @@ -143,7 +144,9 @@ def constrain_quantizer_config_list_for_insertion( """ prior_list = self.quantizer_setup.quantization_points[quantization_point_id].possible_qconfigs if not all(qc in prior_list for qc in constrained_config_list): - raise RuntimeError("Constrained config list is incompatible with the result of the quantizer propagation!") + raise nncf.InternalError( + "Constrained config list is incompatible with the result of the quantizer propagation!" + ) # TODO (vshampor): only allow to constrain 'input-group'-wise? self.quantizer_setup.quantization_points[quantization_point_id].possible_qconfigs = constrained_config_list @@ -188,7 +191,7 @@ def is_final_qconfig_compatible_to_initial(initial_qconfig: QuantizerConfig): ) ) if not compatible_initial_qconfs: - raise RuntimeError( + raise nncf.InternalError( "The final quantizer setup has configurations that were not present in the " "initial proposal!" ) @@ -429,9 +432,11 @@ def __init__( if default_qconfig_list is not None: for op_meta, qconf_list in self._operator_allowed_qconfigs_map.items(): trait = self._operator_quantization_trait_map.get(op_meta, QuantizationTrait.NON_QUANTIZABLE) - if trait == QuantizationTrait.INPUTS_QUANTIZABLE: - if HWConfig.is_qconf_list_corresponding_to_unspecified_op(qconf_list): - self._operator_allowed_qconfigs_map[op_meta] = default_qconfig_list + if ( + trait == QuantizationTrait.INPUTS_QUANTIZABLE + and HWConfig.is_qconf_list_corresponding_to_unspecified_op(qconf_list) + ): + self._operator_allowed_qconfigs_map[op_meta] = default_qconfig_list self._active_propagating_quantizers_queue = deque() self._finished_propagating_quantizers: List[PropagatingQuantizer] = [] self._quantizers_waiting_for_branch_merge = QuantizersWaitingForMergeManager() @@ -587,7 +592,7 @@ def get_final_quantizer_setup( if Counter(final_weight_quantizable_node_names_vs_qconfig_dict.keys()) != Counter( self._weight_quantizable_node_names_vs_qconfigs.keys() ): - raise RuntimeError("Final weight quantizer setup is inconsistent with initial solver assumptions!") + raise nncf.InternalError("Final weight quantizer setup is inconsistent with initial solver assumptions!") multi_setup_with_one_config_per_point = quant_prop_graph.create_quantizer_setup( final_weight_quantizable_node_names_vs_qconfig_dict @@ -728,7 +733,7 @@ def propagation_step( ) ) - unified_scale_path_groups_vs_pqs = {k: [] for k in unified_scale_grouped_paths.keys() if k is not None} + unified_scale_path_groups_vs_pqs = {k: [] for k in unified_scale_grouped_paths if k is not None} existing_pq_assigned = False for gid, path_group in unified_scale_grouped_paths.items(): for _ in path_group: @@ -1014,7 +1019,7 @@ def coalesce_insertion_points( ) ) if len(matching_indices) == 0: - raise RuntimeError( + raise nncf.ValidationError( "No match for linked quantizer entry {} among activation quantizers!".format( group_member_node_name ) @@ -1022,7 +1027,7 @@ def coalesce_insertion_points( for target_idx in matching_indices: if target_idx in insertion_point_indices_vs_group_id: - raise RuntimeError( + raise nncf.InternalError( "Linked activation quantizer groups {} and {} " "overlap!".format(group_idx, insertion_point_indices_vs_group_id[target_idx]) ) @@ -1129,7 +1134,7 @@ def _setup_initial_quantizers_for_operator_node( op_meta_name = metatype.__class__.__name__ if len(per_tensor_qconf_list) != len(qconf_list): if not per_tensor_qconf_list: - raise RuntimeError( + raise nncf.InternalError( "Unified scales currently do not support per-channel configuration - dropping" "per-channel configuration options for {} resulted in no valid quantization " "configs!".format(op_meta_name) @@ -1428,7 +1433,7 @@ def compatible_wo_requant(qconf: QuantizerConfig, other_qconf_list: List[Quantiz elif self._propagation_strategy == PropagationStrategy.MERGE_WITH_SINGLE_FQ_RESULT: compatible_fn = compatible_wo_requant else: - raise RuntimeError(f"Unknown propagation strategy: {self._propagation_strategy}") + raise nncf.ValidationError(f"Unknown propagation strategy: {self._propagation_strategy}") for qconf in qconfigs_union: if all(compatible_fn(qconf, qconf_list) for qconf_list in potential_qconfigs_for_each_branch): diff --git a/nncf/common/quantization/quantizer_propagation/structs.py b/nncf/common/quantization/quantizer_propagation/structs.py index 0831da49f48..37444978d62 100644 --- a/nncf/common/quantization/quantizer_propagation/structs.py +++ b/nncf/common/quantization/quantizer_propagation/structs.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/quantization/quantizer_propagation/visualizer.py b/nncf/common/quantization/quantizer_propagation/visualizer.py index d5a91b2de3b..027beca091e 100644 --- a/nncf/common/quantization/quantizer_propagation/visualizer.py +++ b/nncf/common/quantization/quantizer_propagation/visualizer.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/quantization/quantizer_removal.py b/nncf/common/quantization/quantizer_removal.py index 5855c90c049..edeb2a74e7e 100644 --- a/nncf/common/quantization/quantizer_removal.py +++ b/nncf/common/quantization/quantizer_removal.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -9,7 +9,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -from typing import List, Tuple, TypeVar +from typing import Callable, List, Tuple, TypeVar from nncf.common.factory import CommandCreatorFactory from nncf.common.factory import ModelTransformerFactory @@ -62,6 +62,17 @@ def _parse_node_relatives(node: NNCFNode, is_parents: bool): if relative not in to_cut: to_cut.append(relative) to_see_children.append(relative) + # We should see parents for the `relative` node here only if they are + # all quantizers. This covers the quantize-dequantize case, where we + # should see parents for the dequantize node. + if all(x.metatype in quantizer_metatypes for x in graph.get_previous_nodes(relative)): + to_see_parents.append(relative) + elif node.metatype in quantizer_metatypes: + # `node` is a quantizer (quantize-dequantize case) here, and `relative` + # is the dequantizer. So, we should cut `relative` and look at its children. + if relative not in to_cut: + to_cut.append(relative) + to_see_children.append(relative) else: seen_children.append(relative) elif relative.metatype not in const_metatypes: @@ -94,6 +105,8 @@ def revert_operations_to_floating_point_precision( quantized_model_graph: NNCFGraph, restore_mode: RestoreMode, op_with_weights_metatypes: List[OperatorMetatype], + is_node_with_weight_fn: Callable[[NNCFNode], bool], + get_weight_tensor_port_ids_fn: Callable[[NNCFNode], List[int]], ) -> TModel: """ Reverts provided operations to floating-point precision by removing @@ -109,6 +122,9 @@ def revert_operations_to_floating_point_precision( :param restore_mode: Restore mode. :param op_with_weights_metatypes: List of operation metatypes that can be reverted to representation with int8 weights. + :param is_node_with_weight_fn: Checks if the node has a weight or not. Returns `True` if `node` corresponds + to the operation with weights, `False` otherwise. + :param get_weight_tensor_port_ids_fn: Returns node's input port indices with weights tensors. :return: The model where `operations` were reverted to floating-point precision. """ transformation_layout = TransformationLayout() @@ -144,8 +160,8 @@ def revert_operations_to_floating_point_precision( if not should_revert_weights_to_fp32.get(node.node_name, True): continue - if node.layer_attributes and node.layer_attributes.constant_attributes is not None: - weight_port_ids = node.layer_attributes.get_const_port_ids() + if is_node_with_weight_fn(node): + weight_port_ids = get_weight_tensor_port_ids_fn(node) for port_id in weight_port_ids: original_weight = node.attributes.get(f"original_weight.{port_id}", None) if original_weight is not None: diff --git a/nncf/common/quantization/quantizer_setup.py b/nncf/common/quantization/quantizer_setup.py index 5dd0758aa53..a2d1f3f3ad7 100644 --- a/nncf/common/quantization/quantizer_setup.py +++ b/nncf/common/quantization/quantizer_setup.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -15,6 +15,7 @@ from enum import Enum from typing import Any, Dict, List, Optional, Set +import nncf from nncf.common.graph import NNCFNodeName from nncf.common.logging import nncf_logger from nncf.common.quantization.structs import NonWeightQuantizerId @@ -252,7 +253,7 @@ def register_unified_scale_group(self, qp_group: List[QuantizationPointId]) -> i for qp_id in qp_group: gid = self.get_unified_scale_group_id(qp_id) is not None if gid: - raise RuntimeError("QP id {} is already in unified scale group {}".format(qp_id, gid)) + raise nncf.InternalError("QP id {} is already in unified scale group {}".format(qp_id, gid)) gid = self._next_unified_scale_gid self.unified_scale_groups[self._next_unified_scale_gid] = set(qp_group) self._next_unified_scale_gid += 1 @@ -262,7 +263,7 @@ def register_shared_inputs_group(self, qp_group: List[QuantizationPointId]) -> i for qp_id in qp_group: gid = self.get_shared_inputs_group_id(qp_id) is not None if gid: - raise RuntimeError("QP id {} is already in shared input group {}".format(qp_id, gid)) + raise nncf.InternalError("QP id {} is already in shared input group {}".format(qp_id, gid)) gid = self._next_shared_inputs_gid self.shared_input_operation_set_groups[self._next_shared_inputs_gid] = set(qp_group) self._next_shared_inputs_gid += 1 @@ -308,13 +309,13 @@ def get_shared_inputs_group_id(self, qp_id: QuantizationPointId) -> Optional[int def register_existing_qp_id_in_unified_scale_group(self, qp_id: QuantizationPointId, unified_scale_gid: int): gid = self.get_unified_scale_group_id(qp_id) if gid is not None: - raise RuntimeError("QP id {} is already in unified scale group {}".format(qp_id, gid)) + raise nncf.InternalError("QP id {} is already in unified scale group {}".format(qp_id, gid)) self.unified_scale_groups[unified_scale_gid].add(qp_id) def register_existing_qp_id_in_shared_input_group(self, qp_id: QuantizationPointId, shared_inputs_gid: int): gid = self.get_shared_inputs_group_id(qp_id) if gid is not None: - raise RuntimeError("QP id {} is already in shared inputs group {}".format(qp_id, gid)) + raise nncf.InternalError("QP id {} is already in shared inputs group {}".format(qp_id, gid)) self.shared_input_operation_set_groups[shared_inputs_gid].add(qp_id) def remove_unified_scale_from_point(self, qp_id: QuantizationPointId): diff --git a/nncf/common/quantization/quantizers.py b/nncf/common/quantization/quantizers.py index 4737a62a046..43c9a3f7dd8 100644 --- a/nncf/common/quantization/quantizers.py +++ b/nncf/common/quantization/quantizers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/quantization/statistics.py b/nncf/common/quantization/statistics.py index 38b6334e0fe..e7f9ed4f647 100644 --- a/nncf/common/quantization/statistics.py +++ b/nncf/common/quantization/statistics.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/quantization/structs.py b/nncf/common/quantization/structs.py index 794232c8aaf..aa898b76f23 100644 --- a/nncf/common/quantization/structs.py +++ b/nncf/common/quantization/structs.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -13,6 +13,7 @@ from enum import Enum from typing import Any, Dict, List, Optional +import nncf from nncf.common.graph import NNCFNode from nncf.common.graph import NNCFNodeName from nncf.common.utils.api_marker import api @@ -193,7 +194,9 @@ def __init__(self, **kwargs): """ for attr_name in kwargs: if not hasattr(QuantizationConstraints.REF_QCONF_OBJ, attr_name): - raise RuntimeError("Invalid constraint - QuantizerConfig has no attribute '{}'".format(attr_name)) + raise nncf.ValidationError( + "Invalid constraint - QuantizerConfig has no attribute '{}'".format(attr_name) + ) self.qconf_attr_vs_constraint_dict = kwargs def apply_constraints_to(self, qconfig: QuantizerConfig) -> QuantizerConfig: diff --git a/nncf/common/schedulers.py b/nncf/common/schedulers.py index d9d01ed696e..fe064d4a1fd 100644 --- a/nncf/common/schedulers.py +++ b/nncf/common/schedulers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -161,7 +161,7 @@ class BaseCompressionScheduler(CompressionScheduler): ``` """ - def __init__(self): + def __init__(self) -> None: """ Initializes the internal state of the compression scheduler specified by: - `current_step` is the index of the global training step, counted diff --git a/nncf/common/scopes.py b/nncf/common/scopes.py index ededd3ec158..218a058653b 100644 --- a/nncf/common/scopes.py +++ b/nncf/common/scopes.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -12,6 +12,7 @@ import re from typing import List, Optional, Set, Union +import nncf from nncf.common.graph import NNCFGraph from nncf.common.graph import NNCFNode from nncf.common.graph import NNCFNodeName @@ -137,5 +138,5 @@ def check_scopes_in_graph( ) if validate_scopes: - raise RuntimeError(err_message) + raise nncf.ValidationError(err_message) nncf_logger.info(err_message) diff --git a/nncf/common/sparsity/__init__.py b/nncf/common/sparsity/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/common/sparsity/__init__.py +++ b/nncf/common/sparsity/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/sparsity/collector.py b/nncf/common/sparsity/collector.py index 1555bc10cf7..5001153e279 100644 --- a/nncf/common/sparsity/collector.py +++ b/nncf/common/sparsity/collector.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -72,7 +72,9 @@ def is_sparse(self) -> bool: return self._is_sparse -def _calculate_sparsity_level_for_model(weight_descriptions: List[WeightDescription]) -> float: +def _calculate_sparsity_level_for_model( + weight_descriptions: List[WeightDescription], +) -> float: """ Calculates the sparsity level for the whole model. @@ -121,7 +123,9 @@ def collect(self) -> SparsifiedModelStatistics: sparse_layers_summary.append(SparsifiedLayerSummary(w.name, w.shape, w.sparsity_level, weight_percentage)) sparse_model_stats = SparsifiedModelStatistics( - sparsity_level_for_model, sparsity_level_for_sparse_layers, sparse_layers_summary + sparsity_level_for_model, + sparsity_level_for_sparse_layers, + sparse_layers_summary, ) return sparse_model_stats diff --git a/nncf/common/sparsity/controller.py b/nncf/common/sparsity/controller.py index 0f5825e3380..99ca2ca1136 100644 --- a/nncf/common/sparsity/controller.py +++ b/nncf/common/sparsity/controller.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -9,6 +9,8 @@ # See the License for the specific language governing permissions and # limitations under the License. +from abc import abstractmethod + from nncf.api.compression import CompressionAlgorithmController @@ -17,14 +19,21 @@ class SparsityController(CompressionAlgorithmController): This is the class from which all sparsity controllers inherit. """ - def set_sparsity_level(self, sparsity_level: float): + def set_sparsity_level(self, sparsity_level: float) -> None: """ Sets the sparsity level that should be applied to the model's weights. :param sparsity_level: Sparsity level that should be applied to the model's weights. """ - def freeze(self): + def freeze(self) -> None: """ Freezes all sparsity masks. Sparsity masks will not be trained after calling this method. """ + + @property + @abstractmethod + def current_sparsity_level(self) -> float: + """ + Returns the current sparsity level of the underlying model. + """ diff --git a/nncf/common/sparsity/schedulers.py b/nncf/common/sparsity/schedulers.py index 5569607a9e2..b8acc2bc809 100644 --- a/nncf/common/sparsity/schedulers.py +++ b/nncf/common/sparsity/schedulers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -19,6 +19,7 @@ from nncf.common.sparsity.controller import SparsityController from nncf.common.utils.registry import Registry from nncf.config.schemata.defaults import SPARSITY_FREEZE_EPOCH +from nncf.config.schemata.defaults import SPARSITY_INIT from nncf.config.schemata.defaults import SPARSITY_MULTISTEP_SPARSITY_LEVELS from nncf.config.schemata.defaults import SPARSITY_MULTISTEP_STEPS from nncf.config.schemata.defaults import SPARSITY_SCHEDULER_CONCAVE @@ -28,7 +29,7 @@ from nncf.config.schemata.defaults import SPARSITY_TARGET from nncf.config.schemata.defaults import SPARSITY_TARGET_EPOCH -SPARSITY_SCHEDULERS = Registry("sparsity_schedulers") +SPARSITY_SCHEDULERS: Registry = Registry("sparsity_schedulers") class SparsityScheduler(BaseCompressionScheduler): @@ -50,7 +51,7 @@ class SparsityScheduler(BaseCompressionScheduler): mask will be frozen and will not be trained. """ - def __init__(self, controller: SparsityController, params: dict): + def __init__(self, controller: SparsityController, params: Dict[str, Any]) -> None: """ Initializes the internal state of the sparsity scheduler. @@ -59,8 +60,8 @@ def __init__(self, controller: SparsityController, params: dict): """ super().__init__() self._controller = controller - self.initial_level = params.get("sparsity_init") - self.target_level = params.get("sparsity_target", SPARSITY_TARGET) + self.initial_level: float = params.get("sparsity_init", SPARSITY_INIT) + self.target_level: float = params.get("sparsity_target", SPARSITY_TARGET) self.target_epoch = params.get("sparsity_target_epoch", SPARSITY_TARGET_EPOCH) self.freeze_epoch = params.get("sparsity_freeze_epoch", SPARSITY_FREEZE_EPOCH) @@ -113,7 +114,7 @@ class PolynomialSparsityScheduler(SparsityScheduler): after `steps_per_epoch` will be calculated. """ - def __init__(self, controller: SparsityController, params: dict): + def __init__(self, controller: SparsityController, params: Dict[str, Any]): """ Initializes a sparsity scheduler with a polynomial decay schedule. @@ -181,12 +182,15 @@ def _maybe_should_skip(self) -> None: if self._steps_per_epoch is None and self._steps_in_current_epoch > 0: self._steps_per_epoch = self._steps_in_current_epoch - if self._steps_per_epoch is not None and self._steps_in_current_epoch > 0: - if self._steps_per_epoch != self._steps_in_current_epoch: - raise Exception( - "Actual steps per epoch and steps per epoch from the scheduler " - "parameters are different. Scheduling may be incorrect." - ) + if ( + self._steps_per_epoch is not None + and self._steps_in_current_epoch > 0 + and self._steps_per_epoch != self._steps_in_current_epoch + ): + raise Exception( + "Actual steps per epoch and steps per epoch from the scheduler " + "parameters are different. Scheduling may be incorrect." + ) if self._steps_per_epoch is None: self._should_skip = True @@ -210,7 +214,7 @@ class ExponentialSparsityScheduler(SparsityScheduler): current_density = 1.0 - current_level """ - def __init__(self, controller: SparsityController, params: dict): + def __init__(self, controller: SparsityController, params: Dict[str, Any]): """ Initializes a sparsity scheduler with an exponential decay schedule. @@ -218,6 +222,7 @@ def __init__(self, controller: SparsityController, params: dict): :param params: Parameters of the scheduler. """ super().__init__(controller, params) + initial_density = 1.0 - self.initial_level target_density = 1.0 - self.target_level self.schedule = ExponentialDecaySchedule(initial_density, target_density, self.target_epoch) @@ -227,8 +232,8 @@ def epoch_step(self, next_epoch: Optional[int] = None) -> None: self._update_sparsity_level() def _calculate_sparsity_level(self) -> float: - current_density = self.schedule(self.current_epoch) - current_level = 1.0 - current_density + current_density: float = self.schedule(self.current_epoch) + current_level: float = 1.0 - current_density return min(current_level, self.target_level) @@ -238,7 +243,7 @@ class AdaptiveSparsityScheduler(SparsityScheduler): Sparsity scheduler with an adaptive schedule. """ - def __init__(self, controller: SparsityController, params: dict): + def __init__(self, controller: SparsityController, params: Dict[str, Any]): """ Initializes a sparsity scheduler with an adaptive schedule. @@ -250,7 +255,7 @@ def __init__(self, controller: SparsityController, params: dict): self.eps = params.get("eps", 0.03) self.patience = params.get("patience", SPARSITY_SCHEDULER_PATIENCE) self.num_bad_epochs = 0 - self._current_level = self.initial_level + self._current_level: float = self.initial_level @property def current_sparsity_level(self) -> float: @@ -267,7 +272,7 @@ def epoch_step(self, next_epoch: Optional[int] = None) -> None: self._update_sparsity_level() def _calculate_sparsity_level(self) -> float: - if self._controller.loss.current_sparsity >= self._current_level - self.eps: + if self._controller.current_sparsity_level >= self._current_level - self.eps: self.num_bad_epochs += 1 current_level = self._current_level @@ -297,7 +302,7 @@ class MultiStepSparsityScheduler(SparsityScheduler): Sparsity scheduler with a piecewise constant schedule. """ - def __init__(self, controller: SparsityController, params: dict): + def __init__(self, controller: SparsityController, params: Dict[str, Any]): """ Initializes a sparsity scheduler with a piecewise constant schedule. diff --git a/nncf/common/sparsity/statistics.py b/nncf/common/sparsity/statistics.py index 6d1887dc04c..62ff60aa865 100644 --- a/nncf/common/sparsity/statistics.py +++ b/nncf/common/sparsity/statistics.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -27,7 +27,13 @@ class SparsifiedLayerSummary: :param weight_percentage: Proportion of the layer's weights in the whole model. """ - def __init__(self, name: str, weight_shape: List[int], sparsity_level: float, weight_percentage: float): + def __init__( + self, + name: str, + weight_shape: List[int], + sparsity_level: float, + weight_percentage: float, + ): self.name = name self.weight_shape = weight_shape self.sparsity_level = sparsity_level @@ -60,12 +66,20 @@ def to_str(self) -> str: header=["Statistic's name", "Value"], rows=[ ["Sparsity level of the whole model", self.sparsity_level], - ["Sparsity level of all sparsified layers", self.sparsity_level_for_layers], + [ + "Sparsity level of all sparsified layers", + self.sparsity_level_for_layers, + ], ], ) layers_string = create_table( - header=["Layer's name", "Weight's shape", "Sparsity level", "Weight's percentage"], + header=[ + "Layer's name", + "Weight's shape", + "Sparsity level", + "Weight's percentage", + ], rows=[ [s.name, s.weight_shape, s.sparsity_level, s.weight_percentage] for s in self.sparsified_layers_summary ], @@ -106,13 +120,17 @@ def __init__( def to_str(self) -> str: thresholds_string = create_table( - ["Layer's name", "Sparsity threshold"], [[s.name, s.threshold] for s in self.thresholds] + ["Layer's name", "Sparsity threshold"], + [[s.name, s.threshold] for s in self.thresholds], ) algorithm_string = create_table( header=["Statistic's name", "Value"], rows=[ - ["A target level of the sparsity for the algorithm for the current epoch", self.target_sparsity_level], + [ + "A target level of the sparsity for the algorithm for the current epoch", + self.target_sparsity_level, + ], ], ) @@ -150,7 +168,10 @@ class RBSparsityStatistics(Statistics): """ def __init__( - self, model_statistics: SparsifiedModelStatistics, target_sparsity_level: float, mean_sparse_prob: float + self, + model_statistics: SparsifiedModelStatistics, + target_sparsity_level: float, + mean_sparse_prob: float, ): self.model_statistics = model_statistics self.target_sparsity_level = target_sparsity_level @@ -160,8 +181,14 @@ def to_str(self) -> str: algorithm_string = create_table( header=["Statistic's name", "Value"], rows=[ - ["A target level of the sparsity for the algorithm for the current epoch", self.target_sparsity_level], - ["The probability that one weight will be zeroed", self.mean_sparse_prob], + [ + "A target level of the sparsity for the algorithm for the current epoch", + self.target_sparsity_level, + ], + [ + "The probability that one weight will be zeroed", + self.mean_sparse_prob, + ], ], ) @@ -196,7 +223,10 @@ def to_str(self) -> str: header=["Statistic's name", "Value"], rows=[ ["Mask Importance Threshold", self.importance_threshold], - ["Importance Regularization Factor", self.importance_regularization_factor], + [ + "Importance Regularization Factor", + self.importance_regularization_factor, + ], ], ) diff --git a/nncf/common/stateful_classes_registry.py b/nncf/common/stateful_classes_registry.py index 524b9078779..f608254d488 100644 --- a/nncf/common/stateful_classes_registry.py +++ b/nncf/common/stateful_classes_registry.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/statistics.py b/nncf/common/statistics.py index de9be01125e..aefccfde82b 100644 --- a/nncf/common/statistics.py +++ b/nncf/common/statistics.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/strip.py b/nncf/common/strip.py index 6de10c6b98d..c2cd592ff46 100644 --- a/nncf/common/strip.py +++ b/nncf/common/strip.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -12,6 +12,7 @@ from typing import TypeVar +import nncf from nncf.common.utils.api_marker import api from nncf.common.utils.backend import BackendType from nncf.common.utils.backend import get_backend @@ -36,4 +37,4 @@ def strip(model: TModel, do_copy: bool = True) -> TModel: return strip_pt(model, do_copy) - raise RuntimeError(f"Method `strip` does not support for {model_backend.value} backend.") + raise nncf.UnsupportedBackendError(f"Method `strip` does not support for {model_backend.value} backend.") diff --git a/nncf/common/tensor.py b/nncf/common/tensor.py index 5f766d4fd08..af6fddd46c4 100644 --- a/nncf/common/tensor.py +++ b/nncf/common/tensor.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -9,9 +9,10 @@ # See the License for the specific language governing permissions and # limitations under the License. -from abc import abstractmethod from typing import List, Optional, TypeVar +import nncf + TensorType = TypeVar("TensorType") DeviceType = TypeVar("DeviceType") TensorElementsType = TypeVar("TensorElementsType") @@ -35,13 +36,12 @@ def tensor(self) -> TensorType: @property def shape(self) -> List[int]: if self._tensor is None: - raise RuntimeError("Attempt to get shape of empty NNCFTensor") + raise nncf.InternalError("Attempt to get shape of empty NNCFTensor") return self._tensor.shape @property - @abstractmethod def device(self) -> DeviceType: - pass + raise NotImplementedError def is_empty(self) -> bool: - return False + raise NotImplementedError diff --git a/nncf/common/tensor_statistics/__init__.py b/nncf/common/tensor_statistics/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/common/tensor_statistics/__init__.py +++ b/nncf/common/tensor_statistics/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/tensor_statistics/aggregator.py b/nncf/common/tensor_statistics/aggregator.py index 6ca5178bbf3..cc896b0bee5 100644 --- a/nncf/common/tensor_statistics/aggregator.py +++ b/nncf/common/tensor_statistics/aggregator.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -13,6 +13,7 @@ from itertools import islice from typing import Any, Dict, TypeVar +import nncf from nncf.common import factory from nncf.common.graph.graph import NNCFGraph from nncf.common.graph.transformations.layout import TransformationLayout @@ -70,7 +71,7 @@ def collect_statistics(self, model: TModel, graph: NNCFGraph) -> None: self._register_statistics(processed_outputs, merged_statistics) empty_statistics = False if empty_statistics: - raise RuntimeError( + raise nncf.ValidationError( "Calibration dataset must not be empty. Please provide calibration dataset with at least one sample." ) diff --git a/nncf/common/tensor_statistics/collectors.py b/nncf/common/tensor_statistics/collectors.py index e1b01310216..a74c1f0915c 100644 --- a/nncf/common/tensor_statistics/collectors.py +++ b/nncf/common/tensor_statistics/collectors.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -21,7 +21,7 @@ from nncf.common.tensor import TensorType from nncf.common.tensor_statistics.reduction import get_per_channel_history -ReductionAxes = Tuple[int] +ReductionAxes = Tuple[int, ...] class TensorStatisticCollectorBase(ABC): diff --git a/nncf/common/tensor_statistics/reduction.py b/nncf/common/tensor_statistics/reduction.py index a1cdf89e8b9..3b90b2503ff 100644 --- a/nncf/common/tensor_statistics/reduction.py +++ b/nncf/common/tensor_statistics/reduction.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/tensor_statistics/statistic_point.py b/nncf/common/tensor_statistics/statistic_point.py index 3cf533ac7b6..fd50a100574 100644 --- a/nncf/common/tensor_statistics/statistic_point.py +++ b/nncf/common/tensor_statistics/statistic_point.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -58,7 +58,7 @@ def add_statistic_point(self, statistic_point: StatisticPoint) -> None: else: for _statistic_point in self.data[target_node_name]: if _statistic_point.target_point == statistic_point.target_point: - for algorithm in statistic_point.algorithm_to_tensor_collectors.keys(): + for algorithm in statistic_point.algorithm_to_tensor_collectors: if algorithm in _statistic_point.algorithm_to_tensor_collectors: _statistic_point.algorithm_to_tensor_collectors[algorithm].extend( statistic_point.algorithm_to_tensor_collectors[algorithm] diff --git a/nncf/common/tensor_statistics/statistics.py b/nncf/common/tensor_statistics/statistics.py index 1bc62db3464..2451e25a090 100644 --- a/nncf/common/tensor_statistics/statistics.py +++ b/nncf/common/tensor_statistics/statistics.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -87,7 +87,7 @@ def __init__(self, percentile_vs_values_dict): def __eq__(self, other: "PercentileTensorStatistic", rtol=1e-9) -> bool: if Counter(self.percentile_vs_values_dict.keys()) != Counter(other.percentile_vs_values_dict.keys()): return False - for pct in self.percentile_vs_values_dict.keys(): + for pct in self.percentile_vs_values_dict: if not self.tensor_eq(self.percentile_vs_values_dict[pct], other.percentile_vs_values_dict[pct]): return False return True diff --git a/nncf/common/utils/__init__.py b/nncf/common/utils/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/common/utils/__init__.py +++ b/nncf/common/utils/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/utils/api_marker.py b/nncf/common/utils/api_marker.py index 886a8177626..90e12ff5013 100644 --- a/nncf/common/utils/api_marker.py +++ b/nncf/common/utils/api_marker.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/utils/backend.py b/nncf/common/utils/backend.py index b996772df38..9dcd6a57d71 100644 --- a/nncf/common/utils/backend.py +++ b/nncf/common/utils/backend.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -13,6 +13,8 @@ from enum import Enum from typing import List, TypeVar +import nncf + TModel = TypeVar("TModel") @@ -128,7 +130,7 @@ def get_backend(model: TModel) -> BackendType: if BackendType.OPENVINO in available_backends and is_openvino_model(model): return BackendType.OPENVINO - raise RuntimeError( + raise nncf.UnsupportedBackendError( "Could not infer the backend framework from the model type because " "the framework is not available or the model type is unsupported. " "The available frameworks found: {}.".format(", ".join([b.value for b in available_backends])) diff --git a/nncf/common/utils/debug.py b/nncf/common/utils/debug.py index 25670390414..e88a2778030 100644 --- a/nncf/common/utils/debug.py +++ b/nncf/common/utils/debug.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/utils/decorators.py b/nncf/common/utils/decorators.py index 22d24096914..e5885be5ed0 100644 --- a/nncf/common/utils/decorators.py +++ b/nncf/common/utils/decorators.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/utils/dot_file_rw.py b/nncf/common/utils/dot_file_rw.py index f66699d8c62..be1b8ad0dfb 100644 --- a/nncf/common/utils/dot_file_rw.py +++ b/nncf/common/utils/dot_file_rw.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/utils/helpers.py b/nncf/common/utils/helpers.py index 29308c2dca3..5a789fbdcbb 100644 --- a/nncf/common/utils/helpers.py +++ b/nncf/common/utils/helpers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/utils/logger/__init__.py b/nncf/common/utils/logger/__init__.py index 026057532d4..edd629e0c84 100644 --- a/nncf/common/utils/logger/__init__.py +++ b/nncf/common/utils/logger/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/utils/os.py b/nncf/common/utils/os.py index 78d06d2fb86..471ed6e2a54 100644 --- a/nncf/common/utils/os.py +++ b/nncf/common/utils/os.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -14,10 +14,12 @@ import psutil +import nncf + def fail_if_symlink(file: Path): if file.is_symlink(): - raise RuntimeError("File {} is a symbolic link, aborting.".format(str(file))) + raise nncf.ValidationError("File {} is a symbolic link, aborting.".format(str(file))) @contextmanager diff --git a/nncf/common/utils/patcher.py b/nncf/common/utils/patcher.py index 726e0110ff7..d0f1c2731ed 100644 --- a/nncf/common/utils/patcher.py +++ b/nncf/common/utils/patcher.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/utils/registry.py b/nncf/common/utils/registry.py index 6962861e9d1..b5633bd9ffa 100644 --- a/nncf/common/utils/registry.py +++ b/nncf/common/utils/registry.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -13,7 +13,7 @@ class Registry: REGISTERED_NAME_ATTR = "_registered_name" - def __init__(self, name, add_name_as_attr=False): + def __init__(self, name: str, add_name_as_attr: bool = False): self._name = name self._registry_dict = {} self._add_name_as_attr = add_name_as_attr @@ -30,7 +30,7 @@ def _register(self, obj, name): raise KeyError("{} is already registered in {}".format(name, self._name)) self._registry_dict[name] = obj - def register(self, name=None): + def register(self, name: str = None): def wrap(obj): cls_name = name if cls_name is None: diff --git a/nncf/common/utils/tensorboard.py b/nncf/common/utils/tensorboard.py index 87477939ae6..7a6fe1043c8 100644 --- a/nncf/common/utils/tensorboard.py +++ b/nncf/common/utils/tensorboard.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/common/utils/timer.py b/nncf/common/utils/timer.py index 6e87676ce07..90b47b298be 100644 --- a/nncf/common/utils/timer.py +++ b/nncf/common/utils/timer.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/config/__init__.py b/nncf/config/__init__.py index d98056274d3..8e873e580de 100644 --- a/nncf/config/__init__.py +++ b/nncf/config/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/config/config.py b/nncf/config/config.py index a0d3be2d28a..e6271ac66c1 100644 --- a/nncf/config/config.py +++ b/nncf/config/config.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -16,6 +16,7 @@ import jsonschema import jstyleson as json +import nncf from nncf.common.logging import nncf_logger from nncf.common.utils.api_marker import api from nncf.common.utils.os import safe_open @@ -71,7 +72,7 @@ def register_extra_structs(self, struct_list: List[NNCFExtraConfigStruct]): for struct in struct_list: struct_id = struct.get_id() if struct_id in self.__nncf_extra_structs: - raise RuntimeError(f"{struct_id} is already registered as extra struct in NNCFConfig!") + raise nncf.InternalError(f"{struct_id} is already registered as extra struct in NNCFConfig!") self.__nncf_extra_structs[struct_id] = struct def get_extra_struct(self, struct_cls: Type[NNCFExtraConfigStruct]) -> NNCFExtraConfigStruct: diff --git a/nncf/config/definitions.py b/nncf/config/definitions.py index 475fca220e1..2a6de86434a 100644 --- a/nncf/config/definitions.py +++ b/nncf/config/definitions.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/config/extractors.py b/nncf/config/extractors.py index 3fe5ff6822c..50b9b759e27 100644 --- a/nncf/config/extractors.py +++ b/nncf/config/extractors.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -11,6 +11,7 @@ from typing import Any, Dict, List, Optional +import nncf from nncf.common.logging import nncf_logger from nncf.common.quantization.initialization.range import PerLayerRangeInitConfig from nncf.common.quantization.initialization.range import RangeInitConfig @@ -52,7 +53,7 @@ def extract_algo_specific_config(config: NNCFConfig, algo_name_to_match: str) -> if algo_name_to_match == NO_COMPRESSION_ALGORITHM_NAME: if len(algo_list) > 0: - raise RuntimeError( + raise nncf.ValidationError( f"No algorithm configuration should be specified " f"when you try to extract {algo_name_to_match} from the NNCF config!" ) @@ -65,12 +66,14 @@ def extract_algo_specific_config(config: NNCFConfig, algo_name_to_match: str) -> matches.append(compression_algo_dict) if len(matches) > 1: - raise RuntimeError( + raise nncf.ValidationError( f"Multiple algorithm configurations specified for the same " f"algo {algo_name_to_match} in the NNCF config!" ) if not matches: - raise RuntimeError(f"Did not find an algorithm configuration for algo {algo_name_to_match} in the NNCF config!") + raise nncf.InternalError( + f"Did not find an algorithm configuration for algo {algo_name_to_match} in the NNCF config!" + ) return next(iter(matches)) @@ -203,13 +206,13 @@ def validate_accuracy_aware_schema(config: NNCFConfig, params: Dict[str, object] if NNCFAlgorithmNames.FILTER_PRUNING in algorithms and any( algo in NNCFAlgorithmNames.SPARSITY for algo in algorithms ): - raise RuntimeError( + raise nncf.ValidationError( "adaptive_compression_level mode supports filter_pruning or sparsity algorithms" "separately. Please, choose only one algorithm with adaptive compression level. " "Take a note that you still can use it combined with quantization." ) if len(algorithms) == 1 and algorithms[0] == NNCFAlgorithmNames.QUANTIZATION: - raise RuntimeError("adaptive_compression_level mode doesn't support quantization") + raise nncf.ValidationError("adaptive_compression_level mode doesn't support quantization") accuracy_aware_training_config = config.get("accuracy_aware_training", None) diff --git a/nncf/config/schema.py b/nncf/config/schema.py index 7ad4e085b51..488cd728eeb 100644 --- a/nncf/config/schema.py +++ b/nncf/config/schema.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -78,7 +78,7 @@ "additionalProperties": False, } -TARGET_DEVICE_SCHEMA = {"type": "string", "enum": ["ANY", "CPU", "GPU", "VPU", "TRIAL", "CPU_SPR"]} +TARGET_DEVICE_SCHEMA = {"type": "string", "enum": ["ANY", "CPU", "GPU", "NPU", "TRIAL", "CPU_SPR"]} NNCF_CONFIG_SCHEMA = { diff --git a/nncf/config/schemata/__init__.py b/nncf/config/schemata/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/config/schemata/__init__.py +++ b/nncf/config/schemata/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/config/schemata/accuracy_aware.py b/nncf/config/schemata/accuracy_aware.py index f6fc1c2f24d..decc9db0dd6 100644 --- a/nncf/config/schemata/accuracy_aware.py +++ b/nncf/config/schemata/accuracy_aware.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/config/schemata/algo/__init__.py b/nncf/config/schemata/algo/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/config/schemata/algo/__init__.py +++ b/nncf/config/schemata/algo/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/config/schemata/algo/binarization.py b/nncf/config/schemata/algo/binarization.py index 8e178636ec9..38d0749cd85 100644 --- a/nncf/config/schemata/algo/binarization.py +++ b/nncf/config/schemata/algo/binarization.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/config/schemata/algo/const_sparsity.py b/nncf/config/schemata/algo/const_sparsity.py index 8e529bb5108..51e144d1d71 100644 --- a/nncf/config/schemata/algo/const_sparsity.py +++ b/nncf/config/schemata/algo/const_sparsity.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/config/schemata/algo/filter_pruning.py b/nncf/config/schemata/algo/filter_pruning.py index 3bf94069835..31c184346ac 100644 --- a/nncf/config/schemata/algo/filter_pruning.py +++ b/nncf/config/schemata/algo/filter_pruning.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/config/schemata/algo/knowledge_distillation.py b/nncf/config/schemata/algo/knowledge_distillation.py index f2036627a5d..8f144c76780 100644 --- a/nncf/config/schemata/algo/knowledge_distillation.py +++ b/nncf/config/schemata/algo/knowledge_distillation.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/config/schemata/algo/magnitude_sparsity.py b/nncf/config/schemata/algo/magnitude_sparsity.py index b2dcf8c311b..216b89a2d66 100644 --- a/nncf/config/schemata/algo/magnitude_sparsity.py +++ b/nncf/config/schemata/algo/magnitude_sparsity.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/config/schemata/algo/quantization.py b/nncf/config/schemata/algo/quantization.py index 473642feab2..b3fb3f6ada6 100644 --- a/nncf/config/schemata/algo/quantization.py +++ b/nncf/config/schemata/algo/quantization.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/config/schemata/algo/rb_sparsity.py b/nncf/config/schemata/algo/rb_sparsity.py index d1105fa6028..6489bd3fdfe 100644 --- a/nncf/config/schemata/algo/rb_sparsity.py +++ b/nncf/config/schemata/algo/rb_sparsity.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/config/schemata/basic.py b/nncf/config/schemata/basic.py index d0e8e726675..24635c1f634 100644 --- a/nncf/config/schemata/basic.py +++ b/nncf/config/schemata/basic.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/config/schemata/common/__init__.py b/nncf/config/schemata/common/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/config/schemata/common/__init__.py +++ b/nncf/config/schemata/common/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/config/schemata/common/compression.py b/nncf/config/schemata/common/compression.py index 839550c0454..7c26a7d18be 100644 --- a/nncf/config/schemata/common/compression.py +++ b/nncf/config/schemata/common/compression.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/config/schemata/common/initialization.py b/nncf/config/schemata/common/initialization.py index aca1f0c9753..9043e01c1be 100644 --- a/nncf/config/schemata/common/initialization.py +++ b/nncf/config/schemata/common/initialization.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/config/schemata/common/sparsity.py b/nncf/config/schemata/common/sparsity.py index 8f1a0e7ba94..45a6a7d337e 100644 --- a/nncf/config/schemata/common/sparsity.py +++ b/nncf/config/schemata/common/sparsity.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/config/schemata/common/targeting.py b/nncf/config/schemata/common/targeting.py index e9962ce5711..e0696b55dc8 100644 --- a/nncf/config/schemata/common/targeting.py +++ b/nncf/config/schemata/common/targeting.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/config/schemata/defaults.py b/nncf/config/schemata/defaults.py index a55d098e7cf..6cb9ba15f93 100644 --- a/nncf/config/schemata/defaults.py +++ b/nncf/config/schemata/defaults.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/config/schemata/experimental_schema.py b/nncf/config/schemata/experimental_schema.py index 1b2e0becbef..7d8f80de887 100644 --- a/nncf/config/schemata/experimental_schema.py +++ b/nncf/config/schemata/experimental_schema.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/config/structures.py b/nncf/config/structures.py index c29c6637155..80e7b46c3e4 100644 --- a/nncf/config/structures.py +++ b/nncf/config/structures.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/config/telemetry_extractors.py b/nncf/config/telemetry_extractors.py index 8ad0fcd52eb..d410b4ce0d1 100644 --- a/nncf/config/telemetry_extractors.py +++ b/nncf/config/telemetry_extractors.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/config/utils.py b/nncf/config/utils.py index 906a45f4b8d..bd5b153aa17 100644 --- a/nncf/config/utils.py +++ b/nncf/config/utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/data/__init__.py b/nncf/data/__init__.py index d80c0ca5d52..f6dde64a3e4 100644 --- a/nncf/data/__init__.py +++ b/nncf/data/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/data/dataset.py b/nncf/data/dataset.py index d89fb6fc84c..a6b7f626855 100644 --- a/nncf/data/dataset.py +++ b/nncf/data/dataset.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/definitions.py b/nncf/definitions.py index 560564c00ce..225475fbb6b 100644 --- a/nncf/definitions.py +++ b/nncf/definitions.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/errors.py b/nncf/errors.py new file mode 100644 index 00000000000..d507c3b6d79 --- /dev/null +++ b/nncf/errors.py @@ -0,0 +1,131 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +class ValidationError(Exception): + """ + Raised when an internal NNCF validation check fails, + for example, if the user supplied an invalid or inconsistent set of arguments. + """ + + pass + + +class InternalError(Exception): + """ + Raised when an internal error occurs within the NNCF framework. + + This exception is raised when an unexpected internal error occurs during the execution + of NNCF. It indicates a situation where the code encountered an unexpected condition. + + """ + + pass + + +class UnsupportedDatasetError(Exception): + """ + Raised when an unsupported dataset is encountered + """ + + pass + + +class InvalidPathError(Exception): + """ + Raised when an invalid path is provided + """ + + pass + + +class UnsupportedBackendError(Exception): + """ + Raised when an unsupported backend is specified + """ + + pass + + +class InvalidQuantizerGroupError(Exception): + """ + Raised when an invalid quantizer group is encountered. + """ + + pass + + +class InstallationError(Exception): + """ + Raised when an error occurs during installation. + """ + + pass + + +class UnsupportedModelError(Exception): + """ + Raised when an unsupported model is encountered. + """ + + pass + + +class UnsupportedVersionError(Exception): + """ + Raised when an unsupported version is encountered. + """ + + pass + + +class ModuleNotFoundError(Exception): + """ + Raised when a required module is not found. + """ + + pass + + +class ParameterNotSupportedError(Exception): + """ + Raised when an unsupported parameter is encountered. + + This error indicates that an unsupported parameter was provided, typically in the context + of a compression algorithm's configuration or an argument passed to an API call. + + """ + + pass + + +class InvalidCollectorTypeError(Exception): + """ + Raised when an invalid collector type is encountered. + """ + + pass + + +class BufferFullError(Exception): + """ + Raised when a buffer is full + """ + + pass + + +class UnknownDatasetError(Exception): + """ + Raised when an unknown dataset is encountered. + """ + + pass diff --git a/nncf/experimental/__init__.py b/nncf/experimental/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/experimental/__init__.py +++ b/nncf/experimental/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/common/__init__.py b/nncf/experimental/common/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/experimental/common/__init__.py +++ b/nncf/experimental/common/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/common/graph/__init__.py b/nncf/experimental/common/graph/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/experimental/common/graph/__init__.py +++ b/nncf/experimental/common/graph/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/common/graph/netron.py b/nncf/experimental/common/graph/netron.py index 95c84c112ac..83b3259eda3 100644 --- a/nncf/experimental/common/graph/netron.py +++ b/nncf/experimental/common/graph/netron.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/common/pruning/__init__.py b/nncf/experimental/common/pruning/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/experimental/common/pruning/__init__.py +++ b/nncf/experimental/common/pruning/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/common/pruning/block_hierarchy.py b/nncf/experimental/common/pruning/block_hierarchy.py index 7af693c637b..a65a411f69a 100644 --- a/nncf/experimental/common/pruning/block_hierarchy.py +++ b/nncf/experimental/common/pruning/block_hierarchy.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/common/pruning/nodes_grouping.py b/nncf/experimental/common/pruning/nodes_grouping.py index 3212ecbb103..848c6c0cc7e 100644 --- a/nncf/experimental/common/pruning/nodes_grouping.py +++ b/nncf/experimental/common/pruning/nodes_grouping.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/common/pruning/operations.py b/nncf/experimental/common/pruning/operations.py index e648700dde7..d9b7164f29f 100644 --- a/nncf/experimental/common/pruning/operations.py +++ b/nncf/experimental/common/pruning/operations.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/common/pruning/propagation_data.py b/nncf/experimental/common/pruning/propagation_data.py index 6157d3d26dd..9864f936fac 100644 --- a/nncf/experimental/common/pruning/propagation_data.py +++ b/nncf/experimental/common/pruning/propagation_data.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/common/tensor_statistics/__init__.py b/nncf/experimental/common/tensor_statistics/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/experimental/common/tensor_statistics/__init__.py +++ b/nncf/experimental/common/tensor_statistics/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/common/tensor_statistics/collectors.py b/nncf/experimental/common/tensor_statistics/collectors.py index c1797aadccb..1017877bf24 100644 --- a/nncf/experimental/common/tensor_statistics/collectors.py +++ b/nncf/experimental/common/tensor_statistics/collectors.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -15,6 +15,7 @@ from collections import deque from typing import Any, Dict, List, Optional, Set, Tuple, Type, TypeVar, Union +import nncf from nncf.common.tensor import TensorType from nncf.common.tensor_statistics.collectors import NNCFCollectorTensorProcessor from nncf.common.tensor_statistics.collectors import NNCFTensor @@ -54,6 +55,10 @@ def inplace(self): @property def output_port_id(self) -> int: + """ + Port id of the last node of the reducer subgraph if statistic is inplace. + Port id of the reducer output return node if statistic is not inplace. + """ return 0 @property @@ -73,17 +78,6 @@ def _reduce_out_of_place(self, x: List[TensorType]) -> List[TensorType]: :param x: Tensor to register. """ - @abstractmethod - def get_output_names(self, target_node_name: str, port_id: int) -> List[str]: - """ - Returns target output names from target model that is - modified for statistic collection. - - :param target_node_name: Target node name for reducer. - :param port_id: Target port id for target node name for reducer. - :return: Target output names for reducer. - """ - @abstractmethod def get_inplace_fn(self) -> Optional[InplaceInsertionFNType]: """ @@ -93,6 +87,9 @@ def get_inplace_fn(self) -> Optional[InplaceInsertionFNType]: """ def __call__(self, x: List[NNCFTensor]): + if any(t.is_empty() for t in x): + return None + if self.inplace: return x @@ -259,11 +256,11 @@ def register_statistic_branch( :reducer_output_port_id: Reducer target output port id. """ if container_key in self._stat_container_kwargs_map: - raise RuntimeError( + raise nncf.InternalError( f"Two different statistic branches for one container key {container_key} are encountered" ) if any(aggr is aggregator for aggr in self._aggregators.values()): - raise RuntimeError(f"One aggregator instance {aggregator} for different branches is encountered") + raise nncf.InternalError(f"One aggregator instance {aggregator} for different branches is encountered") self._reducers.add(reducer) key = (hash(reducer), reducer_output_port_id, hash(aggregator)) @@ -299,9 +296,9 @@ def register_inputs(self, inputs: Dict[int, List[NNCFTensor]]) -> None: for reducer in self._reducers: reducer_hash = hash(reducer) input_ = inputs[reducer_hash] - if any(tensor.is_empty() for tensor in input_): - continue - reduced_inputs[reducer_hash] = reducer(input_) + reduced_input = reducer(input_) + if reduced_input is not None: + reduced_inputs[reducer_hash] = reduced_input for ( (reducer_hash, reducer_port_id, _), @@ -430,7 +427,7 @@ def _build_statistic_container(statistic_container_cls: Type[TensorStatistic], k for (_, percentile), value in kwargs.items(): percentile_vs_values_dict[percentile] = value return statistic_container_cls(percentile_vs_values_dict=percentile_vs_values_dict) - raise RuntimeError( + raise nncf.InternalError( f"Statistic collector class {statistic_container_cls} is not supported by the TensorCollector class." ) @@ -482,6 +479,14 @@ def _reduce_out_of_place(self, x: List[TensorType]) -> List[TensorType]: return x +class RawReducer(NoopReducer): + def __init__(self): + super().__init__() + + def __call__(self, x: List[NNCFTensor]): + return self._reduce_out_of_place(x) + + class MinReducer(TensorReducerBase): def _reduce_out_of_place(self, x: List[NNCFTensor]) -> List[NNCFTensor]: x = x[0] @@ -621,9 +626,8 @@ def _register_reduced_input_impl(self, x: NNCFTensor) -> None: self._container.append(reduced) def _aggregate_impl(self) -> NNCFTensor: - if 0 in self._aggregation_axes: - if self._keepdims: - return self._container[0].tensor + if 0 in self._aggregation_axes and self._keepdims: + return self._container[0].tensor return self._tensor_processor.stack(self._container).tensor @abstractmethod @@ -653,9 +657,30 @@ def _register_reduced_input_impl(self, x: TensorType) -> None: self._container.append(x) def _aggregate_impl(self) -> NNCFTensor: - stacked_val = self._tensor_processor.stack(self._container) - aggregated = self._aggregation_fn(stacked_val, axis=self._aggregation_axes, keepdims=self._keepdims) - return self._tensor_processor.squeeze(aggregated, 0).tensor + # Case when all registered tensors have identical shape + if all(self._container[0].shape == x.shape for x in self._container): + stacked_value = self._tensor_processor.stack(self._container) + aggregated = self._aggregation_fn(stacked_value, axis=self._aggregation_axes, keepdims=self._keepdims) + return self._tensor_processor.squeeze(aggregated, 0).tensor + online_axes = tuple(x - 1 for x in self._aggregation_axes if x > 0) + + # Case when some registered tensors have different shapes and + # 0 is present in the aggregation axes + if 0 in self._aggregation_axes: + stacked_value, shape_after_aggregation = _moveaxes_flatten_cat( + self._container, online_axes, self._tensor_processor + ) + aggregated = self._aggregation_fn(stacked_value, axis=0, keepdims=False) + if self._keepdims: + aggregated = self._tensor_processor.reshape(aggregated, shape_after_aggregation) + return aggregated.tensor + + # Case when some registered tensors have different shapes and + # 0 is not present in the aggregation axes + ret_val = [] + for tensor in self._container: + ret_val.append(self._aggregation_fn(tensor, axis=online_axes, keepdims=self._keepdims)) + return self._tensor_processor.stack(ret_val, axis=0).tensor @abstractmethod def _aggregation_fn(self, stacked_value: NNCFTensor, axis: AggregationAxes, keepdims: bool) -> NNCFTensor: @@ -686,25 +711,22 @@ def __init__( self._container = deque(maxlen=window_size) self._quantile = quantile - def _aggregate_impl(self) -> NNCFTensor: - stacked_samples = self._tensor_processor.stack(self._container) + def _aggregation_fn(self, stacked_value: NNCFTensor, axis: int, keepdims: bool) -> NNCFTensor: low_values, high_values = self._tensor_processor.quantile( - stacked_samples, - quantile=(self._quantile, 1 - self._quantile), - axis=self._aggregation_axes, + stacked_value, quantile=(self._quantile, 1 - self._quantile), axis=axis ) tp = self._tensor_processor - outliers_mask = tp.logical_or(tp.less(stacked_samples, low_values), tp.less(high_values, stacked_samples)) - aggregated = self._aggregation_fn( - stacked_samples=stacked_samples, + outliers_mask = tp.logical_or(tp.less(stacked_value, low_values), tp.less(high_values, stacked_value)) + aggregated = self._masked_aggregation_fn( + stacked_samples=stacked_value, mask=outliers_mask, - axis=self._aggregation_axes, - keepdims=self._keepdims, + axis=axis, + keepdims=keepdims, ) - return self._tensor_processor.squeeze(aggregated, 0).tensor + return aggregated @abstractmethod - def _aggregation_fn( + def _masked_aggregation_fn( self, stacked_samples: NNCFTensor, mask: NNCFTensor, axis: AggregationAxes, keepdims: bool ) -> NNCFTensor: pass @@ -717,14 +739,14 @@ def __hash__(self) -> int: class MeanNoOutliersAggregator(NoOutliersAggregatorBase): - def _aggregation_fn( + def _masked_aggregation_fn( self, stacked_samples: NNCFTensor, mask: NNCFTensor, axis: AggregationAxes, keepdims: bool ) -> NNCFTensor: return self._tensor_processor.masked_mean(stacked_samples, axis=axis, mask=mask, keepdims=keepdims) class MedianNoOutliersAggregator(NoOutliersAggregatorBase): - def _aggregation_fn( + def _masked_aggregation_fn( self, stacked_samples: NNCFTensor, mask: NNCFTensor, axis: AggregationAxes, keepdims: bool ) -> NNCFTensor: return self._tensor_processor.masked_median(stacked_samples, axis=axis, mask=mask, keepdims=keepdims) diff --git a/nncf/experimental/common/tensor_statistics/statistical_functions.py b/nncf/experimental/common/tensor_statistics/statistical_functions.py index 13e30f2280d..ea49c58ecd1 100644 --- a/nncf/experimental/common/tensor_statistics/statistical_functions.py +++ b/nncf/experimental/common/tensor_statistics/statistical_functions.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/tensor/__init__.py b/nncf/experimental/tensor/__init__.py index 0555ea139de..0a677404673 100644 --- a/nncf/experimental/tensor/__init__.py +++ b/nncf/experimental/tensor/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/tensor/definitions.py b/nncf/experimental/tensor/definitions.py index 49a64fbbbf3..f622033b318 100644 --- a/nncf/experimental/tensor/definitions.py +++ b/nncf/experimental/tensor/definitions.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/tensor/functions/__init__.py b/nncf/experimental/tensor/functions/__init__.py index 02c57a393e3..b9addc6340f 100644 --- a/nncf/experimental/tensor/functions/__init__.py +++ b/nncf/experimental/tensor/functions/__init__.py @@ -14,6 +14,7 @@ from nncf.experimental.tensor.functions.numeric import all as all from nncf.experimental.tensor.functions.numeric import allclose as allclose from nncf.experimental.tensor.functions.numeric import any as any +from nncf.experimental.tensor.functions.numeric import argsort as argsort from nncf.experimental.tensor.functions.numeric import as_tensor_like as as_tensor_like from nncf.experimental.tensor.functions.numeric import astype as astype from nncf.experimental.tensor.functions.numeric import clip as clip @@ -25,6 +26,7 @@ from nncf.experimental.tensor.functions.numeric import isclose as isclose from nncf.experimental.tensor.functions.numeric import isempty as isempty from nncf.experimental.tensor.functions.numeric import item as item +from nncf.experimental.tensor.functions.numeric import matmul as matmul from nncf.experimental.tensor.functions.numeric import max as max from nncf.experimental.tensor.functions.numeric import maximum as maximum from nncf.experimental.tensor.functions.numeric import mean as mean @@ -33,11 +35,15 @@ from nncf.experimental.tensor.functions.numeric import moveaxis as moveaxis from nncf.experimental.tensor.functions.numeric import multiply as multiply from nncf.experimental.tensor.functions.numeric import ones_like as ones_like +from nncf.experimental.tensor.functions.numeric import power as power +from nncf.experimental.tensor.functions.numeric import quantile as quantile from nncf.experimental.tensor.functions.numeric import reshape as reshape from nncf.experimental.tensor.functions.numeric import round as round from nncf.experimental.tensor.functions.numeric import squeeze as squeeze from nncf.experimental.tensor.functions.numeric import stack as stack from nncf.experimental.tensor.functions.numeric import sum as sum +from nncf.experimental.tensor.functions.numeric import transpose as transpose +from nncf.experimental.tensor.functions.numeric import unsqueeze as unsqueeze from nncf.experimental.tensor.functions.numeric import unstack as unstack from nncf.experimental.tensor.functions.numeric import var as var from nncf.experimental.tensor.functions.numeric import where as where diff --git a/nncf/experimental/tensor/functions/numeric.py b/nncf/experimental/tensor/functions/numeric.py index 395926c8a5e..3dd2c0d8815 100644 --- a/nncf/experimental/tensor/functions/numeric.py +++ b/nncf/experimental/tensor/functions/numeric.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -381,6 +381,45 @@ def round(a: Tensor, decimals=0) -> Tensor: return Tensor(round(a.data, decimals)) +@functools.singledispatch +@tensor_guard +def power(a: Tensor, exponent: Union[Tensor, float]) -> Tensor: + """ + Takes the power of each element in input with exponent and returns a tensor with the result. + Exponent can be either a single float number or a broadcastable Tensor. In case exponent is + a brodcastable tensor, the exponent is being broadcasted and the return tensor contains + the power of each element in input with exponent elementwise. + + :param a: Input data. + :param exponent: Exponent value. + :return: The result of the power of each element in input with given exponent. + """ + return Tensor(power(a.data, unwrap_tensor_data(exponent))) + + +@functools.singledispatch +@tensor_guard +def quantile( + a: Tensor, + q: Union[float, List[float]], + axis: Optional[Union[int, Tuple[int]]] = None, + keepdims: Optional[bool] = None, +) -> Tensor: + """ + Compute the quantile(s) of the data along the specified axis. + + :param a: Given tensor. + :params q: Quantile or sequence of quantiles to compute, which must be between + 0 and 1 inclusive. + :param axis: Axis or axes along which the quantiles are computed. + :param keepdims: If True, the axes which are reduced are left in the result + as dimensions with size one. + :return: An tensor with quantiles, the first axis of the result corresponds + to the quantiles, other axes of the result correspond to the quantiles values. + """ + return Tensor(quantile(a.data, q, axis, keepdims)) + + @functools.singledispatch @tensor_guard def _binary_op_nowarn(a: Tensor, b: Union[Tensor, float], operator_fn: Callable) -> Tensor: @@ -522,3 +561,58 @@ def size(a: Tensor) -> int: :return: The size of the input tensor. """ return size(a.data) + + +@functools.singledispatch +@tensor_guard +def matmul(x1: Tensor, x2: Union[Tensor, float]) -> Tensor: + """ + Matrix multiplication. + + :param x1: The first input tensor. + :param x2: The second input tensor or number. + :return: The product of x1 and x2, matmul. + """ + return Tensor(matmul(x1.data, unwrap_tensor_data(x2))) + + +@functools.singledispatch +@tensor_guard +def unsqueeze(a: Tensor, axis: Optional[Union[int, Tuple[int, ...]]] = None) -> Tensor: + """ + Add axes of length one to a. + + :param a: The input tensor. + :param axis: Selects a subset of the entries of length one in the shape. + :return: The input array, but with expanded shape with len 1 defined in axis. + """ + return Tensor(unsqueeze(a.data, axis=axis)) + + +@functools.singledispatch +@tensor_guard +def transpose(a: Tensor, axes: Optional[Tuple[int, ...]] = None) -> Tensor: + """ + Returns an array with axes transposed. + + :param a: The input tensor. + :param axes: list of permutations or None. + :return: array with permuted axes. + """ + return Tensor(transpose(a.data, axes=axes)) + + +@functools.singledispatch +@tensor_guard +def argsort(a: Tensor, axis: int = -1, descending: bool = False, stable: bool = False) -> Tensor: + """ + Returns the indices that would sort an array. + + :param a: The input tensor. + :param axis: Axis along which to sort. The default is -1 (the last axis). If None, the flattened array is used. + :param descending: Controls the sorting order (ascending or descending). + :param stable: If True then the sorting routine becomes stable, preserving the order of equivalent elements. + If False, the relative order of values which compare equal is not guaranteed. True is slower. + :return: Array of indices that sort a along the specified axis. + """ + return Tensor(argsort(a.data, axis=axis)) diff --git a/nncf/experimental/tensor/functions/numpy_numeric.py b/nncf/experimental/tensor/functions/numpy_numeric.py index c872b377d51..3aef3df1e5f 100644 --- a/nncf/experimental/tensor/functions/numpy_numeric.py +++ b/nncf/experimental/tensor/functions/numpy_numeric.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -179,6 +179,21 @@ def _(a: Union[np.ndarray, np.generic], decimals: int = 0) -> np.ndarray: return np.round(a, decimals=decimals) +@register_numpy_types(numeric.power) +def _(a: Union[np.ndarray, np.generic], exponent: Union[np.ndarray, float]) -> Union[np.ndarray, np.generic]: + return np.power(a, exponent) + + +@register_numpy_types(numeric.quantile) +def _( + a: Union[np.ndarray, np.generic], + q: Union[float, List[float]], + axis: Optional[Union[int, Tuple[int]]] = None, + keepdims: Optional[bool] = None, +) -> Union[np.ndarray, np.generic]: + return np.array(np.quantile(a, q=q, axis=axis, keepdims=keepdims)) + + @register_numpy_types(numeric._binary_op_nowarn) def _( a: Union[np.ndarray, np.generic], b: Union[np.ndarray, np.generic, float], operator_fn: Callable @@ -247,3 +262,27 @@ def _( @register_numpy_types(numeric.size) def _(a: Union[np.ndarray, np.generic]) -> int: return a.size + + +@register_numpy_types(numeric.matmul) +def _(x1: Union[np.ndarray, np.generic], x2: Union[np.ndarray, np.generic, float]) -> np.ndarray: + return np.matmul(x1, x2) + + +@register_numpy_types(numeric.unsqueeze) +def _( + a: Union[np.ndarray, np.generic], axis: Optional[Union[int, Tuple[int, ...]]] = None +) -> Union[np.ndarray, np.generic]: + return np.expand_dims(a, axis=axis) + + +@register_numpy_types(numeric.transpose) +def _(a: Union[np.ndarray, np.generic], axes: Optional[Tuple[int, ...]] = None) -> Union[np.ndarray, np.generic]: + return np.transpose(a, axes=axes) + + +@register_numpy_types(numeric.argsort) +def _( + a: Union[np.ndarray, np.generic], axis: Optional[int] = None, descending=False, stable=False +) -> Union[np.ndarray, np.generic]: + return np.argsort(a, axis=axis) diff --git a/nncf/experimental/tensor/functions/torch_numeric.py b/nncf/experimental/tensor/functions/torch_numeric.py index 2e3c9a1c05f..781e1ce49e8 100644 --- a/nncf/experimental/tensor/functions/torch_numeric.py +++ b/nncf/experimental/tensor/functions/torch_numeric.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -11,6 +11,7 @@ from typing import Any, Callable, List, Optional, Tuple, Union +import numpy as np import torch from nncf.experimental.tensor import TensorDataType @@ -44,7 +45,7 @@ def _(a: torch.Tensor) -> TensorDeviceType: def _(a: torch.Tensor, axis: Optional[Union[int, Tuple[int, ...]]] = None) -> torch.Tensor: if axis is None: return a.squeeze() - if isinstance(axis, Tuple) and any(1 != a.shape[i] for i in axis): + if isinstance(axis, Tuple) and any(a.shape[i] != 1 for i in axis): # Make Numpy behavior, torch.squeeze skips axes that are not equal to one.. raise ValueError("Cannot select an axis to squeeze out which has size not equal to one") return a.squeeze(axis) @@ -191,6 +192,31 @@ def _(a: torch.Tensor, decimals=0) -> torch.Tensor: return torch.round(a, decimals=decimals) +@numeric.power.register(torch.Tensor) +def _(a: torch.Tensor, exponent: Union[torch.Tensor, float]) -> torch.Tensor: + return torch.pow(a, exponent=exponent) + + +@numeric.quantile.register(torch.Tensor) +def _( + a: torch.Tensor, + q: Union[float, List[float]], + axis: Optional[Union[int, Tuple[int]]] = None, + keepdims: Optional[bool] = None, +) -> torch.Tensor: + device = a.device + # See https://github.com/pytorch/pytorch/issues/61582 + # https://github.com/pytorch/pytorch/issues/64947 + if a.numel() <= 16_000_000 and isinstance(axis, int) and a.dtype in [torch.float32, torch.float64]: + return torch.quantile( + a, + torch.tensor(q, dtype=a.dtype, device=a.device), + axis, + keepdims, + ).type(torch.float64) + return torch.tensor(np.quantile(a.detach().cpu().numpy(), q=q, axis=axis, keepdims=keepdims)).to(device) + + @numeric._binary_op_nowarn.register(torch.Tensor) def _(a: torch.Tensor, b: Union[torch.Tensor, float], operator_fn: Callable) -> torch.Tensor: return operator_fn(a, b) @@ -242,3 +268,23 @@ def _( @numeric.size.register(torch.Tensor) def _(a: torch.Tensor) -> int: return torch.numel(a) + + +@numeric.matmul.register(torch.Tensor) +def _(x1: torch.Tensor, x2: torch.Tensor) -> torch.Tensor: + return torch.matmul(x1, x2) + + +@numeric.unsqueeze.register(torch.Tensor) +def _(a: torch.Tensor, axis: Optional[Union[int, Tuple[int, ...]]] = None) -> torch.Tensor: + return torch.unsqueeze(a, dim=axis) + + +@numeric.transpose.register(torch.Tensor) +def _(a: torch.Tensor, axes: Optional[Tuple[int, ...]] = None) -> torch.Tensor: + return a.t() + + +@numeric.argsort.register(torch.Tensor) +def _(a: torch.Tensor, axis: Optional[int] = None, descending=False, stable=False) -> torch.Tensor: + return torch.argsort(a, dim=axis, descending=descending, stable=stable) diff --git a/nncf/experimental/tensor/tensor.py b/nncf/experimental/tensor/tensor.py index d803147ad45..c316b5d267f 100644 --- a/nncf/experimental/tensor/tensor.py +++ b/nncf/experimental/tensor/tensor.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/tensorflow/__init__.py b/nncf/experimental/tensorflow/__init__.py index 4ca939c3d73..df61a96bdda 100644 --- a/nncf/experimental/tensorflow/__init__.py +++ b/nncf/experimental/tensorflow/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/tensorflow/context.py b/nncf/experimental/tensorflow/context.py index d35251a2fc5..3c216081873 100644 --- a/nncf/experimental/tensorflow/context.py +++ b/nncf/experimental/tensorflow/context.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/tensorflow/graph/__init__.py b/nncf/experimental/tensorflow/graph/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/experimental/tensorflow/graph/__init__.py +++ b/nncf/experimental/tensorflow/graph/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/tensorflow/graph/argprovider.py b/nncf/experimental/tensorflow/graph/argprovider.py index dd0d6ef883c..8676b000ac1 100644 --- a/nncf/experimental/tensorflow/graph/argprovider.py +++ b/nncf/experimental/tensorflow/graph/argprovider.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/tensorflow/graph/converter.py b/nncf/experimental/tensorflow/graph/converter.py index 31319fbf0e6..01d1a84b356 100644 --- a/nncf/experimental/tensorflow/graph/converter.py +++ b/nncf/experimental/tensorflow/graph/converter.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -18,6 +18,7 @@ from tensorflow.python.framework import convert_to_constants as _convert_to_constants from tensorflow.python.keras.saving import saving_utils as _saving_utils +import nncf from nncf.common.graph import NNCFGraph from nncf.common.graph.layer_attributes import Dtype from nncf.experimental.tensorflow.graph.node_attributes import TFNodeAttributes @@ -377,7 +378,7 @@ def _convert_dtype_to_nncf_format(dtype: tf.dtypes.DType) -> Dtype: elif dtype.is_integer: tensor_dtype = Dtype.INTEGER else: - raise RuntimeError(f"Unexpected dtype of tensor: {dtype}") + raise nncf.InternalError(f"Unexpected dtype of tensor: {dtype}") return tensor_dtype diff --git a/nncf/experimental/tensorflow/graph/model_transformer.py b/nncf/experimental/tensorflow/graph/model_transformer.py index f621f8283c3..155c41645e7 100644 --- a/nncf/experimental/tensorflow/graph/model_transformer.py +++ b/nncf/experimental/tensorflow/graph/model_transformer.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/tensorflow/graph/node_attributes.py b/nncf/experimental/tensorflow/graph/node_attributes.py index 4f7501dc5ab..50f33c7b5ec 100644 --- a/nncf/experimental/tensorflow/graph/node_attributes.py +++ b/nncf/experimental/tensorflow/graph/node_attributes.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/tensorflow/graph/transformations/__init__.py b/nncf/experimental/tensorflow/graph/transformations/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/experimental/tensorflow/graph/transformations/__init__.py +++ b/nncf/experimental/tensorflow/graph/transformations/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/tensorflow/graph/transformations/commands.py b/nncf/experimental/tensorflow/graph/transformations/commands.py index 2f4ba411fee..bc36cc282df 100644 --- a/nncf/experimental/tensorflow/graph/transformations/commands.py +++ b/nncf/experimental/tensorflow/graph/transformations/commands.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/tensorflow/graph/transformations/layout.py b/nncf/experimental/tensorflow/graph/transformations/layout.py index 501b21f003c..79ac5cfcdca 100644 --- a/nncf/experimental/tensorflow/graph/transformations/layout.py +++ b/nncf/experimental/tensorflow/graph/transformations/layout.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/tensorflow/nncf_network.py b/nncf/experimental/tensorflow/nncf_network.py index 2ced9be22d8..21131dabc86 100644 --- a/nncf/experimental/tensorflow/nncf_network.py +++ b/nncf/experimental/tensorflow/nncf_network.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/tensorflow/patch_tf.py b/nncf/experimental/tensorflow/patch_tf.py index c6f77d69072..05a7433873e 100644 --- a/nncf/experimental/tensorflow/patch_tf.py +++ b/nncf/experimental/tensorflow/patch_tf.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/tensorflow/quantization/__init__.py b/nncf/experimental/tensorflow/quantization/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/experimental/tensorflow/quantization/__init__.py +++ b/nncf/experimental/tensorflow/quantization/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/tensorflow/quantization/algorithm.py b/nncf/experimental/tensorflow/quantization/algorithm.py index 6d7857ce7b0..4afd6970fa6 100644 --- a/nncf/experimental/tensorflow/quantization/algorithm.py +++ b/nncf/experimental/tensorflow/quantization/algorithm.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -11,6 +11,7 @@ from typing import Any, Dict, List, Optional +import nncf from nncf.common.graph import NNCFGraph from nncf.common.graph import NNCFNode from nncf.common.graph.transformations.commands import TargetPoint @@ -190,7 +191,7 @@ def _build_insertion_commands_for_quantizer_setup( for qp_id in unified_scales_group: if was_processed[qp_id]: - raise RuntimeError("Unexpected behavior") + raise nncf.InternalError("Unexpected behavior") was_processed[qp_id] = True curr_qp = quantization_points[qp_id] @@ -271,7 +272,7 @@ def _get_quantizer_setup(self, model: NNCFNetwork) -> TFQuantizationSetupV2: if target_node.node_name in node_name_to_qconfig_map: assigned_qconfig = node_name_to_qconfig_map[target_node.node_name] if qp.qconfig != assigned_qconfig: - raise RuntimeError( + raise nncf.InternalError( "Inconsistent quantizer configurations selected by solver for one " f"and the same quantizable op! Tried to assign {qp.qconfig} to " f"{target_node.node_name} as specified by QP {qp_id}, but the op " @@ -285,7 +286,7 @@ def _get_quantizer_setup(self, model: NNCFNetwork) -> TFQuantizationSetupV2: narrow_range = not half_range target_type = TargetType.OPERATOR_PRE_HOOK if not issubclass(target_node.metatype, TFOpWithWeightsMetatype): - raise RuntimeError(f"Unexpected type of metatype: {type(target_node.metatype)}") + raise nncf.InternalError(f"Unexpected type of metatype: {type(target_node.metatype)}") port_ids = [weight_def.port_id for weight_def in target_node.metatype.weight_definitions] else: @@ -293,7 +294,7 @@ def _get_quantizer_setup(self, model: NNCFNetwork) -> TFQuantizationSetupV2: # Check correctness if not isinstance(qp.insertion_point, ActivationQuantizationInsertionPoint): - raise RuntimeError(f"Unexpected type of insertion point: {type(qp.insertion_point)}") + raise nncf.InternalError(f"Unexpected type of insertion point: {type(qp.insertion_point)}") # Parameters half_range = False diff --git a/nncf/experimental/tensorflow/quantization/init_range.py b/nncf/experimental/tensorflow/quantization/init_range.py index f514177c717..a305231073d 100644 --- a/nncf/experimental/tensorflow/quantization/init_range.py +++ b/nncf/experimental/tensorflow/quantization/init_range.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/tensorflow/quantization/quantizers.py b/nncf/experimental/tensorflow/quantization/quantizers.py index db84fcb0156..62636db21e5 100644 --- a/nncf/experimental/tensorflow/quantization/quantizers.py +++ b/nncf/experimental/tensorflow/quantization/quantizers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/tensorflow/scope.py b/nncf/experimental/tensorflow/scope.py index 4227a71ede8..5dbac1de6ad 100644 --- a/nncf/experimental/tensorflow/scope.py +++ b/nncf/experimental/tensorflow/scope.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/torch/__init__.py b/nncf/experimental/torch/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/experimental/torch/__init__.py +++ b/nncf/experimental/torch/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/torch/nas/__init__.py b/nncf/experimental/torch/nas/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/experimental/torch/nas/__init__.py +++ b/nncf/experimental/torch/nas/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/torch/nas/bootstrapNAS/__init__.py b/nncf/experimental/torch/nas/bootstrapNAS/__init__.py index 7fd77370206..db1b94752bd 100644 --- a/nncf/experimental/torch/nas/bootstrapNAS/__init__.py +++ b/nncf/experimental/torch/nas/bootstrapNAS/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/torch/nas/bootstrapNAS/elasticity/__init__.py b/nncf/experimental/torch/nas/bootstrapNAS/elasticity/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/experimental/torch/nas/bootstrapNAS/elasticity/__init__.py +++ b/nncf/experimental/torch/nas/bootstrapNAS/elasticity/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/torch/nas/bootstrapNAS/elasticity/base_handler.py b/nncf/experimental/torch/nas/bootstrapNAS/elasticity/base_handler.py index 6c8dd3c1cee..aca1999c6a1 100644 --- a/nncf/experimental/torch/nas/bootstrapNAS/elasticity/base_handler.py +++ b/nncf/experimental/torch/nas/bootstrapNAS/elasticity/base_handler.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/torch/nas/bootstrapNAS/elasticity/elastic_depth.py b/nncf/experimental/torch/nas/bootstrapNAS/elasticity/elastic_depth.py index 8599d00f845..8ba82714546 100644 --- a/nncf/experimental/torch/nas/bootstrapNAS/elasticity/elastic_depth.py +++ b/nncf/experimental/torch/nas/bootstrapNAS/elasticity/elastic_depth.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/torch/nas/bootstrapNAS/elasticity/elastic_kernel.py b/nncf/experimental/torch/nas/bootstrapNAS/elasticity/elastic_kernel.py index 383653b0a9f..2d143592c5a 100644 --- a/nncf/experimental/torch/nas/bootstrapNAS/elasticity/elastic_kernel.py +++ b/nncf/experimental/torch/nas/bootstrapNAS/elasticity/elastic_kernel.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/torch/nas/bootstrapNAS/elasticity/elastic_width.py b/nncf/experimental/torch/nas/bootstrapNAS/elasticity/elastic_width.py index de688024e50..227a4d16fee 100644 --- a/nncf/experimental/torch/nas/bootstrapNAS/elasticity/elastic_width.py +++ b/nncf/experimental/torch/nas/bootstrapNAS/elasticity/elastic_width.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -18,6 +18,7 @@ import torch from torch import nn +import nncf from nncf.common.graph import NNCFNodeName from nncf.common.graph.layer_attributes import BaseLayerAttributes from nncf.common.graph.layer_attributes import ConvolutionLayerAttributes @@ -281,11 +282,11 @@ def __init__( if fixed_width_list: fixed_width_list.sort(reverse=True) if fixed_width_list[0] > max_width: - raise RuntimeError( + raise nncf.InternalError( f"Width list for {node_name} contains invalid values: {fixed_width_list}, {max_width}" ) if fixed_width_list[0] != max_width: - raise RuntimeError(f"Max width for {node_name} is not aligned with pre-trained model") + raise nncf.ValidationError(f"Max width for {node_name} is not aligned with pre-trained model") self._width_list = fixed_width_list else: self._width_list = self._generate_width_list(self._max_width, params) @@ -359,7 +360,7 @@ def _generate_width_list(max_width: int, params: ElasticWidthParams) -> List[int if p.max_num_widths == len(width_list): break if 0 >= multiplier > 1: - raise RuntimeError(f"Wrong value for multiplier: {multiplier}") + raise nncf.InternalError(f"Wrong value for multiplier: {multiplier}") w = int(max_width * multiplier) w = w - (w % ALIGNMENT_CONSTANT_FOR_MULTIPLIERS) w = max(w, p.min_width) @@ -578,7 +579,7 @@ def width_num_params_indicator(self): @width_num_params_indicator.setter def width_num_params_indicator(self, width_num_params_indicator): if width_num_params_indicator == 0 or width_num_params_indicator < -1: - raise RuntimeError(f"Invalid width indicator: {width_num_params_indicator}") + raise nncf.InternalError(f"Invalid width indicator: {width_num_params_indicator}") self._width_num_params_indicator = width_num_params_indicator @property @@ -709,35 +710,34 @@ def activate_subnet_for_config(self, config: ElasticWidthConfig) -> None: if not was_set and node_name not in names_of_processed_nodes: nncf_logger.debug(f"input width was not set in scope={node.node_name}") - if self._add_dynamic_inputs: - if node_name in self._add_dynamic_inputs and not was_set: - nncf_logger.debug(f"setting input width by user's request for scope={node_name}") - nodes_to_check = [node] - while any(elem is None for elem in input_masks): - previous_nodes = [] - for node in nodes_to_check: - previous_nodes.append(self._propagation_graph.get_previous_nodes(node)) - nodes_to_check.clear() - previous_nodes = [item for nodes in previous_nodes for item in nodes] - if not previous_nodes: - break - for previous in previous_nodes: - if "output_mask" in previous.attributes: - if previous.attributes["output_mask"] is not None: - input_masks.append(previous.attributes["output_mask"]) - input_masks = [i for i in input_masks if i] - else: - nodes_to_check.append(previous) + if self._add_dynamic_inputs and node_name in self._add_dynamic_inputs and not was_set: + nncf_logger.debug(f"setting input width by user's request for scope={node_name}") + nodes_to_check = [node] + while any(elem is None for elem in input_masks): + previous_nodes = [] + for node in nodes_to_check: + previous_nodes.append(self._propagation_graph.get_previous_nodes(node)) + nodes_to_check.clear() + previous_nodes = [item for nodes in previous_nodes for item in nodes] + if not previous_nodes: + break + for previous in previous_nodes: + if "output_mask" in previous.attributes: + if previous.attributes["output_mask"] is not None: + input_masks.append(previous.attributes["output_mask"]) + input_masks = [i for i in input_masks if i] else: nodes_to_check.append(previous) - if input_masks: - input_mask = input_masks[0] - input_width = self.mask_to_width(input_mask) - if input_width: - dynamic_input_width_op.set_active_width(input_width) - was_set = True - if was_set: - nncf_logger.debug(f"Success setting up user's request for dynamic input at scope={node_name}") + else: + nodes_to_check.append(previous) + if input_masks: + input_mask = input_masks[0] + input_width = self.mask_to_width(input_mask) + if input_width: + dynamic_input_width_op.set_active_width(input_width) + was_set = True + if was_set: + nncf_logger.debug(f"Success setting up user's request for dynamic input at scope={node_name}") def get_active_in_out_width_values( self, @@ -909,7 +909,7 @@ def _collect_ops_data_by_selection_rule(self, selection_rule: Callable) -> Dict[ for cluster in self._pruned_module_groups_info.get_all_clusters(): all_max_out_channels = {el.elastic_op.max_width for el in cluster.elements} if len(all_max_out_channels) != 1: - raise RuntimeError("Invalid grouping of layers with different number of output channels") + raise nncf.InternalError("Invalid grouping of layers with different number of output channels") first_elastic_width_info = next(iter(cluster.elements)) op = first_elastic_width_info.elastic_op @@ -1040,7 +1040,7 @@ def build(self, target_model: NNCFNetwork) -> ElasticWidthHandler: list_of_node_ids.append(node.node_id) layer_attrs = node.layer_attributes if metatype not in metatype_vs_elastic_op_creator: - raise RuntimeError(f"Elastic width is not supported for {metatype}") + raise nncf.InternalError(f"Elastic width is not supported for {metatype}") elastic_op_creator = metatype_vs_elastic_op_creator[metatype] elastic_width_operation = elastic_op_creator( @@ -1130,7 +1130,7 @@ def load_state(self, state: Dict[str, Any]) -> None: self._overwrite_groups_widths = params_from_state[self._state_names.OVERWRITE_GROUP_WIDTHS] self._overwriting_pruning_groups = True if len(self._grouped_node_names_to_prune) != len(self._overwrite_groups_widths): - raise RuntimeError("Mismatch between number of groups for pruning and their corresponding widths") + raise nncf.InternalError("Mismatch between number of groups for pruning and their corresponding widths") if params_from_state.get(self._state_names.ADD_DYNAMIC_INPUTS, None) is not None: self._add_dynamic_inputs = params_from_state[self._state_names.ADD_DYNAMIC_INPUTS] diff --git a/nncf/experimental/torch/nas/bootstrapNAS/elasticity/elasticity_builder.py b/nncf/experimental/torch/nas/bootstrapNAS/elasticity/elasticity_builder.py index a94b67914c0..bc6464b24d5 100644 --- a/nncf/experimental/torch/nas/bootstrapNAS/elasticity/elasticity_builder.py +++ b/nncf/experimental/torch/nas/bootstrapNAS/elasticity/elasticity_builder.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/torch/nas/bootstrapNAS/elasticity/elasticity_controller.py b/nncf/experimental/torch/nas/bootstrapNAS/elasticity/elasticity_controller.py index 3dfe7ac8177..0e4a3b5e400 100644 --- a/nncf/experimental/torch/nas/bootstrapNAS/elasticity/elasticity_controller.py +++ b/nncf/experimental/torch/nas/bootstrapNAS/elasticity/elasticity_controller.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/torch/nas/bootstrapNAS/elasticity/elasticity_dim.py b/nncf/experimental/torch/nas/bootstrapNAS/elasticity/elasticity_dim.py index ca0e4c69a9a..58ceecc4218 100644 --- a/nncf/experimental/torch/nas/bootstrapNAS/elasticity/elasticity_dim.py +++ b/nncf/experimental/torch/nas/bootstrapNAS/elasticity/elasticity_dim.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/torch/nas/bootstrapNAS/elasticity/filter_reorder.py b/nncf/experimental/torch/nas/bootstrapNAS/elasticity/filter_reorder.py index ef10ff53aee..fb9a883f6d3 100644 --- a/nncf/experimental/torch/nas/bootstrapNAS/elasticity/filter_reorder.py +++ b/nncf/experimental/torch/nas/bootstrapNAS/elasticity/filter_reorder.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/torch/nas/bootstrapNAS/elasticity/multi_elasticity_handler.py b/nncf/experimental/torch/nas/bootstrapNAS/elasticity/multi_elasticity_handler.py index 76ae525d275..4daa23a0baf 100644 --- a/nncf/experimental/torch/nas/bootstrapNAS/elasticity/multi_elasticity_handler.py +++ b/nncf/experimental/torch/nas/bootstrapNAS/elasticity/multi_elasticity_handler.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/torch/nas/bootstrapNAS/elasticity/visualization.py b/nncf/experimental/torch/nas/bootstrapNAS/elasticity/visualization.py index 9901fb9060e..4e1e6ddbe56 100644 --- a/nncf/experimental/torch/nas/bootstrapNAS/elasticity/visualization.py +++ b/nncf/experimental/torch/nas/bootstrapNAS/elasticity/visualization.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -12,6 +12,7 @@ import networkx as nx +import nncf from nncf.common.graph import NNCFNode from nncf.common.graph import NNCFNodeName from nncf.common.pruning.utils import get_input_masks @@ -90,6 +91,6 @@ def _get_original_node_from_compressed_node_name( try: propagation_graph: PTNNCFGraph = width_handler.propagation_graph result = propagation_graph.get_node_by_name(node_name) - except RuntimeError: + except nncf.InternalError: result = None return result diff --git a/nncf/experimental/torch/nas/bootstrapNAS/search/__init__.py b/nncf/experimental/torch/nas/bootstrapNAS/search/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/experimental/torch/nas/bootstrapNAS/search/__init__.py +++ b/nncf/experimental/torch/nas/bootstrapNAS/search/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/torch/nas/bootstrapNAS/search/evaluator.py b/nncf/experimental/torch/nas/bootstrapNAS/search/evaluator.py index b7aede09cd4..50902074b50 100644 --- a/nncf/experimental/torch/nas/bootstrapNAS/search/evaluator.py +++ b/nncf/experimental/torch/nas/bootstrapNAS/search/evaluator.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -94,7 +94,7 @@ def retrieve_from_cache(self, subnet_config_repr: Tuple[float, ...]) -> Tuple[bo :param subnet_config_repr: tuple representing the values for the associated design variables. :return: (True if the information is in cache, and corresponding value stored in cache, 0 otherwise) """ - if subnet_config_repr in self.cache.keys(): + if subnet_config_repr in self.cache: return True, self.cache[subnet_config_repr] return False, 0 diff --git a/nncf/experimental/torch/nas/bootstrapNAS/search/evaluator_handler.py b/nncf/experimental/torch/nas/bootstrapNAS/search/evaluator_handler.py index 7ebfbb3f71c..d8ec951fba1 100644 --- a/nncf/experimental/torch/nas/bootstrapNAS/search/evaluator_handler.py +++ b/nncf/experimental/torch/nas/bootstrapNAS/search/evaluator_handler.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/torch/nas/bootstrapNAS/search/search.py b/nncf/experimental/torch/nas/bootstrapNAS/search/search.py index c94036a9212..d5ca9cd55e1 100644 --- a/nncf/experimental/torch/nas/bootstrapNAS/search/search.py +++ b/nncf/experimental/torch/nas/bootstrapNAS/search/search.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -26,6 +26,7 @@ from pymoo.optimize import minimize from torch.utils.data.dataloader import DataLoader +import nncf from nncf import NNCFConfig from nncf.common.initialization.batchnorm_adaptation import BatchnormAdaptationAlgorithm from nncf.common.logging import nncf_logger @@ -333,7 +334,7 @@ def __init__( self._num_vars, self._vars_upper = self._elasticity_ctrl.multi_elasticity_handler.get_design_vars_info() if self._num_vars == 0 or self._vars_lower is None: - raise RuntimeError("Search space is empty") + raise nncf.InternalError("Search space is empty") self._result = None bn_adapt_params = search_config.get("batchnorm_adaptation", {}) @@ -353,7 +354,7 @@ def evaluator_handlers(self) -> List[BaseEvaluatorHandler]: """ if self._evaluator_handlers: return self._evaluator_handlers - raise RuntimeError("Evaluator handlers haven't been defined") + raise nncf.ValidationError("Evaluator handlers haven't been defined") @property def acc_delta(self) -> float: @@ -739,17 +740,14 @@ def _save_checkpoint_best_subnetwork(self, config: SubnetConfig) -> NoReturn: """ acc_within_tolerance = self._accuracy_evaluator_handler.current_value pair_objective = self._efficiency_evaluator_handler.current_value - if acc_within_tolerance < (self._lower_bound_acc * -1.0): - if pair_objective < self._search.best_pair_objective: - self._search.best_pair_objective = pair_objective - self._search.best_config = config - self._search.best_vals = [ - evaluator_handler.current_value for evaluator_handler in self._evaluator_handlers - ] - checkpoint_path = Path(self._search.checkpoint_save_dir, "subnetwork_best.pth") - checkpoint = { - "best_acc1": acc_within_tolerance * -1.0, - "best_efficiency": pair_objective, - "subnet_config": config, - } - torch.save(checkpoint, checkpoint_path) + if acc_within_tolerance < (self._lower_bound_acc * -1.0) and pair_objective < self._search.best_pair_objective: + self._search.best_pair_objective = pair_objective + self._search.best_config = config + self._search.best_vals = [evaluator_handler.current_value for evaluator_handler in self._evaluator_handlers] + checkpoint_path = Path(self._search.checkpoint_save_dir, "subnetwork_best.pth") + checkpoint = { + "best_acc1": acc_within_tolerance * -1.0, + "best_efficiency": pair_objective, + "subnet_config": config, + } + torch.save(checkpoint, checkpoint_path) diff --git a/nncf/experimental/torch/nas/bootstrapNAS/search/supernet.py b/nncf/experimental/torch/nas/bootstrapNAS/search/supernet.py index d64a3269aea..8720bfa0d43 100644 --- a/nncf/experimental/torch/nas/bootstrapNAS/search/supernet.py +++ b/nncf/experimental/torch/nas/bootstrapNAS/search/supernet.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/torch/nas/bootstrapNAS/training/__init__.py b/nncf/experimental/torch/nas/bootstrapNAS/training/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/experimental/torch/nas/bootstrapNAS/training/__init__.py +++ b/nncf/experimental/torch/nas/bootstrapNAS/training/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/torch/nas/bootstrapNAS/training/base_training.py b/nncf/experimental/torch/nas/bootstrapNAS/training/base_training.py index 7479e91cfea..3977e1c9fde 100644 --- a/nncf/experimental/torch/nas/bootstrapNAS/training/base_training.py +++ b/nncf/experimental/torch/nas/bootstrapNAS/training/base_training.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/torch/nas/bootstrapNAS/training/lr_scheduler.py b/nncf/experimental/torch/nas/bootstrapNAS/training/lr_scheduler.py index 4455856a97b..bdb8127d75b 100644 --- a/nncf/experimental/torch/nas/bootstrapNAS/training/lr_scheduler.py +++ b/nncf/experimental/torch/nas/bootstrapNAS/training/lr_scheduler.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/torch/nas/bootstrapNAS/training/model_creator_helpers.py b/nncf/experimental/torch/nas/bootstrapNAS/training/model_creator_helpers.py index a67daba2a8d..cbe79146013 100644 --- a/nncf/experimental/torch/nas/bootstrapNAS/training/model_creator_helpers.py +++ b/nncf/experimental/torch/nas/bootstrapNAS/training/model_creator_helpers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/torch/nas/bootstrapNAS/training/progressive_shrinking_builder.py b/nncf/experimental/torch/nas/bootstrapNAS/training/progressive_shrinking_builder.py index f34cf31c264..92609327fcb 100644 --- a/nncf/experimental/torch/nas/bootstrapNAS/training/progressive_shrinking_builder.py +++ b/nncf/experimental/torch/nas/bootstrapNAS/training/progressive_shrinking_builder.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/torch/nas/bootstrapNAS/training/progressive_shrinking_controller.py b/nncf/experimental/torch/nas/bootstrapNAS/training/progressive_shrinking_controller.py index a447ac57187..4106216d300 100644 --- a/nncf/experimental/torch/nas/bootstrapNAS/training/progressive_shrinking_controller.py +++ b/nncf/experimental/torch/nas/bootstrapNAS/training/progressive_shrinking_controller.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/torch/nas/bootstrapNAS/training/scheduler.py b/nncf/experimental/torch/nas/bootstrapNAS/training/scheduler.py index 63505dda941..3579dbb6dff 100644 --- a/nncf/experimental/torch/nas/bootstrapNAS/training/scheduler.py +++ b/nncf/experimental/torch/nas/bootstrapNAS/training/scheduler.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -175,11 +175,10 @@ def epoch_step(self, next_epoch: Optional[int] = None) -> None: super().epoch_step(next_epoch) self._lr_scheduler.epoch_step(next_epoch) stage_desc, stage_desc_idx = self.get_current_stage_desc() - if stage_desc is not None: - if stage_desc_idx != self.current_stage_idx: - self._lr_scheduler.stage_step(stage_desc) - self._training_ctrl.set_stage(stage_desc) - self.current_stage_idx = stage_desc_idx + if stage_desc is not None and stage_desc_idx != self.current_stage_idx: + self._lr_scheduler.stage_step(stage_desc) + self._training_ctrl.set_stage(stage_desc) + self.current_stage_idx = stage_desc_idx def is_final_stage(self) -> bool: """ diff --git a/nncf/experimental/torch/nas/bootstrapNAS/training/stage_descriptor.py b/nncf/experimental/torch/nas/bootstrapNAS/training/stage_descriptor.py index 038339db9e6..6a4b8537734 100644 --- a/nncf/experimental/torch/nas/bootstrapNAS/training/stage_descriptor.py +++ b/nncf/experimental/torch/nas/bootstrapNAS/training/stage_descriptor.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/torch/nas/bootstrapNAS/training/training_algorithm.py b/nncf/experimental/torch/nas/bootstrapNAS/training/training_algorithm.py index d4107a31554..194db10e903 100644 --- a/nncf/experimental/torch/nas/bootstrapNAS/training/training_algorithm.py +++ b/nncf/experimental/torch/nas/bootstrapNAS/training/training_algorithm.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/torch/pruning/__init__.py b/nncf/experimental/torch/pruning/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/experimental/torch/pruning/__init__.py +++ b/nncf/experimental/torch/pruning/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/torch/pruning/operations.py b/nncf/experimental/torch/pruning/operations.py index aa6ffccc498..e4a84ccd42f 100644 --- a/nncf/experimental/torch/pruning/operations.py +++ b/nncf/experimental/torch/pruning/operations.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/torch/replace_custom_modules/__init__.py b/nncf/experimental/torch/replace_custom_modules/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/experimental/torch/replace_custom_modules/__init__.py +++ b/nncf/experimental/torch/replace_custom_modules/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/torch/replace_custom_modules/timm_custom_modules.py b/nncf/experimental/torch/replace_custom_modules/timm_custom_modules.py index 9628466fb49..7a712279d45 100644 --- a/nncf/experimental/torch/replace_custom_modules/timm_custom_modules.py +++ b/nncf/experimental/torch/replace_custom_modules/timm_custom_modules.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/torch/search_building_blocks/__init__.py b/nncf/experimental/torch/search_building_blocks/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/experimental/torch/search_building_blocks/__init__.py +++ b/nncf/experimental/torch/search_building_blocks/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/torch/search_building_blocks/search_blocks.py b/nncf/experimental/torch/search_building_blocks/search_blocks.py index 0fa75c4d5b7..54e43704f6b 100644 --- a/nncf/experimental/torch/search_building_blocks/search_blocks.py +++ b/nncf/experimental/torch/search_building_blocks/search_blocks.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/torch/search_building_blocks/search_graph.py b/nncf/experimental/torch/search_building_blocks/search_graph.py index 99a146dd890..602940738cf 100644 --- a/nncf/experimental/torch/search_building_blocks/search_graph.py +++ b/nncf/experimental/torch/search_building_blocks/search_graph.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/torch/sparsity/__init__.py b/nncf/experimental/torch/sparsity/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/experimental/torch/sparsity/__init__.py +++ b/nncf/experimental/torch/sparsity/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/torch/sparsity/movement/__init__.py b/nncf/experimental/torch/sparsity/movement/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/experimental/torch/sparsity/movement/__init__.py +++ b/nncf/experimental/torch/sparsity/movement/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/torch/sparsity/movement/algo.py b/nncf/experimental/torch/sparsity/movement/algo.py index a867e76ac5f..863e040098e 100644 --- a/nncf/experimental/torch/sparsity/movement/algo.py +++ b/nncf/experimental/torch/sparsity/movement/algo.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -15,6 +15,7 @@ import torch import torch.distributed as dist +import nncf from nncf import NNCFConfig from nncf.api.compression import CompressionStage from nncf.common.accuracy_aware_training.training_loop import ADAPTIVE_COMPRESSION_CONTROLLERS @@ -69,7 +70,7 @@ def create_weight_sparsifying_operation( sparse_cfg = configs_per_scopes.sparse_config matched_scopes.append(target_scopes) if len(matched_scopes) >= 2: - raise RuntimeError(f'"{node_name}" is matched by multiple items in `sparse_structure_by_scopes`.') + raise nncf.InternalError(f'"{node_name}" is matched by multiple items in `sparse_structure_by_scopes`.') return MovementSparsifier( target_module_node, @@ -109,7 +110,7 @@ def _sparsify_weights(self, target_model: NNCFNetwork) -> List[PTInsertionComman self._sparsified_module_info.append(SparseModuleInfo(node_name, sparsified_module, sparsifying_operation)) if not insertion_commands: - raise RuntimeError("No sparsifiable layer found for movement sparsity algorithm.") + raise nncf.InternalError("No sparsifiable layer found for movement sparsity algorithm.") return insertion_commands def _build_controller(self, model: NNCFNetwork) -> PTCompressionAlgorithmController: @@ -149,7 +150,7 @@ def __init__(self, target_model: NNCFNetwork, sparsified_module_info: List[Spars if self._scheduler.enable_structured_masking: if not is_supported_model_family(self.model): - raise RuntimeError( + raise nncf.UnsupportedModelError( "You set `enable_structured_masking=True`, but no supported model is detected. " f"Supported model families: {MODEL_FAMILIES}." ) diff --git a/nncf/experimental/torch/sparsity/movement/functions.py b/nncf/experimental/torch/sparsity/movement/functions.py index f24c9f35bbc..ef7d558b28e 100644 --- a/nncf/experimental/torch/sparsity/movement/functions.py +++ b/nncf/experimental/torch/sparsity/movement/functions.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/torch/sparsity/movement/layers.py b/nncf/experimental/torch/sparsity/movement/layers.py index 62e09d06c4b..f120a93d0f6 100644 --- a/nncf/experimental/torch/sparsity/movement/layers.py +++ b/nncf/experimental/torch/sparsity/movement/layers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -16,6 +16,7 @@ import torch from torch import nn +import nncf from nncf.common.graph import NNCFNode from nncf.experimental.torch.sparsity.movement.functions import binary_mask_by_threshold from nncf.torch.layer_utils import COMPRESSION_MODULES @@ -304,7 +305,7 @@ def _get_weight_importance_shape( score_shape.append(dim // factor) return tuple(score_shape) - raise RuntimeError("Unknown sparse structure.") + raise nncf.InternalError("Unknown sparse structure.") @staticmethod def _get_sparse_factors(weight_shape: List[int], sparse_config: SparseConfig) -> Tuple[int, int]: diff --git a/nncf/experimental/torch/sparsity/movement/loss.py b/nncf/experimental/torch/sparsity/movement/loss.py index e8dd6f2501a..3146d79e719 100644 --- a/nncf/experimental/torch/sparsity/movement/loss.py +++ b/nncf/experimental/torch/sparsity/movement/loss.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/experimental/torch/sparsity/movement/scheduler.py b/nncf/experimental/torch/sparsity/movement/scheduler.py index fc06c2617c3..4274e4206e5 100644 --- a/nncf/experimental/torch/sparsity/movement/scheduler.py +++ b/nncf/experimental/torch/sparsity/movement/scheduler.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -315,12 +315,15 @@ def _maybe_should_skip(self) -> None: if self._steps_per_epoch is None and self._steps_in_current_epoch > 0: self._steps_per_epoch = self._steps_in_current_epoch - if self._steps_per_epoch is not None and self._steps_in_current_epoch > 0: - if self._steps_per_epoch != self._steps_in_current_epoch: - raise Exception( - "Actual steps per epoch and steps per epoch from the scheduler " - "parameters are different. Scheduling may be incorrect." - ) + if ( + self._steps_per_epoch is not None + and self._steps_in_current_epoch > 0 + and self._steps_per_epoch != self._steps_in_current_epoch + ): + raise Exception( + "Actual steps per epoch and steps per epoch from the scheduler " + "parameters are different. Scheduling may be incorrect." + ) if self._steps_per_epoch is None: self._should_skip = True diff --git a/nncf/experimental/torch/sparsity/movement/structured_mask_handler.py b/nncf/experimental/torch/sparsity/movement/structured_mask_handler.py index f7928708849..ac95f4f11bb 100644 --- a/nncf/experimental/torch/sparsity/movement/structured_mask_handler.py +++ b/nncf/experimental/torch/sparsity/movement/structured_mask_handler.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/onnx/__init__.py b/nncf/onnx/__init__.py index ad92d0721d4..2da3ccba8e2 100644 --- a/nncf/onnx/__init__.py +++ b/nncf/onnx/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/onnx/engine.py b/nncf/onnx/engine.py index ac644d86fe5..a268e617374 100644 --- a/nncf/onnx/engine.py +++ b/nncf/onnx/engine.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/onnx/graph/__init__.py b/nncf/onnx/graph/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/onnx/graph/__init__.py +++ b/nncf/onnx/graph/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/onnx/graph/metatypes/__init__.py b/nncf/onnx/graph/metatypes/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/onnx/graph/metatypes/__init__.py +++ b/nncf/onnx/graph/metatypes/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/onnx/graph/metatypes/groups.py b/nncf/onnx/graph/metatypes/groups.py index 404b65cf8a5..a7f4ffd1ad9 100644 --- a/nncf/onnx/graph/metatypes/groups.py +++ b/nncf/onnx/graph/metatypes/groups.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -121,3 +121,9 @@ onnx_metatypes.ONNXConvolutionMetatype, onnx_metatypes.ONNXDepthwiseConvolutionMetatype, ] + + +QUANTIZE_DEQUANTIZE_OPERATIONS = [ + onnx_metatypes.ONNXQuantizeLinearMetatype, + onnx_metatypes.ONNXDequantizeLinearMetatype, +] diff --git a/nncf/onnx/graph/metatypes/onnx_metatypes.py b/nncf/onnx/graph/metatypes/onnx_metatypes.py index 3ff663a897f..da40b2e8f30 100644 --- a/nncf/onnx/graph/metatypes/onnx_metatypes.py +++ b/nncf/onnx/graph/metatypes/onnx_metatypes.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -13,6 +13,7 @@ import onnx +import nncf from nncf.common.graph.operator_metatypes import OperatorMetatype from nncf.common.graph.operator_metatypes import OperatorMetatypeRegistry from nncf.common.hardware.opset import HWConfigOpName @@ -47,7 +48,7 @@ def determine_subtype(cls, model: onnx.ModelProto, node: onnx.NodeProto) -> Opti if subtype.matches(model, node): matches.append(subtype) if len(matches) > 1: - raise RuntimeError("Multiple subtypes match operator call - cannot determine single subtype.") + raise nncf.InternalError("Multiple subtypes match operator call - cannot determine single subtype.") if not matches: return None return matches[0] diff --git a/nncf/onnx/graph/model_transformer.py b/nncf/onnx/graph/model_transformer.py index 877edb6838b..a584251dab9 100644 --- a/nncf/onnx/graph/model_transformer.py +++ b/nncf/onnx/graph/model_transformer.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -15,6 +15,7 @@ import numpy as np import onnx +import nncf from nncf.common.graph.model_transformer import ModelTransformer from nncf.common.graph.transformations.commands import TargetType from nncf.common.graph.transformations.layout import TransformationLayout @@ -26,7 +27,7 @@ from nncf.onnx.graph.onnx_helper import get_name_to_node_map from nncf.onnx.graph.onnx_helper import get_node_index from nncf.onnx.graph.onnx_helper import get_tensor -from nncf.onnx.graph.transformations.commands import ONNXBiasCorrectionCommand +from nncf.onnx.graph.transformations.commands import ONNXInitializerUpdateCommand from nncf.onnx.graph.transformations.commands import ONNXModelExtractionCommand from nncf.onnx.graph.transformations.commands import ONNXOutputInsertionCommand from nncf.onnx.graph.transformations.commands import ONNXQDQNodeRemovingCommand @@ -87,7 +88,7 @@ def transform(self, transformation_layout: TransformationLayout) -> onnx.ModelPr """ quantizer_insert_transformations = [] output_insert_transformations = [] - bias_correction_transformations = [] + initializer_update_transformations = [] qdq_node_removing_transformations = [] model_extraction_transformation = None transformations = transformation_layout.transformations @@ -99,21 +100,21 @@ def transform(self, transformation_layout: TransformationLayout) -> onnx.ModelPr quantizer_insert_transformations.append(transformation) elif isinstance(transformation, ONNXOutputInsertionCommand): output_insert_transformations.append(transformation) - elif isinstance(transformation, ONNXBiasCorrectionCommand): - bias_correction_transformations.append(transformation) elif isinstance(transformation, ONNXModelExtractionCommand): model_extraction_transformation = transformation elif isinstance(transformation, ONNXQDQNodeRemovingCommand): qdq_node_removing_transformations.append(transformation) + elif isinstance(transformation, ONNXInitializerUpdateCommand): + initializer_update_transformations.append(transformation) # Inplace transformations, using deepcopy of model - if quantizer_insert_transformations or bias_correction_transformations or qdq_node_removing_transformations: + if quantizer_insert_transformations or initializer_update_transformations or qdq_node_removing_transformations: model = deepcopy(self._model) if quantizer_insert_transformations: model = self._apply_quantizer_insertion_transformations(model, quantizer_insert_transformations) - if bias_correction_transformations: - model = self._apply_bias_correction_transformations(model, bias_correction_transformations) if qdq_node_removing_transformations: model = self._apply_qdq_node_removing_transformations(model, qdq_node_removing_transformations) + if initializer_update_transformations: + model = self._apply_initializer_update_transformations(model, initializer_update_transformations) # Transformations that create new model if output_insert_transformations: model = self._apply_output_insertion_transformations(output_insert_transformations) @@ -272,7 +273,7 @@ def _get_scale_zero_point_tensors( elif tensor_type == np.int8: onnx_tensor_type = onnx.TensorProto.INT8 else: - raise RuntimeError(f"Incorrect tensor type - {tensor_type}.") + raise nncf.ValidationError(f"Incorrect tensor type - {tensor_type}.") assert quantizer.input[1] == dequantizer.input[1] and quantizer.input[2] == dequantizer.input[2] scale_tensor_name = quantizer.input[1] zero_point_tensor_name = quantizer.input[2] @@ -326,7 +327,7 @@ def _insert_quantizer_dequantizer( input_nodes = [] input_nodes.extend(children_node_mapping[target_edge_name]) if not input_nodes: - raise RuntimeError( + raise nncf.InternalError( f"Can not add the quantizer to the {target_edge_name} edge. This edge does not have end node." ) @@ -355,8 +356,8 @@ def _insert_quantizer_dequantizer( model.graph.node.insert(insert_index + 1, dequantizer) return model - def _apply_bias_correction_transformations( - self, model: onnx.ModelProto, transformations: List[ONNXBiasCorrectionCommand] + def _apply_initializer_update_transformations( + self, model: onnx.ModelProto, transformations: List[ONNXInitializerUpdateCommand] ) -> onnx.ModelProto: """ Creates a copy of original model and applies bias correction transformations on the model. @@ -365,16 +366,14 @@ def _apply_bias_correction_transformations( :param transformations: Bias correction transformations. :return: Copy of original model with updated biases. """ - node_mapping = get_name_to_node_map(model) + name_to_node_map = get_name_to_node_map(model) for transformation in transformations: - bias_tensor_position = transformation.target_point.port_id - node_name = transformation.target_point.target_node_name - onnx_node = node_mapping[node_name] - bias_initializer_name = onnx_node.input[bias_tensor_position] - bias_initializer = get_tensor(model, bias_initializer_name) + node = name_to_node_map[transformation.target_point.target_node_name] + initializer_name = node.input[transformation.target_point.port_id] + initializer = get_tensor(model, initializer_name) - new_bias_tensor = onnx.numpy_helper.from_array(transformation.bias_value, bias_initializer_name) - bias_initializer.CopyFrom(new_bias_tensor) + new_tensor = onnx.numpy_helper.from_array(transformation.new_value, initializer_name) + initializer.CopyFrom(new_tensor) return model def _apply_model_extraction_transformation(self, transformation: ONNXModelExtractionCommand) -> onnx.ModelProto: @@ -386,14 +385,14 @@ def _apply_model_extraction_transformation(self, transformation: ONNXModelExtrac """ input_tensor_names = [] node_mapping = get_name_to_node_map(self._model) - for input_node_name in transformation.inputs: - input_onnx_node = node_mapping[input_node_name] - input_tensor_names.append(input_onnx_node.input[0]) + for input_name, input_port_id in transformation.input_ids: + input_onnx_node = node_mapping[input_name] + input_tensor_names.append(input_onnx_node.input[input_port_id]) output_tensor_names = [] - for output_node_name in transformation.outputs: - output_onnx_node = node_mapping[output_node_name] - output_tensor_names.append(output_onnx_node.output[0]) + for output_name, output_port_id in transformation.output_ids: + output_onnx_node = node_mapping[output_name] + output_tensor_names.append(output_onnx_node.output[output_port_id]) if not output_tensor_names: output_tensor_names = [n.name for n in self._model.graph.output] @@ -410,24 +409,49 @@ def _apply_qdq_node_removing_transformations( :param transformations: Nodes removing transformations. :return: Model with removed nodes. """ - for transformation in transformations: - node_mapping = get_name_to_node_map(model) - children_node_mapping = get_children_node_mapping(model) - node = node_mapping[transformation.target_point.target_node_name] - - node_children = get_children(node, children_node_mapping) - for node_child in node_children: - for input_id, input_obj in enumerate(node_child.input): - if input_obj == node.output[0]: - node_child.input[input_id] = node.input[0] - - initializers = {i.name: i for i in model.graph.initializer} - value_infos = {i.name: i for i in model.graph.value_info} - for initializer_name in node.input: - if initializer_name in initializers: - model.graph.initializer.remove(initializers[initializer_name]) - if initializer_name in value_infos: - model.graph.value_info.remove(value_infos[initializer_name]) - - model.graph.node.remove(node) + name_to_node_map = get_name_to_node_map(model) + children_node_mapping = get_children_node_mapping(model) + # We combine quantize and dequantize nodes into pairs here because it + # does not make sense to remove only the quantize node or the dequantize + # node. They should be removed together. + was_processed = {t.target_point.target_node_name: False for t in transformations} + quantize_dequantize_pairs = [] + for node_name in was_processed: + if was_processed[node_name]: + continue + quantize_node_proto = name_to_node_map[node_name] + if quantize_node_proto.op_type != "QuantizeLinear": + continue + # `quantize_node_proto` has only one child, which is the dequantize node. + dequantize_node_proto = next(iter(get_children(quantize_node_proto, children_node_mapping))) + assert dequantize_node_proto.op_type == "DequantizeLinear" + + quantize_dequantize_pairs.append((quantize_node_proto, dequantize_node_proto)) + was_processed[quantize_node_proto.name] = True + was_processed[dequantize_node_proto.name] = True + + if not all(was_processed.values()): + raise RuntimeError("Invalid transformation commands.") + + initializers = {i.name: i for i in model.graph.initializer} + value_infos = {i.name: i for i in model.graph.value_info} + + for quantize_node_proto, dequantize_node_proto in quantize_dequantize_pairs: + # Unlink Q-DQ subgraph from graph + children = get_children(dequantize_node_proto, children_node_mapping) + for child in children: + for port_id, input_name in enumerate(child.input): + if input_name == dequantize_node_proto.output[0]: + child.input[port_id] = quantize_node_proto.input[0] + # QuantizeLinear and DequantizeLinear nodes have common initializers in ports 1 and 2. + for i in [1, 2]: + model.graph.initializer.remove(initializers[quantize_node_proto.input[i]]) + model.graph.value_info.remove(value_infos[quantize_node_proto.input[i]]) + + for node_proto in [quantize_node_proto, dequantize_node_proto]: + model.graph.value_info.remove(value_infos[node_proto.output[0]]) + + model.graph.node.remove(quantize_node_proto) + model.graph.node.remove(dequantize_node_proto) + return model diff --git a/nncf/onnx/graph/model_utils.py b/nncf/onnx/graph/model_utils.py index 41c9afcfe54..888fd5b2932 100644 --- a/nncf/onnx/graph/model_utils.py +++ b/nncf/onnx/graph/model_utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/onnx/graph/nncf_graph_builder.py b/nncf/onnx/graph/nncf_graph_builder.py index 148de756c30..79c29928f0c 100644 --- a/nncf/onnx/graph/nncf_graph_builder.py +++ b/nncf/onnx/graph/nncf_graph_builder.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -13,6 +13,7 @@ import onnx +import nncf from nncf.common.graph import NNCFGraph from nncf.common.graph.definitions import MODEL_INPUT_OP_NAME from nncf.common.graph.definitions import MODEL_OUTPUT_OP_NAME @@ -225,7 +226,7 @@ def _replace_empty_node_name(model: onnx.ModelProto) -> onnx.ModelProto: name_counter = Counter([node.name for node in model.graph.node]) if max(name_counter.values()) > 1: - raise RuntimeError( + raise nncf.ValidationError( f"Nodes {[(name, cnt) for name, cnt in name_counter.items() if cnt > 1]} " "(name, counts) occurred more than once. " "NNCF expects every node to have a unique name." diff --git a/nncf/onnx/graph/node_utils.py b/nncf/onnx/graph/node_utils.py index 7d46356904c..29980abc6d9 100644 --- a/nncf/onnx/graph/node_utils.py +++ b/nncf/onnx/graph/node_utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -102,7 +102,7 @@ def is_any_weight_quantized(node: NNCFNode, nncf_graph: NNCFGraph) -> bool: """ is_quantized_weight = False if node.layer_attributes.has_weight(): - for port_id in node.layer_attributes.weight_attrs.keys(): + for port_id in node.layer_attributes.weight_attrs: is_quantized_weight = is_quantized_weight or is_port_quantized(node, nncf_graph, port_id) return is_quantized_weight @@ -159,16 +159,15 @@ def _get_weight_quantization_axis(node: NNCFNode, port_id: int) -> int: :return: Axis, along which quantizer parameters are calculated. """ weight_channel_axis = node.metatype.weight_channel_axis - if node.layer_attributes.has_node_attrs(): - if node.metatype == om.ONNXGemmMetatype: - weight_shape = node.layer_attributes.weight_attrs[port_id]["shape"] - if ( - port_id == 0 - and node.layer_attributes.node_attrs["transA"] == 1 - or port_id == 1 - and node.layer_attributes.node_attrs["transB"] == 1 - ): - weight_channel_axis = transpose_axis(weight_shape, weight_channel_axis) + if node.layer_attributes.has_node_attrs() and node.metatype == om.ONNXGemmMetatype: + weight_shape = node.layer_attributes.weight_attrs[port_id]["shape"] + if ( + port_id == 0 + and node.layer_attributes.node_attrs["transA"] == 1 + or port_id == 1 + and node.layer_attributes.node_attrs["transB"] == 1 + ): + weight_channel_axis = transpose_axis(weight_shape, weight_channel_axis) return weight_channel_axis diff --git a/nncf/onnx/graph/onnx_helper.py b/nncf/onnx/graph/onnx_helper.py index f6b082050a0..24cfd0c7c56 100644 --- a/nncf/onnx/graph/onnx_helper.py +++ b/nncf/onnx/graph/onnx_helper.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -15,6 +15,8 @@ import onnx from onnx import numpy_helper +import nncf + def get_name_to_node_map(model: onnx.ModelProto) -> Dict[str, onnx.NodeProto]: """ @@ -95,7 +97,7 @@ def get_input_port_id_for_node_after_input(input_name: str, to_node: onnx.NodePr for input_port_id, port in enumerate(to_node.input): if port == input_name: return input_port_id - raise RuntimeError(f"The node {to_node} does not have input edge with the name {input_name}") + raise nncf.ValidationError(f"The node {to_node} does not have input edge with the name {input_name}") def get_output_port_id_for_node_before_output(output_name: str, from_node: onnx.NodeProto) -> int: @@ -109,7 +111,7 @@ def get_output_port_id_for_node_before_output(output_name: str, from_node: onnx. for output_port_id, port in enumerate(from_node.output): if port == output_name: return output_port_id - raise RuntimeError(f"The node {from_node} does not have output edge with the name {output_name}") + raise nncf.ValidationError(f"The node {from_node} does not have output edge with the name {output_name}") def get_port_ids_between_nodes(from_node: onnx.NodeProto, to_node: onnx.NodeProto) -> Dict[str, int]: @@ -128,7 +130,7 @@ def get_port_ids_between_nodes(from_node: onnx.NodeProto, to_node: onnx.NodeProt if port in to_node.input: output["output_port_id"] = port_id if output["output_port_id"] is None or output["input_port_id"] is None: - raise RuntimeError(f"The nodes {from_node.name} and {to_node.name} do not have edges between.") + raise nncf.InternalError(f"The nodes {from_node.name} and {to_node.name} do not have edges between.") return output @@ -187,7 +189,7 @@ def get_tensor(model: onnx.ModelProto, tensor_name: str) -> onnx.TensorProto: for tensor in _get_all_tensors(model): if tensor.name == tensor_name: return tensor - raise RuntimeError("There is no tensor with the name {}".format(tensor_name)) + raise nncf.ValidationError("There is no tensor with the name {}".format(tensor_name)) def get_tensor_value(model: onnx.ModelProto, tensor_name: str) -> np.ndarray: diff --git a/nncf/onnx/graph/transformations/__init__.py b/nncf/onnx/graph/transformations/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/onnx/graph/transformations/__init__.py +++ b/nncf/onnx/graph/transformations/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/onnx/graph/transformations/command_creation.py b/nncf/onnx/graph/transformations/command_creation.py index fa418c5d50e..5ff9bc89332 100644 --- a/nncf/onnx/graph/transformations/command_creation.py +++ b/nncf/onnx/graph/transformations/command_creation.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -11,20 +11,51 @@ import numpy as np +from nncf.common.graph.graph import NNCFGraph from nncf.common.graph.graph import NNCFNode +from nncf.common.graph.transformations.command_creation import CommandCreator from nncf.common.graph.transformations.commands import TargetType -from nncf.onnx.graph.transformations.commands import ONNXBiasCorrectionCommand +from nncf.onnx.graph.transformations.commands import ONNXInitializerUpdateCommand +from nncf.onnx.graph.transformations.commands import ONNXQDQNodeRemovingCommand from nncf.onnx.graph.transformations.commands import ONNXTargetPoint -def create_bias_correction_command(node: NNCFNode, bias_value: np.ndarray) -> ONNXBiasCorrectionCommand: +def create_bias_correction_command(node: NNCFNode, bias_value: np.ndarray) -> ONNXInitializerUpdateCommand: """ Creates bias correction command. :param node: The node in the NNCF graph that corresponds to operation with bias. :param bias_value: The new bias value that will be set. - :return: The `ONNXBiasCorrectionCommand` command to update bias. + :return: The `ONNXInitializerUpdateCommand` command to update bias. """ bias_port_id = node.metatype.bias_port_id target_point = ONNXTargetPoint(TargetType.LAYER, node.node_name, bias_port_id) - return ONNXBiasCorrectionCommand(target_point, bias_value) + return ONNXInitializerUpdateCommand(target_point, bias_value) + + +class ONNXCommandCreator(CommandCreator): + """ + Implementation of the `CommandCreator` class for the ONNX backend. + """ + + @staticmethod + def create_command_to_remove_quantizer(quantizer_node: NNCFNode) -> ONNXQDQNodeRemovingCommand: + target_point = ONNXTargetPoint(TargetType.LAYER, quantizer_node.node_name, port_id=None) + return ONNXQDQNodeRemovingCommand(target_point) + + @staticmethod + def create_command_to_update_bias( + node_with_bias: NNCFNode, bias_value: np.ndarray, nncf_graph: NNCFGraph + ) -> ONNXInitializerUpdateCommand: + return create_bias_correction_command(node_with_bias, bias_value) + + @staticmethod + def create_command_to_update_weight( + node_with_weight: NNCFNode, weight_value: np.ndarray, weight_port_id: int + ) -> ONNXInitializerUpdateCommand: + target_point = ONNXTargetPoint(TargetType.LAYER, node_with_weight.node_name, weight_port_id) + return ONNXInitializerUpdateCommand(target_point, weight_value) + + @staticmethod + def create_command_to_insert_bias(node_without_bias, bias_value): + raise NotImplementedError diff --git a/nncf/onnx/graph/transformations/commands.py b/nncf/onnx/graph/transformations/commands.py index 8200fec2c2c..d7778872dc9 100644 --- a/nncf/onnx/graph/transformations/commands.py +++ b/nncf/onnx/graph/transformations/commands.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -72,18 +72,18 @@ class ONNXOutputInsertionCommand(ONNXInsertionCommand): pass -class ONNXBiasCorrectionCommand(TransformationCommand): +class ONNXInitializerUpdateCommand(TransformationCommand): """ - Corrects bias value in the model based on the input value. + Update initializer in the value. """ - def __init__(self, target_point: ONNXTargetPoint, bias_value: np.ndarray): + def __init__(self, target_point: ONNXTargetPoint, new_value: np.ndarray): """ - :param target_point: The TargetPoint instance for the correction that contains layer's information. - :param bias_value: The bias shift value (numpy format) that will be added to the original bias value. + :param target_point: Target point. + :param new_value: New value for initializer. """ super().__init__(TransformationType.CHANGE, target_point) - self.bias_value = bias_value + self.new_value = new_value class ONNXModelExtractionCommand(Command): @@ -91,14 +91,16 @@ class ONNXModelExtractionCommand(Command): Extracts sub-graph based on the sub-model input and output names. """ - def __init__(self, inputs: List[str], outputs: List[str]): + def __init__(self, input_ids: List[Tuple[str, int]], output_ids: List[Tuple[str, int]]): """ - :param inputs: List of the input names that denote the sub-graph beginning. - :param outputs: List of the output names that denote the sub-graph ending. + :param input_ids: List of the input IDs: pairs of node names and correspondent input port ids. + Each pair denotes the sub-graph beginning. + :param output_ids: List of the output IDs: pairs of node names and correspondent output port ids. + Each pair denotes the sub-graph ending. """ super().__init__(TransformationType.EXTRACT) - self.inputs = inputs - self.outputs = outputs + self.input_ids = input_ids + self.output_ids = output_ids class ONNXQDQNodeRemovingCommand(TransformationCommand): @@ -111,15 +113,3 @@ def __init__(self, target_point: ONNXTargetPoint): :param target_point: The TargetPoint instance for the layer that contains information for removing. """ super().__init__(TransformationType.REMOVE, target_point) - - -class ONNXNullBiasInsertionCommand(TransformationCommand): - """ - Inserts null bias for the corresponding node. - """ - - def __init__(self, target_point: ONNXTargetPoint): - """ - :param target_point: The TargetPoint instance for the insertion that contains layer's information. - """ - super().__init__(TransformationType.INSERT, target_point) diff --git a/nncf/onnx/hardware/__init__.py b/nncf/onnx/hardware/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/onnx/hardware/__init__.py +++ b/nncf/onnx/hardware/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/onnx/hardware/config.py b/nncf/onnx/hardware/config.py index 85840435f2e..4800e8ce26b 100644 --- a/nncf/onnx/hardware/config.py +++ b/nncf/onnx/hardware/config.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/onnx/hardware/fused_patterns.py b/nncf/onnx/hardware/fused_patterns.py index 7e2a3efb53d..6040422106e 100644 --- a/nncf/onnx/hardware/fused_patterns.py +++ b/nncf/onnx/hardware/fused_patterns.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/onnx/quantization/__init__.py b/nncf/onnx/quantization/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/onnx/quantization/__init__.py +++ b/nncf/onnx/quantization/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/onnx/quantization/default_quantization.py b/nncf/onnx/quantization/default_quantization.py index 9fbf8aff31d..42ec2ee60a4 100644 --- a/nncf/onnx/quantization/default_quantization.py +++ b/nncf/onnx/quantization/default_quantization.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/onnx/quantization/ignored_patterns.py b/nncf/onnx/quantization/ignored_patterns.py index e18e7b39047..43eb29f0915 100644 --- a/nncf/onnx/quantization/ignored_patterns.py +++ b/nncf/onnx/quantization/ignored_patterns.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/onnx/quantization/quantize_model.py b/nncf/onnx/quantization/quantize_model.py index d448df99377..c89b39a14eb 100644 --- a/nncf/onnx/quantization/quantize_model.py +++ b/nncf/onnx/quantization/quantize_model.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -9,25 +9,34 @@ # See the License for the specific language governing permissions and # limitations under the License. -from typing import Optional +from typing import Any, Callable, Iterable, List, Optional, Tuple, TypeVar, Union import onnx +import nncf from nncf.common.logging.logger import nncf_logger from nncf.common.quantization.structs import QuantizationPreset from nncf.data import Dataset from nncf.onnx.graph.nncf_graph_builder import GraphConverter +from nncf.parameters import DropType from nncf.parameters import ModelType from nncf.parameters import QuantizationMode from nncf.parameters import TargetDevice +from nncf.quantization.advanced_parameters import AdvancedAccuracyRestorerParameters from nncf.quantization.advanced_parameters import AdvancedQuantizationParameters from nncf.quantization.advanced_parameters import QuantizationParameters +from nncf.quantization.algorithms.accuracy_control.algorithm import QuantizationAccuracyRestorer +from nncf.quantization.algorithms.accuracy_control.algorithm import calculate_accuracy_drop +from nncf.quantization.algorithms.accuracy_control.evaluator import Evaluator from nncf.quantization.algorithms.post_training.algorithm import PostTrainingQuantization +from nncf.quantization.quantize_model import quantize_with_tune_hyperparams from nncf.quantization.telemetry_extractors import CompressionStartedWithQuantizeApi from nncf.scopes import IgnoredScope from nncf.telemetry import tracked_function from nncf.telemetry.events import NNCF_ONNX_CATEGORY +TTensor = TypeVar("TTensor") + @tracked_function(NNCF_ONNX_CATEGORY, [CompressionStartedWithQuantizeApi(), "target_device", "preset"]) def quantize_impl( @@ -46,11 +55,11 @@ def quantize_impl( Implementation of the `quantize()` method for the ONNX backend. """ if target_device == TargetDevice.CPU_SPR: - raise RuntimeError("target_device == CPU_SPR is not supported.") + raise nncf.ValidationError("target_device == CPU_SPR is not supported.") if mode is not None: raise ValueError(f"mode={mode} is not supported") if model.opset_import[0].version < 10: - raise RuntimeError("ONNX models with opset version < 10 do not support quantization.") + raise nncf.ValidationError("ONNX models with opset version < 10 do not support quantization.") if model.opset_import[0].version < 13: nncf_logger.warning( "ONNX models with 10 < opset version < 13 do not support per-channel quantization." @@ -75,3 +84,118 @@ def quantize_impl( quantized_model = quantization_algorithm.apply(model, graph, dataset=calibration_dataset) return quantized_model + + +def quantize_with_accuracy_control_impl( + model: onnx.ModelProto, + calibration_dataset: Dataset, + validation_dataset: Dataset, + validation_fn: Callable[[Any, Iterable[Any]], Tuple[float, Union[None, List[float], List[List[TTensor]]]]], + max_drop: float = 0.01, + drop_type: DropType = DropType.ABSOLUTE, + preset: Optional[QuantizationPreset] = None, + target_device: TargetDevice = TargetDevice.ANY, + subset_size: int = 300, + fast_bias_correction: bool = True, + model_type: Optional[ModelType] = None, + ignored_scope: Optional[IgnoredScope] = None, + advanced_quantization_parameters: Optional[AdvancedQuantizationParameters] = None, + advanced_accuracy_restorer_parameters: Optional[AdvancedAccuracyRestorerParameters] = None, +) -> onnx.ModelProto: + """ + Implementation of the `quantize_with_accuracy_control()` method for the ONNX backend. + """ + if advanced_accuracy_restorer_parameters is None: + advanced_accuracy_restorer_parameters = AdvancedAccuracyRestorerParameters() + + if advanced_quantization_parameters is None: + advanced_quantization_parameters = AdvancedQuantizationParameters() + + quantized_model = quantize_impl( + model=model, + calibration_dataset=calibration_dataset, + preset=preset, + target_device=target_device, + subset_size=subset_size, + fast_bias_correction=fast_bias_correction, + model_type=model_type, + ignored_scope=ignored_scope, + advanced_parameters=advanced_quantization_parameters, + ) + + if advanced_accuracy_restorer_parameters.intermediate_model_dir: + quantized_model_path = f"{advanced_accuracy_restorer_parameters.intermediate_model_dir}/intermediate_model.onnx" + onnx.save(quantized_model, quantized_model_path) + + evaluator = Evaluator(validation_fn) + evaluator.enable_iteration_count() + + initial_metric_results = evaluator.collect_metric_results(model, validation_dataset, model_name="initial") + + validation_dataset_size = evaluator.num_passed_iterations + evaluator.disable_iteration_count() + + quantized_metric_results = evaluator.collect_metric_results( + quantized_model, validation_dataset, model_name="quantized" + ) + + should_terminate, accuracy_drop = calculate_accuracy_drop( + initial_metric_results.metric_value, quantized_metric_results.metric_value, max_drop, drop_type + ) + + nncf_logger.info(f"Accuracy drop: {accuracy_drop} ({drop_type})") + + if advanced_accuracy_restorer_parameters.tune_hyperparams and not should_terminate: + tuned_quantized_model = quantize_with_tune_hyperparams( + model, + calibration_dataset, + validation_dataset, + validation_fn, + initial_metric_results, + quantized_metric_results, + subset_size, + preset, + target_device, + subset_size, + fast_bias_correction, + model_type, + ignored_scope, + advanced_quantization_parameters, + ) + tuned_quantized_metric_results = evaluator.collect_metric_results( + tuned_quantized_model, validation_dataset, model_name="tuned" + ) + should_terminate, tuned_accuracy_drop = calculate_accuracy_drop( + initial_metric_results.metric_value, tuned_quantized_metric_results.metric_value, max_drop, drop_type + ) + + nncf_logger.info(f"Accuracy drop (tuned): {tuned_accuracy_drop} ({drop_type})") + + if should_terminate or tuned_accuracy_drop < accuracy_drop: + quantized_model = tuned_quantized_model + quantized_metric_results = tuned_quantized_metric_results + + if not should_terminate: + ranking_subset_size = subset_size + if advanced_accuracy_restorer_parameters.ranking_subset_size is not None: + ranking_subset_size = advanced_accuracy_restorer_parameters.ranking_subset_size + + accuracy_restorer = QuantizationAccuracyRestorer( + ranking_subset_size, + advanced_accuracy_restorer_parameters.max_num_iterations, + max_drop, + drop_type, + advanced_accuracy_restorer_parameters.num_ranking_workers, + advanced_accuracy_restorer_parameters.restore_mode, + ) + quantized_model = accuracy_restorer.apply( + model, + initial_metric_results, + quantized_model, + quantized_metric_results, + validation_dataset, + validation_dataset_size, + evaluator, + ) + + return quantized_model diff --git a/nncf/onnx/quantization/quantizer_parameters.py b/nncf/onnx/quantization/quantizer_parameters.py index 458cbaa5a5f..1a9570d758d 100644 --- a/nncf/onnx/quantization/quantizer_parameters.py +++ b/nncf/onnx/quantization/quantizer_parameters.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -65,6 +65,9 @@ def convert_fq_params_to_onnx_params( level_low, level_high = get_level_low_level_high(tensor_type) narrow_range = levels == 2**num_bits - 1 scale, zero_point = calculate_scale_zero_point(input_low, input_high, level_low, level_high, narrow_range) + # ONNX demands parameters to be a scalar or 1-D Tensor. + scale = np.squeeze(scale) + zero_point = np.squeeze(zero_point) return ONNXQuantizerLayerParameters(scale.data, zero_point.data, tensor_type, axis) diff --git a/nncf/onnx/statistics/__init__.py b/nncf/onnx/statistics/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/onnx/statistics/__init__.py +++ b/nncf/onnx/statistics/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/onnx/statistics/aggregator.py b/nncf/onnx/statistics/aggregator.py index a768a855258..a6296e121ca 100644 --- a/nncf/onnx/statistics/aggregator.py +++ b/nncf/onnx/statistics/aggregator.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -20,6 +20,7 @@ from nncf.common.graph.transformations.layout import TransformationLayout from nncf.common.tensor_statistics.aggregator import StatisticsAggregator from nncf.common.tensor_statistics.statistic_point import StatisticPointsContainer +from nncf.experimental.common.tensor_statistics.collectors import TensorCollector from nncf.onnx.graph.node_utils import get_input_edge from nncf.onnx.graph.node_utils import get_input_edges_mapping from nncf.onnx.graph.onnx_helper import get_name_to_node_map @@ -37,23 +38,31 @@ def collect_statistics(self, model: onnx.ModelProto, graph: NNCFGraph) -> None: def _register_statistics( self, outputs: Dict[str, ONNXNNCFTensor], statistic_points: StatisticPointsContainer ) -> None: - for _statistic_points in statistic_points.values(): - for statistic_point in _statistic_points: - target_point = statistic_point.target_point - port_id = target_point.port_id - if target_point.target_node_name in self.input_edges_mapping: # Input case - edge_name = get_input_edge( - target_point.target_node_name, - self.input_edges_mapping, - self.node_mapping, - ) - elif target_point.type == TargetType.POST_LAYER_OPERATION: - node = self.node_mapping[target_point.target_node_name] - edge_name = node.output[port_id] - elif target_point.type in [TargetType.PRE_LAYER_OPERATION, TargetType.OPERATION_WITH_WEIGHTS]: - node = self.node_mapping[target_point.target_node_name] - edge_name = node.input[port_id] - statistic_point.register_tensor(outputs[edge_name]) + for _, statistic_point, tensor_collector in statistic_points.get_tensor_collectors(): + target_point = statistic_point.target_point + port_id = target_point.port_id + + if target_point.target_node_name in self.input_edges_mapping: # Input case + edge_name = get_input_edge( + target_point.target_node_name, + self.input_edges_mapping, + self.node_mapping, + ) + elif target_point.type == TargetType.POST_LAYER_OPERATION: + node = self.node_mapping[target_point.target_node_name] + edge_name = node.output[port_id] + elif target_point.type in [TargetType.PRE_LAYER_OPERATION, TargetType.OPERATION_WITH_WEIGHTS]: + node = self.node_mapping[target_point.target_node_name] + edge_name = node.input[port_id] + else: + RuntimeError(f"Unsupported target point type for statistic aggregator: {target_point.type}") + + input_info = [] + for reducer in tensor_collector.reducers: + input_info.append((hash(reducer), [edge_name])) + + target_inputs = TensorCollector.get_tensor_collector_inputs(outputs, input_info) + tensor_collector.register_inputs(target_inputs) def _get_transformation_layout_extra_outputs( self, statistic_points: StatisticPointsContainer diff --git a/nncf/onnx/statistics/collectors.py b/nncf/onnx/statistics/collectors.py index e9a43a0593e..c7ea4fbf471 100644 --- a/nncf/onnx/statistics/collectors.py +++ b/nncf/onnx/statistics/collectors.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -13,17 +13,27 @@ import numpy as np -from nncf.common.tensor import NNCFTensor from nncf.common.tensor import TensorElementsType -from nncf.common.tensor_statistics.collectors import MeanMinMaxStatisticCollector -from nncf.common.tensor_statistics.collectors import MeanStatisticCollector -from nncf.common.tensor_statistics.collectors import MinMaxStatisticCollector from nncf.common.tensor_statistics.collectors import NNCFCollectorTensorProcessor -from nncf.common.tensor_statistics.collectors import RawStatisticCollector +from nncf.experimental.common.tensor_statistics.collectors import AbsMaxReducer +from nncf.experimental.common.tensor_statistics.collectors import AbsQuantileReducer +from nncf.experimental.common.tensor_statistics.collectors import BatchMeanReducer +from nncf.experimental.common.tensor_statistics.collectors import MaxReducer +from nncf.experimental.common.tensor_statistics.collectors import MeanAggregator +from nncf.experimental.common.tensor_statistics.collectors import MeanPerChReducer +from nncf.experimental.common.tensor_statistics.collectors import MeanReducer +from nncf.experimental.common.tensor_statistics.collectors import MinReducer +from nncf.experimental.common.tensor_statistics.collectors import NoopAggregator +from nncf.experimental.common.tensor_statistics.collectors import NoopReducer +from nncf.experimental.common.tensor_statistics.collectors import QuantileReducer +from nncf.experimental.common.tensor_statistics.collectors import RawReducer +from nncf.experimental.common.tensor_statistics.collectors import ShapeAggregator +from nncf.experimental.common.tensor_statistics.collectors import TensorCollector +from nncf.experimental.common.tensor_statistics.collectors import TensorReducerBase from nncf.onnx.statistics.statistics import ONNXMeanTensorStatistic -from nncf.onnx.statistics.statistics import ONNXMinMaxTensorStatistic from nncf.onnx.statistics.statistics import ONNXRawTensorStatistic from nncf.onnx.tensor import ONNXNNCFTensor +from nncf.quantization.advanced_parameters import StatisticsType class ONNXNNCFCollectorTensorProcessor(NNCFCollectorTensorProcessor): @@ -32,106 +42,119 @@ class ONNXNNCFCollectorTensorProcessor(NNCFCollectorTensorProcessor): """ @staticmethod - def reduce_min(x: NNCFTensor, axis: Union[int, Tuple[int, ...], List[int]], keepdims: bool = False) -> NNCFTensor: + def reduce_min( + x: ONNXNNCFTensor, axis: Union[int, Tuple[int, ...], List[int]], keepdims: bool = False + ) -> ONNXNNCFTensor: return ONNXNNCFTensor(np.amin(x.tensor, axis=axis, keepdims=keepdims)) @staticmethod - def reduce_max(x: NNCFTensor, axis: Union[int, Tuple[int, ...], List[int]], keepdims: bool = False) -> NNCFTensor: + def reduce_max( + x: ONNXNNCFTensor, axis: Union[int, Tuple[int, ...], List[int]], keepdims: bool = False + ) -> ONNXNNCFTensor: return ONNXNNCFTensor(np.amax(x.tensor, axis=axis, keepdims=keepdims)) @staticmethod - def abs(x: NNCFTensor) -> NNCFTensor: + def abs(x: ONNXNNCFTensor) -> ONNXNNCFTensor: return ONNXNNCFTensor(np.abs(x.tensor)) @staticmethod - def min(x1: NNCFTensor, x2: NNCFTensor) -> NNCFTensor: + def min(x1: ONNXNNCFTensor, x2: ONNXNNCFTensor) -> ONNXNNCFTensor: return ONNXNNCFTensor(np.minimum(x1.tensor, x2.tensor)) @staticmethod - def max(x1: NNCFTensor, x2: NNCFTensor) -> NNCFTensor: + def max(x1: ONNXNNCFTensor, x2: ONNXNNCFTensor) -> ONNXNNCFTensor: return ONNXNNCFTensor(np.maximum(x1.tensor, x2.tensor)) @staticmethod - def mean(x: NNCFTensor, axis: Union[int, Tuple[int, ...], List[int]], keepdims=False) -> NNCFTensor: + def mean(x: ONNXNNCFTensor, axis: Union[int, Tuple[int, ...], List[int]], keepdims: bool = False) -> ONNXNNCFTensor: return ONNXNNCFTensor(np.mean(x.tensor, axis=axis, keepdims=keepdims)) @staticmethod - def median(x: NNCFTensor, axis: Union[int, Tuple[int, ...], List[int]], keepdims=False) -> NNCFTensor: + def median( + x: ONNXNNCFTensor, axis: Union[int, Tuple[int, ...], List[int]], keepdims: bool = False + ) -> ONNXNNCFTensor: return ONNXNNCFTensor(np.median(x.tensor, axis=axis, keepdims=keepdims)) @classmethod def masked_mean( cls, - x: NNCFTensor, + x: ONNXNNCFTensor, axis: Optional[Union[int, Tuple[int, ...], List[int]]], - mask: Optional[NNCFTensor], + mask: Optional[ONNXNNCFTensor], keepdims: bool = False, - ) -> NNCFTensor: + ) -> ONNXNNCFTensor: if mask is None: return cls.mean(x, axis=axis, keepdims=keepdims) masked_x = np.ma.array(x.tensor, mask=mask.tensor) - return ONNXNNCFTensor(np.ma.mean(masked_x, axis=axis, keepdims=False).data) + result = np.ma.mean(masked_x, axis=axis, keepdims=keepdims) + if isinstance(result, np.ma.MaskedArray): + return ONNXNNCFTensor(result.data) + return ONNXNNCFTensor(result) @classmethod def masked_median( cls, - x: NNCFTensor, + x: ONNXNNCFTensor, axis: Optional[Union[int, Tuple[int, ...], List[int]]], - mask: Optional[NNCFTensor], + mask: Optional[ONNXNNCFTensor], keepdims: bool = False, - ) -> NNCFTensor: + ) -> ONNXNNCFTensor: if mask is None: return cls.median(x, axis=axis, keepdims=keepdims) masked_x = np.ma.array(x.tensor, mask=mask.tensor) - return ONNXNNCFTensor(np.ma.median(masked_x, axis=axis, keepdims=keepdims).data) + result = np.ma.median(masked_x, axis=axis, keepdims=keepdims) + if isinstance(result, np.ma.MaskedArray): + return ONNXNNCFTensor(result.data) + return ONNXNNCFTensor(result) @staticmethod - def logical_or(input_: NNCFTensor, other: NNCFTensor) -> NNCFTensor: + def logical_or(input_: ONNXNNCFTensor, other: ONNXNNCFTensor) -> ONNXNNCFTensor: return ONNXNNCFTensor(np.logical_or(input_.tensor, other.tensor)) @staticmethod - def less(input_: NNCFTensor, other: NNCFTensor) -> NNCFTensor: + def less(input_: ONNXNNCFTensor, other: ONNXNNCFTensor) -> ONNXNNCFTensor: return ONNXNNCFTensor(input_.tensor < other.tensor) @staticmethod - def stack(x: Union[List[NNCFTensor], Deque[NNCFTensor]], axis: int = 0) -> NNCFTensor: + def stack(x: Union[List[ONNXNNCFTensor], Deque[ONNXNNCFTensor]], axis: int = 0) -> ONNXNNCFTensor: x = [t.tensor for t in x] return ONNXNNCFTensor(np.stack(x, axis=axis)) @staticmethod - def unstack(x: NNCFTensor, axis: int = 0) -> List[NNCFTensor]: + def unstack(x: ONNXNNCFTensor, axis: int = 0) -> List[ONNXNNCFTensor]: return [ONNXNNCFTensor(np.squeeze(e, axis)) for e in np.split(x.tensor, x.tensor.shape[axis], axis=axis)] @staticmethod - def squeeze(x: NNCFTensor, dim: Optional[Union[int, Tuple[int, ...]]] = None) -> NNCFTensor: - raise NotImplementedError() + def squeeze(x: ONNXNNCFTensor, dim: Optional[Union[int, Tuple[int, ...]]] = None) -> ONNXNNCFTensor: + return ONNXNNCFTensor(np.squeeze(x.tensor, axis=dim)) @staticmethod - def sum(tensor: NNCFTensor) -> TensorElementsType: - return np.sum(tensor.tensor) + def sum(tensor: ONNXNNCFTensor) -> ONNXNNCFTensor: + return ONNXNNCFTensor(np.sum(tensor.tensor)) @staticmethod def quantile( - tensor: NNCFTensor, + tensor: ONNXNNCFTensor, quantile: Union[float, List[float]], axis: Union[int, Tuple[int, ...], List[int]], keepdims: bool = False, - ) -> List[TensorElementsType]: + ) -> List[ONNXNNCFTensor]: result = np.quantile(tensor.tensor, quantile, axis, keepdims=keepdims) return [ONNXNNCFTensor(x) for x in result] @classmethod def percentile( cls, - tensor: NNCFTensor, + tensor: ONNXNNCFTensor, percentile: Union[float, List[float]], axis: Union[int, Tuple[int, ...], List[int]], keepdims: bool = False, ) -> List[TensorElementsType]: - raise NotImplementedError() + quantile = np.true_divide(percentile, 100) + return cls.quantile(tensor, quantile=quantile, axis=axis, keepdims=keepdims) @staticmethod - def mean_per_channel(x: NNCFTensor, axis: int) -> NNCFTensor: + def mean_per_channel(x: ONNXNNCFTensor, axis: int) -> ONNXNNCFTensor: if len(x.shape) < 3: return ONNXNNCFTensor(np.mean(x.tensor, axis=0)) x = np.moveaxis(x.tensor, axis, 1) @@ -139,83 +162,128 @@ def mean_per_channel(x: NNCFTensor, axis: int) -> NNCFTensor: return ONNXNNCFTensor(np.mean(t, axis=(0, 2))) @staticmethod - def transpose(x: NNCFTensor, axes: Tuple[int, ...]) -> NNCFTensor: + def transpose(x: ONNXNNCFTensor, axes: Tuple[int, ...]) -> ONNXNNCFTensor: return ONNXNNCFTensor(np.transpose(x.tensor, axes)) @staticmethod - def reshape(x: NNCFTensor, shape: Tuple[int, ...]) -> NNCFTensor: + def reshape(x: ONNXNNCFTensor, shape: Tuple[int, ...]) -> ONNXNNCFTensor: return ONNXNNCFTensor(np.reshape(x.tensor, shape)) @staticmethod - def cat(x: List[NNCFTensor], axis: int) -> NNCFTensor: + def cat(x: List[ONNXNNCFTensor], axis: int) -> ONNXNNCFTensor: x = [t.tensor for t in x] return ONNXNNCFTensor(np.concatenate(x, axis)) @staticmethod - def batch_mean(x: NNCFTensor) -> NNCFTensor: + def batch_mean(x: ONNXNNCFTensor) -> ONNXNNCFTensor: return ONNXNNCFTensor(np.mean(x.tensor, axis=0, keepdims=True)) @staticmethod - def sub(a: NNCFTensor, b: NNCFTensor) -> NNCFTensor: - raise NotImplementedError() + def sub(a: ONNXNNCFTensor, b: ONNXNNCFTensor) -> ONNXNNCFTensor: + return ONNXNNCFTensor(a.tensor - b.tensor) @staticmethod - def zero_elements(x: NNCFTensor) -> NNCFTensor: - raise NotImplementedError() + def zero_elements(x: ONNXNNCFTensor) -> ONNXNNCFTensor: + np_tensor = x.tensor + eps = np.finfo(np_tensor.dtype).eps + return ONNXNNCFTensor(np.abs(np_tensor) < eps) -class ONNXMinMaxStatisticCollector(MinMaxStatisticCollector): - @staticmethod - def _get_processor() -> NNCFCollectorTensorProcessor: +class ONNXBasicReducer(TensorReducerBase): + def _get_processor(self): return ONNXNNCFCollectorTensorProcessor - def _register_input(self, x: ONNXNNCFTensor): - self._register_input_common(x) + def get_inplace_fn(self): + raise NotImplementedError("ONNX backend has no support of inplace statistics yet.") - def _get_statistics(self) -> ONNXMinMaxTensorStatistic: - return ONNXMinMaxTensorStatistic( - min_values=self._min_values.tensor, - max_values=self._max_values.tensor, - ) +class ONNXMinReducer(ONNXBasicReducer, MinReducer): + pass -class ONNXMeanMinMaxStatisticCollector(MeanMinMaxStatisticCollector): - @staticmethod - def _get_processor() -> NNCFCollectorTensorProcessor: - return ONNXNNCFCollectorTensorProcessor - def _register_input(self, x: ONNXNNCFTensor): - self._register_input_common(x) +class ONNXMaxReducer(ONNXBasicReducer, MaxReducer): + pass - def _get_statistics(self) -> ONNXMinMaxTensorStatistic: - return ONNXMinMaxTensorStatistic( - min_values=self._min_aggregate().tensor, - max_values=self._max_aggregate().tensor, - ) +class ONNXAbsMaxReducer(ONNXBasicReducer, AbsMaxReducer): + pass -class ONNXMeanStatisticCollector(MeanStatisticCollector): - @staticmethod - def _get_processor() -> NNCFCollectorTensorProcessor: - return ONNXNNCFCollectorTensorProcessor - def _register_input(self, x: ONNXNNCFTensor): - self._register_input_common(x) +class ONNXMeanReducer(ONNXBasicReducer, MeanReducer): + pass - def _get_statistics(self) -> ONNXMeanTensorStatistic: - return ONNXMeanTensorStatistic( - mean_values=self._mean_aggregate().tensor, - shape=self._shape(), - ) +class ONNXQuantileReducer(ONNXBasicReducer, QuantileReducer): + pass + + +class ONNXAbsQuantileReducer(ONNXBasicReducer, AbsQuantileReducer): + pass + + +class ONNXBatchMeanReducer(ONNXBasicReducer, BatchMeanReducer): + pass -class ONNXRawStatisticCollector(RawStatisticCollector): - @staticmethod - def _get_processor() -> NNCFCollectorTensorProcessor: - return ONNXNNCFCollectorTensorProcessor - def _register_input(self, x: ONNXNNCFTensor): - self._register_input_common(x) +class ONNXMeanPerChanelReducer(ONNXBasicReducer, MeanPerChReducer): + pass - def _get_statistics(self) -> ONNXRawTensorStatistic: - return ONNXRawTensorStatistic(self._all_values) + +def get_mean_statistic_collector( + num_samples: int, channel_axis: int, window_size: Optional[int] = None, inplace: bool = True +) -> TensorCollector: + """ + Mean statistic collector builder. + + :param num_samples: Maximum number of samples to collect. + :param channel_axis: Channel axis to use during reduction phase. + :param window_size: Number of samples from the end of the list of collected samples to aggregate. + Aggregates all available collected statistics in case parameter is None. + :param inplace: Whether the mean reducer should be calculated inplace or out of place. + :return: Mean statistic collector. + """ + inplace = False + if channel_axis == 0: + reducer = ONNXBatchMeanReducer(inplace) + else: + reducer = ONNXMeanPerChanelReducer(channel_axis=channel_axis, inplace=inplace) + noop_reducer = NoopReducer() + + kwargs = { + "tensor_processor": ONNXNNCFCollectorTensorProcessor, + "num_samples": num_samples, + "window_size": window_size, + } + + aggregate_mean = MeanAggregator(**kwargs) + aggregate_shape = ShapeAggregator() + + collector = TensorCollector(ONNXMeanTensorStatistic) + collector.register_statistic_branch(ONNXMeanTensorStatistic.MEAN_STAT, reducer, aggregate_mean) + collector.register_statistic_branch(ONNXMeanTensorStatistic.SHAPE_STAT, noop_reducer, aggregate_shape) + return collector + + +def get_raw_stat_collector(num_samples: int) -> TensorCollector: + """ + Raw statistic collector builder. + + :param num_samples: Maximum number of samples to collect. + :return: Raw statistic collector. + """ + reducer = RawReducer() + aggregator = NoopAggregator(num_samples) + + collector = TensorCollector(ONNXRawTensorStatistic) + collector.register_statistic_branch(ONNXRawTensorStatistic.VALUES_STATS, reducer, aggregator) + return collector + + +ONNX_REDUCERS_MAP = { + StatisticsType.MIN: ONNXMinReducer, + StatisticsType.MAX: ONNXMaxReducer, + StatisticsType.ABS_MAX: ONNXAbsMaxReducer, + StatisticsType.MEAN: ONNXMeanReducer, + StatisticsType.QUANTILE: ONNXQuantileReducer, + StatisticsType.ABS_QUANTILE: ONNXAbsQuantileReducer, +} diff --git a/nncf/onnx/statistics/statistics.py b/nncf/onnx/statistics/statistics.py index f9d5119201f..1991f4f3659 100644 --- a/nncf/onnx/statistics/statistics.py +++ b/nncf/onnx/statistics/statistics.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/onnx/tensor.py b/nncf/onnx/tensor.py index 785b01044e5..aa25522b765 100644 --- a/nncf/onnx/tensor.py +++ b/nncf/onnx/tensor.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -25,3 +25,6 @@ def __init__(self, tensor: np.ndarray): @property def device(self): return "CPU" + + def is_empty(self) -> bool: + return self.tensor.size == 0 diff --git a/nncf/openvino/__init__.py b/nncf/openvino/__init__.py index 8a07dd5505c..ad1ce49d203 100644 --- a/nncf/openvino/__init__.py +++ b/nncf/openvino/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/openvino/engine.py b/nncf/openvino/engine.py index 248f8912af3..54efab783ca 100644 --- a/nncf/openvino/engine.py +++ b/nncf/openvino/engine.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -14,6 +14,7 @@ import numpy as np import openvino.runtime as ov +import nncf from nncf.common.engine import Engine from nncf.openvino.graph.model_utils import model_has_state from nncf.parameters import TargetDevice @@ -48,11 +49,13 @@ def _check_input_data_format( """ actual_num_inputs = 1 if isinstance(input_data, np.ndarray) else len(input_data) if actual_num_inputs != self.number_of_inputs: - raise RuntimeError(f"Model expects {self.number_of_inputs} inputs, but {actual_num_inputs} are provided.") + raise nncf.ValidationError( + f"Model expects {self.number_of_inputs} inputs, but {actual_num_inputs} are provided." + ) if isinstance(input_data, dict): for name in input_data: if isinstance(name, str) and name not in self.input_tensor_names: - raise RuntimeError(f"Missing a required input: {name} to run the model.") + raise nncf.ValidationError(f"Missing a required input: {name} to run the model.") def infer( self, input_data: Union[np.ndarray, List[np.ndarray], Tuple[np.ndarray], Dict[str, np.ndarray]] diff --git a/nncf/openvino/graph/__init__.py b/nncf/openvino/graph/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/openvino/graph/__init__.py +++ b/nncf/openvino/graph/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/openvino/graph/layer_attributes.py b/nncf/openvino/graph/layer_attributes.py index f0933500a5c..b46650a7665 100644 --- a/nncf/openvino/graph/layer_attributes.py +++ b/nncf/openvino/graph/layer_attributes.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/openvino/graph/layout.py b/nncf/openvino/graph/layout.py index 92152277469..566ae2a8788 100644 --- a/nncf/openvino/graph/layout.py +++ b/nncf/openvino/graph/layout.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -38,11 +38,11 @@ class OVLayoutElem(Enum): _CONV_BASE_CONST_LAYOUT = { - OVConvolutionMetatype: [OVLayoutElem.C_OUT, OVLayoutElem.C_IN], - OVConvolutionBackpropDataMetatype: [OVLayoutElem.C_IN, OVLayoutElem.C_OUT], - OVDepthwiseConvolutionMetatype: [OVLayoutElem.GROUPS, OVLayoutElem.C_OUT, OVLayoutElem.C_IN], - OVGroupConvolutionMetatype: [OVLayoutElem.GROUPS, OVLayoutElem.C_OUT, OVLayoutElem.C_IN], - OVGroupConvolutionBackpropDataMetatype: [OVLayoutElem.GROUPS, OVLayoutElem.C_IN, OVLayoutElem.C_OUT], + OVConvolutionMetatype: (OVLayoutElem.C_OUT, OVLayoutElem.C_IN), + OVConvolutionBackpropDataMetatype: (OVLayoutElem.C_IN, OVLayoutElem.C_OUT), + OVDepthwiseConvolutionMetatype: (OVLayoutElem.GROUPS, OVLayoutElem.C_OUT, OVLayoutElem.C_IN), + OVGroupConvolutionMetatype: (OVLayoutElem.GROUPS, OVLayoutElem.C_OUT, OVLayoutElem.C_IN), + OVGroupConvolutionBackpropDataMetatype: (OVLayoutElem.GROUPS, OVLayoutElem.C_IN, OVLayoutElem.C_OUT), } @@ -85,9 +85,9 @@ def get_conv_weights_layout(ov_metatype: OVOpMetatype, weights_shape: Tuple[int, :param weights_shape: Shape of the target convolution node weight. :return: Target convolution node weights layout. """ - weights_layout = _CONV_BASE_CONST_LAYOUT[ov_metatype] - kernel_size = weights_shape[len(weights_layout) :] - weights_layout += [OVLayoutElem.SPATIAL] * len(kernel_size) + base_layout = _CONV_BASE_CONST_LAYOUT[ov_metatype] + kernel_size = weights_shape[len(base_layout) :] + weights_layout = list(base_layout) + [OVLayoutElem.SPATIAL] * len(kernel_size) return tuple(weights_layout) diff --git a/nncf/openvino/graph/metatypes/__init__.py b/nncf/openvino/graph/metatypes/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/openvino/graph/metatypes/__init__.py +++ b/nncf/openvino/graph/metatypes/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/openvino/graph/metatypes/groups.py b/nncf/openvino/graph/metatypes/groups.py index eef2fba12df..eed716f9f32 100644 --- a/nncf/openvino/graph/metatypes/groups.py +++ b/nncf/openvino/graph/metatypes/groups.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/openvino/graph/metatypes/openvino_metatypes.py b/nncf/openvino/graph/metatypes/openvino_metatypes.py index a51ea1f2187..c884b8621c4 100644 --- a/nncf/openvino/graph/metatypes/openvino_metatypes.py +++ b/nncf/openvino/graph/metatypes/openvino_metatypes.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -14,6 +14,7 @@ import openvino.runtime as ov +import nncf from nncf.common.graph.operator_metatypes import INPUT_NOOP_METATYPES from nncf.common.graph.operator_metatypes import OUTPUT_NOOP_METATYPES from nncf.common.graph.operator_metatypes import OperatorMetatype @@ -47,7 +48,7 @@ def determine_subtype(cls, node: ov.Node) -> Optional[Type[OperatorMetatype]]: if subtype.matches(node): matches.append(subtype) if len(matches) > 1: - raise RuntimeError("Multiple subtypes match operator call - can not determine single subtype.") + raise nncf.InternalError("Multiple subtypes match operator call - can not determine single subtype.") if not matches: return None return matches[0] @@ -787,9 +788,8 @@ def get_node_metatype(node: ov.Node) -> Type[OperatorMetatype]: """ node_type = node.get_type_name() metatype = OV_OPERATOR_METATYPES.get_operator_metatype_by_op_name(node_type) - if metatype is not UnknownMetatype: - if metatype.get_subtypes(): - subtype = metatype.determine_subtype(node) - if subtype is not None: - metatype = subtype + if metatype is not UnknownMetatype and metatype.get_subtypes(): + subtype = metatype.determine_subtype(node) + if subtype is not None: + metatype = subtype return metatype diff --git a/nncf/openvino/graph/model_transformer.py b/nncf/openvino/graph/model_transformer.py index 52c90ddf7b1..71273dcd5fc 100644 --- a/nncf/openvino/graph/model_transformer.py +++ b/nncf/openvino/graph/model_transformer.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -18,10 +18,13 @@ from openvino._pyopenvino import DescriptorTensor from openvino.runtime import opset13 as opset +import nncf from nncf.common.graph.model_transformer import ModelTransformer from nncf.common.graph.model_transformer import TModel from nncf.common.graph.transformations.commands import TargetType from nncf.common.graph.transformations.layout import TransformationLayout +from nncf.openvino.graph.model_utils import copy_rt_info +from nncf.openvino.graph.node_utils import get_parameter_node_name from nncf.openvino.graph.node_utils import get_result_node_name from nncf.openvino.graph.transformations.commands import OVBiasCorrectionCommand from nncf.openvino.graph.transformations.commands import OVBiasInsertionCommand @@ -44,8 +47,9 @@ class OVModelTransformer(ModelTransformer): Applies transformations to an OpenVINO model. """ - def __init__(self, model: TModel): + def __init__(self, model: TModel, inplace: bool = False): super().__init__(model) + self._inplace = inplace self._command_transformation_ordered_pairs = [ (OVFQNodeRemovingCommand, self._apply_fq_nodes_removing_transformation), (OVQuantizerInsertionCommand, self._apply_quantizer_insertion_transformations), @@ -124,7 +128,10 @@ def transform(self, transformation_layout: TransformationLayout) -> ov.Model: for transformation in transformations: aggregated_transformations[transformation.__class__].append(transformation) - model = self._model.clone() + if self._inplace: + model = self._model + else: + model = self._model.clone() # Inplace transformations; Using deepcopy of model for transformation_cls, transformation_fn in self._command_transformation_ordered_pairs: transformations = aggregated_transformations[transformation_cls] @@ -200,9 +207,11 @@ def _insert_outputs(model: ov.Model, outputs: List[Tuple[ov.Output, int, Callabl OVModelTransformer._update_tensor_name([result.get_output_tensor(0)], result_name) extra_model_outputs.append(result) - return ov.Model( + model_with_outputs = ov.Model( results=results + extra_model_outputs, sinks=assign_ops, parameters=params, name=model.friendly_name ) + copy_rt_info(model, model_with_outputs, path=["nncf"]) + return model_with_outputs @staticmethod def _apply_fq_nodes_removing_transformation( @@ -407,7 +416,7 @@ def _insert_fake_quantize_op( for inp_node in target_inputs: inp_node.replace_source_output(fq.output(0)) else: - raise RuntimeError(f"Incorrect target point type {transform_type}") + raise nncf.InternalError(f"Incorrect target point type {transform_type}") @staticmethod def _insert_fake_convert_op( @@ -461,7 +470,7 @@ def _insert_fake_convert_op( for inp_node in target_inputs: inp_node.replace_source_output(fc.output(0)) else: - raise RuntimeError(f"Incorrect target point type {transform_type}") + raise nncf.InternalError(f"Incorrect target point type {transform_type}") @staticmethod def _apply_bias_correction_transformations(model, transformations: List[OVBiasCorrectionCommand]) -> ov.Model: @@ -506,7 +515,7 @@ def _set_const_value(node_with_const: ov.Node, const_port_id: int, const_value: queue.append((curr_node.input(0), curr_node.input_value(0).get_node())) if const_node is None: - raise RuntimeError("Constant node was expected but could not find it.") + raise nncf.InternalError("Constant node was expected but could not find it.") const_shape = const_node.data.shape const_dtype = const_node.data.dtype @@ -546,40 +555,42 @@ def _apply_model_extraction_transformation( """ transformation = transformations[-1] name_to_node_mapping = OVModelTransformer._get_name_to_node_mapping(model) - activation_node_names = OVModelTransformer._get_activation_node_names(model) + params, results = [], [] - for input_name in transformation.inputs: + for input_name, input_port_id in transformation.input_ids: input_node = name_to_node_mapping[input_name] if input_name in [tensor.node.get_friendly_name() for tensor in model.inputs]: params.append(input_node) continue - for input_port in input_node.inputs(): - if input_port.get_source_output().get_node().name not in activation_node_names: - continue - input_node_output = input_port.get_source_output() - parameter_name = f"Parameter_{input_name}" - new_param = opset.parameter( - shape=input_node_output.partial_shape, - dtype=input_node_output.get_element_type(), - name=parameter_name, - ) - input_port.replace_source_output(new_param.output(0)) - new_param_tensors = [o.get_tensor() for o in new_param.outputs()] - OVModelTransformer._update_tensor_name(new_param_tensors, parameter_name) - params.append(new_param) - for output_name in transformation.outputs: + input_port = input_node.input(input_port_id) + input_node_output = input_port.get_source_output() + parameter_name = get_parameter_node_name(input_name, input_port_id) + new_param = opset.parameter( + shape=input_node_output.partial_shape, + dtype=input_node_output.get_element_type(), + name=parameter_name, + ) + input_port.replace_source_output(new_param.output(0)) + new_param_tensors = [o.get_tensor() for o in new_param.outputs()] + OVModelTransformer._update_tensor_name(new_param_tensors, parameter_name) + params.append(new_param) + + for output_name, output_port_id in transformation.output_ids: output_node = name_to_node_mapping[output_name] - for node_out in output_node.outputs(): - result_name = get_result_node_name(output_name, 0) - new_result = opset.result(node_out, name=result_name) - OVModelTransformer._update_tensor_name([new_result.get_output_tensor(0)], result_name) - results.append(new_result) + + output_port = output_node.output(output_port_id) + result_name = get_result_node_name(output_name, output_port_id) + new_result = opset.result(output_port, name=result_name) + OVModelTransformer._update_tensor_name([new_result.get_output_tensor(0)], result_name) + results.append(new_result) if not results: results = model.get_results() - return ov.Model(results, params) + extracted_model = ov.Model(results, params) + copy_rt_info(model, extracted_model, path=["nncf"]) + return extracted_model @staticmethod def _apply_insert_operation(model: ov.Model, transformations: OVInplaceFnInsertionCommand) -> ov.Model: @@ -613,13 +624,15 @@ def _insert_inplace_operation( port_id = transformation.target_point.port_id fn_output_port_id = transformation.fn_output_port_id if transform_type == TargetType.POST_LAYER_OPERATION: - new_node = transformation.inplace_op_fn(target_node, port_id) + new_node = transformation.inplace_op_fn(target_node, port_id, transformation.last_inplace_node_name) return (new_node.output(fn_output_port_id), fn_output_port_id) if transform_type in [TargetType.PRE_LAYER_OPERATION, TargetType.OPERATION_WITH_WEIGHTS]: output = target_node.input_value(port_id) - new_node = transformation.inplace_op_fn(output.get_node(), output.get_index()) + new_node = transformation.inplace_op_fn( + output.get_node(), output.get_index(), transformation.last_inplace_node_name + ) return (new_node.output(fn_output_port_id), fn_output_port_id) - raise RuntimeError(f"Transform type {transform_type} is not supported") + raise nncf.InternalError(f"Transform type {transform_type} is not supported") @staticmethod def _apply_bias_insertion_transformations( diff --git a/nncf/openvino/graph/model_utils.py b/nncf/openvino/graph/model_utils.py index 733a61fa7be..b89c3cba890 100644 --- a/nncf/openvino/graph/model_utils.py +++ b/nncf/openvino/graph/model_utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -62,6 +62,29 @@ def get_start_nodes_for_activation_path_tracing(nncf_graph: NNCFGraph) -> List[N return nncf_graph.get_input_nodes() + nncf_graph.get_nodes_by_metatypes([OVReadValueMetatype]) +def remove_friendly_name_duplicates(model: ov.Model) -> ov.Model: + """ + Removes diplicates of node names (friendly_name attribute) in the model. + + :param model: ov.Model instance to update. + :return: Updated ov.Model without duplicated names. + """ + rt_info_path = ["nncf", "friendly_names_were_updated"] + friendly_names_flag = "True" + if model.has_rt_info(rt_info_path) and model.get_rt_info(rt_info_path).value == friendly_names_flag: + return model + + existing_names = set() + for op in model.get_ops(): + friendly_name = op.get_friendly_name() + if friendly_name in existing_names: + friendly_name = friendly_name + "0" + op.set_friendly_name(friendly_name) + existing_names.add(friendly_name) + model.set_rt_info(friendly_names_flag, rt_info_path) + return model + + def model_has_state(model: ov.Model) -> bool: """ Returns True if model has state else False @@ -70,3 +93,16 @@ def model_has_state(model: ov.Model) -> bool: :return: True if model has state else False """ return len(model.get_sinks()) > 0 + + +def copy_rt_info(model_source: ov.Model, model_dest: ov.Model, path: List[str]) -> None: + """ + Checks and copies the rt_info from the source to destination model. + + :param model_source: ov.Model instance to copy rt_info from. + :param model_dest: ov.Model instance to copy rt_info to. + :param path: Path to rt_info. + """ + if model_source.has_rt_info(path): + source_rt_info = model_source.get_rt_info(path) + model_dest.set_rt_info(source_rt_info, path) diff --git a/nncf/openvino/graph/nncf_graph_builder.py b/nncf/openvino/graph/nncf_graph_builder.py index 86197942032..fa9b80b8937 100644 --- a/nncf/openvino/graph/nncf_graph_builder.py +++ b/nncf/openvino/graph/nncf_graph_builder.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/openvino/graph/node_utils.py b/nncf/openvino/graph/node_utils.py index 74aea17e3f2..59d5e58c45b 100644 --- a/nncf/openvino/graph/node_utils.py +++ b/nncf/openvino/graph/node_utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -15,6 +15,7 @@ import openvino.runtime as ov import openvino.runtime.opset13 as opset +import nncf from nncf.common.graph.graph import NNCFGraph from nncf.common.graph.graph import NNCFNode from nncf.common.graph.layer_attributes import ConvolutionLayerAttributes @@ -155,12 +156,24 @@ def get_result_node_name(output_name: str, port_id: int) -> str: :param output_name: Node name. :param port_id: Node port. - :return: Name of result. + :return: Name of Result. """ return f"Result_{output_name}.{port_id}" +def get_parameter_node_name(parameter_name: str, port_id: int) -> str: + """ + Returns name of Parameter based on node name and its port. + + :param parameter_name: Node name. + :param port_id: Node port. + :return: Name of Parameter. + """ + + return f"Parameter_{parameter_name}.{port_id}" + + def get_ov_model_reduce_node_name(output_name: str, reduce_node_name: str, port_id: int) -> str: """ Returns name of reduce node based on output name, node type and port id. @@ -174,32 +187,26 @@ def get_ov_model_reduce_node_name(output_name: str, reduce_node_name: str, port_ def get_inplace_reduce_op( - op: Type[ov.Node], reduce_node_name: str, reduction_axes: Optional[ReductionAxes], use_abs: bool + op: Type[ov.Node], reduction_axes: Optional[ReductionAxes], use_abs: bool ) -> InplaceInsertionFnType: """ Returns inplace insertion function that adds reduce node to a passed node. :param op: OpenVINO reduction operation type to insert. - :param reduce_node_name: Reduce node name. :param reduction_axes: Target reduction axes for the reduction node. Reduce along all axes in case reduction_axes are None. :param use_abs: Wheather reduce absolute values of input tensors or not. :returns: Inplace insertion function to use in ModelTransformer. """ - def get_reduce_op(node: ov.Node, output_port_id: int) -> ov.Node: - output_name = node.get_friendly_name() + def get_reduce_op(node: ov.Node, output_port_id: int, output_node_name: str) -> ov.Node: reduction_axes_ = reduction_axes - name_output_port_id = output_port_id if reduction_axes_ is None: partial_shape = get_partial_shape_safe(node, output_port_id) reduction_axes_ = np.arange(partial_shape.rank.get_length()).astype(np.int64) if use_abs: - op_input = opset.abs( - node.output(output_port_id), - name=get_ov_model_reduce_node_name(output_name, "abs" + reduce_node_name, name_output_port_id), - ) + op_input = opset.abs(node.output(output_port_id), name="abs_" + output_node_name) output_port_id = 0 else: op_input = node @@ -208,82 +215,73 @@ def get_reduce_op(node: ov.Node, output_port_id: int) -> ov.Node: op_input.output(output_port_id), reduction_axes=np.array(reduction_axes_, dtype=np.int64), keep_dims=True, - name=get_ov_model_reduce_node_name(output_name, reduce_node_name, name_output_port_id), + name=output_node_name, ) return get_reduce_op -def get_inplace_min_op(node_name: str, reduction_axes: Optional[ReductionAxes]) -> InplaceInsertionFnType: +def get_inplace_min_op(reduction_axes: Optional[ReductionAxes]) -> InplaceInsertionFnType: """ Returns inplace min function that adds reduce min node to a passed node. - :param node_name: Min reduce node name. :param reduction_axes: Target reduction axes for the reduction node. Reduce along all axes in case reduction_axes are None. :returns: Inplace insertion function to use in ModelTransformer. """ - return get_inplace_reduce_op(opset.reduce_min, node_name, reduction_axes, False) + return get_inplace_reduce_op(opset.reduce_min, reduction_axes, False) -def get_inplace_max_op( - node_name: str, reduction_axes: Optional[ReductionAxes], use_abs_max: bool -) -> InplaceInsertionFnType: +def get_inplace_max_op(reduction_axes: Optional[ReductionAxes], use_abs_max: bool) -> InplaceInsertionFnType: """ Returns inplace max function that adds reduce max node to a passed node. - :param node_name: Max reduce node name. :param reduction_axes: Target reduction axes for the reduction node. Reduce along all axes in case reduction_axes are None. :param use_abs: Wheather reduce absolute values of input tensors or not. :returns: Inplace insertion function to use in ModelTransformer. """ - return get_inplace_reduce_op(opset.reduce_max, node_name, reduction_axes, use_abs_max) + return get_inplace_reduce_op(opset.reduce_max, reduction_axes, use_abs_max) -def get_inplace_mean_op(node_name: str, reduction_axes: Optional[ReductionAxes]) -> InplaceInsertionFnType: +def get_inplace_mean_op(reduction_axes: Optional[ReductionAxes]) -> InplaceInsertionFnType: """ Returns inplace mean function that adds reduce mean node to a passed node. - :param node_name: Mean reduce node name. :param reduction_axes: Target reduction axes for the reduction node. Reduce along all axes in case reduction_axes are None. :returns: Inplace insertion function to use in ModelTransformer. """ - return get_inplace_reduce_op(opset.reduce_mean, node_name, reduction_axes, False) + return get_inplace_reduce_op(opset.reduce_mean, reduction_axes, False) -def get_inplace_batch_mean_op(node_name: str) -> InplaceInsertionFnType: +def get_inplace_batch_mean_op() -> InplaceInsertionFnType: """ Returns inplace batch mean function that adds reduce batch mean node to a passed node. - :param node_name: Last node of batch mean subgraph name. :returns: Inplace insertion function to use in ModelTransformer. """ - return get_inplace_reduce_op(opset.reduce_mean, node_name, np.array(0), False) + return get_inplace_reduce_op(opset.reduce_mean, np.array(0), False) -def get_inplace_mean_per_ch(op_type: str, axis: int) -> InplaceInsertionFnType: +def get_inplace_mean_per_ch(axis: int) -> InplaceInsertionFnType: """ Returns inplace mean per channel function that adds reduce mean per channel node to a passed node. - :param node_name: Last node of mean per channel subgraph name. :param axis: Channel axis. :returns: Inplace insertion function to use in ModelTransformer. """ - def get_reduce_op(node: ov.Node, output_port_id: int) -> ov.Node: - output_name = node.get_friendly_name() + def get_reduce_op(node: ov.Node, output_port_id: int, output_node_name: str) -> ov.Node: input_shape = get_partial_shape_safe(node, output_port_id) input_shape = [dim.get_length() if dim.is_static else -1 for dim in input_shape] - name_output_port_id = output_port_id if len(input_shape) < 3: return opset.reduce_mean( node.output(output_port_id), reduction_axes=0, keep_dims=False, - name=get_ov_model_reduce_node_name(output_name, op_type, name_output_port_id), + name=output_node_name, ) ch_dim = 1 @@ -299,24 +297,18 @@ def get_reduce_op(node: ov.Node, output_port_id: int) -> ov.Node: transposed_shape = input_shape keeped_dims = transposed_shape[:2] + keeped_dims = [0 if dim < 0 else dim for dim in keeped_dims] squized_dims = -1 if -1 in transposed_shape[2:] else np.prod(transposed_shape[2:]) - if (-1 in keeped_dims and squized_dims == -1) or keeped_dims.count(-1) > 1: - raise RuntimeError( - f"Could not insert mean_per_ch operation inplace" - f" for the node {node} because of" - f" input_shape: {input_shape} -> transposed_shape: {transposed_shape}" - ) - reshape_op = opset.reshape( reshape_input_node.output(output_port_id), output_shape=np.array((keeped_dims[0], keeped_dims[1], squized_dims)), - special_zero=False, + special_zero=True, ) return opset.reduce_mean( reshape_op, reduction_axes=np.array((0, 2)), keep_dims=False, - name=get_ov_model_reduce_node_name(output_name, op_type, name_output_port_id), + name=output_node_name, ) return get_reduce_op @@ -325,7 +317,7 @@ def get_reduce_op(node: ov.Node, output_port_id: int) -> ov.Node: def get_partial_shape_safe(node, port_id) -> Tuple[int, ...]: partial_shape = node.get_output_partial_shape(port_id) if partial_shape.rank.is_dynamic or not partial_shape.all_non_negative: - raise RuntimeError( + raise nncf.ValidationError( f"Could not collect statistics for the node {node} because its output shape rank is dynamic or negative" ) return partial_shape diff --git a/nncf/openvino/graph/transformations/__init__.py b/nncf/openvino/graph/transformations/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/openvino/graph/transformations/__init__.py +++ b/nncf/openvino/graph/transformations/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/openvino/graph/transformations/command_creation.py b/nncf/openvino/graph/transformations/command_creation.py index b4f0f23e580..fe4bfeb974e 100644 --- a/nncf/openvino/graph/transformations/command_creation.py +++ b/nncf/openvino/graph/transformations/command_creation.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/openvino/graph/transformations/commands.py b/nncf/openvino/graph/transformations/commands.py index 8a006371565..29ce412ee30 100644 --- a/nncf/openvino/graph/transformations/commands.py +++ b/nncf/openvino/graph/transformations/commands.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -9,7 +9,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -from typing import List +from typing import List, Tuple import numpy as np import openvino.runtime as ov @@ -54,10 +54,17 @@ def union(self, other: "TransformationCommand") -> "TransformationCommand": class OVInplaceFnInsertionCommand(OVInsertionCommand): - def __init__(self, target_point: OVTargetPoint, inplace_op_fn: InplaceInsertionFnType, fn_output_port_id: int): + def __init__( + self, + target_point: OVTargetPoint, + inplace_op_fn: InplaceInsertionFnType, + fn_output_port_id: int, + last_inplace_node_name: str, + ): super().__init__(target_point) self.inplace_op_fn = inplace_op_fn self.fn_output_port_id = fn_output_port_id + self.last_inplace_node_name = last_inplace_node_name def union(self, other: "TransformationCommand") -> "TransformationCommand": # Have a look at nncf/torch/graph/transformations/commands/PTInsertionCommand @@ -121,14 +128,16 @@ class OVModelExtractionCommand(Command): Extracts sub-graph based on the sub-model input and output names. """ - def __init__(self, inputs: List[str], outputs: List[str]): + def __init__(self, input_ids: List[Tuple[str, int]], output_ids: List[Tuple[str, int]]): """ - :param inputs: List of the input names that denote the sub-graph beginning. - :param outputs: List of the output names that denote the sub-graph ending. + :param input_ids: List of the input IDs: pairs of node names and correspondent input port ids. + Each pair denotes the sub-graph beginning. + :param output_ids: List of the output IDs: pairs of node names and correspondent output port ids. + Each pair denotes the sub-graph ending. """ super().__init__(TransformationType.EXTRACT) - self.inputs = inputs - self.outputs = outputs + self.input_ids = input_ids + self.output_ids = output_ids class OVBiasInsertionCommand(TransformationCommand): diff --git a/nncf/openvino/hardware/__init__.py b/nncf/openvino/hardware/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/openvino/hardware/__init__.py +++ b/nncf/openvino/hardware/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/openvino/hardware/config.py b/nncf/openvino/hardware/config.py index d87ed4d7176..b1ae3e769c8 100644 --- a/nncf/openvino/hardware/config.py +++ b/nncf/openvino/hardware/config.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/openvino/hardware/fused_patterns.py b/nncf/openvino/hardware/fused_patterns.py index df11b83aa4e..12d6052880f 100644 --- a/nncf/openvino/hardware/fused_patterns.py +++ b/nncf/openvino/hardware/fused_patterns.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/openvino/pot/__init__.py b/nncf/openvino/pot/__init__.py deleted file mode 100644 index 9b29b47534a..00000000000 --- a/nncf/openvino/pot/__init__.py +++ /dev/null @@ -1,10 +0,0 @@ -# Copyright (c) 2023 Intel Corporation -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# http://www.apache.org/licenses/LICENSE-2.0 -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/nncf/openvino/pot/engine.py b/nncf/openvino/pot/engine.py deleted file mode 100644 index f9a8b7fc66d..00000000000 --- a/nncf/openvino/pot/engine.py +++ /dev/null @@ -1,199 +0,0 @@ -# Copyright (c) 2023 Intel Corporation -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# http://www.apache.org/licenses/LICENSE-2.0 -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from typing import Any, Callable, Dict, Iterable, List, Optional - -import openvino.runtime as ov -from openvino.tools import pot - -from nncf.api.compression import TModel -from nncf.data import Dataset -from nncf.data.dataset import DataProvider - - -# TODO(andrey-churkin): This class should be removed after refactoring of the OVEngine class. -# We will be able to do that when we will have moved the POT code in the NNCF. -class DummyMetric: - """ - Dummy implementation of the pot.Metric abstract class. It is used for compatibility - with the POT API. All docstring for the methods can be - found [here](https://docs.openvino.ai/latest/pot_compression_api_README.html#metric). - """ - - def __init__(self): - self._avg_value = None - self.name = "original_metric" - - @property - def avg_value(self): - return {self.name: self._avg_value} - - @avg_value.setter - def avg_value(self, value): - self._avg_value = value - - def get_attributes(self): - attributes = { - self.name: { - "direction": "higher-better", - "type": self.name, - } - } - return attributes - - def reset(self): - self.avg_value = None - - -# TODO(andrey-churkin): The algorithms from the POT use the `__len__()` method. -# This class should be removed when we change all algorithms. -class DatasetWrapper: - def __init__(self, iterable): - self._iterable = iterable - self._length = None - - def __iter__(self): - for idx, x in enumerate(self._iterable): - yield (idx, x) - - def __len__(self) -> int: - if self._length is None: - data_source = None - if isinstance(self._iterable, DataProvider): - indices = self._iterable._indices - if indices: - self._length = len(indices) - return self._length - data_source = self._iterable._data_source - iterable = data_source if data_source else self._iterable - self._length = DatasetWrapper._get_length(iterable) - return self._length - - @staticmethod - def _get_length(iterable) -> int: - try: - length = len(iterable) - return length - except (TypeError, AttributeError): - length = None - - length = 0 - for _ in iterable: - length = length + 1 - return length - - -def calc_per_sample_metrics( - compiled_model: ov.CompiledModel, - val_func: Callable[[ov.CompiledModel, Iterable[Any]], float], - dataset: Dataset, - subset_indices: List[int], -) -> List[Dict[str, Any]]: - per_sample_metrics = [] - for inputs in dataset.get_data(subset_indices): - value, _ = val_func(compiled_model, [inputs]) - per_sample_metrics.append( - {"sample_id": len(per_sample_metrics), "metric_name": "original_metric", "result": value} - ) - return per_sample_metrics - - -# TODO(andrey-churkin): This class should be refactored. We will be able to do that when -# we will have POT code in NNCF. -class OVEngine(pot.IEEngine): - """ - Implementation of the engine for OpenVINO backend. - - All docstring for the methods can be found - [here](https://docs.openvino.ai/latest/pot_compression_api_README.html#engine). - """ - - def __init__( - self, - config, - calibration_dataset: Dataset, - validation_dataset: Dataset, - validation_fn: Optional[Callable[[TModel, Iterable[Any]], float]] = None, - use_original_metric: bool = False, - ): - metric = DummyMetric() if validation_fn else None - super().__init__(config, DatasetWrapper(validation_dataset.get_inference_data()), metric) - self._calibration_dataset = calibration_dataset # TODO(andrey-churkin): Not used now. - self._validation_dataset = validation_dataset - self._validation_fn = validation_fn - self.use_original_metric = use_original_metric - self.statistics_collection = False - - def predict( - self, stats_layout=None, sampler=None, stat_aliases=None, metric_per_sample=False, print_progress=False - ): - if self._model is None: - raise Exception("Model was not set in Engine class") - - subset_indices = None - if sampler: - subset_indices = sorted(getattr(sampler, "_subset_indices")) - - is_full_dataset = subset_indices is None or len(subset_indices) == len(self.data_loader) - if self._validation_fn and (is_full_dataset or self.use_original_metric) and not self.statistics_collection: - compiled_model = self._ie.compile_model(model=self._model, device_name=self._device) - self._metric.avg_value, per_sample_metrics = self._validation_fn( - compiled_model, self._validation_dataset.get_data(subset_indices) - ) - if not metric_per_sample and stats_layout is None: - metrics = self._metric.avg_value - self._reset() - return metrics, {} - - if self.use_original_metric and metric_per_sample: - if per_sample_metrics is not None: - self._per_sample_metrics = [ - {"sample_id": i, "metric_name": "original_metric", "result": value} - for i, value in enumerate(per_sample_metrics) - ] - else: - self._per_sample_metrics = self.calculate_per_sample_metrics(subset_indices) - if stats_layout is None: - metrics = self._metric.avg_value - metrics = (sorted(self._per_sample_metrics, key=lambda i: i["sample_id"]), metrics) - self._reset() - return metrics, {} - - dataset_wrapper = DatasetWrapper(self._validation_dataset.get_inference_data(subset_indices)) - return super().predict(stats_layout, dataset_wrapper, stat_aliases, metric_per_sample, print_progress) - - def calculate_per_sample_metrics(self, subset_indices: List[int]): - if subset_indices is None: - subset_indices = list(range(len(self.data_loader))) - - if self.use_original_metric: - compiled_model = self._ie.compile_model(model=self._model, device_name=self._device) - per_sample_metrics = calc_per_sample_metrics( - compiled_model, self._validation_fn, self._validation_dataset, subset_indices - ) - else: - dataset_wrapper = DatasetWrapper(self._validation_dataset.get_inference_data(subset_indices)) - ans = super().predict(None, dataset_wrapper, None, True, None) - (per_sample_metrics, _), *_ = ans - return per_sample_metrics - - def _update_metrics(self, output, annotations, need_metrics_per_sample: bool = False): - if need_metrics_per_sample and not self.use_original_metric: - sample_id, _ = annotations[0] - self._per_sample_metrics.append({"sample_id": sample_id, "result": output[0], "metric_name": "nmse"}) - - @staticmethod - def _process_batch(batch): - index, input_data = batch - return [(index, None)], input_data, None - - def _fill_input(self, model, image_batch): - return image_batch diff --git a/nncf/openvino/pot/quantization/__init__.py b/nncf/openvino/pot/quantization/__init__.py deleted file mode 100644 index 9b29b47534a..00000000000 --- a/nncf/openvino/pot/quantization/__init__.py +++ /dev/null @@ -1,10 +0,0 @@ -# Copyright (c) 2023 Intel Corporation -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# http://www.apache.org/licenses/LICENSE-2.0 -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/nncf/openvino/pot/quantization/accuracy_aware.py b/nncf/openvino/pot/quantization/accuracy_aware.py deleted file mode 100644 index 0e2529740c6..00000000000 --- a/nncf/openvino/pot/quantization/accuracy_aware.py +++ /dev/null @@ -1,91 +0,0 @@ -# Copyright (c) 2023 Intel Corporation -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# http://www.apache.org/licenses/LICENSE-2.0 -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from functools import partial -from typing import List - -import numpy as np -from openvino.tools import pot - -# TODO(andrey-churkin): Should be removed after OpenVINO release. - - -class NMSEBasedAccuracyAware(pot.AccuracyAwareCommon): - """ - NMSE based implementation of the Accuracy Aware algorithm from the POT. - - This implementation works the same way as implementation from the POT in - case when it is possible to calculate the metric for one instance. - When it is not possible NMSE metric is used to select ranking subset and - calculate node importance. - """ - - def __init__(self, config, engine): - super().__init__(config, engine) - if not engine.use_original_metric: - self._metrics_config["original_metric"].persample.clear() - self._metrics_config["original_metric"].persample.update( - { - "name": "nmse", - "type": "nmse", - "is_special": True, - "comparator": lambda a: -a, - "sort_fn": partial( - pot.algorithms.quantization.accuracy_aware_common.utils.sort_by_logit_distance, distance="nmse" - ), - } - ) - self._metrics_config["original_metric"].ranking.clear() - self._metrics_config["original_metric"].ranking.update( - { - "name": "nmse", - "type": "nmse", - "is_special": True, - "comparator": lambda a: -a, - "sort_fn": partial( - pot.algorithms.quantization.accuracy_aware_common.utils.sort_by_logit_distance, distance="nmse" - ), - } - ) - self._need_intermediate_model = False - - def _get_score(self, model, ranking_subset: List[int], metric_name: str) -> float: - if self._engine.use_original_metric: - score = super()._get_score(model, ranking_subset, metric_name) - else: - ranking_metric = self._metrics_config[metric_name].ranking - original_outputs = [(i, self._original_per_sample_metrics[ranking_metric.name][i]) for i in ranking_subset] - _, original_outputs = zip(*sorted(original_outputs, key=lambda x: x[0])) - - self._engine.set_model(model) - per_sample_metrics = self._engine.calculate_per_sample_metrics(ranking_subset) - quantized_outputs = [y["result"] for y in per_sample_metrics] - - nmse_distance = lambda u, v: np.dot(u - v, u - v) / np.dot(u, u) - distance_between_samples = [ - nmse_distance(ui.flatten(), vi.flatten()) for ui, vi in zip(original_outputs, quantized_outputs) - ] - score = np.mean(distance_between_samples).item() - score = ranking_metric.comparator(score) - return score - - def _calculate_per_sample_metrics(self, model, subset_indices): - self._engine.set_model(model) - return self._engine.calculate_per_sample_metrics(subset_indices) - - def _quantize_model(self, model): - self._engine.statistics_collection = True - quantized_model = super()._quantize_model(model) - self._engine.statistics_collection = False - return quantized_model - - def _save_intermediate_model(self, model): - if self._need_intermediate_model: - super()._save_intermediate_model(model) diff --git a/nncf/openvino/pot/quantization/quantize_model.py b/nncf/openvino/pot/quantization/quantize_model.py deleted file mode 100644 index ad15e8ed359..00000000000 --- a/nncf/openvino/pot/quantization/quantize_model.py +++ /dev/null @@ -1,529 +0,0 @@ -# Copyright (c) 2023 Intel Corporation -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# http://www.apache.org/licenses/LICENSE-2.0 -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import logging -import tempfile -from pathlib import Path -from typing import Any, Callable, Dict, Iterable, Optional, Union - -import openvino.runtime as ov -from openvino._offline_transformations import compress_quantize_weights_transformation -from openvino.tools import pot - -from nncf.common.deprecation import warning_deprecated -from nncf.common.logging import nncf_logger -from nncf.common.quantization.structs import QuantizationPreset -from nncf.data import Dataset -from nncf.openvino.pot.engine import OVEngine -from nncf.openvino.pot.quantization.accuracy_aware import NMSEBasedAccuracyAware -from nncf.openvino.pot.telemetry_extractors import POTImplementation -from nncf.openvino.quantization.backend_parameters import BackendParameters -from nncf.openvino.quantization.backend_parameters import is_weight_compression_needed -from nncf.parameters import DropType -from nncf.parameters import ModelType -from nncf.parameters import QuantizationMode -from nncf.parameters import TargetDevice -from nncf.quantization.advanced_parameters import AdvancedAccuracyRestorerParameters -from nncf.quantization.advanced_parameters import AdvancedQuantizationParameters -from nncf.quantization.advanced_parameters import OverflowFix -from nncf.quantization.advanced_parameters import QuantizationParameters -from nncf.quantization.range_estimator import RangeEstimatorParameters -from nncf.quantization.range_estimator import StatisticsCollectorParameters -from nncf.quantization.range_estimator import StatisticsType -from nncf.quantization.telemetry_extractors import CompressionStartedWithQuantizeApi -from nncf.quantization.telemetry_extractors import CompressionStartedWithQuantizeWithAccuracyControlApi -from nncf.scopes import IgnoredScope -from nncf.telemetry import tracked_function -from nncf.telemetry.events import NNCF_OV_CATEGORY - - -def _convert_openvino_model_to_compressed_model( - model: ov.Model, target_device: str -) -> pot.graph.nx_model.CompressedModel: - """ - Serializes the provided OpenVINO model and loads the model in the POT representation. - - :param model: The OpenVINO model. - :param target_device: The target device. - :return: The POT representation of the provided model. - """ - with tempfile.TemporaryDirectory(dir=tempfile.gettempdir()) as tmp_dir: - xml_path = str(Path(tmp_dir) / "model.xml") - bin_path = str(Path(tmp_dir) / "model.bin") - ov.serialize(model, xml_path, bin_path) - model_config = { - "model_name": "model", - "model": xml_path, - "weights": bin_path, - } - pot_model = pot.load_model(model_config, target_device) - - return pot_model - - -def _convert_compressed_model_to_openvino_model(model: pot.graph.nx_model.CompressedModel) -> ov.Model: - """ - Saves the provided POT compressed model and loads it as `openvino.runtime.Model` object. - - :param model: The POT compressed model. - :return: The `openvino.runtime.Model` object which represents the provided model. - """ - with tempfile.TemporaryDirectory(dir=tempfile.gettempdir()) as tmp_dir: - paths = pot.save_model(model, save_path=tmp_dir, model_name="model") - xml_path = paths[0]["model"] - bin_path = paths[0]["weights"] - ie = ov.Core() - ie.set_property({"ENABLE_MMAP": "NO"}) - ov_model = ie.read_model(xml_path, bin_path) - return ov_model - - -def _create_ignored_scope_config(ignored_scope: Optional[IgnoredScope]) -> Dict[str, Any]: - """ - Maps the content of `IgnoredScope` class to the `ignored` section of POT config. - - :param ignored_scope: The ignored scope - :return: A POT ignored scope configuration as dict - """ - if ignored_scope is None: - return {} - - ignored = {} - if ignored_scope.names: - ignored["scope"] = ignored_scope.names - if ignored_scope.patterns: - raise RuntimeError( - "Quantization algorithm from the OpenVINO backend " - "does not support regular expressions in the ignored " - "scopes yet" - ) - if ignored_scope.types: - ignored["operations"] = [{"type": type} for type in ignored_scope.types] - return ignored - - -def _create_statistics_collector_config(statistics_collector_params: StatisticsCollectorParameters) -> Dict[str, Any]: - """ - Creates a statistic collector configuration. - - :param statistics_collector_params: Statistic collector parameters - :return: A POT statistic collector configuration as dict. - """ - config = {} - if statistics_collector_params.statistics_type is not None: - config["type"] = statistics_collector_params.statistics_type.value - if statistics_collector_params.aggregator_type is not None: - config["aggregator"] = statistics_collector_params.aggregator_type.value - if statistics_collector_params.clipping_value is not None: - config["clipping_value"] = statistics_collector_params.clipping_value - if statistics_collector_params.statistics_type in [StatisticsType.QUANTILE, StatisticsType.ABS_QUANTILE]: - config["outlier_prob"] = statistics_collector_params.quantile_outlier_prob - - return config - - -def _create_range_estimator_config(range_estimator_params: RangeEstimatorParameters) -> Dict[str, Any]: - """ - Creates a range estimator configuration. - - :param range_estimator_params: Range estimator parameters. - :return: A POT range estimator configuration as dict. - """ - config = {} - min_config = _create_statistics_collector_config(range_estimator_params.min) - if min_config: - config["min"] = min_config - - max_config = _create_statistics_collector_config(range_estimator_params.max) - if max_config: - config["max"] = max_config - - return config - - -def _create_quantization_group_config( - quantization_params: QuantizationParameters, - range_estimator_params: RangeEstimatorParameters, - backend_params: Dict[str, Any], -) -> Dict[str, Any]: - """ - Creates a configuration for a quantization group such as activations or weights. - - :param quantization_params: Quantization parameters. - :param backend_params: Backend specific parameters. - :return: A POT quantization group configuration as dict. - """ - config = {} - if quantization_params is not None: - if quantization_params.num_bits is not None: - config["bits"] = quantization_params.num_bits - - if quantization_params.mode is not None: - config["mode"] = str(quantization_params.mode) - if quantization_params.per_channel is not None: - config["perchannel"] = quantization_params.per_channel - - not_supported_params = { - "narrow_range": quantization_params.narrow_range, - "signedness_to_force": quantization_params.signedness_to_force, - } - for name, value in not_supported_params.items(): - if value is not None: - raise RuntimeError( - "Quantization algorithm from the OpenVINO backend does not support " - f"{name} directly, please, use backend specific parameters level_low " - "and level_high to specify the quantization levels for activations " - "and weights quantization groups to specify the quantization levels." - 'Example:\n {"activations" : {"level_low": 0, "level_high": 255}}\n' - '{"weights" : {"level_low": -127, "level_high": 127}}' - ) - if BackendParameters.LEVEL_LOW in backend_params: - config["level_low"] = backend_params[BackendParameters.LEVEL_LOW] - if BackendParameters.LEVEL_HIGH in backend_params: - config["level_high"] = backend_params[BackendParameters.LEVEL_HIGH] - config.update(_create_range_estimator_config(range_estimator_params)) - - return config - - -def _create_quantization_config( - preset: Union[QuantizationPreset, None], - target_device: TargetDevice, - subset_size: int, - fast_bias_correction: bool, - model_type: Union[ModelType, None], - ignored_scope: Union[IgnoredScope, None], - advanced_parameters: Union[AdvancedQuantizationParameters, None], -) -> Dict[str, Any]: - """ - Creates a quantization configuration. - - :param preset: A preset controls the quantization mode (symmetric and asymmetric). - It can take the following values: - - `performance`: Symmetric quantization of weights and activations. - - `mixed`: Symmetric quantization of weights and asymmetric quantization of activations. - - `None`: `mixed` preset is used for `transformer` model type otherwise `performace`. - :param target_device: A target device the specificity of which will be - taken into account while compressing in order to obtain the best - performance for this type of device. - :param subset_size: Size of a subset to calculate activations - statistics used for quantization. - :param fast_bias_correction: Setting this option to `False` enables - a different bias correction method which is more accurate, in general, - and takes more time but requires less memory. - :param model_type: Model type is needed to specify additional patterns - in the model. Supported only `transformer` now. - :param ignored_scope: An ignored scope that defined the list of model - control flow graph nodes to be ignored during quantization. - :param advanced_parameters: Advanced quantization parameters for - fine-tuning the quantization algorithm. - :return: A POT quantization configuration as dict. - """ - if preset is None: - preset = QuantizationPreset.MIXED if model_type == ModelType.TRANSFORMER else QuantizationPreset.PERFORMANCE - - config = { - "target_device": target_device.value, - "preset": preset.value, - "stat_subset_size": subset_size, - "use_fast_bias": fast_bias_correction, - } - - if model_type is not None: - config["model_type"] = model_type.value - if ignored_scope is not None: - config["ignored"] = _create_ignored_scope_config(ignored_scope) - - if advanced_parameters is None: - return config - - backend_activations_parameters = advanced_parameters.backend_params.get(BackendParameters.ACTIVATIONS, {}) - activations_config = _create_quantization_group_config( - advanced_parameters.activations_quantization_params, - advanced_parameters.activations_range_estimator_params, - backend_activations_parameters, - ) - if activations_config: - config["activations"] = activations_config - - backend_weights_parameters = advanced_parameters.backend_params.get(BackendParameters.WEIGHTS, {}) - weights_config = _create_quantization_group_config( - advanced_parameters.weights_quantization_params, - advanced_parameters.weights_range_estimator_params, - backend_weights_parameters, - ) - if weights_config: - config["weights"] = weights_config - - if advanced_parameters.overflow_fix == OverflowFix.ENABLE: - config["saturation_fix"] = "all" - elif advanced_parameters.overflow_fix == OverflowFix.FIRST_LAYER: - config["saturation_fix"] = "first_layer" - elif advanced_parameters.overflow_fix == OverflowFix.DISABLE: - config["saturation_fix"] = "no" - - config["inplace_statistics"] = advanced_parameters.inplace_statistics - - if advanced_parameters.quantize_outputs: - raise RuntimeError("Quantization algorithm from the OpenVINO backend does not support output quantization yet") - - bias_correction_params = advanced_parameters.bias_correction_params - if bias_correction_params.apply_for_all_nodes is not None: - config["apply_for_all_nodes"] = bias_correction_params.apply_for_all_nodes - if bias_correction_params.threshold is not None: - config["threshold"] = bias_correction_params.threshold - - return config - - -def _create_engine_config( - device: str, - default_stat_requests_number: int, - default_eval_requests_number: int, - advanced_parameters: AdvancedQuantizationParameters, -) -> Dict[str, Any]: - """ - Creates a POT engine configuration. - - :param device: A target device. - :param stat_requests_number: The default number of infer requests that are used - to collect statistics. - :param default_eval_requests_number: The default number of infer requests that - are used for model evaluation. - :param advanced_parameters: Advanced quantization parameters. - :return: A POT engine configuration as dict. - """ - engine_config = { - "device": device, - "stat_requests_number": default_stat_requests_number, - "eval_requests_number": default_eval_requests_number, - } - - advanced_backend_params = [ - BackendParameters.STAT_REQUESTS_NUMBER, - BackendParameters.EVAL_REQUESTS_NUMBER, - ] - - for param_name in advanced_backend_params: - param_value = advanced_parameters.backend_params.get(param_name) - if param_value is not None: - engine_config[param_name] = param_value - - return engine_config - - -@tracked_function( - NNCF_OV_CATEGORY, [CompressionStartedWithQuantizeApi(), POTImplementation(), "target_device", "preset"] -) -def quantize_impl( - model: ov.Model, - calibration_dataset: Dataset, - mode: Optional[QuantizationMode] = None, - preset: Optional[QuantizationPreset] = None, - target_device: TargetDevice = TargetDevice.ANY, - subset_size: int = 300, - fast_bias_correction: bool = True, - model_type: Optional[ModelType] = None, - ignored_scope: Optional[IgnoredScope] = None, - advanced_parameters: Optional[AdvancedQuantizationParameters] = None, -) -> ov.Model: - """ - Implementation of the `quantize()` method for the OpenVINO backend. - """ - pot.utils.logger.init_logger(level=logging.getLevelName(nncf_logger.getEffectiveLevel())) - - if mode is not None: - raise ValueError(f"mode={mode} is not supported") - - if advanced_parameters is None: - advanced_parameters = AdvancedQuantizationParameters() - - if advanced_parameters.smooth_quant_alpha is not None: - warning_deprecated( - "`AdvancedQuantizationParameters(smooth_quant_alpha=..)` is deprecated." - "Please, use `AdvancedQuantizationParameters(smooth_quant_alphas)` option " - "with AdvancedSmoothQuantParameters(convolution=.., matmul=..) as value instead." - ) - - sq_params = advanced_parameters.smooth_quant_alphas - - if model_type == ModelType.TRANSFORMER and (sq_params.convolution > 0 or sq_params.matmul > 0): - nncf_logger.warning( - "IMPORTANT. The AdvancedSmoothQuantParameters parameter value > 0 IS NOT SUPPORTED for the POT backend!" - "Please, use `AdvancedSmoothQuantParameters(convolution = -1, matmul = -1)`." - ) - - algorithm_parameters = _create_quantization_config( - preset, target_device, subset_size, fast_bias_correction, model_type, ignored_scope, advanced_parameters - ) - - if advanced_parameters.disable_bias_correction: - algorithms = [ - {"name": "ActivationChannelAlignment", "params": algorithm_parameters}, - {"name": "MinMaxQuantization", "params": algorithm_parameters}, - ] - else: - algorithms = [{"name": "DefaultQuantization", "params": algorithm_parameters}] - - pot_model = _convert_openvino_model_to_compressed_model(model, target_device) - - engine_config = _create_engine_config( - device="CPU", - default_stat_requests_number=2, - default_eval_requests_number=2, - advanced_parameters=advanced_parameters, - ) - - engine = OVEngine(engine_config, calibration_dataset, calibration_dataset) - pipeline = pot.create_pipeline(algorithms, engine) - compressed_model = pipeline.run(pot_model) - quantized_model = _convert_compressed_model_to_openvino_model(compressed_model) - - if is_weight_compression_needed(advanced_parameters): - compress_quantize_weights_transformation(quantized_model) - - return quantized_model - - -def _create_accuracy_restorer_config( - max_drop: float, drop_type: DropType, advanced_parameters: Optional[AdvancedAccuracyRestorerParameters] -) -> Dict[str, Any]: - """ - Creates a accuracy restorer configuration. - - :param max_drop: The maximum accuracy drop that should be achieved after - the quantization. - :param drop_type: The accuracy drop type, which determines how the maximum accuracy - drop between the original model and the compressed model is calculated. - :param advanced_parameters: Advanced parameters for fine-tuning the accuracy - restorer algorithm. - :return: A POT accuracy restorer configuration as dict. - """ - config = { - "maximal_drop": max_drop, - "drop_type": drop_type.value, - "metric_subset_ratio": 0.5, - } - - if advanced_parameters is None: - return config - - config["max_num_iterations"] = advanced_parameters.max_num_iterations - config["tune_hyperparams"] = advanced_parameters.tune_hyperparams - if advanced_parameters.ranking_subset_size is not None: - config["ranking_subset_size"] = advanced_parameters.ranking_subset_size - - return config - - -@tracked_function( - NNCF_OV_CATEGORY, - [ - CompressionStartedWithQuantizeWithAccuracyControlApi(), - POTImplementation(), - "target_device", - "preset", - "max_drop", - "drop_type", - ], -) -def quantize_with_accuracy_control_impl( - model: ov.Model, - calibration_dataset: Dataset, - validation_dataset: Dataset, - validation_fn: Callable[[ov.CompiledModel, Iterable[Any]], float], - max_drop: float = 0.01, - drop_type: DropType = DropType.ABSOLUTE, - preset: Optional[QuantizationPreset] = None, - target_device: TargetDevice = TargetDevice.ANY, - subset_size: int = 300, - fast_bias_correction: bool = True, - model_type: Optional[ModelType] = None, - ignored_scope: Optional[IgnoredScope] = None, - advanced_quantization_parameters: Optional[AdvancedQuantizationParameters] = None, - advanced_accuracy_restorer_parameters: Optional[AdvancedAccuracyRestorerParameters] = None, -) -> ov.Model: - """ - Implementation of the `quantize_with_accuracy_control()` method for the OpenVINO backend. - """ - pot.utils.logger.init_logger(level=logging.getLevelName(nncf_logger.getEffectiveLevel())) - - if advanced_quantization_parameters is None: - advanced_quantization_parameters = AdvancedQuantizationParameters() - - if advanced_quantization_parameters.smooth_quant_alpha is not None: - warning_deprecated( - "`AdvancedQuantizationParameters(smooth_quant_alpha=..)` is deprecated." - "Please, use `AdvancedQuantizationParameters(smooth_quant_alphas)` option " - "with AdvancedSmoothQuantParameters(convolution=.., matmul=..) as value instead." - ) - - sq_params = advanced_quantization_parameters.smooth_quant_alphas - - if model_type == ModelType.TRANSFORMER and (sq_params.convolution > 0 or sq_params.matmul > 0): - nncf_logger.warning( - "IMPORTANT. The AdvancedSmoothQuantParameters parameter value > 0 IS NOT SUPPORTED for the POT backend!" - "Please, use `AdvancedSmoothQuantParameters(convolution = -1, matmul = -1)`." - ) - - if advanced_quantization_parameters.disable_bias_correction: - raise ValueError( - "Quantization algorithm with accuracy controll from the OpenVINO backend " - "does not support disabling bias correction algorithm yet" - ) - - pot_model = _convert_openvino_model_to_compressed_model(model, target_device) - - engine_config = _create_engine_config( - device="CPU", - default_stat_requests_number=1, - default_eval_requests_number=1, - advanced_parameters=advanced_quantization_parameters, - ) - - # Check whether it is possible to calculate the metric for one data item. - - use_original_metric = True - try: - ie = ov.Core() - compiled_model = ie.compile_model(model, device_name="CPU") - _ = validation_fn(compiled_model, validation_dataset.get_data(indices=[0])) - except Exception: - use_original_metric = False - compression_algorithms = pot.algorithms.algorithm_selector.COMPRESSION_ALGORITHMS - if "NMSEBasedAccuracyAware" not in compression_algorithms.registry_dict: - compression_algorithms.register("NMSEBasedAccuracyAware")(NMSEBasedAccuracyAware) - - algotrithm_parameters = _create_accuracy_restorer_config(max_drop, drop_type, advanced_accuracy_restorer_parameters) - - algotrithm_parameters.update( - _create_quantization_config( - preset, - target_device, - subset_size, - fast_bias_correction, - model_type, - ignored_scope, - advanced_quantization_parameters, - ) - ) - - algorithms = [{"name": "NMSEBasedAccuracyAware", "params": algotrithm_parameters}] - - engine = OVEngine(engine_config, calibration_dataset, validation_dataset, validation_fn, use_original_metric) - pipeline = pot.create_pipeline(algorithms, engine) - compressed_model = pipeline.run(pot_model) - quantized_model = _convert_compressed_model_to_openvino_model(compressed_model) - - if is_weight_compression_needed(advanced_quantization_parameters): - compress_quantize_weights_transformation(quantized_model) - - return quantized_model diff --git a/nncf/openvino/pot/telemetry_extractors.py b/nncf/openvino/pot/telemetry_extractors.py deleted file mode 100644 index 7b00160c5d0..00000000000 --- a/nncf/openvino/pot/telemetry_extractors.py +++ /dev/null @@ -1,20 +0,0 @@ -# Copyright (c) 2023 Intel Corporation -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# http://www.apache.org/licenses/LICENSE-2.0 -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from typing import Any - -from nncf.telemetry.extractors import CollectedEvent -from nncf.telemetry.extractors import TelemetryExtractor - - -class POTImplementation(TelemetryExtractor): - def extract(self, _: Any) -> CollectedEvent: - return CollectedEvent(name="pot implementation") diff --git a/nncf/openvino/quantization/__init__.py b/nncf/openvino/quantization/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/openvino/quantization/__init__.py +++ b/nncf/openvino/quantization/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/openvino/quantization/backend_parameters.py b/nncf/openvino/quantization/backend_parameters.py index c1cf82c13ba..1099eb88399 100644 --- a/nncf/openvino/quantization/backend_parameters.py +++ b/nncf/openvino/quantization/backend_parameters.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -22,7 +22,6 @@ class BackendParameters: WEIGHTS = "weights" LEVEL_LOW = "level_low" LEVEL_HIGH = "level_high" - USE_POT = "use_pot" def is_weight_compression_needed(advanced_parameters: Optional[AdvancedQuantizationParameters]) -> bool: diff --git a/nncf/openvino/quantization/default_quantization.py b/nncf/openvino/quantization/default_quantization.py index d60358e09fc..f6c838e235f 100644 --- a/nncf/openvino/quantization/default_quantization.py +++ b/nncf/openvino/quantization/default_quantization.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/openvino/quantization/ignored_patterns.py b/nncf/openvino/quantization/ignored_patterns.py index 25591dcd635..11483507d04 100644 --- a/nncf/openvino/quantization/ignored_patterns.py +++ b/nncf/openvino/quantization/ignored_patterns.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/openvino/quantization/quantize_ifmodel.py b/nncf/openvino/quantization/quantize_ifmodel.py index 3280ab4fb54..2dc007201d5 100644 --- a/nncf/openvino/quantization/quantize_ifmodel.py +++ b/nncf/openvino/quantization/quantize_ifmodel.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -28,6 +28,7 @@ from nncf.common.logging.track_progress import track from nncf.common.tensor_statistics.statistic_point import StatisticPointsContainer from nncf.openvino.graph.metatypes.openvino_metatypes import OVIfMetatype +from nncf.openvino.graph.model_utils import remove_friendly_name_duplicates from nncf.openvino.graph.node_utils import get_number_if_op from nncf.openvino.graph.transformations.commands import OVExtractIfBodyCommand from nncf.openvino.graph.transformations.commands import OVOutputInsertionCommand @@ -173,8 +174,12 @@ def apply_algorithm_if_bodies( else_model_input_names, subset_size, ) + then_model = _extract_if_body(model_transformer_fp32, if_node, True) + then_model = remove_friendly_name_duplicates(then_model) else_model = _extract_if_body(model_transformer_fp32, if_node, False) + else_model = remove_friendly_name_duplicates(else_model) + then_quantized_model, current_model_num = apply_algorithm_if_bodies( algorithm, then_model, diff --git a/nncf/openvino/quantization/quantize_model.py b/nncf/openvino/quantization/quantize_model.py index 7ed0ffb05b5..69b1938329b 100644 --- a/nncf/openvino/quantization/quantize_model.py +++ b/nncf/openvino/quantization/quantize_model.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -9,25 +9,28 @@ # See the License for the specific language governing permissions and # limitations under the License. -import importlib from copy import deepcopy from typing import Any, Callable, Iterable, List, Optional, Tuple, TypeVar, Union import openvino.runtime as ov from openvino._offline_transformations import compress_quantize_weights_transformation +from nncf.common.factory import NNCFGraphFactory from nncf.common.logging import nncf_logger from nncf.common.quantization.structs import QuantizationPreset from nncf.data import Dataset +from nncf.openvino.graph.model_utils import remove_friendly_name_duplicates from nncf.openvino.graph.nncf_graph_builder import GraphConverter from nncf.openvino.graph.node_utils import get_number_if_op from nncf.openvino.quantization.backend_parameters import BackendParameters from nncf.openvino.quantization.backend_parameters import is_weight_compression_needed from nncf.openvino.quantization.quantize_ifmodel import apply_algorithm_if_bodies from nncf.openvino.rt_info import dump_parameters +from nncf.parameters import CompressWeightsMode from nncf.parameters import DropType from nncf.parameters import ModelType from nncf.parameters import QuantizationMode +from nncf.parameters import SensitivityMetric from nncf.parameters import TargetDevice from nncf.quantization.advanced_parameters import AdvancedAccuracyRestorerParameters from nncf.quantization.advanced_parameters import AdvancedQuantizationParameters @@ -36,43 +39,16 @@ from nncf.quantization.algorithms.accuracy_control.algorithm import calculate_accuracy_drop from nncf.quantization.algorithms.accuracy_control.evaluator import Evaluator from nncf.quantization.algorithms.post_training.algorithm import PostTrainingQuantization +from nncf.quantization.algorithms.weight_compression.algorithm import WeightCompression from nncf.quantization.quantize_model import quantize_with_tune_hyperparams from nncf.quantization.telemetry_extractors import CompressionStartedWithQuantizeApi from nncf.scopes import IgnoredScope from nncf.telemetry.decorator import tracked_function from nncf.telemetry.events import NNCF_OV_CATEGORY -USE_POT_AS_DEFAULT = False - TTensor = TypeVar("TTensor") -def should_use_pot(advanced_parameters: Optional[AdvancedQuantizationParameters]) -> bool: - """ - Returns True if POT should be used for quantization, False otherwise. - - :param advanced_parameters: Advanced quantization parameters. - :return: True if POT should be used, False otherwise. - :raises ImportError if POT is not found in the Python environment. - """ - use_pot = USE_POT_AS_DEFAULT - if advanced_parameters is not None and advanced_parameters.backend_params is not None: - use_pot = advanced_parameters.backend_params.get(BackendParameters.USE_POT, USE_POT_AS_DEFAULT) - - if not use_pot: - return False - - try: - importlib.import_module("openvino.tools.pot") - except ImportError: - nncf_logger.error( - "OpenVINO POT was not found in your Python environment.\n" - "Please install the openvino-dev package, e.g. via pypi: pip install openvino-dev.\n" - ) - - return True - - @tracked_function(NNCF_OV_CATEGORY, [CompressionStartedWithQuantizeApi(), "target_device", "preset"]) def native_quantize_if_op_impl( model: ov.Model, @@ -249,6 +225,7 @@ def native_quantize_with_accuracy_control_impl( # TODO(andrey-churkin): Collect statistics only once if advanced_accuracy_restorer_parameters.tune_hyperparams and not should_terminate: + model = remove_friendly_name_duplicates(model) tuned_quantized_model = quantize_with_tune_hyperparams( model, calibration_dataset, @@ -337,14 +314,11 @@ def quantize_impl( """ Implementation of the `quantize()` method for the OpenVINO backend. """ - if should_use_pot(advanced_parameters): - from nncf.openvino.pot.quantization.quantize_model import quantize_impl as pot_quantize_impl + model = remove_friendly_name_duplicates(model) - quantize_fn = pot_quantize_impl - else: - quantize_fn = native_quantize_impl - if get_number_if_op(model) > 0: - quantize_fn = native_quantize_if_op_impl + quantize_fn = native_quantize_impl + if get_number_if_op(model) > 0: + quantize_fn = native_quantize_if_op_impl return quantize_fn( model=model, @@ -396,14 +370,8 @@ def quantize_with_accuracy_control_impl( """ Implementation of the `quantize_with_accuracy_control()` method for the OpenVINO backend. """ - if should_use_pot(advanced_quantization_parameters): - from nncf.openvino.pot.quantization.quantize_model import ( - quantize_with_accuracy_control_impl as pot_quantize_with_accuracy_control_impl, - ) - quantize_with_accuracy_control_fn = pot_quantize_with_accuracy_control_impl - else: - quantize_with_accuracy_control_fn = native_quantize_with_accuracy_control_impl + quantize_with_accuracy_control_fn = native_quantize_with_accuracy_control_impl val_func = wrap_validation_fn(validation_fn) @@ -423,3 +391,27 @@ def quantize_with_accuracy_control_impl( advanced_quantization_parameters, advanced_accuracy_restorer_parameters, ) + + +def compress_weights_impl( + model: ov.Model, + dataset: Dataset, + mode: CompressWeightsMode, + ratio: float, + group_size: int, + ignored_scope: IgnoredScope, + all_layers: bool, + sensitivity_metric: SensitivityMetric, + awq: bool, + subset_size: int, +) -> ov.Model: + """ + Implementation of the `compress_weights()` method for the OpenVINO backend. + """ + + model = remove_friendly_name_duplicates(model) + compression_algorithm = WeightCompression( + mode, ratio, group_size, ignored_scope, all_layers, sensitivity_metric, awq, subset_size + ) + graph = NNCFGraphFactory.create(model) + return compression_algorithm.apply(model, graph, dataset=dataset) diff --git a/nncf/openvino/rt_info.py b/nncf/openvino/rt_info.py index 45f61923a70..f6c7bc6ee86 100644 --- a/nncf/openvino/rt_info.py +++ b/nncf/openvino/rt_info.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/openvino/statistics/__init__.py b/nncf/openvino/statistics/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/openvino/statistics/__init__.py +++ b/nncf/openvino/statistics/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/openvino/statistics/aggregator.py b/nncf/openvino/statistics/aggregator.py index 7fd5e26c72d..d7c1a8829e5 100644 --- a/nncf/openvino/statistics/aggregator.py +++ b/nncf/openvino/statistics/aggregator.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -23,6 +23,8 @@ from nncf.common.tensor_statistics.statistic_point import StatisticPointsContainer from nncf.experimental.common.tensor_statistics.collectors import MergedTensorCollector from nncf.experimental.common.tensor_statistics.collectors import TensorCollector +from nncf.openvino.graph.node_utils import get_ov_model_reduce_node_name +from nncf.openvino.graph.node_utils import get_reducer_output_node_names from nncf.openvino.graph.transformations.commands import OVInplaceFnInsertionCommand from nncf.openvino.graph.transformations.commands import OVOutputInsertionCommand from nncf.openvino.tensor import OVNNCFTensor @@ -37,20 +39,17 @@ def _register_statistics( self, outputs: Dict[str, OVNNCFTensor], statistic_points: StatisticPointsContainer ) -> None: for _, statistic_point, tensor_collector in statistic_points.get_tensor_collectors(): - target_point = statistic_point.target_point - node_name = target_point.target_node_name - port_id = target_point.port_id - if target_point.type == TargetType.POST_LAYER_OPERATION: - stat_node_name = node_name - elif target_point.type in [TargetType.PRE_LAYER_OPERATION, TargetType.OPERATION_WITH_WEIGHTS]: - node = self._name_to_node_mapping[node_name] - output = node.input_value(port_id) - stat_node_name = output.get_node().get_friendly_name() - port_id = output.get_index() - else: - RuntimeError(f"Unsupported target point type for statistic aggregator: {target_point.type}") - - input_info = tensor_collector.get_output_info(stat_node_name, port_id) + stat_node_name, port_id, _ = self._translate_to_post_layer_operation(statistic_point) + input_info = [] + for reducer in tensor_collector.reducers: + input_info.append( + [ + hash(reducer), + get_reducer_output_node_names( + reducer.name, stat_node_name, port_id, reducer.output_port_id, reducer.inplace + ), + ] + ) target_inputs = TensorCollector.get_tensor_collector_inputs(outputs, input_info) tensor_collector.register_inputs(target_inputs) @@ -58,18 +57,18 @@ def _get_transformation_layout_extra_outputs( self, statistic_points: StatisticPointsContainer ) -> TransformationLayout: transformation_layout = TransformationLayout() - transformation_commands = [] for _, statistic_point, tensor_collector in statistic_points.get_tensor_collectors(): - for op_fn, fn_out_port_id in tensor_collector.get_inplace_fn_info(): - transformation_commands.append( - OVInplaceFnInsertionCommand(statistic_point.target_point, op_fn, fn_out_port_id) - ) - if tensor_collector.any_stat_out_of_place(): - transformation_commands.append(OVOutputInsertionCommand(statistic_point.target_point)) - - for transformation_command in transformation_commands: - transformation_layout.register(transformation_command) - + for reducer in tensor_collector.reducers: + if reducer.inplace: + target_node_name, target_node_port_id, _ = self._translate_to_post_layer_operation(statistic_point) + output_name = get_ov_model_reduce_node_name(target_node_name, reducer.name, target_node_port_id) + transformation_layout.register( + OVInplaceFnInsertionCommand( + statistic_point.target_point, reducer.get_inplace_fn(), reducer.output_port_id, output_name + ) + ) + else: + transformation_layout.register(OVOutputInsertionCommand(statistic_point.target_point)) return transformation_layout @staticmethod @@ -108,6 +107,23 @@ def _get_merged_statistic_points( merged_statistic_points.add_statistic_point(stat_point) return merged_statistic_points + def _translate_to_post_layer_operation(self, statistic_point: StatisticPoint): + target_point = statistic_point.target_point + node_name = target_point.target_node_name + port_id = target_point.port_id + if target_point.type == TargetType.POST_LAYER_OPERATION: + stat_node_name = node_name + target_point_type = target_point.type + elif target_point.type in [TargetType.PRE_LAYER_OPERATION, TargetType.OPERATION_WITH_WEIGHTS]: + node = self._name_to_node_mapping[node_name] + output = node.input_value(port_id) + stat_node_name = output.get_node().get_friendly_name() + port_id = output.get_index() + target_point_type = TargetType.POST_LAYER_OPERATION + else: + RuntimeError(f"Unsupported target point type for statistic aggregator: {target_point.type}") + return stat_node_name, port_id, target_point_type + @staticmethod def _process_outputs(outputs: Dict[str, np.ndarray]) -> Dict[str, OVNNCFTensor]: return {n: OVNNCFTensor(v) for n, v in outputs.items()} diff --git a/nncf/openvino/statistics/collectors.py b/nncf/openvino/statistics/collectors.py index 5eedd856727..beaac8d2cba 100644 --- a/nncf/openvino/statistics/collectors.py +++ b/nncf/openvino/statistics/collectors.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -28,6 +28,7 @@ from nncf.experimental.common.tensor_statistics.collectors import NoopAggregator from nncf.experimental.common.tensor_statistics.collectors import NoopReducer from nncf.experimental.common.tensor_statistics.collectors import QuantileReducer +from nncf.experimental.common.tensor_statistics.collectors import RawReducer from nncf.experimental.common.tensor_statistics.collectors import ShapeAggregator from nncf.experimental.common.tensor_statistics.collectors import TensorCollector from nncf.openvino.graph.node_utils import get_inplace_batch_mean_op @@ -35,8 +36,6 @@ from nncf.openvino.graph.node_utils import get_inplace_mean_op from nncf.openvino.graph.node_utils import get_inplace_mean_per_ch from nncf.openvino.graph.node_utils import get_inplace_min_op -from nncf.openvino.graph.node_utils import get_reducer_output_node_names -from nncf.openvino.graph.node_utils import get_result_node_name from nncf.openvino.statistics.statistics import OVMeanTensorStatistic from nncf.openvino.statistics.statistics import OVRawTensorStatistic from nncf.openvino.tensor import OVNNCFTensor @@ -190,20 +189,12 @@ def zero_elements(x: NNCFTensor) -> NNCFTensor: return NNCFTensor(np.abs(np_tensor) < eps) -class OVNoopReducer(NoopReducer): - def get_output_names(self, target_node_name: str, port_id: int) -> List[str]: - return [get_result_node_name(target_node_name, port_id)] - - class OVMinReducer(MinReducer): def _get_processor(self): return OVNNCFCollectorTensorProcessor def get_inplace_fn(self): - return get_inplace_min_op(self.name, self._reduction_axes) - - def get_output_names(self, target_node_name: str, port_id: int) -> List[str]: - return get_reducer_output_node_names(self.name, target_node_name, port_id, self.output_port_id, self.inplace) + return get_inplace_min_op(self._reduction_axes) class OVMaxReducer(MaxReducer): @@ -211,10 +202,7 @@ def _get_processor(self): return OVNNCFCollectorTensorProcessor def get_inplace_fn(self): - return get_inplace_max_op(self.name, self._reduction_axes, False) - - def get_output_names(self, target_node_name: str, port_id: int) -> List[str]: - return get_reducer_output_node_names(self.name, target_node_name, port_id, self.output_port_id, self.inplace) + return get_inplace_max_op(self._reduction_axes, False) class OVAbsMaxReducer(AbsMaxReducer): @@ -222,10 +210,7 @@ def _get_processor(self): return OVNNCFCollectorTensorProcessor def get_inplace_fn(self): - return get_inplace_max_op(self.name, self._reduction_axes, True) - - def get_output_names(self, target_node_name: str, port_id: int) -> List[str]: - return get_reducer_output_node_names(self.name, target_node_name, port_id, self.output_port_id, self.inplace) + return get_inplace_max_op(self._reduction_axes, True) class OVMeanReducer(MeanReducer): @@ -233,10 +218,7 @@ def _get_processor(self): return OVNNCFCollectorTensorProcessor def get_inplace_fn(self): - return get_inplace_mean_op(self.name, self._reduction_axes) - - def get_output_names(self, target_node_name: str, port_id: int) -> List[str]: - return get_reducer_output_node_names(self.name, target_node_name, port_id, self.output_port_id, self.inplace) + return get_inplace_mean_op(self._reduction_axes) class OVBatchMeanReducer(BatchMeanReducer): @@ -244,10 +226,7 @@ def _get_processor(self): return OVNNCFCollectorTensorProcessor def get_inplace_fn(self): - return get_inplace_batch_mean_op(self.name) - - def get_output_names(self, target_node_name: str, port_id: int) -> List[str]: - return get_reducer_output_node_names(self.name, target_node_name, port_id, self.output_port_id, self.inplace) + return get_inplace_batch_mean_op() class OVMeanPerChanelReducer(MeanPerChReducer): @@ -255,10 +234,7 @@ def _get_processor(self): return OVNNCFCollectorTensorProcessor def get_inplace_fn(self): - return get_inplace_mean_per_ch(self.name, self._reduction_axes) - - def get_output_names(self, target_node_name: str, port_id: int) -> List[str]: - return get_reducer_output_node_names(self.name, target_node_name, port_id, self.output_port_id, self.inplace) + return get_inplace_mean_per_ch(self._channel_axis) class OVQuantileReducer(QuantileReducer): @@ -268,9 +244,6 @@ def get_inplace_fn(self) -> Optional[InplaceInsertionFNType]: def _get_processor(self): return OVNNCFCollectorTensorProcessor - def get_output_names(self, target_node_name: str, port_id: int) -> List[str]: - return get_reducer_output_node_names(self.name, target_node_name, port_id, self.output_port_id, self.inplace) - class OVAbsQuantileReducer(AbsQuantileReducer): def _get_processor(self): @@ -279,9 +252,6 @@ def _get_processor(self): def get_inplace_fn(self) -> Optional[InplaceInsertionFNType]: return None - def get_output_names(self, target_node_name: str, port_id: int) -> List[str]: - return get_reducer_output_node_names(self.name, target_node_name, port_id, self.output_port_id, self.inplace) - def get_mean_statistic_collector( num_samples: int, channel_axis: int, window_size: Optional[int] = None, inplace: bool = True @@ -296,14 +266,11 @@ def get_mean_statistic_collector( :param inplace: Whether the mean reducer should be calculated inplace or out of place. :return: Mean statistic collector. """ - # TODO(dlyakhov): use inplace OVBatchMeanReducer and OVMeanPerChanelReducer - # after migration on openvino-dev=2023.0 - inplace = False if channel_axis == 0: reducer = OVBatchMeanReducer(inplace) else: reducer = OVMeanPerChanelReducer(channel_axis=channel_axis, inplace=inplace) - noop_reducer = OVNoopReducer() + noop_reducer = NoopReducer() kwargs = { "tensor_processor": OVNNCFCollectorTensorProcessor, @@ -320,7 +287,7 @@ def get_mean_statistic_collector( def get_raw_stat_collector(num_samples: Optional[int] = None) -> TensorCollector: - reducer = OVNoopReducer() + reducer = RawReducer() aggregator = NoopAggregator(num_samples) collector = TensorCollector(OVRawTensorStatistic) diff --git a/nncf/openvino/statistics/statistics.py b/nncf/openvino/statistics/statistics.py index 12a0c82af9c..9e51ed319a3 100644 --- a/nncf/openvino/statistics/statistics.py +++ b/nncf/openvino/statistics/statistics.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/openvino/tensor.py b/nncf/openvino/tensor.py index 3e3832f9771..3d982ac88e6 100644 --- a/nncf/openvino/tensor.py +++ b/nncf/openvino/tensor.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/parameters.py b/nncf/parameters.py index 210a88908f4..e8ef12b4979 100644 --- a/nncf/parameters.py +++ b/nncf/parameters.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -14,8 +14,13 @@ from nncf.common.utils.api_marker import api +class StrEnum(str, Enum): + def __str__(self) -> str: + return self.value + + @api(canonical_alias="nncf.TargetDevice") -class TargetDevice(Enum): +class TargetDevice(StrEnum): """ Target device architecture for compression. @@ -26,12 +31,12 @@ class TargetDevice(Enum): ANY = "ANY" CPU = "CPU" GPU = "GPU" - VPU = "VPU" + NPU = "NPU" CPU_SPR = "CPU_SPR" @api(canonical_alias="nncf.ModelType") -class ModelType(Enum): +class ModelType(StrEnum): """ Describes the model type the specificity of which will be taken into account during compression. @@ -43,7 +48,7 @@ class ModelType(Enum): @api(canonical_alias="nncf.DropType") -class DropType(Enum): +class DropType(StrEnum): """ Describes the accuracy drop type, which determines how the accuracy drop between the original model and the compressed model is calculated. @@ -59,7 +64,7 @@ class DropType(Enum): @api(canonical_alias="nncf.CompressWeightsMode") -class CompressWeightsMode(Enum): +class CompressWeightsMode(StrEnum): """ Defines a mode for weight compression. :param INT8_SYM: Stands for 8-bit integer symmetric quantization of all weights. @@ -90,7 +95,7 @@ class CompressWeightsMode(Enum): @api(canonical_alias="nncf.SensitivityMetric") -class SensitivityMetric(Enum): +class SensitivityMetric(StrEnum): """ Defines a sensitivity metric for assigning quantization precision to layers. In order to preserve the accuracy of the model, the more sensitive layers receives a higher precision. @@ -117,7 +122,7 @@ class SensitivityMetric(Enum): @api(canonical_alias="nncf.QuantizationMode") -class QuantizationMode(Enum): +class QuantizationMode(StrEnum): """ Defines special modes. Currently contains only FP8-related modes (https://arxiv.org/pdf/2209.05433.pdf). diff --git a/nncf/quantization/__init__.py b/nncf/quantization/__init__.py index 259d925f3e2..a1b78c774e1 100644 --- a/nncf/quantization/__init__.py +++ b/nncf/quantization/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/quantization/advanced_parameters.py b/nncf/quantization/advanced_parameters.py index 7c5c564b349..4a244365d57 100644 --- a/nncf/quantization/advanced_parameters.py +++ b/nncf/quantization/advanced_parameters.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -19,6 +19,7 @@ from enum import Enum from typing import Any, Dict, Optional, Union +import nncf from nncf.common.quantization.structs import QuantizationScheme as QuantizationMode from nncf.common.utils.api_marker import api from nncf.quantization.range_estimator import AggregatorType @@ -330,7 +331,7 @@ def convert_quantization_parameters_to_dict(params: QuantizationParameters) -> D if params.per_channel is not None: result["per_channel"] = params.per_channel if params.narrow_range is not None: - raise RuntimeError("narrow_range parameter is not supported in the legacy format") + raise nncf.ParameterNotSupportedError("narrow_range parameter is not supported in the legacy format") return result @@ -342,7 +343,7 @@ def convert_range_estimator_parameters_to_dict(params: RangeEstimatorParameters) :return: range estimator parameters as dict in the legacy format """ if params.min.clipping_value is not None or params.max.clipping_value is not None: - raise RuntimeError("clipping_value parameter is not supported in the legacy format") + raise nncf.ParameterNotSupportedError("clipping_value parameter is not supported in the legacy format") result = {} if ( @@ -378,7 +379,9 @@ def convert_range_estimator_parameters_to_dict(params: RangeEstimatorParameters) ): return {} else: - raise RuntimeError(f"The following range estimator parameters are not supported: {str(params)}") + raise nncf.ParameterNotSupportedError( + f"The following range estimator parameters are not supported: {str(params)}" + ) return result @@ -438,11 +441,13 @@ def apply_advanced_parameters_to_config( config["initializer"] = initializer if params.bias_correction_params.apply_for_all_nodes: - raise RuntimeError( + raise nncf.ParameterNotSupportedError( "apply_for_all_nodes parameter of the BiasCorrection algorithm is not supported in the legacy format" ) if params.bias_correction_params.threshold is not None: - raise RuntimeError("threshold parameter of the BiasCorrection algorithm is not supported in the legacy format") + raise nncf.ParameterNotSupportedError( + "threshold parameter of the BiasCorrection algorithm is not supported in the legacy format" + ) return config diff --git a/nncf/quantization/algorithms/__init__.py b/nncf/quantization/algorithms/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/quantization/algorithms/__init__.py +++ b/nncf/quantization/algorithms/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/quantization/algorithms/accuracy_control/__init__.py b/nncf/quantization/algorithms/accuracy_control/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/quantization/algorithms/accuracy_control/__init__.py +++ b/nncf/quantization/algorithms/accuracy_control/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/quantization/algorithms/accuracy_control/algorithm.py b/nncf/quantization/algorithms/accuracy_control/algorithm.py index dff34aa601a..dca587395ad 100644 --- a/nncf/quantization/algorithms/accuracy_control/algorithm.py +++ b/nncf/quantization/algorithms/accuracy_control/algorithm.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -12,6 +12,7 @@ import sys from typing import Iterable, List, Optional, Tuple, TypeVar +import nncf from nncf.common.factory import NNCFGraphFactory from nncf.common.graph import NNCFGraph from nncf.common.graph import NNCFNode @@ -49,7 +50,12 @@ def get_algo_backend(backend: BackendType) -> AccuracyControlAlgoBackend: return OVAccuracyControlAlgoBackend() - raise RuntimeError( + if backend == BackendType.ONNX: + from nncf.quantization.algorithms.accuracy_control.onnx_backend import ONNXAccuracyControlAlgoBackend + + return ONNXAccuracyControlAlgoBackend() + + raise nncf.UnsupportedBackendError( f"Cannot create the backend for the accuracy control algorithm because {backend} is not supported." ) @@ -310,6 +316,8 @@ def _apply( quantized_model_graph, self.restore_mode, algo_backend.get_op_with_weights_metatypes(), + algo_backend.is_node_with_weight, + algo_backend.get_weight_tensor_port_ids, ) report.removed_groups.append(current_group) diff --git a/nncf/quantization/algorithms/accuracy_control/backend.py b/nncf/quantization/algorithms/accuracy_control/backend.py index de964936880..d757ef73141 100644 --- a/nncf/quantization/algorithms/accuracy_control/backend.py +++ b/nncf/quantization/algorithms/accuracy_control/backend.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -142,7 +142,7 @@ def is_node_with_weight(node: NNCFNode) -> bool: :param node: The node to check. :param nncf_graph: The NNCF graph. - :return: True` if `node` corresponds to the operation with weights, `False` otherwise. + :return: `True` if `node` corresponds to the operation with weights, `False` otherwise. """ @staticmethod diff --git a/nncf/quantization/algorithms/accuracy_control/evaluator.py b/nncf/quantization/algorithms/accuracy_control/evaluator.py index cda9a5e2c9b..c163ffe3d28 100644 --- a/nncf/quantization/algorithms/accuracy_control/evaluator.py +++ b/nncf/quantization/algorithms/accuracy_control/evaluator.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -12,6 +12,7 @@ from dataclasses import dataclass from typing import Any, Callable, Iterable, List, Optional, Tuple, TypeVar, Union +import nncf from nncf.common.logging import nncf_logger from nncf.common.utils.backend import BackendType from nncf.common.utils.backend import get_backend @@ -124,7 +125,14 @@ def prepare_model(self, model: TModel) -> PreparedModel: return OVPreparedModel(model) - raise NotImplementedError(f"The `prepare_model()` method is not implemented for the {backend} backend.") + if backend == BackendType.ONNX: + from nncf.quantization.algorithms.accuracy_control.onnx_backend import ONNXPreparedModel + + return ONNXPreparedModel(model) + + raise NotImplementedError( + f"The `prepare_model_for_inference()` method is not implemented for the {backend} backend." + ) def validate_prepared_model( self, prepared_model: PreparedModel, dataset: Dataset, indices: Optional[List[int]] = None @@ -221,7 +229,7 @@ def determine_mode( try: metric_value = metric_value if metric_value is None else float(metric_value) except Exception as ex: - raise RuntimeError( + raise nncf.InternalError( f"Metric value of {type(metric_value)} type was returned from the `validation_fn` " "but the float value is expected." ) from ex @@ -254,7 +262,7 @@ def determine_mode( if isinstance(metric_value, float) and (values_for_each_item is None or convert_to_float_possible): metric_mode = True elif values_for_each_item is not None and not isinstance(values_for_each_item[0], list): - raise RuntimeError("Unexpected return value from provided validation function.") + raise nncf.InternalError("Unexpected return value from provided validation function.") return metric_mode diff --git a/nncf/quantization/algorithms/accuracy_control/onnx_backend.py b/nncf/quantization/algorithms/accuracy_control/onnx_backend.py new file mode 100644 index 00000000000..4627ca2487d --- /dev/null +++ b/nncf/quantization/algorithms/accuracy_control/onnx_backend.py @@ -0,0 +1,123 @@ +# Copyright (c) 2023 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import List, Optional + +import numpy as np +import onnx + +from nncf.common.graph import NNCFGraph +from nncf.common.graph import NNCFNode +from nncf.onnx.engine import ONNXEngine +from nncf.onnx.graph.metatypes import onnx_metatypes +from nncf.onnx.graph.metatypes.groups import INPUTS_QUANTIZABLE_OPERATIONS +from nncf.onnx.graph.metatypes.groups import OPERATIONS_WITH_WEIGHTS +from nncf.onnx.graph.metatypes.groups import QUANTIZE_AGNOSTIC_OPERATIONS +from nncf.onnx.graph.metatypes.groups import QUANTIZE_DEQUANTIZE_OPERATIONS +from nncf.onnx.graph.metatypes.onnx_metatypes import ONNXOpMetatype +from nncf.onnx.graph.node_utils import get_bias_value +from nncf.onnx.graph.node_utils import is_node_with_bias +from nncf.onnx.graph.onnx_helper import get_tensor_value +from nncf.quantization.algorithms.accuracy_control.backend import AccuracyControlAlgoBackend +from nncf.quantization.algorithms.accuracy_control.backend import PreparedModel + + +class ONNXPreparedModel(PreparedModel): + """ + Implementation of the `PreparedModel` for ONNX backend. + """ + + def __init__(self, model: onnx.ModelProto): + self._model = model + self._engine = None + + @property + def model_for_inference(self) -> onnx.ModelProto: + return self._model + + @property + def engine(self) -> ONNXEngine: + if self._engine is None: + self._engine = ONNXEngine(self._model) + return self._engine + + +class ONNXAccuracyControlAlgoBackend(AccuracyControlAlgoBackend): + """ + Implementation of the `AccuracyControlAlgoBackend` for ONNX backend. + """ + + # Metatypes + + @staticmethod + def get_op_with_weights_metatypes() -> List[ONNXOpMetatype]: + return OPERATIONS_WITH_WEIGHTS + + @staticmethod + def get_quantizer_metatypes() -> List[ONNXOpMetatype]: + return QUANTIZE_DEQUANTIZE_OPERATIONS + + @staticmethod + def get_const_metatypes() -> List[ONNXOpMetatype]: + return [onnx_metatypes.ONNXConstantMetatype] + + @staticmethod + def get_quantizable_metatypes() -> List[ONNXOpMetatype]: + return INPUTS_QUANTIZABLE_OPERATIONS + + @staticmethod + def get_quantize_agnostic_metatypes() -> List[ONNXOpMetatype]: + return QUANTIZE_AGNOSTIC_OPERATIONS + [onnx_metatypes.ONNXConcatMetatype] + + @staticmethod + def get_shapeof_metatypes() -> List[ONNXOpMetatype]: + return [onnx_metatypes.ONNXShapeMetatype] + + @staticmethod + def get_start_nodes_for_activation_path_tracing(nncf_graph: NNCFGraph) -> List[NNCFNode]: + return nncf_graph.get_input_nodes() + + # Manipulations with bias value and weights + + @staticmethod + def is_node_with_bias(node: NNCFNode, nncf_graph: NNCFGraph) -> bool: + return is_node_with_bias(node) + + @staticmethod + def is_node_with_weight(node: NNCFNode) -> bool: + return node.metatype in OPERATIONS_WITH_WEIGHTS and node.layer_attributes.has_weight() + + @staticmethod + def get_bias_value(node_with_bias: NNCFNode, nncf_graph: NNCFGraph, model: onnx.ModelProto) -> np.ndarray: + return get_bias_value(node_with_bias, model) + + @staticmethod + def get_weight_value(node_with_weight: NNCFNode, model: onnx.ModelProto, port_id: int) -> np.ndarray: + assert node_with_weight.layer_attributes.has_weight() + weight_name = node_with_weight.layer_attributes.weight_attrs[port_id]["name"] + return get_tensor_value(model, weight_name) + + @staticmethod + def get_weight_tensor_port_ids(node: NNCFNode) -> List[Optional[int]]: + return list(node.layer_attributes.weight_attrs.keys()) + + @staticmethod + def get_model_size(model: onnx.ModelProto) -> int: + model_size = 0 + for initializer in model.graph.initializer: + model_size += onnx.numpy_helper.to_array(initializer).nbytes + for node in model.graph.node: + for attr in node.attribute: + if attr.HasField("t"): + model_size += onnx.numpy_helper.to_array(attr.t).nbytes + for t in attr.tensors: + model_size += onnx.numpy_helper.to_array(t).nbytes + return model_size diff --git a/nncf/quantization/algorithms/accuracy_control/openvino_backend.py b/nncf/quantization/algorithms/accuracy_control/openvino_backend.py index b330704d54b..024da19d3f8 100644 --- a/nncf/quantization/algorithms/accuracy_control/openvino_backend.py +++ b/nncf/quantization/algorithms/accuracy_control/openvino_backend.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/quantization/algorithms/accuracy_control/rank_functions.py b/nncf/quantization/algorithms/accuracy_control/rank_functions.py index d0961236f0d..3c2abebc5c6 100644 --- a/nncf/quantization/algorithms/accuracy_control/rank_functions.py +++ b/nncf/quantization/algorithms/accuracy_control/rank_functions.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -13,6 +13,7 @@ import numpy as np +import nncf from nncf.common.utils.backend import BackendType TTensor = TypeVar("TTensor") @@ -28,7 +29,9 @@ def create_normalized_mse_func(backend: BackendType) -> Callable[[List[TTensor], if backend == BackendType.OPENVINO: return normalized_mse - raise RuntimeError(f"Could not create backend-specific implementation! {backend} backend is not supported!") + raise nncf.UnsupportedBackendError( + f"Could not create backend-specific implementation! {backend} backend is not supported!" + ) def normalized_mse(ref_outputs: List[np.ndarray], approx_outputs: List[np.ndarray]) -> float: diff --git a/nncf/quantization/algorithms/accuracy_control/ranker.py b/nncf/quantization/algorithms/accuracy_control/ranker.py index c0627a5d912..15b68b79d70 100644 --- a/nncf/quantization/algorithms/accuracy_control/ranker.py +++ b/nncf/quantization/algorithms/accuracy_control/ranker.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -18,6 +18,7 @@ from nncf.common.graph import NNCFGraph from nncf.common.graph import NNCFNode from nncf.common.logging import nncf_logger +from nncf.common.logging.track_progress import track from nncf.common.quantization.quantizer_removal import find_quantizer_nodes_to_cut from nncf.common.quantization.quantizer_removal import revert_operations_to_floating_point_precision from nncf.common.utils.backend import BackendType @@ -93,13 +94,15 @@ def find_groups_of_quantizers_to_rank(self, quantized_model_graph: NNCFGraph) -> :param quantized_model_graph: Graph for quantized model. :return: List of groups of quantizers to rank. """ + quantizer_metatypes = self._algo_backend.get_quantizer_metatypes() + + if len(quantizer_metatypes) == 2: # Quantize-Dequantize case + # Use only Quantize metatype + quantizer_metatypes = quantizer_metatypes[:1] + groups_to_rank = [] processed = {} - quantizers = [ - x - for x in quantized_model_graph.topological_sort() - if x.metatype in self._algo_backend.get_quantizer_metatypes() - ] + quantizers = [x for x in quantized_model_graph.topological_sort() if x.metatype in quantizer_metatypes] quantized_model_graph_without_shapeof = remove_shapeof_subgraphs( deepcopy(quantized_model_graph), @@ -155,7 +158,6 @@ def rank_groups_of_quantizers( self._ranking_fn, ) - nncf_logger.info("Calculating ranking score for groups of quantizers") with timer(): # Calculate ranking score for groups of quantizers. if self._num_workers > 1: @@ -190,7 +192,7 @@ def _sequential_calculation_ranking_score( reference_values_for_each_item: Union[List[float], List[List[TTensor]]], ): ranking_scores = [] # ranking_scores[i] is the ranking score for groups_to_rank[i] - for current_group in groups_to_rank: + for current_group in track(groups_to_rank, description="Calculating ranking scores"): modified_model = revert_operations_to_floating_point_precision( current_group.operations, current_group.quantizers, @@ -198,6 +200,8 @@ def _sequential_calculation_ranking_score( quantized_model_graph, self._restore_mode, self._algo_backend.get_op_with_weights_metatypes(), + self._algo_backend.is_node_with_weight, + self._algo_backend.get_weight_tensor_port_ids, ) prepared_model = self._evaluator.prepare_model(modified_model) @@ -219,6 +223,7 @@ def _multithreading_calculation_ranking_score( ranking_scores = [] # ranking_scores[i] is the ranking score for groups_to_rank[i] prepared_model_queue = [] executor = ThreadPoolExecutor(max_workers=self._num_workers) + nncf_logger.info("Calculating ranking scores") for idx, current_group in enumerate(groups_to_rank): modified_model = revert_operations_to_floating_point_precision( current_group.operations, @@ -227,6 +232,8 @@ def _multithreading_calculation_ranking_score( quantized_model_graph, self._restore_mode, self._algo_backend.get_op_with_weights_metatypes(), + self._algo_backend.is_node_with_weight, + self._algo_backend.get_weight_tensor_port_ids, ) prepared_model_queue.append(executor.submit(self._evaluator.prepare_model, modified_model)) diff --git a/nncf/quantization/algorithms/accuracy_control/subset_selection.py b/nncf/quantization/algorithms/accuracy_control/subset_selection.py index 0715487f248..b4c73eaf469 100644 --- a/nncf/quantization/algorithms/accuracy_control/subset_selection.py +++ b/nncf/quantization/algorithms/accuracy_control/subset_selection.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/quantization/algorithms/algorithm.py b/nncf/quantization/algorithms/algorithm.py index b2bca79a31e..befe0a82f9d 100644 --- a/nncf/quantization/algorithms/algorithm.py +++ b/nncf/quantization/algorithms/algorithm.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/quantization/algorithms/bias_correction/__init__.py b/nncf/quantization/algorithms/bias_correction/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/quantization/algorithms/bias_correction/__init__.py +++ b/nncf/quantization/algorithms/bias_correction/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/quantization/algorithms/bias_correction/algorithm.py b/nncf/quantization/algorithms/bias_correction/algorithm.py index cf5ecf9fd57..4dc605c85e6 100644 --- a/nncf/quantization/algorithms/bias_correction/algorithm.py +++ b/nncf/quantization/algorithms/bias_correction/algorithm.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -14,12 +14,14 @@ import numpy as np +import nncf from nncf import Dataset from nncf import nncf_logger from nncf.common.factory import EngineFactory from nncf.common.factory import ModelTransformerFactory from nncf.common.factory import NNCFGraphFactory from nncf.common.graph import NNCFGraph +from nncf.common.graph import NNCFGraphEdge from nncf.common.graph import NNCFNode from nncf.common.graph.definitions import NNCFGraphNodeType from nncf.common.graph.transformations.commands import TargetType @@ -100,7 +102,7 @@ def __init__( self._algorithm_key = f"BC_{hash(self)}" if self.apply_for_all_nodes: - raise RuntimeError("BiasCorrection algorithm does not support apply_for_all_nodes=True yet") + raise nncf.InternalError("BiasCorrection algorithm does not support apply_for_all_nodes=True yet") @property def available_backends(self) -> List[BackendType]: @@ -122,7 +124,7 @@ def _set_backend_entity(self, model: TModel) -> None: self._backend_entity = OVBiasCorrectionAlgoBackend() else: - raise RuntimeError( + raise nncf.UnsupportedBackendError( "Cannot return backend-specific entity because {} is not supported!".format(model_backend.value) ) @@ -144,9 +146,7 @@ def apply( nodes_with_bias = [] for node in nncf_graph.topological_sort(): - if self._backend_entity.is_node_with_bias(node, nncf_graph) and self._backend_entity.is_quantized_weights( - node, nncf_graph - ): + if self._is_node_correctable(node, nncf_graph): nodes_with_bias.append(node) # We pre-collect information about the subgraph we need in order @@ -204,6 +204,18 @@ def apply( return main_model_transformer.transform(main_transformations_layout) + def _is_node_correctable(self, node: NNCFNode, nncf_graph: NNCFGraph) -> bool: + """ + Verify if node bias can be corrected or not. + + :param node: NNCFNode instance to verify. + :param nncf_graph: NNCFGraph instance. + :return: Boolean value as the result. + """ + return self._backend_entity.is_node_with_bias(node, nncf_graph) and self._backend_entity.is_quantized_weights( + node, nncf_graph + ) + def _get_subgraph_data_for_node(self, node: NNCFNode, nncf_graph: NNCFGraph) -> Dict[str, List[str]]: """ This method collects necessary data for the specified node and its subgraph. @@ -214,69 +226,74 @@ def _get_subgraph_data_for_node(self, node: NNCFNode, nncf_graph: NNCFGraph) -> :param nncf_graph: NNCFGraph instance for graph analysis. :return: A dict with the list of the nodes for the subgraph input and statistics collection. """ - statistic_nodes, subgraph_input_nodes, subgraph_output_nodes, subgraph_output_ids = [], [], [], [] + statistic_nodes = [] + subgraph_input_ids, subgraph_output_ids = [], [] - def fill_statistic_nodes(node): - # A small hack to speed up graph traversal. - if node in statistic_nodes or node in visited_nodes: + def fill_subgraph_output_ids(edge: NNCFGraphEdge): + node = edge.to_node + if node in visited_nodes: return visited_nodes.append(node) + input_id = (node.node_name, edge.input_port_id) + output_id = (edge.from_node.node_name, edge.output_port_id) + # If we found a node with bias, we have to collect it as a statistic node, # and its input for _collected_stat_inputs_map, # which will be used during the collection of statistics for the next node. - if self._backend_entity.is_node_with_bias(node, nncf_graph) and self._backend_entity.is_quantized_weights( - node, nncf_graph - ): - statistic_nodes.append(node) - activation_node, output_port_id = self._get_activation_node_and_port(node, nncf_graph) - subgraph_output_nodes.append(activation_node) - - output_id = (activation_node.node_name, output_port_id) + if self._is_node_correctable(node, nncf_graph): subgraph_output_ids.append(output_id) - self._collected_stat_inputs_map[node.node_name] = output_id + self._collected_stat_inputs_map[input_id] = output_id + + statistic_nodes.append(edge.from_node) return - for next_node in nncf_graph.get_next_nodes(node): - fill_statistic_nodes(next_node) + for output_edge in nncf_graph.get_output_edges(node): + fill_subgraph_output_ids(output_edge) - def fill_subgraph_input_nodes(node): - # A small hack to speed up graph traversal. - if node in subgraph_input_nodes or node in visited_nodes: - return - visited_nodes.append(node) + def fill_subgraph_input_ids(edge: NNCFGraphEdge): + node = edge.from_node + input_id = (edge.to_node.node_name, edge.input_port_id) # Since we need to find the inputs for the subgraph, # we can take only those layers for which we have already collected statistics. - if node.node_name in self._collected_stat_inputs_map and node not in statistic_nodes: - subgraph_input_nodes.append(node) + if input_id in self._collected_stat_inputs_map: + activation_id = self._collected_stat_inputs_map[input_id] + if activation_id not in subgraph_output_ids: + subgraph_input_ids.append(input_id) + return + + if node in visited_nodes: return + visited_nodes.append(node) - for previous_node in nncf_graph.get_previous_nodes(node): - fill_subgraph_input_nodes(previous_node) + for input_edge in nncf_graph.get_input_edges(node): + fill_subgraph_input_ids(input_edge) # First, we need to find out the nodes with bias that follow by main node. # To collect statistics for next nodes. visited_nodes = [] - for next_node in nncf_graph.get_next_nodes(node): - fill_statistic_nodes(next_node) + for output_edge in nncf_graph.get_output_edges(node): + fill_subgraph_output_ids(output_edge) # We then need to find nodes for which statistics have already been collected, # to use them as inputs for the subgraph. statistic_nodes = statistic_nodes if statistic_nodes else nncf_graph.get_next_nodes(node) visited_nodes = [] for stat_node in statistic_nodes: - fill_subgraph_input_nodes(stat_node) + for input_edge in nncf_graph.get_input_edges(stat_node): + fill_subgraph_input_ids(input_edge) + + if not subgraph_output_ids: + for edge in nncf_graph.get_output_edges(node): + next_node_name = edge.to_node.node_name + if NNCFGraphNodeType.OUTPUT_NODE not in next_node_name: + subgraph_output_ids.append((edge.to_node.node_name, OUTPUT_PORT_OF_NODE)) # In case the outputs were not found during the collection of statistics nodes, # we use the latter as the outputs of the subgraph. - subgraph_output_nodes = subgraph_output_nodes if subgraph_output_nodes else statistic_nodes - subgraph_output_names = [ - n.node_name for n in subgraph_output_nodes if NNCFGraphNodeType.OUTPUT_NODE not in n.node_name - ] subgraph_data = { - "subgraph_input_names": set(n.node_name for n in subgraph_input_nodes), - "subgraph_output_names": set(subgraph_output_names), + "subgraph_input_ids": set(subgraph_input_ids), "subgraph_output_ids": set(subgraph_output_ids), } @@ -293,7 +310,7 @@ def _prepare_subgraph(self, node: NNCFNode, model: TModel, nncf_graph: NNCFGraph :return: Backend-specific subgraph extracted from the model. """ extracted_model = self.extract_model( - model, subgraph_data["subgraph_input_names"], subgraph_data["subgraph_output_names"] + model, subgraph_data["subgraph_input_ids"], subgraph_data["subgraph_output_ids"] ) transformation_layout = TransformationLayout() @@ -322,20 +339,20 @@ def _create_feed_dicts( statistics_size = self.subset_size statistics_per_input = {} - for input_node_name in subgraph_data["subgraph_input_names"]: - input_tensor_name = self._backend_entity.get_input_name(model, input_node_name) - activation_name, port_id = self._collected_stat_inputs_map[input_node_name] - input_fp = self._get_fp_inputs(statistic_points, node_name=activation_name, port_id=port_id) + for input_node_name, input_port_id in subgraph_data["subgraph_input_ids"]: + input_tensor_name = self._backend_entity.get_input_name(model, input_node_name, input_port_id) + activation_name, output_port_id = self._collected_stat_inputs_map[(input_node_name, input_port_id)] + input_fp = self._get_fp_inputs(statistic_points, node_name=activation_name, port_id=output_port_id) statistics_per_input[input_tensor_name] = input_fp statistics_size = min(statistics_size, len(input_fp)) for stat_id in range(statistics_size): feed_dict = {} - for input_node_name in subgraph_data["subgraph_input_names"]: - input_tensor_name = self._backend_entity.get_input_name(model, input_node_name) + for input_node_name, input_port_id in subgraph_data["subgraph_input_ids"]: + input_tensor_name = self._backend_entity.get_input_name(model, input_node_name, input_port_id) # Since we do not use as inputs the layers from which the statistics are gathered, # but those that follow them, we need to take this into account when creating feed dicts. - activation_name, port_id = self._collected_stat_inputs_map[input_node_name] + activation_name, _ = self._collected_stat_inputs_map[(input_node_name, input_port_id)] feed_dict[input_tensor_name] = statistics_per_input[input_tensor_name][stat_id] feed_dicts.append(feed_dict) return feed_dicts @@ -375,8 +392,12 @@ def _get_bias_shift_magnitude(current_bias_value: np.ndarray, updated_bias_value :return: Magnitude between original and updated bias values. """ bias_shift_magnitude = np.inf - if np.count_nonzero(current_bias_value == 0) == 0: - bias_shift_magnitude = np.max(np.abs((updated_bias_value - current_bias_value) / current_bias_value)) + bias_shift_magnitude = np.max( + np.abs( + (updated_bias_value - current_bias_value) + / (current_bias_value + np.finfo(current_bias_value.dtype).min) + ) + ) return bias_shift_magnitude def _correct_bias(self, model: TModel, bias_correction_command: TransformationCommand) -> TModel: @@ -419,12 +440,12 @@ def _remove_unnecessary_stats(self, position: int, subgraphs_data: Dict[str, Dic # Collects list of the statistics that needed for the future layers. needed_stats_list = [] for i in range(position + 1, len(subgraphs_data)): - input_names = subgraphs_data[i]["subgraph_input_names"] - needed_stats_list.extend([self._collected_stat_inputs_map[name][0] for name in input_names]) + input_ids = subgraphs_data[i]["subgraph_input_ids"] + needed_stats_list.extend([self._collected_stat_inputs_map[input_id][0] for input_id in input_ids]) - node_inputs_name = subgraphs_data[position]["subgraph_input_names"] - for node_input_name in node_inputs_name: - activation_name, port_id = self._collected_stat_inputs_map[node_input_name] + node_inputs_ids = subgraphs_data[position]["subgraph_input_ids"] + for node_input_id in node_inputs_ids: + activation_name, port_id = self._collected_stat_inputs_map[node_input_id] input_id = (activation_name, port_id) if activation_name not in needed_stats_list and input_id in self._fp_inputs: nncf_logger.debug(f"Dropped {activation_name} output statistics.") @@ -487,14 +508,12 @@ def output_filter_func(point): def get_statistic_points(self, model: TModel, graph: NNCFGraph) -> StatisticPointsContainer: self._set_backend_entity(model) - model_copy = self._backend_entity.remove_fq_from_inputs(copy_model(model), graph) - nncf_graph = NNCFGraphFactory.create(model_copy) statistic_container = StatisticPointsContainer() nodes_with_bias = [ - node for node in nncf_graph.topological_sort() if self._backend_entity.is_node_with_bias(node, nncf_graph) + node for node in graph.topological_sort() if self._backend_entity.is_node_with_bias(node, graph) ] - model_inputs = nncf_graph.get_input_nodes() + model_inputs = graph.get_input_nodes() # Collection of statistics after layers where biases will be corrected. for node in nodes_with_bias: @@ -515,16 +534,22 @@ def get_statistic_points(self, model: TModel, graph: NNCFGraph) -> StatisticPoin ) # We must collect the nodes with biases following the model inputs. - biased_after_input_nodes = self._get_biased_after_nodes(nncf_graph, model_inputs, model_copy) + biased_after_input_nodes = self._get_biased_after_nodes(graph, model_inputs, model) for biased_after_input_node in biased_after_input_nodes: # We need to collect activation input to register it for the biased layer as the layer with statistics. - activation_node, output_port_id = self._get_activation_node_and_port(biased_after_input_node, nncf_graph) - activation_node_name = activation_node.node_name + activation_port = self._backend_entity.get_activation_port_id(node, graph) + edge = graph.get_input_edges(biased_after_input_node)[activation_port] + + input_id = (biased_after_input_node.node_name, edge.input_port_id) + output_id = (edge.from_node.node_name, edge.output_port_id) + + if edge.from_node.node_name in statistic_container: + continue - self._collected_stat_inputs_map[biased_after_input_node.node_name] = (activation_node_name, output_port_id) + self._collected_stat_inputs_map[input_id] = output_id statistic_point = self._backend_entity.target_point( - TargetType.POST_LAYER_OPERATION, activation_node_name, port_id=output_port_id + TargetType.POST_LAYER_OPERATION, edge.from_node.node_name, port_id=edge.output_port_id ) stat_collector = self._backend_entity.raw_statistic_collector(num_samples=self.subset_size) statistic_container.add_statistic_point( @@ -535,12 +560,15 @@ def get_statistic_points(self, model: TModel, graph: NNCFGraph) -> StatisticPoin # Then we need also to collect model input statistics to prevent cases when nodes with bias have no input data. for input_node in model_inputs: - # We assume that input node has only one output port - input_name = input_node.node_name - if input_name in statistic_container: + # We need to map all input node outputs as nodes with statistic + for edge in graph.get_output_edges(input_node): + input_id = (edge.to_node.node_name, edge.input_port_id) + output_id = (input_node.node_name, edge.output_port_id) + self._collected_stat_inputs_map[input_id] = output_id + + if input_node.node_name in statistic_container: continue - for next_layer in nncf_graph.get_next_nodes(input_node): - self._collected_stat_inputs_map[next_layer.node_name] = (input_node.node_name, OUTPUT_PORT_OF_NODE) + statistic_point = self._backend_entity.target_point( TargetType.POST_LAYER_OPERATION, input_node.node_name, port_id=OUTPUT_PORT_OF_NODE ) @@ -553,20 +581,6 @@ def get_statistic_points(self, model: TModel, graph: NNCFGraph) -> StatisticPoin return statistic_container - def _get_activation_node_and_port(self, node: NNCFNode, nncf_graph: NNCFGraph) -> Tuple[NNCFNode, int]: - """ - This method returns the activation layer and corresponding port id for the node. - - :param node: NNCFGraph node for which the activation is sought. - :param nncf_graph: NNCFGraph instance with the node. - :return: Tuple with the activation node and port id. - """ - activation_port = self._backend_entity.get_activation_port_id(node, nncf_graph) - activation_edge = nncf_graph.get_input_edges(node)[activation_port] - activation_node = activation_edge.from_node - port_id = activation_edge.output_port_id - return activation_node, port_id - def _get_biased_after_nodes(self, nncf_graph: NNCFGraph, nodes: List[NNCFNode], model: TModel) -> List[NNCFNode]: """ This method finds and returns nodes with the bias in the model that follows after the input nodes. @@ -611,17 +625,19 @@ def traverse_to_biased(node, condition_container): return list(biased_nodes - dependant_nodes) - def extract_model(self, model: TModel, input_node_names: List[str], output_node_names: List[str]) -> TModel: + def extract_model( + self, model: TModel, input_node_ids: List[Tuple[str, int]], output_node_ids: List[Tuple[str, int]] + ) -> TModel: """ Returns the backend-specific model that bounded by the specified input & output layers. :param model: Backend-specific model. - :param input_node_names: List with the input node names. - :param output_node_names: List with the output node names. + :param input_node_ids: List with the input node IDs. + :param output_node_ids: List with the output node IDs. :return: Extracted backend-specific model. """ transformation_layout = TransformationLayout() model_transformer = ModelTransformerFactory.create(model) - model_extraction_command = self._backend_entity.model_extraction_command(input_node_names, output_node_names) + model_extraction_command = self._backend_entity.model_extraction_command(input_node_ids, output_node_ids) transformation_layout.register(model_extraction_command) return model_transformer.transform(transformation_layout) diff --git a/nncf/quantization/algorithms/bias_correction/backend.py b/nncf/quantization/algorithms/bias_correction/backend.py index 8f1368436aa..5aca3240bc5 100644 --- a/nncf/quantization/algorithms/bias_correction/backend.py +++ b/nncf/quantization/algorithms/bias_correction/backend.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -11,7 +11,7 @@ from abc import ABC from abc import abstractmethod -from typing import List, Optional, TypeVar +from typing import List, Optional, Tuple, TypeVar import numpy as np @@ -60,12 +60,16 @@ def create_bias_correction_command(node: NNCFNode, bias_value: np.ndarray) -> Tr @staticmethod @abstractmethod - def model_extraction_command(inputs: List[str], outputs: List[str]) -> TransformationCommand: + def model_extraction_command( + input_ids: List[Tuple[str, int]], output_ids: List[Tuple[str, int]] + ) -> TransformationCommand: """ Returns backend-specific command to extract sub-model based on input & output names. - :param inputs: List of the input names for sub-model beginning. - :param outputs: List of the output names for sub-model end. + :param input_ids: List of the input IDs: pairs of node names and correspondent input port ids. + Each pair denotes the sub-graph beginning. + :param output_ids: List of the output IDs: pairs of node names and correspondent output port ids. + Each pair denotes the sub-graph ending. :return: Backend-specific TransformationCommand for the model extraction. """ @@ -147,24 +151,25 @@ def get_bias_value(node: NNCFNode, model: TModel, nncf_graph: NNCFGraph) -> np.n @staticmethod @abstractmethod - def get_input_name(model: TModel, node_name: str) -> str: + def get_input_name(model: TModel, node_name: str, input_port_id: int) -> str: """ Returns input tensor name for the specific node. :param model: Backend-specific model for the initializer finding. :param node_name: Name of the backend-specific node. + :param input_port_id: Port Id for input. :return: Input tensor name. """ @staticmethod @abstractmethod - def get_output_name(model: TModel, node_name: str, output_id: int) -> str: + def get_output_name(model: TModel, node_name: str, output_port_id: int) -> str: """ Returns output tensor name for the specific node. :param model: Backend-specific model. :param node_name: Name of the backend-specific node. - :param output_id: Port Id for output. + :param output_port_id: Port Id for output. :return: Output tensor name. """ diff --git a/nncf/quantization/algorithms/bias_correction/onnx_backend.py b/nncf/quantization/algorithms/bias_correction/onnx_backend.py index 71d7dcbd4f8..6636d6b9648 100644 --- a/nncf/quantization/algorithms/bias_correction/onnx_backend.py +++ b/nncf/quantization/algorithms/bias_correction/onnx_backend.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -17,20 +17,20 @@ from nncf.common.graph import NNCFGraph from nncf.common.graph import NNCFNode from nncf.common.graph.transformations.commands import TargetType +from nncf.experimental.common.tensor_statistics.collectors import TensorCollector from nncf.onnx.graph.model_utils import remove_fq_from_inputs from nncf.onnx.graph.node_utils import get_bias_value from nncf.onnx.graph.node_utils import is_any_weight_quantized from nncf.onnx.graph.node_utils import is_node_with_bias from nncf.onnx.graph.onnx_helper import get_name_to_node_map from nncf.onnx.graph.transformations.command_creation import create_bias_correction_command -from nncf.onnx.graph.transformations.commands import ONNXBiasCorrectionCommand +from nncf.onnx.graph.transformations.commands import ONNXInitializerUpdateCommand from nncf.onnx.graph.transformations.commands import ONNXModelExtractionCommand -from nncf.onnx.graph.transformations.commands import ONNXNullBiasInsertionCommand from nncf.onnx.graph.transformations.commands import ONNXOutputInsertionCommand from nncf.onnx.graph.transformations.commands import ONNXTargetPoint -from nncf.onnx.statistics.collectors import ONNXMeanStatisticCollector from nncf.onnx.statistics.collectors import ONNXNNCFCollectorTensorProcessor -from nncf.onnx.statistics.collectors import ONNXRawStatisticCollector +from nncf.onnx.statistics.collectors import get_mean_statistic_collector +from nncf.onnx.statistics.collectors import get_raw_stat_collector from nncf.onnx.tensor import ONNXNNCFTensor from nncf.quantization.algorithms.bias_correction.backend import BiasCorrectionAlgoBackend @@ -40,10 +40,6 @@ class ONNXBiasCorrectionAlgoBackend(BiasCorrectionAlgoBackend): def tensor_processor(self) -> ONNXNNCFCollectorTensorProcessor: return ONNXNNCFCollectorTensorProcessor - @property - def types_to_insert_bias(self): - return [] - @staticmethod def target_point(target_type: TargetType, target_node_name: str, port_id: int) -> ONNXTargetPoint: return ONNXTargetPoint(target_type, target_node_name, port_id) @@ -51,16 +47,14 @@ def target_point(target_type: TargetType, target_node_name: str, port_id: int) - @staticmethod def create_bias_correction_command( node: NNCFNode, bias_value: np.ndarray, nncf_graph: NNCFGraph - ) -> ONNXBiasCorrectionCommand: + ) -> ONNXInitializerUpdateCommand: return create_bias_correction_command(node, bias_value) @staticmethod - def model_extraction_command(inputs: List[str], outputs: List[str]) -> ONNXModelExtractionCommand: - return ONNXModelExtractionCommand(inputs, outputs) - - @staticmethod - def create_bias_insertion_command(node: NNCFNode) -> ONNXNullBiasInsertionCommand: - return ONNXNullBiasInsertionCommand(node) + def model_extraction_command( + input_ids: List[Tuple[str, int]], output_ids: List[Tuple[str, int]] + ) -> ONNXModelExtractionCommand: + return ONNXModelExtractionCommand(input_ids, output_ids) @staticmethod def output_insertion_command(nncf_graph: NNCFGraph, target_point: ONNXTargetPoint) -> ONNXOutputInsertionCommand: @@ -76,12 +70,12 @@ def mean_statistic_collector( inplace: bool, num_samples: Optional[int] = None, window_size: Optional[int] = None, - ) -> ONNXMeanStatisticCollector: - return ONNXMeanStatisticCollector(channel_axis, num_samples, window_size) + ) -> TensorCollector: + return get_mean_statistic_collector(num_samples, channel_axis, window_size, inplace) @staticmethod - def raw_statistic_collector(num_samples: Optional[int] = None) -> ONNXMeanStatisticCollector: - return ONNXRawStatisticCollector(num_samples) + def raw_statistic_collector(num_samples: int = None) -> TensorCollector: + return get_raw_stat_collector(num_samples) @staticmethod def process_model_output(raw_data: Dict, output_name: str) -> ONNXNNCFTensor: @@ -96,14 +90,14 @@ def get_bias_value(node: NNCFNode, model: onnx.ModelProto, nncf_graph: NNCFGraph return get_bias_value(node, model) @staticmethod - def get_input_name(model: onnx.ModelProto, node_name: str) -> str: + def get_input_name(model: onnx.ModelProto, node_name: str, input_port_id: int) -> str: node_mapping = get_name_to_node_map(model) - return node_mapping[node_name].input[0] + return node_mapping[node_name].input[input_port_id] @staticmethod - def get_output_name(model: onnx.ModelProto, node_name: str, output_id: int) -> List[str]: + def get_output_name(model: onnx.ModelProto, node_name: str, output_port_id: int) -> str: node_mapping = get_name_to_node_map(model) - return node_mapping[node_name].output[output_id] + return node_mapping[node_name].output[output_port_id] @staticmethod def is_quantized_weights(node: NNCFNode, nncf_graph: NNCFGraph) -> bool: diff --git a/nncf/quantization/algorithms/bias_correction/openvino_backend.py b/nncf/quantization/algorithms/bias_correction/openvino_backend.py index cae12b535ee..4fbc2d5348f 100644 --- a/nncf/quantization/algorithms/bias_correction/openvino_backend.py +++ b/nncf/quantization/algorithms/bias_correction/openvino_backend.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -9,7 +9,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -from typing import Dict, List, Optional +from typing import Dict, List, Optional, Tuple import numpy as np import openvino.runtime as ov @@ -21,6 +21,8 @@ from nncf.openvino.graph.metatypes.groups import FAKE_QUANTIZE_OPERATIONS from nncf.openvino.graph.model_utils import remove_fq_from_inputs from nncf.openvino.graph.node_utils import get_bias_value +from nncf.openvino.graph.node_utils import get_parameter_node_name +from nncf.openvino.graph.node_utils import get_result_node_name from nncf.openvino.graph.node_utils import is_node_with_bias from nncf.openvino.graph.transformations.command_creation import OVCommandCreator from nncf.openvino.graph.transformations.commands import OVBiasCorrectionCommand @@ -50,8 +52,10 @@ def create_bias_correction_command( return OVCommandCreator.create_command_to_update_bias(node, bias_value, nncf_graph) @staticmethod - def model_extraction_command(inputs: List[str], outputs: List[str]) -> OVModelExtractionCommand: - return OVModelExtractionCommand(inputs, outputs) + def model_extraction_command( + input_ids: List[Tuple[str, int]], output_ids: List[Tuple[str, int]] + ) -> OVModelExtractionCommand: + return OVModelExtractionCommand(input_ids, output_ids) @staticmethod def output_insertion_command(nncf_graph: NNCFGraph, target_point: OVTargetPoint) -> OVOutputInsertionCommand: @@ -88,31 +92,12 @@ def get_bias_value(node: NNCFNode, model: ov.Model, nncf_graph: NNCFGraph) -> np return get_bias_value(node, nncf_graph, model) @staticmethod - def get_input_name(model: ov.Model, node_name: str) -> str: - ops_dict = {op.get_friendly_name(): op for op in model.get_ops()} - - model_input_names = [] - for tensor in model.inputs: - model_input_names.extend(tensor.get_names()) - if node_name in model_input_names: - return node_name - - for input_port in ops_dict[node_name].inputs(): - input_node = input_port.get_source_output().get_node() - if input_node.get_type_name() == "Parameter": - return input_port.get_tensor().get_any_name() - raise RuntimeError(f"Input layer not found for {node_name}") + def get_input_name(model: ov.Model, node_name: str, input_port_id: int) -> str: + return get_parameter_node_name(node_name, input_port_id) @staticmethod - def get_output_name(model: ov.Model, node_name: str, output_id: int) -> str: - ops_dict = {op.get_friendly_name(): op for op in model.get_ops()} - - output_port = ops_dict[node_name].output(output_id) - for output_input_port in output_port.get_target_inputs(): - output_node = output_input_port.get_node() - if output_node.get_type_name() == "Result": - return output_port.get_any_name() - raise RuntimeError(f"Output layer not found for {node_name}") + def get_output_name(model: ov.Model, node_name: str, output_port_id: int) -> str: + return get_result_node_name(node_name, output_port_id) @staticmethod def is_quantized_weights(node: NNCFNode, nncf_graph: NNCFGraph) -> bool: diff --git a/nncf/quantization/algorithms/channel_alignment/__init__.py b/nncf/quantization/algorithms/channel_alignment/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/quantization/algorithms/channel_alignment/__init__.py +++ b/nncf/quantization/algorithms/channel_alignment/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/quantization/algorithms/channel_alignment/algorithm.py b/nncf/quantization/algorithms/channel_alignment/algorithm.py index 46754c903a8..c8582cef551 100644 --- a/nncf/quantization/algorithms/channel_alignment/algorithm.py +++ b/nncf/quantization/algorithms/channel_alignment/algorithm.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/quantization/algorithms/channel_alignment/backend.py b/nncf/quantization/algorithms/channel_alignment/backend.py index 9ab51977d4f..efdfbb7be2f 100644 --- a/nncf/quantization/algorithms/channel_alignment/backend.py +++ b/nncf/quantization/algorithms/channel_alignment/backend.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/quantization/algorithms/channel_alignment/openvino_backend.py b/nncf/quantization/algorithms/channel_alignment/openvino_backend.py index a92a86bb0ae..83512ccd4a4 100644 --- a/nncf/quantization/algorithms/channel_alignment/openvino_backend.py +++ b/nncf/quantization/algorithms/channel_alignment/openvino_backend.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -14,6 +14,7 @@ import numpy as np import openvino.runtime as ov +import nncf from nncf.common.graph import NNCFGraph from nncf.common.graph import NNCFNode from nncf.common.graph.layer_attributes import ConvolutionLayerAttributes @@ -110,7 +111,7 @@ def get_dims_descriptor(node: NNCFNode) -> LayoutDescriptor: elif node.metatype == OVMatMulMetatype: weights_layout = get_linear_weights_layout_from_node(node=node) else: - raise RuntimeError( + raise nncf.InternalError( f"Metatype {node.metatype} of node {node.node_name} dimensions description retrieving is not supported" ) diff --git a/nncf/quantization/algorithms/fast_bias_correction/__init__.py b/nncf/quantization/algorithms/fast_bias_correction/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/quantization/algorithms/fast_bias_correction/__init__.py +++ b/nncf/quantization/algorithms/fast_bias_correction/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/quantization/algorithms/fast_bias_correction/algorithm.py b/nncf/quantization/algorithms/fast_bias_correction/algorithm.py index 821f7475d81..15f9a2f05b1 100644 --- a/nncf/quantization/algorithms/fast_bias_correction/algorithm.py +++ b/nncf/quantization/algorithms/fast_bias_correction/algorithm.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -12,6 +12,7 @@ from math import inf from typing import Any, Dict, List, Optional, Tuple, TypeVar, Union +import nncf from nncf import Dataset from nncf.common.factory import EngineFactory from nncf.common.factory import ModelTransformerFactory @@ -88,7 +89,7 @@ def __init__( self._algorithm_key = f"FBC_{hash(self)}" if self.apply_for_all_nodes: - raise RuntimeError("FastBiasCorrection algorithm does not support apply_for_all_nodes=True yet") + raise nncf.InternalError("FastBiasCorrection algorithm does not support apply_for_all_nodes=True yet") @property def available_backends(self) -> List[BackendType]: @@ -116,7 +117,7 @@ def _set_backend_entity(self, model: TModel) -> None: self._backend_entity = PTFastBiasCorrectionAlgoBackend() else: - raise RuntimeError( + raise nncf.UnsupportedBackendError( "Cannot return backend-specific entity because {} is not supported!".format(model_backend.value) ) @@ -274,7 +275,7 @@ def _extract_submodel(self, model_transformer: ModelTransformer, node_name: str) :param node_name: Name of the node that should be a center of the sub-model. :return: Backend-specific sub-model. """ - model_extraction_command = self._backend_entity.model_extraction_command([node_name], [node_name]) + model_extraction_command = self._backend_entity.model_extraction_command([(node_name, 0)], [(node_name, 0)]) me_transformation_layout = TransformationLayout() me_transformation_layout.register(model_extraction_command) extracted_model = model_transformer.transform(me_transformation_layout) diff --git a/nncf/quantization/algorithms/fast_bias_correction/backend.py b/nncf/quantization/algorithms/fast_bias_correction/backend.py index 057c012f868..49e88a643d1 100644 --- a/nncf/quantization/algorithms/fast_bias_correction/backend.py +++ b/nncf/quantization/algorithms/fast_bias_correction/backend.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -57,12 +57,16 @@ def create_bias_correction_command( @staticmethod @abstractmethod - def model_extraction_command(inputs: List[str], outputs: List[str]) -> TransformationCommand: + def model_extraction_command( + input_ids: List[Tuple[str, int]], output_ids: List[Tuple[str, int]] + ) -> TransformationCommand: """ Returns backend-specific command to extract sub-model based on input & output names. - :param inputs: List of the input names for sub-model beginning. - :param outputs: List of the output names for sub-model end. + :param input_ids: List of the input IDs: pairs of node names and correspondent input port ids. + Each pair denotes the sub-graph beginning. + :param output_ids: List of the output IDs: pairs of node names and correspondent output port ids. + Each pair denotes the sub-graph ending. :return: Backend-specific TransformationCommand for the model extraction. """ diff --git a/nncf/quantization/algorithms/fast_bias_correction/onnx_backend.py b/nncf/quantization/algorithms/fast_bias_correction/onnx_backend.py index 1ae51fcbfe7..f9f2e98d125 100644 --- a/nncf/quantization/algorithms/fast_bias_correction/onnx_backend.py +++ b/nncf/quantization/algorithms/fast_bias_correction/onnx_backend.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -17,41 +17,35 @@ from nncf.common.graph import NNCFGraph from nncf.common.graph import NNCFNode from nncf.common.graph.transformations.commands import TargetType +from nncf.experimental.common.tensor_statistics.collectors import TensorCollector from nncf.experimental.tensor import Tensor from nncf.onnx.graph.node_utils import get_bias_value from nncf.onnx.graph.node_utils import is_any_weight_quantized from nncf.onnx.graph.node_utils import is_node_with_bias from nncf.onnx.graph.transformations.command_creation import create_bias_correction_command -from nncf.onnx.graph.transformations.commands import ONNXBiasCorrectionCommand +from nncf.onnx.graph.transformations.commands import ONNXInitializerUpdateCommand from nncf.onnx.graph.transformations.commands import ONNXModelExtractionCommand -from nncf.onnx.graph.transformations.commands import ONNXNullBiasInsertionCommand from nncf.onnx.graph.transformations.commands import ONNXTargetPoint -from nncf.onnx.statistics.collectors import ONNXMeanStatisticCollector +from nncf.onnx.statistics.collectors import get_mean_statistic_collector from nncf.quantization.algorithms.fast_bias_correction.backend import FastBiasCorrectionAlgoBackend class ONNXFastBiasCorrectionAlgoBackend(FastBiasCorrectionAlgoBackend): - @property - def types_to_insert_bias(self): - return [] - @staticmethod def target_point(target_type: TargetType, target_node_name: str, port_id: int) -> ONNXTargetPoint: return ONNXTargetPoint(target_type, target_node_name, port_id) - @staticmethod - def create_bias_insertion_command(node: NNCFNode) -> ONNXNullBiasInsertionCommand: - return ONNXNullBiasInsertionCommand(node) - @staticmethod def create_bias_correction_command( node: NNCFNode, bias_value: Tensor, nncf_graph: NNCFGraph - ) -> ONNXBiasCorrectionCommand: + ) -> ONNXInitializerUpdateCommand: return create_bias_correction_command(node, bias_value.data) @staticmethod - def model_extraction_command(inputs: List[str], outputs: List[str]) -> ONNXModelExtractionCommand: - return ONNXModelExtractionCommand(inputs, outputs) + def model_extraction_command( + input_ids: List[Tuple[str, int]], output_ids: List[Tuple[str, int]] + ) -> ONNXModelExtractionCommand: + return ONNXModelExtractionCommand(input_ids, output_ids) @staticmethod def mean_statistic_collector( @@ -59,8 +53,8 @@ def mean_statistic_collector( inplace: bool, num_samples: Optional[int] = None, window_size: Optional[int] = None, - ) -> ONNXMeanStatisticCollector: - return ONNXMeanStatisticCollector(channel_axis, num_samples, window_size) + ) -> TensorCollector: + return get_mean_statistic_collector(num_samples, channel_axis, window_size, inplace) @staticmethod def get_sub_input_output_names(subgraph: onnx.ModelProto) -> Tuple[str, str]: diff --git a/nncf/quantization/algorithms/fast_bias_correction/openvino_backend.py b/nncf/quantization/algorithms/fast_bias_correction/openvino_backend.py index ba8d18d1733..58cc3f04ff2 100644 --- a/nncf/quantization/algorithms/fast_bias_correction/openvino_backend.py +++ b/nncf/quantization/algorithms/fast_bias_correction/openvino_backend.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -42,8 +42,10 @@ def create_bias_correction_command( return OVCommandCreator.create_command_to_update_bias(node, bias_value.data, nncf_graph) @staticmethod - def model_extraction_command(inputs: List[str], outputs: List[str]) -> OVModelExtractionCommand: - return OVModelExtractionCommand(inputs, outputs) + def model_extraction_command( + input_ids: List[Tuple[str, int]], output_ids: List[Tuple[str, int]] + ) -> OVModelExtractionCommand: + return OVModelExtractionCommand(input_ids, output_ids) @staticmethod def mean_statistic_collector( diff --git a/nncf/quantization/algorithms/fast_bias_correction/torch_backend.py b/nncf/quantization/algorithms/fast_bias_correction/torch_backend.py index fea39ff068a..7623fdb0012 100644 --- a/nncf/quantization/algorithms/fast_bias_correction/torch_backend.py +++ b/nncf/quantization/algorithms/fast_bias_correction/torch_backend.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -54,8 +54,10 @@ def create_bias_correction_command( return create_bias_correction_command(node, bias_value.data) @staticmethod - def model_extraction_command(inputs: List[str], outputs: List[str]) -> PTModelExtractionWithFusedBiasCommand: - return PTModelExtractionWithFusedBiasCommand(inputs[0]) + def model_extraction_command( + input_ids: List[Tuple[str, int]], output_ids: List[Tuple[str, int]] + ) -> PTModelExtractionWithFusedBiasCommand: + return PTModelExtractionWithFusedBiasCommand(input_ids[0][0]) @staticmethod def mean_statistic_collector( diff --git a/nncf/quantization/algorithms/hyperparameter_tuner/__init__.py b/nncf/quantization/algorithms/hyperparameter_tuner/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/quantization/algorithms/hyperparameter_tuner/__init__.py +++ b/nncf/quantization/algorithms/hyperparameter_tuner/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/quantization/algorithms/hyperparameter_tuner/algorithm.py b/nncf/quantization/algorithms/hyperparameter_tuner/algorithm.py index 2d0e123b1df..4f12fb76f51 100644 --- a/nncf/quantization/algorithms/hyperparameter_tuner/algorithm.py +++ b/nncf/quantization/algorithms/hyperparameter_tuner/algorithm.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/quantization/algorithms/hyperparameter_tuner/param_grid.py b/nncf/quantization/algorithms/hyperparameter_tuner/param_grid.py index 5cc4beb207e..48766ab985c 100644 --- a/nncf/quantization/algorithms/hyperparameter_tuner/param_grid.py +++ b/nncf/quantization/algorithms/hyperparameter_tuner/param_grid.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/quantization/algorithms/min_max/__init__.py b/nncf/quantization/algorithms/min_max/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/quantization/algorithms/min_max/__init__.py +++ b/nncf/quantization/algorithms/min_max/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/quantization/algorithms/min_max/algorithm.py b/nncf/quantization/algorithms/min_max/algorithm.py index d8fb116d403..52ce17dc30f 100644 --- a/nncf/quantization/algorithms/min_max/algorithm.py +++ b/nncf/quantization/algorithms/min_max/algorithm.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -16,6 +16,7 @@ import numpy as np +import nncf from nncf import Dataset from nncf.common.factory import ModelTransformerFactory from nncf.common.graph.graph import NNCFGraph @@ -30,6 +31,7 @@ from nncf.common.insertion_point_graph import InsertionPointGraph from nncf.common.logging import nncf_logger from nncf.common.quantization.config_assignment import assign_qconfig_lists_to_modules +from nncf.common.quantization.initialization.range import RangeInitCollectorParams from nncf.common.quantization.quantizer_propagation.solver import QuantizerPropagationSolver from nncf.common.quantization.quantizer_propagation.structs import IgnoreReason from nncf.common.quantization.quantizer_setup import SingleConfigQuantizationPoint @@ -196,33 +198,35 @@ def _review_defaults_based_on_mode(self): nncf_logger.warning(f"You're using experimental option mode with {self._mode} value.") if self._preset != QuantizationPreset.PERFORMANCE: - raise RuntimeError(f"preset option with {self._preset} value is not supported with the mode option!") + raise nncf.ParameterNotSupportedError( + f"preset option with {self._preset} value is not supported with the mode option!" + ) if self._target_device not in [TargetDevice.CPU, TargetDevice.ANY]: - raise RuntimeError( + raise nncf.ParameterNotSupportedError( f"target_device option with {self._target_device} value is not supported with the mode option!" ) if self._overflow_fix != OverflowFix.DISABLE: - raise RuntimeError( + raise nncf.ParameterNotSupportedError( f"overflow_fix option with {self._overflow_fix} value is not supported with the mode option!" ) if self._quantize_outputs: - raise RuntimeError("quantize_outputs option is not supported with the mode option!") + raise nncf.ParameterNotSupportedError("quantize_outputs option is not supported with the mode option!") if self._backend_params is not None: - raise RuntimeError("backend_params option is not supported with the mode option!") + raise nncf.ParameterNotSupportedError("backend_params option is not supported with the mode option!") if isinstance(self._quantization_params[QuantizerGroup.WEIGHTS], QuantizationParameters): - raise RuntimeError( + raise nncf.ParameterNotSupportedError( "quantization_params option for weights with " f"{self._quantization_params[QuantizerGroup.WEIGHTS]} " "value is not supported with the mode option!" ) if isinstance(self._quantization_params[QuantizerGroup.ACTIVATIONS], QuantizationParameters): - raise RuntimeError( + raise nncf.ParameterNotSupportedError( "quantization_params option for activations with " f"{self._quantization_params[QuantizerGroup.ACTIVATIONS]} " "value is not supported with the mode option!" @@ -274,7 +278,7 @@ def _get_quantizer_constraints( if isinstance(quantization_params, FP8QuantizationParameters): if self._mode is None: - raise RuntimeError( + raise nncf.InternalError( f"FP8QuantizationParameters for {group.value} can not be used without QuantizationMode option!" ) return QuantizationConstraints(**constraints) @@ -310,7 +314,7 @@ def _set_backend_entity(self, model: TModel) -> None: self._backend_entity = PTMinMaxAlgoBackend() else: - raise RuntimeError( + raise nncf.UnsupportedBackendError( "Cannot return backend-specific entity because {} is not supported!".format(model_backend.value) ) @@ -366,11 +370,16 @@ def _get_stat_collector( """ range_estimator_params = self._get_range_estimator_parameters(target_point, quantizer_config) + collector_params = RangeInitCollectorParams( + is_weights=target_point.is_weight_target_point(), + scheme=quantizer_config.mode, + per_channel=quantizer_config.per_channel, + ) return self._backend_entity.get_statistic_collector( range_estimator_params, nncf_graph, target_point, - quantizer_config, + collector_params, inplace=self._inplace_statistics, num_samples=num_samples, ) @@ -650,7 +659,7 @@ def _get_quantization_target_points( elif quantization_point.is_activation_quantization_point(): self._add_activation_quantization_target_point(quantization_point) else: - raise RuntimeError("Incorrect quantization point") + raise nncf.InternalError("Incorrect quantization point") return self._quantization_target_points_to_qconfig, self._unified_scale_groups def _collect_unified_groups( @@ -787,7 +796,7 @@ def filter_func(point: StatisticPoint) -> bool: ): statistics = tensor_collector.get_statistics() if statistics.min_values is None or statistics.max_values is None: - raise RuntimeError(f"Statistics were not collected for the node {target_node_name}") + raise nncf.InternalError(f"Statistics were not collected for the node {target_node_name}") group_statistics.append(statistics) unified_values = self._backend_entity.unify_statistics(group_statistics) @@ -831,7 +840,7 @@ def filter_func(point: StatisticPoint) -> bool: narrow_range = get_quantizer_narrow_range(qconfig, quant_group) statistics = tensor_collector.get_statistics() if statistics.min_values is None or statistics.max_values is None: - raise RuntimeError(f"Statistics were not collected for the node {target_node_name}") + raise nncf.InternalError(f"Statistics were not collected for the node {target_node_name}") if self._mode is not None: destination_type = self._quantization_params[quant_group].destination_type parameters = calculate_convert_parameters( diff --git a/nncf/quantization/algorithms/min_max/backend.py b/nncf/quantization/algorithms/min_max/backend.py index 96ff4be8a68..7f00e8fc62a 100644 --- a/nncf/quantization/algorithms/min_max/backend.py +++ b/nncf/quantization/algorithms/min_max/backend.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -20,6 +20,7 @@ from nncf.common.graph.transformations.commands import TargetType from nncf.common.graph.transformations.commands import TransformationCommand from nncf.common.hardware.config import HWConfig +from nncf.common.quantization.initialization.range import RangeInitCollectorParams from nncf.common.quantization.structs import QuantizerConfig from nncf.common.tensor_statistics.collectors import TensorStatisticCollectorBase from nncf.common.tensor_statistics.statistics import MinMaxTensorStatistic @@ -188,7 +189,7 @@ def get_statistic_collector( range_estimator_params: RangeEstimatorParameters, nncf_graph: NNCFGraph, target_point: TargetPoint, - quantizer_config: QuantizerConfig, + collector_params: RangeInitCollectorParams, inplace: bool, num_samples: int = None, ) -> TensorStatisticCollectorBase: @@ -198,7 +199,7 @@ def get_statistic_collector( :param range_estimator_params: Parameters that specify estimators types. :param nncf_graph: NNCFGraph to get input/output shapes for the target point. :param target_point: Target location for the correction. - :param quantizer_config: QuantizerConfig instance for the current layer. + :param collector_params: RangeInitCollectorParams instance for the current layer. :param inplace: Whether to calculate statistic inplace or not. :param num_samples: Maximum number of samples to collect. :return: Backend-specific TensorStatisticCollectorBase for the statistics calculation. diff --git a/nncf/quantization/algorithms/min_max/onnx_backend.py b/nncf/quantization/algorithms/min_max/onnx_backend.py index 1ea7e2b3042..6f270aa6683 100644 --- a/nncf/quantization/algorithms/min_max/onnx_backend.py +++ b/nncf/quantization/algorithms/min_max/onnx_backend.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -9,18 +9,21 @@ # See the License for the specific language governing permissions and # limitations under the License. -from typing import Dict, List, Optional, Set, Union +from typing import Dict, List, Optional, Set import numpy as np +import nncf from nncf.common.graph.graph import NNCFGraph from nncf.common.graph.graph import NNCFNode from nncf.common.graph.operator_metatypes import OperatorMetatype from nncf.common.graph.transformations.commands import TargetType from nncf.common.graph.transformations.commands import TransformationCommand from nncf.common.hardware.config import HWConfig -from nncf.common.quantization.structs import QuantizationScheme as QuantizationMode +from nncf.common.quantization.initialization.range import RangeInitCollectorParams from nncf.common.quantization.structs import QuantizerConfig +from nncf.experimental.common.tensor_statistics.collectors import AGGREGATORS_MAP +from nncf.experimental.common.tensor_statistics.collectors import TensorCollector from nncf.onnx.graph.metatypes import onnx_metatypes as om from nncf.onnx.graph.metatypes.groups import MATMUL_METATYPES from nncf.onnx.graph.node_utils import get_input_edges_mapping @@ -32,12 +35,11 @@ from nncf.onnx.hardware.config import ONNXHWConfig from nncf.onnx.quantization.default_quantization import DEFAULT_ONNX_QUANT_TRAIT_TO_OP_DICT from nncf.onnx.quantization.quantizer_parameters import convert_fq_params_to_onnx_params -from nncf.onnx.statistics.collectors import ONNXMeanMinMaxStatisticCollector -from nncf.onnx.statistics.collectors import ONNXMinMaxStatisticCollector +from nncf.onnx.statistics.collectors import ONNX_REDUCERS_MAP +from nncf.onnx.statistics.collectors import ONNXNNCFCollectorTensorProcessor from nncf.onnx.statistics.statistics import ONNXMinMaxTensorStatistic from nncf.parameters import ModelType from nncf.parameters import TargetDevice -from nncf.quantization.advanced_parameters import AggregatorType from nncf.quantization.advanced_parameters import StatisticsType from nncf.quantization.algorithms.min_max.backend import MinMaxAlgoBackend from nncf.quantization.fake_quantize import FakeConvertParameters @@ -60,7 +62,11 @@ def conv_metatypes(self) -> List[OperatorMetatype]: @property def overflow_fix_metatypes(self) -> List[OperatorMetatype]: - return [om.ONNXConvolutionMetatype, om.ONNXConvolutionTransposeMetatype, *MATMUL_METATYPES] + return [ + om.ONNXConvolutionMetatype, + om.ONNXConvolutionTransposeMetatype, + *MATMUL_METATYPES, + ] @property def add_metatypes(self) -> List[OperatorMetatype]: @@ -99,7 +105,9 @@ def quant_trait_op_dict(self) -> Dict[int, OperatorMetatype]: return DEFAULT_ONNX_QUANT_TRAIT_TO_OP_DICT @staticmethod - def get_start_nodes_for_activation_path_tracing(nncf_graph: NNCFGraph) -> List[NNCFNode]: + def get_start_nodes_for_activation_path_tracing( + nncf_graph: NNCFGraph, + ) -> List[NNCFNode]: return nncf_graph.get_input_nodes() @staticmethod @@ -127,10 +135,12 @@ def create_convert_insertion_command( target_point: ONNXTargetPoint, parameters: FakeConvertParameters, ) -> TransformationCommand: - raise RuntimeError("FakeConvert insertion not implemented in ONNX backend!") + raise nncf.InternalError("FakeConvert insertion not implemented in ONNX backend!") @staticmethod - def unify_statistics(statistics: List[ONNXMinMaxTensorStatistic]) -> ONNXMinMaxTensorStatistic: + def unify_statistics( + statistics: List[ONNXMinMaxTensorStatistic], + ) -> ONNXMinMaxTensorStatistic: max_values, min_values = [], [] for statistic in statistics: max_values.append(np.array(statistic.max_values).flatten()) @@ -148,44 +158,55 @@ def get_statistic_collector( range_estimator_params: RangeEstimatorParameters, nncf_graph: NNCFGraph, target_point: ONNXTargetPoint, - quantizer_config: QuantizerConfig, + collector_params: RangeInitCollectorParams, inplace: bool, num_samples: int = None, - ) -> Union[ONNXMinMaxStatisticCollector, ONNXMeanMinMaxStatisticCollector]: - is_per_channel = quantizer_config.per_channel + ) -> TensorCollector: + is_per_channel = collector_params.is_per_channel node = nncf_graph.get_node_by_name(target_point.target_node_name) - use_abs_max = quantizer_config.mode == QuantizationMode.SYMMETRIC - reduction_shape = None # Per-Tensor + use_abs_max = collector_params.use_abs_max quantization_axis = get_quantization_axis(is_per_channel, node, target_point) quantized_tensor_shape = get_quantized_tensor_shape(nncf_graph, node, target_point) + reduction_axes = None # Per-Tensor if quantization_axis is not None and quantized_tensor_shape is not None: # Per-Channel - reduction_shape = get_reduction_shape(quantized_tensor_shape, quantization_axis) - - if ( - range_estimator_params.min.statistics_type == StatisticsType.MIN - and range_estimator_params.min.aggregator_type == AggregatorType.MIN - and range_estimator_params.max.statistics_type == StatisticsType.MAX - and range_estimator_params.max.aggregator_type == AggregatorType.MAX + reduction_axes = get_reduction_shape(quantized_tensor_shape, quantization_axis) + collector = TensorCollector(ONNXMinMaxTensorStatistic) + for params, container_key in zip( + [range_estimator_params.min, range_estimator_params.max], + [ONNXMinMaxTensorStatistic.MIN_STAT, ONNXMinMaxTensorStatistic.MAX_STAT], ): - return ONNXMinMaxStatisticCollector(use_abs_max, reduction_shape, num_samples) - - if ( - range_estimator_params.min.statistics_type == StatisticsType.MIN - and range_estimator_params.min.aggregator_type == AggregatorType.MEAN - and range_estimator_params.max.statistics_type == StatisticsType.MAX - and range_estimator_params.max.aggregator_type == AggregatorType.MEAN - ): - return ONNXMeanMinMaxStatisticCollector( - use_per_sample_stats=False, - use_abs_max=use_abs_max, - reduction_shape=reduction_shape, + if params.statistics_type not in ONNX_REDUCERS_MAP: + raise nncf.InternalError( + f"Statistic type: {params.statistics_type} is not supported for ONNX PTQ backend yet." + ) + + if params.aggregator_type not in AGGREGATORS_MAP: + raise nncf.InternalError( + f"Aggregator type: {params.aggregator_type} is not supported for ONNX PTQ backend yet." + ) + + statistic_type = params.statistics_type + kwargs = {"reduction_axes": reduction_axes, "inplace": inplace} + if statistic_type in [StatisticsType.QUANTILE, StatisticsType.ABS_QUANTILE]: + # TODO(dlyakhov): merge two quantile aggregators in one + if container_key == ONNXMinMaxTensorStatistic.MIN_STAT: + quantile = params.quantile_outlier_prob + else: + quantile = 1 - params.quantile_outlier_prob + kwargs.update({"quantile": [quantile]}) + if use_abs_max and statistic_type == StatisticsType.MAX: + statistic_type = StatisticsType.ABS_MAX + reducer = ONNX_REDUCERS_MAP[statistic_type](reduction_axes=reduction_axes) + + aggregation_axes = (0,) + aggregator = AGGREGATORS_MAP[params.aggregator_type]( + aggregation_axes=aggregation_axes, num_samples=num_samples, - window_size=None, + tensor_processor=ONNXNNCFCollectorTensorProcessor, ) - raise RuntimeError( - "The following range estimator parameters are not supported by ONNX backend by now: " - f"{str(range_estimator_params)}" - ) + + collector.register_statistic_branch(container_key, reducer, aggregator) + return collector @staticmethod def get_weight_tensor_port_ids(node: NNCFNode) -> List[Optional[int]]: diff --git a/nncf/quantization/algorithms/min_max/openvino_backend.py b/nncf/quantization/algorithms/min_max/openvino_backend.py index 744ea868a94..1b258618ee5 100644 --- a/nncf/quantization/algorithms/min_max/openvino_backend.py +++ b/nncf/quantization/algorithms/min_max/openvino_backend.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -13,12 +13,13 @@ import numpy as np +import nncf from nncf.common.graph.graph import NNCFGraph from nncf.common.graph.graph import NNCFNode from nncf.common.graph.operator_metatypes import OperatorMetatype from nncf.common.graph.transformations.commands import TargetType from nncf.common.hardware.config import HWConfig -from nncf.common.quantization.structs import QuantizationScheme as QuantizationMode +from nncf.common.quantization.initialization.range import RangeInitCollectorParams from nncf.common.quantization.structs import QuantizerConfig from nncf.common.tensor_statistics.collectors import ReductionAxes from nncf.experimental.common.tensor_statistics.collectors import AGGREGATORS_MAP @@ -140,12 +141,11 @@ def unify_statistics(statistics: List[OVMinMaxTensorStatistic]) -> OVMinMaxTenso return OVMinMaxTensorStatistic(min_values=min_values, max_values=max_values) @staticmethod - def _get_reduction_axes_and_use_abs_max( - nncf_graph: NNCFGraph, target_point: OVTargetPoint, quantizer_config: QuantizerConfig + def _get_reduction_axes( + nncf_graph: NNCFGraph, target_point: OVTargetPoint, collector_params: RangeInitCollectorParams ) -> Tuple[ReductionAxes, bool]: - use_abs_max = quantizer_config.mode == QuantizationMode.SYMMETRIC - if not quantizer_config.per_channel: - return None, use_abs_max + if not collector_params.is_per_channel: + return None node = nncf_graph.get_node_by_name(target_point.target_node_name) if not target_point.is_weight_target_point(): @@ -159,30 +159,28 @@ def _get_reduction_axes_and_use_abs_max( # TODO (l-bat): Disable quantizer propagation through layout changing operations channel_axis = 1 # OpenVINO activations have channel first layout: [N, C, Z, Y, X] axes = get_channel_agnostic_reduction_axes([channel_axis], shape) - return axes, use_abs_max + return axes assert isinstance(node.layer_attributes, OVLayerAttributes) const_shape = node.layer_attributes.constant_attributes[target_point.port_id]["shape"] - if quantizer_config.per_channel: + if collector_params.is_per_channel: channel_axes = get_weight_channel_axes(node) axes = get_channel_agnostic_reduction_axes(channel_axes, const_shape) else: axes = tuple(range(len(const_shape))) - return axes, use_abs_max + return axes @staticmethod def get_statistic_collector( range_estimator_params: RangeEstimatorParameters, nncf_graph: NNCFGraph, target_point: OVTargetPoint, - quantizer_config: QuantizerConfig, + collector_params: RangeInitCollectorParams, inplace: bool, num_samples: int = None, ) -> TensorCollector: - reduction_axes, use_abs_max = OVMinMaxAlgoBackend._get_reduction_axes_and_use_abs_max( - nncf_graph, target_point, quantizer_config - ) + reduction_axes = OVMinMaxAlgoBackend._get_reduction_axes(nncf_graph, target_point, collector_params) collector = TensorCollector(OVMinMaxTensorStatistic) for params, container_key in zip( @@ -190,12 +188,12 @@ def get_statistic_collector( [OVMinMaxTensorStatistic.MIN_STAT, OVMinMaxTensorStatistic.MAX_STAT], ): if params.statistics_type not in OV_REDUCERS_MAP: - raise RuntimeError( + raise nncf.InternalError( f"Statistic type: {params.statistics_type} is not supported for OpenVino PTQ backend yet." ) if params.aggregator_type not in AGGREGATORS_MAP: - raise RuntimeError( + raise nncf.InternalError( f"Aggregator type: {params.aggregator_type} is not supported for OpenVino PTQ backend yet." ) @@ -208,7 +206,7 @@ def get_statistic_collector( kwargs.update({"quantile": [quantile]}) # TODO(dlyakhov): merge two quantile aggregators in one statistic_type = params.statistics_type - if use_abs_max and statistic_type == StatisticsType.MAX: + if collector_params.use_abs_max and statistic_type == StatisticsType.MAX: statistic_type = StatisticsType.ABS_MAX reducer = OV_REDUCERS_MAP[statistic_type](**kwargs) @@ -251,9 +249,11 @@ def get_ignored_names_by_layer_attributes(nncf_graph: NNCFGraph) -> List[str]: ignored_names = [] target_nodes = nncf_graph.get_nodes_by_metatypes([om.OVGRUSequenceMetatype]) for node in target_nodes: - if isinstance(node.layer_attributes, OVLayerAttributes): - if node.layer_attributes.input_attributes["linear_before_reset"]: - ignored_names.append(node.node_name) + if ( + isinstance(node.layer_attributes, OVLayerAttributes) + and node.layer_attributes.input_attributes["linear_before_reset"] + ): + ignored_names.append(node.node_name) return ignored_names @staticmethod diff --git a/nncf/quantization/algorithms/min_max/torch_backend.py b/nncf/quantization/algorithms/min_max/torch_backend.py index 96c8ef87af7..f321a8f70f2 100644 --- a/nncf/quantization/algorithms/min_max/torch_backend.py +++ b/nncf/quantization/algorithms/min_max/torch_backend.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -13,6 +13,7 @@ import torch +import nncf import nncf.torch.graph.operator_metatypes as om from nncf.common.graph.definitions import NNCFGraphNodeType from nncf.common.graph.graph import NNCFGraph @@ -22,6 +23,7 @@ from nncf.common.graph.transformations.commands import TargetType from nncf.common.graph.transformations.commands import TransformationCommand from nncf.common.hardware.config import HWConfig +from nncf.common.quantization.initialization.range import RangeInitCollectorParams from nncf.common.quantization.structs import QuantizationScheme as QuantizationMode from nncf.common.quantization.structs import QuantizerConfig from nncf.experimental.common.tensor_statistics.collectors import AGGREGATORS_MAP @@ -58,7 +60,7 @@ class PTMinMaxAlgoBackend(MinMaxAlgoBackend): @property def mat_mul_metatypes(self) -> List[OperatorMetatype]: - return [om.PTModuleLinearMetatype] + return [om.PTModuleLinearMetatype, om.PTLinearMetatype, om.PTMatMulMetatype] @property def post_processing_metatypes(self) -> List[OperatorMetatype]: @@ -144,7 +146,7 @@ def create_convert_insertion_command( target_point: PTTargetPoint, parameters: FakeConvertParameters, ) -> TransformationCommand: - raise RuntimeError("FakeConvert insertion not implemented in PyTorch backend!") + raise nncf.InternalError("FakeConvert insertion not implemented in PyTorch backend!") @staticmethod def unify_statistics(statistics: List[PTMinMaxTensorStatistic]) -> PTMinMaxTensorStatistic: @@ -161,11 +163,11 @@ def get_statistic_collector( range_estimator_params: RangeEstimatorParameters, nncf_graph: NNCFGraph, target_point: PTTargetPoint, - quantizer_config: QuantizerConfig, + collector_params: RangeInitCollectorParams, inplace: bool, num_samples: int = None, ) -> TensorCollector: - collector_params = PTMinMaxAlgoBackend._default_collector_params(nncf_graph, target_point, quantizer_config) + collector_params = PTMinMaxAlgoBackend._default_collector_params(nncf_graph, target_point, collector_params) reduction_axes = collector_params.get_reduction_axes(per_sample_stats=False) aggregation_axes = collector_params.get_aggregation_axes(per_sample_stats=False) @@ -175,12 +177,12 @@ def get_statistic_collector( [PTMinMaxTensorStatistic.MIN_STAT, PTMinMaxTensorStatistic.MAX_STAT], ): if params.statistics_type not in PT_REDUCERS_MAP: - raise RuntimeError( + raise nncf.InternalError( f"Statistic type: {params.statistics_type} is not supported for Torch PTQ backend yet." ) if params.aggregator_type not in AGGREGATORS_MAP: - raise RuntimeError( + raise nncf.InternalError( f"Aggregator type: {params.aggregator_type} is not supported for Torch PTQ backend yet." ) @@ -225,7 +227,7 @@ def get_weight_config(config: QuantizerConfig, model: NNCFNetwork) -> QuantizerC @staticmethod def _get_input_scale_shape( - nncf_graph: NNCFGraph, target_point: PTTargetPoint, quantization_config: QuantizerConfig + nncf_graph: NNCFGraph, target_point: PTTargetPoint, per_channel: bool ) -> Tuple[Tuple[int, ...], Tuple[int, ...], int]: is_weights = target_point.is_weight_target_point() if is_weights: @@ -239,24 +241,22 @@ def _get_input_scale_shape( channel_idx = 1 # channel dim for activations scale_shape = tuple( - get_scale_shape( - input_shape, is_weights=is_weights, per_channel=quantization_config.per_channel, channel_idx=channel_idx - ) + get_scale_shape(input_shape, is_weights=is_weights, per_channel=per_channel, channel_idx=channel_idx) ) return input_shape, scale_shape, channel_idx @staticmethod def _default_collector_params( - nncf_graph: NNCFGraph, target_point: PTTargetPoint, quantizer_config: QuantizerConfig + nncf_graph: NNCFGraph, target_point: PTTargetPoint, collector_params: RangeInitCollectorParams ) -> PTRangeInitCollectorParams: input_shape, _, channel_idx = PTMinMaxAlgoBackend._get_input_scale_shape( - nncf_graph, target_point, quantizer_config + nncf_graph, target_point, collector_params.is_per_channel ) return PTRangeInitCollectorParams( - is_weights=target_point.is_weight_target_point(), - mode=quantizer_config.mode, - per_channel=quantizer_config.per_channel, + is_weights=collector_params.is_weights, + scheme=collector_params.scheme, + per_channel=collector_params.is_per_channel, input_shape=input_shape, channel_idx=channel_idx, ) @@ -304,7 +304,9 @@ def _create_quantizer_insertion_command( quantizer_config: QuantizerConfig, parameters: FakeQuantizeParameters, ) -> PTQuantizerInsertionCommand: - _, scale_shape, _ = PTMinMaxAlgoBackend._get_input_scale_shape(nncf_graph, target_point, quantizer_config) + _, scale_shape, _ = PTMinMaxAlgoBackend._get_input_scale_shape( + nncf_graph, target_point, quantizer_config.per_channel + ) quantizer = PTMinMaxAlgoBackend._create_quantizer( quantizer_config, scale_shape, parameters, target_point.target_type diff --git a/nncf/quantization/algorithms/pipeline.py b/nncf/quantization/algorithms/pipeline.py index 2c02e3753c6..cd615258553 100644 --- a/nncf/quantization/algorithms/pipeline.py +++ b/nncf/quantization/algorithms/pipeline.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/quantization/algorithms/post_training/__init__.py b/nncf/quantization/algorithms/post_training/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/quantization/algorithms/post_training/__init__.py +++ b/nncf/quantization/algorithms/post_training/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/quantization/algorithms/post_training/algorithm.py b/nncf/quantization/algorithms/post_training/algorithm.py index ff27f06eadb..862dc5d5037 100644 --- a/nncf/quantization/algorithms/post_training/algorithm.py +++ b/nncf/quantization/algorithms/post_training/algorithm.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/quantization/algorithms/post_training/pipeline.py b/nncf/quantization/algorithms/post_training/pipeline.py index 027b03cd07a..739442d3646 100644 --- a/nncf/quantization/algorithms/post_training/pipeline.py +++ b/nncf/quantization/algorithms/post_training/pipeline.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -72,8 +72,6 @@ def create_ptq_pipeline( fine-tuning the quantization algorithm :return: A post-training quantization pipeline. """ - if target_device is TargetDevice.VPU: - warning_deprecated("VPU device is deprecated and will no longer be supported in the future.") if advanced_parameters is None: advanced_parameters = AdvancedQuantizationParameters() diff --git a/nncf/quantization/algorithms/smooth_quant/__init__.py b/nncf/quantization/algorithms/smooth_quant/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/quantization/algorithms/smooth_quant/__init__.py +++ b/nncf/quantization/algorithms/smooth_quant/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/quantization/algorithms/smooth_quant/algorithm.py b/nncf/quantization/algorithms/smooth_quant/algorithm.py index 6f67cf9ee98..3e2994e2bea 100644 --- a/nncf/quantization/algorithms/smooth_quant/algorithm.py +++ b/nncf/quantization/algorithms/smooth_quant/algorithm.py @@ -1,14 +1,4 @@ -# Copyright (c) 2023 Intel Corporation -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# http://www.apache.org/licenses/LICENSE-2.0 -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -24,12 +14,12 @@ from copy import deepcopy from typing import Dict, List, Optional, Tuple, TypeVar +import nncf from nncf import Dataset from nncf.common.factory import ModelTransformerFactory from nncf.common.graph.graph import NNCFGraph from nncf.common.graph.graph import NNCFNode from nncf.common.graph.operator_metatypes import OperatorMetatype -from nncf.common.graph.transformations.commands import TargetType from nncf.common.graph.transformations.layout import TransformationLayout from nncf.common.logging import nncf_logger from nncf.common.logging.track_progress import track @@ -37,6 +27,8 @@ from nncf.common.tensor_statistics.statistic_point import StatisticPointsContainer from nncf.common.utils.backend import BackendType from nncf.common.utils.backend import get_backend +from nncf.experimental.tensor import Tensor +from nncf.experimental.tensor import functions as fns from nncf.quantization.algorithms.algorithm import Algorithm TModel = TypeVar("TModel") @@ -77,7 +69,7 @@ def __init__( @property def available_backends(self) -> List[BackendType]: - return [BackendType.OPENVINO] + return [BackendType.OPENVINO, BackendType.TORCH] def _set_backend_entity(self, model: TModel) -> None: """ @@ -90,8 +82,12 @@ def _set_backend_entity(self, model: TModel) -> None: from nncf.quantization.algorithms.smooth_quant.openvino_backend import OVSmoothQuantAlgoBackend self._backend_entity = OVSmoothQuantAlgoBackend() + elif model_backend == BackendType.TORCH: + from nncf.quantization.algorithms.smooth_quant.torch_backend import PTSmoothQuantAlgoBackend + + self._backend_entity = PTSmoothQuantAlgoBackend() else: - raise RuntimeError( + raise nncf.UnsupportedBackendError( "Cannot return backend-specific entity because {} is not supported!".format(model_backend.value) ) @@ -120,21 +116,26 @@ def apply( activations_value = self._get_statistics_for_node( statistic_points, node_to_smooth.node_name, input_port_id ) - if any(val is None for val in activations_value): + if any(val.data is None for val in activations_value): empty_statistic = True break - activations_value = self._backend_entity.clip_statistics(activations_value) + if len(activations_value) != 1: + raise RuntimeError( + ( + "More than one statistic is collected for one node during" + f"Smooth Quanti algorithm: {node_to_smooth.node_name}" + ) + ) + + activations_value = self._clip_statistics(activations_value) - weight_port = self._backend_entity.get_weight_tensor_port_id(node_to_smooth) - weight_value = self._backend_entity.get_weight_value(node_to_smooth, model, weight_port) + weight_value = self._backend_entity.get_weight_value(node_to_smooth, model) weight_statistics = self._process_weight_statistics(node_to_smooth, weight_value) - weight_statistics = self._backend_entity.clip_statistics(weight_statistics) + weight_statistics = self._clip_statistics([weight_statistics]) alpha = alpha_map[node_to_smooth.metatype] - scales, ratio = self._backend_entity.calculate_scale_and_ratio( - activations_value, weight_statistics, alpha - ) + scales, ratio = self._calculate_scale_and_ratio(activations_value, weight_statistics, alpha) if ratio > best_ratio: best_ratio = ratio @@ -153,13 +154,10 @@ def apply( continue for node_to_smooth in nodes: - weights_scale = self._calculate_weight_scale(best_scale, node_to_smooth) - weight_port = self._backend_entity.get_weight_tensor_port_id(node_to_smooth) - weight_value = self._backend_entity.get_weight_value(node_to_smooth, model, weight_port) + weight_value = self._backend_entity.get_weight_value(node_to_smooth, model) + weights_scale = self._calculate_weight_scale(best_scale, node_to_smooth, weight_value) scaled_weight = weight_value * weights_scale - weight_update_command = self._backend_entity.weight_update_command( - node_to_smooth, scaled_weight, weight_port - ) + weight_update_command = self._backend_entity.weight_update_command(node_to_smooth, scaled_weight.data) transformation_layout.register(weight_update_command) activations_shape = graph.get_output_edges(source_node)[source_output_port_id].tensor_shape @@ -167,13 +165,37 @@ def apply( scale_node_name = self._create_scale_node_name(source_node.node_name, source_output_port_id) scale_insertion_command = self._backend_entity.scale_insertion_command( - source_node, activation_scale, source_output_port_id, nodes, scale_node_name + source_node, activation_scale.data, source_output_port_id, nodes, scale_node_name ) transformation_layout.register(scale_insertion_command) transformed_model = model_transformer.transform(transformation_layout) return transformed_model + @staticmethod + def _calculate_scale_and_ratio( + activations: Tensor, weights: Tensor, alpha: float, quantile: Optional[float] = 0.1 + ) -> Tuple[Tensor, float]: + """ + Calculates base scale value and it's ratio. + + :param activations: Activation statistics value. + :param weights: Weights statistics value. + :param alpha: Base value for exponentiation. + :param quantile: Base quantile value. + :return: Calculated base scale value & ratio. + """ + + eps = fns.finfo(activations).eps + scales = fns.power(activations, alpha) / (fns.power(weights, 1 - alpha) + eps) + + a_min = fns.quantile(scales, quantile, keepdims=False) + a_max = 1e2 + + scales = fns.clip(scales, a_min=a_min, a_max=a_max) + ratio = scales.min() / (scales.max() + eps) + return scales, ratio + def _group_nodes_by_source(self, nodes_to_smooth: List[Dict], nncf_graph: NNCFGraph) -> Dict[tuple, List]: """ Groups nodes that will be smoothed by source (parent node). @@ -208,18 +230,14 @@ def _get_statistics_for_node( :return: List of the TTensor instances. """ - def filter_func(point: StatisticPoint) -> bool: - return ( - self._algorithm_key in point.algorithm_to_tensor_collectors - and point.target_point.type == TargetType.PRE_LAYER_OPERATION - and point.target_point.port_id == act_port - ) - statistics_for_node = [] for tensor_collector in statistic_points.get_algo_statistics_for_node( - node_name, filter_func, self._algorithm_key + node_name, + self._backend_entity.get_filter_fn_for_statistics(act_port, self._algorithm_key), + self._algorithm_key, ): - statistics_for_node.append(tensor_collector.get_statistics()[STATISTIC_BRANCH_KEY]) + statistic = tensor_collector.get_statistics()[STATISTIC_BRANCH_KEY] + statistics_for_node.append(Tensor(statistic)) return statistics_for_node def get_statistic_points(self, model: TModel, graph: NNCFGraph) -> StatisticPointsContainer: @@ -233,7 +251,7 @@ def get_statistic_points(self, model: TModel, graph: NNCFGraph) -> StatisticPoin for node_data in nodes_to_smooth_data: node_to_smooth = node_data["node_to_smooth"] target_point = self._backend_entity.target_point( - TargetType.PRE_LAYER_OPERATION, + target_type=self._backend_entity.pre_layer_target_type(), target_node_name=node_to_smooth.node_name, port_id=node_data["input_act_port"], ) @@ -267,27 +285,26 @@ def _get_nodes_to_smooth_data(self, nncf_graph: NNCFGraph, node_metatypes: List[ if not self._backend_entity.is_node_with_weights(node_with_weight): continue - ports_map = self._backend_entity.get_input_ports_map(node_with_weight, nncf_graph) + activation_port_id = self._backend_entity.get_activations_port_id(node_with_weight, nncf_graph) input_edges = nncf_graph.get_input_edges(node_with_weight) - weight_node = input_edges[ports_map["weight"]].from_node - activation_node = input_edges[ports_map["activation"]].from_node + activation_node = input_edges[activation_port_id].from_node # Skipping agnostic layers as inputs to propagate quantizer # Only for Convolution layers if ( - node_with_weight.metatype == self._backend_entity.convolution_metatype + node_with_weight.metatype in self._backend_entity.convolution_metatypes and activation_node.metatype in self._backend_entity.quantize_agnostic_metatypes ): continue # Skipping shared weights - if len(nncf_graph.get_next_nodes(weight_node)) > 1: + if self._backend_entity.is_node_with_shared_weight(node_with_weight, nncf_graph): continue nodes_to_smooth_data.append( { "node_to_smooth": node_with_weight, - "input_act_port": ports_map["activation"], + "input_act_port": activation_port_id, } ) return nodes_to_smooth_data @@ -303,21 +320,24 @@ def _calculate_activation_scale( :param nodes: List of consumers for Smooth node. :return: Calculated per-channel activation scale. """ - activation_ports_map = { - node: self._backend_entity.get_input_ports_map(node, nncf_graph)["activation"] for node in nodes - } + activation_ports_map = {node: self._backend_entity.get_activations_port_id(node, nncf_graph) for node in nodes} channel_axes = [ self._backend_entity.get_activation_channel_axis(node, port) for node, port in activation_ports_map.items() ] channel_axis = channel_axes[0] if not all(axis == channel_axis for axis in channel_axes): - raise RuntimeError(f"Channel axes for nodes {[n.node_name for n in nodes]} are not identical") + raise nncf.InternalError(f"Channel axes for nodes {[n.node_name for n in nodes]} are not identical") activations_size = len(activations_shape) - return self._backend_entity.calculate_activation_scale(scale_value, activations_size, channel_axis) - - def _calculate_weight_scale(self, scale_value: TTensor, node: NNCFNode) -> TTensor: + activation_scale = scale_value ** (-1) + if activations_size > 1: + reshape_shape = [1 for _ in range(activations_size)] + reshape_shape[channel_axis] = activation_scale.size + activation_scale = activation_scale.reshape(reshape_shape) + return activation_scale + + def _calculate_weight_scale(self, scale_value: Tensor, node: NNCFNode, weights_value: Tensor) -> Tensor: """ Calculates scale for weight tensor. @@ -325,11 +345,15 @@ def _calculate_weight_scale(self, scale_value: TTensor, node: NNCFNode) -> TTens :param node: Consumer for Smooth node. :return: Calculated scale for weights. """ - port_id = self._backend_entity.get_weight_tensor_port_id(node) - weights_size = len(node.layer_attributes.constant_attributes[port_id]["shape"]) + weights_size = len(weights_value.shape) if weights_size > 1: channel_axis = self._backend_entity.get_weight_channel_axis(node) - return self._backend_entity.calculate_weight_scale(scale_value, weights_size, channel_axis) + weight_scale = scale_value + if weights_size > 1: + reshape_shape = [1 for _ in range(weights_size)] + reshape_shape[channel_axis] = scale_value.size + weight_scale = scale_value.reshape(reshape_shape) + return weight_scale return scale_value def _calculate_input_reduction_axes(self, nncf_graph: NNCFGraph, node: NNCFNode, input_port: int) -> Tuple[int]: @@ -348,7 +372,7 @@ def _calculate_input_reduction_axes(self, nncf_graph: NNCFGraph, node: NNCFNode, reduction_axes = self._backend_entity.get_channel_agnostic_reduction_axes(channel_axis, shape) return reduction_axes - def _process_weight_statistics(self, node: NNCFNode, weights: TTensor) -> TTensor: + def _process_weight_statistics(self, node: NNCFNode, weights: Tensor) -> Tensor: """ Returns processed weight statistics for node. @@ -361,7 +385,7 @@ def _process_weight_statistics(self, node: NNCFNode, weights: TTensor) -> TTenso channel_axis = self._backend_entity.get_weight_channel_axis(node) reduction_shape = [i for i, _ in enumerate(weights.shape)] reduction_shape.pop(channel_axis) - return self._backend_entity.process_weight_statistics(weights, tuple(reduction_shape)) + return fns.max(fns.abs(weights), axis=tuple(reduction_shape)) def _create_scale_node_name(self, source_name: str, source_port_id: int) -> str: """ @@ -384,8 +408,8 @@ def _get_alpha_map(self) -> Dict[OperatorMetatype, float]: """ alpha_by_metatype_map = {} name_to_metatype = { - "convolution": self._backend_entity.convolution_metatype, - "matmul": self._backend_entity.matmul_metatype, + "convolution": self._backend_entity.convolution_metatypes, + "matmul": self._backend_entity.matmul_metatypes, } for type_name, alpha_value in self._alpha_map.items(): if alpha_value < 0: @@ -394,6 +418,20 @@ def _get_alpha_map(self) -> Dict[OperatorMetatype, float]: "Skipping these layers." ) continue - metatype = name_to_metatype[type_name] - alpha_by_metatype_map[metatype] = alpha_value + metatypes = name_to_metatype[type_name] + for metatype in metatypes: + alpha_by_metatype_map[metatype] = alpha_value return alpha_by_metatype_map + + @staticmethod + def _clip_statistics(statistics: List[Tensor]) -> Tensor: + """ + Clips statistics for further calculation. + :param statistics: Input statistics. + :return: Clipped statistics. + """ + a_min = 1e-5 + + statistics = fns.stack(statistics) + squeezed = fns.squeeze(statistics) + return fns.clip(squeezed, a_min=a_min, a_max=None) diff --git a/nncf/quantization/algorithms/smooth_quant/backend.py b/nncf/quantization/algorithms/smooth_quant/backend.py index 38605929617..52d5d1a28d5 100644 --- a/nncf/quantization/algorithms/smooth_quant/backend.py +++ b/nncf/quantization/algorithms/smooth_quant/backend.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -11,7 +11,7 @@ from abc import ABC from abc import abstractmethod -from typing import Dict, List, Optional, Tuple, TypeVar +from typing import Callable, List, Tuple, TypeVar from nncf.common.graph import NNCFGraph from nncf.common.graph import NNCFNode @@ -19,7 +19,9 @@ from nncf.common.graph.transformations.commands import TargetPoint from nncf.common.graph.transformations.commands import TargetType from nncf.common.graph.transformations.commands import TransformationCommand +from nncf.common.tensor_statistics.statistic_point import StatisticPoint from nncf.experimental.common.tensor_statistics.collectors import TensorCollector +from nncf.experimental.tensor import Tensor TModel = TypeVar("TModel") TTensor = TypeVar("TTensor") @@ -28,20 +30,20 @@ class SmoothQuantAlgoBackend(ABC): @property @abstractmethod - def convolution_metatype(self) -> OperatorMetatype: + def convolution_metatypes(self) -> List[OperatorMetatype]: """ - Parameter for backend-specific metatype for Convolution. + Parameter for backend-specific metatypes for Convolution. - :return: OperatorMetatype + :return: OperatorMetatype list. """ @property @abstractmethod - def matmul_metatype(self) -> OperatorMetatype: + def matmul_metatypes(self) -> List[OperatorMetatype]: """ - Parameter for backend-specific metatype for MatMul. + Parameter for backend-specific metatypes for MatMul. - :return: OperatorMetatype + :return: OperatorMetatype list. """ @property @@ -53,6 +55,15 @@ def quantize_agnostic_metatypes(self) -> List[OperatorMetatype]: :return: List of OperatorMetatype. """ + @staticmethod + @abstractmethod + def pre_layer_target_type() -> TargetType: + """ + Returns backend-specific pre layer target type. + + :returns: Backend-specific pre layer target type. + """ + @staticmethod @abstractmethod def target_point(target_type: TargetType, target_node_name: str, port_id: int) -> TargetPoint: @@ -77,7 +88,7 @@ def is_node_with_weights(node: NNCFNode) -> bool: @staticmethod @abstractmethod - def get_input_ports_map(node: NNCFNode, nncf_graph: NNCFGraph) -> Dict[str, int]: + def get_activations_port_id(node: NNCFNode, nncf_graph: NNCFGraph) -> int: """ Returns map with activation & weighted ports. @@ -113,18 +124,7 @@ def get_abs_max_channel_collector( @staticmethod @abstractmethod - def process_weight_statistics(weights: TTensor, channel_axis: int) -> TTensor: - """ - Returns processed weight statistics for node. - - :param weights: Weights tensor. - :param channel_axis: Channel axis for calculation. - :return: Weight statistics. - """ - - @staticmethod - @abstractmethod - def get_weight_value(node_with_weight: NNCFNode, model: TModel, port_id: int) -> TTensor: + def get_weight_value(node_with_weight: NNCFNode, model: TModel, port_id: int) -> Tensor: """ Returns the weight value for the node with weight. @@ -144,55 +144,6 @@ def get_weight_tensor_port_id(node: NNCFNode) -> int: :return: Weights input port indices. """ - @staticmethod - @abstractmethod - def clip_statistics(statistics: TTensor) -> TTensor: - """ - Clips statistics for further calculation. - - :param statistics: Input statistics. - :return: Clipped statistics. - """ - - @staticmethod - @abstractmethod - def calculate_scale_and_ratio( - activations: TTensor, weights: TTensor, alpha: float, quantile: Optional[float] - ) -> Tuple[TTensor, TTensor]: - """ - Calculates base scale value and it's ratio. - - :param activations: Activation statistics value. - :param weights: Weights statistics value. - :param alpha: Base value for exponentiation. - :param quantile: Base quantile value. - :return: Calculated base scale value & ratio. - """ - - @staticmethod - @abstractmethod - def calculate_activation_scale(scale_value: TTensor, activations_size: int, channel_axis: int) -> TTensor: - """ - Calculates activation scales for Smooth node. - - :param scale_value: Base scale value. - :param activations_size: Size of the activation shape. - :param channel_axis: Axis for shape calculation. - :return: Calculated activation scale. - """ - - @staticmethod - @abstractmethod - def calculate_weight_scale(scale_value: TTensor, weights_size: int, channel_axis: int) -> TTensor: - """ - Calculates scale for weight tensor. - - :param scale_value: Base scale value. - :param weights_size: Size of the weights shape. - :param channel_axis: Axis for shape calculation. - :return: Calculated scale for weights. - """ - @staticmethod @abstractmethod def weight_update_command( @@ -210,14 +161,14 @@ def weight_update_command( @staticmethod @abstractmethod def scale_insertion_command( - source_node: NNCFNode, scale_value: TTensor, port_id: int, nodes: List[NNCFNode] + source_node: NNCFNode, scale_value: TTensor, source_output_port_id: int, nodes: List[NNCFNode] ) -> TransformationCommand: """ Returns command to insert Smooth Quant node. :param source_node: NNCFNode instance. :param scale_value: Smooth Quant value. - :param port_id: Output port for source node. + :param source_output_port_id: Output port for source node. :param nodes: List of consumers for Smooth node. :return: TransformationCommand instance. """ @@ -245,11 +196,22 @@ def get_weight_channel_axis(node: NNCFNode) -> int: @staticmethod @abstractmethod - def calculate_port_based_channel_axis(port_id: int, transpose: bool) -> int: + def is_node_with_shared_weight(node: NNCFNode, nncf_graph: NNCFGraph) -> bool: """ - Returns port-based channel axis. + Returns true if given node shares constant with a different node. - :param port_id: Specified input port id. - :param transpose: Transpose position. - :return: Channel axis. + :param node: NNCFNode instance. + :param nncf_graph: NNCFGraph instance. + :return: Whether the given node is shares weights with a different node or not. + """ + + @staticmethod + @abstractmethod + def get_filter_fn_for_statistics(activation_port_id: int, algorithm_key: str) -> Callable[[StatisticPoint], bool]: + """ + Returns backend-specific callable to filter statistic containers according to its statistic point. + + :param activation_port_id: Activation port id for the statistic collection target node. + :param algorithm_key: Current algorithm key. + :return: Backend-specific callable to filter statistic containers according to its statistic point. """ diff --git a/nncf/quantization/algorithms/smooth_quant/openvino_backend.py b/nncf/quantization/algorithms/smooth_quant/openvino_backend.py index fd312c202ba..6073a2744cf 100644 --- a/nncf/quantization/algorithms/smooth_quant/openvino_backend.py +++ b/nncf/quantization/algorithms/smooth_quant/openvino_backend.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -9,17 +9,20 @@ # See the License for the specific language governing permissions and # limitations under the License. -from typing import Dict, List, Optional, Tuple +from typing import Callable, List, Tuple import numpy as np import openvino.runtime as ov +import nncf from nncf.common.graph import NNCFGraph from nncf.common.graph import NNCFNode from nncf.common.graph.operator_metatypes import OperatorMetatype from nncf.common.graph.transformations.commands import TargetType +from nncf.common.tensor_statistics.statistic_point import StatisticPoint from nncf.experimental.common.tensor_statistics.collectors import MaxAggregator from nncf.experimental.common.tensor_statistics.collectors import TensorCollector +from nncf.experimental.tensor import Tensor from nncf.openvino.graph.layout import OVLayoutElem from nncf.openvino.graph.layout import get_linear_weights_layout_from_node from nncf.openvino.graph.metatypes.groups import QUANTIZE_AGNOSTIC_OPERATIONS @@ -35,20 +38,26 @@ from nncf.openvino.statistics.collectors import OVNNCFCollectorTensorProcessor from nncf.quantization.algorithms.smooth_quant.backend import SmoothQuantAlgoBackend +OV_PRE_LAYER_TARGET_TYPE = TargetType.PRE_LAYER_OPERATION + class OVSmoothQuantAlgoBackend(SmoothQuantAlgoBackend): @property - def convolution_metatype(self) -> OperatorMetatype: - return OVConvolutionMetatype + def convolution_metatypes(self) -> List[OperatorMetatype]: + return [OVConvolutionMetatype] @property - def matmul_metatype(self) -> OperatorMetatype: - return OVMatMulMetatype + def matmul_metatypes(self) -> List[OperatorMetatype]: + return [OVMatMulMetatype] @property def quantize_agnostic_metatypes(self) -> List[OperatorMetatype]: return QUANTIZE_AGNOSTIC_OPERATIONS + @staticmethod + def pre_layer_target_type() -> TargetType: + return OV_PRE_LAYER_TARGET_TYPE + @staticmethod def target_point(target_type: TargetType, target_node_name: str, port_id: int) -> OVTargetPoint: return OVTargetPoint(target_type, target_node_name, port_id) @@ -58,16 +67,15 @@ def is_node_with_weights(node: NNCFNode) -> bool: return node.layer_attributes and node.layer_attributes.constant_attributes @staticmethod - def get_input_ports_map(node: NNCFNode, nncf_graph: NNCFGraph) -> Dict[str, int]: + def get_activations_port_id(node: NNCFNode, nncf_graph: NNCFGraph) -> int: weight_ports = node.layer_attributes.get_const_port_ids() activation_ports = [ e.input_port_id for e in nncf_graph.get_input_edges(node) if e.input_port_id not in weight_ports ] - if len(weight_ports) != 1 or len(activation_ports) != 1: - raise RuntimeError(f"Too many weight or activation ports for {node.node_name} node") - - return {"activation": activation_ports[0], "weight": weight_ports[0]} + if len(activation_ports) != 1: + raise nncf.InternalError(f"Too many weight or activation ports for {node.node_name} node") + return activation_ports[0] @staticmethod def get_channel_agnostic_reduction_axes(channel_axis: int, shape: Tuple[int]) -> Tuple[int]: @@ -84,84 +92,49 @@ def get_abs_max_channel_collector( return collector @staticmethod - def process_weight_statistics(weights: np.ndarray, reduction_shape: Tuple[int]) -> np.ndarray: - return np.max(np.abs(weights), axis=reduction_shape) - - @staticmethod - def get_weight_value(node_with_weight: NNCFNode, model: ov.Model, port_id: int) -> np.ndarray: - return get_weight_value(node_with_weight, model, port_id) + def get_weight_value(node_with_weight: NNCFNode, model: ov.Model) -> Tensor: + port_id = OVSmoothQuantAlgoBackend.get_weight_tensor_port_id(node_with_weight) + return Tensor(get_weight_value(node_with_weight, model, port_id)) @staticmethod def get_weight_tensor_port_id(node: NNCFNode) -> int: const_ids = node.layer_attributes.get_const_port_ids() if len(const_ids) != 1: - raise RuntimeError(f"Found more than 1 port for {node.node_name} node") + raise nncf.InternalError(f"Found more than 1 port for {node.node_name} node") return const_ids[0] @staticmethod - def clip_statistics(statistics: np.ndarray) -> np.ndarray: - a_min = 1e-5 - squeezed = np.squeeze(statistics) - return np.clip(squeezed, a_min=a_min, a_max=None) - - @staticmethod - def calculate_scale_and_ratio( - activations: np.ndarray, weights: np.ndarray, alpha: float, quantile: Optional[float] = 0.1 - ) -> np.ndarray: - scales = np.power(activations, alpha) / (np.power(weights, 1 - alpha) + np.finfo(float).eps) - - a_min = np.quantile(scales, quantile) - a_max = 1e2 - - scales = np.clip(scales, a_min=a_min, a_max=a_max) - ratio = scales.min() / (scales.max() + np.finfo(float).eps) - return scales, ratio - - @staticmethod - def calculate_activation_scale(scale_value: np.ndarray, activations_size: int, channel_axis: int) -> np.ndarray: - activation_scale = scale_value ** (-1) - if activations_size > 1: - reshape_shape = np.ones(activations_size, dtype=np.int64) - reshape_shape[channel_axis] = activation_scale.size - activation_scale = np.reshape(activation_scale, reshape_shape) - return activation_scale - - @staticmethod - def calculate_weight_scale(scale_value: np.ndarray, weights_size: int, channel_axis: int) -> np.ndarray: - weight_scale = scale_value - if weights_size > 1: - reshape_shape = np.ones(weights_size, dtype=np.int64) - reshape_shape[channel_axis] = scale_value.size - weight_scale = np.reshape(scale_value, reshape_shape) - return weight_scale - - @staticmethod - def weight_update_command( - node_with_weight: NNCFNode, weight_value: np.ndarray, weight_port_id: int - ) -> OVWeightUpdateCommand: + def weight_update_command(node_with_weight: NNCFNode, weight_value: np.ndarray) -> OVWeightUpdateCommand: + weight_port_id = OVSmoothQuantAlgoBackend.get_weight_tensor_port_id(node_with_weight) return OVCommandCreator.create_command_to_update_weight(node_with_weight, weight_value, weight_port_id) @staticmethod def scale_insertion_command( - source_node: NNCFNode, scale_value: np.ndarray, port_id: int, nodes: List[NNCFNode], scale_node_name: str + source_node: NNCFNode, + scale_value: np.ndarray, + source_output_port_id: int, + nodes: List[NNCFNode], + scale_node_name: str, ) -> OVMultiplyInsertionCommand: - return OVCommandCreator.multiply_insertion_command(source_node, nodes, port_id, scale_value, scale_node_name) + return OVCommandCreator.multiply_insertion_command( + source_node, nodes, source_output_port_id, scale_value, scale_node_name + ) @staticmethod def get_activation_channel_axis(node: NNCFNode, port_id: int) -> int: channel_axis = 1 if port_id > 1: - raise RuntimeError(f"{node.metatype.name} can not take more than 2 input tensors.") + raise nncf.InternalError(f"{node.metatype.name} can not take more than 2 input tensors.") - if node.metatype == OVMatMulMetatype: - if ( - node.layer_attributes is not None - and node.layer_attributes.input_attributes is not None - and "transpose" in node.layer_attributes.input_attributes - ): - transpose = node.layer_attributes.input_attributes["transpose"] - channel_axis = OVSmoothQuantAlgoBackend.calculate_port_based_channel_axis(port_id, transpose) + if ( + node.metatype == OVMatMulMetatype + and node.layer_attributes is not None + and node.layer_attributes.input_attributes is not None + and "transpose" in node.layer_attributes.input_attributes + ): + transpose = node.layer_attributes.input_attributes["transpose"] + channel_axis = OVSmoothQuantAlgoBackend.calculate_port_based_channel_axis(port_id, transpose) return channel_axis @@ -176,3 +149,20 @@ def get_weight_channel_axis(node: NNCFNode) -> int: @staticmethod def calculate_port_based_channel_axis(port_id: int, transpose: bool) -> int: return -2 + port_id if transpose else -1 - port_id + + @staticmethod + def is_node_with_shared_weight(node: NNCFNode, nncf_graph: NNCFGraph) -> bool: + weight_port_id = OVSmoothQuantAlgoBackend.get_weight_tensor_port_id(node) + weight_node = nncf_graph.get_input_edges(node)[weight_port_id].from_node + return len(nncf_graph.get_next_nodes(weight_node)) > 1 + + @staticmethod + def get_filter_fn_for_statistics(activation_port_id: int, algorithm_key: str) -> Callable[[StatisticPoint], bool]: + def filter_func(point: StatisticPoint) -> bool: + return ( + algorithm_key in point.algorithm_to_tensor_collectors + and point.target_point.type == OV_PRE_LAYER_TARGET_TYPE + and point.target_point.port_id == activation_port_id + ) + + return filter_func diff --git a/nncf/quantization/algorithms/smooth_quant/torch_backend.py b/nncf/quantization/algorithms/smooth_quant/torch_backend.py new file mode 100644 index 00000000000..a486be98a4f --- /dev/null +++ b/nncf/quantization/algorithms/smooth_quant/torch_backend.py @@ -0,0 +1,162 @@ +# Copyright (c) 2023 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Callable, List, Tuple + +import numpy as np +import torch + +import nncf.torch.graph.operator_metatypes as om +from nncf.common.graph import NNCFGraph +from nncf.common.graph import NNCFNode +from nncf.common.graph.operator_metatypes import OperatorMetatype +from nncf.common.graph.transformations.commands import TargetType +from nncf.common.quantization.quantizer_propagation.structs import QuantizationTrait +from nncf.common.tensor_statistics.statistic_point import StatisticPoint +from nncf.experimental.common.tensor_statistics.collectors import MaxAggregator +from nncf.experimental.common.tensor_statistics.collectors import TensorCollector +from nncf.experimental.tensor import Tensor +from nncf.openvino.graph.node_utils import get_channel_agnostic_reduction_axes +from nncf.openvino.graph.transformations.commands import OVMultiplyInsertionCommand +from nncf.openvino.graph.transformations.commands import OVWeightUpdateCommand +from nncf.quantization.algorithms.smooth_quant.backend import SmoothQuantAlgoBackend +from nncf.torch.graph.transformations.command_creation import create_command_to_update_weight +from nncf.torch.graph.transformations.commands import PTSharedFnInsertionCommand +from nncf.torch.graph.transformations.commands import PTTargetPoint +from nncf.torch.nncf_network import NNCFNetwork +from nncf.torch.quantization.default_quantization import DEFAULT_PT_QUANT_TRAIT_TO_OP_DICT +from nncf.torch.tensor_statistics.collectors import PTAbsMaxReducer +from nncf.torch.tensor_statistics.collectors import PTNNCFCollectorTensorProcessor + + +class SQMultiply(torch.nn.Module): + def __init__(self, scale_value): + super().__init__() + self._scale_value = scale_value + + def forward(self, x): + return torch.mul(x, self._scale_value) + + +PT_PRE_LAYER_TARGET_TYPE = TargetType.OPERATOR_PRE_HOOK + + +class PTSmoothQuantAlgoBackend(SmoothQuantAlgoBackend): + @property + def convolution_metatypes(self) -> List[OperatorMetatype]: + return [ + om.PTModuleConv1dMetatype, + om.PTModuleConv2dMetatype, + om.PTModuleConv3dMetatype, + ] + + @property + def matmul_metatypes(self) -> List[OperatorMetatype]: + return [om.PTModuleLinearMetatype] + + @property + def quantize_agnostic_metatypes(self) -> List[OperatorMetatype]: + return DEFAULT_PT_QUANT_TRAIT_TO_OP_DICT[QuantizationTrait.QUANTIZATION_AGNOSTIC] + + @staticmethod + def pre_layer_target_type() -> TargetType: + return PT_PRE_LAYER_TARGET_TYPE + + @staticmethod + def target_point(target_type: TargetType, target_node_name: str, port_id: int) -> PTTargetPoint: + return PTTargetPoint(target_type, target_node_name, input_port_id=port_id) + + @staticmethod + def is_node_with_weights(node: NNCFNode) -> bool: + # Metatypes of linears and convolutions guarantee + # all nodes with the metatypes have weights, we can skip + # this check by returning True. + return True + + @staticmethod + def get_activations_port_id(node: NNCFNode, nncf_graph: NNCFGraph) -> int: + # Metatypes of linears and convolutions guarantee + # all nodes with the metatypes have 0 activation port id. + return 0 + + @staticmethod + def get_channel_agnostic_reduction_axes(channel_axis: int, shape: Tuple[int]) -> Tuple[int]: + return get_channel_agnostic_reduction_axes([channel_axis], shape) + + @staticmethod + def get_abs_max_channel_collector( + num_samples: int, stats_reduction_axes: Tuple[int], inplace: bool, branch_key: str + ) -> TensorCollector: + collector = TensorCollector() + reducer = PTAbsMaxReducer(reduction_axes=stats_reduction_axes) + aggregator = MaxAggregator(tensor_processor=PTNNCFCollectorTensorProcessor, num_samples=num_samples) + collector.register_statistic_branch(branch_key, reducer, aggregator) + return collector + + @staticmethod + def get_weight_value(node_with_weight: NNCFNode, model: NNCFNetwork) -> Tensor: + node_module = model.nncf.get_containing_module(node_with_weight.node_name) + if node_module.weight is None: + raise RuntimeError(f"{node_module} module has no .weight attribute.") + return Tensor(node_module.weight.data) + + @staticmethod + def get_weight_tensor_port_id(node: NNCFNode) -> int: + const_ids = node.layer_attributes.get_const_port_ids() + if len(const_ids) != 1: + raise RuntimeError(f"Found more than 1 port for {node.node_name} node") + return const_ids[0] + + @staticmethod + def weight_update_command(node_with_weight: NNCFNode, weight_value: np.ndarray) -> OVWeightUpdateCommand: + return create_command_to_update_weight(node_with_weight, weight_value) + + @staticmethod + def scale_insertion_command( + source_node: NNCFNode, + scale_value: np.ndarray, + source_output_port_id: int, + nodes: List[NNCFNode], + scale_node_name: str, + ) -> OVMultiplyInsertionCommand: + input_port_id = 0 + target_points = [] + for node in nodes: + target_points.append(PTTargetPoint(PT_PRE_LAYER_TARGET_TYPE, node.node_name, input_port_id=input_port_id)) + + return PTSharedFnInsertionCommand(target_points, SQMultiply(scale_value), scale_node_name) + + @staticmethod + def get_activation_channel_axis(node: NNCFNode, port_id: int) -> int: + if node.metatype == om.PTModuleLinearMetatype: + return -1 + # TODO: Add activation axis calculation when MatMul will be supported + return 1 + + @staticmethod + def get_weight_channel_axis(node: NNCFNode) -> int: + # TODO: Add activation axis calculation when MatMul will be supported + return 1 + + @staticmethod + def is_node_with_shared_weight(node: NNCFNode, nncf_graph: NNCFGraph) -> bool: + return node.is_shared() + + @staticmethod + def get_filter_fn_for_statistics(activation_port_id: int, algorithm_key: str) -> Callable[[StatisticPoint], bool]: + def filter_func(point: StatisticPoint) -> bool: + return ( + algorithm_key in point.algorithm_to_tensor_collectors + and point.target_point.type == PT_PRE_LAYER_TARGET_TYPE + and point.target_point.input_port_id == activation_port_id + ) + + return filter_func diff --git a/nncf/quantization/algorithms/weight_compression/__init__.py b/nncf/quantization/algorithms/weight_compression/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/quantization/algorithms/weight_compression/__init__.py +++ b/nncf/quantization/algorithms/weight_compression/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/quantization/algorithms/weight_compression/algorithm.py b/nncf/quantization/algorithms/weight_compression/algorithm.py index 3e8d50312e4..a2a80fea257 100644 --- a/nncf/quantization/algorithms/weight_compression/algorithm.py +++ b/nncf/quantization/algorithms/weight_compression/algorithm.py @@ -1,14 +1,4 @@ -# Copyright (c) 2023 Intel Corporation -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# http://www.apache.org/licenses/LICENSE-2.0 -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -22,6 +12,7 @@ from collections import defaultdict from typing import Dict, List, Optional, OrderedDict, Tuple, TypeVar +import nncf from nncf import Dataset from nncf.common.factory import StatisticsAggregatorFactory from nncf.common.graph.graph import NNCFGraph @@ -40,6 +31,7 @@ from nncf.parameters import CompressWeightsMode from nncf.parameters import SensitivityMetric from nncf.quantization.algorithms.algorithm import Algorithm +from nncf.quantization.algorithms.weight_compression.awq import AWQ from nncf.quantization.algorithms.weight_compression.config import WeightCompressionParameters from nncf.quantization.algorithms.weight_compression.mixed_precision import MIXED_PRECISION_CRITERIA from nncf.quantization.algorithms.weight_compression.weight_lowering import WeightCompressionConfig @@ -66,6 +58,8 @@ def __init__( ignored_scope: IgnoredScope, all_layers: bool, sensitivity_metric: SensitivityMetric, + awq: bool, + subset_size: int, ): """ :param mode: Defines a mode for weight compression. @@ -87,10 +81,13 @@ def __init__( that share quantization parameters (scale). The value -1 means no grouping. :param ignored_scope: An ignored scope that defined the list of model control flow graph nodes to be ignored during quantization. - :param all_layers: Indicates whether embeddings and last layers should be compressed to a primary - precision. By default, the backup precision is assigned for the embeddings and last layers. + :param all_layers: Indicates whether embeddings and last MatMul layers should be compressed to a primary + precision. By default, the backup precision is assigned for the embeddings and last MatMul layers. :param sensitivity_metric: The sensitivity metric for assigning quantization precision to layers. In order to preserve the accuracy of the model, the more sensitive layers receives a higher precision. + :param awq: determines whether to use or not modified AWQ algorithm. + :param subset_size: Number of data samples to calculate activation statistics used for assigning different + quantization precision. """ super().__init__() self._mode = mode @@ -102,6 +99,8 @@ def __init__( self._fp_inputs = defaultdict(list) self._all_layers = all_layers self._sensitivity_metric = sensitivity_metric + self._awq = awq + self._subset_size = subset_size @property def available_backends(self) -> List[BackendType]: @@ -123,7 +122,7 @@ def _set_backend_entity(self, model: TModel) -> None: self._backend_entity = PTWeightCompressionAlgoBackend() else: - raise RuntimeError( + raise nncf.UnsupportedBackendError( "Cannot return backend-specific entity because {} is not supported!".format(model_backend.value) ) @@ -134,7 +133,12 @@ def _get_nodes_to_compress(self, nncf_graph: NNCFGraph) -> List[NNCFNode]: :param nncf_graph: NNCFGraph instance. :return: List with the data for each layer. """ - weighted_metatypes = self._backend_entity.matmul_metatypes + self._backend_entity.embedding_metatypes + weighted_metatypes = ( + self._backend_entity.matmul_metatypes + + self._backend_entity.embedding_metatypes + + self._backend_entity.convolution_metatypes + ) + ordered_nodes_to_compress = [] ignored_names = get_ignored_node_names_from_ignored_scope( self._ignored_scope, nncf_graph, strict=self._ignored_scope.validate @@ -158,16 +162,30 @@ def _get_ratio_defining_params( should be quantized or not. :return: Information about each weight node that is considered for mixed precision. """ - if self._mode in [CompressWeightsMode.INT8_SYM, CompressWeightsMode.INT8_ASYM] or self._all_layers: + if self._mode in [CompressWeightsMode.INT8_SYM, CompressWeightsMode.INT8_ASYM]: return all_weight_params + if self._all_layers: + return list(filter(lambda wp: len(wp.reduction_axes) == 1, all_weight_params)) + ratio_defining_params = list( filter( - lambda wp: wp.node_with_weight.metatype not in self._backend_entity.embedding_metatypes, + lambda wp: wp.node_with_weight.metatype in self._backend_entity.matmul_metatypes, all_weight_params, ) ) - if not is_last_layer_shared: + + # Embedding layers are quantized to 4-bits only if all_layers=True. + if self._all_layers: + embedding_params = list( + filter( + lambda wp: wp.node_with_weight.metatype in self._backend_entity.embedding_metatypes, + all_weight_params, + ) + ) + ratio_defining_params.extend(embedding_params) + + if not self._all_layers and not is_last_layer_shared: ratio_defining_params = ratio_defining_params[:-1] return ratio_defining_params @@ -265,7 +283,7 @@ def apply( activations = {} if dataset is not None and self._sensitivity_metric != SensitivityMetric.WEIGHT_QUANTIZATION_ERROR: - activations = self._get_activations(dataset, nodes_to_compress, graph, model) + activations = self._get_activations(dataset, self._subset_size, nodes_to_compress, graph, model) transformed_model = self.do_compression(model, graph, nodes_to_compress, activations) return transformed_model @@ -293,17 +311,25 @@ def do_compression( if weight.dtype not in [TensorDataType.float32, TensorDataType.float16, TensorDataType.float64]: continue reduction_axes = self._backend_entity.get_channel_agnostic_reduction_axes(node, weight_port_id, graph) - if isinstance(reduction_axes, tuple) and len(reduction_axes) != 1: + if ( + self._group_size != -1 + and self._all_layers + and node.metatype in self._backend_entity.embedding_metatypes + and isinstance(reduction_axes, tuple) + and len(reduction_axes) != 1 + ): + # NNCF supports multiple reduction axes only for ops with group_size != -1. + # Convolution ops are always quantized to 8-bits (without groups). + # Embedding layers are quantized to 4-bits only if all_layers=True. + # MatMul ops can't have multiple reduction axes. nncf_logger.warning( f"Weight compression expects a single reduction axis, but {len(reduction_axes)} given. " f"Weight shape: {weight.shape}, reduction axes: {reduction_axes}, " - f"node name: {node.node_name}. The node won't be quantized." + f"node name: {node.node_name}. The node will be asymmetrically quantized to 8 bits." ) - continue - reduction_axis = reduction_axes[0] if isinstance(reduction_axes, tuple) else reduction_axes weight_params = WeightCompressionParameters( - weight_name, node, weight_port_id, weight.size, reduction_axis + weight_name, node, weight_port_id, weight.size, reduction_axes ) all_weight_params.append(weight_params) weight_names.add(weight_name) @@ -312,6 +338,12 @@ def do_compression( self._set_weight_compression_config(ratio_defining_params, model, graph, activations) nncf_logger.info(self._get_bitwidth_distribution_str(all_weight_params, ratio_defining_params)) + if self._awq and activations is not None and self._mode != CompressWeightsMode.NF4: + awq_algo = AWQ( + model, self._backend_entity.name_to_node_mapping, all_weight_params, nodes_to_compress, activations + ) + awq_algo.apply(model, graph) + # Compress model using weight compression parameters transformed_model = self._backend_entity.transform_model( model, graph, track(all_weight_params, description="Applying Weight Compression") @@ -326,6 +358,7 @@ def do_compression( "all_layers": self._all_layers, "ignored_scope": self._ignored_scope, "sensitivity_metric": self._sensitivity_metric.value, + "awq": self._awq, }, algo_name="weight_compression", ) @@ -382,19 +415,19 @@ def input_filter_func(point): return self._fp_inputs[input_id] def _get_activations( - self, dataset: Dataset, nodes_to_compress: List[NNCFNode], graph: NNCFGraph, model: TModel + self, dataset: Dataset, subset_size: int, nodes_to_compress: List[NNCFNode], graph: NNCFGraph, model: TModel ) -> Dict[str, List[Tensor]]: """ Collects input activations for the given nodes on the dataset. :param dataset: Dataset to collect values. + :param subset_size: Number of data samples to calculate activation statistics used for assigning different + quantization precision. :param nodes_to_compress: List of nodes, whose inputs are collected. :param model: Model for statistics collection. :param graph: Model graph. :return: statistics values itself per node name. """ - subset_size = 128 - activations = {} _collected_stat_inputs_map = {} statistic_container = StatisticPointsContainer() diff --git a/nncf/quantization/algorithms/weight_compression/awq.py b/nncf/quantization/algorithms/weight_compression/awq.py new file mode 100644 index 00000000000..91c8dbcce36 --- /dev/null +++ b/nncf/quantization/algorithms/weight_compression/awq.py @@ -0,0 +1,276 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from copy import deepcopy +from dataclasses import dataclass +from typing import Any, Dict, List, Optional, TypeVar + +from nncf import Dataset +from nncf.common.graph.graph import NNCFGraph +from nncf.common.graph.graph import NNCFNode +from nncf.common.graph.graph_matching import find_subgraphs_matching_pattern +from nncf.common.logging.track_progress import track +from nncf.common.tensor_statistics.statistic_point import StatisticPointsContainer +from nncf.common.utils.backend import BackendType +from nncf.common.utils.backend import get_backend +from nncf.experimental.tensor import functions as fns +from nncf.quantization.algorithms.algorithm import Algorithm +from nncf.quantization.algorithms.weight_compression.config import WeightCompressionParameters +from nncf.quantization.algorithms.weight_compression.weight_lowering import do_dequantization +from nncf.quantization.algorithms.weight_compression.weight_lowering import do_integer_quantization +from nncf.quantization.passes import transform_to_inference_graph + +TModel = TypeVar("TModel") +TTensor = TypeVar("TTensor") +TWeightType = TypeVar("TWeightType") + + +@dataclass +class AWQCompressionInfo: + """ + Information on AWQ nodes. + """ + + weight_params: WeightCompressionParameters = None + target_node: NNCFNode = None + merge_node: NNCFNode = None + + +class AWQ(Algorithm): + """ + Modified AWQ algorithm implementation. + """ + + def __init__( + self, + model: TModel, + name_to_node_mapping: Dict[str, Any], + all_weight_params: List[WeightCompressionParameters], + nodes_to_compress: List[NNCFNode], + activations: Optional[Dict[str, TTensor]] = None, + subset_size: int = 32, + percent_to_apply=0.002, + alpha_min=0.01, + alpha_max=1.0, + steps=100, + ): + """ + :param model: Model for applying algorithm. + :param name_to_node_mapping: Name to node mapping for updating node weights. + :param all_weight_params: List of all weight parameters. + :param nodes_to_compress: List of nodes for processing. + :param activations: The input activations of the layers considered for compression. + :param subset_size: The number of samples for AWQ. + :param percent_to_apply: The percent of outliers for correction. + :param alpha_min: Minimum value of smoothness parameter for grid search. + :param alpha_max: Maximal value of smoothness parameter for grid search. + :param steps: The number of the steps in grid search. + """ + super().__init__() + self.name_to_node_mapping = name_to_node_mapping + self._all_weight_params = all_weight_params + self._nodes_to_compress = nodes_to_compress + self._activations = activations + self._subset_size = subset_size + self._percent_to_apply = percent_to_apply + self._alpha_min = alpha_min + self._alpha_max = alpha_max + self._steps = steps + self._backend_entity = None + self._patterns = None + + self._set_backend_entity(model) + + @property + def available_backends(self) -> List[BackendType]: + return [BackendType.OPENVINO] + + def _set_backend_entity(self, model: TModel) -> None: + """ + Creates a helper class with a backed-specific logic of the algorithm. + + :param model: Backend-specific input model. + :param all_weight_params: List of all weight parameters. + :param nodes_to_compress: List of nodes for processing. + :param activations: The input activations of the layers considered for compression. + """ + + model_backend = get_backend(model) + if model_backend == BackendType.OPENVINO: + from nncf.quantization.algorithms.weight_compression.openvino_backend import OVAWQAlgoAlgoBackend + + self._backend_entity = OVAWQAlgoAlgoBackend(model) + self._backend_entity.name_to_node_mapping = self.name_to_node_mapping + self._patterns = self._backend_entity.get_awq_patterns() + else: + raise RuntimeError( + "Cannot return backend-specific AWQ entity because {} is not supported!".format(model_backend.value) + ) + + def apply( + self, + model: TModel, + graph: NNCFGraph, + statistic_points: Optional[StatisticPointsContainer] = None, + dataset: Optional[Dataset] = None, + ) -> TModel: + """ + Applies the algorithm to the model. + + :param model: Model for applying algorithm. + :param graph: Model graph. + :param statistic_points: Statistic points with collected statistics values. + :param dataset: A representative dataset for the calibration process. + :return: A resulting model. + """ + matches = [] + + inference_nncf_graph = transform_to_inference_graph(deepcopy(graph), [], [], []) + nx_graph = inference_nncf_graph.get_nx_graph_copy() + for _, pattern_graph in self._patterns.items(): + matches.extend(find_subgraphs_matching_pattern(nx_graph, pattern_graph(), strict=False)) + + if len(matches) == 0: + return model + + target_node_names = [] + merge_node_names = [] + awq_data = {} + name_mapping = {wp.weight_name: idx for idx, wp in enumerate(self._all_weight_params)} + + for match in matches: + nncf_node = graph.get_node_by_key(match[-1]) + for weight_op_friendly_name, _ in self._backend_entity.get_weight_names_and_port_ids(nncf_node, graph): + target_node_names.append(weight_op_friendly_name) + + nncf_node = graph.get_node_by_key(match[0]) + for weight_op_friendly_name, _ in self._backend_entity.get_weight_names_and_port_ids(nncf_node, graph): + merge_node_names.append(weight_op_friendly_name) + + assert len(target_node_names) == len(merge_node_names) + weight_params = self._all_weight_params[name_mapping[target_node_names[-1]]] + if weight_params.compression_config.num_bits != 4: + continue + target_node = self._nodes_to_compress[name_mapping[target_node_names[-1]]] + merge_node = self._nodes_to_compress[name_mapping[merge_node_names[-1]]] + + awq_data[target_node.node_name] = AWQCompressionInfo(weight_params, target_node, merge_node) + + alpha_step = (self._alpha_max - self._alpha_min) / self._steps + + for k, awq_data_item in track(awq_data.items(), description="Applying AWQ"): + wp = awq_data_item.weight_params + target_node = awq_data_item.target_node + merge_node = awq_data_item.merge_node + + weight_data = self._backend_entity.get_weight_names_and_port_ids(wp.node_with_weight, graph) + if len(weight_data) != 1: # not supported by the algorithm + continue + _, weight_port_id = weight_data[0] + + config = wp.compression_config + + stats = self._activations[k] + X = fns.stack([fns.mean(stat, axis=0) for stat in stats]) + X = fns.transpose(X) + if X.shape[1] > self._subset_size: + X = X[:, : self._subset_size] + + s = fns.max(fns.abs(X), axis=1) + + top_k = max(int(s.shape[0] * self._percent_to_apply), 1) + topk_idxs = fns.argsort(-s)[:top_k] + + groups_to_correct = set() + for idx in topk_idxs: + groups_to_correct.add(idx.data // config.group_size) + + groups_to_correct = list(groups_to_correct) + + weight = self._backend_entity.get_weight( + wp.node_with_weight, weight_port_id, model, graph + ) # get_const_value(wp.weight_node) + assert isinstance(wp.reduction_axes, tuple) and len(wp.reduction_axes) == 1 + reduction_axis = wp.reduction_axes[0] + + if reduction_axis == 0: + weight = fns.transpose(weight) + reduction_axis = 1 + + shape_vector = fns.mean(X, axis=1) + scale = fns.ones_like(shape_vector) + + awq_config = deepcopy(config) + awq_config.group_size = -1 + + for gi in groups_to_correct: + offset = gi * config.group_size + gscale = s[offset : offset + config.group_size] + + a_min = fns.quantile(gscale, 0.1) + a_max = 1e2 + gscale = fns.clip(gscale, a_min=a_min, a_max=a_max) + + gweight = weight[:, offset : offset + config.group_size] + gacts = X[offset : offset + config.group_size, :] + + fp32_out = fns.matmul(gweight, gacts) + min_diff = fns.max(fns.abs(fp32_out)) + best_scale = None + + alpha = self._alpha_min + for _ in range(self._steps): + cur_scale = gscale**alpha + + g_compressed_weighs, g_c_scale, g_c_zp = do_integer_quantization( + gweight * cur_scale, reduction_axis, awq_config + ) + g_decompressed_weighs = do_dequantization(g_compressed_weighs, g_c_scale, g_c_zp) + sacts = gacts / fns.unsqueeze(cur_scale, 1) + + cur_out = fns.matmul(g_decompressed_weighs, sacts) + cur_diff = fns.mean(fns.abs(cur_out - fp32_out)) + if cur_diff < min_diff: + min_diff = cur_diff + best_scale = cur_scale + alpha += alpha_step + + if best_scale is not None: + scale.data[offset : offset + config.group_size] = best_scale.data + + a_scale = scale + w_scale = scale + if wp.reduction_axes[0] == 0: + w_scale = fns.unsqueeze(w_scale, 1) + a_scale = fns.unsqueeze(1.0 / a_scale, 0) + else: + w_scale = fns.unsqueeze(w_scale, 0) + a_scale = fns.unsqueeze(1.0 / a_scale, 1) + + scaled_weight = weight * w_scale + self._backend_entity.set_weight(wp.node_with_weight, weight_port_id, model, graph, scaled_weight) + + for _, port_id in self._backend_entity.get_weight_names_and_port_ids(merge_node, graph): + merge_weight = self._backend_entity.get_weight(merge_node, port_id, model, graph) + merge_weight = merge_weight * a_scale + self._backend_entity.set_weight(merge_node, port_id, model, graph, merge_weight) + + return model + + def get_statistic_points(self, model: TModel, graph: NNCFGraph) -> StatisticPointsContainer: + """ + Returns statistic points, for which StatisticsCollector should collect statistics. + + :param model: Model for statistics collection. + :param graph: Model graph. + :return: Statistic points, for which StatisticsCollector should collect statistics. + """ + return StatisticPointsContainer() diff --git a/nncf/quantization/algorithms/weight_compression/awq_patterns.py b/nncf/quantization/algorithms/weight_compression/awq_patterns.py new file mode 100644 index 00000000000..4e702e5a7ca --- /dev/null +++ b/nncf/quantization/algorithms/weight_compression/awq_patterns.py @@ -0,0 +1,37 @@ +# Copyright (c) 2023 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from functools import partial + +from nncf.common.graph.patterns import GraphPattern +from nncf.common.utils.registry import Registry + +AWQ_PATTERNS = Registry("awq") + + +@AWQ_PATTERNS.register("MatMul_Mul_MatMul") +def create_matmul_mul_matmul(matmul_metatype, multiply_metatype) -> GraphPattern: + pattern = GraphPattern() + linear_node_1 = pattern.add_node(**{GraphPattern.LABEL_ATTR: "LINEAR", GraphPattern.METATYPE_ATTR: matmul_metatype}) + mul_node = pattern.add_node(**{GraphPattern.LABEL_ATTR: "MULTIPLY", GraphPattern.METATYPE_ATTR: multiply_metatype}) + linear_node_2 = pattern.add_node(**{GraphPattern.LABEL_ATTR: "LINEAR", GraphPattern.METATYPE_ATTR: matmul_metatype}) + + pattern.add_edge(linear_node_1, mul_node) + pattern.add_edge(mul_node, linear_node_2) + return pattern + + +def get_awq_patterns(matmul_metatype, multiply_metatype): + res = Registry("awq") + for k, v in AWQ_PATTERNS.registry_dict.items(): + res.registry_dict[k] = partial(v, matmul_metatype, multiply_metatype) + + return res.registry_dict diff --git a/nncf/quantization/algorithms/weight_compression/backend.py b/nncf/quantization/algorithms/weight_compression/backend.py index 557f123eae7..b6d61e7beb4 100644 --- a/nncf/quantization/algorithms/weight_compression/backend.py +++ b/nncf/quantization/algorithms/weight_compression/backend.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -33,6 +33,13 @@ def matmul_metatypes(self) -> List[OperatorMetatype]: Property for the backend-specific metatypes for matmul layers. """ + @property + @abstractmethod + def convolution_metatypes(self) -> List[OperatorMetatype]: + """ + Property for the backend-specific metatypes for convolution layers. + """ + @property @abstractmethod def embedding_metatypes(self) -> List[OperatorMetatype]: @@ -88,6 +95,20 @@ def get_weight(self, node_with_weight: NNCFNode, weight_port_id: int, model: TMo :return: The weight tensor. """ + @abstractmethod + def set_weight( + self, node_with_weight: NNCFNode, weight_port_id: int, model: TModel, graph: NNCFGraph, weight: Tensor + ) -> None: + """ + Update a weight associated with the given node on the given port id. + + :param node_with_weight: The node with weight. + :param weight_port_id: The weight port id for given node with weight. + :param model: The model. + :param graph: The model graph associated with the model. + :param weight: The weight tensor. + """ + @abstractmethod def transform_model( self, model: TModel, graph: NNCFGraph, weight_compression_parameters: Iterable[WeightCompressionParameters] @@ -148,3 +169,11 @@ def dump_parameters( :param parameters: Incoming dictionary with parameters to save. :param path: Optional list of the paths. """ + + +class AWQAlgoBackend(WeightCompressionAlgoBackend): + @staticmethod + def get_awq_patterns() -> Dict: + """ + Returns patterns of nodes in network graph for applying AWQ algorithm. + """ diff --git a/nncf/quantization/algorithms/weight_compression/config.py b/nncf/quantization/algorithms/weight_compression/config.py index 915b9ba23a7..61d976a71e5 100644 --- a/nncf/quantization/algorithms/weight_compression/config.py +++ b/nncf/quantization/algorithms/weight_compression/config.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -9,7 +9,7 @@ # See the License for the specific language governing permissions and # limitations under the License. from dataclasses import dataclass -from typing import Optional, TypeVar +from typing import Optional, Tuple, TypeVar import numpy as np @@ -49,7 +49,7 @@ class WeightCompressionParameters: :param node_with_weight: Node with weight in the NNCF graph. :param weight_port_id: Number of elements in the weight array. :param num_weights: Number of elements in the weight array. - :param reduction_axis: Axis, along which to reduce (collect) different statistics (e.g. min, max). + :param reduction_axes: Axes, along which to reduce (collect) different statistics (e.g. min, max). :param compression_config: Configuration of weight compression for the weight node. """ @@ -57,7 +57,7 @@ class WeightCompressionParameters: node_with_weight: NNCFNode weight_port_id: int num_weights: np.uint64 - reduction_axis: int + reduction_axes: Tuple[int, ...] compression_config = WeightCompressionConfig() def __post_init__(self): diff --git a/nncf/quantization/algorithms/weight_compression/mixed_precision.py b/nncf/quantization/algorithms/weight_compression/mixed_precision.py index aac509bfe7c..29a5fc4bb3a 100644 --- a/nncf/quantization/algorithms/weight_compression/mixed_precision.py +++ b/nncf/quantization/algorithms/weight_compression/mixed_precision.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -102,8 +102,8 @@ def _calc_weight_sensitivity(self, weight_param: WeightCompressionParameters) -> weight_param.node_with_weight, weight_param.weight_port_id, self._model, self._graph ) backup_config = weight_param.compression_config - reduction_axis = weight_param.reduction_axis - int_error = get_integer_quantization_error(weight, reduction_axis, backup_config) + reduction_axes = weight_param.reduction_axes + int_error = get_integer_quantization_error(weight, reduction_axes, backup_config) eps = fns.finfo(weight).eps return 1 / (int_error + eps) @@ -113,7 +113,7 @@ def _calc_score_per_node(self, weight_param: WeightCompressionParameters) -> flo def _calc_sensitivity(self) -> List[float]: scores = [] - for weight_param in track(self._weight_params, description="Searching for Mixed-Precision Configuration"): + for weight_param in track(self._weight_params, description="Mixed-Precision assignment"): scores.append(self._calc_score_per_node(weight_param)) return scores @@ -168,14 +168,14 @@ def _calc_weight_sensitivity(self, weight_param: WeightCompressionParameters) -> weight_param.node_with_weight, weight_param.weight_port_id, self._model, self._graph ) backup_config = weight_param.compression_config - reduction_axis = weight_param.reduction_axis + reduction_axes = weight_param.reduction_axes orig_shape = weight.shape if weight.dtype != TensorDataType.float32: weight = weight.astype(TensorDataType.float32) - compressed_weights, scale, zero_point = do_integer_quantization(weight, reduction_axis, backup_config) + compressed_weights, scale, zero_point = do_integer_quantization(weight, reduction_axes, backup_config) decompressed_weight = (compressed_weights - zero_point).astype(weight.dtype) * scale decompressed_weight = decompressed_weight.reshape(orig_shape) return fns.linalg.norm(decompressed_weight - weight, ord="fro").item() diff --git a/nncf/quantization/algorithms/weight_compression/openvino_backend.py b/nncf/quantization/algorithms/weight_compression/openvino_backend.py index 1ca5a83ef6b..55e6ef4f3d2 100644 --- a/nncf/quantization/algorithms/weight_compression/openvino_backend.py +++ b/nncf/quantization/algorithms/weight_compression/openvino_backend.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -19,8 +19,7 @@ from nncf.common.graph.transformations.commands import TargetType from nncf.experimental.common.tensor_statistics.collectors import TensorCollector from nncf.experimental.tensor.tensor import Tensor -from nncf.openvino.graph.metatypes.openvino_metatypes import OVEmbeddingMetatype -from nncf.openvino.graph.metatypes.openvino_metatypes import OVMatMulMetatype +from nncf.openvino.graph.metatypes import openvino_metatypes as om from nncf.openvino.graph.model_transformer import OVModelTransformer from nncf.openvino.graph.node_utils import get_channel_agnostic_reduction_axes from nncf.openvino.graph.node_utils import get_const_value @@ -29,6 +28,7 @@ from nncf.openvino.rt_info import dump_parameters from nncf.openvino.statistics.collectors import get_raw_stat_collector from nncf.parameters import CompressWeightsMode +from nncf.quantization.algorithms.weight_compression.awq_patterns import get_awq_patterns from nncf.quantization.algorithms.weight_compression.backend import WeightCompressionAlgoBackend from nncf.quantization.algorithms.weight_compression.config import WeightCompressionParameters from nncf.quantization.algorithms.weight_compression.weight_lowering import compress_weight @@ -40,11 +40,21 @@ def __init__(self, model: ov.Model): @property def matmul_metatypes(self) -> List[OperatorMetatype]: - return [OVMatMulMetatype] + return [om.OVMatMulMetatype] + + @property + def convolution_metatypes(self) -> List[OperatorMetatype]: + return [ + om.OVConvolutionMetatype, + om.OVDepthwiseConvolutionMetatype, + om.OVConvolutionBackpropDataMetatype, + om.OVGroupConvolutionMetatype, + om.OVGroupConvolutionBackpropDataMetatype, + ] @property def embedding_metatypes(self) -> List[OperatorMetatype]: - return [OVEmbeddingMetatype] + return [om.OVEmbeddingMetatype] @staticmethod def is_node_with_weights(node: NNCFNode, graph: NNCFGraph) -> bool: @@ -89,6 +99,27 @@ def get_weight(self, node_with_weight: NNCFNode, weight_port_id: int, model: ov. weight_tensor = get_const_value(weight_node) return Tensor(weight_tensor) + def set_weight( + self, node_with_weight: NNCFNode, weight_port_id: int, model: ov.Model, graph: NNCFGraph, weight: Tensor + ): + node_with_const = self.name_to_node_mapping[node_with_weight.node_name] + + const_port = node_with_const.input(weight_port_id) + const_node = node_with_const.input_value(weight_port_id).get_node() + + new_const_node = ov.runtime.op.Constant(weight.data, shared_memory=True) + new_const_node.set_friendly_name(const_node.get_friendly_name()) + const_port.replace_source_output(new_const_node.output(0)) + + const_name = node_with_weight.layer_attributes.constant_attributes[weight_port_id]["name"] + self.name_to_node_mapping[const_name] = new_const_node + + new_output = new_const_node.output(0) + for target_input in const_node.output(0).get_target_inputs(): + target_input.replace_source_output(new_output) + + del const_node + def transform_model( self, model: ov.Model, graph: NNCFGraph, weight_compression_parameters: Iterable[WeightCompressionParameters] ) -> ov.Model: @@ -117,7 +148,7 @@ def transform_model( weight = Tensor(get_const_value(const_node)) original_shape = weight.shape - compressed_weight = compress_weight(weight, wc_params.reduction_axis, compression_config) + compressed_weight = compress_weight(weight, wc_params.reduction_axes, compression_config) compressed_const = opset.constant( compressed_weight.tensor.data, dtype=compression_dtype, name=const_node_name @@ -132,10 +163,12 @@ def transform_model( converted_zero_point = opset.convert(zero_point_const, const_dtype) converted_const = opset.subtract(converted_const, converted_zero_point) - scale_data = compressed_weight.scale.data + scale_const = opset.constant( + compressed_weight.scale.data, dtype=const_dtype, name=f"{const_node_name}/scale" + ) mul = opset.multiply( converted_const, - scale_data.astype(const_dtype), + scale_const, name=f"{const_node_name}/fq_weights_{wc_params.weight_port_id}", ) @@ -156,3 +189,9 @@ def dump_parameters( model: ov.Model, parameters: Dict, algo_name: Optional[str] = "quantization", path: Optional[List] = None ) -> None: dump_parameters(model, parameters, algo_name, path) + + +class OVAWQAlgoAlgoBackend(OVWeightCompressionAlgoBackend): + @staticmethod + def get_awq_patterns(): + return get_awq_patterns(om.OVMatMulMetatype, om.OVMultiplyMetatype) diff --git a/nncf/quantization/algorithms/weight_compression/torch_backend.py b/nncf/quantization/algorithms/weight_compression/torch_backend.py index 89484afa54e..e0c291e81b0 100644 --- a/nncf/quantization/algorithms/weight_compression/torch_backend.py +++ b/nncf/quantization/algorithms/weight_compression/torch_backend.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -13,6 +13,7 @@ import torch +import nncf from nncf.common.graph.definitions import NNCFGraphNodeType from nncf.common.graph.graph import NNCFGraph from nncf.common.graph.graph import NNCFNode @@ -39,12 +40,16 @@ def split_weight_name(weight_name: str) -> Tuple[str, str]: index = weight_name.rfind(".") + if index == -1: + return str(), weight_name module_name = weight_name[:index] weight_attr_name = weight_name[index + 1 :] return module_name, weight_attr_name def get_module_by_name(module_name: str, model: torch.nn.Module) -> torch.nn.Module: + if not module_name: + return model curr_module = model for name in module_name.split("."): for child_name, child_module in curr_module.named_children(): @@ -52,7 +57,7 @@ def get_module_by_name(module_name: str, model: torch.nn.Module) -> torch.nn.Mod curr_module = child_module break else: - raise RuntimeError(f"Could not find the {module_name} module in the model.") + raise nncf.ModuleNotFoundError(f"Could not find the {module_name} module in the model.") return curr_module @@ -73,7 +78,7 @@ def get_weight_node(node_with_weight: NNCFNode, weight_port_id: int, graph: NNCF if edge.input_port_id == weight_port_id: weight_node = find_weight_node_in_constant_subgraph(prev_node, graph) if weight_node is None: - raise RuntimeError("Could not find a constant node in the model graph.") + raise nncf.InternalError("Could not find a constant node in the model graph.") return weight_node @@ -84,6 +89,17 @@ class PTWeightCompressionAlgoBackend(WeightCompressionAlgoBackend): } MATMUL_METATYPES = [om.PTLinearMetatype, om.PTMatMulMetatype, om.PTAddmmMetatype] EMBEDDING_METATYPES = [om.PTEmbeddingMetatype] + CONVOLUTION_METATYPES = [ + om.PTConv1dMetatype, + om.PTConv2dMetatype, + om.PTConv3dMetatype, + om.PTDepthwiseConv1dSubtype, + om.PTDepthwiseConv2dSubtype, + om.PTDepthwiseConv3dSubtype, + om.PTConvTranspose1dMetatype, + om.PTConvTranspose2dMetatype, + om.PTConvTranspose3dMetatype, + ] @property def matmul_metatypes(self) -> List[OperatorMetatype]: @@ -93,11 +109,16 @@ def matmul_metatypes(self) -> List[OperatorMetatype]: def embedding_metatypes(self) -> List[OperatorMetatype]: return PTWeightCompressionAlgoBackend.EMBEDDING_METATYPES + @property + def convolution_metatypes(self) -> List[OperatorMetatype]: + return PTWeightCompressionAlgoBackend.CONVOLUTION_METATYPES + @staticmethod def is_node_with_weights(node: NNCFNode, graph: NNCFGraph) -> bool: if ( node.metatype not in PTWeightCompressionAlgoBackend.MATMUL_METATYPES and node.metatype not in PTWeightCompressionAlgoBackend.EMBEDDING_METATYPES + and node.metatype not in PTWeightCompressionAlgoBackend.CONVOLUTION_METATYPES ): return False for prev_node in graph.get_previous_nodes(node): @@ -128,22 +149,30 @@ def get_channel_agnostic_reduction_axes( weight_node = get_weight_node(node_with_weight, weight_port_id, graph) ndims = len(weight_node.layer_attributes.shape) - reduction_axis = None + reduction_axes = None if node_with_weight.metatype == om.PTEmbeddingMetatype: - reduction_axis = [1] + reduction_axes = [1] elif node_with_weight.metatype == om.PTLinearMetatype: - reduction_axis = [ndims - 1] + reduction_axes = [ndims - 1] elif node_with_weight.metatype == om.PTMatMulMetatype: if weight_port_id == 0: - reduction_axis = [ndims - 1] + reduction_axes = [ndims - 1] elif weight_port_id == 1: - reduction_axis = [max(0, ndims - 2)] + reduction_axes = [max(0, ndims - 2)] elif node_with_weight.metatype == om.PTAddmmMetatype: if weight_port_id == 1: - reduction_axis = [ndims - 1] + reduction_axes = [ndims - 1] elif weight_port_id == 2: - reduction_axis = [max(0, ndims - 2)] - return reduction_axis + reduction_axes = [max(0, ndims - 2)] + elif node_with_weight.metatype in PTWeightCompressionAlgoBackend.CONVOLUTION_METATYPES: + channel_idx = ( + 1 + if node_with_weight.metatype + in [om.PTConvTranspose1dMetatype, om.PTConvTranspose2dMetatype, om.PTConvTranspose3dMetatype] + else 0 + ) + reduction_axes = [i for i in range(ndims) if i != channel_idx] + return tuple(reduction_axes) @staticmethod def target_point(target_type: TargetType, target_node_name: str, port_id: int) -> PTTargetPoint: @@ -177,10 +206,15 @@ def get_weight( module = get_module_by_name(module_name, model) weight = getattr(module, weight_attr_name) if weight is None or not isinstance(weight, torch.nn.Parameter): - raise RuntimeError(f"Could not find a torch.nn.Parameter in the model by name {weight_name}.") + raise nncf.InternalError(f"Could not find a torch.nn.Parameter in the model by name {weight_name}.") return Tensor(weight) + def set_weight( + self, node_with_weight: NNCFNode, weight_port_id: int, model: torch.nn.Module, graph: NNCFGraph, weight: Tensor + ): + pass + def transform_model( self, model: NNCFNetwork, graph: NNCFGraph, weight_compression_parameters: Iterable[WeightCompressionParameters] ) -> NNCFNetwork: @@ -201,10 +235,10 @@ def transform_model( module = get_module_by_name(module_name, model) weight = getattr(module, weight_attr_name) if weight is None or not isinstance(weight, torch.nn.Parameter): - raise RuntimeError(f"Could not find a torch.nn.Parameter in the model by name {weight_name}.") + raise nncf.InternalError(f"Could not find a torch.nn.Parameter in the model by name {weight_name}.") # calculates compressed weights and decompression parameters - compressed_weight = compress_weight(Tensor(weight), wc_params.reduction_axis, compression_config) + compressed_weight = compress_weight(Tensor(weight), wc_params.reduction_axes, compression_config) # pack compressed tensor packed_tensor = compressed_weight.tensor.astype(TensorDataType.uint8) diff --git a/nncf/quantization/algorithms/weight_compression/weight_lowering.py b/nncf/quantization/algorithms/weight_compression/weight_lowering.py index d9358d5a7f7..117640511fc 100644 --- a/nncf/quantization/algorithms/weight_compression/weight_lowering.py +++ b/nncf/quantization/algorithms/weight_compression/weight_lowering.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -12,6 +12,7 @@ from dataclasses import dataclass from typing import Optional, Tuple +import nncf from nncf.experimental.tensor import Tensor from nncf.experimental.tensor.definitions import TensorDataType from nncf.experimental.tensor.functions import numeric as fns @@ -19,6 +20,8 @@ from nncf.quantization.algorithms.weight_compression.config import WeightCompressionConfig from nncf.quantization.fake_quantize import calculate_scale_zero_point +ReductionAxes = Tuple[int, ...] + @dataclass class CompressedWeight: @@ -36,40 +39,47 @@ class CompressedWeight: zero_point: Optional[Tensor] = None -def reshape_weight_for_grouped_quantization(weight: Tensor, reduction_axis: int, group_size: int) -> Tuple[Tensor, int]: +def reshape_weight_for_grouped_quantization( + weight: Tensor, reduction_axes: ReductionAxes, group_size: int +) -> Tuple[Tensor, int]: """ Reshapes weight for group-wise quantization and return a new reduction axis for collecting statistics per group dimension. Having weight with shapes [c_out, c_in] and group size = 128, shape of reshaped weight is [c_out, c_in // 128, 128]. :param weight: Weight array to compress. - :param reduction_axis: Axis, along which to reduce (collect) different statistics (e.g. min, max). + :param reduction_axes: Axes, along which to reduce (collect) different statistics (e.g. min, max). :param group_size: Number of weights (e.g. 128) in the channel dimension that share quantization parameters (scale). :return: reshaped weight and new reduction axis. """ assert group_size != -1 - assert isinstance(reduction_axis, int) - channel_size = weight.shape[reduction_axis] + if isinstance(reduction_axes, tuple) and len(reduction_axes) == 1: + reduction_axes = reduction_axes[0] + if not isinstance(reduction_axes, int): + raise NotImplementedError( + f"Group-wise quantization expects a single reduction axis, but given: {reduction_axes}." + ) + channel_size = weight.shape[reduction_axes] if channel_size % group_size != 0: - raise RuntimeError(f"Channel size {channel_size} should be divisible by size of group {group_size}") + raise nncf.ValidationError(f"Channel size {channel_size} should be divisible by size of group {group_size}") num_groups_per_channel = channel_size // group_size shape = list(weight.shape) # [a1, r, a2] - "r" refers to number of channels along reduction axis - shape[reduction_axis : reduction_axis + 1] = (num_groups_per_channel, group_size) + shape[reduction_axes : reduction_axes + 1] = (num_groups_per_channel, group_size) reshaped_weight = weight.reshape(shape) - reduction_axis += 1 - return reshaped_weight, reduction_axis + reduction_axes += 1 + return reshaped_weight, reduction_axes def calculate_normalized_weight_and_nf4_scale( - weight: Tensor, reduction_axis: int, group_size: int = -1 + weight: Tensor, reduction_axes: ReductionAxes, group_size: int = -1 ) -> Tuple[Tensor, Tensor]: """ Calculates scale for nf4 quantization and normalizes weights by the scale. Weights are reshaped in case of positive value of group size. :param weight: Weight array to compress. - :param reduction_axis: Axis, along which to reduce (collect) different statistics (e.g. min, max). + :param reduction_axes: Axes, along which to reduce (collect) different statistics (e.g. min, max). :param group_size: Number of weights (e.g. 128) in the channel dimension that share quantization parameters (scale). The value -1 means no grouping. Defaults to -1. :return: Normalized weight tensor of float32 type and nf4 scale tensor of float32 type. @@ -79,10 +89,10 @@ def calculate_normalized_weight_and_nf4_scale( if group_size != -1: # weights are reshaped: [a1, r, a2] -> [a1, r//gs, gs, a2] - weight, reduction_axis = reshape_weight_for_grouped_quantization(weight, reduction_axis, group_size) - scale = fns.max(fns.abs(weight), axis=reduction_axis, keepdims=True) # [a1, r//gs, 1, a2] + weight, reduction_axes = reshape_weight_for_grouped_quantization(weight, reduction_axes, group_size) + scale = fns.max(fns.abs(weight), axis=reduction_axes, keepdims=True) # [a1, r//gs, 1, a2] else: - scale = fns.max(fns.abs(weight), axis=reduction_axis, keepdims=True) # [a1, 1, a2] + scale = fns.max(fns.abs(weight), axis=reduction_axes, keepdims=True) # [a1, 1, a2] eps = fns.finfo(weight).eps # NOTE: adding machine epsilon to avoid division by zero scale = fns.where(fns.abs(scale) < eps, eps, scale) @@ -91,7 +101,7 @@ def calculate_normalized_weight_and_nf4_scale( def do_integer_quantization( - weight: Tensor, reduction_axis: int, config: WeightCompressionConfig + weight: Tensor, reduction_axes: ReductionAxes, config: WeightCompressionConfig ) -> Tuple[Tensor, Tensor, Tensor]: """ The method quantizes the given weights to integer data type in accordance with the compression config. @@ -110,7 +120,7 @@ def do_integer_quantization( (scales). :param weight: Weight array to compress. - :param reduction_axis: Axis, along which to reduce (collect) different statistics (e.g. min, max). + :param reduction_axes: Axes, along which to reduce (collect) different statistics (e.g. min, max). :param config: Information on how to compress (quantize) a specific weight. :return: The compressed weights tensor of uint8 type, scale tensor of float32 type and zero point tensor of int32 type that was used for its quantization. @@ -128,16 +138,16 @@ def do_integer_quantization( if group_size != -1: # weights are reshaped from [a1, r, a2] to [a1, r//gs, gs, a2] - weight, reduction_axis = reshape_weight_for_grouped_quantization(weight, reduction_axis, group_size) + weight, reduction_axes = reshape_weight_for_grouped_quantization(weight, reduction_axes, group_size) if mode in [CompressWeightsMode.INT8_ASYM, CompressWeightsMode.INT4_ASYM]: - min_values = fns.min(weight, axis=reduction_axis, keepdims=True) # [a1, r, a2] -> [a1, 1, a2] - max_values = fns.max(weight, axis=reduction_axis, keepdims=True) # [a1, r, a2] -> [a1, 1, a2] + min_values = fns.min(weight, axis=reduction_axes, keepdims=True) # [a1, r, a2] -> [a1, 1, a2] + max_values = fns.max(weight, axis=reduction_axes, keepdims=True) # [a1, r, a2] -> [a1, 1, a2] scale, zero_point = calculate_scale_zero_point( min_values, max_values, level_low, level_high, narrow_range=False ) else: - scale = fns.max(fns.abs(weight), axis=reduction_axis, keepdims=True) # [a1, r//gs, 1, a2] + scale = fns.max(fns.abs(weight), axis=reduction_axes, keepdims=True) # [a1, r//gs, 1, a2] level_low_sym = -(2 ** (num_bits - 1)) level_high_sym = 2 ** (num_bits - 1) - 1 scale = scale / level_high_sym @@ -151,13 +161,15 @@ def do_integer_quantization( return compressed_weights, scale, zero_point -def get_integer_quantization_error(weight: Tensor, reduction_axis: int, config: WeightCompressionConfig) -> float: +def get_integer_quantization_error( + weight: Tensor, reduction_axes: ReductionAxes, config: WeightCompressionConfig +) -> float: """ Calculates a quantity characterizing the difference between floating point weights and fake quantized (compressed and decompressed) to integer ones. :param weight: Weight array to compress. - :param reduction_axis: Axis, along which to reduce (collect) different statistics (e.g. min, max). + :param reduction_axes: Axes, along which to reduce (collect) different statistics (e.g. min, max). :param config: Information on how to compress (quantize) a specific weight. :return: The quantity characterizing the error of integer quantization. """ @@ -166,29 +178,44 @@ def get_integer_quantization_error(weight: Tensor, reduction_axis: int, config: if weight.dtype != TensorDataType.float32: weight = weight.astype(TensorDataType.float32) - compressed_weights, scale, zero_point = do_integer_quantization(weight, reduction_axis, config) + compressed_weights, scale, zero_point = do_integer_quantization(weight, reduction_axes, config) decompressed_weight = (compressed_weights - zero_point).astype(weight.dtype) * scale decompressed_weight = decompressed_weight.reshape(orig_shape) diff = (decompressed_weight - weight) ** 2 - layer_err = fns.mean(diff, axis=reduction_axis) + layer_err = fns.mean(diff, axis=reduction_axes) val = fns.max(layer_err) return val.item() -def compress_weight(weight: Tensor, reduction_axis: int, config: WeightCompressionConfig): +def compress_weight(weight: Tensor, reduction_axes: ReductionAxes, config: WeightCompressionConfig): """ Compress weight using compression configuration. :param weight: The weight to compress. - :param reduction_axis: Axis, along which to reduce (collect) different statistics (e.g. min, max). - :param config: Compresssion configuration. + :param reduction_axes: Axes, along which to reduce (collect) different statistics (e.g. min, max). + :param config: Compression configuration. :return: The compressed weight and decompression parameters as instance of CompressedWeight """ if config.mode == CompressWeightsMode.NF4: - compressed_weight, scale = calculate_normalized_weight_and_nf4_scale(weight, reduction_axis, config.group_size) + compressed_weight, scale = calculate_normalized_weight_and_nf4_scale(weight, reduction_axes, config.group_size) return CompressedWeight(compressed_weight, scale) - compressed_weight, scale, zero_point = do_integer_quantization(weight, reduction_axis, config) + compressed_weight, scale, zero_point = do_integer_quantization(weight, reduction_axes, config) return CompressedWeight(compressed_weight, scale, zero_point) + + +def do_dequantization(compressed_weights: Tensor, scale: Tensor, zero_point: Tensor) -> Tensor: + """ + The method dequantizes the given weights to float point data type in accordance with the scale and + zero_point data type. + + :param compressed_weights: compressed weights. + :param scale: scale in compression/quantization. + :param zero_point: zero point in compression/quantization. + :return: dequantized/decompressed weights. + """ + decompressed_weight = compressed_weights.astype(dtype=scale.dtype) + decompressed_weight = (decompressed_weight - zero_point) * scale + return decompressed_weight diff --git a/nncf/quantization/fake_quantize.py b/nncf/quantization/fake_quantize.py index bd1437d09c5..097ec7355cb 100644 --- a/nncf/quantization/fake_quantize.py +++ b/nncf/quantization/fake_quantize.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -12,6 +12,7 @@ from dataclasses import dataclass from typing import Tuple +import nncf from nncf.common.quantization.quantizers import calculate_asymmetric_level_ranges from nncf.common.quantization.quantizers import calculate_symmetric_level_ranges from nncf.common.quantization.quantizers import get_num_levels @@ -183,7 +184,7 @@ def asymmetric_range( :param max_values: Collected max values for the quantized insertion. :param quantizer_config: Config of the quantization configuration. :param unify_zp: Whether to unify the zero point. - It is `True` for the per-tensor zero point constrain on KMB (vpu2p0). + It is `True` for the per-tensor zero point constrain on KMB. :return: A Tuple level_low - the low quant number level_high - the high quant number @@ -313,9 +314,9 @@ def _calculate_scaled_parameters( levels: Number of quantization levels. """ if quantizer_config.mode == QuantizationMode.ASYMMETRIC: - raise RuntimeError("half_range is only applied to symmetric quantization mode.") + raise nncf.ValidationError("half_range is only applied to symmetric quantization mode.") if quant_group != QuantizerGroup.WEIGHTS: - raise RuntimeError("half_range is only applied to weight quantizers.") + raise nncf.ValidationError("half_range is only applied to weight quantizers.") num_bits = quantizer_config.num_bits level_low, level_high = calculate_symmetric_level_ranges(num_bits - 1, signed=True, narrow_range=False) diff --git a/nncf/quantization/passes.py b/nncf/quantization/passes.py index 9465fa95dec..7c735bab1e3 100644 --- a/nncf/quantization/passes.py +++ b/nncf/quantization/passes.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/quantization/quantize_model.py b/nncf/quantization/quantize_model.py index 2648ae908c8..6982b347600 100644 --- a/nncf/quantization/quantize_model.py +++ b/nncf/quantization/quantize_model.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -11,9 +11,9 @@ from typing import Any, Callable, Iterable, List, Optional, Tuple, TypeVar, Union +import nncf from nncf.api.compression import TModel from nncf.common.deprecation import warning_deprecated -from nncf.common.factory import NNCFGraphFactory from nncf.common.quantization.structs import QuantizationPreset from nncf.common.utils.api_marker import api from nncf.common.utils.backend import BackendType @@ -31,7 +31,6 @@ from nncf.quantization.algorithms.hyperparameter_tuner.algorithm import HyperparameterTuner from nncf.quantization.algorithms.hyperparameter_tuner.param_grid import get_quantization_param_grids from nncf.quantization.algorithms.post_training.pipeline import create_ptq_pipeline -from nncf.quantization.algorithms.weight_compression.algorithm import WeightCompression from nncf.scopes import IgnoredScope TTensor = TypeVar("TTensor") @@ -156,7 +155,7 @@ def quantize( advanced_parameters=advanced_parameters, ) - raise RuntimeError(f"Unsupported type of backend: {backend}") + raise nncf.UnsupportedBackendError(f"Unsupported type of backend: {backend}") @api(canonical_alias="nncf.quantize_with_accuracy_control") @@ -228,6 +227,25 @@ def quantize_with_accuracy_control( if backend == BackendType.OPENVINO: from nncf.openvino.quantization.quantize_model import quantize_with_accuracy_control_impl + return quantize_with_accuracy_control_impl( + model, + calibration_dataset, + validation_dataset, + validation_fn, + max_drop, + drop_type, + preset, + target_device, + subset_size, + fast_bias_correction, + model_type, + ignored_scope, + advanced_quantization_parameters, + advanced_accuracy_restorer_parameters, + ) + if backend == BackendType.ONNX: + from nncf.onnx.quantization.quantize_model import quantize_with_accuracy_control_impl + return quantize_with_accuracy_control_impl( model, calibration_dataset, @@ -245,7 +263,7 @@ def quantize_with_accuracy_control( advanced_accuracy_restorer_parameters, ) - raise RuntimeError(f"Unsupported type of backend: {backend}") + raise nncf.UnsupportedBackendError(f"Unsupported type of backend: {backend}") @api(canonical_alias="nncf.compress_weights") @@ -258,6 +276,9 @@ def compress_weights( all_layers: Optional[bool] = None, dataset: Optional[Dataset] = None, sensitivity_metric: Optional[SensitivityMetric] = None, + *, + subset_size: Optional[int] = 128, + awq: Optional[bool] = None, ) -> TModel: """ Compress model weights. @@ -281,11 +302,14 @@ def compress_weights( The value -1 means no grouping. :param ignored_scope: An ignored scope that defined the list of model control flow graph nodes to be ignored during quantization. - :param all_layers: Indicates whether embeddings and last layers should be compressed to a primary - precision. By default, the backup precision is assigned for the embeddings and last layers. + :param all_layers: Indicates whether embeddings and last MatMul layers should be compressed to a primary + precision. By default, the backup precision is assigned for the embeddings and last MatMul layers. :param dataset: Dataset used for assigning different quantization precision by finding outliers in activations. :param sensitivity_metric: The sensitivity metric for assigning quantization precision to layers. In order to preserve the accuracy of the model, the more sensitive layers receives a higher precision. + :param subset_size: Number of data samples to calculate activation statistics used for assigning different + quantization precision. Defaults to 128. + :param awq: Indicates whether use AWQ weights correction. :return: The non-trainable model with compressed weights. """ if mode == CompressWeightsMode.INT8: @@ -295,9 +319,12 @@ def compress_weights( mode = CompressWeightsMode.INT8_ASYM backend = get_backend(model) + compression_weights_impl = None + if backend == BackendType.TORCH: from nncf.torch.model_creation import is_wrapped_model from nncf.torch.model_creation import wrap_model + from nncf.torch.quantization.quantize_model import compress_weights_impl as pt_compression_weights_impl if mode not in [CompressWeightsMode.INT8_ASYM, CompressWeightsMode.INT8_SYM]: raise AttributeError( @@ -305,6 +332,9 @@ def compress_weights( f"but given {mode.value} mode." ) + if awq is True: + raise AttributeError("Torch backend doesn`t supports AWQ algorithm, but awq=True is specified.") + if is_wrapped_model(model): if not model.nncf.trace_parameters: raise ValueError( @@ -319,6 +349,12 @@ def compress_weights( example_input = next(iter(dataset.get_inference_data())) model = wrap_model(model, example_input=example_input, trace_parameters=True) dataset = None + compression_weights_impl = pt_compression_weights_impl + + if backend == BackendType.OPENVINO: + from nncf.openvino.quantization.quantize_model import compress_weights_impl as ov_compress_weights_impl + + compression_weights_impl = ov_compress_weights_impl if mode in [CompressWeightsMode.INT8_ASYM, CompressWeightsMode.INT8_SYM]: if ratio is None: @@ -330,10 +366,10 @@ def compress_weights( "INT8 mode assumes per-channel quantization of all layers in 8 bit. " "Default values of `ratio` (1) and `group_size` (-1) parameters can not be overridden" ) - options = [all_layers, sensitivity_metric, dataset] + options = [all_layers, sensitivity_metric, dataset, awq] if any(option is not None for option in options): raise AttributeError( - "INT8 modes do not support `all_layers`, `sensitivity_metric` and `dataset` options." + "INT8 modes do not support `all_layers`, `sensitivity_metric`, `awq` and `dataset` options. " "Set them to None." ) @@ -343,6 +379,8 @@ def compress_weights( group_size = 128 if all_layers is None: all_layers = False + if awq is None: + awq = False if ignored_scope is None: ignored_scope = IgnoredScope() if sensitivity_metric is None: @@ -356,13 +394,17 @@ def compress_weights( f"Mixed precision selection based on the given sensitivity metric={sensitivity_metric.value} requires " "a dataset, but it's not provided." ) - if ratio < 0 or ratio > 1: - raise ValueError(f"The ratio should be between 0 and 1, but ration={ratio} is specified.") + raise ValueError(f"The ratio should be between 0 and 1, but ratio={ratio} is specified.") + if subset_size is None or subset_size <= 0: + raise ValueError(f"The subset_size value should be positive, but subset_size={subset_size} is given.") + + if compression_weights_impl is None: + raise nncf.UnsupportedBackendError(f"Unsupported type of backend: {backend}") - compression_algorithm = WeightCompression(mode, ratio, group_size, ignored_scope, all_layers, sensitivity_metric) - graph = NNCFGraphFactory.create(model) - return compression_algorithm.apply(model, graph, dataset=dataset) + return compression_weights_impl( + model, dataset, mode, ratio, group_size, ignored_scope, all_layers, sensitivity_metric, awq, subset_size + ) def quantize_with_tune_hyperparams( diff --git a/nncf/quantization/range_estimator.py b/nncf/quantization/range_estimator.py index 26124e5c305..d0eae69f480 100644 --- a/nncf/quantization/range_estimator.py +++ b/nncf/quantization/range_estimator.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/quantization/telemetry_extractors.py b/nncf/quantization/telemetry_extractors.py index a004460443d..72158e53136 100644 --- a/nncf/quantization/telemetry_extractors.py +++ b/nncf/quantization/telemetry_extractors.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/scopes.py b/nncf/scopes.py index 7145d33a4a3..c052680f343 100644 --- a/nncf/scopes.py +++ b/nncf/scopes.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -14,6 +14,7 @@ from dataclasses import field from typing import List, Optional, Set +import nncf from nncf.common.graph.graph import NNCFGraph from nncf.common.logging import nncf_logger from nncf.common.utils.api_marker import api @@ -86,7 +87,7 @@ def convert_ignored_scope_to_list(ignored_scope: Optional[IgnoredScope]) -> List for p in ignored_scope.patterns: results.append("{re}" + p) if ignored_scope.types: - raise RuntimeError("Legacy ignored scope format does not support operation types") + raise nncf.InternalError("Legacy ignored scope format does not support operation types") return results @@ -118,7 +119,7 @@ def get_ignored_node_names_from_ignored_scope( matched_by_names.append(ignored_node_name) if strict and len(ignored_scope.names) != len(matched_by_names): skipped_names = set(ignored_scope.names) - set(matched_by_names) - raise RuntimeError( + raise nncf.ValidationError( f"Ignored nodes with name {list(skipped_names)} were not found in the NNCFGraph. " + error_msg ) nncf_logger.info(f"{len(matched_by_names)} ignored nodes were found by name in the NNCFGraph") @@ -133,7 +134,9 @@ def get_ignored_node_names_from_ignored_scope( not_matched_patterns.append(str_pattern) matched_by_patterns.extend(matches) if strict and not_matched_patterns: - raise RuntimeError(f"No matches for ignored patterns {not_matched_patterns} in the NNCFGraph. " + error_msg) + raise nncf.ValidationError( + f"No matches for ignored patterns {not_matched_patterns} in the NNCFGraph. " + error_msg + ) nncf_logger.info(f"{len(matched_by_patterns)} ignored nodes were found by patterns in the NNCFGraph") matched_by_types = [] @@ -145,7 +148,7 @@ def get_ignored_node_names_from_ignored_scope( matched_by_types.append(node.node_name) not_matched_types = set(ignored_scope.types) - types_found if strict and not_matched_types: - raise RuntimeError( + raise nncf.ValidationError( f"Nodes with ignored types {list(not_matched_types)} were not found in the NNCFGraph. " + error_msg ) nncf_logger.info(f"{len(matched_by_types)} ignored nodes were found by types in the NNCFGraph") diff --git a/nncf/telemetry/__init__.py b/nncf/telemetry/__init__.py index 3c0c3936eb1..d10f07994f5 100644 --- a/nncf/telemetry/__init__.py +++ b/nncf/telemetry/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/telemetry/decorator.py b/nncf/telemetry/decorator.py index 42ae45f58e3..3ccee0f9235 100644 --- a/nncf/telemetry/decorator.py +++ b/nncf/telemetry/decorator.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/telemetry/events.py b/nncf/telemetry/events.py index fdb6b3b1400..b7f6ebdc95b 100644 --- a/nncf/telemetry/events.py +++ b/nncf/telemetry/events.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/telemetry/extractors.py b/nncf/telemetry/extractors.py index 999e0377afa..b037104de0d 100644 --- a/nncf/telemetry/extractors.py +++ b/nncf/telemetry/extractors.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/telemetry/wrapper.py b/nncf/telemetry/wrapper.py index c27c580ccd5..355e3debb47 100644 --- a/nncf/telemetry/wrapper.py +++ b/nncf/telemetry/wrapper.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/__init__.py b/nncf/tensorflow/__init__.py index 5c746a67aeb..80bc64c2211 100644 --- a/nncf/tensorflow/__init__.py +++ b/nncf/tensorflow/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -15,6 +15,7 @@ import tensorflow from packaging import version +import nncf from nncf import nncf_logger from nncf.common.logging.logger import warn_bkc_version_mismatch from nncf.version import BKC_TF_VERSION @@ -30,7 +31,7 @@ if not tensorflow_version.startswith(BKC_TF_VERSION[:-2]): warn_bkc_version_mismatch("tensorflow", BKC_TF_VERSION, _tf_version) elif not (tensorflow_version_major == 2 and 8 <= tensorflow_version_minor <= 13): - raise RuntimeError( + raise nncf.UnsupportedVersionError( f"NNCF only supports 2.8.4 <= tensorflow <= 2.13.*, while current tensorflow version is {_tf_version}" ) diff --git a/nncf/tensorflow/accuracy_aware_training/__init__.py b/nncf/tensorflow/accuracy_aware_training/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/tensorflow/accuracy_aware_training/__init__.py +++ b/nncf/tensorflow/accuracy_aware_training/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/accuracy_aware_training/keras_model_utils.py b/nncf/tensorflow/accuracy_aware_training/keras_model_utils.py index 2e485f0ef92..5fddbc8d1ec 100644 --- a/nncf/tensorflow/accuracy_aware_training/keras_model_utils.py +++ b/nncf/tensorflow/accuracy_aware_training/keras_model_utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/accuracy_aware_training/runner.py b/nncf/tensorflow/accuracy_aware_training/runner.py index 7e39165271e..d21411835ab 100644 --- a/nncf/tensorflow/accuracy_aware_training/runner.py +++ b/nncf/tensorflow/accuracy_aware_training/runner.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/algorithm_selector.py b/nncf/tensorflow/algorithm_selector.py index 5de0d832382..55666f6cd8b 100644 --- a/nncf/tensorflow/algorithm_selector.py +++ b/nncf/tensorflow/algorithm_selector.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/api/__init__.py b/nncf/tensorflow/api/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/tensorflow/api/__init__.py +++ b/nncf/tensorflow/api/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/api/composite_compression.py b/nncf/tensorflow/api/composite_compression.py index 899cff7de42..883f12da780 100644 --- a/nncf/tensorflow/api/composite_compression.py +++ b/nncf/tensorflow/api/composite_compression.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -11,6 +11,7 @@ from typing import TypeVar +import nncf from nncf import NNCFConfig from nncf.common.composite_compression import CompositeCompressionAlgorithmBuilder from nncf.common.composite_compression import CompositeCompressionAlgorithmController @@ -28,7 +29,7 @@ def __init__(self, config: NNCFConfig, should_init: bool = True): algo_names = extract_algorithm_names(config) if len(algo_names) < 2: - raise RuntimeError( + raise nncf.ValidationError( "Composite algorithm builder must be supplied with a config with more than one " "compression algo specified!" ) diff --git a/nncf/tensorflow/api/compression.py b/nncf/tensorflow/api/compression.py index dba66a3fcbd..c1f4752ecdf 100644 --- a/nncf/tensorflow/api/compression.py +++ b/nncf/tensorflow/api/compression.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/batchnorm_adaptation.py b/nncf/tensorflow/batchnorm_adaptation.py index abb165502f5..58dbdab0f1e 100644 --- a/nncf/tensorflow/batchnorm_adaptation.py +++ b/nncf/tensorflow/batchnorm_adaptation.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/callbacks/__init__.py b/nncf/tensorflow/callbacks/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/tensorflow/callbacks/__init__.py +++ b/nncf/tensorflow/callbacks/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/callbacks/checkpoint_callback.py b/nncf/tensorflow/callbacks/checkpoint_callback.py index bd60e1cb038..854b378902b 100644 --- a/nncf/tensorflow/callbacks/checkpoint_callback.py +++ b/nncf/tensorflow/callbacks/checkpoint_callback.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/callbacks/statistics_callback.py b/nncf/tensorflow/callbacks/statistics_callback.py index 446e997f528..6d5f5e70edc 100644 --- a/nncf/tensorflow/callbacks/statistics_callback.py +++ b/nncf/tensorflow/callbacks/statistics_callback.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/exporter.py b/nncf/tensorflow/exporter.py index 6ec1a653c18..7af21543dfa 100644 --- a/nncf/tensorflow/exporter.py +++ b/nncf/tensorflow/exporter.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/functions.py b/nncf/tensorflow/functions.py index 7552be989d2..cb3901abbe5 100644 --- a/nncf/tensorflow/functions.py +++ b/nncf/tensorflow/functions.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/graph/__init__.py b/nncf/tensorflow/graph/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/tensorflow/graph/__init__.py +++ b/nncf/tensorflow/graph/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/graph/converter.py b/nncf/tensorflow/graph/converter.py index 3708b1c83e7..83c5d9f25cd 100644 --- a/nncf/tensorflow/graph/converter.py +++ b/nncf/tensorflow/graph/converter.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -18,6 +18,7 @@ from tensorflow.core.framework.node_def_pb2 import NodeDef from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2 +import nncf from nncf.common.graph import NNCFGraph from nncf.common.graph import NNCFNodeName from nncf.common.graph.definitions import NNCFGraphNodeType @@ -257,7 +258,7 @@ def _collect_custom_layer_infos( input_graphdef_node_name = splits[0] output_port_id = int(splits[1]) else: - raise RuntimeError("Could not parse NodeDef's input field!") + raise nncf.InternalError("Could not parse NodeDef's input field!") pretty_input_node_name = custom_layer_info.graphdef_node_name_to_pretty_node_name[ input_graphdef_node_name @@ -359,7 +360,7 @@ def get_node_name(graphdef_node_name: str): # Filter control inputs, whatever these are previous_node_names = list(filter(lambda x: "^" not in x, previous_node_names)) if weight_node_name is None: - raise RuntimeError("Could not find a weight node for a weighted node {}".format(weighted_node.name)) + raise nncf.InternalError("Could not find a weight node for a weighted node {}".format(weighted_node.name)) return weight_node_name @staticmethod diff --git a/nncf/tensorflow/graph/metatypes/__init__.py b/nncf/tensorflow/graph/metatypes/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/tensorflow/graph/metatypes/__init__.py +++ b/nncf/tensorflow/graph/metatypes/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/graph/metatypes/common.py b/nncf/tensorflow/graph/metatypes/common.py index e745578adec..e8d140bb744 100644 --- a/nncf/tensorflow/graph/metatypes/common.py +++ b/nncf/tensorflow/graph/metatypes/common.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/graph/metatypes/keras_layers.py b/nncf/tensorflow/graph/metatypes/keras_layers.py index 210e78ceb93..ce65fc63298 100644 --- a/nncf/tensorflow/graph/metatypes/keras_layers.py +++ b/nncf/tensorflow/graph/metatypes/keras_layers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -14,6 +14,7 @@ import tensorflow as tf +import nncf from nncf.common.graph.operator_metatypes import INPUT_NOOP_METATYPES from nncf.common.graph.operator_metatypes import NOOP_METATYPES from nncf.common.graph.operator_metatypes import OperatorMetatype @@ -65,7 +66,7 @@ def _determine_subtype( else: matches.append(subtype) if len(matches) > 1: - raise RuntimeError("Multiple subtypes match operator call - cannot determine single subtype.") + raise nncf.InternalError("Multiple subtypes match operator call - cannot determine single subtype.") if not matches: return None return matches[0] diff --git a/nncf/tensorflow/graph/metatypes/matcher.py b/nncf/tensorflow/graph/metatypes/matcher.py index 9c5fc8c8983..55931f53f4f 100644 --- a/nncf/tensorflow/graph/metatypes/matcher.py +++ b/nncf/tensorflow/graph/metatypes/matcher.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/graph/metatypes/tf_ops.py b/nncf/tensorflow/graph/metatypes/tf_ops.py index ec38997b516..9cd15f37590 100644 --- a/nncf/tensorflow/graph/metatypes/tf_ops.py +++ b/nncf/tensorflow/graph/metatypes/tf_ops.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/graph/model_transformer.py b/nncf/tensorflow/graph/model_transformer.py index 210835f4446..bec81169048 100644 --- a/nncf/tensorflow/graph/model_transformer.py +++ b/nncf/tensorflow/graph/model_transformer.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -15,6 +15,7 @@ import tensorflow as tf +import nncf from nncf.common.graph.model_transformer import ModelTransformer from nncf.common.graph.transformations.commands import TargetPoint from nncf.common.graph.transformations.commands import TargetType @@ -162,7 +163,7 @@ def _shared_insert_layers(self, target_points: List[TargetPoint], layers_to_inse for layer in self._model_config["input_layers"]: for tp in target_points: if isinstance(tp, TFBeforeLayer) and tp.layer_name == layer[0]: - raise RuntimeError(f"Insertion before input layer: {tp.layer_name} is not supported") + raise nncf.InternalError(f"Insertion before input layer: {tp.layer_name} is not supported") layer_configs = [] for layer in layers_to_insert: @@ -209,7 +210,7 @@ def _shared_insert_layers(self, target_points: List[TargetPoint], layers_to_inse tp.layer_name, tp.instance_idx, layer_out_ports, replace_layer_name, i ) if len(layer_out_ports) > 1: - raise RuntimeError( + raise nncf.InternalError( "Insertion after layer ({}) with multiple ports is not supported".format(tp.layer_name) ) @@ -324,7 +325,7 @@ def _insert_layers_before(self, layer_name: str, instance_idx: int, input_port_i if functional_model: for layer in self._model_config["input_layers"]: if layer_name == layer[0]: - raise RuntimeError("Insertion before input layer: {} is not supported".format(layer_name)) + raise nncf.InternalError("Insertion before input layer: {} is not supported".format(layer_name)) layer_configs = [] idx, downstream_layer_cfg = self._find_layer_config(layer_name) @@ -409,13 +410,15 @@ def _insert_layer_after_functional(self, layer_name: str, instance_idx: int, lay self._insert_after_model_outputs(layer_name, instance_idx, layer_out_ports, replace_layer_name) if len(layer_out_ports) > 1: - raise RuntimeError("Insertion after layer ({}) with multiple ports is not supported".format(layer_name)) + raise nncf.InternalError( + "Insertion after layer ({}) with multiple ports is not supported".format(layer_name) + ) self._insert_layer_after_sequential(layer_name, layer_to_insert_config) def _insert_layer_after_sequential(self, layer_name: str, layer_configs): idx, _ = self._find_layer_config(layer_name) if idx is None: - raise RuntimeError("Layer is not found: {}".format(layer_name)) + raise nncf.InternalError("Layer is not found: {}".format(layer_name)) self._model_config["layers"].insert(idx + 1, layer_configs) @staticmethod diff --git a/nncf/tensorflow/graph/pattern_operations.py b/nncf/tensorflow/graph/pattern_operations.py index 708bdc7275b..78dfa28f610 100644 --- a/nncf/tensorflow/graph/pattern_operations.py +++ b/nncf/tensorflow/graph/pattern_operations.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/graph/patterns.py b/nncf/tensorflow/graph/patterns.py index be226639091..58e5806dcc0 100644 --- a/nncf/tensorflow/graph/patterns.py +++ b/nncf/tensorflow/graph/patterns.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/graph/transformations/__init__.py b/nncf/tensorflow/graph/transformations/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/tensorflow/graph/transformations/__init__.py +++ b/nncf/tensorflow/graph/transformations/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/graph/transformations/commands.py b/nncf/tensorflow/graph/transformations/commands.py index a502b7a2b17..90002503a99 100644 --- a/nncf/tensorflow/graph/transformations/commands.py +++ b/nncf/tensorflow/graph/transformations/commands.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/graph/transformations/layout.py b/nncf/tensorflow/graph/transformations/layout.py index 51832e5e371..6a15020a287 100644 --- a/nncf/tensorflow/graph/transformations/layout.py +++ b/nncf/tensorflow/graph/transformations/layout.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/graph/utils.py b/nncf/tensorflow/graph/utils.py index 2ad4a9b8932..0cd696db50f 100644 --- a/nncf/tensorflow/graph/utils.py +++ b/nncf/tensorflow/graph/utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -14,6 +14,7 @@ import tensorflow as tf +import nncf from nncf.common.graph import NNCFGraph from nncf.common.graph import NNCFNode from nncf.common.graph import NNCFNodeName @@ -80,9 +81,8 @@ def get_custom_objects(model): layer = layer.layer if layer.__class__.__name__ not in keras_layers: custom_objects[layer.__class__.__name__] = layer.__class__ - if layer.__class__.__name__ == "Activation": - if layer.activation.__name__ not in keras_activations: - custom_objects[layer.activation.__name__] = layer.activation + if layer.__class__.__name__ == "Activation" and layer.activation.__name__ not in keras_activations: + custom_objects[layer.activation.__name__] = layer.activation return custom_objects @@ -129,7 +129,7 @@ def get_layer_to_graph_nodes_map(model, node_names): for node in node_names: parent_layer_name = node.split("/")[1] # model_name/layer_name/layer_op_name/... if parent_layer_name not in layer_to_nodes_map: - raise RuntimeError("Could not find {} layer in Model".format(parent_layer_name)) + raise nncf.ValidationError("Could not find {} layer in Model".format(parent_layer_name)) layer_to_nodes_map[parent_layer_name]["nodes"].append(node) return layer_to_nodes_map diff --git a/nncf/tensorflow/hardware/__init__.py b/nncf/tensorflow/hardware/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/tensorflow/hardware/__init__.py +++ b/nncf/tensorflow/hardware/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/hardware/config.py b/nncf/tensorflow/hardware/config.py index d74412c709c..e3763b26e46 100644 --- a/nncf/tensorflow/hardware/config.py +++ b/nncf/tensorflow/hardware/config.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/hardware/fused_patterns.py b/nncf/tensorflow/hardware/fused_patterns.py index 5a4081b55bb..85264688d20 100644 --- a/nncf/tensorflow/hardware/fused_patterns.py +++ b/nncf/tensorflow/hardware/fused_patterns.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/helpers/__init__.py b/nncf/tensorflow/helpers/__init__.py index c04c78955a9..9980cc21f75 100644 --- a/nncf/tensorflow/helpers/__init__.py +++ b/nncf/tensorflow/helpers/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/helpers/callback_creation.py b/nncf/tensorflow/helpers/callback_creation.py index c7f91e971e5..3d2a2c345d5 100644 --- a/nncf/tensorflow/helpers/callback_creation.py +++ b/nncf/tensorflow/helpers/callback_creation.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/helpers/model_creation.py b/nncf/tensorflow/helpers/model_creation.py index 653142347df..49cb7228f29 100644 --- a/nncf/tensorflow/helpers/model_creation.py +++ b/nncf/tensorflow/helpers/model_creation.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -14,6 +14,7 @@ import tensorflow as tf +import nncf from nncf import NNCFConfig from nncf.api.compression import CompressionAlgorithmController from nncf.common.compression import BaseCompressionAlgorithmController as BaseController @@ -117,7 +118,7 @@ def get_input_signature(config: NNCFConfig): sample_size = info["sample_size"] samples_sizes.append(sample_size) else: - raise RuntimeError("sample_size must be provided in configuration file") + raise nncf.ValidationError("sample_size must be provided in configuration file") input_signature = [] for sample_size in samples_sizes: diff --git a/nncf/tensorflow/helpers/model_manager.py b/nncf/tensorflow/helpers/model_manager.py index 943ba8ecf04..3398f05939c 100644 --- a/nncf/tensorflow/helpers/model_manager.py +++ b/nncf/tensorflow/helpers/model_manager.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/helpers/utils.py b/nncf/tensorflow/helpers/utils.py index c78ca761bb6..bf68bebc471 100644 --- a/nncf/tensorflow/helpers/utils.py +++ b/nncf/tensorflow/helpers/utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -9,6 +9,8 @@ # See the License for the specific language governing permissions and # limitations under the License. +import nncf + def get_built_model(model, config): if not model.built: @@ -18,7 +20,7 @@ def get_built_model(model, config): else: sample_size = input_info[0].get("sample_size", None) if input_info else None if not sample_size: - raise RuntimeError("sample_size must be provided in configuration file") + raise nncf.ValidationError("sample_size must be provided in configuration file") model.build([None] + list(sample_size[1:])) return model diff --git a/nncf/tensorflow/initialization.py b/nncf/tensorflow/initialization.py index 5bf412fde12..572c2e5e69b 100644 --- a/nncf/tensorflow/initialization.py +++ b/nncf/tensorflow/initialization.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/layers/__init__.py b/nncf/tensorflow/layers/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/tensorflow/layers/__init__.py +++ b/nncf/tensorflow/layers/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/layers/custom_objects.py b/nncf/tensorflow/layers/custom_objects.py index d631fa144dc..18835a254d1 100644 --- a/nncf/tensorflow/layers/custom_objects.py +++ b/nncf/tensorflow/layers/custom_objects.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/layers/data_layout.py b/nncf/tensorflow/layers/data_layout.py index 5e4f6328b8d..6fa2c38f8f3 100644 --- a/nncf/tensorflow/layers/data_layout.py +++ b/nncf/tensorflow/layers/data_layout.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/layers/operation.py b/nncf/tensorflow/layers/operation.py index eabc28fe14e..4e75b89132c 100644 --- a/nncf/tensorflow/layers/operation.py +++ b/nncf/tensorflow/layers/operation.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -11,7 +11,7 @@ from collections import OrderedDict -from nncf.tensorflow.utils.hook_handle import HookHandle +from nncf.common.hook_handle import add_op_to_registry class InputType: @@ -83,9 +83,7 @@ def register_hook_pre_call(self, hook): :return: a handle that can be used to remove the hook form the NNCF operation by calling handle.remove() """ - handle = HookHandle(self._call_pre_hooks) - self._call_pre_hooks[handle.hook_id] = hook - return handle + return add_op_to_registry(self._call_pre_hooks, hook) def __call__(self, *args, **kwargs): inputs = args[0] diff --git a/nncf/tensorflow/layers/wrapper.py b/nncf/tensorflow/layers/wrapper.py index d6e2ab9743b..f3105471d46 100644 --- a/nncf/tensorflow/layers/wrapper.py +++ b/nncf/tensorflow/layers/wrapper.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -15,6 +15,7 @@ import tensorflow as tf +import nncf from nncf.tensorflow.layers.custom_objects import NNCF_CUSTOM_OBJECTS from nncf.tensorflow.layers.custom_objects import get_nncf_custom_objects from nncf.tensorflow.layers.operation import InputType @@ -201,7 +202,9 @@ def registry_weight_operation(self, weights_attr: str, op: NNCFOperation): self.weights_attr_ops[weights_attr] = OrderedDict() if op.name in self.weights_attr_ops[weights_attr]: - raise RuntimeError(f"Attempt to apply an operation with the same name {op.name} on layer weight twice") + raise nncf.InternalError( + f"Attempt to apply an operation with the same name {op.name} on layer weight twice" + ) self.weights_attr_ops[weights_attr][op.name] = op diff --git a/nncf/tensorflow/loss.py b/nncf/tensorflow/loss.py index 20b5ca476a1..abf55e9e768 100644 --- a/nncf/tensorflow/loss.py +++ b/nncf/tensorflow/loss.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/pruning/__init__.py b/nncf/tensorflow/pruning/__init__.py index 23f8e4a89d5..a83b9c34cd3 100644 --- a/nncf/tensorflow/pruning/__init__.py +++ b/nncf/tensorflow/pruning/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/pruning/base_algorithm.py b/nncf/tensorflow/pruning/base_algorithm.py index 12f0cdfc0e4..4e6e090d0e6 100644 --- a/nncf/tensorflow/pruning/base_algorithm.py +++ b/nncf/tensorflow/pruning/base_algorithm.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/pruning/callbacks.py b/nncf/tensorflow/pruning/callbacks.py index 81f32f621fe..a5abb8f1b6b 100644 --- a/nncf/tensorflow/pruning/callbacks.py +++ b/nncf/tensorflow/pruning/callbacks.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/pruning/filter_pruning/__init__.py b/nncf/tensorflow/pruning/filter_pruning/__init__.py index 7f6f2029b35..3fc0fea0047 100644 --- a/nncf/tensorflow/pruning/filter_pruning/__init__.py +++ b/nncf/tensorflow/pruning/filter_pruning/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/pruning/filter_pruning/algorithm.py b/nncf/tensorflow/pruning/filter_pruning/algorithm.py index d095498bee0..a31b5c065df 100644 --- a/nncf/tensorflow/pruning/filter_pruning/algorithm.py +++ b/nncf/tensorflow/pruning/filter_pruning/algorithm.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -15,6 +15,7 @@ import tensorflow as tf +import nncf from nncf import NNCFConfig from nncf.api.compression import CompressionLoss from nncf.api.compression import CompressionStage @@ -437,7 +438,7 @@ def _set_binary_masks_for_pruned_modules_globally_by_flops_target(self, target_f if nncf_node.attributes["output_mask"] is not None: self._set_operation_masks([layer], nncf_node.attributes["output_mask"].tensor) return - raise RuntimeError(f"Unable to prune model to required flops pruning level: {target_flops_pruning_level}") + raise nncf.InternalError(f"Unable to prune model to required flops pruning level: {target_flops_pruning_level}") def _set_operation_masks(self, layers: List[NNCFWrapper], filter_mask): for layer in layers: @@ -465,7 +466,7 @@ def _find_uniform_pruning_level_for_target_flops(self, target_flops_pruning_leve self.current_flops = flops self.current_params_num = params_num return right - raise RuntimeError( + raise nncf.ParameterNotSupportedError( f"Unable to prune the model to get the required pruning level in flops = {target_flops_pruning_level}" ) @@ -535,7 +536,7 @@ def _update_benchmark_statistics(self): def _layer_filter_importance(self, layer: NNCFWrapper): layer_metatype = get_keras_layer_metatype(layer) if len(layer_metatype.weight_definitions) != 1: - raise RuntimeError( + raise nncf.InternalError( f"The layer {layer.layer.name} does not support by the pruning " f"algorithm because it contains several weight attributes." ) diff --git a/nncf/tensorflow/pruning/filter_pruning/functions.py b/nncf/tensorflow/pruning/filter_pruning/functions.py index 673e11dae89..a43564224c8 100644 --- a/nncf/tensorflow/pruning/filter_pruning/functions.py +++ b/nncf/tensorflow/pruning/filter_pruning/functions.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/pruning/operations.py b/nncf/tensorflow/pruning/operations.py index c92e68fada3..abeecd70bc3 100644 --- a/nncf/tensorflow/pruning/operations.py +++ b/nncf/tensorflow/pruning/operations.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/pruning/tensor_processor.py b/nncf/tensorflow/pruning/tensor_processor.py index 7e00722cfdd..198e2af33d5 100644 --- a/nncf/tensorflow/pruning/tensor_processor.py +++ b/nncf/tensorflow/pruning/tensor_processor.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/pruning/utils.py b/nncf/tensorflow/pruning/utils.py index 839fef680bd..c1693bfc7b2 100644 --- a/nncf/tensorflow/pruning/utils.py +++ b/nncf/tensorflow/pruning/utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -14,6 +14,7 @@ import numpy as np import tensorflow as tf +import nncf from nncf.common.graph import NNCFGraph from nncf.common.graph import NNCFNodeName from nncf.common.logging import nncf_logger @@ -94,7 +95,7 @@ def collect_output_shapes(model: "NNCFNetwork", graph: NNCFGraph) -> Dict[NNCFNo out_shape = layer.get_output_shape_at(node_index)[dims_slice] if not is_valid_shape(in_shape) or not is_valid_shape(out_shape): - raise RuntimeError(f"Input/output shape is not defined for layer `{layer.name}` ") + raise nncf.ValidationError(f"Input/output shape is not defined for layer `{layer.name}` ") layers_out_shapes[node.node_name] = out_shape @@ -106,7 +107,7 @@ def collect_output_shapes(model: "NNCFNetwork", graph: NNCFGraph) -> Dict[NNCFNo out_shape = layer.get_output_shape_at(node_index)[1:] if not is_valid_shape(in_shape) or not is_valid_shape(out_shape): - raise RuntimeError(f"Input/output shape is not defined for layer `{layer.name}` ") + raise nncf.ValidationError(f"Input/output shape is not defined for layer `{layer.name}` ") layers_out_shapes[node.node_name] = out_shape return layers_out_shapes diff --git a/nncf/tensorflow/quantization/__init__.py b/nncf/tensorflow/quantization/__init__.py index 6cf84541e81..488ce820964 100644 --- a/nncf/tensorflow/quantization/__init__.py +++ b/nncf/tensorflow/quantization/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/quantization/algorithm.py b/nncf/tensorflow/quantization/algorithm.py index ed8e0a8db67..fab9707ce96 100644 --- a/nncf/tensorflow/quantization/algorithm.py +++ b/nncf/tensorflow/quantization/algorithm.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -13,6 +13,7 @@ import tensorflow as tf +import nncf from nncf import NNCFConfig from nncf.api.compression import CompressionLoss from nncf.api.compression import CompressionScheduler @@ -254,10 +255,10 @@ def __init__(self, config: NNCFConfig, should_init: bool = True): self._overflow_fix = self._algo_config.get("overflow_fix", QUANTIZATION_OVERFLOW_FIX) self._target_device = config.get("target_device", TARGET_DEVICE) algo_config = self._get_algo_specific_config_section() - if self._target_device == "VPU" and "preset" in algo_config: - raise RuntimeError("The VPU target device does not support presets.") + if self._target_device == "NPU" and "preset" in algo_config: + raise nncf.ValidationError("The NPU target device does not support presets.") if self._target_device == "CPU_SPR": - raise RuntimeError("The CPU_SPR target device does not supported.") + raise nncf.ValidationError("The CPU_SPR target device does not supported.") self.global_quantizer_constraints = {} self.ignored_scopes_per_group = {} @@ -341,9 +342,8 @@ def _get_half_range( if self._target_device in ["CPU", "ANY"] and qconfig.num_bits == 8: if self._overflow_fix == "enable": return True - if self._overflow_fix == "first_layer_only": - if target_node in first_conv_nodes: - return True + if self._overflow_fix == "first_layer_only" and target_node in first_conv_nodes: + return True return False def _create_quantizer(self, name: str, qspec: TFQuantizerSpec) -> Quantizer: @@ -468,13 +468,13 @@ def _get_quantizer_setup(self, model: tf.keras.Model) -> TFQuantizationSetup: target_node = nncf_graph.get_node_by_name(qp.insertion_point.target_node_name) is_custom, layer_info = converter.get_layer_info_for_node(target_node.node_name) if is_custom: - raise RuntimeError("Quantizing custom layer weights is currently unsupported!") + raise nncf.InternalError("Quantizing custom layer weights is currently unsupported!") layer_name = layer_info.layer_name qconfig = qp.qconfig if layer_name in quantized_layer_names_vs_qconfigs: assigned_qconfig = quantized_layer_names_vs_qconfigs[layer_name] if qconfig != assigned_qconfig: - raise RuntimeError( + raise nncf.InternalError( f"Inconsistent quantizer configurations selected by solver for one and the " f"same quantizable layer! Tried to assign {qconfig} to {layer_name} as " f"specified by QP {qp_id}, but the layer already has quantizer " @@ -508,7 +508,7 @@ def _get_quantizer_setup(self, model: tf.keras.Model) -> TFQuantizationSetup: is_custom, layer_info = converter.get_layer_info_for_node(target_node_name) if is_custom: - raise RuntimeError("Quantizing custom layer activations is currently unsupported!") + raise nncf.InternalError("Quantizing custom layer activations is currently unsupported!") if input_port_id is not None: target_point = TFBeforeLayer( layer_info.layer_name, instance_idx=layer_info.instance_idx, input_port_id=input_port_id @@ -545,7 +545,7 @@ def _log_if_overflow_fix_was_applied(self, applied_overflow_fix: bool): elif self._overflow_fix == "first_layer_only": quantizers_with_overflow_fix_str = "first convolution weight quantizers" elif self._overflow_fix != "disable": - raise RuntimeError(f"Unknown overflow fix type: {self._overflow_fix}") + raise nncf.InternalError(f"Unknown overflow fix type: {self._overflow_fix}") nncf_logger.info(f"Overflow issue fix was applied to {quantizers_with_overflow_fix_str}.") def _generate_unified_scale_groups( diff --git a/nncf/tensorflow/quantization/collectors.py b/nncf/tensorflow/quantization/collectors.py index 1f44a72d84e..97f5051d76c 100644 --- a/nncf/tensorflow/quantization/collectors.py +++ b/nncf/tensorflow/quantization/collectors.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/quantization/default_quantization.py b/nncf/tensorflow/quantization/default_quantization.py index d184c06cb71..4bdf4d2e2be 100644 --- a/nncf/tensorflow/quantization/default_quantization.py +++ b/nncf/tensorflow/quantization/default_quantization.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/quantization/functions.py b/nncf/tensorflow/quantization/functions.py index 7fb2a07ced0..808a77bbbce 100644 --- a/nncf/tensorflow/quantization/functions.py +++ b/nncf/tensorflow/quantization/functions.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/quantization/init_range.py b/nncf/tensorflow/quantization/init_range.py index 6ff881c026c..3c423bb1837 100644 --- a/nncf/tensorflow/quantization/init_range.py +++ b/nncf/tensorflow/quantization/init_range.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -65,15 +65,15 @@ def get_init_config_for_quantization_point(self, layer: tf.keras.layers.Layer, i def get_init_config_for_scope_and_group(self, node_name: str, group: QuantizerGroup) -> RangeInitConfig: matches: List[RangeInitConfig] = [] for pl_config in self.per_layer_range_init_configs: - if should_consider_scope( + should_be_considered = should_consider_scope( node_name, ignored_scopes=pl_config.ignored_scopes, target_scopes=pl_config.target_scopes - ): - if group == pl_config.target_group or pl_config.target_group is None: - matches.append( - RangeInitConfig( - pl_config.init_type, pl_config.num_init_samples, pl_config.init_type_specific_params - ) + ) + if should_be_considered and (group == pl_config.target_group or pl_config.target_group is None): + matches.append( + RangeInitConfig( + pl_config.init_type, pl_config.num_init_samples, pl_config.init_type_specific_params ) + ) if len(matches) > 1: raise ValueError( "Location {} matches more than one per-layer initialization parameter " diff --git a/nncf/tensorflow/quantization/layers.py b/nncf/tensorflow/quantization/layers.py index 901ec287ab2..b392ffbd0c6 100644 --- a/nncf/tensorflow/quantization/layers.py +++ b/nncf/tensorflow/quantization/layers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/quantization/quantize_model.py b/nncf/tensorflow/quantization/quantize_model.py index 30bfdfe073e..0b22c2f6b2e 100644 --- a/nncf/tensorflow/quantization/quantize_model.py +++ b/nncf/tensorflow/quantization/quantize_model.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -13,6 +13,7 @@ import tensorflow as tf +import nncf from nncf.common.initialization.dataloader import NNCFDataLoader from nncf.common.quantization.structs import QuantizationPreset from nncf.config import NNCFConfig @@ -151,13 +152,13 @@ def quantize_impl( if fast_bias_correction is False: raise ValueError(f"fast_bias_correction={fast_bias_correction} is not supported") if ignored_scope is not None and ignored_scope.types: - raise RuntimeError( + raise nncf.InternalError( "Quantization algorithm form the TensorFlow backend " "does not support operation types in the ignored " "scopes yet" ) if target_device == TargetDevice.CPU_SPR: - raise RuntimeError("target_device == CPU_SPR is not supported.") + raise nncf.InternalError("target_device == CPU_SPR is not supported.") if mode is not None: raise ValueError(f"mode={mode} is not supported") diff --git a/nncf/tensorflow/quantization/quantizers.py b/nncf/tensorflow/quantization/quantizers.py index 8d396bb793a..e0969a88390 100644 --- a/nncf/tensorflow/quantization/quantizers.py +++ b/nncf/tensorflow/quantization/quantizers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -14,6 +14,7 @@ import tensorflow as tf +import nncf from nncf.common.quantization.structs import QuantizationScheme as QuantizationMode from nncf.common.quantization.structs import QuantizerConfig from nncf.common.quantization.structs import QuantizerSpec @@ -326,7 +327,9 @@ def _create_variables(self, layer, input_shape, channel_axes, name: str = ""): def apply_overflow_fix(self, weights): if self.num_bits != 8 or not self._half_range: - raise RuntimeError("Attempt to apply overflow issue fix to quantizer which is not configured for that.") + raise nncf.InternalError( + "Attempt to apply overflow issue fix to quantizer which is not configured for that." + ) # Multiplier to expand scale from 7 bit to 8 bit multiplier = 127 / 63 if self.narrow_range else 255 / 127 @@ -450,7 +453,9 @@ def _create_variables(self, layer, input_shape, channel_axes, name: str = ""): def apply_overflow_fix(self, weights): if self.num_bits != 8 or not self._half_range: - raise RuntimeError("Attempt to apply overflow issue fix to quantizer which is not configured for that.") + raise nncf.InternalError( + "Attempt to apply overflow issue fix to quantizer which is not configured for that." + ) # Low value shift to expand quantize range from 7 bit to 8 bit properly weights["input_low_var"].assign( diff --git a/nncf/tensorflow/quantization/utils.py b/nncf/tensorflow/quantization/utils.py index 5fe691e308a..f9470c0c4bd 100644 --- a/nncf/tensorflow/quantization/utils.py +++ b/nncf/tensorflow/quantization/utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/sparsity/__init__.py b/nncf/tensorflow/sparsity/__init__.py index 3a0b0fe26b2..c8e48c02b12 100644 --- a/nncf/tensorflow/sparsity/__init__.py +++ b/nncf/tensorflow/sparsity/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/sparsity/base_algorithm.py b/nncf/tensorflow/sparsity/base_algorithm.py index 6096e2f0f10..4a7a350d29a 100644 --- a/nncf/tensorflow/sparsity/base_algorithm.py +++ b/nncf/tensorflow/sparsity/base_algorithm.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/sparsity/callbacks.py b/nncf/tensorflow/sparsity/callbacks.py index 3f8f455c084..aa4669cfcd7 100644 --- a/nncf/tensorflow/sparsity/callbacks.py +++ b/nncf/tensorflow/sparsity/callbacks.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/sparsity/collector.py b/nncf/tensorflow/sparsity/collector.py index 215a6a7d19e..440c030ad94 100644 --- a/nncf/tensorflow/sparsity/collector.py +++ b/nncf/tensorflow/sparsity/collector.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/sparsity/magnitude/__init__.py b/nncf/tensorflow/sparsity/magnitude/__init__.py index 9b059339542..07c557252f0 100644 --- a/nncf/tensorflow/sparsity/magnitude/__init__.py +++ b/nncf/tensorflow/sparsity/magnitude/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/sparsity/magnitude/algorithm.py b/nncf/tensorflow/sparsity/magnitude/algorithm.py index 0ef45f5ec9d..8a335cc78e8 100644 --- a/nncf/tensorflow/sparsity/magnitude/algorithm.py +++ b/nncf/tensorflow/sparsity/magnitude/algorithm.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -15,7 +15,6 @@ from nncf import NNCFConfig from nncf.api.compression import CompressionLoss -from nncf.api.compression import CompressionScheduler from nncf.api.compression import CompressionStage from nncf.common.accuracy_aware_training.training_loop import ADAPTIVE_COMPRESSION_CONTROLLERS from nncf.common.graph.operator_metatypes import OUTPUT_NOOP_METATYPES @@ -25,6 +24,7 @@ from nncf.common.scopes import check_scopes_in_graph from nncf.common.scopes import should_consider_scope from nncf.common.sparsity.schedulers import SPARSITY_SCHEDULERS +from nncf.common.sparsity.schedulers import SparsityScheduler from nncf.common.sparsity.statistics import LayerThreshold from nncf.common.sparsity.statistics import MagnitudeSparsityStatistics from nncf.common.statistics import NNCFStatistics @@ -157,16 +157,20 @@ def __init__(self, target_model, config: NNCFConfig, op_names): raise ValueError("Magnitude sparsity algorithm do not support adaptive scheduler") scheduler_cls = SPARSITY_SCHEDULERS.get(scheduler_type) - self._scheduler = scheduler_cls(self, params) + self._scheduler: SparsityScheduler = scheduler_cls(self, params) self._loss = TFZeroCompressionLoss() self._bn_adaptation = None self._config = config self.set_sparsity_level(sparsity_init) @property - def scheduler(self) -> CompressionScheduler: + def scheduler(self) -> SparsityScheduler: return self._scheduler + @property + def current_sparsity_level(self) -> float: + return self._scheduler.current_sparsity_level + @property def loss(self) -> CompressionLoss: return self._loss diff --git a/nncf/tensorflow/sparsity/magnitude/functions.py b/nncf/tensorflow/sparsity/magnitude/functions.py index cdf4972ae38..29a4d8ec1d3 100644 --- a/nncf/tensorflow/sparsity/magnitude/functions.py +++ b/nncf/tensorflow/sparsity/magnitude/functions.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/sparsity/magnitude/operation.py b/nncf/tensorflow/sparsity/magnitude/operation.py index 8b13d80bb0c..4656c72408c 100644 --- a/nncf/tensorflow/sparsity/magnitude/operation.py +++ b/nncf/tensorflow/sparsity/magnitude/operation.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/sparsity/rb/__init__.py b/nncf/tensorflow/sparsity/rb/__init__.py index 6ce683c2d15..2602ae660d5 100644 --- a/nncf/tensorflow/sparsity/rb/__init__.py +++ b/nncf/tensorflow/sparsity/rb/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/sparsity/rb/algorithm.py b/nncf/tensorflow/sparsity/rb/algorithm.py index e014b99b8bc..db68a85fd64 100644 --- a/nncf/tensorflow/sparsity/rb/algorithm.py +++ b/nncf/tensorflow/sparsity/rb/algorithm.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -150,6 +150,12 @@ def loss(self) -> SparseLoss: def set_sparsity_level(self, sparsity_level): self._loss.set_target_sparsity_loss(sparsity_level) + @property + def current_sparsity_level(self) -> float: + # TODO: align with torch where it currently shows the sparsity level as reported by loss object. + # TF does not seem to have this functionality in its SparseLoss right now. + return self.scheduler.current_sparsity_level + def freeze(self): self._loss.disable() diff --git a/nncf/tensorflow/sparsity/rb/functions.py b/nncf/tensorflow/sparsity/rb/functions.py index 6542e2f130f..afa75b89f2a 100644 --- a/nncf/tensorflow/sparsity/rb/functions.py +++ b/nncf/tensorflow/sparsity/rb/functions.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/sparsity/rb/loss.py b/nncf/tensorflow/sparsity/rb/loss.py index 3fa663e732c..1c0f6e9fe2f 100644 --- a/nncf/tensorflow/sparsity/rb/loss.py +++ b/nncf/tensorflow/sparsity/rb/loss.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/sparsity/rb/operation.py b/nncf/tensorflow/sparsity/rb/operation.py index 08607160a02..442c224f387 100644 --- a/nncf/tensorflow/sparsity/rb/operation.py +++ b/nncf/tensorflow/sparsity/rb/operation.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/sparsity/utils.py b/nncf/tensorflow/sparsity/utils.py index 5466edf4b7d..bbb1a72dc99 100644 --- a/nncf/tensorflow/sparsity/utils.py +++ b/nncf/tensorflow/sparsity/utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/tensor.py b/nncf/tensorflow/tensor.py index cf80f4fc1f2..2ab88fcc1dd 100644 --- a/nncf/tensorflow/tensor.py +++ b/nncf/tensorflow/tensor.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -30,3 +30,6 @@ def __init__(self, tensor: tf.Tensor): @property def device(self) -> tf.device: return self._tensor.device + + def is_empty(self) -> bool: + return tf.equal(tf.size(self._tensor), 0) diff --git a/nncf/tensorflow/tensor_statistics/__init__.py b/nncf/tensorflow/tensor_statistics/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/tensorflow/tensor_statistics/__init__.py +++ b/nncf/tensorflow/tensor_statistics/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/tensor_statistics/collectors.py b/nncf/tensorflow/tensor_statistics/collectors.py index 27f4f463230..6cbee5cd914 100644 --- a/nncf/tensorflow/tensor_statistics/collectors.py +++ b/nncf/tensorflow/tensor_statistics/collectors.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/tensor_statistics/reduction.py b/nncf/tensorflow/tensor_statistics/reduction.py index 00229d88be5..499da0fd0d5 100644 --- a/nncf/tensorflow/tensor_statistics/reduction.py +++ b/nncf/tensorflow/tensor_statistics/reduction.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/tensor_statistics/statistics.py b/nncf/tensorflow/tensor_statistics/statistics.py index 724c71019e4..cdeaa810d45 100644 --- a/nncf/tensorflow/tensor_statistics/statistics.py +++ b/nncf/tensorflow/tensor_statistics/statistics.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/tf_internals.py b/nncf/tensorflow/tf_internals.py index b326556af6b..b8521513149 100644 --- a/nncf/tensorflow/tf_internals.py +++ b/nncf/tensorflow/tf_internals.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/utils/__init__.py b/nncf/tensorflow/utils/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/tensorflow/utils/__init__.py +++ b/nncf/tensorflow/utils/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/utils/hook_handle.py b/nncf/tensorflow/utils/hook_handle.py deleted file mode 100644 index 4f4e04540c3..00000000000 --- a/nncf/tensorflow/utils/hook_handle.py +++ /dev/null @@ -1,30 +0,0 @@ -# Copyright (c) 2023 Intel Corporation -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# http://www.apache.org/licenses/LICENSE-2.0 -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import weakref - - -class HookHandle: - """ - A handle to remove a hook - """ - - id = 0 - - def __init__(self, hooks_registry): - self.hooks_registry_ref = weakref.ref(hooks_registry) - self.hook_id = HookHandle.id - HookHandle.id = HookHandle.id + 1 - - def remove(self): - hooks_registry = self.hooks_registry_ref() - if hooks_registry is not None and self.hook_id in hooks_registry: - del hooks_registry[self.hook_id] diff --git a/nncf/tensorflow/utils/node.py b/nncf/tensorflow/utils/node.py index 41db2602805..28d3a0088cf 100644 --- a/nncf/tensorflow/utils/node.py +++ b/nncf/tensorflow/utils/node.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/utils/scopes_handle.py b/nncf/tensorflow/utils/scopes_handle.py index 5fd9435df6a..bca408dbfd3 100644 --- a/nncf/tensorflow/utils/scopes_handle.py +++ b/nncf/tensorflow/utils/scopes_handle.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/tensorflow/utils/state.py b/nncf/tensorflow/utils/state.py index 9c439ca69e6..009d5c8519c 100644 --- a/nncf/tensorflow/utils/state.py +++ b/nncf/tensorflow/utils/state.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/__init__.py b/nncf/torch/__init__.py index 0b6d0ebdc2a..15c32417630 100644 --- a/nncf/torch/__init__.py +++ b/nncf/torch/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/accuracy_aware_training/__init__.py b/nncf/torch/accuracy_aware_training/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/torch/accuracy_aware_training/__init__.py +++ b/nncf/torch/accuracy_aware_training/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/accuracy_aware_training/runner.py b/nncf/torch/accuracy_aware_training/runner.py index 00dac4e7495..8a34c2879ff 100644 --- a/nncf/torch/accuracy_aware_training/runner.py +++ b/nncf/torch/accuracy_aware_training/runner.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -131,14 +131,12 @@ def _make_checkpoint_path(self, is_best, compression_rate=None): return osp.join(self._checkpoint_save_dir, f'acc_aware_checkpoint_{"best" if is_best else "last"}{extension}') def add_tensorboard_scalar(self, key, data, step): - if is_main_process(): - if self.verbose and self._tensorboard_writer is not None: - self._tensorboard_writer.add_scalar(key, data, step) + if is_main_process() and self.verbose and self._tensorboard_writer is not None: + self._tensorboard_writer.add_scalar(key, data, step) def add_tensorboard_image(self, key, data, step): - if is_main_process(): - if self.verbose and self._tensorboard_writer is not None: - self._tensorboard_writer.add_image(key, ToTensor()(data), step) + if is_main_process() and self.verbose and self._tensorboard_writer is not None: + self._tensorboard_writer.add_image(key, ToTensor()(data), step) class PTAdaptiveCompressionLevelTrainingRunner( diff --git a/nncf/torch/accuracy_aware_training/utils.py b/nncf/torch/accuracy_aware_training/utils.py index 415c90e7c39..3023afa75ee 100644 --- a/nncf/torch/accuracy_aware_training/utils.py +++ b/nncf/torch/accuracy_aware_training/utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/algo_selector.py b/nncf/torch/algo_selector.py index dc513843bd6..fd52ffd9d74 100644 --- a/nncf/torch/algo_selector.py +++ b/nncf/torch/algo_selector.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/automl/__init__.py b/nncf/torch/automl/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/torch/automl/__init__.py +++ b/nncf/torch/automl/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/automl/agent/__init__.py b/nncf/torch/automl/agent/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/torch/automl/agent/__init__.py +++ b/nncf/torch/automl/agent/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/automl/agent/ddpg/__init__.py b/nncf/torch/automl/agent/ddpg/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/torch/automl/agent/ddpg/__init__.py +++ b/nncf/torch/automl/agent/ddpg/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/automl/agent/ddpg/ddpg.py b/nncf/torch/automl/agent/ddpg/ddpg.py index bf7c0027e36..ab51f0d510e 100644 --- a/nncf/torch/automl/agent/ddpg/ddpg.py +++ b/nncf/torch/automl/agent/ddpg/ddpg.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/automl/agent/ddpg/memory.py b/nncf/torch/automl/agent/ddpg/memory.py index 14a40719a47..9710515688a 100644 --- a/nncf/torch/automl/agent/ddpg/memory.py +++ b/nncf/torch/automl/agent/ddpg/memory.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -18,6 +18,7 @@ import numpy as np +import nncf from nncf import nncf_logger # [reference] https://github.com/matthiasplappert/keras-rl/blob/master/rl/memory.py @@ -81,7 +82,7 @@ def append(self, v): self.data.append(v) else: # This should never happen. - raise RuntimeError() + raise nncf.BufferFullError() def zeroed_observation(observation): diff --git a/nncf/torch/automl/environment/__init__.py b/nncf/torch/automl/environment/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/torch/automl/environment/__init__.py +++ b/nncf/torch/automl/environment/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/automl/environment/quantization_env.py b/nncf/torch/automl/environment/quantization_env.py index 017378d7a23..4453b489110 100644 --- a/nncf/torch/automl/environment/quantization_env.py +++ b/nncf/torch/automl/environment/quantization_env.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -148,7 +148,7 @@ def __init__( # Check and only proceed if target device is supported by Q.Env self.hw_cfg_type = hw_config_type - assert self.hw_cfg_type in [None, HWConfigType.VPU] + assert self.hw_cfg_type in [None, HWConfigType.NPU] # Set target compression ratio self.compression_ratio = params.compression_ratio @@ -170,7 +170,7 @@ def __init__( # Configure search space for precision according to target device if self.hw_cfg_type is None: self.model_bitwidth_space = params.bits - elif self.hw_cfg_type is HWConfigType.VPU: + elif self.hw_cfg_type is HWConfigType.NPU: self.model_bitwidth_space = self._hw_precision_constraints.get_all_unique_bitwidths() self.model_bitwidth_space = sorted(list(self.model_bitwidth_space)) @@ -179,7 +179,7 @@ def __init__( self.qctrl.all_quantizations.keys() ) if self.hw_cfg_type is None: - for qid in self.qconfig_space_map.keys(): + for qid in self.qconfig_space_map: conf = self.qctrl.all_quantizations[qid].get_quantizer_config() conf_list_to_set = [] for bit in self.model_bitwidth_space: diff --git a/nncf/torch/batchnorm_adaptation.py b/nncf/torch/batchnorm_adaptation.py index 8ead222e55b..4184fb14841 100644 --- a/nncf/torch/batchnorm_adaptation.py +++ b/nncf/torch/batchnorm_adaptation.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/binarization/__init__.py b/nncf/torch/binarization/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/torch/binarization/__init__.py +++ b/nncf/torch/binarization/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/binarization/algo.py b/nncf/torch/binarization/algo.py index 6db1e61bdc3..1afa4d0286c 100644 --- a/nncf/torch/binarization/algo.py +++ b/nncf/torch/binarization/algo.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -17,6 +17,7 @@ from nncf.api.compression import CompressionLoss from nncf.api.compression import CompressionScheduler from nncf.api.compression import CompressionStage +from nncf.common.deprecation import warning_deprecated from nncf.common.graph.transformations.commands import TargetType from nncf.common.graph.transformations.commands import TransformationPriority from nncf.common.logging import nncf_logger @@ -49,6 +50,7 @@ class BinarizationBuilder(PTCompressionAlgorithmBuilder): def __init__(self, config, should_init: bool = True): super().__init__(config, should_init) self.mode = self._algo_config.get("mode", BINARIZATION_MODE) + warning_deprecated("`binarization` algorithm is deprecated and will no longer be supported in the future.") def _get_transformation_layout(self, target_model: NNCFNetwork) -> PTTransformationLayout: layout = PTTransformationLayout() diff --git a/nncf/torch/binarization/binarize_functions.py b/nncf/torch/binarization/binarize_functions.py index 664ed94bd27..4f08b4715a2 100644 --- a/nncf/torch/binarization/binarize_functions.py +++ b/nncf/torch/binarization/binarize_functions.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -127,10 +127,9 @@ def forward(ctx, input_, scale, threshold): @staticmethod def backward(ctx: Any, *grad_outputs: Any) -> Any: grad_output = grad_outputs[0] - if grad_output.is_cuda: - if not grad_output.is_contiguous(): - nncf_logger.debug("grad_output is not contiguous!") - grad_output = grad_output.contiguous() + if grad_output.is_cuda and not grad_output.is_contiguous(): + nncf_logger.debug("grad_output is not contiguous!") + grad_output = grad_output.contiguous() input_, scale, output = ctx.saved_variables diff --git a/nncf/torch/binarization/extensions.py b/nncf/torch/binarization/extensions.py index 6e518429968..5f505519d4f 100644 --- a/nncf/torch/binarization/extensions.py +++ b/nncf/torch/binarization/extensions.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -14,6 +14,7 @@ import torch +import nncf from nncf import nncf_logger from nncf.definitions import NNCF_PACKAGE_ROOT_DIR from nncf.torch.binarization.reference import ReferenceBinarizedFunctions @@ -98,7 +99,7 @@ def load(cls): raise e except (subprocess.CalledProcessError, OSError, RuntimeError) as e: assert torch.cuda.is_available() - raise RuntimeError( + raise nncf.InstallationError( "CUDA is available for PyTorch, but NNCF could not compile " "GPU binarization extensions. Make sure that you have installed CUDA development " "tools (see https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html for " diff --git a/nncf/torch/binarization/layers.py b/nncf/torch/binarization/layers.py index dd7681f71ee..b7154dd285a 100644 --- a/nncf/torch/binarization/layers.py +++ b/nncf/torch/binarization/layers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/binarization/reference.py b/nncf/torch/binarization/reference.py index cbf80d355fa..c43fcfa325a 100644 --- a/nncf/torch/binarization/reference.py +++ b/nncf/torch/binarization/reference.py @@ -1,4 +1,4 @@ -# Copyright (c) 2022 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -15,6 +15,8 @@ import numpy as np import torch +import nncf + GeneralizedTensor = TypeVar("GeneralizedTensor", torch.Tensor, np.ndarray) @@ -30,7 +32,7 @@ def __init__(self, backend_type: ReferenceBackendType): elif backend_type is ReferenceBackendType.TORCH: self.backend = torch else: - raise RuntimeError("Unknown backend for ReferenceQuantize") + raise nncf.UnsupportedBackendError("Unknown backend for ReferenceQuantize") def _astype(self, tensor: GeneralizedTensor, dtype) -> GeneralizedTensor: if self.backend is np: diff --git a/nncf/torch/checkpoint_loading.py b/nncf/torch/checkpoint_loading.py index 62e95c1bbd4..2e28b68cb2c 100644 --- a/nncf/torch/checkpoint_loading.py +++ b/nncf/torch/checkpoint_loading.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -14,6 +14,7 @@ import torch +import nncf from nncf.common.deprecation import warning_deprecated from nncf.common.logging import nncf_logger from nncf.common.utils.api_marker import api @@ -109,7 +110,7 @@ def add_skipped_and_missing_keys(self, model_state_dict: Dict[str, torch.Tensor] for keys in self._keys.values(): all_processed_keys.extend(keys) - for key in model_state_dict.keys(): + for key in model_state_dict: if key not in all_processed_keys: if key.endswith(params_to_skip) or key in optional_param_names: self.add_key(key, ProcessedKeyStatus.SKIPPED) @@ -140,7 +141,7 @@ def add_error_msg(name, keys_): if error_msgs: error_msg = "Error(s) when loading model parameters:\n\t{}".format("\n\t".join(error_msgs)) if is_resume: - raise RuntimeError(error_msg) + raise nncf.InternalError(error_msg) nncf_logger.error(error_msg) diff --git a/nncf/torch/composite_compression.py b/nncf/torch/composite_compression.py index 8173d9ed409..db1399f2741 100644 --- a/nncf/torch/composite_compression.py +++ b/nncf/torch/composite_compression.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -12,6 +12,7 @@ import torch.nn +import nncf from nncf import NNCFConfig from nncf.common.composite_compression import CompositeCompressionAlgorithmBuilder from nncf.common.composite_compression import CompositeCompressionAlgorithmController @@ -44,7 +45,7 @@ def __init__(self, config: NNCFConfig, should_init: bool = True): algo_names = extract_algorithm_names(config) if len(algo_names) < 2: - raise RuntimeError( + raise nncf.ValidationError( "Composite algorithm builder must be supplied with a config with more than one " "compression algo specified!" ) diff --git a/nncf/torch/compression_method_api.py b/nncf/torch/compression_method_api.py index efbbe8b2408..7e6a7940b21 100644 --- a/nncf/torch/compression_method_api.py +++ b/nncf/torch/compression_method_api.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -21,6 +21,7 @@ import torch from torch import nn +import nncf from nncf.api.compression import CompressionLoss from nncf.common.compression import BaseCompressionAlgorithmBuilder from nncf.common.compression import BaseCompressionAlgorithmController @@ -166,7 +167,7 @@ def build_controller(self, model: TModel) -> PTCompressionAlgorithmController: """ ctrl = self._build_controller(model) if not isinstance(ctrl, PTCompressionAlgorithmController): - raise RuntimeError( + raise nncf.InternalError( "Internal error: builder must create controller inherited from " "`PTCompressionAlgorithmController` class" ) @@ -195,9 +196,9 @@ def _get_transformation_layout(self, target_model: NNCFNetwork) -> PTTransformat def _handle_frozen_layers(self, target_model: NNCFNetwork): scopes_of_frozen_layers = [] for weighted_node in target_model.nncf.get_weighted_original_graph_nodes(): - if not weighted_node.layer_attributes.weight_requires_grad: - if self._should_consider_scope(weighted_node.node_name): - scopes_of_frozen_layers.append(weighted_node.node_name) + should_be_considered = self._should_consider_scope(weighted_node.node_name) + if not weighted_node.layer_attributes.weight_requires_grad and should_be_considered: + scopes_of_frozen_layers.append(weighted_node.node_name) scopes_to_print = "\n".join(scopes_of_frozen_layers) if len(scopes_of_frozen_layers) > 0: is_allowed, reason = self._are_frozen_layers_allowed() @@ -206,7 +207,7 @@ def _handle_frozen_layers(self, target_model: NNCFNetwork): f"{reason}, compressing them without tuning weights.\nFrozen layers:\n{scopes_to_print}" ) else: - raise RuntimeError( + raise nncf.InternalError( f"{reason}.\n" f"Please unfreeze them or put into the Ignored Scope.\n" f"Frozen Layers:\n" diff --git a/nncf/torch/debug.py b/nncf/torch/debug.py index 8ced0814eaf..db690d29732 100644 --- a/nncf/torch/debug.py +++ b/nncf/torch/debug.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/dynamic_graph/__init__.py b/nncf/torch/dynamic_graph/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/torch/dynamic_graph/__init__.py +++ b/nncf/torch/dynamic_graph/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/dynamic_graph/context.py b/nncf/torch/dynamic_graph/context.py index 1fdf5fb27d3..723a5a3350e 100644 --- a/nncf/torch/dynamic_graph/context.py +++ b/nncf/torch/dynamic_graph/context.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -11,14 +11,17 @@ import threading import weakref +from collections import OrderedDict from collections import defaultdict from collections import deque from contextlib import contextmanager -from typing import Callable, DefaultDict, List, Optional, Union +from typing import Callable, DefaultDict, Dict, List, Optional, Union import torch from nncf.common.graph.layer_attributes import BaseLayerAttributes +from nncf.common.hook_handle import HookHandle +from nncf.common.hook_handle import add_op_to_registry from nncf.common.utils.api_marker import api from nncf.common.utils.debug import is_debug from nncf.common.utils.patcher import PATCHER @@ -94,8 +97,8 @@ class TracingContext: def __init__(self): self.graph = DynamicGraph() - self._post_hooks: DefaultDict[OperationAddress, List[Callable]] = defaultdict(list) - self._pre_hooks: DefaultDict[PreHookId, List[Callable]] = defaultdict(list) + self._post_hooks: DefaultDict[OperationAddress, Dict[str, Callable]] = defaultdict(OrderedDict) + self._pre_hooks: DefaultDict[PreHookId, Dict[str, Callable]] = defaultdict(OrderedDict) self._num_nested_hooks = 0 self.reused_parameters = [] @@ -282,9 +285,9 @@ def pop_scope(self): self.relative_scopes_stack.pop() self.module_call_stack.pop() - def register_pre_hooks(self, fn_list: List[Callable], op_address: OperationAddress, input_port_id: int): + def register_pre_hook(self, fn: Callable, op_address: OperationAddress, input_port_id: int) -> HookHandle: pre_hook_id = PreHookId(op_address, input_port_id) - self._pre_hooks[pre_hook_id].extend(fn_list) + return add_op_to_registry(self._pre_hooks[pre_hook_id], fn) def execute_pre_hooks(self, op_address: OperationAddress, op_inputs: OperatorInput) -> OperatorInput: in_op = getattr(self, "in_operator", False) @@ -294,7 +297,7 @@ def execute_pre_hooks(self, op_address: OperationAddress, op_inputs: OperatorInp pre_hook_ids_for_curr_op = [x for x in self._pre_hooks if x.op_address == op_address] pre_hook_ids_for_curr_op = sorted(pre_hook_ids_for_curr_op, key=lambda x: x.input_port_id) for pre_hook_id in pre_hook_ids_for_curr_op: - hook_list_for_current_input_port = self._pre_hooks[pre_hook_id] + hook_list_for_current_input_port = self._pre_hooks[pre_hook_id].values() input_arg_to_process = pre_hook_id.input_port_id for hook in hook_list_for_current_input_port: op_inputs[input_arg_to_process] = hook(op_inputs[input_arg_to_process]) @@ -302,15 +305,15 @@ def execute_pre_hooks(self, op_address: OperationAddress, op_inputs: OperatorInp self.in_operator = in_op return op_inputs - def register_post_hooks(self, fn_list: List[Callable], op_address: OperationAddress): - self._post_hooks[op_address].extend(fn_list) + def register_post_hook(self, fn: Callable, op_address: OperationAddress) -> HookHandle: + return add_op_to_registry(self._post_hooks[op_address], fn) def execute_post_hooks(self, op_address: OperationAddress, outputs): in_op = getattr(self, "in_operator", False) self.in_operator = False self._threading.thread_local.num_nested_hooks += 1 if op_address in self._post_hooks: - for hook in self._post_hooks[op_address]: + for hook in self._post_hooks[op_address].values(): outputs = hook(outputs) self._threading.thread_local.num_nested_hooks -= 1 self.in_operator = in_op @@ -454,14 +457,13 @@ def set_active_skipped_block(self, block_indexes: List[int]): self.end_node_name_of_skipped_block.append(self.skipped_blocks[block_index].end_node_name) def set_elastic_blocks(self, blocks: List["BuildingBlock"] = None): # noqa: F821 - if blocks is not None: - if isinstance(blocks, list): - if len(blocks) == 0: - self.skipped_blocks = [] - elif isinstance(blocks[0], str): - self.skipped_blocks = [blocks] - else: - self.skipped_blocks = blocks + if blocks is not None and isinstance(blocks, list): + if len(blocks) == 0: + self.skipped_blocks = [] + elif isinstance(blocks[0], str): + self.skipped_blocks = [blocks] + else: + self.skipped_blocks = blocks def set_current_context(c: TracingContext): diff --git a/nncf/torch/dynamic_graph/graph.py b/nncf/torch/dynamic_graph/graph.py index 158deb3b78e..f8a80a5a0cb 100644 --- a/nncf/torch/dynamic_graph/graph.py +++ b/nncf/torch/dynamic_graph/graph.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/dynamic_graph/graph_tracer.py b/nncf/torch/dynamic_graph/graph_tracer.py index c9cf108dec1..4a86cb68576 100644 --- a/nncf/torch/dynamic_graph/graph_tracer.py +++ b/nncf/torch/dynamic_graph/graph_tracer.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/dynamic_graph/io_handling.py b/nncf/torch/dynamic_graph/io_handling.py index 9d0b7b8e721..c0f3aeb92e5 100644 --- a/nncf/torch/dynamic_graph/io_handling.py +++ b/nncf/torch/dynamic_graph/io_handling.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -17,6 +17,7 @@ import torch +import nncf from nncf import Dataset from nncf import NNCFConfig from nncf.common.graph.definitions import MODEL_INPUT_OP_NAME @@ -178,7 +179,7 @@ def from_nncf_config(cls, config: NNCFConfig): """ input_infos = config.get("input_info") if input_infos is None: - raise RuntimeError("Passed NNCFConfig does not have an 'input_info' field") + raise nncf.ValidationError("Passed NNCFConfig does not have an 'input_info' field") if isinstance(input_infos, dict): return FillerInputInfo( [ @@ -202,7 +203,7 @@ def from_nncf_config(cls, config: NNCFConfig): ) ) return FillerInputInfo(elements) - raise RuntimeError("Invalid input_infos specified in config - should be either dict or list of dicts") + raise nncf.ValidationError("Invalid input_infos specified in config - should be either dict or list of dicts") def get_forward_inputs( self, device: Optional[Union[str, torch.device]] = None diff --git a/nncf/torch/dynamic_graph/layer_attributes_handlers.py b/nncf/torch/dynamic_graph/layer_attributes_handlers.py index fef3601acde..8338e760c17 100644 --- a/nncf/torch/dynamic_graph/layer_attributes_handlers.py +++ b/nncf/torch/dynamic_graph/layer_attributes_handlers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/dynamic_graph/op_input_processing.py b/nncf/torch/dynamic_graph/op_input_processing.py index f4b7a96a067..1eac456e661 100644 --- a/nncf/torch/dynamic_graph/op_input_processing.py +++ b/nncf/torch/dynamic_graph/op_input_processing.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/dynamic_graph/operation_address.py b/nncf/torch/dynamic_graph/operation_address.py index e315240a5d0..e681b18e626 100644 --- a/nncf/torch/dynamic_graph/operation_address.py +++ b/nncf/torch/dynamic_graph/operation_address.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/dynamic_graph/patch_pytorch.py b/nncf/torch/dynamic_graph/patch_pytorch.py index 499eb2c4d1d..b75d3ac604d 100644 --- a/nncf/torch/dynamic_graph/patch_pytorch.py +++ b/nncf/torch/dynamic_graph/patch_pytorch.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -21,6 +21,7 @@ from torch.nn import DataParallel from torch.nn.parallel import DistributedDataParallel +import nncf from nncf import nncf_logger from nncf.common.utils.api_marker import api from nncf.torch.dynamic_graph.structs import NamespaceTarget @@ -39,7 +40,7 @@ def get_namespaces_to_patch(namespace_target: NamespaceTarget) -> Tuple[object, return (TracedTensor, TracedParameter) if namespace_target == NamespaceTarget.TORCH: return (torch,) - raise RuntimeError("{} namespace wasn't found in {}".format(namespace_target, NamespaceTarget)) + raise nncf.ValidationError("{} namespace wasn't found in {}".format(namespace_target, NamespaceTarget)) def get_namespace_to_extract_functions_from(namespace_target: NamespaceTarget) -> object: @@ -49,7 +50,7 @@ def get_namespace_to_extract_functions_from(namespace_target: NamespaceTarget) - return torch.Tensor if namespace_target == NamespaceTarget.TORCH: return torch._C._VariableFunctions - raise RuntimeError("{} namespace wasn't found in {}".format(namespace_target, NamespaceTarget)) + raise nncf.ValidationError("{} namespace wasn't found in {}".format(namespace_target, NamespaceTarget)) class FunctionsToPatchWithoutTracing: diff --git a/nncf/torch/dynamic_graph/scope.py b/nncf/torch/dynamic_graph/scope.py index abc5c3da040..14f78694e5f 100644 --- a/nncf/torch/dynamic_graph/scope.py +++ b/nncf/torch/dynamic_graph/scope.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -12,6 +12,8 @@ from copy import deepcopy from typing import List +import nncf + class ScopeElement: def __init__(self, calling_module_class_name: str, calling_field_name: str = None): @@ -35,12 +37,12 @@ def __hash__(self): def from_str(string: str): matches = re.search(r"(.*)\[(.*)\]|(.*)", string) if matches is None: - raise RuntimeError("Invalid scope element string") + raise nncf.InternalError("Invalid scope element string") if matches.groups()[0] is None and matches.groups()[1] is None: return ScopeElement(matches.groups()[2]) if matches.groups()[0] is not None and matches.groups()[1] is not None: return ScopeElement(matches.groups()[0], matches.groups()[1]) - raise RuntimeError("Could not parse the scope element string") + raise nncf.InternalError("Could not parse the scope element string") class Scope: diff --git a/nncf/torch/dynamic_graph/scope_access.py b/nncf/torch/dynamic_graph/scope_access.py index f84d9e6b0fb..6014c90f3f3 100644 --- a/nncf/torch/dynamic_graph/scope_access.py +++ b/nncf/torch/dynamic_graph/scope_access.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -13,6 +13,7 @@ import torch.nn +import nncf from nncf.torch.dynamic_graph.scope import Scope @@ -26,7 +27,7 @@ def get_module_by_scope(model: torch.nn.Module, scope: Scope) -> Optional[torch. next_module = curr_module._modules.get(scope_element.calling_field_name) if next_module is None: - raise RuntimeError( + raise nncf.InternalError( "Could not find a {} module member in {} module of scope {} during node search".format( scope_element.calling_field_name, scope_element.calling_module_class_name, str(scope) ) diff --git a/nncf/torch/dynamic_graph/structs.py b/nncf/torch/dynamic_graph/structs.py index 20fcbefada2..07d03b18af4 100644 --- a/nncf/torch/dynamic_graph/structs.py +++ b/nncf/torch/dynamic_graph/structs.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/dynamic_graph/trace_functions.py b/nncf/torch/dynamic_graph/trace_functions.py index 3bf3621976a..acf207d78c1 100644 --- a/nncf/torch/dynamic_graph/trace_functions.py +++ b/nncf/torch/dynamic_graph/trace_functions.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -15,6 +15,7 @@ import numpy as np import torch +import nncf from nncf import nncf_logger from nncf.common.graph.layer_attributes import Dtype from nncf.torch.dynamic_graph.context import TracingContext @@ -81,7 +82,7 @@ def forward_trace_only(operator: Callable, *args, **kwargs): forwarded_meta.shape = tuple(result[out_idx].shape) result[out_idx] = TracedTensor.from_torch_tensor(result[out_idx], forwarded_meta) elif len(input_traced_tensor_indices) != len(output_tensors_to_be_traced_indices): - raise RuntimeError( + raise nncf.ValidationError( "Unable to forward trace through operator {} - " "input and output tensor count mismatch!".format(operator.__name__) ) @@ -95,7 +96,7 @@ def forward_trace_only(operator: Callable, *args, **kwargs): if was_tuple: result = tuple(result) elif len(input_traced_tensor_indices) > 1: - raise RuntimeError( + raise nncf.ValidationError( "Unable to forward trace through operator {} - " "input and output tensor count mismatch!".format(operator.__name__) ) diff --git a/nncf/torch/dynamic_graph/trace_tensor.py b/nncf/torch/dynamic_graph/trace_tensor.py index a55e4277900..05958918497 100644 --- a/nncf/torch/dynamic_graph/trace_tensor.py +++ b/nncf/torch/dynamic_graph/trace_tensor.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/dynamic_graph/wrappers.py b/nncf/torch/dynamic_graph/wrappers.py index eb8395a24fd..d844933fd53 100644 --- a/nncf/torch/dynamic_graph/wrappers.py +++ b/nncf/torch/dynamic_graph/wrappers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -15,6 +15,7 @@ import torch from torch.nn import DataParallel +import nncf from nncf.common.graph.definitions import MODEL_CONST_OP_NAME from nncf.common.graph.layer_attributes import BaseLayerAttributes from nncf.common.logging import nncf_logger @@ -34,8 +35,6 @@ from nncf.torch.dynamic_graph.trace_tensor import TracedParameter from nncf.torch.layer_utils import _NNCFModuleMixin from nncf.torch.layers import ITERATION_MODULES -from nncf.torch.return_types import maybe_unwrap_from_torch_return_type -from nncf.torch.return_types import maybe_wrap_to_torch_return_type _IGNORED_SCOPES = [] @@ -61,7 +60,7 @@ def wrap_operator(operator, operator_info: PatchedOperatorInfo): Wraps the input callable object (`operator`) with the functionality that allows the calls to this object to be tracked by the currently set global TracingContext. The wrapped functions can be then intercepted, their arguments and return values modified arbitrarily and, for functions that correspond to operations on - tensors in a DNN, their general position and address in the DNN's model control flow graph can be established. + tensors in a DNN, their general position and address in the DNN's model control flow graph can be established. :param: operator: A callable object to be wrapped. :param: operator_info (PatchedOperatorInfo): An informational struct containing the specifics of wrapping @@ -195,10 +194,8 @@ def _execute_op( if is_debug() and node is not None: ctx.register_node_call(node) - unwrapped_result = maybe_unwrap_from_torch_return_type(result) - unwrapped_result = trace_tensors(unwrapped_result, node, ctx) - unwrapped_result = ctx.execute_post_hooks(op_address, unwrapped_result) - result = maybe_wrap_to_torch_return_type(unwrapped_result, result) + result = trace_tensors(result, node, ctx) + result = ctx.execute_post_hooks(op_address, result) return result @@ -212,7 +209,7 @@ def _collect_module_attrs_and_ignored_algorithms( if op_name in OP_NAMES_WITH_WEIGHTS: curr_module = ctx.get_current_module() if curr_module is None: - raise RuntimeError( + raise nncf.ValidationError( f"Operation {op_name} requires module attributes, but it was executed outside any module" ) layer_attrs = get_layer_attributes_from_module(curr_module, op_name) diff --git a/nncf/torch/engine.py b/nncf/torch/engine.py index 44271123d6b..c2a7c051132 100644 --- a/nncf/torch/engine.py +++ b/nncf/torch/engine.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/exporter.py b/nncf/torch/exporter.py index 4aa35e1bd9e..6d4e548e91c 100644 --- a/nncf/torch/exporter.py +++ b/nncf/torch/exporter.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/extensions/__init__.py b/nncf/torch/extensions/__init__.py index c0549f5cf8b..59edea1ca24 100644 --- a/nncf/torch/extensions/__init__.py +++ b/nncf/torch/extensions/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -22,6 +22,7 @@ import torch from torch.utils.cpp_extension import _get_build_directory +import nncf from nncf.common.logging import nncf_logger from nncf.common.logging.logger import extension_is_loading_info_log from nncf.common.utils.api_marker import api @@ -138,7 +139,7 @@ def force_build_cuda_extensions(): class CudaNotAvailableStub: def __getattr__(self, item): - raise RuntimeError( + raise nncf.InstallationError( f"CUDA is not available on this machine. Check that the machine has a GPU and a proper " f"driver supporting CUDA {torch.version.cuda} is installed." ) diff --git a/nncf/torch/external_hook.py b/nncf/torch/external_hook.py index 367bbe0e888..7983e74da7e 100644 --- a/nncf/torch/external_hook.py +++ b/nncf/torch/external_hook.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/functions.py b/nncf/torch/functions.py index 035e3966e96..425a3f5bc8d 100644 --- a/nncf/torch/functions.py +++ b/nncf/torch/functions.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/graph/__init__.py b/nncf/torch/graph/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/torch/graph/__init__.py +++ b/nncf/torch/graph/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/graph/graph.py b/nncf/torch/graph/graph.py index ba993910ebb..1a651f9a363 100644 --- a/nncf/torch/graph/graph.py +++ b/nncf/torch/graph/graph.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -11,6 +11,7 @@ from typing import Dict, List, Tuple +import nncf from nncf.common.graph import NNCFGraph from nncf.common.graph import NNCFNode from nncf.common.graph import NNCFNodeName @@ -67,7 +68,7 @@ def get_scope_by_node_name(self, node_name: NNCFNodeName) -> Scope: matches.append(Scope.from_str(scope_str)) assert len(matches) <= 1 if not matches: - raise RuntimeError("Node name {} not found in the node-vs-scope dict!".format(node_name)) + raise nncf.InternalError("Node name {} not found in the node-vs-scope dict!".format(node_name)) return matches[0] def get_nodes_with_missed_input_edges(self) -> List[NNCFNode]: diff --git a/nncf/torch/graph/graph_builder.py b/nncf/torch/graph/graph_builder.py index 0412a2449fc..84ece43ee83 100644 --- a/nncf/torch/graph/graph_builder.py +++ b/nncf/torch/graph/graph_builder.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/graph/operator_metatypes.py b/nncf/torch/graph/operator_metatypes.py index 3e287114faa..33d3d628c39 100644 --- a/nncf/torch/graph/operator_metatypes.py +++ b/nncf/torch/graph/operator_metatypes.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/graph/pattern_operations.py b/nncf/torch/graph/pattern_operations.py index 7e0eb1d61b9..d9957871d87 100644 --- a/nncf/torch/graph/pattern_operations.py +++ b/nncf/torch/graph/pattern_operations.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/graph/transformations/__init__.py b/nncf/torch/graph/transformations/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/torch/graph/transformations/__init__.py +++ b/nncf/torch/graph/transformations/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/graph/transformations/command_creation.py b/nncf/torch/graph/transformations/command_creation.py index 8408c92aa4c..ac52802c039 100644 --- a/nncf/torch/graph/transformations/command_creation.py +++ b/nncf/torch/graph/transformations/command_creation.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -15,6 +15,7 @@ from nncf.common.graph.transformations.commands import TargetType from nncf.torch.graph.transformations.commands import PTBiasCorrectionCommand from nncf.torch.graph.transformations.commands import PTTargetPoint +from nncf.torch.graph.transformations.commands import PTWeightUpdateCommand def create_bias_correction_command(node: NNCFNode, bias_value: Tensor) -> PTBiasCorrectionCommand: @@ -27,3 +28,15 @@ def create_bias_correction_command(node: NNCFNode, bias_value: Tensor) -> PTBias """ target_point = PTTargetPoint(TargetType.LAYER, node.node_name) return PTBiasCorrectionCommand(target_point, bias_value) + + +def create_command_to_update_weight(node: NNCFNode, weight_value: Tensor) -> PTWeightUpdateCommand: + """ + Creates weight update command. + + :param node: The node in the NNCF graph that corresponds to operation with weight. + :param weight_value: The new weight value that will be set. + :return: The `PTWeightUpdateCommand` command to update weight. + """ + target_point = PTTargetPoint(TargetType.LAYER, node.node_name) + return PTWeightUpdateCommand(target_point, weight_value) diff --git a/nncf/torch/graph/transformations/commands.py b/nncf/torch/graph/transformations/commands.py index bf61fb858c3..c7793f27a28 100644 --- a/nncf/torch/graph/transformations/commands.py +++ b/nncf/torch/graph/transformations/commands.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -21,6 +21,8 @@ from nncf.common.graph.transformations.commands import TransformationPriority from nncf.common.graph.transformations.commands import TransformationType +DEFAULT_HOOKS_GROUP_NAME = "default_hooks_group" + class PTTargetPointStateNames: TARGET_NODE_NAME = "target_node_name" @@ -136,10 +138,12 @@ def __init__( point: PTTargetPoint, fn: Callable, priority: TransformationPriority = TransformationPriority.DEFAULT_PRIORITY, + hooks_group_name: str = DEFAULT_HOOKS_GROUP_NAME, ): super().__init__(TransformationType.INSERT, point) self.fn: Callable = fn self.priority: TransformationPriority = priority + self.hooks_group_name = hooks_group_name def requires_graph_rebuild(self): # Rebuild graph when adding quantization nodes. @@ -153,12 +157,14 @@ def __init__( fn: Callable, op_unique_name: str, priority: TransformationPriority = TransformationPriority.DEFAULT_PRIORITY, + hooks_group_name: str = DEFAULT_HOOKS_GROUP_NAME, ): super().__init__(TransformationType.INSERT, None) self.target_points = target_points self.fn = fn self.op_name = op_unique_name self.priority = priority + self.hooks_group_name = hooks_group_name def requires_graph_rebuild(self): return True @@ -173,9 +179,11 @@ def __init__( self, point: PTTargetPoint, quantizer: "BaseQuantizer", # noqa: F821 + hooks_group_name: str = DEFAULT_HOOKS_GROUP_NAME, ): super().__init__(TransformationType.INSERT, point) self.quantizer = quantizer + self.hooks_group_name = hooks_group_name def requires_graph_rebuild(self): return True diff --git a/nncf/torch/graph/transformations/layout.py b/nncf/torch/graph/transformations/layout.py index 6d48bdd0c08..e89960505df 100644 --- a/nncf/torch/graph/transformations/layout.py +++ b/nncf/torch/graph/transformations/layout.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/hardware/__init__.py b/nncf/torch/hardware/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/torch/hardware/__init__.py +++ b/nncf/torch/hardware/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/hardware/config.py b/nncf/torch/hardware/config.py index 3fe97e8b960..6b556aa135d 100644 --- a/nncf/torch/hardware/config.py +++ b/nncf/torch/hardware/config.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/hardware/fused_patterns.py b/nncf/torch/hardware/fused_patterns.py index b8db07decd4..3cff4d1ce98 100644 --- a/nncf/torch/hardware/fused_patterns.py +++ b/nncf/torch/hardware/fused_patterns.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/initialization.py b/nncf/torch/initialization.py index 7d5982c3c60..a3702473938 100644 --- a/nncf/torch/initialization.py +++ b/nncf/torch/initialization.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/knowledge_distillation/__init__.py b/nncf/torch/knowledge_distillation/__init__.py index 4682a156e43..7c616e1fe48 100644 --- a/nncf/torch/knowledge_distillation/__init__.py +++ b/nncf/torch/knowledge_distillation/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/knowledge_distillation/algo.py b/nncf/torch/knowledge_distillation/algo.py index 2887b22f40d..3911b0a42d0 100644 --- a/nncf/torch/knowledge_distillation/algo.py +++ b/nncf/torch/knowledge_distillation/algo.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -40,7 +40,7 @@ def __init__(self, config: NNCFConfig, should_init: bool = True): self.kd_type = self._algo_config.get("type") self.scale = self._algo_config.get("scale", KNOWLEDGE_DISTILLATION_SCALE) self.temperature = self._algo_config.get("temperature", KNOWLEDGE_DISTILLATION_TEMPERATURE) - if "temperature" in self._algo_config.keys() and self.kd_type == "mse": + if "temperature" in self._algo_config and self.kd_type == "mse": raise ValueError("Temperature shouldn't be stated for MSE Loss (softmax only feature)") def _get_transformation_layout(self, target_model: NNCFNetwork) -> PTTransformationLayout: diff --git a/nncf/torch/knowledge_distillation/knowledge_distillation_handler.py b/nncf/torch/knowledge_distillation/knowledge_distillation_handler.py index 19ee83cf7d0..08a3351b74d 100644 --- a/nncf/torch/knowledge_distillation/knowledge_distillation_handler.py +++ b/nncf/torch/knowledge_distillation/knowledge_distillation_handler.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/knowledge_distillation/knowledge_distillation_loss.py b/nncf/torch/knowledge_distillation/knowledge_distillation_loss.py index 5dc4013c1f3..f67254be9df 100644 --- a/nncf/torch/knowledge_distillation/knowledge_distillation_loss.py +++ b/nncf/torch/knowledge_distillation/knowledge_distillation_loss.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/layer_utils.py b/nncf/torch/layer_utils.py index 6ff3f361b6b..85756f13dcc 100644 --- a/nncf/torch/layer_utils.py +++ b/nncf/torch/layer_utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -12,6 +12,8 @@ import torch from torch import nn +from nncf.common.hook_handle import HookHandle +from nncf.common.hook_handle import add_op_to_registry from nncf.common.utils.registry import Registry COMPRESSION_MODULES = Registry("compression modules") @@ -61,18 +63,14 @@ def get_pre_op(self, key): def get_post_op(self, key): return self.post_ops[key] - def register_pre_forward_operation(self, op): - key = str(len(self.pre_ops)) - self.pre_ops[key] = op - return key + def register_pre_forward_operation(self, op) -> HookHandle: + return add_op_to_registry(self.pre_ops, op) def remove_pre_forward_operation(self, key): return self.pre_ops.pop(key) - def register_post_forward_operation(self, op): - key = str(len(self.post_ops)) - self.post_ops[key] = op - return key + def register_post_forward_operation(self, op) -> HookHandle: + return add_op_to_registry(self.post_ops, op) def remove_post_forward_operation(self, key): return self.post_ops.pop(key) diff --git a/nncf/torch/layers.py b/nncf/torch/layers.py index c4e671bcb8b..ce27b8cbcd8 100644 --- a/nncf/torch/layers.py +++ b/nncf/torch/layers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -19,6 +19,7 @@ from torch.nn.utils.rnn import PackedSequence from torch.nn.utils.weight_norm import WeightNorm +import nncf from nncf import nncf_logger from nncf.common.graph.layer_attributes import GenericWeightedLayerAttributes from nncf.common.utils.api_marker import api @@ -508,20 +509,20 @@ def extra_repr(self): def check_forward_input(self, input_): if input_.size(1) != self.input_size: - raise RuntimeError( + raise nncf.ValidationError( "input_ has inconsistent input_size: got {}, expected {}".format(input_.size(1), self.input_size) ) def check_forward_hidden(self, input_: torch.Tensor, hx: torch.Tensor, hidden_label: str = ""): if input_.size(0) != hx.size(0): - raise RuntimeError( + raise nncf.ValidationError( "Input batch size {} doesn't match hidden{} batch size {}".format( input_.size(0), hidden_label, hx.size(0) ) ) if hx.size(1) != self.hidden_size: - raise RuntimeError( + raise nncf.ValidationError( "hidden{} has inconsistent hidden_size: got {}, expected {}".format( hidden_label, hx.size(1), self.hidden_size ) @@ -855,9 +856,11 @@ def check_forward_args(self, input_, hidden, batch_sizes): is_input_packed = batch_sizes is not None expected_input_dim = 2 if is_input_packed else 3 if input_.dim() != expected_input_dim: - raise RuntimeError("input_ must have {} dimensions, got {}".format(expected_input_dim, input_.dim())) + raise nncf.ValidationError( + "input_ must have {} dimensions, got {}".format(expected_input_dim, input_.dim()) + ) if self.input_size != input_.size(-1): - raise RuntimeError( + raise nncf.ValidationError( "input_.size(-1) must be equal to input_size. Expected {}, got {}".format( self.input_size, input_.size(-1) ) @@ -873,10 +876,10 @@ def check_forward_args(self, input_, hidden, batch_sizes): def check_hidden_size(hx, expected_hidden_size, msg="Expected hidden size {}, got {}"): expected_size = self.num_layers * self.num_directions if expected_size != len(hx): - raise RuntimeError("Expected number of hidden states {}, got {}".format(expected_size, len(hx))) + raise nncf.InternalError("Expected number of hidden states {}, got {}".format(expected_size, len(hx))) for element in hx: if tuple(element.size()) != expected_hidden_size: - raise RuntimeError(msg.format(expected_hidden_size, tuple(element.size()))) + raise nncf.InternalError(msg.format(expected_hidden_size, tuple(element.size()))) if self.mode == "LSTM": check_hidden_size(hidden[0], expected_hidden_size, "Expected hidden[0] size {}, got {}") diff --git a/nncf/torch/model_analyzer.py b/nncf/torch/model_analyzer.py index bf4a8daf57f..feb6ea2d011 100644 --- a/nncf/torch/model_analyzer.py +++ b/nncf/torch/model_analyzer.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/model_creation.py b/nncf/torch/model_creation.py index d828841eb48..ae5386aa7e7 100644 --- a/nncf/torch/model_creation.py +++ b/nncf/torch/model_creation.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -16,9 +16,9 @@ from torch.distributed import barrier from torch.nn import Module +import nncf from nncf.api.compression import CompressionAlgorithmController from nncf.common.compression import BaseCompressionAlgorithmController as BaseController -from nncf.common.deprecation import warning_deprecated from nncf.common.logging import nncf_logger from nncf.common.utils.api_marker import api from nncf.common.utils.debug import set_debug_log_dir @@ -99,7 +99,7 @@ def create_compressed_model( as an object of NNCFNetwork. """ if isinstance(model, NNCFNetwork): - raise RuntimeError( + raise nncf.InternalError( "The model object has already been compressed.\n" "NNCF for PyTorch modifies the model object in-place, and repeat calls to " "`nncf.torch.create_compressed_model` with the same model object passed as argument " @@ -111,9 +111,6 @@ def create_compressed_model( "is also re-created (via constructor call)." ) - if config.get("target_device") == "VPU": - warning_deprecated("VPU device is deprecated and will no longer be supported in the future.") - set_debug_log_dir(config.get("log_dir", ".")) is_legacy_model_state_dict = ( @@ -171,7 +168,7 @@ def get_input_info_from_config(config: NNCFConfig) -> ModelInputInfo: exact_info = LoaderInputInfo.from_nncf_config_dataloaders(config) if exact_info is not None: return exact_info - raise RuntimeError( + raise nncf.ValidationError( "Could not determine tensor inputs for the model's forward call.\n" "If you are using the `nncf.quantize` API, make sure that you supply the " "calibration dataloader to the `nncf.quantize` call.\n" diff --git a/nncf/torch/model_transformer.py b/nncf/torch/model_transformer.py index b75a8df6a86..df60e730224 100644 --- a/nncf/torch/model_transformer.py +++ b/nncf/torch/model_transformer.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -22,7 +22,6 @@ from nncf.common.graph.transformations.commands import TransformationPriority from nncf.common.quantization.structs import NonWeightQuantizerId from nncf.torch.external_hook import EXTERNAL_OP_STORAGE_NAME -from nncf.torch.external_hook import ExternalOpCallHook from nncf.torch.graph.transformations.commands import PTBiasCorrectionCommand from nncf.torch.graph.transformations.commands import PTInsertionCommand from nncf.torch.graph.transformations.commands import PTModelExtractionWithFusedBiasCommand @@ -36,6 +35,7 @@ from nncf.torch.nncf_network import ExtraCompressionModuleType from nncf.torch.nncf_network import NNCFNetwork from nncf.torch.nncf_network import PTInsertionPoint +from nncf.torch.quantization.external_quantizer import ExternalOpCallHook from nncf.torch.quantization.external_quantizer import ExternalQuantizerCallHook from nncf.torch.utils import get_model_device from nncf.torch.utils import is_multidevice @@ -84,9 +84,10 @@ def _apply_insertion_transformations(model: NNCFNetwork, transformations: List[P :param model: Model to apply transformations. :param transformations: List of the bias correction transformations. + :return: A modified NNCFNetwork. """ node_to_op_address_mapping = model.nncf.get_node_to_op_address_mapping() - fns_grouped_by_points: Dict[PTInsertionPoint, List[Tuple[Callable, TransformationPriority]]] = {} + fns_grouped_by_points: Dict[PTInsertionPoint, List[Tuple[Callable, TransformationPriority]]] = defaultdict(list) for transformation_command in transformations: target_point: PTTargetPoint = transformation_command.target_point @@ -99,15 +100,14 @@ def _apply_insertion_transformations(model: NNCFNetwork, transformations: List[P fn = transformation_command.fn if target_point.type is TargetType.OPERATION_WITH_WEIGHTS: fn = UpdateWeight(fn) - tup = (fn, transformation_command.priority) - if pt_ip not in fns_grouped_by_points: - fns_grouped_by_points[pt_ip] = [tup] - else: - fns_grouped_by_points[pt_ip].append(tup) + tup = (fn, transformation_command) + fns_grouped_by_points[pt_ip].append(tup) for pt_ip, fn_list_with_priority in fns_grouped_by_points.items(): - fn_list_with_priority = sorted(fn_list_with_priority, key=lambda x: x[1]) - model.nncf.insert_at_point(pt_ip, [x[0] for x in fn_list_with_priority]) + fn_list_with_priority = sorted(fn_list_with_priority, key=lambda x: x[1].priority) + for fn, command in fn_list_with_priority: + model.nncf.insert_at_point(pt_ip, fn, command.hooks_group_name) + return model @staticmethod @@ -128,7 +128,14 @@ def _apply_shared_nodes_insertion( fn = ExternalOpCallHook( EXTERNAL_OP_STORAGE_NAME, model.nncf.get_tracing_context(), shared_command.op_name ) - insertion_commands.append(PTInsertionCommand(target_point, fn, priority=shared_command.priority)) + insertion_commands.append( + PTInsertionCommand( + target_point, + fn, + priority=shared_command.priority, + hooks_group_name=shared_command.hooks_group_name, + ) + ) return PTModelTransformer._apply_insertion_transformations(model, insertion_commands) @@ -256,7 +263,6 @@ def extraction_potential_fused_modules(node_name: str, model: NNCFNetwork) -> nn :param node_name: The node name. :param model: The model. - :return nn.Sequential: Copy of the modules. """ extracted_node_names = [node_name] diff --git a/nncf/torch/module_operations.py b/nncf/torch/module_operations.py index b1446fab61c..8aaae0d7d0e 100644 --- a/nncf/torch/module_operations.py +++ b/nncf/torch/module_operations.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/nested_objects_traversal.py b/nncf/torch/nested_objects_traversal.py index 8fa02139405..f8b7f942e7d 100644 --- a/nncf/torch/nested_objects_traversal.py +++ b/nncf/torch/nested_objects_traversal.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/nncf_module_replacement.py b/nncf/torch/nncf_module_replacement.py index 38d6a3c5254..0c0ce5fa2a6 100644 --- a/nncf/torch/nncf_module_replacement.py +++ b/nncf/torch/nncf_module_replacement.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -15,6 +15,7 @@ import torch from torch import nn +import nncf from nncf.common.logging import nncf_logger from nncf.common.scopes import matches_any from nncf.torch.dynamic_graph.scope import Scope @@ -131,7 +132,7 @@ def nncf_module_from(module: nn.Module) -> nn.Module: nncf_module = deepcopy(module) nncf_module = add_nncf_functionality_to_user_module(nncf_module) return nncf_module - raise RuntimeError(f"Could not extend module {module} with NNCF functionality!") + raise nncf.InternalError(f"Could not extend module {module} with NNCF functionality!") def replace_modules_by_nncf_modules( @@ -252,7 +253,7 @@ def _replace_module_by_scope(base_model: torch.nn.Module, scope: Scope, replaced for scope_element in scope[1:]: # omit first scope element which corresponds to base module child_module = curr_module._modules.get(scope_element.calling_field_name) if child_module is None: - raise RuntimeError( + raise nncf.InternalError( "Could not find a {} module member in {} module of scope {} during module replacement".format( scope_element.calling_field_name, scope_element.calling_module_class_name, str(scope) ) diff --git a/nncf/torch/nncf_network.py b/nncf/torch/nncf_network.py index 0954843ce72..c49492a70f7 100644 --- a/nncf/torch/nncf_network.py +++ b/nncf/torch/nncf_network.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -13,6 +13,7 @@ import inspect import types from collections import OrderedDict +from collections import defaultdict from contextlib import contextmanager from copy import deepcopy from dataclasses import dataclass @@ -23,6 +24,7 @@ import torch from torch import nn +import nncf from nncf import nncf_logger from nncf.api.compression import CompressionAlgorithmController from nncf.common.graph import NNCFNode @@ -33,6 +35,7 @@ from nncf.common.graph.operator_metatypes import CONST_NOOP_METATYPES from nncf.common.graph.transformations.commands import TargetType from nncf.common.graph.transformations.commands import TransformationPriority +from nncf.common.hook_handle import HookHandle from nncf.common.insertion_point_graph import InsertionPointGraph from nncf.common.insertion_point_graph import PostHookInsertionPoint from nncf.common.insertion_point_graph import PreHookInsertionPoint @@ -63,6 +66,7 @@ from nncf.torch.graph.graph_builder import GraphConverter from nncf.torch.graph.operator_metatypes import OPERATORS_WITH_WEIGHTS_METATYPES from nncf.torch.graph.operator_metatypes import PTSplitMetatype +from nncf.torch.graph.transformations.commands import DEFAULT_HOOKS_GROUP_NAME from nncf.torch.graph.transformations.commands import PTTargetPoint from nncf.torch.knowledge_distillation.knowledge_distillation_handler import KnowledgeDistillationLossHandler from nncf.torch.layer_utils import _NNCFModuleMixin @@ -97,7 +101,7 @@ def _get_pt_insertion_type(self, target_type: TargetType) -> PTInsertionType: not isinstance(target_type, TargetType) or target_type not in PTInsertionPoint.TARGET_TYPE_VS_PT_INSERTION_TYPE_DICT ): - raise RuntimeError("Unsupported target type for PyTorch: {}".format(target_type)) + raise nncf.InternalError("Unsupported target type for PyTorch: {}".format(target_type)) return PTInsertionPoint.TARGET_TYPE_VS_PT_INSERTION_TYPE_DICT[target_type] def __init__(self, target_type: TargetType, op_address: OperationAddress, input_port_id: int = None): @@ -244,6 +248,7 @@ def __init__( self._target_scopes = target_scopes self._user_dummy_forward_fn = dummy_forward_fn self._kd_loss_handler = None + self._groups_vs_hooks_handlers: Dict[str, List[HookHandle]] = defaultdict(list) if wrap_inputs_fn is not None: self._wrap_inputs_fn = wrap_inputs_fn @@ -400,15 +405,31 @@ def get_modules_in_nncf_modules_by_type(self, class_names: List[str]) -> Dict[Sc retval[nncf_module_scope + relative_scope] = target_module return retval - def insert_at_point(self, point: PTInsertionPoint, fn_list: List[Callable]): + def insert_at_point( + self, + point: PTInsertionPoint, + fn: Callable, + hooks_group_name: Optional[str] = DEFAULT_HOOKS_GROUP_NAME, + ) -> List[HookHandle]: + """ + Inserts given function to the point in the NNCFNetwork, creates hook handle for the inserted function and + stores created hook handle in a group with the given name. A group name could be used late + to remove all hooks from the NNCFNewtork which belongs to the group. + + :param point: Target point to insert function. + :param fn: Function to insert to the NNCFNetwork. + :param hooks_group_name: Name of hooks group for hook handle associated with the inserted function. + :return: Hook handle associated with the inserted function. + """ + handle = None if point.insertion_type == PTInsertionType.OPERATOR_PRE_HOOK: - self._compressed_context.register_pre_hooks(fn_list, point.op_address, point.input_port_id) + handle = self._compressed_context.register_pre_hook(fn, point.op_address, point.input_port_id) elif point.insertion_type == PTInsertionType.OPERATOR_POST_HOOK: - self._compressed_context.register_post_hooks(fn_list, point.op_address) + handle = self._compressed_context.register_post_hook(fn, point.op_address) elif point.insertion_type in [PTInsertionType.NNCF_MODULE_PRE_OP, PTInsertionType.NNCF_MODULE_POST_OP]: nncf_module = self.get_module_by_scope(point.module_scope) if not isinstance(nncf_module, _NNCFModuleMixin): - raise RuntimeError( + raise nncf.ValidationError( f"Failed to insert pre/post op for not registered custom module {point.module_scope}. NNCF only " f"supports native PyTorch modules with respect to trainable parameter (weight) compressed, such " f"as `torch.nn.Conv2d`. If your model contains a custom, non-PyTorch standard module with trainable" @@ -423,13 +444,23 @@ def insert_at_point(self, point: PTInsertionPoint, fn_list: List[Callable]): norm_nncf_scopes.extend([self._normalize_variable_recurrent_scope(x) for x in scope_list_for_module]) assert norm_target_scope in norm_nncf_scopes # Required for proper Recurrent/VariableRecurrent addressing if point.insertion_type == PTInsertionType.NNCF_MODULE_PRE_OP: - for fn in fn_list: - nncf_module.register_pre_forward_operation(fn) + handle = nncf_module.register_pre_forward_operation(fn) elif point.insertion_type == PTInsertionType.NNCF_MODULE_POST_OP: - for fn in fn_list: - nncf_module.register_post_forward_operation(fn) + handle = nncf_module.register_post_forward_operation(fn) else: - raise RuntimeError("Unsupported insertion type: {}".format(point.insertion_type)) + raise nncf.ValidationError("Unsupported insertion type: {}".format(point.insertion_type)) + self._groups_vs_hooks_handlers[hooks_group_name].append(handle) + return handle + + def remove_hooks_group(self, hooks_group_name: str) -> None: + """ + Removes all hooks of given hooks group from the nncf interface. + + :param group: Target hooks group name to remove all hooks from. + """ + for handle in self._groups_vs_hooks_handlers[hooks_group_name]: + handle.remove() + del self._groups_vs_hooks_handlers[hooks_group_name] def get_graph(self) -> PTNNCFGraph: if self._compressed_context.graph.get_nodes_count() == 0 or self._compressed_graphs_pair.nncf_graph is None: @@ -530,7 +561,7 @@ def is_scope_in_nncf_module_scope(self, scope: Scope) -> bool: def register_compression_module_type(self, compression_module_type: ExtraCompressionModuleType): attr_name = self._compression_module_type_to_attr_name(compression_module_type) if compression_module_type in self._extra_module_types: - raise RuntimeError(f"Module type {compression_module_type} is already registered") + raise nncf.ValidationError(f"Module type {compression_module_type} is already registered") self.__setattr__(attr_name, nn.ModuleDict()) self._extra_module_types.append(compression_module_type) @@ -540,16 +571,16 @@ def add_compression_module( ): attr_name = self._compression_module_type_to_attr_name(compression_module_type) if compression_module_type not in self._extra_module_types: - raise RuntimeError(f"Module type {compression_module_type} was not registered") + raise nncf.InternalError(f"Module type {compression_module_type} was not registered") storage = self.__getattr__(attr_name) if module_key in storage: - raise RuntimeError(f"Module {module_key} is already registered under {attr_name}") + raise nncf.InternalError(f"Module {module_key} is already registered under {attr_name}") storage[module_key] = module def get_compression_modules_by_type(self, compression_module_type: ExtraCompressionModuleType) -> nn.ModuleDict: attr_name = self._compression_module_type_to_attr_name(compression_module_type) if compression_module_type not in self._extra_module_types: - raise RuntimeError(f"Module type {compression_module_type} was not registered") + raise nncf.InternalError(f"Module type {compression_module_type} was not registered") return self.__getattr__(attr_name) def is_compression_module_registered(self, compression_module_type: ExtraCompressionModuleType) -> bool: @@ -571,12 +602,12 @@ def _compression_module_type_to_attr_name(compression_module_type: ExtraCompress return EXTERNAL_QUANTIZERS_STORAGE_NAME if compression_module_type == ExtraCompressionModuleType.EXTERNAL_OP: return EXTERNAL_OP_STORAGE_NAME - raise RuntimeError("Unknown extra module type") + raise nncf.ValidationError("Unknown extra module type") def sort_compression_modules(self, compression_module_type: ExtraCompressionModuleType): attr_name = self._compression_module_type_to_attr_name(compression_module_type) if compression_module_type not in self._extra_module_types: - raise RuntimeError("Module type {} was not registered".format(compression_module_type)) + raise nncf.InternalError("Module type {} was not registered".format(compression_module_type)) module_dict = self.__getattr__(attr_name) module_dict._modules = OrderedDict(sorted(module_dict._modules.items())) @@ -610,9 +641,8 @@ def do_dummy_forward(self, force_eval: bool = False): with self._compressed_context as ctx: ctx.base_module_thread_local_replica = self._model_ref self._dummy_forward_fn(self._model_ref) - if force_eval: - if train_mode: - self._model_ref.train() + if force_eval and train_mode: + self._model_ref.train() def get_original_insertion_point_graph(self) -> InsertionPointGraph: # Set up a pre- and post-hooks on almost every op in PyTorch @@ -965,7 +995,7 @@ def __init__(self, *args, **kwargs): achieved by a __call__ method defined in the metaclass `NNCFNetworkMeta`. """ super().__init__() - raise RuntimeError("Direct instantiation of NNCFNetwork objects using __init__ is prohibited.") + raise nncf.InternalError("Direct instantiation of NNCFNetwork objects using __init__ is prohibited.") def __call__(self, *args, **kwargs): """ diff --git a/nncf/torch/pruning/__init__.py b/nncf/torch/pruning/__init__.py index 23f8e4a89d5..a83b9c34cd3 100644 --- a/nncf/torch/pruning/__init__.py +++ b/nncf/torch/pruning/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/pruning/base_algo.py b/nncf/torch/pruning/base_algo.py index 7bf6cffaedb..9031ca59018 100644 --- a/nncf/torch/pruning/base_algo.py +++ b/nncf/torch/pruning/base_algo.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/pruning/export_utils.py b/nncf/torch/pruning/export_utils.py index ca10d830595..9a31fc8d9a4 100644 --- a/nncf/torch/pruning/export_utils.py +++ b/nncf/torch/pruning/export_utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/pruning/filter_pruning/__init__.py b/nncf/torch/pruning/filter_pruning/__init__.py index 7f6f2029b35..3fc0fea0047 100644 --- a/nncf/torch/pruning/filter_pruning/__init__.py +++ b/nncf/torch/pruning/filter_pruning/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/pruning/filter_pruning/algo.py b/nncf/torch/pruning/filter_pruning/algo.py index b94e1d474be..65206c35145 100644 --- a/nncf/torch/pruning/filter_pruning/algo.py +++ b/nncf/torch/pruning/filter_pruning/algo.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -17,6 +17,7 @@ import numpy as np import torch +import nncf from nncf import NNCFConfig from nncf.api.compression import CompressionLoss from nncf.api.compression import CompressionStage @@ -352,7 +353,7 @@ def _find_uniform_pruning_level_for_target_flops(self, target_flops_pruning_leve self.current_flops = flops self.current_params_num = params_num return right - raise RuntimeError( + raise nncf.InternalError( "Can't prune the model to get the required " "pruning level in flops = {}".format(target_flops_pruning_level) ) @@ -377,7 +378,7 @@ def set_pruning_level( with torch.no_grad(): if self.all_weights: if groupwise_pruning_levels_set: - raise RuntimeError("Cannot set group-wise pruning levels with all_weights=True") + raise nncf.InternalError("Cannot set group-wise pruning levels with all_weights=True") # Non-uniform (global) importance-score-based pruning according # to the global pruning level if self.prune_flops: @@ -388,7 +389,9 @@ def set_pruning_level( if groupwise_pruning_levels_set: group_ids = [group.id for group in self.pruned_module_groups_info.get_all_clusters()] if set(pruning_level.keys()) != set(group_ids): - raise RuntimeError("Groupwise pruning level dict keys do not correspond to layer group ids") + raise nncf.InternalError( + "Groupwise pruning level dict keys do not correspond to layer group ids" + ) else: # Pruning uniformly with the same pruning level across layers if self.prune_flops: @@ -592,7 +595,7 @@ def _set_binary_masks_for_pruned_modules_globally_by_flops_target(self, target_f self.current_params_num = params_num return cur_num += 1 - raise RuntimeError("Can't prune model to asked flops pruning level") + raise nncf.InternalError("Can't prune model to asked flops pruning level") def _propagate_masks(self): nncf_logger.debug("Propagating pruning masks") diff --git a/nncf/torch/pruning/filter_pruning/functions.py b/nncf/torch/pruning/filter_pruning/functions.py index ad46dcc6453..778c029f6b2 100644 --- a/nncf/torch/pruning/filter_pruning/functions.py +++ b/nncf/torch/pruning/filter_pruning/functions.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/pruning/filter_pruning/global_ranking/__init__.py b/nncf/torch/pruning/filter_pruning/global_ranking/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/torch/pruning/filter_pruning/global_ranking/__init__.py +++ b/nncf/torch/pruning/filter_pruning/global_ranking/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/pruning/filter_pruning/global_ranking/evolutionary_optimization.py b/nncf/torch/pruning/filter_pruning/global_ranking/evolutionary_optimization.py index 042c3233634..def4266cbd9 100644 --- a/nncf/torch/pruning/filter_pruning/global_ranking/evolutionary_optimization.py +++ b/nncf/torch/pruning/filter_pruning/global_ranking/evolutionary_optimization.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/pruning/filter_pruning/global_ranking/legr.py b/nncf/torch/pruning/filter_pruning/global_ranking/legr.py index 6a8b641d131..2949ded0469 100644 --- a/nncf/torch/pruning/filter_pruning/global_ranking/legr.py +++ b/nncf/torch/pruning/filter_pruning/global_ranking/legr.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/pruning/filter_pruning/layers.py b/nncf/torch/pruning/filter_pruning/layers.py index c300603f8e7..2d95a9d982f 100644 --- a/nncf/torch/pruning/filter_pruning/layers.py +++ b/nncf/torch/pruning/filter_pruning/layers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -12,6 +12,7 @@ import torch from torch import nn +import nncf from nncf.common.graph import NNCFNodeName from nncf.torch.layer_utils import COMPRESSION_MODULES @@ -79,7 +80,7 @@ def apply_filter_binary_mask( :return: result with applied mask """ if filter_mask.size(0) != module_parameter.size(dim): - raise RuntimeError( + raise nncf.InternalError( "Shape of mask = {} for module {} isn't broadcastable to weight shape={}." " ".format(filter_mask.shape, node_name_for_logging, module_parameter.shape) ) diff --git a/nncf/torch/pruning/operations.py b/nncf/torch/pruning/operations.py index 833aa22635b..c774333fb57 100644 --- a/nncf/torch/pruning/operations.py +++ b/nncf/torch/pruning/operations.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -14,6 +14,7 @@ import torch +import nncf from nncf.common.graph import NNCFGraph from nncf.common.graph import NNCFNode from nncf.common.graph.operator_metatypes import UnknownMetatype @@ -574,7 +575,7 @@ def input_prune(cls, model: NNCFNetwork, node: NNCFNode, graph: NNCFGraph, prun_ node_module = model.nncf.get_containing_module(node.node_name) if prun_type == PrunType.CUT_WEIGHTS: - raise RuntimeError("LayerNorm does not support pruning by cutting channels") + raise nncf.InternalError("LayerNorm does not support pruning by cutting channels") node_module.weight = torch.nn.Parameter(apply_filter_binary_mask(input_mask, node_module.weight)) node_module.bias = torch.nn.Parameter(apply_filter_binary_mask(input_mask, node_module.bias)) diff --git a/nncf/torch/pruning/structs.py b/nncf/torch/pruning/structs.py index 2013700fb98..580941047c6 100644 --- a/nncf/torch/pruning/structs.py +++ b/nncf/torch/pruning/structs.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/pruning/tensor_processor.py b/nncf/torch/pruning/tensor_processor.py index 4ca630f1840..c316c036bdb 100644 --- a/nncf/torch/pruning/tensor_processor.py +++ b/nncf/torch/pruning/tensor_processor.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/pruning/utils.py b/nncf/torch/pruning/utils.py index 20ecccdc44a..947e3f9fa37 100644 --- a/nncf/torch/pruning/utils.py +++ b/nncf/torch/pruning/utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -12,6 +12,7 @@ import torch +import nncf from nncf.common.graph import NNCFGraph from nncf.common.graph import NNCFNodeName from nncf.common.graph.layer_attributes import ConvolutionLayerAttributes @@ -87,7 +88,7 @@ def _calculate_output_shape(graph: NNCFGraph, node: NNCFNode) -> Tuple[int, ...] elif isinstance(attrs, LinearLayerAttributes): shape = shape[:-1] + [attrs.out_features] else: - raise RuntimeError(f"Unexpected node type {node.node_type} is fed to _calculate_output_shape") + raise nncf.ValidationError(f"Unexpected node type {node.node_type} is fed to _calculate_output_shape") return tuple(shape) diff --git a/nncf/torch/quantization/__init__.py b/nncf/torch/quantization/__init__.py index 993ee97325a..99098ff8d45 100644 --- a/nncf/torch/quantization/__init__.py +++ b/nncf/torch/quantization/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/quantization/adjust_padding.py b/nncf/torch/quantization/adjust_padding.py index dec435eda07..bff643021a2 100644 --- a/nncf/torch/quantization/adjust_padding.py +++ b/nncf/torch/quantization/adjust_padding.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -14,6 +14,7 @@ import networkx as nx import torch +import nncf from nncf.common.graph import NNCFNodeName from nncf.common.quantization.structs import QuantizationScheme as QuantizationMode from nncf.torch.layers import NNCFConv2d @@ -32,18 +33,18 @@ class AdjustPaddingArgs(NamedTuple): class CalculatePaddingAdjustment: """ - Calculates padding value to perform a workaround for U4 support on VPU. - VPU supports only i4 for weights and activations with zero-point=0 and padding=0. This imposes some limitations on + Calculates padding value to perform a workaround for U4 support on NPU. + NPU supports only i4 for weights and activations with zero-point=0 and padding=0. This imposes some limitations on the quantization scheme we can apply. In case of unsigned input for a quantizer (e.g. output of ReLU) half of i4 range (8 values) is insufficient to preserve the accuracy. To overcome the problem it is proposed - to transform u4 to i4 in the VPU plugin by shifting the input by half of the quantization range to left. Padding + to transform u4 to i4 in the NPU plugin by shifting the input by half of the quantization range to left. Padding value should be shifted as well. And to make it zero after the shift (non-zero padding values are not supported), the model should be trained with padding value equal to the half of the quantization range. """ def __init__(self, activation_quantizer: SymmetricQuantizer): if not isinstance(activation_quantizer, SymmetricQuantizer): - raise RuntimeError("Padding adjustment is not supported for not symmetric quantization") + raise nncf.InternalError("Padding adjustment is not supported for not symmetric quantization") self._activation_quantizer = activation_quantizer self._is_enabled = True diff --git a/nncf/torch/quantization/algo.py b/nncf/torch/quantization/algo.py index c6ab6980fbd..278365d8289 100644 --- a/nncf/torch/quantization/algo.py +++ b/nncf/torch/quantization/algo.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -22,6 +22,7 @@ import torch +import nncf from nncf.api.compression import CompressionLoss from nncf.api.compression import CompressionScheduler from nncf.api.compression import CompressionStage @@ -462,10 +463,10 @@ def __init__(self, config, should_init: bool = True): self.hw_config = PTHWConfig.from_json(hw_config_path) algo_config = self._get_algo_specific_config_section() - if self._target_device == "VPU" and "preset" in algo_config: - raise RuntimeError("The VPU target device does not support presets.") + if self._target_device == "NPU" and "preset" in algo_config: + raise nncf.InternalError("The NPU target device does not support presets.") if self._target_device == "CPU_SPR": - raise RuntimeError("The CPU_SPR target device does not supported.") + raise nncf.InternalError("The CPU_SPR target device does not supported.") self._range_init_params = None self._precision_init_type = None @@ -538,7 +539,7 @@ def _parse_precision_init_params(self, initializer_config: Dict) -> Tuple[str, B return None, None precision_init_type = init_precision_config.get("type", "manual") if precision_init_type not in PRECISION_INIT_TYPES_VS_DESCRIPTION: - raise RuntimeError(f"Unrecognized precision init type: {precision_init_type}") + raise nncf.InternalError(f"Unrecognized precision init type: {precision_init_type}") if precision_init_type == "hawq": try: precision_init_args = self.config.get_extra_struct(QuantizationPrecisionInitArgs) @@ -551,10 +552,10 @@ def _parse_precision_init_params(self, initializer_config: Dict) -> Tuple[str, B ) from e precision_init_params = HAWQPrecisionInitParams.from_config(init_precision_config, precision_init_args) elif precision_init_type == "autoq": - if self.hw_config is not None and self.hw_config.target_device != HWConfigType.VPU.value: + if self.hw_config is not None and self.hw_config.target_device != HWConfigType.NPU.value: raise ValueError( "Unsupported device ({}). Automatic Precision Initialization only supports for " - "target_device NONE or VPU".format(self.hw_config.target_device) + "target_device NONE or NPU".format(self.hw_config.target_device) ) try: precision_init_args = self.config.get_extra_struct(AutoQPrecisionInitArgs) @@ -752,7 +753,7 @@ def _get_quantizer_setup(self, target_model: NNCFNetwork) -> PTQuantizerSetup: half_range = True quantizers_with_overflow_fix_str = "first convolution weight quantizers" elif self._overflow_fix != "disable": - raise RuntimeError(f"Unknown overflow fix type: {self._overflow_fix}") + raise nncf.InternalError(f"Unknown overflow fix type: {self._overflow_fix}") if half_range: nncf_logger.debug(f"Overflow issue fix will be applied to {quantizers_with_overflow_fix_str}") @@ -862,7 +863,7 @@ def _check_and_log_missing_stats_for_setup( minmax_values_for_range_init: Dict[QuantizationPointId, MinMaxTensorStatistic], ): tps_with_uncollected_stats = set() - for qp_id in quantizer_setup.quantization_points.keys(): + for qp_id in quantizer_setup.quantization_points: if qp_id not in minmax_values_for_range_init: tps_with_uncollected_stats.add(quantizer_setup.quantization_points[qp_id].insertion_point) if tps_with_uncollected_stats: @@ -905,7 +906,7 @@ def _build_insertion_commands_list_for_quantizer_setup( for layer_name in shared_weight_quantized_layers_in_group: if layer_name in already_weight_quantized_shared_layers: - raise RuntimeError( + raise nncf.InternalError( "Attempted to assign a unified-scale quantizer to a shared layer node that has " "already had its weights quantized by another unified-scale quantizer!" ) @@ -1074,7 +1075,7 @@ def ip_str_repr_key_lambda(x): linked_qspecs = [quantizer_setup.quantization_points[qp_id].qspec for qp_id in linked_qp_ids] for linked_qspec in linked_qspecs: if not qspec == linked_qspec: - raise RuntimeError("The qspecs for unified scale quantization points should be identical!") + raise nncf.InternalError("The qspecs for unified scale quantization points should be identical!") range_init_minmax_values = None if minmax_values_for_range_init: @@ -1136,7 +1137,7 @@ def _quantize_at_points_by_single_module( target_model_graph = target_model.nncf.get_original_graph() if not insertion_points: - raise RuntimeError("No insertion points to put quantizers into!") + raise nncf.InternalError("No insertion points to put quantizers into!") def is_weights(ip: PTTargetPoint) -> bool: return ip.target_type is TargetType.OPERATION_WITH_WEIGHTS @@ -1194,7 +1195,7 @@ def is_weights(ip: PTTargetPoint) -> bool: insertion_commands = [] for curr_insertion_point in insertion_points: if curr_insertion_point in self._processed_insertion_points: - raise RuntimeError("Insertion point {} already quantized!".format(str(curr_insertion_point))) + raise nncf.InternalError("Insertion point {} already quantized!".format(str(curr_insertion_point))) self._processed_insertion_points.add(curr_insertion_point) if is_weights(curr_insertion_point): diff --git a/nncf/torch/quantization/base_ctrl.py b/nncf/torch/quantization/base_ctrl.py index c9f2e442ab9..c76bf162e6a 100644 --- a/nncf/torch/quantization/base_ctrl.py +++ b/nncf/torch/quantization/base_ctrl.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/quantization/debug_interface.py b/nncf/torch/quantization/debug_interface.py index 2772546eded..60798db1225 100644 --- a/nncf/torch/quantization/debug_interface.py +++ b/nncf/torch/quantization/debug_interface.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -18,6 +18,7 @@ import numpy as np import torch +import nncf from nncf.common.insertion_point_graph import InsertionPointGraph from nncf.common.insertion_point_graph import InsertionPointGraphNodeType from nncf.common.logging import nncf_logger @@ -62,7 +63,7 @@ def init_actual(self, owner_model: NNCFNetwork): quantization_types = [class_type.__name__ for class_type in QUANTIZATION_MODULES.registry_dict.values()] quantizers_in_nncf_modules = owner_model.nncf.get_modules_in_nncf_modules_by_type(quantization_types) - nncf_module_quantizations_id_list: List[str] = [str(scope) for scope in quantizers_in_nncf_modules.keys()] + nncf_module_quantizations_id_list: List[str] = [str(scope) for scope in quantizers_in_nncf_modules] activation_quantizer_id_list: List[str] = owner_model.nncf.get_compression_modules_by_type( ExtraCompressionModuleType.EXTERNAL_QUANTIZER @@ -103,7 +104,9 @@ def post_forward_actions(self, module: NNCFNetwork): if tracker.get_never_called_keys(): # This will always trigger for DataParallel - disregard or disable debug mode # for DataParallel runs - raise RuntimeError(f"{tracker.name} has never called modules: {tracker.get_never_called_keys()}!") + raise nncf.InternalError( + f"{tracker.name} has never called modules: {tracker.get_never_called_keys()}!" + ) def dump_scale(self, quantizer_scale_params: Dict[str, torch.Tensor], quantizer_name: str): import re @@ -163,7 +166,7 @@ def visualize_insertion_point_graph(self, insertion_point_graph: InsertionPointG elif node[InsertionPointGraph.NODE_TYPE_NODE_ATTR] == InsertionPointGraphNodeType.OPERATOR: out_graph.add_node(node_key) else: - raise RuntimeError("Invalid InsertionPointGraph node!") + raise nncf.InternalError("Invalid InsertionPointGraph node!") for u, v in insertion_point_graph.edges: out_graph.add_edge(u, v) diff --git a/nncf/torch/quantization/default_quantization.py b/nncf/torch/quantization/default_quantization.py index 83020fd3107..5a26ce0ed75 100644 --- a/nncf/torch/quantization/default_quantization.py +++ b/nncf/torch/quantization/default_quantization.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/quantization/extensions.py b/nncf/torch/quantization/extensions.py index e59abde027b..8f7acf63b8e 100644 --- a/nncf/torch/quantization/extensions.py +++ b/nncf/torch/quantization/extensions.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -14,6 +14,7 @@ import torch +import nncf from nncf import nncf_logger from nncf.definitions import NNCF_PACKAGE_ROOT_DIR from nncf.torch.extensions import EXTENSIONS @@ -94,7 +95,7 @@ def load(cls): raise e except (subprocess.CalledProcessError, OSError, RuntimeError) as e: assert torch.cuda.is_available() - raise RuntimeError( + raise nncf.InstallationError( "CUDA is available for PyTorch, but NNCF could not compile " "GPU quantization extensions. Make sure that you have installed CUDA development " "tools (see https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html for " diff --git a/nncf/torch/quantization/external_quantizer.py b/nncf/torch/quantization/external_quantizer.py index 68097eada08..128441a7eab 100644 --- a/nncf/torch/quantization/external_quantizer.py +++ b/nncf/torch/quantization/external_quantizer.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/quantization/hessian_trace.py b/nncf/torch/quantization/hessian_trace.py index 477eca9f21d..c7050fd3bf6 100644 --- a/nncf/torch/quantization/hessian_trace.py +++ b/nncf/torch/quantization/hessian_trace.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/quantization/ignored_patterns.py b/nncf/torch/quantization/ignored_patterns.py index c5e87f61e28..b1e6c522ada 100644 --- a/nncf/torch/quantization/ignored_patterns.py +++ b/nncf/torch/quantization/ignored_patterns.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/quantization/init_precision.py b/nncf/torch/quantization/init_precision.py index fa3da5c6d3a..a90fd2245a3 100644 --- a/nncf/torch/quantization/init_precision.py +++ b/nncf/torch/quantization/init_precision.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/quantization/init_range.py b/nncf/torch/quantization/init_range.py index 88a7c03c95f..6ff97bc35c5 100644 --- a/nncf/torch/quantization/init_range.py +++ b/nncf/torch/quantization/init_range.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -16,6 +16,7 @@ import numpy as np import torch +import nncf from nncf.common.graph.layer_attributes import WeightedLayerAttributes from nncf.common.quantization.initialization.range import RangeInitCollectorParams from nncf.common.quantization.initialization.range import RangeInitConfig @@ -23,7 +24,7 @@ from nncf.common.quantization.quantizer_setup import QuantizationPointBase from nncf.common.quantization.quantizer_setup import QuantizerSetupBase from nncf.common.quantization.structs import NonWeightQuantizerId -from nncf.common.quantization.structs import QuantizationScheme as QuantizationMode +from nncf.common.quantization.structs import QuantizationScheme from nncf.common.quantization.structs import QuantizerGroup from nncf.common.quantization.structs import QuantizerId from nncf.common.quantization.structs import WeightQuantizerId @@ -71,13 +72,13 @@ def get_init_config_for_quantization_point(self, qp: QuantizationPointBase) -> R def get_init_config_for_scope_and_group(self, qid: QuantizerId, group: QuantizerGroup) -> RangeInitConfig: matches: List[RangeInitConfig] = [] for pl_config in self.per_layer_range_init_configs: - if should_consider_scope(qid, pl_config.ignored_scopes, pl_config.target_scopes): - if group == pl_config.target_group or pl_config.target_group is None: - matches.append( - RangeInitConfig( - pl_config.init_type, pl_config.num_init_samples, pl_config.init_type_specific_params - ) + should_be_considered = should_consider_scope(qid, pl_config.ignored_scopes, pl_config.target_scopes) + if should_be_considered and (group == pl_config.target_group or pl_config.target_group is None): + matches.append( + RangeInitConfig( + pl_config.init_type, pl_config.num_init_samples, pl_config.init_type_specific_params ) + ) if len(matches) > 1: raise ValueError( "Location {} matches more than one per-layer initialization parameter definition!".format(str(qid)) @@ -94,14 +95,15 @@ def get_init_config_for_scope_and_group(self, qid: QuantizerId, group: Quantizer class PTRangeInitCollectorParams(RangeInitCollectorParams): def __init__( - self, is_weights: bool, mode: QuantizationMode, per_channel: bool, input_shape: tuple, channel_idx: int + self, is_weights: bool, scheme: QuantizationScheme, per_channel: bool, input_shape: tuple, channel_idx: int ): """ - + :param is_weights: Boolean that defines tensor type. True for Weights, False for Activations. + :param scheme: Quantization scheme: symmetric or asymmetric. :param input_shape: Shape of the input tensor. :param channel_idx: Channel dimension. """ - super().__init__(is_weights, mode, per_channel) + super().__init__(is_weights, scheme, per_channel) self._input_shape = input_shape self._channel_idx = channel_idx @@ -114,11 +116,11 @@ def get_reduction_axes(self, per_sample_stats: bool) -> ReductionAxes: """ ndims = len(self._input_shape) reduction_axes: List[int] = list(range(ndims)) - if self._per_channel: + if self.is_per_channel: val = (ndims + self._channel_idx) % ndims reduction_axes.remove(val) if not val and self.use_per_sample_stats(per_sample_stats): - raise RuntimeError("Batch dimension should be equal to zero") + raise nncf.InternalError("Batch dimension should be equal to zero") if self.use_per_sample_stats(per_sample_stats): reduction_axes = reduction_axes[1:] # Assumes batch is the first dimension return tuple(reduction_axes) @@ -175,7 +177,7 @@ def generate_stat_collector_for_range_init_config( if num_samples_to_collect_override is not None: num_samples = num_samples_to_collect_override if init_config.init_type not in RANGE_INIT_TYPES_VS_DESCRIPTIONS: - raise RuntimeError("Unknown range init type: {}".format(init_config.init_type)) + raise nncf.InternalError("Unknown range init type: {}".format(init_config.init_type)) use_per_sample_stats = collector_params.use_per_sample_stats(init_config.init_type == "mixed_min_max") reduction_axes = collector_params.get_reduction_axes(use_per_sample_stats) @@ -307,9 +309,9 @@ def _prepare_initialization(self): num_samples_override = num_batches if isinstance(quantizer_module, SymmetricQuantizer): - mode = QuantizationMode.SYMMETRIC + mode = QuantizationScheme.SYMMETRIC else: - mode = QuantizationMode.ASYMMETRIC + mode = QuantizationScheme.ASYMMETRIC shape = quantizer_module.scale_shape if shape == (1,): # Per-tensor diff --git a/nncf/torch/quantization/layers.py b/nncf/torch/quantization/layers.py index 8d6bab55ac9..937d156c8fe 100644 --- a/nncf/torch/quantization/layers.py +++ b/nncf/torch/quantization/layers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -14,13 +14,14 @@ from abc import abstractmethod from enum import Enum from functools import partial -from typing import Any, Dict, List, Optional, Tuple +from typing import Any, Dict, List, Optional, Tuple, Union import numpy as np import torch from torch import distributed from torch import nn +import nncf from nncf.common.graph import NNCFNodeName from nncf.common.logging import nncf_logger from nncf.common.quantization.quantizer_setup import QuantizationPointId @@ -47,6 +48,8 @@ from nncf.torch.quantization.quantize_functions import decompress from nncf.torch.quantization.quantize_functions import get_scale_zp_from_input_low_input_high from nncf.torch.quantization.quantize_functions import symmetric_quantize +from nncf.torch.return_types import maybe_get_values_from_torch_return_type +from nncf.torch.return_types import maybe_wrap_to_torch_return_type from nncf.torch.utils import get_flat_tensor_contents_string from nncf.torch.utils import get_model_device from nncf.torch.utils import is_tracing_state @@ -319,7 +322,7 @@ def __init__(self, module): def hook_fn( self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs, module ): - for module_key in module.state_dict().keys(): + for module_key in module.state_dict(): candidate = prefix + module_key if candidate in state_dict: module.initialized = True @@ -369,7 +372,16 @@ def disable_quantization(self): self.enabled[0] = 0 self.disable_gradients() - def forward(self, x): + def forward(self, x: Union[torch.Tensor, tuple]): + """ + Method that unwraps return types if it is needed + before acutal quantization forward impl + """ + x_unwrapped = maybe_get_values_from_torch_return_type(x) + result = self._forward_impl(x_unwrapped) + return maybe_wrap_to_torch_return_type(result, x) + + def _forward_impl(self, x: torch.Tensor): if is_debug(): self.call_count += 1 # TODO: refactor to get rid of extra if's and calls on each forward @@ -490,7 +502,7 @@ def _prepare_qdq_export_quantization(self, x: torch.Tensor) -> Tuple[torch.Tenso y_scale, y_zero_point = get_scale_zp_from_input_low_input_high(level_low, level_high, input_low, input_high) possible_axes = self._possible_per_channel_dimensions() if len(possible_axes) > 1: - raise RuntimeError( + raise nncf.InternalError( f"Impossible to determine the per-channel axis for a scale shape {self.scale_shape} - " f"more than one dimension is >1" ) @@ -522,7 +534,7 @@ def run_export_quantization(self, x: torch.Tensor): if self._export_mode == QuantizerExportMode.ONNX_QUANTIZE_DEQUANTIZE_PAIRS: x, y_scale, y_zero_point, axis = self._prepare_qdq_export_quantization(x) return ExportQuantizeToONNXQuantDequant.apply(x, y_scale, y_zero_point, axis) - raise RuntimeError("Unknown export mode") + raise nncf.InternalError("Unknown export mode") def extra_repr(self): return "bit={}, ch={}".format(self.num_bits, self.per_channel) diff --git a/nncf/torch/quantization/metrics.py b/nncf/torch/quantization/metrics.py index ea83f45ff0b..e23cf8c0efd 100644 --- a/nncf/torch/quantization/metrics.py +++ b/nncf/torch/quantization/metrics.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -283,7 +283,7 @@ def collect(self) -> QuantizationConfigurationStatistics: node_name = str(node[NNCFNode.NODE_NAME_ATTR]) matched = False - for aq_key in self._compressed_model.nncf.external_quantizers.keys(): + for aq_key in self._compressed_model.nncf.external_quantizers: if node_name in aq_key: matched = True break diff --git a/nncf/torch/quantization/precision_constraints.py b/nncf/torch/quantization/precision_constraints.py index ce060622cab..6b81e106619 100644 --- a/nncf/torch/quantization/precision_constraints.py +++ b/nncf/torch/quantization/precision_constraints.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/quantization/precision_init/__init__.py b/nncf/torch/quantization/precision_init/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/torch/quantization/precision_init/__init__.py +++ b/nncf/torch/quantization/precision_init/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/quantization/precision_init/adjacent_quantizers.py b/nncf/torch/quantization/precision_init/adjacent_quantizers.py index 21855176d39..0d03163e6b0 100644 --- a/nncf/torch/quantization/precision_init/adjacent_quantizers.py +++ b/nncf/torch/quantization/precision_init/adjacent_quantizers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/quantization/precision_init/autoq_init.py b/nncf/torch/quantization/precision_init/autoq_init.py index a6bc3dadaed..c0d3214d74d 100644 --- a/nncf/torch/quantization/precision_init/autoq_init.py +++ b/nncf/torch/quantization/precision_init/autoq_init.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/quantization/precision_init/base_init.py b/nncf/torch/quantization/precision_init/base_init.py index 652ac2d0389..ef4404c0944 100644 --- a/nncf/torch/quantization/precision_init/base_init.py +++ b/nncf/torch/quantization/precision_init/base_init.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -99,7 +99,7 @@ def __init__( weight_quantizers: Dict[WeightQuantizerId, WeightQuantizerInfo], constraints: HardwareQuantizationConstraints, ): - self._wq_affected_module_node_name_vs_qid_dict = {k.target_node_name: k for k in weight_quantizers.keys()} + self._wq_affected_module_node_name_vs_qid_dict = {k.target_node_name: k for k in weight_quantizers} self._quantizer_module_scope_vs_qid_dict: Dict[Scope, WeightQuantizerId] = {} self._skipped_quantized_weight_node_names = [] self._skipped_weight_quantizers: Dict[WeightQuantizerId, BaseQuantizer] = {} diff --git a/nncf/torch/quantization/precision_init/bitwidth_graph.py b/nncf/torch/quantization/precision_init/bitwidth_graph.py index cd1faf732ac..5d5f51dc914 100644 --- a/nncf/torch/quantization/precision_init/bitwidth_graph.py +++ b/nncf/torch/quantization/precision_init/bitwidth_graph.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/quantization/precision_init/compression_ratio.py b/nncf/torch/quantization/precision_init/compression_ratio.py index 95a923f8a5d..bf719f3a8ed 100644 --- a/nncf/torch/quantization/precision_init/compression_ratio.py +++ b/nncf/torch/quantization/precision_init/compression_ratio.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/quantization/precision_init/definitions.py b/nncf/torch/quantization/precision_init/definitions.py index cdcbe26b5a5..fd867f7634c 100644 --- a/nncf/torch/quantization/precision_init/definitions.py +++ b/nncf/torch/quantization/precision_init/definitions.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/quantization/precision_init/hawq_debug.py b/nncf/torch/quantization/precision_init/hawq_debug.py index 2ebef87350a..697d8367bbc 100644 --- a/nncf/torch/quantization/precision_init/hawq_debug.py +++ b/nncf/torch/quantization/precision_init/hawq_debug.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/quantization/precision_init/hawq_init.py b/nncf/torch/quantization/precision_init/hawq_init.py index 36741f47c15..743e458413a 100644 --- a/nncf/torch/quantization/precision_init/hawq_init.py +++ b/nncf/torch/quantization/precision_init/hawq_init.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -23,6 +23,7 @@ from torch import nn from torch.nn.modules.loss import _Loss +import nncf from nncf.common.graph import NNCFNodeName from nncf.common.logging import nncf_logger from nncf.common.quantization.quantizer_setup import QuantizationPointId @@ -156,7 +157,7 @@ def _generate_covering_qconfig_sequences(observed_qconfs: List[Dict[QuantizerCon optimized_observed_qconfs: List[List[QuantizerConfig]] = [] for qconf_oset in observed_qconfs: variants: List[List[QuantizerConfig]] = [] - for qconf in qconf_oset.keys(): + for qconf in qconf_oset: variants.append(list(filter(qconf.is_a_bitwidth_variant, qconf_oset.keys()))) max_bw_varying_variant = max(variants, key=len) other_qconfs = list(filter(lambda x: x not in max_bw_varying_variant, qconf_oset.keys())) @@ -265,7 +266,7 @@ def apply_init(self) -> SingleConfigQuantizerSetup: traces_per_layer = self._calc_traces(self._criterion_fn, self._criterion, self._iter_number, self._tolerance) if not traces_per_layer: - raise RuntimeError("Failed to calculate hessian traces!") + raise nncf.InternalError("Failed to calculate hessian traces!") traces_order = traces_per_layer.traces_order ( @@ -381,7 +382,7 @@ def _merge_constraints_for_adjacent_quantizers( quantizer_ids.append(quantizer_id) minimal_set_bitwidths = set.intersection(*all_bitwidths_sets) if not minimal_set_bitwidths: - raise RuntimeError( + raise nncf.InternalError( "No bitwidths configurations are left after removing inconsistent groups of weight quantizers" " with adjacent activation quantizers!" ) @@ -491,7 +492,7 @@ def _calc_traces( avg_traces = trace_estimator.get_average_traces(max_iter=iter_number, tolerance=tolerance) except RuntimeError as error: if "cuda out of memory" in error.args[0].lower(): - raise RuntimeError( + raise nncf.InternalError( "Failed to estimate average Hessian traces within precision initialization. Specify " "a smaller batch size via --batch-size-init option in the NNCF samples or register " "a data loader with a smaller batch size. Refer to " @@ -728,7 +729,7 @@ def _set_activations_bitwidth_strictly( weight_bitwidth_set: Set[int], ) -> SingleConfigQuantizerSetup: if len(weight_bitwidth_set) > 1: - raise RuntimeError("Invalid grouping of weight quantizers") + raise nncf.InternalError("Invalid grouping of weight quantizers") all_constraints = set() original_quant_module_ids = [ self._original_qp_id_vs_quantizer_module_id_dict[act_qp_id] for act_qp_id in act_qp_ids @@ -742,7 +743,7 @@ def _set_activations_bitwidth_strictly( if weight_bitwidth_set: common_constraints = common_constraints.intersection(weight_bitwidth_set) if not common_constraints: - raise RuntimeError("No hardware compatible bitwidth for activation quantizers") + raise nncf.InternalError("No hardware compatible bitwidth for activation quantizers") for act_qp_id in act_qp_ids: quant_id = self._original_qp_id_vs_quantizer_module_id_dict[act_qp_id] target_bitwidth = sorted(list(common_constraints))[0] diff --git a/nncf/torch/quantization/precision_init/manual_init.py b/nncf/torch/quantization/precision_init/manual_init.py index 98ae0b23ae6..6b9f82ca45e 100644 --- a/nncf/torch/quantization/precision_init/manual_init.py +++ b/nncf/torch/quantization/precision_init/manual_init.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/quantization/precision_init/perturbations.py b/nncf/torch/quantization/precision_init/perturbations.py index 9daacbfcedd..9195dc08760 100644 --- a/nncf/torch/quantization/precision_init/perturbations.py +++ b/nncf/torch/quantization/precision_init/perturbations.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/quantization/precision_init/traces_order.py b/nncf/torch/quantization/precision_init/traces_order.py index ff59e74ac1e..6dca836d225 100644 --- a/nncf/torch/quantization/precision_init/traces_order.py +++ b/nncf/torch/quantization/precision_init/traces_order.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/quantization/quantize_functions.py b/nncf/torch/quantization/quantize_functions.py index aa8f1f2b9ed..6771d6f38fa 100644 --- a/nncf/torch/quantization/quantize_functions.py +++ b/nncf/torch/quantization/quantize_functions.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -224,7 +224,7 @@ def forward(ctx, input_low, input_range, levels): input_high[input_high < 0] = 0 n = levels - 1 # Need a cast here because fp16 division yields fp32 results sometimes - scale = (levels / (input_high - input_low_copy)).to(dtype=input_high.dtype) + scale = (n / (input_high - input_low_copy)).to(dtype=input_high.dtype) zp = torch.round(-input_low_copy * scale) new_input_low = torch.where(zp < n, zp / (zp - n) * input_high, input_low_copy) diff --git a/nncf/torch/quantization/quantize_model.py b/nncf/torch/quantization/quantize_model.py index 7b277ad4e6f..fe883dabb6e 100644 --- a/nncf/torch/quantization/quantize_model.py +++ b/nncf/torch/quantization/quantize_model.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -14,13 +14,18 @@ import torch +import nncf +from nncf.common.factory import NNCFGraphFactory from nncf.common.quantization.structs import QuantizationPreset from nncf.data import Dataset +from nncf.parameters import CompressWeightsMode from nncf.parameters import ModelType from nncf.parameters import QuantizationMode +from nncf.parameters import SensitivityMetric from nncf.parameters import TargetDevice from nncf.quantization.advanced_parameters import AdvancedQuantizationParameters from nncf.quantization.algorithms.post_training.algorithm import PostTrainingQuantization +from nncf.quantization.algorithms.weight_compression.algorithm import WeightCompression from nncf.scopes import IgnoredScope from nncf.torch.model_creation import wrap_model @@ -45,7 +50,7 @@ def quantize_impl( if fast_bias_correction is False: raise ValueError(f"fast_bias_correction={fast_bias_correction} is not supported") if target_device == TargetDevice.CPU_SPR: - raise RuntimeError("target_device == CPU_SPR is not supported") + raise nncf.InternalError("target_device == CPU_SPR is not supported") if mode is not None: raise ValueError(f"mode={mode} is not supported") @@ -71,3 +76,26 @@ def quantize_impl( quantized_model.nncf.disable_dynamic_graph_building() return quantized_model + + +def compress_weights_impl( + model: torch.nn.Module, + dataset: Dataset, + mode: CompressWeightsMode, + ratio: float, + group_size: int, + ignored_scope: IgnoredScope, + all_layers: bool, + sensitivity_metric: SensitivityMetric, + awq: bool, + subset_size: int, +) -> torch.nn.Module: + """ + Implementation of the `compress_weights()` method for the PyTorch backend. + """ + + compression_algorithm = WeightCompression( + mode, ratio, group_size, ignored_scope, all_layers, sensitivity_metric, awq, subset_size + ) + graph = NNCFGraphFactory.create(model) + return compression_algorithm.apply(model, graph, dataset=dataset) diff --git a/nncf/torch/quantization/reference.py b/nncf/torch/quantization/reference.py index b93b1617a76..4b4cf876751 100644 --- a/nncf/torch/quantization/reference.py +++ b/nncf/torch/quantization/reference.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -15,6 +15,7 @@ import numpy as np import torch +import nncf from nncf.torch.utils import sum_like GeneralizedTensor = TypeVar("GeneralizedTensor", torch.Tensor, np.ndarray) @@ -32,7 +33,7 @@ def __init__(self, backend_type: ReferenceBackendType): elif backend_type is ReferenceBackendType.TORCH: self.backend = torch else: - raise RuntimeError("Unknown backend for ReferenceQuantize") + raise nncf.UnsupportedBackendError("Unknown backend for ReferenceQuantize") def _astype(self, tensor: GeneralizedTensor, dtype) -> GeneralizedTensor: if self.backend is np: @@ -88,7 +89,7 @@ def tune_range( input_low[input_low > 0] = 0 input_high[input_high < 0] = 0 n = levels - 1 - scale = levels / (input_high - input_low) + scale = n / (input_high - input_low) scale = self._astype(scale, input_high.dtype) zp = self.backend.round(-input_low * scale) diff --git a/nncf/torch/quantization/schedulers.py b/nncf/torch/quantization/schedulers.py index 4f726a28f82..6647e2a759e 100644 --- a/nncf/torch/quantization/schedulers.py +++ b/nncf/torch/quantization/schedulers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/quantization/statistics.py b/nncf/torch/quantization/statistics.py index c51aa9c73b3..ef01596d1c1 100644 --- a/nncf/torch/quantization/statistics.py +++ b/nncf/torch/quantization/statistics.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/quantization/strip.py b/nncf/torch/quantization/strip.py index 821059c57cc..76dfe2113bf 100644 --- a/nncf/torch/quantization/strip.py +++ b/nncf/torch/quantization/strip.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -14,6 +14,7 @@ import torch from torch.quantization.fake_quantize import FakeQuantize +import nncf from nncf.torch.nncf_network import ExtraCompressionModuleType from nncf.torch.nncf_network import NNCFNetwork from nncf.torch.quantization.layers import AsymmetricQuantizer @@ -33,7 +34,7 @@ def replace_quantizer_to_torch_native_module(model: NNCFNetwork) -> NNCFNetwork: compression_module_type = ExtraCompressionModuleType.EXTERNAL_QUANTIZER if model.nncf.is_compression_module_registered(compression_module_type): external_quantizers = model.nncf.get_compression_modules_by_type(compression_module_type) - for key in external_quantizers.keys(): + for key in external_quantizers: if external_quantizers[key].is_enabled_quantization(): external_quantizers[key] = convert_to_torch_fakequantizer(external_quantizers[key]) @@ -79,7 +80,7 @@ def convert_to_torch_fakequantizer(nncf_quantizer: BaseQuantizer) -> FakeQuantiz nncf_quantizer.set_levels() if nncf_quantizer.num_bits not in SUPPORTED_NUM_BITS_FOR_STRIP_MODEL: - raise RuntimeError( + raise nncf.InternalError( "Converting nncf quantizer module to torch native only supports " f"for num_bits in {SUPPORTED_NUM_BITS_FOR_STRIP_MODEL}." ) diff --git a/nncf/torch/quantization/structs.py b/nncf/torch/quantization/structs.py index a7e91b67f2f..76a2f404d64 100644 --- a/nncf/torch/quantization/structs.py +++ b/nncf/torch/quantization/structs.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/quantization/translator.py b/nncf/torch/quantization/translator.py index 51af5df9f8b..44d509ef440 100644 --- a/nncf/torch/quantization/translator.py +++ b/nncf/torch/quantization/translator.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/return_types.py b/nncf/torch/return_types.py index 1b8c8e4fd75..36493b83b6e 100644 --- a/nncf/torch/return_types.py +++ b/nncf/torch/return_types.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -10,7 +10,7 @@ # limitations under the License. import inspect -from typing import Any, Optional, Tuple, Type, Union +from typing import Any, Tuple, Type, Union import torch @@ -30,7 +30,7 @@ def __get_supported_torch_return_types() -> Tuple[Type[tuple], ...]: _TORCH_RETURN_TYPES = __get_supported_torch_return_types() -def maybe_unwrap_from_torch_return_type(tensor: Any) -> torch.Tensor: +def maybe_get_values_from_torch_return_type(tensor: Any) -> torch.Tensor: """ Attempts to unwrap the tensor value from one of torch.return_types instances in case torch operation output is wrapped by a torch return_type. @@ -43,7 +43,9 @@ def maybe_unwrap_from_torch_return_type(tensor: Any) -> torch.Tensor: return tensor -def maybe_wrap_to_torch_return_type(tensor: torch.Tensor, wrapped_input: Optional[Union[tuple, torch.Tensor]]) -> Any: +def maybe_wrap_to_torch_return_type( + tensor: torch.Tensor, wrapped_input: Union[tuple, torch.Tensor] +) -> Union[tuple, torch.Tensor]: """ Wraps tensor to wrapped_input wrapper in case wrapped_input is wrapped by a torch.return_value container. diff --git a/nncf/torch/sparsity/__init__.py b/nncf/torch/sparsity/__init__.py index 3a0b0fe26b2..c8e48c02b12 100644 --- a/nncf/torch/sparsity/__init__.py +++ b/nncf/torch/sparsity/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/sparsity/base_algo.py b/nncf/torch/sparsity/base_algo.py index b9df542a4a3..b01ffe92377 100644 --- a/nncf/torch/sparsity/base_algo.py +++ b/nncf/torch/sparsity/base_algo.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -16,7 +16,6 @@ import torch from nncf.api.compression import CompressionLoss -from nncf.api.compression import CompressionScheduler from nncf.api.compression import CompressionStage from nncf.common.graph import NNCFNode from nncf.common.graph import NNCFNodeName @@ -25,6 +24,7 @@ from nncf.common.schedulers import BaseCompressionScheduler from nncf.common.schedulers import StubCompressionScheduler from nncf.common.sparsity.controller import SparsityController +from nncf.common.sparsity.schedulers import SparsityScheduler from nncf.common.utils.api_marker import api from nncf.common.utils.backend import copy_model from nncf.torch.algo_selector import ZeroCompressionLoss @@ -107,12 +107,16 @@ def __init__(self, target_model: NNCFNetwork, sparsified_module_info: List[Spars self._scheduler = BaseCompressionScheduler() self.sparsified_module_info = sparsified_module_info + @property + def current_sparsity_level(self) -> float: + return self._scheduler.current_sparsity_level + @property def loss(self) -> CompressionLoss: return self._loss @property - def scheduler(self) -> CompressionScheduler: + def scheduler(self) -> SparsityScheduler: return self._scheduler def disable_scheduler(self): diff --git a/nncf/torch/sparsity/collector.py b/nncf/torch/sparsity/collector.py index 66b5a5cf02c..4a21f85fb39 100644 --- a/nncf/torch/sparsity/collector.py +++ b/nncf/torch/sparsity/collector.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/sparsity/const/__init__.py b/nncf/torch/sparsity/const/__init__.py index 39635d41f85..0665283223b 100644 --- a/nncf/torch/sparsity/const/__init__.py +++ b/nncf/torch/sparsity/const/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/sparsity/const/algo.py b/nncf/torch/sparsity/const/algo.py index 682d1b600e1..677766d0962 100644 --- a/nncf/torch/sparsity/const/algo.py +++ b/nncf/torch/sparsity/const/algo.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/sparsity/functions.py b/nncf/torch/sparsity/functions.py index 33507662172..473a286cd49 100644 --- a/nncf/torch/sparsity/functions.py +++ b/nncf/torch/sparsity/functions.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/sparsity/layers.py b/nncf/torch/sparsity/layers.py index f9d0e42fe3b..5a506b87a10 100644 --- a/nncf/torch/sparsity/layers.py +++ b/nncf/torch/sparsity/layers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/sparsity/magnitude/__init__.py b/nncf/torch/sparsity/magnitude/__init__.py index 1ad0ebb0026..9ebcefeb533 100644 --- a/nncf/torch/sparsity/magnitude/__init__.py +++ b/nncf/torch/sparsity/magnitude/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/sparsity/magnitude/algo.py b/nncf/torch/sparsity/magnitude/algo.py index 64a5d40f89e..c4615f2ea68 100644 --- a/nncf/torch/sparsity/magnitude/algo.py +++ b/nncf/torch/sparsity/magnitude/algo.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/sparsity/magnitude/functions.py b/nncf/torch/sparsity/magnitude/functions.py index b633e6e469a..7d37cf42ca3 100644 --- a/nncf/torch/sparsity/magnitude/functions.py +++ b/nncf/torch/sparsity/magnitude/functions.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/sparsity/rb/__init__.py b/nncf/torch/sparsity/rb/__init__.py index 6ce683c2d15..2602ae660d5 100644 --- a/nncf/torch/sparsity/rb/__init__.py +++ b/nncf/torch/sparsity/rb/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/sparsity/rb/algo.py b/nncf/torch/sparsity/rb/algo.py index 6e0f17de14a..e7bff81bec2 100644 --- a/nncf/torch/sparsity/rb/algo.py +++ b/nncf/torch/sparsity/rb/algo.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -89,6 +89,10 @@ def set_sparsity_level(self, sparsity_level, target_sparsified_module_info: Spar sparse_op = target_sparsified_module_info.operand self._loss.set_target_sparsity_loss(sparsity_level, sparse_op) + @property + def current_sparsity_level(self) -> float: + return self._loss.current_sparsity + def compression_stage(self) -> CompressionStage: if self._mode == "local": return CompressionStage.FULLY_COMPRESSED diff --git a/nncf/torch/sparsity/rb/functions.py b/nncf/torch/sparsity/rb/functions.py index e07ab547a38..88d94813793 100644 --- a/nncf/torch/sparsity/rb/functions.py +++ b/nncf/torch/sparsity/rb/functions.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/sparsity/rb/layers.py b/nncf/torch/sparsity/rb/layers.py index f6d32872a8a..c1df48ad563 100644 --- a/nncf/torch/sparsity/rb/layers.py +++ b/nncf/torch/sparsity/rb/layers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/sparsity/rb/loss.py b/nncf/torch/sparsity/rb/loss.py index f71e0abb617..ed0f30036ae 100644 --- a/nncf/torch/sparsity/rb/loss.py +++ b/nncf/torch/sparsity/rb/loss.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -22,8 +22,8 @@ def __init__(self, sparse_layers=None, target=1.0, p=0.05): self.target = target self.p = p self.disabled = False - self.current_sparsity = 0 - self.mean_sparse_prob = 0 + self.current_sparsity: float = 0.0 + self.mean_sparse_prob = 0.0 def set_layers(self, sparse_layers): self._sparse_layers = sparse_layers diff --git a/nncf/torch/statistics/__init__.py b/nncf/torch/statistics/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/torch/statistics/__init__.py +++ b/nncf/torch/statistics/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/statistics/aggregator.py b/nncf/torch/statistics/aggregator.py index 41fdc20c4fa..7daae8286fd 100644 --- a/nncf/torch/statistics/aggregator.py +++ b/nncf/torch/statistics/aggregator.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -27,10 +27,12 @@ class PTStatisticsAggregator(StatisticsAggregator): + HOOKS_GROUP_NAME = "statistics_hooks" + def collect_statistics(self, model: NNCFNetwork, graph: NNCFGraph) -> None: with torch.no_grad(): - with model.nncf.temporary_clean_view() as intermediate_model: - super().collect_statistics(intermediate_model, graph) + super().collect_statistics(model, graph) + model.nncf.remove_hooks_group(self.HOOKS_GROUP_NAME) def _register_statistics( self, outputs: Dict[str, PTNNCFTensor], statistic_points: StatisticPointsContainer @@ -52,6 +54,7 @@ def _get_transformation_layout_extra_outputs( _statistic_point.target_point, create_register_input_hook(collector=collector), TransformationPriority.FP32_TENSOR_STATISTICS_OBSERVATION, + hooks_group_name=self.HOOKS_GROUP_NAME, ) ) diff --git a/nncf/torch/strip.py b/nncf/torch/strip.py index ed4959064fd..6c3d2b75df0 100644 --- a/nncf/torch/strip.py +++ b/nncf/torch/strip.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/structures.py b/nncf/torch/structures.py index 567a2c27f17..806a839188b 100644 --- a/nncf/torch/structures.py +++ b/nncf/torch/structures.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/tensor.py b/nncf/torch/tensor.py index edf234734dc..68d58ed0f54 100644 --- a/nncf/torch/tensor.py +++ b/nncf/torch/tensor.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/tensor_statistics/__init__.py b/nncf/torch/tensor_statistics/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/nncf/torch/tensor_statistics/__init__.py +++ b/nncf/torch/tensor_statistics/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/tensor_statistics/algo.py b/nncf/torch/tensor_statistics/algo.py index 55cf2ee4b1b..ebde3ac898e 100644 --- a/nncf/torch/tensor_statistics/algo.py +++ b/nncf/torch/tensor_statistics/algo.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -8,7 +8,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -from typing import Callable, Dict, Set +from typing import Callable, Dict, Set, Union import torch @@ -28,6 +28,7 @@ from nncf.torch.graph.transformations.commands import TransformationPriority from nncf.torch.graph.transformations.layout import PTTransformationLayout from nncf.torch.nncf_network import NNCFNetwork +from nncf.torch.return_types import maybe_get_values_from_torch_return_type from nncf.torch.tensor import PTNNCFTensor @@ -51,7 +52,7 @@ def create_register_input_hook(collector: TensorCollector) -> Callable[[torch.Te :return: Register inputs hook function. """ - def register_inputs_hook(x: torch.Tensor) -> torch.Tensor: + def register_inputs_hook(x: Union[torch.Tensor, tuple]) -> torch.Tensor: """ Register inputs hook function. @@ -59,7 +60,8 @@ def register_inputs_hook(x: torch.Tensor) -> torch.Tensor: :return: tensor to register in hook. """ with no_nncf_trace(): - collector.register_input_for_all_reducers(PTNNCFTensor(x)) + x_unwrapped = maybe_get_values_from_torch_return_type(x) + collector.register_input_for_all_reducers(PTNNCFTensor(x_unwrapped)) return x return register_inputs_hook diff --git a/nncf/torch/tensor_statistics/collectors.py b/nncf/torch/tensor_statistics/collectors.py index 21a801dd659..968a6afbd69 100644 --- a/nncf/torch/tensor_statistics/collectors.py +++ b/nncf/torch/tensor_statistics/collectors.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -34,6 +34,7 @@ from nncf.experimental.common.tensor_statistics.collectors import NoopReducer from nncf.experimental.common.tensor_statistics.collectors import PercentileAggregator from nncf.experimental.common.tensor_statistics.collectors import QuantileReducer +from nncf.experimental.common.tensor_statistics.collectors import RawReducer from nncf.experimental.common.tensor_statistics.collectors import ShapeAggregator from nncf.experimental.common.tensor_statistics.collectors import TensorCollector from nncf.quantization.advanced_parameters import StatisticsType @@ -87,7 +88,7 @@ def median(x: NNCFTensor, axis: Union[int, Tuple[int, ...], List[int]], keepdims @classmethod def masked_mean( - cls, x: NNCFTensor, axis: Union[int, Tuple[int, ...], List[int]], mask: NNCFTensor, keepdims=False + cls, x: NNCFTensor, axis: Union[int, Tuple[int, ...]], mask: NNCFTensor, keepdims: bool = False ) -> NNCFTensor: if mask is None: return cls.mean(x, axis=axis, keepdims=keepdims) @@ -100,7 +101,7 @@ def masked_mean( @classmethod def masked_median( - cls, x: NNCFTensor, axis: Union[int, Tuple[int, ...], List[int]], mask: NNCFTensor, keepdims=False + cls, x: NNCFTensor, axis: Union[int, Tuple[int, ...]], mask: NNCFTensor, keepdims: bool = False ) -> NNCFTensor: # Implemented in numy as torch.masked.median is not implemented yet if mask is None: @@ -223,10 +224,6 @@ def get_output_names(self, target_node_name: str, port_id: int) -> List[str]: return [] -class PTNoopReducer(PTReducerMixIn, NoopReducer): - pass - - class PTMinReducer(PTReducerMixIn, MinReducer): pass @@ -465,7 +462,7 @@ def _get_collection_without_reduction( :return: Target statistic collector. """ tensor_collector = TensorCollector(statistic_cls) - reducer = PTNoopReducer() + reducer = NoopReducer() aggregation_axes = list(set(list(aggregation_axes) + [dim + 1 for dim in reduction_axes])) aggregator = aggregator_cls( PTNNCFCollectorTensorProcessor, @@ -532,7 +529,7 @@ def get_mean_statistic_collector( reducer = PTBatchMeanReducer() else: reducer = PTMeanPerChanelReducer(channel_axis=channel_axis) - noop_reducer = PTNoopReducer() + noop_reducer = NoopReducer() kwargs = { "tensor_processor": PTNNCFCollectorTensorProcessor, @@ -555,7 +552,7 @@ def get_raw_stat_collector(num_samples: Optional[int] = None) -> TensorCollector :param num_samples: Maximum number of samples to collect. :return: Raw statistic collector. """ - reducer = PTNoopReducer() + reducer = RawReducer() aggregator = NoopAggregator(num_samples) collector = TensorCollector(PTRawTensorStatistic) diff --git a/nncf/torch/tensor_statistics/statistics.py b/nncf/torch/tensor_statistics/statistics.py index 2bf942659fb..94312c7f5e8 100644 --- a/nncf/torch/tensor_statistics/statistics.py +++ b/nncf/torch/tensor_statistics/statistics.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/nncf/torch/utils.py b/nncf/torch/utils.py index 94610cacce2..2481823714e 100644 --- a/nncf/torch/utils.py +++ b/nncf/torch/utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -19,6 +19,7 @@ from torch import nn from torch.nn import Module +import nncf from nncf.common.compression import BaseCompressionAlgorithmController as BaseController from nncf.common.deprecation import warning_deprecated from nncf.common.graph import NNCFNodeName @@ -245,7 +246,7 @@ def load_module_state(base_module: Module, state: _ModuleState, strict=False) -> msg = f"Could not find a module to restore state: {name}" nncf_logger.debug(msg) if strict: - raise RuntimeError(msg) from e + raise nncf.InternalError(msg) from e for name, param in base_module.named_parameters(): param.requires_grad = state.requires_grad_state[name] diff --git a/nncf/version.py b/nncf/version.py index c92bad2f07f..bef39c476ba 100644 --- a/nncf/version.py +++ b/nncf/version.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -9,7 +9,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -__version__ = "2.8.1" +__version__ = "2.9.0" BKC_TORCH_VERSION = "2.1.2" BKC_TORCHVISION_VERSION = "0.16.2" diff --git a/ruff.toml b/ruff.toml index fafd792e862..a29a1d4156d 100644 --- a/ruff.toml +++ b/ruff.toml @@ -1,10 +1,18 @@ line-length = 120 ignore-init-module-imports = true -ignore = ["E731"] +ignore = [ + "E731", # lambda-assignment + "SIM108", # if-else-block-instead-of-if-exp + "SIM110", # reimplemented-builtin + "SIM117", # multiple-with-statements +] select = [ - "E", # pycodestyle rules - "F", # pyflakes rules - "CPY001" # copyright check + "E", # pycodestyle rules + "F", # pyflakes rules + "CPY001", # copyright check +] +extend-select = [ + "SIM", # https://pypi.org/project/flake8-simplify ] exclude = ["nncf/tensorflow/__init__.py"] diff --git a/setup.py b/setup.py index ab877f76fc9..375b92d19a3 100644 --- a/setup.py +++ b/setup.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -133,7 +133,7 @@ def find_version(*file_paths): ONNX_EXTRAS = ["onnx~=1.13.1", "onnxruntime~=1.14.1;python_version < '3.11'"] -OPENVINO_EXTRAS = ["openvino==2023.3"] +OPENVINO_EXTRAS = ["openvino==2024.0"] EXTRAS_REQUIRE = { diff --git a/tests/__init__.py b/tests/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/__init__.py +++ b/tests/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/common/__init__.py b/tests/common/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/common/__init__.py +++ b/tests/common/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/common/accuracy_control/__init__.py b/tests/common/accuracy_control/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/common/accuracy_control/__init__.py +++ b/tests/common/accuracy_control/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/common/accuracy_control/backend.py b/tests/common/accuracy_control/backend.py index 116426e89e3..e848bc3f6f3 100644 --- a/tests/common/accuracy_control/backend.py +++ b/tests/common/accuracy_control/backend.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -71,7 +71,3 @@ def get_weight_tensor_port_ids(node: NNCFNode) -> List[Optional[int]]: @staticmethod def get_model_size(model: TModel) -> int: return 0 - - @staticmethod - def prepare_for_inference(model: TModel) -> TModel: - return model diff --git a/tests/common/accuracy_control/test_calculate_drop.py b/tests/common/accuracy_control/test_calculate_drop.py index f67d7284739..50899d3eb3d 100644 --- a/tests/common/accuracy_control/test_calculate_drop.py +++ b/tests/common/accuracy_control/test_calculate_drop.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/common/accuracy_control/test_evaluator.py b/tests/common/accuracy_control/test_evaluator.py index c7c6c5250e8..d3e5f5af93f 100644 --- a/tests/common/accuracy_control/test_evaluator.py +++ b/tests/common/accuracy_control/test_evaluator.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -15,6 +15,7 @@ import numpy as np import pytest +import nncf from nncf.data.dataset import Dataset from nncf.quantization.algorithms.accuracy_control.evaluator import Evaluator @@ -87,7 +88,7 @@ def _validation_fn(dummy_model, dummy_dataset): prepared_model.model_for_inference = None if ts.raise_exception: - with pytest.raises(RuntimeError): + with pytest.raises(nncf.InternalError): _ = Evaluator.determine_mode(prepared_model, Dataset([None]), _validation_fn) else: is_metric_mode = Evaluator.determine_mode(prepared_model, Dataset([None]), _validation_fn) diff --git a/tests/common/accuracy_control/test_ranking.py b/tests/common/accuracy_control/test_ranking.py index c1000d81314..b3cd9227b20 100644 --- a/tests/common/accuracy_control/test_ranking.py +++ b/tests/common/accuracy_control/test_ranking.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -92,17 +92,17 @@ def test_get_subset_indices(errors: List[float], subset_size: int, expected_indi ( "simple_graph", [ - GroupToRank(["quantizer_139", "quantizer_162", "quantizer_119"], ["add_117", "conv2d_161"]), - GroupToRank(["quantizer_153", "quantizer_147"], ["conv2d_146"]), - GroupToRank(["quantizer_134", "quantizer_128"], ["conv2d_127"]), + GroupToRank(["fake_quantize_139", "fake_quantize_162", "fake_quantize_119"], ["add_117", "conv2d_161"]), + GroupToRank(["fake_quantize_153", "fake_quantize_147"], ["conv2d_146"]), + GroupToRank(["fake_quantize_134", "fake_quantize_128"], ["conv2d_127"]), ], ), ( "graph_with_shapeof", [ - GroupToRank(["quantizer_105"], ["interpolate_115"]), - GroupToRank(["quantizer_710", "quantizer_93"], ["multiply_99"]), - GroupToRank(["quantizer_82"], ["power_87"]), + GroupToRank(["fake_quantize_105"], ["interpolate_115"]), + GroupToRank(["fake_quantize_710", "fake_quantize_93"], ["multiply_99"]), + GroupToRank(["fake_quantize_82"], ["power_87"]), ], ), ], diff --git a/tests/common/conftest.py b/tests/common/conftest.py index 8d504511550..18d72418aaa 100644 --- a/tests/common/conftest.py +++ b/tests/common/conftest.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/common/experimental/__init__.py b/tests/common/experimental/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/common/experimental/__init__.py +++ b/tests/common/experimental/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/common/experimental/test_reducers_and_aggregators.py b/tests/common/experimental/test_reducers_and_aggregators.py index b6e76c1e029..ffc36d134e4 100644 --- a/tests/common/experimental/test_reducers_and_aggregators.py +++ b/tests/common/experimental/test_reducers_and_aggregators.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -13,12 +13,14 @@ from dataclasses import dataclass from functools import partial from itertools import product -from typing import Any, List, Optional, Tuple +from typing import Any, Iterator, List, Optional, Tuple import numpy as np import pytest +import nncf from nncf.common.graph.layer_attributes import Dtype +from nncf.common.tensor import NNCFTensor from nncf.common.tensor_statistics.collectors import NNCFCollectorTensorProcessor from nncf.experimental.common.tensor_statistics.collectors import AggregationAxes from nncf.experimental.common.tensor_statistics.collectors import MaxAggregator @@ -29,13 +31,15 @@ from nncf.experimental.common.tensor_statistics.collectors import MedianNoOutliersAggregator from nncf.experimental.common.tensor_statistics.collectors import MinAggregator from nncf.experimental.common.tensor_statistics.collectors import NoopAggregator +from nncf.experimental.common.tensor_statistics.collectors import NoopReducer from nncf.experimental.common.tensor_statistics.collectors import PercentileAggregator +from nncf.experimental.common.tensor_statistics.collectors import RawReducer from nncf.experimental.common.tensor_statistics.collectors import ShapeAggregator -DEFALUT_3D_MEAN_VALUE = [[2503.125, -2493.75, 5009.375], [-4987.5, 7515.625, -7481.25], [10021.875, -9975.0, 12528.125]] +DEFAULT_3D_MEAN_VALUE = [[2503.125, -2493.75, 5009.375], [-4987.5, 7515.625, -7481.25], [10021.875, -9975.0, 12528.125]] -DEFALUT_3D_MEDIAN_VALUE = [[4.5, 5.0, 13.5], [10.0, 22.5, 15.0], [31.5, 20.0, 40.5]] +DEFAULT_3D_MEDIAN_VALUE = [[4.5, 5.0, 13.5], [10.0, 22.5, 15.0], [31.5, 20.0, 40.5]] NO_OUTLIERS_DEFAULT_3D_MEAN_VALUE = [ @@ -77,28 +81,48 @@ class OfflineAggregatorTestCase: min_ref=np.array( [ [[[-50000, 5, 10]]], + [[[-50000, 5, 10]]], + [[[-40000, 4, 8]]], [[[-40000, 4, 8]]], [[[-30000, 3, 6]]], + [[[-30000, 3, 6]]], + [[[-20000, 2, 4]]], [[[-20000, 2, 4]]], [[[-10000, 1, 2]]], + [[[-10000, 1, 2]]], + [[[0, 0, 0]]], [[[0, 0, 0]]], [[[-6, -7, -8]]], + [[[-6, -7, -8]]], [[[-12, -14, -16]]], + [[[-12, -14, -16]]], + [[[-18, -21, -24]]], [[[-18, -21, -24]]], [[[-24, -28, -32]]], + [[[-24, -28, -32]]], ] ), max_ref=np.array( [ + [[[50000, -5, -10]]], [[[50000, -5, -10]]], [[[40000, -4, -8]]], + [[[40000, -4, -8]]], + [[[30000, -3, -6]]], [[[30000, -3, -6]]], [[[20000, -2, -4]]], + [[[20000, -2, -4]]], [[[10000, -1, -2]]], + [[[10000, -1, -2]]], + [[[0, 0, 0]]], [[[0, 0, 0]]], [[[6, 7, 8]]], + [[[6, 7, 8]]], + [[[12, 14, 16]]], [[[12, 14, 16]]], [[[18, 21, 24]]], + [[[18, 21, 24]]], + [[[24, 28, 32]]], [[[24, 28, 32]]], ] ), @@ -149,16 +173,25 @@ def squeeze_tensor(self, ref_tensor: List[Any], axes: Optional[Tuple[int]] = Non def cast_tensor(self, tensor, dtype: Dtype): pass - @abstractmethod - def expand_dims(self, tensor, dims: Tuple[int, ...]): - pass + @pytest.mark.parametrize("reducer_cls", [NoopReducer, RawReducer]) + @pytest.mark.parametrize("input_data", [np.arange(24).reshape((1, 2, 3, 4)), np.array([])]) + def test_other_reducers(self, reducer_cls, input_data): + reducer = reducer_cls() + tensor_data = self.get_nncf_tensor(input_data) + reduced_input = reducer([tensor_data]) + if reducer_cls == NoopReducer and tensor_data.is_empty(): + assert reduced_input is None + else: + assert len(reduced_input) == 1 + assert self.all_close(reduced_input[0].tensor, input_data) - def test_noop_reducer(self, reducers): - reducer = reducers["noop"]() - input_ = np.arange(24).reshape((1, 2, 3, 4)) - reduced_input = reducer([self.get_nncf_tensor(input_)]) - assert len(reduced_input) == 1 - assert self.all_close(reduced_input[0].tensor, input_) + @pytest.mark.parametrize("reducer_cls", [NoopReducer, RawReducer]) + def test_other_reducers_name_hash_equal(self, reducer_cls): + reducers_instances = [reducer_cls() for _ in range(2)] + assert hash(reducers_instances[0]) == hash(reducers_instances[1]) + assert reducers_instances[0] == reducers_instances[1] + assert reducers_instances[0].name == reducers_instances[1].name + assert len(set(reducers_instances)) == 1 @pytest.mark.parametrize( "reducer_name,ref", @@ -193,12 +226,15 @@ def test_quantile_reducers(self, reducer_name, ref, reducers): assert self.all_close(val[i].tensor, self.cast_tensor(ref_, Dtype.FLOAT)) @pytest.mark.parametrize( - "reducer_name,ref", - [("batch_mean", [[[[-12.5, -11.5, -10.5], [-9.5, -8.5, -7.5], [-6.5, -5.5, -4.5]]]]), ("mean_per_ch", [-8.5])], + "reducer_name,ref,kwargs", + [ + ("batch_mean", [[[[-12.5, -11.5, -10.5], [-9.5, -8.5, -7.5], [-6.5, -5.5, -4.5]]]], {}), + ("mean_per_ch", [-22.0, -13.0, -4.0, 5.0], {"channel_axis": 0}), + ], ) - def test_batch_mean_mean_per_ch_reducers(self, reducer_name, ref, reducers): + def test_batch_mean_mean_per_ch_reducers(self, reducer_name, ref, reducers, kwargs): input_ = np.arange(-26, 10).reshape((4, 1, 3, 3)) - reducer = reducers[reducer_name](inplace=False) + reducer = reducers[reducer_name](inplace=False, **kwargs) val = reducer([self.get_nncf_tensor(input_, Dtype.FLOAT)]) assert len(val) == 1 assert self.all_close(val[0].tensor, self.cast_tensor(ref, Dtype.FLOAT)) @@ -243,53 +279,68 @@ def test_min_max_aggregators( min_aggregator.register_reduced_input(self.get_nncf_tensor(input_ * (-i))) max_aggregator.register_reduced_input(self.get_nncf_tensor(input_ * i)) + for axis in aggregation_axes: + if axis: + concatted_input = tensor_processor.cat([self.get_nncf_tensor(input_)] * 2, axis - 1).tensor + min_aggregator.register_reduced_input(self.get_nncf_tensor(concatted_input * (-i))) + max_aggregator.register_reduced_input(self.get_nncf_tensor(concatted_input * i)) + min_ref = offline_aggregators_test_desc.min_ref max_ref = offline_aggregators_test_desc.max_ref - assert self.all_close( - min_aggregator.aggregate(), - min_ref, - ) + + assert self.all_close(min_aggregator.aggregate(), min_ref) assert self.all_close(max_aggregator.aggregate(), max_ref) NO_OUTLIERS_TEST_PARAMS = [ - (MeanAggregator, True, 1, [1404.5138888888905]), - (MedianAggregator, True, 1, [24.0]), + (MeanAggregator, (0, 1), 1, [1404.5138888888905]), + (MedianAggregator, (0, 1), 1, [24.0]), ( MeanAggregator, - False, + (0,), 1, [2503.125, -2493.75, 5009.375, -4987.5, 7515.625, -7481.25, 10021.875, -9975.0, 12528.125], ), - (MedianAggregator, False, 1, [4.5, 5.0, 13.5, 10.0, 22.5, 15.0, 31.5, 20.0, 40.5]), - (MeanAggregator, True, 2, [[2512.5, -1651.04166667, 3352.08333333]]), - (MedianAggregator, True, 2, [[13.0, 12.5, 21.0]]), - (MeanAggregator, False, 2, DEFALUT_3D_MEAN_VALUE), - (MedianAggregator, False, 2, DEFALUT_3D_MEDIAN_VALUE), - (MeanAggregator, True, 3, [DEFALUT_3D_MEAN_VALUE]), - (MedianAggregator, True, 3, [DEFALUT_3D_MEDIAN_VALUE]), - (MeanAggregator, False, 3, [DEFALUT_3D_MEAN_VALUE]), - (MedianAggregator, False, 3, [DEFALUT_3D_MEDIAN_VALUE]), - (default_test_mean_no_outlier, True, 1, [20.0893]), - (default_test_median_no_outlier, True, 1, [30.0]), + (MedianAggregator, (0,), 1, [4.5, 5.0, 13.5, 10.0, 22.5, 15.0, 31.5, 20.0, 40.5]), + ############################################################ + (MeanAggregator, (0, 1), 2, [[2512.5, -1651.04166667, 3352.08333333]]), + (MedianAggregator, (0, 1), 2, [[13.0, 12.5, 21.0]]), + (MeanAggregator, (0,), 2, DEFAULT_3D_MEAN_VALUE), + (MedianAggregator, (0,), 2, DEFAULT_3D_MEDIAN_VALUE), + ############################################################ + (MeanAggregator, (0, 1), 3, [DEFAULT_3D_MEAN_VALUE]), + (MedianAggregator, (0, 1), 3, [DEFAULT_3D_MEDIAN_VALUE]), + (MeanAggregator, (0,), 3, [DEFAULT_3D_MEAN_VALUE]), + (MedianAggregator, (0,), 3, [DEFAULT_3D_MEDIAN_VALUE]), + ############################################################ + (default_test_mean_no_outlier, (0, 1), 1, [20.0893]), + (default_test_median_no_outlier, (0, 1), 1, [30.0]), ( default_test_mean_no_outlier, - False, + (0,), 1, [4.16666667, 8.33333333, 12.5, 16.66666667, 20.83333333, 25.0, 29.16666667, 33.33333333, 37.5], ), - (default_test_median_no_outlier, False, 1, [5.0, 4.0, 15.0, 8.0, 25.0, 12.0, 35.0, 16.0, 45.0]), - (default_test_mean_no_outlier, True, 2, [[16.66666667, 20.83333333, 25.0]]), - (default_test_median_no_outlier, True, 2, [[14.0, 10.0, 24.0]]), - (default_test_mean_no_outlier, False, 2, NO_OUTLIERS_DEFAULT_3D_MEAN_VALUE), - (default_test_median_no_outlier, False, 2, NO_OUTLIERS_DEFAULT_3D_MEDIAN_VALUE), - (default_test_mean_no_outlier, True, 3, [NO_OUTLIERS_DEFAULT_3D_MEAN_VALUE]), - (default_test_median_no_outlier, True, 3, [NO_OUTLIERS_DEFAULT_3D_MEDIAN_VALUE]), - (default_test_mean_no_outlier, False, 3, [NO_OUTLIERS_DEFAULT_3D_MEAN_VALUE]), - (default_test_median_no_outlier, False, 3, [NO_OUTLIERS_DEFAULT_3D_MEDIAN_VALUE]), + (default_test_median_no_outlier, (0,), 1, [5.0, 4.0, 15.0, 8.0, 25.0, 12.0, 35.0, 16.0, 45.0]), + ############################################################ + (default_test_mean_no_outlier, (0, 1), 2, [[16.66666667, 20.83333333, 25.0]]), + (default_test_median_no_outlier, (0, 1), 2, [[14.0, 10.0, 24.0]]), + (default_test_mean_no_outlier, (0,), 2, NO_OUTLIERS_DEFAULT_3D_MEAN_VALUE), + (default_test_median_no_outlier, (0,), 2, NO_OUTLIERS_DEFAULT_3D_MEDIAN_VALUE), + ############################################################ + (default_test_mean_no_outlier, (0, 1), 3, [NO_OUTLIERS_DEFAULT_3D_MEAN_VALUE]), + (default_test_median_no_outlier, (0, 1), 3, [NO_OUTLIERS_DEFAULT_3D_MEDIAN_VALUE]), + (default_test_mean_no_outlier, (0,), 3, [NO_OUTLIERS_DEFAULT_3D_MEAN_VALUE]), + (default_test_median_no_outlier, (0,), 3, [NO_OUTLIERS_DEFAULT_3D_MEDIAN_VALUE]), ] - @pytest.mark.parametrize("aggregator_cls,use_per_sample_stats,dims,refs", NO_OUTLIERS_TEST_PARAMS) - def test_mean_median_agggregators(self, aggregator_cls, refs, tensor_processor, dims, use_per_sample_stats): + def _get_inputs_for_mean_median_aggregators( + self, + dims: int, + aggregation_axes: Tuple[int, ...], + is_median: bool, + tensor_processor: NNCFCollectorTensorProcessor, + different_sizes: bool = False, + ) -> Iterator[NNCFTensor]: input_ = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9]) input_with_outliers = np.array( [100_000, -100_000, 200_000, -200_000, 300_000, -300_000, 400_000, -400_000, 500_000] @@ -301,25 +352,69 @@ def test_mean_median_agggregators(self, aggregator_cls, refs, tensor_processor, input_ = input_.reshape((1, 3, 3)) input_with_outliers = input_with_outliers.reshape((1, 3, 3)) - aggregation_axes = (0, 1) if use_per_sample_stats else (0,) - aggregator = aggregator_cls(tensor_processor=tensor_processor, aggregation_axes=aggregation_axes) for i in range(1, 6): - aggregator.register_reduced_input(self.get_nncf_tensor(input_ * i, Dtype.FLOAT)) + yield self.get_nncf_tensor(input_ * i, Dtype.FLOAT) # this registration is to make diff between mean and median bigger - aggregator.register_reduced_input(self.get_nncf_tensor(input_ * 10, Dtype.FLOAT)) - is_median = isinstance(aggregator, (MedianAggregator, MedianNoOutliersAggregator)) + yield self.get_nncf_tensor(input_ * 10, Dtype.FLOAT) # Outliers registration for i in range(2): # mult is needed to make outlier and no outlier aggreagators differs mult = 2.2 * i - 1 if not is_median else 1 - aggregator.register_reduced_input(self.get_nncf_tensor(input_with_outliers * mult, Dtype.FLOAT)) - if is_median and dims == 1 and use_per_sample_stats: + yield self.get_nncf_tensor(input_with_outliers * mult, Dtype.FLOAT) + if is_median and dims == 1 and aggregation_axes == (0, 1): # To make no outliers and outliers versions return different output - aggregator.register_reduced_input( - self.get_nncf_tensor(np.full(input_with_outliers.shape, input_with_outliers[-1]), Dtype.FLOAT) - ) + yield self.get_nncf_tensor(np.full(input_with_outliers.shape, input_with_outliers[-1]), Dtype.FLOAT) + + if different_sizes: + # Different size input registration: + cat_input = self.get_nncf_tensor(input_, Dtype.FLOAT) + for axis in aggregation_axes: + if axis: + yield tensor_processor.cat([cat_input] * 2, axis=axis - 1) + + @pytest.mark.parametrize("aggregator_cls,aggregation_axes,dims,refs", NO_OUTLIERS_TEST_PARAMS) + def test_mean_median_agggregators(self, aggregator_cls, refs, tensor_processor, dims, aggregation_axes): + aggregator = aggregator_cls(tensor_processor=tensor_processor, aggregation_axes=aggregation_axes) + is_median = isinstance(aggregator, (MedianAggregator, MedianNoOutliersAggregator)) + for input_ in self._get_inputs_for_mean_median_aggregators(dims, aggregation_axes, is_median, tensor_processor): + aggregator.register_reduced_input(input_) + ret_val = aggregator.aggregate() + assert self.all_close(ret_val, self.cast_tensor(refs, Dtype.FLOAT)) + + NO_OUTLIERS_DIFFERENT_SIZES_TEST_PARAMS = [ + (MeanAggregator, (1,), 1, [[5.0], [10.0], [15.0], [20.0], [25.0], [50.0], [-55556.0], [66667.0], [5.0]]), + (MedianAggregator, (1,), 1, [[5.0], [10.0], [15.0], [20.0], [25.0], [50.0], [100_000.0], [100_000.0], [5.0]]), + (MeanAggregator, (0, 1), 1, [1124.6111]), + (MedianAggregator, (0, 1), 1, [15.5]), + ( + default_test_mean_no_outlier, + (1,), + 1, + [[5.0], [10.0], [15.0], [20.0], [25.0], [50.0], [-57143.0], [68571.0], [5.0]], + ), + ( + default_test_median_no_outlier, + (1,), + 1, + [[5.0], [10.0], [15.0], [20.0], [25.0], [50.0], [100_000.0], [100_000.0], [5.0]], + ), + (default_test_mean_no_outlier, (0, 1), 1, [16.8750]), + (default_test_median_no_outlier, (0, 1), 1, [20]), + ] + @pytest.mark.parametrize("aggregator_cls,aggregation_axes,dims,refs", NO_OUTLIERS_DIFFERENT_SIZES_TEST_PARAMS) + def test_mean_median_agggregators_different_sizes( + self, aggregator_cls, refs, tensor_processor, dims, aggregation_axes + ): + aggregator = aggregator_cls(tensor_processor=tensor_processor, aggregation_axes=aggregation_axes) + is_median = isinstance(aggregator, (MedianAggregator, MedianNoOutliersAggregator)) + for input_ in self._get_inputs_for_mean_median_aggregators( + dims, aggregation_axes, is_median, tensor_processor, different_sizes=True + ): + aggregator.register_reduced_input(input_) + + ret_val = aggregator.aggregate() assert self.all_close(ret_val, self.cast_tensor(refs, Dtype.FLOAT)) @pytest.fixture( @@ -419,17 +514,9 @@ def test_mad_percentile_aggregators_not_implemented_aggregation_axes( @pytest.mark.parametrize( "reducer_name", - ["noop", "min", "max", "abs_max", "mean", "quantile", "abs_quantile", "batch_mean", "mean_per_ch"], + ["min", "max", "abs_max", "mean", "quantile", "abs_quantile", "batch_mean", "mean_per_ch"], ) def test_reducers_name_hash_equal(self, reducer_name, reducers): - if reducer_name == "noop": - reducers_instances = [reducers[reducer_name]() for _ in range(2)] - assert hash(reducers_instances[0]) == hash(reducers_instances[1]) - assert reducers_instances[0] == reducers_instances[1] - assert reducers_instances[0].name == reducers_instances[1].name - assert len(set(reducers_instances)) == 1 - return - params = {} if reducer_name in ["min", "max", "abs_max", "mean"]: params["reduction_axes"] = [None, (0, 1, 3), (1, 2, 3)] @@ -443,7 +530,7 @@ def test_reducers_name_hash_equal(self, reducer_name, reducers): params["inplace"] = [False, True] params["channel_axis"] = [1, 2] else: - raise RuntimeError( + raise nncf.ValidationError( "test_min_max_mean_reducer_hash_equal configurated in a wrong way." f" Wrong reducer_name: {reducer_name}" ) diff --git a/tests/common/experimental/test_statistic_collector.py b/tests/common/experimental/test_statistic_collector.py index d0a1344a93f..59b5d4b60aa 100644 --- a/tests/common/experimental/test_statistic_collector.py +++ b/tests/common/experimental/test_statistic_collector.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -15,6 +15,7 @@ import numpy as np import pytest +import nncf from nncf.common.tensor import NNCFTensor from nncf.common.tensor_statistics.statistics import MeanTensorStatistic from nncf.common.tensor_statistics.statistics import MedianMADTensorStatistic @@ -42,6 +43,9 @@ def __init__(self, tensor: np.array, dummy_device: Optional[str] = None): def device(self) -> Optional[str]: return self.dummy_device + def is_empty(self) -> bool: + return self.tensor.size == 0 + class DummyTensorReducer(TensorReducerBase): def __init__(self, output_name: str, inplace: bool = False, inplace_mock=None): @@ -236,7 +240,7 @@ def test_ambigous_container_key(): reducer = DummyTensorReducer("Dummy") aggregator = DummyTensorAggregator(5) collector.register_statistic_branch("A", reducer, aggregator) - with pytest.raises(RuntimeError): + with pytest.raises(nncf.InternalError): collector.register_statistic_branch("A", reducer, aggregator) @@ -245,7 +249,7 @@ def test_ambiguous_branches(): reducer = DummyTensorReducer("Dummy") aggregator = DummyTensorAggregator(5) collector.register_statistic_branch("A", reducer, aggregator) - with pytest.raises(RuntimeError): + with pytest.raises(nncf.InternalError): collector.register_statistic_branch("B", reducer, aggregator) @@ -322,8 +326,8 @@ class BadStatContainer: tensor_collector = TensorCollector(BadStatContainer) tensor_collector.register_statistic_branch("A", DummyTensorReducer("A"), DummyTensorAggregator()) - tensor_collector.register_input_for_all_reducers(NumpyNNCFTensor(1)) - with pytest.raises(RuntimeError): + tensor_collector.register_input_for_all_reducers(NumpyNNCFTensor(np.array(1))) + with pytest.raises(nncf.InternalError): tensor_collector.get_statistics() @@ -402,10 +406,10 @@ def test_min_max_stat_building(self, min_max_statistic_cls: MinMaxTensorStatisti tensor_collector.register_statistic_branch( min_max_statistic_cls.MAX_STAT, DummyTensorReducer("B"), DummyTensorAggregator() ) - tensor_collector.register_input_for_all_reducers(NumpyNNCFTensor(1)) + tensor_collector.register_input_for_all_reducers(NumpyNNCFTensor(np.array(1))) statistic = tensor_collector.get_statistics() assert isinstance(statistic, MinMaxTensorStatistic) - assert statistic.min_values == statistic.max_values == NumpyNNCFTensor(1) + assert statistic.min_values == statistic.max_values == NumpyNNCFTensor(np.array(1)) def test_mean_max_stat_building(self, mean_statistic_cls: MeanTensorStatistic): tensor_collector = TensorCollector(mean_statistic_cls) @@ -415,10 +419,10 @@ def test_mean_max_stat_building(self, mean_statistic_cls: MeanTensorStatistic): tensor_collector.register_statistic_branch( mean_statistic_cls.SHAPE_STAT, DummyTensorReducer("B"), DummyTensorAggregator() ) - tensor_collector.register_input_for_all_reducers(NumpyNNCFTensor(1)) + tensor_collector.register_input_for_all_reducers(NumpyNNCFTensor(np.array(1))) statistic = tensor_collector.get_statistics() assert isinstance(statistic, MeanTensorStatistic) - assert statistic.mean_values == statistic.shape == NumpyNNCFTensor(1) + assert statistic.mean_values == statistic.shape == NumpyNNCFTensor(np.array(1)) def test_median_mad_stat_building(self, median_mad_statistic_cls: MedianMADTensorStatistic): class DummyMADPercentileAggregator(DummyTensorAggregator): @@ -434,10 +438,10 @@ def _aggregate_impl(self): DummyTensorReducer("A"), DummyMADPercentileAggregator(), ) - tensor_collector.register_input_for_all_reducers(NumpyNNCFTensor(1)) + tensor_collector.register_input_for_all_reducers(NumpyNNCFTensor(np.array(1))) statistic = tensor_collector.get_statistics() assert isinstance(statistic, MedianMADTensorStatistic) - assert statistic.median_values == statistic.mad_values == NumpyNNCFTensor(1) + assert statistic.median_values == statistic.mad_values == NumpyNNCFTensor(np.array(1)) def test_percentile_max_stat_building(self, percentile_statistic_cls: PercentileTensorStatistic): class DummyPercentileTensorAggregator(DummyTensorAggregator): @@ -450,10 +454,10 @@ def _aggregate_impl(self): DummyTensorReducer("A"), DummyPercentileTensorAggregator(), ) - tensor_collector.register_input_for_all_reducers(NumpyNNCFTensor(1)) + tensor_collector.register_input_for_all_reducers(NumpyNNCFTensor(np.array(1))) statistic = tensor_collector.get_statistics() assert isinstance(statistic, PercentileTensorStatistic) - assert statistic.percentile_vs_values_dict[0.5] == NumpyNNCFTensor(1) + assert statistic.percentile_vs_values_dict[0.5] == NumpyNNCFTensor(np.array(1)) tensor_collector = TensorCollector(percentile_statistic_cls) qs = [0.3, 0.5, 0.7] @@ -463,19 +467,19 @@ def _aggregate_impl(self): DummyTensorReducer(f"A{q}"), DummyTensorAggregator(), ) - tensor_collector.register_input_for_all_reducers(NumpyNNCFTensor(1)) + tensor_collector.register_input_for_all_reducers(NumpyNNCFTensor(np.array(1))) statistic = tensor_collector.get_statistics() assert isinstance(statistic, PercentileTensorStatistic) assert len(statistic.percentile_vs_values_dict) == len(qs) for q in qs: - assert statistic.percentile_vs_values_dict[q] == NumpyNNCFTensor(1) + assert statistic.percentile_vs_values_dict[q] == NumpyNNCFTensor(np.array(1)) def test_raw_max_stat_building(self, raw_statistic_cls: RawTensorStatistic): tensor_collector = TensorCollector(raw_statistic_cls) tensor_collector.register_statistic_branch( raw_statistic_cls.VALUES_STATS, DummyTensorReducer("A"), DummyTensorAggregator() ) - tensor_collector.register_input_for_all_reducers(NumpyNNCFTensor(1)) + tensor_collector.register_input_for_all_reducers(NumpyNNCFTensor(np.array(1))) statistic = tensor_collector.get_statistics() assert isinstance(statistic, RawTensorStatistic) assert statistic.values == NNCFTensor(1) diff --git a/tests/common/graph/__init__.py b/tests/common/graph/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/common/graph/__init__.py +++ b/tests/common/graph/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/common/graph/test_dot_file_rw.py b/tests/common/graph/test_dot_file_rw.py index 75b965b88b5..f076af33f1a 100644 --- a/tests/common/graph/test_dot_file_rw.py +++ b/tests/common/graph/test_dot_file_rw.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/common/graph/test_graph_matching.py b/tests/common/graph/test_graph_matching.py index e6cdff2be24..c55a15f5cde 100644 --- a/tests/common/graph/test_graph_matching.py +++ b/tests/common/graph/test_graph_matching.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/common/graph/test_graph_pattern.py b/tests/common/graph/test_graph_pattern.py index 21be7755bf3..0fb9654ac12 100644 --- a/tests/common/graph/test_graph_pattern.py +++ b/tests/common/graph/test_graph_pattern.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/common/graph/test_nncf_graph.py b/tests/common/graph/test_nncf_graph.py index a9a6d04b2de..b9380b66dcd 100644 --- a/tests/common/graph/test_nncf_graph.py +++ b/tests/common/graph/test_nncf_graph.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/common/graph/test_utils.py b/tests/common/graph/test_utils.py index c1bc08b4db5..a58b121117b 100644 --- a/tests/common/graph/test_utils.py +++ b/tests/common/graph/test_utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/common/hyperparameter_tuner/__init__.py b/tests/common/hyperparameter_tuner/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/common/hyperparameter_tuner/__init__.py +++ b/tests/common/hyperparameter_tuner/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/common/hyperparameter_tuner/test_hyperparameter_tuner.py b/tests/common/hyperparameter_tuner/test_hyperparameter_tuner.py index f057cfa18e8..2cc0df5bf9c 100644 --- a/tests/common/hyperparameter_tuner/test_hyperparameter_tuner.py +++ b/tests/common/hyperparameter_tuner/test_hyperparameter_tuner.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/common/pruning/__init__.py b/tests/common/pruning/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/common/pruning/__init__.py +++ b/tests/common/pruning/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/common/pruning/dummy_types.py b/tests/common/pruning/dummy_types.py index 844d5caf64a..664349108b3 100644 --- a/tests/common/pruning/dummy_types.py +++ b/tests/common/pruning/dummy_types.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/common/pruning/tensor.py b/tests/common/pruning/tensor.py index 2e57c5de474..7beb25d60ef 100644 --- a/tests/common/pruning/tensor.py +++ b/tests/common/pruning/tensor.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/common/pruning/test_pruning_operations.py b/tests/common/pruning/test_pruning_operations.py index c4ac594f5e7..f1428832349 100644 --- a/tests/common/pruning/test_pruning_operations.py +++ b/tests/common/pruning/test_pruning_operations.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/common/pruning/test_symbolic_mask_processor.py b/tests/common/pruning/test_symbolic_mask_processor.py index bdba57a2623..4d24382e8cc 100644 --- a/tests/common/pruning/test_symbolic_mask_processor.py +++ b/tests/common/pruning/test_symbolic_mask_processor.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -11,6 +11,7 @@ import pytest +import nncf from nncf.common.graph.graph import NNCFGraph from nncf.common.pruning.symbolic_mask import AmbiguousSymbolicMask from nncf.common.pruning.symbolic_mask import SymbolicMask @@ -26,7 +27,7 @@ def test_ones(shape, raise_runtime_error): device = None if raise_runtime_error: - with pytest.raises(RuntimeError): + with pytest.raises(nncf.ValidationError): tensor = SymbolicMaskProcessor.ones(shape, device) else: tensor = SymbolicMaskProcessor.ones(shape, device) diff --git a/tests/common/pruning/test_utils.py b/tests/common/pruning/test_utils.py index 6f04a932387..28a95984436 100644 --- a/tests/common/pruning/test_utils.py +++ b/tests/common/pruning/test_utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/common/quantization/__init__.py b/tests/common/quantization/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/common/quantization/__init__.py +++ b/tests/common/quantization/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/common/quantization/data_generators.py b/tests/common/quantization/data_generators.py index 2db33bc92f9..2bbe4d89067 100644 --- a/tests/common/quantization/data_generators.py +++ b/tests/common/quantization/data_generators.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/common/quantization/metatypes.py b/tests/common/quantization/metatypes.py index a8a8c0a92e4..2c369a52cb6 100644 --- a/tests/common/quantization/metatypes.py +++ b/tests/common/quantization/metatypes.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -163,6 +163,21 @@ class ParameterTestMetatype(TestMetatype): name = "parameter" +@METATYPES_FOR_TEST.register() +class FakeQuantizeTestMetatype(TestMetatype): + name = "fake_quantize" + + +@METATYPES_FOR_TEST.register() +class QuantizeTestMetatype(TestMetatype): + name = "quantize" + + +@METATYPES_FOR_TEST.register() +class DequantizeTestMetatype(TestMetatype): + name = "dequantize" + + WEIGHT_LAYER_METATYPES = [LinearTestMetatype, Conv2dTestMetatype, MatMulTestMetatype] @@ -180,7 +195,9 @@ class ParameterTestMetatype(TestMetatype): QUANTIZER_METATYPES = [ - QuantizerTestMetatype, + FakeQuantizeTestMetatype, + QuantizeTestMetatype, + DequantizeTestMetatype, ] diff --git a/tests/common/quantization/mock_graphs.py b/tests/common/quantization/mock_graphs.py index 46887158950..7fe44a9836f 100644 --- a/tests/common/quantization/mock_graphs.py +++ b/tests/common/quantization/mock_graphs.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -93,22 +93,17 @@ def get_nncf_graph_from_mock_nx_graph(nx_graph: nx.DiGraph, nncf_graph_cls=NNCFG else: node_name = "/" + curr_node_key + "_0" - if NNCFNode.NODE_TYPE_ATTR in node: - node_type = node[NNCFNode.NODE_TYPE_ATTR] - else: - node_type = curr_node_key - + node_type = node.get(NNCFNode.NODE_TYPE_ATTR, curr_node_key) layer_attributes = node.get(NNCFNode.LAYER_ATTRIBUTES) if NNCFNode.METATYPE_ATTR in node: metatype = node[NNCFNode.METATYPE_ATTR] else: metatype = METATYPES_FOR_TEST.get_operator_metatype_by_op_name(node_type) - if metatype is not UnknownMetatype: - if metatype.get_subtypes(): - subtype = metatype.determine_subtype(layer_attributes=layer_attributes) - if subtype is not None: - metatype = subtype + if metatype is not UnknownMetatype and metatype.get_subtypes(): + subtype = metatype.determine_subtype(layer_attributes=layer_attributes) + if subtype is not None: + metatype = subtype node_id = idx node = mock_graph.add_nncf_node( @@ -125,10 +120,7 @@ def get_nncf_graph_from_mock_nx_graph(nx_graph: nx.DiGraph, nncf_graph_cls=NNCFG in_edge = (pred, curr_node_key) out_idx, creator_id = edge_vs_output_idx_and_creator_id[in_edge] edge_data = nx_graph.edges[in_edge] - if NNCFGraph.DTYPE_EDGE_ATTR in edge_data: - dtype = edge_data[NNCFGraph.DTYPE_EDGE_ATTR] - else: - dtype = Dtype.FLOAT + dtype = edge_data.get(NNCFGraph.DTYPE_EDGE_ATTR, Dtype.FLOAT) mock_graph.add_edge_between_nncf_nodes( creator_id, node_id, [1, 1, 1, 1], input_port_id=pred_idx, output_port_id=out_idx, dtype=dtype ) diff --git a/tests/common/quantization/test_filter_constant_nodes.py b/tests/common/quantization/test_filter_constant_nodes.py index e92d271b859..385e9244f4c 100644 --- a/tests/common/quantization/test_filter_constant_nodes.py +++ b/tests/common/quantization/test_filter_constant_nodes.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -237,8 +237,8 @@ def test_constant_nodes_filter(model_to_test): def check_ip_graphs_are_equal(graph_1: InsertionPointGraph, graph_2: InsertionPointGraph): - graph_1_node_keys_without_index = [graph_1_node_key.split(" ")[-1] for graph_1_node_key in graph_1.nodes.keys()] - graph_2_node_keys_without_index = [graph_2_node_key.split(" ")[-1] for graph_2_node_key in graph_2.nodes.keys()] + graph_1_node_keys_without_index = [graph_1_node_key.split(" ")[-1] for graph_1_node_key in graph_1.nodes] + graph_2_node_keys_without_index = [graph_2_node_key.split(" ")[-1] for graph_2_node_key in graph_2.nodes] assert Counter(graph_1_node_keys_without_index) == Counter(graph_2_node_keys_without_index) graph_1_filtered_edges, graph_2_filtered_edges = [], [] diff --git a/tests/common/quantization/test_ignore_post_processing.py b/tests/common/quantization/test_ignore_post_processing.py index a4e92944d68..1d5d3468949 100644 --- a/tests/common/quantization/test_ignore_post_processing.py +++ b/tests/common/quantization/test_ignore_post_processing.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/common/quantization/test_minmax.py b/tests/common/quantization/test_minmax.py index 7d973987c21..d89cd3aa389 100644 --- a/tests/common/quantization/test_minmax.py +++ b/tests/common/quantization/test_minmax.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -11,6 +11,7 @@ import pytest +import nncf from nncf.common.quantization.structs import QuantizationPreset from nncf.common.quantization.structs import QuantizationScheme from nncf.common.quantization.structs import QuantizerGroup @@ -99,8 +100,8 @@ def test_mode_against_default_map(algo_params, is_error): if is_error: try: minmax = MinMaxQuantization(**algo_params) - except RuntimeError: - pytest.xfail("Caught expected RuntimeError") + except nncf.ParameterNotSupportedError: + pytest.xfail("Caught expected error") minmax = MinMaxQuantization(**algo_params) for ref_parameter_name, ref_parameter_value in default_values_to_compare.items(): parameter_value = getattr(minmax, ref_parameter_name) diff --git a/tests/common/quantization/test_passes.py b/tests/common/quantization/test_passes.py index 4113decfc58..d737debcf8d 100644 --- a/tests/common/quantization/test_passes.py +++ b/tests/common/quantization/test_passes.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/common/quantization/test_quantizer_propagation_graph.py b/tests/common/quantization/test_quantizer_propagation_graph.py index f80541d5e39..b957be5b329 100644 --- a/tests/common/quantization/test_quantizer_propagation_graph.py +++ b/tests/common/quantization/test_quantizer_propagation_graph.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -18,6 +18,7 @@ import networkx as nx import pytest +import nncf from nncf.common.graph import NNCFGraph from nncf.common.graph import NNCFNode from nncf.common.graph import NNCFNodeName @@ -141,7 +142,7 @@ def test_add_propagating_quantizer(self, mock_qp_graph): else: assert not edge_data[QPSG.AFFECTING_PROPAGATING_QUANTIZERS_ATTR] - with pytest.raises(RuntimeError): + with pytest.raises(nncf.InternalError): _ = mock_qp_graph.add_propagating_quantizer( ref_qconf_list, InsertionPointGraph.get_post_hook_node_key(target_node_key) ) diff --git a/tests/common/quantization/test_quantizer_propagation_solver.py b/tests/common/quantization/test_quantizer_propagation_solver.py index 6fd3ae45001..9d0171bc643 100644 --- a/tests/common/quantization/test_quantizer_propagation_solver.py +++ b/tests/common/quantization/test_quantizer_propagation_solver.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/common/quantization/test_quantizer_removal.py b/tests/common/quantization/test_quantizer_removal.py index 65baa4aad40..13596083e66 100644 --- a/tests/common/quantization/test_quantizer_removal.py +++ b/tests/common/quantization/test_quantizer_removal.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -24,7 +24,7 @@ from tests.common.quantization.metatypes import QUANTIZABLE_METATYPES from tests.common.quantization.metatypes import QUANTIZE_AGNOSTIC_METATYPES from tests.common.quantization.metatypes import QUANTIZER_METATYPES -from tests.common.quantization.metatypes import ShapeOfTestMetatype +from tests.common.quantization.metatypes import SHAPEOF_METATYPES @dataclass @@ -54,26 +54,26 @@ class Graph: Node(116, "relu"), Node(117, "add"), Node(118, "max_pool2d"), - Node(119, "quantizer"), + Node(119, "fake_quantize"), Node(124, "relu"), Node(125, "add"), Node(126, "constant"), Node(127, "conv2d"), - Node(128, "quantizer"), + Node(128, "fake_quantize"), Node(133, "constant"), - Node(134, "quantizer"), - Node(139, "quantizer"), + Node(134, "fake_quantize"), + Node(139, "fake_quantize"), Node(144, "add"), Node(145, "constant"), Node(146, "conv2d"), - Node(147, "quantizer"), + Node(147, "fake_quantize"), Node(152, "constant"), - Node(153, "quantizer"), + Node(153, "fake_quantize"), Node(158, "relu"), Node(159, "add"), Node(160, "constant"), Node(161, "conv2d"), - Node(162, "quantizer"), + Node(162, "fake_quantize"), Node(167, "constant"), ], [ @@ -107,15 +107,15 @@ class Graph: "graph_with_shapeof": Graph( [ Node(0, "parameter"), - Node(82, "quantizer"), + Node(82, "fake_quantize"), Node(720, "constant"), Node(87, "power"), - Node(93, "quantizer"), + Node(93, "fake_quantize"), Node(99, "multiply"), - Node(710, "quantizer"), + Node(710, "fake_quantize"), Node(715, "constant"), Node(106, "shapeof"), - Node(105, "quantizer"), + Node(105, "fake_quantize"), Node(115, "interpolate"), Node(117, "strided_slice"), Node(746, "constant"), @@ -156,6 +156,40 @@ class Graph: Edge(0, 0, 82, 0), ], ), + "simple_graph_quantize_dequantize": Graph( + [ + Node(0, "parameter"), + Node(37, "quantize"), + Node(38, "dequantize"), + Node(41, "conv2d"), + Node(42, "quantize"), + Node(40, "dequantize"), + Node(43, "dequantize"), + Node(39, "quantize"), + Node(46, "conv2d"), + Node(65, "add"), + Node(45, "dequantize"), + Node(64, "dequantize"), + Node(44, "quantize"), + Node(63, "quantize"), + ], + [ + Edge(0, 0, 37, 0), + Edge(37, 0, 38, 0), + Edge(38, 0, 41, 0), + Edge(41, 0, 42, 0), + Edge(42, 0, 43, 0), + Edge(40, 0, 41, 1), + Edge(43, 0, 46, 0), + Edge(43, 0, 65, 0), + Edge(39, 0, 40, 0), + Edge(45, 0, 46, 1), + Edge(64, 0, 65, 1), + Edge(44, 0, 45, 0), + Edge(63, 0, 64, 0), + Edge(46, 0, 63, 0), + ], + ), } @@ -178,15 +212,47 @@ class TestCase: TEST_CASES = { "simple_graph": [ - TestCase("quantizer_119", ["quantizer_139", "quantizer_162", "quantizer_119"], ["add_117", "conv2d_161"]), - TestCase("quantizer_128", ["quantizer_134", "quantizer_128"], ["conv2d_127"]), - TestCase("quantizer_134", ["quantizer_134", "quantizer_128"], ["conv2d_127"]), - TestCase("quantizer_139", ["quantizer_139", "quantizer_162", "quantizer_119"], ["add_117", "conv2d_161"]), - TestCase("quantizer_147", ["quantizer_153", "quantizer_147"], ["conv2d_146"]), - TestCase("quantizer_153", ["quantizer_153", "quantizer_147"], ["conv2d_146"]), - TestCase("quantizer_162", ["quantizer_139", "quantizer_162", "quantizer_119"], ["add_117", "conv2d_161"]), + TestCase( + "fake_quantize_119", + ["fake_quantize_139", "fake_quantize_162", "fake_quantize_119"], + ["add_117", "conv2d_161"], + ), + TestCase("fake_quantize_128", ["fake_quantize_134", "fake_quantize_128"], ["conv2d_127"]), + TestCase("fake_quantize_134", ["fake_quantize_134", "fake_quantize_128"], ["conv2d_127"]), + TestCase( + "fake_quantize_139", + ["fake_quantize_139", "fake_quantize_162", "fake_quantize_119"], + ["add_117", "conv2d_161"], + ), + TestCase("fake_quantize_147", ["fake_quantize_153", "fake_quantize_147"], ["conv2d_146"]), + TestCase("fake_quantize_153", ["fake_quantize_153", "fake_quantize_147"], ["conv2d_146"]), + TestCase( + "fake_quantize_162", + ["fake_quantize_139", "fake_quantize_162", "fake_quantize_119"], + ["add_117", "conv2d_161"], + ), + ], + "graph_with_shapeof": [TestCase("fake_quantize_105", ["fake_quantize_105"], ["interpolate_115"])], + "simple_graph_quantize_dequantize": [ + TestCase("quantize_37", ["quantize_37", "dequantize_38", "quantize_39", "dequantize_40"], ["conv2d_41"]), + TestCase("quantize_39", ["quantize_37", "dequantize_38", "quantize_39", "dequantize_40"], ["conv2d_41"]), + # + TestCase( + "quantize_42", + ["quantize_42", "dequantize_43", "quantize_44", "dequantize_45", "quantize_63", "dequantize_64"], + ["conv2d_46", "add_65"], + ), + TestCase( + "quantize_44", + ["quantize_42", "dequantize_43", "quantize_44", "dequantize_45", "quantize_63", "dequantize_64"], + ["conv2d_46", "add_65"], + ), + TestCase( + "quantize_63", + ["quantize_42", "dequantize_43", "quantize_44", "dequantize_45", "quantize_63", "dequantize_64"], + ["conv2d_46", "add_65"], + ), ], - "graph_with_shapeof": [TestCase("quantizer_105", ["quantizer_105"], ["interpolate_115"])], } @@ -216,20 +282,25 @@ def create_nncf_graph(graph: Graph) -> NNCFGraph: def create_test_params(): test_params = [] + ids = [] for graph_name, test_cases in TEST_CASES.items(): nncf_graph = create_nncf_graph(GRAPHS[graph_name]) - for test_case in test_cases: + for i, test_case in enumerate(test_cases): + ids.append(f"{graph_name}_{i}") test_params.append((nncf_graph, test_case)) - return test_params + return ids, test_params + + +IDS, TEST_PARAMS = create_test_params() -@pytest.mark.parametrize("nncf_graph,test_case", create_test_params()) +@pytest.mark.parametrize("nncf_graph,test_case", TEST_PARAMS, ids=IDS) def test_find_quantizer_nodes_to_cut(nncf_graph: NNCFGraph, test_case: TestCase): quantizer_node = nncf_graph.get_node_by_name(test_case.node_name) - # As test graphs are fully connected and does not have readvariable metatyep, + # As test graphs are fully connected and does not have readvariable metatype, # this should work input_nodes = nncf_graph.get_input_nodes() - nncf_graph_without_shapeof = remove_shapeof_subgraphs(deepcopy(nncf_graph), [ShapeOfTestMetatype], input_nodes) + nncf_graph_without_shapeof = remove_shapeof_subgraphs(deepcopy(nncf_graph), SHAPEOF_METATYPES, input_nodes) nodes, ops = find_quantizer_nodes_to_cut( nncf_graph_without_shapeof, quantizer_node, diff --git a/tests/common/sdl/__init__.py b/tests/common/sdl/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/common/sdl/__init__.py +++ b/tests/common/sdl/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/common/sdl/test_config_schema.py b/tests/common/sdl/test_config_schema.py index 9dbfcb42780..d3e1183c7af 100644 --- a/tests/common/sdl/test_config_schema.py +++ b/tests/common/sdl/test_config_schema.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/common/sdl/test_symlinks.py b/tests/common/sdl/test_symlinks.py index aa42a83086a..5ab06a68efd 100644 --- a/tests/common/sdl/test_symlinks.py +++ b/tests/common/sdl/test_symlinks.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/common/test_builder_state.py b/tests/common/test_builder_state.py index 2353979abfe..61945e395a6 100644 --- a/tests/common/test_builder_state.py +++ b/tests/common/test_builder_state.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/common/test_ctrl_state.py b/tests/common/test_ctrl_state.py index 9f48ed2ab7e..f3b71d61e34 100644 --- a/tests/common/test_ctrl_state.py +++ b/tests/common/test_ctrl_state.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/common/test_dataset.py b/tests/common/test_dataset.py index 55614716de1..5c89a71dec6 100644 --- a/tests/common/test_dataset.py +++ b/tests/common/test_dataset.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/common/test_deprecation_warnings.py b/tests/common/test_deprecation_warnings.py index ca88245e36d..e62344cd8df 100644 --- a/tests/common/test_deprecation_warnings.py +++ b/tests/common/test_deprecation_warnings.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/common/test_framework_detection.py b/tests/common/test_framework_detection.py index 6df26d0f91d..3511d1d2a3e 100644 --- a/tests/common/test_framework_detection.py +++ b/tests/common/test_framework_detection.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/common/test_hardware_config.py b/tests/common/test_hardware_config.py index fa88a963787..f460a2c2ef2 100644 --- a/tests/common/test_hardware_config.py +++ b/tests/common/test_hardware_config.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -15,7 +15,7 @@ from nncf.parameters import TargetDevice -@pytest.mark.parametrize("target_device", [TargetDevice.ANY, TargetDevice.CPU, TargetDevice.GPU, TargetDevice.VPU]) +@pytest.mark.parametrize("target_device", [TargetDevice.ANY, TargetDevice.CPU, TargetDevice.GPU, TargetDevice.NPU]) def test_get_hw_config_type(target_device): expected = HW_CONFIG_TYPE_TARGET_DEVICE_MAP[target_device.value] mesured = get_hw_config_type(target_device.value) diff --git a/tests/common/test_hook_handle.py b/tests/common/test_hook_handle.py new file mode 100644 index 00000000000..08ac72c6a5e --- /dev/null +++ b/tests/common/test_hook_handle.py @@ -0,0 +1,63 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import gc +from collections import OrderedDict + +from nncf.common.hook_handle import add_op_to_registry + + +def test_hook_handle_adds_removes_unique_keys(): + num_hooks = 3 + hook_registry = OrderedDict() + handles = [] + for _ in range(num_hooks): + handles.append(add_op_to_registry(hook_registry, 0)) + + assert list(hook_registry.keys()) == list(map(str, range(num_hooks))) + assert [h.hook_id for h in handles] == list(map(str, range(num_hooks))) + + handles[0].remove() + assert list(hook_registry.keys()) == list(map(str, range(1, num_hooks))) + + +def test_handle_does_not_fail_if_hook_does_not_exist(): + hook_registry = OrderedDict() + handle = add_op_to_registry(hook_registry, 0) + del hook_registry[handle.hook_id] + handle.remove() + del hook_registry + handle.remove() + + +def test_handle_does_not_remove_op_twice(): + hook_registry = OrderedDict() + handle = add_op_to_registry(hook_registry, 0) + handle.remove() + assert not hook_registry + + second_handle = add_op_to_registry(hook_registry, 1) + assert handle.hook_id == second_handle.hook_id + assert "0" in hook_registry + + handle.remove() + assert "0" in hook_registry + assert hook_registry["0"] == 1 + + +def test_hook_handle_weak_ref(): + def _local_scope_fn(): + hook_registry = OrderedDict() + return add_op_to_registry(hook_registry, 0) + + handle = _local_scope_fn() + gc.collect() + assert handle.hooks_registry_ref() is None diff --git a/tests/common/test_ignored_scope.py b/tests/common/test_ignored_scope.py index c9d295838dc..1bd35b47584 100644 --- a/tests/common/test_ignored_scope.py +++ b/tests/common/test_ignored_scope.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -11,6 +11,7 @@ import pytest +import nncf from nncf.common.graph.operator_metatypes import InputNoopMetatype from nncf.common.graph.operator_metatypes import OutputNoopMetatype from nncf.scopes import IgnoredScope @@ -78,5 +79,5 @@ def test_ignored_scopes(ignored_scope, ref_ignored_names): @pytest.mark.parametrize("ignored_scope", WRONG_IGNORED_SCOPES_TEST_DATA) def test_wrong_ignored_scopes(ignored_scope): nncf_graph = NNCFGraphToTestIgnoredScope(CONV_TYPE, LINEAR_TYPE).nncf_graph - with pytest.raises(RuntimeError): + with pytest.raises(nncf.ValidationError): get_ignored_node_names_from_ignored_scope(ignored_scope, nncf_graph) diff --git a/tests/common/test_logging.py b/tests/common/test_logging.py index 189122ee5ba..298f196508e 100644 --- a/tests/common/test_logging.py +++ b/tests/common/test_logging.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/common/test_monkey_patching.py b/tests/common/test_monkey_patching.py index 245d78862f0..2ee4d1ad7f4 100644 --- a/tests/common/test_monkey_patching.py +++ b/tests/common/test_monkey_patching.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/common/test_parameters.py b/tests/common/test_parameters.py index 3cd35e59bce..c46c0b709be 100644 --- a/tests/common/test_parameters.py +++ b/tests/common/test_parameters.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -11,6 +11,7 @@ import pytest +import nncf from nncf.scopes import IgnoredScope from nncf.scopes import convert_ignored_scope_to_list @@ -35,5 +36,5 @@ def test_convert_ignored_scope_to_list(): def test_create_ignored_scope_config_raise_exception(): ignored_scope = IgnoredScope(types=["type1"]) - with pytest.raises(Exception): + with pytest.raises(nncf.InternalError): _ = convert_ignored_scope_to_list(ignored_scope) diff --git a/tests/common/test_pattern_manager.py b/tests/common/test_pattern_manager.py index 3b05edafc8f..8d87f0aac79 100644 --- a/tests/common/test_pattern_manager.py +++ b/tests/common/test_pattern_manager.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/common/test_progress_bar.py b/tests/common/test_progress_bar.py index 9e6c376fc60..95f2ddcf2ab 100644 --- a/tests/common/test_progress_bar.py +++ b/tests/common/test_progress_bar.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/common/test_scopes.py b/tests/common/test_scopes.py index 0ebfc3914d0..0ae8ea78ba2 100644 --- a/tests/common/test_scopes.py +++ b/tests/common/test_scopes.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/common/test_statistics_aggregator.py b/tests/common/test_statistics_aggregator.py index 4f7cc294df7..f32ebfb74a0 100644 --- a/tests/common/test_statistics_aggregator.py +++ b/tests/common/test_statistics_aggregator.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -18,9 +18,11 @@ import numpy as np import pytest +import nncf from nncf.common.factory import NNCFGraphFactory from nncf.common.graph.transformations.commands import TargetPoint from nncf.common.graph.transformations.commands import TargetType +from nncf.common.quantization.initialization.range import RangeInitCollectorParams from nncf.common.quantization.structs import QuantizationScheme as QuantizationMode from nncf.common.quantization.structs import QuantizerConfig from nncf.common.tensor_statistics.statistic_point import StatisticPoint @@ -91,11 +93,6 @@ def test_params(self): presented in Openvino tests. """ - @abstractmethod - @pytest.fixture - def is_stat_in_shape_of_scale(self) -> bool: - pass - @abstractmethod @pytest.fixture def dataset_samples(self): @@ -338,15 +335,13 @@ def dataset_samples_to_conv_w(self, dataset_sample): ), # Weight collectors ( - ( - MinMaxTestParameters( - RangeEstimatorParametersSet.MINMAX, - TargetType.OPERATION_WITH_WEIGHTS, - QuantizationMode.SYMMETRIC, - False, - 128, - -128, - ) + MinMaxTestParameters( + RangeEstimatorParametersSet.MINMAX, + TargetType.OPERATION_WITH_WEIGHTS, + QuantizationMode.SYMMETRIC, + False, + 128, + -128, ) ), ( @@ -385,7 +380,6 @@ def test_statistics_aggregator_min_max( self, test_parameters: MinMaxTestParameters, dataset_samples, - is_stat_in_shape_of_scale, inplace_statistics, is_backend_support_custom_estimators, ): @@ -436,7 +430,7 @@ def filter_func(point): # Torch and Openvino backends tensor collectors return values in shape of scale # in comparison to ONNX backends. ref_min_val, ref_max_val = test_parameters.ref_min_val, test_parameters.ref_max_val - if isinstance(ref_min_val, np.ndarray) and is_stat_in_shape_of_scale: + if isinstance(ref_min_val, np.ndarray): shape = (1, 3, 1, 1) if test_parameters.target_type == TargetType.OPERATION_WITH_WEIGHTS: shape = (3, 1, 1, 1) @@ -448,7 +442,7 @@ def filter_func(point): assert stat.min_values.shape == ref_min_val.shape assert stat.max_values.shape == ref_max_val.shape else: - ref_shape = (1, 1, 1, 1) if is_stat_in_shape_of_scale else () + ref_shape = (1, 1, 1, 1) assert stat.min_values.shape == ref_shape assert stat.max_values.shape == ref_shape @@ -575,7 +569,7 @@ class BCTestParameters: ], ) def test_statistics_aggregator_bias_correction( - self, dataset_samples, test_params: BCTestParameters, inplace_statistics, is_stat_in_shape_of_scale + self, dataset_samples, test_params: BCTestParameters, inplace_statistics ): name_to_algo_backend_map = { BiasCorrectionAlgos.BIAS_CORRECTION: self.get_bias_correction_algo_backend_cls, @@ -589,7 +583,7 @@ def test_statistics_aggregator_bias_correction( elif test_params.collector_type == BCStatsCollectors.RAW: tensor_collector = algo_backend.raw_statistic_collector(len(dataset_samples)) else: - raise RuntimeError() + raise nncf.InvalidCollectorTypeError(f"Invalid collector type: {test_params.collector_type}") target_point = self.get_target_point(test_params.target_type) @@ -622,10 +616,8 @@ def filter_func(point): elif test_params.collector_type == BCStatsCollectors.RAW: ret_val = stat.values test_params.ref_values = dataset_samples - if not is_stat_in_shape_of_scale: - ret_val = [np.squeeze(x) for x in ret_val] else: - raise RuntimeError() + raise nncf.InvalidCollectorTypeError(f"Invalid collector type: {test_params.collector_type}") for val, ref in zip(ret_val, test_params.ref_values): if isinstance(ref, np.ndarray): @@ -638,11 +630,17 @@ def create_statistics_point( ): algo_backend = cls.get_min_max_algo_backend_cls() nncf_graph = NNCFGraphFactory.create(model) + + collector_params = RangeInitCollectorParams( + is_weights=target_point.is_weight_target_point(), + scheme=q_config.mode, + per_channel=q_config.per_channel, + ) tensor_collector = algo_backend.get_statistic_collector( range_estimator, nncf_graph=nncf_graph, target_point=target_point, - quantizer_config=q_config, + collector_params=collector_params, num_samples=subset_size, inplace=inplace_statistics, ) @@ -759,11 +757,16 @@ def test_statistic_merging(self, test_params, key, dataset_samples, inplace_stat target_point_cls = self.get_target_point_cls() for target_point_args, ref in self.MERGED_TARGET_POINT_AND_REFS[key]: target_point = target_point_cls(*target_point_args) + collector_params = RangeInitCollectorParams( + is_weights=target_point.is_weight_target_point(), + scheme=quantizer_config.mode, + per_channel=quantizer_config.per_channel, + ) min_max_tensor_collector = algo_backend.get_statistic_collector( RangeEstimatorParametersSet.MINMAX, nncf_graph=nncf_graph, target_point=target_point, - quantizer_config=quantizer_config, + collector_params=collector_params, num_samples=len(dataset_samples), inplace=inplace_statistics, ) @@ -771,7 +774,7 @@ def test_statistic_merging(self, test_params, key, dataset_samples, inplace_stat RangeEstimatorParametersSet.MEAN_MINMAX, nncf_graph=nncf_graph, target_point=target_point, - quantizer_config=quantizer_config, + collector_params=collector_params, num_samples=len(dataset_samples), inplace=inplace_statistics, ) @@ -828,11 +831,9 @@ def test_same_collectors_different_attrs_dont_merge(self, statistics_type, test_ params["reduction_axes"] = [None, (0, 1, 3), (1, 2, 3)] params["quantile"] = [[0.01, 0.99], [0.001, 0.999]] elif statistics_type == "batch_mean": - pytest.skip("Inplace statistic woun't work until openvino==2023.0.0 release") params["inplace"] = [False, True] elif statistics_type == "mean_per_ch": - # TODO(dlyakhov) uncoment when nncf will switch to openvino==2023.0.0 - # params["inplace"] = [False, True] + params["inplace"] = [False, True] params["channel_axis"] = [1, 2] def product_dict(**kwargs): @@ -921,6 +922,6 @@ def test_collect_with_empty_dataset(self, dataset_samples): statistics_aggregator = self.get_statistics_aggregator(dataset) statistics_aggregator.register_statistic_points(statistics_points) - with pytest.raises(RuntimeError) as e: + with pytest.raises(nncf.ValidationError) as e: statistics_aggregator.collect_statistics(model, graph) assert "Calibration dataset must not be empty" in e.info diff --git a/tests/common/test_telemetry.py b/tests/common/test_telemetry.py index 6fbb4bd753c..16ee9b503fd 100644 --- a/tests/common/test_telemetry.py +++ b/tests/common/test_telemetry.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/common/test_tensor.py b/tests/common/test_tensor.py index b45f4d725e0..14650c86dd0 100644 --- a/tests/common/test_tensor.py +++ b/tests/common/test_tensor.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -11,6 +11,7 @@ import numpy as np +from nncf.experimental.tensor import TensorDataType from tests.shared.test_templates.template_test_nncf_tensor import TemplateTestNNCFTensorOperators @@ -18,3 +19,11 @@ class TestNPNNCFTensorOperators(TemplateTestNNCFTensorOperators): @staticmethod def to_tensor(x): return np.array(x) + + @staticmethod + def cast_to(x: np.ndarray, dtype: TensorDataType) -> np.ndarray: + if dtype is TensorDataType.float32: + return x.astype(np.float32) + if dtype is TensorDataType.float16: + return x.astype(np.float16) + raise NotImplementedError diff --git a/tests/cross_fw/__init__.py b/tests/cross_fw/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/cross_fw/__init__.py +++ b/tests/cross_fw/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/cross_fw/examples/conftest.py b/tests/cross_fw/examples/conftest.py index 6cac9c21a3e..fa40af4862f 100644 --- a/tests/cross_fw/examples/conftest.py +++ b/tests/cross_fw/examples/conftest.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/cross_fw/examples/example_scope.json b/tests/cross_fw/examples/example_scope.json index 35222ac6452..b87ac562786 100644 --- a/tests/cross_fw/examples/example_scope.json +++ b/tests/cross_fw/examples/example_scope.json @@ -55,18 +55,18 @@ "cpu": "Intel(R) Core(TM) i9-10980XE CPU @ 3.00GHz", "accuracy_metrics": { "fp32_top1": 0.9680365324020386, - "int8_top1": 0.9683257937431335, - "accuracy_drop": -0.0002892613410949707 + "int8_top1": 0.9636363983154297, + "accuracy_drop": 0.004400134086608887 }, "performance_metrics": { - "fp32_fps": 346.23, - "int8_fps": 833.56, - "performance_speed_up": 2.407532565057909 + "fp32_fps": 330.17, + "int8_fps": 838.9, + "performance_speed_up": 2.5408123088106125 }, "model_size_metrics": { "fp32_model_size": 21.33917808532715, - "int8_model_size": 5.725968360900879, - "model_compression_rate": 3.726736988461077 + "int8_model_size": 5.742419242858887, + "model_compression_rate": 3.7160606327836403 } }, "post_training_quantization_openvino_yolo8_quantize_with_accuracy_control": { @@ -75,8 +75,8 @@ "cpu": "Intel(R) Core(TM) i9-10980XE CPU @ 3.00GHz", "accuracy_metrics": { "fp32_mAP": 0.44964819083938, - "int8_mAP": 0.4428344895334926, - "accuracy_drop": 0.006813701305887432 + "int8_mAP": 0.4507030984145927, + "accuracy_drop": -0.0010549075752127046 }, "performance_metrics": { "fp32_fps": 134.48, @@ -84,6 +84,30 @@ "performance_speed_up": 2.0417906008328375 } }, + "post_training_quantization_onnx_yolo8_quantize_with_accuracy_control": { + "backend": "onnx", + "requirements": "examples/post_training_quantization/onnx/yolov8_quantize_with_accuracy_control/requirements.txt", + "cpu": "Intel(R) Core(TM) i9-10980XE CPU @ 3.00GHz", + "accuracy_metrics": { + "onnx_fp32_box_mAP": 0.44964819166629366, + "onnx_fp32_mask_mAP": 0.36509415038708726, + "onnx_int8_box_mAP": 0.45071569411799683, + "onnx_int8_mask_mAP": 0.36805097120147484, + "onnx_drop_box_mAP": -0.0010675024517031728, + "onnx_drop_mask_mAP": -0.002956820814387584, + "ov_fp32_box_mAP": 0.44964819083938, + "ov_fp32_mask_mAP": 0.36509979887281135, + "ov_int8_box_mAP": 0.4462460126164857, + "ov_int8_mask_mAP": 0.3631361727753598, + "ov_drop_box_mAP": 0.003402178222894292, + "ov_drop_mask_mAP": 0.001963626097451543 + }, + "performance_metrics": { + "ov_fp32_fps": 139.32, + "ov_int8_fps": 269.16, + "performance_speed_up": 1.9319552110249787 + } + }, "post_training_quantization_tensorflow_mobilenet_v2": { "backend": "tensorflow", "requirements": "examples/post_training_quantization/tensorflow/mobilenet_v2/requirements.txt", @@ -143,5 +167,23 @@ "int8_model_size": 35.55147361755371, "model_compression_rate": 3.8631822183889652 } + }, + "llm_compression": { + "backend": "openvino", + "requirements": "examples/llm_compression/openvino/tiny_llama/requirements.txt", + "cpu": "Intel(R) Core(TM) i9-10980XE CPU @ 3.00GHz", + "accuracy_metrics": { + "word_count": 71 + } + }, + "llm_tune_params": { + "backend": "openvino", + "requirements": "examples/llm_compression/openvino/tiny_llama_find_hyperparams/requirements.txt", + "cpu": "Intel(R) Core(TM) i9-10980XE CPU @ 3.00GHz", + "accuracy_metrics": { + "awq": true, + "ratio": 0.9, + "group_size": 128 + } } -} +} \ No newline at end of file diff --git a/tests/cross_fw/examples/run_example.py b/tests/cross_fw/examples/run_example.py index afecbce45ea..4b60876e1da 100644 --- a/tests/cross_fw/examples/run_example.py +++ b/tests/cross_fw/examples/run_example.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -87,6 +87,34 @@ def post_training_quantization_openvino_yolo8_quantize_with_accuracy_control() - return format_results(results) +def post_training_quantization_onnx_yolo8_quantize_with_accuracy_control() -> Dict[str, float]: + from examples.post_training_quantization.onnx.yolov8_quantize_with_accuracy_control.main import ( + run_example as yolo8_main, + ) + + onnx_fp32_box_mAP, onnx_fp32_mask_mAP, onnx_int8_box_mAP, onnx_int8_mask_mAP = yolo8_main() + + import examples.post_training_quantization.onnx.yolov8_quantize_with_accuracy_control.deploy as yolov8_deploy + + return { + "onnx_fp32_box_mAP": onnx_fp32_box_mAP, + "onnx_fp32_mask_mAP": onnx_fp32_mask_mAP, + "onnx_int8_box_mAP": onnx_int8_box_mAP, + "onnx_int8_mask_mAP": onnx_int8_mask_mAP, + "onnx_drop_box_mAP": onnx_fp32_box_mAP - onnx_int8_box_mAP, + "onnx_drop_mask_mAP": onnx_fp32_mask_mAP - onnx_int8_mask_mAP, + "ov_fp32_box_mAP": yolov8_deploy.fp32_stats["metrics/mAP50-95(B)"], + "ov_fp32_mask_mAP": yolov8_deploy.fp32_stats["metrics/mAP50-95(M)"], + "ov_int8_box_mAP": yolov8_deploy.int8_stats["metrics/mAP50-95(B)"], + "ov_int8_mask_mAP": yolov8_deploy.int8_stats["metrics/mAP50-95(M)"], + "ov_drop_box_mAP": yolov8_deploy.box_metric_drop, + "ov_drop_mask_mAP": yolov8_deploy.mask_metric_drop, + "ov_fp32_fps": yolov8_deploy.fp32_fps, + "ov_int8_fps": yolov8_deploy.int8_fps, + "performance_speed_up": yolov8_deploy.int8_fps / yolov8_deploy.fp32_fps, + } + + def post_training_quantization_openvino_anomaly_stfpm_quantize_with_accuracy_control() -> Dict[str, float]: sys.path.append( str( @@ -130,6 +158,22 @@ def post_training_quantization_torch_ssd300_vgg16() -> Dict[str, float]: } +def llm_compression() -> Dict[str, float]: + from examples.llm_compression.openvino.tiny_llama.main import main as llm_compression_main + + result = llm_compression_main() + + return {"word_count": len(result.split())} + + +def llm_tune_params() -> Dict[str, float]: + from examples.llm_compression.openvino.tiny_llama_find_hyperparams.main import main as llm_tune_params_main + + awq, ratio, group_size = llm_tune_params_main() + + return {"awq": bool(awq), "ratio": ratio, "group_size": group_size} + + def main(argv): parser = ArgumentParser() parser.add_argument("--name", help="Example name", required=True) diff --git a/tests/cross_fw/examples/test_examples.py b/tests/cross_fw/examples/test_examples.py index e9708d7daaa..a7f82aee702 100644 --- a/tests/cross_fw/examples/test_examples.py +++ b/tests/cross_fw/examples/test_examples.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -52,6 +52,9 @@ def test_examples( is_check_performance: bool, ov_version_override: str, ): + if example_name == "llm_tune_params": + pytest.xfail("ticket 133681") + backend = example_params["backend"] skip_if_backend_not_selected(backend, backends_list) venv_path = create_venv_with_nncf(tmp_path, "pip_e_local", "venv", set([backend])) diff --git a/tests/cross_fw/install/__init__.py b/tests/cross_fw/install/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/cross_fw/install/__init__.py +++ b/tests/cross_fw/install/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/cross_fw/install/common.py b/tests/cross_fw/install/common.py index 4c6a438bcfa..2d3ca760420 100644 --- a/tests/cross_fw/install/common.py +++ b/tests/cross_fw/install/common.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -26,7 +26,7 @@ def excluded_module(name, excluded_modules_patterns): def onerror(name, excluded_modules_patterns): if not excluded_module(name, excluded_modules_patterns): - raise RuntimeError(f"Could not import {name}") + raise nncf.InternalError(f"Could not import {name}") def load_nncf_modules(excluded_modules_patterns, verbose=False): diff --git a/tests/cross_fw/install/conftest.py b/tests/cross_fw/install/conftest.py index 179c2fff682..083fdb8f0f2 100644 --- a/tests/cross_fw/install/conftest.py +++ b/tests/cross_fw/install/conftest.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/cross_fw/install/install_checks_onnx.py b/tests/cross_fw/install/install_checks_onnx.py index 5bc3ada2236..2f246325a40 100644 --- a/tests/cross_fw/install/install_checks_onnx.py +++ b/tests/cross_fw/install/install_checks_onnx.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/cross_fw/install/install_checks_openvino.py b/tests/cross_fw/install/install_checks_openvino.py index ca5e99ff30f..cd6b0bb9f5a 100644 --- a/tests/cross_fw/install/install_checks_openvino.py +++ b/tests/cross_fw/install/install_checks_openvino.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/cross_fw/install/install_checks_tf.py b/tests/cross_fw/install/install_checks_tf.py index fe283bb8d67..a8a293915db 100644 --- a/tests/cross_fw/install/install_checks_tf.py +++ b/tests/cross_fw/install/install_checks_tf.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/cross_fw/install/install_checks_torch.py b/tests/cross_fw/install/install_checks_torch.py index dde6ae290da..83595085c10 100644 --- a/tests/cross_fw/install/install_checks_torch.py +++ b/tests/cross_fw/install/install_checks_torch.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -13,8 +13,12 @@ import torch +import nncf + if len(sys.argv) != 3: - raise RuntimeError("Must be run with an execution type as argument (either 'cpu' or 'gpu') and package type") + raise nncf.ValidationError( + "Must be run with an execution type as argument (either 'cpu' or 'gpu') and package type" + ) execution_type = sys.argv[1] package_type = sys.argv[2] @@ -59,4 +63,4 @@ ) output_tensor = BinarizedFunctionsCUDA.get("WeightBinarize_forward")(output_tensor, True) else: - raise RuntimeError(f"Invalid execution type {execution_type} (expected 'cpu' or 'gpu')!") + raise nncf.ValidationError(f"Invalid execution type {execution_type} (expected 'cpu' or 'gpu')!") diff --git a/tests/cross_fw/install/test_install.py b/tests/cross_fw/install/test_install.py index bcd015c55ba..071ed1d31f3 100644 --- a/tests/cross_fw/install/test_install.py +++ b/tests/cross_fw/install/test_install.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -17,6 +17,7 @@ import pytest +import nncf from nncf.common.utils.os import is_linux from nncf.common.utils.os import is_windows from tests.cross_fw.install.conftest import TESTED_BACKENDS @@ -30,7 +31,7 @@ def run_install_checks(venv_path: Path, tmp_path: Path, package_type: str, backend: str, install_type: str): if install_type.lower() not in ["cpu", "gpu"]: - raise RuntimeError("Unknown installation mode - must be either 'cpu' or 'gpu'") + raise nncf.ValidationError("Unknown installation mode - must be either 'cpu' or 'gpu'") python_executable_with_venv = get_python_executable_with_venv(venv_path) pip_with_venv = get_pip_executable_with_venv(venv_path) diff --git a/tests/cross_fw/sdl/__init__.py b/tests/cross_fw/sdl/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/cross_fw/sdl/__init__.py +++ b/tests/cross_fw/sdl/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/cross_fw/sdl/fuzz/quantize_api.py b/tests/cross_fw/sdl/fuzz/quantize_api.py new file mode 100644 index 00000000000..d48890eda43 --- /dev/null +++ b/tests/cross_fw/sdl/fuzz/quantize_api.py @@ -0,0 +1,116 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import atheris + +from tests.shared.paths import TEST_ROOT + +with atheris.instrument_imports(): + import nncf + import sys + +from random import randint +from random import seed + +import openvino.runtime as ov +import torch +from torchvision import datasets +from torchvision import transforms + +EXPECTED_LEN = 10 + + +def create_mutated_name_list(name_list, data): + if not name_list: + return [] + seed(1) + new_name_list = [] + + for i in range(10): + idx = randint(0, len(name_list) - 1) + new_name_list.append(name_list[idx]) + + mutated_id = data[0] % 10 + + new_name_list[mutated_id] = f"_{new_name_list[mutated_id]}_" + + return new_name_list + + +def execute(model, in_dataset_path, f_mode, f_preset, f_target_device, f_subset_size, f_fast_bias_correction, data): + if len(data) >= EXPECTED_LEN: + dataset = datasets.ImageFolder(in_dataset_path, transforms.Compose([transforms.ToTensor()])) + dataset_loader = torch.utils.data.DataLoader(dataset, batch_size=1) + + # Step 1: Initialize transformation function + def transform_fn(data_item): + images, _ = data_item + return images + + # Step 2: Initialize NNCF Dataset + calibration_dataset = nncf.Dataset(dataset_loader, transform_fn) + # Step 3: Run the quantization pipeline + + ops = model.get_ordered_ops() + name_list = [op.get_name() for op in ops] + + mutated_name_list = create_mutated_name_list(name_list, data) + _ = nncf.IgnoredScope(names=[mutated_name_list]) + + nncf.quantize( + model=model, + calibration_dataset=calibration_dataset, + mode=f_mode, + preset=f_preset, + target_device=f_target_device, + subset_size=f_subset_size, + fast_bias_correction=f_fast_bias_correction, + model_type=nncf.ModelType.TRANSFORMER, + ignored_scope=None, + advanced_parameters=None, + ) + + +def TestOneInput(data): + in_model = ov.Model([], []) + in_dataset_path = TEST_ROOT / "torch" / "data" / "mock_datasets" / "camvid" + + mode_list = [nncf.QuantizationMode.FP8_E4M3, nncf.QuantizationMode.FP8_E5M2] + preset_list = [nncf.QuantizationPreset.PERFORMANCE, nncf.QuantizationPreset.MIXED] + target_device_list = [ + nncf.TargetDevice.ANY, + nncf.TargetDevice.CPU, + nncf.TargetDevice.GPU, + nncf.TargetDevice.NPU, + nncf.TargetDevice.CPU_SPR, + ] + subset_size_list = [10, 11, 17] + fast_bias_correction_list = [True, False] + + for f_mode in mode_list: + for f_preset in preset_list: + for f_target_device in target_device_list: + for f_subset_size in subset_size_list: + for f_fast_bias_correction in fast_bias_correction_list: + execute( + in_model, + in_dataset_path, + f_mode, + f_preset, + f_target_device, + f_subset_size, + f_fast_bias_correction, + data, + ) + + +atheris.Setup(sys.argv, TestOneInput) +atheris.Fuzz() diff --git a/tests/cross_fw/sdl/fuzz/requirements.txt b/tests/cross_fw/sdl/fuzz/requirements.txt new file mode 100644 index 00000000000..1a0aa05cda0 --- /dev/null +++ b/tests/cross_fw/sdl/fuzz/requirements.txt @@ -0,0 +1,2 @@ +atheris +torchvision==0.16 \ No newline at end of file diff --git a/tests/cross_fw/sdl/symlinks.py b/tests/cross_fw/sdl/symlinks.py index 982ac0b8bf7..97d3e603647 100644 --- a/tests/cross_fw/sdl/symlinks.py +++ b/tests/cross_fw/sdl/symlinks.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -14,6 +14,7 @@ import pytest +import nncf from nncf.common.utils.os import is_windows @@ -24,7 +25,7 @@ def check_symlinks_are_not_followed(tmp_path: Path, file_opening_entrypoint: Cal real_file = tmp_path / "real_file" real_file.touch(exist_ok=True) symlink_path.symlink_to(real_file) - with pytest.raises(RuntimeError, match="is a symbolic link, aborting"): + with pytest.raises(nncf.ValidationError, match="is a symbolic link, aborting"): file_opening_entrypoint(str(symlink_path)) diff --git a/tests/onnx/__init__.py b/tests/onnx/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/onnx/__init__.py +++ b/tests/onnx/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/onnx/benchmarking/__init__.py b/tests/onnx/benchmarking/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/onnx/benchmarking/__init__.py +++ b/tests/onnx/benchmarking/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/onnx/benchmarking/accuracy_checker.py b/tests/onnx/benchmarking/ac_wrapper.py similarity index 70% rename from tests/onnx/benchmarking/accuracy_checker.py rename to tests/onnx/benchmarking/ac_wrapper.py index 9f47bc729a2..160fa0a287d 100644 --- a/tests/onnx/benchmarking/accuracy_checker.py +++ b/tests/onnx/benchmarking/ac_wrapper.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -9,9 +9,14 @@ # See the License for the specific language governing permissions and # limitations under the License. -from openvino.tools.accuracy_checker.annotation_converters.ms_coco import COCO_TO_VOC -from openvino.tools.accuracy_checker.annotation_converters.ms_coco import MSCocoSegmentationConverter -from openvino.tools.accuracy_checker.main import main +try: + from accuracy_checker.annotation_converters.ms_coco import COCO_TO_VOC + from accuracy_checker.annotation_converters.ms_coco import MSCocoSegmentationConverter + from accuracy_checker.main import main +except ImportError: + from openvino.tools.accuracy_checker.annotation_converters.ms_coco import COCO_TO_VOC + from openvino.tools.accuracy_checker.annotation_converters.ms_coco import MSCocoSegmentationConverter + from openvino.tools.accuracy_checker.main import main class MSCocoSegmentationToVOCConverter(MSCocoSegmentationConverter): diff --git a/tests/onnx/benchmarking/requirements.txt b/tests/onnx/benchmarking/requirements.txt index de5ee28b0b9..1dda5065283 100644 --- a/tests/onnx/benchmarking/requirements.txt +++ b/tests/onnx/benchmarking/requirements.txt @@ -1,2 +1,2 @@ pycocotools -openvino-dev==2023.3 +openvino-dev==2024.0 diff --git a/tests/onnx/benchmarking/run_ptq.py b/tests/onnx/benchmarking/run_ptq.py index 91c2f7d4728..af9ad887da4 100644 --- a/tests/onnx/benchmarking/run_ptq.py +++ b/tests/onnx/benchmarking/run_ptq.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -15,9 +15,15 @@ import numpy as np import onnx -from openvino.tools.accuracy_checker.argparser import build_arguments_parser -from openvino.tools.accuracy_checker.config import ConfigReader -from openvino.tools.accuracy_checker.evaluators import ModelEvaluator + +try: + from accuracy_checker.argparser import build_arguments_parser + from accuracy_checker.config import ConfigReader + from accuracy_checker.evaluators import ModelEvaluator +except ImportError: + from openvino.tools.accuracy_checker.argparser import build_arguments_parser + from openvino.tools.accuracy_checker.config import ConfigReader + from openvino.tools.accuracy_checker.evaluators import ModelEvaluator import nncf from nncf.scopes import IgnoredScope diff --git a/tests/onnx/common.py b/tests/onnx/common.py index 910490d0159..e2a478a92c3 100644 --- a/tests/onnx/common.py +++ b/tests/onnx/common.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/onnx/conftest.py b/tests/onnx/conftest.py index 161cdce53a9..f7f53424344 100644 --- a/tests/onnx/conftest.py +++ b/tests/onnx/conftest.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/onnx/data/reference_graphs/original_nncf_graph/densenet121.dot b/tests/onnx/data/reference_graphs/original_nncf_graph/densenet121.dot index 985bcb7b73a..7063088f6fc 100644 --- a/tests/onnx/data/reference_graphs/original_nncf_graph/densenet121.dot +++ b/tests/onnx/data/reference_graphs/original_nncf_graph/densenet121.dot @@ -42,343 +42,337 @@ strict digraph { "40 /features/transition1/norm/BatchNormalization" [id=40, type=BatchNormalization]; "41 /features/transition1/relu/Relu" [id=41, type=Relu]; "42 /features/transition1/conv/Conv" [id=42, type=Conv]; -"43 /features/transition1/pool/Constant" [id=43, type=Constant]; -"44 /features/transition1/pool/Pad" [id=44, type=Pad]; -"45 /features/transition1/pool/AveragePool" [id=45, type=AveragePool]; -"46 /features/denseblock2/denselayer1/Concat" [id=46, type=Concat]; -"47 /features/denseblock2/denselayer1/norm1/BatchNormalization" [id=47, type=BatchNormalization]; -"48 /features/denseblock2/denselayer1/relu1/Relu" [id=48, type=Relu]; -"49 /features/denseblock2/denselayer1/conv1/Conv" [id=49, type=Conv]; -"50 /features/denseblock2/denselayer1/relu2/Relu" [id=50, type=Relu]; -"51 /features/denseblock2/denselayer1/conv2/Conv" [id=51, type=Conv]; -"52 /features/denseblock2/denselayer2/Concat" [id=52, type=Concat]; -"53 /features/denseblock2/denselayer2/norm1/BatchNormalization" [id=53, type=BatchNormalization]; -"54 /features/denseblock2/denselayer2/relu1/Relu" [id=54, type=Relu]; -"55 /features/denseblock2/denselayer2/conv1/Conv" [id=55, type=Conv]; -"56 /features/denseblock2/denselayer2/relu2/Relu" [id=56, type=Relu]; -"57 /features/denseblock2/denselayer2/conv2/Conv" [id=57, type=Conv]; -"58 /features/denseblock2/denselayer3/Concat" [id=58, type=Concat]; -"59 /features/denseblock2/denselayer3/norm1/BatchNormalization" [id=59, type=BatchNormalization]; -"60 /features/denseblock2/denselayer3/relu1/Relu" [id=60, type=Relu]; -"61 /features/denseblock2/denselayer3/conv1/Conv" [id=61, type=Conv]; -"62 /features/denseblock2/denselayer3/relu2/Relu" [id=62, type=Relu]; -"63 /features/denseblock2/denselayer3/conv2/Conv" [id=63, type=Conv]; -"64 /features/denseblock2/denselayer4/Concat" [id=64, type=Concat]; -"65 /features/denseblock2/denselayer4/norm1/BatchNormalization" [id=65, type=BatchNormalization]; -"66 /features/denseblock2/denselayer4/relu1/Relu" [id=66, type=Relu]; 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type=BatchNormalization]; -"145 /features/denseblock3/denselayer4/relu1/Relu" [id=145, type=Relu]; -"146 /features/denseblock3/denselayer4/conv1/Conv" [id=146, type=Conv]; -"147 /features/denseblock3/denselayer4/relu2/Relu" [id=147, type=Relu]; -"148 /features/denseblock3/denselayer4/conv2/Conv" [id=148, type=Conv]; -"149 /features/denseblock3/denselayer5/Concat" [id=149, type=Concat]; -"150 /features/denseblock3/denselayer5/norm1/BatchNormalization" [id=150, type=BatchNormalization]; -"151 /features/denseblock3/denselayer5/relu1/Relu" [id=151, type=Relu]; -"152 /features/denseblock3/denselayer5/conv1/Conv" [id=152, type=Conv]; -"153 /features/denseblock3/denselayer5/relu2/Relu" [id=153, type=Relu]; -"154 /features/denseblock3/denselayer5/conv2/Conv" [id=154, type=Conv]; -"155 /features/denseblock3/denselayer6/Concat" [id=155, type=Concat]; -"156 /features/denseblock3/denselayer6/norm1/BatchNormalization" [id=156, type=BatchNormalization]; -"157 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[id=195, type=Relu]; -"196 /features/denseblock3/denselayer12/conv2/Conv" [id=196, type=Conv]; -"197 /features/denseblock3/denselayer13/Concat" [id=197, type=Concat]; -"198 /features/denseblock3/denselayer13/norm1/BatchNormalization" [id=198, type=BatchNormalization]; -"199 /features/denseblock3/denselayer13/relu1/Relu" [id=199, type=Relu]; -"200 /features/denseblock3/denselayer13/conv1/Conv" [id=200, type=Conv]; -"201 /features/denseblock3/denselayer13/relu2/Relu" [id=201, type=Relu]; -"202 /features/denseblock3/denselayer13/conv2/Conv" [id=202, type=Conv]; -"203 /features/denseblock3/denselayer14/Concat" [id=203, type=Concat]; -"204 /features/denseblock3/denselayer14/norm1/BatchNormalization" [id=204, type=BatchNormalization]; -"205 /features/denseblock3/denselayer14/relu1/Relu" [id=205, type=Relu]; -"206 /features/denseblock3/denselayer14/conv1/Conv" [id=206, type=Conv]; -"207 /features/denseblock3/denselayer14/relu2/Relu" [id=207, type=Relu]; -"208 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type=Conv]; -"221 /features/denseblock3/denselayer17/Concat" [id=221, type=Concat]; -"222 /features/denseblock3/denselayer17/norm1/BatchNormalization" [id=222, type=BatchNormalization]; -"223 /features/denseblock3/denselayer17/relu1/Relu" [id=223, type=Relu]; -"224 /features/denseblock3/denselayer17/conv1/Conv" [id=224, type=Conv]; -"225 /features/denseblock3/denselayer17/relu2/Relu" [id=225, type=Relu]; -"226 /features/denseblock3/denselayer17/conv2/Conv" [id=226, type=Conv]; -"227 /features/denseblock3/denselayer18/Concat" [id=227, type=Concat]; -"228 /features/denseblock3/denselayer18/norm1/BatchNormalization" [id=228, type=BatchNormalization]; -"229 /features/denseblock3/denselayer18/relu1/Relu" [id=229, type=Relu]; -"230 /features/denseblock3/denselayer18/conv1/Conv" [id=230, type=Conv]; -"231 /features/denseblock3/denselayer18/relu2/Relu" [id=231, type=Relu]; -"232 /features/denseblock3/denselayer18/conv2/Conv" [id=232, type=Conv]; -"233 /features/denseblock3/denselayer19/Concat" 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/features/denseblock4/denselayer8/conv1/Conv" [label="[1, 736, 7, 7]", style=solid]; +"315 /features/denseblock4/denselayer8/conv1/Conv" -> "316 /features/denseblock4/denselayer8/relu2/Relu" [label="[1, 128, 7, 7]", style=solid]; +"316 /features/denseblock4/denselayer8/relu2/Relu" -> "317 /features/denseblock4/denselayer8/conv2/Conv" [label="[1, 128, 7, 7]", style=solid]; +"317 /features/denseblock4/denselayer8/conv2/Conv" -> "318 /features/denseblock4/denselayer9/Concat" [label="[1, 32, 7, 7]", style=solid]; +"317 /features/denseblock4/denselayer8/conv2/Conv" -> "324 /features/denseblock4/denselayer10/Concat" [label="[1, 32, 7, 7]", style=solid]; +"317 /features/denseblock4/denselayer8/conv2/Conv" -> "330 /features/denseblock4/denselayer11/Concat" [label="[1, 32, 7, 7]", style=solid]; +"317 /features/denseblock4/denselayer8/conv2/Conv" -> "336 /features/denseblock4/denselayer12/Concat" [label="[1, 32, 7, 7]", style=solid]; +"317 /features/denseblock4/denselayer8/conv2/Conv" -> "342 /features/denseblock4/denselayer13/Concat" [label="[1, 32, 7, 7]", style=solid]; +"317 /features/denseblock4/denselayer8/conv2/Conv" -> "348 /features/denseblock4/denselayer14/Concat" [label="[1, 32, 7, 7]", style=solid]; +"317 /features/denseblock4/denselayer8/conv2/Conv" -> "354 /features/denseblock4/denselayer15/Concat" [label="[1, 32, 7, 7]", style=solid]; +"317 /features/denseblock4/denselayer8/conv2/Conv" -> "360 /features/denseblock4/denselayer16/Concat" [label="[1, 32, 7, 7]", style=solid]; +"317 /features/denseblock4/denselayer8/conv2/Conv" -> "366 /features/denseblock4/Concat" [label="[1, 32, 7, 7]", style=solid]; +"318 /features/denseblock4/denselayer9/Concat" -> "319 /features/denseblock4/denselayer9/norm1/BatchNormalization" [label="[1, 768, 7, 7]", style=solid]; +"319 /features/denseblock4/denselayer9/norm1/BatchNormalization" -> "320 /features/denseblock4/denselayer9/relu1/Relu" [label="[1, 768, 7, 7]", style=solid]; +"320 /features/denseblock4/denselayer9/relu1/Relu" -> "321 /features/denseblock4/denselayer9/conv1/Conv" [label="[1, 768, 7, 7]", style=solid]; +"321 /features/denseblock4/denselayer9/conv1/Conv" -> "322 /features/denseblock4/denselayer9/relu2/Relu" [label="[1, 128, 7, 7]", style=solid]; +"322 /features/denseblock4/denselayer9/relu2/Relu" -> "323 /features/denseblock4/denselayer9/conv2/Conv" [label="[1, 128, 7, 7]", style=solid]; +"323 /features/denseblock4/denselayer9/conv2/Conv" -> "324 /features/denseblock4/denselayer10/Concat" [label="[1, 32, 7, 7]", style=solid]; +"323 /features/denseblock4/denselayer9/conv2/Conv" -> "330 /features/denseblock4/denselayer11/Concat" [label="[1, 32, 7, 7]", style=solid]; +"323 /features/denseblock4/denselayer9/conv2/Conv" -> "336 /features/denseblock4/denselayer12/Concat" [label="[1, 32, 7, 7]", style=solid]; +"323 /features/denseblock4/denselayer9/conv2/Conv" -> "342 /features/denseblock4/denselayer13/Concat" [label="[1, 32, 7, 7]", style=solid]; +"323 /features/denseblock4/denselayer9/conv2/Conv" -> "348 /features/denseblock4/denselayer14/Concat" [label="[1, 32, 7, 7]", style=solid]; +"323 /features/denseblock4/denselayer9/conv2/Conv" -> "354 /features/denseblock4/denselayer15/Concat" [label="[1, 32, 7, 7]", style=solid]; +"323 /features/denseblock4/denselayer9/conv2/Conv" -> "360 /features/denseblock4/denselayer16/Concat" [label="[1, 32, 7, 7]", style=solid]; +"323 /features/denseblock4/denselayer9/conv2/Conv" -> "366 /features/denseblock4/Concat" [label="[1, 32, 7, 7]", style=solid]; +"324 /features/denseblock4/denselayer10/Concat" -> "325 /features/denseblock4/denselayer10/norm1/BatchNormalization" [label="[1, 800, 7, 7]", style=solid]; +"325 /features/denseblock4/denselayer10/norm1/BatchNormalization" -> "326 /features/denseblock4/denselayer10/relu1/Relu" [label="[1, 800, 7, 7]", style=solid]; +"326 /features/denseblock4/denselayer10/relu1/Relu" -> "327 /features/denseblock4/denselayer10/conv1/Conv" [label="[1, 800, 7, 7]", style=solid]; +"327 /features/denseblock4/denselayer10/conv1/Conv" -> "328 /features/denseblock4/denselayer10/relu2/Relu" [label="[1, 128, 7, 7]", style=solid]; +"328 /features/denseblock4/denselayer10/relu2/Relu" -> "329 /features/denseblock4/denselayer10/conv2/Conv" [label="[1, 128, 7, 7]", style=solid]; +"329 /features/denseblock4/denselayer10/conv2/Conv" -> "330 /features/denseblock4/denselayer11/Concat" [label="[1, 32, 7, 7]", style=solid]; +"329 /features/denseblock4/denselayer10/conv2/Conv" -> "336 /features/denseblock4/denselayer12/Concat" [label="[1, 32, 7, 7]", style=solid]; +"329 /features/denseblock4/denselayer10/conv2/Conv" -> "342 /features/denseblock4/denselayer13/Concat" [label="[1, 32, 7, 7]", style=solid]; +"329 /features/denseblock4/denselayer10/conv2/Conv" -> "348 /features/denseblock4/denselayer14/Concat" [label="[1, 32, 7, 7]", style=solid]; +"329 /features/denseblock4/denselayer10/conv2/Conv" -> "354 /features/denseblock4/denselayer15/Concat" [label="[1, 32, 7, 7]", style=solid]; +"329 /features/denseblock4/denselayer10/conv2/Conv" -> "360 /features/denseblock4/denselayer16/Concat" [label="[1, 32, 7, 7]", style=solid]; +"329 /features/denseblock4/denselayer10/conv2/Conv" -> "366 /features/denseblock4/Concat" [label="[1, 32, 7, 7]", style=solid]; +"330 /features/denseblock4/denselayer11/Concat" -> "331 /features/denseblock4/denselayer11/norm1/BatchNormalization" [label="[1, 832, 7, 7]", style=solid]; +"331 /features/denseblock4/denselayer11/norm1/BatchNormalization" -> "332 /features/denseblock4/denselayer11/relu1/Relu" [label="[1, 832, 7, 7]", style=solid]; +"332 /features/denseblock4/denselayer11/relu1/Relu" -> "333 /features/denseblock4/denselayer11/conv1/Conv" [label="[1, 832, 7, 7]", style=solid]; +"333 /features/denseblock4/denselayer11/conv1/Conv" -> "334 /features/denseblock4/denselayer11/relu2/Relu" [label="[1, 128, 7, 7]", style=solid]; +"334 /features/denseblock4/denselayer11/relu2/Relu" -> "335 /features/denseblock4/denselayer11/conv2/Conv" [label="[1, 128, 7, 7]", style=solid]; +"335 /features/denseblock4/denselayer11/conv2/Conv" -> "336 /features/denseblock4/denselayer12/Concat" [label="[1, 32, 7, 7]", style=solid]; +"335 /features/denseblock4/denselayer11/conv2/Conv" -> "342 /features/denseblock4/denselayer13/Concat" [label="[1, 32, 7, 7]", style=solid]; +"335 /features/denseblock4/denselayer11/conv2/Conv" -> "348 /features/denseblock4/denselayer14/Concat" [label="[1, 32, 7, 7]", style=solid]; +"335 /features/denseblock4/denselayer11/conv2/Conv" -> "354 /features/denseblock4/denselayer15/Concat" [label="[1, 32, 7, 7]", style=solid]; +"335 /features/denseblock4/denselayer11/conv2/Conv" -> "360 /features/denseblock4/denselayer16/Concat" [label="[1, 32, 7, 7]", style=solid]; +"335 /features/denseblock4/denselayer11/conv2/Conv" -> "366 /features/denseblock4/Concat" [label="[1, 32, 7, 7]", style=solid]; +"336 /features/denseblock4/denselayer12/Concat" -> "337 /features/denseblock4/denselayer12/norm1/BatchNormalization" [label="[1, 864, 7, 7]", style=solid]; +"337 /features/denseblock4/denselayer12/norm1/BatchNormalization" -> "338 /features/denseblock4/denselayer12/relu1/Relu" [label="[1, 864, 7, 7]", style=solid]; +"338 /features/denseblock4/denselayer12/relu1/Relu" -> "339 /features/denseblock4/denselayer12/conv1/Conv" [label="[1, 864, 7, 7]", style=solid]; +"339 /features/denseblock4/denselayer12/conv1/Conv" -> "340 /features/denseblock4/denselayer12/relu2/Relu" [label="[1, 128, 7, 7]", style=solid]; +"340 /features/denseblock4/denselayer12/relu2/Relu" -> "341 /features/denseblock4/denselayer12/conv2/Conv" [label="[1, 128, 7, 7]", style=solid]; +"341 /features/denseblock4/denselayer12/conv2/Conv" -> "342 /features/denseblock4/denselayer13/Concat" [label="[1, 32, 7, 7]", style=solid]; +"341 /features/denseblock4/denselayer12/conv2/Conv" -> "348 /features/denseblock4/denselayer14/Concat" [label="[1, 32, 7, 7]", style=solid]; +"341 /features/denseblock4/denselayer12/conv2/Conv" -> "354 /features/denseblock4/denselayer15/Concat" [label="[1, 32, 7, 7]", style=solid]; +"341 /features/denseblock4/denselayer12/conv2/Conv" -> "360 /features/denseblock4/denselayer16/Concat" [label="[1, 32, 7, 7]", style=solid]; +"341 /features/denseblock4/denselayer12/conv2/Conv" -> "366 /features/denseblock4/Concat" [label="[1, 32, 7, 7]", style=solid]; +"342 /features/denseblock4/denselayer13/Concat" -> "343 /features/denseblock4/denselayer13/norm1/BatchNormalization" [label="[1, 896, 7, 7]", style=solid]; +"343 /features/denseblock4/denselayer13/norm1/BatchNormalization" -> "344 /features/denseblock4/denselayer13/relu1/Relu" [label="[1, 896, 7, 7]", style=solid]; +"344 /features/denseblock4/denselayer13/relu1/Relu" -> "345 /features/denseblock4/denselayer13/conv1/Conv" [label="[1, 896, 7, 7]", style=solid]; +"345 /features/denseblock4/denselayer13/conv1/Conv" -> "346 /features/denseblock4/denselayer13/relu2/Relu" [label="[1, 128, 7, 7]", style=solid]; +"346 /features/denseblock4/denselayer13/relu2/Relu" -> "347 /features/denseblock4/denselayer13/conv2/Conv" [label="[1, 128, 7, 7]", style=solid]; +"347 /features/denseblock4/denselayer13/conv2/Conv" -> "348 /features/denseblock4/denselayer14/Concat" [label="[1, 32, 7, 7]", style=solid]; +"347 /features/denseblock4/denselayer13/conv2/Conv" -> "354 /features/denseblock4/denselayer15/Concat" [label="[1, 32, 7, 7]", style=solid]; +"347 /features/denseblock4/denselayer13/conv2/Conv" -> "360 /features/denseblock4/denselayer16/Concat" [label="[1, 32, 7, 7]", style=solid]; +"347 /features/denseblock4/denselayer13/conv2/Conv" -> "366 /features/denseblock4/Concat" [label="[1, 32, 7, 7]", style=solid]; +"348 /features/denseblock4/denselayer14/Concat" -> "349 /features/denseblock4/denselayer14/norm1/BatchNormalization" [label="[1, 928, 7, 7]", style=solid]; +"349 /features/denseblock4/denselayer14/norm1/BatchNormalization" -> "350 /features/denseblock4/denselayer14/relu1/Relu" [label="[1, 928, 7, 7]", style=solid]; +"350 /features/denseblock4/denselayer14/relu1/Relu" -> "351 /features/denseblock4/denselayer14/conv1/Conv" [label="[1, 928, 7, 7]", style=solid]; +"351 /features/denseblock4/denselayer14/conv1/Conv" -> "352 /features/denseblock4/denselayer14/relu2/Relu" [label="[1, 128, 7, 7]", style=solid]; +"352 /features/denseblock4/denselayer14/relu2/Relu" -> "353 /features/denseblock4/denselayer14/conv2/Conv" [label="[1, 128, 7, 7]", style=solid]; +"353 /features/denseblock4/denselayer14/conv2/Conv" -> "354 /features/denseblock4/denselayer15/Concat" [label="[1, 32, 7, 7]", style=solid]; +"353 /features/denseblock4/denselayer14/conv2/Conv" -> "360 /features/denseblock4/denselayer16/Concat" [label="[1, 32, 7, 7]", style=solid]; +"353 /features/denseblock4/denselayer14/conv2/Conv" -> "366 /features/denseblock4/Concat" [label="[1, 32, 7, 7]", style=solid]; +"354 /features/denseblock4/denselayer15/Concat" -> "355 /features/denseblock4/denselayer15/norm1/BatchNormalization" [label="[1, 960, 7, 7]", style=solid]; +"355 /features/denseblock4/denselayer15/norm1/BatchNormalization" -> "356 /features/denseblock4/denselayer15/relu1/Relu" [label="[1, 960, 7, 7]", style=solid]; +"356 /features/denseblock4/denselayer15/relu1/Relu" -> "357 /features/denseblock4/denselayer15/conv1/Conv" [label="[1, 960, 7, 7]", style=solid]; +"357 /features/denseblock4/denselayer15/conv1/Conv" -> "358 /features/denseblock4/denselayer15/relu2/Relu" [label="[1, 128, 7, 7]", style=solid]; +"358 /features/denseblock4/denselayer15/relu2/Relu" -> "359 /features/denseblock4/denselayer15/conv2/Conv" [label="[1, 128, 7, 7]", style=solid]; +"359 /features/denseblock4/denselayer15/conv2/Conv" -> "360 /features/denseblock4/denselayer16/Concat" [label="[1, 32, 7, 7]", style=solid]; +"359 /features/denseblock4/denselayer15/conv2/Conv" -> "366 /features/denseblock4/Concat" [label="[1, 32, 7, 7]", style=solid]; +"360 /features/denseblock4/denselayer16/Concat" -> "361 /features/denseblock4/denselayer16/norm1/BatchNormalization" [label="[1, 992, 7, 7]", style=solid]; +"361 /features/denseblock4/denselayer16/norm1/BatchNormalization" -> "362 /features/denseblock4/denselayer16/relu1/Relu" [label="[1, 992, 7, 7]", style=solid]; +"362 /features/denseblock4/denselayer16/relu1/Relu" -> "363 /features/denseblock4/denselayer16/conv1/Conv" [label="[1, 992, 7, 7]", style=solid]; +"363 /features/denseblock4/denselayer16/conv1/Conv" -> "364 /features/denseblock4/denselayer16/relu2/Relu" [label="[1, 128, 7, 7]", style=solid]; +"364 /features/denseblock4/denselayer16/relu2/Relu" -> "365 /features/denseblock4/denselayer16/conv2/Conv" [label="[1, 128, 7, 7]", style=solid]; +"365 /features/denseblock4/denselayer16/conv2/Conv" -> "366 /features/denseblock4/Concat" [label="[1, 32, 7, 7]", style=solid]; +"366 /features/denseblock4/Concat" -> "367 /features/norm5/BatchNormalization" [label="[1, 1024, 7, 7]", style=solid]; +"367 /features/norm5/BatchNormalization" -> "368 /Relu" [label="[1, 1024, 7, 7]", style=solid]; +"368 /Relu" -> "369 /GlobalAveragePool" [label="[1, 1024, 7, 7]", style=solid]; +"369 /GlobalAveragePool" -> "370 /Flatten" [label="[1, 1024, 1, 1]", style=solid]; +"370 /Flatten" -> "371 /classifier/Gemm" [label="[1, 1024]", style=solid]; +"371 /classifier/Gemm" -> "373 nncf_model_output_0" [label="[1, 1000]", style=solid]; +"372 nncf_model_input_0" -> "0 /features/conv0/Conv" [label="[1, 3, 224, 224]", style=solid]; } diff --git a/tests/onnx/data/reference_graphs/original_nncf_graph/inception_v3.dot b/tests/onnx/data/reference_graphs/original_nncf_graph/inception_v3.dot index de8ccf7a67a..cae0857e1db 100644 --- a/tests/onnx/data/reference_graphs/original_nncf_graph/inception_v3.dot +++ b/tests/onnx/data/reference_graphs/original_nncf_graph/inception_v3.dot @@ -48,217 +48,199 @@ strict digraph { "46 /Mixed_5b/branch3x3dbl_2/Relu" [id=46, type=Relu]; "47 /Mixed_5b/branch3x3dbl_3/conv/Conv" [id=47, type=Conv]; "48 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/Mixed_6b/branch7x7dbl_1/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; -"100 /Mixed_6a/Concat" -> "120 /Mixed_6b/Pad" [label="[1, 768, 12, 12]", style=solid]; -"101 /Mixed_6b/branch1x1/conv/Conv" -> "102 /Mixed_6b/branch1x1/Relu" [label="[1, 192, 12, 12]", style=solid]; -"102 /Mixed_6b/branch1x1/Relu" -> "124 /Mixed_6b/Concat" [label="[1, 192, 12, 12]", style=solid]; -"103 /Mixed_6b/branch7x7_1/conv/Conv" -> "104 /Mixed_6b/branch7x7_1/Relu" [label="[1, 128, 12, 12]", style=solid]; -"104 /Mixed_6b/branch7x7_1/Relu" -> "105 /Mixed_6b/branch7x7_2/conv/Conv" [label="[1, 128, 12, 12]", style=solid]; -"105 /Mixed_6b/branch7x7_2/conv/Conv" -> "106 /Mixed_6b/branch7x7_2/Relu" [label="[1, 128, 12, 12]", style=solid]; -"106 /Mixed_6b/branch7x7_2/Relu" -> "107 /Mixed_6b/branch7x7_3/conv/Conv" [label="[1, 128, 12, 12]", style=solid]; -"107 /Mixed_6b/branch7x7_3/conv/Conv" -> "108 /Mixed_6b/branch7x7_3/Relu" [label="[1, 192, 12, 12]", style=solid]; -"108 /Mixed_6b/branch7x7_3/Relu" -> "124 /Mixed_6b/Concat" [label="[1, 192, 12, 12]", style=solid]; -"109 /Mixed_6b/branch7x7dbl_1/conv/Conv" -> "110 /Mixed_6b/branch7x7dbl_1/Relu" [label="[1, 128, 12, 12]", style=solid]; -"110 /Mixed_6b/branch7x7dbl_1/Relu" -> "111 /Mixed_6b/branch7x7dbl_2/conv/Conv" [label="[1, 128, 12, 12]", style=solid]; -"111 /Mixed_6b/branch7x7dbl_2/conv/Conv" -> "112 /Mixed_6b/branch7x7dbl_2/Relu" [label="[1, 128, 12, 12]", style=solid]; -"112 /Mixed_6b/branch7x7dbl_2/Relu" -> "113 /Mixed_6b/branch7x7dbl_3/conv/Conv" [label="[1, 128, 12, 12]", style=solid]; -"113 /Mixed_6b/branch7x7dbl_3/conv/Conv" -> "114 /Mixed_6b/branch7x7dbl_3/Relu" [label="[1, 128, 12, 12]", style=solid]; -"114 /Mixed_6b/branch7x7dbl_3/Relu" -> "115 /Mixed_6b/branch7x7dbl_4/conv/Conv" [label="[1, 128, 12, 12]", style=solid]; -"115 /Mixed_6b/branch7x7dbl_4/conv/Conv" -> "116 /Mixed_6b/branch7x7dbl_4/Relu" [label="[1, 128, 12, 12]", style=solid]; -"116 /Mixed_6b/branch7x7dbl_4/Relu" -> "117 /Mixed_6b/branch7x7dbl_5/conv/Conv" [label="[1, 128, 12, 12]", style=solid]; -"117 /Mixed_6b/branch7x7dbl_5/conv/Conv" -> "118 /Mixed_6b/branch7x7dbl_5/Relu" [label="[1, 192, 12, 12]", style=solid]; -"118 /Mixed_6b/branch7x7dbl_5/Relu" -> "124 /Mixed_6b/Concat" [label="[1, 192, 12, 12]", style=solid]; -"119 /Mixed_6b/Constant" -> "120 /Mixed_6b/Pad" [label="[8]", style=dashed]; -"120 /Mixed_6b/Pad" -> "121 /Mixed_6b/AveragePool" [label="[1, 768, 14, 14]", style=solid]; -"121 /Mixed_6b/AveragePool" -> "122 /Mixed_6b/branch_pool/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; -"122 /Mixed_6b/branch_pool/conv/Conv" -> "123 /Mixed_6b/branch_pool/Relu" [label="[1, 192, 12, 12]", style=solid]; -"123 /Mixed_6b/branch_pool/Relu" -> "124 /Mixed_6b/Concat" [label="[1, 192, 12, 12]", style=solid]; -"124 /Mixed_6b/Concat" -> "125 /Mixed_6c/branch1x1/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; -"124 /Mixed_6b/Concat" -> "127 /Mixed_6c/branch7x7_1/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; -"124 /Mixed_6b/Concat" -> "133 /Mixed_6c/branch7x7dbl_1/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; -"124 /Mixed_6b/Concat" -> "144 /Mixed_6c/Pad" [label="[1, 768, 12, 12]", style=solid]; -"125 /Mixed_6c/branch1x1/conv/Conv" -> "126 /Mixed_6c/branch1x1/Relu" [label="[1, 192, 12, 12]", style=solid]; -"126 /Mixed_6c/branch1x1/Relu" -> "148 /Mixed_6c/Concat" [label="[1, 192, 12, 12]", style=solid]; -"127 /Mixed_6c/branch7x7_1/conv/Conv" -> "128 /Mixed_6c/branch7x7_1/Relu" [label="[1, 160, 12, 12]", style=solid]; -"128 /Mixed_6c/branch7x7_1/Relu" -> "129 /Mixed_6c/branch7x7_2/conv/Conv" [label="[1, 160, 12, 12]", style=solid]; -"129 /Mixed_6c/branch7x7_2/conv/Conv" -> "130 /Mixed_6c/branch7x7_2/Relu" [label="[1, 160, 12, 12]", style=solid]; -"130 /Mixed_6c/branch7x7_2/Relu" -> "131 /Mixed_6c/branch7x7_3/conv/Conv" [label="[1, 160, 12, 12]", style=solid]; -"131 /Mixed_6c/branch7x7_3/conv/Conv" -> "132 /Mixed_6c/branch7x7_3/Relu" [label="[1, 192, 12, 12]", style=solid]; -"132 /Mixed_6c/branch7x7_3/Relu" -> "148 /Mixed_6c/Concat" [label="[1, 192, 12, 12]", style=solid]; -"133 /Mixed_6c/branch7x7dbl_1/conv/Conv" -> "134 /Mixed_6c/branch7x7dbl_1/Relu" [label="[1, 160, 12, 12]", style=solid]; -"134 /Mixed_6c/branch7x7dbl_1/Relu" -> "135 /Mixed_6c/branch7x7dbl_2/conv/Conv" [label="[1, 160, 12, 12]", style=solid]; -"135 /Mixed_6c/branch7x7dbl_2/conv/Conv" -> "136 /Mixed_6c/branch7x7dbl_2/Relu" [label="[1, 160, 12, 12]", style=solid]; -"136 /Mixed_6c/branch7x7dbl_2/Relu" -> "137 /Mixed_6c/branch7x7dbl_3/conv/Conv" [label="[1, 160, 12, 12]", style=solid]; -"137 /Mixed_6c/branch7x7dbl_3/conv/Conv" -> "138 /Mixed_6c/branch7x7dbl_3/Relu" [label="[1, 160, 12, 12]", style=solid]; -"138 /Mixed_6c/branch7x7dbl_3/Relu" -> "139 /Mixed_6c/branch7x7dbl_4/conv/Conv" [label="[1, 160, 12, 12]", style=solid]; -"139 /Mixed_6c/branch7x7dbl_4/conv/Conv" -> "140 /Mixed_6c/branch7x7dbl_4/Relu" [label="[1, 160, 12, 12]", style=solid]; -"140 /Mixed_6c/branch7x7dbl_4/Relu" -> "141 /Mixed_6c/branch7x7dbl_5/conv/Conv" [label="[1, 160, 12, 12]", style=solid]; -"141 /Mixed_6c/branch7x7dbl_5/conv/Conv" -> "142 /Mixed_6c/branch7x7dbl_5/Relu" [label="[1, 192, 12, 12]", style=solid]; -"142 /Mixed_6c/branch7x7dbl_5/Relu" -> "148 /Mixed_6c/Concat" [label="[1, 192, 12, 12]", style=solid]; -"143 /Mixed_6c/Constant" -> "144 /Mixed_6c/Pad" [label="[8]", style=dashed]; -"144 /Mixed_6c/Pad" -> "145 /Mixed_6c/AveragePool" [label="[1, 768, 14, 14]", style=solid]; -"145 /Mixed_6c/AveragePool" -> "146 /Mixed_6c/branch_pool/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; -"146 /Mixed_6c/branch_pool/conv/Conv" -> "147 /Mixed_6c/branch_pool/Relu" [label="[1, 192, 12, 12]", style=solid]; -"147 /Mixed_6c/branch_pool/Relu" -> "148 /Mixed_6c/Concat" [label="[1, 192, 12, 12]", style=solid]; -"148 /Mixed_6c/Concat" -> "149 /Mixed_6d/branch1x1/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; -"148 /Mixed_6c/Concat" -> "151 /Mixed_6d/branch7x7_1/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; -"148 /Mixed_6c/Concat" -> "157 /Mixed_6d/branch7x7dbl_1/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; -"148 /Mixed_6c/Concat" -> "168 /Mixed_6d/Pad" [label="[1, 768, 12, 12]", style=solid]; -"149 /Mixed_6d/branch1x1/conv/Conv" -> "150 /Mixed_6d/branch1x1/Relu" [label="[1, 192, 12, 12]", style=solid]; -"150 /Mixed_6d/branch1x1/Relu" -> "172 /Mixed_6d/Concat" [label="[1, 192, 12, 12]", style=solid]; -"151 /Mixed_6d/branch7x7_1/conv/Conv" -> "152 /Mixed_6d/branch7x7_1/Relu" [label="[1, 160, 12, 12]", style=solid]; -"152 /Mixed_6d/branch7x7_1/Relu" -> "153 /Mixed_6d/branch7x7_2/conv/Conv" [label="[1, 160, 12, 12]", style=solid]; -"153 /Mixed_6d/branch7x7_2/conv/Conv" -> "154 /Mixed_6d/branch7x7_2/Relu" [label="[1, 160, 12, 12]", style=solid]; -"154 /Mixed_6d/branch7x7_2/Relu" -> "155 /Mixed_6d/branch7x7_3/conv/Conv" [label="[1, 160, 12, 12]", style=solid]; -"155 /Mixed_6d/branch7x7_3/conv/Conv" -> "156 /Mixed_6d/branch7x7_3/Relu" [label="[1, 192, 12, 12]", style=solid]; -"156 /Mixed_6d/branch7x7_3/Relu" -> "172 /Mixed_6d/Concat" [label="[1, 192, 12, 12]", style=solid]; -"157 /Mixed_6d/branch7x7dbl_1/conv/Conv" -> "158 /Mixed_6d/branch7x7dbl_1/Relu" [label="[1, 160, 12, 12]", style=solid]; -"158 /Mixed_6d/branch7x7dbl_1/Relu" -> "159 /Mixed_6d/branch7x7dbl_2/conv/Conv" [label="[1, 160, 12, 12]", style=solid]; -"159 /Mixed_6d/branch7x7dbl_2/conv/Conv" -> "160 /Mixed_6d/branch7x7dbl_2/Relu" [label="[1, 160, 12, 12]", style=solid]; -"160 /Mixed_6d/branch7x7dbl_2/Relu" -> "161 /Mixed_6d/branch7x7dbl_3/conv/Conv" [label="[1, 160, 12, 12]", style=solid]; -"161 /Mixed_6d/branch7x7dbl_3/conv/Conv" -> "162 /Mixed_6d/branch7x7dbl_3/Relu" [label="[1, 160, 12, 12]", style=solid]; -"162 /Mixed_6d/branch7x7dbl_3/Relu" -> "163 /Mixed_6d/branch7x7dbl_4/conv/Conv" [label="[1, 160, 12, 12]", style=solid]; -"163 /Mixed_6d/branch7x7dbl_4/conv/Conv" -> "164 /Mixed_6d/branch7x7dbl_4/Relu" [label="[1, 160, 12, 12]", style=solid]; -"164 /Mixed_6d/branch7x7dbl_4/Relu" -> "165 /Mixed_6d/branch7x7dbl_5/conv/Conv" [label="[1, 160, 12, 12]", style=solid]; -"165 /Mixed_6d/branch7x7dbl_5/conv/Conv" -> "166 /Mixed_6d/branch7x7dbl_5/Relu" [label="[1, 192, 12, 12]", style=solid]; -"166 /Mixed_6d/branch7x7dbl_5/Relu" -> "172 /Mixed_6d/Concat" [label="[1, 192, 12, 12]", style=solid]; -"167 /Mixed_6d/Constant" -> "168 /Mixed_6d/Pad" [label="[8]", style=dashed]; -"168 /Mixed_6d/Pad" -> "169 /Mixed_6d/AveragePool" [label="[1, 768, 14, 14]", style=solid]; -"169 /Mixed_6d/AveragePool" -> "170 /Mixed_6d/branch_pool/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; -"170 /Mixed_6d/branch_pool/conv/Conv" -> "171 /Mixed_6d/branch_pool/Relu" [label="[1, 192, 12, 12]", style=solid]; -"171 /Mixed_6d/branch_pool/Relu" -> "172 /Mixed_6d/Concat" [label="[1, 192, 12, 12]", style=solid]; -"172 /Mixed_6d/Concat" -> "173 /Mixed_6e/branch1x1/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; -"172 /Mixed_6d/Concat" -> "175 /Mixed_6e/branch7x7_1/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; -"172 /Mixed_6d/Concat" -> "181 /Mixed_6e/branch7x7dbl_1/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; -"172 /Mixed_6d/Concat" -> "192 /Mixed_6e/Pad" [label="[1, 768, 12, 12]", style=solid]; -"173 /Mixed_6e/branch1x1/conv/Conv" -> "174 /Mixed_6e/branch1x1/Relu" [label="[1, 192, 12, 12]", style=solid]; -"174 /Mixed_6e/branch1x1/Relu" -> "196 /Mixed_6e/Concat" [label="[1, 192, 12, 12]", style=solid]; -"175 /Mixed_6e/branch7x7_1/conv/Conv" -> "176 /Mixed_6e/branch7x7_1/Relu" [label="[1, 192, 12, 12]", style=solid]; -"176 /Mixed_6e/branch7x7_1/Relu" -> "177 /Mixed_6e/branch7x7_2/conv/Conv" [label="[1, 192, 12, 12]", style=solid]; -"177 /Mixed_6e/branch7x7_2/conv/Conv" -> "178 /Mixed_6e/branch7x7_2/Relu" [label="[1, 192, 12, 12]", style=solid]; -"178 /Mixed_6e/branch7x7_2/Relu" -> "179 /Mixed_6e/branch7x7_3/conv/Conv" [label="[1, 192, 12, 12]", style=solid]; -"179 /Mixed_6e/branch7x7_3/conv/Conv" -> "180 /Mixed_6e/branch7x7_3/Relu" [label="[1, 192, 12, 12]", style=solid]; -"180 /Mixed_6e/branch7x7_3/Relu" -> "196 /Mixed_6e/Concat" [label="[1, 192, 12, 12]", style=solid]; -"181 /Mixed_6e/branch7x7dbl_1/conv/Conv" -> "182 /Mixed_6e/branch7x7dbl_1/Relu" [label="[1, 192, 12, 12]", style=solid]; -"182 /Mixed_6e/branch7x7dbl_1/Relu" -> "183 /Mixed_6e/branch7x7dbl_2/conv/Conv" [label="[1, 192, 12, 12]", style=solid]; -"183 /Mixed_6e/branch7x7dbl_2/conv/Conv" -> "184 /Mixed_6e/branch7x7dbl_2/Relu" [label="[1, 192, 12, 12]", style=solid]; -"184 /Mixed_6e/branch7x7dbl_2/Relu" -> "185 /Mixed_6e/branch7x7dbl_3/conv/Conv" [label="[1, 192, 12, 12]", style=solid]; -"185 /Mixed_6e/branch7x7dbl_3/conv/Conv" -> "186 /Mixed_6e/branch7x7dbl_3/Relu" [label="[1, 192, 12, 12]", style=solid]; -"186 /Mixed_6e/branch7x7dbl_3/Relu" -> "187 /Mixed_6e/branch7x7dbl_4/conv/Conv" [label="[1, 192, 12, 12]", style=solid]; -"187 /Mixed_6e/branch7x7dbl_4/conv/Conv" -> "188 /Mixed_6e/branch7x7dbl_4/Relu" [label="[1, 192, 12, 12]", style=solid]; -"188 /Mixed_6e/branch7x7dbl_4/Relu" -> "189 /Mixed_6e/branch7x7dbl_5/conv/Conv" [label="[1, 192, 12, 12]", style=solid]; -"189 /Mixed_6e/branch7x7dbl_5/conv/Conv" -> "190 /Mixed_6e/branch7x7dbl_5/Relu" [label="[1, 192, 12, 12]", style=solid]; -"190 /Mixed_6e/branch7x7dbl_5/Relu" -> "196 /Mixed_6e/Concat" [label="[1, 192, 12, 12]", style=solid]; -"191 /Mixed_6e/Constant" -> "192 /Mixed_6e/Pad" [label="[8]", style=dashed]; -"192 /Mixed_6e/Pad" -> "193 /Mixed_6e/AveragePool" [label="[1, 768, 14, 14]", style=solid]; -"193 /Mixed_6e/AveragePool" -> "194 /Mixed_6e/branch_pool/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; -"194 /Mixed_6e/branch_pool/conv/Conv" -> "195 /Mixed_6e/branch_pool/Relu" [label="[1, 192, 12, 12]", style=solid]; -"195 /Mixed_6e/branch_pool/Relu" -> "196 /Mixed_6e/Concat" [label="[1, 192, 12, 12]", style=solid]; -"196 /Mixed_6e/Concat" -> "197 /Mixed_7a/branch3x3_1/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; -"196 /Mixed_6e/Concat" -> "201 /Mixed_7a/branch7x7x3_1/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; -"196 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/Mixed_7b/branch3x3_1/Relu" -> "201 /Mixed_7b/branch3x3_2a/conv/Conv" [label="[1, 384, 5, 5]", style=solid]; +"200 /Mixed_7b/branch3x3_1/Relu" -> "203 /Mixed_7b/branch3x3_2b/conv/Conv" [label="[1, 384, 5, 5]", style=solid]; +"201 /Mixed_7b/branch3x3_2a/conv/Conv" -> "202 /Mixed_7b/branch3x3_2a/Relu" [label="[1, 384, 5, 5]", style=solid]; +"202 /Mixed_7b/branch3x3_2a/Relu" -> "216 /Mixed_7b/Concat" [label="[1, 384, 5, 5]", style=solid]; +"203 /Mixed_7b/branch3x3_2b/conv/Conv" -> "204 /Mixed_7b/branch3x3_2b/Relu" [label="[1, 384, 5, 5]", style=solid]; +"204 /Mixed_7b/branch3x3_2b/Relu" -> "216 /Mixed_7b/Concat" [label="[1, 384, 5, 5]", style=solid]; +"205 /Mixed_7b/branch3x3dbl_1/conv/Conv" -> "206 /Mixed_7b/branch3x3dbl_1/Relu" [label="[1, 448, 5, 5]", style=solid]; +"206 /Mixed_7b/branch3x3dbl_1/Relu" -> "207 /Mixed_7b/branch3x3dbl_2/conv/Conv" [label="[1, 448, 5, 5]", style=solid]; +"207 /Mixed_7b/branch3x3dbl_2/conv/Conv" -> "208 /Mixed_7b/branch3x3dbl_2/Relu" [label="[1, 384, 5, 5]", style=solid]; +"208 /Mixed_7b/branch3x3dbl_2/Relu" -> "209 /Mixed_7b/branch3x3dbl_3a/conv/Conv" [label="[1, 384, 5, 5]", style=solid]; +"208 /Mixed_7b/branch3x3dbl_2/Relu" -> "211 /Mixed_7b/branch3x3dbl_3b/conv/Conv" [label="[1, 384, 5, 5]", style=solid]; +"209 /Mixed_7b/branch3x3dbl_3a/conv/Conv" -> "210 /Mixed_7b/branch3x3dbl_3a/Relu" [label="[1, 384, 5, 5]", style=solid]; +"210 /Mixed_7b/branch3x3dbl_3a/Relu" -> "216 /Mixed_7b/Concat" [label="[1, 384, 5, 5]", style=solid]; +"211 /Mixed_7b/branch3x3dbl_3b/conv/Conv" -> "212 /Mixed_7b/branch3x3dbl_3b/Relu" [label="[1, 384, 5, 5]", style=solid]; +"212 /Mixed_7b/branch3x3dbl_3b/Relu" -> "216 /Mixed_7b/Concat" [label="[1, 384, 5, 5]", style=solid]; +"213 /Mixed_7b/AveragePool" -> "214 /Mixed_7b/branch_pool/conv/Conv" [label="[1, 1280, 5, 5]", style=solid]; +"214 /Mixed_7b/branch_pool/conv/Conv" -> "215 /Mixed_7b/branch_pool/Relu" [label="[1, 192, 5, 5]", style=solid]; +"215 /Mixed_7b/branch_pool/Relu" -> "216 /Mixed_7b/Concat" [label="[1, 192, 5, 5]", style=solid]; +"216 /Mixed_7b/Concat" -> "217 /Mixed_7c/branch1x1/conv/Conv" [label="[1, 2048, 5, 5]", style=solid]; +"216 /Mixed_7b/Concat" -> "219 /Mixed_7c/branch3x3_1/conv/Conv" [label="[1, 2048, 5, 5]", style=solid]; +"216 /Mixed_7b/Concat" -> "225 /Mixed_7c/branch3x3dbl_1/conv/Conv" [label="[1, 2048, 5, 5]", style=solid]; +"216 /Mixed_7b/Concat" -> "233 /Mixed_7c/AveragePool" [label="[1, 2048, 5, 5]", style=solid]; +"217 /Mixed_7c/branch1x1/conv/Conv" -> "218 /Mixed_7c/branch1x1/Relu" [label="[1, 320, 5, 5]", style=solid]; +"218 /Mixed_7c/branch1x1/Relu" -> "236 /Mixed_7c/Concat" [label="[1, 320, 5, 5]", style=solid]; +"219 /Mixed_7c/branch3x3_1/conv/Conv" -> "220 /Mixed_7c/branch3x3_1/Relu" [label="[1, 384, 5, 5]", style=solid]; +"220 /Mixed_7c/branch3x3_1/Relu" -> "221 /Mixed_7c/branch3x3_2a/conv/Conv" [label="[1, 384, 5, 5]", style=solid]; +"220 /Mixed_7c/branch3x3_1/Relu" -> "223 /Mixed_7c/branch3x3_2b/conv/Conv" [label="[1, 384, 5, 5]", style=solid]; +"221 /Mixed_7c/branch3x3_2a/conv/Conv" -> "222 /Mixed_7c/branch3x3_2a/Relu" [label="[1, 384, 5, 5]", style=solid]; +"222 /Mixed_7c/branch3x3_2a/Relu" -> "236 /Mixed_7c/Concat" [label="[1, 384, 5, 5]", style=solid]; +"223 /Mixed_7c/branch3x3_2b/conv/Conv" -> "224 /Mixed_7c/branch3x3_2b/Relu" [label="[1, 384, 5, 5]", style=solid]; +"224 /Mixed_7c/branch3x3_2b/Relu" -> "236 /Mixed_7c/Concat" [label="[1, 384, 5, 5]", style=solid]; +"225 /Mixed_7c/branch3x3dbl_1/conv/Conv" -> "226 /Mixed_7c/branch3x3dbl_1/Relu" [label="[1, 448, 5, 5]", style=solid]; +"226 /Mixed_7c/branch3x3dbl_1/Relu" -> "227 /Mixed_7c/branch3x3dbl_2/conv/Conv" [label="[1, 448, 5, 5]", style=solid]; +"227 /Mixed_7c/branch3x3dbl_2/conv/Conv" -> "228 /Mixed_7c/branch3x3dbl_2/Relu" [label="[1, 384, 5, 5]", style=solid]; +"228 /Mixed_7c/branch3x3dbl_2/Relu" -> "229 /Mixed_7c/branch3x3dbl_3a/conv/Conv" [label="[1, 384, 5, 5]", style=solid]; +"228 /Mixed_7c/branch3x3dbl_2/Relu" -> "231 /Mixed_7c/branch3x3dbl_3b/conv/Conv" [label="[1, 384, 5, 5]", style=solid]; +"229 /Mixed_7c/branch3x3dbl_3a/conv/Conv" -> "230 /Mixed_7c/branch3x3dbl_3a/Relu" [label="[1, 384, 5, 5]", style=solid]; +"230 /Mixed_7c/branch3x3dbl_3a/Relu" -> "236 /Mixed_7c/Concat" [label="[1, 384, 5, 5]", style=solid]; +"231 /Mixed_7c/branch3x3dbl_3b/conv/Conv" -> "232 /Mixed_7c/branch3x3dbl_3b/Relu" [label="[1, 384, 5, 5]", style=solid]; +"232 /Mixed_7c/branch3x3dbl_3b/Relu" -> "236 /Mixed_7c/Concat" [label="[1, 384, 5, 5]", style=solid]; +"233 /Mixed_7c/AveragePool" -> "234 /Mixed_7c/branch_pool/conv/Conv" [label="[1, 2048, 5, 5]", style=solid]; +"234 /Mixed_7c/branch_pool/conv/Conv" -> "235 /Mixed_7c/branch_pool/Relu" [label="[1, 192, 5, 5]", style=solid]; +"235 /Mixed_7c/branch_pool/Relu" -> "236 /Mixed_7c/Concat" [label="[1, 192, 5, 5]", style=solid]; +"236 /Mixed_7c/Concat" -> "237 /avgpool/GlobalAveragePool" [label="[1, 2048, 5, 5]", style=solid]; +"237 /avgpool/GlobalAveragePool" -> "238 /Flatten" [label="[1, 2048, 1, 1]", style=solid]; +"238 /Flatten" -> "239 /fc/Gemm" [label="[1, 2048]", style=solid]; +"239 /fc/Gemm" -> "241 nncf_model_output_0" [label="[1, 1000]", style=solid]; +"240 nncf_model_input_0" -> "2 /Gather" [label="[1, 3, 224, 224]", style=solid]; +"240 nncf_model_input_0" -> "9 /Gather_1" [label="[1, 3, 224, 224]", style=solid]; +"240 nncf_model_input_0" -> "17 /Gather_2" [label="[1, 3, 224, 224]", style=solid]; } diff --git a/tests/onnx/data/reference_graphs/original_nncf_graph/shufflenet_v2_x1_0.dot b/tests/onnx/data/reference_graphs/original_nncf_graph/shufflenet_v2_x1_0.dot index f3ad17eb496..3ffac2720ec 100644 --- a/tests/onnx/data/reference_graphs/original_nncf_graph/shufflenet_v2_x1_0.dot +++ b/tests/onnx/data/reference_graphs/original_nncf_graph/shufflenet_v2_x1_0.dot @@ -409,16 +409,16 @@ strict digraph { "25 /stage2/stage2.1/Constant_4" -> "26 /stage2/stage2.1/Mul" [label="[1]", style=dashed]; "26 /stage2/stage2.1/Mul" -> "27 /stage2/stage2.1/Slice" [label="[1]", style=dashed]; "26 /stage2/stage2.1/Mul" -> "30 /stage2/stage2.1/Slice_1" [label="[1]", style=dashed]; -"27 /stage2/stage2.1/Slice" -> "36 /stage2/stage2.1/Concat" [label="[1]", style=solid]; +"27 /stage2/stage2.1/Slice" -> "36 /stage2/stage2.1/Concat" [label="[]", style=solid]; "28 /stage2/stage2.1/Constant_5" -> "29 /stage2/stage2.1/Mul_1" [label="[1]", style=dashed]; "29 /stage2/stage2.1/Mul_1" -> "30 /stage2/stage2.1/Slice_1" [label="[1]", style=dashed]; -"30 /stage2/stage2.1/Slice_1" -> "31 /stage2/stage2.1/branch2/branch2.0/Conv" [label="[1]", style=solid]; -"31 /stage2/stage2.1/branch2/branch2.0/Conv" -> "32 /stage2/stage2.1/branch2/branch2.2/Relu" [label="[1]", style=solid]; -"32 /stage2/stage2.1/branch2/branch2.2/Relu" -> "33 /stage2/stage2.1/branch2/branch2.3/Conv" [label="[1]", style=solid]; -"33 /stage2/stage2.1/branch2/branch2.3/Conv" -> "34 /stage2/stage2.1/branch2/branch2.5/Conv" [label="[1]", style=solid]; -"34 /stage2/stage2.1/branch2/branch2.5/Conv" -> "35 /stage2/stage2.1/branch2/branch2.7/Relu" [label="[1]", style=solid]; -"35 /stage2/stage2.1/branch2/branch2.7/Relu" -> "36 /stage2/stage2.1/Concat" [label="[1]", style=solid]; -"36 /stage2/stage2.1/Concat" -> "38 /stage2/stage2.1/Reshape" [label="[1]", style=solid]; +"30 /stage2/stage2.1/Slice_1" -> "31 /stage2/stage2.1/branch2/branch2.0/Conv" [label="[]", style=solid]; +"31 /stage2/stage2.1/branch2/branch2.0/Conv" -> "32 /stage2/stage2.1/branch2/branch2.2/Relu" [label="[]", style=solid]; +"32 /stage2/stage2.1/branch2/branch2.2/Relu" -> "33 /stage2/stage2.1/branch2/branch2.3/Conv" [label="[]", style=solid]; +"33 /stage2/stage2.1/branch2/branch2.3/Conv" -> "34 /stage2/stage2.1/branch2/branch2.5/Conv" [label="[]", style=solid]; +"34 /stage2/stage2.1/branch2/branch2.5/Conv" -> "35 /stage2/stage2.1/branch2/branch2.7/Relu" [label="[]", style=solid]; +"35 /stage2/stage2.1/branch2/branch2.7/Relu" -> "36 /stage2/stage2.1/Concat" [label="[]", style=solid]; +"36 /stage2/stage2.1/Concat" -> "38 /stage2/stage2.1/Reshape" [label="[]", style=solid]; "37 /stage2/stage2.1/Constant_6" -> "38 /stage2/stage2.1/Reshape" [label="[5]", style=dashed]; "38 /stage2/stage2.1/Reshape" -> "39 /stage2/stage2.1/Transpose" [label="[1, 2, 58, 28, 28]", style=solid]; "39 /stage2/stage2.1/Transpose" -> "41 /stage2/stage2.1/Reshape_1" [label="[1, 58, 2, 28, 28]", style=solid]; @@ -440,16 +440,16 @@ strict digraph { "50 /stage2/stage2.2/Constant_4" -> "51 /stage2/stage2.2/Mul" [label="[1]", style=dashed]; "51 /stage2/stage2.2/Mul" -> "52 /stage2/stage2.2/Slice" [label="[1]", style=dashed]; "51 /stage2/stage2.2/Mul" -> "55 /stage2/stage2.2/Slice_1" [label="[1]", style=dashed]; -"52 /stage2/stage2.2/Slice" -> "61 /stage2/stage2.2/Concat" [label="[1]", style=solid]; +"52 /stage2/stage2.2/Slice" -> "61 /stage2/stage2.2/Concat" [label="[]", style=solid]; "53 /stage2/stage2.2/Constant_5" -> "54 /stage2/stage2.2/Mul_1" [label="[1]", style=dashed]; "54 /stage2/stage2.2/Mul_1" -> "55 /stage2/stage2.2/Slice_1" [label="[1]", style=dashed]; -"55 /stage2/stage2.2/Slice_1" -> "56 /stage2/stage2.2/branch2/branch2.0/Conv" [label="[1]", style=solid]; -"56 /stage2/stage2.2/branch2/branch2.0/Conv" -> "57 /stage2/stage2.2/branch2/branch2.2/Relu" [label="[1]", style=solid]; -"57 /stage2/stage2.2/branch2/branch2.2/Relu" -> "58 /stage2/stage2.2/branch2/branch2.3/Conv" [label="[1]", style=solid]; -"58 /stage2/stage2.2/branch2/branch2.3/Conv" -> "59 /stage2/stage2.2/branch2/branch2.5/Conv" [label="[1]", style=solid]; -"59 /stage2/stage2.2/branch2/branch2.5/Conv" -> "60 /stage2/stage2.2/branch2/branch2.7/Relu" [label="[1]", style=solid]; -"60 /stage2/stage2.2/branch2/branch2.7/Relu" -> "61 /stage2/stage2.2/Concat" [label="[1]", style=solid]; -"61 /stage2/stage2.2/Concat" -> "63 /stage2/stage2.2/Reshape" [label="[1]", style=solid]; +"55 /stage2/stage2.2/Slice_1" -> "56 /stage2/stage2.2/branch2/branch2.0/Conv" [label="[]", style=solid]; +"56 /stage2/stage2.2/branch2/branch2.0/Conv" -> "57 /stage2/stage2.2/branch2/branch2.2/Relu" [label="[]", style=solid]; +"57 /stage2/stage2.2/branch2/branch2.2/Relu" -> "58 /stage2/stage2.2/branch2/branch2.3/Conv" [label="[]", style=solid]; +"58 /stage2/stage2.2/branch2/branch2.3/Conv" -> "59 /stage2/stage2.2/branch2/branch2.5/Conv" [label="[]", style=solid]; +"59 /stage2/stage2.2/branch2/branch2.5/Conv" -> "60 /stage2/stage2.2/branch2/branch2.7/Relu" [label="[]", style=solid]; +"60 /stage2/stage2.2/branch2/branch2.7/Relu" -> "61 /stage2/stage2.2/Concat" [label="[]", style=solid]; +"61 /stage2/stage2.2/Concat" -> "63 /stage2/stage2.2/Reshape" [label="[]", style=solid]; "62 /stage2/stage2.2/Constant_6" -> "63 /stage2/stage2.2/Reshape" [label="[5]", style=dashed]; "63 /stage2/stage2.2/Reshape" -> "64 /stage2/stage2.2/Transpose" [label="[1, 2, 58, 28, 28]", style=solid]; "64 /stage2/stage2.2/Transpose" -> "66 /stage2/stage2.2/Reshape_1" [label="[1, 58, 2, 28, 28]", style=solid]; @@ -471,16 +471,16 @@ strict digraph { "75 /stage2/stage2.3/Constant_4" -> "76 /stage2/stage2.3/Mul" [label="[1]", style=dashed]; "76 /stage2/stage2.3/Mul" -> "77 /stage2/stage2.3/Slice" [label="[1]", style=dashed]; "76 /stage2/stage2.3/Mul" -> "80 /stage2/stage2.3/Slice_1" [label="[1]", style=dashed]; -"77 /stage2/stage2.3/Slice" -> "86 /stage2/stage2.3/Concat" [label="[1]", style=solid]; +"77 /stage2/stage2.3/Slice" -> "86 /stage2/stage2.3/Concat" [label="[]", style=solid]; "78 /stage2/stage2.3/Constant_5" -> "79 /stage2/stage2.3/Mul_1" [label="[1]", style=dashed]; "79 /stage2/stage2.3/Mul_1" -> "80 /stage2/stage2.3/Slice_1" [label="[1]", style=dashed]; -"80 /stage2/stage2.3/Slice_1" -> "81 /stage2/stage2.3/branch2/branch2.0/Conv" [label="[1]", style=solid]; -"81 /stage2/stage2.3/branch2/branch2.0/Conv" -> "82 /stage2/stage2.3/branch2/branch2.2/Relu" [label="[1]", style=solid]; -"82 /stage2/stage2.3/branch2/branch2.2/Relu" -> "83 /stage2/stage2.3/branch2/branch2.3/Conv" [label="[1]", style=solid]; -"83 /stage2/stage2.3/branch2/branch2.3/Conv" -> "84 /stage2/stage2.3/branch2/branch2.5/Conv" [label="[1]", style=solid]; -"84 /stage2/stage2.3/branch2/branch2.5/Conv" -> "85 /stage2/stage2.3/branch2/branch2.7/Relu" [label="[1]", style=solid]; -"85 /stage2/stage2.3/branch2/branch2.7/Relu" -> "86 /stage2/stage2.3/Concat" [label="[1]", style=solid]; -"86 /stage2/stage2.3/Concat" -> "88 /stage2/stage2.3/Reshape" [label="[1]", style=solid]; +"80 /stage2/stage2.3/Slice_1" -> "81 /stage2/stage2.3/branch2/branch2.0/Conv" [label="[]", style=solid]; +"81 /stage2/stage2.3/branch2/branch2.0/Conv" -> "82 /stage2/stage2.3/branch2/branch2.2/Relu" [label="[]", style=solid]; +"82 /stage2/stage2.3/branch2/branch2.2/Relu" -> "83 /stage2/stage2.3/branch2/branch2.3/Conv" [label="[]", style=solid]; +"83 /stage2/stage2.3/branch2/branch2.3/Conv" -> "84 /stage2/stage2.3/branch2/branch2.5/Conv" [label="[]", style=solid]; +"84 /stage2/stage2.3/branch2/branch2.5/Conv" -> "85 /stage2/stage2.3/branch2/branch2.7/Relu" [label="[]", style=solid]; +"85 /stage2/stage2.3/branch2/branch2.7/Relu" -> "86 /stage2/stage2.3/Concat" [label="[]", style=solid]; +"86 /stage2/stage2.3/Concat" -> "88 /stage2/stage2.3/Reshape" [label="[]", style=solid]; "87 /stage2/stage2.3/Constant_6" -> "88 /stage2/stage2.3/Reshape" [label="[5]", style=dashed]; "88 /stage2/stage2.3/Reshape" -> "89 /stage2/stage2.3/Transpose" [label="[1, 2, 58, 28, 28]", style=solid]; "89 /stage2/stage2.3/Transpose" -> "91 /stage2/stage2.3/Reshape_1" [label="[1, 58, 2, 28, 28]", style=solid]; @@ -517,16 +517,16 @@ strict digraph { "114 /stage3/stage3.1/Constant_4" -> "115 /stage3/stage3.1/Mul" [label="[1]", style=dashed]; "115 /stage3/stage3.1/Mul" -> "116 /stage3/stage3.1/Slice" [label="[1]", style=dashed]; "115 /stage3/stage3.1/Mul" -> "119 /stage3/stage3.1/Slice_1" [label="[1]", style=dashed]; -"116 /stage3/stage3.1/Slice" -> "125 /stage3/stage3.1/Concat" [label="[1]", style=solid]; +"116 /stage3/stage3.1/Slice" -> "125 /stage3/stage3.1/Concat" [label="[]", style=solid]; "117 /stage3/stage3.1/Constant_5" -> "118 /stage3/stage3.1/Mul_1" [label="[1]", style=dashed]; "118 /stage3/stage3.1/Mul_1" -> "119 /stage3/stage3.1/Slice_1" [label="[1]", style=dashed]; -"119 /stage3/stage3.1/Slice_1" -> "120 /stage3/stage3.1/branch2/branch2.0/Conv" [label="[1]", style=solid]; -"120 /stage3/stage3.1/branch2/branch2.0/Conv" -> "121 /stage3/stage3.1/branch2/branch2.2/Relu" [label="[1]", style=solid]; -"121 /stage3/stage3.1/branch2/branch2.2/Relu" -> "122 /stage3/stage3.1/branch2/branch2.3/Conv" [label="[1]", style=solid]; -"122 /stage3/stage3.1/branch2/branch2.3/Conv" -> "123 /stage3/stage3.1/branch2/branch2.5/Conv" [label="[1]", style=solid]; -"123 /stage3/stage3.1/branch2/branch2.5/Conv" -> "124 /stage3/stage3.1/branch2/branch2.7/Relu" [label="[1]", style=solid]; -"124 /stage3/stage3.1/branch2/branch2.7/Relu" -> "125 /stage3/stage3.1/Concat" [label="[1]", style=solid]; -"125 /stage3/stage3.1/Concat" -> "127 /stage3/stage3.1/Reshape" [label="[1]", style=solid]; +"119 /stage3/stage3.1/Slice_1" -> "120 /stage3/stage3.1/branch2/branch2.0/Conv" [label="[]", style=solid]; +"120 /stage3/stage3.1/branch2/branch2.0/Conv" -> "121 /stage3/stage3.1/branch2/branch2.2/Relu" [label="[]", style=solid]; +"121 /stage3/stage3.1/branch2/branch2.2/Relu" -> "122 /stage3/stage3.1/branch2/branch2.3/Conv" [label="[]", style=solid]; +"122 /stage3/stage3.1/branch2/branch2.3/Conv" -> "123 /stage3/stage3.1/branch2/branch2.5/Conv" [label="[]", style=solid]; +"123 /stage3/stage3.1/branch2/branch2.5/Conv" -> "124 /stage3/stage3.1/branch2/branch2.7/Relu" [label="[]", style=solid]; +"124 /stage3/stage3.1/branch2/branch2.7/Relu" -> "125 /stage3/stage3.1/Concat" [label="[]", style=solid]; +"125 /stage3/stage3.1/Concat" -> "127 /stage3/stage3.1/Reshape" [label="[]", style=solid]; "126 /stage3/stage3.1/Constant_6" -> "127 /stage3/stage3.1/Reshape" [label="[5]", style=dashed]; "127 /stage3/stage3.1/Reshape" -> "128 /stage3/stage3.1/Transpose" [label="[1, 2, 116, 14, 14]", style=solid]; "128 /stage3/stage3.1/Transpose" -> "130 /stage3/stage3.1/Reshape_1" [label="[1, 116, 2, 14, 14]", style=solid]; @@ -548,16 +548,16 @@ strict digraph { "139 /stage3/stage3.2/Constant_4" -> "140 /stage3/stage3.2/Mul" [label="[1]", style=dashed]; "140 /stage3/stage3.2/Mul" -> "141 /stage3/stage3.2/Slice" [label="[1]", style=dashed]; "140 /stage3/stage3.2/Mul" -> "144 /stage3/stage3.2/Slice_1" [label="[1]", style=dashed]; -"141 /stage3/stage3.2/Slice" -> "150 /stage3/stage3.2/Concat" [label="[1]", style=solid]; +"141 /stage3/stage3.2/Slice" -> "150 /stage3/stage3.2/Concat" [label="[]", style=solid]; "142 /stage3/stage3.2/Constant_5" -> "143 /stage3/stage3.2/Mul_1" [label="[1]", style=dashed]; "143 /stage3/stage3.2/Mul_1" -> "144 /stage3/stage3.2/Slice_1" [label="[1]", style=dashed]; -"144 /stage3/stage3.2/Slice_1" -> "145 /stage3/stage3.2/branch2/branch2.0/Conv" [label="[1]", style=solid]; -"145 /stage3/stage3.2/branch2/branch2.0/Conv" -> "146 /stage3/stage3.2/branch2/branch2.2/Relu" [label="[1]", style=solid]; -"146 /stage3/stage3.2/branch2/branch2.2/Relu" -> "147 /stage3/stage3.2/branch2/branch2.3/Conv" [label="[1]", style=solid]; -"147 /stage3/stage3.2/branch2/branch2.3/Conv" -> "148 /stage3/stage3.2/branch2/branch2.5/Conv" [label="[1]", style=solid]; -"148 /stage3/stage3.2/branch2/branch2.5/Conv" -> "149 /stage3/stage3.2/branch2/branch2.7/Relu" [label="[1]", style=solid]; -"149 /stage3/stage3.2/branch2/branch2.7/Relu" -> "150 /stage3/stage3.2/Concat" [label="[1]", style=solid]; -"150 /stage3/stage3.2/Concat" -> "152 /stage3/stage3.2/Reshape" [label="[1]", style=solid]; +"144 /stage3/stage3.2/Slice_1" -> "145 /stage3/stage3.2/branch2/branch2.0/Conv" [label="[]", style=solid]; +"145 /stage3/stage3.2/branch2/branch2.0/Conv" -> "146 /stage3/stage3.2/branch2/branch2.2/Relu" [label="[]", style=solid]; +"146 /stage3/stage3.2/branch2/branch2.2/Relu" -> "147 /stage3/stage3.2/branch2/branch2.3/Conv" [label="[]", style=solid]; +"147 /stage3/stage3.2/branch2/branch2.3/Conv" -> "148 /stage3/stage3.2/branch2/branch2.5/Conv" [label="[]", style=solid]; +"148 /stage3/stage3.2/branch2/branch2.5/Conv" -> "149 /stage3/stage3.2/branch2/branch2.7/Relu" [label="[]", style=solid]; +"149 /stage3/stage3.2/branch2/branch2.7/Relu" -> "150 /stage3/stage3.2/Concat" [label="[]", style=solid]; +"150 /stage3/stage3.2/Concat" -> "152 /stage3/stage3.2/Reshape" [label="[]", style=solid]; "151 /stage3/stage3.2/Constant_6" -> "152 /stage3/stage3.2/Reshape" [label="[5]", style=dashed]; "152 /stage3/stage3.2/Reshape" -> "153 /stage3/stage3.2/Transpose" [label="[1, 2, 116, 14, 14]", style=solid]; "153 /stage3/stage3.2/Transpose" -> "155 /stage3/stage3.2/Reshape_1" [label="[1, 116, 2, 14, 14]", style=solid]; @@ -579,16 +579,16 @@ strict digraph { "164 /stage3/stage3.3/Constant_4" -> "165 /stage3/stage3.3/Mul" [label="[1]", style=dashed]; "165 /stage3/stage3.3/Mul" -> "166 /stage3/stage3.3/Slice" [label="[1]", style=dashed]; "165 /stage3/stage3.3/Mul" -> "169 /stage3/stage3.3/Slice_1" [label="[1]", style=dashed]; -"166 /stage3/stage3.3/Slice" -> "175 /stage3/stage3.3/Concat" [label="[1]", style=solid]; +"166 /stage3/stage3.3/Slice" -> "175 /stage3/stage3.3/Concat" [label="[]", style=solid]; "167 /stage3/stage3.3/Constant_5" -> "168 /stage3/stage3.3/Mul_1" [label="[1]", style=dashed]; "168 /stage3/stage3.3/Mul_1" -> "169 /stage3/stage3.3/Slice_1" [label="[1]", style=dashed]; -"169 /stage3/stage3.3/Slice_1" -> "170 /stage3/stage3.3/branch2/branch2.0/Conv" [label="[1]", style=solid]; -"170 /stage3/stage3.3/branch2/branch2.0/Conv" -> "171 /stage3/stage3.3/branch2/branch2.2/Relu" [label="[1]", style=solid]; -"171 /stage3/stage3.3/branch2/branch2.2/Relu" -> "172 /stage3/stage3.3/branch2/branch2.3/Conv" [label="[1]", style=solid]; -"172 /stage3/stage3.3/branch2/branch2.3/Conv" -> "173 /stage3/stage3.3/branch2/branch2.5/Conv" [label="[1]", style=solid]; -"173 /stage3/stage3.3/branch2/branch2.5/Conv" -> "174 /stage3/stage3.3/branch2/branch2.7/Relu" [label="[1]", style=solid]; -"174 /stage3/stage3.3/branch2/branch2.7/Relu" -> "175 /stage3/stage3.3/Concat" [label="[1]", style=solid]; -"175 /stage3/stage3.3/Concat" -> "177 /stage3/stage3.3/Reshape" [label="[1]", style=solid]; +"169 /stage3/stage3.3/Slice_1" -> "170 /stage3/stage3.3/branch2/branch2.0/Conv" [label="[]", style=solid]; +"170 /stage3/stage3.3/branch2/branch2.0/Conv" -> "171 /stage3/stage3.3/branch2/branch2.2/Relu" [label="[]", style=solid]; +"171 /stage3/stage3.3/branch2/branch2.2/Relu" -> "172 /stage3/stage3.3/branch2/branch2.3/Conv" [label="[]", style=solid]; +"172 /stage3/stage3.3/branch2/branch2.3/Conv" -> "173 /stage3/stage3.3/branch2/branch2.5/Conv" [label="[]", style=solid]; +"173 /stage3/stage3.3/branch2/branch2.5/Conv" -> "174 /stage3/stage3.3/branch2/branch2.7/Relu" [label="[]", style=solid]; +"174 /stage3/stage3.3/branch2/branch2.7/Relu" -> "175 /stage3/stage3.3/Concat" [label="[]", style=solid]; +"175 /stage3/stage3.3/Concat" -> "177 /stage3/stage3.3/Reshape" [label="[]", style=solid]; "176 /stage3/stage3.3/Constant_6" -> "177 /stage3/stage3.3/Reshape" [label="[5]", style=dashed]; "177 /stage3/stage3.3/Reshape" -> "178 /stage3/stage3.3/Transpose" [label="[1, 2, 116, 14, 14]", style=solid]; "178 /stage3/stage3.3/Transpose" -> "180 /stage3/stage3.3/Reshape_1" [label="[1, 116, 2, 14, 14]", style=solid]; @@ -610,16 +610,16 @@ strict digraph { "189 /stage3/stage3.4/Constant_4" -> "190 /stage3/stage3.4/Mul" [label="[1]", style=dashed]; "190 /stage3/stage3.4/Mul" -> "191 /stage3/stage3.4/Slice" [label="[1]", style=dashed]; "190 /stage3/stage3.4/Mul" -> "194 /stage3/stage3.4/Slice_1" [label="[1]", style=dashed]; -"191 /stage3/stage3.4/Slice" -> "200 /stage3/stage3.4/Concat" [label="[1]", style=solid]; +"191 /stage3/stage3.4/Slice" -> "200 /stage3/stage3.4/Concat" [label="[]", style=solid]; "192 /stage3/stage3.4/Constant_5" -> "193 /stage3/stage3.4/Mul_1" [label="[1]", style=dashed]; "193 /stage3/stage3.4/Mul_1" -> "194 /stage3/stage3.4/Slice_1" [label="[1]", style=dashed]; -"194 /stage3/stage3.4/Slice_1" -> "195 /stage3/stage3.4/branch2/branch2.0/Conv" [label="[1]", style=solid]; -"195 /stage3/stage3.4/branch2/branch2.0/Conv" -> "196 /stage3/stage3.4/branch2/branch2.2/Relu" [label="[1]", style=solid]; -"196 /stage3/stage3.4/branch2/branch2.2/Relu" -> "197 /stage3/stage3.4/branch2/branch2.3/Conv" [label="[1]", style=solid]; -"197 /stage3/stage3.4/branch2/branch2.3/Conv" -> "198 /stage3/stage3.4/branch2/branch2.5/Conv" [label="[1]", style=solid]; -"198 /stage3/stage3.4/branch2/branch2.5/Conv" -> "199 /stage3/stage3.4/branch2/branch2.7/Relu" [label="[1]", style=solid]; -"199 /stage3/stage3.4/branch2/branch2.7/Relu" -> "200 /stage3/stage3.4/Concat" [label="[1]", style=solid]; -"200 /stage3/stage3.4/Concat" -> "202 /stage3/stage3.4/Reshape" [label="[1]", style=solid]; +"194 /stage3/stage3.4/Slice_1" -> "195 /stage3/stage3.4/branch2/branch2.0/Conv" [label="[]", style=solid]; +"195 /stage3/stage3.4/branch2/branch2.0/Conv" -> "196 /stage3/stage3.4/branch2/branch2.2/Relu" [label="[]", style=solid]; +"196 /stage3/stage3.4/branch2/branch2.2/Relu" -> "197 /stage3/stage3.4/branch2/branch2.3/Conv" [label="[]", style=solid]; +"197 /stage3/stage3.4/branch2/branch2.3/Conv" -> "198 /stage3/stage3.4/branch2/branch2.5/Conv" [label="[]", style=solid]; +"198 /stage3/stage3.4/branch2/branch2.5/Conv" -> "199 /stage3/stage3.4/branch2/branch2.7/Relu" [label="[]", style=solid]; +"199 /stage3/stage3.4/branch2/branch2.7/Relu" -> "200 /stage3/stage3.4/Concat" [label="[]", style=solid]; +"200 /stage3/stage3.4/Concat" -> "202 /stage3/stage3.4/Reshape" [label="[]", style=solid]; "201 /stage3/stage3.4/Constant_6" -> "202 /stage3/stage3.4/Reshape" [label="[5]", style=dashed]; "202 /stage3/stage3.4/Reshape" -> "203 /stage3/stage3.4/Transpose" [label="[1, 2, 116, 14, 14]", style=solid]; "203 /stage3/stage3.4/Transpose" -> "205 /stage3/stage3.4/Reshape_1" [label="[1, 116, 2, 14, 14]", style=solid]; @@ -641,16 +641,16 @@ strict digraph { "214 /stage3/stage3.5/Constant_4" -> "215 /stage3/stage3.5/Mul" [label="[1]", style=dashed]; "215 /stage3/stage3.5/Mul" -> "216 /stage3/stage3.5/Slice" [label="[1]", style=dashed]; "215 /stage3/stage3.5/Mul" -> "219 /stage3/stage3.5/Slice_1" [label="[1]", style=dashed]; -"216 /stage3/stage3.5/Slice" -> "225 /stage3/stage3.5/Concat" [label="[1]", style=solid]; +"216 /stage3/stage3.5/Slice" -> "225 /stage3/stage3.5/Concat" [label="[]", style=solid]; "217 /stage3/stage3.5/Constant_5" -> "218 /stage3/stage3.5/Mul_1" [label="[1]", style=dashed]; "218 /stage3/stage3.5/Mul_1" -> "219 /stage3/stage3.5/Slice_1" [label="[1]", style=dashed]; -"219 /stage3/stage3.5/Slice_1" -> "220 /stage3/stage3.5/branch2/branch2.0/Conv" [label="[1]", style=solid]; -"220 /stage3/stage3.5/branch2/branch2.0/Conv" -> "221 /stage3/stage3.5/branch2/branch2.2/Relu" [label="[1]", style=solid]; -"221 /stage3/stage3.5/branch2/branch2.2/Relu" -> "222 /stage3/stage3.5/branch2/branch2.3/Conv" [label="[1]", style=solid]; -"222 /stage3/stage3.5/branch2/branch2.3/Conv" -> "223 /stage3/stage3.5/branch2/branch2.5/Conv" [label="[1]", style=solid]; -"223 /stage3/stage3.5/branch2/branch2.5/Conv" -> "224 /stage3/stage3.5/branch2/branch2.7/Relu" [label="[1]", style=solid]; -"224 /stage3/stage3.5/branch2/branch2.7/Relu" -> "225 /stage3/stage3.5/Concat" [label="[1]", style=solid]; -"225 /stage3/stage3.5/Concat" -> "227 /stage3/stage3.5/Reshape" [label="[1]", style=solid]; +"219 /stage3/stage3.5/Slice_1" -> "220 /stage3/stage3.5/branch2/branch2.0/Conv" [label="[]", style=solid]; +"220 /stage3/stage3.5/branch2/branch2.0/Conv" -> "221 /stage3/stage3.5/branch2/branch2.2/Relu" [label="[]", style=solid]; +"221 /stage3/stage3.5/branch2/branch2.2/Relu" -> "222 /stage3/stage3.5/branch2/branch2.3/Conv" [label="[]", style=solid]; +"222 /stage3/stage3.5/branch2/branch2.3/Conv" -> "223 /stage3/stage3.5/branch2/branch2.5/Conv" [label="[]", style=solid]; +"223 /stage3/stage3.5/branch2/branch2.5/Conv" -> "224 /stage3/stage3.5/branch2/branch2.7/Relu" [label="[]", style=solid]; +"224 /stage3/stage3.5/branch2/branch2.7/Relu" -> "225 /stage3/stage3.5/Concat" [label="[]", style=solid]; +"225 /stage3/stage3.5/Concat" -> "227 /stage3/stage3.5/Reshape" [label="[]", style=solid]; "226 /stage3/stage3.5/Constant_6" -> "227 /stage3/stage3.5/Reshape" [label="[5]", style=dashed]; "227 /stage3/stage3.5/Reshape" -> "228 /stage3/stage3.5/Transpose" [label="[1, 2, 116, 14, 14]", style=solid]; "228 /stage3/stage3.5/Transpose" -> "230 /stage3/stage3.5/Reshape_1" [label="[1, 116, 2, 14, 14]", style=solid]; @@ -672,16 +672,16 @@ strict digraph { "239 /stage3/stage3.6/Constant_4" -> "240 /stage3/stage3.6/Mul" [label="[1]", style=dashed]; "240 /stage3/stage3.6/Mul" -> "241 /stage3/stage3.6/Slice" [label="[1]", style=dashed]; "240 /stage3/stage3.6/Mul" -> "244 /stage3/stage3.6/Slice_1" [label="[1]", style=dashed]; -"241 /stage3/stage3.6/Slice" -> "250 /stage3/stage3.6/Concat" [label="[1]", style=solid]; +"241 /stage3/stage3.6/Slice" -> "250 /stage3/stage3.6/Concat" [label="[]", style=solid]; "242 /stage3/stage3.6/Constant_5" -> "243 /stage3/stage3.6/Mul_1" [label="[1]", style=dashed]; "243 /stage3/stage3.6/Mul_1" -> "244 /stage3/stage3.6/Slice_1" [label="[1]", style=dashed]; -"244 /stage3/stage3.6/Slice_1" -> "245 /stage3/stage3.6/branch2/branch2.0/Conv" [label="[1]", style=solid]; -"245 /stage3/stage3.6/branch2/branch2.0/Conv" -> "246 /stage3/stage3.6/branch2/branch2.2/Relu" [label="[1]", style=solid]; -"246 /stage3/stage3.6/branch2/branch2.2/Relu" -> "247 /stage3/stage3.6/branch2/branch2.3/Conv" [label="[1]", style=solid]; -"247 /stage3/stage3.6/branch2/branch2.3/Conv" -> "248 /stage3/stage3.6/branch2/branch2.5/Conv" [label="[1]", style=solid]; -"248 /stage3/stage3.6/branch2/branch2.5/Conv" -> "249 /stage3/stage3.6/branch2/branch2.7/Relu" [label="[1]", style=solid]; -"249 /stage3/stage3.6/branch2/branch2.7/Relu" -> "250 /stage3/stage3.6/Concat" [label="[1]", style=solid]; -"250 /stage3/stage3.6/Concat" -> "252 /stage3/stage3.6/Reshape" [label="[1]", style=solid]; +"244 /stage3/stage3.6/Slice_1" -> "245 /stage3/stage3.6/branch2/branch2.0/Conv" [label="[]", style=solid]; +"245 /stage3/stage3.6/branch2/branch2.0/Conv" -> "246 /stage3/stage3.6/branch2/branch2.2/Relu" [label="[]", style=solid]; +"246 /stage3/stage3.6/branch2/branch2.2/Relu" -> "247 /stage3/stage3.6/branch2/branch2.3/Conv" [label="[]", style=solid]; +"247 /stage3/stage3.6/branch2/branch2.3/Conv" -> "248 /stage3/stage3.6/branch2/branch2.5/Conv" [label="[]", style=solid]; +"248 /stage3/stage3.6/branch2/branch2.5/Conv" -> "249 /stage3/stage3.6/branch2/branch2.7/Relu" [label="[]", style=solid]; +"249 /stage3/stage3.6/branch2/branch2.7/Relu" -> "250 /stage3/stage3.6/Concat" [label="[]", style=solid]; +"250 /stage3/stage3.6/Concat" -> "252 /stage3/stage3.6/Reshape" [label="[]", style=solid]; "251 /stage3/stage3.6/Constant_6" -> "252 /stage3/stage3.6/Reshape" [label="[5]", style=dashed]; "252 /stage3/stage3.6/Reshape" -> "253 /stage3/stage3.6/Transpose" [label="[1, 2, 116, 14, 14]", style=solid]; "253 /stage3/stage3.6/Transpose" -> "255 /stage3/stage3.6/Reshape_1" [label="[1, 116, 2, 14, 14]", style=solid]; @@ -703,16 +703,16 @@ strict digraph { "264 /stage3/stage3.7/Constant_4" -> "265 /stage3/stage3.7/Mul" [label="[1]", style=dashed]; "265 /stage3/stage3.7/Mul" -> "266 /stage3/stage3.7/Slice" [label="[1]", style=dashed]; "265 /stage3/stage3.7/Mul" -> "269 /stage3/stage3.7/Slice_1" [label="[1]", style=dashed]; -"266 /stage3/stage3.7/Slice" -> "275 /stage3/stage3.7/Concat" [label="[1]", style=solid]; +"266 /stage3/stage3.7/Slice" -> "275 /stage3/stage3.7/Concat" [label="[]", style=solid]; "267 /stage3/stage3.7/Constant_5" -> "268 /stage3/stage3.7/Mul_1" [label="[1]", style=dashed]; "268 /stage3/stage3.7/Mul_1" -> "269 /stage3/stage3.7/Slice_1" [label="[1]", style=dashed]; -"269 /stage3/stage3.7/Slice_1" -> "270 /stage3/stage3.7/branch2/branch2.0/Conv" [label="[1]", style=solid]; -"270 /stage3/stage3.7/branch2/branch2.0/Conv" -> "271 /stage3/stage3.7/branch2/branch2.2/Relu" [label="[1]", style=solid]; -"271 /stage3/stage3.7/branch2/branch2.2/Relu" -> "272 /stage3/stage3.7/branch2/branch2.3/Conv" [label="[1]", style=solid]; -"272 /stage3/stage3.7/branch2/branch2.3/Conv" -> "273 /stage3/stage3.7/branch2/branch2.5/Conv" [label="[1]", style=solid]; -"273 /stage3/stage3.7/branch2/branch2.5/Conv" -> "274 /stage3/stage3.7/branch2/branch2.7/Relu" [label="[1]", style=solid]; -"274 /stage3/stage3.7/branch2/branch2.7/Relu" -> "275 /stage3/stage3.7/Concat" [label="[1]", style=solid]; -"275 /stage3/stage3.7/Concat" -> "277 /stage3/stage3.7/Reshape" [label="[1]", style=solid]; +"269 /stage3/stage3.7/Slice_1" -> "270 /stage3/stage3.7/branch2/branch2.0/Conv" [label="[]", style=solid]; +"270 /stage3/stage3.7/branch2/branch2.0/Conv" -> "271 /stage3/stage3.7/branch2/branch2.2/Relu" [label="[]", style=solid]; +"271 /stage3/stage3.7/branch2/branch2.2/Relu" -> "272 /stage3/stage3.7/branch2/branch2.3/Conv" [label="[]", style=solid]; +"272 /stage3/stage3.7/branch2/branch2.3/Conv" -> "273 /stage3/stage3.7/branch2/branch2.5/Conv" [label="[]", style=solid]; +"273 /stage3/stage3.7/branch2/branch2.5/Conv" -> "274 /stage3/stage3.7/branch2/branch2.7/Relu" [label="[]", style=solid]; +"274 /stage3/stage3.7/branch2/branch2.7/Relu" -> "275 /stage3/stage3.7/Concat" [label="[]", style=solid]; +"275 /stage3/stage3.7/Concat" -> "277 /stage3/stage3.7/Reshape" [label="[]", style=solid]; "276 /stage3/stage3.7/Constant_6" -> "277 /stage3/stage3.7/Reshape" [label="[5]", style=dashed]; "277 /stage3/stage3.7/Reshape" -> "278 /stage3/stage3.7/Transpose" [label="[1, 2, 116, 14, 14]", style=solid]; "278 /stage3/stage3.7/Transpose" -> "280 /stage3/stage3.7/Reshape_1" [label="[1, 116, 2, 14, 14]", style=solid]; @@ -749,16 +749,16 @@ strict digraph { "303 /stage4/stage4.1/Constant_4" -> "304 /stage4/stage4.1/Mul" [label="[1]", style=dashed]; "304 /stage4/stage4.1/Mul" -> "305 /stage4/stage4.1/Slice" [label="[1]", style=dashed]; "304 /stage4/stage4.1/Mul" -> "308 /stage4/stage4.1/Slice_1" [label="[1]", style=dashed]; -"305 /stage4/stage4.1/Slice" -> "314 /stage4/stage4.1/Concat" [label="[1]", style=solid]; +"305 /stage4/stage4.1/Slice" -> "314 /stage4/stage4.1/Concat" [label="[]", style=solid]; "306 /stage4/stage4.1/Constant_5" -> "307 /stage4/stage4.1/Mul_1" [label="[1]", style=dashed]; "307 /stage4/stage4.1/Mul_1" -> "308 /stage4/stage4.1/Slice_1" [label="[1]", style=dashed]; -"308 /stage4/stage4.1/Slice_1" -> "309 /stage4/stage4.1/branch2/branch2.0/Conv" [label="[1]", style=solid]; -"309 /stage4/stage4.1/branch2/branch2.0/Conv" -> "310 /stage4/stage4.1/branch2/branch2.2/Relu" [label="[1]", style=solid]; -"310 /stage4/stage4.1/branch2/branch2.2/Relu" -> "311 /stage4/stage4.1/branch2/branch2.3/Conv" [label="[1]", style=solid]; -"311 /stage4/stage4.1/branch2/branch2.3/Conv" -> "312 /stage4/stage4.1/branch2/branch2.5/Conv" [label="[1]", style=solid]; -"312 /stage4/stage4.1/branch2/branch2.5/Conv" -> "313 /stage4/stage4.1/branch2/branch2.7/Relu" [label="[1]", style=solid]; -"313 /stage4/stage4.1/branch2/branch2.7/Relu" -> "314 /stage4/stage4.1/Concat" [label="[1]", style=solid]; -"314 /stage4/stage4.1/Concat" -> "316 /stage4/stage4.1/Reshape" [label="[1]", style=solid]; +"308 /stage4/stage4.1/Slice_1" -> "309 /stage4/stage4.1/branch2/branch2.0/Conv" [label="[]", style=solid]; +"309 /stage4/stage4.1/branch2/branch2.0/Conv" -> "310 /stage4/stage4.1/branch2/branch2.2/Relu" [label="[]", style=solid]; +"310 /stage4/stage4.1/branch2/branch2.2/Relu" -> "311 /stage4/stage4.1/branch2/branch2.3/Conv" [label="[]", style=solid]; +"311 /stage4/stage4.1/branch2/branch2.3/Conv" -> "312 /stage4/stage4.1/branch2/branch2.5/Conv" [label="[]", style=solid]; +"312 /stage4/stage4.1/branch2/branch2.5/Conv" -> "313 /stage4/stage4.1/branch2/branch2.7/Relu" [label="[]", style=solid]; +"313 /stage4/stage4.1/branch2/branch2.7/Relu" -> "314 /stage4/stage4.1/Concat" [label="[]", style=solid]; +"314 /stage4/stage4.1/Concat" -> "316 /stage4/stage4.1/Reshape" [label="[]", style=solid]; "315 /stage4/stage4.1/Constant_6" -> "316 /stage4/stage4.1/Reshape" [label="[5]", style=dashed]; "316 /stage4/stage4.1/Reshape" -> "317 /stage4/stage4.1/Transpose" [label="[1, 2, 232, 7, 7]", style=solid]; "317 /stage4/stage4.1/Transpose" -> "319 /stage4/stage4.1/Reshape_1" [label="[1, 232, 2, 7, 7]", style=solid]; @@ -780,16 +780,16 @@ strict digraph { "328 /stage4/stage4.2/Constant_4" -> "329 /stage4/stage4.2/Mul" [label="[1]", style=dashed]; "329 /stage4/stage4.2/Mul" -> "330 /stage4/stage4.2/Slice" [label="[1]", style=dashed]; "329 /stage4/stage4.2/Mul" -> "333 /stage4/stage4.2/Slice_1" [label="[1]", style=dashed]; -"330 /stage4/stage4.2/Slice" -> "339 /stage4/stage4.2/Concat" [label="[1]", style=solid]; +"330 /stage4/stage4.2/Slice" -> "339 /stage4/stage4.2/Concat" [label="[]", style=solid]; "331 /stage4/stage4.2/Constant_5" -> "332 /stage4/stage4.2/Mul_1" [label="[1]", style=dashed]; "332 /stage4/stage4.2/Mul_1" -> "333 /stage4/stage4.2/Slice_1" [label="[1]", style=dashed]; -"333 /stage4/stage4.2/Slice_1" -> "334 /stage4/stage4.2/branch2/branch2.0/Conv" [label="[1]", style=solid]; -"334 /stage4/stage4.2/branch2/branch2.0/Conv" -> "335 /stage4/stage4.2/branch2/branch2.2/Relu" [label="[1]", style=solid]; -"335 /stage4/stage4.2/branch2/branch2.2/Relu" -> "336 /stage4/stage4.2/branch2/branch2.3/Conv" [label="[1]", style=solid]; -"336 /stage4/stage4.2/branch2/branch2.3/Conv" -> "337 /stage4/stage4.2/branch2/branch2.5/Conv" [label="[1]", style=solid]; -"337 /stage4/stage4.2/branch2/branch2.5/Conv" -> "338 /stage4/stage4.2/branch2/branch2.7/Relu" [label="[1]", style=solid]; -"338 /stage4/stage4.2/branch2/branch2.7/Relu" -> "339 /stage4/stage4.2/Concat" [label="[1]", style=solid]; -"339 /stage4/stage4.2/Concat" -> "341 /stage4/stage4.2/Reshape" [label="[1]", style=solid]; +"333 /stage4/stage4.2/Slice_1" -> "334 /stage4/stage4.2/branch2/branch2.0/Conv" [label="[]", style=solid]; +"334 /stage4/stage4.2/branch2/branch2.0/Conv" -> "335 /stage4/stage4.2/branch2/branch2.2/Relu" [label="[]", style=solid]; +"335 /stage4/stage4.2/branch2/branch2.2/Relu" -> "336 /stage4/stage4.2/branch2/branch2.3/Conv" [label="[]", style=solid]; +"336 /stage4/stage4.2/branch2/branch2.3/Conv" -> "337 /stage4/stage4.2/branch2/branch2.5/Conv" [label="[]", style=solid]; +"337 /stage4/stage4.2/branch2/branch2.5/Conv" -> "338 /stage4/stage4.2/branch2/branch2.7/Relu" [label="[]", style=solid]; +"338 /stage4/stage4.2/branch2/branch2.7/Relu" -> "339 /stage4/stage4.2/Concat" [label="[]", style=solid]; +"339 /stage4/stage4.2/Concat" -> "341 /stage4/stage4.2/Reshape" [label="[]", style=solid]; "340 /stage4/stage4.2/Constant_6" -> "341 /stage4/stage4.2/Reshape" [label="[5]", style=dashed]; "341 /stage4/stage4.2/Reshape" -> "342 /stage4/stage4.2/Transpose" [label="[1, 2, 232, 7, 7]", style=solid]; "342 /stage4/stage4.2/Transpose" -> "344 /stage4/stage4.2/Reshape_1" [label="[1, 232, 2, 7, 7]", style=solid]; @@ -811,16 +811,16 @@ strict digraph { "353 /stage4/stage4.3/Constant_4" -> "354 /stage4/stage4.3/Mul" [label="[1]", style=dashed]; "354 /stage4/stage4.3/Mul" -> "355 /stage4/stage4.3/Slice" [label="[1]", style=dashed]; "354 /stage4/stage4.3/Mul" -> "358 /stage4/stage4.3/Slice_1" [label="[1]", style=dashed]; -"355 /stage4/stage4.3/Slice" -> "364 /stage4/stage4.3/Concat" [label="[1]", style=solid]; +"355 /stage4/stage4.3/Slice" -> "364 /stage4/stage4.3/Concat" [label="[]", style=solid]; "356 /stage4/stage4.3/Constant_5" -> "357 /stage4/stage4.3/Mul_1" [label="[1]", style=dashed]; "357 /stage4/stage4.3/Mul_1" -> "358 /stage4/stage4.3/Slice_1" [label="[1]", style=dashed]; -"358 /stage4/stage4.3/Slice_1" -> "359 /stage4/stage4.3/branch2/branch2.0/Conv" [label="[1]", style=solid]; -"359 /stage4/stage4.3/branch2/branch2.0/Conv" -> "360 /stage4/stage4.3/branch2/branch2.2/Relu" [label="[1]", style=solid]; -"360 /stage4/stage4.3/branch2/branch2.2/Relu" -> "361 /stage4/stage4.3/branch2/branch2.3/Conv" [label="[1]", style=solid]; -"361 /stage4/stage4.3/branch2/branch2.3/Conv" -> "362 /stage4/stage4.3/branch2/branch2.5/Conv" [label="[1]", style=solid]; -"362 /stage4/stage4.3/branch2/branch2.5/Conv" -> "363 /stage4/stage4.3/branch2/branch2.7/Relu" [label="[1]", style=solid]; -"363 /stage4/stage4.3/branch2/branch2.7/Relu" -> "364 /stage4/stage4.3/Concat" [label="[1]", style=solid]; -"364 /stage4/stage4.3/Concat" -> "366 /stage4/stage4.3/Reshape" [label="[1]", style=solid]; +"358 /stage4/stage4.3/Slice_1" -> "359 /stage4/stage4.3/branch2/branch2.0/Conv" [label="[]", style=solid]; +"359 /stage4/stage4.3/branch2/branch2.0/Conv" -> "360 /stage4/stage4.3/branch2/branch2.2/Relu" [label="[]", style=solid]; +"360 /stage4/stage4.3/branch2/branch2.2/Relu" -> "361 /stage4/stage4.3/branch2/branch2.3/Conv" [label="[]", style=solid]; +"361 /stage4/stage4.3/branch2/branch2.3/Conv" -> "362 /stage4/stage4.3/branch2/branch2.5/Conv" [label="[]", style=solid]; +"362 /stage4/stage4.3/branch2/branch2.5/Conv" -> "363 /stage4/stage4.3/branch2/branch2.7/Relu" [label="[]", style=solid]; +"363 /stage4/stage4.3/branch2/branch2.7/Relu" -> "364 /stage4/stage4.3/Concat" [label="[]", style=solid]; +"364 /stage4/stage4.3/Concat" -> "366 /stage4/stage4.3/Reshape" [label="[]", style=solid]; "365 /stage4/stage4.3/Constant_6" -> "366 /stage4/stage4.3/Reshape" [label="[5]", style=dashed]; "366 /stage4/stage4.3/Reshape" -> "367 /stage4/stage4.3/Transpose" [label="[1, 2, 232, 7, 7]", style=solid]; "367 /stage4/stage4.3/Transpose" -> "369 /stage4/stage4.3/Reshape_1" [label="[1, 232, 2, 7, 7]", style=solid]; diff --git a/tests/onnx/data/reference_graphs/quantization/densenet121.dot b/tests/onnx/data/reference_graphs/quantization/densenet121.dot index 462ce2a5d67..ca9a70f67d0 100644 --- a/tests/onnx/data/reference_graphs/quantization/densenet121.dot +++ b/tests/onnx/data/reference_graphs/quantization/densenet121.dot @@ -1,8 +1,8 @@ strict digraph { "0 QuantizeLinear_input.1_1" [id=0, type=QuantizeLinear]; "1 DequantizeLinear_input.1_1" [id=1, type=DequantizeLinear]; -"2 QuantizeLinear_onnx^^Conv_1166_1" [id=2, label="2 QuantizeLinear_onnx::Conv_1166_1", type=QuantizeLinear]; -"3 DequantizeLinear_onnx^^Conv_1166_1" [id=3, label="3 DequantizeLinear_onnx::Conv_1166_1", type=DequantizeLinear]; +"2 QuantizeLinear_onnx^^Conv_1160_1" [id=2, label="2 QuantizeLinear_onnx::Conv_1160_1", type=QuantizeLinear]; +"3 DequantizeLinear_onnx^^Conv_1160_1" [id=3, label="3 DequantizeLinear_onnx::Conv_1160_1", type=DequantizeLinear]; "4 /features/conv0/Conv" [id=4, type=Conv]; "5 /features/relu0/Relu" [id=5, type=Relu]; "6 QuantizeLinear_/features/relu0/Relu_output_0_1" [id=6, type=QuantizeLinear]; @@ -13,8 +13,8 @@ strict digraph { "11 /features/denseblock1/denselayer1/relu1/Relu" [id=11, type=Relu]; "12 QuantizeLinear_/features/denseblock1/denselayer1/relu1/Relu_output_0_1" [id=12, type=QuantizeLinear]; "13 DequantizeLinear_/features/denseblock1/denselayer1/relu1/Relu_output_0_1" [id=13, type=DequantizeLinear]; -"14 QuantizeLinear_onnx^^Conv_1169_1" [id=14, label="14 QuantizeLinear_onnx::Conv_1169_1", type=QuantizeLinear]; -"15 DequantizeLinear_onnx^^Conv_1169_1" [id=15, label="15 DequantizeLinear_onnx::Conv_1169_1", type=DequantizeLinear]; +"14 QuantizeLinear_onnx^^Conv_1163_1" [id=14, label="14 QuantizeLinear_onnx::Conv_1163_1", type=QuantizeLinear]; +"15 DequantizeLinear_onnx^^Conv_1163_1" [id=15, label="15 DequantizeLinear_onnx::Conv_1163_1", type=DequantizeLinear]; "16 /features/denseblock1/denselayer1/conv1/Conv" [id=16, type=Conv]; "17 /features/denseblock1/denselayer1/relu2/Relu" [id=17, type=Relu]; "18 QuantizeLinear_/features/denseblock1/denselayer1/relu2/Relu_output_0_1" [id=18, type=QuantizeLinear]; @@ -29,8 +29,8 @@ strict digraph { "27 /features/denseblock1/denselayer2/relu1/Relu" [id=27, type=Relu]; "28 QuantizeLinear_/features/denseblock1/denselayer2/relu1/Relu_output_0_1" [id=28, type=QuantizeLinear]; "29 DequantizeLinear_/features/denseblock1/denselayer2/relu1/Relu_output_0_1" [id=29, type=DequantizeLinear]; -"30 QuantizeLinear_onnx^^Conv_1172_1" [id=30, label="30 QuantizeLinear_onnx::Conv_1172_1", type=QuantizeLinear]; -"31 DequantizeLinear_onnx^^Conv_1172_1" [id=31, label="31 DequantizeLinear_onnx::Conv_1172_1", type=DequantizeLinear]; +"30 QuantizeLinear_onnx^^Conv_1166_1" [id=30, label="30 QuantizeLinear_onnx::Conv_1166_1", type=QuantizeLinear]; +"31 DequantizeLinear_onnx^^Conv_1166_1" [id=31, label="31 DequantizeLinear_onnx::Conv_1166_1", type=DequantizeLinear]; "32 /features/denseblock1/denselayer2/conv1/Conv" [id=32, type=Conv]; "33 /features/denseblock1/denselayer2/relu2/Relu" [id=33, type=Relu]; "34 QuantizeLinear_/features/denseblock1/denselayer2/relu2/Relu_output_0_1" [id=34, type=QuantizeLinear]; @@ -45,8 +45,8 @@ strict digraph { "43 /features/denseblock1/denselayer3/relu1/Relu" [id=43, type=Relu]; "44 QuantizeLinear_/features/denseblock1/denselayer3/relu1/Relu_output_0_1" [id=44, type=QuantizeLinear]; "45 DequantizeLinear_/features/denseblock1/denselayer3/relu1/Relu_output_0_1" [id=45, type=DequantizeLinear]; -"46 QuantizeLinear_onnx^^Conv_1175_1" [id=46, label="46 QuantizeLinear_onnx::Conv_1175_1", type=QuantizeLinear]; -"47 DequantizeLinear_onnx^^Conv_1175_1" [id=47, label="47 DequantizeLinear_onnx::Conv_1175_1", type=DequantizeLinear]; +"46 QuantizeLinear_onnx^^Conv_1169_1" [id=46, label="46 QuantizeLinear_onnx::Conv_1169_1", type=QuantizeLinear]; +"47 DequantizeLinear_onnx^^Conv_1169_1" [id=47, label="47 DequantizeLinear_onnx::Conv_1169_1", type=DequantizeLinear]; "48 /features/denseblock1/denselayer3/conv1/Conv" [id=48, type=Conv]; "49 /features/denseblock1/denselayer3/relu2/Relu" [id=49, type=Relu]; "50 QuantizeLinear_/features/denseblock1/denselayer3/relu2/Relu_output_0_1" [id=50, type=QuantizeLinear]; @@ -61,8 +61,8 @@ strict digraph { "59 /features/denseblock1/denselayer4/relu1/Relu" [id=59, type=Relu]; "60 QuantizeLinear_/features/denseblock1/denselayer4/relu1/Relu_output_0_1" [id=60, type=QuantizeLinear]; "61 DequantizeLinear_/features/denseblock1/denselayer4/relu1/Relu_output_0_1" [id=61, type=DequantizeLinear]; -"62 QuantizeLinear_onnx^^Conv_1178_1" [id=62, label="62 QuantizeLinear_onnx::Conv_1178_1", type=QuantizeLinear]; -"63 DequantizeLinear_onnx^^Conv_1178_1" [id=63, label="63 DequantizeLinear_onnx::Conv_1178_1", type=DequantizeLinear]; +"62 QuantizeLinear_onnx^^Conv_1172_1" [id=62, label="62 QuantizeLinear_onnx::Conv_1172_1", type=QuantizeLinear]; +"63 DequantizeLinear_onnx^^Conv_1172_1" [id=63, label="63 DequantizeLinear_onnx::Conv_1172_1", type=DequantizeLinear]; "64 /features/denseblock1/denselayer4/conv1/Conv" [id=64, type=Conv]; "65 /features/denseblock1/denselayer4/relu2/Relu" [id=65, type=Relu]; "66 QuantizeLinear_/features/denseblock1/denselayer4/relu2/Relu_output_0_1" [id=66, type=QuantizeLinear]; @@ -77,8 +77,8 @@ strict digraph { "75 /features/denseblock1/denselayer5/relu1/Relu" [id=75, type=Relu]; "76 QuantizeLinear_/features/denseblock1/denselayer5/relu1/Relu_output_0_1" [id=76, type=QuantizeLinear]; "77 DequantizeLinear_/features/denseblock1/denselayer5/relu1/Relu_output_0_1" [id=77, type=DequantizeLinear]; -"78 QuantizeLinear_onnx^^Conv_1181_1" [id=78, label="78 QuantizeLinear_onnx::Conv_1181_1", type=QuantizeLinear]; -"79 DequantizeLinear_onnx^^Conv_1181_1" [id=79, label="79 DequantizeLinear_onnx::Conv_1181_1", type=DequantizeLinear]; +"78 QuantizeLinear_onnx^^Conv_1175_1" [id=78, label="78 QuantizeLinear_onnx::Conv_1175_1", type=QuantizeLinear]; +"79 DequantizeLinear_onnx^^Conv_1175_1" [id=79, label="79 DequantizeLinear_onnx::Conv_1175_1", type=DequantizeLinear]; "80 /features/denseblock1/denselayer5/conv1/Conv" [id=80, type=Conv]; "81 /features/denseblock1/denselayer5/relu2/Relu" [id=81, type=Relu]; "82 QuantizeLinear_/features/denseblock1/denselayer5/relu2/Relu_output_0_1" [id=82, type=QuantizeLinear]; @@ -93,8 +93,8 @@ strict digraph { "91 /features/denseblock1/denselayer6/relu1/Relu" [id=91, type=Relu]; "92 QuantizeLinear_/features/denseblock1/denselayer6/relu1/Relu_output_0_1" [id=92, type=QuantizeLinear]; "93 DequantizeLinear_/features/denseblock1/denselayer6/relu1/Relu_output_0_1" [id=93, type=DequantizeLinear]; -"94 QuantizeLinear_onnx^^Conv_1184_1" [id=94, label="94 QuantizeLinear_onnx::Conv_1184_1", type=QuantizeLinear]; -"95 DequantizeLinear_onnx^^Conv_1184_1" [id=95, label="95 DequantizeLinear_onnx::Conv_1184_1", type=DequantizeLinear]; +"94 QuantizeLinear_onnx^^Conv_1178_1" [id=94, label="94 QuantizeLinear_onnx::Conv_1178_1", type=QuantizeLinear]; +"95 DequantizeLinear_onnx^^Conv_1178_1" [id=95, label="95 DequantizeLinear_onnx::Conv_1178_1", type=DequantizeLinear]; "96 /features/denseblock1/denselayer6/conv1/Conv" [id=96, type=Conv]; "97 /features/denseblock1/denselayer6/relu2/Relu" [id=97, type=Relu]; "98 QuantizeLinear_/features/denseblock1/denselayer6/relu2/Relu_output_0_1" [id=98, type=QuantizeLinear]; @@ -112,893 +112,887 @@ strict digraph { "110 QuantizeLinear_features.transition1.conv.weight_1" [id=110, type=QuantizeLinear]; "111 DequantizeLinear_features.transition1.conv.weight_1" [id=111, type=DequantizeLinear]; "112 /features/transition1/conv/Conv" [id=112, type=Conv]; -"113 /features/transition1/pool/Constant" [id=113, type=Constant]; -"114 QuantizeLinear_/features/transition1/conv/Conv_output_0_1" [id=114, type=QuantizeLinear]; -"115 DequantizeLinear_/features/transition1/conv/Conv_output_0_1" [id=115, type=DequantizeLinear]; -"116 /features/transition1/pool/Pad" [id=116, type=Pad]; -"117 /features/transition1/pool/AveragePool" [id=117, type=AveragePool]; -"118 QuantizeLinear_/features/transition1/pool/AveragePool_output_0_1" [id=118, type=QuantizeLinear]; -"119 DequantizeLinear_/features/transition1/pool/AveragePool_output_0_1" [id=119, type=DequantizeLinear]; -"120 /features/denseblock2/denselayer1/Concat" [id=120, type=Concat]; -"121 /features/denseblock2/denselayer1/norm1/BatchNormalization" [id=121, type=BatchNormalization]; -"122 /features/denseblock2/denselayer1/relu1/Relu" [id=122, type=Relu]; -"123 QuantizeLinear_/features/denseblock2/denselayer1/relu1/Relu_output_0_1" [id=123, type=QuantizeLinear]; -"124 DequantizeLinear_/features/denseblock2/denselayer1/relu1/Relu_output_0_1" [id=124, type=DequantizeLinear]; -"125 QuantizeLinear_onnx^^Conv_1187_1" [id=125, label="125 QuantizeLinear_onnx::Conv_1187_1", type=QuantizeLinear]; -"126 DequantizeLinear_onnx^^Conv_1187_1" [id=126, label="126 DequantizeLinear_onnx::Conv_1187_1", type=DequantizeLinear]; -"127 /features/denseblock2/denselayer1/conv1/Conv" [id=127, type=Conv]; -"128 /features/denseblock2/denselayer1/relu2/Relu" [id=128, type=Relu]; -"129 QuantizeLinear_/features/denseblock2/denselayer1/relu2/Relu_output_0_1" [id=129, type=QuantizeLinear]; -"130 DequantizeLinear_/features/denseblock2/denselayer1/relu2/Relu_output_0_1" [id=130, type=DequantizeLinear]; -"131 QuantizeLinear_features.denseblock2.denselayer1.conv2.weight_1" [id=131, type=QuantizeLinear]; -"132 DequantizeLinear_features.denseblock2.denselayer1.conv2.weight_1" [id=132, type=DequantizeLinear]; -"133 /features/denseblock2/denselayer1/conv2/Conv" [id=133, type=Conv]; -"134 QuantizeLinear_/features/denseblock2/denselayer1/conv2/Conv_output_0_1" [id=134, type=QuantizeLinear]; -"135 DequantizeLinear_/features/denseblock2/denselayer1/conv2/Conv_output_0_1" [id=135, type=DequantizeLinear]; -"136 /features/denseblock2/denselayer2/Concat" [id=136, type=Concat]; -"137 /features/denseblock2/denselayer2/norm1/BatchNormalization" [id=137, type=BatchNormalization]; -"138 /features/denseblock2/denselayer2/relu1/Relu" [id=138, type=Relu]; -"139 QuantizeLinear_/features/denseblock2/denselayer2/relu1/Relu_output_0_1" [id=139, type=QuantizeLinear]; -"140 DequantizeLinear_/features/denseblock2/denselayer2/relu1/Relu_output_0_1" [id=140, type=DequantizeLinear]; -"141 QuantizeLinear_onnx^^Conv_1190_1" [id=141, label="141 QuantizeLinear_onnx::Conv_1190_1", type=QuantizeLinear]; -"142 DequantizeLinear_onnx^^Conv_1190_1" [id=142, label="142 DequantizeLinear_onnx::Conv_1190_1", type=DequantizeLinear]; -"143 /features/denseblock2/denselayer2/conv1/Conv" [id=143, type=Conv]; -"144 /features/denseblock2/denselayer2/relu2/Relu" [id=144, type=Relu]; -"145 QuantizeLinear_/features/denseblock2/denselayer2/relu2/Relu_output_0_1" [id=145, type=QuantizeLinear]; -"146 DequantizeLinear_/features/denseblock2/denselayer2/relu2/Relu_output_0_1" [id=146, type=DequantizeLinear]; -"147 QuantizeLinear_features.denseblock2.denselayer2.conv2.weight_1" [id=147, type=QuantizeLinear]; -"148 DequantizeLinear_features.denseblock2.denselayer2.conv2.weight_1" [id=148, type=DequantizeLinear]; -"149 /features/denseblock2/denselayer2/conv2/Conv" [id=149, type=Conv]; -"150 QuantizeLinear_/features/denseblock2/denselayer2/conv2/Conv_output_0_1" [id=150, type=QuantizeLinear]; -"151 DequantizeLinear_/features/denseblock2/denselayer2/conv2/Conv_output_0_1" [id=151, type=DequantizeLinear]; -"152 /features/denseblock2/denselayer3/Concat" [id=152, type=Concat]; -"153 /features/denseblock2/denselayer3/norm1/BatchNormalization" [id=153, type=BatchNormalization]; -"154 /features/denseblock2/denselayer3/relu1/Relu" [id=154, type=Relu]; -"155 QuantizeLinear_/features/denseblock2/denselayer3/relu1/Relu_output_0_1" [id=155, type=QuantizeLinear]; -"156 DequantizeLinear_/features/denseblock2/denselayer3/relu1/Relu_output_0_1" [id=156, type=DequantizeLinear]; -"157 QuantizeLinear_onnx^^Conv_1193_1" [id=157, label="157 QuantizeLinear_onnx::Conv_1193_1", type=QuantizeLinear]; -"158 DequantizeLinear_onnx^^Conv_1193_1" [id=158, label="158 DequantizeLinear_onnx::Conv_1193_1", type=DequantizeLinear]; -"159 /features/denseblock2/denselayer3/conv1/Conv" [id=159, type=Conv]; -"160 /features/denseblock2/denselayer3/relu2/Relu" [id=160, type=Relu]; -"161 QuantizeLinear_/features/denseblock2/denselayer3/relu2/Relu_output_0_1" [id=161, type=QuantizeLinear]; -"162 DequantizeLinear_/features/denseblock2/denselayer3/relu2/Relu_output_0_1" [id=162, type=DequantizeLinear]; -"163 QuantizeLinear_features.denseblock2.denselayer3.conv2.weight_1" [id=163, type=QuantizeLinear]; -"164 DequantizeLinear_features.denseblock2.denselayer3.conv2.weight_1" [id=164, type=DequantizeLinear]; -"165 /features/denseblock2/denselayer3/conv2/Conv" [id=165, type=Conv]; -"166 QuantizeLinear_/features/denseblock2/denselayer3/conv2/Conv_output_0_1" [id=166, type=QuantizeLinear]; -"167 DequantizeLinear_/features/denseblock2/denselayer3/conv2/Conv_output_0_1" [id=167, type=DequantizeLinear]; -"168 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DequantizeLinear_/features/denseblock2/denselayer4/relu2/Relu_output_0_1" [id=178, type=DequantizeLinear]; -"179 QuantizeLinear_features.denseblock2.denselayer4.conv2.weight_1" [id=179, type=QuantizeLinear]; -"180 DequantizeLinear_features.denseblock2.denselayer4.conv2.weight_1" [id=180, type=DequantizeLinear]; -"181 /features/denseblock2/denselayer4/conv2/Conv" [id=181, type=Conv]; -"182 QuantizeLinear_/features/denseblock2/denselayer4/conv2/Conv_output_0_1" [id=182, type=QuantizeLinear]; -"183 DequantizeLinear_/features/denseblock2/denselayer4/conv2/Conv_output_0_1" [id=183, type=DequantizeLinear]; -"184 /features/denseblock2/denselayer5/Concat" [id=184, type=Concat]; -"185 /features/denseblock2/denselayer5/norm1/BatchNormalization" [id=185, type=BatchNormalization]; -"186 /features/denseblock2/denselayer5/relu1/Relu" [id=186, type=Relu]; -"187 QuantizeLinear_/features/denseblock2/denselayer5/relu1/Relu_output_0_1" [id=187, type=QuantizeLinear]; -"188 DequantizeLinear_/features/denseblock2/denselayer5/relu1/Relu_output_0_1" [id=188, type=DequantizeLinear]; -"189 QuantizeLinear_onnx^^Conv_1199_1" [id=189, label="189 QuantizeLinear_onnx::Conv_1199_1", type=QuantizeLinear]; -"190 DequantizeLinear_onnx^^Conv_1199_1" [id=190, label="190 DequantizeLinear_onnx::Conv_1199_1", type=DequantizeLinear]; -"191 /features/denseblock2/denselayer5/conv1/Conv" [id=191, type=Conv]; -"192 /features/denseblock2/denselayer5/relu2/Relu" [id=192, type=Relu]; -"193 QuantizeLinear_/features/denseblock2/denselayer5/relu2/Relu_output_0_1" [id=193, type=QuantizeLinear]; -"194 DequantizeLinear_/features/denseblock2/denselayer5/relu2/Relu_output_0_1" [id=194, type=DequantizeLinear]; -"195 QuantizeLinear_features.denseblock2.denselayer5.conv2.weight_1" [id=195, type=QuantizeLinear]; -"196 DequantizeLinear_features.denseblock2.denselayer5.conv2.weight_1" [id=196, type=DequantizeLinear]; -"197 /features/denseblock2/denselayer5/conv2/Conv" [id=197, type=Conv]; -"198 QuantizeLinear_/features/denseblock2/denselayer5/conv2/Conv_output_0_1" [id=198, type=QuantizeLinear]; -"199 DequantizeLinear_/features/denseblock2/denselayer5/conv2/Conv_output_0_1" [id=199, type=DequantizeLinear]; -"200 /features/denseblock2/denselayer6/Concat" [id=200, type=Concat]; -"201 /features/denseblock2/denselayer6/norm1/BatchNormalization" [id=201, type=BatchNormalization]; -"202 /features/denseblock2/denselayer6/relu1/Relu" [id=202, type=Relu]; -"203 QuantizeLinear_/features/denseblock2/denselayer6/relu1/Relu_output_0_1" [id=203, type=QuantizeLinear]; -"204 DequantizeLinear_/features/denseblock2/denselayer6/relu1/Relu_output_0_1" [id=204, type=DequantizeLinear]; -"205 QuantizeLinear_onnx^^Conv_1202_1" [id=205, label="205 QuantizeLinear_onnx::Conv_1202_1", type=QuantizeLinear]; -"206 DequantizeLinear_onnx^^Conv_1202_1" [id=206, label="206 DequantizeLinear_onnx::Conv_1202_1", type=DequantizeLinear]; -"207 /features/denseblock2/denselayer6/conv1/Conv" [id=207, type=Conv]; 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/features/denseblock2/denselayer7/relu1/Relu" [id=218, type=Relu]; -"219 QuantizeLinear_/features/denseblock2/denselayer7/relu1/Relu_output_0_1" [id=219, type=QuantizeLinear]; -"220 DequantizeLinear_/features/denseblock2/denselayer7/relu1/Relu_output_0_1" [id=220, type=DequantizeLinear]; -"221 QuantizeLinear_onnx^^Conv_1205_1" [id=221, label="221 QuantizeLinear_onnx::Conv_1205_1", type=QuantizeLinear]; -"222 DequantizeLinear_onnx^^Conv_1205_1" [id=222, label="222 DequantizeLinear_onnx::Conv_1205_1", type=DequantizeLinear]; -"223 /features/denseblock2/denselayer7/conv1/Conv" [id=223, type=Conv]; -"224 /features/denseblock2/denselayer7/relu2/Relu" [id=224, type=Relu]; -"225 QuantizeLinear_/features/denseblock2/denselayer7/relu2/Relu_output_0_1" [id=225, type=QuantizeLinear]; -"226 DequantizeLinear_/features/denseblock2/denselayer7/relu2/Relu_output_0_1" [id=226, type=DequantizeLinear]; -"227 QuantizeLinear_features.denseblock2.denselayer7.conv2.weight_1" [id=227, type=QuantizeLinear]; 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DequantizeLinear_onnx^^Conv_1208_1" [id=238, label="238 DequantizeLinear_onnx::Conv_1208_1", type=DequantizeLinear]; -"239 /features/denseblock2/denselayer8/conv1/Conv" [id=239, type=Conv]; -"240 /features/denseblock2/denselayer8/relu2/Relu" [id=240, type=Relu]; -"241 QuantizeLinear_/features/denseblock2/denselayer8/relu2/Relu_output_0_1" [id=241, type=QuantizeLinear]; -"242 DequantizeLinear_/features/denseblock2/denselayer8/relu2/Relu_output_0_1" [id=242, type=DequantizeLinear]; -"243 QuantizeLinear_features.denseblock2.denselayer8.conv2.weight_1" [id=243, type=QuantizeLinear]; -"244 DequantizeLinear_features.denseblock2.denselayer8.conv2.weight_1" [id=244, type=DequantizeLinear]; -"245 /features/denseblock2/denselayer8/conv2/Conv" [id=245, type=Conv]; -"246 QuantizeLinear_/features/denseblock2/denselayer8/conv2/Conv_output_0_1" [id=246, type=QuantizeLinear]; -"247 DequantizeLinear_/features/denseblock2/denselayer8/conv2/Conv_output_0_1" [id=247, type=DequantizeLinear]; -"248 /features/denseblock2/denselayer9/Concat" [id=248, type=Concat]; -"249 /features/denseblock2/denselayer9/norm1/BatchNormalization" [id=249, type=BatchNormalization]; -"250 /features/denseblock2/denselayer9/relu1/Relu" [id=250, type=Relu]; -"251 QuantizeLinear_/features/denseblock2/denselayer9/relu1/Relu_output_0_1" [id=251, type=QuantizeLinear]; -"252 DequantizeLinear_/features/denseblock2/denselayer9/relu1/Relu_output_0_1" [id=252, type=DequantizeLinear]; -"253 QuantizeLinear_onnx^^Conv_1211_1" [id=253, label="253 QuantizeLinear_onnx::Conv_1211_1", type=QuantizeLinear]; -"254 DequantizeLinear_onnx^^Conv_1211_1" [id=254, label="254 DequantizeLinear_onnx::Conv_1211_1", type=DequantizeLinear]; -"255 /features/denseblock2/denselayer9/conv1/Conv" [id=255, type=Conv]; -"256 /features/denseblock2/denselayer9/relu2/Relu" [id=256, type=Relu]; -"257 QuantizeLinear_/features/denseblock2/denselayer9/relu2/Relu_output_0_1" [id=257, type=QuantizeLinear]; -"258 DequantizeLinear_/features/denseblock2/denselayer9/relu2/Relu_output_0_1" [id=258, type=DequantizeLinear]; -"259 QuantizeLinear_features.denseblock2.denselayer9.conv2.weight_1" [id=259, type=QuantizeLinear]; -"260 DequantizeLinear_features.denseblock2.denselayer9.conv2.weight_1" [id=260, type=DequantizeLinear]; -"261 /features/denseblock2/denselayer9/conv2/Conv" [id=261, type=Conv]; -"262 QuantizeLinear_/features/denseblock2/denselayer9/conv2/Conv_output_0_1" [id=262, type=QuantizeLinear]; -"263 DequantizeLinear_/features/denseblock2/denselayer9/conv2/Conv_output_0_1" [id=263, type=DequantizeLinear]; -"264 /features/denseblock2/denselayer10/Concat" [id=264, type=Concat]; -"265 /features/denseblock2/denselayer10/norm1/BatchNormalization" [id=265, type=BatchNormalization]; -"266 /features/denseblock2/denselayer10/relu1/Relu" [id=266, type=Relu]; -"267 QuantizeLinear_/features/denseblock2/denselayer10/relu1/Relu_output_0_1" [id=267, type=QuantizeLinear]; -"268 DequantizeLinear_/features/denseblock2/denselayer10/relu1/Relu_output_0_1" [id=268, type=DequantizeLinear]; -"269 QuantizeLinear_onnx^^Conv_1214_1" [id=269, label="269 QuantizeLinear_onnx::Conv_1214_1", type=QuantizeLinear]; -"270 DequantizeLinear_onnx^^Conv_1214_1" [id=270, label="270 DequantizeLinear_onnx::Conv_1214_1", type=DequantizeLinear]; -"271 /features/denseblock2/denselayer10/conv1/Conv" [id=271, type=Conv]; -"272 /features/denseblock2/denselayer10/relu2/Relu" [id=272, type=Relu]; -"273 QuantizeLinear_/features/denseblock2/denselayer10/relu2/Relu_output_0_1" [id=273, type=QuantizeLinear]; -"274 DequantizeLinear_/features/denseblock2/denselayer10/relu2/Relu_output_0_1" [id=274, type=DequantizeLinear]; -"275 QuantizeLinear_features.denseblock2.denselayer10.conv2.weight_1" [id=275, type=QuantizeLinear]; -"276 DequantizeLinear_features.denseblock2.denselayer10.conv2.weight_1" [id=276, type=DequantizeLinear]; -"277 /features/denseblock2/denselayer10/conv2/Conv" [id=277, type=Conv]; -"278 QuantizeLinear_/features/denseblock2/denselayer10/conv2/Conv_output_0_1" [id=278, type=QuantizeLinear]; -"279 DequantizeLinear_/features/denseblock2/denselayer10/conv2/Conv_output_0_1" [id=279, type=DequantizeLinear]; -"280 /features/denseblock2/denselayer11/Concat" [id=280, type=Concat]; -"281 /features/denseblock2/denselayer11/norm1/BatchNormalization" [id=281, type=BatchNormalization]; -"282 /features/denseblock2/denselayer11/relu1/Relu" [id=282, type=Relu]; -"283 QuantizeLinear_/features/denseblock2/denselayer11/relu1/Relu_output_0_1" [id=283, type=QuantizeLinear]; -"284 DequantizeLinear_/features/denseblock2/denselayer11/relu1/Relu_output_0_1" [id=284, type=DequantizeLinear]; -"285 QuantizeLinear_onnx^^Conv_1217_1" [id=285, label="285 QuantizeLinear_onnx::Conv_1217_1", type=QuantizeLinear]; -"286 DequantizeLinear_onnx^^Conv_1217_1" [id=286, label="286 DequantizeLinear_onnx::Conv_1217_1", type=DequantizeLinear]; -"287 /features/denseblock2/denselayer11/conv1/Conv" [id=287, type=Conv]; -"288 /features/denseblock2/denselayer11/relu2/Relu" [id=288, type=Relu]; -"289 QuantizeLinear_/features/denseblock2/denselayer11/relu2/Relu_output_0_1" [id=289, type=QuantizeLinear]; -"290 DequantizeLinear_/features/denseblock2/denselayer11/relu2/Relu_output_0_1" [id=290, type=DequantizeLinear]; -"291 QuantizeLinear_features.denseblock2.denselayer11.conv2.weight_1" [id=291, type=QuantizeLinear]; -"292 DequantizeLinear_features.denseblock2.denselayer11.conv2.weight_1" [id=292, type=DequantizeLinear]; -"293 /features/denseblock2/denselayer11/conv2/Conv" [id=293, type=Conv]; -"294 QuantizeLinear_/features/denseblock2/denselayer11/conv2/Conv_output_0_1" [id=294, type=QuantizeLinear]; -"295 DequantizeLinear_/features/denseblock2/denselayer11/conv2/Conv_output_0_1" [id=295, type=DequantizeLinear]; -"296 /features/denseblock2/denselayer12/Concat" [id=296, type=Concat]; -"297 /features/denseblock2/denselayer12/norm1/BatchNormalization" [id=297, type=BatchNormalization]; 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type=QuantizeLinear]; -"308 DequantizeLinear_features.denseblock2.denselayer12.conv2.weight_1" [id=308, type=DequantizeLinear]; -"309 /features/denseblock2/denselayer12/conv2/Conv" [id=309, type=Conv]; -"310 QuantizeLinear_/features/denseblock2/denselayer12/conv2/Conv_output_0_1" [id=310, type=QuantizeLinear]; -"311 DequantizeLinear_/features/denseblock2/denselayer12/conv2/Conv_output_0_1" [id=311, type=DequantizeLinear]; -"312 /features/denseblock2/Concat" [id=312, type=Concat]; -"313 /features/transition2/norm/BatchNormalization" [id=313, type=BatchNormalization]; -"314 /features/transition2/relu/Relu" [id=314, type=Relu]; -"315 QuantizeLinear_/features/transition2/relu/Relu_output_0_1" [id=315, type=QuantizeLinear]; -"316 DequantizeLinear_/features/transition2/relu/Relu_output_0_1" [id=316, type=DequantizeLinear]; -"317 QuantizeLinear_features.transition2.conv.weight_1" [id=317, type=QuantizeLinear]; -"318 DequantizeLinear_features.transition2.conv.weight_1" [id=318, type=DequantizeLinear]; -"319 /features/transition2/conv/Conv" [id=319, type=Conv]; -"320 /features/transition2/pool/Constant" [id=320, type=Constant]; -"321 QuantizeLinear_/features/transition2/conv/Conv_output_0_1" [id=321, type=QuantizeLinear]; -"322 DequantizeLinear_/features/transition2/conv/Conv_output_0_1" [id=322, type=DequantizeLinear]; -"323 /features/transition2/pool/Pad" [id=323, type=Pad]; -"324 /features/transition2/pool/AveragePool" [id=324, type=AveragePool]; -"325 QuantizeLinear_/features/transition2/pool/AveragePool_output_0_1" [id=325, type=QuantizeLinear]; -"326 DequantizeLinear_/features/transition2/pool/AveragePool_output_0_1" [id=326, type=DequantizeLinear]; -"327 /features/denseblock3/denselayer1/Concat" [id=327, type=Concat]; -"328 /features/denseblock3/denselayer1/norm1/BatchNormalization" [id=328, type=BatchNormalization]; -"329 /features/denseblock3/denselayer1/relu1/Relu" [id=329, type=Relu]; -"330 QuantizeLinear_/features/denseblock3/denselayer1/relu1/Relu_output_0_1" [id=330, type=QuantizeLinear]; -"331 DequantizeLinear_/features/denseblock3/denselayer1/relu1/Relu_output_0_1" [id=331, type=DequantizeLinear]; -"332 QuantizeLinear_onnx^^Conv_1223_1" [id=332, label="332 QuantizeLinear_onnx::Conv_1223_1", type=QuantizeLinear]; -"333 DequantizeLinear_onnx^^Conv_1223_1" [id=333, label="333 DequantizeLinear_onnx::Conv_1223_1", type=DequantizeLinear]; -"334 /features/denseblock3/denselayer1/conv1/Conv" [id=334, type=Conv]; -"335 /features/denseblock3/denselayer1/relu2/Relu" [id=335, type=Relu]; -"336 QuantizeLinear_/features/denseblock3/denselayer1/relu2/Relu_output_0_1" [id=336, type=QuantizeLinear]; -"337 DequantizeLinear_/features/denseblock3/denselayer1/relu2/Relu_output_0_1" [id=337, type=DequantizeLinear]; -"338 QuantizeLinear_features.denseblock3.denselayer1.conv2.weight_1" [id=338, type=QuantizeLinear]; -"339 DequantizeLinear_features.denseblock3.denselayer1.conv2.weight_1" [id=339, type=DequantizeLinear]; -"340 /features/denseblock3/denselayer1/conv2/Conv" [id=340, type=Conv]; -"341 QuantizeLinear_/features/denseblock3/denselayer1/conv2/Conv_output_0_1" [id=341, type=QuantizeLinear]; -"342 DequantizeLinear_/features/denseblock3/denselayer1/conv2/Conv_output_0_1" [id=342, type=DequantizeLinear]; -"343 /features/denseblock3/denselayer2/Concat" [id=343, type=Concat]; -"344 /features/denseblock3/denselayer2/norm1/BatchNormalization" [id=344, type=BatchNormalization]; -"345 /features/denseblock3/denselayer2/relu1/Relu" [id=345, type=Relu]; -"346 QuantizeLinear_/features/denseblock3/denselayer2/relu1/Relu_output_0_1" [id=346, type=QuantizeLinear]; -"347 DequantizeLinear_/features/denseblock3/denselayer2/relu1/Relu_output_0_1" [id=347, type=DequantizeLinear]; -"348 QuantizeLinear_onnx^^Conv_1226_1" [id=348, label="348 QuantizeLinear_onnx::Conv_1226_1", type=QuantizeLinear]; -"349 DequantizeLinear_onnx^^Conv_1226_1" [id=349, label="349 DequantizeLinear_onnx::Conv_1226_1", type=DequantizeLinear]; -"350 /features/denseblock3/denselayer2/conv1/Conv" [id=350, type=Conv]; -"351 /features/denseblock3/denselayer2/relu2/Relu" [id=351, type=Relu]; -"352 QuantizeLinear_/features/denseblock3/denselayer2/relu2/Relu_output_0_1" [id=352, type=QuantizeLinear]; -"353 DequantizeLinear_/features/denseblock3/denselayer2/relu2/Relu_output_0_1" [id=353, type=DequantizeLinear]; -"354 QuantizeLinear_features.denseblock3.denselayer2.conv2.weight_1" [id=354, type=QuantizeLinear]; -"355 DequantizeLinear_features.denseblock3.denselayer2.conv2.weight_1" [id=355, type=DequantizeLinear]; -"356 /features/denseblock3/denselayer2/conv2/Conv" [id=356, type=Conv]; -"357 QuantizeLinear_/features/denseblock3/denselayer2/conv2/Conv_output_0_1" [id=357, type=QuantizeLinear]; -"358 DequantizeLinear_/features/denseblock3/denselayer2/conv2/Conv_output_0_1" [id=358, type=DequantizeLinear]; -"359 /features/denseblock3/denselayer3/Concat" [id=359, type=Concat]; -"360 /features/denseblock3/denselayer3/norm1/BatchNormalization" [id=360, type=BatchNormalization]; -"361 /features/denseblock3/denselayer3/relu1/Relu" [id=361, type=Relu]; -"362 QuantizeLinear_/features/denseblock3/denselayer3/relu1/Relu_output_0_1" [id=362, type=QuantizeLinear]; -"363 DequantizeLinear_/features/denseblock3/denselayer3/relu1/Relu_output_0_1" [id=363, type=DequantizeLinear]; -"364 QuantizeLinear_onnx^^Conv_1229_1" [id=364, label="364 QuantizeLinear_onnx::Conv_1229_1", type=QuantizeLinear]; -"365 DequantizeLinear_onnx^^Conv_1229_1" [id=365, label="365 DequantizeLinear_onnx::Conv_1229_1", type=DequantizeLinear]; -"366 /features/denseblock3/denselayer3/conv1/Conv" [id=366, type=Conv]; -"367 /features/denseblock3/denselayer3/relu2/Relu" [id=367, type=Relu]; -"368 QuantizeLinear_/features/denseblock3/denselayer3/relu2/Relu_output_0_1" [id=368, type=QuantizeLinear]; -"369 DequantizeLinear_/features/denseblock3/denselayer3/relu2/Relu_output_0_1" [id=369, type=DequantizeLinear]; -"370 QuantizeLinear_features.denseblock3.denselayer3.conv2.weight_1" [id=370, type=QuantizeLinear]; -"371 DequantizeLinear_features.denseblock3.denselayer3.conv2.weight_1" [id=371, type=DequantizeLinear]; -"372 /features/denseblock3/denselayer3/conv2/Conv" [id=372, type=Conv]; -"373 QuantizeLinear_/features/denseblock3/denselayer3/conv2/Conv_output_0_1" [id=373, type=QuantizeLinear]; -"374 DequantizeLinear_/features/denseblock3/denselayer3/conv2/Conv_output_0_1" [id=374, type=DequantizeLinear]; -"375 /features/denseblock3/denselayer4/Concat" [id=375, type=Concat]; -"376 /features/denseblock3/denselayer4/norm1/BatchNormalization" [id=376, type=BatchNormalization]; -"377 /features/denseblock3/denselayer4/relu1/Relu" [id=377, type=Relu]; -"378 QuantizeLinear_/features/denseblock3/denselayer4/relu1/Relu_output_0_1" [id=378, type=QuantizeLinear]; -"379 DequantizeLinear_/features/denseblock3/denselayer4/relu1/Relu_output_0_1" [id=379, type=DequantizeLinear]; -"380 QuantizeLinear_onnx^^Conv_1232_1" [id=380, label="380 QuantizeLinear_onnx::Conv_1232_1", type=QuantizeLinear]; -"381 DequantizeLinear_onnx^^Conv_1232_1" [id=381, label="381 DequantizeLinear_onnx::Conv_1232_1", type=DequantizeLinear]; -"382 /features/denseblock3/denselayer4/conv1/Conv" [id=382, type=Conv]; -"383 /features/denseblock3/denselayer4/relu2/Relu" [id=383, type=Relu]; -"384 QuantizeLinear_/features/denseblock3/denselayer4/relu2/Relu_output_0_1" [id=384, type=QuantizeLinear]; -"385 DequantizeLinear_/features/denseblock3/denselayer4/relu2/Relu_output_0_1" [id=385, type=DequantizeLinear]; -"386 QuantizeLinear_features.denseblock3.denselayer4.conv2.weight_1" [id=386, type=QuantizeLinear]; -"387 DequantizeLinear_features.denseblock3.denselayer4.conv2.weight_1" [id=387, type=DequantizeLinear]; -"388 /features/denseblock3/denselayer4/conv2/Conv" [id=388, type=Conv]; -"389 QuantizeLinear_/features/denseblock3/denselayer4/conv2/Conv_output_0_1" [id=389, type=QuantizeLinear]; -"390 DequantizeLinear_/features/denseblock3/denselayer4/conv2/Conv_output_0_1" [id=390, type=DequantizeLinear]; -"391 /features/denseblock3/denselayer5/Concat" [id=391, type=Concat]; -"392 /features/denseblock3/denselayer5/norm1/BatchNormalization" [id=392, type=BatchNormalization]; -"393 /features/denseblock3/denselayer5/relu1/Relu" [id=393, type=Relu]; -"394 QuantizeLinear_/features/denseblock3/denselayer5/relu1/Relu_output_0_1" [id=394, type=QuantizeLinear]; -"395 DequantizeLinear_/features/denseblock3/denselayer5/relu1/Relu_output_0_1" [id=395, type=DequantizeLinear]; -"396 QuantizeLinear_onnx^^Conv_1235_1" [id=396, label="396 QuantizeLinear_onnx::Conv_1235_1", type=QuantizeLinear]; -"397 DequantizeLinear_onnx^^Conv_1235_1" [id=397, label="397 DequantizeLinear_onnx::Conv_1235_1", type=DequantizeLinear]; -"398 /features/denseblock3/denselayer5/conv1/Conv" [id=398, type=Conv]; -"399 /features/denseblock3/denselayer5/relu2/Relu" [id=399, type=Relu]; -"400 QuantizeLinear_/features/denseblock3/denselayer5/relu2/Relu_output_0_1" [id=400, type=QuantizeLinear]; -"401 DequantizeLinear_/features/denseblock3/denselayer5/relu2/Relu_output_0_1" [id=401, type=DequantizeLinear]; -"402 QuantizeLinear_features.denseblock3.denselayer5.conv2.weight_1" [id=402, type=QuantizeLinear]; -"403 DequantizeLinear_features.denseblock3.denselayer5.conv2.weight_1" [id=403, type=DequantizeLinear]; -"404 /features/denseblock3/denselayer5/conv2/Conv" [id=404, type=Conv]; -"405 QuantizeLinear_/features/denseblock3/denselayer5/conv2/Conv_output_0_1" [id=405, type=QuantizeLinear]; -"406 DequantizeLinear_/features/denseblock3/denselayer5/conv2/Conv_output_0_1" [id=406, type=DequantizeLinear]; -"407 /features/denseblock3/denselayer6/Concat" [id=407, type=Concat]; -"408 /features/denseblock3/denselayer6/norm1/BatchNormalization" [id=408, type=BatchNormalization]; -"409 /features/denseblock3/denselayer6/relu1/Relu" [id=409, type=Relu]; -"410 QuantizeLinear_/features/denseblock3/denselayer6/relu1/Relu_output_0_1" [id=410, type=QuantizeLinear]; -"411 DequantizeLinear_/features/denseblock3/denselayer6/relu1/Relu_output_0_1" [id=411, type=DequantizeLinear]; -"412 QuantizeLinear_onnx^^Conv_1238_1" [id=412, label="412 QuantizeLinear_onnx::Conv_1238_1", type=QuantizeLinear]; -"413 DequantizeLinear_onnx^^Conv_1238_1" [id=413, label="413 DequantizeLinear_onnx::Conv_1238_1", type=DequantizeLinear]; -"414 /features/denseblock3/denselayer6/conv1/Conv" [id=414, type=Conv]; -"415 /features/denseblock3/denselayer6/relu2/Relu" [id=415, type=Relu]; -"416 QuantizeLinear_/features/denseblock3/denselayer6/relu2/Relu_output_0_1" [id=416, type=QuantizeLinear]; -"417 DequantizeLinear_/features/denseblock3/denselayer6/relu2/Relu_output_0_1" [id=417, type=DequantizeLinear]; -"418 QuantizeLinear_features.denseblock3.denselayer6.conv2.weight_1" [id=418, type=QuantizeLinear]; -"419 DequantizeLinear_features.denseblock3.denselayer6.conv2.weight_1" [id=419, type=DequantizeLinear]; -"420 /features/denseblock3/denselayer6/conv2/Conv" [id=420, type=Conv]; -"421 QuantizeLinear_/features/denseblock3/denselayer6/conv2/Conv_output_0_1" [id=421, type=QuantizeLinear]; -"422 DequantizeLinear_/features/denseblock3/denselayer6/conv2/Conv_output_0_1" [id=422, type=DequantizeLinear]; -"423 /features/denseblock3/denselayer7/Concat" [id=423, type=Concat]; -"424 /features/denseblock3/denselayer7/norm1/BatchNormalization" [id=424, type=BatchNormalization]; -"425 /features/denseblock3/denselayer7/relu1/Relu" [id=425, type=Relu]; -"426 QuantizeLinear_/features/denseblock3/denselayer7/relu1/Relu_output_0_1" [id=426, type=QuantizeLinear]; -"427 DequantizeLinear_/features/denseblock3/denselayer7/relu1/Relu_output_0_1" [id=427, type=DequantizeLinear]; -"428 QuantizeLinear_onnx^^Conv_1241_1" [id=428, label="428 QuantizeLinear_onnx::Conv_1241_1", type=QuantizeLinear]; -"429 DequantizeLinear_onnx^^Conv_1241_1" [id=429, label="429 DequantizeLinear_onnx::Conv_1241_1", type=DequantizeLinear]; -"430 /features/denseblock3/denselayer7/conv1/Conv" [id=430, type=Conv]; -"431 /features/denseblock3/denselayer7/relu2/Relu" [id=431, type=Relu]; -"432 QuantizeLinear_/features/denseblock3/denselayer7/relu2/Relu_output_0_1" [id=432, type=QuantizeLinear]; -"433 DequantizeLinear_/features/denseblock3/denselayer7/relu2/Relu_output_0_1" [id=433, type=DequantizeLinear]; -"434 QuantizeLinear_features.denseblock3.denselayer7.conv2.weight_1" [id=434, type=QuantizeLinear]; -"435 DequantizeLinear_features.denseblock3.denselayer7.conv2.weight_1" [id=435, type=DequantizeLinear]; -"436 /features/denseblock3/denselayer7/conv2/Conv" [id=436, type=Conv]; -"437 QuantizeLinear_/features/denseblock3/denselayer7/conv2/Conv_output_0_1" [id=437, type=QuantizeLinear]; -"438 DequantizeLinear_/features/denseblock3/denselayer7/conv2/Conv_output_0_1" [id=438, type=DequantizeLinear]; -"439 /features/denseblock3/denselayer8/Concat" [id=439, type=Concat]; -"440 /features/denseblock3/denselayer8/norm1/BatchNormalization" [id=440, type=BatchNormalization]; -"441 /features/denseblock3/denselayer8/relu1/Relu" [id=441, type=Relu]; -"442 QuantizeLinear_/features/denseblock3/denselayer8/relu1/Relu_output_0_1" [id=442, type=QuantizeLinear]; -"443 DequantizeLinear_/features/denseblock3/denselayer8/relu1/Relu_output_0_1" [id=443, type=DequantizeLinear]; -"444 QuantizeLinear_onnx^^Conv_1244_1" [id=444, label="444 QuantizeLinear_onnx::Conv_1244_1", type=QuantizeLinear]; -"445 DequantizeLinear_onnx^^Conv_1244_1" [id=445, label="445 DequantizeLinear_onnx::Conv_1244_1", type=DequantizeLinear]; -"446 /features/denseblock3/denselayer8/conv1/Conv" [id=446, type=Conv]; -"447 /features/denseblock3/denselayer8/relu2/Relu" [id=447, type=Relu]; -"448 QuantizeLinear_/features/denseblock3/denselayer8/relu2/Relu_output_0_1" [id=448, type=QuantizeLinear]; -"449 DequantizeLinear_/features/denseblock3/denselayer8/relu2/Relu_output_0_1" [id=449, type=DequantizeLinear]; -"450 QuantizeLinear_features.denseblock3.denselayer8.conv2.weight_1" [id=450, type=QuantizeLinear]; -"451 DequantizeLinear_features.denseblock3.denselayer8.conv2.weight_1" [id=451, type=DequantizeLinear]; -"452 /features/denseblock3/denselayer8/conv2/Conv" [id=452, type=Conv]; -"453 QuantizeLinear_/features/denseblock3/denselayer8/conv2/Conv_output_0_1" [id=453, type=QuantizeLinear]; -"454 DequantizeLinear_/features/denseblock3/denselayer8/conv2/Conv_output_0_1" [id=454, type=DequantizeLinear]; -"455 /features/denseblock3/denselayer9/Concat" [id=455, type=Concat]; -"456 /features/denseblock3/denselayer9/norm1/BatchNormalization" [id=456, type=BatchNormalization]; -"457 /features/denseblock3/denselayer9/relu1/Relu" [id=457, type=Relu]; -"458 QuantizeLinear_/features/denseblock3/denselayer9/relu1/Relu_output_0_1" [id=458, type=QuantizeLinear]; -"459 DequantizeLinear_/features/denseblock3/denselayer9/relu1/Relu_output_0_1" [id=459, type=DequantizeLinear]; -"460 QuantizeLinear_onnx^^Conv_1247_1" [id=460, label="460 QuantizeLinear_onnx::Conv_1247_1", type=QuantizeLinear]; -"461 DequantizeLinear_onnx^^Conv_1247_1" [id=461, label="461 DequantizeLinear_onnx::Conv_1247_1", type=DequantizeLinear]; -"462 /features/denseblock3/denselayer9/conv1/Conv" [id=462, type=Conv]; -"463 /features/denseblock3/denselayer9/relu2/Relu" [id=463, type=Relu]; -"464 QuantizeLinear_/features/denseblock3/denselayer9/relu2/Relu_output_0_1" [id=464, type=QuantizeLinear]; -"465 DequantizeLinear_/features/denseblock3/denselayer9/relu2/Relu_output_0_1" [id=465, type=DequantizeLinear]; -"466 QuantizeLinear_features.denseblock3.denselayer9.conv2.weight_1" [id=466, type=QuantizeLinear]; -"467 DequantizeLinear_features.denseblock3.denselayer9.conv2.weight_1" [id=467, type=DequantizeLinear]; -"468 /features/denseblock3/denselayer9/conv2/Conv" [id=468, type=Conv]; -"469 QuantizeLinear_/features/denseblock3/denselayer9/conv2/Conv_output_0_1" [id=469, type=QuantizeLinear]; -"470 DequantizeLinear_/features/denseblock3/denselayer9/conv2/Conv_output_0_1" [id=470, type=DequantizeLinear]; -"471 /features/denseblock3/denselayer10/Concat" [id=471, type=Concat]; -"472 /features/denseblock3/denselayer10/norm1/BatchNormalization" [id=472, type=BatchNormalization]; -"473 /features/denseblock3/denselayer10/relu1/Relu" [id=473, type=Relu]; -"474 QuantizeLinear_/features/denseblock3/denselayer10/relu1/Relu_output_0_1" [id=474, type=QuantizeLinear]; -"475 DequantizeLinear_/features/denseblock3/denselayer10/relu1/Relu_output_0_1" [id=475, type=DequantizeLinear]; -"476 QuantizeLinear_onnx^^Conv_1250_1" [id=476, label="476 QuantizeLinear_onnx::Conv_1250_1", type=QuantizeLinear]; -"477 DequantizeLinear_onnx^^Conv_1250_1" [id=477, label="477 DequantizeLinear_onnx::Conv_1250_1", type=DequantizeLinear]; -"478 /features/denseblock3/denselayer10/conv1/Conv" [id=478, type=Conv]; -"479 /features/denseblock3/denselayer10/relu2/Relu" [id=479, type=Relu]; -"480 QuantizeLinear_/features/denseblock3/denselayer10/relu2/Relu_output_0_1" [id=480, type=QuantizeLinear]; -"481 DequantizeLinear_/features/denseblock3/denselayer10/relu2/Relu_output_0_1" [id=481, type=DequantizeLinear]; -"482 QuantizeLinear_features.denseblock3.denselayer10.conv2.weight_1" [id=482, type=QuantizeLinear]; -"483 DequantizeLinear_features.denseblock3.denselayer10.conv2.weight_1" [id=483, type=DequantizeLinear]; -"484 /features/denseblock3/denselayer10/conv2/Conv" [id=484, type=Conv]; -"485 QuantizeLinear_/features/denseblock3/denselayer10/conv2/Conv_output_0_1" [id=485, type=QuantizeLinear]; -"486 DequantizeLinear_/features/denseblock3/denselayer10/conv2/Conv_output_0_1" [id=486, type=DequantizeLinear]; -"487 /features/denseblock3/denselayer11/Concat" [id=487, type=Concat]; -"488 /features/denseblock3/denselayer11/norm1/BatchNormalization" [id=488, type=BatchNormalization]; -"489 /features/denseblock3/denselayer11/relu1/Relu" [id=489, type=Relu]; -"490 QuantizeLinear_/features/denseblock3/denselayer11/relu1/Relu_output_0_1" [id=490, type=QuantizeLinear]; -"491 DequantizeLinear_/features/denseblock3/denselayer11/relu1/Relu_output_0_1" [id=491, type=DequantizeLinear]; -"492 QuantizeLinear_onnx^^Conv_1253_1" [id=492, label="492 QuantizeLinear_onnx::Conv_1253_1", type=QuantizeLinear]; -"493 DequantizeLinear_onnx^^Conv_1253_1" [id=493, label="493 DequantizeLinear_onnx::Conv_1253_1", type=DequantizeLinear]; -"494 /features/denseblock3/denselayer11/conv1/Conv" [id=494, type=Conv]; -"495 /features/denseblock3/denselayer11/relu2/Relu" [id=495, type=Relu]; -"496 QuantizeLinear_/features/denseblock3/denselayer11/relu2/Relu_output_0_1" [id=496, type=QuantizeLinear]; -"497 DequantizeLinear_/features/denseblock3/denselayer11/relu2/Relu_output_0_1" [id=497, type=DequantizeLinear]; -"498 QuantizeLinear_features.denseblock3.denselayer11.conv2.weight_1" [id=498, type=QuantizeLinear]; -"499 DequantizeLinear_features.denseblock3.denselayer11.conv2.weight_1" [id=499, type=DequantizeLinear]; -"500 /features/denseblock3/denselayer11/conv2/Conv" [id=500, type=Conv]; -"501 QuantizeLinear_/features/denseblock3/denselayer11/conv2/Conv_output_0_1" [id=501, type=QuantizeLinear]; -"502 DequantizeLinear_/features/denseblock3/denselayer11/conv2/Conv_output_0_1" [id=502, type=DequantizeLinear]; -"503 /features/denseblock3/denselayer12/Concat" [id=503, type=Concat]; -"504 /features/denseblock3/denselayer12/norm1/BatchNormalization" [id=504, type=BatchNormalization]; -"505 /features/denseblock3/denselayer12/relu1/Relu" [id=505, type=Relu]; -"506 QuantizeLinear_/features/denseblock3/denselayer12/relu1/Relu_output_0_1" [id=506, type=QuantizeLinear]; -"507 DequantizeLinear_/features/denseblock3/denselayer12/relu1/Relu_output_0_1" [id=507, type=DequantizeLinear]; -"508 QuantizeLinear_onnx^^Conv_1256_1" [id=508, label="508 QuantizeLinear_onnx::Conv_1256_1", type=QuantizeLinear]; -"509 DequantizeLinear_onnx^^Conv_1256_1" [id=509, label="509 DequantizeLinear_onnx::Conv_1256_1", type=DequantizeLinear]; -"510 /features/denseblock3/denselayer12/conv1/Conv" [id=510, type=Conv]; -"511 /features/denseblock3/denselayer12/relu2/Relu" [id=511, type=Relu]; -"512 QuantizeLinear_/features/denseblock3/denselayer12/relu2/Relu_output_0_1" [id=512, type=QuantizeLinear]; -"513 DequantizeLinear_/features/denseblock3/denselayer12/relu2/Relu_output_0_1" [id=513, type=DequantizeLinear]; -"514 QuantizeLinear_features.denseblock3.denselayer12.conv2.weight_1" [id=514, type=QuantizeLinear]; -"515 DequantizeLinear_features.denseblock3.denselayer12.conv2.weight_1" [id=515, type=DequantizeLinear]; -"516 /features/denseblock3/denselayer12/conv2/Conv" [id=516, type=Conv]; -"517 QuantizeLinear_/features/denseblock3/denselayer12/conv2/Conv_output_0_1" [id=517, type=QuantizeLinear]; -"518 DequantizeLinear_/features/denseblock3/denselayer12/conv2/Conv_output_0_1" [id=518, type=DequantizeLinear]; -"519 /features/denseblock3/denselayer13/Concat" [id=519, type=Concat]; -"520 /features/denseblock3/denselayer13/norm1/BatchNormalization" [id=520, type=BatchNormalization]; -"521 /features/denseblock3/denselayer13/relu1/Relu" [id=521, type=Relu]; -"522 QuantizeLinear_/features/denseblock3/denselayer13/relu1/Relu_output_0_1" [id=522, type=QuantizeLinear]; -"523 DequantizeLinear_/features/denseblock3/denselayer13/relu1/Relu_output_0_1" [id=523, type=DequantizeLinear]; -"524 QuantizeLinear_onnx^^Conv_1259_1" [id=524, label="524 QuantizeLinear_onnx::Conv_1259_1", type=QuantizeLinear]; -"525 DequantizeLinear_onnx^^Conv_1259_1" [id=525, label="525 DequantizeLinear_onnx::Conv_1259_1", type=DequantizeLinear]; -"526 /features/denseblock3/denselayer13/conv1/Conv" [id=526, type=Conv]; -"527 /features/denseblock3/denselayer13/relu2/Relu" [id=527, type=Relu]; -"528 QuantizeLinear_/features/denseblock3/denselayer13/relu2/Relu_output_0_1" [id=528, type=QuantizeLinear]; -"529 DequantizeLinear_/features/denseblock3/denselayer13/relu2/Relu_output_0_1" [id=529, type=DequantizeLinear]; -"530 QuantizeLinear_features.denseblock3.denselayer13.conv2.weight_1" [id=530, type=QuantizeLinear]; -"531 DequantizeLinear_features.denseblock3.denselayer13.conv2.weight_1" [id=531, type=DequantizeLinear]; -"532 /features/denseblock3/denselayer13/conv2/Conv" [id=532, type=Conv]; -"533 QuantizeLinear_/features/denseblock3/denselayer13/conv2/Conv_output_0_1" [id=533, type=QuantizeLinear]; -"534 DequantizeLinear_/features/denseblock3/denselayer13/conv2/Conv_output_0_1" [id=534, type=DequantizeLinear]; -"535 /features/denseblock3/denselayer14/Concat" [id=535, type=Concat]; -"536 /features/denseblock3/denselayer14/norm1/BatchNormalization" [id=536, type=BatchNormalization]; -"537 /features/denseblock3/denselayer14/relu1/Relu" [id=537, type=Relu]; -"538 QuantizeLinear_/features/denseblock3/denselayer14/relu1/Relu_output_0_1" [id=538, type=QuantizeLinear]; -"539 DequantizeLinear_/features/denseblock3/denselayer14/relu1/Relu_output_0_1" [id=539, type=DequantizeLinear]; -"540 QuantizeLinear_onnx^^Conv_1262_1" [id=540, label="540 QuantizeLinear_onnx::Conv_1262_1", type=QuantizeLinear]; -"541 DequantizeLinear_onnx^^Conv_1262_1" [id=541, label="541 DequantizeLinear_onnx::Conv_1262_1", type=DequantizeLinear]; -"542 /features/denseblock3/denselayer14/conv1/Conv" [id=542, type=Conv]; -"543 /features/denseblock3/denselayer14/relu2/Relu" [id=543, type=Relu]; -"544 QuantizeLinear_/features/denseblock3/denselayer14/relu2/Relu_output_0_1" [id=544, type=QuantizeLinear]; -"545 DequantizeLinear_/features/denseblock3/denselayer14/relu2/Relu_output_0_1" [id=545, type=DequantizeLinear]; -"546 QuantizeLinear_features.denseblock3.denselayer14.conv2.weight_1" [id=546, type=QuantizeLinear]; -"547 DequantizeLinear_features.denseblock3.denselayer14.conv2.weight_1" [id=547, type=DequantizeLinear]; -"548 /features/denseblock3/denselayer14/conv2/Conv" [id=548, type=Conv]; -"549 QuantizeLinear_/features/denseblock3/denselayer14/conv2/Conv_output_0_1" [id=549, type=QuantizeLinear]; -"550 DequantizeLinear_/features/denseblock3/denselayer14/conv2/Conv_output_0_1" [id=550, type=DequantizeLinear]; -"551 /features/denseblock3/denselayer15/Concat" [id=551, type=Concat]; -"552 /features/denseblock3/denselayer15/norm1/BatchNormalization" [id=552, type=BatchNormalization]; -"553 /features/denseblock3/denselayer15/relu1/Relu" [id=553, type=Relu]; -"554 QuantizeLinear_/features/denseblock3/denselayer15/relu1/Relu_output_0_1" [id=554, type=QuantizeLinear]; -"555 DequantizeLinear_/features/denseblock3/denselayer15/relu1/Relu_output_0_1" [id=555, type=DequantizeLinear]; -"556 QuantizeLinear_onnx^^Conv_1265_1" [id=556, label="556 QuantizeLinear_onnx::Conv_1265_1", type=QuantizeLinear]; -"557 DequantizeLinear_onnx^^Conv_1265_1" [id=557, label="557 DequantizeLinear_onnx::Conv_1265_1", type=DequantizeLinear]; -"558 /features/denseblock3/denselayer15/conv1/Conv" [id=558, type=Conv]; -"559 /features/denseblock3/denselayer15/relu2/Relu" [id=559, type=Relu]; -"560 QuantizeLinear_/features/denseblock3/denselayer15/relu2/Relu_output_0_1" [id=560, type=QuantizeLinear]; -"561 DequantizeLinear_/features/denseblock3/denselayer15/relu2/Relu_output_0_1" [id=561, type=DequantizeLinear]; -"562 QuantizeLinear_features.denseblock3.denselayer15.conv2.weight_1" [id=562, type=QuantizeLinear]; -"563 DequantizeLinear_features.denseblock3.denselayer15.conv2.weight_1" [id=563, type=DequantizeLinear]; -"564 /features/denseblock3/denselayer15/conv2/Conv" [id=564, type=Conv]; -"565 QuantizeLinear_/features/denseblock3/denselayer15/conv2/Conv_output_0_1" [id=565, type=QuantizeLinear]; -"566 DequantizeLinear_/features/denseblock3/denselayer15/conv2/Conv_output_0_1" [id=566, type=DequantizeLinear]; -"567 /features/denseblock3/denselayer16/Concat" [id=567, type=Concat]; -"568 /features/denseblock3/denselayer16/norm1/BatchNormalization" [id=568, type=BatchNormalization]; -"569 /features/denseblock3/denselayer16/relu1/Relu" [id=569, type=Relu]; -"570 QuantizeLinear_/features/denseblock3/denselayer16/relu1/Relu_output_0_1" [id=570, type=QuantizeLinear]; -"571 DequantizeLinear_/features/denseblock3/denselayer16/relu1/Relu_output_0_1" [id=571, type=DequantizeLinear]; -"572 QuantizeLinear_onnx^^Conv_1268_1" [id=572, label="572 QuantizeLinear_onnx::Conv_1268_1", type=QuantizeLinear]; -"573 DequantizeLinear_onnx^^Conv_1268_1" [id=573, label="573 DequantizeLinear_onnx::Conv_1268_1", type=DequantizeLinear]; -"574 /features/denseblock3/denselayer16/conv1/Conv" [id=574, type=Conv]; -"575 /features/denseblock3/denselayer16/relu2/Relu" [id=575, type=Relu]; -"576 QuantizeLinear_/features/denseblock3/denselayer16/relu2/Relu_output_0_1" [id=576, type=QuantizeLinear]; -"577 DequantizeLinear_/features/denseblock3/denselayer16/relu2/Relu_output_0_1" [id=577, type=DequantizeLinear]; -"578 QuantizeLinear_features.denseblock3.denselayer16.conv2.weight_1" [id=578, type=QuantizeLinear]; -"579 DequantizeLinear_features.denseblock3.denselayer16.conv2.weight_1" [id=579, type=DequantizeLinear]; -"580 /features/denseblock3/denselayer16/conv2/Conv" [id=580, type=Conv]; -"581 QuantizeLinear_/features/denseblock3/denselayer16/conv2/Conv_output_0_1" [id=581, type=QuantizeLinear]; -"582 DequantizeLinear_/features/denseblock3/denselayer16/conv2/Conv_output_0_1" [id=582, type=DequantizeLinear]; -"583 /features/denseblock3/denselayer17/Concat" [id=583, type=Concat]; -"584 /features/denseblock3/denselayer17/norm1/BatchNormalization" [id=584, type=BatchNormalization]; -"585 /features/denseblock3/denselayer17/relu1/Relu" [id=585, type=Relu]; -"586 QuantizeLinear_/features/denseblock3/denselayer17/relu1/Relu_output_0_1" [id=586, type=QuantizeLinear]; -"587 DequantizeLinear_/features/denseblock3/denselayer17/relu1/Relu_output_0_1" [id=587, type=DequantizeLinear]; -"588 QuantizeLinear_onnx^^Conv_1271_1" [id=588, label="588 QuantizeLinear_onnx::Conv_1271_1", type=QuantizeLinear]; -"589 DequantizeLinear_onnx^^Conv_1271_1" [id=589, label="589 DequantizeLinear_onnx::Conv_1271_1", type=DequantizeLinear]; -"590 /features/denseblock3/denselayer17/conv1/Conv" [id=590, type=Conv]; -"591 /features/denseblock3/denselayer17/relu2/Relu" [id=591, type=Relu]; -"592 QuantizeLinear_/features/denseblock3/denselayer17/relu2/Relu_output_0_1" [id=592, type=QuantizeLinear]; -"593 DequantizeLinear_/features/denseblock3/denselayer17/relu2/Relu_output_0_1" [id=593, type=DequantizeLinear]; -"594 QuantizeLinear_features.denseblock3.denselayer17.conv2.weight_1" [id=594, type=QuantizeLinear]; -"595 DequantizeLinear_features.denseblock3.denselayer17.conv2.weight_1" [id=595, type=DequantizeLinear]; -"596 /features/denseblock3/denselayer17/conv2/Conv" [id=596, type=Conv]; -"597 QuantizeLinear_/features/denseblock3/denselayer17/conv2/Conv_output_0_1" [id=597, type=QuantizeLinear]; -"598 DequantizeLinear_/features/denseblock3/denselayer17/conv2/Conv_output_0_1" [id=598, type=DequantizeLinear]; -"599 /features/denseblock3/denselayer18/Concat" [id=599, type=Concat]; -"600 /features/denseblock3/denselayer18/norm1/BatchNormalization" [id=600, type=BatchNormalization]; -"601 /features/denseblock3/denselayer18/relu1/Relu" [id=601, type=Relu]; -"602 QuantizeLinear_/features/denseblock3/denselayer18/relu1/Relu_output_0_1" [id=602, type=QuantizeLinear]; -"603 DequantizeLinear_/features/denseblock3/denselayer18/relu1/Relu_output_0_1" [id=603, type=DequantizeLinear]; -"604 QuantizeLinear_onnx^^Conv_1274_1" [id=604, label="604 QuantizeLinear_onnx::Conv_1274_1", type=QuantizeLinear]; -"605 DequantizeLinear_onnx^^Conv_1274_1" [id=605, label="605 DequantizeLinear_onnx::Conv_1274_1", type=DequantizeLinear]; -"606 /features/denseblock3/denselayer18/conv1/Conv" [id=606, type=Conv]; -"607 /features/denseblock3/denselayer18/relu2/Relu" [id=607, type=Relu]; -"608 QuantizeLinear_/features/denseblock3/denselayer18/relu2/Relu_output_0_1" [id=608, type=QuantizeLinear]; -"609 DequantizeLinear_/features/denseblock3/denselayer18/relu2/Relu_output_0_1" [id=609, type=DequantizeLinear]; -"610 QuantizeLinear_features.denseblock3.denselayer18.conv2.weight_1" [id=610, type=QuantizeLinear]; -"611 DequantizeLinear_features.denseblock3.denselayer18.conv2.weight_1" [id=611, type=DequantizeLinear]; -"612 /features/denseblock3/denselayer18/conv2/Conv" [id=612, type=Conv]; -"613 QuantizeLinear_/features/denseblock3/denselayer18/conv2/Conv_output_0_1" [id=613, type=QuantizeLinear]; -"614 DequantizeLinear_/features/denseblock3/denselayer18/conv2/Conv_output_0_1" [id=614, type=DequantizeLinear]; -"615 /features/denseblock3/denselayer19/Concat" [id=615, type=Concat]; -"616 /features/denseblock3/denselayer19/norm1/BatchNormalization" [id=616, type=BatchNormalization]; -"617 /features/denseblock3/denselayer19/relu1/Relu" [id=617, type=Relu]; -"618 QuantizeLinear_/features/denseblock3/denselayer19/relu1/Relu_output_0_1" [id=618, type=QuantizeLinear]; -"619 DequantizeLinear_/features/denseblock3/denselayer19/relu1/Relu_output_0_1" [id=619, type=DequantizeLinear]; -"620 QuantizeLinear_onnx^^Conv_1277_1" [id=620, label="620 QuantizeLinear_onnx::Conv_1277_1", type=QuantizeLinear]; -"621 DequantizeLinear_onnx^^Conv_1277_1" [id=621, label="621 DequantizeLinear_onnx::Conv_1277_1", type=DequantizeLinear]; -"622 /features/denseblock3/denselayer19/conv1/Conv" [id=622, type=Conv]; -"623 /features/denseblock3/denselayer19/relu2/Relu" [id=623, type=Relu]; -"624 QuantizeLinear_/features/denseblock3/denselayer19/relu2/Relu_output_0_1" [id=624, type=QuantizeLinear]; -"625 DequantizeLinear_/features/denseblock3/denselayer19/relu2/Relu_output_0_1" [id=625, type=DequantizeLinear]; -"626 QuantizeLinear_features.denseblock3.denselayer19.conv2.weight_1" [id=626, type=QuantizeLinear]; -"627 DequantizeLinear_features.denseblock3.denselayer19.conv2.weight_1" [id=627, type=DequantizeLinear]; -"628 /features/denseblock3/denselayer19/conv2/Conv" [id=628, type=Conv]; -"629 QuantizeLinear_/features/denseblock3/denselayer19/conv2/Conv_output_0_1" [id=629, type=QuantizeLinear]; -"630 DequantizeLinear_/features/denseblock3/denselayer19/conv2/Conv_output_0_1" [id=630, type=DequantizeLinear]; -"631 /features/denseblock3/denselayer20/Concat" [id=631, type=Concat]; -"632 /features/denseblock3/denselayer20/norm1/BatchNormalization" [id=632, type=BatchNormalization]; -"633 /features/denseblock3/denselayer20/relu1/Relu" [id=633, type=Relu]; -"634 QuantizeLinear_/features/denseblock3/denselayer20/relu1/Relu_output_0_1" [id=634, type=QuantizeLinear]; -"635 DequantizeLinear_/features/denseblock3/denselayer20/relu1/Relu_output_0_1" [id=635, type=DequantizeLinear]; -"636 QuantizeLinear_onnx^^Conv_1280_1" [id=636, label="636 QuantizeLinear_onnx::Conv_1280_1", type=QuantizeLinear]; -"637 DequantizeLinear_onnx^^Conv_1280_1" [id=637, label="637 DequantizeLinear_onnx::Conv_1280_1", type=DequantizeLinear]; -"638 /features/denseblock3/denselayer20/conv1/Conv" [id=638, type=Conv]; -"639 /features/denseblock3/denselayer20/relu2/Relu" [id=639, type=Relu]; -"640 QuantizeLinear_/features/denseblock3/denselayer20/relu2/Relu_output_0_1" [id=640, type=QuantizeLinear]; -"641 DequantizeLinear_/features/denseblock3/denselayer20/relu2/Relu_output_0_1" [id=641, type=DequantizeLinear]; -"642 QuantizeLinear_features.denseblock3.denselayer20.conv2.weight_1" [id=642, type=QuantizeLinear]; -"643 DequantizeLinear_features.denseblock3.denselayer20.conv2.weight_1" [id=643, type=DequantizeLinear]; -"644 /features/denseblock3/denselayer20/conv2/Conv" [id=644, type=Conv]; -"645 QuantizeLinear_/features/denseblock3/denselayer20/conv2/Conv_output_0_1" [id=645, type=QuantizeLinear]; -"646 DequantizeLinear_/features/denseblock3/denselayer20/conv2/Conv_output_0_1" [id=646, type=DequantizeLinear]; -"647 /features/denseblock3/denselayer21/Concat" [id=647, type=Concat]; -"648 /features/denseblock3/denselayer21/norm1/BatchNormalization" [id=648, type=BatchNormalization]; 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type=QuantizeLinear]; -"659 DequantizeLinear_features.denseblock3.denselayer21.conv2.weight_1" [id=659, type=DequantizeLinear]; -"660 /features/denseblock3/denselayer21/conv2/Conv" [id=660, type=Conv]; -"661 QuantizeLinear_/features/denseblock3/denselayer21/conv2/Conv_output_0_1" [id=661, type=QuantizeLinear]; -"662 DequantizeLinear_/features/denseblock3/denselayer21/conv2/Conv_output_0_1" [id=662, type=DequantizeLinear]; -"663 /features/denseblock3/denselayer22/Concat" [id=663, type=Concat]; -"664 /features/denseblock3/denselayer22/norm1/BatchNormalization" [id=664, type=BatchNormalization]; -"665 /features/denseblock3/denselayer22/relu1/Relu" [id=665, type=Relu]; -"666 QuantizeLinear_/features/denseblock3/denselayer22/relu1/Relu_output_0_1" [id=666, type=QuantizeLinear]; -"667 DequantizeLinear_/features/denseblock3/denselayer22/relu1/Relu_output_0_1" [id=667, type=DequantizeLinear]; -"668 QuantizeLinear_onnx^^Conv_1286_1" [id=668, label="668 QuantizeLinear_onnx::Conv_1286_1", type=QuantizeLinear]; -"669 DequantizeLinear_onnx^^Conv_1286_1" [id=669, label="669 DequantizeLinear_onnx::Conv_1286_1", type=DequantizeLinear]; -"670 /features/denseblock3/denselayer22/conv1/Conv" [id=670, type=Conv]; -"671 /features/denseblock3/denselayer22/relu2/Relu" [id=671, type=Relu]; -"672 QuantizeLinear_/features/denseblock3/denselayer22/relu2/Relu_output_0_1" [id=672, type=QuantizeLinear]; -"673 DequantizeLinear_/features/denseblock3/denselayer22/relu2/Relu_output_0_1" [id=673, type=DequantizeLinear]; -"674 QuantizeLinear_features.denseblock3.denselayer22.conv2.weight_1" [id=674, type=QuantizeLinear]; -"675 DequantizeLinear_features.denseblock3.denselayer22.conv2.weight_1" [id=675, type=DequantizeLinear]; -"676 /features/denseblock3/denselayer22/conv2/Conv" [id=676, type=Conv]; -"677 QuantizeLinear_/features/denseblock3/denselayer22/conv2/Conv_output_0_1" [id=677, type=QuantizeLinear]; -"678 DequantizeLinear_/features/denseblock3/denselayer22/conv2/Conv_output_0_1" [id=678, type=DequantizeLinear]; -"679 /features/denseblock3/denselayer23/Concat" [id=679, type=Concat]; -"680 /features/denseblock3/denselayer23/norm1/BatchNormalization" [id=680, type=BatchNormalization]; -"681 /features/denseblock3/denselayer23/relu1/Relu" [id=681, type=Relu]; -"682 QuantizeLinear_/features/denseblock3/denselayer23/relu1/Relu_output_0_1" [id=682, type=QuantizeLinear]; -"683 DequantizeLinear_/features/denseblock3/denselayer23/relu1/Relu_output_0_1" [id=683, type=DequantizeLinear]; -"684 QuantizeLinear_onnx^^Conv_1289_1" [id=684, label="684 QuantizeLinear_onnx::Conv_1289_1", type=QuantizeLinear]; -"685 DequantizeLinear_onnx^^Conv_1289_1" [id=685, label="685 DequantizeLinear_onnx::Conv_1289_1", type=DequantizeLinear]; -"686 /features/denseblock3/denselayer23/conv1/Conv" [id=686, type=Conv]; -"687 /features/denseblock3/denselayer23/relu2/Relu" [id=687, type=Relu]; -"688 QuantizeLinear_/features/denseblock3/denselayer23/relu2/Relu_output_0_1" [id=688, type=QuantizeLinear]; -"689 DequantizeLinear_/features/denseblock3/denselayer23/relu2/Relu_output_0_1" [id=689, type=DequantizeLinear]; -"690 QuantizeLinear_features.denseblock3.denselayer23.conv2.weight_1" [id=690, type=QuantizeLinear]; -"691 DequantizeLinear_features.denseblock3.denselayer23.conv2.weight_1" [id=691, type=DequantizeLinear]; -"692 /features/denseblock3/denselayer23/conv2/Conv" [id=692, type=Conv]; -"693 QuantizeLinear_/features/denseblock3/denselayer23/conv2/Conv_output_0_1" [id=693, type=QuantizeLinear]; -"694 DequantizeLinear_/features/denseblock3/denselayer23/conv2/Conv_output_0_1" [id=694, type=DequantizeLinear]; -"695 /features/denseblock3/denselayer24/Concat" [id=695, type=Concat]; -"696 /features/denseblock3/denselayer24/norm1/BatchNormalization" [id=696, type=BatchNormalization]; -"697 /features/denseblock3/denselayer24/relu1/Relu" [id=697, type=Relu]; -"698 QuantizeLinear_/features/denseblock3/denselayer24/relu1/Relu_output_0_1" [id=698, type=QuantizeLinear]; -"699 DequantizeLinear_/features/denseblock3/denselayer24/relu1/Relu_output_0_1" [id=699, type=DequantizeLinear]; -"700 QuantizeLinear_onnx^^Conv_1292_1" [id=700, label="700 QuantizeLinear_onnx::Conv_1292_1", type=QuantizeLinear]; -"701 DequantizeLinear_onnx^^Conv_1292_1" [id=701, label="701 DequantizeLinear_onnx::Conv_1292_1", type=DequantizeLinear]; -"702 /features/denseblock3/denselayer24/conv1/Conv" [id=702, type=Conv]; -"703 /features/denseblock3/denselayer24/relu2/Relu" [id=703, type=Relu]; -"704 QuantizeLinear_/features/denseblock3/denselayer24/relu2/Relu_output_0_1" [id=704, type=QuantizeLinear]; -"705 DequantizeLinear_/features/denseblock3/denselayer24/relu2/Relu_output_0_1" [id=705, type=DequantizeLinear]; -"706 QuantizeLinear_features.denseblock3.denselayer24.conv2.weight_1" [id=706, type=QuantizeLinear]; -"707 DequantizeLinear_features.denseblock3.denselayer24.conv2.weight_1" [id=707, type=DequantizeLinear]; -"708 /features/denseblock3/denselayer24/conv2/Conv" [id=708, type=Conv]; -"709 QuantizeLinear_/features/denseblock3/denselayer24/conv2/Conv_output_0_1" [id=709, type=QuantizeLinear]; -"710 DequantizeLinear_/features/denseblock3/denselayer24/conv2/Conv_output_0_1" [id=710, type=DequantizeLinear]; -"711 /features/denseblock3/Concat" [id=711, type=Concat]; -"712 /features/transition3/norm/BatchNormalization" [id=712, type=BatchNormalization]; -"713 /features/transition3/relu/Relu" [id=713, type=Relu]; -"714 QuantizeLinear_/features/transition3/relu/Relu_output_0_1" [id=714, type=QuantizeLinear]; -"715 DequantizeLinear_/features/transition3/relu/Relu_output_0_1" [id=715, type=DequantizeLinear]; -"716 QuantizeLinear_features.transition3.conv.weight_1" [id=716, type=QuantizeLinear]; -"717 DequantizeLinear_features.transition3.conv.weight_1" [id=717, type=DequantizeLinear]; -"718 /features/transition3/conv/Conv" [id=718, type=Conv]; -"719 /features/transition3/pool/Constant" [id=719, type=Constant]; -"720 QuantizeLinear_/features/transition3/conv/Conv_output_0_1" [id=720, type=QuantizeLinear]; -"721 DequantizeLinear_/features/transition3/conv/Conv_output_0_1" [id=721, type=DequantizeLinear]; -"722 /features/transition3/pool/Pad" [id=722, type=Pad]; -"723 /features/transition3/pool/AveragePool" [id=723, type=AveragePool]; -"724 QuantizeLinear_/features/transition3/pool/AveragePool_output_0_1" [id=724, type=QuantizeLinear]; -"725 DequantizeLinear_/features/transition3/pool/AveragePool_output_0_1" [id=725, type=DequantizeLinear]; -"726 /features/denseblock4/denselayer1/Concat" [id=726, type=Concat]; -"727 /features/denseblock4/denselayer1/norm1/BatchNormalization" [id=727, type=BatchNormalization]; -"728 /features/denseblock4/denselayer1/relu1/Relu" [id=728, type=Relu]; -"729 QuantizeLinear_/features/denseblock4/denselayer1/relu1/Relu_output_0_1" [id=729, type=QuantizeLinear]; -"730 DequantizeLinear_/features/denseblock4/denselayer1/relu1/Relu_output_0_1" [id=730, type=DequantizeLinear]; 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QuantizeLinear_/features/denseblock4/denselayer3/relu1/Relu_output_0_1" [id=761, type=QuantizeLinear]; -"762 DequantizeLinear_/features/denseblock4/denselayer3/relu1/Relu_output_0_1" [id=762, type=DequantizeLinear]; -"763 QuantizeLinear_onnx^^Conv_1301_1" [id=763, label="763 QuantizeLinear_onnx::Conv_1301_1", type=QuantizeLinear]; -"764 DequantizeLinear_onnx^^Conv_1301_1" [id=764, label="764 DequantizeLinear_onnx::Conv_1301_1", type=DequantizeLinear]; -"765 /features/denseblock4/denselayer3/conv1/Conv" [id=765, type=Conv]; -"766 /features/denseblock4/denselayer3/relu2/Relu" [id=766, type=Relu]; -"767 QuantizeLinear_/features/denseblock4/denselayer3/relu2/Relu_output_0_1" [id=767, type=QuantizeLinear]; -"768 DequantizeLinear_/features/denseblock4/denselayer3/relu2/Relu_output_0_1" [id=768, type=DequantizeLinear]; -"769 QuantizeLinear_features.denseblock4.denselayer3.conv2.weight_1" [id=769, type=QuantizeLinear]; -"770 DequantizeLinear_features.denseblock4.denselayer3.conv2.weight_1" [id=770, type=DequantizeLinear]; -"771 /features/denseblock4/denselayer3/conv2/Conv" [id=771, type=Conv]; -"772 QuantizeLinear_/features/denseblock4/denselayer3/conv2/Conv_output_0_1" [id=772, type=QuantizeLinear]; -"773 DequantizeLinear_/features/denseblock4/denselayer3/conv2/Conv_output_0_1" [id=773, type=DequantizeLinear]; -"774 /features/denseblock4/denselayer4/Concat" [id=774, type=Concat]; -"775 /features/denseblock4/denselayer4/norm1/BatchNormalization" [id=775, type=BatchNormalization]; -"776 /features/denseblock4/denselayer4/relu1/Relu" [id=776, type=Relu]; -"777 QuantizeLinear_/features/denseblock4/denselayer4/relu1/Relu_output_0_1" [id=777, type=QuantizeLinear]; -"778 DequantizeLinear_/features/denseblock4/denselayer4/relu1/Relu_output_0_1" [id=778, type=DequantizeLinear]; -"779 QuantizeLinear_onnx^^Conv_1304_1" [id=779, label="779 QuantizeLinear_onnx::Conv_1304_1", type=QuantizeLinear]; -"780 DequantizeLinear_onnx^^Conv_1304_1" [id=780, label="780 DequantizeLinear_onnx::Conv_1304_1", type=DequantizeLinear]; -"781 /features/denseblock4/denselayer4/conv1/Conv" [id=781, type=Conv]; -"782 /features/denseblock4/denselayer4/relu2/Relu" [id=782, type=Relu]; -"783 QuantizeLinear_/features/denseblock4/denselayer4/relu2/Relu_output_0_1" [id=783, type=QuantizeLinear]; -"784 DequantizeLinear_/features/denseblock4/denselayer4/relu2/Relu_output_0_1" [id=784, type=DequantizeLinear]; -"785 QuantizeLinear_features.denseblock4.denselayer4.conv2.weight_1" [id=785, type=QuantizeLinear]; -"786 DequantizeLinear_features.denseblock4.denselayer4.conv2.weight_1" [id=786, type=DequantizeLinear]; -"787 /features/denseblock4/denselayer4/conv2/Conv" [id=787, type=Conv]; -"788 QuantizeLinear_/features/denseblock4/denselayer4/conv2/Conv_output_0_1" [id=788, type=QuantizeLinear]; -"789 DequantizeLinear_/features/denseblock4/denselayer4/conv2/Conv_output_0_1" [id=789, type=DequantizeLinear]; -"790 /features/denseblock4/denselayer5/Concat" [id=790, type=Concat]; 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type=DequantizeLinear]; -"801 QuantizeLinear_features.denseblock4.denselayer5.conv2.weight_1" [id=801, type=QuantizeLinear]; -"802 DequantizeLinear_features.denseblock4.denselayer5.conv2.weight_1" [id=802, type=DequantizeLinear]; -"803 /features/denseblock4/denselayer5/conv2/Conv" [id=803, type=Conv]; -"804 QuantizeLinear_/features/denseblock4/denselayer5/conv2/Conv_output_0_1" [id=804, type=QuantizeLinear]; -"805 DequantizeLinear_/features/denseblock4/denselayer5/conv2/Conv_output_0_1" [id=805, type=DequantizeLinear]; -"806 /features/denseblock4/denselayer6/Concat" [id=806, type=Concat]; -"807 /features/denseblock4/denselayer6/norm1/BatchNormalization" [id=807, type=BatchNormalization]; -"808 /features/denseblock4/denselayer6/relu1/Relu" [id=808, type=Relu]; -"809 QuantizeLinear_/features/denseblock4/denselayer6/relu1/Relu_output_0_1" [id=809, type=QuantizeLinear]; -"810 DequantizeLinear_/features/denseblock4/denselayer6/relu1/Relu_output_0_1" [id=810, type=DequantizeLinear]; -"811 QuantizeLinear_onnx^^Conv_1310_1" [id=811, label="811 QuantizeLinear_onnx::Conv_1310_1", type=QuantizeLinear]; -"812 DequantizeLinear_onnx^^Conv_1310_1" [id=812, label="812 DequantizeLinear_onnx::Conv_1310_1", type=DequantizeLinear]; -"813 /features/denseblock4/denselayer6/conv1/Conv" [id=813, type=Conv]; -"814 /features/denseblock4/denselayer6/relu2/Relu" [id=814, type=Relu]; -"815 QuantizeLinear_/features/denseblock4/denselayer6/relu2/Relu_output_0_1" [id=815, type=QuantizeLinear]; -"816 DequantizeLinear_/features/denseblock4/denselayer6/relu2/Relu_output_0_1" [id=816, type=DequantizeLinear]; -"817 QuantizeLinear_features.denseblock4.denselayer6.conv2.weight_1" [id=817, type=QuantizeLinear]; -"818 DequantizeLinear_features.denseblock4.denselayer6.conv2.weight_1" [id=818, type=DequantizeLinear]; -"819 /features/denseblock4/denselayer6/conv2/Conv" [id=819, type=Conv]; -"820 QuantizeLinear_/features/denseblock4/denselayer6/conv2/Conv_output_0_1" [id=820, type=QuantizeLinear]; -"821 DequantizeLinear_/features/denseblock4/denselayer6/conv2/Conv_output_0_1" [id=821, type=DequantizeLinear]; -"822 /features/denseblock4/denselayer7/Concat" [id=822, type=Concat]; -"823 /features/denseblock4/denselayer7/norm1/BatchNormalization" [id=823, type=BatchNormalization]; -"824 /features/denseblock4/denselayer7/relu1/Relu" [id=824, type=Relu]; -"825 QuantizeLinear_/features/denseblock4/denselayer7/relu1/Relu_output_0_1" [id=825, type=QuantizeLinear]; -"826 DequantizeLinear_/features/denseblock4/denselayer7/relu1/Relu_output_0_1" [id=826, type=DequantizeLinear]; -"827 QuantizeLinear_onnx^^Conv_1313_1" [id=827, label="827 QuantizeLinear_onnx::Conv_1313_1", type=QuantizeLinear]; -"828 DequantizeLinear_onnx^^Conv_1313_1" [id=828, label="828 DequantizeLinear_onnx::Conv_1313_1", type=DequantizeLinear]; -"829 /features/denseblock4/denselayer7/conv1/Conv" [id=829, type=Conv]; -"830 /features/denseblock4/denselayer7/relu2/Relu" [id=830, type=Relu]; -"831 QuantizeLinear_/features/denseblock4/denselayer7/relu2/Relu_output_0_1" [id=831, type=QuantizeLinear]; -"832 DequantizeLinear_/features/denseblock4/denselayer7/relu2/Relu_output_0_1" [id=832, type=DequantizeLinear]; -"833 QuantizeLinear_features.denseblock4.denselayer7.conv2.weight_1" [id=833, type=QuantizeLinear]; -"834 DequantizeLinear_features.denseblock4.denselayer7.conv2.weight_1" [id=834, type=DequantizeLinear]; -"835 /features/denseblock4/denselayer7/conv2/Conv" [id=835, type=Conv]; -"836 QuantizeLinear_/features/denseblock4/denselayer7/conv2/Conv_output_0_1" [id=836, type=QuantizeLinear]; -"837 DequantizeLinear_/features/denseblock4/denselayer7/conv2/Conv_output_0_1" [id=837, type=DequantizeLinear]; -"838 /features/denseblock4/denselayer8/Concat" [id=838, type=Concat]; -"839 /features/denseblock4/denselayer8/norm1/BatchNormalization" [id=839, type=BatchNormalization]; -"840 /features/denseblock4/denselayer8/relu1/Relu" [id=840, type=Relu]; -"841 QuantizeLinear_/features/denseblock4/denselayer8/relu1/Relu_output_0_1" [id=841, type=QuantizeLinear]; -"842 DequantizeLinear_/features/denseblock4/denselayer8/relu1/Relu_output_0_1" [id=842, type=DequantizeLinear]; -"843 QuantizeLinear_onnx^^Conv_1316_1" [id=843, label="843 QuantizeLinear_onnx::Conv_1316_1", type=QuantizeLinear]; -"844 DequantizeLinear_onnx^^Conv_1316_1" [id=844, label="844 DequantizeLinear_onnx::Conv_1316_1", type=DequantizeLinear]; -"845 /features/denseblock4/denselayer8/conv1/Conv" [id=845, type=Conv]; -"846 /features/denseblock4/denselayer8/relu2/Relu" [id=846, type=Relu]; -"847 QuantizeLinear_/features/denseblock4/denselayer8/relu2/Relu_output_0_1" [id=847, type=QuantizeLinear]; -"848 DequantizeLinear_/features/denseblock4/denselayer8/relu2/Relu_output_0_1" [id=848, type=DequantizeLinear]; -"849 QuantizeLinear_features.denseblock4.denselayer8.conv2.weight_1" [id=849, type=QuantizeLinear]; -"850 DequantizeLinear_features.denseblock4.denselayer8.conv2.weight_1" [id=850, type=DequantizeLinear]; -"851 /features/denseblock4/denselayer8/conv2/Conv" [id=851, type=Conv]; -"852 QuantizeLinear_/features/denseblock4/denselayer8/conv2/Conv_output_0_1" [id=852, type=QuantizeLinear]; -"853 DequantizeLinear_/features/denseblock4/denselayer8/conv2/Conv_output_0_1" [id=853, type=DequantizeLinear]; -"854 /features/denseblock4/denselayer9/Concat" [id=854, type=Concat]; -"855 /features/denseblock4/denselayer9/norm1/BatchNormalization" [id=855, type=BatchNormalization]; -"856 /features/denseblock4/denselayer9/relu1/Relu" [id=856, type=Relu]; -"857 QuantizeLinear_/features/denseblock4/denselayer9/relu1/Relu_output_0_1" [id=857, type=QuantizeLinear]; -"858 DequantizeLinear_/features/denseblock4/denselayer9/relu1/Relu_output_0_1" [id=858, type=DequantizeLinear]; -"859 QuantizeLinear_onnx^^Conv_1319_1" [id=859, label="859 QuantizeLinear_onnx::Conv_1319_1", type=QuantizeLinear]; -"860 DequantizeLinear_onnx^^Conv_1319_1" [id=860, label="860 DequantizeLinear_onnx::Conv_1319_1", type=DequantizeLinear]; -"861 /features/denseblock4/denselayer9/conv1/Conv" [id=861, type=Conv]; -"862 /features/denseblock4/denselayer9/relu2/Relu" [id=862, type=Relu]; -"863 QuantizeLinear_/features/denseblock4/denselayer9/relu2/Relu_output_0_1" [id=863, type=QuantizeLinear]; -"864 DequantizeLinear_/features/denseblock4/denselayer9/relu2/Relu_output_0_1" [id=864, type=DequantizeLinear]; -"865 QuantizeLinear_features.denseblock4.denselayer9.conv2.weight_1" [id=865, type=QuantizeLinear]; -"866 DequantizeLinear_features.denseblock4.denselayer9.conv2.weight_1" [id=866, type=DequantizeLinear]; -"867 /features/denseblock4/denselayer9/conv2/Conv" [id=867, type=Conv]; -"868 QuantizeLinear_/features/denseblock4/denselayer9/conv2/Conv_output_0_1" [id=868, type=QuantizeLinear]; -"869 DequantizeLinear_/features/denseblock4/denselayer9/conv2/Conv_output_0_1" [id=869, type=DequantizeLinear]; -"870 /features/denseblock4/denselayer10/Concat" [id=870, type=Concat]; -"871 /features/denseblock4/denselayer10/norm1/BatchNormalization" [id=871, type=BatchNormalization]; -"872 /features/denseblock4/denselayer10/relu1/Relu" [id=872, type=Relu]; -"873 QuantizeLinear_/features/denseblock4/denselayer10/relu1/Relu_output_0_1" [id=873, type=QuantizeLinear]; -"874 DequantizeLinear_/features/denseblock4/denselayer10/relu1/Relu_output_0_1" [id=874, type=DequantizeLinear]; -"875 QuantizeLinear_onnx^^Conv_1322_1" [id=875, label="875 QuantizeLinear_onnx::Conv_1322_1", type=QuantizeLinear]; -"876 DequantizeLinear_onnx^^Conv_1322_1" [id=876, label="876 DequantizeLinear_onnx::Conv_1322_1", type=DequantizeLinear]; -"877 /features/denseblock4/denselayer10/conv1/Conv" [id=877, type=Conv]; -"878 /features/denseblock4/denselayer10/relu2/Relu" [id=878, type=Relu]; -"879 QuantizeLinear_/features/denseblock4/denselayer10/relu2/Relu_output_0_1" [id=879, type=QuantizeLinear]; -"880 DequantizeLinear_/features/denseblock4/denselayer10/relu2/Relu_output_0_1" [id=880, type=DequantizeLinear]; -"881 QuantizeLinear_features.denseblock4.denselayer10.conv2.weight_1" [id=881, type=QuantizeLinear]; -"882 DequantizeLinear_features.denseblock4.denselayer10.conv2.weight_1" [id=882, type=DequantizeLinear]; -"883 /features/denseblock4/denselayer10/conv2/Conv" [id=883, type=Conv]; -"884 QuantizeLinear_/features/denseblock4/denselayer10/conv2/Conv_output_0_1" [id=884, type=QuantizeLinear]; -"885 DequantizeLinear_/features/denseblock4/denselayer10/conv2/Conv_output_0_1" [id=885, type=DequantizeLinear]; -"886 /features/denseblock4/denselayer11/Concat" [id=886, type=Concat]; -"887 /features/denseblock4/denselayer11/norm1/BatchNormalization" [id=887, type=BatchNormalization]; -"888 /features/denseblock4/denselayer11/relu1/Relu" [id=888, type=Relu]; -"889 QuantizeLinear_/features/denseblock4/denselayer11/relu1/Relu_output_0_1" [id=889, type=QuantizeLinear]; -"890 DequantizeLinear_/features/denseblock4/denselayer11/relu1/Relu_output_0_1" [id=890, type=DequantizeLinear]; -"891 QuantizeLinear_onnx^^Conv_1325_1" [id=891, label="891 QuantizeLinear_onnx::Conv_1325_1", type=QuantizeLinear]; -"892 DequantizeLinear_onnx^^Conv_1325_1" [id=892, label="892 DequantizeLinear_onnx::Conv_1325_1", type=DequantizeLinear]; -"893 /features/denseblock4/denselayer11/conv1/Conv" [id=893, type=Conv]; -"894 /features/denseblock4/denselayer11/relu2/Relu" [id=894, type=Relu]; -"895 QuantizeLinear_/features/denseblock4/denselayer11/relu2/Relu_output_0_1" [id=895, type=QuantizeLinear]; -"896 DequantizeLinear_/features/denseblock4/denselayer11/relu2/Relu_output_0_1" [id=896, type=DequantizeLinear]; -"897 QuantizeLinear_features.denseblock4.denselayer11.conv2.weight_1" [id=897, type=QuantizeLinear]; -"898 DequantizeLinear_features.denseblock4.denselayer11.conv2.weight_1" [id=898, type=DequantizeLinear]; -"899 /features/denseblock4/denselayer11/conv2/Conv" [id=899, type=Conv]; -"900 QuantizeLinear_/features/denseblock4/denselayer11/conv2/Conv_output_0_1" [id=900, type=QuantizeLinear]; -"901 DequantizeLinear_/features/denseblock4/denselayer11/conv2/Conv_output_0_1" [id=901, type=DequantizeLinear]; -"902 /features/denseblock4/denselayer12/Concat" [id=902, type=Concat]; -"903 /features/denseblock4/denselayer12/norm1/BatchNormalization" [id=903, type=BatchNormalization]; -"904 /features/denseblock4/denselayer12/relu1/Relu" [id=904, type=Relu]; -"905 QuantizeLinear_/features/denseblock4/denselayer12/relu1/Relu_output_0_1" [id=905, type=QuantizeLinear]; -"906 DequantizeLinear_/features/denseblock4/denselayer12/relu1/Relu_output_0_1" [id=906, type=DequantizeLinear]; -"907 QuantizeLinear_onnx^^Conv_1328_1" [id=907, label="907 QuantizeLinear_onnx::Conv_1328_1", type=QuantizeLinear]; -"908 DequantizeLinear_onnx^^Conv_1328_1" [id=908, label="908 DequantizeLinear_onnx::Conv_1328_1", type=DequantizeLinear]; -"909 /features/denseblock4/denselayer12/conv1/Conv" [id=909, type=Conv]; -"910 /features/denseblock4/denselayer12/relu2/Relu" [id=910, type=Relu]; -"911 QuantizeLinear_/features/denseblock4/denselayer12/relu2/Relu_output_0_1" [id=911, type=QuantizeLinear]; -"912 DequantizeLinear_/features/denseblock4/denselayer12/relu2/Relu_output_0_1" [id=912, type=DequantizeLinear]; -"913 QuantizeLinear_features.denseblock4.denselayer12.conv2.weight_1" [id=913, type=QuantizeLinear]; -"914 DequantizeLinear_features.denseblock4.denselayer12.conv2.weight_1" [id=914, type=DequantizeLinear]; -"915 /features/denseblock4/denselayer12/conv2/Conv" [id=915, type=Conv]; -"916 QuantizeLinear_/features/denseblock4/denselayer12/conv2/Conv_output_0_1" [id=916, type=QuantizeLinear]; -"917 DequantizeLinear_/features/denseblock4/denselayer12/conv2/Conv_output_0_1" [id=917, type=DequantizeLinear]; -"918 /features/denseblock4/denselayer13/Concat" [id=918, type=Concat]; -"919 /features/denseblock4/denselayer13/norm1/BatchNormalization" [id=919, type=BatchNormalization]; -"920 /features/denseblock4/denselayer13/relu1/Relu" [id=920, type=Relu]; -"921 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DequantizeLinear_onnx^^Conv_1334_1" [id=940, label="940 DequantizeLinear_onnx::Conv_1334_1", type=DequantizeLinear]; -"941 /features/denseblock4/denselayer14/conv1/Conv" [id=941, type=Conv]; -"942 /features/denseblock4/denselayer14/relu2/Relu" [id=942, type=Relu]; -"943 QuantizeLinear_/features/denseblock4/denselayer14/relu2/Relu_output_0_1" [id=943, type=QuantizeLinear]; -"944 DequantizeLinear_/features/denseblock4/denselayer14/relu2/Relu_output_0_1" [id=944, type=DequantizeLinear]; -"945 QuantizeLinear_features.denseblock4.denselayer14.conv2.weight_1" [id=945, type=QuantizeLinear]; -"946 DequantizeLinear_features.denseblock4.denselayer14.conv2.weight_1" [id=946, type=DequantizeLinear]; -"947 /features/denseblock4/denselayer14/conv2/Conv" [id=947, type=Conv]; -"948 QuantizeLinear_/features/denseblock4/denselayer14/conv2/Conv_output_0_1" [id=948, type=QuantizeLinear]; -"949 DequantizeLinear_/features/denseblock4/denselayer14/conv2/Conv_output_0_1" [id=949, type=DequantizeLinear]; -"950 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DequantizeLinear_/features/denseblock4/denselayer15/relu2/Relu_output_0_1" [id=960, type=DequantizeLinear]; -"961 QuantizeLinear_features.denseblock4.denselayer15.conv2.weight_1" [id=961, type=QuantizeLinear]; -"962 DequantizeLinear_features.denseblock4.denselayer15.conv2.weight_1" [id=962, type=DequantizeLinear]; -"963 /features/denseblock4/denselayer15/conv2/Conv" [id=963, type=Conv]; -"964 QuantizeLinear_/features/denseblock4/denselayer15/conv2/Conv_output_0_1" [id=964, type=QuantizeLinear]; -"965 DequantizeLinear_/features/denseblock4/denselayer15/conv2/Conv_output_0_1" [id=965, type=DequantizeLinear]; -"966 /features/denseblock4/denselayer16/Concat" [id=966, type=Concat]; -"967 /features/denseblock4/denselayer16/norm1/BatchNormalization" [id=967, type=BatchNormalization]; -"968 /features/denseblock4/denselayer16/relu1/Relu" [id=968, type=Relu]; -"969 QuantizeLinear_/features/denseblock4/denselayer16/relu1/Relu_output_0_1" [id=969, type=QuantizeLinear]; -"970 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type=Conv]; -"980 QuantizeLinear_/features/denseblock4/denselayer16/conv2/Conv_output_0_1" [id=980, type=QuantizeLinear]; -"981 DequantizeLinear_/features/denseblock4/denselayer16/conv2/Conv_output_0_1" [id=981, type=DequantizeLinear]; -"982 /features/denseblock4/Concat" [id=982, type=Concat]; -"983 /features/norm5/BatchNormalization" [id=983, type=BatchNormalization]; -"984 /Relu" [id=984, type=Relu]; -"985 QuantizeLinear_/Relu_output_0_1" [id=985, type=QuantizeLinear]; -"986 DequantizeLinear_/Relu_output_0_1" [id=986, type=DequantizeLinear]; -"987 /GlobalAveragePool" [id=987, type=GlobalAveragePool]; -"988 QuantizeLinear_/GlobalAveragePool_output_0_1" [id=988, type=QuantizeLinear]; -"989 DequantizeLinear_/GlobalAveragePool_output_0_1" [id=989, type=DequantizeLinear]; -"990 /Flatten" [id=990, type=Flatten]; -"991 QuantizeLinear_classifier.weight_1" [id=991, type=QuantizeLinear]; -"992 DequantizeLinear_classifier.weight_1" [id=992, type=DequantizeLinear]; -"993 /classifier/Gemm" 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DequantizeLinear_/features/denseblock2/denselayer1/relu1/Relu_output_0_1" [id=122, type=DequantizeLinear]; +"123 QuantizeLinear_onnx^^Conv_1181_1" [id=123, label="123 QuantizeLinear_onnx::Conv_1181_1", type=QuantizeLinear]; +"124 DequantizeLinear_onnx^^Conv_1181_1" [id=124, label="124 DequantizeLinear_onnx::Conv_1181_1", type=DequantizeLinear]; +"125 /features/denseblock2/denselayer1/conv1/Conv" [id=125, type=Conv]; +"126 /features/denseblock2/denselayer1/relu2/Relu" [id=126, type=Relu]; +"127 QuantizeLinear_/features/denseblock2/denselayer1/relu2/Relu_output_0_1" [id=127, type=QuantizeLinear]; +"128 DequantizeLinear_/features/denseblock2/denselayer1/relu2/Relu_output_0_1" [id=128, type=DequantizeLinear]; +"129 QuantizeLinear_features.denseblock2.denselayer1.conv2.weight_1" [id=129, type=QuantizeLinear]; +"130 DequantizeLinear_features.denseblock2.denselayer1.conv2.weight_1" [id=130, type=DequantizeLinear]; +"131 /features/denseblock2/denselayer1/conv2/Conv" [id=131, type=Conv]; +"132 QuantizeLinear_/features/denseblock2/denselayer1/conv2/Conv_output_0_1" [id=132, type=QuantizeLinear]; +"133 DequantizeLinear_/features/denseblock2/denselayer1/conv2/Conv_output_0_1" [id=133, type=DequantizeLinear]; +"134 /features/denseblock2/denselayer2/Concat" [id=134, type=Concat]; +"135 /features/denseblock2/denselayer2/norm1/BatchNormalization" [id=135, type=BatchNormalization]; +"136 /features/denseblock2/denselayer2/relu1/Relu" [id=136, type=Relu]; +"137 QuantizeLinear_/features/denseblock2/denselayer2/relu1/Relu_output_0_1" [id=137, type=QuantizeLinear]; +"138 DequantizeLinear_/features/denseblock2/denselayer2/relu1/Relu_output_0_1" [id=138, type=DequantizeLinear]; +"139 QuantizeLinear_onnx^^Conv_1184_1" [id=139, label="139 QuantizeLinear_onnx::Conv_1184_1", type=QuantizeLinear]; +"140 DequantizeLinear_onnx^^Conv_1184_1" [id=140, label="140 DequantizeLinear_onnx::Conv_1184_1", type=DequantizeLinear]; +"141 /features/denseblock2/denselayer2/conv1/Conv" [id=141, type=Conv]; 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DequantizeLinear_onnx^^Conv_1190_1" [id=172, label="172 DequantizeLinear_onnx::Conv_1190_1", type=DequantizeLinear]; +"173 /features/denseblock2/denselayer4/conv1/Conv" [id=173, type=Conv]; +"174 /features/denseblock2/denselayer4/relu2/Relu" [id=174, type=Relu]; +"175 QuantizeLinear_/features/denseblock2/denselayer4/relu2/Relu_output_0_1" [id=175, type=QuantizeLinear]; +"176 DequantizeLinear_/features/denseblock2/denselayer4/relu2/Relu_output_0_1" [id=176, type=DequantizeLinear]; +"177 QuantizeLinear_features.denseblock2.denselayer4.conv2.weight_1" [id=177, type=QuantizeLinear]; +"178 DequantizeLinear_features.denseblock2.denselayer4.conv2.weight_1" [id=178, type=DequantizeLinear]; +"179 /features/denseblock2/denselayer4/conv2/Conv" [id=179, type=Conv]; +"180 QuantizeLinear_/features/denseblock2/denselayer4/conv2/Conv_output_0_1" [id=180, type=QuantizeLinear]; +"181 DequantizeLinear_/features/denseblock2/denselayer4/conv2/Conv_output_0_1" [id=181, type=DequantizeLinear]; +"182 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DequantizeLinear_/features/denseblock2/denselayer5/relu2/Relu_output_0_1" [id=192, type=DequantizeLinear]; +"193 QuantizeLinear_features.denseblock2.denselayer5.conv2.weight_1" [id=193, type=QuantizeLinear]; +"194 DequantizeLinear_features.denseblock2.denselayer5.conv2.weight_1" [id=194, type=DequantizeLinear]; +"195 /features/denseblock2/denselayer5/conv2/Conv" [id=195, type=Conv]; +"196 QuantizeLinear_/features/denseblock2/denselayer5/conv2/Conv_output_0_1" [id=196, type=QuantizeLinear]; +"197 DequantizeLinear_/features/denseblock2/denselayer5/conv2/Conv_output_0_1" [id=197, type=DequantizeLinear]; +"198 /features/denseblock2/denselayer6/Concat" [id=198, type=Concat]; +"199 /features/denseblock2/denselayer6/norm1/BatchNormalization" [id=199, type=BatchNormalization]; +"200 /features/denseblock2/denselayer6/relu1/Relu" [id=200, type=Relu]; +"201 QuantizeLinear_/features/denseblock2/denselayer6/relu1/Relu_output_0_1" [id=201, type=QuantizeLinear]; +"202 DequantizeLinear_/features/denseblock2/denselayer6/relu1/Relu_output_0_1" [id=202, type=DequantizeLinear]; +"203 QuantizeLinear_onnx^^Conv_1196_1" [id=203, label="203 QuantizeLinear_onnx::Conv_1196_1", type=QuantizeLinear]; +"204 DequantizeLinear_onnx^^Conv_1196_1" [id=204, label="204 DequantizeLinear_onnx::Conv_1196_1", type=DequantizeLinear]; +"205 /features/denseblock2/denselayer6/conv1/Conv" [id=205, type=Conv]; +"206 /features/denseblock2/denselayer6/relu2/Relu" [id=206, type=Relu]; +"207 QuantizeLinear_/features/denseblock2/denselayer6/relu2/Relu_output_0_1" [id=207, type=QuantizeLinear]; +"208 DequantizeLinear_/features/denseblock2/denselayer6/relu2/Relu_output_0_1" [id=208, type=DequantizeLinear]; +"209 QuantizeLinear_features.denseblock2.denselayer6.conv2.weight_1" [id=209, type=QuantizeLinear]; +"210 DequantizeLinear_features.denseblock2.denselayer6.conv2.weight_1" [id=210, type=DequantizeLinear]; +"211 /features/denseblock2/denselayer6/conv2/Conv" [id=211, type=Conv]; +"212 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type=Conv]; +"292 QuantizeLinear_/features/denseblock2/denselayer11/conv2/Conv_output_0_1" [id=292, type=QuantizeLinear]; +"293 DequantizeLinear_/features/denseblock2/denselayer11/conv2/Conv_output_0_1" [id=293, type=DequantizeLinear]; +"294 /features/denseblock2/denselayer12/Concat" [id=294, type=Concat]; +"295 /features/denseblock2/denselayer12/norm1/BatchNormalization" [id=295, type=BatchNormalization]; +"296 /features/denseblock2/denselayer12/relu1/Relu" [id=296, type=Relu]; +"297 QuantizeLinear_/features/denseblock2/denselayer12/relu1/Relu_output_0_1" [id=297, type=QuantizeLinear]; +"298 DequantizeLinear_/features/denseblock2/denselayer12/relu1/Relu_output_0_1" [id=298, type=DequantizeLinear]; +"299 QuantizeLinear_onnx^^Conv_1214_1" [id=299, label="299 QuantizeLinear_onnx::Conv_1214_1", type=QuantizeLinear]; +"300 DequantizeLinear_onnx^^Conv_1214_1" [id=300, label="300 DequantizeLinear_onnx::Conv_1214_1", type=DequantizeLinear]; +"301 /features/denseblock2/denselayer12/conv1/Conv" 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type=Conv]; +"513 QuantizeLinear_/features/denseblock3/denselayer12/conv2/Conv_output_0_1" [id=513, type=QuantizeLinear]; +"514 DequantizeLinear_/features/denseblock3/denselayer12/conv2/Conv_output_0_1" [id=514, type=DequantizeLinear]; +"515 /features/denseblock3/denselayer13/Concat" [id=515, type=Concat]; +"516 /features/denseblock3/denselayer13/norm1/BatchNormalization" [id=516, type=BatchNormalization]; +"517 /features/denseblock3/denselayer13/relu1/Relu" [id=517, type=Relu]; +"518 QuantizeLinear_/features/denseblock3/denselayer13/relu1/Relu_output_0_1" [id=518, type=QuantizeLinear]; +"519 DequantizeLinear_/features/denseblock3/denselayer13/relu1/Relu_output_0_1" [id=519, type=DequantizeLinear]; +"520 QuantizeLinear_onnx^^Conv_1253_1" [id=520, label="520 QuantizeLinear_onnx::Conv_1253_1", type=QuantizeLinear]; +"521 DequantizeLinear_onnx^^Conv_1253_1" [id=521, label="521 DequantizeLinear_onnx::Conv_1253_1", type=DequantizeLinear]; +"522 /features/denseblock3/denselayer13/conv1/Conv" 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DequantizeLinear_/features/denseblock4/denselayer16/relu1/Relu_output_0_1" [id=964, type=DequantizeLinear]; +"965 QuantizeLinear_onnx^^Conv_1334_1" [id=965, label="965 QuantizeLinear_onnx::Conv_1334_1", type=QuantizeLinear]; +"966 DequantizeLinear_onnx^^Conv_1334_1" [id=966, label="966 DequantizeLinear_onnx::Conv_1334_1", type=DequantizeLinear]; +"967 /features/denseblock4/denselayer16/conv1/Conv" [id=967, type=Conv]; +"968 /features/denseblock4/denselayer16/relu2/Relu" [id=968, type=Relu]; +"969 QuantizeLinear_/features/denseblock4/denselayer16/relu2/Relu_output_0_1" [id=969, type=QuantizeLinear]; +"970 DequantizeLinear_/features/denseblock4/denselayer16/relu2/Relu_output_0_1" [id=970, type=DequantizeLinear]; +"971 QuantizeLinear_features.denseblock4.denselayer16.conv2.weight_1" [id=971, type=QuantizeLinear]; +"972 DequantizeLinear_features.denseblock4.denselayer16.conv2.weight_1" [id=972, type=DequantizeLinear]; +"973 /features/denseblock4/denselayer16/conv2/Conv" [id=973, type=Conv]; +"974 QuantizeLinear_/features/denseblock4/denselayer16/conv2/Conv_output_0_1" [id=974, type=QuantizeLinear]; +"975 DequantizeLinear_/features/denseblock4/denselayer16/conv2/Conv_output_0_1" [id=975, type=DequantizeLinear]; +"976 /features/denseblock4/Concat" [id=976, type=Concat]; +"977 /features/norm5/BatchNormalization" [id=977, type=BatchNormalization]; +"978 /Relu" [id=978, type=Relu]; +"979 QuantizeLinear_/Relu_output_0_1" [id=979, type=QuantizeLinear]; +"980 DequantizeLinear_/Relu_output_0_1" [id=980, type=DequantizeLinear]; +"981 /GlobalAveragePool" [id=981, type=GlobalAveragePool]; +"982 QuantizeLinear_/GlobalAveragePool_output_0_1" [id=982, type=QuantizeLinear]; +"983 DequantizeLinear_/GlobalAveragePool_output_0_1" [id=983, type=DequantizeLinear]; +"984 /Flatten" [id=984, type=Flatten]; +"985 QuantizeLinear_classifier.weight_1" [id=985, type=QuantizeLinear]; +"986 DequantizeLinear_classifier.weight_1" [id=986, type=DequantizeLinear]; +"987 /classifier/Gemm" [id=987, type=Gemm]; +"988 nncf_model_input_0" [id=988, type=nncf_model_input]; +"989 nncf_model_output_0" [id=989, type=nncf_model_output]; "0 QuantizeLinear_input.1_1" -> "1 DequantizeLinear_input.1_1" [label="[1, 3, 224, 224]", style=dashed]; "1 DequantizeLinear_input.1_1" -> "4 /features/conv0/Conv" [label="[1, 3, 224, 224]", style=solid]; -"2 QuantizeLinear_onnx^^Conv_1166_1" -> "3 DequantizeLinear_onnx^^Conv_1166_1" [label="[64, 3, 7, 7]", style=dashed]; -"3 DequantizeLinear_onnx^^Conv_1166_1" -> "4 /features/conv0/Conv" [label="[64, 3, 7, 7]", style=solid]; +"2 QuantizeLinear_onnx^^Conv_1160_1" -> "3 DequantizeLinear_onnx^^Conv_1160_1" [label="[64, 3, 7, 7]", style=dashed]; +"3 DequantizeLinear_onnx^^Conv_1160_1" -> "4 /features/conv0/Conv" [label="[64, 3, 7, 7]", style=solid]; "4 /features/conv0/Conv" -> "5 /features/relu0/Relu" [label="[1, 64, 112, 112]", style=solid]; "5 /features/relu0/Relu" -> "6 QuantizeLinear_/features/relu0/Relu_output_0_1" [label="[1, 64, 112, 112]", style=solid]; "6 QuantizeLinear_/features/relu0/Relu_output_0_1" -> "7 DequantizeLinear_/features/relu0/Relu_output_0_1" [label="[1, 64, 112, 112]", style=dashed]; @@ -1015,8 +1009,8 @@ strict digraph { "11 /features/denseblock1/denselayer1/relu1/Relu" -> "12 QuantizeLinear_/features/denseblock1/denselayer1/relu1/Relu_output_0_1" [label="[1, 64, 56, 56]", style=solid]; "12 QuantizeLinear_/features/denseblock1/denselayer1/relu1/Relu_output_0_1" -> "13 DequantizeLinear_/features/denseblock1/denselayer1/relu1/Relu_output_0_1" [label="[1, 64, 56, 56]", style=dashed]; "13 DequantizeLinear_/features/denseblock1/denselayer1/relu1/Relu_output_0_1" -> "16 /features/denseblock1/denselayer1/conv1/Conv" [label="[1, 64, 56, 56]", style=solid]; -"14 QuantizeLinear_onnx^^Conv_1169_1" -> "15 DequantizeLinear_onnx^^Conv_1169_1" [label="[128, 64, 1, 1]", style=dashed]; -"15 DequantizeLinear_onnx^^Conv_1169_1" -> "16 /features/denseblock1/denselayer1/conv1/Conv" [label="[128, 64, 1, 1]", style=solid]; +"14 QuantizeLinear_onnx^^Conv_1163_1" -> "15 DequantizeLinear_onnx^^Conv_1163_1" [label="[128, 64, 1, 1]", style=dashed]; +"15 DequantizeLinear_onnx^^Conv_1163_1" -> "16 /features/denseblock1/denselayer1/conv1/Conv" [label="[128, 64, 1, 1]", style=solid]; "16 /features/denseblock1/denselayer1/conv1/Conv" -> "17 /features/denseblock1/denselayer1/relu2/Relu" [label="[1, 128, 56, 56]", style=solid]; "17 /features/denseblock1/denselayer1/relu2/Relu" -> "18 QuantizeLinear_/features/denseblock1/denselayer1/relu2/Relu_output_0_1" [label="[1, 128, 56, 56]", style=solid]; "18 QuantizeLinear_/features/denseblock1/denselayer1/relu2/Relu_output_0_1" -> "19 DequantizeLinear_/features/denseblock1/denselayer1/relu2/Relu_output_0_1" [label="[1, 128, 56, 56]", style=dashed]; @@ -1036,8 +1030,8 @@ strict digraph { "27 /features/denseblock1/denselayer2/relu1/Relu" -> "28 QuantizeLinear_/features/denseblock1/denselayer2/relu1/Relu_output_0_1" [label="[1, 96, 56, 56]", style=solid]; "28 QuantizeLinear_/features/denseblock1/denselayer2/relu1/Relu_output_0_1" -> "29 DequantizeLinear_/features/denseblock1/denselayer2/relu1/Relu_output_0_1" [label="[1, 96, 56, 56]", style=dashed]; "29 DequantizeLinear_/features/denseblock1/denselayer2/relu1/Relu_output_0_1" -> "32 /features/denseblock1/denselayer2/conv1/Conv" [label="[1, 96, 56, 56]", style=solid]; -"30 QuantizeLinear_onnx^^Conv_1172_1" -> "31 DequantizeLinear_onnx^^Conv_1172_1" [label="[128, 96, 1, 1]", style=dashed]; -"31 DequantizeLinear_onnx^^Conv_1172_1" -> "32 /features/denseblock1/denselayer2/conv1/Conv" [label="[128, 96, 1, 1]", style=solid]; +"30 QuantizeLinear_onnx^^Conv_1166_1" -> "31 DequantizeLinear_onnx^^Conv_1166_1" [label="[128, 96, 1, 1]", style=dashed]; +"31 DequantizeLinear_onnx^^Conv_1166_1" -> "32 /features/denseblock1/denselayer2/conv1/Conv" [label="[128, 96, 1, 1]", style=solid]; "32 /features/denseblock1/denselayer2/conv1/Conv" -> "33 /features/denseblock1/denselayer2/relu2/Relu" [label="[1, 128, 56, 56]", style=solid]; "33 /features/denseblock1/denselayer2/relu2/Relu" -> "34 QuantizeLinear_/features/denseblock1/denselayer2/relu2/Relu_output_0_1" [label="[1, 128, 56, 56]", style=solid]; "34 QuantizeLinear_/features/denseblock1/denselayer2/relu2/Relu_output_0_1" -> "35 DequantizeLinear_/features/denseblock1/denselayer2/relu2/Relu_output_0_1" [label="[1, 128, 56, 56]", style=dashed]; @@ -1056,8 +1050,8 @@ strict digraph { "43 /features/denseblock1/denselayer3/relu1/Relu" -> "44 QuantizeLinear_/features/denseblock1/denselayer3/relu1/Relu_output_0_1" [label="[1, 128, 56, 56]", style=solid]; "44 QuantizeLinear_/features/denseblock1/denselayer3/relu1/Relu_output_0_1" -> "45 DequantizeLinear_/features/denseblock1/denselayer3/relu1/Relu_output_0_1" [label="[1, 128, 56, 56]", style=dashed]; "45 DequantizeLinear_/features/denseblock1/denselayer3/relu1/Relu_output_0_1" -> "48 /features/denseblock1/denselayer3/conv1/Conv" [label="[1, 128, 56, 56]", style=solid]; -"46 QuantizeLinear_onnx^^Conv_1175_1" -> "47 DequantizeLinear_onnx^^Conv_1175_1" [label="[128, 128, 1, 1]", style=dashed]; -"47 DequantizeLinear_onnx^^Conv_1175_1" -> "48 /features/denseblock1/denselayer3/conv1/Conv" [label="[128, 128, 1, 1]", style=solid]; +"46 QuantizeLinear_onnx^^Conv_1169_1" -> "47 DequantizeLinear_onnx^^Conv_1169_1" [label="[128, 128, 1, 1]", style=dashed]; +"47 DequantizeLinear_onnx^^Conv_1169_1" -> "48 /features/denseblock1/denselayer3/conv1/Conv" [label="[128, 128, 1, 1]", style=solid]; "48 /features/denseblock1/denselayer3/conv1/Conv" -> "49 /features/denseblock1/denselayer3/relu2/Relu" [label="[1, 128, 56, 56]", style=solid]; "49 /features/denseblock1/denselayer3/relu2/Relu" -> "50 QuantizeLinear_/features/denseblock1/denselayer3/relu2/Relu_output_0_1" [label="[1, 128, 56, 56]", style=solid]; "50 QuantizeLinear_/features/denseblock1/denselayer3/relu2/Relu_output_0_1" -> "51 DequantizeLinear_/features/denseblock1/denselayer3/relu2/Relu_output_0_1" [label="[1, 128, 56, 56]", style=dashed]; @@ -1075,8 +1069,8 @@ strict digraph { "59 /features/denseblock1/denselayer4/relu1/Relu" -> "60 QuantizeLinear_/features/denseblock1/denselayer4/relu1/Relu_output_0_1" [label="[1, 160, 56, 56]", style=solid]; "60 QuantizeLinear_/features/denseblock1/denselayer4/relu1/Relu_output_0_1" -> "61 DequantizeLinear_/features/denseblock1/denselayer4/relu1/Relu_output_0_1" [label="[1, 160, 56, 56]", style=dashed]; "61 DequantizeLinear_/features/denseblock1/denselayer4/relu1/Relu_output_0_1" -> "64 /features/denseblock1/denselayer4/conv1/Conv" [label="[1, 160, 56, 56]", style=solid]; -"62 QuantizeLinear_onnx^^Conv_1178_1" -> "63 DequantizeLinear_onnx^^Conv_1178_1" [label="[128, 160, 1, 1]", style=dashed]; -"63 DequantizeLinear_onnx^^Conv_1178_1" -> "64 /features/denseblock1/denselayer4/conv1/Conv" [label="[128, 160, 1, 1]", style=solid]; +"62 QuantizeLinear_onnx^^Conv_1172_1" -> "63 DequantizeLinear_onnx^^Conv_1172_1" [label="[128, 160, 1, 1]", style=dashed]; +"63 DequantizeLinear_onnx^^Conv_1172_1" -> "64 /features/denseblock1/denselayer4/conv1/Conv" [label="[128, 160, 1, 1]", style=solid]; "64 /features/denseblock1/denselayer4/conv1/Conv" -> "65 /features/denseblock1/denselayer4/relu2/Relu" [label="[1, 128, 56, 56]", style=solid]; "65 /features/denseblock1/denselayer4/relu2/Relu" -> "66 QuantizeLinear_/features/denseblock1/denselayer4/relu2/Relu_output_0_1" [label="[1, 128, 56, 56]", style=solid]; "66 QuantizeLinear_/features/denseblock1/denselayer4/relu2/Relu_output_0_1" -> "67 DequantizeLinear_/features/denseblock1/denselayer4/relu2/Relu_output_0_1" [label="[1, 128, 56, 56]", style=dashed]; @@ -1093,8 +1087,8 @@ strict digraph { "75 /features/denseblock1/denselayer5/relu1/Relu" -> "76 QuantizeLinear_/features/denseblock1/denselayer5/relu1/Relu_output_0_1" [label="[1, 192, 56, 56]", style=solid]; "76 QuantizeLinear_/features/denseblock1/denselayer5/relu1/Relu_output_0_1" -> "77 DequantizeLinear_/features/denseblock1/denselayer5/relu1/Relu_output_0_1" [label="[1, 192, 56, 56]", style=dashed]; "77 DequantizeLinear_/features/denseblock1/denselayer5/relu1/Relu_output_0_1" -> "80 /features/denseblock1/denselayer5/conv1/Conv" [label="[1, 192, 56, 56]", style=solid]; -"78 QuantizeLinear_onnx^^Conv_1181_1" -> "79 DequantizeLinear_onnx^^Conv_1181_1" [label="[128, 192, 1, 1]", style=dashed]; -"79 DequantizeLinear_onnx^^Conv_1181_1" -> "80 /features/denseblock1/denselayer5/conv1/Conv" [label="[128, 192, 1, 1]", style=solid]; +"78 QuantizeLinear_onnx^^Conv_1175_1" -> "79 DequantizeLinear_onnx^^Conv_1175_1" [label="[128, 192, 1, 1]", style=dashed]; +"79 DequantizeLinear_onnx^^Conv_1175_1" -> "80 /features/denseblock1/denselayer5/conv1/Conv" [label="[128, 192, 1, 1]", style=solid]; "80 /features/denseblock1/denselayer5/conv1/Conv" -> "81 /features/denseblock1/denselayer5/relu2/Relu" [label="[1, 128, 56, 56]", style=solid]; "81 /features/denseblock1/denselayer5/relu2/Relu" -> "82 QuantizeLinear_/features/denseblock1/denselayer5/relu2/Relu_output_0_1" [label="[1, 128, 56, 56]", style=solid]; "82 QuantizeLinear_/features/denseblock1/denselayer5/relu2/Relu_output_0_1" -> "83 DequantizeLinear_/features/denseblock1/denselayer5/relu2/Relu_output_0_1" [label="[1, 128, 56, 56]", style=dashed]; @@ -1110,8 +1104,8 @@ strict digraph { "91 /features/denseblock1/denselayer6/relu1/Relu" -> "92 QuantizeLinear_/features/denseblock1/denselayer6/relu1/Relu_output_0_1" [label="[1, 224, 56, 56]", style=solid]; "92 QuantizeLinear_/features/denseblock1/denselayer6/relu1/Relu_output_0_1" -> "93 DequantizeLinear_/features/denseblock1/denselayer6/relu1/Relu_output_0_1" [label="[1, 224, 56, 56]", style=dashed]; "93 DequantizeLinear_/features/denseblock1/denselayer6/relu1/Relu_output_0_1" -> "96 /features/denseblock1/denselayer6/conv1/Conv" [label="[1, 224, 56, 56]", style=solid]; -"94 QuantizeLinear_onnx^^Conv_1184_1" -> "95 DequantizeLinear_onnx^^Conv_1184_1" [label="[128, 224, 1, 1]", style=dashed]; -"95 DequantizeLinear_onnx^^Conv_1184_1" -> "96 /features/denseblock1/denselayer6/conv1/Conv" [label="[128, 224, 1, 1]", style=solid]; +"94 QuantizeLinear_onnx^^Conv_1178_1" -> "95 DequantizeLinear_onnx^^Conv_1178_1" [label="[128, 224, 1, 1]", style=dashed]; +"95 DequantizeLinear_onnx^^Conv_1178_1" -> "96 /features/denseblock1/denselayer6/conv1/Conv" [label="[128, 224, 1, 1]", style=solid]; "96 /features/denseblock1/denselayer6/conv1/Conv" -> "97 /features/denseblock1/denselayer6/relu2/Relu" [label="[1, 128, 56, 56]", style=solid]; "97 /features/denseblock1/denselayer6/relu2/Relu" -> "98 QuantizeLinear_/features/denseblock1/denselayer6/relu2/Relu_output_0_1" [label="[1, 128, 56, 56]", style=solid]; "98 QuantizeLinear_/features/denseblock1/denselayer6/relu2/Relu_output_0_1" -> "99 DequantizeLinear_/features/denseblock1/denselayer6/relu2/Relu_output_0_1" [label="[1, 128, 56, 56]", style=dashed]; @@ -1128,1401 +1122,1395 @@ strict digraph { "109 DequantizeLinear_/features/transition1/relu/Relu_output_0_1" -> "112 /features/transition1/conv/Conv" [label="[1, 256, 56, 56]", style=solid]; "110 QuantizeLinear_features.transition1.conv.weight_1" -> "111 DequantizeLinear_features.transition1.conv.weight_1" [label="[128, 256, 1, 1]", style=dashed]; "111 DequantizeLinear_features.transition1.conv.weight_1" -> "112 /features/transition1/conv/Conv" [label="[128, 256, 1, 1]", style=solid]; -"112 /features/transition1/conv/Conv" -> "114 QuantizeLinear_/features/transition1/conv/Conv_output_0_1" [label="[1, 128, 56, 56]", style=solid]; -"113 /features/transition1/pool/Constant" -> "116 /features/transition1/pool/Pad" [label="[8]", style=dashed]; -"114 QuantizeLinear_/features/transition1/conv/Conv_output_0_1" -> "115 DequantizeLinear_/features/transition1/conv/Conv_output_0_1" [label="[1, 128, 56, 56]", style=dashed]; -"115 DequantizeLinear_/features/transition1/conv/Conv_output_0_1" -> "116 /features/transition1/pool/Pad" [label="[1, 128, 56, 56]", style=solid]; -"116 /features/transition1/pool/Pad" -> "117 /features/transition1/pool/AveragePool" [label="[1, 128, 56, 56]", style=solid]; -"117 /features/transition1/pool/AveragePool" -> "118 QuantizeLinear_/features/transition1/pool/AveragePool_output_0_1" [label="[1, 128, 28, 28]", style=solid]; -"118 QuantizeLinear_/features/transition1/pool/AveragePool_output_0_1" -> "119 DequantizeLinear_/features/transition1/pool/AveragePool_output_0_1" [label="[1, 128, 28, 28]", style=dashed]; -"119 DequantizeLinear_/features/transition1/pool/AveragePool_output_0_1" -> "120 /features/denseblock2/denselayer1/Concat" [label="[1, 128, 28, 28]", style=solid]; -"119 DequantizeLinear_/features/transition1/pool/AveragePool_output_0_1" -> "136 /features/denseblock2/denselayer2/Concat" [label="[1, 128, 28, 28]", style=solid]; -"119 DequantizeLinear_/features/transition1/pool/AveragePool_output_0_1" -> "152 /features/denseblock2/denselayer3/Concat" [label="[1, 128, 28, 28]", style=solid]; -"119 DequantizeLinear_/features/transition1/pool/AveragePool_output_0_1" -> "168 /features/denseblock2/denselayer4/Concat" [label="[1, 128, 28, 28]", style=solid]; -"119 DequantizeLinear_/features/transition1/pool/AveragePool_output_0_1" -> "184 /features/denseblock2/denselayer5/Concat" [label="[1, 128, 28, 28]", style=solid]; -"119 DequantizeLinear_/features/transition1/pool/AveragePool_output_0_1" -> "200 /features/denseblock2/denselayer6/Concat" [label="[1, 128, 28, 28]", style=solid]; -"119 DequantizeLinear_/features/transition1/pool/AveragePool_output_0_1" -> "216 /features/denseblock2/denselayer7/Concat" [label="[1, 128, 28, 28]", style=solid]; -"119 DequantizeLinear_/features/transition1/pool/AveragePool_output_0_1" -> "232 /features/denseblock2/denselayer8/Concat" [label="[1, 128, 28, 28]", style=solid]; -"119 DequantizeLinear_/features/transition1/pool/AveragePool_output_0_1" -> "248 /features/denseblock2/denselayer9/Concat" [label="[1, 128, 28, 28]", style=solid]; -"119 DequantizeLinear_/features/transition1/pool/AveragePool_output_0_1" -> "264 /features/denseblock2/denselayer10/Concat" [label="[1, 128, 28, 28]", style=solid]; -"119 DequantizeLinear_/features/transition1/pool/AveragePool_output_0_1" -> "280 /features/denseblock2/denselayer11/Concat" [label="[1, 128, 28, 28]", style=solid]; -"119 DequantizeLinear_/features/transition1/pool/AveragePool_output_0_1" -> "296 /features/denseblock2/denselayer12/Concat" [label="[1, 128, 28, 28]", style=solid]; -"119 DequantizeLinear_/features/transition1/pool/AveragePool_output_0_1" -> "312 /features/denseblock2/Concat" [label="[1, 128, 28, 28]", style=solid]; -"120 /features/denseblock2/denselayer1/Concat" -> "121 /features/denseblock2/denselayer1/norm1/BatchNormalization" [label="[1, 128, 28, 28]", style=solid]; -"121 /features/denseblock2/denselayer1/norm1/BatchNormalization" -> "122 /features/denseblock2/denselayer1/relu1/Relu" [label="[1, 128, 28, 28]", style=solid]; -"122 /features/denseblock2/denselayer1/relu1/Relu" -> "123 QuantizeLinear_/features/denseblock2/denselayer1/relu1/Relu_output_0_1" [label="[1, 128, 28, 28]", style=solid]; -"123 QuantizeLinear_/features/denseblock2/denselayer1/relu1/Relu_output_0_1" -> "124 DequantizeLinear_/features/denseblock2/denselayer1/relu1/Relu_output_0_1" [label="[1, 128, 28, 28]", style=dashed]; -"124 DequantizeLinear_/features/denseblock2/denselayer1/relu1/Relu_output_0_1" -> "127 /features/denseblock2/denselayer1/conv1/Conv" [label="[1, 128, 28, 28]", style=solid]; -"125 QuantizeLinear_onnx^^Conv_1187_1" -> "126 DequantizeLinear_onnx^^Conv_1187_1" [label="[128, 128, 1, 1]", style=dashed]; -"126 DequantizeLinear_onnx^^Conv_1187_1" -> "127 /features/denseblock2/denselayer1/conv1/Conv" [label="[128, 128, 1, 1]", style=solid]; -"127 /features/denseblock2/denselayer1/conv1/Conv" -> "128 /features/denseblock2/denselayer1/relu2/Relu" [label="[1, 128, 28, 28]", style=solid]; -"128 /features/denseblock2/denselayer1/relu2/Relu" -> "129 QuantizeLinear_/features/denseblock2/denselayer1/relu2/Relu_output_0_1" [label="[1, 128, 28, 28]", style=solid]; -"129 QuantizeLinear_/features/denseblock2/denselayer1/relu2/Relu_output_0_1" -> "130 DequantizeLinear_/features/denseblock2/denselayer1/relu2/Relu_output_0_1" [label="[1, 128, 28, 28]", style=dashed]; -"130 DequantizeLinear_/features/denseblock2/denselayer1/relu2/Relu_output_0_1" -> "133 /features/denseblock2/denselayer1/conv2/Conv" [label="[1, 128, 28, 28]", style=solid]; -"131 QuantizeLinear_features.denseblock2.denselayer1.conv2.weight_1" -> "132 DequantizeLinear_features.denseblock2.denselayer1.conv2.weight_1" [label="[32, 128, 3, 3]", style=dashed]; -"132 DequantizeLinear_features.denseblock2.denselayer1.conv2.weight_1" -> "133 /features/denseblock2/denselayer1/conv2/Conv" [label="[32, 128, 3, 3]", style=solid]; -"133 /features/denseblock2/denselayer1/conv2/Conv" -> "134 QuantizeLinear_/features/denseblock2/denselayer1/conv2/Conv_output_0_1" [label="[1, 32, 28, 28]", style=solid]; -"134 QuantizeLinear_/features/denseblock2/denselayer1/conv2/Conv_output_0_1" -> "135 DequantizeLinear_/features/denseblock2/denselayer1/conv2/Conv_output_0_1" [label="[1, 32, 28, 28]", style=dashed]; -"135 DequantizeLinear_/features/denseblock2/denselayer1/conv2/Conv_output_0_1" -> "136 /features/denseblock2/denselayer2/Concat" [label="[1, 32, 28, 28]", style=solid]; -"135 DequantizeLinear_/features/denseblock2/denselayer1/conv2/Conv_output_0_1" -> "152 /features/denseblock2/denselayer3/Concat" [label="[1, 32, 28, 28]", style=solid]; -"135 DequantizeLinear_/features/denseblock2/denselayer1/conv2/Conv_output_0_1" -> "168 /features/denseblock2/denselayer4/Concat" [label="[1, 32, 28, 28]", style=solid]; -"135 DequantizeLinear_/features/denseblock2/denselayer1/conv2/Conv_output_0_1" -> "184 /features/denseblock2/denselayer5/Concat" [label="[1, 32, 28, 28]", style=solid]; -"135 DequantizeLinear_/features/denseblock2/denselayer1/conv2/Conv_output_0_1" -> "200 /features/denseblock2/denselayer6/Concat" [label="[1, 32, 28, 28]", style=solid]; -"135 DequantizeLinear_/features/denseblock2/denselayer1/conv2/Conv_output_0_1" -> "216 /features/denseblock2/denselayer7/Concat" [label="[1, 32, 28, 28]", style=solid]; -"135 DequantizeLinear_/features/denseblock2/denselayer1/conv2/Conv_output_0_1" -> "232 /features/denseblock2/denselayer8/Concat" [label="[1, 32, 28, 28]", style=solid]; -"135 DequantizeLinear_/features/denseblock2/denselayer1/conv2/Conv_output_0_1" -> "248 /features/denseblock2/denselayer9/Concat" [label="[1, 32, 28, 28]", style=solid]; -"135 DequantizeLinear_/features/denseblock2/denselayer1/conv2/Conv_output_0_1" -> "264 /features/denseblock2/denselayer10/Concat" [label="[1, 32, 28, 28]", style=solid]; -"135 DequantizeLinear_/features/denseblock2/denselayer1/conv2/Conv_output_0_1" -> "280 /features/denseblock2/denselayer11/Concat" [label="[1, 32, 28, 28]", style=solid]; -"135 DequantizeLinear_/features/denseblock2/denselayer1/conv2/Conv_output_0_1" -> "296 /features/denseblock2/denselayer12/Concat" [label="[1, 32, 28, 28]", style=solid]; -"135 DequantizeLinear_/features/denseblock2/denselayer1/conv2/Conv_output_0_1" -> "312 /features/denseblock2/Concat" [label="[1, 32, 28, 28]", style=solid]; -"136 /features/denseblock2/denselayer2/Concat" -> "137 /features/denseblock2/denselayer2/norm1/BatchNormalization" [label="[1, 160, 28, 28]", style=solid]; -"137 /features/denseblock2/denselayer2/norm1/BatchNormalization" -> "138 /features/denseblock2/denselayer2/relu1/Relu" [label="[1, 160, 28, 28]", style=solid]; -"138 /features/denseblock2/denselayer2/relu1/Relu" -> "139 QuantizeLinear_/features/denseblock2/denselayer2/relu1/Relu_output_0_1" [label="[1, 160, 28, 28]", style=solid]; -"139 QuantizeLinear_/features/denseblock2/denselayer2/relu1/Relu_output_0_1" -> "140 DequantizeLinear_/features/denseblock2/denselayer2/relu1/Relu_output_0_1" [label="[1, 160, 28, 28]", style=dashed]; -"140 DequantizeLinear_/features/denseblock2/denselayer2/relu1/Relu_output_0_1" -> "143 /features/denseblock2/denselayer2/conv1/Conv" [label="[1, 160, 28, 28]", style=solid]; -"141 QuantizeLinear_onnx^^Conv_1190_1" -> "142 DequantizeLinear_onnx^^Conv_1190_1" [label="[128, 160, 1, 1]", style=dashed]; -"142 DequantizeLinear_onnx^^Conv_1190_1" -> "143 /features/denseblock2/denselayer2/conv1/Conv" [label="[128, 160, 1, 1]", style=solid]; -"143 /features/denseblock2/denselayer2/conv1/Conv" -> "144 /features/denseblock2/denselayer2/relu2/Relu" [label="[1, 128, 28, 28]", style=solid]; -"144 /features/denseblock2/denselayer2/relu2/Relu" -> "145 QuantizeLinear_/features/denseblock2/denselayer2/relu2/Relu_output_0_1" [label="[1, 128, 28, 28]", style=solid]; -"145 QuantizeLinear_/features/denseblock2/denselayer2/relu2/Relu_output_0_1" -> "146 DequantizeLinear_/features/denseblock2/denselayer2/relu2/Relu_output_0_1" [label="[1, 128, 28, 28]", style=dashed]; -"146 DequantizeLinear_/features/denseblock2/denselayer2/relu2/Relu_output_0_1" -> "149 /features/denseblock2/denselayer2/conv2/Conv" [label="[1, 128, 28, 28]", style=solid]; -"147 QuantizeLinear_features.denseblock2.denselayer2.conv2.weight_1" -> "148 DequantizeLinear_features.denseblock2.denselayer2.conv2.weight_1" [label="[32, 128, 3, 3]", style=dashed]; -"148 DequantizeLinear_features.denseblock2.denselayer2.conv2.weight_1" -> "149 /features/denseblock2/denselayer2/conv2/Conv" [label="[32, 128, 3, 3]", style=solid]; -"149 /features/denseblock2/denselayer2/conv2/Conv" -> "150 QuantizeLinear_/features/denseblock2/denselayer2/conv2/Conv_output_0_1" [label="[1, 32, 28, 28]", style=solid]; -"150 QuantizeLinear_/features/denseblock2/denselayer2/conv2/Conv_output_0_1" -> "151 DequantizeLinear_/features/denseblock2/denselayer2/conv2/Conv_output_0_1" [label="[1, 32, 28, 28]", style=dashed]; -"151 DequantizeLinear_/features/denseblock2/denselayer2/conv2/Conv_output_0_1" -> "152 /features/denseblock2/denselayer3/Concat" [label="[1, 32, 28, 28]", style=solid]; -"151 DequantizeLinear_/features/denseblock2/denselayer2/conv2/Conv_output_0_1" -> "168 /features/denseblock2/denselayer4/Concat" [label="[1, 32, 28, 28]", style=solid]; -"151 DequantizeLinear_/features/denseblock2/denselayer2/conv2/Conv_output_0_1" -> "184 /features/denseblock2/denselayer5/Concat" [label="[1, 32, 28, 28]", style=solid]; -"151 DequantizeLinear_/features/denseblock2/denselayer2/conv2/Conv_output_0_1" -> "200 /features/denseblock2/denselayer6/Concat" [label="[1, 32, 28, 28]", style=solid]; -"151 DequantizeLinear_/features/denseblock2/denselayer2/conv2/Conv_output_0_1" -> "216 /features/denseblock2/denselayer7/Concat" [label="[1, 32, 28, 28]", style=solid]; -"151 DequantizeLinear_/features/denseblock2/denselayer2/conv2/Conv_output_0_1" -> "232 /features/denseblock2/denselayer8/Concat" [label="[1, 32, 28, 28]", style=solid]; -"151 DequantizeLinear_/features/denseblock2/denselayer2/conv2/Conv_output_0_1" -> "248 /features/denseblock2/denselayer9/Concat" [label="[1, 32, 28, 28]", style=solid]; -"151 DequantizeLinear_/features/denseblock2/denselayer2/conv2/Conv_output_0_1" -> "264 /features/denseblock2/denselayer10/Concat" [label="[1, 32, 28, 28]", style=solid]; -"151 DequantizeLinear_/features/denseblock2/denselayer2/conv2/Conv_output_0_1" -> "280 /features/denseblock2/denselayer11/Concat" [label="[1, 32, 28, 28]", style=solid]; -"151 DequantizeLinear_/features/denseblock2/denselayer2/conv2/Conv_output_0_1" -> "296 /features/denseblock2/denselayer12/Concat" [label="[1, 32, 28, 28]", style=solid]; -"151 DequantizeLinear_/features/denseblock2/denselayer2/conv2/Conv_output_0_1" -> "312 /features/denseblock2/Concat" [label="[1, 32, 28, 28]", style=solid]; -"152 /features/denseblock2/denselayer3/Concat" -> "153 /features/denseblock2/denselayer3/norm1/BatchNormalization" [label="[1, 192, 28, 28]", style=solid]; -"153 /features/denseblock2/denselayer3/norm1/BatchNormalization" -> "154 /features/denseblock2/denselayer3/relu1/Relu" [label="[1, 192, 28, 28]", style=solid]; -"154 /features/denseblock2/denselayer3/relu1/Relu" -> "155 QuantizeLinear_/features/denseblock2/denselayer3/relu1/Relu_output_0_1" [label="[1, 192, 28, 28]", style=solid]; -"155 QuantizeLinear_/features/denseblock2/denselayer3/relu1/Relu_output_0_1" -> "156 DequantizeLinear_/features/denseblock2/denselayer3/relu1/Relu_output_0_1" [label="[1, 192, 28, 28]", style=dashed]; -"156 DequantizeLinear_/features/denseblock2/denselayer3/relu1/Relu_output_0_1" -> "159 /features/denseblock2/denselayer3/conv1/Conv" [label="[1, 192, 28, 28]", style=solid]; -"157 QuantizeLinear_onnx^^Conv_1193_1" -> "158 DequantizeLinear_onnx^^Conv_1193_1" [label="[128, 192, 1, 1]", style=dashed]; -"158 DequantizeLinear_onnx^^Conv_1193_1" -> "159 /features/denseblock2/denselayer3/conv1/Conv" [label="[128, 192, 1, 1]", style=solid]; -"159 /features/denseblock2/denselayer3/conv1/Conv" -> "160 /features/denseblock2/denselayer3/relu2/Relu" [label="[1, 128, 28, 28]", style=solid]; -"160 /features/denseblock2/denselayer3/relu2/Relu" -> "161 QuantizeLinear_/features/denseblock2/denselayer3/relu2/Relu_output_0_1" [label="[1, 128, 28, 28]", style=solid]; -"161 QuantizeLinear_/features/denseblock2/denselayer3/relu2/Relu_output_0_1" -> "162 DequantizeLinear_/features/denseblock2/denselayer3/relu2/Relu_output_0_1" [label="[1, 128, 28, 28]", style=dashed]; -"162 DequantizeLinear_/features/denseblock2/denselayer3/relu2/Relu_output_0_1" -> "165 /features/denseblock2/denselayer3/conv2/Conv" [label="[1, 128, 28, 28]", style=solid]; -"163 QuantizeLinear_features.denseblock2.denselayer3.conv2.weight_1" -> "164 DequantizeLinear_features.denseblock2.denselayer3.conv2.weight_1" [label="[32, 128, 3, 3]", style=dashed]; -"164 DequantizeLinear_features.denseblock2.denselayer3.conv2.weight_1" -> "165 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DequantizeLinear_/features/denseblock2/denselayer3/conv2/Conv_output_0_1" -> "216 /features/denseblock2/denselayer7/Concat" [label="[1, 32, 28, 28]", style=solid]; -"167 DequantizeLinear_/features/denseblock2/denselayer3/conv2/Conv_output_0_1" -> "232 /features/denseblock2/denselayer8/Concat" [label="[1, 32, 28, 28]", style=solid]; -"167 DequantizeLinear_/features/denseblock2/denselayer3/conv2/Conv_output_0_1" -> "248 /features/denseblock2/denselayer9/Concat" [label="[1, 32, 28, 28]", style=solid]; -"167 DequantizeLinear_/features/denseblock2/denselayer3/conv2/Conv_output_0_1" -> "264 /features/denseblock2/denselayer10/Concat" [label="[1, 32, 28, 28]", style=solid]; -"167 DequantizeLinear_/features/denseblock2/denselayer3/conv2/Conv_output_0_1" -> "280 /features/denseblock2/denselayer11/Concat" [label="[1, 32, 28, 28]", style=solid]; -"167 DequantizeLinear_/features/denseblock2/denselayer3/conv2/Conv_output_0_1" -> "296 /features/denseblock2/denselayer12/Concat" [label="[1, 32, 28, 28]", style=solid]; -"167 DequantizeLinear_/features/denseblock2/denselayer3/conv2/Conv_output_0_1" -> "312 /features/denseblock2/Concat" [label="[1, 32, 28, 28]", style=solid]; -"168 /features/denseblock2/denselayer4/Concat" -> "169 /features/denseblock2/denselayer4/norm1/BatchNormalization" [label="[1, 224, 28, 28]", style=solid]; -"169 /features/denseblock2/denselayer4/norm1/BatchNormalization" -> "170 /features/denseblock2/denselayer4/relu1/Relu" [label="[1, 224, 28, 28]", style=solid]; -"170 /features/denseblock2/denselayer4/relu1/Relu" -> "171 QuantizeLinear_/features/denseblock2/denselayer4/relu1/Relu_output_0_1" [label="[1, 224, 28, 28]", style=solid]; -"171 QuantizeLinear_/features/denseblock2/denselayer4/relu1/Relu_output_0_1" -> "172 DequantizeLinear_/features/denseblock2/denselayer4/relu1/Relu_output_0_1" [label="[1, 224, 28, 28]", style=dashed]; -"172 DequantizeLinear_/features/denseblock2/denselayer4/relu1/Relu_output_0_1" -> "175 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/features/denseblock2/denselayer4/conv2/Conv" [label="[1, 128, 28, 28]", style=solid]; -"179 QuantizeLinear_features.denseblock2.denselayer4.conv2.weight_1" -> "180 DequantizeLinear_features.denseblock2.denselayer4.conv2.weight_1" [label="[32, 128, 3, 3]", style=dashed]; -"180 DequantizeLinear_features.denseblock2.denselayer4.conv2.weight_1" -> "181 /features/denseblock2/denselayer4/conv2/Conv" [label="[32, 128, 3, 3]", style=solid]; -"181 /features/denseblock2/denselayer4/conv2/Conv" -> "182 QuantizeLinear_/features/denseblock2/denselayer4/conv2/Conv_output_0_1" [label="[1, 32, 28, 28]", style=solid]; -"182 QuantizeLinear_/features/denseblock2/denselayer4/conv2/Conv_output_0_1" -> "183 DequantizeLinear_/features/denseblock2/denselayer4/conv2/Conv_output_0_1" [label="[1, 32, 28, 28]", style=dashed]; -"183 DequantizeLinear_/features/denseblock2/denselayer4/conv2/Conv_output_0_1" -> "184 /features/denseblock2/denselayer5/Concat" [label="[1, 32, 28, 28]", style=solid]; -"183 DequantizeLinear_/features/denseblock2/denselayer4/conv2/Conv_output_0_1" -> "200 /features/denseblock2/denselayer6/Concat" [label="[1, 32, 28, 28]", style=solid]; -"183 DequantizeLinear_/features/denseblock2/denselayer4/conv2/Conv_output_0_1" -> "216 /features/denseblock2/denselayer7/Concat" [label="[1, 32, 28, 28]", style=solid]; -"183 DequantizeLinear_/features/denseblock2/denselayer4/conv2/Conv_output_0_1" -> "232 /features/denseblock2/denselayer8/Concat" [label="[1, 32, 28, 28]", style=solid]; -"183 DequantizeLinear_/features/denseblock2/denselayer4/conv2/Conv_output_0_1" -> "248 /features/denseblock2/denselayer9/Concat" [label="[1, 32, 28, 28]", style=solid]; -"183 DequantizeLinear_/features/denseblock2/denselayer4/conv2/Conv_output_0_1" -> "264 /features/denseblock2/denselayer10/Concat" [label="[1, 32, 28, 28]", style=solid]; -"183 DequantizeLinear_/features/denseblock2/denselayer4/conv2/Conv_output_0_1" -> "280 /features/denseblock2/denselayer11/Concat" [label="[1, 32, 28, 28]", style=solid]; -"183 DequantizeLinear_/features/denseblock2/denselayer4/conv2/Conv_output_0_1" -> "296 /features/denseblock2/denselayer12/Concat" [label="[1, 32, 28, 28]", style=solid]; -"183 DequantizeLinear_/features/denseblock2/denselayer4/conv2/Conv_output_0_1" -> "312 /features/denseblock2/Concat" [label="[1, 32, 28, 28]", style=solid]; -"184 /features/denseblock2/denselayer5/Concat" -> "185 /features/denseblock2/denselayer5/norm1/BatchNormalization" [label="[1, 256, 28, 28]", style=solid]; -"185 /features/denseblock2/denselayer5/norm1/BatchNormalization" -> "186 /features/denseblock2/denselayer5/relu1/Relu" [label="[1, 256, 28, 28]", style=solid]; -"186 /features/denseblock2/denselayer5/relu1/Relu" -> "187 QuantizeLinear_/features/denseblock2/denselayer5/relu1/Relu_output_0_1" [label="[1, 256, 28, 28]", style=solid]; -"187 QuantizeLinear_/features/denseblock2/denselayer5/relu1/Relu_output_0_1" -> "188 DequantizeLinear_/features/denseblock2/denselayer5/relu1/Relu_output_0_1" [label="[1, 256, 28, 28]", style=dashed]; -"188 DequantizeLinear_/features/denseblock2/denselayer5/relu1/Relu_output_0_1" -> "191 /features/denseblock2/denselayer5/conv1/Conv" [label="[1, 256, 28, 28]", style=solid]; -"189 QuantizeLinear_onnx^^Conv_1199_1" -> "190 DequantizeLinear_onnx^^Conv_1199_1" [label="[128, 256, 1, 1]", style=dashed]; -"190 DequantizeLinear_onnx^^Conv_1199_1" -> "191 /features/denseblock2/denselayer5/conv1/Conv" [label="[128, 256, 1, 1]", style=solid]; -"191 /features/denseblock2/denselayer5/conv1/Conv" -> "192 /features/denseblock2/denselayer5/relu2/Relu" [label="[1, 128, 28, 28]", style=solid]; -"192 /features/denseblock2/denselayer5/relu2/Relu" -> "193 QuantizeLinear_/features/denseblock2/denselayer5/relu2/Relu_output_0_1" [label="[1, 128, 28, 28]", style=solid]; -"193 QuantizeLinear_/features/denseblock2/denselayer5/relu2/Relu_output_0_1" -> "194 DequantizeLinear_/features/denseblock2/denselayer5/relu2/Relu_output_0_1" [label="[1, 128, 28, 28]", style=dashed]; -"194 DequantizeLinear_/features/denseblock2/denselayer5/relu2/Relu_output_0_1" -> "197 /features/denseblock2/denselayer5/conv2/Conv" [label="[1, 128, 28, 28]", style=solid]; -"195 QuantizeLinear_features.denseblock2.denselayer5.conv2.weight_1" -> "196 DequantizeLinear_features.denseblock2.denselayer5.conv2.weight_1" [label="[32, 128, 3, 3]", style=dashed]; -"196 DequantizeLinear_features.denseblock2.denselayer5.conv2.weight_1" -> "197 /features/denseblock2/denselayer5/conv2/Conv" [label="[32, 128, 3, 3]", style=solid]; -"197 /features/denseblock2/denselayer5/conv2/Conv" -> "198 QuantizeLinear_/features/denseblock2/denselayer5/conv2/Conv_output_0_1" [label="[1, 32, 28, 28]", style=solid]; -"198 QuantizeLinear_/features/denseblock2/denselayer5/conv2/Conv_output_0_1" -> "199 DequantizeLinear_/features/denseblock2/denselayer5/conv2/Conv_output_0_1" [label="[1, 32, 28, 28]", style=dashed]; -"199 DequantizeLinear_/features/denseblock2/denselayer5/conv2/Conv_output_0_1" -> 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DequantizeLinear_/features/denseblock2/denselayer5/conv2/Conv_output_0_1" -> "296 /features/denseblock2/denselayer12/Concat" [label="[1, 32, 28, 28]", style=solid]; -"199 DequantizeLinear_/features/denseblock2/denselayer5/conv2/Conv_output_0_1" -> "312 /features/denseblock2/Concat" [label="[1, 32, 28, 28]", style=solid]; -"200 /features/denseblock2/denselayer6/Concat" -> "201 /features/denseblock2/denselayer6/norm1/BatchNormalization" [label="[1, 288, 28, 28]", style=solid]; -"201 /features/denseblock2/denselayer6/norm1/BatchNormalization" -> "202 /features/denseblock2/denselayer6/relu1/Relu" [label="[1, 288, 28, 28]", style=solid]; -"202 /features/denseblock2/denselayer6/relu1/Relu" -> "203 QuantizeLinear_/features/denseblock2/denselayer6/relu1/Relu_output_0_1" [label="[1, 288, 28, 28]", style=solid]; -"203 QuantizeLinear_/features/denseblock2/denselayer6/relu1/Relu_output_0_1" -> "204 DequantizeLinear_/features/denseblock2/denselayer6/relu1/Relu_output_0_1" [label="[1, 288, 28, 28]", style=dashed]; -"204 DequantizeLinear_/features/denseblock2/denselayer6/relu1/Relu_output_0_1" -> "207 /features/denseblock2/denselayer6/conv1/Conv" [label="[1, 288, 28, 28]", style=solid]; -"205 QuantizeLinear_onnx^^Conv_1202_1" -> "206 DequantizeLinear_onnx^^Conv_1202_1" [label="[128, 288, 1, 1]", style=dashed]; -"206 DequantizeLinear_onnx^^Conv_1202_1" -> "207 /features/denseblock2/denselayer6/conv1/Conv" [label="[128, 288, 1, 1]", style=solid]; -"207 /features/denseblock2/denselayer6/conv1/Conv" -> "208 /features/denseblock2/denselayer6/relu2/Relu" [label="[1, 128, 28, 28]", style=solid]; -"208 /features/denseblock2/denselayer6/relu2/Relu" -> "209 QuantizeLinear_/features/denseblock2/denselayer6/relu2/Relu_output_0_1" [label="[1, 128, 28, 28]", style=solid]; -"209 QuantizeLinear_/features/denseblock2/denselayer6/relu2/Relu_output_0_1" -> "210 DequantizeLinear_/features/denseblock2/denselayer6/relu2/Relu_output_0_1" [label="[1, 128, 28, 28]", style=dashed]; -"210 DequantizeLinear_/features/denseblock2/denselayer6/relu2/Relu_output_0_1" -> "213 /features/denseblock2/denselayer6/conv2/Conv" [label="[1, 128, 28, 28]", style=solid]; -"211 QuantizeLinear_features.denseblock2.denselayer6.conv2.weight_1" -> "212 DequantizeLinear_features.denseblock2.denselayer6.conv2.weight_1" [label="[32, 128, 3, 3]", style=dashed]; -"212 DequantizeLinear_features.denseblock2.denselayer6.conv2.weight_1" -> "213 /features/denseblock2/denselayer6/conv2/Conv" [label="[32, 128, 3, 3]", style=solid]; -"213 /features/denseblock2/denselayer6/conv2/Conv" -> "214 QuantizeLinear_/features/denseblock2/denselayer6/conv2/Conv_output_0_1" [label="[1, 32, 28, 28]", style=solid]; -"214 QuantizeLinear_/features/denseblock2/denselayer6/conv2/Conv_output_0_1" -> "215 DequantizeLinear_/features/denseblock2/denselayer6/conv2/Conv_output_0_1" [label="[1, 32, 28, 28]", style=dashed]; -"215 DequantizeLinear_/features/denseblock2/denselayer6/conv2/Conv_output_0_1" -> "216 /features/denseblock2/denselayer7/Concat" [label="[1, 32, 28, 28]", style=solid]; -"215 DequantizeLinear_/features/denseblock2/denselayer6/conv2/Conv_output_0_1" -> "232 /features/denseblock2/denselayer8/Concat" [label="[1, 32, 28, 28]", style=solid]; -"215 DequantizeLinear_/features/denseblock2/denselayer6/conv2/Conv_output_0_1" -> "248 /features/denseblock2/denselayer9/Concat" [label="[1, 32, 28, 28]", style=solid]; -"215 DequantizeLinear_/features/denseblock2/denselayer6/conv2/Conv_output_0_1" -> "264 /features/denseblock2/denselayer10/Concat" [label="[1, 32, 28, 28]", style=solid]; -"215 DequantizeLinear_/features/denseblock2/denselayer6/conv2/Conv_output_0_1" -> "280 /features/denseblock2/denselayer11/Concat" [label="[1, 32, 28, 28]", style=solid]; -"215 DequantizeLinear_/features/denseblock2/denselayer6/conv2/Conv_output_0_1" -> "296 /features/denseblock2/denselayer12/Concat" [label="[1, 32, 28, 28]", style=solid]; -"215 DequantizeLinear_/features/denseblock2/denselayer6/conv2/Conv_output_0_1" -> "312 /features/denseblock2/Concat" [label="[1, 32, 28, 28]", style=solid]; -"216 /features/denseblock2/denselayer7/Concat" -> "217 /features/denseblock2/denselayer7/norm1/BatchNormalization" [label="[1, 320, 28, 28]", style=solid]; -"217 /features/denseblock2/denselayer7/norm1/BatchNormalization" -> "218 /features/denseblock2/denselayer7/relu1/Relu" [label="[1, 320, 28, 28]", style=solid]; -"218 /features/denseblock2/denselayer7/relu1/Relu" -> "219 QuantizeLinear_/features/denseblock2/denselayer7/relu1/Relu_output_0_1" [label="[1, 320, 28, 28]", style=solid]; -"219 QuantizeLinear_/features/denseblock2/denselayer7/relu1/Relu_output_0_1" -> "220 DequantizeLinear_/features/denseblock2/denselayer7/relu1/Relu_output_0_1" [label="[1, 320, 28, 28]", style=dashed]; -"220 DequantizeLinear_/features/denseblock2/denselayer7/relu1/Relu_output_0_1" -> "223 /features/denseblock2/denselayer7/conv1/Conv" [label="[1, 320, 28, 28]", style=solid]; -"221 QuantizeLinear_onnx^^Conv_1205_1" -> "222 DequantizeLinear_onnx^^Conv_1205_1" [label="[128, 320, 1, 1]", style=dashed]; -"222 DequantizeLinear_onnx^^Conv_1205_1" -> "223 /features/denseblock2/denselayer7/conv1/Conv" [label="[128, 320, 1, 1]", style=solid]; -"223 /features/denseblock2/denselayer7/conv1/Conv" -> "224 /features/denseblock2/denselayer7/relu2/Relu" [label="[1, 128, 28, 28]", style=solid]; -"224 /features/denseblock2/denselayer7/relu2/Relu" -> "225 QuantizeLinear_/features/denseblock2/denselayer7/relu2/Relu_output_0_1" [label="[1, 128, 28, 28]", style=solid]; -"225 QuantizeLinear_/features/denseblock2/denselayer7/relu2/Relu_output_0_1" -> "226 DequantizeLinear_/features/denseblock2/denselayer7/relu2/Relu_output_0_1" [label="[1, 128, 28, 28]", style=dashed]; -"226 DequantizeLinear_/features/denseblock2/denselayer7/relu2/Relu_output_0_1" -> "229 /features/denseblock2/denselayer7/conv2/Conv" [label="[1, 128, 28, 28]", style=solid]; -"227 QuantizeLinear_features.denseblock2.denselayer7.conv2.weight_1" -> "228 DequantizeLinear_features.denseblock2.denselayer7.conv2.weight_1" [label="[32, 128, 3, 3]", style=dashed]; -"228 DequantizeLinear_features.denseblock2.denselayer7.conv2.weight_1" -> "229 /features/denseblock2/denselayer7/conv2/Conv" [label="[32, 128, 3, 3]", style=solid]; -"229 /features/denseblock2/denselayer7/conv2/Conv" -> "230 QuantizeLinear_/features/denseblock2/denselayer7/conv2/Conv_output_0_1" [label="[1, 32, 28, 28]", style=solid]; -"230 QuantizeLinear_/features/denseblock2/denselayer7/conv2/Conv_output_0_1" -> "231 DequantizeLinear_/features/denseblock2/denselayer7/conv2/Conv_output_0_1" [label="[1, 32, 28, 28]", style=dashed]; -"231 DequantizeLinear_/features/denseblock2/denselayer7/conv2/Conv_output_0_1" -> "232 /features/denseblock2/denselayer8/Concat" [label="[1, 32, 28, 28]", style=solid]; -"231 DequantizeLinear_/features/denseblock2/denselayer7/conv2/Conv_output_0_1" -> "248 /features/denseblock2/denselayer9/Concat" [label="[1, 32, 28, 28]", style=solid]; -"231 DequantizeLinear_/features/denseblock2/denselayer7/conv2/Conv_output_0_1" -> "264 /features/denseblock2/denselayer10/Concat" [label="[1, 32, 28, 28]", style=solid]; -"231 DequantizeLinear_/features/denseblock2/denselayer7/conv2/Conv_output_0_1" -> "280 /features/denseblock2/denselayer11/Concat" [label="[1, 32, 28, 28]", style=solid]; -"231 DequantizeLinear_/features/denseblock2/denselayer7/conv2/Conv_output_0_1" -> "296 /features/denseblock2/denselayer12/Concat" [label="[1, 32, 28, 28]", style=solid]; -"231 DequantizeLinear_/features/denseblock2/denselayer7/conv2/Conv_output_0_1" -> "312 /features/denseblock2/Concat" [label="[1, 32, 28, 28]", style=solid]; -"232 /features/denseblock2/denselayer8/Concat" -> "233 /features/denseblock2/denselayer8/norm1/BatchNormalization" [label="[1, 352, 28, 28]", style=solid]; -"233 /features/denseblock2/denselayer8/norm1/BatchNormalization" -> "234 /features/denseblock2/denselayer8/relu1/Relu" [label="[1, 352, 28, 28]", style=solid]; -"234 /features/denseblock2/denselayer8/relu1/Relu" -> "235 QuantizeLinear_/features/denseblock2/denselayer8/relu1/Relu_output_0_1" [label="[1, 352, 28, 28]", style=solid]; -"235 QuantizeLinear_/features/denseblock2/denselayer8/relu1/Relu_output_0_1" -> "236 DequantizeLinear_/features/denseblock2/denselayer8/relu1/Relu_output_0_1" [label="[1, 352, 28, 28]", style=dashed]; -"236 DequantizeLinear_/features/denseblock2/denselayer8/relu1/Relu_output_0_1" -> "239 /features/denseblock2/denselayer8/conv1/Conv" [label="[1, 352, 28, 28]", style=solid]; -"237 QuantizeLinear_onnx^^Conv_1208_1" -> "238 DequantizeLinear_onnx^^Conv_1208_1" [label="[128, 352, 1, 1]", style=dashed]; -"238 DequantizeLinear_onnx^^Conv_1208_1" -> "239 /features/denseblock2/denselayer8/conv1/Conv" [label="[128, 352, 1, 1]", style=solid]; -"239 /features/denseblock2/denselayer8/conv1/Conv" -> "240 /features/denseblock2/denselayer8/relu2/Relu" [label="[1, 128, 28, 28]", style=solid]; -"240 /features/denseblock2/denselayer8/relu2/Relu" -> "241 QuantizeLinear_/features/denseblock2/denselayer8/relu2/Relu_output_0_1" [label="[1, 128, 28, 28]", style=solid]; -"241 QuantizeLinear_/features/denseblock2/denselayer8/relu2/Relu_output_0_1" -> "242 DequantizeLinear_/features/denseblock2/denselayer8/relu2/Relu_output_0_1" [label="[1, 128, 28, 28]", style=dashed]; -"242 DequantizeLinear_/features/denseblock2/denselayer8/relu2/Relu_output_0_1" -> "245 /features/denseblock2/denselayer8/conv2/Conv" [label="[1, 128, 28, 28]", style=solid]; -"243 QuantizeLinear_features.denseblock2.denselayer8.conv2.weight_1" -> "244 DequantizeLinear_features.denseblock2.denselayer8.conv2.weight_1" [label="[32, 128, 3, 3]", style=dashed]; -"244 DequantizeLinear_features.denseblock2.denselayer8.conv2.weight_1" -> "245 /features/denseblock2/denselayer8/conv2/Conv" [label="[32, 128, 3, 3]", style=solid]; -"245 /features/denseblock2/denselayer8/conv2/Conv" -> "246 QuantizeLinear_/features/denseblock2/denselayer8/conv2/Conv_output_0_1" [label="[1, 32, 28, 28]", style=solid]; -"246 QuantizeLinear_/features/denseblock2/denselayer8/conv2/Conv_output_0_1" -> "247 DequantizeLinear_/features/denseblock2/denselayer8/conv2/Conv_output_0_1" [label="[1, 32, 28, 28]", style=dashed]; -"247 DequantizeLinear_/features/denseblock2/denselayer8/conv2/Conv_output_0_1" -> "248 /features/denseblock2/denselayer9/Concat" [label="[1, 32, 28, 28]", style=solid]; -"247 DequantizeLinear_/features/denseblock2/denselayer8/conv2/Conv_output_0_1" -> "264 /features/denseblock2/denselayer10/Concat" [label="[1, 32, 28, 28]", style=solid]; -"247 DequantizeLinear_/features/denseblock2/denselayer8/conv2/Conv_output_0_1" -> "280 /features/denseblock2/denselayer11/Concat" [label="[1, 32, 28, 28]", style=solid]; -"247 DequantizeLinear_/features/denseblock2/denselayer8/conv2/Conv_output_0_1" -> "296 /features/denseblock2/denselayer12/Concat" [label="[1, 32, 28, 28]", style=solid]; -"247 DequantizeLinear_/features/denseblock2/denselayer8/conv2/Conv_output_0_1" -> "312 /features/denseblock2/Concat" [label="[1, 32, 28, 28]", style=solid]; -"248 /features/denseblock2/denselayer9/Concat" -> "249 /features/denseblock2/denselayer9/norm1/BatchNormalization" [label="[1, 384, 28, 28]", style=solid]; -"249 /features/denseblock2/denselayer9/norm1/BatchNormalization" -> "250 /features/denseblock2/denselayer9/relu1/Relu" [label="[1, 384, 28, 28]", style=solid]; -"250 /features/denseblock2/denselayer9/relu1/Relu" -> "251 QuantizeLinear_/features/denseblock2/denselayer9/relu1/Relu_output_0_1" [label="[1, 384, 28, 28]", style=solid]; -"251 QuantizeLinear_/features/denseblock2/denselayer9/relu1/Relu_output_0_1" -> "252 DequantizeLinear_/features/denseblock2/denselayer9/relu1/Relu_output_0_1" [label="[1, 384, 28, 28]", style=dashed]; -"252 DequantizeLinear_/features/denseblock2/denselayer9/relu1/Relu_output_0_1" -> "255 /features/denseblock2/denselayer9/conv1/Conv" [label="[1, 384, 28, 28]", style=solid]; -"253 QuantizeLinear_onnx^^Conv_1211_1" -> "254 DequantizeLinear_onnx^^Conv_1211_1" [label="[128, 384, 1, 1]", style=dashed]; -"254 DequantizeLinear_onnx^^Conv_1211_1" -> "255 /features/denseblock2/denselayer9/conv1/Conv" [label="[128, 384, 1, 1]", style=solid]; -"255 /features/denseblock2/denselayer9/conv1/Conv" -> "256 /features/denseblock2/denselayer9/relu2/Relu" [label="[1, 128, 28, 28]", style=solid]; -"256 /features/denseblock2/denselayer9/relu2/Relu" -> "257 QuantizeLinear_/features/denseblock2/denselayer9/relu2/Relu_output_0_1" [label="[1, 128, 28, 28]", style=solid]; -"257 QuantizeLinear_/features/denseblock2/denselayer9/relu2/Relu_output_0_1" -> "258 DequantizeLinear_/features/denseblock2/denselayer9/relu2/Relu_output_0_1" [label="[1, 128, 28, 28]", style=dashed]; -"258 DequantizeLinear_/features/denseblock2/denselayer9/relu2/Relu_output_0_1" -> "261 /features/denseblock2/denselayer9/conv2/Conv" [label="[1, 128, 28, 28]", style=solid]; -"259 QuantizeLinear_features.denseblock2.denselayer9.conv2.weight_1" -> "260 DequantizeLinear_features.denseblock2.denselayer9.conv2.weight_1" [label="[32, 128, 3, 3]", style=dashed]; -"260 DequantizeLinear_features.denseblock2.denselayer9.conv2.weight_1" -> "261 /features/denseblock2/denselayer9/conv2/Conv" [label="[32, 128, 3, 3]", style=solid]; -"261 /features/denseblock2/denselayer9/conv2/Conv" -> "262 QuantizeLinear_/features/denseblock2/denselayer9/conv2/Conv_output_0_1" [label="[1, 32, 28, 28]", style=solid]; -"262 QuantizeLinear_/features/denseblock2/denselayer9/conv2/Conv_output_0_1" -> "263 DequantizeLinear_/features/denseblock2/denselayer9/conv2/Conv_output_0_1" [label="[1, 32, 28, 28]", style=dashed]; -"263 DequantizeLinear_/features/denseblock2/denselayer9/conv2/Conv_output_0_1" -> "264 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28, 28]", style=solid]; -"278 QuantizeLinear_/features/denseblock2/denselayer10/conv2/Conv_output_0_1" -> "279 DequantizeLinear_/features/denseblock2/denselayer10/conv2/Conv_output_0_1" [label="[1, 32, 28, 28]", style=dashed]; -"279 DequantizeLinear_/features/denseblock2/denselayer10/conv2/Conv_output_0_1" -> "280 /features/denseblock2/denselayer11/Concat" [label="[1, 32, 28, 28]", style=solid]; -"279 DequantizeLinear_/features/denseblock2/denselayer10/conv2/Conv_output_0_1" -> "296 /features/denseblock2/denselayer12/Concat" [label="[1, 32, 28, 28]", style=solid]; -"279 DequantizeLinear_/features/denseblock2/denselayer10/conv2/Conv_output_0_1" -> "312 /features/denseblock2/Concat" [label="[1, 32, 28, 28]", style=solid]; -"280 /features/denseblock2/denselayer11/Concat" -> "281 /features/denseblock2/denselayer11/norm1/BatchNormalization" [label="[1, 448, 28, 28]", style=solid]; -"281 /features/denseblock2/denselayer11/norm1/BatchNormalization" -> "282 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DequantizeLinear_/features/denseblock3/denselayer2/conv2/Conv_output_0_1" -> "519 /features/denseblock3/denselayer13/Concat" [label="[1, 32, 14, 14]", style=solid]; -"358 DequantizeLinear_/features/denseblock3/denselayer2/conv2/Conv_output_0_1" -> "535 /features/denseblock3/denselayer14/Concat" [label="[1, 32, 14, 14]", style=solid]; -"358 DequantizeLinear_/features/denseblock3/denselayer2/conv2/Conv_output_0_1" -> "551 /features/denseblock3/denselayer15/Concat" [label="[1, 32, 14, 14]", style=solid]; -"358 DequantizeLinear_/features/denseblock3/denselayer2/conv2/Conv_output_0_1" -> "567 /features/denseblock3/denselayer16/Concat" [label="[1, 32, 14, 14]", style=solid]; -"358 DequantizeLinear_/features/denseblock3/denselayer2/conv2/Conv_output_0_1" -> "583 /features/denseblock3/denselayer17/Concat" [label="[1, 32, 14, 14]", style=solid]; -"358 DequantizeLinear_/features/denseblock3/denselayer2/conv2/Conv_output_0_1" -> "599 /features/denseblock3/denselayer18/Concat" [label="[1, 32, 14, 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DequantizeLinear_/features/denseblock3/denselayer3/conv2/Conv_output_0_1" -> "471 /features/denseblock3/denselayer10/Concat" [label="[1, 32, 14, 14]", style=solid]; -"374 DequantizeLinear_/features/denseblock3/denselayer3/conv2/Conv_output_0_1" -> "487 /features/denseblock3/denselayer11/Concat" [label="[1, 32, 14, 14]", style=solid]; -"374 DequantizeLinear_/features/denseblock3/denselayer3/conv2/Conv_output_0_1" -> "503 /features/denseblock3/denselayer12/Concat" [label="[1, 32, 14, 14]", style=solid]; -"374 DequantizeLinear_/features/denseblock3/denselayer3/conv2/Conv_output_0_1" -> "519 /features/denseblock3/denselayer13/Concat" [label="[1, 32, 14, 14]", style=solid]; -"374 DequantizeLinear_/features/denseblock3/denselayer3/conv2/Conv_output_0_1" -> "535 /features/denseblock3/denselayer14/Concat" [label="[1, 32, 14, 14]", style=solid]; -"374 DequantizeLinear_/features/denseblock3/denselayer3/conv2/Conv_output_0_1" -> "551 /features/denseblock3/denselayer15/Concat" [label="[1, 32, 14, 14]", style=solid]; -"374 DequantizeLinear_/features/denseblock3/denselayer3/conv2/Conv_output_0_1" -> "567 /features/denseblock3/denselayer16/Concat" [label="[1, 32, 14, 14]", style=solid]; -"374 DequantizeLinear_/features/denseblock3/denselayer3/conv2/Conv_output_0_1" -> "583 /features/denseblock3/denselayer17/Concat" [label="[1, 32, 14, 14]", style=solid]; -"374 DequantizeLinear_/features/denseblock3/denselayer3/conv2/Conv_output_0_1" -> "599 /features/denseblock3/denselayer18/Concat" [label="[1, 32, 14, 14]", style=solid]; -"374 DequantizeLinear_/features/denseblock3/denselayer3/conv2/Conv_output_0_1" -> "615 /features/denseblock3/denselayer19/Concat" [label="[1, 32, 14, 14]", style=solid]; -"374 DequantizeLinear_/features/denseblock3/denselayer3/conv2/Conv_output_0_1" -> "631 /features/denseblock3/denselayer20/Concat" [label="[1, 32, 14, 14]", style=solid]; -"374 DequantizeLinear_/features/denseblock3/denselayer3/conv2/Conv_output_0_1" -> "647 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14]", style=solid]; -"390 DequantizeLinear_/features/denseblock3/denselayer4/conv2/Conv_output_0_1" -> "631 /features/denseblock3/denselayer20/Concat" [label="[1, 32, 14, 14]", style=solid]; -"390 DequantizeLinear_/features/denseblock3/denselayer4/conv2/Conv_output_0_1" -> "647 /features/denseblock3/denselayer21/Concat" [label="[1, 32, 14, 14]", style=solid]; -"390 DequantizeLinear_/features/denseblock3/denselayer4/conv2/Conv_output_0_1" -> "663 /features/denseblock3/denselayer22/Concat" [label="[1, 32, 14, 14]", style=solid]; -"390 DequantizeLinear_/features/denseblock3/denselayer4/conv2/Conv_output_0_1" -> "679 /features/denseblock3/denselayer23/Concat" [label="[1, 32, 14, 14]", style=solid]; -"390 DequantizeLinear_/features/denseblock3/denselayer4/conv2/Conv_output_0_1" -> "695 /features/denseblock3/denselayer24/Concat" [label="[1, 32, 14, 14]", style=solid]; -"390 DequantizeLinear_/features/denseblock3/denselayer4/conv2/Conv_output_0_1" -> "711 /features/denseblock3/Concat" 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14]", style=solid]; -"406 DequantizeLinear_/features/denseblock3/denselayer5/conv2/Conv_output_0_1" -> "535 /features/denseblock3/denselayer14/Concat" [label="[1, 32, 14, 14]", style=solid]; -"406 DequantizeLinear_/features/denseblock3/denselayer5/conv2/Conv_output_0_1" -> "551 /features/denseblock3/denselayer15/Concat" [label="[1, 32, 14, 14]", style=solid]; -"406 DequantizeLinear_/features/denseblock3/denselayer5/conv2/Conv_output_0_1" -> "567 /features/denseblock3/denselayer16/Concat" [label="[1, 32, 14, 14]", style=solid]; -"406 DequantizeLinear_/features/denseblock3/denselayer5/conv2/Conv_output_0_1" -> "583 /features/denseblock3/denselayer17/Concat" [label="[1, 32, 14, 14]", style=solid]; -"406 DequantizeLinear_/features/denseblock3/denselayer5/conv2/Conv_output_0_1" -> "599 /features/denseblock3/denselayer18/Concat" [label="[1, 32, 14, 14]", style=solid]; -"406 DequantizeLinear_/features/denseblock3/denselayer5/conv2/Conv_output_0_1" -> "615 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14]", style=solid]; -"454 DequantizeLinear_/features/denseblock3/denselayer8/conv2/Conv_output_0_1" -> "695 /features/denseblock3/denselayer24/Concat" [label="[1, 32, 14, 14]", style=solid]; -"454 DequantizeLinear_/features/denseblock3/denselayer8/conv2/Conv_output_0_1" -> "711 /features/denseblock3/Concat" [label="[1, 32, 14, 14]", style=solid]; -"455 /features/denseblock3/denselayer9/Concat" -> "456 /features/denseblock3/denselayer9/norm1/BatchNormalization" [label="[1, 512, 14, 14]", style=solid]; -"456 /features/denseblock3/denselayer9/norm1/BatchNormalization" -> "457 /features/denseblock3/denselayer9/relu1/Relu" [label="[1, 512, 14, 14]", style=solid]; -"457 /features/denseblock3/denselayer9/relu1/Relu" -> "458 QuantizeLinear_/features/denseblock3/denselayer9/relu1/Relu_output_0_1" [label="[1, 512, 14, 14]", style=solid]; -"458 QuantizeLinear_/features/denseblock3/denselayer9/relu1/Relu_output_0_1" -> "459 DequantizeLinear_/features/denseblock3/denselayer9/relu1/Relu_output_0_1" 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style=dashed]; -"465 DequantizeLinear_/features/denseblock3/denselayer9/relu2/Relu_output_0_1" -> "468 /features/denseblock3/denselayer9/conv2/Conv" [label="[1, 128, 14, 14]", style=solid]; -"466 QuantizeLinear_features.denseblock3.denselayer9.conv2.weight_1" -> "467 DequantizeLinear_features.denseblock3.denselayer9.conv2.weight_1" [label="[32, 128, 3, 3]", style=dashed]; -"467 DequantizeLinear_features.denseblock3.denselayer9.conv2.weight_1" -> "468 /features/denseblock3/denselayer9/conv2/Conv" [label="[32, 128, 3, 3]", style=solid]; -"468 /features/denseblock3/denselayer9/conv2/Conv" -> "469 QuantizeLinear_/features/denseblock3/denselayer9/conv2/Conv_output_0_1" [label="[1, 32, 14, 14]", style=solid]; -"469 QuantizeLinear_/features/denseblock3/denselayer9/conv2/Conv_output_0_1" -> "470 DequantizeLinear_/features/denseblock3/denselayer9/conv2/Conv_output_0_1" [label="[1, 32, 14, 14]", style=dashed]; -"470 DequantizeLinear_/features/denseblock3/denselayer9/conv2/Conv_output_0_1" -> 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DequantizeLinear_/features/denseblock3/denselayer9/conv2/Conv_output_0_1" -> "567 /features/denseblock3/denselayer16/Concat" [label="[1, 32, 14, 14]", style=solid]; -"470 DequantizeLinear_/features/denseblock3/denselayer9/conv2/Conv_output_0_1" -> "583 /features/denseblock3/denselayer17/Concat" [label="[1, 32, 14, 14]", style=solid]; -"470 DequantizeLinear_/features/denseblock3/denselayer9/conv2/Conv_output_0_1" -> "599 /features/denseblock3/denselayer18/Concat" [label="[1, 32, 14, 14]", style=solid]; -"470 DequantizeLinear_/features/denseblock3/denselayer9/conv2/Conv_output_0_1" -> "615 /features/denseblock3/denselayer19/Concat" [label="[1, 32, 14, 14]", style=solid]; -"470 DequantizeLinear_/features/denseblock3/denselayer9/conv2/Conv_output_0_1" -> "631 /features/denseblock3/denselayer20/Concat" [label="[1, 32, 14, 14]", style=solid]; -"470 DequantizeLinear_/features/denseblock3/denselayer9/conv2/Conv_output_0_1" -> "647 /features/denseblock3/denselayer21/Concat" [label="[1, 32, 14, 14]", style=solid]; -"470 DequantizeLinear_/features/denseblock3/denselayer9/conv2/Conv_output_0_1" -> "663 /features/denseblock3/denselayer22/Concat" [label="[1, 32, 14, 14]", style=solid]; -"470 DequantizeLinear_/features/denseblock3/denselayer9/conv2/Conv_output_0_1" -> "679 /features/denseblock3/denselayer23/Concat" [label="[1, 32, 14, 14]", style=solid]; -"470 DequantizeLinear_/features/denseblock3/denselayer9/conv2/Conv_output_0_1" -> "695 /features/denseblock3/denselayer24/Concat" [label="[1, 32, 14, 14]", style=solid]; -"470 DequantizeLinear_/features/denseblock3/denselayer9/conv2/Conv_output_0_1" -> "711 /features/denseblock3/Concat" [label="[1, 32, 14, 14]", style=solid]; -"471 /features/denseblock3/denselayer10/Concat" -> "472 /features/denseblock3/denselayer10/norm1/BatchNormalization" [label="[1, 544, 14, 14]", style=solid]; -"472 /features/denseblock3/denselayer10/norm1/BatchNormalization" -> "473 /features/denseblock3/denselayer10/relu1/Relu" [label="[1, 544, 14, 14]", style=solid]; -"473 /features/denseblock3/denselayer10/relu1/Relu" -> "474 QuantizeLinear_/features/denseblock3/denselayer10/relu1/Relu_output_0_1" [label="[1, 544, 14, 14]", style=solid]; -"474 QuantizeLinear_/features/denseblock3/denselayer10/relu1/Relu_output_0_1" -> "475 DequantizeLinear_/features/denseblock3/denselayer10/relu1/Relu_output_0_1" [label="[1, 544, 14, 14]", style=dashed]; -"475 DequantizeLinear_/features/denseblock3/denselayer10/relu1/Relu_output_0_1" -> "478 /features/denseblock3/denselayer10/conv1/Conv" [label="[1, 544, 14, 14]", style=solid]; -"476 QuantizeLinear_onnx^^Conv_1250_1" -> "477 DequantizeLinear_onnx^^Conv_1250_1" [label="[128, 544, 1, 1]", style=dashed]; -"477 DequantizeLinear_onnx^^Conv_1250_1" -> "478 /features/denseblock3/denselayer10/conv1/Conv" [label="[128, 544, 1, 1]", style=solid]; -"478 /features/denseblock3/denselayer10/conv1/Conv" -> "479 /features/denseblock3/denselayer10/relu2/Relu" [label="[1, 128, 14, 14]", style=solid]; -"479 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style=solid]; -"486 DequantizeLinear_/features/denseblock3/denselayer10/conv2/Conv_output_0_1" -> "551 /features/denseblock3/denselayer15/Concat" [label="[1, 32, 14, 14]", style=solid]; -"486 DequantizeLinear_/features/denseblock3/denselayer10/conv2/Conv_output_0_1" -> "567 /features/denseblock3/denselayer16/Concat" [label="[1, 32, 14, 14]", style=solid]; -"486 DequantizeLinear_/features/denseblock3/denselayer10/conv2/Conv_output_0_1" -> "583 /features/denseblock3/denselayer17/Concat" [label="[1, 32, 14, 14]", style=solid]; -"486 DequantizeLinear_/features/denseblock3/denselayer10/conv2/Conv_output_0_1" -> "599 /features/denseblock3/denselayer18/Concat" [label="[1, 32, 14, 14]", style=solid]; -"486 DequantizeLinear_/features/denseblock3/denselayer10/conv2/Conv_output_0_1" -> "615 /features/denseblock3/denselayer19/Concat" [label="[1, 32, 14, 14]", style=solid]; -"486 DequantizeLinear_/features/denseblock3/denselayer10/conv2/Conv_output_0_1" -> "631 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/features/denseblock3/denselayer21/conv2/Conv" [label="[32, 128, 3, 3]", style=solid]; -"660 /features/denseblock3/denselayer21/conv2/Conv" -> "661 QuantizeLinear_/features/denseblock3/denselayer21/conv2/Conv_output_0_1" [label="[1, 32, 14, 14]", style=solid]; -"661 QuantizeLinear_/features/denseblock3/denselayer21/conv2/Conv_output_0_1" -> "662 DequantizeLinear_/features/denseblock3/denselayer21/conv2/Conv_output_0_1" [label="[1, 32, 14, 14]", style=dashed]; -"662 DequantizeLinear_/features/denseblock3/denselayer21/conv2/Conv_output_0_1" -> "663 /features/denseblock3/denselayer22/Concat" [label="[1, 32, 14, 14]", style=solid]; -"662 DequantizeLinear_/features/denseblock3/denselayer21/conv2/Conv_output_0_1" -> "679 /features/denseblock3/denselayer23/Concat" [label="[1, 32, 14, 14]", style=solid]; -"662 DequantizeLinear_/features/denseblock3/denselayer21/conv2/Conv_output_0_1" -> "695 /features/denseblock3/denselayer24/Concat" [label="[1, 32, 14, 14]", style=solid]; -"662 DequantizeLinear_/features/denseblock3/denselayer21/conv2/Conv_output_0_1" -> "711 /features/denseblock3/Concat" [label="[1, 32, 14, 14]", style=solid]; -"663 /features/denseblock3/denselayer22/Concat" -> "664 /features/denseblock3/denselayer22/norm1/BatchNormalization" [label="[1, 928, 14, 14]", style=solid]; -"664 /features/denseblock3/denselayer22/norm1/BatchNormalization" -> "665 /features/denseblock3/denselayer22/relu1/Relu" [label="[1, 928, 14, 14]", style=solid]; -"665 /features/denseblock3/denselayer22/relu1/Relu" -> "666 QuantizeLinear_/features/denseblock3/denselayer22/relu1/Relu_output_0_1" [label="[1, 928, 14, 14]", style=solid]; -"666 QuantizeLinear_/features/denseblock3/denselayer22/relu1/Relu_output_0_1" -> "667 DequantizeLinear_/features/denseblock3/denselayer22/relu1/Relu_output_0_1" [label="[1, 928, 14, 14]", style=dashed]; -"667 DequantizeLinear_/features/denseblock3/denselayer22/relu1/Relu_output_0_1" -> "670 /features/denseblock3/denselayer22/conv1/Conv" [label="[1, 928, 14, 14]", style=solid]; -"668 QuantizeLinear_onnx^^Conv_1286_1" -> "669 DequantizeLinear_onnx^^Conv_1286_1" [label="[128, 928, 1, 1]", style=dashed]; -"669 DequantizeLinear_onnx^^Conv_1286_1" -> "670 /features/denseblock3/denselayer22/conv1/Conv" [label="[128, 928, 1, 1]", style=solid]; -"670 /features/denseblock3/denselayer22/conv1/Conv" -> "671 /features/denseblock3/denselayer22/relu2/Relu" [label="[1, 128, 14, 14]", style=solid]; -"671 /features/denseblock3/denselayer22/relu2/Relu" -> "672 QuantizeLinear_/features/denseblock3/denselayer22/relu2/Relu_output_0_1" [label="[1, 128, 14, 14]", style=solid]; -"672 QuantizeLinear_/features/denseblock3/denselayer22/relu2/Relu_output_0_1" -> "673 DequantizeLinear_/features/denseblock3/denselayer22/relu2/Relu_output_0_1" [label="[1, 128, 14, 14]", style=dashed]; -"673 DequantizeLinear_/features/denseblock3/denselayer22/relu2/Relu_output_0_1" -> "676 /features/denseblock3/denselayer22/conv2/Conv" [label="[1, 128, 14, 14]", style=solid]; -"674 QuantizeLinear_features.denseblock3.denselayer22.conv2.weight_1" -> "675 DequantizeLinear_features.denseblock3.denselayer22.conv2.weight_1" [label="[32, 128, 3, 3]", style=dashed]; -"675 DequantizeLinear_features.denseblock3.denselayer22.conv2.weight_1" -> "676 /features/denseblock3/denselayer22/conv2/Conv" [label="[32, 128, 3, 3]", style=solid]; -"676 /features/denseblock3/denselayer22/conv2/Conv" -> "677 QuantizeLinear_/features/denseblock3/denselayer22/conv2/Conv_output_0_1" [label="[1, 32, 14, 14]", style=solid]; -"677 QuantizeLinear_/features/denseblock3/denselayer22/conv2/Conv_output_0_1" -> "678 DequantizeLinear_/features/denseblock3/denselayer22/conv2/Conv_output_0_1" [label="[1, 32, 14, 14]", style=dashed]; -"678 DequantizeLinear_/features/denseblock3/denselayer22/conv2/Conv_output_0_1" -> "679 /features/denseblock3/denselayer23/Concat" [label="[1, 32, 14, 14]", style=solid]; -"678 DequantizeLinear_/features/denseblock3/denselayer22/conv2/Conv_output_0_1" -> "695 /features/denseblock3/denselayer24/Concat" [label="[1, 32, 14, 14]", style=solid]; -"678 DequantizeLinear_/features/denseblock3/denselayer22/conv2/Conv_output_0_1" -> "711 /features/denseblock3/Concat" [label="[1, 32, 14, 14]", style=solid]; -"679 /features/denseblock3/denselayer23/Concat" -> "680 /features/denseblock3/denselayer23/norm1/BatchNormalization" [label="[1, 960, 14, 14]", style=solid]; -"680 /features/denseblock3/denselayer23/norm1/BatchNormalization" -> "681 /features/denseblock3/denselayer23/relu1/Relu" [label="[1, 960, 14, 14]", style=solid]; -"681 /features/denseblock3/denselayer23/relu1/Relu" -> "682 QuantizeLinear_/features/denseblock3/denselayer23/relu1/Relu_output_0_1" [label="[1, 960, 14, 14]", style=solid]; -"682 QuantizeLinear_/features/denseblock3/denselayer23/relu1/Relu_output_0_1" -> "683 DequantizeLinear_/features/denseblock3/denselayer23/relu1/Relu_output_0_1" [label="[1, 960, 14, 14]", style=dashed]; -"683 DequantizeLinear_/features/denseblock3/denselayer23/relu1/Relu_output_0_1" -> "686 /features/denseblock3/denselayer23/conv1/Conv" [label="[1, 960, 14, 14]", style=solid]; -"684 QuantizeLinear_onnx^^Conv_1289_1" -> "685 DequantizeLinear_onnx^^Conv_1289_1" [label="[128, 960, 1, 1]", style=dashed]; -"685 DequantizeLinear_onnx^^Conv_1289_1" -> "686 /features/denseblock3/denselayer23/conv1/Conv" [label="[128, 960, 1, 1]", style=solid]; -"686 /features/denseblock3/denselayer23/conv1/Conv" -> "687 /features/denseblock3/denselayer23/relu2/Relu" [label="[1, 128, 14, 14]", style=solid]; -"687 /features/denseblock3/denselayer23/relu2/Relu" -> "688 QuantizeLinear_/features/denseblock3/denselayer23/relu2/Relu_output_0_1" [label="[1, 128, 14, 14]", style=solid]; -"688 QuantizeLinear_/features/denseblock3/denselayer23/relu2/Relu_output_0_1" -> "689 DequantizeLinear_/features/denseblock3/denselayer23/relu2/Relu_output_0_1" [label="[1, 128, 14, 14]", style=dashed]; -"689 DequantizeLinear_/features/denseblock3/denselayer23/relu2/Relu_output_0_1" -> "692 /features/denseblock3/denselayer23/conv2/Conv" [label="[1, 128, 14, 14]", style=solid]; -"690 QuantizeLinear_features.denseblock3.denselayer23.conv2.weight_1" -> "691 DequantizeLinear_features.denseblock3.denselayer23.conv2.weight_1" [label="[32, 128, 3, 3]", style=dashed]; -"691 DequantizeLinear_features.denseblock3.denselayer23.conv2.weight_1" -> "692 /features/denseblock3/denselayer23/conv2/Conv" [label="[32, 128, 3, 3]", style=solid]; -"692 /features/denseblock3/denselayer23/conv2/Conv" -> "693 QuantizeLinear_/features/denseblock3/denselayer23/conv2/Conv_output_0_1" [label="[1, 32, 14, 14]", style=solid]; -"693 QuantizeLinear_/features/denseblock3/denselayer23/conv2/Conv_output_0_1" -> "694 DequantizeLinear_/features/denseblock3/denselayer23/conv2/Conv_output_0_1" [label="[1, 32, 14, 14]", style=dashed]; -"694 DequantizeLinear_/features/denseblock3/denselayer23/conv2/Conv_output_0_1" -> "695 /features/denseblock3/denselayer24/Concat" [label="[1, 32, 14, 14]", style=solid]; -"694 DequantizeLinear_/features/denseblock3/denselayer23/conv2/Conv_output_0_1" -> "711 /features/denseblock3/Concat" [label="[1, 32, 14, 14]", style=solid]; -"695 /features/denseblock3/denselayer24/Concat" -> "696 /features/denseblock3/denselayer24/norm1/BatchNormalization" [label="[1, 992, 14, 14]", style=solid]; -"696 /features/denseblock3/denselayer24/norm1/BatchNormalization" -> "697 /features/denseblock3/denselayer24/relu1/Relu" [label="[1, 992, 14, 14]", style=solid]; -"697 /features/denseblock3/denselayer24/relu1/Relu" -> "698 QuantizeLinear_/features/denseblock3/denselayer24/relu1/Relu_output_0_1" [label="[1, 992, 14, 14]", style=solid]; -"698 QuantizeLinear_/features/denseblock3/denselayer24/relu1/Relu_output_0_1" -> "699 DequantizeLinear_/features/denseblock3/denselayer24/relu1/Relu_output_0_1" [label="[1, 992, 14, 14]", style=dashed]; -"699 DequantizeLinear_/features/denseblock3/denselayer24/relu1/Relu_output_0_1" -> "702 /features/denseblock3/denselayer24/conv1/Conv" [label="[1, 992, 14, 14]", style=solid]; -"700 QuantizeLinear_onnx^^Conv_1292_1" -> "701 DequantizeLinear_onnx^^Conv_1292_1" [label="[128, 992, 1, 1]", style=dashed]; -"701 DequantizeLinear_onnx^^Conv_1292_1" -> "702 /features/denseblock3/denselayer24/conv1/Conv" [label="[128, 992, 1, 1]", style=solid]; -"702 /features/denseblock3/denselayer24/conv1/Conv" -> "703 /features/denseblock3/denselayer24/relu2/Relu" [label="[1, 128, 14, 14]", style=solid]; -"703 /features/denseblock3/denselayer24/relu2/Relu" -> "704 QuantizeLinear_/features/denseblock3/denselayer24/relu2/Relu_output_0_1" [label="[1, 128, 14, 14]", style=solid]; -"704 QuantizeLinear_/features/denseblock3/denselayer24/relu2/Relu_output_0_1" -> "705 DequantizeLinear_/features/denseblock3/denselayer24/relu2/Relu_output_0_1" [label="[1, 128, 14, 14]", style=dashed]; -"705 DequantizeLinear_/features/denseblock3/denselayer24/relu2/Relu_output_0_1" -> "708 /features/denseblock3/denselayer24/conv2/Conv" [label="[1, 128, 14, 14]", style=solid]; -"706 QuantizeLinear_features.denseblock3.denselayer24.conv2.weight_1" -> "707 DequantizeLinear_features.denseblock3.denselayer24.conv2.weight_1" [label="[32, 128, 3, 3]", style=dashed]; -"707 DequantizeLinear_features.denseblock3.denselayer24.conv2.weight_1" -> "708 /features/denseblock3/denselayer24/conv2/Conv" [label="[32, 128, 3, 3]", style=solid]; -"708 /features/denseblock3/denselayer24/conv2/Conv" -> "709 QuantizeLinear_/features/denseblock3/denselayer24/conv2/Conv_output_0_1" [label="[1, 32, 14, 14]", style=solid]; -"709 QuantizeLinear_/features/denseblock3/denselayer24/conv2/Conv_output_0_1" -> "710 DequantizeLinear_/features/denseblock3/denselayer24/conv2/Conv_output_0_1" [label="[1, 32, 14, 14]", style=dashed]; -"710 DequantizeLinear_/features/denseblock3/denselayer24/conv2/Conv_output_0_1" -> "711 /features/denseblock3/Concat" [label="[1, 32, 14, 14]", style=solid]; -"711 /features/denseblock3/Concat" -> "712 /features/transition3/norm/BatchNormalization" [label="[1, 1024, 14, 14]", style=solid]; -"712 /features/transition3/norm/BatchNormalization" -> "713 /features/transition3/relu/Relu" [label="[1, 1024, 14, 14]", style=solid]; -"713 /features/transition3/relu/Relu" -> "714 QuantizeLinear_/features/transition3/relu/Relu_output_0_1" [label="[1, 1024, 14, 14]", style=solid]; -"714 QuantizeLinear_/features/transition3/relu/Relu_output_0_1" -> "715 DequantizeLinear_/features/transition3/relu/Relu_output_0_1" [label="[1, 1024, 14, 14]", style=dashed]; -"715 DequantizeLinear_/features/transition3/relu/Relu_output_0_1" -> "718 /features/transition3/conv/Conv" [label="[1, 1024, 14, 14]", style=solid]; -"716 QuantizeLinear_features.transition3.conv.weight_1" -> "717 DequantizeLinear_features.transition3.conv.weight_1" [label="[512, 1024, 1, 1]", style=dashed]; -"717 DequantizeLinear_features.transition3.conv.weight_1" -> "718 /features/transition3/conv/Conv" [label="[512, 1024, 1, 1]", style=solid]; -"718 /features/transition3/conv/Conv" -> "720 QuantizeLinear_/features/transition3/conv/Conv_output_0_1" [label="[1, 512, 14, 14]", style=solid]; -"719 /features/transition3/pool/Constant" -> "722 /features/transition3/pool/Pad" [label="[8]", style=dashed]; -"720 QuantizeLinear_/features/transition3/conv/Conv_output_0_1" -> "721 DequantizeLinear_/features/transition3/conv/Conv_output_0_1" [label="[1, 512, 14, 14]", style=dashed]; -"721 DequantizeLinear_/features/transition3/conv/Conv_output_0_1" -> "722 /features/transition3/pool/Pad" [label="[1, 512, 14, 14]", style=solid]; -"722 /features/transition3/pool/Pad" -> "723 /features/transition3/pool/AveragePool" [label="[1, 512, 14, 14]", style=solid]; -"723 /features/transition3/pool/AveragePool" -> "724 QuantizeLinear_/features/transition3/pool/AveragePool_output_0_1" [label="[1, 512, 7, 7]", style=solid]; -"724 QuantizeLinear_/features/transition3/pool/AveragePool_output_0_1" -> "725 DequantizeLinear_/features/transition3/pool/AveragePool_output_0_1" [label="[1, 512, 7, 7]", style=dashed]; -"725 DequantizeLinear_/features/transition3/pool/AveragePool_output_0_1" -> "726 /features/denseblock4/denselayer1/Concat" [label="[1, 512, 7, 7]", style=solid]; -"725 DequantizeLinear_/features/transition3/pool/AveragePool_output_0_1" -> "742 /features/denseblock4/denselayer2/Concat" [label="[1, 512, 7, 7]", style=solid]; -"725 DequantizeLinear_/features/transition3/pool/AveragePool_output_0_1" -> "758 /features/denseblock4/denselayer3/Concat" [label="[1, 512, 7, 7]", style=solid]; -"725 DequantizeLinear_/features/transition3/pool/AveragePool_output_0_1" -> "774 /features/denseblock4/denselayer4/Concat" [label="[1, 512, 7, 7]", style=solid]; -"725 DequantizeLinear_/features/transition3/pool/AveragePool_output_0_1" -> "790 /features/denseblock4/denselayer5/Concat" [label="[1, 512, 7, 7]", style=solid]; -"725 DequantizeLinear_/features/transition3/pool/AveragePool_output_0_1" -> "806 /features/denseblock4/denselayer6/Concat" [label="[1, 512, 7, 7]", style=solid]; -"725 DequantizeLinear_/features/transition3/pool/AveragePool_output_0_1" -> "822 /features/denseblock4/denselayer7/Concat" [label="[1, 512, 7, 7]", style=solid]; -"725 DequantizeLinear_/features/transition3/pool/AveragePool_output_0_1" -> "838 /features/denseblock4/denselayer8/Concat" [label="[1, 512, 7, 7]", style=solid]; -"725 DequantizeLinear_/features/transition3/pool/AveragePool_output_0_1" -> "854 /features/denseblock4/denselayer9/Concat" [label="[1, 512, 7, 7]", style=solid]; -"725 DequantizeLinear_/features/transition3/pool/AveragePool_output_0_1" -> "870 /features/denseblock4/denselayer10/Concat" [label="[1, 512, 7, 7]", style=solid]; -"725 DequantizeLinear_/features/transition3/pool/AveragePool_output_0_1" -> "886 /features/denseblock4/denselayer11/Concat" [label="[1, 512, 7, 7]", style=solid]; -"725 DequantizeLinear_/features/transition3/pool/AveragePool_output_0_1" -> "902 /features/denseblock4/denselayer12/Concat" [label="[1, 512, 7, 7]", style=solid]; -"725 DequantizeLinear_/features/transition3/pool/AveragePool_output_0_1" -> "918 /features/denseblock4/denselayer13/Concat" [label="[1, 512, 7, 7]", style=solid]; -"725 DequantizeLinear_/features/transition3/pool/AveragePool_output_0_1" -> "934 /features/denseblock4/denselayer14/Concat" [label="[1, 512, 7, 7]", style=solid]; -"725 DequantizeLinear_/features/transition3/pool/AveragePool_output_0_1" -> "950 /features/denseblock4/denselayer15/Concat" [label="[1, 512, 7, 7]", style=solid]; -"725 DequantizeLinear_/features/transition3/pool/AveragePool_output_0_1" -> "966 /features/denseblock4/denselayer16/Concat" [label="[1, 512, 7, 7]", style=solid]; -"725 DequantizeLinear_/features/transition3/pool/AveragePool_output_0_1" -> "982 /features/denseblock4/Concat" [label="[1, 512, 7, 7]", style=solid]; -"726 /features/denseblock4/denselayer1/Concat" -> "727 /features/denseblock4/denselayer1/norm1/BatchNormalization" [label="[1, 512, 7, 7]", style=solid]; -"727 /features/denseblock4/denselayer1/norm1/BatchNormalization" -> "728 /features/denseblock4/denselayer1/relu1/Relu" [label="[1, 512, 7, 7]", style=solid]; -"728 /features/denseblock4/denselayer1/relu1/Relu" -> "729 QuantizeLinear_/features/denseblock4/denselayer1/relu1/Relu_output_0_1" [label="[1, 512, 7, 7]", style=solid]; -"729 QuantizeLinear_/features/denseblock4/denselayer1/relu1/Relu_output_0_1" -> "730 DequantizeLinear_/features/denseblock4/denselayer1/relu1/Relu_output_0_1" [label="[1, 512, 7, 7]", style=dashed]; -"730 DequantizeLinear_/features/denseblock4/denselayer1/relu1/Relu_output_0_1" -> "733 /features/denseblock4/denselayer1/conv1/Conv" [label="[1, 512, 7, 7]", style=solid]; -"731 QuantizeLinear_onnx^^Conv_1295_1" -> "732 DequantizeLinear_onnx^^Conv_1295_1" [label="[128, 512, 1, 1]", style=dashed]; -"732 DequantizeLinear_onnx^^Conv_1295_1" -> "733 /features/denseblock4/denselayer1/conv1/Conv" [label="[128, 512, 1, 1]", style=solid]; -"733 /features/denseblock4/denselayer1/conv1/Conv" -> "734 /features/denseblock4/denselayer1/relu2/Relu" [label="[1, 128, 7, 7]", style=solid]; -"734 /features/denseblock4/denselayer1/relu2/Relu" -> "735 QuantizeLinear_/features/denseblock4/denselayer1/relu2/Relu_output_0_1" [label="[1, 128, 7, 7]", style=solid]; -"735 QuantizeLinear_/features/denseblock4/denselayer1/relu2/Relu_output_0_1" -> "736 DequantizeLinear_/features/denseblock4/denselayer1/relu2/Relu_output_0_1" [label="[1, 128, 7, 7]", style=dashed]; -"736 DequantizeLinear_/features/denseblock4/denselayer1/relu2/Relu_output_0_1" -> "739 /features/denseblock4/denselayer1/conv2/Conv" [label="[1, 128, 7, 7]", style=solid]; -"737 QuantizeLinear_features.denseblock4.denselayer1.conv2.weight_1" -> "738 DequantizeLinear_features.denseblock4.denselayer1.conv2.weight_1" [label="[32, 128, 3, 3]", style=dashed]; -"738 DequantizeLinear_features.denseblock4.denselayer1.conv2.weight_1" -> "739 /features/denseblock4/denselayer1/conv2/Conv" [label="[32, 128, 3, 3]", style=solid]; -"739 /features/denseblock4/denselayer1/conv2/Conv" -> "740 QuantizeLinear_/features/denseblock4/denselayer1/conv2/Conv_output_0_1" [label="[1, 32, 7, 7]", style=solid]; -"740 QuantizeLinear_/features/denseblock4/denselayer1/conv2/Conv_output_0_1" -> "741 DequantizeLinear_/features/denseblock4/denselayer1/conv2/Conv_output_0_1" [label="[1, 32, 7, 7]", style=dashed]; -"741 DequantizeLinear_/features/denseblock4/denselayer1/conv2/Conv_output_0_1" -> "742 /features/denseblock4/denselayer2/Concat" [label="[1, 32, 7, 7]", style=solid]; -"741 DequantizeLinear_/features/denseblock4/denselayer1/conv2/Conv_output_0_1" -> "758 /features/denseblock4/denselayer3/Concat" [label="[1, 32, 7, 7]", style=solid]; -"741 DequantizeLinear_/features/denseblock4/denselayer1/conv2/Conv_output_0_1" -> "774 /features/denseblock4/denselayer4/Concat" [label="[1, 32, 7, 7]", style=solid]; -"741 DequantizeLinear_/features/denseblock4/denselayer1/conv2/Conv_output_0_1" -> "790 /features/denseblock4/denselayer5/Concat" [label="[1, 32, 7, 7]", style=solid]; -"741 DequantizeLinear_/features/denseblock4/denselayer1/conv2/Conv_output_0_1" -> "806 /features/denseblock4/denselayer6/Concat" [label="[1, 32, 7, 7]", style=solid]; -"741 DequantizeLinear_/features/denseblock4/denselayer1/conv2/Conv_output_0_1" -> "822 /features/denseblock4/denselayer7/Concat" [label="[1, 32, 7, 7]", style=solid]; -"741 DequantizeLinear_/features/denseblock4/denselayer1/conv2/Conv_output_0_1" -> "838 /features/denseblock4/denselayer8/Concat" [label="[1, 32, 7, 7]", style=solid]; -"741 DequantizeLinear_/features/denseblock4/denselayer1/conv2/Conv_output_0_1" -> "854 /features/denseblock4/denselayer9/Concat" [label="[1, 32, 7, 7]", style=solid]; -"741 DequantizeLinear_/features/denseblock4/denselayer1/conv2/Conv_output_0_1" -> "870 /features/denseblock4/denselayer10/Concat" [label="[1, 32, 7, 7]", style=solid]; -"741 DequantizeLinear_/features/denseblock4/denselayer1/conv2/Conv_output_0_1" -> "886 /features/denseblock4/denselayer11/Concat" [label="[1, 32, 7, 7]", style=solid]; -"741 DequantizeLinear_/features/denseblock4/denselayer1/conv2/Conv_output_0_1" -> "902 /features/denseblock4/denselayer12/Concat" [label="[1, 32, 7, 7]", style=solid]; -"741 DequantizeLinear_/features/denseblock4/denselayer1/conv2/Conv_output_0_1" -> "918 /features/denseblock4/denselayer13/Concat" [label="[1, 32, 7, 7]", style=solid]; -"741 DequantizeLinear_/features/denseblock4/denselayer1/conv2/Conv_output_0_1" -> "934 /features/denseblock4/denselayer14/Concat" [label="[1, 32, 7, 7]", style=solid]; -"741 DequantizeLinear_/features/denseblock4/denselayer1/conv2/Conv_output_0_1" -> "950 /features/denseblock4/denselayer15/Concat" [label="[1, 32, 7, 7]", style=solid]; -"741 DequantizeLinear_/features/denseblock4/denselayer1/conv2/Conv_output_0_1" -> "966 /features/denseblock4/denselayer16/Concat" [label="[1, 32, 7, 7]", style=solid]; -"741 DequantizeLinear_/features/denseblock4/denselayer1/conv2/Conv_output_0_1" -> "982 /features/denseblock4/Concat" [label="[1, 32, 7, 7]", style=solid]; -"742 /features/denseblock4/denselayer2/Concat" -> "743 /features/denseblock4/denselayer2/norm1/BatchNormalization" [label="[1, 544, 7, 7]", style=solid]; -"743 /features/denseblock4/denselayer2/norm1/BatchNormalization" -> "744 /features/denseblock4/denselayer2/relu1/Relu" [label="[1, 544, 7, 7]", style=solid]; -"744 /features/denseblock4/denselayer2/relu1/Relu" -> "745 QuantizeLinear_/features/denseblock4/denselayer2/relu1/Relu_output_0_1" [label="[1, 544, 7, 7]", style=solid]; -"745 QuantizeLinear_/features/denseblock4/denselayer2/relu1/Relu_output_0_1" -> "746 DequantizeLinear_/features/denseblock4/denselayer2/relu1/Relu_output_0_1" [label="[1, 544, 7, 7]", style=dashed]; -"746 DequantizeLinear_/features/denseblock4/denselayer2/relu1/Relu_output_0_1" -> "749 /features/denseblock4/denselayer2/conv1/Conv" [label="[1, 544, 7, 7]", style=solid]; -"747 QuantizeLinear_onnx^^Conv_1298_1" -> "748 DequantizeLinear_onnx^^Conv_1298_1" [label="[128, 544, 1, 1]", style=dashed]; -"748 DequantizeLinear_onnx^^Conv_1298_1" -> "749 /features/denseblock4/denselayer2/conv1/Conv" [label="[128, 544, 1, 1]", style=solid]; -"749 /features/denseblock4/denselayer2/conv1/Conv" -> "750 /features/denseblock4/denselayer2/relu2/Relu" [label="[1, 128, 7, 7]", style=solid]; -"750 /features/denseblock4/denselayer2/relu2/Relu" -> "751 QuantizeLinear_/features/denseblock4/denselayer2/relu2/Relu_output_0_1" [label="[1, 128, 7, 7]", style=solid]; -"751 QuantizeLinear_/features/denseblock4/denselayer2/relu2/Relu_output_0_1" -> "752 DequantizeLinear_/features/denseblock4/denselayer2/relu2/Relu_output_0_1" [label="[1, 128, 7, 7]", style=dashed]; -"752 DequantizeLinear_/features/denseblock4/denselayer2/relu2/Relu_output_0_1" -> "755 /features/denseblock4/denselayer2/conv2/Conv" [label="[1, 128, 7, 7]", style=solid]; -"753 QuantizeLinear_features.denseblock4.denselayer2.conv2.weight_1" -> "754 DequantizeLinear_features.denseblock4.denselayer2.conv2.weight_1" [label="[32, 128, 3, 3]", style=dashed]; -"754 DequantizeLinear_features.denseblock4.denselayer2.conv2.weight_1" -> "755 /features/denseblock4/denselayer2/conv2/Conv" [label="[32, 128, 3, 3]", style=solid]; -"755 /features/denseblock4/denselayer2/conv2/Conv" -> "756 QuantizeLinear_/features/denseblock4/denselayer2/conv2/Conv_output_0_1" [label="[1, 32, 7, 7]", style=solid]; -"756 QuantizeLinear_/features/denseblock4/denselayer2/conv2/Conv_output_0_1" -> "757 DequantizeLinear_/features/denseblock4/denselayer2/conv2/Conv_output_0_1" [label="[1, 32, 7, 7]", style=dashed]; -"757 DequantizeLinear_/features/denseblock4/denselayer2/conv2/Conv_output_0_1" -> "758 /features/denseblock4/denselayer3/Concat" [label="[1, 32, 7, 7]", style=solid]; -"757 DequantizeLinear_/features/denseblock4/denselayer2/conv2/Conv_output_0_1" -> "774 /features/denseblock4/denselayer4/Concat" [label="[1, 32, 7, 7]", style=solid]; -"757 DequantizeLinear_/features/denseblock4/denselayer2/conv2/Conv_output_0_1" -> "790 /features/denseblock4/denselayer5/Concat" [label="[1, 32, 7, 7]", style=solid]; -"757 DequantizeLinear_/features/denseblock4/denselayer2/conv2/Conv_output_0_1" -> "806 /features/denseblock4/denselayer6/Concat" [label="[1, 32, 7, 7]", style=solid]; -"757 DequantizeLinear_/features/denseblock4/denselayer2/conv2/Conv_output_0_1" -> "822 /features/denseblock4/denselayer7/Concat" [label="[1, 32, 7, 7]", style=solid]; -"757 DequantizeLinear_/features/denseblock4/denselayer2/conv2/Conv_output_0_1" -> "838 /features/denseblock4/denselayer8/Concat" [label="[1, 32, 7, 7]", style=solid]; -"757 DequantizeLinear_/features/denseblock4/denselayer2/conv2/Conv_output_0_1" -> "854 /features/denseblock4/denselayer9/Concat" [label="[1, 32, 7, 7]", style=solid]; -"757 DequantizeLinear_/features/denseblock4/denselayer2/conv2/Conv_output_0_1" -> "870 /features/denseblock4/denselayer10/Concat" [label="[1, 32, 7, 7]", style=solid]; -"757 DequantizeLinear_/features/denseblock4/denselayer2/conv2/Conv_output_0_1" -> "886 /features/denseblock4/denselayer11/Concat" [label="[1, 32, 7, 7]", style=solid]; -"757 DequantizeLinear_/features/denseblock4/denselayer2/conv2/Conv_output_0_1" -> "902 /features/denseblock4/denselayer12/Concat" [label="[1, 32, 7, 7]", style=solid]; -"757 DequantizeLinear_/features/denseblock4/denselayer2/conv2/Conv_output_0_1" -> "918 /features/denseblock4/denselayer13/Concat" [label="[1, 32, 7, 7]", style=solid]; -"757 DequantizeLinear_/features/denseblock4/denselayer2/conv2/Conv_output_0_1" -> "934 /features/denseblock4/denselayer14/Concat" [label="[1, 32, 7, 7]", style=solid]; -"757 DequantizeLinear_/features/denseblock4/denselayer2/conv2/Conv_output_0_1" -> "950 /features/denseblock4/denselayer15/Concat" [label="[1, 32, 7, 7]", style=solid]; -"757 DequantizeLinear_/features/denseblock4/denselayer2/conv2/Conv_output_0_1" -> "966 /features/denseblock4/denselayer16/Concat" [label="[1, 32, 7, 7]", style=solid]; -"757 DequantizeLinear_/features/denseblock4/denselayer2/conv2/Conv_output_0_1" -> "982 /features/denseblock4/Concat" [label="[1, 32, 7, 7]", style=solid]; -"758 /features/denseblock4/denselayer3/Concat" -> "759 /features/denseblock4/denselayer3/norm1/BatchNormalization" [label="[1, 576, 7, 7]", style=solid]; -"759 /features/denseblock4/denselayer3/norm1/BatchNormalization" -> "760 /features/denseblock4/denselayer3/relu1/Relu" [label="[1, 576, 7, 7]", style=solid]; -"760 /features/denseblock4/denselayer3/relu1/Relu" -> "761 QuantizeLinear_/features/denseblock4/denselayer3/relu1/Relu_output_0_1" [label="[1, 576, 7, 7]", style=solid]; -"761 QuantizeLinear_/features/denseblock4/denselayer3/relu1/Relu_output_0_1" -> "762 DequantizeLinear_/features/denseblock4/denselayer3/relu1/Relu_output_0_1" [label="[1, 576, 7, 7]", style=dashed]; -"762 DequantizeLinear_/features/denseblock4/denselayer3/relu1/Relu_output_0_1" -> "765 /features/denseblock4/denselayer3/conv1/Conv" [label="[1, 576, 7, 7]", style=solid]; -"763 QuantizeLinear_onnx^^Conv_1301_1" -> "764 DequantizeLinear_onnx^^Conv_1301_1" [label="[128, 576, 1, 1]", style=dashed]; -"764 DequantizeLinear_onnx^^Conv_1301_1" -> "765 /features/denseblock4/denselayer3/conv1/Conv" [label="[128, 576, 1, 1]", style=solid]; -"765 /features/denseblock4/denselayer3/conv1/Conv" -> "766 /features/denseblock4/denselayer3/relu2/Relu" [label="[1, 128, 7, 7]", style=solid]; -"766 /features/denseblock4/denselayer3/relu2/Relu" -> "767 QuantizeLinear_/features/denseblock4/denselayer3/relu2/Relu_output_0_1" [label="[1, 128, 7, 7]", style=solid]; -"767 QuantizeLinear_/features/denseblock4/denselayer3/relu2/Relu_output_0_1" -> "768 DequantizeLinear_/features/denseblock4/denselayer3/relu2/Relu_output_0_1" [label="[1, 128, 7, 7]", style=dashed]; -"768 DequantizeLinear_/features/denseblock4/denselayer3/relu2/Relu_output_0_1" -> "771 /features/denseblock4/denselayer3/conv2/Conv" [label="[1, 128, 7, 7]", style=solid]; -"769 QuantizeLinear_features.denseblock4.denselayer3.conv2.weight_1" -> "770 DequantizeLinear_features.denseblock4.denselayer3.conv2.weight_1" [label="[32, 128, 3, 3]", style=dashed]; -"770 DequantizeLinear_features.denseblock4.denselayer3.conv2.weight_1" -> "771 /features/denseblock4/denselayer3/conv2/Conv" [label="[32, 128, 3, 3]", style=solid]; -"771 /features/denseblock4/denselayer3/conv2/Conv" -> "772 QuantizeLinear_/features/denseblock4/denselayer3/conv2/Conv_output_0_1" [label="[1, 32, 7, 7]", style=solid]; -"772 QuantizeLinear_/features/denseblock4/denselayer3/conv2/Conv_output_0_1" -> "773 DequantizeLinear_/features/denseblock4/denselayer3/conv2/Conv_output_0_1" [label="[1, 32, 7, 7]", style=dashed]; -"773 DequantizeLinear_/features/denseblock4/denselayer3/conv2/Conv_output_0_1" -> "774 /features/denseblock4/denselayer4/Concat" [label="[1, 32, 7, 7]", style=solid]; -"773 DequantizeLinear_/features/denseblock4/denselayer3/conv2/Conv_output_0_1" -> "790 /features/denseblock4/denselayer5/Concat" [label="[1, 32, 7, 7]", style=solid]; -"773 DequantizeLinear_/features/denseblock4/denselayer3/conv2/Conv_output_0_1" -> "806 /features/denseblock4/denselayer6/Concat" [label="[1, 32, 7, 7]", style=solid]; -"773 DequantizeLinear_/features/denseblock4/denselayer3/conv2/Conv_output_0_1" -> "822 /features/denseblock4/denselayer7/Concat" [label="[1, 32, 7, 7]", style=solid]; -"773 DequantizeLinear_/features/denseblock4/denselayer3/conv2/Conv_output_0_1" -> "838 /features/denseblock4/denselayer8/Concat" [label="[1, 32, 7, 7]", style=solid]; -"773 DequantizeLinear_/features/denseblock4/denselayer3/conv2/Conv_output_0_1" -> "854 /features/denseblock4/denselayer9/Concat" [label="[1, 32, 7, 7]", style=solid]; -"773 DequantizeLinear_/features/denseblock4/denselayer3/conv2/Conv_output_0_1" -> "870 /features/denseblock4/denselayer10/Concat" [label="[1, 32, 7, 7]", style=solid]; -"773 DequantizeLinear_/features/denseblock4/denselayer3/conv2/Conv_output_0_1" -> "886 /features/denseblock4/denselayer11/Concat" [label="[1, 32, 7, 7]", style=solid]; -"773 DequantizeLinear_/features/denseblock4/denselayer3/conv2/Conv_output_0_1" -> "902 /features/denseblock4/denselayer12/Concat" [label="[1, 32, 7, 7]", style=solid]; -"773 DequantizeLinear_/features/denseblock4/denselayer3/conv2/Conv_output_0_1" -> "918 /features/denseblock4/denselayer13/Concat" [label="[1, 32, 7, 7]", style=solid]; -"773 DequantizeLinear_/features/denseblock4/denselayer3/conv2/Conv_output_0_1" -> "934 /features/denseblock4/denselayer14/Concat" [label="[1, 32, 7, 7]", style=solid]; -"773 DequantizeLinear_/features/denseblock4/denselayer3/conv2/Conv_output_0_1" -> "950 /features/denseblock4/denselayer15/Concat" [label="[1, 32, 7, 7]", style=solid]; -"773 DequantizeLinear_/features/denseblock4/denselayer3/conv2/Conv_output_0_1" -> "966 /features/denseblock4/denselayer16/Concat" [label="[1, 32, 7, 7]", style=solid]; -"773 DequantizeLinear_/features/denseblock4/denselayer3/conv2/Conv_output_0_1" -> "982 /features/denseblock4/Concat" [label="[1, 32, 7, 7]", style=solid]; -"774 /features/denseblock4/denselayer4/Concat" -> "775 /features/denseblock4/denselayer4/norm1/BatchNormalization" [label="[1, 608, 7, 7]", style=solid]; -"775 /features/denseblock4/denselayer4/norm1/BatchNormalization" -> "776 /features/denseblock4/denselayer4/relu1/Relu" [label="[1, 608, 7, 7]", style=solid]; -"776 /features/denseblock4/denselayer4/relu1/Relu" -> "777 QuantizeLinear_/features/denseblock4/denselayer4/relu1/Relu_output_0_1" [label="[1, 608, 7, 7]", style=solid]; -"777 QuantizeLinear_/features/denseblock4/denselayer4/relu1/Relu_output_0_1" -> "778 DequantizeLinear_/features/denseblock4/denselayer4/relu1/Relu_output_0_1" [label="[1, 608, 7, 7]", style=dashed]; -"778 DequantizeLinear_/features/denseblock4/denselayer4/relu1/Relu_output_0_1" -> "781 /features/denseblock4/denselayer4/conv1/Conv" [label="[1, 608, 7, 7]", style=solid]; -"779 QuantizeLinear_onnx^^Conv_1304_1" -> "780 DequantizeLinear_onnx^^Conv_1304_1" [label="[128, 608, 1, 1]", style=dashed]; -"780 DequantizeLinear_onnx^^Conv_1304_1" -> "781 /features/denseblock4/denselayer4/conv1/Conv" [label="[128, 608, 1, 1]", style=solid]; -"781 /features/denseblock4/denselayer4/conv1/Conv" -> "782 /features/denseblock4/denselayer4/relu2/Relu" [label="[1, 128, 7, 7]", style=solid]; -"782 /features/denseblock4/denselayer4/relu2/Relu" -> "783 QuantizeLinear_/features/denseblock4/denselayer4/relu2/Relu_output_0_1" [label="[1, 128, 7, 7]", style=solid]; -"783 QuantizeLinear_/features/denseblock4/denselayer4/relu2/Relu_output_0_1" -> "784 DequantizeLinear_/features/denseblock4/denselayer4/relu2/Relu_output_0_1" [label="[1, 128, 7, 7]", style=dashed]; -"784 DequantizeLinear_/features/denseblock4/denselayer4/relu2/Relu_output_0_1" -> "787 /features/denseblock4/denselayer4/conv2/Conv" [label="[1, 128, 7, 7]", style=solid]; -"785 QuantizeLinear_features.denseblock4.denselayer4.conv2.weight_1" -> "786 DequantizeLinear_features.denseblock4.denselayer4.conv2.weight_1" [label="[32, 128, 3, 3]", style=dashed]; -"786 DequantizeLinear_features.denseblock4.denselayer4.conv2.weight_1" -> "787 /features/denseblock4/denselayer4/conv2/Conv" [label="[32, 128, 3, 3]", style=solid]; -"787 /features/denseblock4/denselayer4/conv2/Conv" -> "788 QuantizeLinear_/features/denseblock4/denselayer4/conv2/Conv_output_0_1" [label="[1, 32, 7, 7]", style=solid]; -"788 QuantizeLinear_/features/denseblock4/denselayer4/conv2/Conv_output_0_1" -> "789 DequantizeLinear_/features/denseblock4/denselayer4/conv2/Conv_output_0_1" [label="[1, 32, 7, 7]", style=dashed]; -"789 DequantizeLinear_/features/denseblock4/denselayer4/conv2/Conv_output_0_1" -> "790 /features/denseblock4/denselayer5/Concat" [label="[1, 32, 7, 7]", style=solid]; -"789 DequantizeLinear_/features/denseblock4/denselayer4/conv2/Conv_output_0_1" -> "806 /features/denseblock4/denselayer6/Concat" [label="[1, 32, 7, 7]", style=solid]; -"789 DequantizeLinear_/features/denseblock4/denselayer4/conv2/Conv_output_0_1" -> "822 /features/denseblock4/denselayer7/Concat" [label="[1, 32, 7, 7]", style=solid]; -"789 DequantizeLinear_/features/denseblock4/denselayer4/conv2/Conv_output_0_1" -> "838 /features/denseblock4/denselayer8/Concat" [label="[1, 32, 7, 7]", style=solid]; -"789 DequantizeLinear_/features/denseblock4/denselayer4/conv2/Conv_output_0_1" -> "854 /features/denseblock4/denselayer9/Concat" [label="[1, 32, 7, 7]", style=solid]; -"789 DequantizeLinear_/features/denseblock4/denselayer4/conv2/Conv_output_0_1" -> "870 /features/denseblock4/denselayer10/Concat" [label="[1, 32, 7, 7]", style=solid]; -"789 DequantizeLinear_/features/denseblock4/denselayer4/conv2/Conv_output_0_1" -> "886 /features/denseblock4/denselayer11/Concat" [label="[1, 32, 7, 7]", style=solid]; -"789 DequantizeLinear_/features/denseblock4/denselayer4/conv2/Conv_output_0_1" -> "902 /features/denseblock4/denselayer12/Concat" [label="[1, 32, 7, 7]", style=solid]; -"789 DequantizeLinear_/features/denseblock4/denselayer4/conv2/Conv_output_0_1" -> "918 /features/denseblock4/denselayer13/Concat" [label="[1, 32, 7, 7]", style=solid]; -"789 DequantizeLinear_/features/denseblock4/denselayer4/conv2/Conv_output_0_1" -> "934 /features/denseblock4/denselayer14/Concat" [label="[1, 32, 7, 7]", style=solid]; -"789 DequantizeLinear_/features/denseblock4/denselayer4/conv2/Conv_output_0_1" -> "950 /features/denseblock4/denselayer15/Concat" [label="[1, 32, 7, 7]", style=solid]; -"789 DequantizeLinear_/features/denseblock4/denselayer4/conv2/Conv_output_0_1" -> "966 /features/denseblock4/denselayer16/Concat" [label="[1, 32, 7, 7]", style=solid]; -"789 DequantizeLinear_/features/denseblock4/denselayer4/conv2/Conv_output_0_1" -> "982 /features/denseblock4/Concat" [label="[1, 32, 7, 7]", style=solid]; -"790 /features/denseblock4/denselayer5/Concat" -> "791 /features/denseblock4/denselayer5/norm1/BatchNormalization" [label="[1, 640, 7, 7]", style=solid]; -"791 /features/denseblock4/denselayer5/norm1/BatchNormalization" -> "792 /features/denseblock4/denselayer5/relu1/Relu" [label="[1, 640, 7, 7]", style=solid]; -"792 /features/denseblock4/denselayer5/relu1/Relu" -> "793 QuantizeLinear_/features/denseblock4/denselayer5/relu1/Relu_output_0_1" [label="[1, 640, 7, 7]", style=solid]; -"793 QuantizeLinear_/features/denseblock4/denselayer5/relu1/Relu_output_0_1" -> "794 DequantizeLinear_/features/denseblock4/denselayer5/relu1/Relu_output_0_1" [label="[1, 640, 7, 7]", style=dashed]; -"794 DequantizeLinear_/features/denseblock4/denselayer5/relu1/Relu_output_0_1" -> "797 /features/denseblock4/denselayer5/conv1/Conv" [label="[1, 640, 7, 7]", style=solid]; -"795 QuantizeLinear_onnx^^Conv_1307_1" -> "796 DequantizeLinear_onnx^^Conv_1307_1" [label="[128, 640, 1, 1]", style=dashed]; -"796 DequantizeLinear_onnx^^Conv_1307_1" -> "797 /features/denseblock4/denselayer5/conv1/Conv" [label="[128, 640, 1, 1]", style=solid]; -"797 /features/denseblock4/denselayer5/conv1/Conv" -> "798 /features/denseblock4/denselayer5/relu2/Relu" [label="[1, 128, 7, 7]", style=solid]; -"798 /features/denseblock4/denselayer5/relu2/Relu" -> "799 QuantizeLinear_/features/denseblock4/denselayer5/relu2/Relu_output_0_1" [label="[1, 128, 7, 7]", style=solid]; -"799 QuantizeLinear_/features/denseblock4/denselayer5/relu2/Relu_output_0_1" -> "800 DequantizeLinear_/features/denseblock4/denselayer5/relu2/Relu_output_0_1" [label="[1, 128, 7, 7]", style=dashed]; -"800 DequantizeLinear_/features/denseblock4/denselayer5/relu2/Relu_output_0_1" -> "803 /features/denseblock4/denselayer5/conv2/Conv" [label="[1, 128, 7, 7]", style=solid]; -"801 QuantizeLinear_features.denseblock4.denselayer5.conv2.weight_1" -> "802 DequantizeLinear_features.denseblock4.denselayer5.conv2.weight_1" [label="[32, 128, 3, 3]", style=dashed]; -"802 DequantizeLinear_features.denseblock4.denselayer5.conv2.weight_1" -> "803 /features/denseblock4/denselayer5/conv2/Conv" [label="[32, 128, 3, 3]", style=solid]; -"803 /features/denseblock4/denselayer5/conv2/Conv" -> "804 QuantizeLinear_/features/denseblock4/denselayer5/conv2/Conv_output_0_1" [label="[1, 32, 7, 7]", style=solid]; -"804 QuantizeLinear_/features/denseblock4/denselayer5/conv2/Conv_output_0_1" -> "805 DequantizeLinear_/features/denseblock4/denselayer5/conv2/Conv_output_0_1" [label="[1, 32, 7, 7]", style=dashed]; -"805 DequantizeLinear_/features/denseblock4/denselayer5/conv2/Conv_output_0_1" -> "806 /features/denseblock4/denselayer6/Concat" [label="[1, 32, 7, 7]", style=solid]; -"805 DequantizeLinear_/features/denseblock4/denselayer5/conv2/Conv_output_0_1" -> "822 /features/denseblock4/denselayer7/Concat" [label="[1, 32, 7, 7]", style=solid]; -"805 DequantizeLinear_/features/denseblock4/denselayer5/conv2/Conv_output_0_1" -> "838 /features/denseblock4/denselayer8/Concat" [label="[1, 32, 7, 7]", style=solid]; -"805 DequantizeLinear_/features/denseblock4/denselayer5/conv2/Conv_output_0_1" -> "854 /features/denseblock4/denselayer9/Concat" [label="[1, 32, 7, 7]", style=solid]; -"805 DequantizeLinear_/features/denseblock4/denselayer5/conv2/Conv_output_0_1" -> "870 /features/denseblock4/denselayer10/Concat" [label="[1, 32, 7, 7]", style=solid]; -"805 DequantizeLinear_/features/denseblock4/denselayer5/conv2/Conv_output_0_1" -> "886 /features/denseblock4/denselayer11/Concat" [label="[1, 32, 7, 7]", style=solid]; -"805 DequantizeLinear_/features/denseblock4/denselayer5/conv2/Conv_output_0_1" -> "902 /features/denseblock4/denselayer12/Concat" [label="[1, 32, 7, 7]", style=solid]; -"805 DequantizeLinear_/features/denseblock4/denselayer5/conv2/Conv_output_0_1" -> "918 /features/denseblock4/denselayer13/Concat" [label="[1, 32, 7, 7]", style=solid]; -"805 DequantizeLinear_/features/denseblock4/denselayer5/conv2/Conv_output_0_1" -> "934 /features/denseblock4/denselayer14/Concat" [label="[1, 32, 7, 7]", style=solid]; -"805 DequantizeLinear_/features/denseblock4/denselayer5/conv2/Conv_output_0_1" -> "950 /features/denseblock4/denselayer15/Concat" [label="[1, 32, 7, 7]", style=solid]; -"805 DequantizeLinear_/features/denseblock4/denselayer5/conv2/Conv_output_0_1" -> "966 /features/denseblock4/denselayer16/Concat" [label="[1, 32, 7, 7]", style=solid]; -"805 DequantizeLinear_/features/denseblock4/denselayer5/conv2/Conv_output_0_1" -> "982 /features/denseblock4/Concat" [label="[1, 32, 7, 7]", style=solid]; -"806 /features/denseblock4/denselayer6/Concat" -> "807 /features/denseblock4/denselayer6/norm1/BatchNormalization" [label="[1, 672, 7, 7]", style=solid]; -"807 /features/denseblock4/denselayer6/norm1/BatchNormalization" -> "808 /features/denseblock4/denselayer6/relu1/Relu" [label="[1, 672, 7, 7]", style=solid]; -"808 /features/denseblock4/denselayer6/relu1/Relu" -> "809 QuantizeLinear_/features/denseblock4/denselayer6/relu1/Relu_output_0_1" [label="[1, 672, 7, 7]", style=solid]; -"809 QuantizeLinear_/features/denseblock4/denselayer6/relu1/Relu_output_0_1" -> "810 DequantizeLinear_/features/denseblock4/denselayer6/relu1/Relu_output_0_1" [label="[1, 672, 7, 7]", style=dashed]; -"810 DequantizeLinear_/features/denseblock4/denselayer6/relu1/Relu_output_0_1" -> "813 /features/denseblock4/denselayer6/conv1/Conv" [label="[1, 672, 7, 7]", style=solid]; -"811 QuantizeLinear_onnx^^Conv_1310_1" -> "812 DequantizeLinear_onnx^^Conv_1310_1" [label="[128, 672, 1, 1]", style=dashed]; -"812 DequantizeLinear_onnx^^Conv_1310_1" -> "813 /features/denseblock4/denselayer6/conv1/Conv" [label="[128, 672, 1, 1]", style=solid]; -"813 /features/denseblock4/denselayer6/conv1/Conv" -> "814 /features/denseblock4/denselayer6/relu2/Relu" [label="[1, 128, 7, 7]", style=solid]; -"814 /features/denseblock4/denselayer6/relu2/Relu" -> "815 QuantizeLinear_/features/denseblock4/denselayer6/relu2/Relu_output_0_1" [label="[1, 128, 7, 7]", style=solid]; -"815 QuantizeLinear_/features/denseblock4/denselayer6/relu2/Relu_output_0_1" -> "816 DequantizeLinear_/features/denseblock4/denselayer6/relu2/Relu_output_0_1" [label="[1, 128, 7, 7]", style=dashed]; -"816 DequantizeLinear_/features/denseblock4/denselayer6/relu2/Relu_output_0_1" -> "819 /features/denseblock4/denselayer6/conv2/Conv" [label="[1, 128, 7, 7]", style=solid]; -"817 QuantizeLinear_features.denseblock4.denselayer6.conv2.weight_1" -> "818 DequantizeLinear_features.denseblock4.denselayer6.conv2.weight_1" [label="[32, 128, 3, 3]", style=dashed]; -"818 DequantizeLinear_features.denseblock4.denselayer6.conv2.weight_1" -> "819 /features/denseblock4/denselayer6/conv2/Conv" [label="[32, 128, 3, 3]", style=solid]; -"819 /features/denseblock4/denselayer6/conv2/Conv" -> "820 QuantizeLinear_/features/denseblock4/denselayer6/conv2/Conv_output_0_1" [label="[1, 32, 7, 7]", style=solid]; -"820 QuantizeLinear_/features/denseblock4/denselayer6/conv2/Conv_output_0_1" -> "821 DequantizeLinear_/features/denseblock4/denselayer6/conv2/Conv_output_0_1" [label="[1, 32, 7, 7]", style=dashed]; -"821 DequantizeLinear_/features/denseblock4/denselayer6/conv2/Conv_output_0_1" -> "822 /features/denseblock4/denselayer7/Concat" [label="[1, 32, 7, 7]", style=solid]; -"821 DequantizeLinear_/features/denseblock4/denselayer6/conv2/Conv_output_0_1" -> "838 /features/denseblock4/denselayer8/Concat" [label="[1, 32, 7, 7]", style=solid]; -"821 DequantizeLinear_/features/denseblock4/denselayer6/conv2/Conv_output_0_1" -> "854 /features/denseblock4/denselayer9/Concat" [label="[1, 32, 7, 7]", style=solid]; -"821 DequantizeLinear_/features/denseblock4/denselayer6/conv2/Conv_output_0_1" -> "870 /features/denseblock4/denselayer10/Concat" [label="[1, 32, 7, 7]", style=solid]; -"821 DequantizeLinear_/features/denseblock4/denselayer6/conv2/Conv_output_0_1" -> "886 /features/denseblock4/denselayer11/Concat" [label="[1, 32, 7, 7]", style=solid]; -"821 DequantizeLinear_/features/denseblock4/denselayer6/conv2/Conv_output_0_1" -> "902 /features/denseblock4/denselayer12/Concat" [label="[1, 32, 7, 7]", style=solid]; -"821 DequantizeLinear_/features/denseblock4/denselayer6/conv2/Conv_output_0_1" -> "918 /features/denseblock4/denselayer13/Concat" [label="[1, 32, 7, 7]", style=solid]; -"821 DequantizeLinear_/features/denseblock4/denselayer6/conv2/Conv_output_0_1" -> "934 /features/denseblock4/denselayer14/Concat" [label="[1, 32, 7, 7]", style=solid]; -"821 DequantizeLinear_/features/denseblock4/denselayer6/conv2/Conv_output_0_1" -> "950 /features/denseblock4/denselayer15/Concat" [label="[1, 32, 7, 7]", style=solid]; -"821 DequantizeLinear_/features/denseblock4/denselayer6/conv2/Conv_output_0_1" -> "966 /features/denseblock4/denselayer16/Concat" [label="[1, 32, 7, 7]", style=solid]; -"821 DequantizeLinear_/features/denseblock4/denselayer6/conv2/Conv_output_0_1" -> "982 /features/denseblock4/Concat" [label="[1, 32, 7, 7]", style=solid]; -"822 /features/denseblock4/denselayer7/Concat" -> "823 /features/denseblock4/denselayer7/norm1/BatchNormalization" [label="[1, 704, 7, 7]", style=solid]; -"823 /features/denseblock4/denselayer7/norm1/BatchNormalization" -> "824 /features/denseblock4/denselayer7/relu1/Relu" [label="[1, 704, 7, 7]", style=solid]; -"824 /features/denseblock4/denselayer7/relu1/Relu" -> "825 QuantizeLinear_/features/denseblock4/denselayer7/relu1/Relu_output_0_1" [label="[1, 704, 7, 7]", style=solid]; -"825 QuantizeLinear_/features/denseblock4/denselayer7/relu1/Relu_output_0_1" -> "826 DequantizeLinear_/features/denseblock4/denselayer7/relu1/Relu_output_0_1" [label="[1, 704, 7, 7]", style=dashed]; -"826 DequantizeLinear_/features/denseblock4/denselayer7/relu1/Relu_output_0_1" -> "829 /features/denseblock4/denselayer7/conv1/Conv" [label="[1, 704, 7, 7]", style=solid]; -"827 QuantizeLinear_onnx^^Conv_1313_1" -> "828 DequantizeLinear_onnx^^Conv_1313_1" [label="[128, 704, 1, 1]", style=dashed]; -"828 DequantizeLinear_onnx^^Conv_1313_1" -> "829 /features/denseblock4/denselayer7/conv1/Conv" [label="[128, 704, 1, 1]", style=solid]; -"829 /features/denseblock4/denselayer7/conv1/Conv" -> "830 /features/denseblock4/denselayer7/relu2/Relu" [label="[1, 128, 7, 7]", style=solid]; -"830 /features/denseblock4/denselayer7/relu2/Relu" -> "831 QuantizeLinear_/features/denseblock4/denselayer7/relu2/Relu_output_0_1" [label="[1, 128, 7, 7]", style=solid]; -"831 QuantizeLinear_/features/denseblock4/denselayer7/relu2/Relu_output_0_1" -> "832 DequantizeLinear_/features/denseblock4/denselayer7/relu2/Relu_output_0_1" [label="[1, 128, 7, 7]", style=dashed]; -"832 DequantizeLinear_/features/denseblock4/denselayer7/relu2/Relu_output_0_1" -> "835 /features/denseblock4/denselayer7/conv2/Conv" [label="[1, 128, 7, 7]", style=solid]; -"833 QuantizeLinear_features.denseblock4.denselayer7.conv2.weight_1" -> "834 DequantizeLinear_features.denseblock4.denselayer7.conv2.weight_1" [label="[32, 128, 3, 3]", style=dashed]; -"834 DequantizeLinear_features.denseblock4.denselayer7.conv2.weight_1" -> "835 /features/denseblock4/denselayer7/conv2/Conv" [label="[32, 128, 3, 3]", style=solid]; -"835 /features/denseblock4/denselayer7/conv2/Conv" -> "836 QuantizeLinear_/features/denseblock4/denselayer7/conv2/Conv_output_0_1" [label="[1, 32, 7, 7]", style=solid]; -"836 QuantizeLinear_/features/denseblock4/denselayer7/conv2/Conv_output_0_1" -> "837 DequantizeLinear_/features/denseblock4/denselayer7/conv2/Conv_output_0_1" [label="[1, 32, 7, 7]", style=dashed]; -"837 DequantizeLinear_/features/denseblock4/denselayer7/conv2/Conv_output_0_1" -> "838 /features/denseblock4/denselayer8/Concat" [label="[1, 32, 7, 7]", style=solid]; -"837 DequantizeLinear_/features/denseblock4/denselayer7/conv2/Conv_output_0_1" -> "854 /features/denseblock4/denselayer9/Concat" [label="[1, 32, 7, 7]", style=solid]; -"837 DequantizeLinear_/features/denseblock4/denselayer7/conv2/Conv_output_0_1" -> "870 /features/denseblock4/denselayer10/Concat" [label="[1, 32, 7, 7]", style=solid]; -"837 DequantizeLinear_/features/denseblock4/denselayer7/conv2/Conv_output_0_1" -> "886 /features/denseblock4/denselayer11/Concat" [label="[1, 32, 7, 7]", style=solid]; -"837 DequantizeLinear_/features/denseblock4/denselayer7/conv2/Conv_output_0_1" -> "902 /features/denseblock4/denselayer12/Concat" [label="[1, 32, 7, 7]", style=solid]; -"837 DequantizeLinear_/features/denseblock4/denselayer7/conv2/Conv_output_0_1" -> "918 /features/denseblock4/denselayer13/Concat" [label="[1, 32, 7, 7]", style=solid]; -"837 DequantizeLinear_/features/denseblock4/denselayer7/conv2/Conv_output_0_1" -> "934 /features/denseblock4/denselayer14/Concat" [label="[1, 32, 7, 7]", style=solid]; -"837 DequantizeLinear_/features/denseblock4/denselayer7/conv2/Conv_output_0_1" -> "950 /features/denseblock4/denselayer15/Concat" [label="[1, 32, 7, 7]", style=solid]; -"837 DequantizeLinear_/features/denseblock4/denselayer7/conv2/Conv_output_0_1" -> "966 /features/denseblock4/denselayer16/Concat" [label="[1, 32, 7, 7]", style=solid]; -"837 DequantizeLinear_/features/denseblock4/denselayer7/conv2/Conv_output_0_1" -> "982 /features/denseblock4/Concat" [label="[1, 32, 7, 7]", style=solid]; -"838 /features/denseblock4/denselayer8/Concat" -> "839 /features/denseblock4/denselayer8/norm1/BatchNormalization" [label="[1, 736, 7, 7]", style=solid]; -"839 /features/denseblock4/denselayer8/norm1/BatchNormalization" -> "840 /features/denseblock4/denselayer8/relu1/Relu" [label="[1, 736, 7, 7]", style=solid]; -"840 /features/denseblock4/denselayer8/relu1/Relu" -> "841 QuantizeLinear_/features/denseblock4/denselayer8/relu1/Relu_output_0_1" [label="[1, 736, 7, 7]", style=solid]; -"841 QuantizeLinear_/features/denseblock4/denselayer8/relu1/Relu_output_0_1" -> "842 DequantizeLinear_/features/denseblock4/denselayer8/relu1/Relu_output_0_1" [label="[1, 736, 7, 7]", style=dashed]; -"842 DequantizeLinear_/features/denseblock4/denselayer8/relu1/Relu_output_0_1" -> "845 /features/denseblock4/denselayer8/conv1/Conv" [label="[1, 736, 7, 7]", style=solid]; -"843 QuantizeLinear_onnx^^Conv_1316_1" -> "844 DequantizeLinear_onnx^^Conv_1316_1" [label="[128, 736, 1, 1]", style=dashed]; -"844 DequantizeLinear_onnx^^Conv_1316_1" -> "845 /features/denseblock4/denselayer8/conv1/Conv" [label="[128, 736, 1, 1]", style=solid]; -"845 /features/denseblock4/denselayer8/conv1/Conv" -> "846 /features/denseblock4/denselayer8/relu2/Relu" [label="[1, 128, 7, 7]", style=solid]; -"846 /features/denseblock4/denselayer8/relu2/Relu" -> "847 QuantizeLinear_/features/denseblock4/denselayer8/relu2/Relu_output_0_1" [label="[1, 128, 7, 7]", style=solid]; -"847 QuantizeLinear_/features/denseblock4/denselayer8/relu2/Relu_output_0_1" -> "848 DequantizeLinear_/features/denseblock4/denselayer8/relu2/Relu_output_0_1" [label="[1, 128, 7, 7]", style=dashed]; -"848 DequantizeLinear_/features/denseblock4/denselayer8/relu2/Relu_output_0_1" -> "851 /features/denseblock4/denselayer8/conv2/Conv" [label="[1, 128, 7, 7]", style=solid]; -"849 QuantizeLinear_features.denseblock4.denselayer8.conv2.weight_1" -> "850 DequantizeLinear_features.denseblock4.denselayer8.conv2.weight_1" [label="[32, 128, 3, 3]", style=dashed]; -"850 DequantizeLinear_features.denseblock4.denselayer8.conv2.weight_1" -> "851 /features/denseblock4/denselayer8/conv2/Conv" [label="[32, 128, 3, 3]", style=solid]; -"851 /features/denseblock4/denselayer8/conv2/Conv" -> "852 QuantizeLinear_/features/denseblock4/denselayer8/conv2/Conv_output_0_1" [label="[1, 32, 7, 7]", style=solid]; -"852 QuantizeLinear_/features/denseblock4/denselayer8/conv2/Conv_output_0_1" -> "853 DequantizeLinear_/features/denseblock4/denselayer8/conv2/Conv_output_0_1" [label="[1, 32, 7, 7]", style=dashed]; -"853 DequantizeLinear_/features/denseblock4/denselayer8/conv2/Conv_output_0_1" -> "854 /features/denseblock4/denselayer9/Concat" [label="[1, 32, 7, 7]", style=solid]; -"853 DequantizeLinear_/features/denseblock4/denselayer8/conv2/Conv_output_0_1" -> "870 /features/denseblock4/denselayer10/Concat" [label="[1, 32, 7, 7]", style=solid]; -"853 DequantizeLinear_/features/denseblock4/denselayer8/conv2/Conv_output_0_1" -> "886 /features/denseblock4/denselayer11/Concat" [label="[1, 32, 7, 7]", style=solid]; -"853 DequantizeLinear_/features/denseblock4/denselayer8/conv2/Conv_output_0_1" -> 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/features/denseblock4/denselayer9/norm1/BatchNormalization" [label="[1, 768, 7, 7]", style=solid]; -"855 /features/denseblock4/denselayer9/norm1/BatchNormalization" -> "856 /features/denseblock4/denselayer9/relu1/Relu" [label="[1, 768, 7, 7]", style=solid]; -"856 /features/denseblock4/denselayer9/relu1/Relu" -> "857 QuantizeLinear_/features/denseblock4/denselayer9/relu1/Relu_output_0_1" [label="[1, 768, 7, 7]", style=solid]; -"857 QuantizeLinear_/features/denseblock4/denselayer9/relu1/Relu_output_0_1" -> "858 DequantizeLinear_/features/denseblock4/denselayer9/relu1/Relu_output_0_1" [label="[1, 768, 7, 7]", style=dashed]; -"858 DequantizeLinear_/features/denseblock4/denselayer9/relu1/Relu_output_0_1" -> "861 /features/denseblock4/denselayer9/conv1/Conv" [label="[1, 768, 7, 7]", style=solid]; -"859 QuantizeLinear_onnx^^Conv_1319_1" -> "860 DequantizeLinear_onnx^^Conv_1319_1" [label="[128, 768, 1, 1]", style=dashed]; -"860 DequantizeLinear_onnx^^Conv_1319_1" -> "861 /features/denseblock4/denselayer9/conv1/Conv" [label="[128, 768, 1, 1]", style=solid]; -"861 /features/denseblock4/denselayer9/conv1/Conv" -> "862 /features/denseblock4/denselayer9/relu2/Relu" [label="[1, 128, 7, 7]", style=solid]; -"862 /features/denseblock4/denselayer9/relu2/Relu" -> "863 QuantizeLinear_/features/denseblock4/denselayer9/relu2/Relu_output_0_1" [label="[1, 128, 7, 7]", style=solid]; -"863 QuantizeLinear_/features/denseblock4/denselayer9/relu2/Relu_output_0_1" -> "864 DequantizeLinear_/features/denseblock4/denselayer9/relu2/Relu_output_0_1" [label="[1, 128, 7, 7]", style=dashed]; -"864 DequantizeLinear_/features/denseblock4/denselayer9/relu2/Relu_output_0_1" -> "867 /features/denseblock4/denselayer9/conv2/Conv" [label="[1, 128, 7, 7]", style=solid]; -"865 QuantizeLinear_features.denseblock4.denselayer9.conv2.weight_1" -> "866 DequantizeLinear_features.denseblock4.denselayer9.conv2.weight_1" [label="[32, 128, 3, 3]", style=dashed]; -"866 DequantizeLinear_features.denseblock4.denselayer9.conv2.weight_1" -> "867 /features/denseblock4/denselayer9/conv2/Conv" [label="[32, 128, 3, 3]", style=solid]; -"867 /features/denseblock4/denselayer9/conv2/Conv" -> "868 QuantizeLinear_/features/denseblock4/denselayer9/conv2/Conv_output_0_1" [label="[1, 32, 7, 7]", style=solid]; -"868 QuantizeLinear_/features/denseblock4/denselayer9/conv2/Conv_output_0_1" -> "869 DequantizeLinear_/features/denseblock4/denselayer9/conv2/Conv_output_0_1" [label="[1, 32, 7, 7]", style=dashed]; -"869 DequantizeLinear_/features/denseblock4/denselayer9/conv2/Conv_output_0_1" -> "870 /features/denseblock4/denselayer10/Concat" [label="[1, 32, 7, 7]", style=solid]; -"869 DequantizeLinear_/features/denseblock4/denselayer9/conv2/Conv_output_0_1" -> "886 /features/denseblock4/denselayer11/Concat" [label="[1, 32, 7, 7]", style=solid]; -"869 DequantizeLinear_/features/denseblock4/denselayer9/conv2/Conv_output_0_1" -> "902 /features/denseblock4/denselayer12/Concat" [label="[1, 32, 7, 7]", style=solid]; -"869 DequantizeLinear_/features/denseblock4/denselayer9/conv2/Conv_output_0_1" -> "918 /features/denseblock4/denselayer13/Concat" [label="[1, 32, 7, 7]", style=solid]; -"869 DequantizeLinear_/features/denseblock4/denselayer9/conv2/Conv_output_0_1" -> "934 /features/denseblock4/denselayer14/Concat" [label="[1, 32, 7, 7]", style=solid]; -"869 DequantizeLinear_/features/denseblock4/denselayer9/conv2/Conv_output_0_1" -> "950 /features/denseblock4/denselayer15/Concat" [label="[1, 32, 7, 7]", style=solid]; -"869 DequantizeLinear_/features/denseblock4/denselayer9/conv2/Conv_output_0_1" -> "966 /features/denseblock4/denselayer16/Concat" [label="[1, 32, 7, 7]", style=solid]; -"869 DequantizeLinear_/features/denseblock4/denselayer9/conv2/Conv_output_0_1" -> "982 /features/denseblock4/Concat" [label="[1, 32, 7, 7]", style=solid]; -"870 /features/denseblock4/denselayer10/Concat" -> "871 /features/denseblock4/denselayer10/norm1/BatchNormalization" [label="[1, 800, 7, 7]", style=solid]; -"871 /features/denseblock4/denselayer10/norm1/BatchNormalization" -> "872 /features/denseblock4/denselayer10/relu1/Relu" [label="[1, 800, 7, 7]", style=solid]; -"872 /features/denseblock4/denselayer10/relu1/Relu" -> "873 QuantizeLinear_/features/denseblock4/denselayer10/relu1/Relu_output_0_1" [label="[1, 800, 7, 7]", style=solid]; -"873 QuantizeLinear_/features/denseblock4/denselayer10/relu1/Relu_output_0_1" -> "874 DequantizeLinear_/features/denseblock4/denselayer10/relu1/Relu_output_0_1" [label="[1, 800, 7, 7]", style=dashed]; -"874 DequantizeLinear_/features/denseblock4/denselayer10/relu1/Relu_output_0_1" -> "877 /features/denseblock4/denselayer10/conv1/Conv" [label="[1, 800, 7, 7]", style=solid]; -"875 QuantizeLinear_onnx^^Conv_1322_1" -> "876 DequantizeLinear_onnx^^Conv_1322_1" [label="[128, 800, 1, 1]", style=dashed]; -"876 DequantizeLinear_onnx^^Conv_1322_1" -> "877 /features/denseblock4/denselayer10/conv1/Conv" [label="[128, 800, 1, 1]", style=solid]; -"877 /features/denseblock4/denselayer10/conv1/Conv" -> "878 /features/denseblock4/denselayer10/relu2/Relu" [label="[1, 128, 7, 7]", style=solid]; -"878 /features/denseblock4/denselayer10/relu2/Relu" -> "879 QuantizeLinear_/features/denseblock4/denselayer10/relu2/Relu_output_0_1" [label="[1, 128, 7, 7]", style=solid]; -"879 QuantizeLinear_/features/denseblock4/denselayer10/relu2/Relu_output_0_1" -> "880 DequantizeLinear_/features/denseblock4/denselayer10/relu2/Relu_output_0_1" [label="[1, 128, 7, 7]", style=dashed]; -"880 DequantizeLinear_/features/denseblock4/denselayer10/relu2/Relu_output_0_1" -> "883 /features/denseblock4/denselayer10/conv2/Conv" [label="[1, 128, 7, 7]", style=solid]; -"881 QuantizeLinear_features.denseblock4.denselayer10.conv2.weight_1" -> "882 DequantizeLinear_features.denseblock4.denselayer10.conv2.weight_1" [label="[32, 128, 3, 3]", style=dashed]; -"882 DequantizeLinear_features.denseblock4.denselayer10.conv2.weight_1" -> "883 /features/denseblock4/denselayer10/conv2/Conv" [label="[32, 128, 3, 3]", style=solid]; -"883 /features/denseblock4/denselayer10/conv2/Conv" -> "884 QuantizeLinear_/features/denseblock4/denselayer10/conv2/Conv_output_0_1" [label="[1, 32, 7, 7]", style=solid]; -"884 QuantizeLinear_/features/denseblock4/denselayer10/conv2/Conv_output_0_1" -> "885 DequantizeLinear_/features/denseblock4/denselayer10/conv2/Conv_output_0_1" [label="[1, 32, 7, 7]", style=dashed]; -"885 DequantizeLinear_/features/denseblock4/denselayer10/conv2/Conv_output_0_1" -> "886 /features/denseblock4/denselayer11/Concat" [label="[1, 32, 7, 7]", style=solid]; -"885 DequantizeLinear_/features/denseblock4/denselayer10/conv2/Conv_output_0_1" -> "902 /features/denseblock4/denselayer12/Concat" [label="[1, 32, 7, 7]", style=solid]; -"885 DequantizeLinear_/features/denseblock4/denselayer10/conv2/Conv_output_0_1" -> "918 /features/denseblock4/denselayer13/Concat" [label="[1, 32, 7, 7]", style=solid]; -"885 DequantizeLinear_/features/denseblock4/denselayer10/conv2/Conv_output_0_1" -> "934 /features/denseblock4/denselayer14/Concat" [label="[1, 32, 7, 7]", style=solid]; -"885 DequantizeLinear_/features/denseblock4/denselayer10/conv2/Conv_output_0_1" -> "950 /features/denseblock4/denselayer15/Concat" [label="[1, 32, 7, 7]", style=solid]; -"885 DequantizeLinear_/features/denseblock4/denselayer10/conv2/Conv_output_0_1" -> "966 /features/denseblock4/denselayer16/Concat" [label="[1, 32, 7, 7]", style=solid]; -"885 DequantizeLinear_/features/denseblock4/denselayer10/conv2/Conv_output_0_1" -> "982 /features/denseblock4/Concat" [label="[1, 32, 7, 7]", style=solid]; -"886 /features/denseblock4/denselayer11/Concat" -> "887 /features/denseblock4/denselayer11/norm1/BatchNormalization" [label="[1, 832, 7, 7]", style=solid]; -"887 /features/denseblock4/denselayer11/norm1/BatchNormalization" -> "888 /features/denseblock4/denselayer11/relu1/Relu" [label="[1, 832, 7, 7]", style=solid]; -"888 /features/denseblock4/denselayer11/relu1/Relu" -> "889 QuantizeLinear_/features/denseblock4/denselayer11/relu1/Relu_output_0_1" [label="[1, 832, 7, 7]", style=solid]; -"889 QuantizeLinear_/features/denseblock4/denselayer11/relu1/Relu_output_0_1" -> "890 DequantizeLinear_/features/denseblock4/denselayer11/relu1/Relu_output_0_1" [label="[1, 832, 7, 7]", style=dashed]; -"890 DequantizeLinear_/features/denseblock4/denselayer11/relu1/Relu_output_0_1" -> "893 /features/denseblock4/denselayer11/conv1/Conv" [label="[1, 832, 7, 7]", style=solid]; -"891 QuantizeLinear_onnx^^Conv_1325_1" -> "892 DequantizeLinear_onnx^^Conv_1325_1" [label="[128, 832, 1, 1]", style=dashed]; -"892 DequantizeLinear_onnx^^Conv_1325_1" -> "893 /features/denseblock4/denselayer11/conv1/Conv" [label="[128, 832, 1, 1]", style=solid]; -"893 /features/denseblock4/denselayer11/conv1/Conv" -> "894 /features/denseblock4/denselayer11/relu2/Relu" [label="[1, 128, 7, 7]", style=solid]; -"894 /features/denseblock4/denselayer11/relu2/Relu" -> "895 QuantizeLinear_/features/denseblock4/denselayer11/relu2/Relu_output_0_1" [label="[1, 128, 7, 7]", style=solid]; -"895 QuantizeLinear_/features/denseblock4/denselayer11/relu2/Relu_output_0_1" -> "896 DequantizeLinear_/features/denseblock4/denselayer11/relu2/Relu_output_0_1" [label="[1, 128, 7, 7]", style=dashed]; -"896 DequantizeLinear_/features/denseblock4/denselayer11/relu2/Relu_output_0_1" -> "899 /features/denseblock4/denselayer11/conv2/Conv" [label="[1, 128, 7, 7]", style=solid]; -"897 QuantizeLinear_features.denseblock4.denselayer11.conv2.weight_1" -> "898 DequantizeLinear_features.denseblock4.denselayer11.conv2.weight_1" [label="[32, 128, 3, 3]", style=dashed]; -"898 DequantizeLinear_features.denseblock4.denselayer11.conv2.weight_1" -> "899 /features/denseblock4/denselayer11/conv2/Conv" [label="[32, 128, 3, 3]", style=solid]; -"899 /features/denseblock4/denselayer11/conv2/Conv" -> "900 QuantizeLinear_/features/denseblock4/denselayer11/conv2/Conv_output_0_1" [label="[1, 32, 7, 7]", style=solid]; -"900 QuantizeLinear_/features/denseblock4/denselayer11/conv2/Conv_output_0_1" -> "901 DequantizeLinear_/features/denseblock4/denselayer11/conv2/Conv_output_0_1" [label="[1, 32, 7, 7]", style=dashed]; -"901 DequantizeLinear_/features/denseblock4/denselayer11/conv2/Conv_output_0_1" -> "902 /features/denseblock4/denselayer12/Concat" [label="[1, 32, 7, 7]", style=solid]; -"901 DequantizeLinear_/features/denseblock4/denselayer11/conv2/Conv_output_0_1" -> "918 /features/denseblock4/denselayer13/Concat" [label="[1, 32, 7, 7]", style=solid]; -"901 DequantizeLinear_/features/denseblock4/denselayer11/conv2/Conv_output_0_1" -> "934 /features/denseblock4/denselayer14/Concat" [label="[1, 32, 7, 7]", style=solid]; -"901 DequantizeLinear_/features/denseblock4/denselayer11/conv2/Conv_output_0_1" -> "950 /features/denseblock4/denselayer15/Concat" [label="[1, 32, 7, 7]", style=solid]; -"901 DequantizeLinear_/features/denseblock4/denselayer11/conv2/Conv_output_0_1" -> "966 /features/denseblock4/denselayer16/Concat" [label="[1, 32, 7, 7]", style=solid]; -"901 DequantizeLinear_/features/denseblock4/denselayer11/conv2/Conv_output_0_1" -> "982 /features/denseblock4/Concat" [label="[1, 32, 7, 7]", style=solid]; -"902 /features/denseblock4/denselayer12/Concat" -> "903 /features/denseblock4/denselayer12/norm1/BatchNormalization" [label="[1, 864, 7, 7]", style=solid]; -"903 /features/denseblock4/denselayer12/norm1/BatchNormalization" -> "904 /features/denseblock4/denselayer12/relu1/Relu" [label="[1, 864, 7, 7]", style=solid]; -"904 /features/denseblock4/denselayer12/relu1/Relu" -> "905 QuantizeLinear_/features/denseblock4/denselayer12/relu1/Relu_output_0_1" [label="[1, 864, 7, 7]", style=solid]; -"905 QuantizeLinear_/features/denseblock4/denselayer12/relu1/Relu_output_0_1" -> "906 DequantizeLinear_/features/denseblock4/denselayer12/relu1/Relu_output_0_1" [label="[1, 864, 7, 7]", style=dashed]; -"906 DequantizeLinear_/features/denseblock4/denselayer12/relu1/Relu_output_0_1" -> "909 /features/denseblock4/denselayer12/conv1/Conv" [label="[1, 864, 7, 7]", style=solid]; -"907 QuantizeLinear_onnx^^Conv_1328_1" -> "908 DequantizeLinear_onnx^^Conv_1328_1" [label="[128, 864, 1, 1]", style=dashed]; -"908 DequantizeLinear_onnx^^Conv_1328_1" -> "909 /features/denseblock4/denselayer12/conv1/Conv" [label="[128, 864, 1, 1]", style=solid]; -"909 /features/denseblock4/denselayer12/conv1/Conv" -> "910 /features/denseblock4/denselayer12/relu2/Relu" [label="[1, 128, 7, 7]", style=solid]; -"910 /features/denseblock4/denselayer12/relu2/Relu" -> "911 QuantizeLinear_/features/denseblock4/denselayer12/relu2/Relu_output_0_1" [label="[1, 128, 7, 7]", style=solid]; -"911 QuantizeLinear_/features/denseblock4/denselayer12/relu2/Relu_output_0_1" -> "912 DequantizeLinear_/features/denseblock4/denselayer12/relu2/Relu_output_0_1" [label="[1, 128, 7, 7]", style=dashed]; -"912 DequantizeLinear_/features/denseblock4/denselayer12/relu2/Relu_output_0_1" -> "915 /features/denseblock4/denselayer12/conv2/Conv" [label="[1, 128, 7, 7]", style=solid]; -"913 QuantizeLinear_features.denseblock4.denselayer12.conv2.weight_1" -> "914 DequantizeLinear_features.denseblock4.denselayer12.conv2.weight_1" [label="[32, 128, 3, 3]", style=dashed]; -"914 DequantizeLinear_features.denseblock4.denselayer12.conv2.weight_1" -> "915 /features/denseblock4/denselayer12/conv2/Conv" [label="[32, 128, 3, 3]", style=solid]; -"915 /features/denseblock4/denselayer12/conv2/Conv" -> "916 QuantizeLinear_/features/denseblock4/denselayer12/conv2/Conv_output_0_1" [label="[1, 32, 7, 7]", style=solid]; -"916 QuantizeLinear_/features/denseblock4/denselayer12/conv2/Conv_output_0_1" -> "917 DequantizeLinear_/features/denseblock4/denselayer12/conv2/Conv_output_0_1" [label="[1, 32, 7, 7]", style=dashed]; -"917 DequantizeLinear_/features/denseblock4/denselayer12/conv2/Conv_output_0_1" -> "918 /features/denseblock4/denselayer13/Concat" [label="[1, 32, 7, 7]", style=solid]; -"917 DequantizeLinear_/features/denseblock4/denselayer12/conv2/Conv_output_0_1" -> "934 /features/denseblock4/denselayer14/Concat" [label="[1, 32, 7, 7]", style=solid]; -"917 DequantizeLinear_/features/denseblock4/denselayer12/conv2/Conv_output_0_1" -> "950 /features/denseblock4/denselayer15/Concat" [label="[1, 32, 7, 7]", style=solid]; -"917 DequantizeLinear_/features/denseblock4/denselayer12/conv2/Conv_output_0_1" -> "966 /features/denseblock4/denselayer16/Concat" [label="[1, 32, 7, 7]", style=solid]; -"917 DequantizeLinear_/features/denseblock4/denselayer12/conv2/Conv_output_0_1" -> "982 /features/denseblock4/Concat" [label="[1, 32, 7, 7]", style=solid]; -"918 /features/denseblock4/denselayer13/Concat" -> "919 /features/denseblock4/denselayer13/norm1/BatchNormalization" [label="[1, 896, 7, 7]", style=solid]; -"919 /features/denseblock4/denselayer13/norm1/BatchNormalization" -> "920 /features/denseblock4/denselayer13/relu1/Relu" [label="[1, 896, 7, 7]", style=solid]; -"920 /features/denseblock4/denselayer13/relu1/Relu" -> "921 QuantizeLinear_/features/denseblock4/denselayer13/relu1/Relu_output_0_1" [label="[1, 896, 7, 7]", style=solid]; -"921 QuantizeLinear_/features/denseblock4/denselayer13/relu1/Relu_output_0_1" -> "922 DequantizeLinear_/features/denseblock4/denselayer13/relu1/Relu_output_0_1" [label="[1, 896, 7, 7]", style=dashed]; -"922 DequantizeLinear_/features/denseblock4/denselayer13/relu1/Relu_output_0_1" -> "925 /features/denseblock4/denselayer13/conv1/Conv" [label="[1, 896, 7, 7]", style=solid]; -"923 QuantizeLinear_onnx^^Conv_1331_1" -> "924 DequantizeLinear_onnx^^Conv_1331_1" [label="[128, 896, 1, 1]", style=dashed]; -"924 DequantizeLinear_onnx^^Conv_1331_1" -> "925 /features/denseblock4/denselayer13/conv1/Conv" [label="[128, 896, 1, 1]", style=solid]; -"925 /features/denseblock4/denselayer13/conv1/Conv" -> "926 /features/denseblock4/denselayer13/relu2/Relu" [label="[1, 128, 7, 7]", style=solid]; -"926 /features/denseblock4/denselayer13/relu2/Relu" -> "927 QuantizeLinear_/features/denseblock4/denselayer13/relu2/Relu_output_0_1" [label="[1, 128, 7, 7]", style=solid]; -"927 QuantizeLinear_/features/denseblock4/denselayer13/relu2/Relu_output_0_1" -> "928 DequantizeLinear_/features/denseblock4/denselayer13/relu2/Relu_output_0_1" [label="[1, 128, 7, 7]", style=dashed]; -"928 DequantizeLinear_/features/denseblock4/denselayer13/relu2/Relu_output_0_1" -> "931 /features/denseblock4/denselayer13/conv2/Conv" [label="[1, 128, 7, 7]", style=solid]; -"929 QuantizeLinear_features.denseblock4.denselayer13.conv2.weight_1" -> "930 DequantizeLinear_features.denseblock4.denselayer13.conv2.weight_1" [label="[32, 128, 3, 3]", style=dashed]; -"930 DequantizeLinear_features.denseblock4.denselayer13.conv2.weight_1" -> "931 /features/denseblock4/denselayer13/conv2/Conv" [label="[32, 128, 3, 3]", style=solid]; -"931 /features/denseblock4/denselayer13/conv2/Conv" -> "932 QuantizeLinear_/features/denseblock4/denselayer13/conv2/Conv_output_0_1" [label="[1, 32, 7, 7]", style=solid]; -"932 QuantizeLinear_/features/denseblock4/denselayer13/conv2/Conv_output_0_1" -> "933 DequantizeLinear_/features/denseblock4/denselayer13/conv2/Conv_output_0_1" [label="[1, 32, 7, 7]", style=dashed]; -"933 DequantizeLinear_/features/denseblock4/denselayer13/conv2/Conv_output_0_1" -> "934 /features/denseblock4/denselayer14/Concat" [label="[1, 32, 7, 7]", style=solid]; -"933 DequantizeLinear_/features/denseblock4/denselayer13/conv2/Conv_output_0_1" -> "950 /features/denseblock4/denselayer15/Concat" [label="[1, 32, 7, 7]", style=solid]; -"933 DequantizeLinear_/features/denseblock4/denselayer13/conv2/Conv_output_0_1" -> "966 /features/denseblock4/denselayer16/Concat" [label="[1, 32, 7, 7]", style=solid]; -"933 DequantizeLinear_/features/denseblock4/denselayer13/conv2/Conv_output_0_1" -> "982 /features/denseblock4/Concat" [label="[1, 32, 7, 7]", style=solid]; -"934 /features/denseblock4/denselayer14/Concat" -> "935 /features/denseblock4/denselayer14/norm1/BatchNormalization" [label="[1, 928, 7, 7]", style=solid]; -"935 /features/denseblock4/denselayer14/norm1/BatchNormalization" -> "936 /features/denseblock4/denselayer14/relu1/Relu" [label="[1, 928, 7, 7]", style=solid]; -"936 /features/denseblock4/denselayer14/relu1/Relu" -> "937 QuantizeLinear_/features/denseblock4/denselayer14/relu1/Relu_output_0_1" [label="[1, 928, 7, 7]", style=solid]; -"937 QuantizeLinear_/features/denseblock4/denselayer14/relu1/Relu_output_0_1" -> "938 DequantizeLinear_/features/denseblock4/denselayer14/relu1/Relu_output_0_1" [label="[1, 928, 7, 7]", style=dashed]; -"938 DequantizeLinear_/features/denseblock4/denselayer14/relu1/Relu_output_0_1" -> "941 /features/denseblock4/denselayer14/conv1/Conv" [label="[1, 928, 7, 7]", style=solid]; -"939 QuantizeLinear_onnx^^Conv_1334_1" -> "940 DequantizeLinear_onnx^^Conv_1334_1" [label="[128, 928, 1, 1]", style=dashed]; -"940 DequantizeLinear_onnx^^Conv_1334_1" -> "941 /features/denseblock4/denselayer14/conv1/Conv" [label="[128, 928, 1, 1]", style=solid]; -"941 /features/denseblock4/denselayer14/conv1/Conv" -> "942 /features/denseblock4/denselayer14/relu2/Relu" [label="[1, 128, 7, 7]", style=solid]; -"942 /features/denseblock4/denselayer14/relu2/Relu" -> "943 QuantizeLinear_/features/denseblock4/denselayer14/relu2/Relu_output_0_1" [label="[1, 128, 7, 7]", style=solid]; -"943 QuantizeLinear_/features/denseblock4/denselayer14/relu2/Relu_output_0_1" -> "944 DequantizeLinear_/features/denseblock4/denselayer14/relu2/Relu_output_0_1" [label="[1, 128, 7, 7]", style=dashed]; -"944 DequantizeLinear_/features/denseblock4/denselayer14/relu2/Relu_output_0_1" -> "947 /features/denseblock4/denselayer14/conv2/Conv" [label="[1, 128, 7, 7]", style=solid]; -"945 QuantizeLinear_features.denseblock4.denselayer14.conv2.weight_1" -> "946 DequantizeLinear_features.denseblock4.denselayer14.conv2.weight_1" [label="[32, 128, 3, 3]", style=dashed]; -"946 DequantizeLinear_features.denseblock4.denselayer14.conv2.weight_1" -> "947 /features/denseblock4/denselayer14/conv2/Conv" [label="[32, 128, 3, 3]", style=solid]; -"947 /features/denseblock4/denselayer14/conv2/Conv" -> "948 QuantizeLinear_/features/denseblock4/denselayer14/conv2/Conv_output_0_1" [label="[1, 32, 7, 7]", style=solid]; -"948 QuantizeLinear_/features/denseblock4/denselayer14/conv2/Conv_output_0_1" -> "949 DequantizeLinear_/features/denseblock4/denselayer14/conv2/Conv_output_0_1" [label="[1, 32, 7, 7]", style=dashed]; -"949 DequantizeLinear_/features/denseblock4/denselayer14/conv2/Conv_output_0_1" -> "950 /features/denseblock4/denselayer15/Concat" [label="[1, 32, 7, 7]", style=solid]; -"949 DequantizeLinear_/features/denseblock4/denselayer14/conv2/Conv_output_0_1" -> "966 /features/denseblock4/denselayer16/Concat" [label="[1, 32, 7, 7]", style=solid]; -"949 DequantizeLinear_/features/denseblock4/denselayer14/conv2/Conv_output_0_1" -> "982 /features/denseblock4/Concat" [label="[1, 32, 7, 7]", style=solid]; -"950 /features/denseblock4/denselayer15/Concat" -> "951 /features/denseblock4/denselayer15/norm1/BatchNormalization" [label="[1, 960, 7, 7]", style=solid]; -"951 /features/denseblock4/denselayer15/norm1/BatchNormalization" -> "952 /features/denseblock4/denselayer15/relu1/Relu" [label="[1, 960, 7, 7]", style=solid]; -"952 /features/denseblock4/denselayer15/relu1/Relu" -> "953 QuantizeLinear_/features/denseblock4/denselayer15/relu1/Relu_output_0_1" [label="[1, 960, 7, 7]", style=solid]; -"953 QuantizeLinear_/features/denseblock4/denselayer15/relu1/Relu_output_0_1" -> "954 DequantizeLinear_/features/denseblock4/denselayer15/relu1/Relu_output_0_1" [label="[1, 960, 7, 7]", style=dashed]; -"954 DequantizeLinear_/features/denseblock4/denselayer15/relu1/Relu_output_0_1" -> "957 /features/denseblock4/denselayer15/conv1/Conv" [label="[1, 960, 7, 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DequantizeLinear_/features/denseblock2/denselayer3/conv2/Conv_output_0_1" -> "246 /features/denseblock2/denselayer9/Concat" [label="[1, 32, 28, 28]", style=solid]; +"165 DequantizeLinear_/features/denseblock2/denselayer3/conv2/Conv_output_0_1" -> "262 /features/denseblock2/denselayer10/Concat" [label="[1, 32, 28, 28]", style=solid]; +"165 DequantizeLinear_/features/denseblock2/denselayer3/conv2/Conv_output_0_1" -> "278 /features/denseblock2/denselayer11/Concat" [label="[1, 32, 28, 28]", style=solid]; +"165 DequantizeLinear_/features/denseblock2/denselayer3/conv2/Conv_output_0_1" -> "294 /features/denseblock2/denselayer12/Concat" [label="[1, 32, 28, 28]", style=solid]; +"165 DequantizeLinear_/features/denseblock2/denselayer3/conv2/Conv_output_0_1" -> "310 /features/denseblock2/Concat" [label="[1, 32, 28, 28]", style=solid]; +"166 /features/denseblock2/denselayer4/Concat" -> "167 /features/denseblock2/denselayer4/norm1/BatchNormalization" [label="[1, 224, 28, 28]", style=solid]; +"167 /features/denseblock2/denselayer4/norm1/BatchNormalization" -> "168 /features/denseblock2/denselayer4/relu1/Relu" [label="[1, 224, 28, 28]", style=solid]; +"168 /features/denseblock2/denselayer4/relu1/Relu" -> "169 QuantizeLinear_/features/denseblock2/denselayer4/relu1/Relu_output_0_1" [label="[1, 224, 28, 28]", style=solid]; +"169 QuantizeLinear_/features/denseblock2/denselayer4/relu1/Relu_output_0_1" -> "170 DequantizeLinear_/features/denseblock2/denselayer4/relu1/Relu_output_0_1" [label="[1, 224, 28, 28]", style=dashed]; +"170 DequantizeLinear_/features/denseblock2/denselayer4/relu1/Relu_output_0_1" -> "173 /features/denseblock2/denselayer4/conv1/Conv" [label="[1, 224, 28, 28]", style=solid]; +"171 QuantizeLinear_onnx^^Conv_1190_1" -> "172 DequantizeLinear_onnx^^Conv_1190_1" [label="[128, 224, 1, 1]", style=dashed]; +"172 DequantizeLinear_onnx^^Conv_1190_1" -> "173 /features/denseblock2/denselayer4/conv1/Conv" [label="[128, 224, 1, 1]", style=solid]; +"173 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DequantizeLinear_/features/denseblock2/denselayer4/conv2/Conv_output_0_1" -> "230 /features/denseblock2/denselayer8/Concat" [label="[1, 32, 28, 28]", style=solid]; +"181 DequantizeLinear_/features/denseblock2/denselayer4/conv2/Conv_output_0_1" -> "246 /features/denseblock2/denselayer9/Concat" [label="[1, 32, 28, 28]", style=solid]; +"181 DequantizeLinear_/features/denseblock2/denselayer4/conv2/Conv_output_0_1" -> "262 /features/denseblock2/denselayer10/Concat" [label="[1, 32, 28, 28]", style=solid]; +"181 DequantizeLinear_/features/denseblock2/denselayer4/conv2/Conv_output_0_1" -> "278 /features/denseblock2/denselayer11/Concat" [label="[1, 32, 28, 28]", style=solid]; +"181 DequantizeLinear_/features/denseblock2/denselayer4/conv2/Conv_output_0_1" -> "294 /features/denseblock2/denselayer12/Concat" [label="[1, 32, 28, 28]", style=solid]; +"181 DequantizeLinear_/features/denseblock2/denselayer4/conv2/Conv_output_0_1" -> "310 /features/denseblock2/Concat" [label="[1, 32, 28, 28]", 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[label="[32, 128, 3, 3]", style=dashed]; +"194 DequantizeLinear_features.denseblock2.denselayer5.conv2.weight_1" -> "195 /features/denseblock2/denselayer5/conv2/Conv" [label="[32, 128, 3, 3]", style=solid]; +"195 /features/denseblock2/denselayer5/conv2/Conv" -> "196 QuantizeLinear_/features/denseblock2/denselayer5/conv2/Conv_output_0_1" [label="[1, 32, 28, 28]", style=solid]; +"196 QuantizeLinear_/features/denseblock2/denselayer5/conv2/Conv_output_0_1" -> "197 DequantizeLinear_/features/denseblock2/denselayer5/conv2/Conv_output_0_1" [label="[1, 32, 28, 28]", style=dashed]; +"197 DequantizeLinear_/features/denseblock2/denselayer5/conv2/Conv_output_0_1" -> "198 /features/denseblock2/denselayer6/Concat" [label="[1, 32, 28, 28]", style=solid]; +"197 DequantizeLinear_/features/denseblock2/denselayer5/conv2/Conv_output_0_1" -> "214 /features/denseblock2/denselayer7/Concat" [label="[1, 32, 28, 28]", style=solid]; +"197 DequantizeLinear_/features/denseblock2/denselayer5/conv2/Conv_output_0_1" -> "230 /features/denseblock2/denselayer8/Concat" [label="[1, 32, 28, 28]", style=solid]; +"197 DequantizeLinear_/features/denseblock2/denselayer5/conv2/Conv_output_0_1" -> "246 /features/denseblock2/denselayer9/Concat" [label="[1, 32, 28, 28]", style=solid]; +"197 DequantizeLinear_/features/denseblock2/denselayer5/conv2/Conv_output_0_1" -> "262 /features/denseblock2/denselayer10/Concat" [label="[1, 32, 28, 28]", style=solid]; +"197 DequantizeLinear_/features/denseblock2/denselayer5/conv2/Conv_output_0_1" -> "278 /features/denseblock2/denselayer11/Concat" [label="[1, 32, 28, 28]", style=solid]; +"197 DequantizeLinear_/features/denseblock2/denselayer5/conv2/Conv_output_0_1" -> "294 /features/denseblock2/denselayer12/Concat" [label="[1, 32, 28, 28]", style=solid]; +"197 DequantizeLinear_/features/denseblock2/denselayer5/conv2/Conv_output_0_1" -> "310 /features/denseblock2/Concat" [label="[1, 32, 28, 28]", style=solid]; +"198 /features/denseblock2/denselayer6/Concat" -> "199 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[label="[1, 32, 14, 14]", style=solid]; +"546 DequantizeLinear_/features/denseblock3/denselayer14/conv2/Conv_output_0_1" -> "675 /features/denseblock3/denselayer23/Concat" [label="[1, 32, 14, 14]", style=solid]; +"546 DequantizeLinear_/features/denseblock3/denselayer14/conv2/Conv_output_0_1" -> "691 /features/denseblock3/denselayer24/Concat" [label="[1, 32, 14, 14]", style=solid]; +"546 DequantizeLinear_/features/denseblock3/denselayer14/conv2/Conv_output_0_1" -> "707 /features/denseblock3/Concat" [label="[1, 32, 14, 14]", style=solid]; +"547 /features/denseblock3/denselayer15/Concat" -> "548 /features/denseblock3/denselayer15/norm1/BatchNormalization" [label="[1, 704, 14, 14]", style=solid]; +"548 /features/denseblock3/denselayer15/norm1/BatchNormalization" -> "549 /features/denseblock3/denselayer15/relu1/Relu" [label="[1, 704, 14, 14]", style=solid]; +"549 /features/denseblock3/denselayer15/relu1/Relu" -> "550 QuantizeLinear_/features/denseblock3/denselayer15/relu1/Relu_output_0_1" [label="[1, 704, 14, 14]", style=solid]; +"550 QuantizeLinear_/features/denseblock3/denselayer15/relu1/Relu_output_0_1" -> "551 DequantizeLinear_/features/denseblock3/denselayer15/relu1/Relu_output_0_1" [label="[1, 704, 14, 14]", style=dashed]; +"551 DequantizeLinear_/features/denseblock3/denselayer15/relu1/Relu_output_0_1" -> "554 /features/denseblock3/denselayer15/conv1/Conv" [label="[1, 704, 14, 14]", style=solid]; +"552 QuantizeLinear_onnx^^Conv_1259_1" -> "553 DequantizeLinear_onnx^^Conv_1259_1" [label="[128, 704, 1, 1]", style=dashed]; +"553 DequantizeLinear_onnx^^Conv_1259_1" -> "554 /features/denseblock3/denselayer15/conv1/Conv" [label="[128, 704, 1, 1]", style=solid]; +"554 /features/denseblock3/denselayer15/conv1/Conv" -> "555 /features/denseblock3/denselayer15/relu2/Relu" [label="[1, 128, 14, 14]", style=solid]; +"555 /features/denseblock3/denselayer15/relu2/Relu" -> "556 QuantizeLinear_/features/denseblock3/denselayer15/relu2/Relu_output_0_1" [label="[1, 128, 14, 14]", style=solid]; +"556 QuantizeLinear_/features/denseblock3/denselayer15/relu2/Relu_output_0_1" -> "557 DequantizeLinear_/features/denseblock3/denselayer15/relu2/Relu_output_0_1" [label="[1, 128, 14, 14]", style=dashed]; +"557 DequantizeLinear_/features/denseblock3/denselayer15/relu2/Relu_output_0_1" -> "560 /features/denseblock3/denselayer15/conv2/Conv" [label="[1, 128, 14, 14]", style=solid]; +"558 QuantizeLinear_features.denseblock3.denselayer15.conv2.weight_1" -> "559 DequantizeLinear_features.denseblock3.denselayer15.conv2.weight_1" [label="[32, 128, 3, 3]", style=dashed]; +"559 DequantizeLinear_features.denseblock3.denselayer15.conv2.weight_1" -> "560 /features/denseblock3/denselayer15/conv2/Conv" [label="[32, 128, 3, 3]", style=solid]; +"560 /features/denseblock3/denselayer15/conv2/Conv" -> "561 QuantizeLinear_/features/denseblock3/denselayer15/conv2/Conv_output_0_1" [label="[1, 32, 14, 14]", style=solid]; +"561 QuantizeLinear_/features/denseblock3/denselayer15/conv2/Conv_output_0_1" -> "562 DequantizeLinear_/features/denseblock3/denselayer15/conv2/Conv_output_0_1" [label="[1, 32, 14, 14]", style=dashed]; +"562 DequantizeLinear_/features/denseblock3/denselayer15/conv2/Conv_output_0_1" -> "563 /features/denseblock3/denselayer16/Concat" [label="[1, 32, 14, 14]", style=solid]; +"562 DequantizeLinear_/features/denseblock3/denselayer15/conv2/Conv_output_0_1" -> "579 /features/denseblock3/denselayer17/Concat" [label="[1, 32, 14, 14]", style=solid]; +"562 DequantizeLinear_/features/denseblock3/denselayer15/conv2/Conv_output_0_1" -> "595 /features/denseblock3/denselayer18/Concat" [label="[1, 32, 14, 14]", style=solid]; +"562 DequantizeLinear_/features/denseblock3/denselayer15/conv2/Conv_output_0_1" -> "611 /features/denseblock3/denselayer19/Concat" [label="[1, 32, 14, 14]", style=solid]; +"562 DequantizeLinear_/features/denseblock3/denselayer15/conv2/Conv_output_0_1" -> "627 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/features/denseblock3/denselayer16/norm1/BatchNormalization" [label="[1, 736, 14, 14]", style=solid]; +"564 /features/denseblock3/denselayer16/norm1/BatchNormalization" -> "565 /features/denseblock3/denselayer16/relu1/Relu" [label="[1, 736, 14, 14]", style=solid]; +"565 /features/denseblock3/denselayer16/relu1/Relu" -> "566 QuantizeLinear_/features/denseblock3/denselayer16/relu1/Relu_output_0_1" [label="[1, 736, 14, 14]", style=solid]; +"566 QuantizeLinear_/features/denseblock3/denselayer16/relu1/Relu_output_0_1" -> "567 DequantizeLinear_/features/denseblock3/denselayer16/relu1/Relu_output_0_1" [label="[1, 736, 14, 14]", style=dashed]; +"567 DequantizeLinear_/features/denseblock3/denselayer16/relu1/Relu_output_0_1" -> "570 /features/denseblock3/denselayer16/conv1/Conv" [label="[1, 736, 14, 14]", style=solid]; +"568 QuantizeLinear_onnx^^Conv_1262_1" -> "569 DequantizeLinear_onnx^^Conv_1262_1" [label="[128, 736, 1, 1]", style=dashed]; +"569 DequantizeLinear_onnx^^Conv_1262_1" -> "570 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style=solid]; +"606 QuantizeLinear_features.denseblock3.denselayer18.conv2.weight_1" -> "607 DequantizeLinear_features.denseblock3.denselayer18.conv2.weight_1" [label="[32, 128, 3, 3]", style=dashed]; +"607 DequantizeLinear_features.denseblock3.denselayer18.conv2.weight_1" -> "608 /features/denseblock3/denselayer18/conv2/Conv" [label="[32, 128, 3, 3]", style=solid]; +"608 /features/denseblock3/denselayer18/conv2/Conv" -> "609 QuantizeLinear_/features/denseblock3/denselayer18/conv2/Conv_output_0_1" [label="[1, 32, 14, 14]", style=solid]; +"609 QuantizeLinear_/features/denseblock3/denselayer18/conv2/Conv_output_0_1" -> "610 DequantizeLinear_/features/denseblock3/denselayer18/conv2/Conv_output_0_1" [label="[1, 32, 14, 14]", style=dashed]; +"610 DequantizeLinear_/features/denseblock3/denselayer18/conv2/Conv_output_0_1" -> "611 /features/denseblock3/denselayer19/Concat" [label="[1, 32, 14, 14]", style=solid]; +"610 DequantizeLinear_/features/denseblock3/denselayer18/conv2/Conv_output_0_1" -> "627 /features/denseblock3/denselayer20/Concat" [label="[1, 32, 14, 14]", style=solid]; +"610 DequantizeLinear_/features/denseblock3/denselayer18/conv2/Conv_output_0_1" -> "643 /features/denseblock3/denselayer21/Concat" [label="[1, 32, 14, 14]", style=solid]; +"610 DequantizeLinear_/features/denseblock3/denselayer18/conv2/Conv_output_0_1" -> "659 /features/denseblock3/denselayer22/Concat" [label="[1, 32, 14, 14]", style=solid]; +"610 DequantizeLinear_/features/denseblock3/denselayer18/conv2/Conv_output_0_1" -> "675 /features/denseblock3/denselayer23/Concat" [label="[1, 32, 14, 14]", style=solid]; +"610 DequantizeLinear_/features/denseblock3/denselayer18/conv2/Conv_output_0_1" -> "691 /features/denseblock3/denselayer24/Concat" [label="[1, 32, 14, 14]", style=solid]; +"610 DequantizeLinear_/features/denseblock3/denselayer18/conv2/Conv_output_0_1" -> "707 /features/denseblock3/Concat" [label="[1, 32, 14, 14]", style=solid]; +"611 /features/denseblock3/denselayer19/Concat" -> "612 /features/denseblock3/denselayer19/norm1/BatchNormalization" [label="[1, 832, 14, 14]", style=solid]; +"612 /features/denseblock3/denselayer19/norm1/BatchNormalization" -> "613 /features/denseblock3/denselayer19/relu1/Relu" [label="[1, 832, 14, 14]", style=solid]; +"613 /features/denseblock3/denselayer19/relu1/Relu" -> "614 QuantizeLinear_/features/denseblock3/denselayer19/relu1/Relu_output_0_1" [label="[1, 832, 14, 14]", style=solid]; +"614 QuantizeLinear_/features/denseblock3/denselayer19/relu1/Relu_output_0_1" -> "615 DequantizeLinear_/features/denseblock3/denselayer19/relu1/Relu_output_0_1" [label="[1, 832, 14, 14]", style=dashed]; +"615 DequantizeLinear_/features/denseblock3/denselayer19/relu1/Relu_output_0_1" -> "618 /features/denseblock3/denselayer19/conv1/Conv" [label="[1, 832, 14, 14]", style=solid]; +"616 QuantizeLinear_onnx^^Conv_1271_1" -> "617 DequantizeLinear_onnx^^Conv_1271_1" [label="[128, 832, 1, 1]", style=dashed]; +"617 DequantizeLinear_onnx^^Conv_1271_1" -> "618 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QuantizeLinear_/features/denseblock3/denselayer24/relu1/Relu_output_0_1" -> "695 DequantizeLinear_/features/denseblock3/denselayer24/relu1/Relu_output_0_1" [label="[1, 992, 14, 14]", style=dashed]; +"695 DequantizeLinear_/features/denseblock3/denselayer24/relu1/Relu_output_0_1" -> "698 /features/denseblock3/denselayer24/conv1/Conv" [label="[1, 992, 14, 14]", style=solid]; +"696 QuantizeLinear_onnx^^Conv_1286_1" -> "697 DequantizeLinear_onnx^^Conv_1286_1" [label="[128, 992, 1, 1]", style=dashed]; +"697 DequantizeLinear_onnx^^Conv_1286_1" -> "698 /features/denseblock3/denselayer24/conv1/Conv" [label="[128, 992, 1, 1]", style=solid]; +"698 /features/denseblock3/denselayer24/conv1/Conv" -> "699 /features/denseblock3/denselayer24/relu2/Relu" [label="[1, 128, 14, 14]", style=solid]; +"699 /features/denseblock3/denselayer24/relu2/Relu" -> "700 QuantizeLinear_/features/denseblock3/denselayer24/relu2/Relu_output_0_1" [label="[1, 128, 14, 14]", style=solid]; +"700 QuantizeLinear_/features/denseblock3/denselayer24/relu2/Relu_output_0_1" -> "701 DequantizeLinear_/features/denseblock3/denselayer24/relu2/Relu_output_0_1" [label="[1, 128, 14, 14]", style=dashed]; +"701 DequantizeLinear_/features/denseblock3/denselayer24/relu2/Relu_output_0_1" -> "704 /features/denseblock3/denselayer24/conv2/Conv" [label="[1, 128, 14, 14]", style=solid]; +"702 QuantizeLinear_features.denseblock3.denselayer24.conv2.weight_1" -> "703 DequantizeLinear_features.denseblock3.denselayer24.conv2.weight_1" [label="[32, 128, 3, 3]", style=dashed]; +"703 DequantizeLinear_features.denseblock3.denselayer24.conv2.weight_1" -> "704 /features/denseblock3/denselayer24/conv2/Conv" [label="[32, 128, 3, 3]", style=solid]; +"704 /features/denseblock3/denselayer24/conv2/Conv" -> "705 QuantizeLinear_/features/denseblock3/denselayer24/conv2/Conv_output_0_1" [label="[1, 32, 14, 14]", style=solid]; +"705 QuantizeLinear_/features/denseblock3/denselayer24/conv2/Conv_output_0_1" -> "706 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QuantizeLinear_/features/transition3/pool/AveragePool_output_0_1" -> "719 DequantizeLinear_/features/transition3/pool/AveragePool_output_0_1" [label="[1, 512, 7, 7]", style=dashed]; +"719 DequantizeLinear_/features/transition3/pool/AveragePool_output_0_1" -> "720 /features/denseblock4/denselayer1/Concat" [label="[1, 512, 7, 7]", style=solid]; +"719 DequantizeLinear_/features/transition3/pool/AveragePool_output_0_1" -> "736 /features/denseblock4/denselayer2/Concat" [label="[1, 512, 7, 7]", style=solid]; +"719 DequantizeLinear_/features/transition3/pool/AveragePool_output_0_1" -> "752 /features/denseblock4/denselayer3/Concat" [label="[1, 512, 7, 7]", style=solid]; +"719 DequantizeLinear_/features/transition3/pool/AveragePool_output_0_1" -> "768 /features/denseblock4/denselayer4/Concat" [label="[1, 512, 7, 7]", style=solid]; +"719 DequantizeLinear_/features/transition3/pool/AveragePool_output_0_1" -> "784 /features/denseblock4/denselayer5/Concat" [label="[1, 512, 7, 7]", style=solid]; 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DequantizeLinear_/features/transition3/pool/AveragePool_output_0_1" -> "896 /features/denseblock4/denselayer12/Concat" [label="[1, 512, 7, 7]", style=solid]; +"719 DequantizeLinear_/features/transition3/pool/AveragePool_output_0_1" -> "912 /features/denseblock4/denselayer13/Concat" [label="[1, 512, 7, 7]", style=solid]; +"719 DequantizeLinear_/features/transition3/pool/AveragePool_output_0_1" -> "928 /features/denseblock4/denselayer14/Concat" [label="[1, 512, 7, 7]", style=solid]; +"719 DequantizeLinear_/features/transition3/pool/AveragePool_output_0_1" -> "944 /features/denseblock4/denselayer15/Concat" [label="[1, 512, 7, 7]", style=solid]; +"719 DequantizeLinear_/features/transition3/pool/AveragePool_output_0_1" -> "960 /features/denseblock4/denselayer16/Concat" [label="[1, 512, 7, 7]", style=solid]; +"719 DequantizeLinear_/features/transition3/pool/AveragePool_output_0_1" -> "976 /features/denseblock4/Concat" [label="[1, 512, 7, 7]", style=solid]; +"720 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DequantizeLinear_onnx^^Conv_1289_1" -> "727 /features/denseblock4/denselayer1/conv1/Conv" [label="[128, 512, 1, 1]", style=solid]; +"727 /features/denseblock4/denselayer1/conv1/Conv" -> "728 /features/denseblock4/denselayer1/relu2/Relu" [label="[1, 128, 7, 7]", style=solid]; +"728 /features/denseblock4/denselayer1/relu2/Relu" -> "729 QuantizeLinear_/features/denseblock4/denselayer1/relu2/Relu_output_0_1" [label="[1, 128, 7, 7]", style=solid]; +"729 QuantizeLinear_/features/denseblock4/denselayer1/relu2/Relu_output_0_1" -> "730 DequantizeLinear_/features/denseblock4/denselayer1/relu2/Relu_output_0_1" [label="[1, 128, 7, 7]", style=dashed]; +"730 DequantizeLinear_/features/denseblock4/denselayer1/relu2/Relu_output_0_1" -> "733 /features/denseblock4/denselayer1/conv2/Conv" [label="[1, 128, 7, 7]", style=solid]; +"731 QuantizeLinear_features.denseblock4.denselayer1.conv2.weight_1" -> "732 DequantizeLinear_features.denseblock4.denselayer1.conv2.weight_1" [label="[32, 128, 3, 3]", 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DequantizeLinear_/features/denseblock4/denselayer7/conv2/Conv_output_0_1" -> "912 /features/denseblock4/denselayer13/Concat" [label="[1, 32, 7, 7]", style=solid]; +"831 DequantizeLinear_/features/denseblock4/denselayer7/conv2/Conv_output_0_1" -> "928 /features/denseblock4/denselayer14/Concat" [label="[1, 32, 7, 7]", style=solid]; +"831 DequantizeLinear_/features/denseblock4/denselayer7/conv2/Conv_output_0_1" -> "944 /features/denseblock4/denselayer15/Concat" [label="[1, 32, 7, 7]", style=solid]; +"831 DequantizeLinear_/features/denseblock4/denselayer7/conv2/Conv_output_0_1" -> "960 /features/denseblock4/denselayer16/Concat" [label="[1, 32, 7, 7]", style=solid]; +"831 DequantizeLinear_/features/denseblock4/denselayer7/conv2/Conv_output_0_1" -> "976 /features/denseblock4/Concat" [label="[1, 32, 7, 7]", style=solid]; +"832 /features/denseblock4/denselayer8/Concat" -> "833 /features/denseblock4/denselayer8/norm1/BatchNormalization" [label="[1, 736, 7, 7]", style=solid]; +"833 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[label="[32, 128, 3, 3]", style=solid]; +"845 /features/denseblock4/denselayer8/conv2/Conv" -> "846 QuantizeLinear_/features/denseblock4/denselayer8/conv2/Conv_output_0_1" [label="[1, 32, 7, 7]", style=solid]; +"846 QuantizeLinear_/features/denseblock4/denselayer8/conv2/Conv_output_0_1" -> "847 DequantizeLinear_/features/denseblock4/denselayer8/conv2/Conv_output_0_1" [label="[1, 32, 7, 7]", style=dashed]; +"847 DequantizeLinear_/features/denseblock4/denselayer8/conv2/Conv_output_0_1" -> "848 /features/denseblock4/denselayer9/Concat" [label="[1, 32, 7, 7]", style=solid]; +"847 DequantizeLinear_/features/denseblock4/denselayer8/conv2/Conv_output_0_1" -> "864 /features/denseblock4/denselayer10/Concat" [label="[1, 32, 7, 7]", style=solid]; +"847 DequantizeLinear_/features/denseblock4/denselayer8/conv2/Conv_output_0_1" -> "880 /features/denseblock4/denselayer11/Concat" [label="[1, 32, 7, 7]", style=solid]; +"847 DequantizeLinear_/features/denseblock4/denselayer8/conv2/Conv_output_0_1" -> 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QuantizeLinear_/features/denseblock4/denselayer11/conv2/Conv_output_0_1" [label="[1, 32, 7, 7]", style=solid]; +"894 QuantizeLinear_/features/denseblock4/denselayer11/conv2/Conv_output_0_1" -> "895 DequantizeLinear_/features/denseblock4/denselayer11/conv2/Conv_output_0_1" [label="[1, 32, 7, 7]", style=dashed]; +"895 DequantizeLinear_/features/denseblock4/denselayer11/conv2/Conv_output_0_1" -> "896 /features/denseblock4/denselayer12/Concat" [label="[1, 32, 7, 7]", style=solid]; +"895 DequantizeLinear_/features/denseblock4/denselayer11/conv2/Conv_output_0_1" -> "912 /features/denseblock4/denselayer13/Concat" [label="[1, 32, 7, 7]", style=solid]; +"895 DequantizeLinear_/features/denseblock4/denselayer11/conv2/Conv_output_0_1" -> "928 /features/denseblock4/denselayer14/Concat" [label="[1, 32, 7, 7]", style=solid]; +"895 DequantizeLinear_/features/denseblock4/denselayer11/conv2/Conv_output_0_1" -> "944 /features/denseblock4/denselayer15/Concat" [label="[1, 32, 7, 7]", style=solid]; +"895 DequantizeLinear_/features/denseblock4/denselayer11/conv2/Conv_output_0_1" -> "960 /features/denseblock4/denselayer16/Concat" [label="[1, 32, 7, 7]", style=solid]; +"895 DequantizeLinear_/features/denseblock4/denselayer11/conv2/Conv_output_0_1" -> "976 /features/denseblock4/Concat" [label="[1, 32, 7, 7]", style=solid]; +"896 /features/denseblock4/denselayer12/Concat" -> "897 /features/denseblock4/denselayer12/norm1/BatchNormalization" [label="[1, 864, 7, 7]", style=solid]; +"897 /features/denseblock4/denselayer12/norm1/BatchNormalization" -> "898 /features/denseblock4/denselayer12/relu1/Relu" [label="[1, 864, 7, 7]", style=solid]; +"898 /features/denseblock4/denselayer12/relu1/Relu" -> "899 QuantizeLinear_/features/denseblock4/denselayer12/relu1/Relu_output_0_1" [label="[1, 864, 7, 7]", style=solid]; +"899 QuantizeLinear_/features/denseblock4/denselayer12/relu1/Relu_output_0_1" -> "900 DequantizeLinear_/features/denseblock4/denselayer12/relu1/Relu_output_0_1" [label="[1, 864, 7, 7]", style=dashed]; +"900 DequantizeLinear_/features/denseblock4/denselayer12/relu1/Relu_output_0_1" -> "903 /features/denseblock4/denselayer12/conv1/Conv" [label="[1, 864, 7, 7]", style=solid]; +"901 QuantizeLinear_onnx^^Conv_1322_1" -> "902 DequantizeLinear_onnx^^Conv_1322_1" [label="[128, 864, 1, 1]", style=dashed]; +"902 DequantizeLinear_onnx^^Conv_1322_1" -> "903 /features/denseblock4/denselayer12/conv1/Conv" [label="[128, 864, 1, 1]", style=solid]; +"903 /features/denseblock4/denselayer12/conv1/Conv" -> "904 /features/denseblock4/denselayer12/relu2/Relu" [label="[1, 128, 7, 7]", style=solid]; +"904 /features/denseblock4/denselayer12/relu2/Relu" -> "905 QuantizeLinear_/features/denseblock4/denselayer12/relu2/Relu_output_0_1" [label="[1, 128, 7, 7]", style=solid]; +"905 QuantizeLinear_/features/denseblock4/denselayer12/relu2/Relu_output_0_1" -> "906 DequantizeLinear_/features/denseblock4/denselayer12/relu2/Relu_output_0_1" [label="[1, 128, 7, 7]", style=dashed]; +"906 DequantizeLinear_/features/denseblock4/denselayer12/relu2/Relu_output_0_1" -> "909 /features/denseblock4/denselayer12/conv2/Conv" [label="[1, 128, 7, 7]", style=solid]; +"907 QuantizeLinear_features.denseblock4.denselayer12.conv2.weight_1" -> "908 DequantizeLinear_features.denseblock4.denselayer12.conv2.weight_1" [label="[32, 128, 3, 3]", style=dashed]; +"908 DequantizeLinear_features.denseblock4.denselayer12.conv2.weight_1" -> "909 /features/denseblock4/denselayer12/conv2/Conv" [label="[32, 128, 3, 3]", style=solid]; +"909 /features/denseblock4/denselayer12/conv2/Conv" -> "910 QuantizeLinear_/features/denseblock4/denselayer12/conv2/Conv_output_0_1" [label="[1, 32, 7, 7]", style=solid]; +"910 QuantizeLinear_/features/denseblock4/denselayer12/conv2/Conv_output_0_1" -> "911 DequantizeLinear_/features/denseblock4/denselayer12/conv2/Conv_output_0_1" [label="[1, 32, 7, 7]", style=dashed]; +"911 DequantizeLinear_/features/denseblock4/denselayer12/conv2/Conv_output_0_1" -> "912 /features/denseblock4/denselayer13/Concat" [label="[1, 32, 7, 7]", style=solid]; +"911 DequantizeLinear_/features/denseblock4/denselayer12/conv2/Conv_output_0_1" -> "928 /features/denseblock4/denselayer14/Concat" [label="[1, 32, 7, 7]", style=solid]; +"911 DequantizeLinear_/features/denseblock4/denselayer12/conv2/Conv_output_0_1" -> "944 /features/denseblock4/denselayer15/Concat" [label="[1, 32, 7, 7]", style=solid]; +"911 DequantizeLinear_/features/denseblock4/denselayer12/conv2/Conv_output_0_1" -> "960 /features/denseblock4/denselayer16/Concat" [label="[1, 32, 7, 7]", style=solid]; +"911 DequantizeLinear_/features/denseblock4/denselayer12/conv2/Conv_output_0_1" -> "976 /features/denseblock4/Concat" [label="[1, 32, 7, 7]", style=solid]; +"912 /features/denseblock4/denselayer13/Concat" -> "913 /features/denseblock4/denselayer13/norm1/BatchNormalization" [label="[1, 896, 7, 7]", style=solid]; +"913 /features/denseblock4/denselayer13/norm1/BatchNormalization" -> "914 /features/denseblock4/denselayer13/relu1/Relu" [label="[1, 896, 7, 7]", style=solid]; +"914 /features/denseblock4/denselayer13/relu1/Relu" -> "915 QuantizeLinear_/features/denseblock4/denselayer13/relu1/Relu_output_0_1" [label="[1, 896, 7, 7]", style=solid]; +"915 QuantizeLinear_/features/denseblock4/denselayer13/relu1/Relu_output_0_1" -> "916 DequantizeLinear_/features/denseblock4/denselayer13/relu1/Relu_output_0_1" [label="[1, 896, 7, 7]", style=dashed]; +"916 DequantizeLinear_/features/denseblock4/denselayer13/relu1/Relu_output_0_1" -> "919 /features/denseblock4/denselayer13/conv1/Conv" [label="[1, 896, 7, 7]", style=solid]; +"917 QuantizeLinear_onnx^^Conv_1325_1" -> "918 DequantizeLinear_onnx^^Conv_1325_1" [label="[128, 896, 1, 1]", style=dashed]; +"918 DequantizeLinear_onnx^^Conv_1325_1" -> "919 /features/denseblock4/denselayer13/conv1/Conv" [label="[128, 896, 1, 1]", style=solid]; +"919 /features/denseblock4/denselayer13/conv1/Conv" -> "920 /features/denseblock4/denselayer13/relu2/Relu" [label="[1, 128, 7, 7]", style=solid]; +"920 /features/denseblock4/denselayer13/relu2/Relu" -> "921 QuantizeLinear_/features/denseblock4/denselayer13/relu2/Relu_output_0_1" [label="[1, 128, 7, 7]", style=solid]; +"921 QuantizeLinear_/features/denseblock4/denselayer13/relu2/Relu_output_0_1" -> "922 DequantizeLinear_/features/denseblock4/denselayer13/relu2/Relu_output_0_1" [label="[1, 128, 7, 7]", style=dashed]; +"922 DequantizeLinear_/features/denseblock4/denselayer13/relu2/Relu_output_0_1" -> "925 /features/denseblock4/denselayer13/conv2/Conv" [label="[1, 128, 7, 7]", style=solid]; +"923 QuantizeLinear_features.denseblock4.denselayer13.conv2.weight_1" -> "924 DequantizeLinear_features.denseblock4.denselayer13.conv2.weight_1" [label="[32, 128, 3, 3]", style=dashed]; +"924 DequantizeLinear_features.denseblock4.denselayer13.conv2.weight_1" -> "925 /features/denseblock4/denselayer13/conv2/Conv" [label="[32, 128, 3, 3]", style=solid]; +"925 /features/denseblock4/denselayer13/conv2/Conv" -> "926 QuantizeLinear_/features/denseblock4/denselayer13/conv2/Conv_output_0_1" [label="[1, 32, 7, 7]", style=solid]; +"926 QuantizeLinear_/features/denseblock4/denselayer13/conv2/Conv_output_0_1" -> "927 DequantizeLinear_/features/denseblock4/denselayer13/conv2/Conv_output_0_1" [label="[1, 32, 7, 7]", style=dashed]; +"927 DequantizeLinear_/features/denseblock4/denselayer13/conv2/Conv_output_0_1" -> "928 /features/denseblock4/denselayer14/Concat" [label="[1, 32, 7, 7]", style=solid]; +"927 DequantizeLinear_/features/denseblock4/denselayer13/conv2/Conv_output_0_1" -> "944 /features/denseblock4/denselayer15/Concat" [label="[1, 32, 7, 7]", style=solid]; +"927 DequantizeLinear_/features/denseblock4/denselayer13/conv2/Conv_output_0_1" -> "960 /features/denseblock4/denselayer16/Concat" [label="[1, 32, 7, 7]", style=solid]; +"927 DequantizeLinear_/features/denseblock4/denselayer13/conv2/Conv_output_0_1" -> "976 /features/denseblock4/Concat" [label="[1, 32, 7, 7]", style=solid]; +"928 /features/denseblock4/denselayer14/Concat" -> "929 /features/denseblock4/denselayer14/norm1/BatchNormalization" [label="[1, 928, 7, 7]", style=solid]; +"929 /features/denseblock4/denselayer14/norm1/BatchNormalization" -> "930 /features/denseblock4/denselayer14/relu1/Relu" [label="[1, 928, 7, 7]", style=solid]; +"930 /features/denseblock4/denselayer14/relu1/Relu" -> "931 QuantizeLinear_/features/denseblock4/denselayer14/relu1/Relu_output_0_1" [label="[1, 928, 7, 7]", style=solid]; +"931 QuantizeLinear_/features/denseblock4/denselayer14/relu1/Relu_output_0_1" -> "932 DequantizeLinear_/features/denseblock4/denselayer14/relu1/Relu_output_0_1" [label="[1, 928, 7, 7]", style=dashed]; +"932 DequantizeLinear_/features/denseblock4/denselayer14/relu1/Relu_output_0_1" -> "935 /features/denseblock4/denselayer14/conv1/Conv" [label="[1, 928, 7, 7]", style=solid]; +"933 QuantizeLinear_onnx^^Conv_1328_1" -> "934 DequantizeLinear_onnx^^Conv_1328_1" [label="[128, 928, 1, 1]", style=dashed]; +"934 DequantizeLinear_onnx^^Conv_1328_1" -> "935 /features/denseblock4/denselayer14/conv1/Conv" [label="[128, 928, 1, 1]", style=solid]; +"935 /features/denseblock4/denselayer14/conv1/Conv" -> "936 /features/denseblock4/denselayer14/relu2/Relu" [label="[1, 128, 7, 7]", style=solid]; +"936 /features/denseblock4/denselayer14/relu2/Relu" -> "937 QuantizeLinear_/features/denseblock4/denselayer14/relu2/Relu_output_0_1" [label="[1, 128, 7, 7]", style=solid]; +"937 QuantizeLinear_/features/denseblock4/denselayer14/relu2/Relu_output_0_1" -> "938 DequantizeLinear_/features/denseblock4/denselayer14/relu2/Relu_output_0_1" [label="[1, 128, 7, 7]", style=dashed]; +"938 DequantizeLinear_/features/denseblock4/denselayer14/relu2/Relu_output_0_1" -> "941 /features/denseblock4/denselayer14/conv2/Conv" [label="[1, 128, 7, 7]", style=solid]; +"939 QuantizeLinear_features.denseblock4.denselayer14.conv2.weight_1" -> "940 DequantizeLinear_features.denseblock4.denselayer14.conv2.weight_1" [label="[32, 128, 3, 3]", style=dashed]; +"940 DequantizeLinear_features.denseblock4.denselayer14.conv2.weight_1" -> "941 /features/denseblock4/denselayer14/conv2/Conv" [label="[32, 128, 3, 3]", style=solid]; +"941 /features/denseblock4/denselayer14/conv2/Conv" -> "942 QuantizeLinear_/features/denseblock4/denselayer14/conv2/Conv_output_0_1" [label="[1, 32, 7, 7]", style=solid]; +"942 QuantizeLinear_/features/denseblock4/denselayer14/conv2/Conv_output_0_1" -> "943 DequantizeLinear_/features/denseblock4/denselayer14/conv2/Conv_output_0_1" [label="[1, 32, 7, 7]", style=dashed]; +"943 DequantizeLinear_/features/denseblock4/denselayer14/conv2/Conv_output_0_1" -> "944 /features/denseblock4/denselayer15/Concat" [label="[1, 32, 7, 7]", style=solid]; +"943 DequantizeLinear_/features/denseblock4/denselayer14/conv2/Conv_output_0_1" -> "960 /features/denseblock4/denselayer16/Concat" [label="[1, 32, 7, 7]", style=solid]; +"943 DequantizeLinear_/features/denseblock4/denselayer14/conv2/Conv_output_0_1" -> "976 /features/denseblock4/Concat" [label="[1, 32, 7, 7]", style=solid]; +"944 /features/denseblock4/denselayer15/Concat" -> "945 /features/denseblock4/denselayer15/norm1/BatchNormalization" [label="[1, 960, 7, 7]", style=solid]; +"945 /features/denseblock4/denselayer15/norm1/BatchNormalization" -> "946 /features/denseblock4/denselayer15/relu1/Relu" [label="[1, 960, 7, 7]", style=solid]; +"946 /features/denseblock4/denselayer15/relu1/Relu" -> "947 QuantizeLinear_/features/denseblock4/denselayer15/relu1/Relu_output_0_1" [label="[1, 960, 7, 7]", style=solid]; +"947 QuantizeLinear_/features/denseblock4/denselayer15/relu1/Relu_output_0_1" -> "948 DequantizeLinear_/features/denseblock4/denselayer15/relu1/Relu_output_0_1" [label="[1, 960, 7, 7]", style=dashed]; +"948 DequantizeLinear_/features/denseblock4/denselayer15/relu1/Relu_output_0_1" -> "951 /features/denseblock4/denselayer15/conv1/Conv" [label="[1, 960, 7, 7]", style=solid]; +"949 QuantizeLinear_onnx^^Conv_1331_1" -> "950 DequantizeLinear_onnx^^Conv_1331_1" [label="[128, 960, 1, 1]", style=dashed]; +"950 DequantizeLinear_onnx^^Conv_1331_1" -> "951 /features/denseblock4/denselayer15/conv1/Conv" [label="[128, 960, 1, 1]", style=solid]; +"951 /features/denseblock4/denselayer15/conv1/Conv" -> "952 /features/denseblock4/denselayer15/relu2/Relu" [label="[1, 128, 7, 7]", style=solid]; +"952 /features/denseblock4/denselayer15/relu2/Relu" -> "953 QuantizeLinear_/features/denseblock4/denselayer15/relu2/Relu_output_0_1" [label="[1, 128, 7, 7]", style=solid]; +"953 QuantizeLinear_/features/denseblock4/denselayer15/relu2/Relu_output_0_1" -> "954 DequantizeLinear_/features/denseblock4/denselayer15/relu2/Relu_output_0_1" [label="[1, 128, 7, 7]", style=dashed]; +"954 DequantizeLinear_/features/denseblock4/denselayer15/relu2/Relu_output_0_1" -> "957 /features/denseblock4/denselayer15/conv2/Conv" [label="[1, 128, 7, 7]", style=solid]; +"955 QuantizeLinear_features.denseblock4.denselayer15.conv2.weight_1" -> "956 DequantizeLinear_features.denseblock4.denselayer15.conv2.weight_1" [label="[32, 128, 3, 3]", style=dashed]; +"956 DequantizeLinear_features.denseblock4.denselayer15.conv2.weight_1" -> "957 /features/denseblock4/denselayer15/conv2/Conv" [label="[32, 128, 3, 3]", style=solid]; +"957 /features/denseblock4/denselayer15/conv2/Conv" -> "958 QuantizeLinear_/features/denseblock4/denselayer15/conv2/Conv_output_0_1" [label="[1, 32, 7, 7]", style=solid]; +"958 QuantizeLinear_/features/denseblock4/denselayer15/conv2/Conv_output_0_1" -> "959 DequantizeLinear_/features/denseblock4/denselayer15/conv2/Conv_output_0_1" [label="[1, 32, 7, 7]", style=dashed]; +"959 DequantizeLinear_/features/denseblock4/denselayer15/conv2/Conv_output_0_1" -> "960 /features/denseblock4/denselayer16/Concat" [label="[1, 32, 7, 7]", style=solid]; +"959 DequantizeLinear_/features/denseblock4/denselayer15/conv2/Conv_output_0_1" -> "976 /features/denseblock4/Concat" [label="[1, 32, 7, 7]", style=solid]; +"960 /features/denseblock4/denselayer16/Concat" -> "961 /features/denseblock4/denselayer16/norm1/BatchNormalization" [label="[1, 992, 7, 7]", style=solid]; +"961 /features/denseblock4/denselayer16/norm1/BatchNormalization" -> "962 /features/denseblock4/denselayer16/relu1/Relu" [label="[1, 992, 7, 7]", style=solid]; +"962 /features/denseblock4/denselayer16/relu1/Relu" -> "963 QuantizeLinear_/features/denseblock4/denselayer16/relu1/Relu_output_0_1" [label="[1, 992, 7, 7]", style=solid]; +"963 QuantizeLinear_/features/denseblock4/denselayer16/relu1/Relu_output_0_1" -> "964 DequantizeLinear_/features/denseblock4/denselayer16/relu1/Relu_output_0_1" [label="[1, 992, 7, 7]", style=dashed]; +"964 DequantizeLinear_/features/denseblock4/denselayer16/relu1/Relu_output_0_1" -> "967 /features/denseblock4/denselayer16/conv1/Conv" [label="[1, 992, 7, 7]", style=solid]; +"965 QuantizeLinear_onnx^^Conv_1334_1" -> "966 DequantizeLinear_onnx^^Conv_1334_1" [label="[128, 992, 1, 1]", style=dashed]; +"966 DequantizeLinear_onnx^^Conv_1334_1" -> "967 /features/denseblock4/denselayer16/conv1/Conv" [label="[128, 992, 1, 1]", style=solid]; +"967 /features/denseblock4/denselayer16/conv1/Conv" -> "968 /features/denseblock4/denselayer16/relu2/Relu" [label="[1, 128, 7, 7]", style=solid]; +"968 /features/denseblock4/denselayer16/relu2/Relu" -> "969 QuantizeLinear_/features/denseblock4/denselayer16/relu2/Relu_output_0_1" [label="[1, 128, 7, 7]", style=solid]; +"969 QuantizeLinear_/features/denseblock4/denselayer16/relu2/Relu_output_0_1" -> "970 DequantizeLinear_/features/denseblock4/denselayer16/relu2/Relu_output_0_1" [label="[1, 128, 7, 7]", style=dashed]; +"970 DequantizeLinear_/features/denseblock4/denselayer16/relu2/Relu_output_0_1" -> "973 /features/denseblock4/denselayer16/conv2/Conv" [label="[1, 128, 7, 7]", style=solid]; +"971 QuantizeLinear_features.denseblock4.denselayer16.conv2.weight_1" -> "972 DequantizeLinear_features.denseblock4.denselayer16.conv2.weight_1" [label="[32, 128, 3, 3]", style=dashed]; +"972 DequantizeLinear_features.denseblock4.denselayer16.conv2.weight_1" -> "973 /features/denseblock4/denselayer16/conv2/Conv" [label="[32, 128, 3, 3]", style=solid]; +"973 /features/denseblock4/denselayer16/conv2/Conv" -> "974 QuantizeLinear_/features/denseblock4/denselayer16/conv2/Conv_output_0_1" [label="[1, 32, 7, 7]", style=solid]; +"974 QuantizeLinear_/features/denseblock4/denselayer16/conv2/Conv_output_0_1" -> "975 DequantizeLinear_/features/denseblock4/denselayer16/conv2/Conv_output_0_1" [label="[1, 32, 7, 7]", style=dashed]; +"975 DequantizeLinear_/features/denseblock4/denselayer16/conv2/Conv_output_0_1" -> "976 /features/denseblock4/Concat" [label="[1, 32, 7, 7]", style=solid]; +"976 /features/denseblock4/Concat" -> "977 /features/norm5/BatchNormalization" [label="[1, 1024, 7, 7]", style=solid]; +"977 /features/norm5/BatchNormalization" -> "978 /Relu" [label="[1, 1024, 7, 7]", style=solid]; +"978 /Relu" -> "979 QuantizeLinear_/Relu_output_0_1" [label="[1, 1024, 7, 7]", style=solid]; +"979 QuantizeLinear_/Relu_output_0_1" -> "980 DequantizeLinear_/Relu_output_0_1" [label="[1, 1024, 7, 7]", style=dashed]; +"980 DequantizeLinear_/Relu_output_0_1" -> "981 /GlobalAveragePool" [label="[1, 1024, 7, 7]", style=solid]; +"981 /GlobalAveragePool" -> "982 QuantizeLinear_/GlobalAveragePool_output_0_1" [label="[1, 1024, 1, 1]", style=solid]; +"982 QuantizeLinear_/GlobalAveragePool_output_0_1" -> "983 DequantizeLinear_/GlobalAveragePool_output_0_1" [label="[1, 1024, 1, 1]", style=dashed]; +"983 DequantizeLinear_/GlobalAveragePool_output_0_1" -> "984 /Flatten" [label="[1, 1024, 1, 1]", style=solid]; +"984 /Flatten" -> "987 /classifier/Gemm" [label="[1, 1024]", style=solid]; +"985 QuantizeLinear_classifier.weight_1" -> "986 DequantizeLinear_classifier.weight_1" [label="[1000, 1024]", style=dashed]; +"986 DequantizeLinear_classifier.weight_1" -> "987 /classifier/Gemm" [label="[1000, 1024]", style=solid]; +"987 /classifier/Gemm" -> "989 nncf_model_output_0" [label="[1, 1000]", style=solid]; +"988 nncf_model_input_0" -> "0 QuantizeLinear_input.1_1" [label="[1, 3, 224, 224]", style=solid]; } diff --git a/tests/onnx/data/reference_graphs/quantization/inception_v3.dot b/tests/onnx/data/reference_graphs/quantization/inception_v3.dot index f2480d42f8b..550f9f5027b 100644 --- a/tests/onnx/data/reference_graphs/quantization/inception_v3.dot +++ b/tests/onnx/data/reference_graphs/quantization/inception_v3.dot @@ -32,639 +32,621 @@ strict digraph { "30 QuantizeLinear_/Add_2_output_0_1" [id=30, type=QuantizeLinear]; "31 DequantizeLinear_/Add_2_output_0_1" [id=31, type=DequantizeLinear]; "32 /Concat" [id=32, type=Concat]; -"33 QuantizeLinear_onnx^^Conv_934_1" [id=33, label="33 QuantizeLinear_onnx::Conv_934_1", type=QuantizeLinear]; -"34 DequantizeLinear_onnx^^Conv_934_1" [id=34, label="34 DequantizeLinear_onnx::Conv_934_1", type=DequantizeLinear]; +"33 QuantizeLinear_onnx^^Conv_916_1" [id=33, label="33 QuantizeLinear_onnx::Conv_916_1", type=QuantizeLinear]; +"34 DequantizeLinear_onnx^^Conv_916_1" [id=34, label="34 DequantizeLinear_onnx::Conv_916_1", type=DequantizeLinear]; "35 /Conv2d_1a_3x3/conv/Conv" [id=35, type=Conv]; "36 /Conv2d_1a_3x3/Relu" [id=36, type=Relu]; "37 QuantizeLinear_/Conv2d_1a_3x3/Relu_output_0_1" [id=37, type=QuantizeLinear]; "38 DequantizeLinear_/Conv2d_1a_3x3/Relu_output_0_1" [id=38, type=DequantizeLinear]; -"39 QuantizeLinear_onnx^^Conv_937_1" [id=39, label="39 QuantizeLinear_onnx::Conv_937_1", type=QuantizeLinear]; -"40 DequantizeLinear_onnx^^Conv_937_1" [id=40, label="40 DequantizeLinear_onnx::Conv_937_1", type=DequantizeLinear]; +"39 QuantizeLinear_onnx^^Conv_919_1" [id=39, label="39 QuantizeLinear_onnx::Conv_919_1", type=QuantizeLinear]; +"40 DequantizeLinear_onnx^^Conv_919_1" [id=40, label="40 DequantizeLinear_onnx::Conv_919_1", type=DequantizeLinear]; "41 /Conv2d_2a_3x3/conv/Conv" [id=41, type=Conv]; "42 /Conv2d_2a_3x3/Relu" [id=42, type=Relu]; "43 QuantizeLinear_/Conv2d_2a_3x3/Relu_output_0_1" [id=43, type=QuantizeLinear]; "44 DequantizeLinear_/Conv2d_2a_3x3/Relu_output_0_1" [id=44, type=DequantizeLinear]; -"45 QuantizeLinear_onnx^^Conv_940_1" [id=45, label="45 QuantizeLinear_onnx::Conv_940_1", type=QuantizeLinear]; -"46 DequantizeLinear_onnx^^Conv_940_1" [id=46, label="46 DequantizeLinear_onnx::Conv_940_1", type=DequantizeLinear]; +"45 QuantizeLinear_onnx^^Conv_922_1" [id=45, label="45 QuantizeLinear_onnx::Conv_922_1", type=QuantizeLinear]; +"46 DequantizeLinear_onnx^^Conv_922_1" [id=46, label="46 DequantizeLinear_onnx::Conv_922_1", type=DequantizeLinear]; "47 /Conv2d_2b_3x3/conv/Conv" [id=47, type=Conv]; "48 /Conv2d_2b_3x3/Relu" [id=48, type=Relu]; "49 QuantizeLinear_/Conv2d_2b_3x3/Relu_output_0_1" [id=49, type=QuantizeLinear]; "50 DequantizeLinear_/Conv2d_2b_3x3/Relu_output_0_1" [id=50, type=DequantizeLinear]; "51 /maxpool1/MaxPool" [id=51, type=MaxPool]; -"52 QuantizeLinear_onnx^^Conv_943_1" [id=52, label="52 QuantizeLinear_onnx::Conv_943_1", type=QuantizeLinear]; -"53 DequantizeLinear_onnx^^Conv_943_1" [id=53, label="53 DequantizeLinear_onnx::Conv_943_1", type=DequantizeLinear]; +"52 QuantizeLinear_onnx^^Conv_925_1" [id=52, label="52 QuantizeLinear_onnx::Conv_925_1", type=QuantizeLinear]; +"53 DequantizeLinear_onnx^^Conv_925_1" [id=53, label="53 DequantizeLinear_onnx::Conv_925_1", type=DequantizeLinear]; "54 /Conv2d_3b_1x1/conv/Conv" [id=54, type=Conv]; "55 /Conv2d_3b_1x1/Relu" [id=55, type=Relu]; "56 QuantizeLinear_/Conv2d_3b_1x1/Relu_output_0_1" [id=56, type=QuantizeLinear]; "57 DequantizeLinear_/Conv2d_3b_1x1/Relu_output_0_1" [id=57, type=DequantizeLinear]; -"58 QuantizeLinear_onnx^^Conv_946_1" [id=58, label="58 QuantizeLinear_onnx::Conv_946_1", type=QuantizeLinear]; -"59 DequantizeLinear_onnx^^Conv_946_1" [id=59, label="59 DequantizeLinear_onnx::Conv_946_1", type=DequantizeLinear]; +"58 QuantizeLinear_onnx^^Conv_928_1" [id=58, label="58 QuantizeLinear_onnx::Conv_928_1", type=QuantizeLinear]; +"59 DequantizeLinear_onnx^^Conv_928_1" [id=59, label="59 DequantizeLinear_onnx::Conv_928_1", type=DequantizeLinear]; "60 /Conv2d_4a_3x3/conv/Conv" [id=60, type=Conv]; "61 /Conv2d_4a_3x3/Relu" [id=61, type=Relu]; "62 QuantizeLinear_/Conv2d_4a_3x3/Relu_output_0_1" [id=62, type=QuantizeLinear]; "63 DequantizeLinear_/Conv2d_4a_3x3/Relu_output_0_1" [id=63, type=DequantizeLinear]; "64 /maxpool2/MaxPool" [id=64, type=MaxPool]; -"65 QuantizeLinear_onnx^^Conv_949_1" [id=65, label="65 QuantizeLinear_onnx::Conv_949_1", type=QuantizeLinear]; -"66 DequantizeLinear_onnx^^Conv_949_1" [id=66, label="66 DequantizeLinear_onnx::Conv_949_1", type=DequantizeLinear]; +"65 QuantizeLinear_onnx^^Conv_931_1" [id=65, label="65 QuantizeLinear_onnx::Conv_931_1", type=QuantizeLinear]; +"66 DequantizeLinear_onnx^^Conv_931_1" [id=66, label="66 DequantizeLinear_onnx::Conv_931_1", type=DequantizeLinear]; "67 /Mixed_5b/branch1x1/conv/Conv" [id=67, type=Conv]; "68 /Mixed_5b/branch1x1/Relu" [id=68, type=Relu]; -"69 QuantizeLinear_onnx^^Conv_952_1" [id=69, label="69 QuantizeLinear_onnx::Conv_952_1", type=QuantizeLinear]; -"70 DequantizeLinear_onnx^^Conv_952_1" [id=70, label="70 DequantizeLinear_onnx::Conv_952_1", type=DequantizeLinear]; +"69 QuantizeLinear_onnx^^Conv_934_1" [id=69, label="69 QuantizeLinear_onnx::Conv_934_1", type=QuantizeLinear]; +"70 DequantizeLinear_onnx^^Conv_934_1" [id=70, label="70 DequantizeLinear_onnx::Conv_934_1", type=DequantizeLinear]; "71 /Mixed_5b/branch5x5_1/conv/Conv" [id=71, type=Conv]; "72 /Mixed_5b/branch5x5_1/Relu" [id=72, type=Relu]; "73 QuantizeLinear_/Mixed_5b/branch5x5_1/Relu_output_0_1" [id=73, type=QuantizeLinear]; "74 DequantizeLinear_/Mixed_5b/branch5x5_1/Relu_output_0_1" [id=74, type=DequantizeLinear]; -"75 QuantizeLinear_onnx^^Conv_955_1" [id=75, label="75 QuantizeLinear_onnx::Conv_955_1", type=QuantizeLinear]; -"76 DequantizeLinear_onnx^^Conv_955_1" [id=76, label="76 DequantizeLinear_onnx::Conv_955_1", type=DequantizeLinear]; +"75 QuantizeLinear_onnx^^Conv_937_1" [id=75, label="75 QuantizeLinear_onnx::Conv_937_1", type=QuantizeLinear]; +"76 DequantizeLinear_onnx^^Conv_937_1" [id=76, label="76 DequantizeLinear_onnx::Conv_937_1", type=DequantizeLinear]; "77 /Mixed_5b/branch5x5_2/conv/Conv" [id=77, type=Conv]; "78 /Mixed_5b/branch5x5_2/Relu" [id=78, type=Relu]; -"79 QuantizeLinear_onnx^^Conv_958_1" [id=79, label="79 QuantizeLinear_onnx::Conv_958_1", type=QuantizeLinear]; -"80 DequantizeLinear_onnx^^Conv_958_1" [id=80, label="80 DequantizeLinear_onnx::Conv_958_1", type=DequantizeLinear]; +"79 QuantizeLinear_onnx^^Conv_940_1" [id=79, label="79 QuantizeLinear_onnx::Conv_940_1", type=QuantizeLinear]; +"80 DequantizeLinear_onnx^^Conv_940_1" [id=80, label="80 DequantizeLinear_onnx::Conv_940_1", type=DequantizeLinear]; "81 /Mixed_5b/branch3x3dbl_1/conv/Conv" [id=81, type=Conv]; "82 /Mixed_5b/branch3x3dbl_1/Relu" [id=82, type=Relu]; "83 QuantizeLinear_/Mixed_5b/branch3x3dbl_1/Relu_output_0_1" [id=83, type=QuantizeLinear]; "84 DequantizeLinear_/Mixed_5b/branch3x3dbl_1/Relu_output_0_1" [id=84, type=DequantizeLinear]; -"85 QuantizeLinear_onnx^^Conv_961_1" [id=85, label="85 QuantizeLinear_onnx::Conv_961_1", type=QuantizeLinear]; -"86 DequantizeLinear_onnx^^Conv_961_1" [id=86, label="86 DequantizeLinear_onnx::Conv_961_1", type=DequantizeLinear]; +"85 QuantizeLinear_onnx^^Conv_943_1" [id=85, label="85 QuantizeLinear_onnx::Conv_943_1", type=QuantizeLinear]; +"86 DequantizeLinear_onnx^^Conv_943_1" [id=86, label="86 DequantizeLinear_onnx::Conv_943_1", type=DequantizeLinear]; "87 /Mixed_5b/branch3x3dbl_2/conv/Conv" [id=87, type=Conv]; "88 /Mixed_5b/branch3x3dbl_2/Relu" [id=88, type=Relu]; "89 QuantizeLinear_/Mixed_5b/branch3x3dbl_2/Relu_output_0_1" [id=89, type=QuantizeLinear]; "90 DequantizeLinear_/Mixed_5b/branch3x3dbl_2/Relu_output_0_1" [id=90, type=DequantizeLinear]; -"91 QuantizeLinear_onnx^^Conv_964_1" [id=91, label="91 QuantizeLinear_onnx::Conv_964_1", type=QuantizeLinear]; -"92 DequantizeLinear_onnx^^Conv_964_1" [id=92, label="92 DequantizeLinear_onnx::Conv_964_1", type=DequantizeLinear]; +"91 QuantizeLinear_onnx^^Conv_946_1" [id=91, label="91 QuantizeLinear_onnx::Conv_946_1", type=QuantizeLinear]; +"92 DequantizeLinear_onnx^^Conv_946_1" [id=92, label="92 DequantizeLinear_onnx::Conv_946_1", type=DequantizeLinear]; "93 /Mixed_5b/branch3x3dbl_3/conv/Conv" [id=93, type=Conv]; "94 /Mixed_5b/branch3x3dbl_3/Relu" [id=94, type=Relu]; -"95 /Mixed_5b/Constant" [id=95, type=Constant]; -"96 /Mixed_5b/Pad" [id=96, type=Pad]; -"97 /Mixed_5b/AveragePool" [id=97, type=AveragePool]; -"98 QuantizeLinear_/Mixed_5b/AveragePool_output_0_1" [id=98, type=QuantizeLinear]; -"99 DequantizeLinear_/Mixed_5b/AveragePool_output_0_1" [id=99, type=DequantizeLinear]; -"100 QuantizeLinear_onnx^^Conv_967_1" [id=100, label="100 QuantizeLinear_onnx::Conv_967_1", type=QuantizeLinear]; -"101 DequantizeLinear_onnx^^Conv_967_1" [id=101, label="101 DequantizeLinear_onnx::Conv_967_1", type=DequantizeLinear]; -"102 /Mixed_5b/branch_pool/conv/Conv" [id=102, type=Conv]; -"103 /Mixed_5b/branch_pool/Relu" [id=103, type=Relu]; -"104 QuantizeLinear_/Mixed_5b/branch1x1/Relu_output_0_1" [id=104, type=QuantizeLinear]; -"105 DequantizeLinear_/Mixed_5b/branch1x1/Relu_output_0_1" [id=105, type=DequantizeLinear]; -"106 QuantizeLinear_/Mixed_5b/branch5x5_2/Relu_output_0_1" [id=106, type=QuantizeLinear]; -"107 DequantizeLinear_/Mixed_5b/branch5x5_2/Relu_output_0_1" [id=107, type=DequantizeLinear]; +"95 /Mixed_5b/AveragePool" [id=95, type=AveragePool]; +"96 QuantizeLinear_/Mixed_5b/AveragePool_output_0_1" [id=96, type=QuantizeLinear]; +"97 DequantizeLinear_/Mixed_5b/AveragePool_output_0_1" [id=97, type=DequantizeLinear]; +"98 QuantizeLinear_onnx^^Conv_949_1" [id=98, label="98 QuantizeLinear_onnx::Conv_949_1", type=QuantizeLinear]; +"99 DequantizeLinear_onnx^^Conv_949_1" [id=99, label="99 DequantizeLinear_onnx::Conv_949_1", type=DequantizeLinear]; +"100 /Mixed_5b/branch_pool/conv/Conv" [id=100, type=Conv]; +"101 /Mixed_5b/branch_pool/Relu" [id=101, type=Relu]; +"102 QuantizeLinear_/Mixed_5b/branch1x1/Relu_output_0_1" [id=102, type=QuantizeLinear]; +"103 DequantizeLinear_/Mixed_5b/branch1x1/Relu_output_0_1" [id=103, type=DequantizeLinear]; +"104 QuantizeLinear_/Mixed_5b/branch5x5_2/Relu_output_0_1" [id=104, type=QuantizeLinear]; +"105 DequantizeLinear_/Mixed_5b/branch5x5_2/Relu_output_0_1" [id=105, type=DequantizeLinear]; +"106 QuantizeLinear_/Mixed_5b/branch3x3dbl_3/Relu_output_0_1" [id=106, type=QuantizeLinear]; +"107 DequantizeLinear_/Mixed_5b/branch3x3dbl_3/Relu_output_0_1" [id=107, type=DequantizeLinear]; "108 QuantizeLinear_/Mixed_5b/branch_pool/Relu_output_0_1" [id=108, type=QuantizeLinear]; "109 DequantizeLinear_/Mixed_5b/branch_pool/Relu_output_0_1" [id=109, type=DequantizeLinear]; -"110 QuantizeLinear_/Mixed_5b/branch3x3dbl_3/Relu_output_0_1" [id=110, type=QuantizeLinear]; -"111 DequantizeLinear_/Mixed_5b/branch3x3dbl_3/Relu_output_0_1" [id=111, type=DequantizeLinear]; -"112 /Mixed_5b/Concat" [id=112, type=Concat]; -"113 QuantizeLinear_onnx^^Conv_970_1" [id=113, label="113 QuantizeLinear_onnx::Conv_970_1", type=QuantizeLinear]; -"114 DequantizeLinear_onnx^^Conv_970_1" [id=114, label="114 DequantizeLinear_onnx::Conv_970_1", type=DequantizeLinear]; -"115 /Mixed_5c/branch1x1/conv/Conv" [id=115, type=Conv]; -"116 /Mixed_5c/branch1x1/Relu" [id=116, type=Relu]; -"117 QuantizeLinear_onnx^^Conv_973_1" [id=117, label="117 QuantizeLinear_onnx::Conv_973_1", type=QuantizeLinear]; -"118 DequantizeLinear_onnx^^Conv_973_1" [id=118, label="118 DequantizeLinear_onnx::Conv_973_1", type=DequantizeLinear]; -"119 /Mixed_5c/branch5x5_1/conv/Conv" [id=119, type=Conv]; -"120 /Mixed_5c/branch5x5_1/Relu" [id=120, type=Relu]; -"121 QuantizeLinear_/Mixed_5c/branch5x5_1/Relu_output_0_1" [id=121, type=QuantizeLinear]; -"122 DequantizeLinear_/Mixed_5c/branch5x5_1/Relu_output_0_1" [id=122, type=DequantizeLinear]; -"123 QuantizeLinear_onnx^^Conv_976_1" [id=123, label="123 QuantizeLinear_onnx::Conv_976_1", type=QuantizeLinear]; -"124 DequantizeLinear_onnx^^Conv_976_1" [id=124, label="124 DequantizeLinear_onnx::Conv_976_1", type=DequantizeLinear]; -"125 /Mixed_5c/branch5x5_2/conv/Conv" [id=125, type=Conv]; -"126 /Mixed_5c/branch5x5_2/Relu" [id=126, type=Relu]; -"127 QuantizeLinear_onnx^^Conv_979_1" [id=127, label="127 QuantizeLinear_onnx::Conv_979_1", type=QuantizeLinear]; -"128 DequantizeLinear_onnx^^Conv_979_1" [id=128, label="128 DequantizeLinear_onnx::Conv_979_1", type=DequantizeLinear]; -"129 /Mixed_5c/branch3x3dbl_1/conv/Conv" [id=129, type=Conv]; -"130 /Mixed_5c/branch3x3dbl_1/Relu" [id=130, type=Relu]; -"131 QuantizeLinear_/Mixed_5c/branch3x3dbl_1/Relu_output_0_1" [id=131, type=QuantizeLinear]; -"132 DequantizeLinear_/Mixed_5c/branch3x3dbl_1/Relu_output_0_1" [id=132, type=DequantizeLinear]; -"133 QuantizeLinear_onnx^^Conv_982_1" [id=133, label="133 QuantizeLinear_onnx::Conv_982_1", type=QuantizeLinear]; -"134 DequantizeLinear_onnx^^Conv_982_1" [id=134, label="134 DequantizeLinear_onnx::Conv_982_1", type=DequantizeLinear]; -"135 /Mixed_5c/branch3x3dbl_2/conv/Conv" [id=135, type=Conv]; -"136 /Mixed_5c/branch3x3dbl_2/Relu" [id=136, type=Relu]; -"137 QuantizeLinear_/Mixed_5c/branch3x3dbl_2/Relu_output_0_1" [id=137, type=QuantizeLinear]; -"138 DequantizeLinear_/Mixed_5c/branch3x3dbl_2/Relu_output_0_1" [id=138, type=DequantizeLinear]; -"139 QuantizeLinear_onnx^^Conv_985_1" [id=139, label="139 QuantizeLinear_onnx::Conv_985_1", type=QuantizeLinear]; -"140 DequantizeLinear_onnx^^Conv_985_1" [id=140, label="140 DequantizeLinear_onnx::Conv_985_1", type=DequantizeLinear]; -"141 /Mixed_5c/branch3x3dbl_3/conv/Conv" [id=141, type=Conv]; -"142 /Mixed_5c/branch3x3dbl_3/Relu" [id=142, type=Relu]; -"143 /Mixed_5c/Constant" [id=143, type=Constant]; -"144 /Mixed_5c/Pad" [id=144, type=Pad]; -"145 /Mixed_5c/AveragePool" [id=145, type=AveragePool]; -"146 QuantizeLinear_/Mixed_5c/AveragePool_output_0_1" [id=146, type=QuantizeLinear]; -"147 DequantizeLinear_/Mixed_5c/AveragePool_output_0_1" [id=147, type=DequantizeLinear]; -"148 QuantizeLinear_onnx^^Conv_988_1" [id=148, label="148 QuantizeLinear_onnx::Conv_988_1", type=QuantizeLinear]; -"149 DequantizeLinear_onnx^^Conv_988_1" [id=149, label="149 DequantizeLinear_onnx::Conv_988_1", type=DequantizeLinear]; -"150 /Mixed_5c/branch_pool/conv/Conv" [id=150, type=Conv]; -"151 /Mixed_5c/branch_pool/Relu" [id=151, type=Relu]; -"152 QuantizeLinear_/Mixed_5c/branch1x1/Relu_output_0_1" [id=152, type=QuantizeLinear]; -"153 DequantizeLinear_/Mixed_5c/branch1x1/Relu_output_0_1" [id=153, type=DequantizeLinear]; -"154 QuantizeLinear_/Mixed_5c/branch5x5_2/Relu_output_0_1" [id=154, type=QuantizeLinear]; -"155 DequantizeLinear_/Mixed_5c/branch5x5_2/Relu_output_0_1" [id=155, type=DequantizeLinear]; -"156 QuantizeLinear_/Mixed_5c/branch3x3dbl_3/Relu_output_0_1" [id=156, type=QuantizeLinear]; -"157 DequantizeLinear_/Mixed_5c/branch3x3dbl_3/Relu_output_0_1" [id=157, type=DequantizeLinear]; -"158 QuantizeLinear_/Mixed_5c/branch_pool/Relu_output_0_1" [id=158, type=QuantizeLinear]; -"159 DequantizeLinear_/Mixed_5c/branch_pool/Relu_output_0_1" [id=159, type=DequantizeLinear]; -"160 /Mixed_5c/Concat" [id=160, type=Concat]; -"161 QuantizeLinear_onnx^^Conv_991_1" [id=161, label="161 QuantizeLinear_onnx::Conv_991_1", type=QuantizeLinear]; -"162 DequantizeLinear_onnx^^Conv_991_1" [id=162, label="162 DequantizeLinear_onnx::Conv_991_1", type=DequantizeLinear]; -"163 /Mixed_5d/branch1x1/conv/Conv" [id=163, type=Conv]; -"164 /Mixed_5d/branch1x1/Relu" [id=164, type=Relu]; -"165 QuantizeLinear_onnx^^Conv_994_1" [id=165, label="165 QuantizeLinear_onnx::Conv_994_1", type=QuantizeLinear]; -"166 DequantizeLinear_onnx^^Conv_994_1" [id=166, label="166 DequantizeLinear_onnx::Conv_994_1", type=DequantizeLinear]; -"167 /Mixed_5d/branch5x5_1/conv/Conv" [id=167, type=Conv]; -"168 /Mixed_5d/branch5x5_1/Relu" [id=168, type=Relu]; -"169 QuantizeLinear_/Mixed_5d/branch5x5_1/Relu_output_0_1" [id=169, type=QuantizeLinear]; -"170 DequantizeLinear_/Mixed_5d/branch5x5_1/Relu_output_0_1" [id=170, type=DequantizeLinear]; -"171 QuantizeLinear_onnx^^Conv_997_1" [id=171, label="171 QuantizeLinear_onnx::Conv_997_1", type=QuantizeLinear]; -"172 DequantizeLinear_onnx^^Conv_997_1" [id=172, label="172 DequantizeLinear_onnx::Conv_997_1", type=DequantizeLinear]; -"173 /Mixed_5d/branch5x5_2/conv/Conv" [id=173, type=Conv]; -"174 /Mixed_5d/branch5x5_2/Relu" [id=174, type=Relu]; -"175 QuantizeLinear_onnx^^Conv_1000_1" [id=175, label="175 QuantizeLinear_onnx::Conv_1000_1", type=QuantizeLinear]; -"176 DequantizeLinear_onnx^^Conv_1000_1" [id=176, label="176 DequantizeLinear_onnx::Conv_1000_1", type=DequantizeLinear]; -"177 /Mixed_5d/branch3x3dbl_1/conv/Conv" [id=177, type=Conv]; -"178 /Mixed_5d/branch3x3dbl_1/Relu" [id=178, type=Relu]; -"179 QuantizeLinear_/Mixed_5d/branch3x3dbl_1/Relu_output_0_1" [id=179, type=QuantizeLinear]; -"180 DequantizeLinear_/Mixed_5d/branch3x3dbl_1/Relu_output_0_1" [id=180, type=DequantizeLinear]; -"181 QuantizeLinear_onnx^^Conv_1003_1" [id=181, label="181 QuantizeLinear_onnx::Conv_1003_1", type=QuantizeLinear]; -"182 DequantizeLinear_onnx^^Conv_1003_1" [id=182, label="182 DequantizeLinear_onnx::Conv_1003_1", type=DequantizeLinear]; -"183 /Mixed_5d/branch3x3dbl_2/conv/Conv" [id=183, type=Conv]; -"184 /Mixed_5d/branch3x3dbl_2/Relu" [id=184, type=Relu]; -"185 QuantizeLinear_/Mixed_5d/branch3x3dbl_2/Relu_output_0_1" [id=185, type=QuantizeLinear]; -"186 DequantizeLinear_/Mixed_5d/branch3x3dbl_2/Relu_output_0_1" [id=186, type=DequantizeLinear]; -"187 QuantizeLinear_onnx^^Conv_1006_1" [id=187, label="187 QuantizeLinear_onnx::Conv_1006_1", type=QuantizeLinear]; -"188 DequantizeLinear_onnx^^Conv_1006_1" [id=188, label="188 DequantizeLinear_onnx::Conv_1006_1", type=DequantizeLinear]; -"189 /Mixed_5d/branch3x3dbl_3/conv/Conv" [id=189, type=Conv]; -"190 /Mixed_5d/branch3x3dbl_3/Relu" [id=190, type=Relu]; -"191 /Mixed_5d/Constant" [id=191, type=Constant]; -"192 /Mixed_5d/Pad" [id=192, type=Pad]; -"193 /Mixed_5d/AveragePool" [id=193, type=AveragePool]; -"194 QuantizeLinear_/Mixed_5d/AveragePool_output_0_1" [id=194, type=QuantizeLinear]; -"195 DequantizeLinear_/Mixed_5d/AveragePool_output_0_1" [id=195, type=DequantizeLinear]; -"196 QuantizeLinear_onnx^^Conv_1009_1" [id=196, label="196 QuantizeLinear_onnx::Conv_1009_1", type=QuantizeLinear]; -"197 DequantizeLinear_onnx^^Conv_1009_1" [id=197, label="197 DequantizeLinear_onnx::Conv_1009_1", type=DequantizeLinear]; -"198 /Mixed_5d/branch_pool/conv/Conv" [id=198, type=Conv]; -"199 /Mixed_5d/branch_pool/Relu" [id=199, type=Relu]; -"200 QuantizeLinear_/Mixed_5d/branch1x1/Relu_output_0_1" [id=200, type=QuantizeLinear]; -"201 DequantizeLinear_/Mixed_5d/branch1x1/Relu_output_0_1" [id=201, type=DequantizeLinear]; -"202 QuantizeLinear_/Mixed_5d/branch5x5_2/Relu_output_0_1" [id=202, type=QuantizeLinear]; -"203 DequantizeLinear_/Mixed_5d/branch5x5_2/Relu_output_0_1" [id=203, type=DequantizeLinear]; -"204 QuantizeLinear_/Mixed_5d/branch3x3dbl_3/Relu_output_0_1" [id=204, type=QuantizeLinear]; -"205 DequantizeLinear_/Mixed_5d/branch3x3dbl_3/Relu_output_0_1" [id=205, type=DequantizeLinear]; -"206 QuantizeLinear_/Mixed_5d/branch_pool/Relu_output_0_1" [id=206, type=QuantizeLinear]; -"207 DequantizeLinear_/Mixed_5d/branch_pool/Relu_output_0_1" [id=207, type=DequantizeLinear]; -"208 /Mixed_5d/Concat" [id=208, type=Concat]; -"209 QuantizeLinear_onnx^^Conv_1012_1" [id=209, label="209 QuantizeLinear_onnx::Conv_1012_1", type=QuantizeLinear]; -"210 DequantizeLinear_onnx^^Conv_1012_1" [id=210, label="210 DequantizeLinear_onnx::Conv_1012_1", type=DequantizeLinear]; -"211 /Mixed_6a/branch3x3/conv/Conv" [id=211, type=Conv]; -"212 /Mixed_6a/branch3x3/Relu" [id=212, type=Relu]; -"213 QuantizeLinear_onnx^^Conv_1015_1" [id=213, label="213 QuantizeLinear_onnx::Conv_1015_1", type=QuantizeLinear]; -"214 DequantizeLinear_onnx^^Conv_1015_1" [id=214, label="214 DequantizeLinear_onnx::Conv_1015_1", type=DequantizeLinear]; -"215 /Mixed_6a/branch3x3dbl_1/conv/Conv" [id=215, type=Conv]; -"216 /Mixed_6a/branch3x3dbl_1/Relu" [id=216, type=Relu]; -"217 QuantizeLinear_/Mixed_6a/branch3x3dbl_1/Relu_output_0_1" [id=217, type=QuantizeLinear]; -"218 DequantizeLinear_/Mixed_6a/branch3x3dbl_1/Relu_output_0_1" [id=218, type=DequantizeLinear]; -"219 QuantizeLinear_onnx^^Conv_1018_1" [id=219, label="219 QuantizeLinear_onnx::Conv_1018_1", type=QuantizeLinear]; -"220 DequantizeLinear_onnx^^Conv_1018_1" [id=220, label="220 DequantizeLinear_onnx::Conv_1018_1", type=DequantizeLinear]; -"221 /Mixed_6a/branch3x3dbl_2/conv/Conv" [id=221, type=Conv]; -"222 /Mixed_6a/branch3x3dbl_2/Relu" [id=222, type=Relu]; -"223 QuantizeLinear_/Mixed_6a/branch3x3dbl_2/Relu_output_0_1" [id=223, type=QuantizeLinear]; -"224 DequantizeLinear_/Mixed_6a/branch3x3dbl_2/Relu_output_0_1" [id=224, type=DequantizeLinear]; -"225 QuantizeLinear_onnx^^Conv_1021_1" [id=225, label="225 QuantizeLinear_onnx::Conv_1021_1", type=QuantizeLinear]; -"226 DequantizeLinear_onnx^^Conv_1021_1" [id=226, label="226 DequantizeLinear_onnx::Conv_1021_1", type=DequantizeLinear]; -"227 /Mixed_6a/branch3x3dbl_3/conv/Conv" [id=227, type=Conv]; -"228 /Mixed_6a/branch3x3dbl_3/Relu" [id=228, type=Relu]; -"229 /Mixed_6a/MaxPool" [id=229, type=MaxPool]; -"230 QuantizeLinear_/Mixed_6a/branch3x3dbl_3/Relu_output_0_1" [id=230, type=QuantizeLinear]; -"231 DequantizeLinear_/Mixed_6a/branch3x3dbl_3/Relu_output_0_1" [id=231, type=DequantizeLinear]; -"232 QuantizeLinear_/Mixed_6a/branch3x3/Relu_output_0_1" [id=232, type=QuantizeLinear]; -"233 DequantizeLinear_/Mixed_6a/branch3x3/Relu_output_0_1" [id=233, type=DequantizeLinear]; -"234 /Mixed_6a/Concat" [id=234, type=Concat]; -"235 QuantizeLinear_onnx^^Conv_1024_1" [id=235, label="235 QuantizeLinear_onnx::Conv_1024_1", type=QuantizeLinear]; -"236 DequantizeLinear_onnx^^Conv_1024_1" [id=236, label="236 DequantizeLinear_onnx::Conv_1024_1", type=DequantizeLinear]; -"237 /Mixed_6b/branch1x1/conv/Conv" [id=237, type=Conv]; -"238 /Mixed_6b/branch1x1/Relu" [id=238, type=Relu]; -"239 QuantizeLinear_onnx^^Conv_1027_1" [id=239, label="239 QuantizeLinear_onnx::Conv_1027_1", type=QuantizeLinear]; -"240 DequantizeLinear_onnx^^Conv_1027_1" [id=240, label="240 DequantizeLinear_onnx::Conv_1027_1", type=DequantizeLinear]; -"241 /Mixed_6b/branch7x7_1/conv/Conv" [id=241, type=Conv]; -"242 /Mixed_6b/branch7x7_1/Relu" [id=242, type=Relu]; -"243 QuantizeLinear_/Mixed_6b/branch7x7_1/Relu_output_0_1" [id=243, type=QuantizeLinear]; -"244 DequantizeLinear_/Mixed_6b/branch7x7_1/Relu_output_0_1" [id=244, type=DequantizeLinear]; -"245 QuantizeLinear_onnx^^Conv_1030_1" [id=245, label="245 QuantizeLinear_onnx::Conv_1030_1", type=QuantizeLinear]; -"246 DequantizeLinear_onnx^^Conv_1030_1" [id=246, label="246 DequantizeLinear_onnx::Conv_1030_1", type=DequantizeLinear]; -"247 /Mixed_6b/branch7x7_2/conv/Conv" [id=247, type=Conv]; -"248 /Mixed_6b/branch7x7_2/Relu" [id=248, type=Relu]; -"249 QuantizeLinear_/Mixed_6b/branch7x7_2/Relu_output_0_1" [id=249, type=QuantizeLinear]; -"250 DequantizeLinear_/Mixed_6b/branch7x7_2/Relu_output_0_1" [id=250, type=DequantizeLinear]; -"251 QuantizeLinear_onnx^^Conv_1033_1" [id=251, label="251 QuantizeLinear_onnx::Conv_1033_1", type=QuantizeLinear]; -"252 DequantizeLinear_onnx^^Conv_1033_1" [id=252, label="252 DequantizeLinear_onnx::Conv_1033_1", type=DequantizeLinear]; -"253 /Mixed_6b/branch7x7_3/conv/Conv" [id=253, type=Conv]; -"254 /Mixed_6b/branch7x7_3/Relu" [id=254, type=Relu]; -"255 QuantizeLinear_onnx^^Conv_1036_1" [id=255, label="255 QuantizeLinear_onnx::Conv_1036_1", type=QuantizeLinear]; -"256 DequantizeLinear_onnx^^Conv_1036_1" [id=256, label="256 DequantizeLinear_onnx::Conv_1036_1", type=DequantizeLinear]; -"257 /Mixed_6b/branch7x7dbl_1/conv/Conv" [id=257, type=Conv]; -"258 /Mixed_6b/branch7x7dbl_1/Relu" [id=258, type=Relu]; -"259 QuantizeLinear_/Mixed_6b/branch7x7dbl_1/Relu_output_0_1" [id=259, type=QuantizeLinear]; -"260 DequantizeLinear_/Mixed_6b/branch7x7dbl_1/Relu_output_0_1" [id=260, type=DequantizeLinear]; -"261 QuantizeLinear_onnx^^Conv_1039_1" [id=261, label="261 QuantizeLinear_onnx::Conv_1039_1", type=QuantizeLinear]; -"262 DequantizeLinear_onnx^^Conv_1039_1" [id=262, label="262 DequantizeLinear_onnx::Conv_1039_1", type=DequantizeLinear]; -"263 /Mixed_6b/branch7x7dbl_2/conv/Conv" [id=263, type=Conv]; -"264 /Mixed_6b/branch7x7dbl_2/Relu" [id=264, type=Relu]; -"265 QuantizeLinear_/Mixed_6b/branch7x7dbl_2/Relu_output_0_1" [id=265, type=QuantizeLinear]; -"266 DequantizeLinear_/Mixed_6b/branch7x7dbl_2/Relu_output_0_1" [id=266, type=DequantizeLinear]; -"267 QuantizeLinear_onnx^^Conv_1042_1" [id=267, label="267 QuantizeLinear_onnx::Conv_1042_1", type=QuantizeLinear]; -"268 DequantizeLinear_onnx^^Conv_1042_1" [id=268, label="268 DequantizeLinear_onnx::Conv_1042_1", type=DequantizeLinear]; -"269 /Mixed_6b/branch7x7dbl_3/conv/Conv" [id=269, type=Conv]; -"270 /Mixed_6b/branch7x7dbl_3/Relu" [id=270, type=Relu]; -"271 QuantizeLinear_/Mixed_6b/branch7x7dbl_3/Relu_output_0_1" [id=271, type=QuantizeLinear]; -"272 DequantizeLinear_/Mixed_6b/branch7x7dbl_3/Relu_output_0_1" [id=272, type=DequantizeLinear]; -"273 QuantizeLinear_onnx^^Conv_1045_1" [id=273, label="273 QuantizeLinear_onnx::Conv_1045_1", type=QuantizeLinear]; -"274 DequantizeLinear_onnx^^Conv_1045_1" [id=274, label="274 DequantizeLinear_onnx::Conv_1045_1", type=DequantizeLinear]; -"275 /Mixed_6b/branch7x7dbl_4/conv/Conv" [id=275, type=Conv]; -"276 /Mixed_6b/branch7x7dbl_4/Relu" [id=276, type=Relu]; -"277 QuantizeLinear_/Mixed_6b/branch7x7dbl_4/Relu_output_0_1" [id=277, type=QuantizeLinear]; -"278 DequantizeLinear_/Mixed_6b/branch7x7dbl_4/Relu_output_0_1" [id=278, type=DequantizeLinear]; -"279 QuantizeLinear_onnx^^Conv_1048_1" [id=279, label="279 QuantizeLinear_onnx::Conv_1048_1", type=QuantizeLinear]; -"280 DequantizeLinear_onnx^^Conv_1048_1" [id=280, label="280 DequantizeLinear_onnx::Conv_1048_1", type=DequantizeLinear]; -"281 /Mixed_6b/branch7x7dbl_5/conv/Conv" [id=281, type=Conv]; -"282 /Mixed_6b/branch7x7dbl_5/Relu" [id=282, type=Relu]; -"283 /Mixed_6b/Constant" [id=283, type=Constant]; -"284 /Mixed_6b/Pad" [id=284, type=Pad]; -"285 /Mixed_6b/AveragePool" [id=285, type=AveragePool]; -"286 QuantizeLinear_/Mixed_6b/AveragePool_output_0_1" [id=286, type=QuantizeLinear]; -"287 DequantizeLinear_/Mixed_6b/AveragePool_output_0_1" [id=287, type=DequantizeLinear]; -"288 QuantizeLinear_onnx^^Conv_1051_1" [id=288, label="288 QuantizeLinear_onnx::Conv_1051_1", type=QuantizeLinear]; -"289 DequantizeLinear_onnx^^Conv_1051_1" [id=289, label="289 DequantizeLinear_onnx::Conv_1051_1", type=DequantizeLinear]; -"290 /Mixed_6b/branch_pool/conv/Conv" [id=290, type=Conv]; -"291 /Mixed_6b/branch_pool/Relu" [id=291, type=Relu]; -"292 QuantizeLinear_/Mixed_6b/branch7x7_3/Relu_output_0_1" [id=292, type=QuantizeLinear]; -"293 DequantizeLinear_/Mixed_6b/branch7x7_3/Relu_output_0_1" [id=293, type=DequantizeLinear]; -"294 QuantizeLinear_/Mixed_6b/branch7x7dbl_5/Relu_output_0_1" [id=294, type=QuantizeLinear]; -"295 DequantizeLinear_/Mixed_6b/branch7x7dbl_5/Relu_output_0_1" [id=295, type=DequantizeLinear]; -"296 QuantizeLinear_/Mixed_6b/branch_pool/Relu_output_0_1" [id=296, type=QuantizeLinear]; -"297 DequantizeLinear_/Mixed_6b/branch_pool/Relu_output_0_1" [id=297, type=DequantizeLinear]; -"298 QuantizeLinear_/Mixed_6b/branch1x1/Relu_output_0_1" [id=298, type=QuantizeLinear]; -"299 DequantizeLinear_/Mixed_6b/branch1x1/Relu_output_0_1" [id=299, type=DequantizeLinear]; -"300 /Mixed_6b/Concat" [id=300, type=Concat]; -"301 QuantizeLinear_onnx^^Conv_1054_1" [id=301, label="301 QuantizeLinear_onnx::Conv_1054_1", type=QuantizeLinear]; -"302 DequantizeLinear_onnx^^Conv_1054_1" [id=302, label="302 DequantizeLinear_onnx::Conv_1054_1", type=DequantizeLinear]; -"303 /Mixed_6c/branch1x1/conv/Conv" [id=303, type=Conv]; -"304 /Mixed_6c/branch1x1/Relu" [id=304, type=Relu]; -"305 QuantizeLinear_onnx^^Conv_1057_1" [id=305, label="305 QuantizeLinear_onnx::Conv_1057_1", type=QuantizeLinear]; -"306 DequantizeLinear_onnx^^Conv_1057_1" [id=306, label="306 DequantizeLinear_onnx::Conv_1057_1", type=DequantizeLinear]; -"307 /Mixed_6c/branch7x7_1/conv/Conv" [id=307, type=Conv]; -"308 /Mixed_6c/branch7x7_1/Relu" [id=308, type=Relu]; -"309 QuantizeLinear_/Mixed_6c/branch7x7_1/Relu_output_0_1" [id=309, type=QuantizeLinear]; -"310 DequantizeLinear_/Mixed_6c/branch7x7_1/Relu_output_0_1" [id=310, type=DequantizeLinear]; -"311 QuantizeLinear_onnx^^Conv_1060_1" [id=311, label="311 QuantizeLinear_onnx::Conv_1060_1", type=QuantizeLinear]; -"312 DequantizeLinear_onnx^^Conv_1060_1" [id=312, label="312 DequantizeLinear_onnx::Conv_1060_1", type=DequantizeLinear]; -"313 /Mixed_6c/branch7x7_2/conv/Conv" [id=313, type=Conv]; -"314 /Mixed_6c/branch7x7_2/Relu" [id=314, type=Relu]; -"315 QuantizeLinear_/Mixed_6c/branch7x7_2/Relu_output_0_1" [id=315, type=QuantizeLinear]; -"316 DequantizeLinear_/Mixed_6c/branch7x7_2/Relu_output_0_1" [id=316, type=DequantizeLinear]; -"317 QuantizeLinear_onnx^^Conv_1063_1" [id=317, label="317 QuantizeLinear_onnx::Conv_1063_1", type=QuantizeLinear]; -"318 DequantizeLinear_onnx^^Conv_1063_1" [id=318, label="318 DequantizeLinear_onnx::Conv_1063_1", type=DequantizeLinear]; -"319 /Mixed_6c/branch7x7_3/conv/Conv" [id=319, type=Conv]; -"320 /Mixed_6c/branch7x7_3/Relu" [id=320, type=Relu]; -"321 QuantizeLinear_onnx^^Conv_1066_1" [id=321, label="321 QuantizeLinear_onnx::Conv_1066_1", type=QuantizeLinear]; -"322 DequantizeLinear_onnx^^Conv_1066_1" [id=322, label="322 DequantizeLinear_onnx::Conv_1066_1", type=DequantizeLinear]; -"323 /Mixed_6c/branch7x7dbl_1/conv/Conv" [id=323, type=Conv]; -"324 /Mixed_6c/branch7x7dbl_1/Relu" [id=324, type=Relu]; -"325 QuantizeLinear_/Mixed_6c/branch7x7dbl_1/Relu_output_0_1" [id=325, type=QuantizeLinear]; -"326 DequantizeLinear_/Mixed_6c/branch7x7dbl_1/Relu_output_0_1" [id=326, type=DequantizeLinear]; -"327 QuantizeLinear_onnx^^Conv_1069_1" [id=327, label="327 QuantizeLinear_onnx::Conv_1069_1", type=QuantizeLinear]; -"328 DequantizeLinear_onnx^^Conv_1069_1" [id=328, label="328 DequantizeLinear_onnx::Conv_1069_1", type=DequantizeLinear]; -"329 /Mixed_6c/branch7x7dbl_2/conv/Conv" [id=329, type=Conv]; -"330 /Mixed_6c/branch7x7dbl_2/Relu" [id=330, type=Relu]; -"331 QuantizeLinear_/Mixed_6c/branch7x7dbl_2/Relu_output_0_1" [id=331, type=QuantizeLinear]; -"332 DequantizeLinear_/Mixed_6c/branch7x7dbl_2/Relu_output_0_1" [id=332, type=DequantizeLinear]; -"333 QuantizeLinear_onnx^^Conv_1072_1" [id=333, label="333 QuantizeLinear_onnx::Conv_1072_1", type=QuantizeLinear]; -"334 DequantizeLinear_onnx^^Conv_1072_1" [id=334, label="334 DequantizeLinear_onnx::Conv_1072_1", type=DequantizeLinear]; -"335 /Mixed_6c/branch7x7dbl_3/conv/Conv" [id=335, type=Conv]; -"336 /Mixed_6c/branch7x7dbl_3/Relu" [id=336, type=Relu]; -"337 QuantizeLinear_/Mixed_6c/branch7x7dbl_3/Relu_output_0_1" [id=337, type=QuantizeLinear]; -"338 DequantizeLinear_/Mixed_6c/branch7x7dbl_3/Relu_output_0_1" [id=338, type=DequantizeLinear]; -"339 QuantizeLinear_onnx^^Conv_1075_1" [id=339, label="339 QuantizeLinear_onnx::Conv_1075_1", type=QuantizeLinear]; -"340 DequantizeLinear_onnx^^Conv_1075_1" [id=340, label="340 DequantizeLinear_onnx::Conv_1075_1", type=DequantizeLinear]; -"341 /Mixed_6c/branch7x7dbl_4/conv/Conv" [id=341, type=Conv]; -"342 /Mixed_6c/branch7x7dbl_4/Relu" [id=342, type=Relu]; -"343 QuantizeLinear_/Mixed_6c/branch7x7dbl_4/Relu_output_0_1" [id=343, type=QuantizeLinear]; -"344 DequantizeLinear_/Mixed_6c/branch7x7dbl_4/Relu_output_0_1" [id=344, type=DequantizeLinear]; -"345 QuantizeLinear_onnx^^Conv_1078_1" [id=345, label="345 QuantizeLinear_onnx::Conv_1078_1", type=QuantizeLinear]; -"346 DequantizeLinear_onnx^^Conv_1078_1" [id=346, label="346 DequantizeLinear_onnx::Conv_1078_1", type=DequantizeLinear]; -"347 /Mixed_6c/branch7x7dbl_5/conv/Conv" [id=347, type=Conv]; -"348 /Mixed_6c/branch7x7dbl_5/Relu" [id=348, type=Relu]; -"349 /Mixed_6c/Constant" [id=349, type=Constant]; -"350 /Mixed_6c/Pad" [id=350, type=Pad]; -"351 /Mixed_6c/AveragePool" [id=351, type=AveragePool]; -"352 QuantizeLinear_/Mixed_6c/AveragePool_output_0_1" [id=352, type=QuantizeLinear]; -"353 DequantizeLinear_/Mixed_6c/AveragePool_output_0_1" [id=353, type=DequantizeLinear]; -"354 QuantizeLinear_onnx^^Conv_1081_1" [id=354, label="354 QuantizeLinear_onnx::Conv_1081_1", type=QuantizeLinear]; -"355 DequantizeLinear_onnx^^Conv_1081_1" [id=355, label="355 DequantizeLinear_onnx::Conv_1081_1", type=DequantizeLinear]; -"356 /Mixed_6c/branch_pool/conv/Conv" [id=356, type=Conv]; -"357 /Mixed_6c/branch_pool/Relu" [id=357, type=Relu]; -"358 QuantizeLinear_/Mixed_6c/branch7x7_3/Relu_output_0_1" [id=358, type=QuantizeLinear]; -"359 DequantizeLinear_/Mixed_6c/branch7x7_3/Relu_output_0_1" [id=359, type=DequantizeLinear]; -"360 QuantizeLinear_/Mixed_6c/branch1x1/Relu_output_0_1" [id=360, type=QuantizeLinear]; -"361 DequantizeLinear_/Mixed_6c/branch1x1/Relu_output_0_1" [id=361, type=DequantizeLinear]; -"362 QuantizeLinear_/Mixed_6c/branch_pool/Relu_output_0_1" [id=362, type=QuantizeLinear]; -"363 DequantizeLinear_/Mixed_6c/branch_pool/Relu_output_0_1" [id=363, type=DequantizeLinear]; -"364 QuantizeLinear_/Mixed_6c/branch7x7dbl_5/Relu_output_0_1" [id=364, type=QuantizeLinear]; -"365 DequantizeLinear_/Mixed_6c/branch7x7dbl_5/Relu_output_0_1" [id=365, type=DequantizeLinear]; -"366 /Mixed_6c/Concat" [id=366, type=Concat]; -"367 QuantizeLinear_onnx^^Conv_1084_1" [id=367, label="367 QuantizeLinear_onnx::Conv_1084_1", type=QuantizeLinear]; -"368 DequantizeLinear_onnx^^Conv_1084_1" [id=368, label="368 DequantizeLinear_onnx::Conv_1084_1", type=DequantizeLinear]; -"369 /Mixed_6d/branch1x1/conv/Conv" [id=369, type=Conv]; -"370 /Mixed_6d/branch1x1/Relu" [id=370, type=Relu]; -"371 QuantizeLinear_onnx^^Conv_1087_1" [id=371, label="371 QuantizeLinear_onnx::Conv_1087_1", type=QuantizeLinear]; -"372 DequantizeLinear_onnx^^Conv_1087_1" [id=372, label="372 DequantizeLinear_onnx::Conv_1087_1", type=DequantizeLinear]; -"373 /Mixed_6d/branch7x7_1/conv/Conv" [id=373, type=Conv]; -"374 /Mixed_6d/branch7x7_1/Relu" [id=374, type=Relu]; -"375 QuantizeLinear_/Mixed_6d/branch7x7_1/Relu_output_0_1" [id=375, type=QuantizeLinear]; -"376 DequantizeLinear_/Mixed_6d/branch7x7_1/Relu_output_0_1" [id=376, type=DequantizeLinear]; -"377 QuantizeLinear_onnx^^Conv_1090_1" [id=377, label="377 QuantizeLinear_onnx::Conv_1090_1", type=QuantizeLinear]; -"378 DequantizeLinear_onnx^^Conv_1090_1" [id=378, label="378 DequantizeLinear_onnx::Conv_1090_1", type=DequantizeLinear]; -"379 /Mixed_6d/branch7x7_2/conv/Conv" [id=379, type=Conv]; -"380 /Mixed_6d/branch7x7_2/Relu" [id=380, type=Relu]; -"381 QuantizeLinear_/Mixed_6d/branch7x7_2/Relu_output_0_1" [id=381, type=QuantizeLinear]; -"382 DequantizeLinear_/Mixed_6d/branch7x7_2/Relu_output_0_1" [id=382, type=DequantizeLinear]; -"383 QuantizeLinear_onnx^^Conv_1093_1" [id=383, label="383 QuantizeLinear_onnx::Conv_1093_1", type=QuantizeLinear]; -"384 DequantizeLinear_onnx^^Conv_1093_1" [id=384, label="384 DequantizeLinear_onnx::Conv_1093_1", type=DequantizeLinear]; -"385 /Mixed_6d/branch7x7_3/conv/Conv" [id=385, type=Conv]; -"386 /Mixed_6d/branch7x7_3/Relu" [id=386, type=Relu]; -"387 QuantizeLinear_onnx^^Conv_1096_1" [id=387, label="387 QuantizeLinear_onnx::Conv_1096_1", type=QuantizeLinear]; -"388 DequantizeLinear_onnx^^Conv_1096_1" [id=388, label="388 DequantizeLinear_onnx::Conv_1096_1", type=DequantizeLinear]; -"389 /Mixed_6d/branch7x7dbl_1/conv/Conv" [id=389, type=Conv]; -"390 /Mixed_6d/branch7x7dbl_1/Relu" [id=390, type=Relu]; -"391 QuantizeLinear_/Mixed_6d/branch7x7dbl_1/Relu_output_0_1" [id=391, type=QuantizeLinear]; -"392 DequantizeLinear_/Mixed_6d/branch7x7dbl_1/Relu_output_0_1" [id=392, type=DequantizeLinear]; -"393 QuantizeLinear_onnx^^Conv_1099_1" [id=393, label="393 QuantizeLinear_onnx::Conv_1099_1", type=QuantizeLinear]; -"394 DequantizeLinear_onnx^^Conv_1099_1" [id=394, label="394 DequantizeLinear_onnx::Conv_1099_1", type=DequantizeLinear]; -"395 /Mixed_6d/branch7x7dbl_2/conv/Conv" [id=395, type=Conv]; -"396 /Mixed_6d/branch7x7dbl_2/Relu" [id=396, type=Relu]; -"397 QuantizeLinear_/Mixed_6d/branch7x7dbl_2/Relu_output_0_1" [id=397, type=QuantizeLinear]; -"398 DequantizeLinear_/Mixed_6d/branch7x7dbl_2/Relu_output_0_1" [id=398, type=DequantizeLinear]; -"399 QuantizeLinear_onnx^^Conv_1102_1" [id=399, label="399 QuantizeLinear_onnx::Conv_1102_1", type=QuantizeLinear]; -"400 DequantizeLinear_onnx^^Conv_1102_1" [id=400, label="400 DequantizeLinear_onnx::Conv_1102_1", type=DequantizeLinear]; -"401 /Mixed_6d/branch7x7dbl_3/conv/Conv" [id=401, type=Conv]; -"402 /Mixed_6d/branch7x7dbl_3/Relu" [id=402, type=Relu]; -"403 QuantizeLinear_/Mixed_6d/branch7x7dbl_3/Relu_output_0_1" [id=403, type=QuantizeLinear]; -"404 DequantizeLinear_/Mixed_6d/branch7x7dbl_3/Relu_output_0_1" [id=404, type=DequantizeLinear]; -"405 QuantizeLinear_onnx^^Conv_1105_1" [id=405, label="405 QuantizeLinear_onnx::Conv_1105_1", type=QuantizeLinear]; -"406 DequantizeLinear_onnx^^Conv_1105_1" [id=406, label="406 DequantizeLinear_onnx::Conv_1105_1", type=DequantizeLinear]; -"407 /Mixed_6d/branch7x7dbl_4/conv/Conv" [id=407, type=Conv]; -"408 /Mixed_6d/branch7x7dbl_4/Relu" [id=408, type=Relu]; -"409 QuantizeLinear_/Mixed_6d/branch7x7dbl_4/Relu_output_0_1" [id=409, type=QuantizeLinear]; -"410 DequantizeLinear_/Mixed_6d/branch7x7dbl_4/Relu_output_0_1" [id=410, type=DequantizeLinear]; -"411 QuantizeLinear_onnx^^Conv_1108_1" [id=411, label="411 QuantizeLinear_onnx::Conv_1108_1", type=QuantizeLinear]; -"412 DequantizeLinear_onnx^^Conv_1108_1" [id=412, label="412 DequantizeLinear_onnx::Conv_1108_1", type=DequantizeLinear]; -"413 /Mixed_6d/branch7x7dbl_5/conv/Conv" [id=413, type=Conv]; -"414 /Mixed_6d/branch7x7dbl_5/Relu" [id=414, type=Relu]; -"415 /Mixed_6d/Constant" [id=415, type=Constant]; -"416 /Mixed_6d/Pad" [id=416, type=Pad]; -"417 /Mixed_6d/AveragePool" [id=417, type=AveragePool]; -"418 QuantizeLinear_/Mixed_6d/AveragePool_output_0_1" [id=418, type=QuantizeLinear]; -"419 DequantizeLinear_/Mixed_6d/AveragePool_output_0_1" [id=419, type=DequantizeLinear]; -"420 QuantizeLinear_onnx^^Conv_1111_1" [id=420, label="420 QuantizeLinear_onnx::Conv_1111_1", type=QuantizeLinear]; -"421 DequantizeLinear_onnx^^Conv_1111_1" [id=421, label="421 DequantizeLinear_onnx::Conv_1111_1", type=DequantizeLinear]; -"422 /Mixed_6d/branch_pool/conv/Conv" [id=422, type=Conv]; -"423 /Mixed_6d/branch_pool/Relu" [id=423, type=Relu]; -"424 QuantizeLinear_/Mixed_6d/branch7x7dbl_5/Relu_output_0_1" [id=424, type=QuantizeLinear]; -"425 DequantizeLinear_/Mixed_6d/branch7x7dbl_5/Relu_output_0_1" [id=425, type=DequantizeLinear]; -"426 QuantizeLinear_/Mixed_6d/branch_pool/Relu_output_0_1" [id=426, type=QuantizeLinear]; -"427 DequantizeLinear_/Mixed_6d/branch_pool/Relu_output_0_1" [id=427, type=DequantizeLinear]; -"428 QuantizeLinear_/Mixed_6d/branch1x1/Relu_output_0_1" [id=428, type=QuantizeLinear]; -"429 DequantizeLinear_/Mixed_6d/branch1x1/Relu_output_0_1" [id=429, type=DequantizeLinear]; -"430 QuantizeLinear_/Mixed_6d/branch7x7_3/Relu_output_0_1" [id=430, type=QuantizeLinear]; -"431 DequantizeLinear_/Mixed_6d/branch7x7_3/Relu_output_0_1" [id=431, type=DequantizeLinear]; -"432 /Mixed_6d/Concat" [id=432, type=Concat]; -"433 QuantizeLinear_onnx^^Conv_1114_1" [id=433, label="433 QuantizeLinear_onnx::Conv_1114_1", type=QuantizeLinear]; -"434 DequantizeLinear_onnx^^Conv_1114_1" [id=434, label="434 DequantizeLinear_onnx::Conv_1114_1", type=DequantizeLinear]; -"435 /Mixed_6e/branch1x1/conv/Conv" [id=435, type=Conv]; -"436 /Mixed_6e/branch1x1/Relu" [id=436, type=Relu]; -"437 QuantizeLinear_onnx^^Conv_1117_1" [id=437, label="437 QuantizeLinear_onnx::Conv_1117_1", type=QuantizeLinear]; -"438 DequantizeLinear_onnx^^Conv_1117_1" [id=438, label="438 DequantizeLinear_onnx::Conv_1117_1", type=DequantizeLinear]; -"439 /Mixed_6e/branch7x7_1/conv/Conv" [id=439, type=Conv]; -"440 /Mixed_6e/branch7x7_1/Relu" [id=440, type=Relu]; -"441 QuantizeLinear_/Mixed_6e/branch7x7_1/Relu_output_0_1" [id=441, type=QuantizeLinear]; -"442 DequantizeLinear_/Mixed_6e/branch7x7_1/Relu_output_0_1" [id=442, type=DequantizeLinear]; -"443 QuantizeLinear_onnx^^Conv_1120_1" [id=443, label="443 QuantizeLinear_onnx::Conv_1120_1", type=QuantizeLinear]; -"444 DequantizeLinear_onnx^^Conv_1120_1" [id=444, label="444 DequantizeLinear_onnx::Conv_1120_1", type=DequantizeLinear]; -"445 /Mixed_6e/branch7x7_2/conv/Conv" [id=445, type=Conv]; -"446 /Mixed_6e/branch7x7_2/Relu" [id=446, type=Relu]; -"447 QuantizeLinear_/Mixed_6e/branch7x7_2/Relu_output_0_1" [id=447, type=QuantizeLinear]; -"448 DequantizeLinear_/Mixed_6e/branch7x7_2/Relu_output_0_1" [id=448, type=DequantizeLinear]; -"449 QuantizeLinear_onnx^^Conv_1123_1" [id=449, label="449 QuantizeLinear_onnx::Conv_1123_1", type=QuantizeLinear]; -"450 DequantizeLinear_onnx^^Conv_1123_1" [id=450, label="450 DequantizeLinear_onnx::Conv_1123_1", type=DequantizeLinear]; -"451 /Mixed_6e/branch7x7_3/conv/Conv" [id=451, type=Conv]; -"452 /Mixed_6e/branch7x7_3/Relu" [id=452, type=Relu]; -"453 QuantizeLinear_onnx^^Conv_1126_1" [id=453, label="453 QuantizeLinear_onnx::Conv_1126_1", type=QuantizeLinear]; -"454 DequantizeLinear_onnx^^Conv_1126_1" [id=454, label="454 DequantizeLinear_onnx::Conv_1126_1", type=DequantizeLinear]; -"455 /Mixed_6e/branch7x7dbl_1/conv/Conv" [id=455, type=Conv]; -"456 /Mixed_6e/branch7x7dbl_1/Relu" [id=456, type=Relu]; -"457 QuantizeLinear_/Mixed_6e/branch7x7dbl_1/Relu_output_0_1" [id=457, type=QuantizeLinear]; -"458 DequantizeLinear_/Mixed_6e/branch7x7dbl_1/Relu_output_0_1" [id=458, type=DequantizeLinear]; -"459 QuantizeLinear_onnx^^Conv_1129_1" [id=459, label="459 QuantizeLinear_onnx::Conv_1129_1", type=QuantizeLinear]; -"460 DequantizeLinear_onnx^^Conv_1129_1" [id=460, label="460 DequantizeLinear_onnx::Conv_1129_1", type=DequantizeLinear]; -"461 /Mixed_6e/branch7x7dbl_2/conv/Conv" [id=461, type=Conv]; -"462 /Mixed_6e/branch7x7dbl_2/Relu" [id=462, type=Relu]; -"463 QuantizeLinear_/Mixed_6e/branch7x7dbl_2/Relu_output_0_1" [id=463, type=QuantizeLinear]; -"464 DequantizeLinear_/Mixed_6e/branch7x7dbl_2/Relu_output_0_1" [id=464, type=DequantizeLinear]; -"465 QuantizeLinear_onnx^^Conv_1132_1" [id=465, label="465 QuantizeLinear_onnx::Conv_1132_1", type=QuantizeLinear]; -"466 DequantizeLinear_onnx^^Conv_1132_1" [id=466, label="466 DequantizeLinear_onnx::Conv_1132_1", type=DequantizeLinear]; -"467 /Mixed_6e/branch7x7dbl_3/conv/Conv" [id=467, type=Conv]; -"468 /Mixed_6e/branch7x7dbl_3/Relu" [id=468, type=Relu]; -"469 QuantizeLinear_/Mixed_6e/branch7x7dbl_3/Relu_output_0_1" [id=469, type=QuantizeLinear]; -"470 DequantizeLinear_/Mixed_6e/branch7x7dbl_3/Relu_output_0_1" [id=470, type=DequantizeLinear]; -"471 QuantizeLinear_onnx^^Conv_1135_1" [id=471, label="471 QuantizeLinear_onnx::Conv_1135_1", type=QuantizeLinear]; -"472 DequantizeLinear_onnx^^Conv_1135_1" [id=472, label="472 DequantizeLinear_onnx::Conv_1135_1", type=DequantizeLinear]; -"473 /Mixed_6e/branch7x7dbl_4/conv/Conv" [id=473, type=Conv]; -"474 /Mixed_6e/branch7x7dbl_4/Relu" [id=474, type=Relu]; -"475 QuantizeLinear_/Mixed_6e/branch7x7dbl_4/Relu_output_0_1" [id=475, type=QuantizeLinear]; -"476 DequantizeLinear_/Mixed_6e/branch7x7dbl_4/Relu_output_0_1" [id=476, type=DequantizeLinear]; -"477 QuantizeLinear_onnx^^Conv_1138_1" [id=477, label="477 QuantizeLinear_onnx::Conv_1138_1", type=QuantizeLinear]; -"478 DequantizeLinear_onnx^^Conv_1138_1" [id=478, label="478 DequantizeLinear_onnx::Conv_1138_1", type=DequantizeLinear]; -"479 /Mixed_6e/branch7x7dbl_5/conv/Conv" [id=479, type=Conv]; -"480 /Mixed_6e/branch7x7dbl_5/Relu" [id=480, type=Relu]; -"481 /Mixed_6e/Constant" [id=481, type=Constant]; -"482 /Mixed_6e/Pad" [id=482, type=Pad]; -"483 /Mixed_6e/AveragePool" [id=483, type=AveragePool]; -"484 QuantizeLinear_/Mixed_6e/AveragePool_output_0_1" [id=484, type=QuantizeLinear]; -"485 DequantizeLinear_/Mixed_6e/AveragePool_output_0_1" [id=485, type=DequantizeLinear]; -"486 QuantizeLinear_onnx^^Conv_1141_1" [id=486, label="486 QuantizeLinear_onnx::Conv_1141_1", type=QuantizeLinear]; -"487 DequantizeLinear_onnx^^Conv_1141_1" [id=487, label="487 DequantizeLinear_onnx::Conv_1141_1", type=DequantizeLinear]; -"488 /Mixed_6e/branch_pool/conv/Conv" [id=488, type=Conv]; -"489 /Mixed_6e/branch_pool/Relu" [id=489, type=Relu]; -"490 QuantizeLinear_/Mixed_6e/branch7x7dbl_5/Relu_output_0_1" [id=490, type=QuantizeLinear]; -"491 DequantizeLinear_/Mixed_6e/branch7x7dbl_5/Relu_output_0_1" [id=491, type=DequantizeLinear]; -"492 QuantizeLinear_/Mixed_6e/branch_pool/Relu_output_0_1" [id=492, type=QuantizeLinear]; -"493 DequantizeLinear_/Mixed_6e/branch_pool/Relu_output_0_1" [id=493, type=DequantizeLinear]; -"494 QuantizeLinear_/Mixed_6e/branch7x7_3/Relu_output_0_1" [id=494, type=QuantizeLinear]; -"495 DequantizeLinear_/Mixed_6e/branch7x7_3/Relu_output_0_1" [id=495, type=DequantizeLinear]; -"496 QuantizeLinear_/Mixed_6e/branch1x1/Relu_output_0_1" [id=496, type=QuantizeLinear]; -"497 DequantizeLinear_/Mixed_6e/branch1x1/Relu_output_0_1" [id=497, type=DequantizeLinear]; -"498 /Mixed_6e/Concat" [id=498, type=Concat]; -"499 QuantizeLinear_onnx^^Conv_1144_1" [id=499, label="499 QuantizeLinear_onnx::Conv_1144_1", type=QuantizeLinear]; -"500 DequantizeLinear_onnx^^Conv_1144_1" [id=500, label="500 DequantizeLinear_onnx::Conv_1144_1", type=DequantizeLinear]; -"501 /Mixed_7a/branch3x3_1/conv/Conv" [id=501, type=Conv]; -"502 /Mixed_7a/branch3x3_1/Relu" [id=502, type=Relu]; -"503 QuantizeLinear_/Mixed_7a/branch3x3_1/Relu_output_0_1" [id=503, type=QuantizeLinear]; -"504 DequantizeLinear_/Mixed_7a/branch3x3_1/Relu_output_0_1" [id=504, type=DequantizeLinear]; -"505 QuantizeLinear_onnx^^Conv_1147_1" [id=505, label="505 QuantizeLinear_onnx::Conv_1147_1", type=QuantizeLinear]; -"506 DequantizeLinear_onnx^^Conv_1147_1" [id=506, label="506 DequantizeLinear_onnx::Conv_1147_1", type=DequantizeLinear]; -"507 /Mixed_7a/branch3x3_2/conv/Conv" [id=507, type=Conv]; -"508 /Mixed_7a/branch3x3_2/Relu" [id=508, type=Relu]; -"509 QuantizeLinear_onnx^^Conv_1150_1" [id=509, label="509 QuantizeLinear_onnx::Conv_1150_1", type=QuantizeLinear]; -"510 DequantizeLinear_onnx^^Conv_1150_1" [id=510, label="510 DequantizeLinear_onnx::Conv_1150_1", type=DequantizeLinear]; -"511 /Mixed_7a/branch7x7x3_1/conv/Conv" [id=511, type=Conv]; -"512 /Mixed_7a/branch7x7x3_1/Relu" [id=512, type=Relu]; -"513 QuantizeLinear_/Mixed_7a/branch7x7x3_1/Relu_output_0_1" [id=513, type=QuantizeLinear]; -"514 DequantizeLinear_/Mixed_7a/branch7x7x3_1/Relu_output_0_1" [id=514, type=DequantizeLinear]; -"515 QuantizeLinear_onnx^^Conv_1153_1" [id=515, label="515 QuantizeLinear_onnx::Conv_1153_1", type=QuantizeLinear]; -"516 DequantizeLinear_onnx^^Conv_1153_1" [id=516, label="516 DequantizeLinear_onnx::Conv_1153_1", type=DequantizeLinear]; -"517 /Mixed_7a/branch7x7x3_2/conv/Conv" [id=517, type=Conv]; -"518 /Mixed_7a/branch7x7x3_2/Relu" [id=518, type=Relu]; -"519 QuantizeLinear_/Mixed_7a/branch7x7x3_2/Relu_output_0_1" [id=519, type=QuantizeLinear]; -"520 DequantizeLinear_/Mixed_7a/branch7x7x3_2/Relu_output_0_1" [id=520, type=DequantizeLinear]; -"521 QuantizeLinear_onnx^^Conv_1156_1" [id=521, label="521 QuantizeLinear_onnx::Conv_1156_1", type=QuantizeLinear]; -"522 DequantizeLinear_onnx^^Conv_1156_1" [id=522, label="522 DequantizeLinear_onnx::Conv_1156_1", type=DequantizeLinear]; -"523 /Mixed_7a/branch7x7x3_3/conv/Conv" [id=523, type=Conv]; -"524 /Mixed_7a/branch7x7x3_3/Relu" [id=524, type=Relu]; -"525 QuantizeLinear_/Mixed_7a/branch7x7x3_3/Relu_output_0_1" [id=525, type=QuantizeLinear]; -"526 DequantizeLinear_/Mixed_7a/branch7x7x3_3/Relu_output_0_1" [id=526, type=DequantizeLinear]; -"527 QuantizeLinear_onnx^^Conv_1159_1" [id=527, label="527 QuantizeLinear_onnx::Conv_1159_1", type=QuantizeLinear]; -"528 DequantizeLinear_onnx^^Conv_1159_1" [id=528, label="528 DequantizeLinear_onnx::Conv_1159_1", type=DequantizeLinear]; -"529 /Mixed_7a/branch7x7x3_4/conv/Conv" [id=529, type=Conv]; -"530 /Mixed_7a/branch7x7x3_4/Relu" [id=530, type=Relu]; -"531 /Mixed_7a/MaxPool" [id=531, type=MaxPool]; -"532 QuantizeLinear_/Mixed_7a/branch7x7x3_4/Relu_output_0_1" [id=532, type=QuantizeLinear]; -"533 DequantizeLinear_/Mixed_7a/branch7x7x3_4/Relu_output_0_1" [id=533, type=DequantizeLinear]; -"534 QuantizeLinear_/Mixed_7a/branch3x3_2/Relu_output_0_1" [id=534, type=QuantizeLinear]; -"535 DequantizeLinear_/Mixed_7a/branch3x3_2/Relu_output_0_1" [id=535, type=DequantizeLinear]; -"536 /Mixed_7a/Concat" [id=536, type=Concat]; -"537 QuantizeLinear_onnx^^Conv_1162_1" [id=537, label="537 QuantizeLinear_onnx::Conv_1162_1", type=QuantizeLinear]; -"538 DequantizeLinear_onnx^^Conv_1162_1" [id=538, label="538 DequantizeLinear_onnx::Conv_1162_1", type=DequantizeLinear]; -"539 /Mixed_7b/branch1x1/conv/Conv" [id=539, type=Conv]; -"540 /Mixed_7b/branch1x1/Relu" [id=540, type=Relu]; -"541 QuantizeLinear_onnx^^Conv_1165_1" [id=541, label="541 QuantizeLinear_onnx::Conv_1165_1", type=QuantizeLinear]; -"542 DequantizeLinear_onnx^^Conv_1165_1" [id=542, label="542 DequantizeLinear_onnx::Conv_1165_1", type=DequantizeLinear]; -"543 /Mixed_7b/branch3x3_1/conv/Conv" [id=543, type=Conv]; -"544 /Mixed_7b/branch3x3_1/Relu" [id=544, type=Relu]; -"545 QuantizeLinear_/Mixed_7b/branch3x3_1/Relu_output_0_1" [id=545, type=QuantizeLinear]; -"546 DequantizeLinear_/Mixed_7b/branch3x3_1/Relu_output_0_1" [id=546, type=DequantizeLinear]; -"547 QuantizeLinear_onnx^^Conv_1168_1" [id=547, label="547 QuantizeLinear_onnx::Conv_1168_1", type=QuantizeLinear]; -"548 DequantizeLinear_onnx^^Conv_1168_1" [id=548, label="548 DequantizeLinear_onnx::Conv_1168_1", type=DequantizeLinear]; -"549 /Mixed_7b/branch3x3_2a/conv/Conv" [id=549, type=Conv]; -"550 /Mixed_7b/branch3x3_2a/Relu" [id=550, type=Relu]; -"551 QuantizeLinear_onnx^^Conv_1171_1" [id=551, label="551 QuantizeLinear_onnx::Conv_1171_1", type=QuantizeLinear]; -"552 DequantizeLinear_onnx^^Conv_1171_1" [id=552, label="552 DequantizeLinear_onnx::Conv_1171_1", type=DequantizeLinear]; -"553 /Mixed_7b/branch3x3_2b/conv/Conv" [id=553, type=Conv]; -"554 /Mixed_7b/branch3x3_2b/Relu" [id=554, type=Relu]; -"555 QuantizeLinear_onnx^^Conv_1174_1" [id=555, label="555 QuantizeLinear_onnx::Conv_1174_1", type=QuantizeLinear]; -"556 DequantizeLinear_onnx^^Conv_1174_1" [id=556, label="556 DequantizeLinear_onnx::Conv_1174_1", type=DequantizeLinear]; -"557 /Mixed_7b/branch3x3dbl_1/conv/Conv" [id=557, type=Conv]; -"558 /Mixed_7b/branch3x3dbl_1/Relu" [id=558, type=Relu]; -"559 QuantizeLinear_/Mixed_7b/branch3x3dbl_1/Relu_output_0_1" [id=559, type=QuantizeLinear]; -"560 DequantizeLinear_/Mixed_7b/branch3x3dbl_1/Relu_output_0_1" [id=560, type=DequantizeLinear]; -"561 QuantizeLinear_onnx^^Conv_1177_1" [id=561, label="561 QuantizeLinear_onnx::Conv_1177_1", type=QuantizeLinear]; -"562 DequantizeLinear_onnx^^Conv_1177_1" [id=562, label="562 DequantizeLinear_onnx::Conv_1177_1", type=DequantizeLinear]; -"563 /Mixed_7b/branch3x3dbl_2/conv/Conv" [id=563, type=Conv]; -"564 /Mixed_7b/branch3x3dbl_2/Relu" [id=564, type=Relu]; -"565 QuantizeLinear_/Mixed_7b/branch3x3dbl_2/Relu_output_0_1" [id=565, type=QuantizeLinear]; -"566 DequantizeLinear_/Mixed_7b/branch3x3dbl_2/Relu_output_0_1" [id=566, type=DequantizeLinear]; -"567 QuantizeLinear_onnx^^Conv_1180_1" [id=567, label="567 QuantizeLinear_onnx::Conv_1180_1", type=QuantizeLinear]; -"568 DequantizeLinear_onnx^^Conv_1180_1" [id=568, label="568 DequantizeLinear_onnx::Conv_1180_1", type=DequantizeLinear]; -"569 /Mixed_7b/branch3x3dbl_3a/conv/Conv" [id=569, type=Conv]; -"570 /Mixed_7b/branch3x3dbl_3a/Relu" [id=570, type=Relu]; -"571 QuantizeLinear_onnx^^Conv_1183_1" [id=571, label="571 QuantizeLinear_onnx::Conv_1183_1", type=QuantizeLinear]; -"572 DequantizeLinear_onnx^^Conv_1183_1" [id=572, label="572 DequantizeLinear_onnx::Conv_1183_1", type=DequantizeLinear]; -"573 /Mixed_7b/branch3x3dbl_3b/conv/Conv" [id=573, type=Conv]; -"574 /Mixed_7b/branch3x3dbl_3b/Relu" [id=574, type=Relu]; -"575 /Mixed_7b/Constant" [id=575, type=Constant]; -"576 /Mixed_7b/Pad" [id=576, type=Pad]; -"577 /Mixed_7b/AveragePool" [id=577, type=AveragePool]; -"578 QuantizeLinear_/Mixed_7b/AveragePool_output_0_1" [id=578, type=QuantizeLinear]; -"579 DequantizeLinear_/Mixed_7b/AveragePool_output_0_1" [id=579, type=DequantizeLinear]; -"580 QuantizeLinear_onnx^^Conv_1186_1" [id=580, label="580 QuantizeLinear_onnx::Conv_1186_1", type=QuantizeLinear]; -"581 DequantizeLinear_onnx^^Conv_1186_1" [id=581, label="581 DequantizeLinear_onnx::Conv_1186_1", type=DequantizeLinear]; -"582 /Mixed_7b/branch_pool/conv/Conv" [id=582, type=Conv]; -"583 /Mixed_7b/branch_pool/Relu" [id=583, type=Relu]; -"584 QuantizeLinear_/Mixed_7b/branch1x1/Relu_output_0_1" [id=584, type=QuantizeLinear]; -"585 DequantizeLinear_/Mixed_7b/branch1x1/Relu_output_0_1" [id=585, type=DequantizeLinear]; -"586 QuantizeLinear_/Mixed_7b/branch3x3_2a/Relu_output_0_1" [id=586, type=QuantizeLinear]; -"587 DequantizeLinear_/Mixed_7b/branch3x3_2a/Relu_output_0_1" [id=587, type=DequantizeLinear]; -"588 QuantizeLinear_/Mixed_7b/branch3x3_2b/Relu_output_0_1" [id=588, type=QuantizeLinear]; -"589 DequantizeLinear_/Mixed_7b/branch3x3_2b/Relu_output_0_1" [id=589, type=DequantizeLinear]; -"590 QuantizeLinear_/Mixed_7b/branch3x3dbl_3a/Relu_output_0_1" [id=590, type=QuantizeLinear]; -"591 DequantizeLinear_/Mixed_7b/branch3x3dbl_3a/Relu_output_0_1" [id=591, type=DequantizeLinear]; -"592 QuantizeLinear_/Mixed_7b/branch3x3dbl_3b/Relu_output_0_1" [id=592, type=QuantizeLinear]; -"593 DequantizeLinear_/Mixed_7b/branch3x3dbl_3b/Relu_output_0_1" [id=593, type=DequantizeLinear]; -"594 QuantizeLinear_/Mixed_7b/branch_pool/Relu_output_0_1" [id=594, type=QuantizeLinear]; -"595 DequantizeLinear_/Mixed_7b/branch_pool/Relu_output_0_1" [id=595, type=DequantizeLinear]; -"596 /Mixed_7b/Concat" [id=596, type=Concat]; -"597 QuantizeLinear_onnx^^Conv_1189_1" [id=597, label="597 QuantizeLinear_onnx::Conv_1189_1", type=QuantizeLinear]; -"598 DequantizeLinear_onnx^^Conv_1189_1" [id=598, label="598 DequantizeLinear_onnx::Conv_1189_1", type=DequantizeLinear]; -"599 /Mixed_7c/branch1x1/conv/Conv" [id=599, type=Conv]; -"600 /Mixed_7c/branch1x1/Relu" [id=600, type=Relu]; -"601 QuantizeLinear_onnx^^Conv_1192_1" [id=601, label="601 QuantizeLinear_onnx::Conv_1192_1", type=QuantizeLinear]; -"602 DequantizeLinear_onnx^^Conv_1192_1" [id=602, label="602 DequantizeLinear_onnx::Conv_1192_1", type=DequantizeLinear]; -"603 /Mixed_7c/branch3x3_1/conv/Conv" [id=603, type=Conv]; -"604 /Mixed_7c/branch3x3_1/Relu" [id=604, type=Relu]; -"605 QuantizeLinear_/Mixed_7c/branch3x3_1/Relu_output_0_1" [id=605, type=QuantizeLinear]; -"606 DequantizeLinear_/Mixed_7c/branch3x3_1/Relu_output_0_1" [id=606, type=DequantizeLinear]; -"607 QuantizeLinear_onnx^^Conv_1195_1" [id=607, label="607 QuantizeLinear_onnx::Conv_1195_1", type=QuantizeLinear]; -"608 DequantizeLinear_onnx^^Conv_1195_1" [id=608, label="608 DequantizeLinear_onnx::Conv_1195_1", type=DequantizeLinear]; -"609 /Mixed_7c/branch3x3_2a/conv/Conv" [id=609, type=Conv]; -"610 /Mixed_7c/branch3x3_2a/Relu" [id=610, type=Relu]; -"611 QuantizeLinear_onnx^^Conv_1198_1" [id=611, label="611 QuantizeLinear_onnx::Conv_1198_1", type=QuantizeLinear]; -"612 DequantizeLinear_onnx^^Conv_1198_1" [id=612, label="612 DequantizeLinear_onnx::Conv_1198_1", type=DequantizeLinear]; -"613 /Mixed_7c/branch3x3_2b/conv/Conv" [id=613, type=Conv]; -"614 /Mixed_7c/branch3x3_2b/Relu" [id=614, type=Relu]; -"615 QuantizeLinear_onnx^^Conv_1201_1" [id=615, label="615 QuantizeLinear_onnx::Conv_1201_1", type=QuantizeLinear]; -"616 DequantizeLinear_onnx^^Conv_1201_1" [id=616, label="616 DequantizeLinear_onnx::Conv_1201_1", type=DequantizeLinear]; -"617 /Mixed_7c/branch3x3dbl_1/conv/Conv" [id=617, type=Conv]; -"618 /Mixed_7c/branch3x3dbl_1/Relu" [id=618, type=Relu]; -"619 QuantizeLinear_/Mixed_7c/branch3x3dbl_1/Relu_output_0_1" [id=619, type=QuantizeLinear]; -"620 DequantizeLinear_/Mixed_7c/branch3x3dbl_1/Relu_output_0_1" [id=620, type=DequantizeLinear]; -"621 QuantizeLinear_onnx^^Conv_1204_1" [id=621, label="621 QuantizeLinear_onnx::Conv_1204_1", type=QuantizeLinear]; -"622 DequantizeLinear_onnx^^Conv_1204_1" [id=622, label="622 DequantizeLinear_onnx::Conv_1204_1", type=DequantizeLinear]; -"623 /Mixed_7c/branch3x3dbl_2/conv/Conv" [id=623, type=Conv]; -"624 /Mixed_7c/branch3x3dbl_2/Relu" [id=624, type=Relu]; -"625 QuantizeLinear_/Mixed_7c/branch3x3dbl_2/Relu_output_0_1" [id=625, type=QuantizeLinear]; -"626 DequantizeLinear_/Mixed_7c/branch3x3dbl_2/Relu_output_0_1" [id=626, type=DequantizeLinear]; -"627 QuantizeLinear_onnx^^Conv_1207_1" [id=627, label="627 QuantizeLinear_onnx::Conv_1207_1", type=QuantizeLinear]; -"628 DequantizeLinear_onnx^^Conv_1207_1" [id=628, label="628 DequantizeLinear_onnx::Conv_1207_1", type=DequantizeLinear]; -"629 /Mixed_7c/branch3x3dbl_3a/conv/Conv" [id=629, type=Conv]; -"630 /Mixed_7c/branch3x3dbl_3a/Relu" [id=630, type=Relu]; -"631 QuantizeLinear_onnx^^Conv_1210_1" [id=631, label="631 QuantizeLinear_onnx::Conv_1210_1", type=QuantizeLinear]; -"632 DequantizeLinear_onnx^^Conv_1210_1" [id=632, label="632 DequantizeLinear_onnx::Conv_1210_1", type=DequantizeLinear]; -"633 /Mixed_7c/branch3x3dbl_3b/conv/Conv" [id=633, type=Conv]; -"634 /Mixed_7c/branch3x3dbl_3b/Relu" [id=634, type=Relu]; -"635 /Mixed_7c/Constant" [id=635, type=Constant]; -"636 /Mixed_7c/Pad" [id=636, type=Pad]; -"637 /Mixed_7c/AveragePool" [id=637, type=AveragePool]; -"638 QuantizeLinear_/Mixed_7c/AveragePool_output_0_1" [id=638, type=QuantizeLinear]; -"639 DequantizeLinear_/Mixed_7c/AveragePool_output_0_1" [id=639, type=DequantizeLinear]; -"640 QuantizeLinear_onnx^^Conv_1213_1" [id=640, label="640 QuantizeLinear_onnx::Conv_1213_1", type=QuantizeLinear]; -"641 DequantizeLinear_onnx^^Conv_1213_1" [id=641, label="641 DequantizeLinear_onnx::Conv_1213_1", type=DequantizeLinear]; -"642 /Mixed_7c/branch_pool/conv/Conv" [id=642, type=Conv]; -"643 /Mixed_7c/branch_pool/Relu" [id=643, type=Relu]; -"644 QuantizeLinear_/Mixed_7c/branch1x1/Relu_output_0_1" [id=644, type=QuantizeLinear]; -"645 DequantizeLinear_/Mixed_7c/branch1x1/Relu_output_0_1" [id=645, type=DequantizeLinear]; -"646 QuantizeLinear_/Mixed_7c/branch3x3_2a/Relu_output_0_1" [id=646, type=QuantizeLinear]; -"647 DequantizeLinear_/Mixed_7c/branch3x3_2a/Relu_output_0_1" [id=647, type=DequantizeLinear]; -"648 QuantizeLinear_/Mixed_7c/branch3x3_2b/Relu_output_0_1" [id=648, type=QuantizeLinear]; -"649 DequantizeLinear_/Mixed_7c/branch3x3_2b/Relu_output_0_1" [id=649, type=DequantizeLinear]; -"650 QuantizeLinear_/Mixed_7c/branch3x3dbl_3a/Relu_output_0_1" [id=650, type=QuantizeLinear]; -"651 DequantizeLinear_/Mixed_7c/branch3x3dbl_3a/Relu_output_0_1" [id=651, type=DequantizeLinear]; -"652 QuantizeLinear_/Mixed_7c/branch3x3dbl_3b/Relu_output_0_1" [id=652, type=QuantizeLinear]; -"653 DequantizeLinear_/Mixed_7c/branch3x3dbl_3b/Relu_output_0_1" [id=653, type=DequantizeLinear]; -"654 QuantizeLinear_/Mixed_7c/branch_pool/Relu_output_0_1" [id=654, type=QuantizeLinear]; -"655 DequantizeLinear_/Mixed_7c/branch_pool/Relu_output_0_1" [id=655, type=DequantizeLinear]; -"656 /Mixed_7c/Concat" [id=656, type=Concat]; -"657 /avgpool/GlobalAveragePool" [id=657, type=GlobalAveragePool]; -"658 QuantizeLinear_/avgpool/GlobalAveragePool_output_0_1" [id=658, type=QuantizeLinear]; -"659 DequantizeLinear_/avgpool/GlobalAveragePool_output_0_1" [id=659, type=DequantizeLinear]; -"660 /Flatten" [id=660, type=Flatten]; -"661 QuantizeLinear_fc.weight_1" [id=661, type=QuantizeLinear]; -"662 DequantizeLinear_fc.weight_1" [id=662, type=DequantizeLinear]; -"663 /fc/Gemm" [id=663, type=Gemm]; -"664 nncf_model_input_0" [id=664, type=nncf_model_input]; -"665 nncf_model_output_0" [id=665, type=nncf_model_output]; +"110 /Mixed_5b/Concat" [id=110, type=Concat]; +"111 QuantizeLinear_onnx^^Conv_952_1" [id=111, label="111 QuantizeLinear_onnx::Conv_952_1", type=QuantizeLinear]; +"112 DequantizeLinear_onnx^^Conv_952_1" [id=112, label="112 DequantizeLinear_onnx::Conv_952_1", type=DequantizeLinear]; +"113 /Mixed_5c/branch1x1/conv/Conv" [id=113, type=Conv]; +"114 /Mixed_5c/branch1x1/Relu" [id=114, type=Relu]; +"115 QuantizeLinear_onnx^^Conv_955_1" [id=115, label="115 QuantizeLinear_onnx::Conv_955_1", type=QuantizeLinear]; +"116 DequantizeLinear_onnx^^Conv_955_1" [id=116, label="116 DequantizeLinear_onnx::Conv_955_1", type=DequantizeLinear]; +"117 /Mixed_5c/branch5x5_1/conv/Conv" [id=117, type=Conv]; +"118 /Mixed_5c/branch5x5_1/Relu" [id=118, type=Relu]; +"119 QuantizeLinear_/Mixed_5c/branch5x5_1/Relu_output_0_1" [id=119, type=QuantizeLinear]; +"120 DequantizeLinear_/Mixed_5c/branch5x5_1/Relu_output_0_1" [id=120, type=DequantizeLinear]; +"121 QuantizeLinear_onnx^^Conv_958_1" [id=121, label="121 QuantizeLinear_onnx::Conv_958_1", type=QuantizeLinear]; +"122 DequantizeLinear_onnx^^Conv_958_1" [id=122, label="122 DequantizeLinear_onnx::Conv_958_1", type=DequantizeLinear]; +"123 /Mixed_5c/branch5x5_2/conv/Conv" [id=123, type=Conv]; +"124 /Mixed_5c/branch5x5_2/Relu" [id=124, type=Relu]; +"125 QuantizeLinear_onnx^^Conv_961_1" [id=125, label="125 QuantizeLinear_onnx::Conv_961_1", type=QuantizeLinear]; +"126 DequantizeLinear_onnx^^Conv_961_1" [id=126, label="126 DequantizeLinear_onnx::Conv_961_1", type=DequantizeLinear]; +"127 /Mixed_5c/branch3x3dbl_1/conv/Conv" [id=127, type=Conv]; +"128 /Mixed_5c/branch3x3dbl_1/Relu" [id=128, type=Relu]; +"129 QuantizeLinear_/Mixed_5c/branch3x3dbl_1/Relu_output_0_1" [id=129, type=QuantizeLinear]; +"130 DequantizeLinear_/Mixed_5c/branch3x3dbl_1/Relu_output_0_1" [id=130, type=DequantizeLinear]; +"131 QuantizeLinear_onnx^^Conv_964_1" [id=131, label="131 QuantizeLinear_onnx::Conv_964_1", type=QuantizeLinear]; +"132 DequantizeLinear_onnx^^Conv_964_1" [id=132, label="132 DequantizeLinear_onnx::Conv_964_1", type=DequantizeLinear]; +"133 /Mixed_5c/branch3x3dbl_2/conv/Conv" [id=133, type=Conv]; +"134 /Mixed_5c/branch3x3dbl_2/Relu" [id=134, type=Relu]; +"135 QuantizeLinear_/Mixed_5c/branch3x3dbl_2/Relu_output_0_1" [id=135, type=QuantizeLinear]; +"136 DequantizeLinear_/Mixed_5c/branch3x3dbl_2/Relu_output_0_1" [id=136, type=DequantizeLinear]; +"137 QuantizeLinear_onnx^^Conv_967_1" [id=137, label="137 QuantizeLinear_onnx::Conv_967_1", type=QuantizeLinear]; +"138 DequantizeLinear_onnx^^Conv_967_1" [id=138, label="138 DequantizeLinear_onnx::Conv_967_1", type=DequantizeLinear]; +"139 /Mixed_5c/branch3x3dbl_3/conv/Conv" [id=139, type=Conv]; +"140 /Mixed_5c/branch3x3dbl_3/Relu" [id=140, type=Relu]; +"141 /Mixed_5c/AveragePool" [id=141, type=AveragePool]; +"142 QuantizeLinear_/Mixed_5c/AveragePool_output_0_1" [id=142, type=QuantizeLinear]; +"143 DequantizeLinear_/Mixed_5c/AveragePool_output_0_1" [id=143, type=DequantizeLinear]; +"144 QuantizeLinear_onnx^^Conv_970_1" [id=144, label="144 QuantizeLinear_onnx::Conv_970_1", type=QuantizeLinear]; +"145 DequantizeLinear_onnx^^Conv_970_1" [id=145, label="145 DequantizeLinear_onnx::Conv_970_1", type=DequantizeLinear]; +"146 /Mixed_5c/branch_pool/conv/Conv" [id=146, type=Conv]; +"147 /Mixed_5c/branch_pool/Relu" [id=147, type=Relu]; +"148 QuantizeLinear_/Mixed_5c/branch1x1/Relu_output_0_1" [id=148, type=QuantizeLinear]; +"149 DequantizeLinear_/Mixed_5c/branch1x1/Relu_output_0_1" [id=149, type=DequantizeLinear]; +"150 QuantizeLinear_/Mixed_5c/branch5x5_2/Relu_output_0_1" [id=150, type=QuantizeLinear]; +"151 DequantizeLinear_/Mixed_5c/branch5x5_2/Relu_output_0_1" [id=151, type=DequantizeLinear]; +"152 QuantizeLinear_/Mixed_5c/branch3x3dbl_3/Relu_output_0_1" [id=152, type=QuantizeLinear]; +"153 DequantizeLinear_/Mixed_5c/branch3x3dbl_3/Relu_output_0_1" [id=153, type=DequantizeLinear]; +"154 QuantizeLinear_/Mixed_5c/branch_pool/Relu_output_0_1" [id=154, type=QuantizeLinear]; +"155 DequantizeLinear_/Mixed_5c/branch_pool/Relu_output_0_1" [id=155, type=DequantizeLinear]; +"156 /Mixed_5c/Concat" [id=156, type=Concat]; +"157 QuantizeLinear_onnx^^Conv_973_1" [id=157, label="157 QuantizeLinear_onnx::Conv_973_1", type=QuantizeLinear]; +"158 DequantizeLinear_onnx^^Conv_973_1" [id=158, label="158 DequantizeLinear_onnx::Conv_973_1", type=DequantizeLinear]; +"159 /Mixed_5d/branch1x1/conv/Conv" [id=159, type=Conv]; +"160 /Mixed_5d/branch1x1/Relu" [id=160, type=Relu]; +"161 QuantizeLinear_onnx^^Conv_976_1" [id=161, label="161 QuantizeLinear_onnx::Conv_976_1", type=QuantizeLinear]; +"162 DequantizeLinear_onnx^^Conv_976_1" [id=162, label="162 DequantizeLinear_onnx::Conv_976_1", type=DequantizeLinear]; +"163 /Mixed_5d/branch5x5_1/conv/Conv" [id=163, type=Conv]; +"164 /Mixed_5d/branch5x5_1/Relu" [id=164, type=Relu]; +"165 QuantizeLinear_/Mixed_5d/branch5x5_1/Relu_output_0_1" [id=165, type=QuantizeLinear]; +"166 DequantizeLinear_/Mixed_5d/branch5x5_1/Relu_output_0_1" [id=166, type=DequantizeLinear]; +"167 QuantizeLinear_onnx^^Conv_979_1" [id=167, label="167 QuantizeLinear_onnx::Conv_979_1", type=QuantizeLinear]; +"168 DequantizeLinear_onnx^^Conv_979_1" [id=168, label="168 DequantizeLinear_onnx::Conv_979_1", type=DequantizeLinear]; +"169 /Mixed_5d/branch5x5_2/conv/Conv" [id=169, type=Conv]; +"170 /Mixed_5d/branch5x5_2/Relu" [id=170, type=Relu]; +"171 QuantizeLinear_onnx^^Conv_982_1" [id=171, label="171 QuantizeLinear_onnx::Conv_982_1", type=QuantizeLinear]; +"172 DequantizeLinear_onnx^^Conv_982_1" [id=172, label="172 DequantizeLinear_onnx::Conv_982_1", type=DequantizeLinear]; +"173 /Mixed_5d/branch3x3dbl_1/conv/Conv" [id=173, type=Conv]; +"174 /Mixed_5d/branch3x3dbl_1/Relu" [id=174, type=Relu]; +"175 QuantizeLinear_/Mixed_5d/branch3x3dbl_1/Relu_output_0_1" [id=175, type=QuantizeLinear]; +"176 DequantizeLinear_/Mixed_5d/branch3x3dbl_1/Relu_output_0_1" [id=176, type=DequantizeLinear]; +"177 QuantizeLinear_onnx^^Conv_985_1" [id=177, label="177 QuantizeLinear_onnx::Conv_985_1", type=QuantizeLinear]; +"178 DequantizeLinear_onnx^^Conv_985_1" [id=178, label="178 DequantizeLinear_onnx::Conv_985_1", type=DequantizeLinear]; +"179 /Mixed_5d/branch3x3dbl_2/conv/Conv" [id=179, type=Conv]; +"180 /Mixed_5d/branch3x3dbl_2/Relu" [id=180, type=Relu]; +"181 QuantizeLinear_/Mixed_5d/branch3x3dbl_2/Relu_output_0_1" [id=181, type=QuantizeLinear]; +"182 DequantizeLinear_/Mixed_5d/branch3x3dbl_2/Relu_output_0_1" [id=182, type=DequantizeLinear]; +"183 QuantizeLinear_onnx^^Conv_988_1" [id=183, label="183 QuantizeLinear_onnx::Conv_988_1", type=QuantizeLinear]; +"184 DequantizeLinear_onnx^^Conv_988_1" [id=184, label="184 DequantizeLinear_onnx::Conv_988_1", type=DequantizeLinear]; +"185 /Mixed_5d/branch3x3dbl_3/conv/Conv" [id=185, type=Conv]; +"186 /Mixed_5d/branch3x3dbl_3/Relu" [id=186, type=Relu]; +"187 /Mixed_5d/AveragePool" [id=187, type=AveragePool]; +"188 QuantizeLinear_/Mixed_5d/AveragePool_output_0_1" [id=188, type=QuantizeLinear]; +"189 DequantizeLinear_/Mixed_5d/AveragePool_output_0_1" [id=189, type=DequantizeLinear]; +"190 QuantizeLinear_onnx^^Conv_991_1" [id=190, label="190 QuantizeLinear_onnx::Conv_991_1", type=QuantizeLinear]; +"191 DequantizeLinear_onnx^^Conv_991_1" [id=191, label="191 DequantizeLinear_onnx::Conv_991_1", type=DequantizeLinear]; +"192 /Mixed_5d/branch_pool/conv/Conv" [id=192, type=Conv]; +"193 /Mixed_5d/branch_pool/Relu" [id=193, type=Relu]; +"194 QuantizeLinear_/Mixed_5d/branch1x1/Relu_output_0_1" [id=194, type=QuantizeLinear]; +"195 DequantizeLinear_/Mixed_5d/branch1x1/Relu_output_0_1" [id=195, type=DequantizeLinear]; +"196 QuantizeLinear_/Mixed_5d/branch5x5_2/Relu_output_0_1" [id=196, type=QuantizeLinear]; +"197 DequantizeLinear_/Mixed_5d/branch5x5_2/Relu_output_0_1" [id=197, type=DequantizeLinear]; +"198 QuantizeLinear_/Mixed_5d/branch3x3dbl_3/Relu_output_0_1" [id=198, type=QuantizeLinear]; +"199 DequantizeLinear_/Mixed_5d/branch3x3dbl_3/Relu_output_0_1" [id=199, type=DequantizeLinear]; +"200 QuantizeLinear_/Mixed_5d/branch_pool/Relu_output_0_1" [id=200, type=QuantizeLinear]; +"201 DequantizeLinear_/Mixed_5d/branch_pool/Relu_output_0_1" [id=201, type=DequantizeLinear]; +"202 /Mixed_5d/Concat" [id=202, type=Concat]; +"203 QuantizeLinear_onnx^^Conv_994_1" [id=203, label="203 QuantizeLinear_onnx::Conv_994_1", type=QuantizeLinear]; +"204 DequantizeLinear_onnx^^Conv_994_1" [id=204, label="204 DequantizeLinear_onnx::Conv_994_1", type=DequantizeLinear]; +"205 /Mixed_6a/branch3x3/conv/Conv" [id=205, type=Conv]; +"206 /Mixed_6a/branch3x3/Relu" [id=206, type=Relu]; +"207 QuantizeLinear_onnx^^Conv_997_1" [id=207, label="207 QuantizeLinear_onnx::Conv_997_1", type=QuantizeLinear]; +"208 DequantizeLinear_onnx^^Conv_997_1" [id=208, label="208 DequantizeLinear_onnx::Conv_997_1", type=DequantizeLinear]; +"209 /Mixed_6a/branch3x3dbl_1/conv/Conv" [id=209, type=Conv]; +"210 /Mixed_6a/branch3x3dbl_1/Relu" [id=210, type=Relu]; +"211 QuantizeLinear_/Mixed_6a/branch3x3dbl_1/Relu_output_0_1" [id=211, type=QuantizeLinear]; +"212 DequantizeLinear_/Mixed_6a/branch3x3dbl_1/Relu_output_0_1" [id=212, type=DequantizeLinear]; +"213 QuantizeLinear_onnx^^Conv_1000_1" [id=213, label="213 QuantizeLinear_onnx::Conv_1000_1", type=QuantizeLinear]; +"214 DequantizeLinear_onnx^^Conv_1000_1" [id=214, label="214 DequantizeLinear_onnx::Conv_1000_1", type=DequantizeLinear]; +"215 /Mixed_6a/branch3x3dbl_2/conv/Conv" [id=215, type=Conv]; +"216 /Mixed_6a/branch3x3dbl_2/Relu" [id=216, type=Relu]; +"217 QuantizeLinear_/Mixed_6a/branch3x3dbl_2/Relu_output_0_1" [id=217, type=QuantizeLinear]; +"218 DequantizeLinear_/Mixed_6a/branch3x3dbl_2/Relu_output_0_1" [id=218, type=DequantizeLinear]; +"219 QuantizeLinear_onnx^^Conv_1003_1" [id=219, label="219 QuantizeLinear_onnx::Conv_1003_1", type=QuantizeLinear]; +"220 DequantizeLinear_onnx^^Conv_1003_1" [id=220, label="220 DequantizeLinear_onnx::Conv_1003_1", type=DequantizeLinear]; +"221 /Mixed_6a/branch3x3dbl_3/conv/Conv" [id=221, type=Conv]; +"222 /Mixed_6a/branch3x3dbl_3/Relu" [id=222, type=Relu]; +"223 /Mixed_6a/MaxPool" [id=223, type=MaxPool]; +"224 QuantizeLinear_/Mixed_6a/branch3x3dbl_3/Relu_output_0_1" [id=224, type=QuantizeLinear]; +"225 DequantizeLinear_/Mixed_6a/branch3x3dbl_3/Relu_output_0_1" [id=225, type=DequantizeLinear]; +"226 QuantizeLinear_/Mixed_6a/branch3x3/Relu_output_0_1" [id=226, type=QuantizeLinear]; +"227 DequantizeLinear_/Mixed_6a/branch3x3/Relu_output_0_1" [id=227, type=DequantizeLinear]; +"228 /Mixed_6a/Concat" [id=228, type=Concat]; +"229 QuantizeLinear_onnx^^Conv_1006_1" [id=229, label="229 QuantizeLinear_onnx::Conv_1006_1", type=QuantizeLinear]; +"230 DequantizeLinear_onnx^^Conv_1006_1" [id=230, label="230 DequantizeLinear_onnx::Conv_1006_1", type=DequantizeLinear]; +"231 /Mixed_6b/branch1x1/conv/Conv" [id=231, type=Conv]; +"232 /Mixed_6b/branch1x1/Relu" [id=232, type=Relu]; +"233 QuantizeLinear_onnx^^Conv_1009_1" [id=233, label="233 QuantizeLinear_onnx::Conv_1009_1", type=QuantizeLinear]; +"234 DequantizeLinear_onnx^^Conv_1009_1" [id=234, label="234 DequantizeLinear_onnx::Conv_1009_1", type=DequantizeLinear]; +"235 /Mixed_6b/branch7x7_1/conv/Conv" [id=235, type=Conv]; +"236 /Mixed_6b/branch7x7_1/Relu" [id=236, type=Relu]; +"237 QuantizeLinear_/Mixed_6b/branch7x7_1/Relu_output_0_1" [id=237, type=QuantizeLinear]; +"238 DequantizeLinear_/Mixed_6b/branch7x7_1/Relu_output_0_1" [id=238, type=DequantizeLinear]; +"239 QuantizeLinear_onnx^^Conv_1012_1" [id=239, label="239 QuantizeLinear_onnx::Conv_1012_1", type=QuantizeLinear]; +"240 DequantizeLinear_onnx^^Conv_1012_1" [id=240, label="240 DequantizeLinear_onnx::Conv_1012_1", type=DequantizeLinear]; +"241 /Mixed_6b/branch7x7_2/conv/Conv" [id=241, type=Conv]; +"242 /Mixed_6b/branch7x7_2/Relu" [id=242, type=Relu]; +"243 QuantizeLinear_/Mixed_6b/branch7x7_2/Relu_output_0_1" [id=243, type=QuantizeLinear]; +"244 DequantizeLinear_/Mixed_6b/branch7x7_2/Relu_output_0_1" [id=244, type=DequantizeLinear]; +"245 QuantizeLinear_onnx^^Conv_1015_1" [id=245, label="245 QuantizeLinear_onnx::Conv_1015_1", type=QuantizeLinear]; +"246 DequantizeLinear_onnx^^Conv_1015_1" [id=246, label="246 DequantizeLinear_onnx::Conv_1015_1", type=DequantizeLinear]; +"247 /Mixed_6b/branch7x7_3/conv/Conv" [id=247, type=Conv]; +"248 /Mixed_6b/branch7x7_3/Relu" [id=248, type=Relu]; +"249 QuantizeLinear_onnx^^Conv_1018_1" [id=249, label="249 QuantizeLinear_onnx::Conv_1018_1", type=QuantizeLinear]; +"250 DequantizeLinear_onnx^^Conv_1018_1" [id=250, label="250 DequantizeLinear_onnx::Conv_1018_1", type=DequantizeLinear]; +"251 /Mixed_6b/branch7x7dbl_1/conv/Conv" [id=251, type=Conv]; +"252 /Mixed_6b/branch7x7dbl_1/Relu" [id=252, type=Relu]; +"253 QuantizeLinear_/Mixed_6b/branch7x7dbl_1/Relu_output_0_1" [id=253, type=QuantizeLinear]; +"254 DequantizeLinear_/Mixed_6b/branch7x7dbl_1/Relu_output_0_1" [id=254, type=DequantizeLinear]; +"255 QuantizeLinear_onnx^^Conv_1021_1" [id=255, label="255 QuantizeLinear_onnx::Conv_1021_1", type=QuantizeLinear]; +"256 DequantizeLinear_onnx^^Conv_1021_1" [id=256, label="256 DequantizeLinear_onnx::Conv_1021_1", type=DequantizeLinear]; +"257 /Mixed_6b/branch7x7dbl_2/conv/Conv" [id=257, type=Conv]; +"258 /Mixed_6b/branch7x7dbl_2/Relu" [id=258, type=Relu]; +"259 QuantizeLinear_/Mixed_6b/branch7x7dbl_2/Relu_output_0_1" [id=259, type=QuantizeLinear]; +"260 DequantizeLinear_/Mixed_6b/branch7x7dbl_2/Relu_output_0_1" [id=260, type=DequantizeLinear]; +"261 QuantizeLinear_onnx^^Conv_1024_1" [id=261, label="261 QuantizeLinear_onnx::Conv_1024_1", type=QuantizeLinear]; +"262 DequantizeLinear_onnx^^Conv_1024_1" [id=262, label="262 DequantizeLinear_onnx::Conv_1024_1", type=DequantizeLinear]; +"263 /Mixed_6b/branch7x7dbl_3/conv/Conv" [id=263, type=Conv]; +"264 /Mixed_6b/branch7x7dbl_3/Relu" [id=264, type=Relu]; +"265 QuantizeLinear_/Mixed_6b/branch7x7dbl_3/Relu_output_0_1" [id=265, type=QuantizeLinear]; +"266 DequantizeLinear_/Mixed_6b/branch7x7dbl_3/Relu_output_0_1" [id=266, type=DequantizeLinear]; +"267 QuantizeLinear_onnx^^Conv_1027_1" [id=267, label="267 QuantizeLinear_onnx::Conv_1027_1", type=QuantizeLinear]; +"268 DequantizeLinear_onnx^^Conv_1027_1" [id=268, label="268 DequantizeLinear_onnx::Conv_1027_1", type=DequantizeLinear]; +"269 /Mixed_6b/branch7x7dbl_4/conv/Conv" [id=269, type=Conv]; +"270 /Mixed_6b/branch7x7dbl_4/Relu" [id=270, type=Relu]; +"271 QuantizeLinear_/Mixed_6b/branch7x7dbl_4/Relu_output_0_1" [id=271, type=QuantizeLinear]; +"272 DequantizeLinear_/Mixed_6b/branch7x7dbl_4/Relu_output_0_1" [id=272, type=DequantizeLinear]; +"273 QuantizeLinear_onnx^^Conv_1030_1" [id=273, label="273 QuantizeLinear_onnx::Conv_1030_1", type=QuantizeLinear]; +"274 DequantizeLinear_onnx^^Conv_1030_1" [id=274, label="274 DequantizeLinear_onnx::Conv_1030_1", type=DequantizeLinear]; +"275 /Mixed_6b/branch7x7dbl_5/conv/Conv" [id=275, type=Conv]; +"276 /Mixed_6b/branch7x7dbl_5/Relu" [id=276, type=Relu]; +"277 /Mixed_6b/AveragePool" [id=277, type=AveragePool]; +"278 QuantizeLinear_/Mixed_6b/AveragePool_output_0_1" [id=278, type=QuantizeLinear]; +"279 DequantizeLinear_/Mixed_6b/AveragePool_output_0_1" [id=279, type=DequantizeLinear]; +"280 QuantizeLinear_onnx^^Conv_1033_1" [id=280, label="280 QuantizeLinear_onnx::Conv_1033_1", type=QuantizeLinear]; +"281 DequantizeLinear_onnx^^Conv_1033_1" [id=281, label="281 DequantizeLinear_onnx::Conv_1033_1", type=DequantizeLinear]; +"282 /Mixed_6b/branch_pool/conv/Conv" [id=282, type=Conv]; +"283 /Mixed_6b/branch_pool/Relu" [id=283, type=Relu]; +"284 QuantizeLinear_/Mixed_6b/branch7x7_3/Relu_output_0_1" [id=284, type=QuantizeLinear]; +"285 DequantizeLinear_/Mixed_6b/branch7x7_3/Relu_output_0_1" [id=285, type=DequantizeLinear]; +"286 QuantizeLinear_/Mixed_6b/branch7x7dbl_5/Relu_output_0_1" [id=286, type=QuantizeLinear]; +"287 DequantizeLinear_/Mixed_6b/branch7x7dbl_5/Relu_output_0_1" [id=287, type=DequantizeLinear]; +"288 QuantizeLinear_/Mixed_6b/branch1x1/Relu_output_0_1" [id=288, type=QuantizeLinear]; +"289 DequantizeLinear_/Mixed_6b/branch1x1/Relu_output_0_1" [id=289, type=DequantizeLinear]; +"290 QuantizeLinear_/Mixed_6b/branch_pool/Relu_output_0_1" [id=290, type=QuantizeLinear]; +"291 DequantizeLinear_/Mixed_6b/branch_pool/Relu_output_0_1" [id=291, type=DequantizeLinear]; +"292 /Mixed_6b/Concat" [id=292, type=Concat]; +"293 QuantizeLinear_onnx^^Conv_1036_1" [id=293, label="293 QuantizeLinear_onnx::Conv_1036_1", type=QuantizeLinear]; +"294 DequantizeLinear_onnx^^Conv_1036_1" [id=294, label="294 DequantizeLinear_onnx::Conv_1036_1", type=DequantizeLinear]; +"295 /Mixed_6c/branch1x1/conv/Conv" [id=295, type=Conv]; +"296 /Mixed_6c/branch1x1/Relu" [id=296, type=Relu]; +"297 QuantizeLinear_onnx^^Conv_1039_1" [id=297, label="297 QuantizeLinear_onnx::Conv_1039_1", type=QuantizeLinear]; +"298 DequantizeLinear_onnx^^Conv_1039_1" [id=298, label="298 DequantizeLinear_onnx::Conv_1039_1", type=DequantizeLinear]; +"299 /Mixed_6c/branch7x7_1/conv/Conv" [id=299, type=Conv]; +"300 /Mixed_6c/branch7x7_1/Relu" [id=300, type=Relu]; +"301 QuantizeLinear_/Mixed_6c/branch7x7_1/Relu_output_0_1" [id=301, type=QuantizeLinear]; +"302 DequantizeLinear_/Mixed_6c/branch7x7_1/Relu_output_0_1" [id=302, type=DequantizeLinear]; +"303 QuantizeLinear_onnx^^Conv_1042_1" [id=303, label="303 QuantizeLinear_onnx::Conv_1042_1", type=QuantizeLinear]; +"304 DequantizeLinear_onnx^^Conv_1042_1" [id=304, label="304 DequantizeLinear_onnx::Conv_1042_1", type=DequantizeLinear]; +"305 /Mixed_6c/branch7x7_2/conv/Conv" [id=305, type=Conv]; +"306 /Mixed_6c/branch7x7_2/Relu" [id=306, type=Relu]; +"307 QuantizeLinear_/Mixed_6c/branch7x7_2/Relu_output_0_1" [id=307, type=QuantizeLinear]; +"308 DequantizeLinear_/Mixed_6c/branch7x7_2/Relu_output_0_1" [id=308, type=DequantizeLinear]; +"309 QuantizeLinear_onnx^^Conv_1045_1" [id=309, label="309 QuantizeLinear_onnx::Conv_1045_1", type=QuantizeLinear]; +"310 DequantizeLinear_onnx^^Conv_1045_1" [id=310, label="310 DequantizeLinear_onnx::Conv_1045_1", type=DequantizeLinear]; +"311 /Mixed_6c/branch7x7_3/conv/Conv" [id=311, type=Conv]; +"312 /Mixed_6c/branch7x7_3/Relu" [id=312, type=Relu]; +"313 QuantizeLinear_onnx^^Conv_1048_1" [id=313, label="313 QuantizeLinear_onnx::Conv_1048_1", type=QuantizeLinear]; +"314 DequantizeLinear_onnx^^Conv_1048_1" [id=314, label="314 DequantizeLinear_onnx::Conv_1048_1", type=DequantizeLinear]; +"315 /Mixed_6c/branch7x7dbl_1/conv/Conv" [id=315, type=Conv]; +"316 /Mixed_6c/branch7x7dbl_1/Relu" [id=316, type=Relu]; +"317 QuantizeLinear_/Mixed_6c/branch7x7dbl_1/Relu_output_0_1" [id=317, type=QuantizeLinear]; +"318 DequantizeLinear_/Mixed_6c/branch7x7dbl_1/Relu_output_0_1" [id=318, type=DequantizeLinear]; +"319 QuantizeLinear_onnx^^Conv_1051_1" [id=319, label="319 QuantizeLinear_onnx::Conv_1051_1", type=QuantizeLinear]; +"320 DequantizeLinear_onnx^^Conv_1051_1" [id=320, label="320 DequantizeLinear_onnx::Conv_1051_1", type=DequantizeLinear]; +"321 /Mixed_6c/branch7x7dbl_2/conv/Conv" [id=321, type=Conv]; +"322 /Mixed_6c/branch7x7dbl_2/Relu" [id=322, type=Relu]; +"323 QuantizeLinear_/Mixed_6c/branch7x7dbl_2/Relu_output_0_1" [id=323, type=QuantizeLinear]; +"324 DequantizeLinear_/Mixed_6c/branch7x7dbl_2/Relu_output_0_1" [id=324, type=DequantizeLinear]; +"325 QuantizeLinear_onnx^^Conv_1054_1" [id=325, label="325 QuantizeLinear_onnx::Conv_1054_1", type=QuantizeLinear]; +"326 DequantizeLinear_onnx^^Conv_1054_1" [id=326, label="326 DequantizeLinear_onnx::Conv_1054_1", type=DequantizeLinear]; +"327 /Mixed_6c/branch7x7dbl_3/conv/Conv" [id=327, type=Conv]; +"328 /Mixed_6c/branch7x7dbl_3/Relu" [id=328, type=Relu]; +"329 QuantizeLinear_/Mixed_6c/branch7x7dbl_3/Relu_output_0_1" [id=329, type=QuantizeLinear]; +"330 DequantizeLinear_/Mixed_6c/branch7x7dbl_3/Relu_output_0_1" [id=330, type=DequantizeLinear]; +"331 QuantizeLinear_onnx^^Conv_1057_1" [id=331, label="331 QuantizeLinear_onnx::Conv_1057_1", type=QuantizeLinear]; +"332 DequantizeLinear_onnx^^Conv_1057_1" [id=332, label="332 DequantizeLinear_onnx::Conv_1057_1", type=DequantizeLinear]; +"333 /Mixed_6c/branch7x7dbl_4/conv/Conv" [id=333, type=Conv]; +"334 /Mixed_6c/branch7x7dbl_4/Relu" [id=334, type=Relu]; +"335 QuantizeLinear_/Mixed_6c/branch7x7dbl_4/Relu_output_0_1" [id=335, type=QuantizeLinear]; +"336 DequantizeLinear_/Mixed_6c/branch7x7dbl_4/Relu_output_0_1" [id=336, type=DequantizeLinear]; +"337 QuantizeLinear_onnx^^Conv_1060_1" [id=337, label="337 QuantizeLinear_onnx::Conv_1060_1", type=QuantizeLinear]; +"338 DequantizeLinear_onnx^^Conv_1060_1" [id=338, label="338 DequantizeLinear_onnx::Conv_1060_1", type=DequantizeLinear]; +"339 /Mixed_6c/branch7x7dbl_5/conv/Conv" [id=339, type=Conv]; +"340 /Mixed_6c/branch7x7dbl_5/Relu" [id=340, type=Relu]; +"341 /Mixed_6c/AveragePool" [id=341, type=AveragePool]; +"342 QuantizeLinear_/Mixed_6c/AveragePool_output_0_1" [id=342, type=QuantizeLinear]; +"343 DequantizeLinear_/Mixed_6c/AveragePool_output_0_1" [id=343, type=DequantizeLinear]; +"344 QuantizeLinear_onnx^^Conv_1063_1" [id=344, label="344 QuantizeLinear_onnx::Conv_1063_1", type=QuantizeLinear]; +"345 DequantizeLinear_onnx^^Conv_1063_1" [id=345, label="345 DequantizeLinear_onnx::Conv_1063_1", type=DequantizeLinear]; +"346 /Mixed_6c/branch_pool/conv/Conv" [id=346, type=Conv]; +"347 /Mixed_6c/branch_pool/Relu" [id=347, type=Relu]; +"348 QuantizeLinear_/Mixed_6c/branch1x1/Relu_output_0_1" [id=348, type=QuantizeLinear]; +"349 DequantizeLinear_/Mixed_6c/branch1x1/Relu_output_0_1" [id=349, type=DequantizeLinear]; +"350 QuantizeLinear_/Mixed_6c/branch7x7_3/Relu_output_0_1" [id=350, type=QuantizeLinear]; +"351 DequantizeLinear_/Mixed_6c/branch7x7_3/Relu_output_0_1" [id=351, type=DequantizeLinear]; +"352 QuantizeLinear_/Mixed_6c/branch7x7dbl_5/Relu_output_0_1" [id=352, type=QuantizeLinear]; +"353 DequantizeLinear_/Mixed_6c/branch7x7dbl_5/Relu_output_0_1" [id=353, type=DequantizeLinear]; +"354 QuantizeLinear_/Mixed_6c/branch_pool/Relu_output_0_1" [id=354, type=QuantizeLinear]; +"355 DequantizeLinear_/Mixed_6c/branch_pool/Relu_output_0_1" [id=355, type=DequantizeLinear]; +"356 /Mixed_6c/Concat" [id=356, type=Concat]; +"357 QuantizeLinear_onnx^^Conv_1066_1" [id=357, label="357 QuantizeLinear_onnx::Conv_1066_1", type=QuantizeLinear]; +"358 DequantizeLinear_onnx^^Conv_1066_1" [id=358, label="358 DequantizeLinear_onnx::Conv_1066_1", type=DequantizeLinear]; +"359 /Mixed_6d/branch1x1/conv/Conv" [id=359, type=Conv]; +"360 /Mixed_6d/branch1x1/Relu" [id=360, type=Relu]; +"361 QuantizeLinear_onnx^^Conv_1069_1" [id=361, label="361 QuantizeLinear_onnx::Conv_1069_1", type=QuantizeLinear]; +"362 DequantizeLinear_onnx^^Conv_1069_1" [id=362, label="362 DequantizeLinear_onnx::Conv_1069_1", type=DequantizeLinear]; +"363 /Mixed_6d/branch7x7_1/conv/Conv" [id=363, type=Conv]; +"364 /Mixed_6d/branch7x7_1/Relu" [id=364, type=Relu]; +"365 QuantizeLinear_/Mixed_6d/branch7x7_1/Relu_output_0_1" [id=365, type=QuantizeLinear]; +"366 DequantizeLinear_/Mixed_6d/branch7x7_1/Relu_output_0_1" [id=366, type=DequantizeLinear]; +"367 QuantizeLinear_onnx^^Conv_1072_1" [id=367, label="367 QuantizeLinear_onnx::Conv_1072_1", type=QuantizeLinear]; +"368 DequantizeLinear_onnx^^Conv_1072_1" [id=368, label="368 DequantizeLinear_onnx::Conv_1072_1", type=DequantizeLinear]; +"369 /Mixed_6d/branch7x7_2/conv/Conv" [id=369, type=Conv]; +"370 /Mixed_6d/branch7x7_2/Relu" [id=370, type=Relu]; +"371 QuantizeLinear_/Mixed_6d/branch7x7_2/Relu_output_0_1" [id=371, type=QuantizeLinear]; +"372 DequantizeLinear_/Mixed_6d/branch7x7_2/Relu_output_0_1" [id=372, type=DequantizeLinear]; +"373 QuantizeLinear_onnx^^Conv_1075_1" [id=373, label="373 QuantizeLinear_onnx::Conv_1075_1", type=QuantizeLinear]; +"374 DequantizeLinear_onnx^^Conv_1075_1" [id=374, label="374 DequantizeLinear_onnx::Conv_1075_1", type=DequantizeLinear]; +"375 /Mixed_6d/branch7x7_3/conv/Conv" [id=375, type=Conv]; +"376 /Mixed_6d/branch7x7_3/Relu" [id=376, type=Relu]; +"377 QuantizeLinear_onnx^^Conv_1078_1" [id=377, label="377 QuantizeLinear_onnx::Conv_1078_1", type=QuantizeLinear]; +"378 DequantizeLinear_onnx^^Conv_1078_1" [id=378, label="378 DequantizeLinear_onnx::Conv_1078_1", type=DequantizeLinear]; +"379 /Mixed_6d/branch7x7dbl_1/conv/Conv" [id=379, type=Conv]; +"380 /Mixed_6d/branch7x7dbl_1/Relu" [id=380, type=Relu]; +"381 QuantizeLinear_/Mixed_6d/branch7x7dbl_1/Relu_output_0_1" [id=381, type=QuantizeLinear]; +"382 DequantizeLinear_/Mixed_6d/branch7x7dbl_1/Relu_output_0_1" [id=382, type=DequantizeLinear]; +"383 QuantizeLinear_onnx^^Conv_1081_1" [id=383, label="383 QuantizeLinear_onnx::Conv_1081_1", type=QuantizeLinear]; +"384 DequantizeLinear_onnx^^Conv_1081_1" [id=384, label="384 DequantizeLinear_onnx::Conv_1081_1", type=DequantizeLinear]; +"385 /Mixed_6d/branch7x7dbl_2/conv/Conv" [id=385, type=Conv]; +"386 /Mixed_6d/branch7x7dbl_2/Relu" [id=386, type=Relu]; +"387 QuantizeLinear_/Mixed_6d/branch7x7dbl_2/Relu_output_0_1" [id=387, type=QuantizeLinear]; +"388 DequantizeLinear_/Mixed_6d/branch7x7dbl_2/Relu_output_0_1" [id=388, type=DequantizeLinear]; +"389 QuantizeLinear_onnx^^Conv_1084_1" [id=389, label="389 QuantizeLinear_onnx::Conv_1084_1", type=QuantizeLinear]; +"390 DequantizeLinear_onnx^^Conv_1084_1" [id=390, label="390 DequantizeLinear_onnx::Conv_1084_1", type=DequantizeLinear]; +"391 /Mixed_6d/branch7x7dbl_3/conv/Conv" [id=391, type=Conv]; +"392 /Mixed_6d/branch7x7dbl_3/Relu" [id=392, type=Relu]; +"393 QuantizeLinear_/Mixed_6d/branch7x7dbl_3/Relu_output_0_1" [id=393, type=QuantizeLinear]; +"394 DequantizeLinear_/Mixed_6d/branch7x7dbl_3/Relu_output_0_1" [id=394, type=DequantizeLinear]; +"395 QuantizeLinear_onnx^^Conv_1087_1" [id=395, label="395 QuantizeLinear_onnx::Conv_1087_1", type=QuantizeLinear]; +"396 DequantizeLinear_onnx^^Conv_1087_1" [id=396, label="396 DequantizeLinear_onnx::Conv_1087_1", type=DequantizeLinear]; +"397 /Mixed_6d/branch7x7dbl_4/conv/Conv" [id=397, type=Conv]; +"398 /Mixed_6d/branch7x7dbl_4/Relu" [id=398, type=Relu]; +"399 QuantizeLinear_/Mixed_6d/branch7x7dbl_4/Relu_output_0_1" [id=399, type=QuantizeLinear]; +"400 DequantizeLinear_/Mixed_6d/branch7x7dbl_4/Relu_output_0_1" [id=400, type=DequantizeLinear]; +"401 QuantizeLinear_onnx^^Conv_1090_1" [id=401, label="401 QuantizeLinear_onnx::Conv_1090_1", type=QuantizeLinear]; +"402 DequantizeLinear_onnx^^Conv_1090_1" [id=402, label="402 DequantizeLinear_onnx::Conv_1090_1", type=DequantizeLinear]; +"403 /Mixed_6d/branch7x7dbl_5/conv/Conv" [id=403, type=Conv]; +"404 /Mixed_6d/branch7x7dbl_5/Relu" [id=404, type=Relu]; +"405 /Mixed_6d/AveragePool" [id=405, type=AveragePool]; +"406 QuantizeLinear_/Mixed_6d/AveragePool_output_0_1" [id=406, type=QuantizeLinear]; +"407 DequantizeLinear_/Mixed_6d/AveragePool_output_0_1" [id=407, type=DequantizeLinear]; +"408 QuantizeLinear_onnx^^Conv_1093_1" [id=408, label="408 QuantizeLinear_onnx::Conv_1093_1", type=QuantizeLinear]; +"409 DequantizeLinear_onnx^^Conv_1093_1" [id=409, label="409 DequantizeLinear_onnx::Conv_1093_1", type=DequantizeLinear]; +"410 /Mixed_6d/branch_pool/conv/Conv" [id=410, type=Conv]; +"411 /Mixed_6d/branch_pool/Relu" [id=411, type=Relu]; +"412 QuantizeLinear_/Mixed_6d/branch7x7dbl_5/Relu_output_0_1" [id=412, type=QuantizeLinear]; +"413 DequantizeLinear_/Mixed_6d/branch7x7dbl_5/Relu_output_0_1" [id=413, type=DequantizeLinear]; +"414 QuantizeLinear_/Mixed_6d/branch_pool/Relu_output_0_1" [id=414, type=QuantizeLinear]; +"415 DequantizeLinear_/Mixed_6d/branch_pool/Relu_output_0_1" [id=415, type=DequantizeLinear]; +"416 QuantizeLinear_/Mixed_6d/branch1x1/Relu_output_0_1" [id=416, type=QuantizeLinear]; +"417 DequantizeLinear_/Mixed_6d/branch1x1/Relu_output_0_1" [id=417, type=DequantizeLinear]; +"418 QuantizeLinear_/Mixed_6d/branch7x7_3/Relu_output_0_1" [id=418, type=QuantizeLinear]; +"419 DequantizeLinear_/Mixed_6d/branch7x7_3/Relu_output_0_1" [id=419, type=DequantizeLinear]; +"420 /Mixed_6d/Concat" [id=420, type=Concat]; +"421 QuantizeLinear_onnx^^Conv_1096_1" [id=421, label="421 QuantizeLinear_onnx::Conv_1096_1", type=QuantizeLinear]; +"422 DequantizeLinear_onnx^^Conv_1096_1" [id=422, label="422 DequantizeLinear_onnx::Conv_1096_1", type=DequantizeLinear]; +"423 /Mixed_6e/branch1x1/conv/Conv" [id=423, type=Conv]; +"424 /Mixed_6e/branch1x1/Relu" [id=424, type=Relu]; +"425 QuantizeLinear_onnx^^Conv_1099_1" [id=425, label="425 QuantizeLinear_onnx::Conv_1099_1", type=QuantizeLinear]; +"426 DequantizeLinear_onnx^^Conv_1099_1" [id=426, label="426 DequantizeLinear_onnx::Conv_1099_1", type=DequantizeLinear]; +"427 /Mixed_6e/branch7x7_1/conv/Conv" [id=427, type=Conv]; +"428 /Mixed_6e/branch7x7_1/Relu" [id=428, type=Relu]; +"429 QuantizeLinear_/Mixed_6e/branch7x7_1/Relu_output_0_1" [id=429, type=QuantizeLinear]; +"430 DequantizeLinear_/Mixed_6e/branch7x7_1/Relu_output_0_1" [id=430, type=DequantizeLinear]; +"431 QuantizeLinear_onnx^^Conv_1102_1" [id=431, label="431 QuantizeLinear_onnx::Conv_1102_1", type=QuantizeLinear]; +"432 DequantizeLinear_onnx^^Conv_1102_1" [id=432, label="432 DequantizeLinear_onnx::Conv_1102_1", type=DequantizeLinear]; +"433 /Mixed_6e/branch7x7_2/conv/Conv" [id=433, type=Conv]; +"434 /Mixed_6e/branch7x7_2/Relu" [id=434, type=Relu]; +"435 QuantizeLinear_/Mixed_6e/branch7x7_2/Relu_output_0_1" [id=435, type=QuantizeLinear]; +"436 DequantizeLinear_/Mixed_6e/branch7x7_2/Relu_output_0_1" [id=436, type=DequantizeLinear]; +"437 QuantizeLinear_onnx^^Conv_1105_1" [id=437, label="437 QuantizeLinear_onnx::Conv_1105_1", type=QuantizeLinear]; +"438 DequantizeLinear_onnx^^Conv_1105_1" [id=438, label="438 DequantizeLinear_onnx::Conv_1105_1", type=DequantizeLinear]; +"439 /Mixed_6e/branch7x7_3/conv/Conv" [id=439, type=Conv]; +"440 /Mixed_6e/branch7x7_3/Relu" [id=440, type=Relu]; +"441 QuantizeLinear_onnx^^Conv_1108_1" [id=441, label="441 QuantizeLinear_onnx::Conv_1108_1", type=QuantizeLinear]; +"442 DequantizeLinear_onnx^^Conv_1108_1" [id=442, label="442 DequantizeLinear_onnx::Conv_1108_1", type=DequantizeLinear]; +"443 /Mixed_6e/branch7x7dbl_1/conv/Conv" [id=443, type=Conv]; +"444 /Mixed_6e/branch7x7dbl_1/Relu" [id=444, type=Relu]; +"445 QuantizeLinear_/Mixed_6e/branch7x7dbl_1/Relu_output_0_1" [id=445, type=QuantizeLinear]; +"446 DequantizeLinear_/Mixed_6e/branch7x7dbl_1/Relu_output_0_1" [id=446, type=DequantizeLinear]; +"447 QuantizeLinear_onnx^^Conv_1111_1" [id=447, label="447 QuantizeLinear_onnx::Conv_1111_1", type=QuantizeLinear]; +"448 DequantizeLinear_onnx^^Conv_1111_1" [id=448, label="448 DequantizeLinear_onnx::Conv_1111_1", type=DequantizeLinear]; +"449 /Mixed_6e/branch7x7dbl_2/conv/Conv" [id=449, type=Conv]; +"450 /Mixed_6e/branch7x7dbl_2/Relu" [id=450, type=Relu]; +"451 QuantizeLinear_/Mixed_6e/branch7x7dbl_2/Relu_output_0_1" [id=451, type=QuantizeLinear]; +"452 DequantizeLinear_/Mixed_6e/branch7x7dbl_2/Relu_output_0_1" [id=452, type=DequantizeLinear]; +"453 QuantizeLinear_onnx^^Conv_1114_1" [id=453, label="453 QuantizeLinear_onnx::Conv_1114_1", type=QuantizeLinear]; +"454 DequantizeLinear_onnx^^Conv_1114_1" [id=454, label="454 DequantizeLinear_onnx::Conv_1114_1", type=DequantizeLinear]; +"455 /Mixed_6e/branch7x7dbl_3/conv/Conv" [id=455, type=Conv]; +"456 /Mixed_6e/branch7x7dbl_3/Relu" [id=456, type=Relu]; +"457 QuantizeLinear_/Mixed_6e/branch7x7dbl_3/Relu_output_0_1" [id=457, type=QuantizeLinear]; +"458 DequantizeLinear_/Mixed_6e/branch7x7dbl_3/Relu_output_0_1" [id=458, type=DequantizeLinear]; +"459 QuantizeLinear_onnx^^Conv_1117_1" [id=459, label="459 QuantizeLinear_onnx::Conv_1117_1", type=QuantizeLinear]; +"460 DequantizeLinear_onnx^^Conv_1117_1" [id=460, label="460 DequantizeLinear_onnx::Conv_1117_1", type=DequantizeLinear]; +"461 /Mixed_6e/branch7x7dbl_4/conv/Conv" [id=461, type=Conv]; +"462 /Mixed_6e/branch7x7dbl_4/Relu" [id=462, type=Relu]; +"463 QuantizeLinear_/Mixed_6e/branch7x7dbl_4/Relu_output_0_1" [id=463, type=QuantizeLinear]; +"464 DequantizeLinear_/Mixed_6e/branch7x7dbl_4/Relu_output_0_1" [id=464, type=DequantizeLinear]; +"465 QuantizeLinear_onnx^^Conv_1120_1" [id=465, label="465 QuantizeLinear_onnx::Conv_1120_1", type=QuantizeLinear]; +"466 DequantizeLinear_onnx^^Conv_1120_1" [id=466, label="466 DequantizeLinear_onnx::Conv_1120_1", type=DequantizeLinear]; +"467 /Mixed_6e/branch7x7dbl_5/conv/Conv" [id=467, type=Conv]; +"468 /Mixed_6e/branch7x7dbl_5/Relu" [id=468, type=Relu]; +"469 /Mixed_6e/AveragePool" [id=469, type=AveragePool]; +"470 QuantizeLinear_/Mixed_6e/AveragePool_output_0_1" [id=470, type=QuantizeLinear]; +"471 DequantizeLinear_/Mixed_6e/AveragePool_output_0_1" [id=471, type=DequantizeLinear]; +"472 QuantizeLinear_onnx^^Conv_1123_1" [id=472, label="472 QuantizeLinear_onnx::Conv_1123_1", type=QuantizeLinear]; +"473 DequantizeLinear_onnx^^Conv_1123_1" [id=473, label="473 DequantizeLinear_onnx::Conv_1123_1", type=DequantizeLinear]; +"474 /Mixed_6e/branch_pool/conv/Conv" [id=474, type=Conv]; +"475 /Mixed_6e/branch_pool/Relu" [id=475, type=Relu]; +"476 QuantizeLinear_/Mixed_6e/branch7x7dbl_5/Relu_output_0_1" [id=476, type=QuantizeLinear]; +"477 DequantizeLinear_/Mixed_6e/branch7x7dbl_5/Relu_output_0_1" [id=477, type=DequantizeLinear]; +"478 QuantizeLinear_/Mixed_6e/branch_pool/Relu_output_0_1" [id=478, type=QuantizeLinear]; +"479 DequantizeLinear_/Mixed_6e/branch_pool/Relu_output_0_1" [id=479, type=DequantizeLinear]; +"480 QuantizeLinear_/Mixed_6e/branch1x1/Relu_output_0_1" [id=480, type=QuantizeLinear]; +"481 DequantizeLinear_/Mixed_6e/branch1x1/Relu_output_0_1" [id=481, type=DequantizeLinear]; +"482 QuantizeLinear_/Mixed_6e/branch7x7_3/Relu_output_0_1" [id=482, type=QuantizeLinear]; +"483 DequantizeLinear_/Mixed_6e/branch7x7_3/Relu_output_0_1" [id=483, type=DequantizeLinear]; +"484 /Mixed_6e/Concat" [id=484, type=Concat]; +"485 QuantizeLinear_onnx^^Conv_1126_1" [id=485, label="485 QuantizeLinear_onnx::Conv_1126_1", type=QuantizeLinear]; +"486 DequantizeLinear_onnx^^Conv_1126_1" [id=486, label="486 DequantizeLinear_onnx::Conv_1126_1", type=DequantizeLinear]; +"487 /Mixed_7a/branch3x3_1/conv/Conv" [id=487, type=Conv]; +"488 /Mixed_7a/branch3x3_1/Relu" [id=488, type=Relu]; +"489 QuantizeLinear_/Mixed_7a/branch3x3_1/Relu_output_0_1" [id=489, type=QuantizeLinear]; +"490 DequantizeLinear_/Mixed_7a/branch3x3_1/Relu_output_0_1" [id=490, type=DequantizeLinear]; +"491 QuantizeLinear_onnx^^Conv_1129_1" [id=491, label="491 QuantizeLinear_onnx::Conv_1129_1", type=QuantizeLinear]; +"492 DequantizeLinear_onnx^^Conv_1129_1" [id=492, label="492 DequantizeLinear_onnx::Conv_1129_1", type=DequantizeLinear]; +"493 /Mixed_7a/branch3x3_2/conv/Conv" [id=493, type=Conv]; +"494 /Mixed_7a/branch3x3_2/Relu" [id=494, type=Relu]; +"495 QuantizeLinear_onnx^^Conv_1132_1" [id=495, label="495 QuantizeLinear_onnx::Conv_1132_1", type=QuantizeLinear]; +"496 DequantizeLinear_onnx^^Conv_1132_1" [id=496, label="496 DequantizeLinear_onnx::Conv_1132_1", type=DequantizeLinear]; +"497 /Mixed_7a/branch7x7x3_1/conv/Conv" [id=497, type=Conv]; +"498 /Mixed_7a/branch7x7x3_1/Relu" [id=498, type=Relu]; +"499 QuantizeLinear_/Mixed_7a/branch7x7x3_1/Relu_output_0_1" [id=499, type=QuantizeLinear]; +"500 DequantizeLinear_/Mixed_7a/branch7x7x3_1/Relu_output_0_1" [id=500, type=DequantizeLinear]; +"501 QuantizeLinear_onnx^^Conv_1135_1" [id=501, label="501 QuantizeLinear_onnx::Conv_1135_1", type=QuantizeLinear]; +"502 DequantizeLinear_onnx^^Conv_1135_1" [id=502, label="502 DequantizeLinear_onnx::Conv_1135_1", type=DequantizeLinear]; +"503 /Mixed_7a/branch7x7x3_2/conv/Conv" [id=503, type=Conv]; +"504 /Mixed_7a/branch7x7x3_2/Relu" [id=504, type=Relu]; +"505 QuantizeLinear_/Mixed_7a/branch7x7x3_2/Relu_output_0_1" [id=505, type=QuantizeLinear]; +"506 DequantizeLinear_/Mixed_7a/branch7x7x3_2/Relu_output_0_1" [id=506, type=DequantizeLinear]; +"507 QuantizeLinear_onnx^^Conv_1138_1" [id=507, label="507 QuantizeLinear_onnx::Conv_1138_1", type=QuantizeLinear]; +"508 DequantizeLinear_onnx^^Conv_1138_1" [id=508, label="508 DequantizeLinear_onnx::Conv_1138_1", type=DequantizeLinear]; +"509 /Mixed_7a/branch7x7x3_3/conv/Conv" [id=509, type=Conv]; +"510 /Mixed_7a/branch7x7x3_3/Relu" [id=510, type=Relu]; +"511 QuantizeLinear_/Mixed_7a/branch7x7x3_3/Relu_output_0_1" [id=511, type=QuantizeLinear]; +"512 DequantizeLinear_/Mixed_7a/branch7x7x3_3/Relu_output_0_1" [id=512, type=DequantizeLinear]; +"513 QuantizeLinear_onnx^^Conv_1141_1" [id=513, label="513 QuantizeLinear_onnx::Conv_1141_1", type=QuantizeLinear]; +"514 DequantizeLinear_onnx^^Conv_1141_1" [id=514, label="514 DequantizeLinear_onnx::Conv_1141_1", type=DequantizeLinear]; +"515 /Mixed_7a/branch7x7x3_4/conv/Conv" [id=515, type=Conv]; +"516 /Mixed_7a/branch7x7x3_4/Relu" [id=516, type=Relu]; +"517 /Mixed_7a/MaxPool" [id=517, type=MaxPool]; +"518 QuantizeLinear_/Mixed_7a/branch7x7x3_4/Relu_output_0_1" [id=518, type=QuantizeLinear]; +"519 DequantizeLinear_/Mixed_7a/branch7x7x3_4/Relu_output_0_1" [id=519, type=DequantizeLinear]; +"520 QuantizeLinear_/Mixed_7a/branch3x3_2/Relu_output_0_1" [id=520, type=QuantizeLinear]; +"521 DequantizeLinear_/Mixed_7a/branch3x3_2/Relu_output_0_1" [id=521, type=DequantizeLinear]; +"522 /Mixed_7a/Concat" [id=522, type=Concat]; +"523 QuantizeLinear_onnx^^Conv_1144_1" [id=523, label="523 QuantizeLinear_onnx::Conv_1144_1", type=QuantizeLinear]; +"524 DequantizeLinear_onnx^^Conv_1144_1" [id=524, label="524 DequantizeLinear_onnx::Conv_1144_1", type=DequantizeLinear]; +"525 /Mixed_7b/branch1x1/conv/Conv" [id=525, type=Conv]; +"526 /Mixed_7b/branch1x1/Relu" [id=526, type=Relu]; +"527 QuantizeLinear_onnx^^Conv_1147_1" [id=527, label="527 QuantizeLinear_onnx::Conv_1147_1", type=QuantizeLinear]; +"528 DequantizeLinear_onnx^^Conv_1147_1" [id=528, label="528 DequantizeLinear_onnx::Conv_1147_1", type=DequantizeLinear]; +"529 /Mixed_7b/branch3x3_1/conv/Conv" [id=529, type=Conv]; +"530 /Mixed_7b/branch3x3_1/Relu" [id=530, type=Relu]; +"531 QuantizeLinear_/Mixed_7b/branch3x3_1/Relu_output_0_1" [id=531, type=QuantizeLinear]; +"532 DequantizeLinear_/Mixed_7b/branch3x3_1/Relu_output_0_1" [id=532, type=DequantizeLinear]; +"533 QuantizeLinear_onnx^^Conv_1150_1" [id=533, label="533 QuantizeLinear_onnx::Conv_1150_1", type=QuantizeLinear]; +"534 DequantizeLinear_onnx^^Conv_1150_1" [id=534, label="534 DequantizeLinear_onnx::Conv_1150_1", type=DequantizeLinear]; +"535 /Mixed_7b/branch3x3_2a/conv/Conv" [id=535, type=Conv]; +"536 /Mixed_7b/branch3x3_2a/Relu" [id=536, type=Relu]; +"537 QuantizeLinear_onnx^^Conv_1153_1" [id=537, label="537 QuantizeLinear_onnx::Conv_1153_1", type=QuantizeLinear]; +"538 DequantizeLinear_onnx^^Conv_1153_1" [id=538, label="538 DequantizeLinear_onnx::Conv_1153_1", type=DequantizeLinear]; +"539 /Mixed_7b/branch3x3_2b/conv/Conv" [id=539, type=Conv]; +"540 /Mixed_7b/branch3x3_2b/Relu" [id=540, type=Relu]; +"541 QuantizeLinear_onnx^^Conv_1156_1" [id=541, label="541 QuantizeLinear_onnx::Conv_1156_1", type=QuantizeLinear]; +"542 DequantizeLinear_onnx^^Conv_1156_1" [id=542, label="542 DequantizeLinear_onnx::Conv_1156_1", type=DequantizeLinear]; +"543 /Mixed_7b/branch3x3dbl_1/conv/Conv" [id=543, type=Conv]; +"544 /Mixed_7b/branch3x3dbl_1/Relu" [id=544, type=Relu]; +"545 QuantizeLinear_/Mixed_7b/branch3x3dbl_1/Relu_output_0_1" [id=545, type=QuantizeLinear]; +"546 DequantizeLinear_/Mixed_7b/branch3x3dbl_1/Relu_output_0_1" [id=546, type=DequantizeLinear]; +"547 QuantizeLinear_onnx^^Conv_1159_1" [id=547, label="547 QuantizeLinear_onnx::Conv_1159_1", type=QuantizeLinear]; +"548 DequantizeLinear_onnx^^Conv_1159_1" [id=548, label="548 DequantizeLinear_onnx::Conv_1159_1", type=DequantizeLinear]; +"549 /Mixed_7b/branch3x3dbl_2/conv/Conv" [id=549, type=Conv]; +"550 /Mixed_7b/branch3x3dbl_2/Relu" [id=550, type=Relu]; +"551 QuantizeLinear_/Mixed_7b/branch3x3dbl_2/Relu_output_0_1" [id=551, type=QuantizeLinear]; +"552 DequantizeLinear_/Mixed_7b/branch3x3dbl_2/Relu_output_0_1" [id=552, type=DequantizeLinear]; +"553 QuantizeLinear_onnx^^Conv_1162_1" [id=553, label="553 QuantizeLinear_onnx::Conv_1162_1", type=QuantizeLinear]; +"554 DequantizeLinear_onnx^^Conv_1162_1" [id=554, label="554 DequantizeLinear_onnx::Conv_1162_1", type=DequantizeLinear]; +"555 /Mixed_7b/branch3x3dbl_3a/conv/Conv" [id=555, type=Conv]; +"556 /Mixed_7b/branch3x3dbl_3a/Relu" [id=556, type=Relu]; +"557 QuantizeLinear_onnx^^Conv_1165_1" [id=557, label="557 QuantizeLinear_onnx::Conv_1165_1", type=QuantizeLinear]; +"558 DequantizeLinear_onnx^^Conv_1165_1" [id=558, label="558 DequantizeLinear_onnx::Conv_1165_1", type=DequantizeLinear]; +"559 /Mixed_7b/branch3x3dbl_3b/conv/Conv" [id=559, type=Conv]; +"560 /Mixed_7b/branch3x3dbl_3b/Relu" [id=560, type=Relu]; +"561 /Mixed_7b/AveragePool" [id=561, type=AveragePool]; +"562 QuantizeLinear_/Mixed_7b/AveragePool_output_0_1" [id=562, type=QuantizeLinear]; +"563 DequantizeLinear_/Mixed_7b/AveragePool_output_0_1" [id=563, type=DequantizeLinear]; +"564 QuantizeLinear_onnx^^Conv_1168_1" [id=564, label="564 QuantizeLinear_onnx::Conv_1168_1", type=QuantizeLinear]; +"565 DequantizeLinear_onnx^^Conv_1168_1" [id=565, label="565 DequantizeLinear_onnx::Conv_1168_1", type=DequantizeLinear]; +"566 /Mixed_7b/branch_pool/conv/Conv" [id=566, type=Conv]; +"567 /Mixed_7b/branch_pool/Relu" [id=567, type=Relu]; +"568 QuantizeLinear_/Mixed_7b/branch3x3_2a/Relu_output_0_1" [id=568, type=QuantizeLinear]; +"569 DequantizeLinear_/Mixed_7b/branch3x3_2a/Relu_output_0_1" [id=569, type=DequantizeLinear]; +"570 QuantizeLinear_/Mixed_7b/branch3x3_2b/Relu_output_0_1" [id=570, type=QuantizeLinear]; +"571 DequantizeLinear_/Mixed_7b/branch3x3_2b/Relu_output_0_1" [id=571, type=DequantizeLinear]; +"572 QuantizeLinear_/Mixed_7b/branch3x3dbl_3a/Relu_output_0_1" [id=572, type=QuantizeLinear]; +"573 DequantizeLinear_/Mixed_7b/branch3x3dbl_3a/Relu_output_0_1" [id=573, type=DequantizeLinear]; +"574 QuantizeLinear_/Mixed_7b/branch3x3dbl_3b/Relu_output_0_1" [id=574, type=QuantizeLinear]; +"575 DequantizeLinear_/Mixed_7b/branch3x3dbl_3b/Relu_output_0_1" [id=575, type=DequantizeLinear]; +"576 QuantizeLinear_/Mixed_7b/branch_pool/Relu_output_0_1" [id=576, type=QuantizeLinear]; +"577 DequantizeLinear_/Mixed_7b/branch_pool/Relu_output_0_1" [id=577, type=DequantizeLinear]; +"578 QuantizeLinear_/Mixed_7b/branch1x1/Relu_output_0_1" [id=578, type=QuantizeLinear]; +"579 DequantizeLinear_/Mixed_7b/branch1x1/Relu_output_0_1" [id=579, type=DequantizeLinear]; +"580 /Mixed_7b/Concat" [id=580, type=Concat]; +"581 QuantizeLinear_onnx^^Conv_1171_1" [id=581, label="581 QuantizeLinear_onnx::Conv_1171_1", type=QuantizeLinear]; +"582 DequantizeLinear_onnx^^Conv_1171_1" [id=582, label="582 DequantizeLinear_onnx::Conv_1171_1", type=DequantizeLinear]; +"583 /Mixed_7c/branch1x1/conv/Conv" [id=583, type=Conv]; +"584 /Mixed_7c/branch1x1/Relu" [id=584, type=Relu]; +"585 QuantizeLinear_onnx^^Conv_1174_1" [id=585, label="585 QuantizeLinear_onnx::Conv_1174_1", type=QuantizeLinear]; +"586 DequantizeLinear_onnx^^Conv_1174_1" [id=586, label="586 DequantizeLinear_onnx::Conv_1174_1", type=DequantizeLinear]; +"587 /Mixed_7c/branch3x3_1/conv/Conv" [id=587, type=Conv]; +"588 /Mixed_7c/branch3x3_1/Relu" [id=588, type=Relu]; +"589 QuantizeLinear_/Mixed_7c/branch3x3_1/Relu_output_0_1" [id=589, type=QuantizeLinear]; +"590 DequantizeLinear_/Mixed_7c/branch3x3_1/Relu_output_0_1" [id=590, type=DequantizeLinear]; +"591 QuantizeLinear_onnx^^Conv_1177_1" [id=591, label="591 QuantizeLinear_onnx::Conv_1177_1", type=QuantizeLinear]; +"592 DequantizeLinear_onnx^^Conv_1177_1" [id=592, label="592 DequantizeLinear_onnx::Conv_1177_1", type=DequantizeLinear]; +"593 /Mixed_7c/branch3x3_2a/conv/Conv" [id=593, type=Conv]; +"594 /Mixed_7c/branch3x3_2a/Relu" [id=594, type=Relu]; +"595 QuantizeLinear_onnx^^Conv_1180_1" [id=595, label="595 QuantizeLinear_onnx::Conv_1180_1", type=QuantizeLinear]; +"596 DequantizeLinear_onnx^^Conv_1180_1" [id=596, label="596 DequantizeLinear_onnx::Conv_1180_1", type=DequantizeLinear]; +"597 /Mixed_7c/branch3x3_2b/conv/Conv" [id=597, type=Conv]; +"598 /Mixed_7c/branch3x3_2b/Relu" [id=598, type=Relu]; +"599 QuantizeLinear_onnx^^Conv_1183_1" [id=599, label="599 QuantizeLinear_onnx::Conv_1183_1", type=QuantizeLinear]; +"600 DequantizeLinear_onnx^^Conv_1183_1" [id=600, label="600 DequantizeLinear_onnx::Conv_1183_1", type=DequantizeLinear]; +"601 /Mixed_7c/branch3x3dbl_1/conv/Conv" [id=601, type=Conv]; +"602 /Mixed_7c/branch3x3dbl_1/Relu" [id=602, type=Relu]; +"603 QuantizeLinear_/Mixed_7c/branch3x3dbl_1/Relu_output_0_1" [id=603, type=QuantizeLinear]; +"604 DequantizeLinear_/Mixed_7c/branch3x3dbl_1/Relu_output_0_1" [id=604, type=DequantizeLinear]; +"605 QuantizeLinear_onnx^^Conv_1186_1" [id=605, label="605 QuantizeLinear_onnx::Conv_1186_1", type=QuantizeLinear]; +"606 DequantizeLinear_onnx^^Conv_1186_1" [id=606, label="606 DequantizeLinear_onnx::Conv_1186_1", type=DequantizeLinear]; +"607 /Mixed_7c/branch3x3dbl_2/conv/Conv" [id=607, type=Conv]; +"608 /Mixed_7c/branch3x3dbl_2/Relu" [id=608, type=Relu]; +"609 QuantizeLinear_/Mixed_7c/branch3x3dbl_2/Relu_output_0_1" [id=609, type=QuantizeLinear]; +"610 DequantizeLinear_/Mixed_7c/branch3x3dbl_2/Relu_output_0_1" [id=610, type=DequantizeLinear]; +"611 QuantizeLinear_onnx^^Conv_1189_1" [id=611, label="611 QuantizeLinear_onnx::Conv_1189_1", type=QuantizeLinear]; +"612 DequantizeLinear_onnx^^Conv_1189_1" [id=612, label="612 DequantizeLinear_onnx::Conv_1189_1", type=DequantizeLinear]; +"613 /Mixed_7c/branch3x3dbl_3a/conv/Conv" [id=613, type=Conv]; +"614 /Mixed_7c/branch3x3dbl_3a/Relu" [id=614, type=Relu]; +"615 QuantizeLinear_onnx^^Conv_1192_1" [id=615, label="615 QuantizeLinear_onnx::Conv_1192_1", type=QuantizeLinear]; +"616 DequantizeLinear_onnx^^Conv_1192_1" [id=616, label="616 DequantizeLinear_onnx::Conv_1192_1", type=DequantizeLinear]; +"617 /Mixed_7c/branch3x3dbl_3b/conv/Conv" [id=617, type=Conv]; +"618 /Mixed_7c/branch3x3dbl_3b/Relu" [id=618, type=Relu]; +"619 /Mixed_7c/AveragePool" [id=619, type=AveragePool]; +"620 QuantizeLinear_/Mixed_7c/AveragePool_output_0_1" [id=620, type=QuantizeLinear]; +"621 DequantizeLinear_/Mixed_7c/AveragePool_output_0_1" [id=621, type=DequantizeLinear]; +"622 QuantizeLinear_onnx^^Conv_1195_1" [id=622, label="622 QuantizeLinear_onnx::Conv_1195_1", type=QuantizeLinear]; +"623 DequantizeLinear_onnx^^Conv_1195_1" [id=623, label="623 DequantizeLinear_onnx::Conv_1195_1", type=DequantizeLinear]; +"624 /Mixed_7c/branch_pool/conv/Conv" [id=624, type=Conv]; +"625 /Mixed_7c/branch_pool/Relu" [id=625, type=Relu]; +"626 QuantizeLinear_/Mixed_7c/branch1x1/Relu_output_0_1" [id=626, type=QuantizeLinear]; +"627 DequantizeLinear_/Mixed_7c/branch1x1/Relu_output_0_1" [id=627, type=DequantizeLinear]; +"628 QuantizeLinear_/Mixed_7c/branch3x3_2a/Relu_output_0_1" [id=628, type=QuantizeLinear]; +"629 DequantizeLinear_/Mixed_7c/branch3x3_2a/Relu_output_0_1" [id=629, type=DequantizeLinear]; +"630 QuantizeLinear_/Mixed_7c/branch3x3_2b/Relu_output_0_1" [id=630, type=QuantizeLinear]; +"631 DequantizeLinear_/Mixed_7c/branch3x3_2b/Relu_output_0_1" [id=631, type=DequantizeLinear]; +"632 QuantizeLinear_/Mixed_7c/branch3x3dbl_3a/Relu_output_0_1" [id=632, type=QuantizeLinear]; +"633 DequantizeLinear_/Mixed_7c/branch3x3dbl_3a/Relu_output_0_1" [id=633, type=DequantizeLinear]; +"634 QuantizeLinear_/Mixed_7c/branch3x3dbl_3b/Relu_output_0_1" [id=634, type=QuantizeLinear]; +"635 DequantizeLinear_/Mixed_7c/branch3x3dbl_3b/Relu_output_0_1" [id=635, type=DequantizeLinear]; +"636 QuantizeLinear_/Mixed_7c/branch_pool/Relu_output_0_1" [id=636, type=QuantizeLinear]; +"637 DequantizeLinear_/Mixed_7c/branch_pool/Relu_output_0_1" [id=637, type=DequantizeLinear]; +"638 /Mixed_7c/Concat" [id=638, type=Concat]; +"639 /avgpool/GlobalAveragePool" [id=639, type=GlobalAveragePool]; +"640 QuantizeLinear_/avgpool/GlobalAveragePool_output_0_1" [id=640, type=QuantizeLinear]; +"641 DequantizeLinear_/avgpool/GlobalAveragePool_output_0_1" [id=641, type=DequantizeLinear]; +"642 /Flatten" [id=642, type=Flatten]; +"643 QuantizeLinear_fc.weight_1" [id=643, type=QuantizeLinear]; +"644 DequantizeLinear_fc.weight_1" [id=644, type=DequantizeLinear]; +"645 /fc/Gemm" [id=645, type=Gemm]; +"646 nncf_model_input_0" [id=646, type=nncf_model_input]; +"647 nncf_model_output_0" [id=647, type=nncf_model_output]; "0 /Constant" -> "4 /Gather" [label="[]", style=dashed]; "1 /Constant_1" -> "11 /Gather_1" [label="[]", style=dashed]; "2 QuantizeLinear_x.1_1" -> "3 DequantizeLinear_x.1_1" [label="[1, 3, 224, 224]", style=dashed]; @@ -700,33 +682,33 @@ strict digraph { "30 QuantizeLinear_/Add_2_output_0_1" -> "31 DequantizeLinear_/Add_2_output_0_1" [label="[1, 1, 224, 224]", style=dashed]; "31 DequantizeLinear_/Add_2_output_0_1" -> "32 /Concat" [label="[1, 1, 224, 224]", style=solid]; "32 /Concat" -> "35 /Conv2d_1a_3x3/conv/Conv" [label="[1, 3, 224, 224]", style=solid]; -"33 QuantizeLinear_onnx^^Conv_934_1" -> "34 DequantizeLinear_onnx^^Conv_934_1" [label="[32, 3, 3, 3]", style=dashed]; -"34 DequantizeLinear_onnx^^Conv_934_1" -> "35 /Conv2d_1a_3x3/conv/Conv" [label="[32, 3, 3, 3]", style=solid]; +"33 QuantizeLinear_onnx^^Conv_916_1" -> "34 DequantizeLinear_onnx^^Conv_916_1" [label="[32, 3, 3, 3]", style=dashed]; +"34 DequantizeLinear_onnx^^Conv_916_1" -> "35 /Conv2d_1a_3x3/conv/Conv" [label="[32, 3, 3, 3]", style=solid]; "35 /Conv2d_1a_3x3/conv/Conv" -> "36 /Conv2d_1a_3x3/Relu" [label="[1, 32, 111, 111]", style=solid]; "36 /Conv2d_1a_3x3/Relu" -> "37 QuantizeLinear_/Conv2d_1a_3x3/Relu_output_0_1" [label="[1, 32, 111, 111]", style=solid]; "37 QuantizeLinear_/Conv2d_1a_3x3/Relu_output_0_1" -> "38 DequantizeLinear_/Conv2d_1a_3x3/Relu_output_0_1" [label="[1, 32, 111, 111]", style=dashed]; "38 DequantizeLinear_/Conv2d_1a_3x3/Relu_output_0_1" -> "41 /Conv2d_2a_3x3/conv/Conv" [label="[1, 32, 111, 111]", style=solid]; -"39 QuantizeLinear_onnx^^Conv_937_1" -> "40 DequantizeLinear_onnx^^Conv_937_1" [label="[32, 32, 3, 3]", style=dashed]; -"40 DequantizeLinear_onnx^^Conv_937_1" -> "41 /Conv2d_2a_3x3/conv/Conv" [label="[32, 32, 3, 3]", style=solid]; +"39 QuantizeLinear_onnx^^Conv_919_1" -> "40 DequantizeLinear_onnx^^Conv_919_1" [label="[32, 32, 3, 3]", style=dashed]; +"40 DequantizeLinear_onnx^^Conv_919_1" -> "41 /Conv2d_2a_3x3/conv/Conv" [label="[32, 32, 3, 3]", style=solid]; "41 /Conv2d_2a_3x3/conv/Conv" -> "42 /Conv2d_2a_3x3/Relu" [label="[1, 32, 109, 109]", style=solid]; "42 /Conv2d_2a_3x3/Relu" -> "43 QuantizeLinear_/Conv2d_2a_3x3/Relu_output_0_1" [label="[1, 32, 109, 109]", style=solid]; "43 QuantizeLinear_/Conv2d_2a_3x3/Relu_output_0_1" -> "44 DequantizeLinear_/Conv2d_2a_3x3/Relu_output_0_1" [label="[1, 32, 109, 109]", style=dashed]; "44 DequantizeLinear_/Conv2d_2a_3x3/Relu_output_0_1" -> "47 /Conv2d_2b_3x3/conv/Conv" [label="[1, 32, 109, 109]", style=solid]; -"45 QuantizeLinear_onnx^^Conv_940_1" -> "46 DequantizeLinear_onnx^^Conv_940_1" [label="[64, 32, 3, 3]", style=dashed]; -"46 DequantizeLinear_onnx^^Conv_940_1" -> "47 /Conv2d_2b_3x3/conv/Conv" [label="[64, 32, 3, 3]", style=solid]; +"45 QuantizeLinear_onnx^^Conv_922_1" -> "46 DequantizeLinear_onnx^^Conv_922_1" [label="[64, 32, 3, 3]", style=dashed]; +"46 DequantizeLinear_onnx^^Conv_922_1" -> "47 /Conv2d_2b_3x3/conv/Conv" [label="[64, 32, 3, 3]", style=solid]; "47 /Conv2d_2b_3x3/conv/Conv" -> "48 /Conv2d_2b_3x3/Relu" [label="[1, 64, 109, 109]", style=solid]; "48 /Conv2d_2b_3x3/Relu" -> "49 QuantizeLinear_/Conv2d_2b_3x3/Relu_output_0_1" [label="[1, 64, 109, 109]", style=solid]; "49 QuantizeLinear_/Conv2d_2b_3x3/Relu_output_0_1" -> "50 DequantizeLinear_/Conv2d_2b_3x3/Relu_output_0_1" [label="[1, 64, 109, 109]", style=dashed]; "50 DequantizeLinear_/Conv2d_2b_3x3/Relu_output_0_1" -> "51 /maxpool1/MaxPool" [label="[1, 64, 109, 109]", style=solid]; "51 /maxpool1/MaxPool" -> "54 /Conv2d_3b_1x1/conv/Conv" [label="[1, 64, 54, 54]", style=solid]; -"52 QuantizeLinear_onnx^^Conv_943_1" -> "53 DequantizeLinear_onnx^^Conv_943_1" [label="[80, 64, 1, 1]", style=dashed]; -"53 DequantizeLinear_onnx^^Conv_943_1" -> "54 /Conv2d_3b_1x1/conv/Conv" [label="[80, 64, 1, 1]", style=solid]; +"52 QuantizeLinear_onnx^^Conv_925_1" -> "53 DequantizeLinear_onnx^^Conv_925_1" [label="[80, 64, 1, 1]", style=dashed]; +"53 DequantizeLinear_onnx^^Conv_925_1" -> "54 /Conv2d_3b_1x1/conv/Conv" [label="[80, 64, 1, 1]", style=solid]; "54 /Conv2d_3b_1x1/conv/Conv" -> "55 /Conv2d_3b_1x1/Relu" [label="[1, 80, 54, 54]", style=solid]; "55 /Conv2d_3b_1x1/Relu" -> "56 QuantizeLinear_/Conv2d_3b_1x1/Relu_output_0_1" [label="[1, 80, 54, 54]", style=solid]; "56 QuantizeLinear_/Conv2d_3b_1x1/Relu_output_0_1" -> "57 DequantizeLinear_/Conv2d_3b_1x1/Relu_output_0_1" [label="[1, 80, 54, 54]", style=dashed]; "57 DequantizeLinear_/Conv2d_3b_1x1/Relu_output_0_1" -> "60 /Conv2d_4a_3x3/conv/Conv" [label="[1, 80, 54, 54]", style=solid]; -"58 QuantizeLinear_onnx^^Conv_946_1" -> "59 DequantizeLinear_onnx^^Conv_946_1" [label="[192, 80, 3, 3]", style=dashed]; -"59 DequantizeLinear_onnx^^Conv_946_1" -> "60 /Conv2d_4a_3x3/conv/Conv" [label="[192, 80, 3, 3]", style=solid]; +"58 QuantizeLinear_onnx^^Conv_928_1" -> "59 DequantizeLinear_onnx^^Conv_928_1" [label="[192, 80, 3, 3]", style=dashed]; +"59 DequantizeLinear_onnx^^Conv_928_1" -> "60 /Conv2d_4a_3x3/conv/Conv" [label="[192, 80, 3, 3]", style=solid]; "60 /Conv2d_4a_3x3/conv/Conv" -> "61 /Conv2d_4a_3x3/Relu" [label="[1, 192, 52, 52]", style=solid]; "61 /Conv2d_4a_3x3/Relu" -> "62 QuantizeLinear_/Conv2d_4a_3x3/Relu_output_0_1" [label="[1, 192, 52, 52]", style=solid]; "62 QuantizeLinear_/Conv2d_4a_3x3/Relu_output_0_1" -> "63 DequantizeLinear_/Conv2d_4a_3x3/Relu_output_0_1" [label="[1, 192, 52, 52]", style=dashed]; @@ -734,637 +716,619 @@ strict digraph { "64 /maxpool2/MaxPool" -> "67 /Mixed_5b/branch1x1/conv/Conv" [label="[1, 192, 25, 25]", style=solid]; "64 /maxpool2/MaxPool" -> "71 /Mixed_5b/branch5x5_1/conv/Conv" [label="[1, 192, 25, 25]", style=solid]; "64 /maxpool2/MaxPool" -> "81 /Mixed_5b/branch3x3dbl_1/conv/Conv" [label="[1, 192, 25, 25]", style=solid]; -"64 /maxpool2/MaxPool" -> "96 /Mixed_5b/Pad" [label="[1, 192, 25, 25]", style=solid]; -"65 QuantizeLinear_onnx^^Conv_949_1" -> "66 DequantizeLinear_onnx^^Conv_949_1" [label="[64, 192, 1, 1]", style=dashed]; -"66 DequantizeLinear_onnx^^Conv_949_1" -> "67 /Mixed_5b/branch1x1/conv/Conv" [label="[64, 192, 1, 1]", style=solid]; +"64 /maxpool2/MaxPool" -> "95 /Mixed_5b/AveragePool" [label="[1, 192, 25, 25]", style=solid]; +"65 QuantizeLinear_onnx^^Conv_931_1" -> "66 DequantizeLinear_onnx^^Conv_931_1" [label="[64, 192, 1, 1]", style=dashed]; +"66 DequantizeLinear_onnx^^Conv_931_1" -> "67 /Mixed_5b/branch1x1/conv/Conv" [label="[64, 192, 1, 1]", style=solid]; "67 /Mixed_5b/branch1x1/conv/Conv" -> "68 /Mixed_5b/branch1x1/Relu" [label="[1, 64, 25, 25]", style=solid]; -"68 /Mixed_5b/branch1x1/Relu" -> "104 QuantizeLinear_/Mixed_5b/branch1x1/Relu_output_0_1" [label="[1, 64, 25, 25]", style=solid]; -"69 QuantizeLinear_onnx^^Conv_952_1" -> "70 DequantizeLinear_onnx^^Conv_952_1" [label="[48, 192, 1, 1]", style=dashed]; -"70 DequantizeLinear_onnx^^Conv_952_1" -> "71 /Mixed_5b/branch5x5_1/conv/Conv" [label="[48, 192, 1, 1]", style=solid]; +"68 /Mixed_5b/branch1x1/Relu" -> "102 QuantizeLinear_/Mixed_5b/branch1x1/Relu_output_0_1" [label="[1, 64, 25, 25]", style=solid]; +"69 QuantizeLinear_onnx^^Conv_934_1" -> "70 DequantizeLinear_onnx^^Conv_934_1" [label="[48, 192, 1, 1]", style=dashed]; +"70 DequantizeLinear_onnx^^Conv_934_1" -> "71 /Mixed_5b/branch5x5_1/conv/Conv" [label="[48, 192, 1, 1]", style=solid]; "71 /Mixed_5b/branch5x5_1/conv/Conv" -> "72 /Mixed_5b/branch5x5_1/Relu" [label="[1, 48, 25, 25]", style=solid]; "72 /Mixed_5b/branch5x5_1/Relu" -> "73 QuantizeLinear_/Mixed_5b/branch5x5_1/Relu_output_0_1" [label="[1, 48, 25, 25]", style=solid]; "73 QuantizeLinear_/Mixed_5b/branch5x5_1/Relu_output_0_1" -> "74 DequantizeLinear_/Mixed_5b/branch5x5_1/Relu_output_0_1" [label="[1, 48, 25, 25]", style=dashed]; "74 DequantizeLinear_/Mixed_5b/branch5x5_1/Relu_output_0_1" -> "77 /Mixed_5b/branch5x5_2/conv/Conv" [label="[1, 48, 25, 25]", style=solid]; -"75 QuantizeLinear_onnx^^Conv_955_1" -> "76 DequantizeLinear_onnx^^Conv_955_1" [label="[64, 48, 5, 5]", style=dashed]; -"76 DequantizeLinear_onnx^^Conv_955_1" -> "77 /Mixed_5b/branch5x5_2/conv/Conv" [label="[64, 48, 5, 5]", style=solid]; +"75 QuantizeLinear_onnx^^Conv_937_1" -> "76 DequantizeLinear_onnx^^Conv_937_1" [label="[64, 48, 5, 5]", style=dashed]; +"76 DequantizeLinear_onnx^^Conv_937_1" -> "77 /Mixed_5b/branch5x5_2/conv/Conv" [label="[64, 48, 5, 5]", style=solid]; "77 /Mixed_5b/branch5x5_2/conv/Conv" -> "78 /Mixed_5b/branch5x5_2/Relu" [label="[1, 64, 25, 25]", style=solid]; -"78 /Mixed_5b/branch5x5_2/Relu" -> "106 QuantizeLinear_/Mixed_5b/branch5x5_2/Relu_output_0_1" [label="[1, 64, 25, 25]", style=solid]; -"79 QuantizeLinear_onnx^^Conv_958_1" -> "80 DequantizeLinear_onnx^^Conv_958_1" [label="[64, 192, 1, 1]", style=dashed]; -"80 DequantizeLinear_onnx^^Conv_958_1" -> "81 /Mixed_5b/branch3x3dbl_1/conv/Conv" [label="[64, 192, 1, 1]", style=solid]; +"78 /Mixed_5b/branch5x5_2/Relu" -> "104 QuantizeLinear_/Mixed_5b/branch5x5_2/Relu_output_0_1" [label="[1, 64, 25, 25]", style=solid]; +"79 QuantizeLinear_onnx^^Conv_940_1" -> "80 DequantizeLinear_onnx^^Conv_940_1" [label="[64, 192, 1, 1]", style=dashed]; +"80 DequantizeLinear_onnx^^Conv_940_1" -> "81 /Mixed_5b/branch3x3dbl_1/conv/Conv" [label="[64, 192, 1, 1]", style=solid]; "81 /Mixed_5b/branch3x3dbl_1/conv/Conv" -> "82 /Mixed_5b/branch3x3dbl_1/Relu" [label="[1, 64, 25, 25]", style=solid]; "82 /Mixed_5b/branch3x3dbl_1/Relu" -> "83 QuantizeLinear_/Mixed_5b/branch3x3dbl_1/Relu_output_0_1" [label="[1, 64, 25, 25]", style=solid]; "83 QuantizeLinear_/Mixed_5b/branch3x3dbl_1/Relu_output_0_1" -> "84 DequantizeLinear_/Mixed_5b/branch3x3dbl_1/Relu_output_0_1" [label="[1, 64, 25, 25]", style=dashed]; "84 DequantizeLinear_/Mixed_5b/branch3x3dbl_1/Relu_output_0_1" -> "87 /Mixed_5b/branch3x3dbl_2/conv/Conv" [label="[1, 64, 25, 25]", style=solid]; -"85 QuantizeLinear_onnx^^Conv_961_1" -> "86 DequantizeLinear_onnx^^Conv_961_1" [label="[96, 64, 3, 3]", style=dashed]; -"86 DequantizeLinear_onnx^^Conv_961_1" -> "87 /Mixed_5b/branch3x3dbl_2/conv/Conv" [label="[96, 64, 3, 3]", style=solid]; +"85 QuantizeLinear_onnx^^Conv_943_1" -> "86 DequantizeLinear_onnx^^Conv_943_1" [label="[96, 64, 3, 3]", style=dashed]; +"86 DequantizeLinear_onnx^^Conv_943_1" -> "87 /Mixed_5b/branch3x3dbl_2/conv/Conv" [label="[96, 64, 3, 3]", style=solid]; "87 /Mixed_5b/branch3x3dbl_2/conv/Conv" -> "88 /Mixed_5b/branch3x3dbl_2/Relu" [label="[1, 96, 25, 25]", style=solid]; "88 /Mixed_5b/branch3x3dbl_2/Relu" -> "89 QuantizeLinear_/Mixed_5b/branch3x3dbl_2/Relu_output_0_1" [label="[1, 96, 25, 25]", style=solid]; "89 QuantizeLinear_/Mixed_5b/branch3x3dbl_2/Relu_output_0_1" -> "90 DequantizeLinear_/Mixed_5b/branch3x3dbl_2/Relu_output_0_1" [label="[1, 96, 25, 25]", style=dashed]; "90 DequantizeLinear_/Mixed_5b/branch3x3dbl_2/Relu_output_0_1" -> "93 /Mixed_5b/branch3x3dbl_3/conv/Conv" [label="[1, 96, 25, 25]", style=solid]; -"91 QuantizeLinear_onnx^^Conv_964_1" -> "92 DequantizeLinear_onnx^^Conv_964_1" [label="[96, 96, 3, 3]", style=dashed]; -"92 DequantizeLinear_onnx^^Conv_964_1" -> "93 /Mixed_5b/branch3x3dbl_3/conv/Conv" [label="[96, 96, 3, 3]", style=solid]; +"91 QuantizeLinear_onnx^^Conv_946_1" -> "92 DequantizeLinear_onnx^^Conv_946_1" [label="[96, 96, 3, 3]", style=dashed]; +"92 DequantizeLinear_onnx^^Conv_946_1" -> "93 /Mixed_5b/branch3x3dbl_3/conv/Conv" [label="[96, 96, 3, 3]", style=solid]; "93 /Mixed_5b/branch3x3dbl_3/conv/Conv" -> "94 /Mixed_5b/branch3x3dbl_3/Relu" [label="[1, 96, 25, 25]", style=solid]; -"94 /Mixed_5b/branch3x3dbl_3/Relu" -> "110 QuantizeLinear_/Mixed_5b/branch3x3dbl_3/Relu_output_0_1" [label="[1, 96, 25, 25]", style=solid]; -"95 /Mixed_5b/Constant" -> "96 /Mixed_5b/Pad" [label="[8]", style=dashed]; -"96 /Mixed_5b/Pad" -> "97 /Mixed_5b/AveragePool" [label="[1, 192, 27, 27]", style=solid]; -"97 /Mixed_5b/AveragePool" -> "98 QuantizeLinear_/Mixed_5b/AveragePool_output_0_1" [label="[1, 192, 25, 25]", style=solid]; -"98 QuantizeLinear_/Mixed_5b/AveragePool_output_0_1" -> "99 DequantizeLinear_/Mixed_5b/AveragePool_output_0_1" [label="[1, 192, 25, 25]", style=dashed]; -"99 DequantizeLinear_/Mixed_5b/AveragePool_output_0_1" -> "102 /Mixed_5b/branch_pool/conv/Conv" [label="[1, 192, 25, 25]", style=solid]; -"100 QuantizeLinear_onnx^^Conv_967_1" -> "101 DequantizeLinear_onnx^^Conv_967_1" [label="[32, 192, 1, 1]", style=dashed]; -"101 DequantizeLinear_onnx^^Conv_967_1" -> "102 /Mixed_5b/branch_pool/conv/Conv" [label="[32, 192, 1, 1]", style=solid]; -"102 /Mixed_5b/branch_pool/conv/Conv" -> "103 /Mixed_5b/branch_pool/Relu" [label="[1, 32, 25, 25]", style=solid]; -"103 /Mixed_5b/branch_pool/Relu" -> "108 QuantizeLinear_/Mixed_5b/branch_pool/Relu_output_0_1" [label="[1, 32, 25, 25]", style=solid]; -"104 QuantizeLinear_/Mixed_5b/branch1x1/Relu_output_0_1" -> "105 DequantizeLinear_/Mixed_5b/branch1x1/Relu_output_0_1" [label="[1, 64, 25, 25]", style=dashed]; -"105 DequantizeLinear_/Mixed_5b/branch1x1/Relu_output_0_1" -> "112 /Mixed_5b/Concat" [label="[1, 64, 25, 25]", style=solid]; -"106 QuantizeLinear_/Mixed_5b/branch5x5_2/Relu_output_0_1" -> "107 DequantizeLinear_/Mixed_5b/branch5x5_2/Relu_output_0_1" [label="[1, 64, 25, 25]", style=dashed]; -"107 DequantizeLinear_/Mixed_5b/branch5x5_2/Relu_output_0_1" -> "112 /Mixed_5b/Concat" [label="[1, 64, 25, 25]", style=solid]; +"94 /Mixed_5b/branch3x3dbl_3/Relu" -> "106 QuantizeLinear_/Mixed_5b/branch3x3dbl_3/Relu_output_0_1" [label="[1, 96, 25, 25]", style=solid]; +"95 /Mixed_5b/AveragePool" -> "96 QuantizeLinear_/Mixed_5b/AveragePool_output_0_1" [label="[1, 192, 25, 25]", style=solid]; +"96 QuantizeLinear_/Mixed_5b/AveragePool_output_0_1" -> "97 DequantizeLinear_/Mixed_5b/AveragePool_output_0_1" [label="[1, 192, 25, 25]", style=dashed]; +"97 DequantizeLinear_/Mixed_5b/AveragePool_output_0_1" -> "100 /Mixed_5b/branch_pool/conv/Conv" [label="[1, 192, 25, 25]", style=solid]; +"98 QuantizeLinear_onnx^^Conv_949_1" -> "99 DequantizeLinear_onnx^^Conv_949_1" [label="[32, 192, 1, 1]", style=dashed]; +"99 DequantizeLinear_onnx^^Conv_949_1" -> "100 /Mixed_5b/branch_pool/conv/Conv" [label="[32, 192, 1, 1]", style=solid]; +"100 /Mixed_5b/branch_pool/conv/Conv" -> "101 /Mixed_5b/branch_pool/Relu" [label="[1, 32, 25, 25]", style=solid]; +"101 /Mixed_5b/branch_pool/Relu" -> "108 QuantizeLinear_/Mixed_5b/branch_pool/Relu_output_0_1" [label="[1, 32, 25, 25]", style=solid]; +"102 QuantizeLinear_/Mixed_5b/branch1x1/Relu_output_0_1" -> "103 DequantizeLinear_/Mixed_5b/branch1x1/Relu_output_0_1" [label="[1, 64, 25, 25]", style=dashed]; +"103 DequantizeLinear_/Mixed_5b/branch1x1/Relu_output_0_1" -> "110 /Mixed_5b/Concat" [label="[1, 64, 25, 25]", style=solid]; +"104 QuantizeLinear_/Mixed_5b/branch5x5_2/Relu_output_0_1" -> "105 DequantizeLinear_/Mixed_5b/branch5x5_2/Relu_output_0_1" [label="[1, 64, 25, 25]", style=dashed]; +"105 DequantizeLinear_/Mixed_5b/branch5x5_2/Relu_output_0_1" -> "110 /Mixed_5b/Concat" [label="[1, 64, 25, 25]", style=solid]; +"106 QuantizeLinear_/Mixed_5b/branch3x3dbl_3/Relu_output_0_1" -> "107 DequantizeLinear_/Mixed_5b/branch3x3dbl_3/Relu_output_0_1" [label="[1, 96, 25, 25]", style=dashed]; +"107 DequantizeLinear_/Mixed_5b/branch3x3dbl_3/Relu_output_0_1" -> "110 /Mixed_5b/Concat" [label="[1, 96, 25, 25]", style=solid]; "108 QuantizeLinear_/Mixed_5b/branch_pool/Relu_output_0_1" -> "109 DequantizeLinear_/Mixed_5b/branch_pool/Relu_output_0_1" [label="[1, 32, 25, 25]", style=dashed]; -"109 DequantizeLinear_/Mixed_5b/branch_pool/Relu_output_0_1" -> "112 /Mixed_5b/Concat" [label="[1, 32, 25, 25]", style=solid]; -"110 QuantizeLinear_/Mixed_5b/branch3x3dbl_3/Relu_output_0_1" -> "111 DequantizeLinear_/Mixed_5b/branch3x3dbl_3/Relu_output_0_1" [label="[1, 96, 25, 25]", style=dashed]; -"111 DequantizeLinear_/Mixed_5b/branch3x3dbl_3/Relu_output_0_1" -> "112 /Mixed_5b/Concat" [label="[1, 96, 25, 25]", style=solid]; -"112 /Mixed_5b/Concat" -> "115 /Mixed_5c/branch1x1/conv/Conv" [label="[1, 256, 25, 25]", style=solid]; -"112 /Mixed_5b/Concat" -> "119 /Mixed_5c/branch5x5_1/conv/Conv" [label="[1, 256, 25, 25]", style=solid]; -"112 /Mixed_5b/Concat" -> "129 /Mixed_5c/branch3x3dbl_1/conv/Conv" [label="[1, 256, 25, 25]", style=solid]; -"112 /Mixed_5b/Concat" -> "144 /Mixed_5c/Pad" [label="[1, 256, 25, 25]", style=solid]; -"113 QuantizeLinear_onnx^^Conv_970_1" -> "114 DequantizeLinear_onnx^^Conv_970_1" [label="[64, 256, 1, 1]", style=dashed]; -"114 DequantizeLinear_onnx^^Conv_970_1" -> "115 /Mixed_5c/branch1x1/conv/Conv" [label="[64, 256, 1, 1]", style=solid]; -"115 /Mixed_5c/branch1x1/conv/Conv" -> "116 /Mixed_5c/branch1x1/Relu" [label="[1, 64, 25, 25]", style=solid]; -"116 /Mixed_5c/branch1x1/Relu" -> "152 QuantizeLinear_/Mixed_5c/branch1x1/Relu_output_0_1" [label="[1, 64, 25, 25]", style=solid]; -"117 QuantizeLinear_onnx^^Conv_973_1" -> "118 DequantizeLinear_onnx^^Conv_973_1" [label="[48, 256, 1, 1]", style=dashed]; -"118 DequantizeLinear_onnx^^Conv_973_1" -> "119 /Mixed_5c/branch5x5_1/conv/Conv" [label="[48, 256, 1, 1]", style=solid]; -"119 /Mixed_5c/branch5x5_1/conv/Conv" -> "120 /Mixed_5c/branch5x5_1/Relu" [label="[1, 48, 25, 25]", style=solid]; -"120 /Mixed_5c/branch5x5_1/Relu" -> "121 QuantizeLinear_/Mixed_5c/branch5x5_1/Relu_output_0_1" [label="[1, 48, 25, 25]", style=solid]; -"121 QuantizeLinear_/Mixed_5c/branch5x5_1/Relu_output_0_1" -> "122 DequantizeLinear_/Mixed_5c/branch5x5_1/Relu_output_0_1" [label="[1, 48, 25, 25]", style=dashed]; -"122 DequantizeLinear_/Mixed_5c/branch5x5_1/Relu_output_0_1" -> "125 /Mixed_5c/branch5x5_2/conv/Conv" [label="[1, 48, 25, 25]", style=solid]; -"123 QuantizeLinear_onnx^^Conv_976_1" -> "124 DequantizeLinear_onnx^^Conv_976_1" [label="[64, 48, 5, 5]", style=dashed]; -"124 DequantizeLinear_onnx^^Conv_976_1" -> "125 /Mixed_5c/branch5x5_2/conv/Conv" [label="[64, 48, 5, 5]", style=solid]; -"125 /Mixed_5c/branch5x5_2/conv/Conv" -> "126 /Mixed_5c/branch5x5_2/Relu" [label="[1, 64, 25, 25]", style=solid]; -"126 /Mixed_5c/branch5x5_2/Relu" -> "154 QuantizeLinear_/Mixed_5c/branch5x5_2/Relu_output_0_1" [label="[1, 64, 25, 25]", style=solid]; -"127 QuantizeLinear_onnx^^Conv_979_1" -> "128 DequantizeLinear_onnx^^Conv_979_1" [label="[64, 256, 1, 1]", style=dashed]; -"128 DequantizeLinear_onnx^^Conv_979_1" -> "129 /Mixed_5c/branch3x3dbl_1/conv/Conv" [label="[64, 256, 1, 1]", style=solid]; -"129 /Mixed_5c/branch3x3dbl_1/conv/Conv" -> "130 /Mixed_5c/branch3x3dbl_1/Relu" [label="[1, 64, 25, 25]", style=solid]; -"130 /Mixed_5c/branch3x3dbl_1/Relu" -> "131 QuantizeLinear_/Mixed_5c/branch3x3dbl_1/Relu_output_0_1" [label="[1, 64, 25, 25]", style=solid]; -"131 QuantizeLinear_/Mixed_5c/branch3x3dbl_1/Relu_output_0_1" -> "132 DequantizeLinear_/Mixed_5c/branch3x3dbl_1/Relu_output_0_1" [label="[1, 64, 25, 25]", style=dashed]; -"132 DequantizeLinear_/Mixed_5c/branch3x3dbl_1/Relu_output_0_1" -> "135 /Mixed_5c/branch3x3dbl_2/conv/Conv" [label="[1, 64, 25, 25]", style=solid]; -"133 QuantizeLinear_onnx^^Conv_982_1" -> "134 DequantizeLinear_onnx^^Conv_982_1" [label="[96, 64, 3, 3]", style=dashed]; -"134 DequantizeLinear_onnx^^Conv_982_1" -> "135 /Mixed_5c/branch3x3dbl_2/conv/Conv" [label="[96, 64, 3, 3]", style=solid]; -"135 /Mixed_5c/branch3x3dbl_2/conv/Conv" -> "136 /Mixed_5c/branch3x3dbl_2/Relu" [label="[1, 96, 25, 25]", style=solid]; -"136 /Mixed_5c/branch3x3dbl_2/Relu" -> "137 QuantizeLinear_/Mixed_5c/branch3x3dbl_2/Relu_output_0_1" [label="[1, 96, 25, 25]", style=solid]; -"137 QuantizeLinear_/Mixed_5c/branch3x3dbl_2/Relu_output_0_1" -> "138 DequantizeLinear_/Mixed_5c/branch3x3dbl_2/Relu_output_0_1" [label="[1, 96, 25, 25]", style=dashed]; -"138 DequantizeLinear_/Mixed_5c/branch3x3dbl_2/Relu_output_0_1" -> "141 /Mixed_5c/branch3x3dbl_3/conv/Conv" [label="[1, 96, 25, 25]", style=solid]; -"139 QuantizeLinear_onnx^^Conv_985_1" -> "140 DequantizeLinear_onnx^^Conv_985_1" [label="[96, 96, 3, 3]", style=dashed]; -"140 DequantizeLinear_onnx^^Conv_985_1" -> "141 /Mixed_5c/branch3x3dbl_3/conv/Conv" [label="[96, 96, 3, 3]", style=solid]; -"141 /Mixed_5c/branch3x3dbl_3/conv/Conv" -> "142 /Mixed_5c/branch3x3dbl_3/Relu" [label="[1, 96, 25, 25]", style=solid]; -"142 /Mixed_5c/branch3x3dbl_3/Relu" -> "156 QuantizeLinear_/Mixed_5c/branch3x3dbl_3/Relu_output_0_1" [label="[1, 96, 25, 25]", style=solid]; -"143 /Mixed_5c/Constant" -> "144 /Mixed_5c/Pad" [label="[8]", style=dashed]; -"144 /Mixed_5c/Pad" -> "145 /Mixed_5c/AveragePool" [label="[1, 256, 27, 27]", style=solid]; -"145 /Mixed_5c/AveragePool" -> "146 QuantizeLinear_/Mixed_5c/AveragePool_output_0_1" [label="[1, 256, 25, 25]", style=solid]; -"146 QuantizeLinear_/Mixed_5c/AveragePool_output_0_1" -> "147 DequantizeLinear_/Mixed_5c/AveragePool_output_0_1" [label="[1, 256, 25, 25]", style=dashed]; -"147 DequantizeLinear_/Mixed_5c/AveragePool_output_0_1" -> "150 /Mixed_5c/branch_pool/conv/Conv" [label="[1, 256, 25, 25]", style=solid]; -"148 QuantizeLinear_onnx^^Conv_988_1" -> "149 DequantizeLinear_onnx^^Conv_988_1" [label="[64, 256, 1, 1]", style=dashed]; -"149 DequantizeLinear_onnx^^Conv_988_1" -> "150 /Mixed_5c/branch_pool/conv/Conv" [label="[64, 256, 1, 1]", style=solid]; -"150 /Mixed_5c/branch_pool/conv/Conv" -> "151 /Mixed_5c/branch_pool/Relu" [label="[1, 64, 25, 25]", style=solid]; -"151 /Mixed_5c/branch_pool/Relu" -> "158 QuantizeLinear_/Mixed_5c/branch_pool/Relu_output_0_1" [label="[1, 64, 25, 25]", style=solid]; -"152 QuantizeLinear_/Mixed_5c/branch1x1/Relu_output_0_1" -> "153 DequantizeLinear_/Mixed_5c/branch1x1/Relu_output_0_1" [label="[1, 64, 25, 25]", style=dashed]; -"153 DequantizeLinear_/Mixed_5c/branch1x1/Relu_output_0_1" -> "160 /Mixed_5c/Concat" [label="[1, 64, 25, 25]", style=solid]; -"154 QuantizeLinear_/Mixed_5c/branch5x5_2/Relu_output_0_1" -> "155 DequantizeLinear_/Mixed_5c/branch5x5_2/Relu_output_0_1" [label="[1, 64, 25, 25]", style=dashed]; -"155 DequantizeLinear_/Mixed_5c/branch5x5_2/Relu_output_0_1" -> "160 /Mixed_5c/Concat" [label="[1, 64, 25, 25]", style=solid]; -"156 QuantizeLinear_/Mixed_5c/branch3x3dbl_3/Relu_output_0_1" -> "157 DequantizeLinear_/Mixed_5c/branch3x3dbl_3/Relu_output_0_1" [label="[1, 96, 25, 25]", style=dashed]; -"157 DequantizeLinear_/Mixed_5c/branch3x3dbl_3/Relu_output_0_1" -> "160 /Mixed_5c/Concat" [label="[1, 96, 25, 25]", style=solid]; -"158 QuantizeLinear_/Mixed_5c/branch_pool/Relu_output_0_1" -> "159 DequantizeLinear_/Mixed_5c/branch_pool/Relu_output_0_1" [label="[1, 64, 25, 25]", style=dashed]; -"159 DequantizeLinear_/Mixed_5c/branch_pool/Relu_output_0_1" -> "160 /Mixed_5c/Concat" [label="[1, 64, 25, 25]", style=solid]; -"160 /Mixed_5c/Concat" -> "163 /Mixed_5d/branch1x1/conv/Conv" [label="[1, 288, 25, 25]", style=solid]; -"160 /Mixed_5c/Concat" -> "167 /Mixed_5d/branch5x5_1/conv/Conv" [label="[1, 288, 25, 25]", style=solid]; -"160 /Mixed_5c/Concat" -> "177 /Mixed_5d/branch3x3dbl_1/conv/Conv" [label="[1, 288, 25, 25]", style=solid]; -"160 /Mixed_5c/Concat" -> "192 /Mixed_5d/Pad" [label="[1, 288, 25, 25]", style=solid]; -"161 QuantizeLinear_onnx^^Conv_991_1" -> "162 DequantizeLinear_onnx^^Conv_991_1" [label="[64, 288, 1, 1]", style=dashed]; -"162 DequantizeLinear_onnx^^Conv_991_1" -> "163 /Mixed_5d/branch1x1/conv/Conv" [label="[64, 288, 1, 1]", style=solid]; -"163 /Mixed_5d/branch1x1/conv/Conv" -> "164 /Mixed_5d/branch1x1/Relu" [label="[1, 64, 25, 25]", style=solid]; -"164 /Mixed_5d/branch1x1/Relu" -> "200 QuantizeLinear_/Mixed_5d/branch1x1/Relu_output_0_1" [label="[1, 64, 25, 25]", style=solid]; -"165 QuantizeLinear_onnx^^Conv_994_1" -> "166 DequantizeLinear_onnx^^Conv_994_1" [label="[48, 288, 1, 1]", style=dashed]; -"166 DequantizeLinear_onnx^^Conv_994_1" -> "167 /Mixed_5d/branch5x5_1/conv/Conv" [label="[48, 288, 1, 1]", style=solid]; -"167 /Mixed_5d/branch5x5_1/conv/Conv" -> "168 /Mixed_5d/branch5x5_1/Relu" [label="[1, 48, 25, 25]", style=solid]; -"168 /Mixed_5d/branch5x5_1/Relu" -> "169 QuantizeLinear_/Mixed_5d/branch5x5_1/Relu_output_0_1" [label="[1, 48, 25, 25]", style=solid]; -"169 QuantizeLinear_/Mixed_5d/branch5x5_1/Relu_output_0_1" -> "170 DequantizeLinear_/Mixed_5d/branch5x5_1/Relu_output_0_1" [label="[1, 48, 25, 25]", style=dashed]; -"170 DequantizeLinear_/Mixed_5d/branch5x5_1/Relu_output_0_1" -> "173 /Mixed_5d/branch5x5_2/conv/Conv" [label="[1, 48, 25, 25]", style=solid]; -"171 QuantizeLinear_onnx^^Conv_997_1" -> "172 DequantizeLinear_onnx^^Conv_997_1" [label="[64, 48, 5, 5]", style=dashed]; -"172 DequantizeLinear_onnx^^Conv_997_1" -> "173 /Mixed_5d/branch5x5_2/conv/Conv" [label="[64, 48, 5, 5]", style=solid]; -"173 /Mixed_5d/branch5x5_2/conv/Conv" -> "174 /Mixed_5d/branch5x5_2/Relu" [label="[1, 64, 25, 25]", style=solid]; -"174 /Mixed_5d/branch5x5_2/Relu" -> "202 QuantizeLinear_/Mixed_5d/branch5x5_2/Relu_output_0_1" [label="[1, 64, 25, 25]", style=solid]; -"175 QuantizeLinear_onnx^^Conv_1000_1" -> "176 DequantizeLinear_onnx^^Conv_1000_1" [label="[64, 288, 1, 1]", style=dashed]; -"176 DequantizeLinear_onnx^^Conv_1000_1" -> "177 /Mixed_5d/branch3x3dbl_1/conv/Conv" [label="[64, 288, 1, 1]", style=solid]; -"177 /Mixed_5d/branch3x3dbl_1/conv/Conv" -> "178 /Mixed_5d/branch3x3dbl_1/Relu" [label="[1, 64, 25, 25]", style=solid]; -"178 /Mixed_5d/branch3x3dbl_1/Relu" -> "179 QuantizeLinear_/Mixed_5d/branch3x3dbl_1/Relu_output_0_1" [label="[1, 64, 25, 25]", style=solid]; -"179 QuantizeLinear_/Mixed_5d/branch3x3dbl_1/Relu_output_0_1" -> "180 DequantizeLinear_/Mixed_5d/branch3x3dbl_1/Relu_output_0_1" [label="[1, 64, 25, 25]", style=dashed]; -"180 DequantizeLinear_/Mixed_5d/branch3x3dbl_1/Relu_output_0_1" -> "183 /Mixed_5d/branch3x3dbl_2/conv/Conv" [label="[1, 64, 25, 25]", style=solid]; -"181 QuantizeLinear_onnx^^Conv_1003_1" -> "182 DequantizeLinear_onnx^^Conv_1003_1" [label="[96, 64, 3, 3]", style=dashed]; -"182 DequantizeLinear_onnx^^Conv_1003_1" -> "183 /Mixed_5d/branch3x3dbl_2/conv/Conv" [label="[96, 64, 3, 3]", style=solid]; -"183 /Mixed_5d/branch3x3dbl_2/conv/Conv" -> "184 /Mixed_5d/branch3x3dbl_2/Relu" [label="[1, 96, 25, 25]", style=solid]; -"184 /Mixed_5d/branch3x3dbl_2/Relu" -> "185 QuantizeLinear_/Mixed_5d/branch3x3dbl_2/Relu_output_0_1" [label="[1, 96, 25, 25]", style=solid]; -"185 QuantizeLinear_/Mixed_5d/branch3x3dbl_2/Relu_output_0_1" -> "186 DequantizeLinear_/Mixed_5d/branch3x3dbl_2/Relu_output_0_1" [label="[1, 96, 25, 25]", style=dashed]; -"186 DequantizeLinear_/Mixed_5d/branch3x3dbl_2/Relu_output_0_1" -> "189 /Mixed_5d/branch3x3dbl_3/conv/Conv" [label="[1, 96, 25, 25]", style=solid]; -"187 QuantizeLinear_onnx^^Conv_1006_1" -> "188 DequantizeLinear_onnx^^Conv_1006_1" [label="[96, 96, 3, 3]", style=dashed]; -"188 DequantizeLinear_onnx^^Conv_1006_1" -> "189 /Mixed_5d/branch3x3dbl_3/conv/Conv" [label="[96, 96, 3, 3]", style=solid]; -"189 /Mixed_5d/branch3x3dbl_3/conv/Conv" -> "190 /Mixed_5d/branch3x3dbl_3/Relu" [label="[1, 96, 25, 25]", style=solid]; -"190 /Mixed_5d/branch3x3dbl_3/Relu" -> "204 QuantizeLinear_/Mixed_5d/branch3x3dbl_3/Relu_output_0_1" [label="[1, 96, 25, 25]", style=solid]; -"191 /Mixed_5d/Constant" -> "192 /Mixed_5d/Pad" [label="[8]", style=dashed]; -"192 /Mixed_5d/Pad" -> "193 /Mixed_5d/AveragePool" [label="[1, 288, 27, 27]", style=solid]; -"193 /Mixed_5d/AveragePool" -> "194 QuantizeLinear_/Mixed_5d/AveragePool_output_0_1" [label="[1, 288, 25, 25]", style=solid]; -"194 QuantizeLinear_/Mixed_5d/AveragePool_output_0_1" -> "195 DequantizeLinear_/Mixed_5d/AveragePool_output_0_1" [label="[1, 288, 25, 25]", style=dashed]; -"195 DequantizeLinear_/Mixed_5d/AveragePool_output_0_1" -> "198 /Mixed_5d/branch_pool/conv/Conv" [label="[1, 288, 25, 25]", style=solid]; -"196 QuantizeLinear_onnx^^Conv_1009_1" -> "197 DequantizeLinear_onnx^^Conv_1009_1" [label="[64, 288, 1, 1]", style=dashed]; -"197 DequantizeLinear_onnx^^Conv_1009_1" -> "198 /Mixed_5d/branch_pool/conv/Conv" [label="[64, 288, 1, 1]", style=solid]; -"198 /Mixed_5d/branch_pool/conv/Conv" -> "199 /Mixed_5d/branch_pool/Relu" [label="[1, 64, 25, 25]", style=solid]; -"199 /Mixed_5d/branch_pool/Relu" -> "206 QuantizeLinear_/Mixed_5d/branch_pool/Relu_output_0_1" [label="[1, 64, 25, 25]", style=solid]; -"200 QuantizeLinear_/Mixed_5d/branch1x1/Relu_output_0_1" -> "201 DequantizeLinear_/Mixed_5d/branch1x1/Relu_output_0_1" [label="[1, 64, 25, 25]", style=dashed]; -"201 DequantizeLinear_/Mixed_5d/branch1x1/Relu_output_0_1" -> "208 /Mixed_5d/Concat" [label="[1, 64, 25, 25]", style=solid]; -"202 QuantizeLinear_/Mixed_5d/branch5x5_2/Relu_output_0_1" -> "203 DequantizeLinear_/Mixed_5d/branch5x5_2/Relu_output_0_1" [label="[1, 64, 25, 25]", style=dashed]; -"203 DequantizeLinear_/Mixed_5d/branch5x5_2/Relu_output_0_1" -> "208 /Mixed_5d/Concat" [label="[1, 64, 25, 25]", style=solid]; -"204 QuantizeLinear_/Mixed_5d/branch3x3dbl_3/Relu_output_0_1" -> "205 DequantizeLinear_/Mixed_5d/branch3x3dbl_3/Relu_output_0_1" [label="[1, 96, 25, 25]", style=dashed]; -"205 DequantizeLinear_/Mixed_5d/branch3x3dbl_3/Relu_output_0_1" -> "208 /Mixed_5d/Concat" [label="[1, 96, 25, 25]", style=solid]; -"206 QuantizeLinear_/Mixed_5d/branch_pool/Relu_output_0_1" -> "207 DequantizeLinear_/Mixed_5d/branch_pool/Relu_output_0_1" [label="[1, 64, 25, 25]", style=dashed]; -"207 DequantizeLinear_/Mixed_5d/branch_pool/Relu_output_0_1" -> "208 /Mixed_5d/Concat" [label="[1, 64, 25, 25]", style=solid]; -"208 /Mixed_5d/Concat" -> "211 /Mixed_6a/branch3x3/conv/Conv" [label="[1, 288, 25, 25]", style=solid]; -"208 /Mixed_5d/Concat" -> "215 /Mixed_6a/branch3x3dbl_1/conv/Conv" [label="[1, 288, 25, 25]", style=solid]; -"208 /Mixed_5d/Concat" -> "229 /Mixed_6a/MaxPool" [label="[1, 288, 25, 25]", style=solid]; -"209 QuantizeLinear_onnx^^Conv_1012_1" -> "210 DequantizeLinear_onnx^^Conv_1012_1" [label="[384, 288, 3, 3]", style=dashed]; -"210 DequantizeLinear_onnx^^Conv_1012_1" -> "211 /Mixed_6a/branch3x3/conv/Conv" [label="[384, 288, 3, 3]", style=solid]; -"211 /Mixed_6a/branch3x3/conv/Conv" -> "212 /Mixed_6a/branch3x3/Relu" [label="[1, 384, 12, 12]", style=solid]; -"212 /Mixed_6a/branch3x3/Relu" -> "232 QuantizeLinear_/Mixed_6a/branch3x3/Relu_output_0_1" [label="[1, 384, 12, 12]", style=solid]; -"213 QuantizeLinear_onnx^^Conv_1015_1" -> "214 DequantizeLinear_onnx^^Conv_1015_1" [label="[64, 288, 1, 1]", style=dashed]; -"214 DequantizeLinear_onnx^^Conv_1015_1" -> "215 /Mixed_6a/branch3x3dbl_1/conv/Conv" [label="[64, 288, 1, 1]", style=solid]; -"215 /Mixed_6a/branch3x3dbl_1/conv/Conv" -> "216 /Mixed_6a/branch3x3dbl_1/Relu" [label="[1, 64, 25, 25]", style=solid]; -"216 /Mixed_6a/branch3x3dbl_1/Relu" -> "217 QuantizeLinear_/Mixed_6a/branch3x3dbl_1/Relu_output_0_1" [label="[1, 64, 25, 25]", style=solid]; -"217 QuantizeLinear_/Mixed_6a/branch3x3dbl_1/Relu_output_0_1" -> "218 DequantizeLinear_/Mixed_6a/branch3x3dbl_1/Relu_output_0_1" [label="[1, 64, 25, 25]", style=dashed]; -"218 DequantizeLinear_/Mixed_6a/branch3x3dbl_1/Relu_output_0_1" -> "221 /Mixed_6a/branch3x3dbl_2/conv/Conv" [label="[1, 64, 25, 25]", style=solid]; -"219 QuantizeLinear_onnx^^Conv_1018_1" -> "220 DequantizeLinear_onnx^^Conv_1018_1" [label="[96, 64, 3, 3]", style=dashed]; -"220 DequantizeLinear_onnx^^Conv_1018_1" -> "221 /Mixed_6a/branch3x3dbl_2/conv/Conv" [label="[96, 64, 3, 3]", style=solid]; -"221 /Mixed_6a/branch3x3dbl_2/conv/Conv" -> "222 /Mixed_6a/branch3x3dbl_2/Relu" [label="[1, 96, 25, 25]", style=solid]; -"222 /Mixed_6a/branch3x3dbl_2/Relu" -> "223 QuantizeLinear_/Mixed_6a/branch3x3dbl_2/Relu_output_0_1" [label="[1, 96, 25, 25]", style=solid]; -"223 QuantizeLinear_/Mixed_6a/branch3x3dbl_2/Relu_output_0_1" -> "224 DequantizeLinear_/Mixed_6a/branch3x3dbl_2/Relu_output_0_1" [label="[1, 96, 25, 25]", style=dashed]; -"224 DequantizeLinear_/Mixed_6a/branch3x3dbl_2/Relu_output_0_1" -> "227 /Mixed_6a/branch3x3dbl_3/conv/Conv" [label="[1, 96, 25, 25]", style=solid]; -"225 QuantizeLinear_onnx^^Conv_1021_1" -> "226 DequantizeLinear_onnx^^Conv_1021_1" [label="[96, 96, 3, 3]", style=dashed]; -"226 DequantizeLinear_onnx^^Conv_1021_1" -> "227 /Mixed_6a/branch3x3dbl_3/conv/Conv" [label="[96, 96, 3, 3]", style=solid]; -"227 /Mixed_6a/branch3x3dbl_3/conv/Conv" -> "228 /Mixed_6a/branch3x3dbl_3/Relu" [label="[1, 96, 12, 12]", style=solid]; -"228 /Mixed_6a/branch3x3dbl_3/Relu" -> "230 QuantizeLinear_/Mixed_6a/branch3x3dbl_3/Relu_output_0_1" [label="[1, 96, 12, 12]", style=solid]; -"229 /Mixed_6a/MaxPool" -> "234 /Mixed_6a/Concat" [label="[1, 288, 12, 12]", style=solid]; -"230 QuantizeLinear_/Mixed_6a/branch3x3dbl_3/Relu_output_0_1" -> "231 DequantizeLinear_/Mixed_6a/branch3x3dbl_3/Relu_output_0_1" [label="[1, 96, 12, 12]", style=dashed]; -"231 DequantizeLinear_/Mixed_6a/branch3x3dbl_3/Relu_output_0_1" -> "234 /Mixed_6a/Concat" [label="[1, 96, 12, 12]", style=solid]; -"232 QuantizeLinear_/Mixed_6a/branch3x3/Relu_output_0_1" -> "233 DequantizeLinear_/Mixed_6a/branch3x3/Relu_output_0_1" [label="[1, 384, 12, 12]", style=dashed]; -"233 DequantizeLinear_/Mixed_6a/branch3x3/Relu_output_0_1" -> "234 /Mixed_6a/Concat" [label="[1, 384, 12, 12]", style=solid]; -"234 /Mixed_6a/Concat" -> "237 /Mixed_6b/branch1x1/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; -"234 /Mixed_6a/Concat" -> "241 /Mixed_6b/branch7x7_1/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; -"234 /Mixed_6a/Concat" -> "257 /Mixed_6b/branch7x7dbl_1/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; -"234 /Mixed_6a/Concat" -> "284 /Mixed_6b/Pad" [label="[1, 768, 12, 12]", style=solid]; -"235 QuantizeLinear_onnx^^Conv_1024_1" -> "236 DequantizeLinear_onnx^^Conv_1024_1" [label="[192, 768, 1, 1]", style=dashed]; -"236 DequantizeLinear_onnx^^Conv_1024_1" -> "237 /Mixed_6b/branch1x1/conv/Conv" [label="[192, 768, 1, 1]", style=solid]; -"237 /Mixed_6b/branch1x1/conv/Conv" -> "238 /Mixed_6b/branch1x1/Relu" [label="[1, 192, 12, 12]", style=solid]; -"238 /Mixed_6b/branch1x1/Relu" -> "298 QuantizeLinear_/Mixed_6b/branch1x1/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; -"239 QuantizeLinear_onnx^^Conv_1027_1" -> "240 DequantizeLinear_onnx^^Conv_1027_1" [label="[128, 768, 1, 1]", style=dashed]; -"240 DequantizeLinear_onnx^^Conv_1027_1" -> "241 /Mixed_6b/branch7x7_1/conv/Conv" [label="[128, 768, 1, 1]", style=solid]; -"241 /Mixed_6b/branch7x7_1/conv/Conv" -> "242 /Mixed_6b/branch7x7_1/Relu" [label="[1, 128, 12, 12]", style=solid]; -"242 /Mixed_6b/branch7x7_1/Relu" -> "243 QuantizeLinear_/Mixed_6b/branch7x7_1/Relu_output_0_1" [label="[1, 128, 12, 12]", style=solid]; -"243 QuantizeLinear_/Mixed_6b/branch7x7_1/Relu_output_0_1" -> "244 DequantizeLinear_/Mixed_6b/branch7x7_1/Relu_output_0_1" [label="[1, 128, 12, 12]", style=dashed]; -"244 DequantizeLinear_/Mixed_6b/branch7x7_1/Relu_output_0_1" -> "247 /Mixed_6b/branch7x7_2/conv/Conv" [label="[1, 128, 12, 12]", style=solid]; -"245 QuantizeLinear_onnx^^Conv_1030_1" -> "246 DequantizeLinear_onnx^^Conv_1030_1" [label="[128, 128, 1, 7]", style=dashed]; -"246 DequantizeLinear_onnx^^Conv_1030_1" -> "247 /Mixed_6b/branch7x7_2/conv/Conv" [label="[128, 128, 1, 7]", style=solid]; -"247 /Mixed_6b/branch7x7_2/conv/Conv" -> "248 /Mixed_6b/branch7x7_2/Relu" [label="[1, 128, 12, 12]", style=solid]; -"248 /Mixed_6b/branch7x7_2/Relu" -> "249 QuantizeLinear_/Mixed_6b/branch7x7_2/Relu_output_0_1" [label="[1, 128, 12, 12]", style=solid]; -"249 QuantizeLinear_/Mixed_6b/branch7x7_2/Relu_output_0_1" -> "250 DequantizeLinear_/Mixed_6b/branch7x7_2/Relu_output_0_1" [label="[1, 128, 12, 12]", style=dashed]; -"250 DequantizeLinear_/Mixed_6b/branch7x7_2/Relu_output_0_1" -> "253 /Mixed_6b/branch7x7_3/conv/Conv" [label="[1, 128, 12, 12]", style=solid]; -"251 QuantizeLinear_onnx^^Conv_1033_1" -> "252 DequantizeLinear_onnx^^Conv_1033_1" [label="[192, 128, 7, 1]", style=dashed]; -"252 DequantizeLinear_onnx^^Conv_1033_1" -> "253 /Mixed_6b/branch7x7_3/conv/Conv" [label="[192, 128, 7, 1]", style=solid]; -"253 /Mixed_6b/branch7x7_3/conv/Conv" -> "254 /Mixed_6b/branch7x7_3/Relu" [label="[1, 192, 12, 12]", style=solid]; -"254 /Mixed_6b/branch7x7_3/Relu" -> "292 QuantizeLinear_/Mixed_6b/branch7x7_3/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; -"255 QuantizeLinear_onnx^^Conv_1036_1" -> "256 DequantizeLinear_onnx^^Conv_1036_1" [label="[128, 768, 1, 1]", style=dashed]; -"256 DequantizeLinear_onnx^^Conv_1036_1" -> "257 /Mixed_6b/branch7x7dbl_1/conv/Conv" [label="[128, 768, 1, 1]", style=solid]; -"257 /Mixed_6b/branch7x7dbl_1/conv/Conv" -> "258 /Mixed_6b/branch7x7dbl_1/Relu" [label="[1, 128, 12, 12]", style=solid]; -"258 /Mixed_6b/branch7x7dbl_1/Relu" -> "259 QuantizeLinear_/Mixed_6b/branch7x7dbl_1/Relu_output_0_1" [label="[1, 128, 12, 12]", style=solid]; -"259 QuantizeLinear_/Mixed_6b/branch7x7dbl_1/Relu_output_0_1" -> "260 DequantizeLinear_/Mixed_6b/branch7x7dbl_1/Relu_output_0_1" [label="[1, 128, 12, 12]", style=dashed]; -"260 DequantizeLinear_/Mixed_6b/branch7x7dbl_1/Relu_output_0_1" -> "263 /Mixed_6b/branch7x7dbl_2/conv/Conv" [label="[1, 128, 12, 12]", style=solid]; -"261 QuantizeLinear_onnx^^Conv_1039_1" -> "262 DequantizeLinear_onnx^^Conv_1039_1" [label="[128, 128, 7, 1]", style=dashed]; -"262 DequantizeLinear_onnx^^Conv_1039_1" -> "263 /Mixed_6b/branch7x7dbl_2/conv/Conv" [label="[128, 128, 7, 1]", style=solid]; -"263 /Mixed_6b/branch7x7dbl_2/conv/Conv" -> "264 /Mixed_6b/branch7x7dbl_2/Relu" [label="[1, 128, 12, 12]", style=solid]; -"264 /Mixed_6b/branch7x7dbl_2/Relu" -> "265 QuantizeLinear_/Mixed_6b/branch7x7dbl_2/Relu_output_0_1" [label="[1, 128, 12, 12]", style=solid]; -"265 QuantizeLinear_/Mixed_6b/branch7x7dbl_2/Relu_output_0_1" -> "266 DequantizeLinear_/Mixed_6b/branch7x7dbl_2/Relu_output_0_1" [label="[1, 128, 12, 12]", style=dashed]; -"266 DequantizeLinear_/Mixed_6b/branch7x7dbl_2/Relu_output_0_1" -> "269 /Mixed_6b/branch7x7dbl_3/conv/Conv" [label="[1, 128, 12, 12]", style=solid]; -"267 QuantizeLinear_onnx^^Conv_1042_1" -> "268 DequantizeLinear_onnx^^Conv_1042_1" [label="[128, 128, 1, 7]", style=dashed]; -"268 DequantizeLinear_onnx^^Conv_1042_1" -> "269 /Mixed_6b/branch7x7dbl_3/conv/Conv" [label="[128, 128, 1, 7]", style=solid]; -"269 /Mixed_6b/branch7x7dbl_3/conv/Conv" -> "270 /Mixed_6b/branch7x7dbl_3/Relu" [label="[1, 128, 12, 12]", style=solid]; -"270 /Mixed_6b/branch7x7dbl_3/Relu" -> "271 QuantizeLinear_/Mixed_6b/branch7x7dbl_3/Relu_output_0_1" [label="[1, 128, 12, 12]", style=solid]; -"271 QuantizeLinear_/Mixed_6b/branch7x7dbl_3/Relu_output_0_1" -> "272 DequantizeLinear_/Mixed_6b/branch7x7dbl_3/Relu_output_0_1" [label="[1, 128, 12, 12]", style=dashed]; -"272 DequantizeLinear_/Mixed_6b/branch7x7dbl_3/Relu_output_0_1" -> "275 /Mixed_6b/branch7x7dbl_4/conv/Conv" [label="[1, 128, 12, 12]", style=solid]; -"273 QuantizeLinear_onnx^^Conv_1045_1" -> "274 DequantizeLinear_onnx^^Conv_1045_1" [label="[128, 128, 7, 1]", style=dashed]; -"274 DequantizeLinear_onnx^^Conv_1045_1" -> "275 /Mixed_6b/branch7x7dbl_4/conv/Conv" [label="[128, 128, 7, 1]", style=solid]; -"275 /Mixed_6b/branch7x7dbl_4/conv/Conv" -> "276 /Mixed_6b/branch7x7dbl_4/Relu" [label="[1, 128, 12, 12]", style=solid]; -"276 /Mixed_6b/branch7x7dbl_4/Relu" -> "277 QuantizeLinear_/Mixed_6b/branch7x7dbl_4/Relu_output_0_1" [label="[1, 128, 12, 12]", style=solid]; -"277 QuantizeLinear_/Mixed_6b/branch7x7dbl_4/Relu_output_0_1" -> "278 DequantizeLinear_/Mixed_6b/branch7x7dbl_4/Relu_output_0_1" [label="[1, 128, 12, 12]", style=dashed]; -"278 DequantizeLinear_/Mixed_6b/branch7x7dbl_4/Relu_output_0_1" -> "281 /Mixed_6b/branch7x7dbl_5/conv/Conv" [label="[1, 128, 12, 12]", style=solid]; -"279 QuantizeLinear_onnx^^Conv_1048_1" -> "280 DequantizeLinear_onnx^^Conv_1048_1" [label="[192, 128, 1, 7]", style=dashed]; -"280 DequantizeLinear_onnx^^Conv_1048_1" -> "281 /Mixed_6b/branch7x7dbl_5/conv/Conv" [label="[192, 128, 1, 7]", style=solid]; -"281 /Mixed_6b/branch7x7dbl_5/conv/Conv" -> "282 /Mixed_6b/branch7x7dbl_5/Relu" [label="[1, 192, 12, 12]", style=solid]; -"282 /Mixed_6b/branch7x7dbl_5/Relu" -> "294 QuantizeLinear_/Mixed_6b/branch7x7dbl_5/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; -"283 /Mixed_6b/Constant" -> "284 /Mixed_6b/Pad" [label="[8]", style=dashed]; -"284 /Mixed_6b/Pad" -> "285 /Mixed_6b/AveragePool" [label="[1, 768, 14, 14]", style=solid]; -"285 /Mixed_6b/AveragePool" -> "286 QuantizeLinear_/Mixed_6b/AveragePool_output_0_1" [label="[1, 768, 12, 12]", style=solid]; -"286 QuantizeLinear_/Mixed_6b/AveragePool_output_0_1" -> "287 DequantizeLinear_/Mixed_6b/AveragePool_output_0_1" [label="[1, 768, 12, 12]", style=dashed]; -"287 DequantizeLinear_/Mixed_6b/AveragePool_output_0_1" -> "290 /Mixed_6b/branch_pool/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; -"288 QuantizeLinear_onnx^^Conv_1051_1" -> "289 DequantizeLinear_onnx^^Conv_1051_1" [label="[192, 768, 1, 1]", style=dashed]; -"289 DequantizeLinear_onnx^^Conv_1051_1" -> "290 /Mixed_6b/branch_pool/conv/Conv" [label="[192, 768, 1, 1]", style=solid]; -"290 /Mixed_6b/branch_pool/conv/Conv" -> "291 /Mixed_6b/branch_pool/Relu" [label="[1, 192, 12, 12]", style=solid]; -"291 /Mixed_6b/branch_pool/Relu" -> "296 QuantizeLinear_/Mixed_6b/branch_pool/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; -"292 QuantizeLinear_/Mixed_6b/branch7x7_3/Relu_output_0_1" -> "293 DequantizeLinear_/Mixed_6b/branch7x7_3/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; -"293 DequantizeLinear_/Mixed_6b/branch7x7_3/Relu_output_0_1" -> "300 /Mixed_6b/Concat" [label="[1, 192, 12, 12]", style=solid]; -"294 QuantizeLinear_/Mixed_6b/branch7x7dbl_5/Relu_output_0_1" -> "295 DequantizeLinear_/Mixed_6b/branch7x7dbl_5/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; -"295 DequantizeLinear_/Mixed_6b/branch7x7dbl_5/Relu_output_0_1" -> "300 /Mixed_6b/Concat" [label="[1, 192, 12, 12]", style=solid]; -"296 QuantizeLinear_/Mixed_6b/branch_pool/Relu_output_0_1" -> "297 DequantizeLinear_/Mixed_6b/branch_pool/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; -"297 DequantizeLinear_/Mixed_6b/branch_pool/Relu_output_0_1" -> "300 /Mixed_6b/Concat" [label="[1, 192, 12, 12]", style=solid]; -"298 QuantizeLinear_/Mixed_6b/branch1x1/Relu_output_0_1" -> "299 DequantizeLinear_/Mixed_6b/branch1x1/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; -"299 DequantizeLinear_/Mixed_6b/branch1x1/Relu_output_0_1" -> "300 /Mixed_6b/Concat" [label="[1, 192, 12, 12]", style=solid]; -"300 /Mixed_6b/Concat" -> "303 /Mixed_6c/branch1x1/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; -"300 /Mixed_6b/Concat" -> "307 /Mixed_6c/branch7x7_1/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; -"300 /Mixed_6b/Concat" -> "323 /Mixed_6c/branch7x7dbl_1/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; -"300 /Mixed_6b/Concat" -> "350 /Mixed_6c/Pad" [label="[1, 768, 12, 12]", style=solid]; -"301 QuantizeLinear_onnx^^Conv_1054_1" -> "302 DequantizeLinear_onnx^^Conv_1054_1" [label="[192, 768, 1, 1]", style=dashed]; -"302 DequantizeLinear_onnx^^Conv_1054_1" -> "303 /Mixed_6c/branch1x1/conv/Conv" [label="[192, 768, 1, 1]", style=solid]; -"303 /Mixed_6c/branch1x1/conv/Conv" -> "304 /Mixed_6c/branch1x1/Relu" [label="[1, 192, 12, 12]", style=solid]; -"304 /Mixed_6c/branch1x1/Relu" -> "360 QuantizeLinear_/Mixed_6c/branch1x1/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; -"305 QuantizeLinear_onnx^^Conv_1057_1" -> "306 DequantizeLinear_onnx^^Conv_1057_1" [label="[160, 768, 1, 1]", style=dashed]; -"306 DequantizeLinear_onnx^^Conv_1057_1" -> "307 /Mixed_6c/branch7x7_1/conv/Conv" [label="[160, 768, 1, 1]", style=solid]; -"307 /Mixed_6c/branch7x7_1/conv/Conv" -> "308 /Mixed_6c/branch7x7_1/Relu" [label="[1, 160, 12, 12]", style=solid]; -"308 /Mixed_6c/branch7x7_1/Relu" -> "309 QuantizeLinear_/Mixed_6c/branch7x7_1/Relu_output_0_1" [label="[1, 160, 12, 12]", style=solid]; -"309 QuantizeLinear_/Mixed_6c/branch7x7_1/Relu_output_0_1" -> "310 DequantizeLinear_/Mixed_6c/branch7x7_1/Relu_output_0_1" [label="[1, 160, 12, 12]", style=dashed]; -"310 DequantizeLinear_/Mixed_6c/branch7x7_1/Relu_output_0_1" -> "313 /Mixed_6c/branch7x7_2/conv/Conv" [label="[1, 160, 12, 12]", style=solid]; -"311 QuantizeLinear_onnx^^Conv_1060_1" -> "312 DequantizeLinear_onnx^^Conv_1060_1" [label="[160, 160, 1, 7]", style=dashed]; -"312 DequantizeLinear_onnx^^Conv_1060_1" -> "313 /Mixed_6c/branch7x7_2/conv/Conv" [label="[160, 160, 1, 7]", style=solid]; -"313 /Mixed_6c/branch7x7_2/conv/Conv" -> "314 /Mixed_6c/branch7x7_2/Relu" [label="[1, 160, 12, 12]", style=solid]; -"314 /Mixed_6c/branch7x7_2/Relu" -> "315 QuantizeLinear_/Mixed_6c/branch7x7_2/Relu_output_0_1" [label="[1, 160, 12, 12]", style=solid]; -"315 QuantizeLinear_/Mixed_6c/branch7x7_2/Relu_output_0_1" -> "316 DequantizeLinear_/Mixed_6c/branch7x7_2/Relu_output_0_1" [label="[1, 160, 12, 12]", style=dashed]; -"316 DequantizeLinear_/Mixed_6c/branch7x7_2/Relu_output_0_1" -> "319 /Mixed_6c/branch7x7_3/conv/Conv" [label="[1, 160, 12, 12]", style=solid]; -"317 QuantizeLinear_onnx^^Conv_1063_1" -> "318 DequantizeLinear_onnx^^Conv_1063_1" [label="[192, 160, 7, 1]", style=dashed]; -"318 DequantizeLinear_onnx^^Conv_1063_1" -> "319 /Mixed_6c/branch7x7_3/conv/Conv" [label="[192, 160, 7, 1]", style=solid]; -"319 /Mixed_6c/branch7x7_3/conv/Conv" -> "320 /Mixed_6c/branch7x7_3/Relu" [label="[1, 192, 12, 12]", style=solid]; -"320 /Mixed_6c/branch7x7_3/Relu" -> "358 QuantizeLinear_/Mixed_6c/branch7x7_3/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; -"321 QuantizeLinear_onnx^^Conv_1066_1" -> "322 DequantizeLinear_onnx^^Conv_1066_1" [label="[160, 768, 1, 1]", style=dashed]; -"322 DequantizeLinear_onnx^^Conv_1066_1" -> "323 /Mixed_6c/branch7x7dbl_1/conv/Conv" [label="[160, 768, 1, 1]", style=solid]; -"323 /Mixed_6c/branch7x7dbl_1/conv/Conv" -> "324 /Mixed_6c/branch7x7dbl_1/Relu" [label="[1, 160, 12, 12]", style=solid]; -"324 /Mixed_6c/branch7x7dbl_1/Relu" -> "325 QuantizeLinear_/Mixed_6c/branch7x7dbl_1/Relu_output_0_1" [label="[1, 160, 12, 12]", style=solid]; -"325 QuantizeLinear_/Mixed_6c/branch7x7dbl_1/Relu_output_0_1" -> "326 DequantizeLinear_/Mixed_6c/branch7x7dbl_1/Relu_output_0_1" [label="[1, 160, 12, 12]", style=dashed]; -"326 DequantizeLinear_/Mixed_6c/branch7x7dbl_1/Relu_output_0_1" -> "329 /Mixed_6c/branch7x7dbl_2/conv/Conv" [label="[1, 160, 12, 12]", style=solid]; -"327 QuantizeLinear_onnx^^Conv_1069_1" -> "328 DequantizeLinear_onnx^^Conv_1069_1" [label="[160, 160, 7, 1]", style=dashed]; -"328 DequantizeLinear_onnx^^Conv_1069_1" -> "329 /Mixed_6c/branch7x7dbl_2/conv/Conv" [label="[160, 160, 7, 1]", style=solid]; -"329 /Mixed_6c/branch7x7dbl_2/conv/Conv" -> "330 /Mixed_6c/branch7x7dbl_2/Relu" [label="[1, 160, 12, 12]", style=solid]; -"330 /Mixed_6c/branch7x7dbl_2/Relu" -> "331 QuantizeLinear_/Mixed_6c/branch7x7dbl_2/Relu_output_0_1" [label="[1, 160, 12, 12]", style=solid]; -"331 QuantizeLinear_/Mixed_6c/branch7x7dbl_2/Relu_output_0_1" -> "332 DequantizeLinear_/Mixed_6c/branch7x7dbl_2/Relu_output_0_1" [label="[1, 160, 12, 12]", style=dashed]; -"332 DequantizeLinear_/Mixed_6c/branch7x7dbl_2/Relu_output_0_1" -> "335 /Mixed_6c/branch7x7dbl_3/conv/Conv" [label="[1, 160, 12, 12]", style=solid]; -"333 QuantizeLinear_onnx^^Conv_1072_1" -> "334 DequantizeLinear_onnx^^Conv_1072_1" [label="[160, 160, 1, 7]", style=dashed]; -"334 DequantizeLinear_onnx^^Conv_1072_1" -> "335 /Mixed_6c/branch7x7dbl_3/conv/Conv" [label="[160, 160, 1, 7]", style=solid]; -"335 /Mixed_6c/branch7x7dbl_3/conv/Conv" -> "336 /Mixed_6c/branch7x7dbl_3/Relu" [label="[1, 160, 12, 12]", style=solid]; -"336 /Mixed_6c/branch7x7dbl_3/Relu" -> "337 QuantizeLinear_/Mixed_6c/branch7x7dbl_3/Relu_output_0_1" [label="[1, 160, 12, 12]", style=solid]; -"337 QuantizeLinear_/Mixed_6c/branch7x7dbl_3/Relu_output_0_1" -> "338 DequantizeLinear_/Mixed_6c/branch7x7dbl_3/Relu_output_0_1" [label="[1, 160, 12, 12]", style=dashed]; -"338 DequantizeLinear_/Mixed_6c/branch7x7dbl_3/Relu_output_0_1" -> "341 /Mixed_6c/branch7x7dbl_4/conv/Conv" [label="[1, 160, 12, 12]", style=solid]; -"339 QuantizeLinear_onnx^^Conv_1075_1" -> "340 DequantizeLinear_onnx^^Conv_1075_1" [label="[160, 160, 7, 1]", style=dashed]; -"340 DequantizeLinear_onnx^^Conv_1075_1" -> "341 /Mixed_6c/branch7x7dbl_4/conv/Conv" [label="[160, 160, 7, 1]", style=solid]; -"341 /Mixed_6c/branch7x7dbl_4/conv/Conv" -> "342 /Mixed_6c/branch7x7dbl_4/Relu" [label="[1, 160, 12, 12]", style=solid]; -"342 /Mixed_6c/branch7x7dbl_4/Relu" -> "343 QuantizeLinear_/Mixed_6c/branch7x7dbl_4/Relu_output_0_1" [label="[1, 160, 12, 12]", style=solid]; -"343 QuantizeLinear_/Mixed_6c/branch7x7dbl_4/Relu_output_0_1" -> "344 DequantizeLinear_/Mixed_6c/branch7x7dbl_4/Relu_output_0_1" [label="[1, 160, 12, 12]", style=dashed]; -"344 DequantizeLinear_/Mixed_6c/branch7x7dbl_4/Relu_output_0_1" -> "347 /Mixed_6c/branch7x7dbl_5/conv/Conv" [label="[1, 160, 12, 12]", style=solid]; -"345 QuantizeLinear_onnx^^Conv_1078_1" -> "346 DequantizeLinear_onnx^^Conv_1078_1" [label="[192, 160, 1, 7]", style=dashed]; -"346 DequantizeLinear_onnx^^Conv_1078_1" -> "347 /Mixed_6c/branch7x7dbl_5/conv/Conv" [label="[192, 160, 1, 7]", style=solid]; -"347 /Mixed_6c/branch7x7dbl_5/conv/Conv" -> "348 /Mixed_6c/branch7x7dbl_5/Relu" [label="[1, 192, 12, 12]", style=solid]; -"348 /Mixed_6c/branch7x7dbl_5/Relu" -> "364 QuantizeLinear_/Mixed_6c/branch7x7dbl_5/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; -"349 /Mixed_6c/Constant" -> "350 /Mixed_6c/Pad" [label="[8]", style=dashed]; -"350 /Mixed_6c/Pad" -> "351 /Mixed_6c/AveragePool" [label="[1, 768, 14, 14]", style=solid]; -"351 /Mixed_6c/AveragePool" -> "352 QuantizeLinear_/Mixed_6c/AveragePool_output_0_1" [label="[1, 768, 12, 12]", style=solid]; -"352 QuantizeLinear_/Mixed_6c/AveragePool_output_0_1" -> "353 DequantizeLinear_/Mixed_6c/AveragePool_output_0_1" [label="[1, 768, 12, 12]", style=dashed]; -"353 DequantizeLinear_/Mixed_6c/AveragePool_output_0_1" -> "356 /Mixed_6c/branch_pool/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; -"354 QuantizeLinear_onnx^^Conv_1081_1" -> "355 DequantizeLinear_onnx^^Conv_1081_1" [label="[192, 768, 1, 1]", style=dashed]; -"355 DequantizeLinear_onnx^^Conv_1081_1" -> "356 /Mixed_6c/branch_pool/conv/Conv" [label="[192, 768, 1, 1]", style=solid]; -"356 /Mixed_6c/branch_pool/conv/Conv" -> "357 /Mixed_6c/branch_pool/Relu" [label="[1, 192, 12, 12]", style=solid]; -"357 /Mixed_6c/branch_pool/Relu" -> "362 QuantizeLinear_/Mixed_6c/branch_pool/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; -"358 QuantizeLinear_/Mixed_6c/branch7x7_3/Relu_output_0_1" -> "359 DequantizeLinear_/Mixed_6c/branch7x7_3/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; -"359 DequantizeLinear_/Mixed_6c/branch7x7_3/Relu_output_0_1" -> "366 /Mixed_6c/Concat" [label="[1, 192, 12, 12]", style=solid]; -"360 QuantizeLinear_/Mixed_6c/branch1x1/Relu_output_0_1" -> "361 DequantizeLinear_/Mixed_6c/branch1x1/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; -"361 DequantizeLinear_/Mixed_6c/branch1x1/Relu_output_0_1" -> "366 /Mixed_6c/Concat" [label="[1, 192, 12, 12]", style=solid]; -"362 QuantizeLinear_/Mixed_6c/branch_pool/Relu_output_0_1" -> "363 DequantizeLinear_/Mixed_6c/branch_pool/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; -"363 DequantizeLinear_/Mixed_6c/branch_pool/Relu_output_0_1" -> "366 /Mixed_6c/Concat" [label="[1, 192, 12, 12]", style=solid]; -"364 QuantizeLinear_/Mixed_6c/branch7x7dbl_5/Relu_output_0_1" -> "365 DequantizeLinear_/Mixed_6c/branch7x7dbl_5/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; -"365 DequantizeLinear_/Mixed_6c/branch7x7dbl_5/Relu_output_0_1" -> "366 /Mixed_6c/Concat" [label="[1, 192, 12, 12]", style=solid]; -"366 /Mixed_6c/Concat" -> "369 /Mixed_6d/branch1x1/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; -"366 /Mixed_6c/Concat" -> "373 /Mixed_6d/branch7x7_1/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; -"366 /Mixed_6c/Concat" -> "389 /Mixed_6d/branch7x7dbl_1/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; -"366 /Mixed_6c/Concat" -> "416 /Mixed_6d/Pad" [label="[1, 768, 12, 12]", style=solid]; -"367 QuantizeLinear_onnx^^Conv_1084_1" -> "368 DequantizeLinear_onnx^^Conv_1084_1" [label="[192, 768, 1, 1]", style=dashed]; -"368 DequantizeLinear_onnx^^Conv_1084_1" -> "369 /Mixed_6d/branch1x1/conv/Conv" [label="[192, 768, 1, 1]", style=solid]; -"369 /Mixed_6d/branch1x1/conv/Conv" -> "370 /Mixed_6d/branch1x1/Relu" [label="[1, 192, 12, 12]", style=solid]; -"370 /Mixed_6d/branch1x1/Relu" -> "428 QuantizeLinear_/Mixed_6d/branch1x1/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; -"371 QuantizeLinear_onnx^^Conv_1087_1" -> "372 DequantizeLinear_onnx^^Conv_1087_1" [label="[160, 768, 1, 1]", style=dashed]; -"372 DequantizeLinear_onnx^^Conv_1087_1" -> "373 /Mixed_6d/branch7x7_1/conv/Conv" [label="[160, 768, 1, 1]", style=solid]; -"373 /Mixed_6d/branch7x7_1/conv/Conv" -> "374 /Mixed_6d/branch7x7_1/Relu" [label="[1, 160, 12, 12]", style=solid]; -"374 /Mixed_6d/branch7x7_1/Relu" -> "375 QuantizeLinear_/Mixed_6d/branch7x7_1/Relu_output_0_1" [label="[1, 160, 12, 12]", style=solid]; -"375 QuantizeLinear_/Mixed_6d/branch7x7_1/Relu_output_0_1" -> "376 DequantizeLinear_/Mixed_6d/branch7x7_1/Relu_output_0_1" [label="[1, 160, 12, 12]", style=dashed]; -"376 DequantizeLinear_/Mixed_6d/branch7x7_1/Relu_output_0_1" -> "379 /Mixed_6d/branch7x7_2/conv/Conv" [label="[1, 160, 12, 12]", style=solid]; -"377 QuantizeLinear_onnx^^Conv_1090_1" -> "378 DequantizeLinear_onnx^^Conv_1090_1" [label="[160, 160, 1, 7]", style=dashed]; -"378 DequantizeLinear_onnx^^Conv_1090_1" -> "379 /Mixed_6d/branch7x7_2/conv/Conv" [label="[160, 160, 1, 7]", style=solid]; -"379 /Mixed_6d/branch7x7_2/conv/Conv" -> "380 /Mixed_6d/branch7x7_2/Relu" [label="[1, 160, 12, 12]", style=solid]; -"380 /Mixed_6d/branch7x7_2/Relu" -> "381 QuantizeLinear_/Mixed_6d/branch7x7_2/Relu_output_0_1" [label="[1, 160, 12, 12]", style=solid]; -"381 QuantizeLinear_/Mixed_6d/branch7x7_2/Relu_output_0_1" -> "382 DequantizeLinear_/Mixed_6d/branch7x7_2/Relu_output_0_1" [label="[1, 160, 12, 12]", style=dashed]; -"382 DequantizeLinear_/Mixed_6d/branch7x7_2/Relu_output_0_1" -> "385 /Mixed_6d/branch7x7_3/conv/Conv" [label="[1, 160, 12, 12]", style=solid]; -"383 QuantizeLinear_onnx^^Conv_1093_1" -> "384 DequantizeLinear_onnx^^Conv_1093_1" [label="[192, 160, 7, 1]", style=dashed]; -"384 DequantizeLinear_onnx^^Conv_1093_1" -> "385 /Mixed_6d/branch7x7_3/conv/Conv" [label="[192, 160, 7, 1]", style=solid]; -"385 /Mixed_6d/branch7x7_3/conv/Conv" -> "386 /Mixed_6d/branch7x7_3/Relu" [label="[1, 192, 12, 12]", style=solid]; -"386 /Mixed_6d/branch7x7_3/Relu" -> "430 QuantizeLinear_/Mixed_6d/branch7x7_3/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; -"387 QuantizeLinear_onnx^^Conv_1096_1" -> "388 DequantizeLinear_onnx^^Conv_1096_1" [label="[160, 768, 1, 1]", style=dashed]; -"388 DequantizeLinear_onnx^^Conv_1096_1" -> "389 /Mixed_6d/branch7x7dbl_1/conv/Conv" [label="[160, 768, 1, 1]", style=solid]; -"389 /Mixed_6d/branch7x7dbl_1/conv/Conv" -> "390 /Mixed_6d/branch7x7dbl_1/Relu" [label="[1, 160, 12, 12]", style=solid]; -"390 /Mixed_6d/branch7x7dbl_1/Relu" -> "391 QuantizeLinear_/Mixed_6d/branch7x7dbl_1/Relu_output_0_1" [label="[1, 160, 12, 12]", style=solid]; -"391 QuantizeLinear_/Mixed_6d/branch7x7dbl_1/Relu_output_0_1" -> "392 DequantizeLinear_/Mixed_6d/branch7x7dbl_1/Relu_output_0_1" [label="[1, 160, 12, 12]", style=dashed]; -"392 DequantizeLinear_/Mixed_6d/branch7x7dbl_1/Relu_output_0_1" -> "395 /Mixed_6d/branch7x7dbl_2/conv/Conv" [label="[1, 160, 12, 12]", style=solid]; -"393 QuantizeLinear_onnx^^Conv_1099_1" -> "394 DequantizeLinear_onnx^^Conv_1099_1" [label="[160, 160, 7, 1]", style=dashed]; -"394 DequantizeLinear_onnx^^Conv_1099_1" -> "395 /Mixed_6d/branch7x7dbl_2/conv/Conv" [label="[160, 160, 7, 1]", style=solid]; -"395 /Mixed_6d/branch7x7dbl_2/conv/Conv" -> "396 /Mixed_6d/branch7x7dbl_2/Relu" [label="[1, 160, 12, 12]", style=solid]; -"396 /Mixed_6d/branch7x7dbl_2/Relu" -> "397 QuantizeLinear_/Mixed_6d/branch7x7dbl_2/Relu_output_0_1" [label="[1, 160, 12, 12]", style=solid]; -"397 QuantizeLinear_/Mixed_6d/branch7x7dbl_2/Relu_output_0_1" -> "398 DequantizeLinear_/Mixed_6d/branch7x7dbl_2/Relu_output_0_1" [label="[1, 160, 12, 12]", style=dashed]; -"398 DequantizeLinear_/Mixed_6d/branch7x7dbl_2/Relu_output_0_1" -> "401 /Mixed_6d/branch7x7dbl_3/conv/Conv" [label="[1, 160, 12, 12]", style=solid]; -"399 QuantizeLinear_onnx^^Conv_1102_1" -> "400 DequantizeLinear_onnx^^Conv_1102_1" [label="[160, 160, 1, 7]", style=dashed]; -"400 DequantizeLinear_onnx^^Conv_1102_1" -> "401 /Mixed_6d/branch7x7dbl_3/conv/Conv" [label="[160, 160, 1, 7]", style=solid]; -"401 /Mixed_6d/branch7x7dbl_3/conv/Conv" -> "402 /Mixed_6d/branch7x7dbl_3/Relu" [label="[1, 160, 12, 12]", style=solid]; -"402 /Mixed_6d/branch7x7dbl_3/Relu" -> "403 QuantizeLinear_/Mixed_6d/branch7x7dbl_3/Relu_output_0_1" [label="[1, 160, 12, 12]", style=solid]; -"403 QuantizeLinear_/Mixed_6d/branch7x7dbl_3/Relu_output_0_1" -> "404 DequantizeLinear_/Mixed_6d/branch7x7dbl_3/Relu_output_0_1" [label="[1, 160, 12, 12]", style=dashed]; -"404 DequantizeLinear_/Mixed_6d/branch7x7dbl_3/Relu_output_0_1" -> "407 /Mixed_6d/branch7x7dbl_4/conv/Conv" [label="[1, 160, 12, 12]", style=solid]; -"405 QuantizeLinear_onnx^^Conv_1105_1" -> "406 DequantizeLinear_onnx^^Conv_1105_1" [label="[160, 160, 7, 1]", style=dashed]; -"406 DequantizeLinear_onnx^^Conv_1105_1" -> "407 /Mixed_6d/branch7x7dbl_4/conv/Conv" [label="[160, 160, 7, 1]", style=solid]; -"407 /Mixed_6d/branch7x7dbl_4/conv/Conv" -> "408 /Mixed_6d/branch7x7dbl_4/Relu" [label="[1, 160, 12, 12]", style=solid]; -"408 /Mixed_6d/branch7x7dbl_4/Relu" -> "409 QuantizeLinear_/Mixed_6d/branch7x7dbl_4/Relu_output_0_1" [label="[1, 160, 12, 12]", style=solid]; -"409 QuantizeLinear_/Mixed_6d/branch7x7dbl_4/Relu_output_0_1" -> "410 DequantizeLinear_/Mixed_6d/branch7x7dbl_4/Relu_output_0_1" [label="[1, 160, 12, 12]", style=dashed]; -"410 DequantizeLinear_/Mixed_6d/branch7x7dbl_4/Relu_output_0_1" -> "413 /Mixed_6d/branch7x7dbl_5/conv/Conv" [label="[1, 160, 12, 12]", style=solid]; -"411 QuantizeLinear_onnx^^Conv_1108_1" -> "412 DequantizeLinear_onnx^^Conv_1108_1" [label="[192, 160, 1, 7]", style=dashed]; -"412 DequantizeLinear_onnx^^Conv_1108_1" -> "413 /Mixed_6d/branch7x7dbl_5/conv/Conv" [label="[192, 160, 1, 7]", style=solid]; -"413 /Mixed_6d/branch7x7dbl_5/conv/Conv" -> "414 /Mixed_6d/branch7x7dbl_5/Relu" [label="[1, 192, 12, 12]", style=solid]; -"414 /Mixed_6d/branch7x7dbl_5/Relu" -> "424 QuantizeLinear_/Mixed_6d/branch7x7dbl_5/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; -"415 /Mixed_6d/Constant" -> "416 /Mixed_6d/Pad" [label="[8]", style=dashed]; -"416 /Mixed_6d/Pad" -> "417 /Mixed_6d/AveragePool" [label="[1, 768, 14, 14]", style=solid]; -"417 /Mixed_6d/AveragePool" -> "418 QuantizeLinear_/Mixed_6d/AveragePool_output_0_1" [label="[1, 768, 12, 12]", style=solid]; -"418 QuantizeLinear_/Mixed_6d/AveragePool_output_0_1" -> "419 DequantizeLinear_/Mixed_6d/AveragePool_output_0_1" [label="[1, 768, 12, 12]", style=dashed]; -"419 DequantizeLinear_/Mixed_6d/AveragePool_output_0_1" -> "422 /Mixed_6d/branch_pool/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; -"420 QuantizeLinear_onnx^^Conv_1111_1" -> "421 DequantizeLinear_onnx^^Conv_1111_1" [label="[192, 768, 1, 1]", style=dashed]; -"421 DequantizeLinear_onnx^^Conv_1111_1" -> "422 /Mixed_6d/branch_pool/conv/Conv" [label="[192, 768, 1, 1]", style=solid]; -"422 /Mixed_6d/branch_pool/conv/Conv" -> "423 /Mixed_6d/branch_pool/Relu" [label="[1, 192, 12, 12]", style=solid]; -"423 /Mixed_6d/branch_pool/Relu" -> "426 QuantizeLinear_/Mixed_6d/branch_pool/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; -"424 QuantizeLinear_/Mixed_6d/branch7x7dbl_5/Relu_output_0_1" -> "425 DequantizeLinear_/Mixed_6d/branch7x7dbl_5/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; -"425 DequantizeLinear_/Mixed_6d/branch7x7dbl_5/Relu_output_0_1" -> "432 /Mixed_6d/Concat" [label="[1, 192, 12, 12]", style=solid]; -"426 QuantizeLinear_/Mixed_6d/branch_pool/Relu_output_0_1" -> "427 DequantizeLinear_/Mixed_6d/branch_pool/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; -"427 DequantizeLinear_/Mixed_6d/branch_pool/Relu_output_0_1" -> "432 /Mixed_6d/Concat" [label="[1, 192, 12, 12]", style=solid]; -"428 QuantizeLinear_/Mixed_6d/branch1x1/Relu_output_0_1" -> "429 DequantizeLinear_/Mixed_6d/branch1x1/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; -"429 DequantizeLinear_/Mixed_6d/branch1x1/Relu_output_0_1" -> "432 /Mixed_6d/Concat" [label="[1, 192, 12, 12]", style=solid]; -"430 QuantizeLinear_/Mixed_6d/branch7x7_3/Relu_output_0_1" -> "431 DequantizeLinear_/Mixed_6d/branch7x7_3/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; -"431 DequantizeLinear_/Mixed_6d/branch7x7_3/Relu_output_0_1" -> "432 /Mixed_6d/Concat" [label="[1, 192, 12, 12]", style=solid]; -"432 /Mixed_6d/Concat" -> "435 /Mixed_6e/branch1x1/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; -"432 /Mixed_6d/Concat" -> "439 /Mixed_6e/branch7x7_1/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; -"432 /Mixed_6d/Concat" -> "455 /Mixed_6e/branch7x7dbl_1/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; -"432 /Mixed_6d/Concat" -> "482 /Mixed_6e/Pad" [label="[1, 768, 12, 12]", style=solid]; -"433 QuantizeLinear_onnx^^Conv_1114_1" -> "434 DequantizeLinear_onnx^^Conv_1114_1" [label="[192, 768, 1, 1]", style=dashed]; -"434 DequantizeLinear_onnx^^Conv_1114_1" -> "435 /Mixed_6e/branch1x1/conv/Conv" [label="[192, 768, 1, 1]", style=solid]; -"435 /Mixed_6e/branch1x1/conv/Conv" -> "436 /Mixed_6e/branch1x1/Relu" [label="[1, 192, 12, 12]", style=solid]; -"436 /Mixed_6e/branch1x1/Relu" -> "496 QuantizeLinear_/Mixed_6e/branch1x1/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; -"437 QuantizeLinear_onnx^^Conv_1117_1" -> "438 DequantizeLinear_onnx^^Conv_1117_1" [label="[192, 768, 1, 1]", style=dashed]; -"438 DequantizeLinear_onnx^^Conv_1117_1" -> "439 /Mixed_6e/branch7x7_1/conv/Conv" [label="[192, 768, 1, 1]", style=solid]; -"439 /Mixed_6e/branch7x7_1/conv/Conv" -> "440 /Mixed_6e/branch7x7_1/Relu" [label="[1, 192, 12, 12]", style=solid]; -"440 /Mixed_6e/branch7x7_1/Relu" -> "441 QuantizeLinear_/Mixed_6e/branch7x7_1/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; -"441 QuantizeLinear_/Mixed_6e/branch7x7_1/Relu_output_0_1" -> "442 DequantizeLinear_/Mixed_6e/branch7x7_1/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; -"442 DequantizeLinear_/Mixed_6e/branch7x7_1/Relu_output_0_1" -> "445 /Mixed_6e/branch7x7_2/conv/Conv" [label="[1, 192, 12, 12]", style=solid]; -"443 QuantizeLinear_onnx^^Conv_1120_1" -> "444 DequantizeLinear_onnx^^Conv_1120_1" [label="[192, 192, 1, 7]", style=dashed]; -"444 DequantizeLinear_onnx^^Conv_1120_1" -> "445 /Mixed_6e/branch7x7_2/conv/Conv" [label="[192, 192, 1, 7]", style=solid]; -"445 /Mixed_6e/branch7x7_2/conv/Conv" -> "446 /Mixed_6e/branch7x7_2/Relu" [label="[1, 192, 12, 12]", style=solid]; -"446 /Mixed_6e/branch7x7_2/Relu" -> "447 QuantizeLinear_/Mixed_6e/branch7x7_2/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; -"447 QuantizeLinear_/Mixed_6e/branch7x7_2/Relu_output_0_1" -> "448 DequantizeLinear_/Mixed_6e/branch7x7_2/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; -"448 DequantizeLinear_/Mixed_6e/branch7x7_2/Relu_output_0_1" -> "451 /Mixed_6e/branch7x7_3/conv/Conv" [label="[1, 192, 12, 12]", style=solid]; -"449 QuantizeLinear_onnx^^Conv_1123_1" -> "450 DequantizeLinear_onnx^^Conv_1123_1" [label="[192, 192, 7, 1]", style=dashed]; -"450 DequantizeLinear_onnx^^Conv_1123_1" -> "451 /Mixed_6e/branch7x7_3/conv/Conv" [label="[192, 192, 7, 1]", style=solid]; -"451 /Mixed_6e/branch7x7_3/conv/Conv" -> "452 /Mixed_6e/branch7x7_3/Relu" [label="[1, 192, 12, 12]", style=solid]; -"452 /Mixed_6e/branch7x7_3/Relu" -> "494 QuantizeLinear_/Mixed_6e/branch7x7_3/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; -"453 QuantizeLinear_onnx^^Conv_1126_1" -> "454 DequantizeLinear_onnx^^Conv_1126_1" [label="[192, 768, 1, 1]", style=dashed]; -"454 DequantizeLinear_onnx^^Conv_1126_1" -> "455 /Mixed_6e/branch7x7dbl_1/conv/Conv" [label="[192, 768, 1, 1]", style=solid]; -"455 /Mixed_6e/branch7x7dbl_1/conv/Conv" -> "456 /Mixed_6e/branch7x7dbl_1/Relu" [label="[1, 192, 12, 12]", style=solid]; -"456 /Mixed_6e/branch7x7dbl_1/Relu" -> "457 QuantizeLinear_/Mixed_6e/branch7x7dbl_1/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; -"457 QuantizeLinear_/Mixed_6e/branch7x7dbl_1/Relu_output_0_1" -> "458 DequantizeLinear_/Mixed_6e/branch7x7dbl_1/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; -"458 DequantizeLinear_/Mixed_6e/branch7x7dbl_1/Relu_output_0_1" -> "461 /Mixed_6e/branch7x7dbl_2/conv/Conv" [label="[1, 192, 12, 12]", style=solid]; -"459 QuantizeLinear_onnx^^Conv_1129_1" -> "460 DequantizeLinear_onnx^^Conv_1129_1" [label="[192, 192, 7, 1]", style=dashed]; -"460 DequantizeLinear_onnx^^Conv_1129_1" -> "461 /Mixed_6e/branch7x7dbl_2/conv/Conv" [label="[192, 192, 7, 1]", style=solid]; -"461 /Mixed_6e/branch7x7dbl_2/conv/Conv" -> "462 /Mixed_6e/branch7x7dbl_2/Relu" [label="[1, 192, 12, 12]", style=solid]; -"462 /Mixed_6e/branch7x7dbl_2/Relu" -> "463 QuantizeLinear_/Mixed_6e/branch7x7dbl_2/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; -"463 QuantizeLinear_/Mixed_6e/branch7x7dbl_2/Relu_output_0_1" -> "464 DequantizeLinear_/Mixed_6e/branch7x7dbl_2/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; -"464 DequantizeLinear_/Mixed_6e/branch7x7dbl_2/Relu_output_0_1" -> "467 /Mixed_6e/branch7x7dbl_3/conv/Conv" [label="[1, 192, 12, 12]", style=solid]; -"465 QuantizeLinear_onnx^^Conv_1132_1" -> "466 DequantizeLinear_onnx^^Conv_1132_1" [label="[192, 192, 1, 7]", style=dashed]; -"466 DequantizeLinear_onnx^^Conv_1132_1" -> "467 /Mixed_6e/branch7x7dbl_3/conv/Conv" [label="[192, 192, 1, 7]", style=solid]; -"467 /Mixed_6e/branch7x7dbl_3/conv/Conv" -> "468 /Mixed_6e/branch7x7dbl_3/Relu" [label="[1, 192, 12, 12]", style=solid]; -"468 /Mixed_6e/branch7x7dbl_3/Relu" -> "469 QuantizeLinear_/Mixed_6e/branch7x7dbl_3/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; -"469 QuantizeLinear_/Mixed_6e/branch7x7dbl_3/Relu_output_0_1" -> "470 DequantizeLinear_/Mixed_6e/branch7x7dbl_3/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; -"470 DequantizeLinear_/Mixed_6e/branch7x7dbl_3/Relu_output_0_1" -> "473 /Mixed_6e/branch7x7dbl_4/conv/Conv" [label="[1, 192, 12, 12]", style=solid]; -"471 QuantizeLinear_onnx^^Conv_1135_1" -> "472 DequantizeLinear_onnx^^Conv_1135_1" [label="[192, 192, 7, 1]", style=dashed]; -"472 DequantizeLinear_onnx^^Conv_1135_1" -> "473 /Mixed_6e/branch7x7dbl_4/conv/Conv" [label="[192, 192, 7, 1]", style=solid]; -"473 /Mixed_6e/branch7x7dbl_4/conv/Conv" -> "474 /Mixed_6e/branch7x7dbl_4/Relu" [label="[1, 192, 12, 12]", style=solid]; -"474 /Mixed_6e/branch7x7dbl_4/Relu" -> "475 QuantizeLinear_/Mixed_6e/branch7x7dbl_4/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; -"475 QuantizeLinear_/Mixed_6e/branch7x7dbl_4/Relu_output_0_1" -> "476 DequantizeLinear_/Mixed_6e/branch7x7dbl_4/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; -"476 DequantizeLinear_/Mixed_6e/branch7x7dbl_4/Relu_output_0_1" -> "479 /Mixed_6e/branch7x7dbl_5/conv/Conv" [label="[1, 192, 12, 12]", style=solid]; -"477 QuantizeLinear_onnx^^Conv_1138_1" -> "478 DequantizeLinear_onnx^^Conv_1138_1" [label="[192, 192, 1, 7]", style=dashed]; -"478 DequantizeLinear_onnx^^Conv_1138_1" -> "479 /Mixed_6e/branch7x7dbl_5/conv/Conv" [label="[192, 192, 1, 7]", style=solid]; -"479 /Mixed_6e/branch7x7dbl_5/conv/Conv" -> "480 /Mixed_6e/branch7x7dbl_5/Relu" [label="[1, 192, 12, 12]", style=solid]; -"480 /Mixed_6e/branch7x7dbl_5/Relu" -> "490 QuantizeLinear_/Mixed_6e/branch7x7dbl_5/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; -"481 /Mixed_6e/Constant" -> "482 /Mixed_6e/Pad" [label="[8]", style=dashed]; -"482 /Mixed_6e/Pad" -> "483 /Mixed_6e/AveragePool" [label="[1, 768, 14, 14]", style=solid]; -"483 /Mixed_6e/AveragePool" -> "484 QuantizeLinear_/Mixed_6e/AveragePool_output_0_1" [label="[1, 768, 12, 12]", style=solid]; -"484 QuantizeLinear_/Mixed_6e/AveragePool_output_0_1" -> "485 DequantizeLinear_/Mixed_6e/AveragePool_output_0_1" [label="[1, 768, 12, 12]", style=dashed]; -"485 DequantizeLinear_/Mixed_6e/AveragePool_output_0_1" -> "488 /Mixed_6e/branch_pool/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; -"486 QuantizeLinear_onnx^^Conv_1141_1" -> "487 DequantizeLinear_onnx^^Conv_1141_1" [label="[192, 768, 1, 1]", style=dashed]; -"487 DequantizeLinear_onnx^^Conv_1141_1" -> "488 /Mixed_6e/branch_pool/conv/Conv" [label="[192, 768, 1, 1]", style=solid]; -"488 /Mixed_6e/branch_pool/conv/Conv" -> "489 /Mixed_6e/branch_pool/Relu" [label="[1, 192, 12, 12]", style=solid]; -"489 /Mixed_6e/branch_pool/Relu" -> "492 QuantizeLinear_/Mixed_6e/branch_pool/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; -"490 QuantizeLinear_/Mixed_6e/branch7x7dbl_5/Relu_output_0_1" -> "491 DequantizeLinear_/Mixed_6e/branch7x7dbl_5/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; -"491 DequantizeLinear_/Mixed_6e/branch7x7dbl_5/Relu_output_0_1" -> "498 /Mixed_6e/Concat" [label="[1, 192, 12, 12]", style=solid]; -"492 QuantizeLinear_/Mixed_6e/branch_pool/Relu_output_0_1" -> "493 DequantizeLinear_/Mixed_6e/branch_pool/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; -"493 DequantizeLinear_/Mixed_6e/branch_pool/Relu_output_0_1" -> "498 /Mixed_6e/Concat" [label="[1, 192, 12, 12]", style=solid]; -"494 QuantizeLinear_/Mixed_6e/branch7x7_3/Relu_output_0_1" -> "495 DequantizeLinear_/Mixed_6e/branch7x7_3/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; -"495 DequantizeLinear_/Mixed_6e/branch7x7_3/Relu_output_0_1" -> "498 /Mixed_6e/Concat" [label="[1, 192, 12, 12]", style=solid]; -"496 QuantizeLinear_/Mixed_6e/branch1x1/Relu_output_0_1" -> "497 DequantizeLinear_/Mixed_6e/branch1x1/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; -"497 DequantizeLinear_/Mixed_6e/branch1x1/Relu_output_0_1" -> "498 /Mixed_6e/Concat" [label="[1, 192, 12, 12]", style=solid]; -"498 /Mixed_6e/Concat" -> "501 /Mixed_7a/branch3x3_1/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; -"498 /Mixed_6e/Concat" -> "511 /Mixed_7a/branch7x7x3_1/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; -"498 /Mixed_6e/Concat" -> "531 /Mixed_7a/MaxPool" [label="[1, 768, 12, 12]", style=solid]; -"499 QuantizeLinear_onnx^^Conv_1144_1" -> "500 DequantizeLinear_onnx^^Conv_1144_1" [label="[192, 768, 1, 1]", style=dashed]; -"500 DequantizeLinear_onnx^^Conv_1144_1" -> "501 /Mixed_7a/branch3x3_1/conv/Conv" [label="[192, 768, 1, 1]", style=solid]; -"501 /Mixed_7a/branch3x3_1/conv/Conv" -> "502 /Mixed_7a/branch3x3_1/Relu" [label="[1, 192, 12, 12]", style=solid]; -"502 /Mixed_7a/branch3x3_1/Relu" -> "503 QuantizeLinear_/Mixed_7a/branch3x3_1/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; -"503 QuantizeLinear_/Mixed_7a/branch3x3_1/Relu_output_0_1" -> "504 DequantizeLinear_/Mixed_7a/branch3x3_1/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; -"504 DequantizeLinear_/Mixed_7a/branch3x3_1/Relu_output_0_1" -> "507 /Mixed_7a/branch3x3_2/conv/Conv" [label="[1, 192, 12, 12]", style=solid]; -"505 QuantizeLinear_onnx^^Conv_1147_1" -> "506 DequantizeLinear_onnx^^Conv_1147_1" [label="[320, 192, 3, 3]", style=dashed]; -"506 DequantizeLinear_onnx^^Conv_1147_1" -> "507 /Mixed_7a/branch3x3_2/conv/Conv" [label="[320, 192, 3, 3]", style=solid]; -"507 /Mixed_7a/branch3x3_2/conv/Conv" -> "508 /Mixed_7a/branch3x3_2/Relu" [label="[1, 320, 5, 5]", style=solid]; -"508 /Mixed_7a/branch3x3_2/Relu" -> "534 QuantizeLinear_/Mixed_7a/branch3x3_2/Relu_output_0_1" [label="[1, 320, 5, 5]", style=solid]; -"509 QuantizeLinear_onnx^^Conv_1150_1" -> "510 DequantizeLinear_onnx^^Conv_1150_1" [label="[192, 768, 1, 1]", style=dashed]; -"510 DequantizeLinear_onnx^^Conv_1150_1" -> "511 /Mixed_7a/branch7x7x3_1/conv/Conv" [label="[192, 768, 1, 1]", style=solid]; -"511 /Mixed_7a/branch7x7x3_1/conv/Conv" -> "512 /Mixed_7a/branch7x7x3_1/Relu" [label="[1, 192, 12, 12]", style=solid]; -"512 /Mixed_7a/branch7x7x3_1/Relu" -> "513 QuantizeLinear_/Mixed_7a/branch7x7x3_1/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; -"513 QuantizeLinear_/Mixed_7a/branch7x7x3_1/Relu_output_0_1" -> "514 DequantizeLinear_/Mixed_7a/branch7x7x3_1/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; -"514 DequantizeLinear_/Mixed_7a/branch7x7x3_1/Relu_output_0_1" -> "517 /Mixed_7a/branch7x7x3_2/conv/Conv" [label="[1, 192, 12, 12]", style=solid]; -"515 QuantizeLinear_onnx^^Conv_1153_1" -> "516 DequantizeLinear_onnx^^Conv_1153_1" [label="[192, 192, 1, 7]", style=dashed]; -"516 DequantizeLinear_onnx^^Conv_1153_1" -> "517 /Mixed_7a/branch7x7x3_2/conv/Conv" [label="[192, 192, 1, 7]", style=solid]; -"517 /Mixed_7a/branch7x7x3_2/conv/Conv" -> "518 /Mixed_7a/branch7x7x3_2/Relu" [label="[1, 192, 12, 12]", style=solid]; -"518 /Mixed_7a/branch7x7x3_2/Relu" -> "519 QuantizeLinear_/Mixed_7a/branch7x7x3_2/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; -"519 QuantizeLinear_/Mixed_7a/branch7x7x3_2/Relu_output_0_1" -> "520 DequantizeLinear_/Mixed_7a/branch7x7x3_2/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; -"520 DequantizeLinear_/Mixed_7a/branch7x7x3_2/Relu_output_0_1" -> "523 /Mixed_7a/branch7x7x3_3/conv/Conv" [label="[1, 192, 12, 12]", style=solid]; -"521 QuantizeLinear_onnx^^Conv_1156_1" -> "522 DequantizeLinear_onnx^^Conv_1156_1" [label="[192, 192, 7, 1]", style=dashed]; -"522 DequantizeLinear_onnx^^Conv_1156_1" -> "523 /Mixed_7a/branch7x7x3_3/conv/Conv" [label="[192, 192, 7, 1]", style=solid]; -"523 /Mixed_7a/branch7x7x3_3/conv/Conv" -> "524 /Mixed_7a/branch7x7x3_3/Relu" [label="[1, 192, 12, 12]", style=solid]; -"524 /Mixed_7a/branch7x7x3_3/Relu" -> "525 QuantizeLinear_/Mixed_7a/branch7x7x3_3/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; -"525 QuantizeLinear_/Mixed_7a/branch7x7x3_3/Relu_output_0_1" -> "526 DequantizeLinear_/Mixed_7a/branch7x7x3_3/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; -"526 DequantizeLinear_/Mixed_7a/branch7x7x3_3/Relu_output_0_1" -> "529 /Mixed_7a/branch7x7x3_4/conv/Conv" [label="[1, 192, 12, 12]", style=solid]; -"527 QuantizeLinear_onnx^^Conv_1159_1" -> "528 DequantizeLinear_onnx^^Conv_1159_1" [label="[192, 192, 3, 3]", style=dashed]; -"528 DequantizeLinear_onnx^^Conv_1159_1" -> "529 /Mixed_7a/branch7x7x3_4/conv/Conv" [label="[192, 192, 3, 3]", style=solid]; -"529 /Mixed_7a/branch7x7x3_4/conv/Conv" -> "530 /Mixed_7a/branch7x7x3_4/Relu" [label="[1, 192, 5, 5]", style=solid]; -"530 /Mixed_7a/branch7x7x3_4/Relu" -> "532 QuantizeLinear_/Mixed_7a/branch7x7x3_4/Relu_output_0_1" [label="[1, 192, 5, 5]", style=solid]; -"531 /Mixed_7a/MaxPool" -> "536 /Mixed_7a/Concat" [label="[1, 768, 5, 5]", style=solid]; -"532 QuantizeLinear_/Mixed_7a/branch7x7x3_4/Relu_output_0_1" -> "533 DequantizeLinear_/Mixed_7a/branch7x7x3_4/Relu_output_0_1" [label="[1, 192, 5, 5]", style=dashed]; -"533 DequantizeLinear_/Mixed_7a/branch7x7x3_4/Relu_output_0_1" -> "536 /Mixed_7a/Concat" [label="[1, 192, 5, 5]", style=solid]; -"534 QuantizeLinear_/Mixed_7a/branch3x3_2/Relu_output_0_1" -> "535 DequantizeLinear_/Mixed_7a/branch3x3_2/Relu_output_0_1" [label="[1, 320, 5, 5]", style=dashed]; -"535 DequantizeLinear_/Mixed_7a/branch3x3_2/Relu_output_0_1" -> "536 /Mixed_7a/Concat" [label="[1, 320, 5, 5]", style=solid]; -"536 /Mixed_7a/Concat" -> "539 /Mixed_7b/branch1x1/conv/Conv" [label="[1, 1280, 5, 5]", style=solid]; -"536 /Mixed_7a/Concat" -> "543 /Mixed_7b/branch3x3_1/conv/Conv" [label="[1, 1280, 5, 5]", style=solid]; -"536 /Mixed_7a/Concat" -> "557 /Mixed_7b/branch3x3dbl_1/conv/Conv" [label="[1, 1280, 5, 5]", style=solid]; -"536 /Mixed_7a/Concat" -> "576 /Mixed_7b/Pad" [label="[1, 1280, 5, 5]", style=solid]; -"537 QuantizeLinear_onnx^^Conv_1162_1" -> "538 DequantizeLinear_onnx^^Conv_1162_1" [label="[320, 1280, 1, 1]", style=dashed]; -"538 DequantizeLinear_onnx^^Conv_1162_1" -> "539 /Mixed_7b/branch1x1/conv/Conv" [label="[320, 1280, 1, 1]", style=solid]; -"539 /Mixed_7b/branch1x1/conv/Conv" -> "540 /Mixed_7b/branch1x1/Relu" [label="[1, 320, 5, 5]", style=solid]; -"540 /Mixed_7b/branch1x1/Relu" -> "584 QuantizeLinear_/Mixed_7b/branch1x1/Relu_output_0_1" [label="[1, 320, 5, 5]", style=solid]; -"541 QuantizeLinear_onnx^^Conv_1165_1" -> "542 DequantizeLinear_onnx^^Conv_1165_1" [label="[384, 1280, 1, 1]", style=dashed]; -"542 DequantizeLinear_onnx^^Conv_1165_1" -> "543 /Mixed_7b/branch3x3_1/conv/Conv" [label="[384, 1280, 1, 1]", style=solid]; -"543 /Mixed_7b/branch3x3_1/conv/Conv" -> "544 /Mixed_7b/branch3x3_1/Relu" [label="[1, 384, 5, 5]", style=solid]; -"544 /Mixed_7b/branch3x3_1/Relu" -> "545 QuantizeLinear_/Mixed_7b/branch3x3_1/Relu_output_0_1" [label="[1, 384, 5, 5]", style=solid]; -"545 QuantizeLinear_/Mixed_7b/branch3x3_1/Relu_output_0_1" -> "546 DequantizeLinear_/Mixed_7b/branch3x3_1/Relu_output_0_1" [label="[1, 384, 5, 5]", style=dashed]; -"546 DequantizeLinear_/Mixed_7b/branch3x3_1/Relu_output_0_1" -> "549 /Mixed_7b/branch3x3_2a/conv/Conv" [label="[1, 384, 5, 5]", style=solid]; -"546 DequantizeLinear_/Mixed_7b/branch3x3_1/Relu_output_0_1" -> "553 /Mixed_7b/branch3x3_2b/conv/Conv" [label="[1, 384, 5, 5]", style=solid]; -"547 QuantizeLinear_onnx^^Conv_1168_1" -> "548 DequantizeLinear_onnx^^Conv_1168_1" [label="[384, 384, 1, 3]", style=dashed]; -"548 DequantizeLinear_onnx^^Conv_1168_1" -> "549 /Mixed_7b/branch3x3_2a/conv/Conv" [label="[384, 384, 1, 3]", style=solid]; -"549 /Mixed_7b/branch3x3_2a/conv/Conv" -> "550 /Mixed_7b/branch3x3_2a/Relu" [label="[1, 384, 5, 5]", style=solid]; -"550 /Mixed_7b/branch3x3_2a/Relu" -> "586 QuantizeLinear_/Mixed_7b/branch3x3_2a/Relu_output_0_1" [label="[1, 384, 5, 5]", style=solid]; -"551 QuantizeLinear_onnx^^Conv_1171_1" -> "552 DequantizeLinear_onnx^^Conv_1171_1" [label="[384, 384, 3, 1]", style=dashed]; -"552 DequantizeLinear_onnx^^Conv_1171_1" -> "553 /Mixed_7b/branch3x3_2b/conv/Conv" [label="[384, 384, 3, 1]", style=solid]; -"553 /Mixed_7b/branch3x3_2b/conv/Conv" -> "554 /Mixed_7b/branch3x3_2b/Relu" [label="[1, 384, 5, 5]", style=solid]; -"554 /Mixed_7b/branch3x3_2b/Relu" -> "588 QuantizeLinear_/Mixed_7b/branch3x3_2b/Relu_output_0_1" [label="[1, 384, 5, 5]", style=solid]; -"555 QuantizeLinear_onnx^^Conv_1174_1" -> "556 DequantizeLinear_onnx^^Conv_1174_1" [label="[448, 1280, 1, 1]", style=dashed]; -"556 DequantizeLinear_onnx^^Conv_1174_1" -> "557 /Mixed_7b/branch3x3dbl_1/conv/Conv" [label="[448, 1280, 1, 1]", style=solid]; -"557 /Mixed_7b/branch3x3dbl_1/conv/Conv" -> "558 /Mixed_7b/branch3x3dbl_1/Relu" [label="[1, 448, 5, 5]", style=solid]; -"558 /Mixed_7b/branch3x3dbl_1/Relu" -> "559 QuantizeLinear_/Mixed_7b/branch3x3dbl_1/Relu_output_0_1" [label="[1, 448, 5, 5]", style=solid]; -"559 QuantizeLinear_/Mixed_7b/branch3x3dbl_1/Relu_output_0_1" -> "560 DequantizeLinear_/Mixed_7b/branch3x3dbl_1/Relu_output_0_1" [label="[1, 448, 5, 5]", style=dashed]; -"560 DequantizeLinear_/Mixed_7b/branch3x3dbl_1/Relu_output_0_1" -> "563 /Mixed_7b/branch3x3dbl_2/conv/Conv" [label="[1, 448, 5, 5]", style=solid]; -"561 QuantizeLinear_onnx^^Conv_1177_1" -> "562 DequantizeLinear_onnx^^Conv_1177_1" [label="[384, 448, 3, 3]", style=dashed]; -"562 DequantizeLinear_onnx^^Conv_1177_1" -> "563 /Mixed_7b/branch3x3dbl_2/conv/Conv" [label="[384, 448, 3, 3]", style=solid]; -"563 /Mixed_7b/branch3x3dbl_2/conv/Conv" -> "564 /Mixed_7b/branch3x3dbl_2/Relu" [label="[1, 384, 5, 5]", style=solid]; -"564 /Mixed_7b/branch3x3dbl_2/Relu" -> "565 QuantizeLinear_/Mixed_7b/branch3x3dbl_2/Relu_output_0_1" [label="[1, 384, 5, 5]", style=solid]; -"565 QuantizeLinear_/Mixed_7b/branch3x3dbl_2/Relu_output_0_1" -> "566 DequantizeLinear_/Mixed_7b/branch3x3dbl_2/Relu_output_0_1" [label="[1, 384, 5, 5]", style=dashed]; -"566 DequantizeLinear_/Mixed_7b/branch3x3dbl_2/Relu_output_0_1" -> "569 /Mixed_7b/branch3x3dbl_3a/conv/Conv" [label="[1, 384, 5, 5]", style=solid]; -"566 DequantizeLinear_/Mixed_7b/branch3x3dbl_2/Relu_output_0_1" -> "573 /Mixed_7b/branch3x3dbl_3b/conv/Conv" [label="[1, 384, 5, 5]", style=solid]; -"567 QuantizeLinear_onnx^^Conv_1180_1" -> "568 DequantizeLinear_onnx^^Conv_1180_1" [label="[384, 384, 1, 3]", style=dashed]; -"568 DequantizeLinear_onnx^^Conv_1180_1" -> "569 /Mixed_7b/branch3x3dbl_3a/conv/Conv" [label="[384, 384, 1, 3]", style=solid]; -"569 /Mixed_7b/branch3x3dbl_3a/conv/Conv" -> "570 /Mixed_7b/branch3x3dbl_3a/Relu" [label="[1, 384, 5, 5]", style=solid]; -"570 /Mixed_7b/branch3x3dbl_3a/Relu" -> "590 QuantizeLinear_/Mixed_7b/branch3x3dbl_3a/Relu_output_0_1" [label="[1, 384, 5, 5]", style=solid]; -"571 QuantizeLinear_onnx^^Conv_1183_1" -> "572 DequantizeLinear_onnx^^Conv_1183_1" [label="[384, 384, 3, 1]", style=dashed]; -"572 DequantizeLinear_onnx^^Conv_1183_1" -> "573 /Mixed_7b/branch3x3dbl_3b/conv/Conv" [label="[384, 384, 3, 1]", style=solid]; -"573 /Mixed_7b/branch3x3dbl_3b/conv/Conv" -> "574 /Mixed_7b/branch3x3dbl_3b/Relu" [label="[1, 384, 5, 5]", style=solid]; -"574 /Mixed_7b/branch3x3dbl_3b/Relu" -> "592 QuantizeLinear_/Mixed_7b/branch3x3dbl_3b/Relu_output_0_1" [label="[1, 384, 5, 5]", style=solid]; -"575 /Mixed_7b/Constant" -> "576 /Mixed_7b/Pad" [label="[8]", style=dashed]; -"576 /Mixed_7b/Pad" -> "577 /Mixed_7b/AveragePool" [label="[1, 1280, 7, 7]", style=solid]; -"577 /Mixed_7b/AveragePool" -> "578 QuantizeLinear_/Mixed_7b/AveragePool_output_0_1" [label="[1, 1280, 5, 5]", style=solid]; -"578 QuantizeLinear_/Mixed_7b/AveragePool_output_0_1" -> "579 DequantizeLinear_/Mixed_7b/AveragePool_output_0_1" [label="[1, 1280, 5, 5]", style=dashed]; -"579 DequantizeLinear_/Mixed_7b/AveragePool_output_0_1" -> "582 /Mixed_7b/branch_pool/conv/Conv" [label="[1, 1280, 5, 5]", style=solid]; -"580 QuantizeLinear_onnx^^Conv_1186_1" -> "581 DequantizeLinear_onnx^^Conv_1186_1" [label="[192, 1280, 1, 1]", style=dashed]; -"581 DequantizeLinear_onnx^^Conv_1186_1" -> "582 /Mixed_7b/branch_pool/conv/Conv" [label="[192, 1280, 1, 1]", style=solid]; -"582 /Mixed_7b/branch_pool/conv/Conv" -> "583 /Mixed_7b/branch_pool/Relu" [label="[1, 192, 5, 5]", style=solid]; -"583 /Mixed_7b/branch_pool/Relu" -> "594 QuantizeLinear_/Mixed_7b/branch_pool/Relu_output_0_1" [label="[1, 192, 5, 5]", style=solid]; -"584 QuantizeLinear_/Mixed_7b/branch1x1/Relu_output_0_1" -> "585 DequantizeLinear_/Mixed_7b/branch1x1/Relu_output_0_1" [label="[1, 320, 5, 5]", style=dashed]; -"585 DequantizeLinear_/Mixed_7b/branch1x1/Relu_output_0_1" -> "596 /Mixed_7b/Concat" [label="[1, 320, 5, 5]", style=solid]; -"586 QuantizeLinear_/Mixed_7b/branch3x3_2a/Relu_output_0_1" -> "587 DequantizeLinear_/Mixed_7b/branch3x3_2a/Relu_output_0_1" [label="[1, 384, 5, 5]", style=dashed]; -"587 DequantizeLinear_/Mixed_7b/branch3x3_2a/Relu_output_0_1" -> "596 /Mixed_7b/Concat" [label="[1, 384, 5, 5]", style=solid]; -"588 QuantizeLinear_/Mixed_7b/branch3x3_2b/Relu_output_0_1" -> "589 DequantizeLinear_/Mixed_7b/branch3x3_2b/Relu_output_0_1" [label="[1, 384, 5, 5]", style=dashed]; -"589 DequantizeLinear_/Mixed_7b/branch3x3_2b/Relu_output_0_1" -> "596 /Mixed_7b/Concat" [label="[1, 384, 5, 5]", style=solid]; -"590 QuantizeLinear_/Mixed_7b/branch3x3dbl_3a/Relu_output_0_1" -> "591 DequantizeLinear_/Mixed_7b/branch3x3dbl_3a/Relu_output_0_1" [label="[1, 384, 5, 5]", style=dashed]; -"591 DequantizeLinear_/Mixed_7b/branch3x3dbl_3a/Relu_output_0_1" -> "596 /Mixed_7b/Concat" [label="[1, 384, 5, 5]", style=solid]; -"592 QuantizeLinear_/Mixed_7b/branch3x3dbl_3b/Relu_output_0_1" -> "593 DequantizeLinear_/Mixed_7b/branch3x3dbl_3b/Relu_output_0_1" [label="[1, 384, 5, 5]", style=dashed]; -"593 DequantizeLinear_/Mixed_7b/branch3x3dbl_3b/Relu_output_0_1" -> "596 /Mixed_7b/Concat" [label="[1, 384, 5, 5]", style=solid]; -"594 QuantizeLinear_/Mixed_7b/branch_pool/Relu_output_0_1" -> "595 DequantizeLinear_/Mixed_7b/branch_pool/Relu_output_0_1" [label="[1, 192, 5, 5]", style=dashed]; -"595 DequantizeLinear_/Mixed_7b/branch_pool/Relu_output_0_1" -> "596 /Mixed_7b/Concat" [label="[1, 192, 5, 5]", style=solid]; -"596 /Mixed_7b/Concat" -> "599 /Mixed_7c/branch1x1/conv/Conv" [label="[1, 2048, 5, 5]", style=solid]; -"596 /Mixed_7b/Concat" -> "603 /Mixed_7c/branch3x3_1/conv/Conv" [label="[1, 2048, 5, 5]", style=solid]; -"596 /Mixed_7b/Concat" -> "617 /Mixed_7c/branch3x3dbl_1/conv/Conv" [label="[1, 2048, 5, 5]", style=solid]; -"596 /Mixed_7b/Concat" -> "636 /Mixed_7c/Pad" [label="[1, 2048, 5, 5]", style=solid]; -"597 QuantizeLinear_onnx^^Conv_1189_1" -> "598 DequantizeLinear_onnx^^Conv_1189_1" [label="[320, 2048, 1, 1]", style=dashed]; -"598 DequantizeLinear_onnx^^Conv_1189_1" -> "599 /Mixed_7c/branch1x1/conv/Conv" [label="[320, 2048, 1, 1]", style=solid]; -"599 /Mixed_7c/branch1x1/conv/Conv" -> "600 /Mixed_7c/branch1x1/Relu" [label="[1, 320, 5, 5]", style=solid]; -"600 /Mixed_7c/branch1x1/Relu" -> "644 QuantizeLinear_/Mixed_7c/branch1x1/Relu_output_0_1" [label="[1, 320, 5, 5]", style=solid]; -"601 QuantizeLinear_onnx^^Conv_1192_1" -> "602 DequantizeLinear_onnx^^Conv_1192_1" [label="[384, 2048, 1, 1]", style=dashed]; -"602 DequantizeLinear_onnx^^Conv_1192_1" -> "603 /Mixed_7c/branch3x3_1/conv/Conv" [label="[384, 2048, 1, 1]", style=solid]; -"603 /Mixed_7c/branch3x3_1/conv/Conv" -> "604 /Mixed_7c/branch3x3_1/Relu" [label="[1, 384, 5, 5]", style=solid]; -"604 /Mixed_7c/branch3x3_1/Relu" -> "605 QuantizeLinear_/Mixed_7c/branch3x3_1/Relu_output_0_1" [label="[1, 384, 5, 5]", style=solid]; -"605 QuantizeLinear_/Mixed_7c/branch3x3_1/Relu_output_0_1" -> "606 DequantizeLinear_/Mixed_7c/branch3x3_1/Relu_output_0_1" [label="[1, 384, 5, 5]", style=dashed]; -"606 DequantizeLinear_/Mixed_7c/branch3x3_1/Relu_output_0_1" -> "609 /Mixed_7c/branch3x3_2a/conv/Conv" [label="[1, 384, 5, 5]", style=solid]; -"606 DequantizeLinear_/Mixed_7c/branch3x3_1/Relu_output_0_1" -> "613 /Mixed_7c/branch3x3_2b/conv/Conv" [label="[1, 384, 5, 5]", style=solid]; -"607 QuantizeLinear_onnx^^Conv_1195_1" -> "608 DequantizeLinear_onnx^^Conv_1195_1" [label="[384, 384, 1, 3]", style=dashed]; -"608 DequantizeLinear_onnx^^Conv_1195_1" -> "609 /Mixed_7c/branch3x3_2a/conv/Conv" [label="[384, 384, 1, 3]", style=solid]; -"609 /Mixed_7c/branch3x3_2a/conv/Conv" -> "610 /Mixed_7c/branch3x3_2a/Relu" [label="[1, 384, 5, 5]", style=solid]; -"610 /Mixed_7c/branch3x3_2a/Relu" -> "646 QuantizeLinear_/Mixed_7c/branch3x3_2a/Relu_output_0_1" [label="[1, 384, 5, 5]", style=solid]; -"611 QuantizeLinear_onnx^^Conv_1198_1" -> "612 DequantizeLinear_onnx^^Conv_1198_1" [label="[384, 384, 3, 1]", style=dashed]; -"612 DequantizeLinear_onnx^^Conv_1198_1" -> "613 /Mixed_7c/branch3x3_2b/conv/Conv" [label="[384, 384, 3, 1]", style=solid]; -"613 /Mixed_7c/branch3x3_2b/conv/Conv" -> "614 /Mixed_7c/branch3x3_2b/Relu" [label="[1, 384, 5, 5]", style=solid]; -"614 /Mixed_7c/branch3x3_2b/Relu" -> "648 QuantizeLinear_/Mixed_7c/branch3x3_2b/Relu_output_0_1" [label="[1, 384, 5, 5]", style=solid]; -"615 QuantizeLinear_onnx^^Conv_1201_1" -> "616 DequantizeLinear_onnx^^Conv_1201_1" [label="[448, 2048, 1, 1]", style=dashed]; -"616 DequantizeLinear_onnx^^Conv_1201_1" -> "617 /Mixed_7c/branch3x3dbl_1/conv/Conv" [label="[448, 2048, 1, 1]", style=solid]; -"617 /Mixed_7c/branch3x3dbl_1/conv/Conv" -> "618 /Mixed_7c/branch3x3dbl_1/Relu" [label="[1, 448, 5, 5]", style=solid]; -"618 /Mixed_7c/branch3x3dbl_1/Relu" -> "619 QuantizeLinear_/Mixed_7c/branch3x3dbl_1/Relu_output_0_1" [label="[1, 448, 5, 5]", style=solid]; -"619 QuantizeLinear_/Mixed_7c/branch3x3dbl_1/Relu_output_0_1" -> "620 DequantizeLinear_/Mixed_7c/branch3x3dbl_1/Relu_output_0_1" [label="[1, 448, 5, 5]", style=dashed]; -"620 DequantizeLinear_/Mixed_7c/branch3x3dbl_1/Relu_output_0_1" -> "623 /Mixed_7c/branch3x3dbl_2/conv/Conv" [label="[1, 448, 5, 5]", style=solid]; -"621 QuantizeLinear_onnx^^Conv_1204_1" -> "622 DequantizeLinear_onnx^^Conv_1204_1" [label="[384, 448, 3, 3]", style=dashed]; -"622 DequantizeLinear_onnx^^Conv_1204_1" -> "623 /Mixed_7c/branch3x3dbl_2/conv/Conv" [label="[384, 448, 3, 3]", style=solid]; -"623 /Mixed_7c/branch3x3dbl_2/conv/Conv" -> "624 /Mixed_7c/branch3x3dbl_2/Relu" [label="[1, 384, 5, 5]", style=solid]; -"624 /Mixed_7c/branch3x3dbl_2/Relu" -> "625 QuantizeLinear_/Mixed_7c/branch3x3dbl_2/Relu_output_0_1" [label="[1, 384, 5, 5]", style=solid]; -"625 QuantizeLinear_/Mixed_7c/branch3x3dbl_2/Relu_output_0_1" -> "626 DequantizeLinear_/Mixed_7c/branch3x3dbl_2/Relu_output_0_1" [label="[1, 384, 5, 5]", style=dashed]; -"626 DequantizeLinear_/Mixed_7c/branch3x3dbl_2/Relu_output_0_1" -> "629 /Mixed_7c/branch3x3dbl_3a/conv/Conv" [label="[1, 384, 5, 5]", style=solid]; -"626 DequantizeLinear_/Mixed_7c/branch3x3dbl_2/Relu_output_0_1" -> "633 /Mixed_7c/branch3x3dbl_3b/conv/Conv" [label="[1, 384, 5, 5]", style=solid]; -"627 QuantizeLinear_onnx^^Conv_1207_1" -> "628 DequantizeLinear_onnx^^Conv_1207_1" [label="[384, 384, 1, 3]", style=dashed]; -"628 DequantizeLinear_onnx^^Conv_1207_1" -> "629 /Mixed_7c/branch3x3dbl_3a/conv/Conv" [label="[384, 384, 1, 3]", style=solid]; -"629 /Mixed_7c/branch3x3dbl_3a/conv/Conv" -> "630 /Mixed_7c/branch3x3dbl_3a/Relu" [label="[1, 384, 5, 5]", style=solid]; -"630 /Mixed_7c/branch3x3dbl_3a/Relu" -> "650 QuantizeLinear_/Mixed_7c/branch3x3dbl_3a/Relu_output_0_1" [label="[1, 384, 5, 5]", style=solid]; -"631 QuantizeLinear_onnx^^Conv_1210_1" -> "632 DequantizeLinear_onnx^^Conv_1210_1" [label="[384, 384, 3, 1]", style=dashed]; -"632 DequantizeLinear_onnx^^Conv_1210_1" -> "633 /Mixed_7c/branch3x3dbl_3b/conv/Conv" [label="[384, 384, 3, 1]", style=solid]; -"633 /Mixed_7c/branch3x3dbl_3b/conv/Conv" -> "634 /Mixed_7c/branch3x3dbl_3b/Relu" [label="[1, 384, 5, 5]", style=solid]; -"634 /Mixed_7c/branch3x3dbl_3b/Relu" -> "652 QuantizeLinear_/Mixed_7c/branch3x3dbl_3b/Relu_output_0_1" [label="[1, 384, 5, 5]", style=solid]; -"635 /Mixed_7c/Constant" -> "636 /Mixed_7c/Pad" [label="[8]", style=dashed]; -"636 /Mixed_7c/Pad" -> "637 /Mixed_7c/AveragePool" [label="[1, 2048, 7, 7]", style=solid]; -"637 /Mixed_7c/AveragePool" -> "638 QuantizeLinear_/Mixed_7c/AveragePool_output_0_1" [label="[1, 2048, 5, 5]", style=solid]; -"638 QuantizeLinear_/Mixed_7c/AveragePool_output_0_1" -> "639 DequantizeLinear_/Mixed_7c/AveragePool_output_0_1" [label="[1, 2048, 5, 5]", style=dashed]; -"639 DequantizeLinear_/Mixed_7c/AveragePool_output_0_1" -> "642 /Mixed_7c/branch_pool/conv/Conv" [label="[1, 2048, 5, 5]", style=solid]; -"640 QuantizeLinear_onnx^^Conv_1213_1" -> "641 DequantizeLinear_onnx^^Conv_1213_1" [label="[192, 2048, 1, 1]", style=dashed]; -"641 DequantizeLinear_onnx^^Conv_1213_1" -> "642 /Mixed_7c/branch_pool/conv/Conv" [label="[192, 2048, 1, 1]", style=solid]; -"642 /Mixed_7c/branch_pool/conv/Conv" -> "643 /Mixed_7c/branch_pool/Relu" [label="[1, 192, 5, 5]", style=solid]; -"643 /Mixed_7c/branch_pool/Relu" -> "654 QuantizeLinear_/Mixed_7c/branch_pool/Relu_output_0_1" [label="[1, 192, 5, 5]", style=solid]; -"644 QuantizeLinear_/Mixed_7c/branch1x1/Relu_output_0_1" -> "645 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DequantizeLinear_/Mixed_7c/branch3x3dbl_3a/Relu_output_0_1" -> "656 /Mixed_7c/Concat" [label="[1, 384, 5, 5]", style=solid]; -"652 QuantizeLinear_/Mixed_7c/branch3x3dbl_3b/Relu_output_0_1" -> "653 DequantizeLinear_/Mixed_7c/branch3x3dbl_3b/Relu_output_0_1" [label="[1, 384, 5, 5]", style=dashed]; -"653 DequantizeLinear_/Mixed_7c/branch3x3dbl_3b/Relu_output_0_1" -> "656 /Mixed_7c/Concat" [label="[1, 384, 5, 5]", style=solid]; -"654 QuantizeLinear_/Mixed_7c/branch_pool/Relu_output_0_1" -> "655 DequantizeLinear_/Mixed_7c/branch_pool/Relu_output_0_1" [label="[1, 192, 5, 5]", style=dashed]; -"655 DequantizeLinear_/Mixed_7c/branch_pool/Relu_output_0_1" -> "656 /Mixed_7c/Concat" [label="[1, 192, 5, 5]", style=solid]; -"656 /Mixed_7c/Concat" -> "657 /avgpool/GlobalAveragePool" [label="[1, 2048, 5, 5]", style=solid]; -"657 /avgpool/GlobalAveragePool" -> "658 QuantizeLinear_/avgpool/GlobalAveragePool_output_0_1" [label="[1, 2048, 1, 1]", style=solid]; -"658 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/Mixed_5c/branch5x5_1/conv/Conv" [label="[1, 256, 25, 25]", style=solid]; +"110 /Mixed_5b/Concat" -> "127 /Mixed_5c/branch3x3dbl_1/conv/Conv" [label="[1, 256, 25, 25]", style=solid]; +"110 /Mixed_5b/Concat" -> "141 /Mixed_5c/AveragePool" [label="[1, 256, 25, 25]", style=solid]; +"111 QuantizeLinear_onnx^^Conv_952_1" -> "112 DequantizeLinear_onnx^^Conv_952_1" [label="[64, 256, 1, 1]", style=dashed]; +"112 DequantizeLinear_onnx^^Conv_952_1" -> "113 /Mixed_5c/branch1x1/conv/Conv" [label="[64, 256, 1, 1]", style=solid]; +"113 /Mixed_5c/branch1x1/conv/Conv" -> "114 /Mixed_5c/branch1x1/Relu" [label="[1, 64, 25, 25]", style=solid]; +"114 /Mixed_5c/branch1x1/Relu" -> "148 QuantizeLinear_/Mixed_5c/branch1x1/Relu_output_0_1" [label="[1, 64, 25, 25]", style=solid]; +"115 QuantizeLinear_onnx^^Conv_955_1" -> "116 DequantizeLinear_onnx^^Conv_955_1" [label="[48, 256, 1, 1]", style=dashed]; +"116 DequantizeLinear_onnx^^Conv_955_1" -> "117 /Mixed_5c/branch5x5_1/conv/Conv" [label="[48, 256, 1, 1]", style=solid]; +"117 /Mixed_5c/branch5x5_1/conv/Conv" -> "118 /Mixed_5c/branch5x5_1/Relu" [label="[1, 48, 25, 25]", style=solid]; +"118 /Mixed_5c/branch5x5_1/Relu" -> "119 QuantizeLinear_/Mixed_5c/branch5x5_1/Relu_output_0_1" [label="[1, 48, 25, 25]", style=solid]; +"119 QuantizeLinear_/Mixed_5c/branch5x5_1/Relu_output_0_1" -> "120 DequantizeLinear_/Mixed_5c/branch5x5_1/Relu_output_0_1" [label="[1, 48, 25, 25]", style=dashed]; +"120 DequantizeLinear_/Mixed_5c/branch5x5_1/Relu_output_0_1" -> "123 /Mixed_5c/branch5x5_2/conv/Conv" [label="[1, 48, 25, 25]", style=solid]; +"121 QuantizeLinear_onnx^^Conv_958_1" -> "122 DequantizeLinear_onnx^^Conv_958_1" [label="[64, 48, 5, 5]", style=dashed]; +"122 DequantizeLinear_onnx^^Conv_958_1" -> "123 /Mixed_5c/branch5x5_2/conv/Conv" [label="[64, 48, 5, 5]", style=solid]; +"123 /Mixed_5c/branch5x5_2/conv/Conv" -> "124 /Mixed_5c/branch5x5_2/Relu" [label="[1, 64, 25, 25]", style=solid]; +"124 /Mixed_5c/branch5x5_2/Relu" -> "150 QuantizeLinear_/Mixed_5c/branch5x5_2/Relu_output_0_1" [label="[1, 64, 25, 25]", style=solid]; +"125 QuantizeLinear_onnx^^Conv_961_1" -> "126 DequantizeLinear_onnx^^Conv_961_1" [label="[64, 256, 1, 1]", style=dashed]; +"126 DequantizeLinear_onnx^^Conv_961_1" -> "127 /Mixed_5c/branch3x3dbl_1/conv/Conv" [label="[64, 256, 1, 1]", style=solid]; +"127 /Mixed_5c/branch3x3dbl_1/conv/Conv" -> "128 /Mixed_5c/branch3x3dbl_1/Relu" [label="[1, 64, 25, 25]", style=solid]; +"128 /Mixed_5c/branch3x3dbl_1/Relu" -> "129 QuantizeLinear_/Mixed_5c/branch3x3dbl_1/Relu_output_0_1" [label="[1, 64, 25, 25]", style=solid]; +"129 QuantizeLinear_/Mixed_5c/branch3x3dbl_1/Relu_output_0_1" -> "130 DequantizeLinear_/Mixed_5c/branch3x3dbl_1/Relu_output_0_1" [label="[1, 64, 25, 25]", style=dashed]; +"130 DequantizeLinear_/Mixed_5c/branch3x3dbl_1/Relu_output_0_1" -> "133 /Mixed_5c/branch3x3dbl_2/conv/Conv" [label="[1, 64, 25, 25]", style=solid]; +"131 QuantizeLinear_onnx^^Conv_964_1" -> "132 DequantizeLinear_onnx^^Conv_964_1" [label="[96, 64, 3, 3]", style=dashed]; +"132 DequantizeLinear_onnx^^Conv_964_1" -> "133 /Mixed_5c/branch3x3dbl_2/conv/Conv" [label="[96, 64, 3, 3]", style=solid]; +"133 /Mixed_5c/branch3x3dbl_2/conv/Conv" -> "134 /Mixed_5c/branch3x3dbl_2/Relu" [label="[1, 96, 25, 25]", style=solid]; +"134 /Mixed_5c/branch3x3dbl_2/Relu" -> "135 QuantizeLinear_/Mixed_5c/branch3x3dbl_2/Relu_output_0_1" [label="[1, 96, 25, 25]", style=solid]; +"135 QuantizeLinear_/Mixed_5c/branch3x3dbl_2/Relu_output_0_1" -> "136 DequantizeLinear_/Mixed_5c/branch3x3dbl_2/Relu_output_0_1" [label="[1, 96, 25, 25]", style=dashed]; +"136 DequantizeLinear_/Mixed_5c/branch3x3dbl_2/Relu_output_0_1" -> "139 /Mixed_5c/branch3x3dbl_3/conv/Conv" [label="[1, 96, 25, 25]", style=solid]; +"137 QuantizeLinear_onnx^^Conv_967_1" -> "138 DequantizeLinear_onnx^^Conv_967_1" [label="[96, 96, 3, 3]", style=dashed]; +"138 DequantizeLinear_onnx^^Conv_967_1" -> "139 /Mixed_5c/branch3x3dbl_3/conv/Conv" [label="[96, 96, 3, 3]", style=solid]; +"139 /Mixed_5c/branch3x3dbl_3/conv/Conv" -> "140 /Mixed_5c/branch3x3dbl_3/Relu" [label="[1, 96, 25, 25]", style=solid]; +"140 /Mixed_5c/branch3x3dbl_3/Relu" -> "152 QuantizeLinear_/Mixed_5c/branch3x3dbl_3/Relu_output_0_1" [label="[1, 96, 25, 25]", style=solid]; +"141 /Mixed_5c/AveragePool" -> "142 QuantizeLinear_/Mixed_5c/AveragePool_output_0_1" [label="[1, 256, 25, 25]", style=solid]; +"142 QuantizeLinear_/Mixed_5c/AveragePool_output_0_1" -> "143 DequantizeLinear_/Mixed_5c/AveragePool_output_0_1" [label="[1, 256, 25, 25]", style=dashed]; +"143 DequantizeLinear_/Mixed_5c/AveragePool_output_0_1" -> "146 /Mixed_5c/branch_pool/conv/Conv" [label="[1, 256, 25, 25]", style=solid]; +"144 QuantizeLinear_onnx^^Conv_970_1" -> "145 DequantizeLinear_onnx^^Conv_970_1" [label="[64, 256, 1, 1]", style=dashed]; +"145 DequantizeLinear_onnx^^Conv_970_1" -> "146 /Mixed_5c/branch_pool/conv/Conv" [label="[64, 256, 1, 1]", style=solid]; +"146 /Mixed_5c/branch_pool/conv/Conv" -> "147 /Mixed_5c/branch_pool/Relu" [label="[1, 64, 25, 25]", style=solid]; +"147 /Mixed_5c/branch_pool/Relu" -> "154 QuantizeLinear_/Mixed_5c/branch_pool/Relu_output_0_1" [label="[1, 64, 25, 25]", style=solid]; +"148 QuantizeLinear_/Mixed_5c/branch1x1/Relu_output_0_1" -> "149 DequantizeLinear_/Mixed_5c/branch1x1/Relu_output_0_1" [label="[1, 64, 25, 25]", style=dashed]; +"149 DequantizeLinear_/Mixed_5c/branch1x1/Relu_output_0_1" -> "156 /Mixed_5c/Concat" [label="[1, 64, 25, 25]", style=solid]; +"150 QuantizeLinear_/Mixed_5c/branch5x5_2/Relu_output_0_1" -> "151 DequantizeLinear_/Mixed_5c/branch5x5_2/Relu_output_0_1" [label="[1, 64, 25, 25]", style=dashed]; +"151 DequantizeLinear_/Mixed_5c/branch5x5_2/Relu_output_0_1" -> "156 /Mixed_5c/Concat" [label="[1, 64, 25, 25]", style=solid]; +"152 QuantizeLinear_/Mixed_5c/branch3x3dbl_3/Relu_output_0_1" -> "153 DequantizeLinear_/Mixed_5c/branch3x3dbl_3/Relu_output_0_1" [label="[1, 96, 25, 25]", style=dashed]; 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DequantizeLinear_onnx^^Conv_973_1" -> "159 /Mixed_5d/branch1x1/conv/Conv" [label="[64, 288, 1, 1]", style=solid]; +"159 /Mixed_5d/branch1x1/conv/Conv" -> "160 /Mixed_5d/branch1x1/Relu" [label="[1, 64, 25, 25]", style=solid]; +"160 /Mixed_5d/branch1x1/Relu" -> "194 QuantizeLinear_/Mixed_5d/branch1x1/Relu_output_0_1" [label="[1, 64, 25, 25]", style=solid]; +"161 QuantizeLinear_onnx^^Conv_976_1" -> "162 DequantizeLinear_onnx^^Conv_976_1" [label="[48, 288, 1, 1]", style=dashed]; +"162 DequantizeLinear_onnx^^Conv_976_1" -> "163 /Mixed_5d/branch5x5_1/conv/Conv" [label="[48, 288, 1, 1]", style=solid]; +"163 /Mixed_5d/branch5x5_1/conv/Conv" -> "164 /Mixed_5d/branch5x5_1/Relu" [label="[1, 48, 25, 25]", style=solid]; +"164 /Mixed_5d/branch5x5_1/Relu" -> "165 QuantizeLinear_/Mixed_5d/branch5x5_1/Relu_output_0_1" [label="[1, 48, 25, 25]", style=solid]; +"165 QuantizeLinear_/Mixed_5d/branch5x5_1/Relu_output_0_1" -> "166 DequantizeLinear_/Mixed_5d/branch5x5_1/Relu_output_0_1" [label="[1, 48, 25, 25]", style=dashed]; +"166 DequantizeLinear_/Mixed_5d/branch5x5_1/Relu_output_0_1" -> "169 /Mixed_5d/branch5x5_2/conv/Conv" [label="[1, 48, 25, 25]", style=solid]; +"167 QuantizeLinear_onnx^^Conv_979_1" -> "168 DequantizeLinear_onnx^^Conv_979_1" [label="[64, 48, 5, 5]", style=dashed]; +"168 DequantizeLinear_onnx^^Conv_979_1" -> "169 /Mixed_5d/branch5x5_2/conv/Conv" [label="[64, 48, 5, 5]", style=solid]; +"169 /Mixed_5d/branch5x5_2/conv/Conv" -> "170 /Mixed_5d/branch5x5_2/Relu" [label="[1, 64, 25, 25]", style=solid]; +"170 /Mixed_5d/branch5x5_2/Relu" -> "196 QuantizeLinear_/Mixed_5d/branch5x5_2/Relu_output_0_1" [label="[1, 64, 25, 25]", style=solid]; +"171 QuantizeLinear_onnx^^Conv_982_1" -> "172 DequantizeLinear_onnx^^Conv_982_1" [label="[64, 288, 1, 1]", style=dashed]; +"172 DequantizeLinear_onnx^^Conv_982_1" -> "173 /Mixed_5d/branch3x3dbl_1/conv/Conv" [label="[64, 288, 1, 1]", style=solid]; +"173 /Mixed_5d/branch3x3dbl_1/conv/Conv" -> "174 /Mixed_5d/branch3x3dbl_1/Relu" [label="[1, 64, 25, 25]", style=solid]; +"174 /Mixed_5d/branch3x3dbl_1/Relu" -> "175 QuantizeLinear_/Mixed_5d/branch3x3dbl_1/Relu_output_0_1" [label="[1, 64, 25, 25]", style=solid]; +"175 QuantizeLinear_/Mixed_5d/branch3x3dbl_1/Relu_output_0_1" -> "176 DequantizeLinear_/Mixed_5d/branch3x3dbl_1/Relu_output_0_1" [label="[1, 64, 25, 25]", style=dashed]; +"176 DequantizeLinear_/Mixed_5d/branch3x3dbl_1/Relu_output_0_1" -> "179 /Mixed_5d/branch3x3dbl_2/conv/Conv" [label="[1, 64, 25, 25]", style=solid]; +"177 QuantizeLinear_onnx^^Conv_985_1" -> "178 DequantizeLinear_onnx^^Conv_985_1" [label="[96, 64, 3, 3]", style=dashed]; +"178 DequantizeLinear_onnx^^Conv_985_1" -> "179 /Mixed_5d/branch3x3dbl_2/conv/Conv" [label="[96, 64, 3, 3]", style=solid]; +"179 /Mixed_5d/branch3x3dbl_2/conv/Conv" -> "180 /Mixed_5d/branch3x3dbl_2/Relu" [label="[1, 96, 25, 25]", style=solid]; +"180 /Mixed_5d/branch3x3dbl_2/Relu" -> "181 QuantizeLinear_/Mixed_5d/branch3x3dbl_2/Relu_output_0_1" [label="[1, 96, 25, 25]", style=solid]; 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QuantizeLinear_/Mixed_5d/AveragePool_output_0_1" -> "189 DequantizeLinear_/Mixed_5d/AveragePool_output_0_1" [label="[1, 288, 25, 25]", style=dashed]; +"189 DequantizeLinear_/Mixed_5d/AveragePool_output_0_1" -> "192 /Mixed_5d/branch_pool/conv/Conv" [label="[1, 288, 25, 25]", style=solid]; +"190 QuantizeLinear_onnx^^Conv_991_1" -> "191 DequantizeLinear_onnx^^Conv_991_1" [label="[64, 288, 1, 1]", style=dashed]; +"191 DequantizeLinear_onnx^^Conv_991_1" -> "192 /Mixed_5d/branch_pool/conv/Conv" [label="[64, 288, 1, 1]", style=solid]; +"192 /Mixed_5d/branch_pool/conv/Conv" -> "193 /Mixed_5d/branch_pool/Relu" [label="[1, 64, 25, 25]", style=solid]; +"193 /Mixed_5d/branch_pool/Relu" -> "200 QuantizeLinear_/Mixed_5d/branch_pool/Relu_output_0_1" [label="[1, 64, 25, 25]", style=solid]; +"194 QuantizeLinear_/Mixed_5d/branch1x1/Relu_output_0_1" -> "195 DequantizeLinear_/Mixed_5d/branch1x1/Relu_output_0_1" [label="[1, 64, 25, 25]", style=dashed]; +"195 DequantizeLinear_/Mixed_5d/branch1x1/Relu_output_0_1" -> "202 /Mixed_5d/Concat" [label="[1, 64, 25, 25]", style=solid]; +"196 QuantizeLinear_/Mixed_5d/branch5x5_2/Relu_output_0_1" -> "197 DequantizeLinear_/Mixed_5d/branch5x5_2/Relu_output_0_1" [label="[1, 64, 25, 25]", style=dashed]; +"197 DequantizeLinear_/Mixed_5d/branch5x5_2/Relu_output_0_1" -> "202 /Mixed_5d/Concat" [label="[1, 64, 25, 25]", style=solid]; +"198 QuantizeLinear_/Mixed_5d/branch3x3dbl_3/Relu_output_0_1" -> "199 DequantizeLinear_/Mixed_5d/branch3x3dbl_3/Relu_output_0_1" [label="[1, 96, 25, 25]", style=dashed]; +"199 DequantizeLinear_/Mixed_5d/branch3x3dbl_3/Relu_output_0_1" -> "202 /Mixed_5d/Concat" [label="[1, 96, 25, 25]", style=solid]; +"200 QuantizeLinear_/Mixed_5d/branch_pool/Relu_output_0_1" -> "201 DequantizeLinear_/Mixed_5d/branch_pool/Relu_output_0_1" [label="[1, 64, 25, 25]", style=dashed]; +"201 DequantizeLinear_/Mixed_5d/branch_pool/Relu_output_0_1" -> "202 /Mixed_5d/Concat" [label="[1, 64, 25, 25]", style=solid]; +"202 /Mixed_5d/Concat" -> "205 /Mixed_6a/branch3x3/conv/Conv" [label="[1, 288, 25, 25]", style=solid]; +"202 /Mixed_5d/Concat" -> "209 /Mixed_6a/branch3x3dbl_1/conv/Conv" [label="[1, 288, 25, 25]", style=solid]; +"202 /Mixed_5d/Concat" -> "223 /Mixed_6a/MaxPool" [label="[1, 288, 25, 25]", style=solid]; +"203 QuantizeLinear_onnx^^Conv_994_1" -> "204 DequantizeLinear_onnx^^Conv_994_1" [label="[384, 288, 3, 3]", style=dashed]; +"204 DequantizeLinear_onnx^^Conv_994_1" -> "205 /Mixed_6a/branch3x3/conv/Conv" [label="[384, 288, 3, 3]", style=solid]; +"205 /Mixed_6a/branch3x3/conv/Conv" -> "206 /Mixed_6a/branch3x3/Relu" [label="[1, 384, 12, 12]", style=solid]; +"206 /Mixed_6a/branch3x3/Relu" -> "226 QuantizeLinear_/Mixed_6a/branch3x3/Relu_output_0_1" [label="[1, 384, 12, 12]", style=solid]; +"207 QuantizeLinear_onnx^^Conv_997_1" -> "208 DequantizeLinear_onnx^^Conv_997_1" [label="[64, 288, 1, 1]", style=dashed]; +"208 DequantizeLinear_onnx^^Conv_997_1" -> "209 /Mixed_6a/branch3x3dbl_1/conv/Conv" [label="[64, 288, 1, 1]", style=solid]; +"209 /Mixed_6a/branch3x3dbl_1/conv/Conv" -> "210 /Mixed_6a/branch3x3dbl_1/Relu" [label="[1, 64, 25, 25]", style=solid]; +"210 /Mixed_6a/branch3x3dbl_1/Relu" -> "211 QuantizeLinear_/Mixed_6a/branch3x3dbl_1/Relu_output_0_1" [label="[1, 64, 25, 25]", style=solid]; +"211 QuantizeLinear_/Mixed_6a/branch3x3dbl_1/Relu_output_0_1" -> "212 DequantizeLinear_/Mixed_6a/branch3x3dbl_1/Relu_output_0_1" [label="[1, 64, 25, 25]", style=dashed]; +"212 DequantizeLinear_/Mixed_6a/branch3x3dbl_1/Relu_output_0_1" -> "215 /Mixed_6a/branch3x3dbl_2/conv/Conv" [label="[1, 64, 25, 25]", style=solid]; +"213 QuantizeLinear_onnx^^Conv_1000_1" -> "214 DequantizeLinear_onnx^^Conv_1000_1" [label="[96, 64, 3, 3]", style=dashed]; +"214 DequantizeLinear_onnx^^Conv_1000_1" -> "215 /Mixed_6a/branch3x3dbl_2/conv/Conv" [label="[96, 64, 3, 3]", style=solid]; +"215 /Mixed_6a/branch3x3dbl_2/conv/Conv" -> "216 /Mixed_6a/branch3x3dbl_2/Relu" [label="[1, 96, 25, 25]", style=solid]; +"216 /Mixed_6a/branch3x3dbl_2/Relu" -> "217 QuantizeLinear_/Mixed_6a/branch3x3dbl_2/Relu_output_0_1" [label="[1, 96, 25, 25]", style=solid]; +"217 QuantizeLinear_/Mixed_6a/branch3x3dbl_2/Relu_output_0_1" -> "218 DequantizeLinear_/Mixed_6a/branch3x3dbl_2/Relu_output_0_1" [label="[1, 96, 25, 25]", style=dashed]; +"218 DequantizeLinear_/Mixed_6a/branch3x3dbl_2/Relu_output_0_1" -> "221 /Mixed_6a/branch3x3dbl_3/conv/Conv" [label="[1, 96, 25, 25]", style=solid]; +"219 QuantizeLinear_onnx^^Conv_1003_1" -> "220 DequantizeLinear_onnx^^Conv_1003_1" [label="[96, 96, 3, 3]", style=dashed]; +"220 DequantizeLinear_onnx^^Conv_1003_1" -> "221 /Mixed_6a/branch3x3dbl_3/conv/Conv" [label="[96, 96, 3, 3]", style=solid]; +"221 /Mixed_6a/branch3x3dbl_3/conv/Conv" -> "222 /Mixed_6a/branch3x3dbl_3/Relu" [label="[1, 96, 12, 12]", style=solid]; +"222 /Mixed_6a/branch3x3dbl_3/Relu" -> "224 QuantizeLinear_/Mixed_6a/branch3x3dbl_3/Relu_output_0_1" [label="[1, 96, 12, 12]", style=solid]; +"223 /Mixed_6a/MaxPool" -> "228 /Mixed_6a/Concat" [label="[1, 288, 12, 12]", style=solid]; +"224 QuantizeLinear_/Mixed_6a/branch3x3dbl_3/Relu_output_0_1" -> "225 DequantizeLinear_/Mixed_6a/branch3x3dbl_3/Relu_output_0_1" [label="[1, 96, 12, 12]", style=dashed]; +"225 DequantizeLinear_/Mixed_6a/branch3x3dbl_3/Relu_output_0_1" -> "228 /Mixed_6a/Concat" [label="[1, 96, 12, 12]", style=solid]; +"226 QuantizeLinear_/Mixed_6a/branch3x3/Relu_output_0_1" -> "227 DequantizeLinear_/Mixed_6a/branch3x3/Relu_output_0_1" [label="[1, 384, 12, 12]", style=dashed]; +"227 DequantizeLinear_/Mixed_6a/branch3x3/Relu_output_0_1" -> "228 /Mixed_6a/Concat" [label="[1, 384, 12, 12]", style=solid]; +"228 /Mixed_6a/Concat" -> "231 /Mixed_6b/branch1x1/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; +"228 /Mixed_6a/Concat" -> "235 /Mixed_6b/branch7x7_1/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; +"228 /Mixed_6a/Concat" -> "251 /Mixed_6b/branch7x7dbl_1/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; +"228 /Mixed_6a/Concat" -> "277 /Mixed_6b/AveragePool" [label="[1, 768, 12, 12]", style=solid]; +"229 QuantizeLinear_onnx^^Conv_1006_1" -> "230 DequantizeLinear_onnx^^Conv_1006_1" [label="[192, 768, 1, 1]", style=dashed]; +"230 DequantizeLinear_onnx^^Conv_1006_1" -> "231 /Mixed_6b/branch1x1/conv/Conv" [label="[192, 768, 1, 1]", style=solid]; +"231 /Mixed_6b/branch1x1/conv/Conv" -> "232 /Mixed_6b/branch1x1/Relu" [label="[1, 192, 12, 12]", style=solid]; +"232 /Mixed_6b/branch1x1/Relu" -> "288 QuantizeLinear_/Mixed_6b/branch1x1/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; +"233 QuantizeLinear_onnx^^Conv_1009_1" -> "234 DequantizeLinear_onnx^^Conv_1009_1" [label="[128, 768, 1, 1]", style=dashed]; +"234 DequantizeLinear_onnx^^Conv_1009_1" -> "235 /Mixed_6b/branch7x7_1/conv/Conv" [label="[128, 768, 1, 1]", style=solid]; +"235 /Mixed_6b/branch7x7_1/conv/Conv" -> "236 /Mixed_6b/branch7x7_1/Relu" [label="[1, 128, 12, 12]", style=solid]; +"236 /Mixed_6b/branch7x7_1/Relu" -> "237 QuantizeLinear_/Mixed_6b/branch7x7_1/Relu_output_0_1" [label="[1, 128, 12, 12]", style=solid]; +"237 QuantizeLinear_/Mixed_6b/branch7x7_1/Relu_output_0_1" -> "238 DequantizeLinear_/Mixed_6b/branch7x7_1/Relu_output_0_1" [label="[1, 128, 12, 12]", style=dashed]; +"238 DequantizeLinear_/Mixed_6b/branch7x7_1/Relu_output_0_1" -> "241 /Mixed_6b/branch7x7_2/conv/Conv" [label="[1, 128, 12, 12]", style=solid]; +"239 QuantizeLinear_onnx^^Conv_1012_1" -> "240 DequantizeLinear_onnx^^Conv_1012_1" [label="[128, 128, 1, 7]", style=dashed]; +"240 DequantizeLinear_onnx^^Conv_1012_1" -> "241 /Mixed_6b/branch7x7_2/conv/Conv" [label="[128, 128, 1, 7]", style=solid]; +"241 /Mixed_6b/branch7x7_2/conv/Conv" -> "242 /Mixed_6b/branch7x7_2/Relu" [label="[1, 128, 12, 12]", style=solid]; +"242 /Mixed_6b/branch7x7_2/Relu" -> "243 QuantizeLinear_/Mixed_6b/branch7x7_2/Relu_output_0_1" [label="[1, 128, 12, 12]", style=solid]; +"243 QuantizeLinear_/Mixed_6b/branch7x7_2/Relu_output_0_1" -> "244 DequantizeLinear_/Mixed_6b/branch7x7_2/Relu_output_0_1" [label="[1, 128, 12, 12]", style=dashed]; +"244 DequantizeLinear_/Mixed_6b/branch7x7_2/Relu_output_0_1" -> "247 /Mixed_6b/branch7x7_3/conv/Conv" [label="[1, 128, 12, 12]", style=solid]; +"245 QuantizeLinear_onnx^^Conv_1015_1" -> "246 DequantizeLinear_onnx^^Conv_1015_1" [label="[192, 128, 7, 1]", style=dashed]; +"246 DequantizeLinear_onnx^^Conv_1015_1" -> "247 /Mixed_6b/branch7x7_3/conv/Conv" [label="[192, 128, 7, 1]", style=solid]; +"247 /Mixed_6b/branch7x7_3/conv/Conv" -> "248 /Mixed_6b/branch7x7_3/Relu" [label="[1, 192, 12, 12]", style=solid]; +"248 /Mixed_6b/branch7x7_3/Relu" -> "284 QuantizeLinear_/Mixed_6b/branch7x7_3/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; +"249 QuantizeLinear_onnx^^Conv_1018_1" -> "250 DequantizeLinear_onnx^^Conv_1018_1" [label="[128, 768, 1, 1]", style=dashed]; +"250 DequantizeLinear_onnx^^Conv_1018_1" -> "251 /Mixed_6b/branch7x7dbl_1/conv/Conv" [label="[128, 768, 1, 1]", style=solid]; +"251 /Mixed_6b/branch7x7dbl_1/conv/Conv" -> "252 /Mixed_6b/branch7x7dbl_1/Relu" [label="[1, 128, 12, 12]", style=solid]; +"252 /Mixed_6b/branch7x7dbl_1/Relu" -> "253 QuantizeLinear_/Mixed_6b/branch7x7dbl_1/Relu_output_0_1" [label="[1, 128, 12, 12]", style=solid]; +"253 QuantizeLinear_/Mixed_6b/branch7x7dbl_1/Relu_output_0_1" -> "254 DequantizeLinear_/Mixed_6b/branch7x7dbl_1/Relu_output_0_1" [label="[1, 128, 12, 12]", style=dashed]; +"254 DequantizeLinear_/Mixed_6b/branch7x7dbl_1/Relu_output_0_1" -> "257 /Mixed_6b/branch7x7dbl_2/conv/Conv" [label="[1, 128, 12, 12]", style=solid]; +"255 QuantizeLinear_onnx^^Conv_1021_1" -> "256 DequantizeLinear_onnx^^Conv_1021_1" [label="[128, 128, 7, 1]", style=dashed]; +"256 DequantizeLinear_onnx^^Conv_1021_1" -> "257 /Mixed_6b/branch7x7dbl_2/conv/Conv" [label="[128, 128, 7, 1]", style=solid]; +"257 /Mixed_6b/branch7x7dbl_2/conv/Conv" -> "258 /Mixed_6b/branch7x7dbl_2/Relu" [label="[1, 128, 12, 12]", style=solid]; +"258 /Mixed_6b/branch7x7dbl_2/Relu" -> "259 QuantizeLinear_/Mixed_6b/branch7x7dbl_2/Relu_output_0_1" [label="[1, 128, 12, 12]", style=solid]; +"259 QuantizeLinear_/Mixed_6b/branch7x7dbl_2/Relu_output_0_1" -> "260 DequantizeLinear_/Mixed_6b/branch7x7dbl_2/Relu_output_0_1" [label="[1, 128, 12, 12]", style=dashed]; +"260 DequantizeLinear_/Mixed_6b/branch7x7dbl_2/Relu_output_0_1" -> "263 /Mixed_6b/branch7x7dbl_3/conv/Conv" [label="[1, 128, 12, 12]", style=solid]; +"261 QuantizeLinear_onnx^^Conv_1024_1" -> "262 DequantizeLinear_onnx^^Conv_1024_1" [label="[128, 128, 1, 7]", style=dashed]; +"262 DequantizeLinear_onnx^^Conv_1024_1" -> "263 /Mixed_6b/branch7x7dbl_3/conv/Conv" [label="[128, 128, 1, 7]", style=solid]; +"263 /Mixed_6b/branch7x7dbl_3/conv/Conv" -> "264 /Mixed_6b/branch7x7dbl_3/Relu" [label="[1, 128, 12, 12]", style=solid]; +"264 /Mixed_6b/branch7x7dbl_3/Relu" -> "265 QuantizeLinear_/Mixed_6b/branch7x7dbl_3/Relu_output_0_1" [label="[1, 128, 12, 12]", style=solid]; +"265 QuantizeLinear_/Mixed_6b/branch7x7dbl_3/Relu_output_0_1" -> "266 DequantizeLinear_/Mixed_6b/branch7x7dbl_3/Relu_output_0_1" [label="[1, 128, 12, 12]", style=dashed]; +"266 DequantizeLinear_/Mixed_6b/branch7x7dbl_3/Relu_output_0_1" -> "269 /Mixed_6b/branch7x7dbl_4/conv/Conv" [label="[1, 128, 12, 12]", style=solid]; +"267 QuantizeLinear_onnx^^Conv_1027_1" -> "268 DequantizeLinear_onnx^^Conv_1027_1" [label="[128, 128, 7, 1]", style=dashed]; +"268 DequantizeLinear_onnx^^Conv_1027_1" -> "269 /Mixed_6b/branch7x7dbl_4/conv/Conv" [label="[128, 128, 7, 1]", style=solid]; +"269 /Mixed_6b/branch7x7dbl_4/conv/Conv" -> "270 /Mixed_6b/branch7x7dbl_4/Relu" [label="[1, 128, 12, 12]", style=solid]; +"270 /Mixed_6b/branch7x7dbl_4/Relu" -> "271 QuantizeLinear_/Mixed_6b/branch7x7dbl_4/Relu_output_0_1" [label="[1, 128, 12, 12]", style=solid]; +"271 QuantizeLinear_/Mixed_6b/branch7x7dbl_4/Relu_output_0_1" -> "272 DequantizeLinear_/Mixed_6b/branch7x7dbl_4/Relu_output_0_1" [label="[1, 128, 12, 12]", style=dashed]; +"272 DequantizeLinear_/Mixed_6b/branch7x7dbl_4/Relu_output_0_1" -> "275 /Mixed_6b/branch7x7dbl_5/conv/Conv" [label="[1, 128, 12, 12]", style=solid]; +"273 QuantizeLinear_onnx^^Conv_1030_1" -> "274 DequantizeLinear_onnx^^Conv_1030_1" [label="[192, 128, 1, 7]", style=dashed]; +"274 DequantizeLinear_onnx^^Conv_1030_1" -> "275 /Mixed_6b/branch7x7dbl_5/conv/Conv" [label="[192, 128, 1, 7]", style=solid]; +"275 /Mixed_6b/branch7x7dbl_5/conv/Conv" -> "276 /Mixed_6b/branch7x7dbl_5/Relu" [label="[1, 192, 12, 12]", style=solid]; +"276 /Mixed_6b/branch7x7dbl_5/Relu" -> "286 QuantizeLinear_/Mixed_6b/branch7x7dbl_5/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; +"277 /Mixed_6b/AveragePool" -> "278 QuantizeLinear_/Mixed_6b/AveragePool_output_0_1" [label="[1, 768, 12, 12]", style=solid]; +"278 QuantizeLinear_/Mixed_6b/AveragePool_output_0_1" -> "279 DequantizeLinear_/Mixed_6b/AveragePool_output_0_1" [label="[1, 768, 12, 12]", style=dashed]; +"279 DequantizeLinear_/Mixed_6b/AveragePool_output_0_1" -> "282 /Mixed_6b/branch_pool/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; +"280 QuantizeLinear_onnx^^Conv_1033_1" -> "281 DequantizeLinear_onnx^^Conv_1033_1" [label="[192, 768, 1, 1]", style=dashed]; +"281 DequantizeLinear_onnx^^Conv_1033_1" -> "282 /Mixed_6b/branch_pool/conv/Conv" [label="[192, 768, 1, 1]", style=solid]; +"282 /Mixed_6b/branch_pool/conv/Conv" -> "283 /Mixed_6b/branch_pool/Relu" [label="[1, 192, 12, 12]", style=solid]; +"283 /Mixed_6b/branch_pool/Relu" -> "290 QuantizeLinear_/Mixed_6b/branch_pool/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; +"284 QuantizeLinear_/Mixed_6b/branch7x7_3/Relu_output_0_1" -> "285 DequantizeLinear_/Mixed_6b/branch7x7_3/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; +"285 DequantizeLinear_/Mixed_6b/branch7x7_3/Relu_output_0_1" -> "292 /Mixed_6b/Concat" [label="[1, 192, 12, 12]", style=solid]; +"286 QuantizeLinear_/Mixed_6b/branch7x7dbl_5/Relu_output_0_1" -> "287 DequantizeLinear_/Mixed_6b/branch7x7dbl_5/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; +"287 DequantizeLinear_/Mixed_6b/branch7x7dbl_5/Relu_output_0_1" -> "292 /Mixed_6b/Concat" [label="[1, 192, 12, 12]", style=solid]; +"288 QuantizeLinear_/Mixed_6b/branch1x1/Relu_output_0_1" -> "289 DequantizeLinear_/Mixed_6b/branch1x1/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; +"289 DequantizeLinear_/Mixed_6b/branch1x1/Relu_output_0_1" -> "292 /Mixed_6b/Concat" [label="[1, 192, 12, 12]", style=solid]; +"290 QuantizeLinear_/Mixed_6b/branch_pool/Relu_output_0_1" -> "291 DequantizeLinear_/Mixed_6b/branch_pool/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; +"291 DequantizeLinear_/Mixed_6b/branch_pool/Relu_output_0_1" -> "292 /Mixed_6b/Concat" [label="[1, 192, 12, 12]", style=solid]; +"292 /Mixed_6b/Concat" -> "295 /Mixed_6c/branch1x1/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; +"292 /Mixed_6b/Concat" -> "299 /Mixed_6c/branch7x7_1/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; +"292 /Mixed_6b/Concat" -> "315 /Mixed_6c/branch7x7dbl_1/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; +"292 /Mixed_6b/Concat" -> "341 /Mixed_6c/AveragePool" [label="[1, 768, 12, 12]", style=solid]; +"293 QuantizeLinear_onnx^^Conv_1036_1" -> "294 DequantizeLinear_onnx^^Conv_1036_1" [label="[192, 768, 1, 1]", style=dashed]; +"294 DequantizeLinear_onnx^^Conv_1036_1" -> "295 /Mixed_6c/branch1x1/conv/Conv" [label="[192, 768, 1, 1]", style=solid]; +"295 /Mixed_6c/branch1x1/conv/Conv" -> "296 /Mixed_6c/branch1x1/Relu" [label="[1, 192, 12, 12]", style=solid]; +"296 /Mixed_6c/branch1x1/Relu" -> "348 QuantizeLinear_/Mixed_6c/branch1x1/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; +"297 QuantizeLinear_onnx^^Conv_1039_1" -> "298 DequantizeLinear_onnx^^Conv_1039_1" [label="[160, 768, 1, 1]", style=dashed]; +"298 DequantizeLinear_onnx^^Conv_1039_1" -> "299 /Mixed_6c/branch7x7_1/conv/Conv" [label="[160, 768, 1, 1]", style=solid]; +"299 /Mixed_6c/branch7x7_1/conv/Conv" -> "300 /Mixed_6c/branch7x7_1/Relu" [label="[1, 160, 12, 12]", style=solid]; +"300 /Mixed_6c/branch7x7_1/Relu" -> "301 QuantizeLinear_/Mixed_6c/branch7x7_1/Relu_output_0_1" [label="[1, 160, 12, 12]", style=solid]; +"301 QuantizeLinear_/Mixed_6c/branch7x7_1/Relu_output_0_1" -> "302 DequantizeLinear_/Mixed_6c/branch7x7_1/Relu_output_0_1" [label="[1, 160, 12, 12]", style=dashed]; +"302 DequantizeLinear_/Mixed_6c/branch7x7_1/Relu_output_0_1" -> "305 /Mixed_6c/branch7x7_2/conv/Conv" [label="[1, 160, 12, 12]", style=solid]; +"303 QuantizeLinear_onnx^^Conv_1042_1" -> "304 DequantizeLinear_onnx^^Conv_1042_1" [label="[160, 160, 1, 7]", style=dashed]; +"304 DequantizeLinear_onnx^^Conv_1042_1" -> "305 /Mixed_6c/branch7x7_2/conv/Conv" [label="[160, 160, 1, 7]", style=solid]; +"305 /Mixed_6c/branch7x7_2/conv/Conv" -> "306 /Mixed_6c/branch7x7_2/Relu" [label="[1, 160, 12, 12]", style=solid]; +"306 /Mixed_6c/branch7x7_2/Relu" -> "307 QuantizeLinear_/Mixed_6c/branch7x7_2/Relu_output_0_1" [label="[1, 160, 12, 12]", style=solid]; +"307 QuantizeLinear_/Mixed_6c/branch7x7_2/Relu_output_0_1" -> "308 DequantizeLinear_/Mixed_6c/branch7x7_2/Relu_output_0_1" [label="[1, 160, 12, 12]", style=dashed]; +"308 DequantizeLinear_/Mixed_6c/branch7x7_2/Relu_output_0_1" -> "311 /Mixed_6c/branch7x7_3/conv/Conv" [label="[1, 160, 12, 12]", style=solid]; +"309 QuantizeLinear_onnx^^Conv_1045_1" -> "310 DequantizeLinear_onnx^^Conv_1045_1" [label="[192, 160, 7, 1]", style=dashed]; +"310 DequantizeLinear_onnx^^Conv_1045_1" -> "311 /Mixed_6c/branch7x7_3/conv/Conv" [label="[192, 160, 7, 1]", style=solid]; +"311 /Mixed_6c/branch7x7_3/conv/Conv" -> "312 /Mixed_6c/branch7x7_3/Relu" [label="[1, 192, 12, 12]", style=solid]; +"312 /Mixed_6c/branch7x7_3/Relu" -> "350 QuantizeLinear_/Mixed_6c/branch7x7_3/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; +"313 QuantizeLinear_onnx^^Conv_1048_1" -> "314 DequantizeLinear_onnx^^Conv_1048_1" [label="[160, 768, 1, 1]", style=dashed]; +"314 DequantizeLinear_onnx^^Conv_1048_1" -> "315 /Mixed_6c/branch7x7dbl_1/conv/Conv" [label="[160, 768, 1, 1]", style=solid]; +"315 /Mixed_6c/branch7x7dbl_1/conv/Conv" -> "316 /Mixed_6c/branch7x7dbl_1/Relu" [label="[1, 160, 12, 12]", style=solid]; +"316 /Mixed_6c/branch7x7dbl_1/Relu" -> "317 QuantizeLinear_/Mixed_6c/branch7x7dbl_1/Relu_output_0_1" [label="[1, 160, 12, 12]", style=solid]; +"317 QuantizeLinear_/Mixed_6c/branch7x7dbl_1/Relu_output_0_1" -> "318 DequantizeLinear_/Mixed_6c/branch7x7dbl_1/Relu_output_0_1" [label="[1, 160, 12, 12]", style=dashed]; +"318 DequantizeLinear_/Mixed_6c/branch7x7dbl_1/Relu_output_0_1" -> "321 /Mixed_6c/branch7x7dbl_2/conv/Conv" [label="[1, 160, 12, 12]", style=solid]; +"319 QuantizeLinear_onnx^^Conv_1051_1" -> "320 DequantizeLinear_onnx^^Conv_1051_1" [label="[160, 160, 7, 1]", style=dashed]; +"320 DequantizeLinear_onnx^^Conv_1051_1" -> "321 /Mixed_6c/branch7x7dbl_2/conv/Conv" [label="[160, 160, 7, 1]", style=solid]; +"321 /Mixed_6c/branch7x7dbl_2/conv/Conv" -> "322 /Mixed_6c/branch7x7dbl_2/Relu" [label="[1, 160, 12, 12]", style=solid]; +"322 /Mixed_6c/branch7x7dbl_2/Relu" -> "323 QuantizeLinear_/Mixed_6c/branch7x7dbl_2/Relu_output_0_1" [label="[1, 160, 12, 12]", style=solid]; +"323 QuantizeLinear_/Mixed_6c/branch7x7dbl_2/Relu_output_0_1" -> "324 DequantizeLinear_/Mixed_6c/branch7x7dbl_2/Relu_output_0_1" [label="[1, 160, 12, 12]", style=dashed]; +"324 DequantizeLinear_/Mixed_6c/branch7x7dbl_2/Relu_output_0_1" -> "327 /Mixed_6c/branch7x7dbl_3/conv/Conv" [label="[1, 160, 12, 12]", style=solid]; +"325 QuantizeLinear_onnx^^Conv_1054_1" -> "326 DequantizeLinear_onnx^^Conv_1054_1" [label="[160, 160, 1, 7]", style=dashed]; +"326 DequantizeLinear_onnx^^Conv_1054_1" -> "327 /Mixed_6c/branch7x7dbl_3/conv/Conv" [label="[160, 160, 1, 7]", style=solid]; +"327 /Mixed_6c/branch7x7dbl_3/conv/Conv" -> "328 /Mixed_6c/branch7x7dbl_3/Relu" [label="[1, 160, 12, 12]", style=solid]; +"328 /Mixed_6c/branch7x7dbl_3/Relu" -> "329 QuantizeLinear_/Mixed_6c/branch7x7dbl_3/Relu_output_0_1" [label="[1, 160, 12, 12]", style=solid]; +"329 QuantizeLinear_/Mixed_6c/branch7x7dbl_3/Relu_output_0_1" -> "330 DequantizeLinear_/Mixed_6c/branch7x7dbl_3/Relu_output_0_1" [label="[1, 160, 12, 12]", style=dashed]; +"330 DequantizeLinear_/Mixed_6c/branch7x7dbl_3/Relu_output_0_1" -> "333 /Mixed_6c/branch7x7dbl_4/conv/Conv" [label="[1, 160, 12, 12]", style=solid]; +"331 QuantizeLinear_onnx^^Conv_1057_1" -> "332 DequantizeLinear_onnx^^Conv_1057_1" [label="[160, 160, 7, 1]", style=dashed]; +"332 DequantizeLinear_onnx^^Conv_1057_1" -> "333 /Mixed_6c/branch7x7dbl_4/conv/Conv" [label="[160, 160, 7, 1]", style=solid]; +"333 /Mixed_6c/branch7x7dbl_4/conv/Conv" -> "334 /Mixed_6c/branch7x7dbl_4/Relu" [label="[1, 160, 12, 12]", style=solid]; +"334 /Mixed_6c/branch7x7dbl_4/Relu" -> "335 QuantizeLinear_/Mixed_6c/branch7x7dbl_4/Relu_output_0_1" [label="[1, 160, 12, 12]", style=solid]; +"335 QuantizeLinear_/Mixed_6c/branch7x7dbl_4/Relu_output_0_1" -> "336 DequantizeLinear_/Mixed_6c/branch7x7dbl_4/Relu_output_0_1" [label="[1, 160, 12, 12]", style=dashed]; +"336 DequantizeLinear_/Mixed_6c/branch7x7dbl_4/Relu_output_0_1" -> "339 /Mixed_6c/branch7x7dbl_5/conv/Conv" [label="[1, 160, 12, 12]", style=solid]; +"337 QuantizeLinear_onnx^^Conv_1060_1" -> "338 DequantizeLinear_onnx^^Conv_1060_1" [label="[192, 160, 1, 7]", style=dashed]; +"338 DequantizeLinear_onnx^^Conv_1060_1" -> "339 /Mixed_6c/branch7x7dbl_5/conv/Conv" [label="[192, 160, 1, 7]", style=solid]; +"339 /Mixed_6c/branch7x7dbl_5/conv/Conv" -> "340 /Mixed_6c/branch7x7dbl_5/Relu" [label="[1, 192, 12, 12]", style=solid]; +"340 /Mixed_6c/branch7x7dbl_5/Relu" -> "352 QuantizeLinear_/Mixed_6c/branch7x7dbl_5/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; +"341 /Mixed_6c/AveragePool" -> "342 QuantizeLinear_/Mixed_6c/AveragePool_output_0_1" [label="[1, 768, 12, 12]", style=solid]; +"342 QuantizeLinear_/Mixed_6c/AveragePool_output_0_1" -> "343 DequantizeLinear_/Mixed_6c/AveragePool_output_0_1" [label="[1, 768, 12, 12]", style=dashed]; +"343 DequantizeLinear_/Mixed_6c/AveragePool_output_0_1" -> "346 /Mixed_6c/branch_pool/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; +"344 QuantizeLinear_onnx^^Conv_1063_1" -> "345 DequantizeLinear_onnx^^Conv_1063_1" [label="[192, 768, 1, 1]", style=dashed]; +"345 DequantizeLinear_onnx^^Conv_1063_1" -> "346 /Mixed_6c/branch_pool/conv/Conv" [label="[192, 768, 1, 1]", style=solid]; +"346 /Mixed_6c/branch_pool/conv/Conv" -> "347 /Mixed_6c/branch_pool/Relu" [label="[1, 192, 12, 12]", style=solid]; +"347 /Mixed_6c/branch_pool/Relu" -> "354 QuantizeLinear_/Mixed_6c/branch_pool/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; +"348 QuantizeLinear_/Mixed_6c/branch1x1/Relu_output_0_1" -> "349 DequantizeLinear_/Mixed_6c/branch1x1/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; +"349 DequantizeLinear_/Mixed_6c/branch1x1/Relu_output_0_1" -> "356 /Mixed_6c/Concat" [label="[1, 192, 12, 12]", style=solid]; +"350 QuantizeLinear_/Mixed_6c/branch7x7_3/Relu_output_0_1" -> "351 DequantizeLinear_/Mixed_6c/branch7x7_3/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; +"351 DequantizeLinear_/Mixed_6c/branch7x7_3/Relu_output_0_1" -> "356 /Mixed_6c/Concat" [label="[1, 192, 12, 12]", style=solid]; +"352 QuantizeLinear_/Mixed_6c/branch7x7dbl_5/Relu_output_0_1" -> "353 DequantizeLinear_/Mixed_6c/branch7x7dbl_5/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; +"353 DequantizeLinear_/Mixed_6c/branch7x7dbl_5/Relu_output_0_1" -> "356 /Mixed_6c/Concat" [label="[1, 192, 12, 12]", style=solid]; +"354 QuantizeLinear_/Mixed_6c/branch_pool/Relu_output_0_1" -> "355 DequantizeLinear_/Mixed_6c/branch_pool/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; +"355 DequantizeLinear_/Mixed_6c/branch_pool/Relu_output_0_1" -> "356 /Mixed_6c/Concat" [label="[1, 192, 12, 12]", style=solid]; +"356 /Mixed_6c/Concat" -> "359 /Mixed_6d/branch1x1/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; +"356 /Mixed_6c/Concat" -> "363 /Mixed_6d/branch7x7_1/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; +"356 /Mixed_6c/Concat" -> "379 /Mixed_6d/branch7x7dbl_1/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; +"356 /Mixed_6c/Concat" -> "405 /Mixed_6d/AveragePool" [label="[1, 768, 12, 12]", style=solid]; +"357 QuantizeLinear_onnx^^Conv_1066_1" -> "358 DequantizeLinear_onnx^^Conv_1066_1" [label="[192, 768, 1, 1]", style=dashed]; +"358 DequantizeLinear_onnx^^Conv_1066_1" -> "359 /Mixed_6d/branch1x1/conv/Conv" [label="[192, 768, 1, 1]", style=solid]; +"359 /Mixed_6d/branch1x1/conv/Conv" -> "360 /Mixed_6d/branch1x1/Relu" [label="[1, 192, 12, 12]", style=solid]; +"360 /Mixed_6d/branch1x1/Relu" -> "416 QuantizeLinear_/Mixed_6d/branch1x1/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; +"361 QuantizeLinear_onnx^^Conv_1069_1" -> "362 DequantizeLinear_onnx^^Conv_1069_1" [label="[160, 768, 1, 1]", style=dashed]; +"362 DequantizeLinear_onnx^^Conv_1069_1" -> "363 /Mixed_6d/branch7x7_1/conv/Conv" [label="[160, 768, 1, 1]", style=solid]; +"363 /Mixed_6d/branch7x7_1/conv/Conv" -> "364 /Mixed_6d/branch7x7_1/Relu" [label="[1, 160, 12, 12]", style=solid]; +"364 /Mixed_6d/branch7x7_1/Relu" -> "365 QuantizeLinear_/Mixed_6d/branch7x7_1/Relu_output_0_1" [label="[1, 160, 12, 12]", style=solid]; +"365 QuantizeLinear_/Mixed_6d/branch7x7_1/Relu_output_0_1" -> "366 DequantizeLinear_/Mixed_6d/branch7x7_1/Relu_output_0_1" [label="[1, 160, 12, 12]", style=dashed]; +"366 DequantizeLinear_/Mixed_6d/branch7x7_1/Relu_output_0_1" -> "369 /Mixed_6d/branch7x7_2/conv/Conv" [label="[1, 160, 12, 12]", style=solid]; +"367 QuantizeLinear_onnx^^Conv_1072_1" -> "368 DequantizeLinear_onnx^^Conv_1072_1" [label="[160, 160, 1, 7]", style=dashed]; +"368 DequantizeLinear_onnx^^Conv_1072_1" -> "369 /Mixed_6d/branch7x7_2/conv/Conv" [label="[160, 160, 1, 7]", style=solid]; +"369 /Mixed_6d/branch7x7_2/conv/Conv" -> "370 /Mixed_6d/branch7x7_2/Relu" [label="[1, 160, 12, 12]", style=solid]; +"370 /Mixed_6d/branch7x7_2/Relu" -> "371 QuantizeLinear_/Mixed_6d/branch7x7_2/Relu_output_0_1" [label="[1, 160, 12, 12]", style=solid]; +"371 QuantizeLinear_/Mixed_6d/branch7x7_2/Relu_output_0_1" -> "372 DequantizeLinear_/Mixed_6d/branch7x7_2/Relu_output_0_1" [label="[1, 160, 12, 12]", style=dashed]; +"372 DequantizeLinear_/Mixed_6d/branch7x7_2/Relu_output_0_1" -> "375 /Mixed_6d/branch7x7_3/conv/Conv" [label="[1, 160, 12, 12]", style=solid]; +"373 QuantizeLinear_onnx^^Conv_1075_1" -> "374 DequantizeLinear_onnx^^Conv_1075_1" [label="[192, 160, 7, 1]", style=dashed]; +"374 DequantizeLinear_onnx^^Conv_1075_1" -> "375 /Mixed_6d/branch7x7_3/conv/Conv" [label="[192, 160, 7, 1]", style=solid]; +"375 /Mixed_6d/branch7x7_3/conv/Conv" -> "376 /Mixed_6d/branch7x7_3/Relu" [label="[1, 192, 12, 12]", style=solid]; +"376 /Mixed_6d/branch7x7_3/Relu" -> "418 QuantizeLinear_/Mixed_6d/branch7x7_3/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; +"377 QuantizeLinear_onnx^^Conv_1078_1" -> "378 DequantizeLinear_onnx^^Conv_1078_1" [label="[160, 768, 1, 1]", style=dashed]; +"378 DequantizeLinear_onnx^^Conv_1078_1" -> "379 /Mixed_6d/branch7x7dbl_1/conv/Conv" [label="[160, 768, 1, 1]", style=solid]; +"379 /Mixed_6d/branch7x7dbl_1/conv/Conv" -> "380 /Mixed_6d/branch7x7dbl_1/Relu" [label="[1, 160, 12, 12]", style=solid]; +"380 /Mixed_6d/branch7x7dbl_1/Relu" -> "381 QuantizeLinear_/Mixed_6d/branch7x7dbl_1/Relu_output_0_1" [label="[1, 160, 12, 12]", style=solid]; +"381 QuantizeLinear_/Mixed_6d/branch7x7dbl_1/Relu_output_0_1" -> "382 DequantizeLinear_/Mixed_6d/branch7x7dbl_1/Relu_output_0_1" [label="[1, 160, 12, 12]", style=dashed]; +"382 DequantizeLinear_/Mixed_6d/branch7x7dbl_1/Relu_output_0_1" -> "385 /Mixed_6d/branch7x7dbl_2/conv/Conv" [label="[1, 160, 12, 12]", style=solid]; +"383 QuantizeLinear_onnx^^Conv_1081_1" -> "384 DequantizeLinear_onnx^^Conv_1081_1" [label="[160, 160, 7, 1]", style=dashed]; +"384 DequantizeLinear_onnx^^Conv_1081_1" -> "385 /Mixed_6d/branch7x7dbl_2/conv/Conv" [label="[160, 160, 7, 1]", style=solid]; +"385 /Mixed_6d/branch7x7dbl_2/conv/Conv" -> "386 /Mixed_6d/branch7x7dbl_2/Relu" [label="[1, 160, 12, 12]", style=solid]; +"386 /Mixed_6d/branch7x7dbl_2/Relu" -> "387 QuantizeLinear_/Mixed_6d/branch7x7dbl_2/Relu_output_0_1" [label="[1, 160, 12, 12]", style=solid]; +"387 QuantizeLinear_/Mixed_6d/branch7x7dbl_2/Relu_output_0_1" -> "388 DequantizeLinear_/Mixed_6d/branch7x7dbl_2/Relu_output_0_1" [label="[1, 160, 12, 12]", style=dashed]; +"388 DequantizeLinear_/Mixed_6d/branch7x7dbl_2/Relu_output_0_1" -> "391 /Mixed_6d/branch7x7dbl_3/conv/Conv" [label="[1, 160, 12, 12]", style=solid]; +"389 QuantizeLinear_onnx^^Conv_1084_1" -> "390 DequantizeLinear_onnx^^Conv_1084_1" [label="[160, 160, 1, 7]", style=dashed]; +"390 DequantizeLinear_onnx^^Conv_1084_1" -> "391 /Mixed_6d/branch7x7dbl_3/conv/Conv" [label="[160, 160, 1, 7]", style=solid]; +"391 /Mixed_6d/branch7x7dbl_3/conv/Conv" -> "392 /Mixed_6d/branch7x7dbl_3/Relu" [label="[1, 160, 12, 12]", style=solid]; +"392 /Mixed_6d/branch7x7dbl_3/Relu" -> "393 QuantizeLinear_/Mixed_6d/branch7x7dbl_3/Relu_output_0_1" [label="[1, 160, 12, 12]", style=solid]; +"393 QuantizeLinear_/Mixed_6d/branch7x7dbl_3/Relu_output_0_1" -> "394 DequantizeLinear_/Mixed_6d/branch7x7dbl_3/Relu_output_0_1" [label="[1, 160, 12, 12]", style=dashed]; +"394 DequantizeLinear_/Mixed_6d/branch7x7dbl_3/Relu_output_0_1" -> "397 /Mixed_6d/branch7x7dbl_4/conv/Conv" [label="[1, 160, 12, 12]", style=solid]; +"395 QuantizeLinear_onnx^^Conv_1087_1" -> "396 DequantizeLinear_onnx^^Conv_1087_1" [label="[160, 160, 7, 1]", style=dashed]; +"396 DequantizeLinear_onnx^^Conv_1087_1" -> "397 /Mixed_6d/branch7x7dbl_4/conv/Conv" [label="[160, 160, 7, 1]", style=solid]; +"397 /Mixed_6d/branch7x7dbl_4/conv/Conv" -> "398 /Mixed_6d/branch7x7dbl_4/Relu" [label="[1, 160, 12, 12]", style=solid]; +"398 /Mixed_6d/branch7x7dbl_4/Relu" -> "399 QuantizeLinear_/Mixed_6d/branch7x7dbl_4/Relu_output_0_1" [label="[1, 160, 12, 12]", style=solid]; +"399 QuantizeLinear_/Mixed_6d/branch7x7dbl_4/Relu_output_0_1" -> "400 DequantizeLinear_/Mixed_6d/branch7x7dbl_4/Relu_output_0_1" [label="[1, 160, 12, 12]", style=dashed]; +"400 DequantizeLinear_/Mixed_6d/branch7x7dbl_4/Relu_output_0_1" -> "403 /Mixed_6d/branch7x7dbl_5/conv/Conv" [label="[1, 160, 12, 12]", style=solid]; +"401 QuantizeLinear_onnx^^Conv_1090_1" -> "402 DequantizeLinear_onnx^^Conv_1090_1" [label="[192, 160, 1, 7]", style=dashed]; +"402 DequantizeLinear_onnx^^Conv_1090_1" -> "403 /Mixed_6d/branch7x7dbl_5/conv/Conv" [label="[192, 160, 1, 7]", style=solid]; +"403 /Mixed_6d/branch7x7dbl_5/conv/Conv" -> "404 /Mixed_6d/branch7x7dbl_5/Relu" [label="[1, 192, 12, 12]", style=solid]; +"404 /Mixed_6d/branch7x7dbl_5/Relu" -> "412 QuantizeLinear_/Mixed_6d/branch7x7dbl_5/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; +"405 /Mixed_6d/AveragePool" -> "406 QuantizeLinear_/Mixed_6d/AveragePool_output_0_1" [label="[1, 768, 12, 12]", style=solid]; +"406 QuantizeLinear_/Mixed_6d/AveragePool_output_0_1" -> "407 DequantizeLinear_/Mixed_6d/AveragePool_output_0_1" [label="[1, 768, 12, 12]", style=dashed]; +"407 DequantizeLinear_/Mixed_6d/AveragePool_output_0_1" -> "410 /Mixed_6d/branch_pool/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; +"408 QuantizeLinear_onnx^^Conv_1093_1" -> "409 DequantizeLinear_onnx^^Conv_1093_1" [label="[192, 768, 1, 1]", style=dashed]; +"409 DequantizeLinear_onnx^^Conv_1093_1" -> "410 /Mixed_6d/branch_pool/conv/Conv" [label="[192, 768, 1, 1]", style=solid]; +"410 /Mixed_6d/branch_pool/conv/Conv" -> "411 /Mixed_6d/branch_pool/Relu" [label="[1, 192, 12, 12]", style=solid]; +"411 /Mixed_6d/branch_pool/Relu" -> "414 QuantizeLinear_/Mixed_6d/branch_pool/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; +"412 QuantizeLinear_/Mixed_6d/branch7x7dbl_5/Relu_output_0_1" -> "413 DequantizeLinear_/Mixed_6d/branch7x7dbl_5/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; +"413 DequantizeLinear_/Mixed_6d/branch7x7dbl_5/Relu_output_0_1" -> "420 /Mixed_6d/Concat" [label="[1, 192, 12, 12]", style=solid]; +"414 QuantizeLinear_/Mixed_6d/branch_pool/Relu_output_0_1" -> "415 DequantizeLinear_/Mixed_6d/branch_pool/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; +"415 DequantizeLinear_/Mixed_6d/branch_pool/Relu_output_0_1" -> "420 /Mixed_6d/Concat" [label="[1, 192, 12, 12]", style=solid]; +"416 QuantizeLinear_/Mixed_6d/branch1x1/Relu_output_0_1" -> "417 DequantizeLinear_/Mixed_6d/branch1x1/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; +"417 DequantizeLinear_/Mixed_6d/branch1x1/Relu_output_0_1" -> "420 /Mixed_6d/Concat" [label="[1, 192, 12, 12]", style=solid]; +"418 QuantizeLinear_/Mixed_6d/branch7x7_3/Relu_output_0_1" -> "419 DequantizeLinear_/Mixed_6d/branch7x7_3/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; +"419 DequantizeLinear_/Mixed_6d/branch7x7_3/Relu_output_0_1" -> "420 /Mixed_6d/Concat" [label="[1, 192, 12, 12]", style=solid]; +"420 /Mixed_6d/Concat" -> "423 /Mixed_6e/branch1x1/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; +"420 /Mixed_6d/Concat" -> "427 /Mixed_6e/branch7x7_1/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; +"420 /Mixed_6d/Concat" -> "443 /Mixed_6e/branch7x7dbl_1/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; +"420 /Mixed_6d/Concat" -> "469 /Mixed_6e/AveragePool" [label="[1, 768, 12, 12]", style=solid]; +"421 QuantizeLinear_onnx^^Conv_1096_1" -> "422 DequantizeLinear_onnx^^Conv_1096_1" [label="[192, 768, 1, 1]", style=dashed]; +"422 DequantizeLinear_onnx^^Conv_1096_1" -> "423 /Mixed_6e/branch1x1/conv/Conv" [label="[192, 768, 1, 1]", style=solid]; +"423 /Mixed_6e/branch1x1/conv/Conv" -> "424 /Mixed_6e/branch1x1/Relu" [label="[1, 192, 12, 12]", style=solid]; +"424 /Mixed_6e/branch1x1/Relu" -> "480 QuantizeLinear_/Mixed_6e/branch1x1/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; +"425 QuantizeLinear_onnx^^Conv_1099_1" -> "426 DequantizeLinear_onnx^^Conv_1099_1" [label="[192, 768, 1, 1]", style=dashed]; +"426 DequantizeLinear_onnx^^Conv_1099_1" -> "427 /Mixed_6e/branch7x7_1/conv/Conv" [label="[192, 768, 1, 1]", style=solid]; +"427 /Mixed_6e/branch7x7_1/conv/Conv" -> "428 /Mixed_6e/branch7x7_1/Relu" [label="[1, 192, 12, 12]", style=solid]; +"428 /Mixed_6e/branch7x7_1/Relu" -> "429 QuantizeLinear_/Mixed_6e/branch7x7_1/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; +"429 QuantizeLinear_/Mixed_6e/branch7x7_1/Relu_output_0_1" -> "430 DequantizeLinear_/Mixed_6e/branch7x7_1/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; +"430 DequantizeLinear_/Mixed_6e/branch7x7_1/Relu_output_0_1" -> "433 /Mixed_6e/branch7x7_2/conv/Conv" [label="[1, 192, 12, 12]", style=solid]; +"431 QuantizeLinear_onnx^^Conv_1102_1" -> "432 DequantizeLinear_onnx^^Conv_1102_1" [label="[192, 192, 1, 7]", style=dashed]; +"432 DequantizeLinear_onnx^^Conv_1102_1" -> "433 /Mixed_6e/branch7x7_2/conv/Conv" [label="[192, 192, 1, 7]", style=solid]; +"433 /Mixed_6e/branch7x7_2/conv/Conv" -> "434 /Mixed_6e/branch7x7_2/Relu" [label="[1, 192, 12, 12]", style=solid]; +"434 /Mixed_6e/branch7x7_2/Relu" -> "435 QuantizeLinear_/Mixed_6e/branch7x7_2/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; +"435 QuantizeLinear_/Mixed_6e/branch7x7_2/Relu_output_0_1" -> "436 DequantizeLinear_/Mixed_6e/branch7x7_2/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; +"436 DequantizeLinear_/Mixed_6e/branch7x7_2/Relu_output_0_1" -> "439 /Mixed_6e/branch7x7_3/conv/Conv" [label="[1, 192, 12, 12]", style=solid]; +"437 QuantizeLinear_onnx^^Conv_1105_1" -> "438 DequantizeLinear_onnx^^Conv_1105_1" [label="[192, 192, 7, 1]", style=dashed]; +"438 DequantizeLinear_onnx^^Conv_1105_1" -> "439 /Mixed_6e/branch7x7_3/conv/Conv" [label="[192, 192, 7, 1]", style=solid]; +"439 /Mixed_6e/branch7x7_3/conv/Conv" -> "440 /Mixed_6e/branch7x7_3/Relu" [label="[1, 192, 12, 12]", style=solid]; +"440 /Mixed_6e/branch7x7_3/Relu" -> "482 QuantizeLinear_/Mixed_6e/branch7x7_3/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; +"441 QuantizeLinear_onnx^^Conv_1108_1" -> "442 DequantizeLinear_onnx^^Conv_1108_1" [label="[192, 768, 1, 1]", style=dashed]; +"442 DequantizeLinear_onnx^^Conv_1108_1" -> "443 /Mixed_6e/branch7x7dbl_1/conv/Conv" [label="[192, 768, 1, 1]", style=solid]; +"443 /Mixed_6e/branch7x7dbl_1/conv/Conv" -> "444 /Mixed_6e/branch7x7dbl_1/Relu" [label="[1, 192, 12, 12]", style=solid]; +"444 /Mixed_6e/branch7x7dbl_1/Relu" -> "445 QuantizeLinear_/Mixed_6e/branch7x7dbl_1/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; +"445 QuantizeLinear_/Mixed_6e/branch7x7dbl_1/Relu_output_0_1" -> "446 DequantizeLinear_/Mixed_6e/branch7x7dbl_1/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; +"446 DequantizeLinear_/Mixed_6e/branch7x7dbl_1/Relu_output_0_1" -> "449 /Mixed_6e/branch7x7dbl_2/conv/Conv" [label="[1, 192, 12, 12]", style=solid]; +"447 QuantizeLinear_onnx^^Conv_1111_1" -> "448 DequantizeLinear_onnx^^Conv_1111_1" [label="[192, 192, 7, 1]", style=dashed]; +"448 DequantizeLinear_onnx^^Conv_1111_1" -> "449 /Mixed_6e/branch7x7dbl_2/conv/Conv" [label="[192, 192, 7, 1]", style=solid]; +"449 /Mixed_6e/branch7x7dbl_2/conv/Conv" -> "450 /Mixed_6e/branch7x7dbl_2/Relu" [label="[1, 192, 12, 12]", style=solid]; +"450 /Mixed_6e/branch7x7dbl_2/Relu" -> "451 QuantizeLinear_/Mixed_6e/branch7x7dbl_2/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; +"451 QuantizeLinear_/Mixed_6e/branch7x7dbl_2/Relu_output_0_1" -> "452 DequantizeLinear_/Mixed_6e/branch7x7dbl_2/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; +"452 DequantizeLinear_/Mixed_6e/branch7x7dbl_2/Relu_output_0_1" -> "455 /Mixed_6e/branch7x7dbl_3/conv/Conv" [label="[1, 192, 12, 12]", style=solid]; +"453 QuantizeLinear_onnx^^Conv_1114_1" -> "454 DequantizeLinear_onnx^^Conv_1114_1" [label="[192, 192, 1, 7]", style=dashed]; +"454 DequantizeLinear_onnx^^Conv_1114_1" -> "455 /Mixed_6e/branch7x7dbl_3/conv/Conv" [label="[192, 192, 1, 7]", style=solid]; +"455 /Mixed_6e/branch7x7dbl_3/conv/Conv" -> "456 /Mixed_6e/branch7x7dbl_3/Relu" [label="[1, 192, 12, 12]", style=solid]; +"456 /Mixed_6e/branch7x7dbl_3/Relu" -> "457 QuantizeLinear_/Mixed_6e/branch7x7dbl_3/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; +"457 QuantizeLinear_/Mixed_6e/branch7x7dbl_3/Relu_output_0_1" -> "458 DequantizeLinear_/Mixed_6e/branch7x7dbl_3/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; +"458 DequantizeLinear_/Mixed_6e/branch7x7dbl_3/Relu_output_0_1" -> "461 /Mixed_6e/branch7x7dbl_4/conv/Conv" [label="[1, 192, 12, 12]", style=solid]; +"459 QuantizeLinear_onnx^^Conv_1117_1" -> "460 DequantizeLinear_onnx^^Conv_1117_1" [label="[192, 192, 7, 1]", style=dashed]; +"460 DequantizeLinear_onnx^^Conv_1117_1" -> "461 /Mixed_6e/branch7x7dbl_4/conv/Conv" [label="[192, 192, 7, 1]", style=solid]; +"461 /Mixed_6e/branch7x7dbl_4/conv/Conv" -> "462 /Mixed_6e/branch7x7dbl_4/Relu" [label="[1, 192, 12, 12]", style=solid]; +"462 /Mixed_6e/branch7x7dbl_4/Relu" -> "463 QuantizeLinear_/Mixed_6e/branch7x7dbl_4/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; +"463 QuantizeLinear_/Mixed_6e/branch7x7dbl_4/Relu_output_0_1" -> "464 DequantizeLinear_/Mixed_6e/branch7x7dbl_4/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; +"464 DequantizeLinear_/Mixed_6e/branch7x7dbl_4/Relu_output_0_1" -> "467 /Mixed_6e/branch7x7dbl_5/conv/Conv" [label="[1, 192, 12, 12]", style=solid]; +"465 QuantizeLinear_onnx^^Conv_1120_1" -> "466 DequantizeLinear_onnx^^Conv_1120_1" [label="[192, 192, 1, 7]", style=dashed]; +"466 DequantizeLinear_onnx^^Conv_1120_1" -> "467 /Mixed_6e/branch7x7dbl_5/conv/Conv" [label="[192, 192, 1, 7]", style=solid]; +"467 /Mixed_6e/branch7x7dbl_5/conv/Conv" -> "468 /Mixed_6e/branch7x7dbl_5/Relu" [label="[1, 192, 12, 12]", style=solid]; +"468 /Mixed_6e/branch7x7dbl_5/Relu" -> "476 QuantizeLinear_/Mixed_6e/branch7x7dbl_5/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; +"469 /Mixed_6e/AveragePool" -> "470 QuantizeLinear_/Mixed_6e/AveragePool_output_0_1" [label="[1, 768, 12, 12]", style=solid]; +"470 QuantizeLinear_/Mixed_6e/AveragePool_output_0_1" -> "471 DequantizeLinear_/Mixed_6e/AveragePool_output_0_1" [label="[1, 768, 12, 12]", style=dashed]; +"471 DequantizeLinear_/Mixed_6e/AveragePool_output_0_1" -> "474 /Mixed_6e/branch_pool/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; +"472 QuantizeLinear_onnx^^Conv_1123_1" -> "473 DequantizeLinear_onnx^^Conv_1123_1" [label="[192, 768, 1, 1]", style=dashed]; +"473 DequantizeLinear_onnx^^Conv_1123_1" -> "474 /Mixed_6e/branch_pool/conv/Conv" [label="[192, 768, 1, 1]", style=solid]; +"474 /Mixed_6e/branch_pool/conv/Conv" -> "475 /Mixed_6e/branch_pool/Relu" [label="[1, 192, 12, 12]", style=solid]; +"475 /Mixed_6e/branch_pool/Relu" -> "478 QuantizeLinear_/Mixed_6e/branch_pool/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; +"476 QuantizeLinear_/Mixed_6e/branch7x7dbl_5/Relu_output_0_1" -> "477 DequantizeLinear_/Mixed_6e/branch7x7dbl_5/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; +"477 DequantizeLinear_/Mixed_6e/branch7x7dbl_5/Relu_output_0_1" -> "484 /Mixed_6e/Concat" [label="[1, 192, 12, 12]", style=solid]; +"478 QuantizeLinear_/Mixed_6e/branch_pool/Relu_output_0_1" -> "479 DequantizeLinear_/Mixed_6e/branch_pool/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; +"479 DequantizeLinear_/Mixed_6e/branch_pool/Relu_output_0_1" -> "484 /Mixed_6e/Concat" [label="[1, 192, 12, 12]", style=solid]; +"480 QuantizeLinear_/Mixed_6e/branch1x1/Relu_output_0_1" -> "481 DequantizeLinear_/Mixed_6e/branch1x1/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; +"481 DequantizeLinear_/Mixed_6e/branch1x1/Relu_output_0_1" -> "484 /Mixed_6e/Concat" [label="[1, 192, 12, 12]", style=solid]; +"482 QuantizeLinear_/Mixed_6e/branch7x7_3/Relu_output_0_1" -> "483 DequantizeLinear_/Mixed_6e/branch7x7_3/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; +"483 DequantizeLinear_/Mixed_6e/branch7x7_3/Relu_output_0_1" -> "484 /Mixed_6e/Concat" [label="[1, 192, 12, 12]", style=solid]; +"484 /Mixed_6e/Concat" -> "487 /Mixed_7a/branch3x3_1/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; +"484 /Mixed_6e/Concat" -> "497 /Mixed_7a/branch7x7x3_1/conv/Conv" [label="[1, 768, 12, 12]", style=solid]; +"484 /Mixed_6e/Concat" -> "517 /Mixed_7a/MaxPool" [label="[1, 768, 12, 12]", style=solid]; +"485 QuantizeLinear_onnx^^Conv_1126_1" -> "486 DequantizeLinear_onnx^^Conv_1126_1" [label="[192, 768, 1, 1]", style=dashed]; +"486 DequantizeLinear_onnx^^Conv_1126_1" -> "487 /Mixed_7a/branch3x3_1/conv/Conv" [label="[192, 768, 1, 1]", style=solid]; +"487 /Mixed_7a/branch3x3_1/conv/Conv" -> "488 /Mixed_7a/branch3x3_1/Relu" [label="[1, 192, 12, 12]", style=solid]; +"488 /Mixed_7a/branch3x3_1/Relu" -> "489 QuantizeLinear_/Mixed_7a/branch3x3_1/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; +"489 QuantizeLinear_/Mixed_7a/branch3x3_1/Relu_output_0_1" -> "490 DequantizeLinear_/Mixed_7a/branch3x3_1/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; +"490 DequantizeLinear_/Mixed_7a/branch3x3_1/Relu_output_0_1" -> "493 /Mixed_7a/branch3x3_2/conv/Conv" [label="[1, 192, 12, 12]", style=solid]; +"491 QuantizeLinear_onnx^^Conv_1129_1" -> "492 DequantizeLinear_onnx^^Conv_1129_1" [label="[320, 192, 3, 3]", style=dashed]; +"492 DequantizeLinear_onnx^^Conv_1129_1" -> "493 /Mixed_7a/branch3x3_2/conv/Conv" [label="[320, 192, 3, 3]", style=solid]; +"493 /Mixed_7a/branch3x3_2/conv/Conv" -> "494 /Mixed_7a/branch3x3_2/Relu" [label="[1, 320, 5, 5]", style=solid]; +"494 /Mixed_7a/branch3x3_2/Relu" -> "520 QuantizeLinear_/Mixed_7a/branch3x3_2/Relu_output_0_1" [label="[1, 320, 5, 5]", style=solid]; +"495 QuantizeLinear_onnx^^Conv_1132_1" -> "496 DequantizeLinear_onnx^^Conv_1132_1" [label="[192, 768, 1, 1]", style=dashed]; +"496 DequantizeLinear_onnx^^Conv_1132_1" -> "497 /Mixed_7a/branch7x7x3_1/conv/Conv" [label="[192, 768, 1, 1]", style=solid]; +"497 /Mixed_7a/branch7x7x3_1/conv/Conv" -> "498 /Mixed_7a/branch7x7x3_1/Relu" [label="[1, 192, 12, 12]", style=solid]; +"498 /Mixed_7a/branch7x7x3_1/Relu" -> "499 QuantizeLinear_/Mixed_7a/branch7x7x3_1/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; +"499 QuantizeLinear_/Mixed_7a/branch7x7x3_1/Relu_output_0_1" -> "500 DequantizeLinear_/Mixed_7a/branch7x7x3_1/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; +"500 DequantizeLinear_/Mixed_7a/branch7x7x3_1/Relu_output_0_1" -> "503 /Mixed_7a/branch7x7x3_2/conv/Conv" [label="[1, 192, 12, 12]", style=solid]; +"501 QuantizeLinear_onnx^^Conv_1135_1" -> "502 DequantizeLinear_onnx^^Conv_1135_1" [label="[192, 192, 1, 7]", style=dashed]; +"502 DequantizeLinear_onnx^^Conv_1135_1" -> "503 /Mixed_7a/branch7x7x3_2/conv/Conv" [label="[192, 192, 1, 7]", style=solid]; +"503 /Mixed_7a/branch7x7x3_2/conv/Conv" -> "504 /Mixed_7a/branch7x7x3_2/Relu" [label="[1, 192, 12, 12]", style=solid]; +"504 /Mixed_7a/branch7x7x3_2/Relu" -> "505 QuantizeLinear_/Mixed_7a/branch7x7x3_2/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; +"505 QuantizeLinear_/Mixed_7a/branch7x7x3_2/Relu_output_0_1" -> "506 DequantizeLinear_/Mixed_7a/branch7x7x3_2/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; +"506 DequantizeLinear_/Mixed_7a/branch7x7x3_2/Relu_output_0_1" -> "509 /Mixed_7a/branch7x7x3_3/conv/Conv" [label="[1, 192, 12, 12]", style=solid]; +"507 QuantizeLinear_onnx^^Conv_1138_1" -> "508 DequantizeLinear_onnx^^Conv_1138_1" [label="[192, 192, 7, 1]", style=dashed]; +"508 DequantizeLinear_onnx^^Conv_1138_1" -> "509 /Mixed_7a/branch7x7x3_3/conv/Conv" [label="[192, 192, 7, 1]", style=solid]; +"509 /Mixed_7a/branch7x7x3_3/conv/Conv" -> "510 /Mixed_7a/branch7x7x3_3/Relu" [label="[1, 192, 12, 12]", style=solid]; +"510 /Mixed_7a/branch7x7x3_3/Relu" -> "511 QuantizeLinear_/Mixed_7a/branch7x7x3_3/Relu_output_0_1" [label="[1, 192, 12, 12]", style=solid]; +"511 QuantizeLinear_/Mixed_7a/branch7x7x3_3/Relu_output_0_1" -> "512 DequantizeLinear_/Mixed_7a/branch7x7x3_3/Relu_output_0_1" [label="[1, 192, 12, 12]", style=dashed]; +"512 DequantizeLinear_/Mixed_7a/branch7x7x3_3/Relu_output_0_1" -> "515 /Mixed_7a/branch7x7x3_4/conv/Conv" [label="[1, 192, 12, 12]", style=solid]; +"513 QuantizeLinear_onnx^^Conv_1141_1" -> "514 DequantizeLinear_onnx^^Conv_1141_1" [label="[192, 192, 3, 3]", style=dashed]; +"514 DequantizeLinear_onnx^^Conv_1141_1" -> "515 /Mixed_7a/branch7x7x3_4/conv/Conv" [label="[192, 192, 3, 3]", style=solid]; +"515 /Mixed_7a/branch7x7x3_4/conv/Conv" -> "516 /Mixed_7a/branch7x7x3_4/Relu" [label="[1, 192, 5, 5]", style=solid]; +"516 /Mixed_7a/branch7x7x3_4/Relu" -> "518 QuantizeLinear_/Mixed_7a/branch7x7x3_4/Relu_output_0_1" [label="[1, 192, 5, 5]", style=solid]; +"517 /Mixed_7a/MaxPool" -> "522 /Mixed_7a/Concat" [label="[1, 768, 5, 5]", style=solid]; +"518 QuantizeLinear_/Mixed_7a/branch7x7x3_4/Relu_output_0_1" -> "519 DequantizeLinear_/Mixed_7a/branch7x7x3_4/Relu_output_0_1" [label="[1, 192, 5, 5]", style=dashed]; +"519 DequantizeLinear_/Mixed_7a/branch7x7x3_4/Relu_output_0_1" -> "522 /Mixed_7a/Concat" [label="[1, 192, 5, 5]", style=solid]; +"520 QuantizeLinear_/Mixed_7a/branch3x3_2/Relu_output_0_1" -> "521 DequantizeLinear_/Mixed_7a/branch3x3_2/Relu_output_0_1" [label="[1, 320, 5, 5]", style=dashed]; +"521 DequantizeLinear_/Mixed_7a/branch3x3_2/Relu_output_0_1" -> "522 /Mixed_7a/Concat" [label="[1, 320, 5, 5]", style=solid]; +"522 /Mixed_7a/Concat" -> "525 /Mixed_7b/branch1x1/conv/Conv" [label="[1, 1280, 5, 5]", style=solid]; +"522 /Mixed_7a/Concat" -> "529 /Mixed_7b/branch3x3_1/conv/Conv" [label="[1, 1280, 5, 5]", style=solid]; +"522 /Mixed_7a/Concat" -> "543 /Mixed_7b/branch3x3dbl_1/conv/Conv" [label="[1, 1280, 5, 5]", style=solid]; +"522 /Mixed_7a/Concat" -> "561 /Mixed_7b/AveragePool" [label="[1, 1280, 5, 5]", style=solid]; +"523 QuantizeLinear_onnx^^Conv_1144_1" -> "524 DequantizeLinear_onnx^^Conv_1144_1" [label="[320, 1280, 1, 1]", style=dashed]; +"524 DequantizeLinear_onnx^^Conv_1144_1" -> "525 /Mixed_7b/branch1x1/conv/Conv" [label="[320, 1280, 1, 1]", style=solid]; +"525 /Mixed_7b/branch1x1/conv/Conv" -> "526 /Mixed_7b/branch1x1/Relu" [label="[1, 320, 5, 5]", style=solid]; +"526 /Mixed_7b/branch1x1/Relu" -> "578 QuantizeLinear_/Mixed_7b/branch1x1/Relu_output_0_1" [label="[1, 320, 5, 5]", style=solid]; +"527 QuantizeLinear_onnx^^Conv_1147_1" -> "528 DequantizeLinear_onnx^^Conv_1147_1" [label="[384, 1280, 1, 1]", style=dashed]; +"528 DequantizeLinear_onnx^^Conv_1147_1" -> "529 /Mixed_7b/branch3x3_1/conv/Conv" [label="[384, 1280, 1, 1]", style=solid]; +"529 /Mixed_7b/branch3x3_1/conv/Conv" -> "530 /Mixed_7b/branch3x3_1/Relu" [label="[1, 384, 5, 5]", style=solid]; +"530 /Mixed_7b/branch3x3_1/Relu" -> "531 QuantizeLinear_/Mixed_7b/branch3x3_1/Relu_output_0_1" [label="[1, 384, 5, 5]", style=solid]; +"531 QuantizeLinear_/Mixed_7b/branch3x3_1/Relu_output_0_1" -> "532 DequantizeLinear_/Mixed_7b/branch3x3_1/Relu_output_0_1" [label="[1, 384, 5, 5]", style=dashed]; +"532 DequantizeLinear_/Mixed_7b/branch3x3_1/Relu_output_0_1" -> "535 /Mixed_7b/branch3x3_2a/conv/Conv" [label="[1, 384, 5, 5]", style=solid]; +"532 DequantizeLinear_/Mixed_7b/branch3x3_1/Relu_output_0_1" -> "539 /Mixed_7b/branch3x3_2b/conv/Conv" [label="[1, 384, 5, 5]", style=solid]; +"533 QuantizeLinear_onnx^^Conv_1150_1" -> "534 DequantizeLinear_onnx^^Conv_1150_1" [label="[384, 384, 1, 3]", style=dashed]; +"534 DequantizeLinear_onnx^^Conv_1150_1" -> "535 /Mixed_7b/branch3x3_2a/conv/Conv" [label="[384, 384, 1, 3]", style=solid]; +"535 /Mixed_7b/branch3x3_2a/conv/Conv" -> "536 /Mixed_7b/branch3x3_2a/Relu" [label="[1, 384, 5, 5]", style=solid]; +"536 /Mixed_7b/branch3x3_2a/Relu" -> "568 QuantizeLinear_/Mixed_7b/branch3x3_2a/Relu_output_0_1" [label="[1, 384, 5, 5]", style=solid]; +"537 QuantizeLinear_onnx^^Conv_1153_1" -> "538 DequantizeLinear_onnx^^Conv_1153_1" [label="[384, 384, 3, 1]", style=dashed]; +"538 DequantizeLinear_onnx^^Conv_1153_1" -> "539 /Mixed_7b/branch3x3_2b/conv/Conv" [label="[384, 384, 3, 1]", style=solid]; +"539 /Mixed_7b/branch3x3_2b/conv/Conv" -> "540 /Mixed_7b/branch3x3_2b/Relu" [label="[1, 384, 5, 5]", style=solid]; +"540 /Mixed_7b/branch3x3_2b/Relu" -> "570 QuantizeLinear_/Mixed_7b/branch3x3_2b/Relu_output_0_1" [label="[1, 384, 5, 5]", style=solid]; +"541 QuantizeLinear_onnx^^Conv_1156_1" -> "542 DequantizeLinear_onnx^^Conv_1156_1" [label="[448, 1280, 1, 1]", style=dashed]; +"542 DequantizeLinear_onnx^^Conv_1156_1" -> "543 /Mixed_7b/branch3x3dbl_1/conv/Conv" [label="[448, 1280, 1, 1]", style=solid]; +"543 /Mixed_7b/branch3x3dbl_1/conv/Conv" -> "544 /Mixed_7b/branch3x3dbl_1/Relu" [label="[1, 448, 5, 5]", style=solid]; +"544 /Mixed_7b/branch3x3dbl_1/Relu" -> "545 QuantizeLinear_/Mixed_7b/branch3x3dbl_1/Relu_output_0_1" [label="[1, 448, 5, 5]", style=solid]; +"545 QuantizeLinear_/Mixed_7b/branch3x3dbl_1/Relu_output_0_1" -> "546 DequantizeLinear_/Mixed_7b/branch3x3dbl_1/Relu_output_0_1" [label="[1, 448, 5, 5]", style=dashed]; +"546 DequantizeLinear_/Mixed_7b/branch3x3dbl_1/Relu_output_0_1" -> "549 /Mixed_7b/branch3x3dbl_2/conv/Conv" [label="[1, 448, 5, 5]", style=solid]; +"547 QuantizeLinear_onnx^^Conv_1159_1" -> "548 DequantizeLinear_onnx^^Conv_1159_1" [label="[384, 448, 3, 3]", style=dashed]; +"548 DequantizeLinear_onnx^^Conv_1159_1" -> "549 /Mixed_7b/branch3x3dbl_2/conv/Conv" [label="[384, 448, 3, 3]", style=solid]; +"549 /Mixed_7b/branch3x3dbl_2/conv/Conv" -> "550 /Mixed_7b/branch3x3dbl_2/Relu" [label="[1, 384, 5, 5]", style=solid]; +"550 /Mixed_7b/branch3x3dbl_2/Relu" -> "551 QuantizeLinear_/Mixed_7b/branch3x3dbl_2/Relu_output_0_1" [label="[1, 384, 5, 5]", style=solid]; +"551 QuantizeLinear_/Mixed_7b/branch3x3dbl_2/Relu_output_0_1" -> "552 DequantizeLinear_/Mixed_7b/branch3x3dbl_2/Relu_output_0_1" [label="[1, 384, 5, 5]", style=dashed]; +"552 DequantizeLinear_/Mixed_7b/branch3x3dbl_2/Relu_output_0_1" -> "555 /Mixed_7b/branch3x3dbl_3a/conv/Conv" [label="[1, 384, 5, 5]", style=solid]; +"552 DequantizeLinear_/Mixed_7b/branch3x3dbl_2/Relu_output_0_1" -> "559 /Mixed_7b/branch3x3dbl_3b/conv/Conv" [label="[1, 384, 5, 5]", style=solid]; +"553 QuantizeLinear_onnx^^Conv_1162_1" -> "554 DequantizeLinear_onnx^^Conv_1162_1" [label="[384, 384, 1, 3]", style=dashed]; +"554 DequantizeLinear_onnx^^Conv_1162_1" -> "555 /Mixed_7b/branch3x3dbl_3a/conv/Conv" [label="[384, 384, 1, 3]", style=solid]; +"555 /Mixed_7b/branch3x3dbl_3a/conv/Conv" -> "556 /Mixed_7b/branch3x3dbl_3a/Relu" [label="[1, 384, 5, 5]", style=solid]; +"556 /Mixed_7b/branch3x3dbl_3a/Relu" -> "572 QuantizeLinear_/Mixed_7b/branch3x3dbl_3a/Relu_output_0_1" [label="[1, 384, 5, 5]", style=solid]; +"557 QuantizeLinear_onnx^^Conv_1165_1" -> "558 DequantizeLinear_onnx^^Conv_1165_1" [label="[384, 384, 3, 1]", style=dashed]; +"558 DequantizeLinear_onnx^^Conv_1165_1" -> "559 /Mixed_7b/branch3x3dbl_3b/conv/Conv" [label="[384, 384, 3, 1]", style=solid]; +"559 /Mixed_7b/branch3x3dbl_3b/conv/Conv" -> "560 /Mixed_7b/branch3x3dbl_3b/Relu" [label="[1, 384, 5, 5]", style=solid]; +"560 /Mixed_7b/branch3x3dbl_3b/Relu" -> "574 QuantizeLinear_/Mixed_7b/branch3x3dbl_3b/Relu_output_0_1" [label="[1, 384, 5, 5]", style=solid]; +"561 /Mixed_7b/AveragePool" -> "562 QuantizeLinear_/Mixed_7b/AveragePool_output_0_1" [label="[1, 1280, 5, 5]", style=solid]; +"562 QuantizeLinear_/Mixed_7b/AveragePool_output_0_1" -> "563 DequantizeLinear_/Mixed_7b/AveragePool_output_0_1" [label="[1, 1280, 5, 5]", style=dashed]; +"563 DequantizeLinear_/Mixed_7b/AveragePool_output_0_1" -> "566 /Mixed_7b/branch_pool/conv/Conv" [label="[1, 1280, 5, 5]", style=solid]; +"564 QuantizeLinear_onnx^^Conv_1168_1" -> "565 DequantizeLinear_onnx^^Conv_1168_1" [label="[192, 1280, 1, 1]", style=dashed]; +"565 DequantizeLinear_onnx^^Conv_1168_1" -> "566 /Mixed_7b/branch_pool/conv/Conv" [label="[192, 1280, 1, 1]", style=solid]; +"566 /Mixed_7b/branch_pool/conv/Conv" -> "567 /Mixed_7b/branch_pool/Relu" [label="[1, 192, 5, 5]", style=solid]; +"567 /Mixed_7b/branch_pool/Relu" -> "576 QuantizeLinear_/Mixed_7b/branch_pool/Relu_output_0_1" [label="[1, 192, 5, 5]", style=solid]; +"568 QuantizeLinear_/Mixed_7b/branch3x3_2a/Relu_output_0_1" -> "569 DequantizeLinear_/Mixed_7b/branch3x3_2a/Relu_output_0_1" [label="[1, 384, 5, 5]", style=dashed]; +"569 DequantizeLinear_/Mixed_7b/branch3x3_2a/Relu_output_0_1" -> "580 /Mixed_7b/Concat" [label="[1, 384, 5, 5]", style=solid]; +"570 QuantizeLinear_/Mixed_7b/branch3x3_2b/Relu_output_0_1" -> "571 DequantizeLinear_/Mixed_7b/branch3x3_2b/Relu_output_0_1" [label="[1, 384, 5, 5]", style=dashed]; +"571 DequantizeLinear_/Mixed_7b/branch3x3_2b/Relu_output_0_1" -> "580 /Mixed_7b/Concat" [label="[1, 384, 5, 5]", style=solid]; +"572 QuantizeLinear_/Mixed_7b/branch3x3dbl_3a/Relu_output_0_1" -> "573 DequantizeLinear_/Mixed_7b/branch3x3dbl_3a/Relu_output_0_1" [label="[1, 384, 5, 5]", style=dashed]; +"573 DequantizeLinear_/Mixed_7b/branch3x3dbl_3a/Relu_output_0_1" -> "580 /Mixed_7b/Concat" [label="[1, 384, 5, 5]", style=solid]; +"574 QuantizeLinear_/Mixed_7b/branch3x3dbl_3b/Relu_output_0_1" -> "575 DequantizeLinear_/Mixed_7b/branch3x3dbl_3b/Relu_output_0_1" [label="[1, 384, 5, 5]", style=dashed]; +"575 DequantizeLinear_/Mixed_7b/branch3x3dbl_3b/Relu_output_0_1" -> "580 /Mixed_7b/Concat" [label="[1, 384, 5, 5]", style=solid]; +"576 QuantizeLinear_/Mixed_7b/branch_pool/Relu_output_0_1" -> "577 DequantizeLinear_/Mixed_7b/branch_pool/Relu_output_0_1" [label="[1, 192, 5, 5]", style=dashed]; +"577 DequantizeLinear_/Mixed_7b/branch_pool/Relu_output_0_1" -> "580 /Mixed_7b/Concat" [label="[1, 192, 5, 5]", style=solid]; +"578 QuantizeLinear_/Mixed_7b/branch1x1/Relu_output_0_1" -> "579 DequantizeLinear_/Mixed_7b/branch1x1/Relu_output_0_1" [label="[1, 320, 5, 5]", style=dashed]; +"579 DequantizeLinear_/Mixed_7b/branch1x1/Relu_output_0_1" -> "580 /Mixed_7b/Concat" [label="[1, 320, 5, 5]", style=solid]; +"580 /Mixed_7b/Concat" -> "583 /Mixed_7c/branch1x1/conv/Conv" [label="[1, 2048, 5, 5]", style=solid]; +"580 /Mixed_7b/Concat" -> "587 /Mixed_7c/branch3x3_1/conv/Conv" [label="[1, 2048, 5, 5]", style=solid]; +"580 /Mixed_7b/Concat" -> "601 /Mixed_7c/branch3x3dbl_1/conv/Conv" [label="[1, 2048, 5, 5]", style=solid]; +"580 /Mixed_7b/Concat" -> "619 /Mixed_7c/AveragePool" [label="[1, 2048, 5, 5]", style=solid]; +"581 QuantizeLinear_onnx^^Conv_1171_1" -> "582 DequantizeLinear_onnx^^Conv_1171_1" [label="[320, 2048, 1, 1]", style=dashed]; +"582 DequantizeLinear_onnx^^Conv_1171_1" -> "583 /Mixed_7c/branch1x1/conv/Conv" [label="[320, 2048, 1, 1]", style=solid]; +"583 /Mixed_7c/branch1x1/conv/Conv" -> "584 /Mixed_7c/branch1x1/Relu" [label="[1, 320, 5, 5]", style=solid]; +"584 /Mixed_7c/branch1x1/Relu" -> "626 QuantizeLinear_/Mixed_7c/branch1x1/Relu_output_0_1" [label="[1, 320, 5, 5]", style=solid]; +"585 QuantizeLinear_onnx^^Conv_1174_1" -> "586 DequantizeLinear_onnx^^Conv_1174_1" [label="[384, 2048, 1, 1]", style=dashed]; +"586 DequantizeLinear_onnx^^Conv_1174_1" -> "587 /Mixed_7c/branch3x3_1/conv/Conv" [label="[384, 2048, 1, 1]", style=solid]; +"587 /Mixed_7c/branch3x3_1/conv/Conv" -> "588 /Mixed_7c/branch3x3_1/Relu" [label="[1, 384, 5, 5]", style=solid]; +"588 /Mixed_7c/branch3x3_1/Relu" -> "589 QuantizeLinear_/Mixed_7c/branch3x3_1/Relu_output_0_1" [label="[1, 384, 5, 5]", style=solid]; +"589 QuantizeLinear_/Mixed_7c/branch3x3_1/Relu_output_0_1" -> "590 DequantizeLinear_/Mixed_7c/branch3x3_1/Relu_output_0_1" [label="[1, 384, 5, 5]", style=dashed]; +"590 DequantizeLinear_/Mixed_7c/branch3x3_1/Relu_output_0_1" -> "593 /Mixed_7c/branch3x3_2a/conv/Conv" [label="[1, 384, 5, 5]", style=solid]; +"590 DequantizeLinear_/Mixed_7c/branch3x3_1/Relu_output_0_1" -> "597 /Mixed_7c/branch3x3_2b/conv/Conv" [label="[1, 384, 5, 5]", style=solid]; +"591 QuantizeLinear_onnx^^Conv_1177_1" -> "592 DequantizeLinear_onnx^^Conv_1177_1" [label="[384, 384, 1, 3]", style=dashed]; +"592 DequantizeLinear_onnx^^Conv_1177_1" -> "593 /Mixed_7c/branch3x3_2a/conv/Conv" [label="[384, 384, 1, 3]", style=solid]; +"593 /Mixed_7c/branch3x3_2a/conv/Conv" -> "594 /Mixed_7c/branch3x3_2a/Relu" [label="[1, 384, 5, 5]", style=solid]; +"594 /Mixed_7c/branch3x3_2a/Relu" -> "628 QuantizeLinear_/Mixed_7c/branch3x3_2a/Relu_output_0_1" [label="[1, 384, 5, 5]", style=solid]; +"595 QuantizeLinear_onnx^^Conv_1180_1" -> "596 DequantizeLinear_onnx^^Conv_1180_1" [label="[384, 384, 3, 1]", style=dashed]; +"596 DequantizeLinear_onnx^^Conv_1180_1" -> "597 /Mixed_7c/branch3x3_2b/conv/Conv" [label="[384, 384, 3, 1]", style=solid]; +"597 /Mixed_7c/branch3x3_2b/conv/Conv" -> "598 /Mixed_7c/branch3x3_2b/Relu" [label="[1, 384, 5, 5]", style=solid]; +"598 /Mixed_7c/branch3x3_2b/Relu" -> "630 QuantizeLinear_/Mixed_7c/branch3x3_2b/Relu_output_0_1" [label="[1, 384, 5, 5]", style=solid]; +"599 QuantizeLinear_onnx^^Conv_1183_1" -> "600 DequantizeLinear_onnx^^Conv_1183_1" [label="[448, 2048, 1, 1]", style=dashed]; +"600 DequantizeLinear_onnx^^Conv_1183_1" -> "601 /Mixed_7c/branch3x3dbl_1/conv/Conv" [label="[448, 2048, 1, 1]", style=solid]; +"601 /Mixed_7c/branch3x3dbl_1/conv/Conv" -> "602 /Mixed_7c/branch3x3dbl_1/Relu" [label="[1, 448, 5, 5]", style=solid]; +"602 /Mixed_7c/branch3x3dbl_1/Relu" -> "603 QuantizeLinear_/Mixed_7c/branch3x3dbl_1/Relu_output_0_1" [label="[1, 448, 5, 5]", style=solid]; +"603 QuantizeLinear_/Mixed_7c/branch3x3dbl_1/Relu_output_0_1" -> "604 DequantizeLinear_/Mixed_7c/branch3x3dbl_1/Relu_output_0_1" [label="[1, 448, 5, 5]", style=dashed]; +"604 DequantizeLinear_/Mixed_7c/branch3x3dbl_1/Relu_output_0_1" -> "607 /Mixed_7c/branch3x3dbl_2/conv/Conv" [label="[1, 448, 5, 5]", style=solid]; +"605 QuantizeLinear_onnx^^Conv_1186_1" -> "606 DequantizeLinear_onnx^^Conv_1186_1" [label="[384, 448, 3, 3]", style=dashed]; +"606 DequantizeLinear_onnx^^Conv_1186_1" -> "607 /Mixed_7c/branch3x3dbl_2/conv/Conv" [label="[384, 448, 3, 3]", style=solid]; +"607 /Mixed_7c/branch3x3dbl_2/conv/Conv" -> "608 /Mixed_7c/branch3x3dbl_2/Relu" [label="[1, 384, 5, 5]", style=solid]; +"608 /Mixed_7c/branch3x3dbl_2/Relu" -> "609 QuantizeLinear_/Mixed_7c/branch3x3dbl_2/Relu_output_0_1" [label="[1, 384, 5, 5]", style=solid]; +"609 QuantizeLinear_/Mixed_7c/branch3x3dbl_2/Relu_output_0_1" -> "610 DequantizeLinear_/Mixed_7c/branch3x3dbl_2/Relu_output_0_1" [label="[1, 384, 5, 5]", style=dashed]; +"610 DequantizeLinear_/Mixed_7c/branch3x3dbl_2/Relu_output_0_1" -> "613 /Mixed_7c/branch3x3dbl_3a/conv/Conv" [label="[1, 384, 5, 5]", style=solid]; +"610 DequantizeLinear_/Mixed_7c/branch3x3dbl_2/Relu_output_0_1" -> "617 /Mixed_7c/branch3x3dbl_3b/conv/Conv" [label="[1, 384, 5, 5]", style=solid]; +"611 QuantizeLinear_onnx^^Conv_1189_1" -> "612 DequantizeLinear_onnx^^Conv_1189_1" [label="[384, 384, 1, 3]", style=dashed]; +"612 DequantizeLinear_onnx^^Conv_1189_1" -> "613 /Mixed_7c/branch3x3dbl_3a/conv/Conv" [label="[384, 384, 1, 3]", style=solid]; +"613 /Mixed_7c/branch3x3dbl_3a/conv/Conv" -> "614 /Mixed_7c/branch3x3dbl_3a/Relu" [label="[1, 384, 5, 5]", style=solid]; +"614 /Mixed_7c/branch3x3dbl_3a/Relu" -> "632 QuantizeLinear_/Mixed_7c/branch3x3dbl_3a/Relu_output_0_1" [label="[1, 384, 5, 5]", style=solid]; +"615 QuantizeLinear_onnx^^Conv_1192_1" -> "616 DequantizeLinear_onnx^^Conv_1192_1" [label="[384, 384, 3, 1]", style=dashed]; +"616 DequantizeLinear_onnx^^Conv_1192_1" -> "617 /Mixed_7c/branch3x3dbl_3b/conv/Conv" [label="[384, 384, 3, 1]", style=solid]; +"617 /Mixed_7c/branch3x3dbl_3b/conv/Conv" -> "618 /Mixed_7c/branch3x3dbl_3b/Relu" [label="[1, 384, 5, 5]", style=solid]; +"618 /Mixed_7c/branch3x3dbl_3b/Relu" -> "634 QuantizeLinear_/Mixed_7c/branch3x3dbl_3b/Relu_output_0_1" [label="[1, 384, 5, 5]", style=solid]; +"619 /Mixed_7c/AveragePool" -> "620 QuantizeLinear_/Mixed_7c/AveragePool_output_0_1" [label="[1, 2048, 5, 5]", style=solid]; +"620 QuantizeLinear_/Mixed_7c/AveragePool_output_0_1" -> "621 DequantizeLinear_/Mixed_7c/AveragePool_output_0_1" [label="[1, 2048, 5, 5]", style=dashed]; +"621 DequantizeLinear_/Mixed_7c/AveragePool_output_0_1" -> "624 /Mixed_7c/branch_pool/conv/Conv" [label="[1, 2048, 5, 5]", style=solid]; +"622 QuantizeLinear_onnx^^Conv_1195_1" -> "623 DequantizeLinear_onnx^^Conv_1195_1" [label="[192, 2048, 1, 1]", style=dashed]; +"623 DequantizeLinear_onnx^^Conv_1195_1" -> "624 /Mixed_7c/branch_pool/conv/Conv" [label="[192, 2048, 1, 1]", style=solid]; +"624 /Mixed_7c/branch_pool/conv/Conv" -> "625 /Mixed_7c/branch_pool/Relu" [label="[1, 192, 5, 5]", style=solid]; +"625 /Mixed_7c/branch_pool/Relu" -> "636 QuantizeLinear_/Mixed_7c/branch_pool/Relu_output_0_1" [label="[1, 192, 5, 5]", style=solid]; +"626 QuantizeLinear_/Mixed_7c/branch1x1/Relu_output_0_1" -> "627 DequantizeLinear_/Mixed_7c/branch1x1/Relu_output_0_1" [label="[1, 320, 5, 5]", style=dashed]; +"627 DequantizeLinear_/Mixed_7c/branch1x1/Relu_output_0_1" -> "638 /Mixed_7c/Concat" [label="[1, 320, 5, 5]", style=solid]; +"628 QuantizeLinear_/Mixed_7c/branch3x3_2a/Relu_output_0_1" -> "629 DequantizeLinear_/Mixed_7c/branch3x3_2a/Relu_output_0_1" [label="[1, 384, 5, 5]", style=dashed]; +"629 DequantizeLinear_/Mixed_7c/branch3x3_2a/Relu_output_0_1" -> "638 /Mixed_7c/Concat" [label="[1, 384, 5, 5]", style=solid]; +"630 QuantizeLinear_/Mixed_7c/branch3x3_2b/Relu_output_0_1" -> "631 DequantizeLinear_/Mixed_7c/branch3x3_2b/Relu_output_0_1" [label="[1, 384, 5, 5]", style=dashed]; +"631 DequantizeLinear_/Mixed_7c/branch3x3_2b/Relu_output_0_1" -> "638 /Mixed_7c/Concat" [label="[1, 384, 5, 5]", style=solid]; +"632 QuantizeLinear_/Mixed_7c/branch3x3dbl_3a/Relu_output_0_1" -> "633 DequantizeLinear_/Mixed_7c/branch3x3dbl_3a/Relu_output_0_1" [label="[1, 384, 5, 5]", style=dashed]; +"633 DequantizeLinear_/Mixed_7c/branch3x3dbl_3a/Relu_output_0_1" -> "638 /Mixed_7c/Concat" [label="[1, 384, 5, 5]", style=solid]; +"634 QuantizeLinear_/Mixed_7c/branch3x3dbl_3b/Relu_output_0_1" -> "635 DequantizeLinear_/Mixed_7c/branch3x3dbl_3b/Relu_output_0_1" [label="[1, 384, 5, 5]", style=dashed]; +"635 DequantizeLinear_/Mixed_7c/branch3x3dbl_3b/Relu_output_0_1" -> "638 /Mixed_7c/Concat" [label="[1, 384, 5, 5]", style=solid]; +"636 QuantizeLinear_/Mixed_7c/branch_pool/Relu_output_0_1" -> "637 DequantizeLinear_/Mixed_7c/branch_pool/Relu_output_0_1" [label="[1, 192, 5, 5]", style=dashed]; +"637 DequantizeLinear_/Mixed_7c/branch_pool/Relu_output_0_1" -> "638 /Mixed_7c/Concat" [label="[1, 192, 5, 5]", style=solid]; +"638 /Mixed_7c/Concat" -> "639 /avgpool/GlobalAveragePool" [label="[1, 2048, 5, 5]", style=solid]; +"639 /avgpool/GlobalAveragePool" -> "640 QuantizeLinear_/avgpool/GlobalAveragePool_output_0_1" [label="[1, 2048, 1, 1]", style=solid]; +"640 QuantizeLinear_/avgpool/GlobalAveragePool_output_0_1" -> "641 DequantizeLinear_/avgpool/GlobalAveragePool_output_0_1" [label="[1, 2048, 1, 1]", style=dashed]; +"641 DequantizeLinear_/avgpool/GlobalAveragePool_output_0_1" -> "642 /Flatten" [label="[1, 2048, 1, 1]", style=solid]; +"642 /Flatten" -> "645 /fc/Gemm" [label="[1, 2048]", style=solid]; +"643 QuantizeLinear_fc.weight_1" -> "644 DequantizeLinear_fc.weight_1" [label="[1000, 2048]", style=dashed]; +"644 DequantizeLinear_fc.weight_1" -> "645 /fc/Gemm" [label="[1000, 2048]", style=solid]; +"645 /fc/Gemm" -> "647 nncf_model_output_0" [label="[1, 1000]", style=solid]; +"646 nncf_model_input_0" -> "2 QuantizeLinear_x.1_1" [label="[1, 3, 224, 224]", style=solid]; } diff --git a/tests/onnx/data/reference_graphs/quantization/shufflenet_v2_x1_0.dot b/tests/onnx/data/reference_graphs/quantization/shufflenet_v2_x1_0.dot index 08bb363d7f7..275ea36935a 100644 --- a/tests/onnx/data/reference_graphs/quantization/shufflenet_v2_x1_0.dot +++ b/tests/onnx/data/reference_graphs/quantization/shufflenet_v2_x1_0.dot @@ -1,35 +1,35 @@ strict digraph { "0 QuantizeLinear_input.1_1" [id=0, type=QuantizeLinear]; "1 DequantizeLinear_input.1_1" [id=1, type=DequantizeLinear]; -"2 QuantizeLinear_onnx^^Conv_1124_1" [id=2, label="2 QuantizeLinear_onnx::Conv_1124_1", type=QuantizeLinear]; -"3 DequantizeLinear_onnx^^Conv_1124_1" [id=3, label="3 DequantizeLinear_onnx::Conv_1124_1", type=DequantizeLinear]; +"2 QuantizeLinear_onnx^^Conv_1139_1" [id=2, label="2 QuantizeLinear_onnx::Conv_1139_1", type=QuantizeLinear]; +"3 DequantizeLinear_onnx^^Conv_1139_1" [id=3, label="3 DequantizeLinear_onnx::Conv_1139_1", type=DequantizeLinear]; "4 /conv1/conv1.0/Conv" [id=4, type=Conv]; "5 /conv1/conv1.2/Relu" [id=5, type=Relu]; "6 QuantizeLinear_/conv1/conv1.2/Relu_output_0_1" [id=6, type=QuantizeLinear]; "7 DequantizeLinear_/conv1/conv1.2/Relu_output_0_1" [id=7, type=DequantizeLinear]; "8 /maxpool/MaxPool" [id=8, type=MaxPool]; -"9 QuantizeLinear_onnx^^Conv_1127_1" [id=9, label="9 QuantizeLinear_onnx::Conv_1127_1", type=QuantizeLinear]; -"10 DequantizeLinear_onnx^^Conv_1127_1" [id=10, label="10 DequantizeLinear_onnx::Conv_1127_1", type=DequantizeLinear]; +"9 QuantizeLinear_onnx^^Conv_1142_1" [id=9, label="9 QuantizeLinear_onnx::Conv_1142_1", type=QuantizeLinear]; +"10 DequantizeLinear_onnx^^Conv_1142_1" [id=10, label="10 DequantizeLinear_onnx::Conv_1142_1", type=DequantizeLinear]; "11 /stage2/stage2.0/branch1/branch1.0/Conv" [id=11, type=Conv]; "12 QuantizeLinear_/stage2/stage2.0/branch1/branch1.0/Conv_output_0_1" [id=12, type=QuantizeLinear]; "13 DequantizeLinear_/stage2/stage2.0/branch1/branch1.0/Conv_output_0_1" [id=13, type=DequantizeLinear]; -"14 QuantizeLinear_onnx^^Conv_1130_1" [id=14, label="14 QuantizeLinear_onnx::Conv_1130_1", type=QuantizeLinear]; -"15 DequantizeLinear_onnx^^Conv_1130_1" [id=15, label="15 DequantizeLinear_onnx::Conv_1130_1", type=DequantizeLinear]; +"14 QuantizeLinear_onnx^^Conv_1145_1" [id=14, label="14 QuantizeLinear_onnx::Conv_1145_1", type=QuantizeLinear]; +"15 DequantizeLinear_onnx^^Conv_1145_1" [id=15, label="15 DequantizeLinear_onnx::Conv_1145_1", type=DequantizeLinear]; "16 /stage2/stage2.0/branch1/branch1.2/Conv" [id=16, type=Conv]; "17 /stage2/stage2.0/branch1/branch1.4/Relu" [id=17, type=Relu]; -"18 QuantizeLinear_onnx^^Conv_1133_1" [id=18, label="18 QuantizeLinear_onnx::Conv_1133_1", type=QuantizeLinear]; -"19 DequantizeLinear_onnx^^Conv_1133_1" [id=19, label="19 DequantizeLinear_onnx::Conv_1133_1", type=DequantizeLinear]; +"18 QuantizeLinear_onnx^^Conv_1148_1" [id=18, label="18 QuantizeLinear_onnx::Conv_1148_1", type=QuantizeLinear]; +"19 DequantizeLinear_onnx^^Conv_1148_1" [id=19, label="19 DequantizeLinear_onnx::Conv_1148_1", type=DequantizeLinear]; "20 /stage2/stage2.0/branch2/branch2.0/Conv" [id=20, type=Conv]; "21 /stage2/stage2.0/branch2/branch2.2/Relu" [id=21, type=Relu]; "22 QuantizeLinear_/stage2/stage2.0/branch2/branch2.2/Relu_output_0_1" [id=22, type=QuantizeLinear]; "23 DequantizeLinear_/stage2/stage2.0/branch2/branch2.2/Relu_output_0_1" [id=23, type=DequantizeLinear]; -"24 QuantizeLinear_onnx^^Conv_1136_1" [id=24, label="24 QuantizeLinear_onnx::Conv_1136_1", type=QuantizeLinear]; -"25 DequantizeLinear_onnx^^Conv_1136_1" [id=25, label="25 DequantizeLinear_onnx::Conv_1136_1", type=DequantizeLinear]; +"24 QuantizeLinear_onnx^^Conv_1151_1" [id=24, label="24 QuantizeLinear_onnx::Conv_1151_1", type=QuantizeLinear]; +"25 DequantizeLinear_onnx^^Conv_1151_1" [id=25, label="25 DequantizeLinear_onnx::Conv_1151_1", type=DequantizeLinear]; "26 /stage2/stage2.0/branch2/branch2.3/Conv" [id=26, type=Conv]; "27 QuantizeLinear_/stage2/stage2.0/branch2/branch2.3/Conv_output_0_1" [id=27, type=QuantizeLinear]; "28 DequantizeLinear_/stage2/stage2.0/branch2/branch2.3/Conv_output_0_1" [id=28, type=DequantizeLinear]; -"29 QuantizeLinear_onnx^^Conv_1139_1" [id=29, label="29 QuantizeLinear_onnx::Conv_1139_1", type=QuantizeLinear]; -"30 DequantizeLinear_onnx^^Conv_1139_1" [id=30, label="30 DequantizeLinear_onnx::Conv_1139_1", type=DequantizeLinear]; +"29 QuantizeLinear_onnx^^Conv_1154_1" [id=29, label="29 QuantizeLinear_onnx::Conv_1154_1", type=QuantizeLinear]; +"30 DequantizeLinear_onnx^^Conv_1154_1" [id=30, label="30 DequantizeLinear_onnx::Conv_1154_1", type=DequantizeLinear]; "31 /stage2/stage2.0/branch2/branch2.5/Conv" [id=31, type=Conv]; "32 /stage2/stage2.0/branch2/branch2.7/Relu" [id=32, type=Relu]; "33 QuantizeLinear_/stage2/stage2.0/branch1/branch1.4/Relu_output_0_1" [id=33, type=QuantizeLinear]; @@ -56,19 +56,19 @@ strict digraph { "54 /stage2/stage2.1/Constant_5" [id=54, type=Constant]; "55 /stage2/stage2.1/Mul_1" [id=55, type=Mul]; "56 /stage2/stage2.1/Slice_1" [id=56, type=Slice]; -"57 QuantizeLinear_onnx^^Conv_1142_1" [id=57, label="57 QuantizeLinear_onnx::Conv_1142_1", type=QuantizeLinear]; -"58 DequantizeLinear_onnx^^Conv_1142_1" [id=58, label="58 DequantizeLinear_onnx::Conv_1142_1", type=DequantizeLinear]; +"57 QuantizeLinear_onnx^^Conv_1157_1" [id=57, label="57 QuantizeLinear_onnx::Conv_1157_1", type=QuantizeLinear]; +"58 DequantizeLinear_onnx^^Conv_1157_1" [id=58, label="58 DequantizeLinear_onnx::Conv_1157_1", type=DequantizeLinear]; "59 /stage2/stage2.1/branch2/branch2.0/Conv" [id=59, type=Conv]; "60 /stage2/stage2.1/branch2/branch2.2/Relu" [id=60, type=Relu]; "61 QuantizeLinear_/stage2/stage2.1/branch2/branch2.2/Relu_output_0_1" [id=61, type=QuantizeLinear]; "62 DequantizeLinear_/stage2/stage2.1/branch2/branch2.2/Relu_output_0_1" [id=62, type=DequantizeLinear]; -"63 QuantizeLinear_onnx^^Conv_1145_1" [id=63, label="63 QuantizeLinear_onnx::Conv_1145_1", type=QuantizeLinear]; -"64 DequantizeLinear_onnx^^Conv_1145_1" [id=64, label="64 DequantizeLinear_onnx::Conv_1145_1", type=DequantizeLinear]; +"63 QuantizeLinear_onnx^^Conv_1160_1" [id=63, label="63 QuantizeLinear_onnx::Conv_1160_1", type=QuantizeLinear]; +"64 DequantizeLinear_onnx^^Conv_1160_1" [id=64, label="64 DequantizeLinear_onnx::Conv_1160_1", type=DequantizeLinear]; "65 /stage2/stage2.1/branch2/branch2.3/Conv" [id=65, type=Conv]; "66 QuantizeLinear_/stage2/stage2.1/branch2/branch2.3/Conv_output_0_1" [id=66, type=QuantizeLinear]; "67 DequantizeLinear_/stage2/stage2.1/branch2/branch2.3/Conv_output_0_1" [id=67, type=DequantizeLinear]; -"68 QuantizeLinear_onnx^^Conv_1148_1" [id=68, label="68 QuantizeLinear_onnx::Conv_1148_1", type=QuantizeLinear]; -"69 DequantizeLinear_onnx^^Conv_1148_1" [id=69, label="69 DequantizeLinear_onnx::Conv_1148_1", type=DequantizeLinear]; +"68 QuantizeLinear_onnx^^Conv_1163_1" [id=68, label="68 QuantizeLinear_onnx::Conv_1163_1", type=QuantizeLinear]; +"69 DequantizeLinear_onnx^^Conv_1163_1" [id=69, label="69 DequantizeLinear_onnx::Conv_1163_1", type=DequantizeLinear]; "70 /stage2/stage2.1/branch2/branch2.5/Conv" [id=70, type=Conv]; "71 /stage2/stage2.1/branch2/branch2.7/Relu" [id=71, type=Relu]; "72 QuantizeLinear_/stage2/stage2.1/branch2/branch2.7/Relu_output_0_1" [id=72, type=QuantizeLinear]; @@ -93,19 +93,19 @@ strict digraph { "91 /stage2/stage2.2/Constant_5" [id=91, type=Constant]; "92 /stage2/stage2.2/Mul_1" [id=92, type=Mul]; "93 /stage2/stage2.2/Slice_1" [id=93, type=Slice]; -"94 QuantizeLinear_onnx^^Conv_1151_1" [id=94, label="94 QuantizeLinear_onnx::Conv_1151_1", type=QuantizeLinear]; -"95 DequantizeLinear_onnx^^Conv_1151_1" [id=95, label="95 DequantizeLinear_onnx::Conv_1151_1", type=DequantizeLinear]; +"94 QuantizeLinear_onnx^^Conv_1166_1" [id=94, label="94 QuantizeLinear_onnx::Conv_1166_1", type=QuantizeLinear]; +"95 DequantizeLinear_onnx^^Conv_1166_1" [id=95, label="95 DequantizeLinear_onnx::Conv_1166_1", type=DequantizeLinear]; "96 /stage2/stage2.2/branch2/branch2.0/Conv" [id=96, type=Conv]; "97 /stage2/stage2.2/branch2/branch2.2/Relu" [id=97, type=Relu]; "98 QuantizeLinear_/stage2/stage2.2/branch2/branch2.2/Relu_output_0_1" [id=98, type=QuantizeLinear]; "99 DequantizeLinear_/stage2/stage2.2/branch2/branch2.2/Relu_output_0_1" [id=99, type=DequantizeLinear]; -"100 QuantizeLinear_onnx^^Conv_1154_1" [id=100, label="100 QuantizeLinear_onnx::Conv_1154_1", type=QuantizeLinear]; -"101 DequantizeLinear_onnx^^Conv_1154_1" [id=101, label="101 DequantizeLinear_onnx::Conv_1154_1", type=DequantizeLinear]; +"100 QuantizeLinear_onnx^^Conv_1169_1" [id=100, label="100 QuantizeLinear_onnx::Conv_1169_1", type=QuantizeLinear]; +"101 DequantizeLinear_onnx^^Conv_1169_1" [id=101, label="101 DequantizeLinear_onnx::Conv_1169_1", type=DequantizeLinear]; "102 /stage2/stage2.2/branch2/branch2.3/Conv" [id=102, type=Conv]; "103 QuantizeLinear_/stage2/stage2.2/branch2/branch2.3/Conv_output_0_1" [id=103, type=QuantizeLinear]; "104 DequantizeLinear_/stage2/stage2.2/branch2/branch2.3/Conv_output_0_1" [id=104, type=DequantizeLinear]; -"105 QuantizeLinear_onnx^^Conv_1157_1" [id=105, label="105 QuantizeLinear_onnx::Conv_1157_1", type=QuantizeLinear]; -"106 DequantizeLinear_onnx^^Conv_1157_1" [id=106, label="106 DequantizeLinear_onnx::Conv_1157_1", type=DequantizeLinear]; +"105 QuantizeLinear_onnx^^Conv_1172_1" [id=105, label="105 QuantizeLinear_onnx::Conv_1172_1", type=QuantizeLinear]; +"106 DequantizeLinear_onnx^^Conv_1172_1" [id=106, label="106 DequantizeLinear_onnx::Conv_1172_1", type=DequantizeLinear]; "107 /stage2/stage2.2/branch2/branch2.5/Conv" [id=107, type=Conv]; "108 /stage2/stage2.2/branch2/branch2.7/Relu" [id=108, type=Relu]; "109 QuantizeLinear_/stage2/stage2.2/branch2/branch2.7/Relu_output_0_1" [id=109, type=QuantizeLinear]; @@ -130,19 +130,19 @@ strict digraph { "128 /stage2/stage2.3/Constant_5" [id=128, type=Constant]; "129 /stage2/stage2.3/Mul_1" [id=129, type=Mul]; "130 /stage2/stage2.3/Slice_1" [id=130, type=Slice]; -"131 QuantizeLinear_onnx^^Conv_1160_1" [id=131, label="131 QuantizeLinear_onnx::Conv_1160_1", type=QuantizeLinear]; -"132 DequantizeLinear_onnx^^Conv_1160_1" [id=132, label="132 DequantizeLinear_onnx::Conv_1160_1", type=DequantizeLinear]; +"131 QuantizeLinear_onnx^^Conv_1175_1" [id=131, label="131 QuantizeLinear_onnx::Conv_1175_1", type=QuantizeLinear]; +"132 DequantizeLinear_onnx^^Conv_1175_1" [id=132, label="132 DequantizeLinear_onnx::Conv_1175_1", type=DequantizeLinear]; "133 /stage2/stage2.3/branch2/branch2.0/Conv" [id=133, type=Conv]; "134 /stage2/stage2.3/branch2/branch2.2/Relu" [id=134, type=Relu]; "135 QuantizeLinear_/stage2/stage2.3/branch2/branch2.2/Relu_output_0_1" [id=135, type=QuantizeLinear]; "136 DequantizeLinear_/stage2/stage2.3/branch2/branch2.2/Relu_output_0_1" [id=136, type=DequantizeLinear]; -"137 QuantizeLinear_onnx^^Conv_1163_1" [id=137, label="137 QuantizeLinear_onnx::Conv_1163_1", type=QuantizeLinear]; -"138 DequantizeLinear_onnx^^Conv_1163_1" [id=138, label="138 DequantizeLinear_onnx::Conv_1163_1", type=DequantizeLinear]; +"137 QuantizeLinear_onnx^^Conv_1178_1" [id=137, label="137 QuantizeLinear_onnx::Conv_1178_1", type=QuantizeLinear]; +"138 DequantizeLinear_onnx^^Conv_1178_1" [id=138, label="138 DequantizeLinear_onnx::Conv_1178_1", type=DequantizeLinear]; "139 /stage2/stage2.3/branch2/branch2.3/Conv" [id=139, type=Conv]; "140 QuantizeLinear_/stage2/stage2.3/branch2/branch2.3/Conv_output_0_1" [id=140, type=QuantizeLinear]; "141 DequantizeLinear_/stage2/stage2.3/branch2/branch2.3/Conv_output_0_1" [id=141, type=DequantizeLinear]; -"142 QuantizeLinear_onnx^^Conv_1166_1" [id=142, label="142 QuantizeLinear_onnx::Conv_1166_1", type=QuantizeLinear]; -"143 DequantizeLinear_onnx^^Conv_1166_1" [id=143, label="143 DequantizeLinear_onnx::Conv_1166_1", type=DequantizeLinear]; +"142 QuantizeLinear_onnx^^Conv_1181_1" [id=142, label="142 QuantizeLinear_onnx::Conv_1181_1", type=QuantizeLinear]; +"143 DequantizeLinear_onnx^^Conv_1181_1" [id=143, label="143 DequantizeLinear_onnx::Conv_1181_1", type=DequantizeLinear]; "144 /stage2/stage2.3/branch2/branch2.5/Conv" [id=144, type=Conv]; "145 /stage2/stage2.3/branch2/branch2.7/Relu" [id=145, type=Relu]; "146 QuantizeLinear_/stage2/stage2.3/branch2/branch2.7/Relu_output_0_1" [id=146, type=QuantizeLinear]; @@ -153,28 +153,28 @@ strict digraph { "151 /stage2/stage2.3/Transpose" [id=151, type=Transpose]; "152 /stage2/stage2.3/Constant_7" [id=152, type=Constant]; "153 /stage2/stage2.3/Reshape_1" [id=153, type=Reshape]; -"154 QuantizeLinear_onnx^^Conv_1169_1" [id=154, label="154 QuantizeLinear_onnx::Conv_1169_1", type=QuantizeLinear]; -"155 DequantizeLinear_onnx^^Conv_1169_1" [id=155, label="155 DequantizeLinear_onnx::Conv_1169_1", type=DequantizeLinear]; +"154 QuantizeLinear_onnx^^Conv_1184_1" [id=154, label="154 QuantizeLinear_onnx::Conv_1184_1", type=QuantizeLinear]; +"155 DequantizeLinear_onnx^^Conv_1184_1" [id=155, label="155 DequantizeLinear_onnx::Conv_1184_1", type=DequantizeLinear]; "156 /stage3/stage3.0/branch1/branch1.0/Conv" [id=156, type=Conv]; "157 QuantizeLinear_/stage3/stage3.0/branch1/branch1.0/Conv_output_0_1" [id=157, type=QuantizeLinear]; "158 DequantizeLinear_/stage3/stage3.0/branch1/branch1.0/Conv_output_0_1" [id=158, type=DequantizeLinear]; -"159 QuantizeLinear_onnx^^Conv_1172_1" [id=159, label="159 QuantizeLinear_onnx::Conv_1172_1", type=QuantizeLinear]; -"160 DequantizeLinear_onnx^^Conv_1172_1" [id=160, label="160 DequantizeLinear_onnx::Conv_1172_1", type=DequantizeLinear]; +"159 QuantizeLinear_onnx^^Conv_1187_1" [id=159, label="159 QuantizeLinear_onnx::Conv_1187_1", type=QuantizeLinear]; +"160 DequantizeLinear_onnx^^Conv_1187_1" [id=160, label="160 DequantizeLinear_onnx::Conv_1187_1", type=DequantizeLinear]; "161 /stage3/stage3.0/branch1/branch1.2/Conv" [id=161, type=Conv]; "162 /stage3/stage3.0/branch1/branch1.4/Relu" [id=162, type=Relu]; -"163 QuantizeLinear_onnx^^Conv_1175_1" [id=163, label="163 QuantizeLinear_onnx::Conv_1175_1", type=QuantizeLinear]; -"164 DequantizeLinear_onnx^^Conv_1175_1" [id=164, label="164 DequantizeLinear_onnx::Conv_1175_1", type=DequantizeLinear]; +"163 QuantizeLinear_onnx^^Conv_1190_1" [id=163, label="163 QuantizeLinear_onnx::Conv_1190_1", type=QuantizeLinear]; +"164 DequantizeLinear_onnx^^Conv_1190_1" [id=164, label="164 DequantizeLinear_onnx::Conv_1190_1", type=DequantizeLinear]; "165 /stage3/stage3.0/branch2/branch2.0/Conv" [id=165, type=Conv]; "166 /stage3/stage3.0/branch2/branch2.2/Relu" [id=166, type=Relu]; "167 QuantizeLinear_/stage3/stage3.0/branch2/branch2.2/Relu_output_0_1" [id=167, type=QuantizeLinear]; "168 DequantizeLinear_/stage3/stage3.0/branch2/branch2.2/Relu_output_0_1" [id=168, type=DequantizeLinear]; -"169 QuantizeLinear_onnx^^Conv_1178_1" [id=169, label="169 QuantizeLinear_onnx::Conv_1178_1", type=QuantizeLinear]; -"170 DequantizeLinear_onnx^^Conv_1178_1" [id=170, label="170 DequantizeLinear_onnx::Conv_1178_1", type=DequantizeLinear]; +"169 QuantizeLinear_onnx^^Conv_1193_1" [id=169, label="169 QuantizeLinear_onnx::Conv_1193_1", type=QuantizeLinear]; +"170 DequantizeLinear_onnx^^Conv_1193_1" [id=170, label="170 DequantizeLinear_onnx::Conv_1193_1", type=DequantizeLinear]; "171 /stage3/stage3.0/branch2/branch2.3/Conv" [id=171, type=Conv]; "172 QuantizeLinear_/stage3/stage3.0/branch2/branch2.3/Conv_output_0_1" [id=172, type=QuantizeLinear]; "173 DequantizeLinear_/stage3/stage3.0/branch2/branch2.3/Conv_output_0_1" [id=173, type=DequantizeLinear]; -"174 QuantizeLinear_onnx^^Conv_1181_1" [id=174, label="174 QuantizeLinear_onnx::Conv_1181_1", type=QuantizeLinear]; -"175 DequantizeLinear_onnx^^Conv_1181_1" [id=175, label="175 DequantizeLinear_onnx::Conv_1181_1", type=DequantizeLinear]; +"174 QuantizeLinear_onnx^^Conv_1196_1" [id=174, label="174 QuantizeLinear_onnx::Conv_1196_1", type=QuantizeLinear]; +"175 DequantizeLinear_onnx^^Conv_1196_1" [id=175, label="175 DequantizeLinear_onnx::Conv_1196_1", type=DequantizeLinear]; "176 /stage3/stage3.0/branch2/branch2.5/Conv" [id=176, type=Conv]; "177 /stage3/stage3.0/branch2/branch2.7/Relu" [id=177, type=Relu]; "178 QuantizeLinear_/stage3/stage3.0/branch1/branch1.4/Relu_output_0_1" [id=178, type=QuantizeLinear]; @@ -201,19 +201,19 @@ strict digraph { "199 /stage3/stage3.1/Constant_5" [id=199, type=Constant]; "200 /stage3/stage3.1/Mul_1" [id=200, type=Mul]; "201 /stage3/stage3.1/Slice_1" [id=201, type=Slice]; -"202 QuantizeLinear_onnx^^Conv_1184_1" [id=202, label="202 QuantizeLinear_onnx::Conv_1184_1", type=QuantizeLinear]; -"203 DequantizeLinear_onnx^^Conv_1184_1" [id=203, label="203 DequantizeLinear_onnx::Conv_1184_1", type=DequantizeLinear]; +"202 QuantizeLinear_onnx^^Conv_1199_1" [id=202, label="202 QuantizeLinear_onnx::Conv_1199_1", type=QuantizeLinear]; +"203 DequantizeLinear_onnx^^Conv_1199_1" [id=203, label="203 DequantizeLinear_onnx::Conv_1199_1", type=DequantizeLinear]; "204 /stage3/stage3.1/branch2/branch2.0/Conv" [id=204, type=Conv]; "205 /stage3/stage3.1/branch2/branch2.2/Relu" [id=205, type=Relu]; "206 QuantizeLinear_/stage3/stage3.1/branch2/branch2.2/Relu_output_0_1" [id=206, type=QuantizeLinear]; "207 DequantizeLinear_/stage3/stage3.1/branch2/branch2.2/Relu_output_0_1" [id=207, type=DequantizeLinear]; -"208 QuantizeLinear_onnx^^Conv_1187_1" [id=208, label="208 QuantizeLinear_onnx::Conv_1187_1", type=QuantizeLinear]; -"209 DequantizeLinear_onnx^^Conv_1187_1" [id=209, label="209 DequantizeLinear_onnx::Conv_1187_1", type=DequantizeLinear]; +"208 QuantizeLinear_onnx^^Conv_1202_1" [id=208, label="208 QuantizeLinear_onnx::Conv_1202_1", type=QuantizeLinear]; +"209 DequantizeLinear_onnx^^Conv_1202_1" [id=209, label="209 DequantizeLinear_onnx::Conv_1202_1", type=DequantizeLinear]; "210 /stage3/stage3.1/branch2/branch2.3/Conv" [id=210, type=Conv]; "211 QuantizeLinear_/stage3/stage3.1/branch2/branch2.3/Conv_output_0_1" [id=211, type=QuantizeLinear]; "212 DequantizeLinear_/stage3/stage3.1/branch2/branch2.3/Conv_output_0_1" [id=212, type=DequantizeLinear]; -"213 QuantizeLinear_onnx^^Conv_1190_1" [id=213, label="213 QuantizeLinear_onnx::Conv_1190_1", type=QuantizeLinear]; -"214 DequantizeLinear_onnx^^Conv_1190_1" [id=214, label="214 DequantizeLinear_onnx::Conv_1190_1", type=DequantizeLinear]; +"213 QuantizeLinear_onnx^^Conv_1205_1" [id=213, label="213 QuantizeLinear_onnx::Conv_1205_1", type=QuantizeLinear]; +"214 DequantizeLinear_onnx^^Conv_1205_1" [id=214, label="214 DequantizeLinear_onnx::Conv_1205_1", type=DequantizeLinear]; "215 /stage3/stage3.1/branch2/branch2.5/Conv" [id=215, type=Conv]; "216 /stage3/stage3.1/branch2/branch2.7/Relu" [id=216, type=Relu]; "217 QuantizeLinear_/stage3/stage3.1/branch2/branch2.7/Relu_output_0_1" [id=217, type=QuantizeLinear]; @@ -238,19 +238,19 @@ strict digraph { "236 /stage3/stage3.2/Constant_5" [id=236, type=Constant]; "237 /stage3/stage3.2/Mul_1" [id=237, type=Mul]; "238 /stage3/stage3.2/Slice_1" [id=238, type=Slice]; -"239 QuantizeLinear_onnx^^Conv_1193_1" [id=239, label="239 QuantizeLinear_onnx::Conv_1193_1", type=QuantizeLinear]; -"240 DequantizeLinear_onnx^^Conv_1193_1" [id=240, label="240 DequantizeLinear_onnx::Conv_1193_1", type=DequantizeLinear]; +"239 QuantizeLinear_onnx^^Conv_1208_1" [id=239, label="239 QuantizeLinear_onnx::Conv_1208_1", type=QuantizeLinear]; +"240 DequantizeLinear_onnx^^Conv_1208_1" [id=240, label="240 DequantizeLinear_onnx::Conv_1208_1", type=DequantizeLinear]; "241 /stage3/stage3.2/branch2/branch2.0/Conv" [id=241, type=Conv]; "242 /stage3/stage3.2/branch2/branch2.2/Relu" [id=242, type=Relu]; "243 QuantizeLinear_/stage3/stage3.2/branch2/branch2.2/Relu_output_0_1" [id=243, type=QuantizeLinear]; "244 DequantizeLinear_/stage3/stage3.2/branch2/branch2.2/Relu_output_0_1" [id=244, type=DequantizeLinear]; -"245 QuantizeLinear_onnx^^Conv_1196_1" [id=245, label="245 QuantizeLinear_onnx::Conv_1196_1", type=QuantizeLinear]; -"246 DequantizeLinear_onnx^^Conv_1196_1" [id=246, label="246 DequantizeLinear_onnx::Conv_1196_1", type=DequantizeLinear]; +"245 QuantizeLinear_onnx^^Conv_1211_1" [id=245, label="245 QuantizeLinear_onnx::Conv_1211_1", type=QuantizeLinear]; +"246 DequantizeLinear_onnx^^Conv_1211_1" [id=246, label="246 DequantizeLinear_onnx::Conv_1211_1", type=DequantizeLinear]; "247 /stage3/stage3.2/branch2/branch2.3/Conv" [id=247, type=Conv]; "248 QuantizeLinear_/stage3/stage3.2/branch2/branch2.3/Conv_output_0_1" [id=248, type=QuantizeLinear]; "249 DequantizeLinear_/stage3/stage3.2/branch2/branch2.3/Conv_output_0_1" [id=249, type=DequantizeLinear]; -"250 QuantizeLinear_onnx^^Conv_1199_1" [id=250, label="250 QuantizeLinear_onnx::Conv_1199_1", type=QuantizeLinear]; -"251 DequantizeLinear_onnx^^Conv_1199_1" [id=251, label="251 DequantizeLinear_onnx::Conv_1199_1", type=DequantizeLinear]; +"250 QuantizeLinear_onnx^^Conv_1214_1" [id=250, label="250 QuantizeLinear_onnx::Conv_1214_1", type=QuantizeLinear]; +"251 DequantizeLinear_onnx^^Conv_1214_1" [id=251, label="251 DequantizeLinear_onnx::Conv_1214_1", type=DequantizeLinear]; "252 /stage3/stage3.2/branch2/branch2.5/Conv" [id=252, type=Conv]; "253 /stage3/stage3.2/branch2/branch2.7/Relu" [id=253, type=Relu]; "254 QuantizeLinear_/stage3/stage3.2/branch2/branch2.7/Relu_output_0_1" [id=254, type=QuantizeLinear]; @@ -275,19 +275,19 @@ strict digraph { "273 /stage3/stage3.3/Constant_5" [id=273, type=Constant]; "274 /stage3/stage3.3/Mul_1" [id=274, type=Mul]; "275 /stage3/stage3.3/Slice_1" [id=275, type=Slice]; -"276 QuantizeLinear_onnx^^Conv_1202_1" [id=276, label="276 QuantizeLinear_onnx::Conv_1202_1", type=QuantizeLinear]; -"277 DequantizeLinear_onnx^^Conv_1202_1" [id=277, label="277 DequantizeLinear_onnx::Conv_1202_1", type=DequantizeLinear]; +"276 QuantizeLinear_onnx^^Conv_1217_1" [id=276, label="276 QuantizeLinear_onnx::Conv_1217_1", type=QuantizeLinear]; +"277 DequantizeLinear_onnx^^Conv_1217_1" [id=277, label="277 DequantizeLinear_onnx::Conv_1217_1", type=DequantizeLinear]; "278 /stage3/stage3.3/branch2/branch2.0/Conv" [id=278, type=Conv]; "279 /stage3/stage3.3/branch2/branch2.2/Relu" [id=279, type=Relu]; "280 QuantizeLinear_/stage3/stage3.3/branch2/branch2.2/Relu_output_0_1" [id=280, type=QuantizeLinear]; "281 DequantizeLinear_/stage3/stage3.3/branch2/branch2.2/Relu_output_0_1" [id=281, type=DequantizeLinear]; -"282 QuantizeLinear_onnx^^Conv_1205_1" [id=282, label="282 QuantizeLinear_onnx::Conv_1205_1", type=QuantizeLinear]; -"283 DequantizeLinear_onnx^^Conv_1205_1" [id=283, label="283 DequantizeLinear_onnx::Conv_1205_1", type=DequantizeLinear]; +"282 QuantizeLinear_onnx^^Conv_1220_1" [id=282, label="282 QuantizeLinear_onnx::Conv_1220_1", type=QuantizeLinear]; +"283 DequantizeLinear_onnx^^Conv_1220_1" [id=283, label="283 DequantizeLinear_onnx::Conv_1220_1", type=DequantizeLinear]; "284 /stage3/stage3.3/branch2/branch2.3/Conv" [id=284, type=Conv]; "285 QuantizeLinear_/stage3/stage3.3/branch2/branch2.3/Conv_output_0_1" [id=285, type=QuantizeLinear]; "286 DequantizeLinear_/stage3/stage3.3/branch2/branch2.3/Conv_output_0_1" [id=286, type=DequantizeLinear]; -"287 QuantizeLinear_onnx^^Conv_1208_1" [id=287, label="287 QuantizeLinear_onnx::Conv_1208_1", type=QuantizeLinear]; -"288 DequantizeLinear_onnx^^Conv_1208_1" [id=288, label="288 DequantizeLinear_onnx::Conv_1208_1", type=DequantizeLinear]; +"287 QuantizeLinear_onnx^^Conv_1223_1" [id=287, label="287 QuantizeLinear_onnx::Conv_1223_1", type=QuantizeLinear]; +"288 DequantizeLinear_onnx^^Conv_1223_1" [id=288, label="288 DequantizeLinear_onnx::Conv_1223_1", type=DequantizeLinear]; "289 /stage3/stage3.3/branch2/branch2.5/Conv" [id=289, type=Conv]; "290 /stage3/stage3.3/branch2/branch2.7/Relu" [id=290, type=Relu]; "291 QuantizeLinear_/stage3/stage3.3/branch2/branch2.7/Relu_output_0_1" [id=291, type=QuantizeLinear]; @@ -312,19 +312,19 @@ strict digraph { "310 /stage3/stage3.4/Constant_5" [id=310, type=Constant]; "311 /stage3/stage3.4/Mul_1" [id=311, type=Mul]; "312 /stage3/stage3.4/Slice_1" [id=312, type=Slice]; -"313 QuantizeLinear_onnx^^Conv_1211_1" [id=313, label="313 QuantizeLinear_onnx::Conv_1211_1", type=QuantizeLinear]; -"314 DequantizeLinear_onnx^^Conv_1211_1" [id=314, label="314 DequantizeLinear_onnx::Conv_1211_1", type=DequantizeLinear]; +"313 QuantizeLinear_onnx^^Conv_1226_1" [id=313, label="313 QuantizeLinear_onnx::Conv_1226_1", type=QuantizeLinear]; +"314 DequantizeLinear_onnx^^Conv_1226_1" [id=314, label="314 DequantizeLinear_onnx::Conv_1226_1", type=DequantizeLinear]; "315 /stage3/stage3.4/branch2/branch2.0/Conv" [id=315, type=Conv]; "316 /stage3/stage3.4/branch2/branch2.2/Relu" [id=316, type=Relu]; "317 QuantizeLinear_/stage3/stage3.4/branch2/branch2.2/Relu_output_0_1" [id=317, type=QuantizeLinear]; "318 DequantizeLinear_/stage3/stage3.4/branch2/branch2.2/Relu_output_0_1" [id=318, type=DequantizeLinear]; -"319 QuantizeLinear_onnx^^Conv_1214_1" [id=319, label="319 QuantizeLinear_onnx::Conv_1214_1", type=QuantizeLinear]; -"320 DequantizeLinear_onnx^^Conv_1214_1" [id=320, label="320 DequantizeLinear_onnx::Conv_1214_1", type=DequantizeLinear]; +"319 QuantizeLinear_onnx^^Conv_1229_1" [id=319, label="319 QuantizeLinear_onnx::Conv_1229_1", type=QuantizeLinear]; +"320 DequantizeLinear_onnx^^Conv_1229_1" [id=320, label="320 DequantizeLinear_onnx::Conv_1229_1", type=DequantizeLinear]; "321 /stage3/stage3.4/branch2/branch2.3/Conv" [id=321, type=Conv]; "322 QuantizeLinear_/stage3/stage3.4/branch2/branch2.3/Conv_output_0_1" [id=322, type=QuantizeLinear]; "323 DequantizeLinear_/stage3/stage3.4/branch2/branch2.3/Conv_output_0_1" [id=323, type=DequantizeLinear]; -"324 QuantizeLinear_onnx^^Conv_1217_1" [id=324, label="324 QuantizeLinear_onnx::Conv_1217_1", type=QuantizeLinear]; -"325 DequantizeLinear_onnx^^Conv_1217_1" [id=325, label="325 DequantizeLinear_onnx::Conv_1217_1", type=DequantizeLinear]; +"324 QuantizeLinear_onnx^^Conv_1232_1" [id=324, label="324 QuantizeLinear_onnx::Conv_1232_1", type=QuantizeLinear]; +"325 DequantizeLinear_onnx^^Conv_1232_1" [id=325, label="325 DequantizeLinear_onnx::Conv_1232_1", type=DequantizeLinear]; "326 /stage3/stage3.4/branch2/branch2.5/Conv" [id=326, type=Conv]; "327 /stage3/stage3.4/branch2/branch2.7/Relu" [id=327, type=Relu]; "328 QuantizeLinear_/stage3/stage3.4/branch2/branch2.7/Relu_output_0_1" [id=328, type=QuantizeLinear]; @@ -349,19 +349,19 @@ strict digraph { "347 /stage3/stage3.5/Constant_5" [id=347, type=Constant]; "348 /stage3/stage3.5/Mul_1" [id=348, type=Mul]; "349 /stage3/stage3.5/Slice_1" [id=349, type=Slice]; -"350 QuantizeLinear_onnx^^Conv_1220_1" [id=350, label="350 QuantizeLinear_onnx::Conv_1220_1", type=QuantizeLinear]; -"351 DequantizeLinear_onnx^^Conv_1220_1" [id=351, label="351 DequantizeLinear_onnx::Conv_1220_1", type=DequantizeLinear]; +"350 QuantizeLinear_onnx^^Conv_1235_1" [id=350, label="350 QuantizeLinear_onnx::Conv_1235_1", type=QuantizeLinear]; +"351 DequantizeLinear_onnx^^Conv_1235_1" [id=351, label="351 DequantizeLinear_onnx::Conv_1235_1", type=DequantizeLinear]; "352 /stage3/stage3.5/branch2/branch2.0/Conv" [id=352, type=Conv]; "353 /stage3/stage3.5/branch2/branch2.2/Relu" [id=353, type=Relu]; "354 QuantizeLinear_/stage3/stage3.5/branch2/branch2.2/Relu_output_0_1" [id=354, type=QuantizeLinear]; "355 DequantizeLinear_/stage3/stage3.5/branch2/branch2.2/Relu_output_0_1" [id=355, type=DequantizeLinear]; -"356 QuantizeLinear_onnx^^Conv_1223_1" [id=356, label="356 QuantizeLinear_onnx::Conv_1223_1", type=QuantizeLinear]; -"357 DequantizeLinear_onnx^^Conv_1223_1" [id=357, label="357 DequantizeLinear_onnx::Conv_1223_1", type=DequantizeLinear]; +"356 QuantizeLinear_onnx^^Conv_1238_1" [id=356, label="356 QuantizeLinear_onnx::Conv_1238_1", type=QuantizeLinear]; +"357 DequantizeLinear_onnx^^Conv_1238_1" [id=357, label="357 DequantizeLinear_onnx::Conv_1238_1", type=DequantizeLinear]; "358 /stage3/stage3.5/branch2/branch2.3/Conv" [id=358, type=Conv]; "359 QuantizeLinear_/stage3/stage3.5/branch2/branch2.3/Conv_output_0_1" [id=359, type=QuantizeLinear]; "360 DequantizeLinear_/stage3/stage3.5/branch2/branch2.3/Conv_output_0_1" [id=360, type=DequantizeLinear]; -"361 QuantizeLinear_onnx^^Conv_1226_1" [id=361, label="361 QuantizeLinear_onnx::Conv_1226_1", type=QuantizeLinear]; -"362 DequantizeLinear_onnx^^Conv_1226_1" [id=362, label="362 DequantizeLinear_onnx::Conv_1226_1", type=DequantizeLinear]; +"361 QuantizeLinear_onnx^^Conv_1241_1" [id=361, label="361 QuantizeLinear_onnx::Conv_1241_1", type=QuantizeLinear]; +"362 DequantizeLinear_onnx^^Conv_1241_1" [id=362, label="362 DequantizeLinear_onnx::Conv_1241_1", type=DequantizeLinear]; "363 /stage3/stage3.5/branch2/branch2.5/Conv" [id=363, type=Conv]; "364 /stage3/stage3.5/branch2/branch2.7/Relu" [id=364, type=Relu]; "365 QuantizeLinear_/stage3/stage3.5/branch2/branch2.7/Relu_output_0_1" [id=365, type=QuantizeLinear]; @@ -386,19 +386,19 @@ strict digraph { "384 /stage3/stage3.6/Constant_5" [id=384, type=Constant]; "385 /stage3/stage3.6/Mul_1" [id=385, type=Mul]; "386 /stage3/stage3.6/Slice_1" [id=386, type=Slice]; -"387 QuantizeLinear_onnx^^Conv_1229_1" [id=387, label="387 QuantizeLinear_onnx::Conv_1229_1", type=QuantizeLinear]; -"388 DequantizeLinear_onnx^^Conv_1229_1" [id=388, label="388 DequantizeLinear_onnx::Conv_1229_1", type=DequantizeLinear]; +"387 QuantizeLinear_onnx^^Conv_1244_1" [id=387, label="387 QuantizeLinear_onnx::Conv_1244_1", type=QuantizeLinear]; +"388 DequantizeLinear_onnx^^Conv_1244_1" [id=388, label="388 DequantizeLinear_onnx::Conv_1244_1", type=DequantizeLinear]; "389 /stage3/stage3.6/branch2/branch2.0/Conv" [id=389, type=Conv]; "390 /stage3/stage3.6/branch2/branch2.2/Relu" [id=390, type=Relu]; "391 QuantizeLinear_/stage3/stage3.6/branch2/branch2.2/Relu_output_0_1" [id=391, type=QuantizeLinear]; "392 DequantizeLinear_/stage3/stage3.6/branch2/branch2.2/Relu_output_0_1" [id=392, type=DequantizeLinear]; -"393 QuantizeLinear_onnx^^Conv_1232_1" [id=393, label="393 QuantizeLinear_onnx::Conv_1232_1", type=QuantizeLinear]; -"394 DequantizeLinear_onnx^^Conv_1232_1" [id=394, label="394 DequantizeLinear_onnx::Conv_1232_1", type=DequantizeLinear]; +"393 QuantizeLinear_onnx^^Conv_1247_1" [id=393, label="393 QuantizeLinear_onnx::Conv_1247_1", type=QuantizeLinear]; +"394 DequantizeLinear_onnx^^Conv_1247_1" [id=394, label="394 DequantizeLinear_onnx::Conv_1247_1", type=DequantizeLinear]; "395 /stage3/stage3.6/branch2/branch2.3/Conv" [id=395, type=Conv]; "396 QuantizeLinear_/stage3/stage3.6/branch2/branch2.3/Conv_output_0_1" [id=396, type=QuantizeLinear]; "397 DequantizeLinear_/stage3/stage3.6/branch2/branch2.3/Conv_output_0_1" [id=397, type=DequantizeLinear]; -"398 QuantizeLinear_onnx^^Conv_1235_1" [id=398, label="398 QuantizeLinear_onnx::Conv_1235_1", type=QuantizeLinear]; -"399 DequantizeLinear_onnx^^Conv_1235_1" [id=399, label="399 DequantizeLinear_onnx::Conv_1235_1", type=DequantizeLinear]; +"398 QuantizeLinear_onnx^^Conv_1250_1" [id=398, label="398 QuantizeLinear_onnx::Conv_1250_1", type=QuantizeLinear]; +"399 DequantizeLinear_onnx^^Conv_1250_1" [id=399, label="399 DequantizeLinear_onnx::Conv_1250_1", type=DequantizeLinear]; "400 /stage3/stage3.6/branch2/branch2.5/Conv" [id=400, type=Conv]; "401 /stage3/stage3.6/branch2/branch2.7/Relu" [id=401, type=Relu]; "402 QuantizeLinear_/stage3/stage3.6/branch2/branch2.7/Relu_output_0_1" [id=402, type=QuantizeLinear]; @@ -423,19 +423,19 @@ strict digraph { "421 /stage3/stage3.7/Constant_5" [id=421, type=Constant]; "422 /stage3/stage3.7/Mul_1" [id=422, type=Mul]; "423 /stage3/stage3.7/Slice_1" [id=423, type=Slice]; -"424 QuantizeLinear_onnx^^Conv_1238_1" [id=424, label="424 QuantizeLinear_onnx::Conv_1238_1", type=QuantizeLinear]; -"425 DequantizeLinear_onnx^^Conv_1238_1" [id=425, label="425 DequantizeLinear_onnx::Conv_1238_1", type=DequantizeLinear]; +"424 QuantizeLinear_onnx^^Conv_1253_1" [id=424, label="424 QuantizeLinear_onnx::Conv_1253_1", type=QuantizeLinear]; +"425 DequantizeLinear_onnx^^Conv_1253_1" [id=425, label="425 DequantizeLinear_onnx::Conv_1253_1", type=DequantizeLinear]; "426 /stage3/stage3.7/branch2/branch2.0/Conv" [id=426, type=Conv]; "427 /stage3/stage3.7/branch2/branch2.2/Relu" [id=427, type=Relu]; "428 QuantizeLinear_/stage3/stage3.7/branch2/branch2.2/Relu_output_0_1" [id=428, type=QuantizeLinear]; "429 DequantizeLinear_/stage3/stage3.7/branch2/branch2.2/Relu_output_0_1" [id=429, type=DequantizeLinear]; -"430 QuantizeLinear_onnx^^Conv_1241_1" [id=430, label="430 QuantizeLinear_onnx::Conv_1241_1", type=QuantizeLinear]; -"431 DequantizeLinear_onnx^^Conv_1241_1" [id=431, label="431 DequantizeLinear_onnx::Conv_1241_1", type=DequantizeLinear]; +"430 QuantizeLinear_onnx^^Conv_1256_1" [id=430, label="430 QuantizeLinear_onnx::Conv_1256_1", type=QuantizeLinear]; +"431 DequantizeLinear_onnx^^Conv_1256_1" [id=431, label="431 DequantizeLinear_onnx::Conv_1256_1", type=DequantizeLinear]; "432 /stage3/stage3.7/branch2/branch2.3/Conv" [id=432, type=Conv]; "433 QuantizeLinear_/stage3/stage3.7/branch2/branch2.3/Conv_output_0_1" [id=433, type=QuantizeLinear]; "434 DequantizeLinear_/stage3/stage3.7/branch2/branch2.3/Conv_output_0_1" [id=434, type=DequantizeLinear]; -"435 QuantizeLinear_onnx^^Conv_1244_1" [id=435, label="435 QuantizeLinear_onnx::Conv_1244_1", type=QuantizeLinear]; -"436 DequantizeLinear_onnx^^Conv_1244_1" [id=436, label="436 DequantizeLinear_onnx::Conv_1244_1", type=DequantizeLinear]; +"435 QuantizeLinear_onnx^^Conv_1259_1" [id=435, label="435 QuantizeLinear_onnx::Conv_1259_1", type=QuantizeLinear]; +"436 DequantizeLinear_onnx^^Conv_1259_1" [id=436, label="436 DequantizeLinear_onnx::Conv_1259_1", type=DequantizeLinear]; "437 /stage3/stage3.7/branch2/branch2.5/Conv" [id=437, type=Conv]; "438 /stage3/stage3.7/branch2/branch2.7/Relu" [id=438, type=Relu]; "439 QuantizeLinear_/stage3/stage3.7/branch2/branch2.7/Relu_output_0_1" [id=439, type=QuantizeLinear]; @@ -446,28 +446,28 @@ strict digraph { "444 /stage3/stage3.7/Transpose" [id=444, type=Transpose]; "445 /stage3/stage3.7/Constant_7" [id=445, type=Constant]; "446 /stage3/stage3.7/Reshape_1" [id=446, type=Reshape]; -"447 QuantizeLinear_onnx^^Conv_1247_1" [id=447, label="447 QuantizeLinear_onnx::Conv_1247_1", type=QuantizeLinear]; -"448 DequantizeLinear_onnx^^Conv_1247_1" [id=448, label="448 DequantizeLinear_onnx::Conv_1247_1", type=DequantizeLinear]; +"447 QuantizeLinear_onnx^^Conv_1262_1" [id=447, label="447 QuantizeLinear_onnx::Conv_1262_1", type=QuantizeLinear]; +"448 DequantizeLinear_onnx^^Conv_1262_1" [id=448, label="448 DequantizeLinear_onnx::Conv_1262_1", type=DequantizeLinear]; "449 /stage4/stage4.0/branch1/branch1.0/Conv" [id=449, type=Conv]; "450 QuantizeLinear_/stage4/stage4.0/branch1/branch1.0/Conv_output_0_1" [id=450, type=QuantizeLinear]; "451 DequantizeLinear_/stage4/stage4.0/branch1/branch1.0/Conv_output_0_1" [id=451, type=DequantizeLinear]; -"452 QuantizeLinear_onnx^^Conv_1250_1" [id=452, label="452 QuantizeLinear_onnx::Conv_1250_1", type=QuantizeLinear]; -"453 DequantizeLinear_onnx^^Conv_1250_1" [id=453, label="453 DequantizeLinear_onnx::Conv_1250_1", type=DequantizeLinear]; +"452 QuantizeLinear_onnx^^Conv_1265_1" [id=452, label="452 QuantizeLinear_onnx::Conv_1265_1", type=QuantizeLinear]; +"453 DequantizeLinear_onnx^^Conv_1265_1" [id=453, label="453 DequantizeLinear_onnx::Conv_1265_1", type=DequantizeLinear]; "454 /stage4/stage4.0/branch1/branch1.2/Conv" [id=454, type=Conv]; "455 /stage4/stage4.0/branch1/branch1.4/Relu" [id=455, type=Relu]; -"456 QuantizeLinear_onnx^^Conv_1253_1" [id=456, label="456 QuantizeLinear_onnx::Conv_1253_1", type=QuantizeLinear]; -"457 DequantizeLinear_onnx^^Conv_1253_1" [id=457, label="457 DequantizeLinear_onnx::Conv_1253_1", type=DequantizeLinear]; +"456 QuantizeLinear_onnx^^Conv_1268_1" [id=456, label="456 QuantizeLinear_onnx::Conv_1268_1", type=QuantizeLinear]; +"457 DequantizeLinear_onnx^^Conv_1268_1" [id=457, label="457 DequantizeLinear_onnx::Conv_1268_1", type=DequantizeLinear]; "458 /stage4/stage4.0/branch2/branch2.0/Conv" [id=458, type=Conv]; "459 /stage4/stage4.0/branch2/branch2.2/Relu" [id=459, type=Relu]; "460 QuantizeLinear_/stage4/stage4.0/branch2/branch2.2/Relu_output_0_1" [id=460, type=QuantizeLinear]; "461 DequantizeLinear_/stage4/stage4.0/branch2/branch2.2/Relu_output_0_1" [id=461, type=DequantizeLinear]; -"462 QuantizeLinear_onnx^^Conv_1256_1" [id=462, label="462 QuantizeLinear_onnx::Conv_1256_1", type=QuantizeLinear]; -"463 DequantizeLinear_onnx^^Conv_1256_1" [id=463, label="463 DequantizeLinear_onnx::Conv_1256_1", type=DequantizeLinear]; +"462 QuantizeLinear_onnx^^Conv_1271_1" [id=462, label="462 QuantizeLinear_onnx::Conv_1271_1", type=QuantizeLinear]; +"463 DequantizeLinear_onnx^^Conv_1271_1" [id=463, label="463 DequantizeLinear_onnx::Conv_1271_1", type=DequantizeLinear]; "464 /stage4/stage4.0/branch2/branch2.3/Conv" [id=464, type=Conv]; "465 QuantizeLinear_/stage4/stage4.0/branch2/branch2.3/Conv_output_0_1" [id=465, type=QuantizeLinear]; "466 DequantizeLinear_/stage4/stage4.0/branch2/branch2.3/Conv_output_0_1" [id=466, type=DequantizeLinear]; -"467 QuantizeLinear_onnx^^Conv_1259_1" [id=467, label="467 QuantizeLinear_onnx::Conv_1259_1", type=QuantizeLinear]; -"468 DequantizeLinear_onnx^^Conv_1259_1" [id=468, label="468 DequantizeLinear_onnx::Conv_1259_1", type=DequantizeLinear]; +"467 QuantizeLinear_onnx^^Conv_1274_1" [id=467, label="467 QuantizeLinear_onnx::Conv_1274_1", type=QuantizeLinear]; +"468 DequantizeLinear_onnx^^Conv_1274_1" [id=468, label="468 DequantizeLinear_onnx::Conv_1274_1", type=DequantizeLinear]; "469 /stage4/stage4.0/branch2/branch2.5/Conv" [id=469, type=Conv]; "470 /stage4/stage4.0/branch2/branch2.7/Relu" [id=470, type=Relu]; "471 QuantizeLinear_/stage4/stage4.0/branch1/branch1.4/Relu_output_0_1" [id=471, type=QuantizeLinear]; @@ -494,19 +494,19 @@ strict digraph { "492 /stage4/stage4.1/Constant_5" [id=492, type=Constant]; "493 /stage4/stage4.1/Mul_1" [id=493, type=Mul]; "494 /stage4/stage4.1/Slice_1" [id=494, type=Slice]; -"495 QuantizeLinear_onnx^^Conv_1262_1" [id=495, label="495 QuantizeLinear_onnx::Conv_1262_1", type=QuantizeLinear]; -"496 DequantizeLinear_onnx^^Conv_1262_1" [id=496, label="496 DequantizeLinear_onnx::Conv_1262_1", type=DequantizeLinear]; +"495 QuantizeLinear_onnx^^Conv_1277_1" [id=495, label="495 QuantizeLinear_onnx::Conv_1277_1", type=QuantizeLinear]; +"496 DequantizeLinear_onnx^^Conv_1277_1" [id=496, label="496 DequantizeLinear_onnx::Conv_1277_1", type=DequantizeLinear]; "497 /stage4/stage4.1/branch2/branch2.0/Conv" [id=497, type=Conv]; "498 /stage4/stage4.1/branch2/branch2.2/Relu" [id=498, type=Relu]; "499 QuantizeLinear_/stage4/stage4.1/branch2/branch2.2/Relu_output_0_1" [id=499, type=QuantizeLinear]; "500 DequantizeLinear_/stage4/stage4.1/branch2/branch2.2/Relu_output_0_1" [id=500, type=DequantizeLinear]; -"501 QuantizeLinear_onnx^^Conv_1265_1" [id=501, label="501 QuantizeLinear_onnx::Conv_1265_1", type=QuantizeLinear]; -"502 DequantizeLinear_onnx^^Conv_1265_1" [id=502, label="502 DequantizeLinear_onnx::Conv_1265_1", type=DequantizeLinear]; +"501 QuantizeLinear_onnx^^Conv_1280_1" [id=501, label="501 QuantizeLinear_onnx::Conv_1280_1", type=QuantizeLinear]; +"502 DequantizeLinear_onnx^^Conv_1280_1" [id=502, label="502 DequantizeLinear_onnx::Conv_1280_1", type=DequantizeLinear]; "503 /stage4/stage4.1/branch2/branch2.3/Conv" [id=503, type=Conv]; "504 QuantizeLinear_/stage4/stage4.1/branch2/branch2.3/Conv_output_0_1" [id=504, type=QuantizeLinear]; "505 DequantizeLinear_/stage4/stage4.1/branch2/branch2.3/Conv_output_0_1" [id=505, type=DequantizeLinear]; -"506 QuantizeLinear_onnx^^Conv_1268_1" [id=506, label="506 QuantizeLinear_onnx::Conv_1268_1", type=QuantizeLinear]; -"507 DequantizeLinear_onnx^^Conv_1268_1" [id=507, label="507 DequantizeLinear_onnx::Conv_1268_1", type=DequantizeLinear]; +"506 QuantizeLinear_onnx^^Conv_1283_1" [id=506, label="506 QuantizeLinear_onnx::Conv_1283_1", type=QuantizeLinear]; +"507 DequantizeLinear_onnx^^Conv_1283_1" [id=507, label="507 DequantizeLinear_onnx::Conv_1283_1", type=DequantizeLinear]; "508 /stage4/stage4.1/branch2/branch2.5/Conv" [id=508, type=Conv]; "509 /stage4/stage4.1/branch2/branch2.7/Relu" [id=509, type=Relu]; "510 QuantizeLinear_/stage4/stage4.1/branch2/branch2.7/Relu_output_0_1" [id=510, type=QuantizeLinear]; @@ -531,19 +531,19 @@ strict digraph { "529 /stage4/stage4.2/Constant_5" [id=529, type=Constant]; "530 /stage4/stage4.2/Mul_1" [id=530, type=Mul]; "531 /stage4/stage4.2/Slice_1" [id=531, type=Slice]; -"532 QuantizeLinear_onnx^^Conv_1271_1" [id=532, label="532 QuantizeLinear_onnx::Conv_1271_1", type=QuantizeLinear]; -"533 DequantizeLinear_onnx^^Conv_1271_1" [id=533, label="533 DequantizeLinear_onnx::Conv_1271_1", type=DequantizeLinear]; +"532 QuantizeLinear_onnx^^Conv_1286_1" [id=532, label="532 QuantizeLinear_onnx::Conv_1286_1", type=QuantizeLinear]; +"533 DequantizeLinear_onnx^^Conv_1286_1" [id=533, label="533 DequantizeLinear_onnx::Conv_1286_1", type=DequantizeLinear]; "534 /stage4/stage4.2/branch2/branch2.0/Conv" [id=534, type=Conv]; "535 /stage4/stage4.2/branch2/branch2.2/Relu" [id=535, type=Relu]; "536 QuantizeLinear_/stage4/stage4.2/branch2/branch2.2/Relu_output_0_1" [id=536, type=QuantizeLinear]; "537 DequantizeLinear_/stage4/stage4.2/branch2/branch2.2/Relu_output_0_1" [id=537, type=DequantizeLinear]; -"538 QuantizeLinear_onnx^^Conv_1274_1" [id=538, label="538 QuantizeLinear_onnx::Conv_1274_1", type=QuantizeLinear]; -"539 DequantizeLinear_onnx^^Conv_1274_1" [id=539, label="539 DequantizeLinear_onnx::Conv_1274_1", type=DequantizeLinear]; +"538 QuantizeLinear_onnx^^Conv_1289_1" [id=538, label="538 QuantizeLinear_onnx::Conv_1289_1", type=QuantizeLinear]; +"539 DequantizeLinear_onnx^^Conv_1289_1" [id=539, label="539 DequantizeLinear_onnx::Conv_1289_1", type=DequantizeLinear]; "540 /stage4/stage4.2/branch2/branch2.3/Conv" [id=540, type=Conv]; "541 QuantizeLinear_/stage4/stage4.2/branch2/branch2.3/Conv_output_0_1" [id=541, type=QuantizeLinear]; "542 DequantizeLinear_/stage4/stage4.2/branch2/branch2.3/Conv_output_0_1" [id=542, type=DequantizeLinear]; -"543 QuantizeLinear_onnx^^Conv_1277_1" [id=543, label="543 QuantizeLinear_onnx::Conv_1277_1", type=QuantizeLinear]; -"544 DequantizeLinear_onnx^^Conv_1277_1" [id=544, label="544 DequantizeLinear_onnx::Conv_1277_1", type=DequantizeLinear]; +"543 QuantizeLinear_onnx^^Conv_1292_1" [id=543, label="543 QuantizeLinear_onnx::Conv_1292_1", type=QuantizeLinear]; +"544 DequantizeLinear_onnx^^Conv_1292_1" [id=544, label="544 DequantizeLinear_onnx::Conv_1292_1", type=DequantizeLinear]; "545 /stage4/stage4.2/branch2/branch2.5/Conv" [id=545, type=Conv]; "546 /stage4/stage4.2/branch2/branch2.7/Relu" [id=546, type=Relu]; "547 QuantizeLinear_/stage4/stage4.2/branch2/branch2.7/Relu_output_0_1" [id=547, type=QuantizeLinear]; @@ -568,19 +568,19 @@ strict digraph { "566 /stage4/stage4.3/Constant_5" [id=566, type=Constant]; "567 /stage4/stage4.3/Mul_1" [id=567, type=Mul]; "568 /stage4/stage4.3/Slice_1" [id=568, type=Slice]; -"569 QuantizeLinear_onnx^^Conv_1280_1" [id=569, label="569 QuantizeLinear_onnx::Conv_1280_1", type=QuantizeLinear]; -"570 DequantizeLinear_onnx^^Conv_1280_1" [id=570, label="570 DequantizeLinear_onnx::Conv_1280_1", type=DequantizeLinear]; +"569 QuantizeLinear_onnx^^Conv_1295_1" [id=569, label="569 QuantizeLinear_onnx::Conv_1295_1", type=QuantizeLinear]; +"570 DequantizeLinear_onnx^^Conv_1295_1" [id=570, label="570 DequantizeLinear_onnx::Conv_1295_1", type=DequantizeLinear]; "571 /stage4/stage4.3/branch2/branch2.0/Conv" [id=571, type=Conv]; "572 /stage4/stage4.3/branch2/branch2.2/Relu" [id=572, type=Relu]; "573 QuantizeLinear_/stage4/stage4.3/branch2/branch2.2/Relu_output_0_1" [id=573, type=QuantizeLinear]; "574 DequantizeLinear_/stage4/stage4.3/branch2/branch2.2/Relu_output_0_1" [id=574, type=DequantizeLinear]; -"575 QuantizeLinear_onnx^^Conv_1283_1" [id=575, label="575 QuantizeLinear_onnx::Conv_1283_1", type=QuantizeLinear]; -"576 DequantizeLinear_onnx^^Conv_1283_1" [id=576, label="576 DequantizeLinear_onnx::Conv_1283_1", type=DequantizeLinear]; +"575 QuantizeLinear_onnx^^Conv_1298_1" [id=575, label="575 QuantizeLinear_onnx::Conv_1298_1", type=QuantizeLinear]; +"576 DequantizeLinear_onnx^^Conv_1298_1" [id=576, label="576 DequantizeLinear_onnx::Conv_1298_1", type=DequantizeLinear]; "577 /stage4/stage4.3/branch2/branch2.3/Conv" [id=577, type=Conv]; "578 QuantizeLinear_/stage4/stage4.3/branch2/branch2.3/Conv_output_0_1" [id=578, type=QuantizeLinear]; "579 DequantizeLinear_/stage4/stage4.3/branch2/branch2.3/Conv_output_0_1" [id=579, type=DequantizeLinear]; -"580 QuantizeLinear_onnx^^Conv_1286_1" [id=580, label="580 QuantizeLinear_onnx::Conv_1286_1", type=QuantizeLinear]; -"581 DequantizeLinear_onnx^^Conv_1286_1" [id=581, label="581 DequantizeLinear_onnx::Conv_1286_1", type=DequantizeLinear]; +"580 QuantizeLinear_onnx^^Conv_1301_1" [id=580, label="580 QuantizeLinear_onnx::Conv_1301_1", type=QuantizeLinear]; +"581 DequantizeLinear_onnx^^Conv_1301_1" [id=581, label="581 DequantizeLinear_onnx::Conv_1301_1", type=DequantizeLinear]; "582 /stage4/stage4.3/branch2/branch2.5/Conv" [id=582, type=Conv]; "583 /stage4/stage4.3/branch2/branch2.7/Relu" [id=583, type=Relu]; "584 QuantizeLinear_/stage4/stage4.3/branch2/branch2.7/Relu_output_0_1" [id=584, type=QuantizeLinear]; @@ -591,8 +591,8 @@ strict digraph { "589 /stage4/stage4.3/Transpose" [id=589, type=Transpose]; "590 /stage4/stage4.3/Constant_7" [id=590, type=Constant]; "591 /stage4/stage4.3/Reshape_1" [id=591, type=Reshape]; -"592 QuantizeLinear_onnx^^Conv_1289_1" [id=592, label="592 QuantizeLinear_onnx::Conv_1289_1", type=QuantizeLinear]; -"593 DequantizeLinear_onnx^^Conv_1289_1" [id=593, label="593 DequantizeLinear_onnx::Conv_1289_1", type=DequantizeLinear]; +"592 QuantizeLinear_onnx^^Conv_1304_1" [id=592, label="592 QuantizeLinear_onnx::Conv_1304_1", type=QuantizeLinear]; +"593 DequantizeLinear_onnx^^Conv_1304_1" [id=593, label="593 DequantizeLinear_onnx::Conv_1304_1", type=DequantizeLinear]; "594 /conv5/conv5.0/Conv" [id=594, type=Conv]; "595 /conv5/conv5.2/Relu" [id=595, type=Relu]; "596 QuantizeLinear_/conv5/conv5.2/Relu_output_0_1" [id=596, type=QuantizeLinear]; @@ -607,36 +607,36 @@ strict digraph { "605 nncf_model_output_0" [id=605, type=nncf_model_output]; "0 QuantizeLinear_input.1_1" -> "1 DequantizeLinear_input.1_1" [label="[1, 3, 224, 224]", style=dashed]; "1 DequantizeLinear_input.1_1" -> "4 /conv1/conv1.0/Conv" [label="[1, 3, 224, 224]", style=solid]; -"2 QuantizeLinear_onnx^^Conv_1124_1" -> "3 DequantizeLinear_onnx^^Conv_1124_1" [label="[24, 3, 3, 3]", style=dashed]; -"3 DequantizeLinear_onnx^^Conv_1124_1" -> "4 /conv1/conv1.0/Conv" [label="[24, 3, 3, 3]", style=solid]; +"2 QuantizeLinear_onnx^^Conv_1139_1" -> "3 DequantizeLinear_onnx^^Conv_1139_1" [label="[24, 3, 3, 3]", style=dashed]; +"3 DequantizeLinear_onnx^^Conv_1139_1" -> "4 /conv1/conv1.0/Conv" [label="[24, 3, 3, 3]", style=solid]; "4 /conv1/conv1.0/Conv" -> "5 /conv1/conv1.2/Relu" [label="[1, 24, 112, 112]", style=solid]; "5 /conv1/conv1.2/Relu" -> "6 QuantizeLinear_/conv1/conv1.2/Relu_output_0_1" [label="[1, 24, 112, 112]", style=solid]; "6 QuantizeLinear_/conv1/conv1.2/Relu_output_0_1" -> "7 DequantizeLinear_/conv1/conv1.2/Relu_output_0_1" [label="[1, 24, 112, 112]", style=dashed]; "7 DequantizeLinear_/conv1/conv1.2/Relu_output_0_1" -> "8 /maxpool/MaxPool" [label="[1, 24, 112, 112]", style=solid]; "8 /maxpool/MaxPool" -> "11 /stage2/stage2.0/branch1/branch1.0/Conv" [label="[1, 24, 56, 56]", style=solid]; "8 /maxpool/MaxPool" -> "20 /stage2/stage2.0/branch2/branch2.0/Conv" [label="[1, 24, 56, 56]", style=solid]; -"9 QuantizeLinear_onnx^^Conv_1127_1" -> "10 DequantizeLinear_onnx^^Conv_1127_1" [label="[24, 1, 3, 3]", style=dashed]; -"10 DequantizeLinear_onnx^^Conv_1127_1" -> "11 /stage2/stage2.0/branch1/branch1.0/Conv" [label="[24, 1, 3, 3]", style=solid]; +"9 QuantizeLinear_onnx^^Conv_1142_1" -> "10 DequantizeLinear_onnx^^Conv_1142_1" [label="[24, 1, 3, 3]", style=dashed]; +"10 DequantizeLinear_onnx^^Conv_1142_1" -> "11 /stage2/stage2.0/branch1/branch1.0/Conv" [label="[24, 1, 3, 3]", style=solid]; "11 /stage2/stage2.0/branch1/branch1.0/Conv" -> "12 QuantizeLinear_/stage2/stage2.0/branch1/branch1.0/Conv_output_0_1" [label="[1, 24, 28, 28]", style=solid]; "12 QuantizeLinear_/stage2/stage2.0/branch1/branch1.0/Conv_output_0_1" -> "13 DequantizeLinear_/stage2/stage2.0/branch1/branch1.0/Conv_output_0_1" [label="[1, 24, 28, 28]", style=dashed]; "13 DequantizeLinear_/stage2/stage2.0/branch1/branch1.0/Conv_output_0_1" -> "16 /stage2/stage2.0/branch1/branch1.2/Conv" [label="[1, 24, 28, 28]", style=solid]; -"14 QuantizeLinear_onnx^^Conv_1130_1" -> "15 DequantizeLinear_onnx^^Conv_1130_1" [label="[58, 24, 1, 1]", style=dashed]; -"15 DequantizeLinear_onnx^^Conv_1130_1" -> "16 /stage2/stage2.0/branch1/branch1.2/Conv" [label="[58, 24, 1, 1]", style=solid]; +"14 QuantizeLinear_onnx^^Conv_1145_1" -> "15 DequantizeLinear_onnx^^Conv_1145_1" [label="[58, 24, 1, 1]", style=dashed]; +"15 DequantizeLinear_onnx^^Conv_1145_1" -> "16 /stage2/stage2.0/branch1/branch1.2/Conv" [label="[58, 24, 1, 1]", style=solid]; "16 /stage2/stage2.0/branch1/branch1.2/Conv" -> "17 /stage2/stage2.0/branch1/branch1.4/Relu" [label="[1, 58, 28, 28]", style=solid]; "17 /stage2/stage2.0/branch1/branch1.4/Relu" -> "33 QuantizeLinear_/stage2/stage2.0/branch1/branch1.4/Relu_output_0_1" [label="[1, 58, 28, 28]", style=solid]; -"18 QuantizeLinear_onnx^^Conv_1133_1" -> "19 DequantizeLinear_onnx^^Conv_1133_1" [label="[58, 24, 1, 1]", style=dashed]; -"19 DequantizeLinear_onnx^^Conv_1133_1" -> "20 /stage2/stage2.0/branch2/branch2.0/Conv" [label="[58, 24, 1, 1]", style=solid]; +"18 QuantizeLinear_onnx^^Conv_1148_1" -> "19 DequantizeLinear_onnx^^Conv_1148_1" [label="[58, 24, 1, 1]", style=dashed]; +"19 DequantizeLinear_onnx^^Conv_1148_1" -> "20 /stage2/stage2.0/branch2/branch2.0/Conv" [label="[58, 24, 1, 1]", style=solid]; "20 /stage2/stage2.0/branch2/branch2.0/Conv" -> "21 /stage2/stage2.0/branch2/branch2.2/Relu" [label="[1, 58, 56, 56]", style=solid]; "21 /stage2/stage2.0/branch2/branch2.2/Relu" -> "22 QuantizeLinear_/stage2/stage2.0/branch2/branch2.2/Relu_output_0_1" [label="[1, 58, 56, 56]", style=solid]; "22 QuantizeLinear_/stage2/stage2.0/branch2/branch2.2/Relu_output_0_1" -> "23 DequantizeLinear_/stage2/stage2.0/branch2/branch2.2/Relu_output_0_1" [label="[1, 58, 56, 56]", style=dashed]; "23 DequantizeLinear_/stage2/stage2.0/branch2/branch2.2/Relu_output_0_1" -> "26 /stage2/stage2.0/branch2/branch2.3/Conv" [label="[1, 58, 56, 56]", style=solid]; -"24 QuantizeLinear_onnx^^Conv_1136_1" -> "25 DequantizeLinear_onnx^^Conv_1136_1" [label="[58, 1, 3, 3]", style=dashed]; -"25 DequantizeLinear_onnx^^Conv_1136_1" -> "26 /stage2/stage2.0/branch2/branch2.3/Conv" [label="[58, 1, 3, 3]", style=solid]; +"24 QuantizeLinear_onnx^^Conv_1151_1" -> "25 DequantizeLinear_onnx^^Conv_1151_1" [label="[58, 1, 3, 3]", style=dashed]; +"25 DequantizeLinear_onnx^^Conv_1151_1" -> "26 /stage2/stage2.0/branch2/branch2.3/Conv" [label="[58, 1, 3, 3]", style=solid]; "26 /stage2/stage2.0/branch2/branch2.3/Conv" -> "27 QuantizeLinear_/stage2/stage2.0/branch2/branch2.3/Conv_output_0_1" [label="[1, 58, 28, 28]", style=solid]; "27 QuantizeLinear_/stage2/stage2.0/branch2/branch2.3/Conv_output_0_1" -> "28 DequantizeLinear_/stage2/stage2.0/branch2/branch2.3/Conv_output_0_1" [label="[1, 58, 28, 28]", style=dashed]; "28 DequantizeLinear_/stage2/stage2.0/branch2/branch2.3/Conv_output_0_1" -> "31 /stage2/stage2.0/branch2/branch2.5/Conv" [label="[1, 58, 28, 28]", style=solid]; -"29 QuantizeLinear_onnx^^Conv_1139_1" -> "30 DequantizeLinear_onnx^^Conv_1139_1" [label="[58, 58, 1, 1]", style=dashed]; -"30 DequantizeLinear_onnx^^Conv_1139_1" -> "31 /stage2/stage2.0/branch2/branch2.5/Conv" [label="[58, 58, 1, 1]", style=solid]; +"29 QuantizeLinear_onnx^^Conv_1154_1" -> "30 DequantizeLinear_onnx^^Conv_1154_1" [label="[58, 58, 1, 1]", style=dashed]; +"30 DequantizeLinear_onnx^^Conv_1154_1" -> "31 /stage2/stage2.0/branch2/branch2.5/Conv" [label="[58, 58, 1, 1]", style=solid]; "31 /stage2/stage2.0/branch2/branch2.5/Conv" -> "32 /stage2/stage2.0/branch2/branch2.7/Relu" [label="[1, 58, 28, 28]", style=solid]; "32 /stage2/stage2.0/branch2/branch2.7/Relu" -> "35 QuantizeLinear_/stage2/stage2.0/branch2/branch2.7/Relu_output_0_1" [label="[1, 58, 28, 28]", style=solid]; "33 QuantizeLinear_/stage2/stage2.0/branch1/branch1.4/Relu_output_0_1" -> "34 DequantizeLinear_/stage2/stage2.0/branch1/branch1.4/Relu_output_0_1" [label="[1, 58, 28, 28]", style=dashed]; @@ -665,28 +665,28 @@ strict digraph { "51 /stage2/stage2.1/Constant_4" -> "52 /stage2/stage2.1/Mul" [label="[1]", style=dashed]; "52 /stage2/stage2.1/Mul" -> "53 /stage2/stage2.1/Slice" [label="[1]", style=dashed]; "52 /stage2/stage2.1/Mul" -> "56 /stage2/stage2.1/Slice_1" [label="[1]", style=dashed]; -"53 /stage2/stage2.1/Slice" -> "74 /stage2/stage2.1/Concat" [label="[1]", style=solid]; +"53 /stage2/stage2.1/Slice" -> "74 /stage2/stage2.1/Concat" [label="[]", style=solid]; "54 /stage2/stage2.1/Constant_5" -> "55 /stage2/stage2.1/Mul_1" [label="[1]", style=dashed]; "55 /stage2/stage2.1/Mul_1" -> "56 /stage2/stage2.1/Slice_1" [label="[1]", style=dashed]; -"56 /stage2/stage2.1/Slice_1" -> "59 /stage2/stage2.1/branch2/branch2.0/Conv" [label="[1]", style=solid]; -"57 QuantizeLinear_onnx^^Conv_1142_1" -> "58 DequantizeLinear_onnx^^Conv_1142_1" [label="[58, 58, 1, 1]", style=dashed]; -"58 DequantizeLinear_onnx^^Conv_1142_1" -> "59 /stage2/stage2.1/branch2/branch2.0/Conv" [label="[58, 58, 1, 1]", style=solid]; -"59 /stage2/stage2.1/branch2/branch2.0/Conv" -> "60 /stage2/stage2.1/branch2/branch2.2/Relu" [label="[1]", style=solid]; -"60 /stage2/stage2.1/branch2/branch2.2/Relu" -> "61 QuantizeLinear_/stage2/stage2.1/branch2/branch2.2/Relu_output_0_1" [label="[1]", style=solid]; -"61 QuantizeLinear_/stage2/stage2.1/branch2/branch2.2/Relu_output_0_1" -> "62 DequantizeLinear_/stage2/stage2.1/branch2/branch2.2/Relu_output_0_1" [label="[1]", style=dashed]; -"62 DequantizeLinear_/stage2/stage2.1/branch2/branch2.2/Relu_output_0_1" -> "65 /stage2/stage2.1/branch2/branch2.3/Conv" [label="[1]", style=solid]; -"63 QuantizeLinear_onnx^^Conv_1145_1" -> "64 DequantizeLinear_onnx^^Conv_1145_1" [label="[58, 1, 3, 3]", style=dashed]; -"64 DequantizeLinear_onnx^^Conv_1145_1" -> "65 /stage2/stage2.1/branch2/branch2.3/Conv" [label="[58, 1, 3, 3]", style=solid]; -"65 /stage2/stage2.1/branch2/branch2.3/Conv" -> "66 QuantizeLinear_/stage2/stage2.1/branch2/branch2.3/Conv_output_0_1" [label="[1]", style=solid]; -"66 QuantizeLinear_/stage2/stage2.1/branch2/branch2.3/Conv_output_0_1" -> "67 DequantizeLinear_/stage2/stage2.1/branch2/branch2.3/Conv_output_0_1" [label="[1]", style=dashed]; -"67 DequantizeLinear_/stage2/stage2.1/branch2/branch2.3/Conv_output_0_1" -> "70 /stage2/stage2.1/branch2/branch2.5/Conv" [label="[1]", style=solid]; -"68 QuantizeLinear_onnx^^Conv_1148_1" -> "69 DequantizeLinear_onnx^^Conv_1148_1" [label="[58, 58, 1, 1]", style=dashed]; -"69 DequantizeLinear_onnx^^Conv_1148_1" -> "70 /stage2/stage2.1/branch2/branch2.5/Conv" [label="[58, 58, 1, 1]", style=solid]; -"70 /stage2/stage2.1/branch2/branch2.5/Conv" -> "71 /stage2/stage2.1/branch2/branch2.7/Relu" [label="[1]", style=solid]; -"71 /stage2/stage2.1/branch2/branch2.7/Relu" -> "72 QuantizeLinear_/stage2/stage2.1/branch2/branch2.7/Relu_output_0_1" [label="[1]", style=solid]; -"72 QuantizeLinear_/stage2/stage2.1/branch2/branch2.7/Relu_output_0_1" -> "73 DequantizeLinear_/stage2/stage2.1/branch2/branch2.7/Relu_output_0_1" [label="[1]", style=dashed]; -"73 DequantizeLinear_/stage2/stage2.1/branch2/branch2.7/Relu_output_0_1" -> "74 /stage2/stage2.1/Concat" [label="[1]", style=solid]; -"74 /stage2/stage2.1/Concat" -> "76 /stage2/stage2.1/Reshape" [label="[1]", style=solid]; +"56 /stage2/stage2.1/Slice_1" -> "59 /stage2/stage2.1/branch2/branch2.0/Conv" [label="[]", style=solid]; +"57 QuantizeLinear_onnx^^Conv_1157_1" -> "58 DequantizeLinear_onnx^^Conv_1157_1" [label="[58, 58, 1, 1]", style=dashed]; +"58 DequantizeLinear_onnx^^Conv_1157_1" -> "59 /stage2/stage2.1/branch2/branch2.0/Conv" [label="[58, 58, 1, 1]", style=solid]; +"59 /stage2/stage2.1/branch2/branch2.0/Conv" -> "60 /stage2/stage2.1/branch2/branch2.2/Relu" [label="[]", style=solid]; +"60 /stage2/stage2.1/branch2/branch2.2/Relu" -> "61 QuantizeLinear_/stage2/stage2.1/branch2/branch2.2/Relu_output_0_1" [label="[]", style=solid]; +"61 QuantizeLinear_/stage2/stage2.1/branch2/branch2.2/Relu_output_0_1" -> "62 DequantizeLinear_/stage2/stage2.1/branch2/branch2.2/Relu_output_0_1" [label="[]", style=dashed]; +"62 DequantizeLinear_/stage2/stage2.1/branch2/branch2.2/Relu_output_0_1" -> "65 /stage2/stage2.1/branch2/branch2.3/Conv" [label="[]", style=solid]; +"63 QuantizeLinear_onnx^^Conv_1160_1" -> "64 DequantizeLinear_onnx^^Conv_1160_1" [label="[58, 1, 3, 3]", style=dashed]; +"64 DequantizeLinear_onnx^^Conv_1160_1" -> "65 /stage2/stage2.1/branch2/branch2.3/Conv" [label="[58, 1, 3, 3]", style=solid]; +"65 /stage2/stage2.1/branch2/branch2.3/Conv" -> "66 QuantizeLinear_/stage2/stage2.1/branch2/branch2.3/Conv_output_0_1" [label="[]", style=solid]; +"66 QuantizeLinear_/stage2/stage2.1/branch2/branch2.3/Conv_output_0_1" -> "67 DequantizeLinear_/stage2/stage2.1/branch2/branch2.3/Conv_output_0_1" [label="[]", style=dashed]; +"67 DequantizeLinear_/stage2/stage2.1/branch2/branch2.3/Conv_output_0_1" -> "70 /stage2/stage2.1/branch2/branch2.5/Conv" [label="[]", style=solid]; +"68 QuantizeLinear_onnx^^Conv_1163_1" -> "69 DequantizeLinear_onnx^^Conv_1163_1" [label="[58, 58, 1, 1]", style=dashed]; +"69 DequantizeLinear_onnx^^Conv_1163_1" -> "70 /stage2/stage2.1/branch2/branch2.5/Conv" [label="[58, 58, 1, 1]", style=solid]; +"70 /stage2/stage2.1/branch2/branch2.5/Conv" -> "71 /stage2/stage2.1/branch2/branch2.7/Relu" [label="[]", style=solid]; +"71 /stage2/stage2.1/branch2/branch2.7/Relu" -> "72 QuantizeLinear_/stage2/stage2.1/branch2/branch2.7/Relu_output_0_1" [label="[]", style=solid]; +"72 QuantizeLinear_/stage2/stage2.1/branch2/branch2.7/Relu_output_0_1" -> "73 DequantizeLinear_/stage2/stage2.1/branch2/branch2.7/Relu_output_0_1" [label="[]", style=dashed]; +"73 DequantizeLinear_/stage2/stage2.1/branch2/branch2.7/Relu_output_0_1" -> "74 /stage2/stage2.1/Concat" [label="[]", style=solid]; +"74 /stage2/stage2.1/Concat" -> "76 /stage2/stage2.1/Reshape" [label="[]", style=solid]; "75 /stage2/stage2.1/Constant_6" -> "76 /stage2/stage2.1/Reshape" [label="[5]", style=dashed]; "76 /stage2/stage2.1/Reshape" -> "77 /stage2/stage2.1/Transpose" [label="[1, 2, 58, 28, 28]", style=solid]; "77 /stage2/stage2.1/Transpose" -> "79 /stage2/stage2.1/Reshape_1" [label="[1, 58, 2, 28, 28]", style=solid]; @@ -708,28 +708,28 @@ strict digraph { "88 /stage2/stage2.2/Constant_4" -> "89 /stage2/stage2.2/Mul" [label="[1]", style=dashed]; "89 /stage2/stage2.2/Mul" -> "90 /stage2/stage2.2/Slice" [label="[1]", style=dashed]; "89 /stage2/stage2.2/Mul" -> "93 /stage2/stage2.2/Slice_1" [label="[1]", style=dashed]; -"90 /stage2/stage2.2/Slice" -> "111 /stage2/stage2.2/Concat" [label="[1]", style=solid]; +"90 /stage2/stage2.2/Slice" -> "111 /stage2/stage2.2/Concat" [label="[]", style=solid]; "91 /stage2/stage2.2/Constant_5" -> "92 /stage2/stage2.2/Mul_1" [label="[1]", style=dashed]; "92 /stage2/stage2.2/Mul_1" -> "93 /stage2/stage2.2/Slice_1" [label="[1]", style=dashed]; -"93 /stage2/stage2.2/Slice_1" -> "96 /stage2/stage2.2/branch2/branch2.0/Conv" [label="[1]", style=solid]; -"94 QuantizeLinear_onnx^^Conv_1151_1" -> "95 DequantizeLinear_onnx^^Conv_1151_1" [label="[58, 58, 1, 1]", style=dashed]; -"95 DequantizeLinear_onnx^^Conv_1151_1" -> "96 /stage2/stage2.2/branch2/branch2.0/Conv" [label="[58, 58, 1, 1]", style=solid]; -"96 /stage2/stage2.2/branch2/branch2.0/Conv" -> "97 /stage2/stage2.2/branch2/branch2.2/Relu" [label="[1]", style=solid]; -"97 /stage2/stage2.2/branch2/branch2.2/Relu" -> "98 QuantizeLinear_/stage2/stage2.2/branch2/branch2.2/Relu_output_0_1" [label="[1]", style=solid]; -"98 QuantizeLinear_/stage2/stage2.2/branch2/branch2.2/Relu_output_0_1" -> "99 DequantizeLinear_/stage2/stage2.2/branch2/branch2.2/Relu_output_0_1" [label="[1]", style=dashed]; -"99 DequantizeLinear_/stage2/stage2.2/branch2/branch2.2/Relu_output_0_1" -> "102 /stage2/stage2.2/branch2/branch2.3/Conv" [label="[1]", style=solid]; -"100 QuantizeLinear_onnx^^Conv_1154_1" -> "101 DequantizeLinear_onnx^^Conv_1154_1" [label="[58, 1, 3, 3]", style=dashed]; -"101 DequantizeLinear_onnx^^Conv_1154_1" -> "102 /stage2/stage2.2/branch2/branch2.3/Conv" [label="[58, 1, 3, 3]", style=solid]; -"102 /stage2/stage2.2/branch2/branch2.3/Conv" -> "103 QuantizeLinear_/stage2/stage2.2/branch2/branch2.3/Conv_output_0_1" [label="[1]", style=solid]; -"103 QuantizeLinear_/stage2/stage2.2/branch2/branch2.3/Conv_output_0_1" -> "104 DequantizeLinear_/stage2/stage2.2/branch2/branch2.3/Conv_output_0_1" [label="[1]", style=dashed]; -"104 DequantizeLinear_/stage2/stage2.2/branch2/branch2.3/Conv_output_0_1" -> "107 /stage2/stage2.2/branch2/branch2.5/Conv" [label="[1]", style=solid]; -"105 QuantizeLinear_onnx^^Conv_1157_1" -> "106 DequantizeLinear_onnx^^Conv_1157_1" [label="[58, 58, 1, 1]", style=dashed]; -"106 DequantizeLinear_onnx^^Conv_1157_1" -> "107 /stage2/stage2.2/branch2/branch2.5/Conv" [label="[58, 58, 1, 1]", style=solid]; -"107 /stage2/stage2.2/branch2/branch2.5/Conv" -> "108 /stage2/stage2.2/branch2/branch2.7/Relu" [label="[1]", style=solid]; -"108 /stage2/stage2.2/branch2/branch2.7/Relu" -> "109 QuantizeLinear_/stage2/stage2.2/branch2/branch2.7/Relu_output_0_1" [label="[1]", style=solid]; -"109 QuantizeLinear_/stage2/stage2.2/branch2/branch2.7/Relu_output_0_1" -> "110 DequantizeLinear_/stage2/stage2.2/branch2/branch2.7/Relu_output_0_1" [label="[1]", style=dashed]; -"110 DequantizeLinear_/stage2/stage2.2/branch2/branch2.7/Relu_output_0_1" -> "111 /stage2/stage2.2/Concat" [label="[1]", style=solid]; -"111 /stage2/stage2.2/Concat" -> "113 /stage2/stage2.2/Reshape" [label="[1]", style=solid]; +"93 /stage2/stage2.2/Slice_1" -> "96 /stage2/stage2.2/branch2/branch2.0/Conv" [label="[]", style=solid]; +"94 QuantizeLinear_onnx^^Conv_1166_1" -> "95 DequantizeLinear_onnx^^Conv_1166_1" [label="[58, 58, 1, 1]", style=dashed]; +"95 DequantizeLinear_onnx^^Conv_1166_1" -> "96 /stage2/stage2.2/branch2/branch2.0/Conv" [label="[58, 58, 1, 1]", style=solid]; +"96 /stage2/stage2.2/branch2/branch2.0/Conv" -> "97 /stage2/stage2.2/branch2/branch2.2/Relu" [label="[]", style=solid]; +"97 /stage2/stage2.2/branch2/branch2.2/Relu" -> "98 QuantizeLinear_/stage2/stage2.2/branch2/branch2.2/Relu_output_0_1" [label="[]", style=solid]; +"98 QuantizeLinear_/stage2/stage2.2/branch2/branch2.2/Relu_output_0_1" -> "99 DequantizeLinear_/stage2/stage2.2/branch2/branch2.2/Relu_output_0_1" [label="[]", style=dashed]; +"99 DequantizeLinear_/stage2/stage2.2/branch2/branch2.2/Relu_output_0_1" -> "102 /stage2/stage2.2/branch2/branch2.3/Conv" [label="[]", style=solid]; +"100 QuantizeLinear_onnx^^Conv_1169_1" -> "101 DequantizeLinear_onnx^^Conv_1169_1" [label="[58, 1, 3, 3]", style=dashed]; +"101 DequantizeLinear_onnx^^Conv_1169_1" -> "102 /stage2/stage2.2/branch2/branch2.3/Conv" [label="[58, 1, 3, 3]", style=solid]; +"102 /stage2/stage2.2/branch2/branch2.3/Conv" -> "103 QuantizeLinear_/stage2/stage2.2/branch2/branch2.3/Conv_output_0_1" [label="[]", style=solid]; +"103 QuantizeLinear_/stage2/stage2.2/branch2/branch2.3/Conv_output_0_1" -> "104 DequantizeLinear_/stage2/stage2.2/branch2/branch2.3/Conv_output_0_1" [label="[]", style=dashed]; +"104 DequantizeLinear_/stage2/stage2.2/branch2/branch2.3/Conv_output_0_1" -> "107 /stage2/stage2.2/branch2/branch2.5/Conv" [label="[]", style=solid]; +"105 QuantizeLinear_onnx^^Conv_1172_1" -> "106 DequantizeLinear_onnx^^Conv_1172_1" [label="[58, 58, 1, 1]", style=dashed]; +"106 DequantizeLinear_onnx^^Conv_1172_1" -> "107 /stage2/stage2.2/branch2/branch2.5/Conv" [label="[58, 58, 1, 1]", style=solid]; +"107 /stage2/stage2.2/branch2/branch2.5/Conv" -> "108 /stage2/stage2.2/branch2/branch2.7/Relu" [label="[]", style=solid]; +"108 /stage2/stage2.2/branch2/branch2.7/Relu" -> "109 QuantizeLinear_/stage2/stage2.2/branch2/branch2.7/Relu_output_0_1" [label="[]", style=solid]; +"109 QuantizeLinear_/stage2/stage2.2/branch2/branch2.7/Relu_output_0_1" -> "110 DequantizeLinear_/stage2/stage2.2/branch2/branch2.7/Relu_output_0_1" [label="[]", style=dashed]; +"110 DequantizeLinear_/stage2/stage2.2/branch2/branch2.7/Relu_output_0_1" -> "111 /stage2/stage2.2/Concat" [label="[]", style=solid]; +"111 /stage2/stage2.2/Concat" -> "113 /stage2/stage2.2/Reshape" [label="[]", style=solid]; "112 /stage2/stage2.2/Constant_6" -> "113 /stage2/stage2.2/Reshape" [label="[5]", style=dashed]; "113 /stage2/stage2.2/Reshape" -> "114 /stage2/stage2.2/Transpose" [label="[1, 2, 58, 28, 28]", style=solid]; "114 /stage2/stage2.2/Transpose" -> "116 /stage2/stage2.2/Reshape_1" [label="[1, 58, 2, 28, 28]", style=solid]; @@ -751,56 +751,56 @@ strict digraph { "125 /stage2/stage2.3/Constant_4" -> "126 /stage2/stage2.3/Mul" [label="[1]", style=dashed]; "126 /stage2/stage2.3/Mul" -> "127 /stage2/stage2.3/Slice" [label="[1]", style=dashed]; "126 /stage2/stage2.3/Mul" -> "130 /stage2/stage2.3/Slice_1" [label="[1]", style=dashed]; -"127 /stage2/stage2.3/Slice" -> "148 /stage2/stage2.3/Concat" [label="[1]", style=solid]; +"127 /stage2/stage2.3/Slice" -> "148 /stage2/stage2.3/Concat" [label="[]", style=solid]; "128 /stage2/stage2.3/Constant_5" -> "129 /stage2/stage2.3/Mul_1" [label="[1]", style=dashed]; "129 /stage2/stage2.3/Mul_1" -> "130 /stage2/stage2.3/Slice_1" [label="[1]", style=dashed]; -"130 /stage2/stage2.3/Slice_1" -> "133 /stage2/stage2.3/branch2/branch2.0/Conv" [label="[1]", style=solid]; -"131 QuantizeLinear_onnx^^Conv_1160_1" -> "132 DequantizeLinear_onnx^^Conv_1160_1" [label="[58, 58, 1, 1]", style=dashed]; -"132 DequantizeLinear_onnx^^Conv_1160_1" -> "133 /stage2/stage2.3/branch2/branch2.0/Conv" [label="[58, 58, 1, 1]", style=solid]; -"133 /stage2/stage2.3/branch2/branch2.0/Conv" -> "134 /stage2/stage2.3/branch2/branch2.2/Relu" [label="[1]", style=solid]; -"134 /stage2/stage2.3/branch2/branch2.2/Relu" -> "135 QuantizeLinear_/stage2/stage2.3/branch2/branch2.2/Relu_output_0_1" [label="[1]", style=solid]; -"135 QuantizeLinear_/stage2/stage2.3/branch2/branch2.2/Relu_output_0_1" -> "136 DequantizeLinear_/stage2/stage2.3/branch2/branch2.2/Relu_output_0_1" [label="[1]", style=dashed]; -"136 DequantizeLinear_/stage2/stage2.3/branch2/branch2.2/Relu_output_0_1" -> "139 /stage2/stage2.3/branch2/branch2.3/Conv" [label="[1]", style=solid]; -"137 QuantizeLinear_onnx^^Conv_1163_1" -> "138 DequantizeLinear_onnx^^Conv_1163_1" [label="[58, 1, 3, 3]", style=dashed]; -"138 DequantizeLinear_onnx^^Conv_1163_1" -> "139 /stage2/stage2.3/branch2/branch2.3/Conv" [label="[58, 1, 3, 3]", style=solid]; -"139 /stage2/stage2.3/branch2/branch2.3/Conv" -> "140 QuantizeLinear_/stage2/stage2.3/branch2/branch2.3/Conv_output_0_1" [label="[1]", style=solid]; -"140 QuantizeLinear_/stage2/stage2.3/branch2/branch2.3/Conv_output_0_1" -> "141 DequantizeLinear_/stage2/stage2.3/branch2/branch2.3/Conv_output_0_1" [label="[1]", style=dashed]; -"141 DequantizeLinear_/stage2/stage2.3/branch2/branch2.3/Conv_output_0_1" -> "144 /stage2/stage2.3/branch2/branch2.5/Conv" [label="[1]", style=solid]; -"142 QuantizeLinear_onnx^^Conv_1166_1" -> "143 DequantizeLinear_onnx^^Conv_1166_1" [label="[58, 58, 1, 1]", style=dashed]; -"143 DequantizeLinear_onnx^^Conv_1166_1" -> "144 /stage2/stage2.3/branch2/branch2.5/Conv" [label="[58, 58, 1, 1]", style=solid]; -"144 /stage2/stage2.3/branch2/branch2.5/Conv" -> "145 /stage2/stage2.3/branch2/branch2.7/Relu" [label="[1]", style=solid]; -"145 /stage2/stage2.3/branch2/branch2.7/Relu" -> "146 QuantizeLinear_/stage2/stage2.3/branch2/branch2.7/Relu_output_0_1" [label="[1]", style=solid]; -"146 QuantizeLinear_/stage2/stage2.3/branch2/branch2.7/Relu_output_0_1" -> "147 DequantizeLinear_/stage2/stage2.3/branch2/branch2.7/Relu_output_0_1" [label="[1]", style=dashed]; -"147 DequantizeLinear_/stage2/stage2.3/branch2/branch2.7/Relu_output_0_1" -> "148 /stage2/stage2.3/Concat" [label="[1]", style=solid]; -"148 /stage2/stage2.3/Concat" -> "150 /stage2/stage2.3/Reshape" [label="[1]", style=solid]; +"130 /stage2/stage2.3/Slice_1" -> "133 /stage2/stage2.3/branch2/branch2.0/Conv" [label="[]", style=solid]; +"131 QuantizeLinear_onnx^^Conv_1175_1" -> "132 DequantizeLinear_onnx^^Conv_1175_1" [label="[58, 58, 1, 1]", style=dashed]; +"132 DequantizeLinear_onnx^^Conv_1175_1" -> "133 /stage2/stage2.3/branch2/branch2.0/Conv" [label="[58, 58, 1, 1]", style=solid]; +"133 /stage2/stage2.3/branch2/branch2.0/Conv" -> "134 /stage2/stage2.3/branch2/branch2.2/Relu" [label="[]", style=solid]; +"134 /stage2/stage2.3/branch2/branch2.2/Relu" -> "135 QuantizeLinear_/stage2/stage2.3/branch2/branch2.2/Relu_output_0_1" [label="[]", style=solid]; +"135 QuantizeLinear_/stage2/stage2.3/branch2/branch2.2/Relu_output_0_1" -> "136 DequantizeLinear_/stage2/stage2.3/branch2/branch2.2/Relu_output_0_1" [label="[]", style=dashed]; +"136 DequantizeLinear_/stage2/stage2.3/branch2/branch2.2/Relu_output_0_1" -> "139 /stage2/stage2.3/branch2/branch2.3/Conv" [label="[]", style=solid]; +"137 QuantizeLinear_onnx^^Conv_1178_1" -> "138 DequantizeLinear_onnx^^Conv_1178_1" [label="[58, 1, 3, 3]", style=dashed]; +"138 DequantizeLinear_onnx^^Conv_1178_1" -> "139 /stage2/stage2.3/branch2/branch2.3/Conv" [label="[58, 1, 3, 3]", style=solid]; +"139 /stage2/stage2.3/branch2/branch2.3/Conv" -> "140 QuantizeLinear_/stage2/stage2.3/branch2/branch2.3/Conv_output_0_1" [label="[]", style=solid]; +"140 QuantizeLinear_/stage2/stage2.3/branch2/branch2.3/Conv_output_0_1" -> "141 DequantizeLinear_/stage2/stage2.3/branch2/branch2.3/Conv_output_0_1" [label="[]", style=dashed]; +"141 DequantizeLinear_/stage2/stage2.3/branch2/branch2.3/Conv_output_0_1" -> "144 /stage2/stage2.3/branch2/branch2.5/Conv" [label="[]", style=solid]; +"142 QuantizeLinear_onnx^^Conv_1181_1" -> "143 DequantizeLinear_onnx^^Conv_1181_1" [label="[58, 58, 1, 1]", style=dashed]; +"143 DequantizeLinear_onnx^^Conv_1181_1" -> "144 /stage2/stage2.3/branch2/branch2.5/Conv" [label="[58, 58, 1, 1]", style=solid]; +"144 /stage2/stage2.3/branch2/branch2.5/Conv" -> "145 /stage2/stage2.3/branch2/branch2.7/Relu" [label="[]", style=solid]; +"145 /stage2/stage2.3/branch2/branch2.7/Relu" -> "146 QuantizeLinear_/stage2/stage2.3/branch2/branch2.7/Relu_output_0_1" [label="[]", style=solid]; +"146 QuantizeLinear_/stage2/stage2.3/branch2/branch2.7/Relu_output_0_1" -> "147 DequantizeLinear_/stage2/stage2.3/branch2/branch2.7/Relu_output_0_1" [label="[]", style=dashed]; +"147 DequantizeLinear_/stage2/stage2.3/branch2/branch2.7/Relu_output_0_1" -> "148 /stage2/stage2.3/Concat" [label="[]", style=solid]; +"148 /stage2/stage2.3/Concat" -> "150 /stage2/stage2.3/Reshape" [label="[]", style=solid]; "149 /stage2/stage2.3/Constant_6" -> "150 /stage2/stage2.3/Reshape" [label="[5]", style=dashed]; "150 /stage2/stage2.3/Reshape" -> "151 /stage2/stage2.3/Transpose" [label="[1, 2, 58, 28, 28]", style=solid]; "151 /stage2/stage2.3/Transpose" -> "153 /stage2/stage2.3/Reshape_1" [label="[1, 58, 2, 28, 28]", style=solid]; "152 /stage2/stage2.3/Constant_7" -> "153 /stage2/stage2.3/Reshape_1" [label="[4]", style=dashed]; "153 /stage2/stage2.3/Reshape_1" -> "156 /stage3/stage3.0/branch1/branch1.0/Conv" [label="[1, 116, 28, 28]", style=solid]; "153 /stage2/stage2.3/Reshape_1" -> "165 /stage3/stage3.0/branch2/branch2.0/Conv" [label="[1, 116, 28, 28]", style=solid]; -"154 QuantizeLinear_onnx^^Conv_1169_1" -> "155 DequantizeLinear_onnx^^Conv_1169_1" [label="[116, 1, 3, 3]", style=dashed]; -"155 DequantizeLinear_onnx^^Conv_1169_1" -> "156 /stage3/stage3.0/branch1/branch1.0/Conv" [label="[116, 1, 3, 3]", style=solid]; +"154 QuantizeLinear_onnx^^Conv_1184_1" -> "155 DequantizeLinear_onnx^^Conv_1184_1" [label="[116, 1, 3, 3]", style=dashed]; +"155 DequantizeLinear_onnx^^Conv_1184_1" -> "156 /stage3/stage3.0/branch1/branch1.0/Conv" [label="[116, 1, 3, 3]", style=solid]; "156 /stage3/stage3.0/branch1/branch1.0/Conv" -> "157 QuantizeLinear_/stage3/stage3.0/branch1/branch1.0/Conv_output_0_1" [label="[1, 116, 14, 14]", style=solid]; "157 QuantizeLinear_/stage3/stage3.0/branch1/branch1.0/Conv_output_0_1" -> "158 DequantizeLinear_/stage3/stage3.0/branch1/branch1.0/Conv_output_0_1" [label="[1, 116, 14, 14]", style=dashed]; "158 DequantizeLinear_/stage3/stage3.0/branch1/branch1.0/Conv_output_0_1" -> "161 /stage3/stage3.0/branch1/branch1.2/Conv" [label="[1, 116, 14, 14]", style=solid]; -"159 QuantizeLinear_onnx^^Conv_1172_1" -> "160 DequantizeLinear_onnx^^Conv_1172_1" [label="[116, 116, 1, 1]", style=dashed]; -"160 DequantizeLinear_onnx^^Conv_1172_1" -> "161 /stage3/stage3.0/branch1/branch1.2/Conv" [label="[116, 116, 1, 1]", style=solid]; +"159 QuantizeLinear_onnx^^Conv_1187_1" -> "160 DequantizeLinear_onnx^^Conv_1187_1" [label="[116, 116, 1, 1]", style=dashed]; +"160 DequantizeLinear_onnx^^Conv_1187_1" -> "161 /stage3/stage3.0/branch1/branch1.2/Conv" [label="[116, 116, 1, 1]", style=solid]; "161 /stage3/stage3.0/branch1/branch1.2/Conv" -> "162 /stage3/stage3.0/branch1/branch1.4/Relu" [label="[1, 116, 14, 14]", style=solid]; "162 /stage3/stage3.0/branch1/branch1.4/Relu" -> "178 QuantizeLinear_/stage3/stage3.0/branch1/branch1.4/Relu_output_0_1" [label="[1, 116, 14, 14]", style=solid]; -"163 QuantizeLinear_onnx^^Conv_1175_1" -> "164 DequantizeLinear_onnx^^Conv_1175_1" [label="[116, 116, 1, 1]", style=dashed]; -"164 DequantizeLinear_onnx^^Conv_1175_1" -> "165 /stage3/stage3.0/branch2/branch2.0/Conv" [label="[116, 116, 1, 1]", style=solid]; +"163 QuantizeLinear_onnx^^Conv_1190_1" -> "164 DequantizeLinear_onnx^^Conv_1190_1" [label="[116, 116, 1, 1]", style=dashed]; +"164 DequantizeLinear_onnx^^Conv_1190_1" -> "165 /stage3/stage3.0/branch2/branch2.0/Conv" [label="[116, 116, 1, 1]", style=solid]; "165 /stage3/stage3.0/branch2/branch2.0/Conv" -> "166 /stage3/stage3.0/branch2/branch2.2/Relu" [label="[1, 116, 28, 28]", style=solid]; "166 /stage3/stage3.0/branch2/branch2.2/Relu" -> "167 QuantizeLinear_/stage3/stage3.0/branch2/branch2.2/Relu_output_0_1" [label="[1, 116, 28, 28]", style=solid]; "167 QuantizeLinear_/stage3/stage3.0/branch2/branch2.2/Relu_output_0_1" -> "168 DequantizeLinear_/stage3/stage3.0/branch2/branch2.2/Relu_output_0_1" [label="[1, 116, 28, 28]", style=dashed]; "168 DequantizeLinear_/stage3/stage3.0/branch2/branch2.2/Relu_output_0_1" -> "171 /stage3/stage3.0/branch2/branch2.3/Conv" [label="[1, 116, 28, 28]", style=solid]; -"169 QuantizeLinear_onnx^^Conv_1178_1" -> "170 DequantizeLinear_onnx^^Conv_1178_1" [label="[116, 1, 3, 3]", style=dashed]; -"170 DequantizeLinear_onnx^^Conv_1178_1" -> "171 /stage3/stage3.0/branch2/branch2.3/Conv" [label="[116, 1, 3, 3]", style=solid]; +"169 QuantizeLinear_onnx^^Conv_1193_1" -> "170 DequantizeLinear_onnx^^Conv_1193_1" [label="[116, 1, 3, 3]", style=dashed]; +"170 DequantizeLinear_onnx^^Conv_1193_1" -> "171 /stage3/stage3.0/branch2/branch2.3/Conv" [label="[116, 1, 3, 3]", style=solid]; "171 /stage3/stage3.0/branch2/branch2.3/Conv" -> "172 QuantizeLinear_/stage3/stage3.0/branch2/branch2.3/Conv_output_0_1" [label="[1, 116, 14, 14]", style=solid]; "172 QuantizeLinear_/stage3/stage3.0/branch2/branch2.3/Conv_output_0_1" -> "173 DequantizeLinear_/stage3/stage3.0/branch2/branch2.3/Conv_output_0_1" [label="[1, 116, 14, 14]", style=dashed]; "173 DequantizeLinear_/stage3/stage3.0/branch2/branch2.3/Conv_output_0_1" -> "176 /stage3/stage3.0/branch2/branch2.5/Conv" [label="[1, 116, 14, 14]", style=solid]; -"174 QuantizeLinear_onnx^^Conv_1181_1" -> "175 DequantizeLinear_onnx^^Conv_1181_1" [label="[116, 116, 1, 1]", style=dashed]; -"175 DequantizeLinear_onnx^^Conv_1181_1" -> "176 /stage3/stage3.0/branch2/branch2.5/Conv" [label="[116, 116, 1, 1]", style=solid]; +"174 QuantizeLinear_onnx^^Conv_1196_1" -> "175 DequantizeLinear_onnx^^Conv_1196_1" [label="[116, 116, 1, 1]", style=dashed]; +"175 DequantizeLinear_onnx^^Conv_1196_1" -> "176 /stage3/stage3.0/branch2/branch2.5/Conv" [label="[116, 116, 1, 1]", style=solid]; "176 /stage3/stage3.0/branch2/branch2.5/Conv" -> "177 /stage3/stage3.0/branch2/branch2.7/Relu" [label="[1, 116, 14, 14]", style=solid]; "177 /stage3/stage3.0/branch2/branch2.7/Relu" -> "180 QuantizeLinear_/stage3/stage3.0/branch2/branch2.7/Relu_output_0_1" [label="[1, 116, 14, 14]", style=solid]; "178 QuantizeLinear_/stage3/stage3.0/branch1/branch1.4/Relu_output_0_1" -> "179 DequantizeLinear_/stage3/stage3.0/branch1/branch1.4/Relu_output_0_1" [label="[1, 116, 14, 14]", style=dashed]; @@ -829,28 +829,28 @@ strict digraph { "196 /stage3/stage3.1/Constant_4" -> "197 /stage3/stage3.1/Mul" [label="[1]", style=dashed]; "197 /stage3/stage3.1/Mul" -> "198 /stage3/stage3.1/Slice" [label="[1]", style=dashed]; "197 /stage3/stage3.1/Mul" -> "201 /stage3/stage3.1/Slice_1" [label="[1]", style=dashed]; -"198 /stage3/stage3.1/Slice" -> "219 /stage3/stage3.1/Concat" [label="[1]", style=solid]; +"198 /stage3/stage3.1/Slice" -> "219 /stage3/stage3.1/Concat" [label="[]", style=solid]; "199 /stage3/stage3.1/Constant_5" -> "200 /stage3/stage3.1/Mul_1" [label="[1]", style=dashed]; "200 /stage3/stage3.1/Mul_1" -> "201 /stage3/stage3.1/Slice_1" [label="[1]", style=dashed]; -"201 /stage3/stage3.1/Slice_1" -> "204 /stage3/stage3.1/branch2/branch2.0/Conv" [label="[1]", style=solid]; -"202 QuantizeLinear_onnx^^Conv_1184_1" -> "203 DequantizeLinear_onnx^^Conv_1184_1" [label="[116, 116, 1, 1]", style=dashed]; -"203 DequantizeLinear_onnx^^Conv_1184_1" -> "204 /stage3/stage3.1/branch2/branch2.0/Conv" [label="[116, 116, 1, 1]", style=solid]; -"204 /stage3/stage3.1/branch2/branch2.0/Conv" -> "205 /stage3/stage3.1/branch2/branch2.2/Relu" [label="[1]", style=solid]; -"205 /stage3/stage3.1/branch2/branch2.2/Relu" -> "206 QuantizeLinear_/stage3/stage3.1/branch2/branch2.2/Relu_output_0_1" [label="[1]", style=solid]; -"206 QuantizeLinear_/stage3/stage3.1/branch2/branch2.2/Relu_output_0_1" -> "207 DequantizeLinear_/stage3/stage3.1/branch2/branch2.2/Relu_output_0_1" [label="[1]", style=dashed]; -"207 DequantizeLinear_/stage3/stage3.1/branch2/branch2.2/Relu_output_0_1" -> "210 /stage3/stage3.1/branch2/branch2.3/Conv" [label="[1]", style=solid]; -"208 QuantizeLinear_onnx^^Conv_1187_1" -> "209 DequantizeLinear_onnx^^Conv_1187_1" [label="[116, 1, 3, 3]", style=dashed]; -"209 DequantizeLinear_onnx^^Conv_1187_1" -> "210 /stage3/stage3.1/branch2/branch2.3/Conv" [label="[116, 1, 3, 3]", style=solid]; -"210 /stage3/stage3.1/branch2/branch2.3/Conv" -> "211 QuantizeLinear_/stage3/stage3.1/branch2/branch2.3/Conv_output_0_1" [label="[1]", style=solid]; -"211 QuantizeLinear_/stage3/stage3.1/branch2/branch2.3/Conv_output_0_1" -> "212 DequantizeLinear_/stage3/stage3.1/branch2/branch2.3/Conv_output_0_1" [label="[1]", style=dashed]; -"212 DequantizeLinear_/stage3/stage3.1/branch2/branch2.3/Conv_output_0_1" -> "215 /stage3/stage3.1/branch2/branch2.5/Conv" [label="[1]", style=solid]; -"213 QuantizeLinear_onnx^^Conv_1190_1" -> "214 DequantizeLinear_onnx^^Conv_1190_1" [label="[116, 116, 1, 1]", style=dashed]; -"214 DequantizeLinear_onnx^^Conv_1190_1" -> "215 /stage3/stage3.1/branch2/branch2.5/Conv" [label="[116, 116, 1, 1]", style=solid]; -"215 /stage3/stage3.1/branch2/branch2.5/Conv" -> "216 /stage3/stage3.1/branch2/branch2.7/Relu" [label="[1]", style=solid]; -"216 /stage3/stage3.1/branch2/branch2.7/Relu" -> "217 QuantizeLinear_/stage3/stage3.1/branch2/branch2.7/Relu_output_0_1" [label="[1]", style=solid]; -"217 QuantizeLinear_/stage3/stage3.1/branch2/branch2.7/Relu_output_0_1" -> "218 DequantizeLinear_/stage3/stage3.1/branch2/branch2.7/Relu_output_0_1" [label="[1]", style=dashed]; -"218 DequantizeLinear_/stage3/stage3.1/branch2/branch2.7/Relu_output_0_1" -> "219 /stage3/stage3.1/Concat" [label="[1]", style=solid]; -"219 /stage3/stage3.1/Concat" -> "221 /stage3/stage3.1/Reshape" [label="[1]", style=solid]; +"201 /stage3/stage3.1/Slice_1" -> "204 /stage3/stage3.1/branch2/branch2.0/Conv" [label="[]", style=solid]; +"202 QuantizeLinear_onnx^^Conv_1199_1" -> "203 DequantizeLinear_onnx^^Conv_1199_1" [label="[116, 116, 1, 1]", style=dashed]; +"203 DequantizeLinear_onnx^^Conv_1199_1" -> "204 /stage3/stage3.1/branch2/branch2.0/Conv" [label="[116, 116, 1, 1]", style=solid]; +"204 /stage3/stage3.1/branch2/branch2.0/Conv" -> "205 /stage3/stage3.1/branch2/branch2.2/Relu" [label="[]", style=solid]; +"205 /stage3/stage3.1/branch2/branch2.2/Relu" -> "206 QuantizeLinear_/stage3/stage3.1/branch2/branch2.2/Relu_output_0_1" [label="[]", style=solid]; +"206 QuantizeLinear_/stage3/stage3.1/branch2/branch2.2/Relu_output_0_1" -> "207 DequantizeLinear_/stage3/stage3.1/branch2/branch2.2/Relu_output_0_1" [label="[]", style=dashed]; +"207 DequantizeLinear_/stage3/stage3.1/branch2/branch2.2/Relu_output_0_1" -> "210 /stage3/stage3.1/branch2/branch2.3/Conv" [label="[]", style=solid]; +"208 QuantizeLinear_onnx^^Conv_1202_1" -> "209 DequantizeLinear_onnx^^Conv_1202_1" [label="[116, 1, 3, 3]", style=dashed]; +"209 DequantizeLinear_onnx^^Conv_1202_1" -> "210 /stage3/stage3.1/branch2/branch2.3/Conv" [label="[116, 1, 3, 3]", style=solid]; +"210 /stage3/stage3.1/branch2/branch2.3/Conv" -> "211 QuantizeLinear_/stage3/stage3.1/branch2/branch2.3/Conv_output_0_1" [label="[]", style=solid]; +"211 QuantizeLinear_/stage3/stage3.1/branch2/branch2.3/Conv_output_0_1" -> "212 DequantizeLinear_/stage3/stage3.1/branch2/branch2.3/Conv_output_0_1" [label="[]", style=dashed]; +"212 DequantizeLinear_/stage3/stage3.1/branch2/branch2.3/Conv_output_0_1" -> "215 /stage3/stage3.1/branch2/branch2.5/Conv" [label="[]", style=solid]; +"213 QuantizeLinear_onnx^^Conv_1205_1" -> "214 DequantizeLinear_onnx^^Conv_1205_1" [label="[116, 116, 1, 1]", style=dashed]; +"214 DequantizeLinear_onnx^^Conv_1205_1" -> "215 /stage3/stage3.1/branch2/branch2.5/Conv" [label="[116, 116, 1, 1]", style=solid]; +"215 /stage3/stage3.1/branch2/branch2.5/Conv" -> "216 /stage3/stage3.1/branch2/branch2.7/Relu" [label="[]", style=solid]; +"216 /stage3/stage3.1/branch2/branch2.7/Relu" -> "217 QuantizeLinear_/stage3/stage3.1/branch2/branch2.7/Relu_output_0_1" [label="[]", style=solid]; +"217 QuantizeLinear_/stage3/stage3.1/branch2/branch2.7/Relu_output_0_1" -> "218 DequantizeLinear_/stage3/stage3.1/branch2/branch2.7/Relu_output_0_1" [label="[]", style=dashed]; +"218 DequantizeLinear_/stage3/stage3.1/branch2/branch2.7/Relu_output_0_1" -> "219 /stage3/stage3.1/Concat" [label="[]", style=solid]; +"219 /stage3/stage3.1/Concat" -> "221 /stage3/stage3.1/Reshape" [label="[]", style=solid]; "220 /stage3/stage3.1/Constant_6" -> "221 /stage3/stage3.1/Reshape" [label="[5]", style=dashed]; "221 /stage3/stage3.1/Reshape" -> "222 /stage3/stage3.1/Transpose" [label="[1, 2, 116, 14, 14]", style=solid]; "222 /stage3/stage3.1/Transpose" -> "224 /stage3/stage3.1/Reshape_1" [label="[1, 116, 2, 14, 14]", style=solid]; @@ -872,28 +872,28 @@ strict digraph { "233 /stage3/stage3.2/Constant_4" -> "234 /stage3/stage3.2/Mul" [label="[1]", style=dashed]; "234 /stage3/stage3.2/Mul" -> "235 /stage3/stage3.2/Slice" [label="[1]", style=dashed]; "234 /stage3/stage3.2/Mul" -> "238 /stage3/stage3.2/Slice_1" [label="[1]", style=dashed]; -"235 /stage3/stage3.2/Slice" -> "256 /stage3/stage3.2/Concat" [label="[1]", style=solid]; +"235 /stage3/stage3.2/Slice" -> "256 /stage3/stage3.2/Concat" [label="[]", style=solid]; "236 /stage3/stage3.2/Constant_5" -> "237 /stage3/stage3.2/Mul_1" [label="[1]", style=dashed]; "237 /stage3/stage3.2/Mul_1" -> "238 /stage3/stage3.2/Slice_1" [label="[1]", style=dashed]; -"238 /stage3/stage3.2/Slice_1" -> "241 /stage3/stage3.2/branch2/branch2.0/Conv" [label="[1]", style=solid]; -"239 QuantizeLinear_onnx^^Conv_1193_1" -> "240 DequantizeLinear_onnx^^Conv_1193_1" [label="[116, 116, 1, 1]", style=dashed]; -"240 DequantizeLinear_onnx^^Conv_1193_1" -> "241 /stage3/stage3.2/branch2/branch2.0/Conv" [label="[116, 116, 1, 1]", style=solid]; -"241 /stage3/stage3.2/branch2/branch2.0/Conv" -> "242 /stage3/stage3.2/branch2/branch2.2/Relu" [label="[1]", style=solid]; -"242 /stage3/stage3.2/branch2/branch2.2/Relu" -> "243 QuantizeLinear_/stage3/stage3.2/branch2/branch2.2/Relu_output_0_1" [label="[1]", style=solid]; -"243 QuantizeLinear_/stage3/stage3.2/branch2/branch2.2/Relu_output_0_1" -> "244 DequantizeLinear_/stage3/stage3.2/branch2/branch2.2/Relu_output_0_1" [label="[1]", style=dashed]; -"244 DequantizeLinear_/stage3/stage3.2/branch2/branch2.2/Relu_output_0_1" -> "247 /stage3/stage3.2/branch2/branch2.3/Conv" [label="[1]", style=solid]; -"245 QuantizeLinear_onnx^^Conv_1196_1" -> "246 DequantizeLinear_onnx^^Conv_1196_1" [label="[116, 1, 3, 3]", style=dashed]; -"246 DequantizeLinear_onnx^^Conv_1196_1" -> "247 /stage3/stage3.2/branch2/branch2.3/Conv" [label="[116, 1, 3, 3]", style=solid]; -"247 /stage3/stage3.2/branch2/branch2.3/Conv" -> "248 QuantizeLinear_/stage3/stage3.2/branch2/branch2.3/Conv_output_0_1" [label="[1]", style=solid]; -"248 QuantizeLinear_/stage3/stage3.2/branch2/branch2.3/Conv_output_0_1" -> "249 DequantizeLinear_/stage3/stage3.2/branch2/branch2.3/Conv_output_0_1" [label="[1]", style=dashed]; -"249 DequantizeLinear_/stage3/stage3.2/branch2/branch2.3/Conv_output_0_1" -> "252 /stage3/stage3.2/branch2/branch2.5/Conv" [label="[1]", style=solid]; -"250 QuantizeLinear_onnx^^Conv_1199_1" -> "251 DequantizeLinear_onnx^^Conv_1199_1" [label="[116, 116, 1, 1]", style=dashed]; -"251 DequantizeLinear_onnx^^Conv_1199_1" -> "252 /stage3/stage3.2/branch2/branch2.5/Conv" [label="[116, 116, 1, 1]", style=solid]; -"252 /stage3/stage3.2/branch2/branch2.5/Conv" -> "253 /stage3/stage3.2/branch2/branch2.7/Relu" [label="[1]", style=solid]; -"253 /stage3/stage3.2/branch2/branch2.7/Relu" -> "254 QuantizeLinear_/stage3/stage3.2/branch2/branch2.7/Relu_output_0_1" [label="[1]", style=solid]; -"254 QuantizeLinear_/stage3/stage3.2/branch2/branch2.7/Relu_output_0_1" -> "255 DequantizeLinear_/stage3/stage3.2/branch2/branch2.7/Relu_output_0_1" [label="[1]", style=dashed]; -"255 DequantizeLinear_/stage3/stage3.2/branch2/branch2.7/Relu_output_0_1" -> "256 /stage3/stage3.2/Concat" [label="[1]", style=solid]; -"256 /stage3/stage3.2/Concat" -> "258 /stage3/stage3.2/Reshape" [label="[1]", style=solid]; +"238 /stage3/stage3.2/Slice_1" -> "241 /stage3/stage3.2/branch2/branch2.0/Conv" [label="[]", style=solid]; +"239 QuantizeLinear_onnx^^Conv_1208_1" -> "240 DequantizeLinear_onnx^^Conv_1208_1" [label="[116, 116, 1, 1]", style=dashed]; +"240 DequantizeLinear_onnx^^Conv_1208_1" -> "241 /stage3/stage3.2/branch2/branch2.0/Conv" [label="[116, 116, 1, 1]", style=solid]; +"241 /stage3/stage3.2/branch2/branch2.0/Conv" -> "242 /stage3/stage3.2/branch2/branch2.2/Relu" [label="[]", style=solid]; +"242 /stage3/stage3.2/branch2/branch2.2/Relu" -> "243 QuantizeLinear_/stage3/stage3.2/branch2/branch2.2/Relu_output_0_1" [label="[]", style=solid]; +"243 QuantizeLinear_/stage3/stage3.2/branch2/branch2.2/Relu_output_0_1" -> "244 DequantizeLinear_/stage3/stage3.2/branch2/branch2.2/Relu_output_0_1" [label="[]", style=dashed]; +"244 DequantizeLinear_/stage3/stage3.2/branch2/branch2.2/Relu_output_0_1" -> "247 /stage3/stage3.2/branch2/branch2.3/Conv" [label="[]", style=solid]; +"245 QuantizeLinear_onnx^^Conv_1211_1" -> "246 DequantizeLinear_onnx^^Conv_1211_1" [label="[116, 1, 3, 3]", style=dashed]; +"246 DequantizeLinear_onnx^^Conv_1211_1" -> "247 /stage3/stage3.2/branch2/branch2.3/Conv" [label="[116, 1, 3, 3]", style=solid]; +"247 /stage3/stage3.2/branch2/branch2.3/Conv" -> "248 QuantizeLinear_/stage3/stage3.2/branch2/branch2.3/Conv_output_0_1" [label="[]", style=solid]; +"248 QuantizeLinear_/stage3/stage3.2/branch2/branch2.3/Conv_output_0_1" -> "249 DequantizeLinear_/stage3/stage3.2/branch2/branch2.3/Conv_output_0_1" [label="[]", style=dashed]; +"249 DequantizeLinear_/stage3/stage3.2/branch2/branch2.3/Conv_output_0_1" -> "252 /stage3/stage3.2/branch2/branch2.5/Conv" [label="[]", style=solid]; +"250 QuantizeLinear_onnx^^Conv_1214_1" -> "251 DequantizeLinear_onnx^^Conv_1214_1" [label="[116, 116, 1, 1]", style=dashed]; +"251 DequantizeLinear_onnx^^Conv_1214_1" -> "252 /stage3/stage3.2/branch2/branch2.5/Conv" [label="[116, 116, 1, 1]", style=solid]; +"252 /stage3/stage3.2/branch2/branch2.5/Conv" -> "253 /stage3/stage3.2/branch2/branch2.7/Relu" [label="[]", style=solid]; +"253 /stage3/stage3.2/branch2/branch2.7/Relu" -> "254 QuantizeLinear_/stage3/stage3.2/branch2/branch2.7/Relu_output_0_1" [label="[]", style=solid]; +"254 QuantizeLinear_/stage3/stage3.2/branch2/branch2.7/Relu_output_0_1" -> "255 DequantizeLinear_/stage3/stage3.2/branch2/branch2.7/Relu_output_0_1" [label="[]", style=dashed]; +"255 DequantizeLinear_/stage3/stage3.2/branch2/branch2.7/Relu_output_0_1" -> "256 /stage3/stage3.2/Concat" [label="[]", style=solid]; +"256 /stage3/stage3.2/Concat" -> "258 /stage3/stage3.2/Reshape" [label="[]", style=solid]; "257 /stage3/stage3.2/Constant_6" -> "258 /stage3/stage3.2/Reshape" [label="[5]", style=dashed]; "258 /stage3/stage3.2/Reshape" -> "259 /stage3/stage3.2/Transpose" [label="[1, 2, 116, 14, 14]", style=solid]; "259 /stage3/stage3.2/Transpose" -> "261 /stage3/stage3.2/Reshape_1" [label="[1, 116, 2, 14, 14]", style=solid]; @@ -915,28 +915,28 @@ strict digraph { "270 /stage3/stage3.3/Constant_4" -> "271 /stage3/stage3.3/Mul" [label="[1]", style=dashed]; "271 /stage3/stage3.3/Mul" -> "272 /stage3/stage3.3/Slice" [label="[1]", style=dashed]; "271 /stage3/stage3.3/Mul" -> "275 /stage3/stage3.3/Slice_1" [label="[1]", style=dashed]; -"272 /stage3/stage3.3/Slice" -> "293 /stage3/stage3.3/Concat" [label="[1]", style=solid]; +"272 /stage3/stage3.3/Slice" -> "293 /stage3/stage3.3/Concat" [label="[]", style=solid]; "273 /stage3/stage3.3/Constant_5" -> "274 /stage3/stage3.3/Mul_1" [label="[1]", style=dashed]; "274 /stage3/stage3.3/Mul_1" -> "275 /stage3/stage3.3/Slice_1" [label="[1]", style=dashed]; -"275 /stage3/stage3.3/Slice_1" -> "278 /stage3/stage3.3/branch2/branch2.0/Conv" [label="[1]", style=solid]; -"276 QuantizeLinear_onnx^^Conv_1202_1" -> "277 DequantizeLinear_onnx^^Conv_1202_1" [label="[116, 116, 1, 1]", style=dashed]; -"277 DequantizeLinear_onnx^^Conv_1202_1" -> "278 /stage3/stage3.3/branch2/branch2.0/Conv" [label="[116, 116, 1, 1]", style=solid]; -"278 /stage3/stage3.3/branch2/branch2.0/Conv" -> "279 /stage3/stage3.3/branch2/branch2.2/Relu" [label="[1]", style=solid]; -"279 /stage3/stage3.3/branch2/branch2.2/Relu" -> "280 QuantizeLinear_/stage3/stage3.3/branch2/branch2.2/Relu_output_0_1" [label="[1]", style=solid]; -"280 QuantizeLinear_/stage3/stage3.3/branch2/branch2.2/Relu_output_0_1" -> "281 DequantizeLinear_/stage3/stage3.3/branch2/branch2.2/Relu_output_0_1" [label="[1]", style=dashed]; -"281 DequantizeLinear_/stage3/stage3.3/branch2/branch2.2/Relu_output_0_1" -> "284 /stage3/stage3.3/branch2/branch2.3/Conv" [label="[1]", style=solid]; -"282 QuantizeLinear_onnx^^Conv_1205_1" -> "283 DequantizeLinear_onnx^^Conv_1205_1" [label="[116, 1, 3, 3]", style=dashed]; -"283 DequantizeLinear_onnx^^Conv_1205_1" -> "284 /stage3/stage3.3/branch2/branch2.3/Conv" [label="[116, 1, 3, 3]", style=solid]; -"284 /stage3/stage3.3/branch2/branch2.3/Conv" -> "285 QuantizeLinear_/stage3/stage3.3/branch2/branch2.3/Conv_output_0_1" [label="[1]", style=solid]; -"285 QuantizeLinear_/stage3/stage3.3/branch2/branch2.3/Conv_output_0_1" -> "286 DequantizeLinear_/stage3/stage3.3/branch2/branch2.3/Conv_output_0_1" [label="[1]", style=dashed]; -"286 DequantizeLinear_/stage3/stage3.3/branch2/branch2.3/Conv_output_0_1" -> "289 /stage3/stage3.3/branch2/branch2.5/Conv" [label="[1]", style=solid]; -"287 QuantizeLinear_onnx^^Conv_1208_1" -> "288 DequantizeLinear_onnx^^Conv_1208_1" [label="[116, 116, 1, 1]", style=dashed]; -"288 DequantizeLinear_onnx^^Conv_1208_1" -> "289 /stage3/stage3.3/branch2/branch2.5/Conv" [label="[116, 116, 1, 1]", style=solid]; -"289 /stage3/stage3.3/branch2/branch2.5/Conv" -> "290 /stage3/stage3.3/branch2/branch2.7/Relu" [label="[1]", style=solid]; -"290 /stage3/stage3.3/branch2/branch2.7/Relu" -> "291 QuantizeLinear_/stage3/stage3.3/branch2/branch2.7/Relu_output_0_1" [label="[1]", style=solid]; -"291 QuantizeLinear_/stage3/stage3.3/branch2/branch2.7/Relu_output_0_1" -> "292 DequantizeLinear_/stage3/stage3.3/branch2/branch2.7/Relu_output_0_1" [label="[1]", style=dashed]; -"292 DequantizeLinear_/stage3/stage3.3/branch2/branch2.7/Relu_output_0_1" -> "293 /stage3/stage3.3/Concat" [label="[1]", style=solid]; -"293 /stage3/stage3.3/Concat" -> "295 /stage3/stage3.3/Reshape" [label="[1]", style=solid]; +"275 /stage3/stage3.3/Slice_1" -> "278 /stage3/stage3.3/branch2/branch2.0/Conv" [label="[]", style=solid]; +"276 QuantizeLinear_onnx^^Conv_1217_1" -> "277 DequantizeLinear_onnx^^Conv_1217_1" [label="[116, 116, 1, 1]", style=dashed]; +"277 DequantizeLinear_onnx^^Conv_1217_1" -> "278 /stage3/stage3.3/branch2/branch2.0/Conv" [label="[116, 116, 1, 1]", style=solid]; +"278 /stage3/stage3.3/branch2/branch2.0/Conv" -> "279 /stage3/stage3.3/branch2/branch2.2/Relu" [label="[]", style=solid]; +"279 /stage3/stage3.3/branch2/branch2.2/Relu" -> "280 QuantizeLinear_/stage3/stage3.3/branch2/branch2.2/Relu_output_0_1" [label="[]", style=solid]; +"280 QuantizeLinear_/stage3/stage3.3/branch2/branch2.2/Relu_output_0_1" -> "281 DequantizeLinear_/stage3/stage3.3/branch2/branch2.2/Relu_output_0_1" [label="[]", style=dashed]; +"281 DequantizeLinear_/stage3/stage3.3/branch2/branch2.2/Relu_output_0_1" -> "284 /stage3/stage3.3/branch2/branch2.3/Conv" [label="[]", style=solid]; +"282 QuantizeLinear_onnx^^Conv_1220_1" -> "283 DequantizeLinear_onnx^^Conv_1220_1" [label="[116, 1, 3, 3]", style=dashed]; +"283 DequantizeLinear_onnx^^Conv_1220_1" -> "284 /stage3/stage3.3/branch2/branch2.3/Conv" [label="[116, 1, 3, 3]", style=solid]; +"284 /stage3/stage3.3/branch2/branch2.3/Conv" -> "285 QuantizeLinear_/stage3/stage3.3/branch2/branch2.3/Conv_output_0_1" [label="[]", style=solid]; +"285 QuantizeLinear_/stage3/stage3.3/branch2/branch2.3/Conv_output_0_1" -> "286 DequantizeLinear_/stage3/stage3.3/branch2/branch2.3/Conv_output_0_1" [label="[]", style=dashed]; +"286 DequantizeLinear_/stage3/stage3.3/branch2/branch2.3/Conv_output_0_1" -> "289 /stage3/stage3.3/branch2/branch2.5/Conv" [label="[]", style=solid]; +"287 QuantizeLinear_onnx^^Conv_1223_1" -> "288 DequantizeLinear_onnx^^Conv_1223_1" [label="[116, 116, 1, 1]", style=dashed]; +"288 DequantizeLinear_onnx^^Conv_1223_1" -> "289 /stage3/stage3.3/branch2/branch2.5/Conv" [label="[116, 116, 1, 1]", style=solid]; +"289 /stage3/stage3.3/branch2/branch2.5/Conv" -> "290 /stage3/stage3.3/branch2/branch2.7/Relu" [label="[]", style=solid]; +"290 /stage3/stage3.3/branch2/branch2.7/Relu" -> "291 QuantizeLinear_/stage3/stage3.3/branch2/branch2.7/Relu_output_0_1" [label="[]", style=solid]; +"291 QuantizeLinear_/stage3/stage3.3/branch2/branch2.7/Relu_output_0_1" -> "292 DequantizeLinear_/stage3/stage3.3/branch2/branch2.7/Relu_output_0_1" [label="[]", style=dashed]; +"292 DequantizeLinear_/stage3/stage3.3/branch2/branch2.7/Relu_output_0_1" -> "293 /stage3/stage3.3/Concat" [label="[]", style=solid]; +"293 /stage3/stage3.3/Concat" -> "295 /stage3/stage3.3/Reshape" [label="[]", style=solid]; "294 /stage3/stage3.3/Constant_6" -> "295 /stage3/stage3.3/Reshape" [label="[5]", style=dashed]; "295 /stage3/stage3.3/Reshape" -> "296 /stage3/stage3.3/Transpose" [label="[1, 2, 116, 14, 14]", style=solid]; "296 /stage3/stage3.3/Transpose" -> "298 /stage3/stage3.3/Reshape_1" [label="[1, 116, 2, 14, 14]", style=solid]; @@ -958,28 +958,28 @@ strict digraph { "307 /stage3/stage3.4/Constant_4" -> "308 /stage3/stage3.4/Mul" [label="[1]", style=dashed]; "308 /stage3/stage3.4/Mul" -> "309 /stage3/stage3.4/Slice" [label="[1]", style=dashed]; "308 /stage3/stage3.4/Mul" -> "312 /stage3/stage3.4/Slice_1" [label="[1]", style=dashed]; -"309 /stage3/stage3.4/Slice" -> "330 /stage3/stage3.4/Concat" [label="[1]", style=solid]; +"309 /stage3/stage3.4/Slice" -> "330 /stage3/stage3.4/Concat" [label="[]", style=solid]; "310 /stage3/stage3.4/Constant_5" -> "311 /stage3/stage3.4/Mul_1" [label="[1]", style=dashed]; "311 /stage3/stage3.4/Mul_1" -> "312 /stage3/stage3.4/Slice_1" [label="[1]", style=dashed]; -"312 /stage3/stage3.4/Slice_1" -> "315 /stage3/stage3.4/branch2/branch2.0/Conv" [label="[1]", style=solid]; -"313 QuantizeLinear_onnx^^Conv_1211_1" -> "314 DequantizeLinear_onnx^^Conv_1211_1" [label="[116, 116, 1, 1]", style=dashed]; -"314 DequantizeLinear_onnx^^Conv_1211_1" -> "315 /stage3/stage3.4/branch2/branch2.0/Conv" [label="[116, 116, 1, 1]", style=solid]; -"315 /stage3/stage3.4/branch2/branch2.0/Conv" -> "316 /stage3/stage3.4/branch2/branch2.2/Relu" [label="[1]", style=solid]; -"316 /stage3/stage3.4/branch2/branch2.2/Relu" -> "317 QuantizeLinear_/stage3/stage3.4/branch2/branch2.2/Relu_output_0_1" [label="[1]", style=solid]; -"317 QuantizeLinear_/stage3/stage3.4/branch2/branch2.2/Relu_output_0_1" -> "318 DequantizeLinear_/stage3/stage3.4/branch2/branch2.2/Relu_output_0_1" [label="[1]", style=dashed]; -"318 DequantizeLinear_/stage3/stage3.4/branch2/branch2.2/Relu_output_0_1" -> "321 /stage3/stage3.4/branch2/branch2.3/Conv" [label="[1]", style=solid]; -"319 QuantizeLinear_onnx^^Conv_1214_1" -> "320 DequantizeLinear_onnx^^Conv_1214_1" [label="[116, 1, 3, 3]", style=dashed]; -"320 DequantizeLinear_onnx^^Conv_1214_1" -> "321 /stage3/stage3.4/branch2/branch2.3/Conv" [label="[116, 1, 3, 3]", style=solid]; -"321 /stage3/stage3.4/branch2/branch2.3/Conv" -> "322 QuantizeLinear_/stage3/stage3.4/branch2/branch2.3/Conv_output_0_1" [label="[1]", style=solid]; -"322 QuantizeLinear_/stage3/stage3.4/branch2/branch2.3/Conv_output_0_1" -> "323 DequantizeLinear_/stage3/stage3.4/branch2/branch2.3/Conv_output_0_1" [label="[1]", style=dashed]; -"323 DequantizeLinear_/stage3/stage3.4/branch2/branch2.3/Conv_output_0_1" -> "326 /stage3/stage3.4/branch2/branch2.5/Conv" [label="[1]", style=solid]; -"324 QuantizeLinear_onnx^^Conv_1217_1" -> "325 DequantizeLinear_onnx^^Conv_1217_1" [label="[116, 116, 1, 1]", style=dashed]; -"325 DequantizeLinear_onnx^^Conv_1217_1" -> "326 /stage3/stage3.4/branch2/branch2.5/Conv" [label="[116, 116, 1, 1]", style=solid]; -"326 /stage3/stage3.4/branch2/branch2.5/Conv" -> "327 /stage3/stage3.4/branch2/branch2.7/Relu" [label="[1]", style=solid]; -"327 /stage3/stage3.4/branch2/branch2.7/Relu" -> "328 QuantizeLinear_/stage3/stage3.4/branch2/branch2.7/Relu_output_0_1" [label="[1]", style=solid]; -"328 QuantizeLinear_/stage3/stage3.4/branch2/branch2.7/Relu_output_0_1" -> "329 DequantizeLinear_/stage3/stage3.4/branch2/branch2.7/Relu_output_0_1" [label="[1]", style=dashed]; -"329 DequantizeLinear_/stage3/stage3.4/branch2/branch2.7/Relu_output_0_1" -> "330 /stage3/stage3.4/Concat" [label="[1]", style=solid]; -"330 /stage3/stage3.4/Concat" -> "332 /stage3/stage3.4/Reshape" [label="[1]", style=solid]; +"312 /stage3/stage3.4/Slice_1" -> "315 /stage3/stage3.4/branch2/branch2.0/Conv" [label="[]", style=solid]; +"313 QuantizeLinear_onnx^^Conv_1226_1" -> "314 DequantizeLinear_onnx^^Conv_1226_1" [label="[116, 116, 1, 1]", style=dashed]; +"314 DequantizeLinear_onnx^^Conv_1226_1" -> "315 /stage3/stage3.4/branch2/branch2.0/Conv" [label="[116, 116, 1, 1]", style=solid]; +"315 /stage3/stage3.4/branch2/branch2.0/Conv" -> "316 /stage3/stage3.4/branch2/branch2.2/Relu" [label="[]", style=solid]; +"316 /stage3/stage3.4/branch2/branch2.2/Relu" -> "317 QuantizeLinear_/stage3/stage3.4/branch2/branch2.2/Relu_output_0_1" [label="[]", style=solid]; +"317 QuantizeLinear_/stage3/stage3.4/branch2/branch2.2/Relu_output_0_1" -> "318 DequantizeLinear_/stage3/stage3.4/branch2/branch2.2/Relu_output_0_1" [label="[]", style=dashed]; +"318 DequantizeLinear_/stage3/stage3.4/branch2/branch2.2/Relu_output_0_1" -> "321 /stage3/stage3.4/branch2/branch2.3/Conv" [label="[]", style=solid]; +"319 QuantizeLinear_onnx^^Conv_1229_1" -> "320 DequantizeLinear_onnx^^Conv_1229_1" [label="[116, 1, 3, 3]", style=dashed]; +"320 DequantizeLinear_onnx^^Conv_1229_1" -> "321 /stage3/stage3.4/branch2/branch2.3/Conv" [label="[116, 1, 3, 3]", style=solid]; +"321 /stage3/stage3.4/branch2/branch2.3/Conv" -> "322 QuantizeLinear_/stage3/stage3.4/branch2/branch2.3/Conv_output_0_1" [label="[]", style=solid]; +"322 QuantizeLinear_/stage3/stage3.4/branch2/branch2.3/Conv_output_0_1" -> "323 DequantizeLinear_/stage3/stage3.4/branch2/branch2.3/Conv_output_0_1" [label="[]", style=dashed]; +"323 DequantizeLinear_/stage3/stage3.4/branch2/branch2.3/Conv_output_0_1" -> "326 /stage3/stage3.4/branch2/branch2.5/Conv" [label="[]", style=solid]; +"324 QuantizeLinear_onnx^^Conv_1232_1" -> "325 DequantizeLinear_onnx^^Conv_1232_1" [label="[116, 116, 1, 1]", style=dashed]; +"325 DequantizeLinear_onnx^^Conv_1232_1" -> "326 /stage3/stage3.4/branch2/branch2.5/Conv" [label="[116, 116, 1, 1]", style=solid]; +"326 /stage3/stage3.4/branch2/branch2.5/Conv" -> "327 /stage3/stage3.4/branch2/branch2.7/Relu" [label="[]", style=solid]; +"327 /stage3/stage3.4/branch2/branch2.7/Relu" -> "328 QuantizeLinear_/stage3/stage3.4/branch2/branch2.7/Relu_output_0_1" [label="[]", style=solid]; +"328 QuantizeLinear_/stage3/stage3.4/branch2/branch2.7/Relu_output_0_1" -> "329 DequantizeLinear_/stage3/stage3.4/branch2/branch2.7/Relu_output_0_1" [label="[]", style=dashed]; +"329 DequantizeLinear_/stage3/stage3.4/branch2/branch2.7/Relu_output_0_1" -> "330 /stage3/stage3.4/Concat" [label="[]", style=solid]; +"330 /stage3/stage3.4/Concat" -> "332 /stage3/stage3.4/Reshape" [label="[]", style=solid]; "331 /stage3/stage3.4/Constant_6" -> "332 /stage3/stage3.4/Reshape" [label="[5]", style=dashed]; "332 /stage3/stage3.4/Reshape" -> "333 /stage3/stage3.4/Transpose" [label="[1, 2, 116, 14, 14]", style=solid]; "333 /stage3/stage3.4/Transpose" -> "335 /stage3/stage3.4/Reshape_1" [label="[1, 116, 2, 14, 14]", style=solid]; @@ -1001,28 +1001,28 @@ strict digraph { "344 /stage3/stage3.5/Constant_4" -> "345 /stage3/stage3.5/Mul" [label="[1]", style=dashed]; "345 /stage3/stage3.5/Mul" -> "346 /stage3/stage3.5/Slice" [label="[1]", style=dashed]; "345 /stage3/stage3.5/Mul" -> "349 /stage3/stage3.5/Slice_1" [label="[1]", style=dashed]; -"346 /stage3/stage3.5/Slice" -> "367 /stage3/stage3.5/Concat" [label="[1]", style=solid]; +"346 /stage3/stage3.5/Slice" -> "367 /stage3/stage3.5/Concat" [label="[]", style=solid]; "347 /stage3/stage3.5/Constant_5" -> "348 /stage3/stage3.5/Mul_1" [label="[1]", style=dashed]; "348 /stage3/stage3.5/Mul_1" -> "349 /stage3/stage3.5/Slice_1" [label="[1]", style=dashed]; -"349 /stage3/stage3.5/Slice_1" -> "352 /stage3/stage3.5/branch2/branch2.0/Conv" [label="[1]", style=solid]; -"350 QuantizeLinear_onnx^^Conv_1220_1" -> "351 DequantizeLinear_onnx^^Conv_1220_1" [label="[116, 116, 1, 1]", style=dashed]; -"351 DequantizeLinear_onnx^^Conv_1220_1" -> "352 /stage3/stage3.5/branch2/branch2.0/Conv" [label="[116, 116, 1, 1]", style=solid]; -"352 /stage3/stage3.5/branch2/branch2.0/Conv" -> "353 /stage3/stage3.5/branch2/branch2.2/Relu" [label="[1]", style=solid]; -"353 /stage3/stage3.5/branch2/branch2.2/Relu" -> "354 QuantizeLinear_/stage3/stage3.5/branch2/branch2.2/Relu_output_0_1" [label="[1]", style=solid]; -"354 QuantizeLinear_/stage3/stage3.5/branch2/branch2.2/Relu_output_0_1" -> "355 DequantizeLinear_/stage3/stage3.5/branch2/branch2.2/Relu_output_0_1" [label="[1]", style=dashed]; -"355 DequantizeLinear_/stage3/stage3.5/branch2/branch2.2/Relu_output_0_1" -> "358 /stage3/stage3.5/branch2/branch2.3/Conv" [label="[1]", style=solid]; -"356 QuantizeLinear_onnx^^Conv_1223_1" -> "357 DequantizeLinear_onnx^^Conv_1223_1" [label="[116, 1, 3, 3]", style=dashed]; -"357 DequantizeLinear_onnx^^Conv_1223_1" -> "358 /stage3/stage3.5/branch2/branch2.3/Conv" [label="[116, 1, 3, 3]", style=solid]; -"358 /stage3/stage3.5/branch2/branch2.3/Conv" -> "359 QuantizeLinear_/stage3/stage3.5/branch2/branch2.3/Conv_output_0_1" [label="[1]", style=solid]; -"359 QuantizeLinear_/stage3/stage3.5/branch2/branch2.3/Conv_output_0_1" -> "360 DequantizeLinear_/stage3/stage3.5/branch2/branch2.3/Conv_output_0_1" [label="[1]", style=dashed]; -"360 DequantizeLinear_/stage3/stage3.5/branch2/branch2.3/Conv_output_0_1" -> "363 /stage3/stage3.5/branch2/branch2.5/Conv" [label="[1]", style=solid]; -"361 QuantizeLinear_onnx^^Conv_1226_1" -> "362 DequantizeLinear_onnx^^Conv_1226_1" [label="[116, 116, 1, 1]", style=dashed]; -"362 DequantizeLinear_onnx^^Conv_1226_1" -> "363 /stage3/stage3.5/branch2/branch2.5/Conv" [label="[116, 116, 1, 1]", style=solid]; -"363 /stage3/stage3.5/branch2/branch2.5/Conv" -> "364 /stage3/stage3.5/branch2/branch2.7/Relu" [label="[1]", style=solid]; -"364 /stage3/stage3.5/branch2/branch2.7/Relu" -> "365 QuantizeLinear_/stage3/stage3.5/branch2/branch2.7/Relu_output_0_1" [label="[1]", style=solid]; -"365 QuantizeLinear_/stage3/stage3.5/branch2/branch2.7/Relu_output_0_1" -> "366 DequantizeLinear_/stage3/stage3.5/branch2/branch2.7/Relu_output_0_1" [label="[1]", style=dashed]; -"366 DequantizeLinear_/stage3/stage3.5/branch2/branch2.7/Relu_output_0_1" -> "367 /stage3/stage3.5/Concat" [label="[1]", style=solid]; -"367 /stage3/stage3.5/Concat" -> "369 /stage3/stage3.5/Reshape" [label="[1]", style=solid]; +"349 /stage3/stage3.5/Slice_1" -> "352 /stage3/stage3.5/branch2/branch2.0/Conv" [label="[]", style=solid]; +"350 QuantizeLinear_onnx^^Conv_1235_1" -> "351 DequantizeLinear_onnx^^Conv_1235_1" [label="[116, 116, 1, 1]", style=dashed]; +"351 DequantizeLinear_onnx^^Conv_1235_1" -> "352 /stage3/stage3.5/branch2/branch2.0/Conv" [label="[116, 116, 1, 1]", style=solid]; +"352 /stage3/stage3.5/branch2/branch2.0/Conv" -> "353 /stage3/stage3.5/branch2/branch2.2/Relu" [label="[]", style=solid]; +"353 /stage3/stage3.5/branch2/branch2.2/Relu" -> "354 QuantizeLinear_/stage3/stage3.5/branch2/branch2.2/Relu_output_0_1" [label="[]", style=solid]; +"354 QuantizeLinear_/stage3/stage3.5/branch2/branch2.2/Relu_output_0_1" -> "355 DequantizeLinear_/stage3/stage3.5/branch2/branch2.2/Relu_output_0_1" [label="[]", style=dashed]; +"355 DequantizeLinear_/stage3/stage3.5/branch2/branch2.2/Relu_output_0_1" -> "358 /stage3/stage3.5/branch2/branch2.3/Conv" [label="[]", style=solid]; +"356 QuantizeLinear_onnx^^Conv_1238_1" -> "357 DequantizeLinear_onnx^^Conv_1238_1" [label="[116, 1, 3, 3]", style=dashed]; +"357 DequantizeLinear_onnx^^Conv_1238_1" -> "358 /stage3/stage3.5/branch2/branch2.3/Conv" [label="[116, 1, 3, 3]", style=solid]; +"358 /stage3/stage3.5/branch2/branch2.3/Conv" -> "359 QuantizeLinear_/stage3/stage3.5/branch2/branch2.3/Conv_output_0_1" [label="[]", style=solid]; +"359 QuantizeLinear_/stage3/stage3.5/branch2/branch2.3/Conv_output_0_1" -> "360 DequantizeLinear_/stage3/stage3.5/branch2/branch2.3/Conv_output_0_1" [label="[]", style=dashed]; +"360 DequantizeLinear_/stage3/stage3.5/branch2/branch2.3/Conv_output_0_1" -> "363 /stage3/stage3.5/branch2/branch2.5/Conv" [label="[]", style=solid]; +"361 QuantizeLinear_onnx^^Conv_1241_1" -> "362 DequantizeLinear_onnx^^Conv_1241_1" [label="[116, 116, 1, 1]", style=dashed]; +"362 DequantizeLinear_onnx^^Conv_1241_1" -> "363 /stage3/stage3.5/branch2/branch2.5/Conv" [label="[116, 116, 1, 1]", style=solid]; +"363 /stage3/stage3.5/branch2/branch2.5/Conv" -> "364 /stage3/stage3.5/branch2/branch2.7/Relu" [label="[]", style=solid]; +"364 /stage3/stage3.5/branch2/branch2.7/Relu" -> "365 QuantizeLinear_/stage3/stage3.5/branch2/branch2.7/Relu_output_0_1" [label="[]", style=solid]; +"365 QuantizeLinear_/stage3/stage3.5/branch2/branch2.7/Relu_output_0_1" -> "366 DequantizeLinear_/stage3/stage3.5/branch2/branch2.7/Relu_output_0_1" [label="[]", style=dashed]; +"366 DequantizeLinear_/stage3/stage3.5/branch2/branch2.7/Relu_output_0_1" -> "367 /stage3/stage3.5/Concat" [label="[]", style=solid]; +"367 /stage3/stage3.5/Concat" -> "369 /stage3/stage3.5/Reshape" [label="[]", style=solid]; "368 /stage3/stage3.5/Constant_6" -> "369 /stage3/stage3.5/Reshape" [label="[5]", style=dashed]; "369 /stage3/stage3.5/Reshape" -> "370 /stage3/stage3.5/Transpose" [label="[1, 2, 116, 14, 14]", style=solid]; "370 /stage3/stage3.5/Transpose" -> "372 /stage3/stage3.5/Reshape_1" [label="[1, 116, 2, 14, 14]", style=solid]; @@ -1044,28 +1044,28 @@ strict digraph { "381 /stage3/stage3.6/Constant_4" -> "382 /stage3/stage3.6/Mul" [label="[1]", style=dashed]; "382 /stage3/stage3.6/Mul" -> "383 /stage3/stage3.6/Slice" [label="[1]", style=dashed]; "382 /stage3/stage3.6/Mul" -> "386 /stage3/stage3.6/Slice_1" [label="[1]", style=dashed]; -"383 /stage3/stage3.6/Slice" -> "404 /stage3/stage3.6/Concat" [label="[1]", style=solid]; +"383 /stage3/stage3.6/Slice" -> "404 /stage3/stage3.6/Concat" [label="[]", style=solid]; "384 /stage3/stage3.6/Constant_5" -> "385 /stage3/stage3.6/Mul_1" [label="[1]", style=dashed]; "385 /stage3/stage3.6/Mul_1" -> "386 /stage3/stage3.6/Slice_1" [label="[1]", style=dashed]; -"386 /stage3/stage3.6/Slice_1" -> "389 /stage3/stage3.6/branch2/branch2.0/Conv" [label="[1]", style=solid]; -"387 QuantizeLinear_onnx^^Conv_1229_1" -> "388 DequantizeLinear_onnx^^Conv_1229_1" [label="[116, 116, 1, 1]", style=dashed]; -"388 DequantizeLinear_onnx^^Conv_1229_1" -> "389 /stage3/stage3.6/branch2/branch2.0/Conv" [label="[116, 116, 1, 1]", style=solid]; -"389 /stage3/stage3.6/branch2/branch2.0/Conv" -> "390 /stage3/stage3.6/branch2/branch2.2/Relu" [label="[1]", style=solid]; -"390 /stage3/stage3.6/branch2/branch2.2/Relu" -> "391 QuantizeLinear_/stage3/stage3.6/branch2/branch2.2/Relu_output_0_1" [label="[1]", style=solid]; -"391 QuantizeLinear_/stage3/stage3.6/branch2/branch2.2/Relu_output_0_1" -> "392 DequantizeLinear_/stage3/stage3.6/branch2/branch2.2/Relu_output_0_1" [label="[1]", style=dashed]; -"392 DequantizeLinear_/stage3/stage3.6/branch2/branch2.2/Relu_output_0_1" -> "395 /stage3/stage3.6/branch2/branch2.3/Conv" [label="[1]", style=solid]; -"393 QuantizeLinear_onnx^^Conv_1232_1" -> "394 DequantizeLinear_onnx^^Conv_1232_1" [label="[116, 1, 3, 3]", style=dashed]; -"394 DequantizeLinear_onnx^^Conv_1232_1" -> "395 /stage3/stage3.6/branch2/branch2.3/Conv" [label="[116, 1, 3, 3]", style=solid]; -"395 /stage3/stage3.6/branch2/branch2.3/Conv" -> "396 QuantizeLinear_/stage3/stage3.6/branch2/branch2.3/Conv_output_0_1" [label="[1]", style=solid]; -"396 QuantizeLinear_/stage3/stage3.6/branch2/branch2.3/Conv_output_0_1" -> "397 DequantizeLinear_/stage3/stage3.6/branch2/branch2.3/Conv_output_0_1" [label="[1]", style=dashed]; -"397 DequantizeLinear_/stage3/stage3.6/branch2/branch2.3/Conv_output_0_1" -> "400 /stage3/stage3.6/branch2/branch2.5/Conv" [label="[1]", style=solid]; -"398 QuantizeLinear_onnx^^Conv_1235_1" -> "399 DequantizeLinear_onnx^^Conv_1235_1" [label="[116, 116, 1, 1]", style=dashed]; -"399 DequantizeLinear_onnx^^Conv_1235_1" -> "400 /stage3/stage3.6/branch2/branch2.5/Conv" [label="[116, 116, 1, 1]", style=solid]; -"400 /stage3/stage3.6/branch2/branch2.5/Conv" -> "401 /stage3/stage3.6/branch2/branch2.7/Relu" [label="[1]", style=solid]; -"401 /stage3/stage3.6/branch2/branch2.7/Relu" -> "402 QuantizeLinear_/stage3/stage3.6/branch2/branch2.7/Relu_output_0_1" [label="[1]", style=solid]; -"402 QuantizeLinear_/stage3/stage3.6/branch2/branch2.7/Relu_output_0_1" -> "403 DequantizeLinear_/stage3/stage3.6/branch2/branch2.7/Relu_output_0_1" [label="[1]", style=dashed]; -"403 DequantizeLinear_/stage3/stage3.6/branch2/branch2.7/Relu_output_0_1" -> "404 /stage3/stage3.6/Concat" [label="[1]", style=solid]; -"404 /stage3/stage3.6/Concat" -> "406 /stage3/stage3.6/Reshape" [label="[1]", style=solid]; +"386 /stage3/stage3.6/Slice_1" -> "389 /stage3/stage3.6/branch2/branch2.0/Conv" [label="[]", style=solid]; +"387 QuantizeLinear_onnx^^Conv_1244_1" -> "388 DequantizeLinear_onnx^^Conv_1244_1" [label="[116, 116, 1, 1]", style=dashed]; +"388 DequantizeLinear_onnx^^Conv_1244_1" -> "389 /stage3/stage3.6/branch2/branch2.0/Conv" [label="[116, 116, 1, 1]", style=solid]; +"389 /stage3/stage3.6/branch2/branch2.0/Conv" -> "390 /stage3/stage3.6/branch2/branch2.2/Relu" [label="[]", style=solid]; +"390 /stage3/stage3.6/branch2/branch2.2/Relu" -> "391 QuantizeLinear_/stage3/stage3.6/branch2/branch2.2/Relu_output_0_1" [label="[]", style=solid]; +"391 QuantizeLinear_/stage3/stage3.6/branch2/branch2.2/Relu_output_0_1" -> "392 DequantizeLinear_/stage3/stage3.6/branch2/branch2.2/Relu_output_0_1" [label="[]", style=dashed]; +"392 DequantizeLinear_/stage3/stage3.6/branch2/branch2.2/Relu_output_0_1" -> "395 /stage3/stage3.6/branch2/branch2.3/Conv" [label="[]", style=solid]; +"393 QuantizeLinear_onnx^^Conv_1247_1" -> "394 DequantizeLinear_onnx^^Conv_1247_1" [label="[116, 1, 3, 3]", style=dashed]; +"394 DequantizeLinear_onnx^^Conv_1247_1" -> "395 /stage3/stage3.6/branch2/branch2.3/Conv" [label="[116, 1, 3, 3]", style=solid]; +"395 /stage3/stage3.6/branch2/branch2.3/Conv" -> "396 QuantizeLinear_/stage3/stage3.6/branch2/branch2.3/Conv_output_0_1" [label="[]", style=solid]; +"396 QuantizeLinear_/stage3/stage3.6/branch2/branch2.3/Conv_output_0_1" -> "397 DequantizeLinear_/stage3/stage3.6/branch2/branch2.3/Conv_output_0_1" [label="[]", style=dashed]; +"397 DequantizeLinear_/stage3/stage3.6/branch2/branch2.3/Conv_output_0_1" -> "400 /stage3/stage3.6/branch2/branch2.5/Conv" [label="[]", style=solid]; +"398 QuantizeLinear_onnx^^Conv_1250_1" -> "399 DequantizeLinear_onnx^^Conv_1250_1" [label="[116, 116, 1, 1]", style=dashed]; +"399 DequantizeLinear_onnx^^Conv_1250_1" -> "400 /stage3/stage3.6/branch2/branch2.5/Conv" [label="[116, 116, 1, 1]", style=solid]; +"400 /stage3/stage3.6/branch2/branch2.5/Conv" -> "401 /stage3/stage3.6/branch2/branch2.7/Relu" [label="[]", style=solid]; +"401 /stage3/stage3.6/branch2/branch2.7/Relu" -> "402 QuantizeLinear_/stage3/stage3.6/branch2/branch2.7/Relu_output_0_1" [label="[]", style=solid]; +"402 QuantizeLinear_/stage3/stage3.6/branch2/branch2.7/Relu_output_0_1" -> "403 DequantizeLinear_/stage3/stage3.6/branch2/branch2.7/Relu_output_0_1" [label="[]", style=dashed]; +"403 DequantizeLinear_/stage3/stage3.6/branch2/branch2.7/Relu_output_0_1" -> "404 /stage3/stage3.6/Concat" [label="[]", style=solid]; +"404 /stage3/stage3.6/Concat" -> "406 /stage3/stage3.6/Reshape" [label="[]", style=solid]; "405 /stage3/stage3.6/Constant_6" -> "406 /stage3/stage3.6/Reshape" [label="[5]", style=dashed]; "406 /stage3/stage3.6/Reshape" -> "407 /stage3/stage3.6/Transpose" [label="[1, 2, 116, 14, 14]", style=solid]; "407 /stage3/stage3.6/Transpose" -> "409 /stage3/stage3.6/Reshape_1" [label="[1, 116, 2, 14, 14]", style=solid]; @@ -1087,56 +1087,56 @@ strict digraph { "418 /stage3/stage3.7/Constant_4" -> "419 /stage3/stage3.7/Mul" [label="[1]", style=dashed]; "419 /stage3/stage3.7/Mul" -> "420 /stage3/stage3.7/Slice" [label="[1]", style=dashed]; "419 /stage3/stage3.7/Mul" -> "423 /stage3/stage3.7/Slice_1" [label="[1]", style=dashed]; -"420 /stage3/stage3.7/Slice" -> "441 /stage3/stage3.7/Concat" [label="[1]", style=solid]; +"420 /stage3/stage3.7/Slice" -> "441 /stage3/stage3.7/Concat" [label="[]", style=solid]; "421 /stage3/stage3.7/Constant_5" -> "422 /stage3/stage3.7/Mul_1" [label="[1]", style=dashed]; "422 /stage3/stage3.7/Mul_1" -> "423 /stage3/stage3.7/Slice_1" [label="[1]", style=dashed]; -"423 /stage3/stage3.7/Slice_1" -> "426 /stage3/stage3.7/branch2/branch2.0/Conv" [label="[1]", style=solid]; -"424 QuantizeLinear_onnx^^Conv_1238_1" -> "425 DequantizeLinear_onnx^^Conv_1238_1" [label="[116, 116, 1, 1]", style=dashed]; -"425 DequantizeLinear_onnx^^Conv_1238_1" -> "426 /stage3/stage3.7/branch2/branch2.0/Conv" [label="[116, 116, 1, 1]", style=solid]; -"426 /stage3/stage3.7/branch2/branch2.0/Conv" -> "427 /stage3/stage3.7/branch2/branch2.2/Relu" [label="[1]", style=solid]; -"427 /stage3/stage3.7/branch2/branch2.2/Relu" -> "428 QuantizeLinear_/stage3/stage3.7/branch2/branch2.2/Relu_output_0_1" [label="[1]", style=solid]; -"428 QuantizeLinear_/stage3/stage3.7/branch2/branch2.2/Relu_output_0_1" -> "429 DequantizeLinear_/stage3/stage3.7/branch2/branch2.2/Relu_output_0_1" [label="[1]", style=dashed]; -"429 DequantizeLinear_/stage3/stage3.7/branch2/branch2.2/Relu_output_0_1" -> "432 /stage3/stage3.7/branch2/branch2.3/Conv" [label="[1]", style=solid]; -"430 QuantizeLinear_onnx^^Conv_1241_1" -> "431 DequantizeLinear_onnx^^Conv_1241_1" [label="[116, 1, 3, 3]", style=dashed]; -"431 DequantizeLinear_onnx^^Conv_1241_1" -> "432 /stage3/stage3.7/branch2/branch2.3/Conv" [label="[116, 1, 3, 3]", style=solid]; -"432 /stage3/stage3.7/branch2/branch2.3/Conv" -> "433 QuantizeLinear_/stage3/stage3.7/branch2/branch2.3/Conv_output_0_1" [label="[1]", style=solid]; -"433 QuantizeLinear_/stage3/stage3.7/branch2/branch2.3/Conv_output_0_1" -> "434 DequantizeLinear_/stage3/stage3.7/branch2/branch2.3/Conv_output_0_1" [label="[1]", style=dashed]; -"434 DequantizeLinear_/stage3/stage3.7/branch2/branch2.3/Conv_output_0_1" -> "437 /stage3/stage3.7/branch2/branch2.5/Conv" [label="[1]", style=solid]; -"435 QuantizeLinear_onnx^^Conv_1244_1" -> "436 DequantizeLinear_onnx^^Conv_1244_1" [label="[116, 116, 1, 1]", style=dashed]; -"436 DequantizeLinear_onnx^^Conv_1244_1" -> "437 /stage3/stage3.7/branch2/branch2.5/Conv" [label="[116, 116, 1, 1]", style=solid]; -"437 /stage3/stage3.7/branch2/branch2.5/Conv" -> "438 /stage3/stage3.7/branch2/branch2.7/Relu" [label="[1]", style=solid]; -"438 /stage3/stage3.7/branch2/branch2.7/Relu" -> "439 QuantizeLinear_/stage3/stage3.7/branch2/branch2.7/Relu_output_0_1" [label="[1]", style=solid]; -"439 QuantizeLinear_/stage3/stage3.7/branch2/branch2.7/Relu_output_0_1" -> "440 DequantizeLinear_/stage3/stage3.7/branch2/branch2.7/Relu_output_0_1" [label="[1]", style=dashed]; -"440 DequantizeLinear_/stage3/stage3.7/branch2/branch2.7/Relu_output_0_1" -> "441 /stage3/stage3.7/Concat" [label="[1]", style=solid]; -"441 /stage3/stage3.7/Concat" -> "443 /stage3/stage3.7/Reshape" [label="[1]", style=solid]; +"423 /stage3/stage3.7/Slice_1" -> "426 /stage3/stage3.7/branch2/branch2.0/Conv" [label="[]", style=solid]; +"424 QuantizeLinear_onnx^^Conv_1253_1" -> "425 DequantizeLinear_onnx^^Conv_1253_1" [label="[116, 116, 1, 1]", style=dashed]; +"425 DequantizeLinear_onnx^^Conv_1253_1" -> "426 /stage3/stage3.7/branch2/branch2.0/Conv" [label="[116, 116, 1, 1]", style=solid]; +"426 /stage3/stage3.7/branch2/branch2.0/Conv" -> "427 /stage3/stage3.7/branch2/branch2.2/Relu" [label="[]", style=solid]; +"427 /stage3/stage3.7/branch2/branch2.2/Relu" -> "428 QuantizeLinear_/stage3/stage3.7/branch2/branch2.2/Relu_output_0_1" [label="[]", style=solid]; +"428 QuantizeLinear_/stage3/stage3.7/branch2/branch2.2/Relu_output_0_1" -> "429 DequantizeLinear_/stage3/stage3.7/branch2/branch2.2/Relu_output_0_1" [label="[]", style=dashed]; +"429 DequantizeLinear_/stage3/stage3.7/branch2/branch2.2/Relu_output_0_1" -> "432 /stage3/stage3.7/branch2/branch2.3/Conv" [label="[]", style=solid]; +"430 QuantizeLinear_onnx^^Conv_1256_1" -> "431 DequantizeLinear_onnx^^Conv_1256_1" [label="[116, 1, 3, 3]", style=dashed]; +"431 DequantizeLinear_onnx^^Conv_1256_1" -> "432 /stage3/stage3.7/branch2/branch2.3/Conv" [label="[116, 1, 3, 3]", style=solid]; +"432 /stage3/stage3.7/branch2/branch2.3/Conv" -> "433 QuantizeLinear_/stage3/stage3.7/branch2/branch2.3/Conv_output_0_1" [label="[]", style=solid]; +"433 QuantizeLinear_/stage3/stage3.7/branch2/branch2.3/Conv_output_0_1" -> "434 DequantizeLinear_/stage3/stage3.7/branch2/branch2.3/Conv_output_0_1" [label="[]", style=dashed]; +"434 DequantizeLinear_/stage3/stage3.7/branch2/branch2.3/Conv_output_0_1" -> "437 /stage3/stage3.7/branch2/branch2.5/Conv" [label="[]", style=solid]; +"435 QuantizeLinear_onnx^^Conv_1259_1" -> "436 DequantizeLinear_onnx^^Conv_1259_1" [label="[116, 116, 1, 1]", style=dashed]; +"436 DequantizeLinear_onnx^^Conv_1259_1" -> "437 /stage3/stage3.7/branch2/branch2.5/Conv" [label="[116, 116, 1, 1]", style=solid]; +"437 /stage3/stage3.7/branch2/branch2.5/Conv" -> "438 /stage3/stage3.7/branch2/branch2.7/Relu" [label="[]", style=solid]; +"438 /stage3/stage3.7/branch2/branch2.7/Relu" -> "439 QuantizeLinear_/stage3/stage3.7/branch2/branch2.7/Relu_output_0_1" [label="[]", style=solid]; +"439 QuantizeLinear_/stage3/stage3.7/branch2/branch2.7/Relu_output_0_1" -> "440 DequantizeLinear_/stage3/stage3.7/branch2/branch2.7/Relu_output_0_1" [label="[]", style=dashed]; +"440 DequantizeLinear_/stage3/stage3.7/branch2/branch2.7/Relu_output_0_1" -> "441 /stage3/stage3.7/Concat" [label="[]", style=solid]; +"441 /stage3/stage3.7/Concat" -> "443 /stage3/stage3.7/Reshape" [label="[]", style=solid]; "442 /stage3/stage3.7/Constant_6" -> "443 /stage3/stage3.7/Reshape" [label="[5]", style=dashed]; "443 /stage3/stage3.7/Reshape" -> "444 /stage3/stage3.7/Transpose" [label="[1, 2, 116, 14, 14]", style=solid]; "444 /stage3/stage3.7/Transpose" -> "446 /stage3/stage3.7/Reshape_1" [label="[1, 116, 2, 14, 14]", style=solid]; "445 /stage3/stage3.7/Constant_7" -> "446 /stage3/stage3.7/Reshape_1" [label="[4]", style=dashed]; "446 /stage3/stage3.7/Reshape_1" -> "449 /stage4/stage4.0/branch1/branch1.0/Conv" [label="[1, 232, 14, 14]", style=solid]; "446 /stage3/stage3.7/Reshape_1" -> "458 /stage4/stage4.0/branch2/branch2.0/Conv" [label="[1, 232, 14, 14]", style=solid]; -"447 QuantizeLinear_onnx^^Conv_1247_1" -> "448 DequantizeLinear_onnx^^Conv_1247_1" [label="[232, 1, 3, 3]", style=dashed]; -"448 DequantizeLinear_onnx^^Conv_1247_1" -> "449 /stage4/stage4.0/branch1/branch1.0/Conv" [label="[232, 1, 3, 3]", style=solid]; +"447 QuantizeLinear_onnx^^Conv_1262_1" -> "448 DequantizeLinear_onnx^^Conv_1262_1" [label="[232, 1, 3, 3]", style=dashed]; +"448 DequantizeLinear_onnx^^Conv_1262_1" -> "449 /stage4/stage4.0/branch1/branch1.0/Conv" [label="[232, 1, 3, 3]", style=solid]; "449 /stage4/stage4.0/branch1/branch1.0/Conv" -> "450 QuantizeLinear_/stage4/stage4.0/branch1/branch1.0/Conv_output_0_1" [label="[1, 232, 7, 7]", style=solid]; "450 QuantizeLinear_/stage4/stage4.0/branch1/branch1.0/Conv_output_0_1" -> "451 DequantizeLinear_/stage4/stage4.0/branch1/branch1.0/Conv_output_0_1" [label="[1, 232, 7, 7]", style=dashed]; "451 DequantizeLinear_/stage4/stage4.0/branch1/branch1.0/Conv_output_0_1" -> "454 /stage4/stage4.0/branch1/branch1.2/Conv" [label="[1, 232, 7, 7]", style=solid]; -"452 QuantizeLinear_onnx^^Conv_1250_1" -> "453 DequantizeLinear_onnx^^Conv_1250_1" [label="[232, 232, 1, 1]", style=dashed]; -"453 DequantizeLinear_onnx^^Conv_1250_1" -> "454 /stage4/stage4.0/branch1/branch1.2/Conv" [label="[232, 232, 1, 1]", style=solid]; +"452 QuantizeLinear_onnx^^Conv_1265_1" -> "453 DequantizeLinear_onnx^^Conv_1265_1" [label="[232, 232, 1, 1]", style=dashed]; +"453 DequantizeLinear_onnx^^Conv_1265_1" -> "454 /stage4/stage4.0/branch1/branch1.2/Conv" [label="[232, 232, 1, 1]", style=solid]; "454 /stage4/stage4.0/branch1/branch1.2/Conv" -> "455 /stage4/stage4.0/branch1/branch1.4/Relu" [label="[1, 232, 7, 7]", style=solid]; "455 /stage4/stage4.0/branch1/branch1.4/Relu" -> "471 QuantizeLinear_/stage4/stage4.0/branch1/branch1.4/Relu_output_0_1" [label="[1, 232, 7, 7]", style=solid]; -"456 QuantizeLinear_onnx^^Conv_1253_1" -> "457 DequantizeLinear_onnx^^Conv_1253_1" [label="[232, 232, 1, 1]", style=dashed]; -"457 DequantizeLinear_onnx^^Conv_1253_1" -> "458 /stage4/stage4.0/branch2/branch2.0/Conv" [label="[232, 232, 1, 1]", style=solid]; +"456 QuantizeLinear_onnx^^Conv_1268_1" -> "457 DequantizeLinear_onnx^^Conv_1268_1" [label="[232, 232, 1, 1]", style=dashed]; +"457 DequantizeLinear_onnx^^Conv_1268_1" -> "458 /stage4/stage4.0/branch2/branch2.0/Conv" [label="[232, 232, 1, 1]", style=solid]; "458 /stage4/stage4.0/branch2/branch2.0/Conv" -> "459 /stage4/stage4.0/branch2/branch2.2/Relu" [label="[1, 232, 14, 14]", style=solid]; "459 /stage4/stage4.0/branch2/branch2.2/Relu" -> "460 QuantizeLinear_/stage4/stage4.0/branch2/branch2.2/Relu_output_0_1" [label="[1, 232, 14, 14]", style=solid]; "460 QuantizeLinear_/stage4/stage4.0/branch2/branch2.2/Relu_output_0_1" -> "461 DequantizeLinear_/stage4/stage4.0/branch2/branch2.2/Relu_output_0_1" [label="[1, 232, 14, 14]", style=dashed]; "461 DequantizeLinear_/stage4/stage4.0/branch2/branch2.2/Relu_output_0_1" -> "464 /stage4/stage4.0/branch2/branch2.3/Conv" [label="[1, 232, 14, 14]", style=solid]; -"462 QuantizeLinear_onnx^^Conv_1256_1" -> "463 DequantizeLinear_onnx^^Conv_1256_1" [label="[232, 1, 3, 3]", style=dashed]; -"463 DequantizeLinear_onnx^^Conv_1256_1" -> "464 /stage4/stage4.0/branch2/branch2.3/Conv" [label="[232, 1, 3, 3]", style=solid]; +"462 QuantizeLinear_onnx^^Conv_1271_1" -> "463 DequantizeLinear_onnx^^Conv_1271_1" [label="[232, 1, 3, 3]", style=dashed]; +"463 DequantizeLinear_onnx^^Conv_1271_1" -> "464 /stage4/stage4.0/branch2/branch2.3/Conv" [label="[232, 1, 3, 3]", style=solid]; "464 /stage4/stage4.0/branch2/branch2.3/Conv" -> "465 QuantizeLinear_/stage4/stage4.0/branch2/branch2.3/Conv_output_0_1" [label="[1, 232, 7, 7]", style=solid]; "465 QuantizeLinear_/stage4/stage4.0/branch2/branch2.3/Conv_output_0_1" -> "466 DequantizeLinear_/stage4/stage4.0/branch2/branch2.3/Conv_output_0_1" [label="[1, 232, 7, 7]", style=dashed]; "466 DequantizeLinear_/stage4/stage4.0/branch2/branch2.3/Conv_output_0_1" -> "469 /stage4/stage4.0/branch2/branch2.5/Conv" [label="[1, 232, 7, 7]", style=solid]; -"467 QuantizeLinear_onnx^^Conv_1259_1" -> "468 DequantizeLinear_onnx^^Conv_1259_1" [label="[232, 232, 1, 1]", style=dashed]; -"468 DequantizeLinear_onnx^^Conv_1259_1" -> "469 /stage4/stage4.0/branch2/branch2.5/Conv" [label="[232, 232, 1, 1]", style=solid]; +"467 QuantizeLinear_onnx^^Conv_1274_1" -> "468 DequantizeLinear_onnx^^Conv_1274_1" [label="[232, 232, 1, 1]", style=dashed]; +"468 DequantizeLinear_onnx^^Conv_1274_1" -> "469 /stage4/stage4.0/branch2/branch2.5/Conv" [label="[232, 232, 1, 1]", style=solid]; "469 /stage4/stage4.0/branch2/branch2.5/Conv" -> "470 /stage4/stage4.0/branch2/branch2.7/Relu" [label="[1, 232, 7, 7]", style=solid]; "470 /stage4/stage4.0/branch2/branch2.7/Relu" -> "473 QuantizeLinear_/stage4/stage4.0/branch2/branch2.7/Relu_output_0_1" [label="[1, 232, 7, 7]", style=solid]; "471 QuantizeLinear_/stage4/stage4.0/branch1/branch1.4/Relu_output_0_1" -> "472 DequantizeLinear_/stage4/stage4.0/branch1/branch1.4/Relu_output_0_1" [label="[1, 232, 7, 7]", style=dashed]; @@ -1165,28 +1165,28 @@ strict digraph { "489 /stage4/stage4.1/Constant_4" -> "490 /stage4/stage4.1/Mul" [label="[1]", style=dashed]; "490 /stage4/stage4.1/Mul" -> "491 /stage4/stage4.1/Slice" [label="[1]", style=dashed]; "490 /stage4/stage4.1/Mul" -> "494 /stage4/stage4.1/Slice_1" [label="[1]", style=dashed]; -"491 /stage4/stage4.1/Slice" -> "512 /stage4/stage4.1/Concat" [label="[1]", style=solid]; +"491 /stage4/stage4.1/Slice" -> "512 /stage4/stage4.1/Concat" [label="[]", style=solid]; "492 /stage4/stage4.1/Constant_5" -> "493 /stage4/stage4.1/Mul_1" [label="[1]", style=dashed]; "493 /stage4/stage4.1/Mul_1" -> "494 /stage4/stage4.1/Slice_1" [label="[1]", style=dashed]; -"494 /stage4/stage4.1/Slice_1" -> "497 /stage4/stage4.1/branch2/branch2.0/Conv" [label="[1]", style=solid]; -"495 QuantizeLinear_onnx^^Conv_1262_1" -> "496 DequantizeLinear_onnx^^Conv_1262_1" [label="[232, 232, 1, 1]", style=dashed]; -"496 DequantizeLinear_onnx^^Conv_1262_1" -> "497 /stage4/stage4.1/branch2/branch2.0/Conv" [label="[232, 232, 1, 1]", style=solid]; -"497 /stage4/stage4.1/branch2/branch2.0/Conv" -> "498 /stage4/stage4.1/branch2/branch2.2/Relu" [label="[1]", style=solid]; -"498 /stage4/stage4.1/branch2/branch2.2/Relu" -> "499 QuantizeLinear_/stage4/stage4.1/branch2/branch2.2/Relu_output_0_1" [label="[1]", style=solid]; -"499 QuantizeLinear_/stage4/stage4.1/branch2/branch2.2/Relu_output_0_1" -> "500 DequantizeLinear_/stage4/stage4.1/branch2/branch2.2/Relu_output_0_1" [label="[1]", style=dashed]; -"500 DequantizeLinear_/stage4/stage4.1/branch2/branch2.2/Relu_output_0_1" -> "503 /stage4/stage4.1/branch2/branch2.3/Conv" [label="[1]", style=solid]; -"501 QuantizeLinear_onnx^^Conv_1265_1" -> "502 DequantizeLinear_onnx^^Conv_1265_1" [label="[232, 1, 3, 3]", style=dashed]; -"502 DequantizeLinear_onnx^^Conv_1265_1" -> "503 /stage4/stage4.1/branch2/branch2.3/Conv" [label="[232, 1, 3, 3]", style=solid]; -"503 /stage4/stage4.1/branch2/branch2.3/Conv" -> "504 QuantizeLinear_/stage4/stage4.1/branch2/branch2.3/Conv_output_0_1" [label="[1]", style=solid]; -"504 QuantizeLinear_/stage4/stage4.1/branch2/branch2.3/Conv_output_0_1" -> "505 DequantizeLinear_/stage4/stage4.1/branch2/branch2.3/Conv_output_0_1" [label="[1]", style=dashed]; -"505 DequantizeLinear_/stage4/stage4.1/branch2/branch2.3/Conv_output_0_1" -> "508 /stage4/stage4.1/branch2/branch2.5/Conv" [label="[1]", style=solid]; -"506 QuantizeLinear_onnx^^Conv_1268_1" -> "507 DequantizeLinear_onnx^^Conv_1268_1" [label="[232, 232, 1, 1]", style=dashed]; -"507 DequantizeLinear_onnx^^Conv_1268_1" -> "508 /stage4/stage4.1/branch2/branch2.5/Conv" [label="[232, 232, 1, 1]", style=solid]; -"508 /stage4/stage4.1/branch2/branch2.5/Conv" -> "509 /stage4/stage4.1/branch2/branch2.7/Relu" [label="[1]", style=solid]; -"509 /stage4/stage4.1/branch2/branch2.7/Relu" -> "510 QuantizeLinear_/stage4/stage4.1/branch2/branch2.7/Relu_output_0_1" [label="[1]", style=solid]; -"510 QuantizeLinear_/stage4/stage4.1/branch2/branch2.7/Relu_output_0_1" -> "511 DequantizeLinear_/stage4/stage4.1/branch2/branch2.7/Relu_output_0_1" [label="[1]", style=dashed]; -"511 DequantizeLinear_/stage4/stage4.1/branch2/branch2.7/Relu_output_0_1" -> "512 /stage4/stage4.1/Concat" [label="[1]", style=solid]; -"512 /stage4/stage4.1/Concat" -> "514 /stage4/stage4.1/Reshape" [label="[1]", style=solid]; +"494 /stage4/stage4.1/Slice_1" -> "497 /stage4/stage4.1/branch2/branch2.0/Conv" [label="[]", style=solid]; +"495 QuantizeLinear_onnx^^Conv_1277_1" -> "496 DequantizeLinear_onnx^^Conv_1277_1" [label="[232, 232, 1, 1]", style=dashed]; +"496 DequantizeLinear_onnx^^Conv_1277_1" -> "497 /stage4/stage4.1/branch2/branch2.0/Conv" [label="[232, 232, 1, 1]", style=solid]; +"497 /stage4/stage4.1/branch2/branch2.0/Conv" -> "498 /stage4/stage4.1/branch2/branch2.2/Relu" [label="[]", style=solid]; +"498 /stage4/stage4.1/branch2/branch2.2/Relu" -> "499 QuantizeLinear_/stage4/stage4.1/branch2/branch2.2/Relu_output_0_1" [label="[]", style=solid]; +"499 QuantizeLinear_/stage4/stage4.1/branch2/branch2.2/Relu_output_0_1" -> "500 DequantizeLinear_/stage4/stage4.1/branch2/branch2.2/Relu_output_0_1" [label="[]", style=dashed]; +"500 DequantizeLinear_/stage4/stage4.1/branch2/branch2.2/Relu_output_0_1" -> "503 /stage4/stage4.1/branch2/branch2.3/Conv" [label="[]", style=solid]; +"501 QuantizeLinear_onnx^^Conv_1280_1" -> "502 DequantizeLinear_onnx^^Conv_1280_1" [label="[232, 1, 3, 3]", style=dashed]; +"502 DequantizeLinear_onnx^^Conv_1280_1" -> "503 /stage4/stage4.1/branch2/branch2.3/Conv" [label="[232, 1, 3, 3]", style=solid]; +"503 /stage4/stage4.1/branch2/branch2.3/Conv" -> "504 QuantizeLinear_/stage4/stage4.1/branch2/branch2.3/Conv_output_0_1" [label="[]", style=solid]; +"504 QuantizeLinear_/stage4/stage4.1/branch2/branch2.3/Conv_output_0_1" -> "505 DequantizeLinear_/stage4/stage4.1/branch2/branch2.3/Conv_output_0_1" [label="[]", style=dashed]; +"505 DequantizeLinear_/stage4/stage4.1/branch2/branch2.3/Conv_output_0_1" -> "508 /stage4/stage4.1/branch2/branch2.5/Conv" [label="[]", style=solid]; +"506 QuantizeLinear_onnx^^Conv_1283_1" -> "507 DequantizeLinear_onnx^^Conv_1283_1" [label="[232, 232, 1, 1]", style=dashed]; +"507 DequantizeLinear_onnx^^Conv_1283_1" -> "508 /stage4/stage4.1/branch2/branch2.5/Conv" [label="[232, 232, 1, 1]", style=solid]; +"508 /stage4/stage4.1/branch2/branch2.5/Conv" -> "509 /stage4/stage4.1/branch2/branch2.7/Relu" [label="[]", style=solid]; +"509 /stage4/stage4.1/branch2/branch2.7/Relu" -> "510 QuantizeLinear_/stage4/stage4.1/branch2/branch2.7/Relu_output_0_1" [label="[]", style=solid]; +"510 QuantizeLinear_/stage4/stage4.1/branch2/branch2.7/Relu_output_0_1" -> "511 DequantizeLinear_/stage4/stage4.1/branch2/branch2.7/Relu_output_0_1" [label="[]", style=dashed]; +"511 DequantizeLinear_/stage4/stage4.1/branch2/branch2.7/Relu_output_0_1" -> "512 /stage4/stage4.1/Concat" [label="[]", style=solid]; +"512 /stage4/stage4.1/Concat" -> "514 /stage4/stage4.1/Reshape" [label="[]", style=solid]; "513 /stage4/stage4.1/Constant_6" -> "514 /stage4/stage4.1/Reshape" [label="[5]", style=dashed]; "514 /stage4/stage4.1/Reshape" -> "515 /stage4/stage4.1/Transpose" [label="[1, 2, 232, 7, 7]", style=solid]; "515 /stage4/stage4.1/Transpose" -> "517 /stage4/stage4.1/Reshape_1" [label="[1, 232, 2, 7, 7]", style=solid]; @@ -1208,28 +1208,28 @@ strict digraph { "526 /stage4/stage4.2/Constant_4" -> "527 /stage4/stage4.2/Mul" [label="[1]", style=dashed]; "527 /stage4/stage4.2/Mul" -> "528 /stage4/stage4.2/Slice" [label="[1]", style=dashed]; "527 /stage4/stage4.2/Mul" -> "531 /stage4/stage4.2/Slice_1" [label="[1]", style=dashed]; -"528 /stage4/stage4.2/Slice" -> "549 /stage4/stage4.2/Concat" [label="[1]", style=solid]; +"528 /stage4/stage4.2/Slice" -> "549 /stage4/stage4.2/Concat" [label="[]", style=solid]; "529 /stage4/stage4.2/Constant_5" -> "530 /stage4/stage4.2/Mul_1" [label="[1]", style=dashed]; "530 /stage4/stage4.2/Mul_1" -> "531 /stage4/stage4.2/Slice_1" [label="[1]", style=dashed]; -"531 /stage4/stage4.2/Slice_1" -> "534 /stage4/stage4.2/branch2/branch2.0/Conv" [label="[1]", style=solid]; -"532 QuantizeLinear_onnx^^Conv_1271_1" -> "533 DequantizeLinear_onnx^^Conv_1271_1" [label="[232, 232, 1, 1]", style=dashed]; -"533 DequantizeLinear_onnx^^Conv_1271_1" -> "534 /stage4/stage4.2/branch2/branch2.0/Conv" [label="[232, 232, 1, 1]", style=solid]; -"534 /stage4/stage4.2/branch2/branch2.0/Conv" -> "535 /stage4/stage4.2/branch2/branch2.2/Relu" [label="[1]", style=solid]; -"535 /stage4/stage4.2/branch2/branch2.2/Relu" -> "536 QuantizeLinear_/stage4/stage4.2/branch2/branch2.2/Relu_output_0_1" [label="[1]", style=solid]; -"536 QuantizeLinear_/stage4/stage4.2/branch2/branch2.2/Relu_output_0_1" -> "537 DequantizeLinear_/stage4/stage4.2/branch2/branch2.2/Relu_output_0_1" [label="[1]", style=dashed]; -"537 DequantizeLinear_/stage4/stage4.2/branch2/branch2.2/Relu_output_0_1" -> "540 /stage4/stage4.2/branch2/branch2.3/Conv" [label="[1]", style=solid]; -"538 QuantizeLinear_onnx^^Conv_1274_1" -> "539 DequantizeLinear_onnx^^Conv_1274_1" [label="[232, 1, 3, 3]", style=dashed]; -"539 DequantizeLinear_onnx^^Conv_1274_1" -> "540 /stage4/stage4.2/branch2/branch2.3/Conv" [label="[232, 1, 3, 3]", style=solid]; -"540 /stage4/stage4.2/branch2/branch2.3/Conv" -> "541 QuantizeLinear_/stage4/stage4.2/branch2/branch2.3/Conv_output_0_1" [label="[1]", style=solid]; -"541 QuantizeLinear_/stage4/stage4.2/branch2/branch2.3/Conv_output_0_1" -> "542 DequantizeLinear_/stage4/stage4.2/branch2/branch2.3/Conv_output_0_1" [label="[1]", style=dashed]; -"542 DequantizeLinear_/stage4/stage4.2/branch2/branch2.3/Conv_output_0_1" -> "545 /stage4/stage4.2/branch2/branch2.5/Conv" [label="[1]", style=solid]; -"543 QuantizeLinear_onnx^^Conv_1277_1" -> "544 DequantizeLinear_onnx^^Conv_1277_1" [label="[232, 232, 1, 1]", style=dashed]; -"544 DequantizeLinear_onnx^^Conv_1277_1" -> "545 /stage4/stage4.2/branch2/branch2.5/Conv" [label="[232, 232, 1, 1]", style=solid]; -"545 /stage4/stage4.2/branch2/branch2.5/Conv" -> "546 /stage4/stage4.2/branch2/branch2.7/Relu" [label="[1]", style=solid]; -"546 /stage4/stage4.2/branch2/branch2.7/Relu" -> "547 QuantizeLinear_/stage4/stage4.2/branch2/branch2.7/Relu_output_0_1" [label="[1]", style=solid]; -"547 QuantizeLinear_/stage4/stage4.2/branch2/branch2.7/Relu_output_0_1" -> "548 DequantizeLinear_/stage4/stage4.2/branch2/branch2.7/Relu_output_0_1" [label="[1]", style=dashed]; -"548 DequantizeLinear_/stage4/stage4.2/branch2/branch2.7/Relu_output_0_1" -> "549 /stage4/stage4.2/Concat" [label="[1]", style=solid]; -"549 /stage4/stage4.2/Concat" -> "551 /stage4/stage4.2/Reshape" [label="[1]", style=solid]; +"531 /stage4/stage4.2/Slice_1" -> "534 /stage4/stage4.2/branch2/branch2.0/Conv" [label="[]", style=solid]; +"532 QuantizeLinear_onnx^^Conv_1286_1" -> "533 DequantizeLinear_onnx^^Conv_1286_1" [label="[232, 232, 1, 1]", style=dashed]; +"533 DequantizeLinear_onnx^^Conv_1286_1" -> "534 /stage4/stage4.2/branch2/branch2.0/Conv" [label="[232, 232, 1, 1]", style=solid]; +"534 /stage4/stage4.2/branch2/branch2.0/Conv" -> "535 /stage4/stage4.2/branch2/branch2.2/Relu" [label="[]", style=solid]; +"535 /stage4/stage4.2/branch2/branch2.2/Relu" -> "536 QuantizeLinear_/stage4/stage4.2/branch2/branch2.2/Relu_output_0_1" [label="[]", style=solid]; +"536 QuantizeLinear_/stage4/stage4.2/branch2/branch2.2/Relu_output_0_1" -> "537 DequantizeLinear_/stage4/stage4.2/branch2/branch2.2/Relu_output_0_1" [label="[]", style=dashed]; +"537 DequantizeLinear_/stage4/stage4.2/branch2/branch2.2/Relu_output_0_1" -> "540 /stage4/stage4.2/branch2/branch2.3/Conv" [label="[]", style=solid]; +"538 QuantizeLinear_onnx^^Conv_1289_1" -> "539 DequantizeLinear_onnx^^Conv_1289_1" [label="[232, 1, 3, 3]", style=dashed]; +"539 DequantizeLinear_onnx^^Conv_1289_1" -> "540 /stage4/stage4.2/branch2/branch2.3/Conv" [label="[232, 1, 3, 3]", style=solid]; +"540 /stage4/stage4.2/branch2/branch2.3/Conv" -> "541 QuantizeLinear_/stage4/stage4.2/branch2/branch2.3/Conv_output_0_1" [label="[]", style=solid]; +"541 QuantizeLinear_/stage4/stage4.2/branch2/branch2.3/Conv_output_0_1" -> "542 DequantizeLinear_/stage4/stage4.2/branch2/branch2.3/Conv_output_0_1" [label="[]", style=dashed]; +"542 DequantizeLinear_/stage4/stage4.2/branch2/branch2.3/Conv_output_0_1" -> "545 /stage4/stage4.2/branch2/branch2.5/Conv" [label="[]", style=solid]; +"543 QuantizeLinear_onnx^^Conv_1292_1" -> "544 DequantizeLinear_onnx^^Conv_1292_1" [label="[232, 232, 1, 1]", style=dashed]; +"544 DequantizeLinear_onnx^^Conv_1292_1" -> "545 /stage4/stage4.2/branch2/branch2.5/Conv" [label="[232, 232, 1, 1]", style=solid]; +"545 /stage4/stage4.2/branch2/branch2.5/Conv" -> "546 /stage4/stage4.2/branch2/branch2.7/Relu" [label="[]", style=solid]; +"546 /stage4/stage4.2/branch2/branch2.7/Relu" -> "547 QuantizeLinear_/stage4/stage4.2/branch2/branch2.7/Relu_output_0_1" [label="[]", style=solid]; +"547 QuantizeLinear_/stage4/stage4.2/branch2/branch2.7/Relu_output_0_1" -> "548 DequantizeLinear_/stage4/stage4.2/branch2/branch2.7/Relu_output_0_1" [label="[]", style=dashed]; +"548 DequantizeLinear_/stage4/stage4.2/branch2/branch2.7/Relu_output_0_1" -> "549 /stage4/stage4.2/Concat" [label="[]", style=solid]; +"549 /stage4/stage4.2/Concat" -> "551 /stage4/stage4.2/Reshape" [label="[]", style=solid]; "550 /stage4/stage4.2/Constant_6" -> "551 /stage4/stage4.2/Reshape" [label="[5]", style=dashed]; "551 /stage4/stage4.2/Reshape" -> "552 /stage4/stage4.2/Transpose" [label="[1, 2, 232, 7, 7]", style=solid]; "552 /stage4/stage4.2/Transpose" -> "554 /stage4/stage4.2/Reshape_1" [label="[1, 232, 2, 7, 7]", style=solid]; @@ -1251,35 +1251,35 @@ strict digraph { "563 /stage4/stage4.3/Constant_4" -> "564 /stage4/stage4.3/Mul" [label="[1]", style=dashed]; "564 /stage4/stage4.3/Mul" -> "565 /stage4/stage4.3/Slice" [label="[1]", style=dashed]; "564 /stage4/stage4.3/Mul" -> "568 /stage4/stage4.3/Slice_1" [label="[1]", style=dashed]; -"565 /stage4/stage4.3/Slice" -> "586 /stage4/stage4.3/Concat" [label="[1]", style=solid]; +"565 /stage4/stage4.3/Slice" -> "586 /stage4/stage4.3/Concat" [label="[]", style=solid]; "566 /stage4/stage4.3/Constant_5" -> "567 /stage4/stage4.3/Mul_1" [label="[1]", style=dashed]; "567 /stage4/stage4.3/Mul_1" -> "568 /stage4/stage4.3/Slice_1" [label="[1]", style=dashed]; -"568 /stage4/stage4.3/Slice_1" -> "571 /stage4/stage4.3/branch2/branch2.0/Conv" [label="[1]", style=solid]; -"569 QuantizeLinear_onnx^^Conv_1280_1" -> "570 DequantizeLinear_onnx^^Conv_1280_1" [label="[232, 232, 1, 1]", style=dashed]; -"570 DequantizeLinear_onnx^^Conv_1280_1" -> "571 /stage4/stage4.3/branch2/branch2.0/Conv" [label="[232, 232, 1, 1]", style=solid]; -"571 /stage4/stage4.3/branch2/branch2.0/Conv" -> "572 /stage4/stage4.3/branch2/branch2.2/Relu" [label="[1]", style=solid]; -"572 /stage4/stage4.3/branch2/branch2.2/Relu" -> "573 QuantizeLinear_/stage4/stage4.3/branch2/branch2.2/Relu_output_0_1" [label="[1]", style=solid]; -"573 QuantizeLinear_/stage4/stage4.3/branch2/branch2.2/Relu_output_0_1" -> "574 DequantizeLinear_/stage4/stage4.3/branch2/branch2.2/Relu_output_0_1" [label="[1]", style=dashed]; -"574 DequantizeLinear_/stage4/stage4.3/branch2/branch2.2/Relu_output_0_1" -> "577 /stage4/stage4.3/branch2/branch2.3/Conv" [label="[1]", style=solid]; -"575 QuantizeLinear_onnx^^Conv_1283_1" -> "576 DequantizeLinear_onnx^^Conv_1283_1" [label="[232, 1, 3, 3]", style=dashed]; -"576 DequantizeLinear_onnx^^Conv_1283_1" -> "577 /stage4/stage4.3/branch2/branch2.3/Conv" [label="[232, 1, 3, 3]", style=solid]; -"577 /stage4/stage4.3/branch2/branch2.3/Conv" -> "578 QuantizeLinear_/stage4/stage4.3/branch2/branch2.3/Conv_output_0_1" [label="[1]", style=solid]; -"578 QuantizeLinear_/stage4/stage4.3/branch2/branch2.3/Conv_output_0_1" -> "579 DequantizeLinear_/stage4/stage4.3/branch2/branch2.3/Conv_output_0_1" [label="[1]", style=dashed]; -"579 DequantizeLinear_/stage4/stage4.3/branch2/branch2.3/Conv_output_0_1" -> "582 /stage4/stage4.3/branch2/branch2.5/Conv" [label="[1]", style=solid]; -"580 QuantizeLinear_onnx^^Conv_1286_1" -> "581 DequantizeLinear_onnx^^Conv_1286_1" [label="[232, 232, 1, 1]", style=dashed]; -"581 DequantizeLinear_onnx^^Conv_1286_1" -> "582 /stage4/stage4.3/branch2/branch2.5/Conv" [label="[232, 232, 1, 1]", style=solid]; -"582 /stage4/stage4.3/branch2/branch2.5/Conv" -> "583 /stage4/stage4.3/branch2/branch2.7/Relu" [label="[1]", style=solid]; -"583 /stage4/stage4.3/branch2/branch2.7/Relu" -> "584 QuantizeLinear_/stage4/stage4.3/branch2/branch2.7/Relu_output_0_1" [label="[1]", style=solid]; -"584 QuantizeLinear_/stage4/stage4.3/branch2/branch2.7/Relu_output_0_1" -> "585 DequantizeLinear_/stage4/stage4.3/branch2/branch2.7/Relu_output_0_1" [label="[1]", style=dashed]; -"585 DequantizeLinear_/stage4/stage4.3/branch2/branch2.7/Relu_output_0_1" -> "586 /stage4/stage4.3/Concat" [label="[1]", style=solid]; -"586 /stage4/stage4.3/Concat" -> "588 /stage4/stage4.3/Reshape" [label="[1]", style=solid]; +"568 /stage4/stage4.3/Slice_1" -> "571 /stage4/stage4.3/branch2/branch2.0/Conv" [label="[]", style=solid]; +"569 QuantizeLinear_onnx^^Conv_1295_1" -> "570 DequantizeLinear_onnx^^Conv_1295_1" [label="[232, 232, 1, 1]", style=dashed]; +"570 DequantizeLinear_onnx^^Conv_1295_1" -> "571 /stage4/stage4.3/branch2/branch2.0/Conv" [label="[232, 232, 1, 1]", style=solid]; +"571 /stage4/stage4.3/branch2/branch2.0/Conv" -> "572 /stage4/stage4.3/branch2/branch2.2/Relu" [label="[]", style=solid]; +"572 /stage4/stage4.3/branch2/branch2.2/Relu" -> "573 QuantizeLinear_/stage4/stage4.3/branch2/branch2.2/Relu_output_0_1" [label="[]", style=solid]; +"573 QuantizeLinear_/stage4/stage4.3/branch2/branch2.2/Relu_output_0_1" -> "574 DequantizeLinear_/stage4/stage4.3/branch2/branch2.2/Relu_output_0_1" [label="[]", style=dashed]; +"574 DequantizeLinear_/stage4/stage4.3/branch2/branch2.2/Relu_output_0_1" -> "577 /stage4/stage4.3/branch2/branch2.3/Conv" [label="[]", style=solid]; +"575 QuantizeLinear_onnx^^Conv_1298_1" -> "576 DequantizeLinear_onnx^^Conv_1298_1" [label="[232, 1, 3, 3]", style=dashed]; +"576 DequantizeLinear_onnx^^Conv_1298_1" -> "577 /stage4/stage4.3/branch2/branch2.3/Conv" [label="[232, 1, 3, 3]", style=solid]; +"577 /stage4/stage4.3/branch2/branch2.3/Conv" -> "578 QuantizeLinear_/stage4/stage4.3/branch2/branch2.3/Conv_output_0_1" [label="[]", style=solid]; +"578 QuantizeLinear_/stage4/stage4.3/branch2/branch2.3/Conv_output_0_1" -> "579 DequantizeLinear_/stage4/stage4.3/branch2/branch2.3/Conv_output_0_1" [label="[]", style=dashed]; +"579 DequantizeLinear_/stage4/stage4.3/branch2/branch2.3/Conv_output_0_1" -> "582 /stage4/stage4.3/branch2/branch2.5/Conv" [label="[]", style=solid]; +"580 QuantizeLinear_onnx^^Conv_1301_1" -> "581 DequantizeLinear_onnx^^Conv_1301_1" [label="[232, 232, 1, 1]", style=dashed]; +"581 DequantizeLinear_onnx^^Conv_1301_1" -> "582 /stage4/stage4.3/branch2/branch2.5/Conv" [label="[232, 232, 1, 1]", style=solid]; +"582 /stage4/stage4.3/branch2/branch2.5/Conv" -> "583 /stage4/stage4.3/branch2/branch2.7/Relu" [label="[]", style=solid]; +"583 /stage4/stage4.3/branch2/branch2.7/Relu" -> "584 QuantizeLinear_/stage4/stage4.3/branch2/branch2.7/Relu_output_0_1" [label="[]", style=solid]; +"584 QuantizeLinear_/stage4/stage4.3/branch2/branch2.7/Relu_output_0_1" -> "585 DequantizeLinear_/stage4/stage4.3/branch2/branch2.7/Relu_output_0_1" [label="[]", style=dashed]; +"585 DequantizeLinear_/stage4/stage4.3/branch2/branch2.7/Relu_output_0_1" -> "586 /stage4/stage4.3/Concat" [label="[]", style=solid]; +"586 /stage4/stage4.3/Concat" -> "588 /stage4/stage4.3/Reshape" [label="[]", style=solid]; "587 /stage4/stage4.3/Constant_6" -> "588 /stage4/stage4.3/Reshape" [label="[5]", style=dashed]; "588 /stage4/stage4.3/Reshape" -> "589 /stage4/stage4.3/Transpose" [label="[1, 2, 232, 7, 7]", style=solid]; "589 /stage4/stage4.3/Transpose" -> "591 /stage4/stage4.3/Reshape_1" [label="[1, 232, 2, 7, 7]", style=solid]; "590 /stage4/stage4.3/Constant_7" -> "591 /stage4/stage4.3/Reshape_1" [label="[4]", style=dashed]; "591 /stage4/stage4.3/Reshape_1" -> "594 /conv5/conv5.0/Conv" [label="[1, 464, 7, 7]", style=solid]; -"592 QuantizeLinear_onnx^^Conv_1289_1" -> "593 DequantizeLinear_onnx^^Conv_1289_1" [label="[1024, 464, 1, 1]", style=dashed]; -"593 DequantizeLinear_onnx^^Conv_1289_1" -> "594 /conv5/conv5.0/Conv" [label="[1024, 464, 1, 1]", style=solid]; +"592 QuantizeLinear_onnx^^Conv_1304_1" -> "593 DequantizeLinear_onnx^^Conv_1304_1" [label="[1024, 464, 1, 1]", style=dashed]; +"593 DequantizeLinear_onnx^^Conv_1304_1" -> "594 /conv5/conv5.0/Conv" [label="[1024, 464, 1, 1]", style=solid]; "594 /conv5/conv5.0/Conv" -> "595 /conv5/conv5.2/Relu" [label="[1, 1024, 7, 7]", style=solid]; "595 /conv5/conv5.2/Relu" -> "596 QuantizeLinear_/conv5/conv5.2/Relu_output_0_1" [label="[1, 1024, 7, 7]", style=solid]; "596 QuantizeLinear_/conv5/conv5.2/Relu_output_0_1" -> "597 DequantizeLinear_/conv5/conv5.2/Relu_output_0_1" [label="[1, 1024, 7, 7]", style=dashed]; diff --git a/tests/onnx/models.py b/tests/onnx/models.py index 72516621cfe..98a43bfe984 100644 --- a/tests/onnx/models.py +++ b/tests/onnx/models.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/onnx/opset_converter.py b/tests/onnx/opset_converter.py index 512535b0512..16557e1a369 100644 --- a/tests/onnx/opset_converter.py +++ b/tests/onnx/opset_converter.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/onnx/quantization/__init__.py b/tests/onnx/quantization/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/onnx/quantization/__init__.py +++ b/tests/onnx/quantization/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/onnx/quantization/common.py b/tests/onnx/quantization/common.py index e46b131a928..7423b910efd 100644 --- a/tests/onnx/quantization/common.py +++ b/tests/onnx/quantization/common.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -53,7 +53,7 @@ def mock_collect_statistics(mocker): "nncf.common.tensor_statistics.aggregator.StatisticsAggregator.collect_statistics", return_value=None ) _ = mocker.patch( - "nncf.common.tensor_statistics.collectors.TensorStatisticCollectorBase.get_statistics", + "nncf.experimental.common.tensor_statistics.collectors.TensorCollector.get_statistics", return_value=get_statistics_value, ) @@ -74,8 +74,9 @@ def transform_fn(i): input_dtype = get_edge_dtype(edge) input_np_dtype = onnx.helper.tensor_dtype_to_np_dtype(input_dtype) shape = get_edge_shape(edge) + static_shape = [axis if axis > 0 else 1 for axis in shape] rng = get_random_generator() - tensor = rng.uniform(-1, 1, shape).astype(input_np_dtype) + tensor = rng.uniform(-1, 1, static_shape).astype(input_np_dtype) if has_batch_dim: tensor = np.squeeze(tensor, axis=0) output[key] = tensor @@ -113,8 +114,9 @@ def min_max_quantize_model( advanced_parameters = quantization_params.get("advanced_parameters", AdvancedQuantizationParameters()) - # ONNX backend does not support these algorithms + # Turn off bias correction to test only MinMax advanced_parameters.disable_bias_correction = True + # ONNX backend does not support these algorithms advanced_parameters.disable_channel_alignment = True advanced_parameters.smooth_quant_alphas = AdvancedSmoothQuantParameters(convolution=-1, matmul=-1) quantization_params["advanced_parameters"] = advanced_parameters diff --git a/tests/onnx/quantization/test_bias_correction.py b/tests/onnx/quantization/test_bias_correction.py index 2e09c072868..948519ad39d 100644 --- a/tests/onnx/quantization/test_bias_correction.py +++ b/tests/onnx/quantization/test_bias_correction.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -22,6 +22,9 @@ from nncf.onnx.graph.node_utils import get_bias_value from nncf.quantization.algorithms.bias_correction.onnx_backend import ONNXBiasCorrectionAlgoBackend from tests.onnx.quantization.common import compare_nncf_graph +from tests.post_training.test_templates.helpers import ConvTestModel +from tests.post_training.test_templates.helpers import MultipleConvTestModel +from tests.post_training.test_templates.helpers import SplittedModel from tests.post_training.test_templates.test_bias_correction import TemplateTestBCAlgorithm from tests.shared.paths import TEST_ROOT @@ -90,10 +93,12 @@ def check_bias(model: onnx.ModelProto, ref_biases: Dict) -> None: ( "/conv_1/Conv", { - "collected_inputs": {"/conv_1/Conv": ("input.1", 0)}, + "collected_inputs": { + ("/Concat", 1): ("nncf_model_input_0", 0), + ("/conv_1/Conv", 0): ("nncf_model_input_0", 0), + }, "subgraph_data": { - "subgraph_input_names": {"/conv_1/Conv"}, - "subgraph_output_names": {"/maxpool_1/MaxPool", "/Split"}, + "subgraph_input_ids": {("/conv_1/Conv", 0)}, "subgraph_output_ids": {("/Split", 0), ("/maxpool_1/MaxPool", 0), ("/Split", 1)}, }, }, @@ -102,14 +107,13 @@ def check_bias(model: onnx.ModelProto, ref_biases: Dict) -> None: "/conv_2/Conv", { "collected_inputs": { - "/conv_1/Conv": ("input.1", 0), - "/conv_2/Conv": ("/maxpool_1/MaxPool", 0), - "/conv_4/Conv": ("/Split", 0), - "/conv_6/Conv": ("/Split", 1), + ("/conv_1/Conv", 0): ("nncf_model_input_0", 0), + ("/conv_2/Conv", 0): ("/maxpool_1/MaxPool", 0), + ("/conv_4/Conv", 0): ("/Split", 0), + ("/conv_6/Conv", 0): ("/Split", 1), }, "subgraph_data": { - "subgraph_input_names": {"/conv_2/Conv"}, - "subgraph_output_names": {"/Relu_1"}, + "subgraph_input_ids": {("/conv_2/Conv", 0)}, "subgraph_output_ids": {("/Relu_1", 0)}, }, }, @@ -118,15 +122,14 @@ def check_bias(model: onnx.ModelProto, ref_biases: Dict) -> None: "/conv_3/Conv", { "collected_inputs": { - "/conv_1/Conv": ("input.1", 0), - "/conv_2/Conv": ("/maxpool_1/MaxPool", 0), - "/conv_3/Conv": ("/Relu_1", 0), - "/conv_4/Conv": ("/Split", 0), - "/conv_6/Conv": ("/Split", 1), + ("/conv_1/Conv", 0): ("nncf_model_input_0", 0), + ("/conv_2/Conv", 0): ("/maxpool_1/MaxPool", 0), + ("/conv_3/Conv", 0): ("/Relu_1", 0), + ("/conv_4/Conv", 0): ("/Split", 0), + ("/conv_6/Conv", 0): ("/Split", 1), }, "subgraph_data": { - "subgraph_input_names": {"/conv_1/Conv", "/conv_3/Conv"}, - "subgraph_output_names": {"/Split"}, + "subgraph_input_ids": {("/conv_1/Conv", 0), ("/conv_3/Conv", 0)}, "subgraph_output_ids": {("/Split", 0), ("/Split", 1)}, }, }, @@ -135,12 +138,11 @@ def check_bias(model: onnx.ModelProto, ref_biases: Dict) -> None: "/conv_4/Conv", { "collected_inputs": { - "/conv_4/Conv": ("/Split", 0), - "/conv_6/Conv": ("/Split", 1), + ("/conv_4/Conv", 0): ("/Split", 0), + ("/conv_6/Conv", 0): ("/Split", 1), }, "subgraph_data": { - "subgraph_input_names": {"/conv_4/Conv"}, - "subgraph_output_names": {"/Relu_2"}, + "subgraph_input_ids": {("/conv_4/Conv", 0)}, "subgraph_output_ids": {("/Relu_2", 0)}, }, }, @@ -149,13 +151,12 @@ def check_bias(model: onnx.ModelProto, ref_biases: Dict) -> None: "/conv_6/Conv", { "collected_inputs": { - "/conv_5/Conv": ("/Relu_2", 0), - "/conv_6/Conv": ("/Split", 1), + ("/conv_5/Conv", 0): ("/Relu_2", 0), + ("/conv_6/Conv", 0): ("/Split", 1), }, "subgraph_data": { - "subgraph_input_names": {"/conv_5/Conv", "/conv_6/Conv"}, - "subgraph_output_names": {"/Add_3", "/Concat"}, - "subgraph_output_ids": {("/Add_3", 0), ("/Concat", 0)}, + "subgraph_input_ids": {("/conv_5/Conv", 0), ("/conv_6/Conv", 0)}, + "subgraph_output_ids": {("/Add_3", 0), ("/Concat_1", 0)}, }, }, ), @@ -163,18 +164,17 @@ def check_bias(model: onnx.ModelProto, ref_biases: Dict) -> None: "/conv_10/Conv", { "collected_inputs": { - "/conv_8/Conv": ("/conv_7/Conv", 0), - "/conv_9/Conv": ("/Add_3", 0), - "/conv_10/Conv": ("/Concat", 0), + ("/conv_8/Conv", 0): ("/conv_7/Conv", 0), + ("/conv_9/Conv", 0): ("/Add_3", 0), + ("/conv_10/Conv", 0): ("/Concat_1", 0), }, "subgraph_data": { - "subgraph_input_names": { - "/conv_8/Conv", - "/conv_9/Conv", - "/conv_10/Conv", + "subgraph_input_ids": { + ("/conv_8/Conv", 0), + ("/conv_9/Conv", 0), + ("/conv_10/Conv", 0), }, - "subgraph_output_names": {"/Concat_1"}, - "subgraph_output_ids": {("/Concat_1", 0)}, + "subgraph_output_ids": {("/Concat_2", 0)}, }, }, ), @@ -197,3 +197,26 @@ def check_bias(model: onnx.ModelProto, ref_biases: Dict) -> None: ) def test__get_subgraph_data_for_node(self, quantized_test_model, layer_name, ref_data): return super().test__get_subgraph_data_for_node(quantized_test_model, layer_name, ref_data) + + @pytest.mark.parametrize( + "model_cls, ref_stat_inputs_map", + ( + ( + SplittedModel, + { + ("/conv_1/Conv", 0): ("/Concat", 0), + ("/Concat", 1): ("nncf_model_input_0", 0), + }, + ), + ( + MultipleConvTestModel, + { + ("/conv_1/Conv", 0): ("nncf_model_input_0", 0), + ("/conv_3/Conv", 0): ("nncf_model_input_0", 1), + }, + ), + (ConvTestModel, {("/conv/Conv", 0): ("nncf_model_input_0", 0)}), + ), + ) + def test_verify_collected_stat_inputs_map(self, model_cls, ref_stat_inputs_map, tmpdir): + return super().test_verify_collected_stat_inputs_map(model_cls, ref_stat_inputs_map, tmpdir) diff --git a/tests/onnx/quantization/test_calculation_quantizer_params.py b/tests/onnx/quantization/test_calculation_quantizer_params.py index 1e00f38129e..f49a6eea662 100644 --- a/tests/onnx/quantization/test_calculation_quantizer_params.py +++ b/tests/onnx/quantization/test_calculation_quantizer_params.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -14,7 +14,7 @@ from nncf.experimental.tensor import Tensor from nncf.onnx.quantization.quantizer_parameters import get_level_low_level_high -from nncf.onnx.statistics.collectors import ONNXMinMaxTensorStatistic +from nncf.onnx.statistics.statistics import ONNXMinMaxTensorStatistic from nncf.quantization.fake_quantize import calculate_scale_zero_point from tests.post_training.test_templates.test_calculate_quantizer_parameters import TemplateTestFQParams diff --git a/tests/onnx/quantization/test_classification_models_graph.py b/tests/onnx/quantization/test_classification_models_graph.py index 7db2d90d091..fb678852e7a 100644 --- a/tests/onnx/quantization/test_classification_models_graph.py +++ b/tests/onnx/quantization/test_classification_models_graph.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/onnx/quantization/test_detection_models_graph.py b/tests/onnx/quantization/test_detection_models_graph.py index d074961ac1a..e45336b13fc 100644 --- a/tests/onnx/quantization/test_detection_models_graph.py +++ b/tests/onnx/quantization/test_detection_models_graph.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/onnx/quantization/test_fast_bias_correction.py b/tests/onnx/quantization/test_fast_bias_correction.py index c294723bdbc..9a6322e9b76 100644 --- a/tests/onnx/quantization/test_fast_bias_correction.py +++ b/tests/onnx/quantization/test_fast_bias_correction.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/onnx/quantization/test_graphs.py b/tests/onnx/quantization/test_graphs.py index 47eafc94699..9eccc145b42 100644 --- a/tests/onnx/quantization/test_graphs.py +++ b/tests/onnx/quantization/test_graphs.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/onnx/quantization/test_min_max.py b/tests/onnx/quantization/test_min_max.py index 2c6a1cf78e2..53a6700f3a7 100644 --- a/tests/onnx/quantization/test_min_max.py +++ b/tests/onnx/quantization/test_min_max.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/onnx/quantization/test_opset_versions.py b/tests/onnx/quantization/test_opset_versions.py index 845c8b5d650..37ca24e8fc7 100644 --- a/tests/onnx/quantization/test_opset_versions.py +++ b/tests/onnx/quantization/test_opset_versions.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/onnx/quantization/test_ptq_params.py b/tests/onnx/quantization/test_ptq_params.py index 99277cdd6c7..daadc8a8337 100644 --- a/tests/onnx/quantization/test_ptq_params.py +++ b/tests/onnx/quantization/test_ptq_params.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -21,8 +21,6 @@ from nncf.onnx.graph.nncf_graph_builder import GraphConverter from nncf.onnx.graph.nncf_graph_builder import ONNXLayerAttributes from nncf.onnx.graph.transformations.commands import ONNXTargetPoint -from nncf.onnx.statistics.collectors import ONNXMeanMinMaxStatisticCollector -from nncf.onnx.statistics.collectors import ONNXMinMaxStatisticCollector from nncf.parameters import TargetDevice from nncf.quantization.algorithms.min_max.algorithm import MinMaxQuantization from nncf.quantization.algorithms.min_max.onnx_backend import ONNXMinMaxAlgoBackend @@ -56,12 +54,6 @@ class TestPTQParams(TemplateTestPTQParams): def get_algo_backend(self): return ONNXMinMaxAlgoBackend() - def check_is_min_max_statistic_collector(self, tensor_collector): - assert isinstance(tensor_collector, ONNXMinMaxStatisticCollector) - - def check_is_mean_min_max_statistic_collector(self, tensor_collector): - assert isinstance(tensor_collector, ONNXMeanMinMaxStatisticCollector) - def check_quantize_outputs_fq_num(self, quantize_outputs, act_num_q, weight_num_q): if quantize_outputs: assert act_num_q == 2 diff --git a/tests/onnx/quantization/test_ptq_regression.py b/tests/onnx/quantization/test_ptq_regression.py index 3c3b3ee7a4c..0d8d3cbcc02 100644 --- a/tests/onnx/quantization/test_ptq_regression.py +++ b/tests/onnx/quantization/test_ptq_regression.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/onnx/quantization/test_qdq_params_calculation.py b/tests/onnx/quantization/test_qdq_params_calculation.py index 59a8b84dafe..00d8bd73a52 100644 --- a/tests/onnx/quantization/test_qdq_params_calculation.py +++ b/tests/onnx/quantization/test_qdq_params_calculation.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/onnx/quantization/test_quantizer_config.py b/tests/onnx/quantization/test_quantizer_config.py index eb24f752b16..dd778d853ab 100644 --- a/tests/onnx/quantization/test_quantizer_config.py +++ b/tests/onnx/quantization/test_quantizer_config.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -12,14 +12,10 @@ import pytest from nncf.common.graph.transformations.commands import TargetType -from nncf.common.tensor_statistics.collectors import ReductionAxes -from nncf.common.tensor_statistics.collectors import TensorStatisticCollectorBase from nncf.onnx.graph.metatypes.onnx_metatypes import ONNXAddLayerMetatype from nncf.onnx.graph.metatypes.onnx_metatypes import ONNXConvolutionMetatype from nncf.onnx.graph.metatypes.onnx_metatypes import ONNXDepthwiseConvolutionMetatype from nncf.onnx.graph.nncf_graph_builder import ONNXLayerAttributes -from nncf.onnx.statistics.collectors import ONNXMeanMinMaxStatisticCollector -from nncf.onnx.statistics.collectors import ONNXMinMaxStatisticCollector from nncf.quantization.algorithms.min_max.onnx_backend import ONNXMinMaxAlgoBackend from tests.post_training.test_templates.models import NNCFGraphToTest from tests.post_training.test_templates.models import NNCFGraphToTestDepthwiseConv @@ -33,15 +29,6 @@ class TestQuantizerConfig(TemplateTestQuantizerConfig): def get_algo_backend(self): return ONNXMinMaxAlgoBackend() - def check_is_min_max_statistic_collector(self, tensor_collector): - assert isinstance(tensor_collector, ONNXMinMaxStatisticCollector) - - def check_is_mean_min_max_statistic_collector(self, tensor_collector): - assert isinstance(tensor_collector, ONNXMeanMinMaxStatisticCollector) - - def get_reduction_axes(self, reducer: TensorStatisticCollectorBase) -> ReductionAxes: - return reducer._reduction_shape - @pytest.fixture( params=[ pytest.param( diff --git a/tests/onnx/quantization/test_reducers_and_aggregators.py b/tests/onnx/quantization/test_reducers_and_aggregators.py new file mode 100644 index 00000000000..3e1c3ac988a --- /dev/null +++ b/tests/onnx/quantization/test_reducers_and_aggregators.py @@ -0,0 +1,65 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Any, List, Optional, Tuple + +import numpy as np +import pytest + +from nncf.common.graph.layer_attributes import Dtype +from nncf.onnx.statistics.collectors import ONNXAbsMaxReducer +from nncf.onnx.statistics.collectors import ONNXAbsQuantileReducer +from nncf.onnx.statistics.collectors import ONNXBatchMeanReducer +from nncf.onnx.statistics.collectors import ONNXMaxReducer +from nncf.onnx.statistics.collectors import ONNXMeanPerChanelReducer +from nncf.onnx.statistics.collectors import ONNXMeanReducer +from nncf.onnx.statistics.collectors import ONNXMinReducer +from nncf.onnx.statistics.collectors import ONNXNNCFCollectorTensorProcessor +from nncf.onnx.statistics.collectors import ONNXQuantileReducer +from nncf.onnx.tensor import ONNXNNCFTensor +from tests.common.experimental.test_reducers_and_aggregators import TemplateTestReducersAggreagtors + + +class TestReducersAggregators(TemplateTestReducersAggreagtors): + @pytest.fixture + def tensor_processor(self): + return ONNXNNCFCollectorTensorProcessor + + def get_nncf_tensor(self, x: np.array, dtype: Optional[Dtype] = None): + if dtype is Dtype.INTEGER: + x = x.astype(np.int64) + if dtype is Dtype.FLOAT: + x = x.astype(np.float32) + return ONNXNNCFTensor(x) + + @pytest.fixture(scope="module") + def reducers(self): + return { + "min": ONNXMinReducer, + "max": ONNXMaxReducer, + "abs_max": ONNXAbsMaxReducer, + "mean": ONNXMeanReducer, + "quantile": ONNXQuantileReducer, + "abs_quantile": ONNXAbsQuantileReducer, + "batch_mean": ONNXBatchMeanReducer, + "mean_per_ch": ONNXMeanPerChanelReducer, + } + + def all_close(self, val, ref) -> bool: + val_ = np.array(val) + ref_ = np.array(ref) + return np.allclose(val_, ref_) and val_.shape == ref_.shape + + def squeeze_tensor(self, ref_tensor: List[Any], axes: Optional[Tuple[int]] = None): + return np.squeeze(np.array(ref_tensor), axes) + + def cast_tensor(self, tensor, dtype: Dtype): + return tensor diff --git a/tests/onnx/quantization/test_segmentation_models_graph.py b/tests/onnx/quantization/test_segmentation_models_graph.py index 7861de76d69..d606671b128 100644 --- a/tests/onnx/quantization/test_segmentation_models_graph.py +++ b/tests/onnx/quantization/test_segmentation_models_graph.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/onnx/quantization/test_transform_fn.py b/tests/onnx/quantization/test_transform_fn.py index 56ab6f73971..f25598bbb13 100644 --- a/tests/onnx/quantization/test_transform_fn.py +++ b/tests/onnx/quantization/test_transform_fn.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/onnx/quantization/test_transformer_models_graph.py b/tests/onnx/quantization/test_transformer_models_graph.py index f28a7d23108..73ad08a04a7 100644 --- a/tests/onnx/quantization/test_transformer_models_graph.py +++ b/tests/onnx/quantization/test_transformer_models_graph.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/onnx/requirements.txt b/tests/onnx/requirements.txt index fb44d6257dd..6a3ef1e77fa 100644 --- a/tests/onnx/requirements.txt +++ b/tests/onnx/requirements.txt @@ -2,11 +2,9 @@ pytest-mock>=3.3.1 pytest-cov pytest-dependency>=0.5.1 pytest-ordering>=0.6 -torch~=1.13.0 -torchvision~=0.14.0 +torch==2.1.0 +torchvision==0.16.0 yattag>=1.14.0 -openvino-dev==2023.3 +openvino-dev==2024.0 fastdownload scikit-learn -# Pandas requires it for styling in e2e report generation -Jinja2==3.1.1 diff --git a/tests/onnx/test_e2e_ptq.py b/tests/onnx/test_e2e_ptq.py index b3604af8c45..bfd58ab0878 100644 --- a/tests/onnx/test_e2e_ptq.py +++ b/tests/onnx/test_e2e_ptq.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -184,7 +184,7 @@ def is_ov(request): def _read_accuracy_checker_result(root_dir: Path, key: str) -> pd.DataFrame: dfs = [] for task in TASKS: - csv_fp = str(root_dir / task / f"accuracy_checker-{key}.csv") + csv_fp = str(root_dir / task / f"ac_wrapper-{key}.csv") dfs += [pd.read_csv(csv_fp)] df = pd.concat(dfs, axis=0) df = df[["model", "metric_value", "metric_name", "tags"]] @@ -402,7 +402,7 @@ def test_reference_model_accuracy( anno_dir, output_dir, eval_size, - program="accuracy_checker.py", + program="ac_wrapper.py", is_quantized=False, is_ov_ep=False, is_cpu_ep=True, @@ -445,7 +445,7 @@ def test_onnx_rt_quantized_model_accuracy( anno_dir, output_dir, eval_size, - program="accuracy_checker.py", + program="ac_wrapper.py", is_quantized=True, is_ov_ep=is_ov_ep, is_cpu_ep=is_cpu_ep, @@ -474,7 +474,7 @@ def test_ov_quantized_model_accuracy( anno_dir, output_dir, eval_size, - program="accuracy_checker.py", + program="ac_wrapper.py", is_quantized=True, ) run_command(command) diff --git a/tests/onnx/test_engine.py b/tests/onnx/test_engine.py index d11ced20f0a..2697f474c10 100644 --- a/tests/onnx/test_engine.py +++ b/tests/onnx/test_engine.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/onnx/test_layer_attributes.py b/tests/onnx/test_layer_attributes.py index 3b793256136..518b1768ac7 100644 --- a/tests/onnx/test_layer_attributes.py +++ b/tests/onnx/test_layer_attributes.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/onnx/test_metatypes.py b/tests/onnx/test_metatypes.py index 041b4a38e55..7017fff61d8 100644 --- a/tests/onnx/test_metatypes.py +++ b/tests/onnx/test_metatypes.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/onnx/test_model_transformer.py b/tests/onnx/test_model_transformer.py index 3d1290da13c..0e2a3d562f3 100644 --- a/tests/onnx/test_model_transformer.py +++ b/tests/onnx/test_model_transformer.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -22,7 +22,7 @@ from nncf.onnx.graph.nncf_graph_builder import GraphConverter from nncf.onnx.graph.onnx_helper import get_tensor from nncf.onnx.graph.onnx_helper import get_tensor_value -from nncf.onnx.graph.transformations.commands import ONNXBiasCorrectionCommand +from nncf.onnx.graph.transformations.commands import ONNXInitializerUpdateCommand from nncf.onnx.graph.transformations.commands import ONNXOutputInsertionCommand from nncf.onnx.graph.transformations.commands import ONNXQDQNodeRemovingCommand from nncf.onnx.graph.transformations.commands import ONNXQuantizerInsertionCommand @@ -173,7 +173,7 @@ def test_bias_correction(layers, values, refs): for conv_layer, bias_value in zip(layers, values): bias_port_id = 2 target_point = ONNXTargetPoint(TargetType.LAYER, conv_layer, bias_port_id) - command = ONNXBiasCorrectionCommand(target_point, bias_value) + command = ONNXInitializerUpdateCommand(target_point, bias_value) transformation_layout.register(command) model_transformer = ONNXModelTransformer(model) @@ -209,6 +209,7 @@ def test_node_removing(target_layers): model_transformer = ONNXModelTransformer(quantized_model) transformed_model = model_transformer.transform(transformation_layout) + onnx.checker.check_model(transformed_model) compare_nncf_graph(transformed_model, "synthetic/" + "removed_nodes_in_" + model_to_test.path_ref_graph) diff --git a/tests/onnx/test_nncf_graph_builder.py b/tests/onnx/test_nncf_graph_builder.py index fc13d10b3fe..0797197c9c4 100644 --- a/tests/onnx/test_nncf_graph_builder.py +++ b/tests/onnx/test_nncf_graph_builder.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/onnx/test_node_utils.py b/tests/onnx/test_node_utils.py index 2c4ec41c5d9..09ebfe7de9f 100644 --- a/tests/onnx/test_node_utils.py +++ b/tests/onnx/test_node_utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/onnx/test_pattern_manager.py b/tests/onnx/test_pattern_manager.py index a21ec96d119..51b611489c8 100644 --- a/tests/onnx/test_pattern_manager.py +++ b/tests/onnx/test_pattern_manager.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/onnx/test_statistics_aggregator.py b/tests/onnx/test_statistics_aggregator.py index 4e97a301ad4..af4c8ea7f89 100644 --- a/tests/onnx/test_statistics_aggregator.py +++ b/tests/onnx/test_statistics_aggregator.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -28,7 +28,7 @@ INPUT_NAME = "X" IDENTITY_NODE_NAME = "Identity" CONV_NODE_NAME = "Conv1" -INPUT_SHAPE = [3, 3, 3] +INPUT_SHAPE = [1, 3, 3, 3] class TestStatisticsAggregator(TemplateTestStatisticsAggregator): @@ -43,9 +43,10 @@ def get_fast_bias_correction_algo_backend_cls(self) -> Type[ONNXFastBiasCorrecti return ONNXFastBiasCorrectionAlgoBackend def get_backend_model(self, dataset_samples): - conv_w = self.dataset_samples_to_conv_w(dataset_samples[0]) + sample = dataset_samples[0].reshape(INPUT_SHAPE[1:]) + conv_w = self.dataset_samples_to_conv_w(sample) return IdentityConvolutionalModel( - input_shape=[1] + INPUT_SHAPE, inp_ch=3, out_ch=3, kernel_size=3, conv_w=conv_w + input_shape=INPUT_SHAPE, inp_ch=3, out_ch=3, kernel_size=3, conv_w=conv_w ).onnx_model def get_statistics_aggregator(self, dataset): @@ -62,7 +63,7 @@ def test_params(self): def get_dataset(self, samples): def transform_fn(data_item): inputs = data_item - return {INPUT_NAME: [inputs]} + return {INPUT_NAME: inputs} return Dataset(samples, transform_fn) @@ -84,18 +85,13 @@ def reducers_map(self) -> List[TensorReducerBase]: @pytest.fixture def dataset_samples(self, dataset_values): input_shape = INPUT_SHAPE - dataset_samples = [np.zeros(input_shape), np.ones(input_shape)] + dataset_samples = [np.zeros(input_shape, dtype=np.float32), np.ones(input_shape, dtype=np.float32)] for i, value in enumerate(dataset_values): - dataset_samples[0][i, 0, 0] = value["max"] - dataset_samples[0][i, 0, 1] = value["min"] - + dataset_samples[0][0, i, 0, 0] = value["max"] + dataset_samples[0][0, i, 0, 1] = value["min"] return dataset_samples - @pytest.fixture - def is_stat_in_shape_of_scale(self) -> bool: - return False - @pytest.fixture(params=[False], ids=["out_of_palce"]) def inplace_statistics(self, request) -> bool: return request.param diff --git a/tests/onnx/test_telemetry.py b/tests/onnx/test_telemetry.py index 62c10bacf35..33c839e282e 100644 --- a/tests/onnx/test_telemetry.py +++ b/tests/onnx/test_telemetry.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/onnx/test_weightless_model.py b/tests/onnx/test_weightless_model.py index 77138522b49..7dec68d87be 100644 --- a/tests/onnx/test_weightless_model.py +++ b/tests/onnx/test_weightless_model.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/onnx/tools/save_model_without_tensors.py b/tests/onnx/tools/save_model_without_tensors.py index 88891d97d95..82e682ea932 100644 --- a/tests/onnx/tools/save_model_without_tensors.py +++ b/tests/onnx/tools/save_model_without_tensors.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/onnx/weightless_model.py b/tests/onnx/weightless_model.py index cf5c8597e6e..73d0bf9252e 100644 --- a/tests/onnx/weightless_model.py +++ b/tests/onnx/weightless_model.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/openvino/__init__.py b/tests/openvino/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/openvino/__init__.py +++ b/tests/openvino/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/openvino/conftest.py b/tests/openvino/conftest.py index 58d250f6bc1..0331e5930cc 100644 --- a/tests/openvino/conftest.py +++ b/tests/openvino/conftest.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/openvino/datasets_helpers.py b/tests/openvino/datasets_helpers.py index 631c2114a04..14ba14d671e 100644 --- a/tests/openvino/datasets_helpers.py +++ b/tests/openvino/datasets_helpers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -15,10 +15,17 @@ import requests from fastdownload import FastDownload -from openvino.tools.accuracy_checker.argparser import build_arguments_parser -from openvino.tools.accuracy_checker.config import ConfigReader -from openvino.tools.accuracy_checker.evaluators import ModelEvaluator +try: + from accuracy_checker.argparser import build_arguments_parser + from accuracy_checker.config import ConfigReader + from accuracy_checker.evaluators import ModelEvaluator +except ImportError: + from openvino.tools.accuracy_checker.argparser import build_arguments_parser + from openvino.tools.accuracy_checker.config import ConfigReader + from openvino.tools.accuracy_checker.evaluators import ModelEvaluator + +import nncf from nncf import Dataset from tests.openvino.omz_helpers import OPENVINO_DATASET_DEFINITIONS_PATH @@ -97,7 +104,9 @@ def get_dataset_for_test(dataset_name: str, data_dir: Path) -> Path: if dataset_name == "wider": return prepare_wider_for_test(data_dir) - raise RuntimeError(f"Unknown dataset: {dataset_name}.") + raise nncf.ValidationError( + f"Unknown dataset: {dataset_name}. Supported datasets are: 'imagenette2-320' and 'wider' " + ) def get_nncf_dataset_from_ac_config(model_path, config_path, data_dir, framework="openvino", device="CPU"): diff --git a/tests/openvino/native/__init__.py b/tests/openvino/native/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/openvino/native/__init__.py +++ b/tests/openvino/native/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/openvino/native/common.py b/tests/openvino/native/common.py index f31751581e7..201b8f7e4da 100644 --- a/tests/openvino/native/common.py +++ b/tests/openvino/native/common.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -10,14 +10,17 @@ # limitations under the License. import json from copy import deepcopy +from pathlib import Path from typing import Tuple import numpy as np import openvino.runtime as ov from packaging import version +import nncf from nncf import Dataset from nncf.openvino.graph.nncf_graph_builder import GraphConverter +from tests.openvino.conftest import OPENVINO_NATIVE_TEST_ROOT from tests.shared.nx_graph import compare_nx_graph_with_reference @@ -80,3 +83,33 @@ def get_openvino_version() -> str: major_verison, minor_version = get_openvino_major_minor_version() return f"{major_verison}.{minor_version}" + + +def get_actual_reference_for_current_openvino(rel_path: Path) -> Path: + """ + Get path to actual reference file. + + :param rel_path: Relative path to reference file. + + :return: Path to reference file or raise RuntimeError. + """ + root_dir = OPENVINO_NATIVE_TEST_ROOT / "data" + current_ov_version = version.parse(get_openvino_version()) + + def is_valid_version(dir_path: Path) -> bool: + try: + version.parse(dir_path.name) + except version.InvalidVersion: + return False + return True + + ref_versions = filter(is_valid_version, root_dir.iterdir()) + ref_versions = sorted(ref_versions, key=lambda x: version.parse(x.name), reverse=True) + ref_versions = filter(lambda x: version.parse(x.name) <= current_ov_version, ref_versions) + + for root_version in ref_versions: + file_name = root_version / rel_path + if file_name.is_file(): + return file_name + + raise nncf.InternalError(f"Not found file {root_dir}/{current_ov_version}/{rel_path}") diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/ComparisonBinaryModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/ComparisonBinaryModel.dot deleted file mode 100644 index 467811ae36f..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/ComparisonBinaryModel.dot +++ /dev/null @@ -1,19 +0,0 @@ -strict digraph { -"0 Input" [id=0, type=Parameter]; -"1 Add" [id=1, type=Add]; -"2 Gather_6" [id=2, type=Gather]; -"3 GreaterEqual" [id=3, type=GreaterEqual]; -"4 Result_Add" [id=4, type=Result]; -"5 Convert" [id=5, type=Convert]; -"6 Constant_5" [id=6, type=Constant]; -"7 Constant_2" [id=7, type=Constant]; -"0 Input" -> "1 Add" [label="[1, 3, 4, 2]", style=solid]; -"0 Input" -> "2 Gather_6" [label="[1, 3, 4, 2]", style=solid]; -"0 Input" -> "3 GreaterEqual" [label="[1, 3, 4, 2]", style=solid]; -"1 Add" -> "4 Result_Add" [label="[1, 3, 4, 2, 3, 4, 2]", style=solid]; -"2 Gather_6" -> "1 Add" [label="[1, 3, 4, 2, 3, 4, 2]", style=solid]; -"3 GreaterEqual" -> "5 Convert" [label="[1, 3, 4, 2]", style=dashed]; -"5 Convert" -> "2 Gather_6" [label="[1, 3, 4, 2]", style=dashed]; -"6 Constant_5" -> "2 Gather_6" [label="[]", style=dashed]; -"7 Constant_2" -> "3 GreaterEqual" [label="[1, 3, 4, 2]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/ConvModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/ConvModel.dot deleted file mode 100644 index dcc2423384f..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/ConvModel.dot +++ /dev/null @@ -1,35 +0,0 @@ -strict digraph { -"0 Input_1" [id=0, type=Parameter]; -"1 Input_2" [id=1, type=Parameter]; -"2 Sub" [id=2, type=Subtract]; -"3 Add" [id=3, type=Add]; -"4 Conv" [id=4, type=Convolution]; -"5 Mul" [id=5, type=Multiply]; -"6 Conv_Add" [id=6, type=Add]; -"7 Transpose" [id=7, type=Transpose]; -"8 Relu" [id=8, type=Relu]; -"9 Concat_15" [id=9, type=Concat]; -"10 Result" [id=10, type=Result]; -"11 Constant_13" [id=11, type=Constant]; -"12 Constant_11" [id=12, type=Constant]; -"13 Constant_9" [id=13, type=Constant]; -"14 Bias" [id=14, type=Constant]; -"15 Constant_3" [id=15, type=Constant]; -"16 Constant_1" [id=16, type=Constant]; -"0 Input_1" -> "2 Sub" [label="[1, 3, 4, 2]", style=solid]; -"1 Input_2" -> "3 Add" [label="[1, 3, 2, 4]", style=solid]; -"2 Sub" -> "4 Conv" [label="[1, 3, 4, 2]", style=solid]; -"3 Add" -> "5 Mul" [label="[1, 3, 2, 4]", style=solid]; -"4 Conv" -> "6 Conv_Add" [label="[1, 3, 4, 2]", style=solid]; -"5 Mul" -> "7 Transpose" [label="[1, 3, 2, 4]", style=solid]; -"6 Conv_Add" -> "8 Relu" [label="[1, 3, 4, 2]", style=solid]; -"7 Transpose" -> "9 Concat_15" [label="[1, 3, 4, 2]", style=solid]; -"8 Relu" -> "9 Concat_15" [label="[1, 3, 4, 2]", style=solid]; -"9 Concat_15" -> "10 Result" [label="[2, 3, 4, 2]", style=solid]; -"11 Constant_13" -> "7 Transpose" [label="[4]", style=dashed]; -"12 Constant_11" -> "5 Mul" [label="[1, 3, 1, 1]", style=solid]; -"13 Constant_9" -> "3 Add" [label="[1, 3, 1, 1]", style=solid]; -"14 Bias" -> "6 Conv_Add" [label="[1, 3, 1, 1]", style=solid]; -"15 Constant_3" -> "4 Conv" [label="[3, 3, 1, 1]", style=solid]; -"16 Constant_1" -> "2 Sub" [label="[1, 3, 1, 1]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/ConvNotBiasModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/ConvNotBiasModel.dot deleted file mode 100644 index a53e7699cce..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/ConvNotBiasModel.dot +++ /dev/null @@ -1,19 +0,0 @@ -strict digraph { -"0 Input_1" [id=0, type=Parameter]; -"1 Sub" [id=1, type=Subtract]; -"2 Conv" [id=2, type=Convolution]; -"3 Conv_Add" [id=3, type=Add]; -"4 Relu" [id=4, type=Relu]; -"5 Result" [id=5, type=Result]; -"6 NotBias" [id=6, type=Constant]; -"7 Constant_143" [id=7, type=Constant]; -"8 Constant_141" [id=8, type=Constant]; -"0 Input_1" -> "1 Sub" [label="[1, 3, 4, 2]", style=solid]; -"1 Sub" -> "2 Conv" [label="[1, 3, 4, 2]", style=solid]; -"2 Conv" -> "3 Conv_Add" [label="[1, 3, 4, 2]", style=solid]; -"3 Conv_Add" -> "4 Relu" [label="[1, 3, 4, 2]", style=solid]; -"4 Relu" -> "5 Result" [label="[1, 3, 4, 2]", style=solid]; -"6 NotBias" -> "3 Conv_Add" [label="[1, 3, 4, 2]", style=solid]; -"7 Constant_143" -> "2 Conv" [label="[3, 3, 1, 1]", style=solid]; -"8 Constant_141" -> "1 Sub" [label="[1, 3, 1, 1]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/DepthwiseConvModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/DepthwiseConvModel.dot deleted file mode 100644 index 884ed9bce29..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/DepthwiseConvModel.dot +++ /dev/null @@ -1,15 +0,0 @@ -strict digraph { -"0 Input_1" [id=0, type=Parameter]; -"1 Conv" [id=1, type=GroupConvolution]; -"2 Add" [id=2, type=Add]; -"3 Relu" [id=3, type=Relu]; -"4 Result" [id=4, type=Result]; -"5 Constant_4" [id=5, type=Constant]; -"6 Constant_2" [id=6, type=Constant]; -"0 Input_1" -> "1 Conv" [label="[1, 3, 5, 5]", style=solid]; -"1 Conv" -> "2 Add" [label="[1, 3, 3, 3]", style=solid]; -"2 Add" -> "3 Relu" [label="[1, 3, 3, 3]", style=solid]; -"3 Relu" -> "4 Result" [label="[1, 3, 3, 3]", style=solid]; -"5 Constant_4" -> "2 Add" [label="[1, 3, 1, 1]", style=solid]; -"6 Constant_2" -> "1 Conv" [label="[3, 1, 1, 3, 3]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/DynamicModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/DynamicModel.dot deleted file mode 100644 index bf3926ef1ad..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/DynamicModel.dot +++ /dev/null @@ -1,35 +0,0 @@ -strict digraph { -"0 Input_1" [id=0, type=Parameter]; -"1 Input_2" [id=1, type=Parameter]; -"2 Sub" [id=2, type=Subtract]; -"3 Add" [id=3, type=Add]; -"4 Conv" [id=4, type=Convolution]; -"5 Mul" [id=5, type=Multiply]; -"6 Conv_Add" [id=6, type=Add]; -"7 Transpose" [id=7, type=Transpose]; -"8 Relu" [id=8, type=Relu]; -"9 Concat_58" [id=9, type=Concat]; -"10 Result" [id=10, type=Result]; -"11 Constant_56" [id=11, type=Constant]; -"12 Constant_54" [id=12, type=Constant]; -"13 Constant_52" [id=13, type=Constant]; -"14 Bias" [id=14, type=Constant]; -"15 Constant_46" [id=15, type=Constant]; -"16 Constant_44" [id=16, type=Constant]; -"0 Input_1" -> "2 Sub" [label="[99, 3, 4, 2]", style=solid]; -"1 Input_2" -> "3 Add" [label="[99, 3, 2, 4]", style=solid]; -"2 Sub" -> "4 Conv" [label="[99, 3, 4, 2]", style=solid]; -"3 Add" -> "5 Mul" [label="[99, 3, 2, 4]", style=solid]; -"4 Conv" -> "6 Conv_Add" [label="[99, 3, 4, 2]", style=solid]; -"5 Mul" -> "7 Transpose" [label="[99, 3, 2, 4]", style=solid]; -"6 Conv_Add" -> "8 Relu" [label="[99, 3, 4, 2]", style=solid]; -"7 Transpose" -> "9 Concat_58" [label="[99, 3, 4, 2]", style=solid]; -"8 Relu" -> "9 Concat_58" [label="[99, 3, 4, 2]", style=solid]; -"9 Concat_58" -> "10 Result" [label="[198, 3, 4, 2]", style=solid]; -"11 Constant_56" -> "7 Transpose" [label="[4]", style=dashed]; -"12 Constant_54" -> "5 Mul" [label="[1, 3, 1, 1]", style=solid]; -"13 Constant_52" -> "3 Add" [label="[1, 3, 1, 1]", style=solid]; -"14 Bias" -> "6 Conv_Add" [label="[1, 3, 1, 1]", style=solid]; -"15 Constant_46" -> "4 Conv" [label="[3, 3, 1, 1]", style=solid]; -"16 Constant_44" -> "2 Sub" [label="[1, 3, 1, 1]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/GroupNormalizationModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/GroupNormalizationModel.dot deleted file mode 100644 index 44ba7698a92..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/GroupNormalizationModel.dot +++ /dev/null @@ -1,29 +0,0 @@ -strict digraph { -"0 Input_1" [id=0, type=Parameter]; -"1 Conv" [id=1, type=Convolution]; -"2 Conv_Add" [id=2, type=Add]; -"3 GroupNormalization_169" [id=3, type=GroupNormalization]; -"4 Relu" [id=4, type=Relu]; -"5 Mul" [id=5, type=Multiply]; -"6 Add" [id=6, type=Add]; -"7 Result" [id=7, type=Result]; -"8 Constant_173" [id=8, type=Constant]; -"9 Constant_171" [id=9, type=Constant]; -"10 Constant_168" [id=10, type=Constant]; -"11 Constant_167" [id=11, type=Constant]; -"12 Bias" [id=12, type=Constant]; -"13 Constant_163" [id=13, type=Constant]; -"0 Input_1" -> "1 Conv" [label="[1, 2, 3, 4, 4]", style=solid]; -"1 Conv" -> "2 Conv_Add" [label="[1, 4, 1, 2, 2]", style=solid]; -"2 Conv_Add" -> "3 GroupNormalization_169" [label="[1, 4, 3, 2, 2]", style=solid]; -"3 GroupNormalization_169" -> "4 Relu" [label="[1, 4, 3, 2, 2]", style=solid]; -"4 Relu" -> "5 Mul" [label="[1, 4, 3, 2, 2]", style=solid]; -"5 Mul" -> "6 Add" [label="[1, 4, 3, 2, 2]", style=solid]; -"6 Add" -> "7 Result" [label="[1, 4, 3, 2, 2]", style=solid]; -"8 Constant_173" -> "6 Add" [label="[1, 4, 1, 1, 1]", style=solid]; -"9 Constant_171" -> "5 Mul" [label="[1, 4, 1, 1, 1]", style=solid]; -"10 Constant_168" -> "3 GroupNormalization_169" [label="[4]", style=solid]; -"11 Constant_167" -> "3 GroupNormalization_169" [label="[4]", style=solid]; -"12 Bias" -> "2 Conv_Add" [label="[1, 1, 3, 1, 1]", style=solid]; -"13 Constant_163" -> "1 Conv" [label="[4, 2, 3, 3, 3]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/IntegerModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/IntegerModel.dot deleted file mode 100644 index 63cc63bfa23..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/IntegerModel.dot +++ /dev/null @@ -1,42 +0,0 @@ -strict digraph { -"0 Input" [id=0, type=Parameter]; -"1 Convert_1" [id=1, type=Convert]; -"2 Gather_15" [id=2, type=Gather]; -"3 Gather_4" [id=3, type=Gather]; -"4 MatMul_2" [id=4, type=MatMul]; -"5 Gather_7" [id=5, type=Gather]; -"6 Add_1" [id=6, type=Add]; -"7 Gather_10" [id=7, type=Gather]; -"8 Result" [id=8, type=Result]; -"9 MatMul_1" [id=9, type=MatMul]; -"10 Constant_16" [id=10, type=Constant]; -"11 Constant_14" [id=11, type=Constant]; -"12 Constant_13" [id=12, type=Constant]; -"13 Constant_11" [id=13, type=Constant]; -"14 Constant_9" [id=14, type=Constant]; -"15 Constant_8" [id=15, type=Constant]; -"16 Constant_6" [id=16, type=Constant]; -"17 Constant_3" [id=17, type=Constant]; -"18 Constant_2" [id=18, type=Constant]; -"19 Constant_5" [id=19, type=Constant]; -"0 Input" -> "1 Convert_1" [label="[1, 192, 1]", style=solid]; -"0 Input" -> "2 Gather_15" [label="[1, 192, 1]", style=solid]; -"1 Convert_1" -> "3 Gather_4" [label="[1, 192, 1]", style=dashed]; -"2 Gather_15" -> "4 MatMul_2" [label="[1, 192]", style=solid]; -"3 Gather_4" -> "5 Gather_7" [label="[192, 1]", style=dashed]; -"4 MatMul_2" -> "6 Add_1" [label="[1, 160]", style=solid]; -"5 Gather_7" -> "7 Gather_10" [label="[192, 1, 160]", style=solid]; -"6 Add_1" -> "8 Result" [label="[1, 160]", style=solid]; -"7 Gather_10" -> "9 MatMul_1" [label="[1, 160]", style=solid]; -"9 MatMul_1" -> "6 Add_1" [label="[1, 160]", style=solid]; -"10 Constant_16" -> "4 MatMul_2" [label="[160, 192]", style=solid]; -"11 Constant_14" -> "2 Gather_15" [label="[]", style=dashed]; -"12 Constant_13" -> "2 Gather_15" [label="[]", style=dashed]; -"13 Constant_11" -> "9 MatMul_1" [label="[160, 160]", style=solid]; -"14 Constant_9" -> "7 Gather_10" [label="[]", style=dashed]; -"15 Constant_8" -> "7 Gather_10" [label="[]", style=dashed]; -"16 Constant_6" -> "5 Gather_7" [label="[]", style=dashed]; -"17 Constant_3" -> "3 Gather_4" [label="[]", style=dashed]; -"18 Constant_2" -> "3 Gather_4" [label="[]", style=dashed]; -"19 Constant_5" -> "5 Gather_7" [label="[369, 160]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/LSTMModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/LSTMModel.dot deleted file mode 100644 index b23d980fed3..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/LSTMModel.dot +++ /dev/null @@ -1,60 +0,0 @@ -strict digraph { -"0 Input" [id=0, type=Parameter]; -"1 ReadValue_1" [id=1, type=ReadValue]; -"2 Squeeze_1" [id=2, type=Squeeze]; -"3 Add" [id=3, type=Add]; -"4 MatMul_1" [id=4, type=MatMul]; -"5 MatMul_2" [id=5, type=MatMul]; -"6 Add_1" [id=6, type=Add]; -"7 Split_13" [id=7, type=Split]; -"8 Sigmoid_1" [id=8, type=Sigmoid]; -"9 Sigmoid_2" [id=9, type=Sigmoid]; -"10 Sigmoid_3" [id=10, type=Sigmoid]; -"11 Tanh_1" [id=11, type=Tanh]; -"12 Multiply_1" [id=12, type=Multiply]; -"13 Multiply_2" [id=13, type=Multiply]; -"14 Multiply_3" [id=14, type=Multiply]; -"15 Add_2" [id=15, type=Add]; -"16 Assign_1" [id=16, type=Assign]; -"17 MatMul_3" [id=17, type=MatMul]; -"18 Tanh_2" [id=18, type=Tanh]; -"19 Result" [id=19, type=Result]; -"20 Constant_12" [id=20, type=Constant]; -"21 Constant_9" [id=21, type=Constant]; -"22 Constant_7" [id=22, type=Constant]; -"23 Constant_5" [id=23, type=Constant]; -"24 Constant_3" [id=24, type=Constant]; -"25 Constant_1" [id=25, type=Constant]; -"26 Constant_18" [id=26, type=Constant]; -"27 Constant_25" [id=27, type=Constant]; -"0 Input" -> "2 Squeeze_1" [label="[1, 1, 128]", style=solid]; -"1 ReadValue_1" -> "3 Add" [label="[1, 64]", style=solid]; -"2 Squeeze_1" -> "4 MatMul_1" [label="[1, 128]", style=solid]; -"3 Add" -> "5 MatMul_2" [label="[1, 64]", style=solid]; -"4 MatMul_1" -> "6 Add_1" [label="[1, 256]", style=solid]; -"5 MatMul_2" -> "6 Add_1" [label="[1, 256]", style=solid]; -"6 Add_1" -> "7 Split_13" [label="[1, 256]", style=solid]; -"7 Split_13" -> "8 Sigmoid_1" [label="[1, 64]", style=solid]; -"7 Split_13" -> "9 Sigmoid_2" [label="[1, 64]", style=solid]; -"7 Split_13" -> "10 Sigmoid_3" [label="[1, 64]", style=solid]; -"7 Split_13" -> "11 Tanh_1" [label="[1, 64]", style=solid]; -"8 Sigmoid_1" -> "12 Multiply_1" [label="[1, 64]", style=solid]; -"9 Sigmoid_2" -> "13 Multiply_2" [label="[1, 64]", style=solid]; -"10 Sigmoid_3" -> "14 Multiply_3" [label="[1, 64]", style=solid]; -"11 Tanh_1" -> "13 Multiply_2" [label="[1, 64]", style=solid]; -"12 Multiply_1" -> "15 Add_2" [label="[1, 64]", style=solid]; -"13 Multiply_2" -> "15 Add_2" [label="[1, 64]", style=solid]; -"14 Multiply_3" -> "16 Assign_1" [label="[1, 64]", style=solid]; -"14 Multiply_3" -> "17 MatMul_3" [label="[1, 64]", style=solid]; -"15 Add_2" -> "18 Tanh_2" [label="[1, 64]", style=solid]; -"17 MatMul_3" -> "19 Result" [label="[1, 128]", style=solid]; -"18 Tanh_2" -> "14 Multiply_3" [label="[1, 64]", style=solid]; -"20 Constant_12" -> "7 Split_13" [label="[]", style=dashed]; -"21 Constant_9" -> "5 MatMul_2" [label="[256, 64]", style=solid]; -"22 Constant_7" -> "3 Add" [label="[1, 64]", style=solid]; -"23 Constant_5" -> "1 ReadValue_1" [label="[1, 64]", style=solid]; -"24 Constant_3" -> "4 MatMul_1" [label="[256, 128]", style=solid]; -"25 Constant_1" -> "2 Squeeze_1" [label="[]", style=dashed]; -"26 Constant_18" -> "12 Multiply_1" [label="[1, 64]", style=solid]; -"27 Constant_25" -> "17 MatMul_3" [label="[128, 64]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/LSTMSequenceModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/LSTMSequenceModel.dot deleted file mode 100644 index 91f5b08577c..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/LSTMSequenceModel.dot +++ /dev/null @@ -1,23 +0,0 @@ -strict digraph { -"0 X" [id=0, type=Parameter]; -"1 initial_hidden_state" [id=1, type=Parameter]; -"2 initial_cell_state" [id=2, type=Parameter]; -"3 LSTMSequence_7" [id=3, type=LSTMSequence]; -"4 MatMul" [id=4, type=MatMul]; -"5 Result" [id=5, type=Result]; -"6 Constant_8" [id=6, type=Constant]; -"7 Constant_6" [id=7, type=Constant]; -"8 Constant_5" [id=8, type=Constant]; -"9 Constant_4" [id=9, type=Constant]; -"10 Constant_3" [id=10, type=Constant]; -"0 X" -> "3 LSTMSequence_7" [label="[1, 2, 16]", style=solid]; -"1 initial_hidden_state" -> "3 LSTMSequence_7" [label="[1, 1, 128]", style=solid]; -"2 initial_cell_state" -> "3 LSTMSequence_7" [label="[1, 1, 128]", style=solid]; -"3 LSTMSequence_7" -> "4 MatMul" [label="[1, 1, 2, 128]", style=solid]; -"4 MatMul" -> "5 Result" [label="[1, 1, 2, 3]", style=solid]; -"6 Constant_8" -> "4 MatMul" [label="[1, 1, 128, 3]", style=solid]; -"7 Constant_6" -> "3 LSTMSequence_7" [label="[1, 512]", style=solid]; -"8 Constant_5" -> "3 LSTMSequence_7" [label="[1, 512, 128]", style=solid]; -"9 Constant_4" -> "3 LSTMSequence_7" [label="[1, 512, 16]", style=solid]; -"10 Constant_3" -> "3 LSTMSequence_7" [label="[1]", style=dashed]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/LinearModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/LinearModel.dot deleted file mode 100644 index 50c34d96235..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/LinearModel.dot +++ /dev/null @@ -1,19 +0,0 @@ -strict digraph { -"0 Input" [id=0, type=Parameter]; -"1 Reshape" [id=1, type=Reshape]; -"2 Add" [id=2, type=Add]; -"3 MatMul" [id=3, type=MatMul]; -"4 Result_Add" [id=4, type=Result]; -"5 Result_MatMul" [id=5, type=Result]; -"6 Constant_10" [id=6, type=Constant]; -"7 Constant_1" [id=7, type=Constant]; -"8 Constant_8" [id=8, type=Constant]; -"0 Input" -> "1 Reshape" [label="[1, 3, 4, 2]", style=solid]; -"1 Reshape" -> "2 Add" [label="[1, 3, 2, 4]", style=solid]; -"1 Reshape" -> "3 MatMul" [label="[1, 3, 2, 4]", style=solid]; -"2 Add" -> "4 Result_Add" [label="[1, 3, 2, 4]", style=solid]; -"3 MatMul" -> "5 Result_MatMul" [label="[1, 3, 2, 5]", style=solid]; -"6 Constant_10" -> "2 Add" [label="[1, 3, 2, 4]", style=solid]; -"7 Constant_1" -> "1 Reshape" [label="[4]", style=dashed]; -"8 Constant_8" -> "3 MatMul" [label="[1, 3, 4, 5]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/MatMul2DModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/MatMul2DModel.dot deleted file mode 100644 index d7bd2d4b57d..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/MatMul2DModel.dot +++ /dev/null @@ -1,13 +0,0 @@ -strict digraph { -"0 Input" [id=0, type=Parameter]; -"1 MatMul" [id=1, type=MatMul]; -"2 Add" [id=2, type=Add]; -"3 Result" [id=3, type=Result]; -"4 Constant_32" [id=4, type=Constant]; -"5 Constant_30" [id=5, type=Constant]; -"0 Input" -> "1 MatMul" [label="[3, 5]", style=solid]; -"1 MatMul" -> "2 Add" [label="[3, 2]", style=solid]; -"2 Add" -> "3 Result" [label="[3, 2]", style=solid]; -"4 Constant_32" -> "2 Add" [label="[1, 2]", style=solid]; -"5 Constant_30" -> "1 MatMul" [label="[5, 2]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/MatmulSoftmaxMatmulBlock.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/MatmulSoftmaxMatmulBlock.dot deleted file mode 100644 index 55cb4dfe072..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/MatmulSoftmaxMatmulBlock.dot +++ /dev/null @@ -1,18 +0,0 @@ -strict digraph { -"0 Input" [id=0, type=Parameter]; -"1 MatMul_1" [id=1, type=MatMul]; -"2 Squeeze_1" [id=2, type=Squeeze]; -"3 Softmax_1" [id=3, type=Softmax]; -"4 MatMul_2" [id=4, type=MatMul]; -"5 Result" [id=5, type=Result]; -"6 Constant_178" [id=6, type=Constant]; -"7 Constant_180" [id=7, type=Constant]; -"0 Input" -> "1 MatMul_1" [label="[1, 1, 1]", style=solid]; -"0 Input" -> "2 Squeeze_1" [label="[1, 1, 1]", style=solid]; -"1 MatMul_1" -> "3 Softmax_1" [label="[1, 1, 1]", style=solid]; -"2 Squeeze_1" -> "4 MatMul_2" [label="[1, 1]", style=solid]; -"3 Softmax_1" -> "4 MatMul_2" [label="[1, 1, 1]", style=solid]; -"4 MatMul_2" -> "5 Result" [label="[1, 1, 1]", style=solid]; -"6 Constant_178" -> "2 Squeeze_1" [label="[]", style=dashed]; -"7 Constant_180" -> "1 MatMul_1" [label="[1, 1]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/ScaleShiftReluModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/ScaleShiftReluModel.dot deleted file mode 100644 index a811bd55053..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/ScaleShiftReluModel.dot +++ /dev/null @@ -1,23 +0,0 @@ -strict digraph { -"0 Input" [id=0, type=Parameter]; -"1 MatMul" [id=1, type=MatMul]; -"2 Mul" [id=2, type=Multiply]; -"3 Add" [id=3, type=Add]; -"4 Relu" [id=4, type=Relu]; -"5 MatMul2" [id=5, type=MatMul]; -"6 Result" [id=6, type=Result]; -"7 Constant_45" [id=7, type=Constant]; -"8 Constant_42" [id=8, type=Constant]; -"9 Constant_40" [id=9, type=Constant]; -"10 Constant_38" [id=10, type=Constant]; -"0 Input" -> "1 MatMul" [label="[3, 5]", style=solid]; -"1 MatMul" -> "2 Mul" [label="[3, 2]", style=solid]; -"2 Mul" -> "3 Add" [label="[3, 2]", style=solid]; -"3 Add" -> "4 Relu" [label="[3, 2]", style=solid]; -"4 Relu" -> "5 MatMul2" [label="[3, 2]", style=solid]; -"5 MatMul2" -> "6 Result" [label="[3, 4]", style=solid]; -"7 Constant_45" -> "5 MatMul2" [label="[2, 4]", style=solid]; -"8 Constant_42" -> "3 Add" [label="[1, 2]", style=solid]; -"9 Constant_40" -> "2 Mul" [label="[1, 2]", style=solid]; -"10 Constant_38" -> "1 MatMul" [label="[5, 2]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/SeBlockModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/SeBlockModel.dot deleted file mode 100644 index f3ba7b4bedb..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/SeBlockModel.dot +++ /dev/null @@ -1,44 +0,0 @@ -strict digraph { -"0 Input" [id=0, type=Parameter]; -"1 Conv0" [id=1, type=Convolution]; -"2 Add0" [id=2, type=Add]; -"3 Mul" [id=3, type=Multiply]; -"4 ReduceMean_6" [id=4, type=ReduceMean]; -"5 MatMul" [id=5, type=MatMul]; -"6 Conv" [id=6, type=Convolution]; -"7 Result" [id=7, type=Result]; -"8 Add" [id=8, type=Add]; -"9 Relu" [id=9, type=Relu]; -"10 Conv2" [id=10, type=Convolution]; -"11 Add2" [id=11, type=Add]; -"12 Sigmoid" [id=12, type=Sigmoid]; -"13 Constant_18" [id=13, type=Constant]; -"14 Constant_14" [id=14, type=Constant]; -"15 Constant_12" [id=15, type=Constant]; -"16 Constant_9" [id=16, type=Constant]; -"17 Constant_7" [id=17, type=Constant]; -"18 Constant_5" [id=18, type=Constant]; -"19 Constant_3" [id=19, type=Constant]; -"20 Constant_1" [id=20, type=Constant]; -"0 Input" -> "1 Conv0" [label="[1, 3, 5, 6]", style=solid]; -"1 Conv0" -> "2 Add0" [label="[1, 3, 5, 6]", style=solid]; -"2 Add0" -> "3 Mul" [label="[1, 3, 5, 6]", style=solid]; -"2 Add0" -> "4 ReduceMean_6" [label="[1, 3, 5, 6]", style=solid]; -"3 Mul" -> "5 MatMul" [label="[1, 3, 5, 6]", style=solid]; -"4 ReduceMean_6" -> "6 Conv" [label="[1, 3, 1, 1]", style=solid]; -"5 MatMul" -> "7 Result" [label="[1, 3, 5, 5]", style=solid]; -"6 Conv" -> "8 Add" [label="[1, 5, 1, 1]", style=solid]; -"8 Add" -> "9 Relu" [label="[1, 5, 1, 1]", style=solid]; -"9 Relu" -> "10 Conv2" [label="[1, 5, 1, 1]", style=solid]; -"10 Conv2" -> "11 Add2" [label="[1, 3, 1, 1]", style=solid]; -"11 Add2" -> "12 Sigmoid" [label="[1, 3, 1, 1]", style=solid]; -"12 Sigmoid" -> "3 Mul" [label="[1, 3, 1, 1]", style=solid]; -"13 Constant_18" -> "5 MatMul" [label="[1, 3, 6, 5]", style=solid]; -"14 Constant_14" -> "11 Add2" [label="[1, 3, 1, 1]", style=solid]; -"15 Constant_12" -> "10 Conv2" [label="[3, 5, 1, 1]", style=solid]; -"16 Constant_9" -> "8 Add" [label="[1, 5, 1, 1]", style=solid]; -"17 Constant_7" -> "6 Conv" [label="[5, 3, 1, 1]", style=solid]; -"18 Constant_5" -> "4 ReduceMean_6" [label="[2]", style=dashed]; -"19 Constant_3" -> "2 Add0" [label="[1, 3, 1, 1]", style=solid]; -"20 Constant_1" -> "1 Conv0" [label="[3, 3, 1, 1]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/ShapeOfModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/ShapeOfModel.dot deleted file mode 100644 index 234d08dc31d..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/ShapeOfModel.dot +++ /dev/null @@ -1,63 +0,0 @@ -strict digraph { -"0 Input" [id=0, type=Parameter]; -"1 Conv_1" [id=1, type=Convolution]; -"2 Conv_Add_1" [id=2, type=Add]; -"3 Reshape_1" [id=3, type=Reshape]; -"4 ShapeOf_1" [id=4, type=ShapeOf]; -"5 Transpose" [id=5, type=Transpose]; -"6 Gather_33092" [id=6, type=Gather]; -"7 Conv_2" [id=7, type=Convolution]; -"8 Concat_33095" [id=8, type=Concat]; -"9 Conv_Add_2" [id=9, type=Add]; -"10 Reshape_2" [id=10, type=Reshape]; -"11 ShapeOf_2" [id=11, type=ShapeOf]; -"12 Conv_3" [id=12, type=Convolution]; -"13 Convert_1" [id=13, type=Convert]; -"14 Conv_Add_3" [id=14, type=Add]; -"15 Multiply" [id=15, type=Multiply]; -"16 Result" [id=16, type=Result]; -"17 Convert_2" [id=17, type=Convert]; -"18 Bias_3" [id=18, type=Constant]; -"19 Constant_33121" [id=19, type=Constant]; -"20 Constant_33107" [id=20, type=Constant]; -"21 Bias_2" [id=21, type=Constant]; -"22 Constant_33101" [id=22, type=Constant]; -"23 Constant_33099" [id=23, type=Constant]; -"24 Constant_33091" [id=24, type=Constant]; -"25 Constant_33090" [id=25, type=Constant]; -"26 Bias_1" [id=26, type=Constant]; -"27 Constant_33085" [id=27, type=Constant]; -"28 Constant_33094" [id=28, type=Constant]; -"29 Constant_33093" [id=29, type=Constant]; -"0 Input" -> "1 Conv_1" [label="[1, 3, 4, 2]", style=solid]; -"1 Conv_1" -> "2 Conv_Add_1" [label="[1, 3, 4, 2]", style=solid]; -"2 Conv_Add_1" -> "3 Reshape_1" [label="[1, 3, 4, 2]", style=solid]; -"2 Conv_Add_1" -> "4 ShapeOf_1" [label="[1, 3, 4, 2]", style=solid]; -"3 Reshape_1" -> "5 Transpose" [label="[1, 3, 4, 2]", style=solid]; -"4 ShapeOf_1" -> "6 Gather_33092" [label="[4]", style=dashed]; -"5 Transpose" -> "7 Conv_2" [label="[1, 3, 2, 4]", style=solid]; -"6 Gather_33092" -> "8 Concat_33095" [label="[2]", style=dashed]; -"7 Conv_2" -> "9 Conv_Add_2" [label="[1, 3, 2, 4]", style=solid]; -"8 Concat_33095" -> "3 Reshape_1" [label="[4]", style=dashed]; -"9 Conv_Add_2" -> "10 Reshape_2" [label="[1, 3, 2, 4]", style=solid]; -"9 Conv_Add_2" -> "11 ShapeOf_2" [label="[1, 3, 2, 4]", style=solid]; -"10 Reshape_2" -> "12 Conv_3" [label="[1, 3, 2, 4]", style=solid]; -"11 ShapeOf_2" -> "13 Convert_1" [label="[4]", style=dashed]; -"12 Conv_3" -> "14 Conv_Add_3" [label="[1, 3, 2, 4]", style=solid]; -"13 Convert_1" -> "15 Multiply" [label="[4]", style=solid]; -"14 Conv_Add_3" -> "16 Result" [label="[1, 3, 2, 4]", style=solid]; -"15 Multiply" -> "17 Convert_2" [label="[4]", style=solid]; -"17 Convert_2" -> "10 Reshape_2" [label="[4]", style=dashed]; -"18 Bias_3" -> "14 Conv_Add_3" [label="[1, 3, 1, 1]", style=solid]; -"19 Constant_33121" -> "12 Conv_3" [label="[3, 3, 1, 1]", style=solid]; -"20 Constant_33107" -> "15 Multiply" [label="[4]", style=solid]; -"21 Bias_2" -> "9 Conv_Add_2" [label="[1, 3, 1, 1]", style=solid]; -"22 Constant_33101" -> "7 Conv_2" [label="[3, 3, 1, 1]", style=solid]; -"23 Constant_33099" -> "5 Transpose" [label="[4]", style=dashed]; -"24 Constant_33091" -> "6 Gather_33092" [label="[]", style=dashed]; -"25 Constant_33090" -> "6 Gather_33092" [label="[2]", style=dashed]; -"26 Bias_1" -> "2 Conv_Add_1" [label="[1, 3, 1, 1]", style=solid]; -"27 Constant_33085" -> "1 Conv_1" [label="[3, 3, 1, 1]", style=solid]; -"28 Constant_33094" -> "8 Concat_33095" [label="[1]", style=dashed]; -"29 Constant_33093" -> "8 Concat_33095" [label="[1]", style=dashed]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/SharedConvModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/SharedConvModel.dot deleted file mode 100644 index 2bc26e8711a..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/SharedConvModel.dot +++ /dev/null @@ -1,14 +0,0 @@ -strict digraph { -"0 Input" [id=0, type=Parameter]; -"1 Conv_1" [id=1, type=Convolution]; -"2 Conv_2" [id=2, type=Convolution]; -"3 Result_1" [id=3, type=Result]; -"4 Result_2" [id=4, type=Result]; -"5 Shared_conv_w" [id=5, type=Constant]; -"0 Input" -> "1 Conv_1" [label="[1, 3, 3, 3]", style=solid]; -"0 Input" -> "2 Conv_2" [label="[1, 3, 3, 3]", style=solid]; -"1 Conv_1" -> "3 Result_1" [label="[1, 3, 3, 3]", style=solid]; -"2 Conv_2" -> "4 Result_2" [label="[1, 3, 3, 3]", style=solid]; -"5 Shared_conv_w" -> "1 Conv_1" [label="[3, 3, 1, 1]", style=solid]; -"5 Shared_conv_w" -> "2 Conv_2" [label="[3, 3, 1, 1]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/UnifiedEmbeddingModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/UnifiedEmbeddingModel.dot deleted file mode 100644 index b779ab79695..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/UnifiedEmbeddingModel.dot +++ /dev/null @@ -1,28 +0,0 @@ -strict digraph { -"0 Input" [id=0, type=Parameter]; -"1 Convert_1" [id=1, type=Convert]; -"2 MatMul_1" [id=2, type=MatMul]; -"3 Gather_1" [id=3, type=Gather]; -"4 Reshape_1" [id=4, type=Reshape]; -"5 Concat_12" [id=5, type=Concat]; -"6 MatMul_2" [id=6, type=MatMul]; -"7 Result" [id=7, type=Result]; -"8 matmul_2_data" [id=8, type=Constant]; -"9 Constant_8" [id=9, type=Constant]; -"10 matmul_1_data" [id=10, type=Constant]; -"11 Constant_4" [id=11, type=Constant]; -"12 gather_1_data" [id=12, type=Constant]; -"0 Input" -> "1 Convert_1" [label="[1, 3]", style=solid]; -"0 Input" -> "2 MatMul_1" [label="[1, 3]", style=solid]; -"1 Convert_1" -> "3 Gather_1" [label="[1, 3]", style=dashed]; -"2 MatMul_1" -> "4 Reshape_1" [label="[3, 1, 5]", style=solid]; -"3 Gather_1" -> "5 Concat_12" [label="[1, 3, 5]", style=solid]; -"4 Reshape_1" -> "5 Concat_12" [label="[1, 3, 5]", style=solid]; -"5 Concat_12" -> "6 MatMul_2" [label="[1, 6, 5]", style=solid]; -"6 MatMul_2" -> "7 Result" [label="[1, 6, 1]", style=solid]; -"8 matmul_2_data" -> "6 MatMul_2" [label="[1, 5]", style=solid]; -"9 Constant_8" -> "4 Reshape_1" [label="[3]", style=dashed]; -"10 matmul_1_data" -> "2 MatMul_1" [label="[3, 3, 5]", style=solid]; -"11 Constant_4" -> "3 Gather_1" [label="[]", style=dashed]; -"12 gather_1_data" -> "3 Gather_1" [label="[4, 5]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/WeightsModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/WeightsModel.dot deleted file mode 100644 index fe530ba099a..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/WeightsModel.dot +++ /dev/null @@ -1,32 +0,0 @@ -strict digraph { -"0 Input_1" [id=0, type=Parameter]; -"1 Conv" [id=1, type=Convolution]; -"2 Conv_backprop" [id=2, type=ConvolutionBackpropData]; -"3 MatMul_1" [id=3, type=MatMul]; -"4 MatMul" [id=4, type=MatMul]; -"5 MatMul_0" [id=5, type=MatMul]; -"6 Add_15" [id=6, type=Add]; -"7 Result" [id=7, type=Result]; -"8 weights_1" [id=8, type=Constant]; -"9 Constant_5" [id=9, type=Constant]; -"10 Constant_4" [id=10, type=Constant]; -"11 Constant_2" [id=11, type=Constant]; -"12 weights_0" [id=12, type=Constant]; -"13 MatMul_const" [id=13, type=MatMul]; -"0 Input_1" -> "1 Conv" [label="[1, 3, 5, 5]", style=solid]; -"1 Conv" -> "2 Conv_backprop" [label="[1, 3, 5, 5]", style=solid]; -"2 Conv_backprop" -> "3 MatMul_1" [label="[1, 3, 1, 1]", style=solid]; -"3 MatMul_1" -> "4 MatMul" [label="[1, 3, 1, 4]", style=solid]; -"3 MatMul_1" -> "5 MatMul_0" [label="[1, 3, 1, 4]", style=solid]; -"4 MatMul" -> "6 Add_15" [label="[1, 3, 1, 1]", style=solid]; -"5 MatMul_0" -> "4 MatMul" [label="[1, 3, 1, 4]", style=solid]; -"6 Add_15" -> "7 Result" [label="[1, 3, 4, 1]", style=solid]; -"8 weights_1" -> "3 MatMul_1" [label="[1, 4]", style=solid]; -"8 weights_1" -> "13 MatMul_const" [label="[1, 4]", style=solid]; -"9 Constant_5" -> "2 Conv_backprop" [label="[2]", style=dashed]; -"10 Constant_4" -> "2 Conv_backprop" [label="[3, 3, 1, 1]", style=solid]; -"11 Constant_2" -> "1 Conv" [label="[3, 3, 1, 1]", style=solid]; -"12 weights_0" -> "5 MatMul_0" [label="[1, 1]", style=solid]; -"12 weights_0" -> "13 MatMul_const" [label="[1, 1]", style=solid]; -"13 MatMul_const" -> "6 Add_15" [label="[4, 1]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/exctracted_ConvModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/exctracted_ConvModel.dot deleted file mode 100644 index fc05c2e496a..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/exctracted_ConvModel.dot +++ /dev/null @@ -1,9 +0,0 @@ -strict digraph { -"0 Parameter_Conv" [id=0, type=Parameter]; -"1 Conv" [id=1, type=Convolution]; -"2 Result_Conv.0" [id=2, type=Result]; -"3 Constant_58" [id=3, type=Constant]; -"0 Parameter_Conv" -> "1 Conv" [label="[1, 3, 4, 2]", style=solid]; -"1 Conv" -> "2 Result_Conv.0" [label="[1, 3, 4, 2]", style=solid]; -"3 Constant_58" -> "1 Conv" [label="[3, 3, 1, 1]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/exctracted_QuantizedModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/exctracted_QuantizedModel.dot deleted file mode 100644 index cb59d32c930..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/exctracted_QuantizedModel.dot +++ /dev/null @@ -1,81 +0,0 @@ -strict digraph { -"0 Parameter_Relu_1" [id=0, type=Parameter]; -"1 Parameter_Transpose" [id=1, type=Parameter]; -"2 Relu_1" [id=2, type=Relu]; -"3 Transpose" [id=3, type=Transpose]; -"4 Concat_1/fq_input_0" [id=4, type=FakeQuantize]; -"5 Concat_1" [id=5, type=Concat]; -"6 Add_2" [id=6, type=Add]; -"7 Conv_2" [id=7, type=Convolution]; -"8 Result_Add_2.0" [id=8, type=Result]; -"9 Relu_2" [id=9, type=Relu]; -"10 Conv_3/fq_input_0" [id=10, type=FakeQuantize]; -"11 Conv_3" [id=11, type=Convolution]; -"12 Result_Conv_3.0" [id=12, type=Result]; -"13 Add_2/fq_weights_0" [id=13, type=FakeQuantize]; -"14 Constant_128" [id=14, type=Constant]; -"15 Constant_127" [id=15, type=Constant]; -"16 Constant_126" [id=16, type=Constant]; -"17 Constant_125" [id=17, type=Constant]; -"18 Constant_124" [id=18, type=Constant]; -"19 Constant_96" [id=19, type=Constant]; -"20 Constant_101" [id=20, type=Constant]; -"21 Constant_100" [id=21, type=Constant]; -"22 Constant_99" [id=22, type=Constant]; -"23 Constant_98" [id=23, type=Constant]; -"24 Conv_3/fq_weights_0" [id=24, type=FakeQuantize]; -"25 Constant_121" [id=25, type=Constant]; -"26 Constant_120" [id=26, type=Constant]; -"27 Constant_119" [id=27, type=Constant]; -"28 Constant_118" [id=28, type=Constant]; -"29 Constant_117" [id=29, type=Constant]; -"30 Constant_115" [id=30, type=Constant]; -"31 Constant_114" [id=31, type=Constant]; -"32 Constant_113" [id=32, type=Constant]; -"33 Constant_112" [id=33, type=Constant]; -"34 Conv_2/fq_weights_0" [id=34, type=FakeQuantize]; -"35 Constant_108" [id=35, type=Constant]; -"36 Constant_107" [id=36, type=Constant]; -"37 Constant_106" [id=37, type=Constant]; -"38 Constant_105" [id=38, type=Constant]; -"39 Constant_104" [id=39, type=Constant]; -"0 Parameter_Relu_1" -> "2 Relu_1" [label="[1, 3, 14, 28]", style=solid]; -"1 Parameter_Transpose" -> "3 Transpose" [label="[1, 3, 28, 14]", style=solid]; -"2 Relu_1" -> "4 Concat_1/fq_input_0" [label="[1, 3, 14, 28]", style=solid]; -"3 Transpose" -> "5 Concat_1" [label="[1, 3, 14, 28]", style=solid]; -"4 Concat_1/fq_input_0" -> "5 Concat_1" [label="[1, 3, 14, 28]", style=solid]; -"5 Concat_1" -> "6 Add_2" [label="[1, 6, 14, 28]", style=solid]; -"5 Concat_1" -> "7 Conv_2" [label="[1, 6, 14, 28]", style=solid]; -"6 Add_2" -> "8 Result_Add_2.0" [label="[1, 6, 14, 28]", style=solid]; -"7 Conv_2" -> "9 Relu_2" [label="[1, 12, 14, 28]", style=solid]; -"9 Relu_2" -> "10 Conv_3/fq_input_0" [label="[1, 12, 14, 28]", style=solid]; -"10 Conv_3/fq_input_0" -> "11 Conv_3" [label="[1, 12, 14, 28]", style=solid]; -"11 Conv_3" -> "12 Result_Conv_3.0" [label="[1, 6, 14, 28]", style=solid]; -"13 Add_2/fq_weights_0" -> "6 Add_2" [label="[1, 6, 1, 1]", style=solid]; -"14 Constant_128" -> "13 Add_2/fq_weights_0" [label="[]", style=solid]; -"15 Constant_127" -> "13 Add_2/fq_weights_0" [label="[]", style=solid]; -"16 Constant_126" -> "13 Add_2/fq_weights_0" [label="[]", style=solid]; -"17 Constant_125" -> "13 Add_2/fq_weights_0" [label="[]", style=solid]; -"18 Constant_124" -> "13 Add_2/fq_weights_0" [label="[1, 6, 1, 1]", style=solid]; -"19 Constant_96" -> "3 Transpose" [label="[4]", style=dashed]; -"20 Constant_101" -> "4 Concat_1/fq_input_0" [label="[]", style=solid]; -"21 Constant_100" -> "4 Concat_1/fq_input_0" [label="[]", style=solid]; -"22 Constant_99" -> "4 Concat_1/fq_input_0" [label="[]", style=solid]; -"23 Constant_98" -> "4 Concat_1/fq_input_0" [label="[]", style=solid]; -"24 Conv_3/fq_weights_0" -> "11 Conv_3" [label="[6, 12, 1, 1]", style=solid]; -"25 Constant_121" -> "24 Conv_3/fq_weights_0" [label="[]", style=solid]; -"26 Constant_120" -> "24 Conv_3/fq_weights_0" [label="[]", style=solid]; -"27 Constant_119" -> "24 Conv_3/fq_weights_0" [label="[]", style=solid]; -"28 Constant_118" -> "24 Conv_3/fq_weights_0" [label="[]", style=solid]; -"29 Constant_117" -> "24 Conv_3/fq_weights_0" [label="[6, 12, 1, 1]", style=solid]; -"30 Constant_115" -> "10 Conv_3/fq_input_0" [label="[]", style=solid]; -"31 Constant_114" -> "10 Conv_3/fq_input_0" [label="[]", style=solid]; -"32 Constant_113" -> "10 Conv_3/fq_input_0" [label="[]", style=solid]; -"33 Constant_112" -> "10 Conv_3/fq_input_0" [label="[]", style=solid]; -"34 Conv_2/fq_weights_0" -> "7 Conv_2" [label="[12, 6, 1, 1]", style=solid]; -"35 Constant_108" -> "34 Conv_2/fq_weights_0" [label="[]", style=solid]; -"36 Constant_107" -> "34 Conv_2/fq_weights_0" [label="[]", style=solid]; -"37 Constant_106" -> "34 Conv_2/fq_weights_0" [label="[]", style=solid]; -"38 Constant_105" -> "34 Conv_2/fq_weights_0" [label="[]", style=solid]; -"39 Constant_104" -> "34 Conv_2/fq_weights_0" [label="[12, 6, 1, 1]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/googlenet-v3-pytorch.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/googlenet-v3-pytorch.dot deleted file mode 100644 index 508b31bcc30..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/googlenet-v3-pytorch.dot +++ /dev/null @@ -1,1052 +0,0 @@ -strict digraph { -"0 data" [id=0, type=Parameter]; -"1 Multiply_4031" [id=1, type=Multiply]; -"2 Divide_1751" [id=2, type=Add]; -"3 /Conv2d_1a_3x3/conv/Conv/WithoutBiases" [id=3, type=Convolution]; -"4 /Conv2d_1a_3x3/conv/Conv" [id=4, type=Add]; -"5 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type=Convolution]; -"23 /Mixed_5b/branch5x5_1/conv/Conv/WithoutBiases" [id=23, type=Convolution]; -"24 /Mixed_5b/branch_pool/conv/Conv/WithoutBiases" [id=24, type=Convolution]; -"25 /Mixed_5b/branch1x1/conv/Conv" [id=25, type=Add]; -"26 /Mixed_5b/branch3x3dbl_1/conv/Conv" [id=26, type=Add]; -"27 /Mixed_5b/branch5x5_1/conv/Conv" [id=27, type=Add]; -"28 /Mixed_5b/branch_pool/conv/Conv" [id=28, type=Add]; -"29 /Mixed_5b/branch1x1/Relu" [id=29, type=Relu]; -"30 /Mixed_5b/branch3x3dbl_1/Relu" [id=30, type=Relu]; -"31 /Mixed_5b/branch5x5_1/Relu" [id=31, type=Relu]; -"32 /Mixed_5b/branch_pool/Relu" [id=32, type=Relu]; -"33 /Mixed_5b/Concat" [id=33, type=Concat]; -"34 /Mixed_5b/branch3x3dbl_2/conv/Conv/WithoutBiases" [id=34, type=Convolution]; -"35 /Mixed_5b/branch5x5_2/conv/Conv/WithoutBiases" [id=35, type=Convolution]; -"36 /Mixed_5c/AveragePool" [id=36, type=AvgPool]; -"37 /Mixed_5c/branch1x1/conv/Conv/WithoutBiases" [id=37, type=Convolution]; -"38 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type=Constant]; -"500 Reshape_1633" [id=500, type=Constant]; -"501 onnx^^Conv_1155" [id=501, label="501 onnx::Conv_1155", type=Constant]; -"502 Reshape_1601" [id=502, type=Constant]; -"503 onnx^^Conv_1149" [id=503, label="503 onnx::Conv_1149", type=Constant]; -"504 Reshape_1617" [id=504, type=Constant]; -"505 onnx^^Conv_1152" [id=505, label="505 onnx::Conv_1152", type=Constant]; -"506 Reshape_1585" [id=506, type=Constant]; -"507 onnx^^Conv_1146" [id=507, label="507 onnx::Conv_1146", type=Constant]; -"0 data" -> "1 Multiply_4031" [label="[1, 3, 299, 299]", style=solid]; -"1 Multiply_4031" -> "2 Divide_1751" [label="[1, 3, 299, 299]", style=solid]; -"2 Divide_1751" -> "3 /Conv2d_1a_3x3/conv/Conv/WithoutBiases" [label="[1, 3, 299, 299]", style=solid]; -"3 /Conv2d_1a_3x3/conv/Conv/WithoutBiases" -> "4 /Conv2d_1a_3x3/conv/Conv" [label="[1, 32, 149, 149]", style=solid]; -"4 /Conv2d_1a_3x3/conv/Conv" -> "5 /Conv2d_1a_3x3/Relu" [label="[1, 32, 149, 149]", style=solid]; -"5 /Conv2d_1a_3x3/Relu" -> "6 /Conv2d_2a_3x3/conv/Conv/WithoutBiases" [label="[1, 32, 149, 149]", style=solid]; -"6 /Conv2d_2a_3x3/conv/Conv/WithoutBiases" -> "7 /Conv2d_2a_3x3/conv/Conv" [label="[1, 32, 147, 147]", style=solid]; -"7 /Conv2d_2a_3x3/conv/Conv" -> "8 /Conv2d_2a_3x3/Relu" [label="[1, 32, 147, 147]", style=solid]; -"8 /Conv2d_2a_3x3/Relu" -> "9 /Conv2d_2b_3x3/conv/Conv/WithoutBiases" [label="[1, 32, 147, 147]", style=solid]; -"9 /Conv2d_2b_3x3/conv/Conv/WithoutBiases" -> "10 /Conv2d_2b_3x3/conv/Conv" [label="[1, 64, 147, 147]", style=solid]; -"10 /Conv2d_2b_3x3/conv/Conv" -> "11 /Conv2d_2b_3x3/Relu" [label="[1, 64, 147, 147]", style=solid]; -"11 /Conv2d_2b_3x3/Relu" -> "12 /maxpool1/MaxPool" [label="[1, 64, 147, 147]", style=solid]; -"12 /maxpool1/MaxPool" -> "13 /Conv2d_3b_1x1/conv/Conv/WithoutBiases" [label="[1, 64, 73, 73]", style=solid]; -"13 /Conv2d_3b_1x1/conv/Conv/WithoutBiases" -> "14 /Conv2d_3b_1x1/conv/Conv" [label="[1, 80, 73, 73]", style=solid]; -"14 /Conv2d_3b_1x1/conv/Conv" -> "15 /Conv2d_3b_1x1/Relu" [label="[1, 80, 73, 73]", style=solid]; -"15 /Conv2d_3b_1x1/Relu" -> "16 /Conv2d_4a_3x3/conv/Conv/WithoutBiases" [label="[1, 80, 73, 73]", style=solid]; -"16 /Conv2d_4a_3x3/conv/Conv/WithoutBiases" -> "17 /Conv2d_4a_3x3/conv/Conv" [label="[1, 192, 71, 71]", style=solid]; -"17 /Conv2d_4a_3x3/conv/Conv" -> "18 /Conv2d_4a_3x3/Relu" [label="[1, 192, 71, 71]", style=solid]; -"18 /Conv2d_4a_3x3/Relu" -> "19 /maxpool2/MaxPool" [label="[1, 192, 71, 71]", style=solid]; -"19 /maxpool2/MaxPool" -> "20 /Mixed_5b/AveragePool" [label="[1, 192, 35, 35]", style=solid]; -"19 /maxpool2/MaxPool" -> "21 /Mixed_5b/branch1x1/conv/Conv/WithoutBiases" [label="[1, 192, 35, 35]", style=solid]; -"19 /maxpool2/MaxPool" -> "22 /Mixed_5b/branch3x3dbl_1/conv/Conv/WithoutBiases" [label="[1, 192, 35, 35]", style=solid]; -"19 /maxpool2/MaxPool" -> "23 /Mixed_5b/branch5x5_1/conv/Conv/WithoutBiases" [label="[1, 192, 35, 35]", style=solid]; -"20 /Mixed_5b/AveragePool" -> "24 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"32 /Mixed_5b/branch_pool/Relu" [label="[1, 32, 35, 35]", style=solid]; -"29 /Mixed_5b/branch1x1/Relu" -> "33 /Mixed_5b/Concat" [label="[1, 64, 35, 35]", style=solid]; -"30 /Mixed_5b/branch3x3dbl_1/Relu" -> "34 /Mixed_5b/branch3x3dbl_2/conv/Conv/WithoutBiases" [label="[1, 64, 35, 35]", style=solid]; -"31 /Mixed_5b/branch5x5_1/Relu" -> "35 /Mixed_5b/branch5x5_2/conv/Conv/WithoutBiases" [label="[1, 48, 35, 35]", style=solid]; -"32 /Mixed_5b/branch_pool/Relu" -> "33 /Mixed_5b/Concat" [label="[1, 32, 35, 35]", style=solid]; -"33 /Mixed_5b/Concat" -> "36 /Mixed_5c/AveragePool" [label="[1, 256, 35, 35]", style=solid]; -"33 /Mixed_5b/Concat" -> "37 /Mixed_5c/branch1x1/conv/Conv/WithoutBiases" [label="[1, 256, 35, 35]", style=solid]; -"33 /Mixed_5b/Concat" -> "38 /Mixed_5c/branch3x3dbl_1/conv/Conv/WithoutBiases" [label="[1, 256, 35, 35]", style=solid]; -"33 /Mixed_5b/Concat" -> "39 /Mixed_5c/branch5x5_1/conv/Conv/WithoutBiases" [label="[1, 256, 35, 35]", style=solid]; -"34 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[label="[1, 96, 35, 35]", style=solid]; -"56 /Mixed_5c/branch5x5_2/conv/Conv/WithoutBiases" -> "63 /Mixed_5c/branch5x5_2/conv/Conv" [label="[1, 64, 35, 35]", style=solid]; -"57 /Mixed_5b/branch3x3dbl_3/conv/Conv" -> "64 /Mixed_5b/branch3x3dbl_3/Relu" [label="[1, 96, 35, 35]", style=solid]; -"58 /Mixed_5d/AveragePool" -> "65 /Mixed_5d/branch_pool/conv/Conv/WithoutBiases" [label="[1, 288, 35, 35]", style=solid]; -"59 /Mixed_5d/branch1x1/conv/Conv/WithoutBiases" -> "66 /Mixed_5d/branch1x1/conv/Conv" [label="[1, 64, 35, 35]", style=solid]; -"60 /Mixed_5d/branch3x3dbl_1/conv/Conv/WithoutBiases" -> "67 /Mixed_5d/branch3x3dbl_1/conv/Conv" [label="[1, 64, 35, 35]", style=solid]; -"61 /Mixed_5d/branch5x5_1/conv/Conv/WithoutBiases" -> "68 /Mixed_5d/branch5x5_1/conv/Conv" [label="[1, 48, 35, 35]", style=solid]; -"62 /Mixed_5c/branch3x3dbl_2/conv/Conv" -> "69 /Mixed_5c/branch3x3dbl_2/Relu" [label="[1, 96, 35, 35]", style=solid]; -"63 /Mixed_5c/branch5x5_2/conv/Conv" -> "70 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/Mixed_6b/branch7x7dbl_2/conv/Conv/WithoutBiases" [label="[1, 128, 17, 17]", style=solid]; -"109 /Mixed_6a/branch3x3dbl_2/conv/Conv" -> "115 /Mixed_6a/branch3x3dbl_2/Relu" [label="[1, 96, 35, 35]", style=solid]; -"110 /Mixed_5d/branch3x3dbl_3/Relu" -> "77 /Mixed_5d/Concat" [label="[1, 96, 35, 35]", style=solid]; -"111 /Mixed_6b/branch_pool/Relu" -> "112 /Mixed_6b/Concat" [label="[1, 192, 17, 17]", style=solid]; -"112 /Mixed_6b/Concat" -> "116 /Mixed_6c/AveragePool" [label="[1, 768, 17, 17]", style=solid]; -"112 /Mixed_6b/Concat" -> "117 /Mixed_6c/branch1x1/conv/Conv/WithoutBiases" [label="[1, 768, 17, 17]", style=solid]; -"112 /Mixed_6b/Concat" -> "118 /Mixed_6c/branch7x7_1/conv/Conv/WithoutBiases" [label="[1, 768, 17, 17]", style=solid]; -"112 /Mixed_6b/Concat" -> "119 /Mixed_6c/branch7x7dbl_1/conv/Conv/WithoutBiases" [label="[1, 768, 17, 17]", style=solid]; -"113 /Mixed_6b/branch7x7_2/conv/Conv/WithoutBiases" -> "120 /Mixed_6b/branch7x7_2/conv/Conv" [label="[1, 128, 17, 17]", style=solid]; -"114 /Mixed_6b/branch7x7dbl_2/conv/Conv/WithoutBiases" -> "121 /Mixed_6b/branch7x7dbl_2/conv/Conv" [label="[1, 128, 17, 17]", style=solid]; -"115 /Mixed_6a/branch3x3dbl_2/Relu" -> "122 /Mixed_6a/branch3x3dbl_3/conv/Conv/WithoutBiases" [label="[1, 96, 35, 35]", style=solid]; -"116 /Mixed_6c/AveragePool" -> "123 /Mixed_6c/branch_pool/conv/Conv/WithoutBiases" [label="[1, 768, 17, 17]", style=solid]; -"117 /Mixed_6c/branch1x1/conv/Conv/WithoutBiases" -> "124 /Mixed_6c/branch1x1/conv/Conv" [label="[1, 192, 17, 17]", style=solid]; -"118 /Mixed_6c/branch7x7_1/conv/Conv/WithoutBiases" -> "125 /Mixed_6c/branch7x7_1/conv/Conv" [label="[1, 160, 17, 17]", style=solid]; -"119 /Mixed_6c/branch7x7dbl_1/conv/Conv/WithoutBiases" -> "126 /Mixed_6c/branch7x7dbl_1/conv/Conv" [label="[1, 160, 17, 17]", style=solid]; -"120 /Mixed_6b/branch7x7_2/conv/Conv" -> "127 /Mixed_6b/branch7x7_2/Relu" [label="[1, 128, 17, 17]", style=solid]; -"121 /Mixed_6b/branch7x7dbl_2/conv/Conv" -> "128 /Mixed_6b/branch7x7dbl_2/Relu" [label="[1, 128, 17, 17]", style=solid]; -"122 /Mixed_6a/branch3x3dbl_3/conv/Conv/WithoutBiases" -> "129 /Mixed_6a/branch3x3dbl_3/conv/Conv" [label="[1, 96, 17, 17]", style=solid]; -"123 /Mixed_6c/branch_pool/conv/Conv/WithoutBiases" -> "130 /Mixed_6c/branch_pool/conv/Conv" [label="[1, 192, 17, 17]", style=solid]; -"124 /Mixed_6c/branch1x1/conv/Conv" -> "131 /Mixed_6c/branch1x1/Relu" [label="[1, 192, 17, 17]", style=solid]; -"125 /Mixed_6c/branch7x7_1/conv/Conv" -> "132 /Mixed_6c/branch7x7_1/Relu" [label="[1, 160, 17, 17]", style=solid]; -"126 /Mixed_6c/branch7x7dbl_1/conv/Conv" -> "133 /Mixed_6c/branch7x7dbl_1/Relu" [label="[1, 160, 17, 17]", style=solid]; -"127 /Mixed_6b/branch7x7_2/Relu" -> "134 /Mixed_6b/branch7x7_3/conv/Conv/WithoutBiases" [label="[1, 128, 17, 17]", style=solid]; -"128 /Mixed_6b/branch7x7dbl_2/Relu" -> "135 /Mixed_6b/branch7x7dbl_3/conv/Conv/WithoutBiases" [label="[1, 128, 17, 17]", style=solid]; -"129 /Mixed_6a/branch3x3dbl_3/conv/Conv" -> "136 /Mixed_6a/branch3x3dbl_3/Relu" [label="[1, 96, 17, 17]", style=solid]; -"130 /Mixed_6c/branch_pool/conv/Conv" -> "137 /Mixed_6c/branch_pool/Relu" [label="[1, 192, 17, 17]", style=solid]; -"131 /Mixed_6c/branch1x1/Relu" -> "138 /Mixed_6c/Concat" [label="[1, 192, 17, 17]", style=solid]; -"132 /Mixed_6c/branch7x7_1/Relu" -> "139 /Mixed_6c/branch7x7_2/conv/Conv/WithoutBiases" [label="[1, 160, 17, 17]", style=solid]; -"133 /Mixed_6c/branch7x7dbl_1/Relu" -> "140 /Mixed_6c/branch7x7dbl_2/conv/Conv/WithoutBiases" [label="[1, 160, 17, 17]", style=solid]; -"134 /Mixed_6b/branch7x7_3/conv/Conv/WithoutBiases" -> "141 /Mixed_6b/branch7x7_3/conv/Conv" [label="[1, 192, 17, 17]", style=solid]; -"135 /Mixed_6b/branch7x7dbl_3/conv/Conv/WithoutBiases" -> "142 /Mixed_6b/branch7x7dbl_3/conv/Conv" [label="[1, 128, 17, 17]", style=solid]; -"136 /Mixed_6a/branch3x3dbl_3/Relu" -> "87 /Mixed_6a/Concat" [label="[1, 96, 17, 17]", style=solid]; -"137 /Mixed_6c/branch_pool/Relu" -> "138 /Mixed_6c/Concat" [label="[1, 192, 17, 17]", style=solid]; -"138 /Mixed_6c/Concat" -> "143 /Mixed_6d/AveragePool" [label="[1, 768, 17, 17]", style=solid]; -"138 /Mixed_6c/Concat" -> "144 /Mixed_6d/branch1x1/conv/Conv/WithoutBiases" [label="[1, 768, 17, 17]", style=solid]; -"138 /Mixed_6c/Concat" -> "145 /Mixed_6d/branch7x7_1/conv/Conv/WithoutBiases" [label="[1, 768, 17, 17]", style=solid]; -"138 /Mixed_6c/Concat" -> "146 /Mixed_6d/branch7x7dbl_1/conv/Conv/WithoutBiases" [label="[1, 768, 17, 17]", style=solid]; -"139 /Mixed_6c/branch7x7_2/conv/Conv/WithoutBiases" -> "147 /Mixed_6c/branch7x7_2/conv/Conv" [label="[1, 160, 17, 17]", style=solid]; -"140 /Mixed_6c/branch7x7dbl_2/conv/Conv/WithoutBiases" -> "148 /Mixed_6c/branch7x7dbl_2/conv/Conv" [label="[1, 160, 17, 17]", style=solid]; -"141 /Mixed_6b/branch7x7_3/conv/Conv" -> "149 /Mixed_6b/branch7x7_3/Relu" [label="[1, 192, 17, 17]", style=solid]; -"142 /Mixed_6b/branch7x7dbl_3/conv/Conv" -> "150 /Mixed_6b/branch7x7dbl_3/Relu" [label="[1, 128, 17, 17]", style=solid]; -"143 /Mixed_6d/AveragePool" -> "151 /Mixed_6d/branch_pool/conv/Conv/WithoutBiases" [label="[1, 768, 17, 17]", style=solid]; -"144 /Mixed_6d/branch1x1/conv/Conv/WithoutBiases" -> "152 /Mixed_6d/branch1x1/conv/Conv" [label="[1, 192, 17, 17]", style=solid]; -"145 /Mixed_6d/branch7x7_1/conv/Conv/WithoutBiases" -> "153 /Mixed_6d/branch7x7_1/conv/Conv" [label="[1, 160, 17, 17]", style=solid]; -"146 /Mixed_6d/branch7x7dbl_1/conv/Conv/WithoutBiases" -> "154 /Mixed_6d/branch7x7dbl_1/conv/Conv" [label="[1, 160, 17, 17]", style=solid]; -"147 /Mixed_6c/branch7x7_2/conv/Conv" -> "155 /Mixed_6c/branch7x7_2/Relu" [label="[1, 160, 17, 17]", style=solid]; -"148 /Mixed_6c/branch7x7dbl_2/conv/Conv" -> "156 /Mixed_6c/branch7x7dbl_2/Relu" [label="[1, 160, 17, 17]", style=solid]; -"149 /Mixed_6b/branch7x7_3/Relu" -> "112 /Mixed_6b/Concat" [label="[1, 192, 17, 17]", style=solid]; -"150 /Mixed_6b/branch7x7dbl_3/Relu" -> "157 /Mixed_6b/branch7x7dbl_4/conv/Conv/WithoutBiases" [label="[1, 128, 17, 17]", style=solid]; -"151 /Mixed_6d/branch_pool/conv/Conv/WithoutBiases" -> "158 /Mixed_6d/branch_pool/conv/Conv" [label="[1, 192, 17, 17]", style=solid]; -"152 /Mixed_6d/branch1x1/conv/Conv" -> "159 /Mixed_6d/branch1x1/Relu" [label="[1, 192, 17, 17]", style=solid]; -"153 /Mixed_6d/branch7x7_1/conv/Conv" -> "160 /Mixed_6d/branch7x7_1/Relu" [label="[1, 160, 17, 17]", style=solid]; -"154 /Mixed_6d/branch7x7dbl_1/conv/Conv" -> "161 /Mixed_6d/branch7x7dbl_1/Relu" [label="[1, 160, 17, 17]", style=solid]; -"155 /Mixed_6c/branch7x7_2/Relu" -> "162 /Mixed_6c/branch7x7_3/conv/Conv/WithoutBiases" [label="[1, 160, 17, 17]", style=solid]; -"156 /Mixed_6c/branch7x7dbl_2/Relu" -> "163 /Mixed_6c/branch7x7dbl_3/conv/Conv/WithoutBiases" [label="[1, 160, 17, 17]", style=solid]; -"157 /Mixed_6b/branch7x7dbl_4/conv/Conv/WithoutBiases" -> "164 /Mixed_6b/branch7x7dbl_4/conv/Conv" [label="[1, 128, 17, 17]", style=solid]; -"158 /Mixed_6d/branch_pool/conv/Conv" -> "165 /Mixed_6d/branch_pool/Relu" [label="[1, 192, 17, 17]", style=solid]; -"159 /Mixed_6d/branch1x1/Relu" -> "166 /Mixed_6d/Concat" [label="[1, 192, 17, 17]", style=solid]; -"160 /Mixed_6d/branch7x7_1/Relu" -> "167 /Mixed_6d/branch7x7_2/conv/Conv/WithoutBiases" [label="[1, 160, 17, 17]", style=solid]; -"161 /Mixed_6d/branch7x7dbl_1/Relu" -> "168 /Mixed_6d/branch7x7dbl_2/conv/Conv/WithoutBiases" [label="[1, 160, 17, 17]", style=solid]; -"162 /Mixed_6c/branch7x7_3/conv/Conv/WithoutBiases" -> "169 /Mixed_6c/branch7x7_3/conv/Conv" [label="[1, 192, 17, 17]", style=solid]; -"163 /Mixed_6c/branch7x7dbl_3/conv/Conv/WithoutBiases" -> "170 /Mixed_6c/branch7x7dbl_3/conv/Conv" [label="[1, 160, 17, 17]", style=solid]; -"164 /Mixed_6b/branch7x7dbl_4/conv/Conv" -> "171 /Mixed_6b/branch7x7dbl_4/Relu" [label="[1, 128, 17, 17]", style=solid]; -"165 /Mixed_6d/branch_pool/Relu" -> "166 /Mixed_6d/Concat" [label="[1, 192, 17, 17]", style=solid]; -"166 /Mixed_6d/Concat" -> "172 /Mixed_6e/AveragePool" [label="[1, 768, 17, 17]", style=solid]; -"166 /Mixed_6d/Concat" -> "173 /Mixed_6e/branch1x1/conv/Conv/WithoutBiases" [label="[1, 768, 17, 17]", style=solid]; -"166 /Mixed_6d/Concat" -> "174 /Mixed_6e/branch7x7_1/conv/Conv/WithoutBiases" [label="[1, 768, 17, 17]", style=solid]; -"166 /Mixed_6d/Concat" -> "175 /Mixed_6e/branch7x7dbl_1/conv/Conv/WithoutBiases" [label="[1, 768, 17, 17]", style=solid]; -"167 /Mixed_6d/branch7x7_2/conv/Conv/WithoutBiases" -> "176 /Mixed_6d/branch7x7_2/conv/Conv" [label="[1, 160, 17, 17]", style=solid]; -"168 /Mixed_6d/branch7x7dbl_2/conv/Conv/WithoutBiases" -> "177 /Mixed_6d/branch7x7dbl_2/conv/Conv" [label="[1, 160, 17, 17]", style=solid]; -"169 /Mixed_6c/branch7x7_3/conv/Conv" -> "178 /Mixed_6c/branch7x7_3/Relu" [label="[1, 192, 17, 17]", style=solid]; -"170 /Mixed_6c/branch7x7dbl_3/conv/Conv" -> "179 /Mixed_6c/branch7x7dbl_3/Relu" [label="[1, 160, 17, 17]", style=solid]; -"171 /Mixed_6b/branch7x7dbl_4/Relu" -> "180 /Mixed_6b/branch7x7dbl_5/conv/Conv/WithoutBiases" [label="[1, 128, 17, 17]", style=solid]; -"172 /Mixed_6e/AveragePool" -> "181 /Mixed_6e/branch_pool/conv/Conv/WithoutBiases" [label="[1, 768, 17, 17]", style=solid]; -"173 /Mixed_6e/branch1x1/conv/Conv/WithoutBiases" -> "182 /Mixed_6e/branch1x1/conv/Conv" [label="[1, 192, 17, 17]", style=solid]; -"174 /Mixed_6e/branch7x7_1/conv/Conv/WithoutBiases" -> "183 /Mixed_6e/branch7x7_1/conv/Conv" [label="[1, 192, 17, 17]", style=solid]; -"175 /Mixed_6e/branch7x7dbl_1/conv/Conv/WithoutBiases" -> "184 /Mixed_6e/branch7x7dbl_1/conv/Conv" [label="[1, 192, 17, 17]", style=solid]; -"176 /Mixed_6d/branch7x7_2/conv/Conv" -> "185 /Mixed_6d/branch7x7_2/Relu" [label="[1, 160, 17, 17]", style=solid]; -"177 /Mixed_6d/branch7x7dbl_2/conv/Conv" -> "186 /Mixed_6d/branch7x7dbl_2/Relu" [label="[1, 160, 17, 17]", style=solid]; -"178 /Mixed_6c/branch7x7_3/Relu" -> "138 /Mixed_6c/Concat" [label="[1, 192, 17, 17]", style=solid]; -"179 /Mixed_6c/branch7x7dbl_3/Relu" -> "187 /Mixed_6c/branch7x7dbl_4/conv/Conv/WithoutBiases" [label="[1, 160, 17, 17]", style=solid]; -"180 /Mixed_6b/branch7x7dbl_5/conv/Conv/WithoutBiases" -> "188 /Mixed_6b/branch7x7dbl_5/conv/Conv" [label="[1, 192, 17, 17]", style=solid]; -"181 /Mixed_6e/branch_pool/conv/Conv/WithoutBiases" -> "189 /Mixed_6e/branch_pool/conv/Conv" [label="[1, 192, 17, 17]", style=solid]; -"182 /Mixed_6e/branch1x1/conv/Conv" -> "190 /Mixed_6e/branch1x1/Relu" [label="[1, 192, 17, 17]", style=solid]; -"183 /Mixed_6e/branch7x7_1/conv/Conv" -> "191 /Mixed_6e/branch7x7_1/Relu" [label="[1, 192, 17, 17]", style=solid]; -"184 /Mixed_6e/branch7x7dbl_1/conv/Conv" -> "192 /Mixed_6e/branch7x7dbl_1/Relu" [label="[1, 192, 17, 17]", style=solid]; -"185 /Mixed_6d/branch7x7_2/Relu" -> "193 /Mixed_6d/branch7x7_3/conv/Conv/WithoutBiases" [label="[1, 160, 17, 17]", style=solid]; -"186 /Mixed_6d/branch7x7dbl_2/Relu" -> "194 /Mixed_6d/branch7x7dbl_3/conv/Conv/WithoutBiases" [label="[1, 160, 17, 17]", style=solid]; -"187 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384, 1, 1]", style=solid]; -"220 Reshape_844" -> "74 /features/features.8/conv/conv.1/conv.1.0/Conv/WithoutBiases" [label="[384, 1, 1, 3, 3]", style=solid]; -"221 Reshape_829" -> "66 /features/features.8/conv/conv.0/conv.0.0/Conv" [label="[1, 384, 1, 1]", style=solid]; -"222 onnx^^Conv_601" -> "63 /features/features.8/conv/conv.0/conv.0.0/Conv/WithoutBiases" [label="[384, 64, 1, 1]", style=solid]; -"223 Reshape_814" -> "61 /features/features.7/conv/conv.2/Conv" [label="[1, 64, 1, 1]", style=solid]; -"224 onnx^^Conv_598" -> "59 /features/features.7/conv/conv.2/Conv/WithoutBiases" [label="[64, 192, 1, 1]", style=solid]; -"225 Reshape_795" -> "53 /features/features.7/conv/conv.1/conv.1.0/Conv" [label="[1, 192, 1, 1]", style=solid]; -"226 Reshape_743" -> "50 /features/features.7/conv/conv.1/conv.1.0/Conv/WithoutBiases" [label="[192, 1, 1, 3, 3]", style=solid]; -"227 Reshape_728" -> "44 /features/features.7/conv/conv.0/conv.0.0/Conv" [label="[1, 192, 1, 1]", style=solid]; -"228 onnx^^Conv_592" -> "41 /features/features.7/conv/conv.0/conv.0.0/Conv/WithoutBiases" [label="[192, 32, 1, 1]", style=solid]; -"229 Reshape_712" -> "60 /features/features.6/conv/conv.2/Conv" [label="[1, 32, 1, 1]", style=solid]; -"230 onnx^^Conv_589" -> "57 /features/features.6/conv/conv.2/Conv/WithoutBiases" [label="[32, 192, 1, 1]", style=solid]; -"231 Reshape_693" -> "51 /features/features.6/conv/conv.1/conv.1.0/Conv" [label="[1, 192, 1, 1]", style=solid]; -"232 Reshape_641" -> "48 /features/features.6/conv/conv.1/conv.1.0/Conv/WithoutBiases" [label="[192, 1, 1, 3, 3]", style=solid]; -"233 Reshape_626" -> "42 /features/features.6/conv/conv.0/conv.0.0/Conv" [label="[1, 192, 1, 1]", style=solid]; -"234 onnx^^Conv_583" -> "39 /features/features.6/conv/conv.0/conv.0.0/Conv/WithoutBiases" [label="[192, 32, 1, 1]", style=solid]; -"235 Reshape_610" -> "58 /features/features.5/conv/conv.2/Conv" [label="[1, 32, 1, 1]", style=solid]; -"236 onnx^^Conv_580" -> "55 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/features/features.4/conv/conv.1/conv.1.0/Conv/WithoutBiases" [label="[144, 1, 1, 3, 3]", style=solid]; -"245 Reshape_423" -> "23 /features/features.4/conv/conv.0/conv.0.0/Conv" [label="[1, 144, 1, 1]", style=solid]; -"246 onnx^^Conv_565" -> "21 /features/features.4/conv/conv.0/conv.0.0/Conv/WithoutBiases" [label="[144, 24, 1, 1]", style=solid]; -"247 Reshape_407" -> "34 /features/features.3/conv/conv.2/Conv" [label="[1, 24, 1, 1]", style=solid]; -"248 onnx^^Conv_562" -> "32 /features/features.3/conv/conv.2/Conv/WithoutBiases" [label="[24, 144, 1, 1]", style=solid]; -"249 Reshape_388" -> "28 /features/features.3/conv/conv.1/conv.1.0/Conv" [label="[1, 144, 1, 1]", style=solid]; -"250 Reshape_336" -> "26 /features/features.3/conv/conv.1/conv.1.0/Conv/WithoutBiases" [label="[144, 1, 1, 3, 3]", style=solid]; -"251 Reshape_321" -> "22 /features/features.3/conv/conv.0/conv.0.0/Conv" [label="[1, 144, 1, 1]", style=solid]; -"252 onnx^^Conv_556" -> "20 /features/features.3/conv/conv.0/conv.0.0/Conv/WithoutBiases" [label="[144, 24, 1, 1]", style=solid]; -"253 Reshape_306" -> "18 /features/features.2/conv/conv.2/Conv" [label="[1, 24, 1, 1]", style=solid]; -"254 onnx^^Conv_553" -> "17 /features/features.2/conv/conv.2/Conv/WithoutBiases" [label="[24, 96, 1, 1]", style=solid]; -"255 Reshape_287" -> "15 /features/features.2/conv/conv.1/conv.1.0/Conv" [label="[1, 96, 1, 1]", style=solid]; -"256 Reshape_235" -> "14 /features/features.2/conv/conv.1/conv.1.0/Conv/WithoutBiases" [label="[96, 1, 1, 3, 3]", style=solid]; -"257 Reshape_220" -> "12 /features/features.2/conv/conv.0/conv.0.0/Conv" [label="[1, 96, 1, 1]", style=solid]; -"258 onnx^^Conv_547" -> "11 /features/features.2/conv/conv.0/conv.0.0/Conv/WithoutBiases" [label="[96, 16, 1, 1]", style=solid]; -"259 Reshape_205" -> "10 /features/features.1/conv/conv.1/Conv" [label="[1, 16, 1, 1]", style=solid]; -"260 onnx^^Conv_544" -> "9 /features/features.1/conv/conv.1/Conv/WithoutBiases" [label="[16, 32, 1, 1]", style=solid]; -"261 Reshape_186" -> "7 /features/features.1/conv/conv.0/conv.0.0/Conv" [label="[1, 32, 1, 1]", style=solid]; -"262 Reshape_134" -> "6 /features/features.1/conv/conv.0/conv.0.0/Conv/WithoutBiases" [label="[32, 1, 1, 3, 3]", style=solid]; -"263 Reshape_119" -> "4 /features/features.0/features.0.0/Conv" [label="[1, 32, 1, 1]", style=solid]; -"264 Gather_5168" -> "3 /features/features.0/features.0.0/Conv/WithoutBiases" [label="[32, 3, 3, 3]", style=solid]; -"265 Gather_5165" -> "2 Divide_1885" [label="[1, 3, 1, 1]", style=solid]; -"266 Gather_5162" -> "1 Multiply_5095" [label="[1, 3, 1, 1]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/mobilenet-v3-small-1.0-224-tf.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/mobilenet-v3-small-1.0-224-tf.dot deleted file mode 100644 index 195becdc1a1..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/mobilenet-v3-small-1.0-224-tf.dot +++ /dev/null @@ -1,628 +0,0 @@ -strict digraph { -"0 input_1" [id=0, type=Parameter]; -"1 Transpose_7780" [id=1, type=Transpose]; -"2 Transpose_7774" [id=2, type=Multiply]; -"3 Transpose_710" [id=3, type=Add]; -"4 Multiply_9167" [id=4, type=Convolution]; -"5 Transpose_5170" [id=5, type=Add]; -"6 Transpose_5188" [id=6, type=HSwish]; -"7 Multiply_9181" [id=7, type=GroupConvolution]; -"8 Transpose_5239" [id=8, type=Add]; -"9 Transpose_5241" [id=9, type=Relu]; -"10 Transpose_5245" [id=10, type=ReduceMean]; -"11 Transpose_5277" [id=11, type=Multiply]; -"12 Convolution_801" [id=12, type=Convolution]; -"13 Multiply_9195" [id=13, type=Convolution]; -"14 Transpose_5251" [id=14, type=Add]; -"15 Transpose_5301" [id=15, type=Add]; -"16 Transpose_5253" [id=16, type=Relu]; -"17 Multiply_9209" [id=17, type=Convolution]; -"18 Convolution_810" [id=18, type=Convolution]; -"19 Transpose_5325" [id=19, type=Add]; -"20 Transpose_5259" [id=20, type=Add]; -"21 Transpose_5327" [id=21, type=Relu]; -"22 Transpose_5273" [id=22, type=HSigmoid]; -"23 Multiply_9223" [id=23, type=GroupConvolution]; -"24 Transpose_5378" [id=24, type=Add]; -"25 Transpose_5380" [id=25, type=Relu]; -"26 Multiply_9237" [id=26, type=Convolution]; -"27 Transpose_5404" [id=27, type=Add]; -"28 Multiply_9251" [id=28, type=Convolution]; -"29 Transpose_5484" [id=29, type=Add]; -"30 Transpose_5428" [id=30, type=Add]; -"31 Multiply_9293" [id=31, type=Convolution]; -"32 Transpose_5430" [id=32, type=Relu]; -"33 Transpose_5508" [id=33, type=Add]; -"34 Multiply_9265" [id=34, type=GroupConvolution]; -"35 Transpose_5526" [id=35, type=HSwish]; -"36 Transpose_5454" [id=36, type=Add]; -"37 Multiply_9307" [id=37, type=GroupConvolution]; -"38 Transpose_5456" [id=38, type=Relu]; -"39 Transpose_5577" [id=39, type=Add]; -"40 Multiply_9279" [id=40, type=Convolution]; -"41 Transpose_5595" [id=41, type=HSwish]; -"42 Transpose_5480" [id=42, type=Add]; -"43 Transpose_5599" [id=43, type=ReduceMean]; -"44 Transpose_5631" [id=44, type=Multiply]; -"45 Convolution_1132" [id=45, type=Convolution]; -"46 Multiply_9321" [id=46, type=Convolution]; -"47 Transpose_5605" [id=47, type=Add]; -"48 Transpose_5655" [id=48, type=Add]; -"49 Transpose_5607" [id=49, type=Relu]; -"50 Multiply_9335" [id=50, type=Convolution]; -"51 Transpose_5803" [id=51, type=Add]; -"52 Convolution_1141" [id=52, type=Convolution]; -"53 Transpose_5679" [id=53, type=Add]; -"54 Multiply_9377" [id=54, type=Convolution]; -"55 Transpose_5951" [id=55, type=Add]; -"56 Transpose_5613" [id=56, type=Add]; -"57 Transpose_5697" [id=57, type=HSwish]; -"58 Transpose_5827" [id=58, type=Add]; -"59 Multiply_9419" [id=59, type=Convolution]; -"60 Transpose_5627" [id=60, type=HSigmoid]; -"61 Multiply_9349" [id=61, type=GroupConvolution]; -"62 Transpose_5845" [id=62, type=HSwish]; -"63 Transpose_5975" [id=63, type=Add]; -"64 Transpose_5721" [id=64, type=Add]; -"65 Multiply_9391" [id=65, type=GroupConvolution]; -"66 Transpose_5993" [id=66, type=HSwish]; -"67 Transpose_5739" [id=67, type=HSwish]; -"68 Transpose_5869" [id=68, type=Add]; -"69 Multiply_9433" [id=69, type=GroupConvolution]; -"70 Transpose_5743" [id=70, type=ReduceMean]; -"71 Transpose_5775" [id=71, type=Multiply]; -"72 Transpose_5887" [id=72, type=HSwish]; -"73 Transpose_6017" [id=73, type=Add]; -"74 Convolution_1242" [id=74, type=Convolution]; -"75 Multiply_9363" [id=75, type=Convolution]; -"76 Transpose_5891" [id=76, type=ReduceMean]; -"77 Transpose_5923" [id=77, type=Multiply]; -"78 Transpose_6035" [id=78, type=HSwish]; -"79 Transpose_5749" [id=79, type=Add]; -"80 Transpose_5799" [id=80, type=Add]; -"81 Convolution_1353" [id=81, type=Convolution]; -"82 Multiply_9405" [id=82, type=Convolution]; -"83 Transpose_6039" [id=83, type=ReduceMean]; -"84 Transpose_6071" [id=84, type=Multiply]; -"85 Transpose_5751" [id=85, type=Relu]; -"86 Transpose_5897" [id=86, type=Add]; -"87 Transpose_5947" [id=87, type=Add]; -"88 Convolution_1464" [id=88, type=Convolution]; -"89 Multiply_9447" [id=89, type=Convolution]; -"90 Convolution_1251" [id=90, type=Convolution]; -"91 Transpose_5899" [id=91, type=Relu]; -"92 Transpose_6045" [id=92, type=Add]; -"93 Transpose_6095" [id=93, type=Add]; -"94 Transpose_5757" [id=94, type=Add]; -"95 Convolution_1362" [id=95, type=Convolution]; -"96 Transpose_6047" [id=96, type=Relu]; -"97 Multiply_9461" [id=97, type=Convolution]; -"98 Transpose_6243" [id=98, type=Add]; -"99 Transpose_5771" [id=99, type=HSigmoid]; -"100 Transpose_5905" [id=100, type=Add]; -"101 Convolution_1473" [id=101, type=Convolution]; -"102 Transpose_6119" [id=102, type=Add]; -"103 Multiply_9503" [id=103, type=Convolution]; -"104 Transpose_5919" [id=104, type=HSigmoid]; -"105 Transpose_6053" [id=105, type=Add]; -"106 Transpose_6137" [id=106, type=HSwish]; -"107 Transpose_6267" [id=107, type=Add]; -"108 Transpose_6067" [id=108, type=HSigmoid]; -"109 Multiply_9475" [id=109, type=GroupConvolution]; -"110 Transpose_6285" [id=110, type=HSwish]; -"111 Transpose_6161" [id=111, type=Add]; -"112 Multiply_9517" [id=112, type=GroupConvolution]; -"113 Transpose_6179" [id=113, type=HSwish]; -"114 Transpose_6336" [id=114, type=Add]; -"115 Transpose_6183" [id=115, type=ReduceMean]; -"116 Transpose_6215" [id=116, type=Multiply]; -"117 Transpose_6354" [id=117, type=HSwish]; -"118 Convolution_1574" [id=118, type=Convolution]; -"119 Multiply_9489" [id=119, type=Convolution]; -"120 Transpose_6358" [id=120, type=ReduceMean]; -"121 Transpose_6390" [id=121, type=Multiply]; -"122 Transpose_6189" [id=122, type=Add]; -"123 Transpose_6239" [id=123, type=Add]; -"124 Convolution_1713" [id=124, type=Convolution]; -"125 Multiply_9531" [id=125, type=Convolution]; -"126 Transpose_6191" [id=126, type=Relu]; -"127 Transpose_6364" [id=127, type=Add]; -"128 Transpose_6414" [id=128, type=Add]; -"129 Convolution_1583" [id=129, type=Convolution]; -"130 Transpose_6366" [id=130, type=Relu]; -"131 Multiply_9545" [id=131, type=Convolution]; -"132 Transpose_6562" [id=132, type=Add]; -"133 Transpose_6197" [id=133, type=Add]; -"134 Convolution_1722" [id=134, type=Convolution]; -"135 Transpose_6438" [id=135, type=Add]; -"136 Multiply_9587" [id=136, type=Convolution]; -"137 Transpose_6710" [id=137, type=Add]; -"138 Transpose_6211" [id=138, type=HSigmoid]; -"139 Transpose_6372" [id=139, type=Add]; -"140 Transpose_6456" [id=140, type=HSwish]; -"141 Transpose_6586" [id=141, type=Add]; -"142 Multiply_9629" [id=142, type=Convolution]; -"143 Transpose_6386" [id=143, type=HSigmoid]; -"144 Multiply_9559" [id=144, type=GroupConvolution]; -"145 Transpose_6604" [id=145, type=HSwish]; -"146 Transpose_6734" [id=146, type=Add]; -"147 Transpose_6480" [id=147, type=Add]; -"148 Multiply_9601" [id=148, type=GroupConvolution]; -"149 Transpose_6752" [id=149, type=HSwish]; -"150 Transpose_6498" [id=150, type=HSwish]; -"151 Transpose_6628" [id=151, type=Add]; -"152 Transpose_6756" [id=152, type=ReduceMean]; -"153 Transpose_6502" [id=153, type=ReduceMean]; -"154 Transpose_6534" [id=154, type=Multiply]; -"155 Transpose_6646" [id=155, type=HSwish]; -"156 Convolution_2013" [id=156, type=Convolution]; -"157 Convolution_1823" [id=157, type=Convolution]; -"158 Multiply_9573" [id=158, type=Convolution]; -"159 Transpose_6650" [id=159, type=ReduceMean]; -"160 Transpose_6682" [id=160, type=Multiply]; -"161 Transpose_6762" [id=161, type=Add]; -"162 Transpose_6508" [id=162, type=Add]; -"163 Transpose_6558" [id=163, type=Add]; -"164 Convolution_1934" [id=164, type=Convolution]; -"165 Multiply_9615" [id=165, type=Convolution]; -"166 Transpose_6780" [id=166, type=HSwish]; -"167 Transpose_6510" [id=167, type=Relu]; -"168 Transpose_6656" [id=168, type=Add]; -"169 Transpose_6706" [id=169, type=Add]; -"170 Convolution_2025" [id=170, type=Convolution]; -"171 Convolution_1832" [id=171, type=Convolution]; -"172 Transpose_6658" [id=172, type=Relu]; -"173 Transpose_6786" [id=173, type=Add]; -"174 Transpose_6516" [id=174, type=Add]; -"175 Convolution_1943" [id=175, type=Convolution]; -"176 MobilenetV3small/Logits/BiasAdd" [id=176, type=Reshape]; -"177 Transpose_6530" [id=177, type=HSigmoid]; -"178 Transpose_6664" [id=178, type=Add]; -"179 MobilenetV3small/flatten/Reshape" [id=179, type=Reshape]; -"180 Transpose_6678" [id=180, type=HSigmoid]; -"181 MobilenetV3small/Predictions/Softmax" [id=181, type=Softmax]; -"182 Predictions" [id=182, type=Result]; -"183 MobilenetV3small/flatten/Const" [id=183, type=Constant]; -"184 Constant_8887" [id=184, type=Constant]; -"185 Transpose_6784" [id=185, type=Constant]; -"186 Transpose_2024" [id=186, type=Constant]; -"187 Transpose_6760" [id=187, type=Constant]; -"188 Transpose_2012" [id=188, type=Constant]; -"189 Constant_6754" [id=189, type=Constant]; -"190 Constant_9637" [id=190, type=Constant]; -"191 Multiply_9830" [id=191, type=Constant]; -"192 Constant_9623" [id=192, type=Constant]; -"193 Multiply_9824" [id=193, type=Constant]; -"194 Transpose_6662" [id=194, type=Constant]; -"195 Transpose_1942" [id=195, type=Constant]; -"196 Transpose_6654" [id=196, type=Constant]; -"197 Transpose_1933" [id=197, type=Constant]; -"198 Constant_6648" [id=198, type=Constant]; -"199 Constant_9609" [id=199, type=Constant]; -"200 Multiply_9819" [id=200, type=Constant]; -"201 Constant_9595" [id=201, type=Constant]; -"202 Multiply_9813" [id=202, type=Constant]; -"203 Constant_9581" [id=203, type=Constant]; -"204 Multiply_9807" [id=204, type=Constant]; -"205 Transpose_6514" [id=205, type=Constant]; -"206 Transpose_1831" [id=206, type=Constant]; -"207 Transpose_6506" [id=207, type=Constant]; -"208 Transpose_1822" [id=208, type=Constant]; -"209 Constant_6500" [id=209, type=Constant]; -"210 Constant_9567" [id=210, type=Constant]; -"211 Multiply_9802" [id=211, type=Constant]; -"212 Constant_9553" [id=212, type=Constant]; -"213 Multiply_9796" [id=213, type=Constant]; -"214 Constant_9539" [id=214, type=Constant]; -"215 Multiply_9790" [id=215, type=Constant]; -"216 Transpose_6370" [id=216, type=Constant]; -"217 Transpose_1721" [id=217, type=Constant]; -"218 Transpose_6362" [id=218, type=Constant]; -"219 Transpose_1712" [id=219, type=Constant]; -"220 Constant_6356" [id=220, type=Constant]; -"221 Constant_9525" [id=221, type=Constant]; -"222 Multiply_9785" [id=222, type=Constant]; -"223 Constant_9511" [id=223, type=Constant]; -"224 Multiply_9779" [id=224, type=Constant]; -"225 Constant_9497" [id=225, type=Constant]; -"226 Multiply_9773" [id=226, type=Constant]; -"227 Transpose_6195" [id=227, type=Constant]; -"228 Transpose_1582" [id=228, type=Constant]; -"229 Transpose_6187" [id=229, type=Constant]; -"230 Transpose_1573" [id=230, type=Constant]; -"231 Constant_6181" [id=231, type=Constant]; -"232 Constant_9483" [id=232, type=Constant]; -"233 Multiply_9768" [id=233, type=Constant]; -"234 Constant_9469" [id=234, type=Constant]; -"235 Multiply_9762" [id=235, type=Constant]; -"236 Constant_9455" [id=236, type=Constant]; -"237 Multiply_9756" [id=237, type=Constant]; -"238 Transpose_6051" [id=238, type=Constant]; -"239 Transpose_1472" [id=239, type=Constant]; -"240 Transpose_6043" [id=240, type=Constant]; -"241 Transpose_1463" [id=241, type=Constant]; -"242 Constant_6037" [id=242, type=Constant]; -"243 Constant_9441" [id=243, type=Constant]; -"244 Multiply_9751" [id=244, type=Constant]; -"245 Constant_9427" [id=245, type=Constant]; -"246 Multiply_9745" [id=246, type=Constant]; -"247 Constant_9413" [id=247, type=Constant]; -"248 Multiply_9739" [id=248, type=Constant]; -"249 Transpose_5903" [id=249, type=Constant]; -"250 Transpose_1361" [id=250, type=Constant]; -"251 Transpose_5895" [id=251, type=Constant]; -"252 Transpose_1352" [id=252, type=Constant]; -"253 Constant_5889" [id=253, type=Constant]; -"254 Constant_9399" [id=254, type=Constant]; -"255 Multiply_9734" [id=255, type=Constant]; -"256 Constant_9385" [id=256, type=Constant]; -"257 Multiply_9728" [id=257, type=Constant]; -"258 Constant_9371" [id=258, type=Constant]; -"259 Multiply_9722" [id=259, type=Constant]; -"260 Transpose_5755" [id=260, type=Constant]; -"261 Transpose_1250" [id=261, type=Constant]; -"262 Transpose_5747" [id=262, type=Constant]; -"263 Transpose_1241" [id=263, type=Constant]; -"264 Constant_5741" [id=264, type=Constant]; -"265 Constant_9357" [id=265, type=Constant]; -"266 Multiply_9717" [id=266, type=Constant]; -"267 Constant_9343" [id=267, type=Constant]; -"268 Multiply_9711" [id=268, type=Constant]; -"269 Constant_9329" [id=269, type=Constant]; -"270 Multiply_9705" [id=270, type=Constant]; -"271 Transpose_5611" [id=271, type=Constant]; -"272 Transpose_1140" [id=272, type=Constant]; -"273 Transpose_5603" [id=273, type=Constant]; -"274 Transpose_1131" [id=274, type=Constant]; -"275 Constant_5597" [id=275, type=Constant]; -"276 Constant_9315" [id=276, type=Constant]; -"277 Multiply_9700" [id=277, type=Constant]; -"278 Constant_9301" [id=278, type=Constant]; -"279 Multiply_9694" [id=279, type=Constant]; -"280 Constant_9287" [id=280, type=Constant]; -"281 Multiply_9688" [id=281, type=Constant]; -"282 Constant_9273" [id=282, type=Constant]; -"283 Multiply_9683" [id=283, type=Constant]; -"284 Constant_9259" [id=284, type=Constant]; -"285 Multiply_9677" [id=285, type=Constant]; -"286 Constant_9245" [id=286, type=Constant]; -"287 Multiply_9671" [id=287, type=Constant]; -"288 Constant_9231" [id=288, type=Constant]; -"289 Multiply_9666" [id=289, type=Constant]; -"290 Constant_9217" [id=290, type=Constant]; -"291 Multiply_9660" [id=291, type=Constant]; -"292 Constant_9203" [id=292, type=Constant]; -"293 Multiply_9654" [id=293, type=Constant]; -"294 Transpose_5257" [id=294, type=Constant]; -"295 Transpose_809" [id=295, type=Constant]; -"296 Transpose_5249" [id=296, type=Constant]; -"297 Transpose_800" [id=297, type=Constant]; -"298 Constant_5243" [id=298, type=Constant]; -"299 Constant_9189" [id=299, type=Constant]; -"300 Multiply_9649" [id=300, type=Constant]; -"301 Constant_9175" [id=301, type=Constant]; -"302 Gather_10068" [id=302, type=Constant]; -"303 Unsqueeze_7776" [id=303, type=Constant]; -"304 Unsqueeze_7782" [id=304, type=Constant]; -"305 Constant_7779" [id=305, type=Constant]; -"0 input_1" -> "1 Transpose_7780" [label="[1, 224, 224, 3]", style=solid]; -"1 Transpose_7780" -> "2 Transpose_7774" [label="[1, 3, 224, 224]", style=solid]; -"2 Transpose_7774" -> "3 Transpose_710" [label="[1, 3, 224, 224]", style=solid]; -"3 Transpose_710" -> "4 Multiply_9167" [label="[1, 3, 224, 224]", style=solid]; -"4 Multiply_9167" -> "5 Transpose_5170" [label="[1, 16, 112, 112]", style=solid]; -"5 Transpose_5170" -> "6 Transpose_5188" [label="[1, 16, 112, 112]", style=solid]; -"6 Transpose_5188" -> "7 Multiply_9181" [label="[1, 16, 112, 112]", style=solid]; -"7 Multiply_9181" -> "8 Transpose_5239" [label="[1, 16, 56, 56]", style=solid]; -"8 Transpose_5239" -> "9 Transpose_5241" [label="[1, 16, 56, 56]", style=solid]; -"9 Transpose_5241" -> "10 Transpose_5245" [label="[1, 16, 56, 56]", style=solid]; -"9 Transpose_5241" -> "11 Transpose_5277" [label="[1, 16, 56, 56]", style=solid]; -"10 Transpose_5245" -> "12 Convolution_801" [label="[1, 16, 1, 1]", style=solid]; -"11 Transpose_5277" -> "13 Multiply_9195" [label="[1, 16, 56, 56]", style=solid]; -"12 Convolution_801" -> "14 Transpose_5251" [label="[1, 8, 1, 1]", style=solid]; -"13 Multiply_9195" -> "15 Transpose_5301" [label="[1, 16, 56, 56]", style=solid]; -"14 Transpose_5251" -> "16 Transpose_5253" [label="[1, 8, 1, 1]", style=solid]; -"15 Transpose_5301" -> "17 Multiply_9209" [label="[1, 16, 56, 56]", style=solid]; -"16 Transpose_5253" -> "18 Convolution_810" [label="[1, 8, 1, 1]", style=solid]; -"17 Multiply_9209" -> "19 Transpose_5325" [label="[1, 72, 56, 56]", style=solid]; -"18 Convolution_810" -> "20 Transpose_5259" [label="[1, 16, 1, 1]", style=solid]; -"19 Transpose_5325" -> "21 Transpose_5327" [label="[1, 72, 56, 56]", style=solid]; -"20 Transpose_5259" -> "22 Transpose_5273" [label="[1, 16, 1, 1]", style=solid]; -"21 Transpose_5327" -> "23 Multiply_9223" [label="[1, 72, 56, 56]", style=solid]; -"22 Transpose_5273" -> "11 Transpose_5277" [label="[1, 16, 1, 1]", style=solid]; -"23 Multiply_9223" -> "24 Transpose_5378" [label="[1, 72, 28, 28]", style=solid]; -"24 Transpose_5378" -> "25 Transpose_5380" [label="[1, 72, 28, 28]", style=solid]; -"25 Transpose_5380" -> "26 Multiply_9237" [label="[1, 72, 28, 28]", style=solid]; -"26 Multiply_9237" -> "27 Transpose_5404" [label="[1, 24, 28, 28]", style=solid]; -"27 Transpose_5404" -> "28 Multiply_9251" [label="[1, 24, 28, 28]", style=solid]; -"27 Transpose_5404" -> "29 Transpose_5484" [label="[1, 24, 28, 28]", style=solid]; -"28 Multiply_9251" -> "30 Transpose_5428" [label="[1, 88, 28, 28]", style=solid]; -"29 Transpose_5484" -> "31 Multiply_9293" [label="[1, 24, 28, 28]", style=solid]; -"30 Transpose_5428" -> "32 Transpose_5430" [label="[1, 88, 28, 28]", style=solid]; -"31 Multiply_9293" -> "33 Transpose_5508" [label="[1, 96, 28, 28]", style=solid]; -"32 Transpose_5430" -> "34 Multiply_9265" [label="[1, 88, 28, 28]", style=solid]; -"33 Transpose_5508" -> "35 Transpose_5526" [label="[1, 96, 28, 28]", style=solid]; -"34 Multiply_9265" -> "36 Transpose_5454" [label="[1, 88, 28, 28]", style=solid]; -"35 Transpose_5526" -> "37 Multiply_9307" [label="[1, 96, 28, 28]", style=solid]; -"36 Transpose_5454" -> "38 Transpose_5456" [label="[1, 88, 28, 28]", style=solid]; -"37 Multiply_9307" -> "39 Transpose_5577" [label="[1, 96, 14, 14]", style=solid]; -"38 Transpose_5456" -> "40 Multiply_9279" [label="[1, 88, 28, 28]", style=solid]; -"39 Transpose_5577" -> "41 Transpose_5595" [label="[1, 96, 14, 14]", style=solid]; -"40 Multiply_9279" -> "42 Transpose_5480" [label="[1, 24, 28, 28]", style=solid]; -"41 Transpose_5595" -> "43 Transpose_5599" [label="[1, 96, 14, 14]", style=solid]; -"41 Transpose_5595" -> "44 Transpose_5631" [label="[1, 96, 14, 14]", style=solid]; -"42 Transpose_5480" -> "29 Transpose_5484" [label="[1, 24, 28, 28]", style=solid]; -"43 Transpose_5599" -> "45 Convolution_1132" [label="[1, 96, 1, 1]", style=solid]; -"44 Transpose_5631" -> "46 Multiply_9321" [label="[1, 96, 14, 14]", style=solid]; -"45 Convolution_1132" -> "47 Transpose_5605" [label="[1, 24, 1, 1]", style=solid]; -"46 Multiply_9321" -> "48 Transpose_5655" [label="[1, 40, 14, 14]", style=solid]; -"47 Transpose_5605" -> "49 Transpose_5607" [label="[1, 24, 1, 1]", style=solid]; -"48 Transpose_5655" -> "50 Multiply_9335" [label="[1, 40, 14, 14]", style=solid]; -"48 Transpose_5655" -> "51 Transpose_5803" [label="[1, 40, 14, 14]", style=solid]; -"49 Transpose_5607" -> "52 Convolution_1141" [label="[1, 24, 1, 1]", style=solid]; -"50 Multiply_9335" -> "53 Transpose_5679" [label="[1, 240, 14, 14]", style=solid]; -"51 Transpose_5803" -> "54 Multiply_9377" [label="[1, 40, 14, 14]", style=solid]; -"51 Transpose_5803" -> "55 Transpose_5951" [label="[1, 40, 14, 14]", style=solid]; -"52 Convolution_1141" -> "56 Transpose_5613" [label="[1, 96, 1, 1]", style=solid]; -"53 Transpose_5679" -> "57 Transpose_5697" [label="[1, 240, 14, 14]", style=solid]; -"54 Multiply_9377" -> "58 Transpose_5827" [label="[1, 240, 14, 14]", style=solid]; -"55 Transpose_5951" -> "59 Multiply_9419" [label="[1, 40, 14, 14]", style=solid]; -"56 Transpose_5613" -> "60 Transpose_5627" [label="[1, 96, 1, 1]", style=solid]; -"57 Transpose_5697" -> "61 Multiply_9349" [label="[1, 240, 14, 14]", style=solid]; -"58 Transpose_5827" -> "62 Transpose_5845" [label="[1, 240, 14, 14]", style=solid]; -"59 Multiply_9419" -> "63 Transpose_5975" [label="[1, 120, 14, 14]", style=solid]; -"60 Transpose_5627" -> "44 Transpose_5631" [label="[1, 96, 1, 1]", style=solid]; -"61 Multiply_9349" -> "64 Transpose_5721" [label="[1, 240, 14, 14]", style=solid]; -"62 Transpose_5845" -> "65 Multiply_9391" [label="[1, 240, 14, 14]", style=solid]; -"63 Transpose_5975" -> "66 Transpose_5993" [label="[1, 120, 14, 14]", style=solid]; -"64 Transpose_5721" -> "67 Transpose_5739" [label="[1, 240, 14, 14]", style=solid]; -"65 Multiply_9391" -> "68 Transpose_5869" [label="[1, 240, 14, 14]", style=solid]; -"66 Transpose_5993" -> "69 Multiply_9433" [label="[1, 120, 14, 14]", style=solid]; -"67 Transpose_5739" -> "70 Transpose_5743" [label="[1, 240, 14, 14]", style=solid]; -"67 Transpose_5739" -> "71 Transpose_5775" [label="[1, 240, 14, 14]", style=solid]; -"68 Transpose_5869" -> "72 Transpose_5887" [label="[1, 240, 14, 14]", style=solid]; -"69 Multiply_9433" -> "73 Transpose_6017" [label="[1, 120, 14, 14]", style=solid]; -"70 Transpose_5743" -> "74 Convolution_1242" [label="[1, 240, 1, 1]", style=solid]; -"71 Transpose_5775" -> "75 Multiply_9363" [label="[1, 240, 14, 14]", style=solid]; -"72 Transpose_5887" -> "76 Transpose_5891" [label="[1, 240, 14, 14]", style=solid]; -"72 Transpose_5887" -> "77 Transpose_5923" [label="[1, 240, 14, 14]", style=solid]; -"73 Transpose_6017" -> "78 Transpose_6035" [label="[1, 120, 14, 14]", style=solid]; -"74 Convolution_1242" -> "79 Transpose_5749" [label="[1, 64, 1, 1]", style=solid]; -"75 Multiply_9363" -> "80 Transpose_5799" [label="[1, 40, 14, 14]", style=solid]; -"76 Transpose_5891" -> "81 Convolution_1353" [label="[1, 240, 1, 1]", style=solid]; -"77 Transpose_5923" -> "82 Multiply_9405" [label="[1, 240, 14, 14]", style=solid]; -"78 Transpose_6035" -> "83 Transpose_6039" [label="[1, 120, 14, 14]", style=solid]; -"78 Transpose_6035" -> "84 Transpose_6071" [label="[1, 120, 14, 14]", style=solid]; -"79 Transpose_5749" -> "85 Transpose_5751" [label="[1, 64, 1, 1]", style=solid]; -"80 Transpose_5799" -> "51 Transpose_5803" [label="[1, 40, 14, 14]", style=solid]; -"81 Convolution_1353" -> "86 Transpose_5897" [label="[1, 64, 1, 1]", style=solid]; -"82 Multiply_9405" -> "87 Transpose_5947" [label="[1, 40, 14, 14]", style=solid]; -"83 Transpose_6039" -> "88 Convolution_1464" [label="[1, 120, 1, 1]", style=solid]; -"84 Transpose_6071" -> "89 Multiply_9447" [label="[1, 120, 14, 14]", style=solid]; -"85 Transpose_5751" -> "90 Convolution_1251" [label="[1, 64, 1, 1]", style=solid]; -"86 Transpose_5897" -> "91 Transpose_5899" [label="[1, 64, 1, 1]", style=solid]; -"87 Transpose_5947" -> "55 Transpose_5951" [label="[1, 40, 14, 14]", style=solid]; -"88 Convolution_1464" -> "92 Transpose_6045" [label="[1, 32, 1, 1]", style=solid]; -"89 Multiply_9447" -> "93 Transpose_6095" [label="[1, 48, 14, 14]", style=solid]; -"90 Convolution_1251" -> "94 Transpose_5757" [label="[1, 240, 1, 1]", style=solid]; -"91 Transpose_5899" -> "95 Convolution_1362" [label="[1, 64, 1, 1]", style=solid]; -"92 Transpose_6045" -> "96 Transpose_6047" [label="[1, 32, 1, 1]", style=solid]; -"93 Transpose_6095" -> "97 Multiply_9461" [label="[1, 48, 14, 14]", style=solid]; -"93 Transpose_6095" -> "98 Transpose_6243" [label="[1, 48, 14, 14]", style=solid]; -"94 Transpose_5757" -> "99 Transpose_5771" [label="[1, 240, 1, 1]", style=solid]; -"95 Convolution_1362" -> "100 Transpose_5905" [label="[1, 240, 1, 1]", style=solid]; -"96 Transpose_6047" -> "101 Convolution_1473" [label="[1, 32, 1, 1]", style=solid]; -"97 Multiply_9461" -> "102 Transpose_6119" [label="[1, 144, 14, 14]", style=solid]; -"98 Transpose_6243" -> "103 Multiply_9503" [label="[1, 48, 14, 14]", style=solid]; -"99 Transpose_5771" -> "71 Transpose_5775" [label="[1, 240, 1, 1]", style=solid]; -"100 Transpose_5905" -> "104 Transpose_5919" [label="[1, 240, 1, 1]", style=solid]; -"101 Convolution_1473" -> "105 Transpose_6053" [label="[1, 120, 1, 1]", style=solid]; -"102 Transpose_6119" -> "106 Transpose_6137" [label="[1, 144, 14, 14]", style=solid]; -"103 Multiply_9503" -> "107 Transpose_6267" [label="[1, 288, 14, 14]", style=solid]; -"104 Transpose_5919" -> "77 Transpose_5923" [label="[1, 240, 1, 1]", style=solid]; -"105 Transpose_6053" -> "108 Transpose_6067" [label="[1, 120, 1, 1]", style=solid]; -"106 Transpose_6137" -> "109 Multiply_9475" [label="[1, 144, 14, 14]", style=solid]; -"107 Transpose_6267" -> "110 Transpose_6285" [label="[1, 288, 14, 14]", style=solid]; -"108 Transpose_6067" -> "84 Transpose_6071" [label="[1, 120, 1, 1]", style=solid]; -"109 Multiply_9475" -> "111 Transpose_6161" [label="[1, 144, 14, 14]", style=solid]; -"110 Transpose_6285" -> "112 Multiply_9517" [label="[1, 288, 14, 14]", style=solid]; -"111 Transpose_6161" -> "113 Transpose_6179" [label="[1, 144, 14, 14]", style=solid]; -"112 Multiply_9517" -> "114 Transpose_6336" [label="[1, 288, 7, 7]", style=solid]; -"113 Transpose_6179" -> "115 Transpose_6183" [label="[1, 144, 14, 14]", style=solid]; -"113 Transpose_6179" -> "116 Transpose_6215" [label="[1, 144, 14, 14]", style=solid]; -"114 Transpose_6336" -> "117 Transpose_6354" [label="[1, 288, 7, 7]", style=solid]; -"115 Transpose_6183" -> "118 Convolution_1574" [label="[1, 144, 1, 1]", style=solid]; -"116 Transpose_6215" -> "119 Multiply_9489" [label="[1, 144, 14, 14]", style=solid]; -"117 Transpose_6354" -> "120 Transpose_6358" [label="[1, 288, 7, 7]", style=solid]; -"117 Transpose_6354" -> "121 Transpose_6390" [label="[1, 288, 7, 7]", style=solid]; -"118 Convolution_1574" -> "122 Transpose_6189" [label="[1, 40, 1, 1]", style=solid]; -"119 Multiply_9489" -> "123 Transpose_6239" [label="[1, 48, 14, 14]", style=solid]; -"120 Transpose_6358" -> "124 Convolution_1713" [label="[1, 288, 1, 1]", style=solid]; -"121 Transpose_6390" -> "125 Multiply_9531" [label="[1, 288, 7, 7]", style=solid]; -"122 Transpose_6189" -> "126 Transpose_6191" [label="[1, 40, 1, 1]", style=solid]; -"123 Transpose_6239" -> "98 Transpose_6243" [label="[1, 48, 14, 14]", style=solid]; -"124 Convolution_1713" -> "127 Transpose_6364" [label="[1, 72, 1, 1]", style=solid]; -"125 Multiply_9531" -> "128 Transpose_6414" [label="[1, 96, 7, 7]", style=solid]; -"126 Transpose_6191" -> "129 Convolution_1583" [label="[1, 40, 1, 1]", style=solid]; -"127 Transpose_6364" -> "130 Transpose_6366" [label="[1, 72, 1, 1]", style=solid]; -"128 Transpose_6414" -> "131 Multiply_9545" [label="[1, 96, 7, 7]", style=solid]; -"128 Transpose_6414" -> "132 Transpose_6562" [label="[1, 96, 7, 7]", style=solid]; -"129 Convolution_1583" -> "133 Transpose_6197" [label="[1, 144, 1, 1]", style=solid]; -"130 Transpose_6366" -> "134 Convolution_1722" [label="[1, 72, 1, 1]", style=solid]; -"131 Multiply_9545" -> "135 Transpose_6438" [label="[1, 576, 7, 7]", style=solid]; -"132 Transpose_6562" -> "136 Multiply_9587" [label="[1, 96, 7, 7]", style=solid]; -"132 Transpose_6562" -> "137 Transpose_6710" [label="[1, 96, 7, 7]", style=solid]; -"133 Transpose_6197" -> "138 Transpose_6211" [label="[1, 144, 1, 1]", style=solid]; -"134 Convolution_1722" -> "139 Transpose_6372" [label="[1, 288, 1, 1]", style=solid]; -"135 Transpose_6438" -> "140 Transpose_6456" [label="[1, 576, 7, 7]", style=solid]; -"136 Multiply_9587" -> "141 Transpose_6586" [label="[1, 576, 7, 7]", style=solid]; -"137 Transpose_6710" -> "142 Multiply_9629" [label="[1, 96, 7, 7]", style=solid]; -"138 Transpose_6211" -> "116 Transpose_6215" [label="[1, 144, 1, 1]", style=solid]; -"139 Transpose_6372" -> "143 Transpose_6386" [label="[1, 288, 1, 1]", style=solid]; -"140 Transpose_6456" -> "144 Multiply_9559" [label="[1, 576, 7, 7]", style=solid]; -"141 Transpose_6586" -> "145 Transpose_6604" [label="[1, 576, 7, 7]", style=solid]; -"142 Multiply_9629" -> "146 Transpose_6734" [label="[1, 576, 7, 7]", style=solid]; -"143 Transpose_6386" -> "121 Transpose_6390" [label="[1, 288, 1, 1]", style=solid]; -"144 Multiply_9559" -> "147 Transpose_6480" [label="[1, 576, 7, 7]", style=solid]; -"145 Transpose_6604" -> "148 Multiply_9601" [label="[1, 576, 7, 7]", style=solid]; -"146 Transpose_6734" -> "149 Transpose_6752" [label="[1, 576, 7, 7]", style=solid]; -"147 Transpose_6480" -> "150 Transpose_6498" [label="[1, 576, 7, 7]", style=solid]; -"148 Multiply_9601" -> "151 Transpose_6628" [label="[1, 576, 7, 7]", style=solid]; -"149 Transpose_6752" -> "152 Transpose_6756" [label="[1, 576, 7, 7]", style=solid]; -"150 Transpose_6498" -> "153 Transpose_6502" [label="[1, 576, 7, 7]", style=solid]; -"150 Transpose_6498" -> "154 Transpose_6534" [label="[1, 576, 7, 7]", style=solid]; -"151 Transpose_6628" -> "155 Transpose_6646" [label="[1, 576, 7, 7]", style=solid]; -"152 Transpose_6756" -> "156 Convolution_2013" [label="[1, 576, 1, 1]", style=solid]; -"153 Transpose_6502" -> "157 Convolution_1823" [label="[1, 576, 1, 1]", style=solid]; -"154 Transpose_6534" -> "158 Multiply_9573" [label="[1, 576, 7, 7]", style=solid]; -"155 Transpose_6646" -> "159 Transpose_6650" [label="[1, 576, 7, 7]", style=solid]; -"155 Transpose_6646" -> "160 Transpose_6682" [label="[1, 576, 7, 7]", style=solid]; -"156 Convolution_2013" -> "161 Transpose_6762" [label="[1, 1024, 1, 1]", style=solid]; -"157 Convolution_1823" -> "162 Transpose_6508" [label="[1, 144, 1, 1]", style=solid]; -"158 Multiply_9573" -> "163 Transpose_6558" [label="[1, 96, 7, 7]", style=solid]; -"159 Transpose_6650" -> "164 Convolution_1934" [label="[1, 576, 1, 1]", style=solid]; -"160 Transpose_6682" -> "165 Multiply_9615" [label="[1, 576, 7, 7]", style=solid]; -"161 Transpose_6762" -> "166 Transpose_6780" [label="[1, 1024, 1, 1]", style=solid]; -"162 Transpose_6508" -> "167 Transpose_6510" [label="[1, 144, 1, 1]", style=solid]; -"163 Transpose_6558" -> "132 Transpose_6562" [label="[1, 96, 7, 7]", style=solid]; -"164 Convolution_1934" -> "168 Transpose_6656" [label="[1, 144, 1, 1]", style=solid]; -"165 Multiply_9615" -> "169 Transpose_6706" [label="[1, 96, 7, 7]", style=solid]; -"166 Transpose_6780" -> "170 Convolution_2025" [label="[1, 1024, 1, 1]", style=solid]; -"167 Transpose_6510" -> "171 Convolution_1832" [label="[1, 144, 1, 1]", style=solid]; -"168 Transpose_6656" -> "172 Transpose_6658" [label="[1, 144, 1, 1]", style=solid]; -"169 Transpose_6706" -> "137 Transpose_6710" [label="[1, 96, 7, 7]", style=solid]; -"170 Convolution_2025" -> "173 Transpose_6786" [label="[1, 1000, 1, 1]", style=solid]; -"171 Convolution_1832" -> "174 Transpose_6516" [label="[1, 576, 1, 1]", style=solid]; -"172 Transpose_6658" -> "175 Convolution_1943" [label="[1, 144, 1, 1]", style=solid]; -"173 Transpose_6786" -> "176 MobilenetV3small/Logits/BiasAdd" [label="[1, 1000, 1, 1]", style=solid]; -"174 Transpose_6516" -> "177 Transpose_6530" [label="[1, 576, 1, 1]", style=solid]; -"175 Convolution_1943" -> "178 Transpose_6664" [label="[1, 576, 1, 1]", style=solid]; -"176 MobilenetV3small/Logits/BiasAdd" -> "179 MobilenetV3small/flatten/Reshape" [label="[1, 1, 1, 1000]", style=solid]; -"177 Transpose_6530" -> "154 Transpose_6534" [label="[1, 576, 1, 1]", style=solid]; -"178 Transpose_6664" -> "180 Transpose_6678" [label="[1, 576, 1, 1]", style=solid]; -"179 MobilenetV3small/flatten/Reshape" -> "181 MobilenetV3small/Predictions/Softmax" [label="[1, 1000]", style=solid]; -"180 Transpose_6678" -> "160 Transpose_6682" [label="[1, 576, 1, 1]", style=solid]; -"181 MobilenetV3small/Predictions/Softmax" -> "182 Predictions" [label="[1, 1000]", style=solid]; -"183 MobilenetV3small/flatten/Const" -> "179 MobilenetV3small/flatten/Reshape" [label="[2]", style=dashed]; -"184 Constant_8887" -> "176 MobilenetV3small/Logits/BiasAdd" [label="[4]", style=dashed]; -"185 Transpose_6784" -> "173 Transpose_6786" [label="[1, 1000, 1, 1]", style=solid]; -"186 Transpose_2024" -> "170 Convolution_2025" [label="[1000, 1024, 1, 1]", style=solid]; -"187 Transpose_6760" -> "161 Transpose_6762" [label="[1, 1024, 1, 1]", style=solid]; -"188 Transpose_2012" -> "156 Convolution_2013" [label="[1024, 576, 1, 1]", style=solid]; -"189 Constant_6754" -> "152 Transpose_6756" [label="[2]", style=dashed]; -"190 Constant_9637" -> "146 Transpose_6734" [label="[1, 576, 1, 1]", style=solid]; -"191 Multiply_9830" -> "142 Multiply_9629" [label="[576, 96, 1, 1]", style=solid]; -"192 Constant_9623" -> "169 Transpose_6706" [label="[1, 96, 1, 1]", style=solid]; -"193 Multiply_9824" -> "165 Multiply_9615" [label="[96, 576, 1, 1]", style=solid]; -"194 Transpose_6662" -> "178 Transpose_6664" [label="[1, 576, 1, 1]", style=solid]; -"195 Transpose_1942" -> "175 Convolution_1943" [label="[576, 144, 1, 1]", style=solid]; -"196 Transpose_6654" -> "168 Transpose_6656" [label="[1, 144, 1, 1]", style=solid]; -"197 Transpose_1933" -> "164 Convolution_1934" [label="[144, 576, 1, 1]", style=solid]; -"198 Constant_6648" -> "159 Transpose_6650" [label="[2]", style=dashed]; -"199 Constant_9609" -> "151 Transpose_6628" [label="[1, 576, 1, 1]", style=solid]; -"200 Multiply_9819" -> "148 Multiply_9601" [label="[576, 1, 1, 5, 5]", style=solid]; -"201 Constant_9595" -> "141 Transpose_6586" [label="[1, 576, 1, 1]", style=solid]; -"202 Multiply_9813" -> "136 Multiply_9587" [label="[576, 96, 1, 1]", style=solid]; -"203 Constant_9581" -> "163 Transpose_6558" [label="[1, 96, 1, 1]", style=solid]; -"204 Multiply_9807" -> "158 Multiply_9573" [label="[96, 576, 1, 1]", style=solid]; -"205 Transpose_6514" -> "174 Transpose_6516" [label="[1, 576, 1, 1]", style=solid]; -"206 Transpose_1831" -> "171 Convolution_1832" [label="[576, 144, 1, 1]", style=solid]; -"207 Transpose_6506" -> "162 Transpose_6508" [label="[1, 144, 1, 1]", style=solid]; -"208 Transpose_1822" -> "157 Convolution_1823" [label="[144, 576, 1, 1]", style=solid]; -"209 Constant_6500" -> "153 Transpose_6502" [label="[2]", style=dashed]; -"210 Constant_9567" -> "147 Transpose_6480" [label="[1, 576, 1, 1]", style=solid]; -"211 Multiply_9802" -> "144 Multiply_9559" [label="[576, 1, 1, 5, 5]", style=solid]; -"212 Constant_9553" -> "135 Transpose_6438" [label="[1, 576, 1, 1]", style=solid]; -"213 Multiply_9796" -> "131 Multiply_9545" [label="[576, 96, 1, 1]", style=solid]; -"214 Constant_9539" -> "128 Transpose_6414" [label="[1, 96, 1, 1]", style=solid]; -"215 Multiply_9790" -> "125 Multiply_9531" [label="[96, 288, 1, 1]", style=solid]; -"216 Transpose_6370" -> "139 Transpose_6372" [label="[1, 288, 1, 1]", style=solid]; -"217 Transpose_1721" -> "134 Convolution_1722" [label="[288, 72, 1, 1]", style=solid]; -"218 Transpose_6362" -> "127 Transpose_6364" [label="[1, 72, 1, 1]", style=solid]; -"219 Transpose_1712" -> "124 Convolution_1713" [label="[72, 288, 1, 1]", style=solid]; -"220 Constant_6356" -> "120 Transpose_6358" [label="[2]", style=dashed]; -"221 Constant_9525" -> "114 Transpose_6336" [label="[1, 288, 1, 1]", style=solid]; -"222 Multiply_9785" -> "112 Multiply_9517" [label="[288, 1, 1, 5, 5]", style=solid]; -"223 Constant_9511" -> "107 Transpose_6267" [label="[1, 288, 1, 1]", style=solid]; -"224 Multiply_9779" -> "103 Multiply_9503" [label="[288, 48, 1, 1]", style=solid]; -"225 Constant_9497" -> "123 Transpose_6239" [label="[1, 48, 1, 1]", style=solid]; -"226 Multiply_9773" -> "119 Multiply_9489" [label="[48, 144, 1, 1]", style=solid]; -"227 Transpose_6195" -> "133 Transpose_6197" [label="[1, 144, 1, 1]", style=solid]; -"228 Transpose_1582" -> "129 Convolution_1583" [label="[144, 40, 1, 1]", style=solid]; -"229 Transpose_6187" -> "122 Transpose_6189" [label="[1, 40, 1, 1]", style=solid]; -"230 Transpose_1573" -> "118 Convolution_1574" [label="[40, 144, 1, 1]", style=solid]; -"231 Constant_6181" -> "115 Transpose_6183" [label="[2]", style=dashed]; -"232 Constant_9483" -> "111 Transpose_6161" [label="[1, 144, 1, 1]", style=solid]; -"233 Multiply_9768" -> "109 Multiply_9475" [label="[144, 1, 1, 5, 5]", style=solid]; -"234 Constant_9469" -> "102 Transpose_6119" [label="[1, 144, 1, 1]", style=solid]; -"235 Multiply_9762" -> "97 Multiply_9461" [label="[144, 48, 1, 1]", style=solid]; -"236 Constant_9455" -> "93 Transpose_6095" [label="[1, 48, 1, 1]", style=solid]; -"237 Multiply_9756" -> "89 Multiply_9447" [label="[48, 120, 1, 1]", style=solid]; -"238 Transpose_6051" -> "105 Transpose_6053" [label="[1, 120, 1, 1]", style=solid]; -"239 Transpose_1472" -> "101 Convolution_1473" [label="[120, 32, 1, 1]", style=solid]; -"240 Transpose_6043" -> "92 Transpose_6045" [label="[1, 32, 1, 1]", style=solid]; -"241 Transpose_1463" -> "88 Convolution_1464" [label="[32, 120, 1, 1]", style=solid]; -"242 Constant_6037" -> "83 Transpose_6039" [label="[2]", style=dashed]; -"243 Constant_9441" -> "73 Transpose_6017" [label="[1, 120, 1, 1]", style=solid]; -"244 Multiply_9751" -> "69 Multiply_9433" [label="[120, 1, 1, 5, 5]", style=solid]; -"245 Constant_9427" -> "63 Transpose_5975" [label="[1, 120, 1, 1]", style=solid]; -"246 Multiply_9745" -> "59 Multiply_9419" [label="[120, 40, 1, 1]", style=solid]; -"247 Constant_9413" -> "87 Transpose_5947" [label="[1, 40, 1, 1]", style=solid]; -"248 Multiply_9739" -> "82 Multiply_9405" [label="[40, 240, 1, 1]", style=solid]; -"249 Transpose_5903" -> "100 Transpose_5905" [label="[1, 240, 1, 1]", style=solid]; -"250 Transpose_1361" -> "95 Convolution_1362" [label="[240, 64, 1, 1]", style=solid]; -"251 Transpose_5895" -> "86 Transpose_5897" [label="[1, 64, 1, 1]", style=solid]; -"252 Transpose_1352" -> "81 Convolution_1353" [label="[64, 240, 1, 1]", style=solid]; -"253 Constant_5889" -> "76 Transpose_5891" [label="[2]", style=dashed]; -"254 Constant_9399" -> "68 Transpose_5869" [label="[1, 240, 1, 1]", style=solid]; -"255 Multiply_9734" -> "65 Multiply_9391" [label="[240, 1, 1, 5, 5]", style=solid]; -"256 Constant_9385" -> "58 Transpose_5827" [label="[1, 240, 1, 1]", style=solid]; -"257 Multiply_9728" -> "54 Multiply_9377" [label="[240, 40, 1, 1]", style=solid]; -"258 Constant_9371" -> "80 Transpose_5799" [label="[1, 40, 1, 1]", style=solid]; -"259 Multiply_9722" -> "75 Multiply_9363" [label="[40, 240, 1, 1]", style=solid]; -"260 Transpose_5755" -> "94 Transpose_5757" [label="[1, 240, 1, 1]", style=solid]; -"261 Transpose_1250" -> "90 Convolution_1251" [label="[240, 64, 1, 1]", style=solid]; -"262 Transpose_5747" -> "79 Transpose_5749" [label="[1, 64, 1, 1]", style=solid]; -"263 Transpose_1241" -> "74 Convolution_1242" [label="[64, 240, 1, 1]", style=solid]; -"264 Constant_5741" -> "70 Transpose_5743" [label="[2]", style=dashed]; -"265 Constant_9357" -> "64 Transpose_5721" [label="[1, 240, 1, 1]", style=solid]; -"266 Multiply_9717" -> "61 Multiply_9349" [label="[240, 1, 1, 5, 5]", style=solid]; -"267 Constant_9343" -> "53 Transpose_5679" [label="[1, 240, 1, 1]", style=solid]; -"268 Multiply_9711" -> "50 Multiply_9335" [label="[240, 40, 1, 1]", style=solid]; -"269 Constant_9329" -> "48 Transpose_5655" [label="[1, 40, 1, 1]", style=solid]; -"270 Multiply_9705" -> "46 Multiply_9321" [label="[40, 96, 1, 1]", style=solid]; -"271 Transpose_5611" -> "56 Transpose_5613" [label="[1, 96, 1, 1]", style=solid]; -"272 Transpose_1140" -> "52 Convolution_1141" [label="[96, 24, 1, 1]", style=solid]; -"273 Transpose_5603" -> "47 Transpose_5605" [label="[1, 24, 1, 1]", style=solid]; -"274 Transpose_1131" -> "45 Convolution_1132" [label="[24, 96, 1, 1]", style=solid]; -"275 Constant_5597" -> "43 Transpose_5599" [label="[2]", style=dashed]; -"276 Constant_9315" -> "39 Transpose_5577" [label="[1, 96, 1, 1]", style=solid]; -"277 Multiply_9700" -> "37 Multiply_9307" [label="[96, 1, 1, 5, 5]", style=solid]; -"278 Constant_9301" -> "33 Transpose_5508" [label="[1, 96, 1, 1]", style=solid]; -"279 Multiply_9694" -> "31 Multiply_9293" [label="[96, 24, 1, 1]", style=solid]; -"280 Constant_9287" -> "42 Transpose_5480" [label="[1, 24, 1, 1]", style=solid]; -"281 Multiply_9688" -> "40 Multiply_9279" [label="[24, 88, 1, 1]", style=solid]; -"282 Constant_9273" -> "36 Transpose_5454" [label="[1, 88, 1, 1]", style=solid]; -"283 Multiply_9683" -> "34 Multiply_9265" [label="[88, 1, 1, 3, 3]", style=solid]; -"284 Constant_9259" -> "30 Transpose_5428" [label="[1, 88, 1, 1]", style=solid]; -"285 Multiply_9677" -> "28 Multiply_9251" [label="[88, 24, 1, 1]", style=solid]; -"286 Constant_9245" -> "27 Transpose_5404" [label="[1, 24, 1, 1]", style=solid]; -"287 Multiply_9671" -> "26 Multiply_9237" [label="[24, 72, 1, 1]", style=solid]; -"288 Constant_9231" -> "24 Transpose_5378" [label="[1, 72, 1, 1]", style=solid]; -"289 Multiply_9666" -> "23 Multiply_9223" [label="[72, 1, 1, 3, 3]", style=solid]; -"290 Constant_9217" -> "19 Transpose_5325" [label="[1, 72, 1, 1]", style=solid]; -"291 Multiply_9660" -> "17 Multiply_9209" [label="[72, 16, 1, 1]", style=solid]; -"292 Constant_9203" -> "15 Transpose_5301" [label="[1, 16, 1, 1]", style=solid]; -"293 Multiply_9654" -> "13 Multiply_9195" [label="[16, 16, 1, 1]", style=solid]; -"294 Transpose_5257" -> "20 Transpose_5259" [label="[1, 16, 1, 1]", style=solid]; -"295 Transpose_809" -> "18 Convolution_810" [label="[16, 8, 1, 1]", style=solid]; -"296 Transpose_5249" -> "14 Transpose_5251" [label="[1, 8, 1, 1]", style=solid]; -"297 Transpose_800" -> "12 Convolution_801" [label="[8, 16, 1, 1]", style=solid]; -"298 Constant_5243" -> "10 Transpose_5245" [label="[2]", style=dashed]; -"299 Constant_9189" -> "8 Transpose_5239" [label="[1, 16, 1, 1]", style=solid]; -"300 Multiply_9649" -> "7 Multiply_9181" [label="[16, 1, 1, 3, 3]", style=solid]; -"301 Constant_9175" -> "5 Transpose_5170" [label="[1, 16, 1, 1]", style=solid]; -"302 Gather_10068" -> "4 Multiply_9167" [label="[16, 3, 3, 3]", style=solid]; -"303 Unsqueeze_7776" -> "3 Transpose_710" [label="[1, 1, 1, 1]", style=solid]; -"304 Unsqueeze_7782" -> "2 Transpose_7774" [label="[1, 1, 1, 1]", style=solid]; -"305 Constant_7779" -> "1 Transpose_7780" [label="[4]", style=dashed]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/removed_nodes_in_QuantizedModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/removed_nodes_in_QuantizedModel.dot deleted file mode 100644 index 7ab98ea6599..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/removed_nodes_in_QuantizedModel.dot +++ /dev/null @@ -1,92 +0,0 @@ -strict digraph { -"0 Input_1" [id=0, type=Parameter]; -"1 Input_2" [id=1, type=Parameter]; -"2 Conv_1" [id=2, type=Convolution]; -"3 Mul" [id=3, type=Multiply]; -"4 Relu_1" [id=4, type=Relu]; -"5 Add_1" [id=5, type=Add]; -"6 Concat_1" [id=6, type=Concat]; -"7 Transpose/fq_input_0" [id=7, type=FakeQuantize]; -"8 Add_2" [id=8, type=Add]; -"9 Conv_2" [id=9, type=Convolution]; -"10 Transpose" [id=10, type=Transpose]; -"11 Concat_2" [id=11, type=Concat]; -"12 Relu_2" [id=12, type=Relu]; -"13 Reshape_237" [id=13, type=Reshape]; -"14 Conv_3/fq_input_0" [id=14, type=FakeQuantize]; -"15 MatMul_244" [id=15, type=MatMul]; -"16 Conv_3" [id=16, type=Convolution]; -"17 Result" [id=17, type=Result]; -"18 Constant_243" [id=18, type=Constant]; -"19 Constant_236" [id=19, type=Constant]; -"20 Constant_228" [id=20, type=Constant]; -"21 Constant_200" [id=21, type=Constant]; -"22 Constant_198" [id=22, type=Constant]; -"23 Constant_197" [id=23, type=Constant]; -"24 Constant_196" [id=24, type=Constant]; -"25 Constant_195" [id=25, type=Constant]; -"26 Constant_193" [id=26, type=Constant]; -"27 Constant_191" [id=27, type=Constant]; -"28 Conv_1/fq_weights_0" [id=28, type=FakeQuantize]; -"29 Constant_186" [id=29, type=Constant]; -"30 Constant_185" [id=30, type=Constant]; -"31 Constant_184" [id=31, type=Constant]; -"32 Constant_183" [id=32, type=Constant]; -"33 Constant_182" [id=33, type=Constant]; -"34 Constant_221" [id=34, type=Constant]; -"35 Constant_219" [id=35, type=Constant]; -"36 Constant_218" [id=36, type=Constant]; -"37 Constant_217" [id=37, type=Constant]; -"38 Constant_216" [id=38, type=Constant]; -"39 Conv_2/fq_weights_0" [id=39, type=FakeQuantize]; -"40 Constant_212" [id=40, type=Constant]; -"41 Constant_211" [id=41, type=Constant]; -"42 Constant_210" [id=42, type=Constant]; -"43 Constant_209" [id=43, type=Constant]; -"44 Constant_208" [id=44, type=Constant]; -"0 Input_1" -> "2 Conv_1" [label="[1, 3, 14, 28]", style=solid]; -"1 Input_2" -> "3 Mul" [label="[1, 3, 28, 14]", style=solid]; -"2 Conv_1" -> "4 Relu_1" [label="[1, 3, 14, 28]", style=solid]; -"3 Mul" -> "5 Add_1" [label="[1, 3, 28, 14]", style=solid]; -"4 Relu_1" -> "6 Concat_1" [label="[1, 3, 14, 28]", style=solid]; -"5 Add_1" -> "7 Transpose/fq_input_0" [label="[1, 3, 28, 14]", style=solid]; -"6 Concat_1" -> "8 Add_2" [label="[1, 6, 14, 28]", style=solid]; -"6 Concat_1" -> "9 Conv_2" [label="[1, 6, 14, 28]", style=solid]; -"7 Transpose/fq_input_0" -> "10 Transpose" [label="[1, 3, 28, 14]", style=solid]; -"8 Add_2" -> "11 Concat_2" [label="[1, 6, 14, 28]", style=solid]; -"9 Conv_2" -> "12 Relu_2" [label="[1, 12, 14, 28]", style=solid]; -"10 Transpose" -> "6 Concat_1" [label="[1, 3, 14, 28]", style=solid]; -"11 Concat_2" -> "13 Reshape_237" [label="[1, 12, 14, 28]", style=solid]; -"12 Relu_2" -> "14 Conv_3/fq_input_0" [label="[1, 12, 14, 28]", style=solid]; -"13 Reshape_237" -> "15 MatMul_244" [label="[2, 2352]", style=solid]; -"14 Conv_3/fq_input_0" -> "16 Conv_3" [label="[1, 12, 14, 28]", style=solid]; -"15 MatMul_244" -> "17 Result" [label="[2, 100]", style=solid]; -"16 Conv_3" -> "11 Concat_2" [label="[1, 6, 14, 28]", style=solid]; -"18 Constant_243" -> "15 MatMul_244" [label="[100, 2352]", style=solid]; -"19 Constant_236" -> "13 Reshape_237" [label="[2]", style=dashed]; -"20 Constant_228" -> "8 Add_2" [label="[1, 6, 1, 1]", style=solid]; -"21 Constant_200" -> "10 Transpose" [label="[4]", style=dashed]; -"22 Constant_198" -> "7 Transpose/fq_input_0" [label="[]", style=solid]; -"23 Constant_197" -> "7 Transpose/fq_input_0" [label="[]", style=solid]; -"24 Constant_196" -> "7 Transpose/fq_input_0" [label="[]", style=solid]; -"25 Constant_195" -> "7 Transpose/fq_input_0" [label="[]", style=solid]; -"26 Constant_193" -> "5 Add_1" [label="[1, 3, 1, 1]", style=solid]; -"27 Constant_191" -> "3 Mul" [label="[1, 3, 1, 1]", style=solid]; -"28 Conv_1/fq_weights_0" -> "2 Conv_1" [label="[3, 3, 1, 1]", style=solid]; -"29 Constant_186" -> "28 Conv_1/fq_weights_0" [label="[]", style=solid]; -"30 Constant_185" -> "28 Conv_1/fq_weights_0" [label="[]", style=solid]; -"31 Constant_184" -> "28 Conv_1/fq_weights_0" [label="[]", style=solid]; -"32 Constant_183" -> "28 Conv_1/fq_weights_0" [label="[]", style=solid]; -"33 Constant_182" -> "28 Conv_1/fq_weights_0" [label="[3, 3, 1, 1]", style=solid]; -"34 Constant_221" -> "16 Conv_3" [label="[6, 12, 1, 1]", style=solid]; -"35 Constant_219" -> "14 Conv_3/fq_input_0" [label="[]", style=solid]; -"36 Constant_218" -> "14 Conv_3/fq_input_0" [label="[]", style=solid]; -"37 Constant_217" -> "14 Conv_3/fq_input_0" [label="[]", style=solid]; -"38 Constant_216" -> "14 Conv_3/fq_input_0" [label="[]", style=solid]; -"39 Conv_2/fq_weights_0" -> "9 Conv_2" [label="[12, 6, 1, 1]", style=solid]; -"40 Constant_212" -> "39 Conv_2/fq_weights_0" [label="[]", style=solid]; -"41 Constant_211" -> "39 Conv_2/fq_weights_0" [label="[]", style=solid]; -"42 Constant_210" -> "39 Conv_2/fq_weights_0" [label="[]", style=solid]; -"43 Constant_209" -> "39 Conv_2/fq_weights_0" [label="[]", style=solid]; -"44 Constant_208" -> "39 Conv_2/fq_weights_0" [label="[12, 6, 1, 1]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/resnet-18-pytorch.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/resnet-18-pytorch.dot deleted file mode 100644 index 24fecd18ab3..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/resnet-18-pytorch.dot +++ /dev/null @@ -1,249 +0,0 @@ -strict digraph { -"0 data" [id=0, type=Parameter]; -"1 Multiply_1715" [id=1, type=Multiply]; -"2 Divide_401" [id=2, type=Add]; -"3 /conv1/Conv/WithoutBiases" [id=3, type=Convolution]; -"4 /conv1/Conv" [id=4, type=Add]; -"5 /relu/Relu" [id=5, type=Relu]; -"6 /maxpool/MaxPool" [id=6, type=MaxPool]; -"7 /layer1/layer1.0/Add" [id=7, type=Add]; -"8 /layer1/layer1.0/conv1/Conv/WithoutBiases" [id=8, type=Convolution]; -"9 /layer1/layer1.0/relu_1/Relu" [id=9, type=Relu]; -"10 /layer1/layer1.0/conv1/Conv" [id=10, type=Add]; -"11 /layer1/layer1.1/Add" [id=11, type=Add]; -"12 /layer1/layer1.1/conv1/Conv/WithoutBiases" [id=12, type=Convolution]; -"13 /layer1/layer1.0/relu/Relu" [id=13, type=Relu]; -"14 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style=solid]; -"118 Reshape_348" -> "65 /layer4/layer4.1/conv1/Conv" [label="[1, 512, 1, 1]", style=solid]; -"119 onnx^^Conv_247" -> "63 /layer4/layer4.1/conv1/Conv/WithoutBiases" [label="[512, 512, 3, 3]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/ssd_mobilenet_v1_coco.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/ssd_mobilenet_v1_coco.dot deleted file mode 100644 index a6a110d28df..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/ssd_mobilenet_v1_coco.dot +++ /dev/null @@ -1,752 +0,0 @@ -strict digraph { -"0 image_tensor" [id=0, type=Parameter]; -"1 Convert_2817" [id=1, type=Convert]; -"2 ToFloat" [id=2, type=Transpose]; -"3 Preprocessor/mul" [id=3, type=Multiply]; -"4 PriorBoxClustered_0/1_port" [id=4, type=ShapeOf]; -"5 Preprocessor/sub" [id=5, type=Add]; -"6 PriorBoxClustered_0/ss_1_port" [id=6, type=StridedSlice]; -"7 PriorBoxClustered_1/ss_1_port" 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style=solid]; -"10 Transpose_1265" -> "11 Transpose_1368" [label="[1, 64, 104, 104]", style=solid]; -"10 Transpose_1265" -> "12 group_route_3/split" [label="[1, 64, 104, 104]", style=solid]; -"11 Transpose_1368" -> "13 MaxPool_307" [label="[1, 128, 104, 104]", style=solid]; -"12 group_route_3/split" -> "14 Multiply_3789" [label="[1, 32, 104, 104]", style=solid]; -"13 MaxPool_307" -> "15 Multiply_3831" [label="[1, 128, 52, 52]", style=solid]; -"14 Multiply_3789" -> "16 Transpose_1294" [label="[1, 32, 104, 104]", style=solid]; -"15 Multiply_3831" -> "17 Transpose_1392" [label="[1, 128, 52, 52]", style=solid]; -"16 Transpose_1294" -> "18 Transpose_1300" [label="[1, 32, 104, 104]", style=solid]; -"17 Transpose_1392" -> "19 Transpose_1398" [label="[1, 128, 52, 52]", style=solid]; -"18 Transpose_1300" -> "20 Multiply_3803" [label="[1, 32, 104, 104]", style=solid]; -"18 Transpose_1300" -> "21 Transpose_1334" [label="[1, 32, 104, 104]", style=solid]; -"19 Transpose_1398" -> "22 Transpose_1501" [label="[1, 128, 52, 52]", style=solid]; -"19 Transpose_1398" -> "23 group_route_11/split" [label="[1, 128, 52, 52]", style=solid]; -"20 Multiply_3803" -> "24 Transpose_1324" [label="[1, 32, 104, 104]", style=solid]; -"21 Transpose_1334" -> "25 Multiply_3817" [label="[1, 64, 104, 104]", style=solid]; -"22 Transpose_1501" -> "26 MaxPool_433" [label="[1, 256, 52, 52]", style=solid]; -"23 group_route_11/split" -> "27 Multiply_3845" [label="[1, 64, 52, 52]", style=solid]; -"24 Transpose_1324" -> "28 Transpose_1330" [label="[1, 32, 104, 104]", style=solid]; -"25 Multiply_3817" -> "29 Transpose_1358" [label="[1, 64, 104, 104]", style=solid]; -"26 MaxPool_433" -> "30 Multiply_3887" [label="[1, 256, 26, 26]", style=solid]; -"27 Multiply_3845" -> "31 Transpose_1427" [label="[1, 64, 52, 52]", style=solid]; -"28 Transpose_1330" -> "21 Transpose_1334" [label="[1, 32, 104, 104]", style=solid]; -"29 Transpose_1358" -> "32 Transpose_1364" [label="[1, 64, 104, 104]", style=solid]; -"30 Multiply_3887" -> "33 Transpose_1525" [label="[1, 256, 26, 26]", style=solid]; -"31 Transpose_1427" -> "34 Transpose_1433" [label="[1, 64, 52, 52]", style=solid]; -"32 Transpose_1364" -> "11 Transpose_1368" [label="[1, 64, 104, 104]", style=solid]; -"33 Transpose_1525" -> "35 Transpose_1531" [label="[1, 256, 26, 26]", style=solid]; -"34 Transpose_1433" -> "36 Multiply_3859" [label="[1, 64, 52, 52]", style=solid]; -"34 Transpose_1433" -> "37 Transpose_1467" [label="[1, 64, 52, 52]", style=solid]; -"35 Transpose_1531" -> "38 Transpose_1634" [label="[1, 256, 26, 26]", style=solid]; -"35 Transpose_1531" -> "39 group_route_19/split" [label="[1, 256, 26, 26]", style=solid]; -"36 Multiply_3859" -> "40 Transpose_1457" [label="[1, 64, 52, 52]", style=solid]; -"37 Transpose_1467" -> "41 Multiply_3873" [label="[1, 128, 52, 52]", style=solid]; -"38 Transpose_1634" -> "42 MaxPool_579" [label="[1, 512, 26, 26]", style=solid]; -"39 group_route_19/split" -> "43 Multiply_3901" [label="[1, 128, 26, 26]", style=solid]; -"40 Transpose_1457" -> "44 Transpose_1463" [label="[1, 64, 52, 52]", style=solid]; -"41 Multiply_3873" -> "45 Transpose_1491" [label="[1, 128, 52, 52]", style=solid]; -"42 MaxPool_579" -> "46 Multiply_3943" [label="[1, 512, 13, 13]", style=solid]; -"43 Multiply_3901" -> "47 Transpose_1560" [label="[1, 128, 26, 26]", style=solid]; -"44 Transpose_1463" -> "37 Transpose_1467" [label="[1, 64, 52, 52]", style=solid]; -"45 Transpose_1491" -> "48 Transpose_1497" [label="[1, 128, 52, 52]", style=solid]; -"46 Multiply_3943" -> "49 Transpose_1658" [label="[1, 512, 13, 13]", style=solid]; -"47 Transpose_1560" -> "50 Transpose_1566" [label="[1, 128, 26, 26]", style=solid]; -"48 Transpose_1497" -> "22 Transpose_1501" [label="[1, 128, 52, 52]", style=solid]; -"49 Transpose_1658" -> "51 Transpose_1664" [label="[1, 512, 13, 13]", style=solid]; -"50 Transpose_1566" -> "52 Multiply_3915" [label="[1, 128, 26, 26]", style=solid]; -"50 Transpose_1566" -> "53 Transpose_1600" [label="[1, 128, 26, 26]", style=solid]; -"51 Transpose_1664" -> "54 Multiply_3957" [label="[1, 512, 13, 13]", style=solid]; -"52 Multiply_3915" -> "55 Transpose_1590" [label="[1, 128, 26, 26]", style=solid]; -"53 Transpose_1600" -> "56 Multiply_3929" [label="[1, 256, 26, 26]", style=solid]; -"54 Multiply_3957" -> "57 Transpose_1688" [label="[1, 256, 13, 13]", style=solid]; -"55 Transpose_1590" -> "58 Transpose_1596" [label="[1, 128, 26, 26]", style=solid]; -"56 Multiply_3929" -> "59 Transpose_1624" [label="[1, 256, 26, 26]", style=solid]; -"57 Transpose_1688" -> "60 Transpose_1694" [label="[1, 256, 13, 13]", style=solid]; -"58 Transpose_1596" -> "53 Transpose_1600" [label="[1, 128, 26, 26]", style=solid]; -"59 Transpose_1624" -> "61 Transpose_1630" [label="[1, 256, 26, 26]", style=solid]; -"60 Transpose_1694" -> "62 Multiply_3971" [label="[1, 256, 13, 13]", style=solid]; -"60 Transpose_1694" -> "63 Multiply_3999" [label="[1, 256, 13, 13]", style=solid]; -"61 Transpose_1630" -> "38 Transpose_1634" [label="[1, 256, 26, 26]", style=solid]; -"61 Transpose_1630" -> "64 Transpose_1730" [label="[1, 256, 26, 26]", style=solid]; -"62 Multiply_3971" -> "65 Transpose_1718" [label="[1, 128, 13, 13]", style=solid]; -"63 Multiply_3999" -> "66 Transpose_1790" [label="[1, 512, 13, 13]", style=solid]; -"64 Transpose_1730" -> "67 Multiply_3985" [label="[1, 384, 26, 26]", style=solid]; -"65 Transpose_1718" -> "68 Transpose_1724" [label="[1, 128, 13, 13]", style=solid]; -"66 Transpose_1790" -> "69 Transpose_1796" [label="[1, 512, 13, 13]", style=solid]; -"67 Multiply_3985" -> "70 Transpose_1754" [label="[1, 256, 26, 26]", style=solid]; -"68 Transpose_1724" -> "71 Gather_1726" [label="[1, 128, 13, 13]", style=solid]; -"68 Transpose_1724" -> "72 leaky_re_lu_17/LeakyRelu" [label="[1, 128, 13, 13]", style=solid]; -"69 Transpose_1796" -> "73 Convolution_749" [label="[1, 512, 13, 13]", style=solid]; -"70 Transpose_1754" -> "74 Transpose_1760" [label="[1, 256, 26, 26]", style=solid]; -"71 Gather_1726" -> "75 up_sampling2d/Shape" [label="[4]", style=dashed]; -"72 leaky_re_lu_17/LeakyRelu" -> "76 up_sampling2d/resize/ResizeNearestNeighbor" [label="[1, 13, 13, 128]", style=solid]; -"73 Convolution_749" -> "77 Transpose_1802" [label="[1, 255, 13, 13]", style=solid]; -"74 Transpose_1760" -> "78 Convolution_706" [label="[1, 256, 26, 26]", style=solid]; -"75 up_sampling2d/Shape" -> "79 up_sampling2d/strided_slice" [label="[4]", style=dashed]; -"76 up_sampling2d/resize/ResizeNearestNeighbor" -> "80 Transpose_1728" [label="[1, 26, 26, 128]", style=solid]; -"77 Transpose_1802" -> "81 conv2d_17/BiasAdd" [label="[1, 255, 13, 13]", style=solid]; -"78 Convolution_706" -> "82 Transpose_1766" [label="[1, 255, 26, 26]", style=solid]; -"79 up_sampling2d/strided_slice" -> "83 up_sampling2d/mul" [label="[2]", style=dashed]; -"80 Transpose_1728" -> "64 Transpose_1730" [label="[1, 128, 26, 26]", style=solid]; -"81 conv2d_17/BiasAdd" -> "84 conv2d_17/BiasAdd^0" [label="[1, 13, 13, 255]", style=solid]; -"82 Transpose_1766" -> "85 conv2d_20/BiasAdd" [label="[1, 255, 26, 26]", style=solid]; -"83 up_sampling2d/mul" -> "76 up_sampling2d/resize/ResizeNearestNeighbor" [label="[2]", style=dashed]; -"85 conv2d_20/BiasAdd" -> "86 conv2d_20/BiasAdd^0" [label="[1, 26, 26, 255]", style=solid]; -"87 Constant_1765" -> "85 conv2d_20/BiasAdd" [label="[4]", style=dashed]; -"88 Transpose_1764" -> "82 Transpose_1766" [label="[1, 255, 1, 1]", style=solid]; -"89 Transpose_705" -> "78 Convolution_706" [label="[255, 256, 1, 1]", style=solid]; -"90 Transpose_1758" -> "74 Transpose_1760" [label="[1, 1, 1, 1]", style=solid]; -"91 Constant_3993" -> "70 Transpose_1754" [label="[1, 256, 1, 1]", style=solid]; -"92 Multiply_4118" -> "67 Multiply_3985" [label="[256, 384, 3, 3]", style=solid]; -"93 Transpose_1628" -> "61 Transpose_1630" [label="[1, 1, 1, 1]", style=solid]; -"94 Constant_3937" -> "59 Transpose_1624" [label="[1, 256, 1, 1]", style=solid]; -"95 Multiply_4094" -> "56 Multiply_3929" [label="[256, 256, 1, 1]", style=solid]; -"96 Transpose_1564" -> "50 Transpose_1566" [label="[1, 1, 1, 1]", style=solid]; -"97 Constant_3909" -> "47 Transpose_1560" [label="[1, 128, 1, 1]", style=solid]; -"98 Multiply_4082" -> "43 Multiply_3901" [label="[128, 128, 3, 3]", style=solid]; -"99 Constant_1532" -> "39 group_route_19/split" [label="[]", style=dashed]; -"100 Transpose_1529" -> "35 Transpose_1531" [label="[1, 1, 1, 1]", style=solid]; -"101 Constant_3895" -> "33 Transpose_1525" [label="[1, 256, 1, 1]", style=solid]; -"102 Multiply_4076" -> "30 Multiply_3887" [label="[256, 256, 3, 3]", style=solid]; -"103 Transpose_1495" -> "48 Transpose_1497" [label="[1, 1, 1, 1]", style=solid]; -"104 Constant_3881" -> "45 Transpose_1491" [label="[1, 128, 1, 1]", style=solid]; -"105 Multiply_4070" -> "41 Multiply_3873" [label="[128, 128, 1, 1]", style=solid]; -"106 Transpose_1431" -> "34 Transpose_1433" [label="[1, 1, 1, 1]", style=solid]; -"107 Constant_3853" -> "31 Transpose_1427" [label="[1, 64, 1, 1]", style=solid]; -"108 Multiply_4058" -> "27 Multiply_3845" [label="[64, 64, 3, 3]", style=solid]; -"109 Constant_1399" -> "23 group_route_11/split" [label="[]", style=dashed]; -"110 Transpose_1396" -> "19 Transpose_1398" [label="[1, 1, 1, 1]", style=solid]; -"111 Constant_3839" -> "17 Transpose_1392" [label="[1, 128, 1, 1]", style=solid]; -"112 Multiply_4052" -> "15 Multiply_3831" [label="[128, 128, 3, 3]", style=solid]; -"113 Transpose_1362" -> "32 Transpose_1364" [label="[1, 1, 1, 1]", style=solid]; -"114 Constant_3825" -> "29 Transpose_1358" [label="[1, 64, 1, 1]", style=solid]; -"115 Multiply_4046" -> "25 Multiply_3817" [label="[64, 64, 1, 1]", style=solid]; -"116 Transpose_1298" -> "18 Transpose_1300" [label="[1, 1, 1, 1]", style=solid]; -"117 Constant_3797" -> "16 Transpose_1294" [label="[1, 32, 1, 1]", style=solid]; -"118 Multiply_4034" -> "14 Multiply_3789" [label="[32, 32, 3, 3]", style=solid]; -"119 Constant_1266" -> "12 group_route_3/split" [label="[]", style=dashed]; -"120 Transpose_1263" -> "10 Transpose_1265" [label="[1, 1, 1, 1]", style=solid]; -"121 Constant_3783" -> "9 Transpose_1259" [label="[1, 64, 1, 1]", style=solid]; -"122 Multiply_4028" -> "8 Multiply_3775" [label="[64, 64, 3, 3]", style=solid]; -"123 Transpose_1233" -> "7 Transpose_1235" [label="[1, 1, 1, 1]", style=solid]; -"124 Constant_3769" -> "6 Transpose_1229" [label="[1, 64, 1, 1]", style=solid]; -"125 Multiply_4022" -> "5 Multiply_3761" [label="[64, 32, 3, 3]", style=solid]; -"126 Transpose_1176" -> "4 Transpose_1178" [label="[1, 1, 1, 1]", style=solid]; -"127 Constant_3755" -> "3 Transpose_1172" [label="[1, 32, 1, 1]", style=solid]; -"128 Gather_4293" -> "2 Multiply_3747" [label="[32, 3, 3, 3]", style=solid]; -"129 Constant_2333" -> "1 Divide_2378" [label="[4]", style=dashed]; -"130 Transpose_1328" -> "28 Transpose_1330" [label="[1, 1, 1, 1]", style=solid]; -"131 Constant_3811" -> "24 Transpose_1324" [label="[1, 32, 1, 1]", style=solid]; -"132 Multiply_4040" -> "20 Multiply_3803" [label="[32, 32, 3, 3]", style=solid]; -"133 Transpose_1461" -> "44 Transpose_1463" [label="[1, 1, 1, 1]", style=solid]; -"134 Constant_3867" -> "40 Transpose_1457" [label="[1, 64, 1, 1]", style=solid]; -"135 Multiply_4064" -> "36 Multiply_3859" [label="[64, 64, 3, 3]", style=solid]; -"136 Transpose_1594" -> "58 Transpose_1596" [label="[1, 1, 1, 1]", style=solid]; -"137 Constant_3923" -> "55 Transpose_1590" [label="[1, 128, 1, 1]", style=solid]; -"138 Multiply_4088" -> "52 Multiply_3915" [label="[128, 128, 3, 3]", style=solid]; -"139 Constant_1727" -> "80 Transpose_1728" [label="[4]", style=dashed]; -"140 Constant_669" -> "76 up_sampling2d/resize/ResizeNearestNeighbor" [label="[2]", style=dashed]; -"141 up_sampling2d/Const" -> "83 up_sampling2d/mul" [label="[2]", style=dashed]; -"142 up_sampling2d/strided_slice/stack_2" -> "79 up_sampling2d/strided_slice" [label="[1]", style=dashed]; -"143 up_sampling2d/strided_slice/stack_1" -> "79 up_sampling2d/strided_slice" [label="[1]", style=dashed]; -"144 up_sampling2d/strided_slice/stack" -> "79 up_sampling2d/strided_slice" [label="[1]", style=dashed]; -"145 Constant_1725" -> "75 up_sampling2d/Shape" [label="[]", style=dashed]; -"146 Constant_1723" -> "72 leaky_re_lu_17/LeakyRelu" [label="[4]", style=dashed]; -"146 Constant_1723" -> "75 up_sampling2d/Shape" [label="[4]", style=dashed]; -"147 Transpose_1722" -> "68 Transpose_1724" [label="[1, 1, 1, 1]", style=solid]; -"148 Constant_3979" -> "65 Transpose_1718" [label="[1, 128, 1, 1]", style=solid]; -"149 Multiply_4112" -> "62 Multiply_3971" [label="[128, 256, 1, 1]", style=solid]; -"150 Transpose_1692" -> "60 Transpose_1694" [label="[1, 1, 1, 1]", style=solid]; -"151 Constant_3965" -> "57 Transpose_1688" [label="[1, 256, 1, 1]", style=solid]; -"152 Multiply_4106" -> "54 Multiply_3957" [label="[256, 512, 1, 1]", style=solid]; -"153 Transpose_1662" -> "51 Transpose_1664" [label="[1, 1, 1, 1]", style=solid]; -"154 Constant_3951" -> "49 Transpose_1658" [label="[1, 512, 1, 1]", style=solid]; -"155 Multiply_4100" -> "46 Multiply_3943" [label="[512, 512, 3, 3]", style=solid]; -"156 Constant_1801" -> "81 conv2d_17/BiasAdd" [label="[4]", style=dashed]; -"157 Transpose_1800" -> "77 Transpose_1802" [label="[1, 255, 1, 1]", style=solid]; -"158 Transpose_748" -> "73 Convolution_749" [label="[255, 512, 1, 1]", style=solid]; -"159 Transpose_1794" -> "69 Transpose_1796" [label="[1, 1, 1, 1]", style=solid]; -"160 Constant_4007" -> "66 Transpose_1790" [label="[1, 512, 1, 1]", style=solid]; -"161 Multiply_4124" -> "63 Multiply_3999" [label="[512, 256, 3, 3]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/ComparisonBinaryModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/ComparisonBinaryModel.dot deleted file mode 100644 index bc031f16aaa..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/quantized/ComparisonBinaryModel.dot +++ /dev/null @@ -1,29 +0,0 @@ -strict digraph { -"0 Input" [id=0, type=Parameter]; -"1 Input/fq_output_0" [id=1, type=FakeQuantize]; -"2 Add" [id=2, type=Add]; -"3 Gather_6" [id=3, type=Gather]; -"4 GreaterEqual_3" [id=4, type=GreaterEqual]; -"5 Result_Add" [id=5, type=Result]; -"6 Convert_4" [id=6, type=Convert]; -"7 Constant_5" [id=7, type=Constant]; -"8 Constant_2" [id=8, type=Constant]; -"9 Constant_2140" [id=9, type=Constant]; -"10 Constant_2139" [id=10, type=Constant]; -"11 Constant_2138" [id=11, type=Constant]; -"12 Constant_2137" [id=12, type=Constant]; -"0 Input" -> "1 Input/fq_output_0" [label="[1, 3, 4, 2]", style=solid]; -"1 Input/fq_output_0" -> "2 Add" [label="[1, 3, 4, 2]", style=solid]; -"1 Input/fq_output_0" -> "3 Gather_6" [label="[1, 3, 4, 2]", style=solid]; -"1 Input/fq_output_0" -> "4 GreaterEqual_3" [label="[1, 3, 4, 2]", style=solid]; -"2 Add" -> "5 Result_Add" [label="[1, 3, 4, 2, 3, 4, 2]", style=solid]; -"3 Gather_6" -> "2 Add" [label="[1, 3, 4, 2, 3, 4, 2]", style=solid]; -"4 GreaterEqual_3" -> "6 Convert_4" [label="[1, 3, 4, 2]", style=dashed]; -"6 Convert_4" -> "3 Gather_6" [label="[1, 3, 4, 2]", style=dashed]; -"7 Constant_5" -> "3 Gather_6" [label="[]", style=dashed]; -"8 Constant_2" -> "4 GreaterEqual_3" [label="[1, 3, 4, 2]", style=solid]; -"9 Constant_2140" -> "1 Input/fq_output_0" [label="[]", style=solid]; -"10 Constant_2139" -> "1 Input/fq_output_0" [label="[]", style=solid]; -"11 Constant_2138" -> "1 Input/fq_output_0" [label="[]", style=solid]; -"12 Constant_2137" -> "1 Input/fq_output_0" [label="[]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/ConvModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/ConvModel.dot deleted file mode 100644 index 125ee7ec102..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/quantized/ConvModel.dot +++ /dev/null @@ -1,55 +0,0 @@ -strict digraph { -"0 Input_1" [id=0, type=Parameter]; -"1 Input_2" [id=1, type=Parameter]; -"2 Sub" [id=2, type=Subtract]; -"3 Add" [id=3, type=Add]; -"4 Sub/fq_output_0" [id=4, type=FakeQuantize]; -"5 Mul" [id=5, type=Multiply]; -"6 Conv" [id=6, type=Convolution]; -"7 Transpose" [id=7, type=Transpose]; -"8 Conv_Add" [id=8, type=Add]; -"9 Concat_32" [id=9, type=Concat]; -"10 Relu" [id=10, type=Relu]; -"11 Result" [id=11, type=Result]; -"12 Constant_30" [id=12, type=Constant]; -"13 Constant_28" [id=13, type=Constant]; -"14 Constant_26" [id=14, type=Constant]; -"15 Bias" [id=15, type=Constant]; -"16 Conv/fq_weights_1" [id=16, type=FakeQuantize]; -"17 Constant_2268" [id=17, type=Constant]; -"18 Constant_2267" [id=18, type=Constant]; -"19 Constant_2266" [id=19, type=Constant]; -"20 Constant_2265" [id=20, type=Constant]; -"21 Constant_20" [id=21, type=Constant]; -"22 Constant_2263" [id=22, type=Constant]; -"23 Constant_2262" [id=23, type=Constant]; -"24 Constant_2261" [id=24, type=Constant]; -"25 Constant_2260" [id=25, type=Constant]; -"26 Constant_18" [id=26, type=Constant]; -"0 Input_1" -> "2 Sub" [label="[1, 3, 4, 2]", style=solid]; -"1 Input_2" -> "3 Add" [label="[1, 3, 2, 4]", style=solid]; -"2 Sub" -> "4 Sub/fq_output_0" [label="[1, 3, 4, 2]", style=solid]; -"3 Add" -> "5 Mul" [label="[1, 3, 2, 4]", style=solid]; -"4 Sub/fq_output_0" -> "6 Conv" [label="[1, 3, 4, 2]", style=solid]; -"5 Mul" -> "7 Transpose" [label="[1, 3, 2, 4]", style=solid]; -"6 Conv" -> "8 Conv_Add" [label="[1, 3, 4, 2]", style=solid]; -"7 Transpose" -> "9 Concat_32" [label="[1, 3, 4, 2]", style=solid]; -"8 Conv_Add" -> "10 Relu" [label="[1, 3, 4, 2]", style=solid]; -"9 Concat_32" -> "11 Result" [label="[2, 3, 4, 2]", style=solid]; -"10 Relu" -> "9 Concat_32" [label="[1, 3, 4, 2]", style=solid]; -"12 Constant_30" -> "7 Transpose" [label="[4]", style=dashed]; -"13 Constant_28" -> "5 Mul" [label="[1, 3, 1, 1]", style=solid]; -"14 Constant_26" -> "3 Add" [label="[1, 3, 1, 1]", style=solid]; -"15 Bias" -> "8 Conv_Add" [label="[1, 3, 1, 1]", style=solid]; -"16 Conv/fq_weights_1" -> "6 Conv" [label="[3, 3, 1, 1]", style=solid]; -"17 Constant_2268" -> "16 Conv/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; -"18 Constant_2267" -> "16 Conv/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; -"19 Constant_2266" -> "16 Conv/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; -"20 Constant_2265" -> "16 Conv/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; -"21 Constant_20" -> "16 Conv/fq_weights_1" [label="[3, 3, 1, 1]", style=solid]; -"22 Constant_2263" -> "4 Sub/fq_output_0" [label="[]", style=solid]; -"23 Constant_2262" -> "4 Sub/fq_output_0" [label="[]", style=solid]; -"24 Constant_2261" -> "4 Sub/fq_output_0" [label="[]", style=solid]; -"25 Constant_2260" -> "4 Sub/fq_output_0" [label="[]", style=solid]; -"26 Constant_18" -> "2 Sub" [label="[1, 3, 1, 1]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/ConvNotBiasModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/ConvNotBiasModel.dot deleted file mode 100644 index 9d7e0778387..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/quantized/ConvNotBiasModel.dot +++ /dev/null @@ -1,39 +0,0 @@ -strict digraph { -"0 Input_1" [id=0, type=Parameter]; -"1 Sub" [id=1, type=Subtract]; -"2 Sub/fq_output_0" [id=2, type=FakeQuantize]; -"3 Conv" [id=3, type=Convolution]; -"4 Conv_Add" [id=4, type=Add]; -"5 Relu" [id=5, type=Relu]; -"6 Result" [id=6, type=Result]; -"7 NotBias" [id=7, type=Constant]; -"8 Conv/fq_weights_1" [id=8, type=FakeQuantize]; -"9 Constant_14951" [id=9, type=Constant]; -"10 Constant_14950" [id=10, type=Constant]; -"11 Constant_14949" [id=11, type=Constant]; -"12 Constant_14948" [id=12, type=Constant]; -"13 Constant_12903" [id=13, type=Constant]; -"14 Constant_14946" [id=14, type=Constant]; -"15 Constant_14945" [id=15, type=Constant]; -"16 Constant_14944" [id=16, type=Constant]; -"17 Constant_14943" [id=17, type=Constant]; -"18 Constant_12901" [id=18, type=Constant]; -"0 Input_1" -> "1 Sub" [label="[1, 3, 4, 2]", style=solid]; -"1 Sub" -> "2 Sub/fq_output_0" [label="[1, 3, 4, 2]", style=solid]; -"2 Sub/fq_output_0" -> "3 Conv" [label="[1, 3, 4, 2]", style=solid]; -"3 Conv" -> "4 Conv_Add" [label="[1, 3, 4, 2]", style=solid]; -"4 Conv_Add" -> "5 Relu" [label="[1, 3, 4, 2]", style=solid]; -"5 Relu" -> "6 Result" [label="[1, 3, 4, 2]", style=solid]; -"7 NotBias" -> "4 Conv_Add" [label="[1, 3, 4, 2]", style=solid]; -"8 Conv/fq_weights_1" -> "3 Conv" [label="[3, 3, 1, 1]", style=solid]; -"9 Constant_14951" -> "8 Conv/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; -"10 Constant_14950" -> "8 Conv/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; -"11 Constant_14949" -> "8 Conv/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; -"12 Constant_14948" -> "8 Conv/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; -"13 Constant_12903" -> "8 Conv/fq_weights_1" [label="[3, 3, 1, 1]", style=solid]; -"14 Constant_14946" -> "2 Sub/fq_output_0" [label="[]", style=solid]; -"15 Constant_14945" -> "2 Sub/fq_output_0" [label="[]", style=solid]; -"16 Constant_14944" -> "2 Sub/fq_output_0" [label="[]", style=solid]; -"17 Constant_14943" -> "2 Sub/fq_output_0" [label="[]", style=solid]; -"18 Constant_12901" -> "1 Sub" [label="[1, 3, 1, 1]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/DepthwiseConv3DModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/DepthwiseConv3DModel.dot deleted file mode 100644 index e485b5780d1..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/quantized/DepthwiseConv3DModel.dot +++ /dev/null @@ -1,33 +0,0 @@ -strict digraph { -"0 Input_1" [id=0, type=Parameter]; -"1 Input_1/fq_output_0" [id=1, type=FakeQuantize]; -"2 Conv3D" [id=2, type=GroupConvolution]; -"3 Add" [id=3, type=Add]; -"4 Result" [id=4, type=Result]; -"5 Constant_4" [id=5, type=Constant]; -"6 Conv3D/fq_weights_1" [id=6, type=FakeQuantize]; -"7 Constant_2046" [id=7, type=Constant]; -"8 Constant_2045" [id=8, type=Constant]; -"9 Constant_2044" [id=9, type=Constant]; -"10 Constant_2043" [id=10, type=Constant]; -"11 Constant_2" [id=11, type=Constant]; -"12 Constant_2041" [id=12, type=Constant]; -"13 Constant_2040" [id=13, type=Constant]; -"14 Constant_2039" [id=14, type=Constant]; -"15 Constant_2038" [id=15, type=Constant]; -"0 Input_1" -> "1 Input_1/fq_output_0" [label="[1, 3, 7]", style=solid]; -"1 Input_1/fq_output_0" -> "2 Conv3D" [label="[1, 3, 7]", style=solid]; -"2 Conv3D" -> "3 Add" [label="[1, 3, 3]", style=solid]; -"3 Add" -> "4 Result" [label="[1, 3, 3]", style=solid]; -"5 Constant_4" -> "3 Add" [label="[1, 3, 1]", style=solid]; -"6 Conv3D/fq_weights_1" -> "2 Conv3D" [label="[3, 1, 1, 5]", style=solid]; -"7 Constant_2046" -> "6 Conv3D/fq_weights_1" [label="[1, 1, 1, 1]", style=solid]; -"8 Constant_2045" -> "6 Conv3D/fq_weights_1" [label="[1, 1, 1, 1]", style=solid]; -"9 Constant_2044" -> "6 Conv3D/fq_weights_1" [label="[1, 1, 1, 1]", style=solid]; -"10 Constant_2043" -> "6 Conv3D/fq_weights_1" [label="[1, 1, 1, 1]", style=solid]; -"11 Constant_2" -> "6 Conv3D/fq_weights_1" [label="[3, 1, 1, 5]", style=solid]; -"12 Constant_2041" -> "1 Input_1/fq_output_0" [label="[1, 3, 1]", style=solid]; -"13 Constant_2040" -> "1 Input_1/fq_output_0" [label="[1, 3, 1]", style=solid]; -"14 Constant_2039" -> "1 Input_1/fq_output_0" [label="[1, 3, 1]", style=solid]; -"15 Constant_2038" -> "1 Input_1/fq_output_0" [label="[1, 3, 1]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/DepthwiseConv4DModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/DepthwiseConv4DModel.dot deleted file mode 100644 index 4f85164604c..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/quantized/DepthwiseConv4DModel.dot +++ /dev/null @@ -1,35 +0,0 @@ -strict digraph { -"0 Input_1" [id=0, type=Parameter]; -"1 Input_1/fq_output_0" [id=1, type=FakeQuantize]; -"2 Conv4D" [id=2, type=GroupConvolution]; -"3 Add" [id=3, type=Add]; -"4 Relu" [id=4, type=Relu]; -"5 Result" [id=5, type=Result]; -"6 Constant_4" [id=6, type=Constant]; -"7 Conv4D/fq_weights_1" [id=7, type=FakeQuantize]; -"8 Constant_2051" [id=8, type=Constant]; -"9 Constant_2050" [id=9, type=Constant]; -"10 Constant_2049" [id=10, type=Constant]; -"11 Constant_2048" [id=11, type=Constant]; -"12 Constant_2" [id=12, type=Constant]; -"13 Constant_2046" [id=13, type=Constant]; -"14 Constant_2045" [id=14, type=Constant]; -"15 Constant_2044" [id=15, type=Constant]; -"16 Constant_2043" [id=16, type=Constant]; -"0 Input_1" -> "1 Input_1/fq_output_0" [label="[1, 3, 5, 5]", style=solid]; -"1 Input_1/fq_output_0" -> "2 Conv4D" [label="[1, 3, 5, 5]", style=solid]; -"2 Conv4D" -> "3 Add" [label="[1, 3, 3, 3]", style=solid]; -"3 Add" -> "4 Relu" [label="[1, 3, 3, 3]", style=solid]; -"4 Relu" -> "5 Result" [label="[1, 3, 3, 3]", style=solid]; -"6 Constant_4" -> "3 Add" [label="[1, 3, 1, 1]", style=solid]; -"7 Conv4D/fq_weights_1" -> "2 Conv4D" [label="[3, 1, 1, 3, 3]", style=solid]; -"8 Constant_2051" -> "7 Conv4D/fq_weights_1" [label="[1, 1, 1, 1, 1]", style=solid]; -"9 Constant_2050" -> "7 Conv4D/fq_weights_1" [label="[1, 1, 1, 1, 1]", style=solid]; -"10 Constant_2049" -> "7 Conv4D/fq_weights_1" [label="[1, 1, 1, 1, 1]", style=solid]; -"11 Constant_2048" -> "7 Conv4D/fq_weights_1" [label="[1, 1, 1, 1, 1]", style=solid]; -"12 Constant_2" -> "7 Conv4D/fq_weights_1" [label="[3, 1, 1, 3, 3]", style=solid]; -"13 Constant_2046" -> "1 Input_1/fq_output_0" [label="[1, 3, 1, 1]", style=solid]; -"14 Constant_2045" -> "1 Input_1/fq_output_0" [label="[1, 3, 1, 1]", style=solid]; -"15 Constant_2044" -> "1 Input_1/fq_output_0" [label="[1, 3, 1, 1]", style=solid]; -"16 Constant_2043" -> "1 Input_1/fq_output_0" [label="[1, 3, 1, 1]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/DepthwiseConv5DModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/DepthwiseConv5DModel.dot deleted file mode 100644 index a5bc30bfccb..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/quantized/DepthwiseConv5DModel.dot +++ /dev/null @@ -1,33 +0,0 @@ -strict digraph { -"0 Input_1" [id=0, type=Parameter]; -"1 Input_1/fq_output_0" [id=1, type=FakeQuantize]; -"2 Conv5D" [id=2, type=GroupConvolution]; -"3 Add" [id=3, type=Add]; -"4 Result" [id=4, type=Result]; -"5 Constant_4" [id=5, type=Constant]; -"6 Conv5D/fq_weights_1" [id=6, type=FakeQuantize]; -"7 Constant_2046" [id=7, type=Constant]; -"8 Constant_2045" [id=8, type=Constant]; -"9 Constant_2044" [id=9, type=Constant]; -"10 Constant_2043" [id=10, type=Constant]; -"11 Constant_2" [id=11, type=Constant]; -"12 Constant_2041" [id=12, type=Constant]; -"13 Constant_2040" [id=13, type=Constant]; -"14 Constant_2039" [id=14, type=Constant]; -"15 Constant_2038" [id=15, type=Constant]; -"0 Input_1" -> "1 Input_1/fq_output_0" [label="[1, 3, 7, 6, 5]", style=solid]; -"1 Input_1/fq_output_0" -> "2 Conv5D" [label="[1, 3, 7, 6, 5]", style=solid]; -"2 Conv5D" -> "3 Add" [label="[1, 3, 3, 3, 3]", style=solid]; -"3 Add" -> "4 Result" [label="[1, 3, 3, 3, 3]", style=solid]; -"5 Constant_4" -> "3 Add" [label="[1, 3, 1, 1, 1]", style=solid]; -"6 Conv5D/fq_weights_1" -> "2 Conv5D" [label="[3, 1, 1, 5, 4, 3]", style=solid]; -"7 Constant_2046" -> "6 Conv5D/fq_weights_1" [label="[1, 1, 1, 1, 1, 1]", style=solid]; -"8 Constant_2045" -> "6 Conv5D/fq_weights_1" [label="[1, 1, 1, 1, 1, 1]", style=solid]; -"9 Constant_2044" -> "6 Conv5D/fq_weights_1" [label="[1, 1, 1, 1, 1, 1]", style=solid]; -"10 Constant_2043" -> "6 Conv5D/fq_weights_1" [label="[1, 1, 1, 1, 1, 1]", style=solid]; -"11 Constant_2" -> "6 Conv5D/fq_weights_1" [label="[3, 1, 1, 5, 4, 3]", style=solid]; -"12 Constant_2041" -> "1 Input_1/fq_output_0" [label="[1, 3, 1, 1, 1]", style=solid]; -"13 Constant_2040" -> "1 Input_1/fq_output_0" [label="[1, 3, 1, 1, 1]", style=solid]; -"14 Constant_2039" -> "1 Input_1/fq_output_0" [label="[1, 3, 1, 1, 1]", style=solid]; -"15 Constant_2038" -> "1 Input_1/fq_output_0" [label="[1, 3, 1, 1, 1]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/DepthwiseConvModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/DepthwiseConvModel.dot deleted file mode 100644 index ad694143041..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/quantized/DepthwiseConvModel.dot +++ /dev/null @@ -1,35 +0,0 @@ -strict digraph { -"0 Input_1" [id=0, type=Parameter]; -"1 Input_1/fq_output_0" [id=1, type=FakeQuantize]; -"2 Conv" [id=2, type=GroupConvolution]; -"3 Add" [id=3, type=Add]; -"4 Relu" [id=4, type=Relu]; -"5 Result" [id=5, type=Result]; -"6 Constant_11" [id=6, type=Constant]; -"7 Conv/fq_weights_1" [id=7, type=FakeQuantize]; -"8 Constant_2060" [id=8, type=Constant]; -"9 Constant_2059" [id=9, type=Constant]; -"10 Constant_2058" [id=10, type=Constant]; -"11 Constant_2057" [id=11, type=Constant]; -"12 Constant_9" [id=12, type=Constant]; -"13 Constant_2055" [id=13, type=Constant]; -"14 Constant_2054" [id=14, type=Constant]; -"15 Constant_2053" [id=15, type=Constant]; -"16 Constant_2052" [id=16, type=Constant]; -"0 Input_1" -> "1 Input_1/fq_output_0" [label="[1, 3, 5, 5]", style=solid]; -"1 Input_1/fq_output_0" -> "2 Conv" [label="[1, 3, 5, 5]", style=solid]; -"2 Conv" -> "3 Add" [label="[1, 3, 3, 3]", style=solid]; -"3 Add" -> "4 Relu" [label="[1, 3, 3, 3]", style=solid]; -"4 Relu" -> "5 Result" [label="[1, 3, 3, 3]", style=solid]; -"6 Constant_11" -> "3 Add" [label="[1, 3, 1, 1]", style=solid]; -"7 Conv/fq_weights_1" -> "2 Conv" [label="[3, 1, 1, 3, 3]", style=solid]; -"8 Constant_2060" -> "7 Conv/fq_weights_1" [label="[1, 1, 1, 1, 1]", style=solid]; -"9 Constant_2059" -> "7 Conv/fq_weights_1" [label="[1, 1, 1, 1, 1]", style=solid]; -"10 Constant_2058" -> "7 Conv/fq_weights_1" [label="[1, 1, 1, 1, 1]", style=solid]; -"11 Constant_2057" -> "7 Conv/fq_weights_1" [label="[1, 1, 1, 1, 1]", style=solid]; -"12 Constant_9" -> "7 Conv/fq_weights_1" [label="[3, 1, 1, 3, 3]", style=solid]; -"13 Constant_2055" -> "1 Input_1/fq_output_0" [label="[1, 3, 1, 1]", style=solid]; -"14 Constant_2054" -> "1 Input_1/fq_output_0" [label="[1, 3, 1, 1]", style=solid]; -"15 Constant_2053" -> "1 Input_1/fq_output_0" [label="[1, 3, 1, 1]", style=solid]; -"16 Constant_2052" -> "1 Input_1/fq_output_0" [label="[1, 3, 1, 1]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/DynamicModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/DynamicModel.dot deleted file mode 100644 index 1633fc0a5b4..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/quantized/DynamicModel.dot +++ /dev/null @@ -1,55 +0,0 @@ -strict digraph { -"0 Input_1" [id=0, type=Parameter]; -"1 Input_2" [id=1, type=Parameter]; -"2 Sub" [id=2, type=Subtract]; -"3 Add" [id=3, type=Add]; -"4 Sub/fq_output_0" [id=4, type=FakeQuantize]; -"5 Mul" [id=5, type=Multiply]; -"6 Conv" [id=6, type=Convolution]; -"7 Transpose" [id=7, type=Transpose]; -"8 Conv_Add" [id=8, type=Add]; -"9 Concat_8690" [id=9, type=Concat]; -"10 Relu" [id=10, type=Relu]; -"11 Result" [id=11, type=Result]; -"12 Constant_8688" [id=12, type=Constant]; -"13 Constant_8686" [id=13, type=Constant]; -"14 Constant_8684" [id=14, type=Constant]; -"15 Bias" [id=15, type=Constant]; -"16 Conv/fq_weights_1" [id=16, type=FakeQuantize]; -"17 Constant_10833" [id=17, type=Constant]; -"18 Constant_10832" [id=18, type=Constant]; -"19 Constant_10831" [id=19, type=Constant]; -"20 Constant_10830" [id=20, type=Constant]; -"21 Constant_8678" [id=21, type=Constant]; -"22 Constant_10828" [id=22, type=Constant]; -"23 Constant_10827" [id=23, type=Constant]; -"24 Constant_10826" [id=24, type=Constant]; -"25 Constant_10825" [id=25, type=Constant]; -"26 Constant_8676" [id=26, type=Constant]; -"0 Input_1" -> "2 Sub" [label="[99, 3, 4, 2]", style=solid]; -"1 Input_2" -> "3 Add" [label="[99, 3, 2, 4]", style=solid]; -"2 Sub" -> "4 Sub/fq_output_0" [label="[99, 3, 4, 2]", style=solid]; -"3 Add" -> "5 Mul" [label="[99, 3, 2, 4]", style=solid]; -"4 Sub/fq_output_0" -> "6 Conv" [label="[99, 3, 4, 2]", style=solid]; -"5 Mul" -> "7 Transpose" [label="[99, 3, 2, 4]", style=solid]; -"6 Conv" -> "8 Conv_Add" [label="[99, 3, 4, 2]", style=solid]; -"7 Transpose" -> "9 Concat_8690" [label="[99, 3, 4, 2]", style=solid]; -"8 Conv_Add" -> "10 Relu" [label="[99, 3, 4, 2]", style=solid]; -"9 Concat_8690" -> "11 Result" [label="[198, 3, 4, 2]", style=solid]; -"10 Relu" -> "9 Concat_8690" [label="[99, 3, 4, 2]", style=solid]; -"12 Constant_8688" -> "7 Transpose" [label="[4]", style=dashed]; -"13 Constant_8686" -> "5 Mul" [label="[1, 3, 1, 1]", style=solid]; -"14 Constant_8684" -> "3 Add" [label="[1, 3, 1, 1]", style=solid]; -"15 Bias" -> "8 Conv_Add" [label="[1, 3, 1, 1]", style=solid]; -"16 Conv/fq_weights_1" -> "6 Conv" [label="[3, 3, 1, 1]", style=solid]; -"17 Constant_10833" -> "16 Conv/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; -"18 Constant_10832" -> "16 Conv/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; -"19 Constant_10831" -> "16 Conv/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; -"20 Constant_10830" -> "16 Conv/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; -"21 Constant_8678" -> "16 Conv/fq_weights_1" [label="[3, 3, 1, 1]", style=solid]; -"22 Constant_10828" -> "4 Sub/fq_output_0" [label="[]", style=solid]; -"23 Constant_10827" -> "4 Sub/fq_output_0" [label="[]", style=solid]; -"24 Constant_10826" -> "4 Sub/fq_output_0" [label="[]", style=solid]; -"25 Constant_10825" -> "4 Sub/fq_output_0" [label="[]", style=solid]; -"26 Constant_8676" -> "2 Sub" [label="[1, 3, 1, 1]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/GRUSequenceModel_linear_before_reset_F.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/GRUSequenceModel_linear_before_reset_F.dot deleted file mode 100644 index f97ac8a2d68..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/quantized/GRUSequenceModel_linear_before_reset_F.dot +++ /dev/null @@ -1,81 +0,0 @@ -strict digraph { -"0 X" [id=0, type=Parameter]; -"1 initial_hidden_state" [id=1, type=Parameter]; -"2 X/fq_output_0" [id=2, type=FakeQuantize]; -"3 initial_hidden_state/fq_output_0" [id=3, type=FakeQuantize]; -"4 GRUSequence" [id=4, type=GRUSequence]; -"5 GRUSequence/fq_output_0" [id=5, type=FakeQuantize]; -"6 MatMul" [id=6, type=MatMul]; -"7 Result" [id=7, type=Result]; -"8 MatMul/fq_weights_1" [id=8, type=FakeQuantize]; -"9 Constant_2541" [id=9, type=Constant]; -"10 Constant_2540" [id=10, type=Constant]; -"11 Constant_2539" [id=11, type=Constant]; -"12 Constant_2538" [id=12, type=Constant]; -"13 Constant_8" [id=13, type=Constant]; -"14 Constant_2526" [id=14, type=Constant]; -"15 Constant_2525" [id=15, type=Constant]; -"16 Constant_2524" [id=16, type=Constant]; -"17 Constant_2523" [id=17, type=Constant]; -"18 Constant_6" [id=18, type=Constant]; -"19 GRUSequence/fq_weights_4" [id=19, type=FakeQuantize]; -"20 Constant_2536" [id=20, type=Constant]; -"21 Constant_2535" [id=21, type=Constant]; -"22 Constant_2534" [id=22, type=Constant]; -"23 Constant_2533" [id=23, type=Constant]; -"24 Constant_5" [id=24, type=Constant]; -"25 GRUSequence/fq_weights_3" [id=25, type=FakeQuantize]; -"26 Constant_2531" [id=26, type=Constant]; -"27 Constant_2530" [id=27, type=Constant]; -"28 Constant_2529" [id=28, type=Constant]; -"29 Constant_2528" [id=29, type=Constant]; -"30 Constant_4" [id=30, type=Constant]; -"31 Constant_3" [id=31, type=Constant]; -"32 Constant_2521" [id=32, type=Constant]; -"33 Constant_2520" [id=33, type=Constant]; -"34 Constant_2519" [id=34, type=Constant]; -"35 Constant_2518" [id=35, type=Constant]; -"36 Constant_2516" [id=36, type=Constant]; -"37 Constant_2515" [id=37, type=Constant]; -"38 Constant_2514" [id=38, type=Constant]; -"39 Constant_2513" [id=39, type=Constant]; -"0 X" -> "2 X/fq_output_0" [label="[3, 2, 16]", style=solid]; -"1 initial_hidden_state" -> "3 initial_hidden_state/fq_output_0" [label="[3, 1, 128]", style=solid]; -"2 X/fq_output_0" -> "4 GRUSequence" [label="[3, 2, 16]", style=solid]; -"3 initial_hidden_state/fq_output_0" -> "4 GRUSequence" [label="[3, 1, 128]", style=solid]; -"4 GRUSequence" -> "5 GRUSequence/fq_output_0" [label="[3, 1, 2, 128]", style=solid]; -"5 GRUSequence/fq_output_0" -> "6 MatMul" [label="[3, 1, 2, 128]", style=solid]; -"6 MatMul" -> "7 Result" [label="[3, 1, 2, 3]", style=solid]; -"8 MatMul/fq_weights_1" -> "6 MatMul" [label="[3, 1, 128, 3]", style=solid]; -"9 Constant_2541" -> "8 MatMul/fq_weights_1" [label="[3, 1, 1, 3]", style=solid]; -"10 Constant_2540" -> "8 MatMul/fq_weights_1" [label="[3, 1, 1, 3]", style=solid]; -"11 Constant_2539" -> "8 MatMul/fq_weights_1" [label="[3, 1, 1, 3]", style=solid]; -"12 Constant_2538" -> "8 MatMul/fq_weights_1" [label="[3, 1, 1, 3]", style=solid]; -"13 Constant_8" -> "8 MatMul/fq_weights_1" [label="[3, 1, 128, 3]", style=solid]; -"14 Constant_2526" -> "5 GRUSequence/fq_output_0" [label="[]", style=solid]; -"15 Constant_2525" -> "5 GRUSequence/fq_output_0" [label="[]", style=solid]; -"16 Constant_2524" -> "5 GRUSequence/fq_output_0" [label="[]", style=solid]; -"17 Constant_2523" -> "5 GRUSequence/fq_output_0" [label="[]", style=solid]; -"18 Constant_6" -> "4 GRUSequence" [label="[1, 384]", style=solid]; -"19 GRUSequence/fq_weights_4" -> "4 GRUSequence" [label="[1, 384, 128]", style=solid]; -"20 Constant_2536" -> "19 GRUSequence/fq_weights_4" [label="[1, 384, 1]", style=solid]; -"21 Constant_2535" -> "19 GRUSequence/fq_weights_4" [label="[1, 384, 1]", style=solid]; -"22 Constant_2534" -> "19 GRUSequence/fq_weights_4" [label="[1, 384, 1]", style=solid]; -"23 Constant_2533" -> "19 GRUSequence/fq_weights_4" [label="[1, 384, 1]", style=solid]; -"24 Constant_5" -> "19 GRUSequence/fq_weights_4" [label="[1, 384, 128]", style=solid]; -"25 GRUSequence/fq_weights_3" -> "4 GRUSequence" [label="[1, 384, 16]", style=solid]; -"26 Constant_2531" -> "25 GRUSequence/fq_weights_3" [label="[1, 384, 1]", style=solid]; -"27 Constant_2530" -> "25 GRUSequence/fq_weights_3" [label="[1, 384, 1]", style=solid]; -"28 Constant_2529" -> "25 GRUSequence/fq_weights_3" [label="[1, 384, 1]", style=solid]; -"29 Constant_2528" -> "25 GRUSequence/fq_weights_3" [label="[1, 384, 1]", style=solid]; -"30 Constant_4" -> "25 GRUSequence/fq_weights_3" [label="[1, 384, 16]", style=solid]; -"31 Constant_3" -> "4 GRUSequence" [label="[3]", style=dashed]; -"32 Constant_2521" -> "3 initial_hidden_state/fq_output_0" [label="[]", style=solid]; -"33 Constant_2520" -> "3 initial_hidden_state/fq_output_0" [label="[]", style=solid]; -"34 Constant_2519" -> "3 initial_hidden_state/fq_output_0" [label="[]", style=solid]; -"35 Constant_2518" -> "3 initial_hidden_state/fq_output_0" [label="[]", style=solid]; -"36 Constant_2516" -> "2 X/fq_output_0" [label="[]", style=solid]; -"37 Constant_2515" -> "2 X/fq_output_0" [label="[]", style=solid]; -"38 Constant_2514" -> "2 X/fq_output_0" [label="[]", style=solid]; -"39 Constant_2513" -> "2 X/fq_output_0" [label="[]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/GRUSequenceModel_linear_before_reset_T.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/GRUSequenceModel_linear_before_reset_T.dot deleted file mode 100644 index a9819020b25..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/quantized/GRUSequenceModel_linear_before_reset_T.dot +++ /dev/null @@ -1,41 +0,0 @@ -strict digraph { -"0 X" [id=0, type=Parameter]; -"1 initial_hidden_state" [id=1, type=Parameter]; -"2 GRUSequence" [id=2, type=GRUSequence]; -"3 GRUSequence/fq_output_0" [id=3, type=FakeQuantize]; -"4 MatMul" [id=4, type=MatMul]; -"5 Result" [id=5, type=Result]; -"6 MatMul/fq_weights_1" [id=6, type=FakeQuantize]; -"7 Constant_2205" [id=7, type=Constant]; -"8 Constant_2204" [id=8, type=Constant]; -"9 Constant_2203" [id=9, type=Constant]; -"10 Constant_2202" [id=10, type=Constant]; -"11 Constant_8" [id=11, type=Constant]; -"12 Constant_2200" [id=12, type=Constant]; -"13 Constant_2199" [id=13, type=Constant]; -"14 Constant_2198" [id=14, type=Constant]; -"15 Constant_2197" [id=15, type=Constant]; -"16 Constant_6" [id=16, type=Constant]; -"17 Constant_5" [id=17, type=Constant]; -"18 Constant_4" [id=18, type=Constant]; -"19 Constant_3" [id=19, type=Constant]; -"0 X" -> "2 GRUSequence" [label="[3, 2, 16]", style=solid]; -"1 initial_hidden_state" -> "2 GRUSequence" [label="[3, 1, 128]", style=solid]; -"2 GRUSequence" -> "3 GRUSequence/fq_output_0" [label="[3, 1, 2, 128]", style=solid]; -"3 GRUSequence/fq_output_0" -> "4 MatMul" [label="[3, 1, 2, 128]", style=solid]; -"4 MatMul" -> "5 Result" [label="[3, 1, 2, 3]", style=solid]; -"6 MatMul/fq_weights_1" -> "4 MatMul" [label="[3, 1, 128, 3]", style=solid]; -"7 Constant_2205" -> "6 MatMul/fq_weights_1" [label="[3, 1, 1, 3]", style=solid]; -"8 Constant_2204" -> "6 MatMul/fq_weights_1" [label="[3, 1, 1, 3]", style=solid]; -"9 Constant_2203" -> "6 MatMul/fq_weights_1" [label="[3, 1, 1, 3]", style=solid]; -"10 Constant_2202" -> "6 MatMul/fq_weights_1" [label="[3, 1, 1, 3]", style=solid]; -"11 Constant_8" -> "6 MatMul/fq_weights_1" [label="[3, 1, 128, 3]", style=solid]; -"12 Constant_2200" -> "3 GRUSequence/fq_output_0" [label="[]", style=solid]; -"13 Constant_2199" -> "3 GRUSequence/fq_output_0" [label="[]", style=solid]; -"14 Constant_2198" -> "3 GRUSequence/fq_output_0" [label="[]", style=solid]; -"15 Constant_2197" -> "3 GRUSequence/fq_output_0" [label="[]", style=solid]; -"16 Constant_6" -> "2 GRUSequence" [label="[1, 512]", style=solid]; -"17 Constant_5" -> "2 GRUSequence" [label="[1, 384, 128]", style=solid]; -"18 Constant_4" -> "2 GRUSequence" [label="[1, 384, 16]", style=solid]; -"19 Constant_3" -> "2 GRUSequence" [label="[3]", style=dashed]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/GroupNormalizationModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/GroupNormalizationModel.dot deleted file mode 100644 index 90d267ee9dd..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/quantized/GroupNormalizationModel.dot +++ /dev/null @@ -1,69 +0,0 @@ -strict digraph { -"0 Input_1" [id=0, type=Parameter]; -"1 Input_1/fq_output_0" [id=1, type=FakeQuantize]; -"2 Conv" [id=2, type=Convolution]; -"3 Conv_Add" [id=3, type=Add]; -"4 Conv_Add/fq_output_0" [id=4, type=FakeQuantize]; -"5 GroupNormalization_27318" [id=5, type=GroupNormalization]; -"6 Relu" [id=6, type=Relu]; -"7 Relu/fq_output_0" [id=7, type=FakeQuantize]; -"8 Mul" [id=8, type=Multiply]; -"9 Add" [id=9, type=Add]; -"10 Result" [id=10, type=Result]; -"11 Constant_27322" [id=11, type=Constant]; -"12 Constant_27320" [id=12, type=Constant]; -"13 Constant_29930" [id=13, type=Constant]; -"14 Constant_29929" [id=14, type=Constant]; -"15 Constant_29928" [id=15, type=Constant]; -"16 Constant_29927" [id=16, type=Constant]; -"17 Constant_27317" [id=17, type=Constant]; -"18 Constant_27316" [id=18, type=Constant]; -"19 Constant_29925" [id=19, type=Constant]; -"20 Constant_29924" [id=20, type=Constant]; -"21 Constant_29923" [id=21, type=Constant]; -"22 Constant_29922" [id=22, type=Constant]; -"23 Bias" [id=23, type=Constant]; -"24 Conv/fq_weights_1" [id=24, type=FakeQuantize]; -"25 Constant_29920" [id=25, type=Constant]; -"26 Constant_29919" [id=26, type=Constant]; -"27 Constant_29918" [id=27, type=Constant]; -"28 Constant_29917" [id=28, type=Constant]; -"29 Constant_27312" [id=29, type=Constant]; -"30 Constant_29915" [id=30, type=Constant]; -"31 Constant_29914" [id=31, type=Constant]; -"32 Constant_29913" [id=32, type=Constant]; -"33 Constant_29912" [id=33, type=Constant]; -"0 Input_1" -> "1 Input_1/fq_output_0" [label="[1, 2, 3, 4, 4]", style=solid]; -"1 Input_1/fq_output_0" -> "2 Conv" [label="[1, 2, 3, 4, 4]", style=solid]; -"2 Conv" -> "3 Conv_Add" [label="[1, 4, 1, 2, 2]", style=solid]; -"3 Conv_Add" -> "4 Conv_Add/fq_output_0" [label="[1, 4, 3, 2, 2]", style=solid]; -"4 Conv_Add/fq_output_0" -> "5 GroupNormalization_27318" [label="[1, 4, 3, 2, 2]", style=solid]; -"5 GroupNormalization_27318" -> "6 Relu" [label="[1, 4, 3, 2, 2]", style=solid]; -"6 Relu" -> "7 Relu/fq_output_0" [label="[1, 4, 3, 2, 2]", style=solid]; -"7 Relu/fq_output_0" -> "8 Mul" [label="[1, 4, 3, 2, 2]", style=solid]; -"8 Mul" -> "9 Add" [label="[1, 4, 3, 2, 2]", style=solid]; -"9 Add" -> "10 Result" [label="[1, 4, 3, 2, 2]", style=solid]; -"11 Constant_27322" -> "9 Add" [label="[1, 4, 1, 1, 1]", style=solid]; -"12 Constant_27320" -> "8 Mul" [label="[1, 4, 1, 1, 1]", style=solid]; -"13 Constant_29930" -> "7 Relu/fq_output_0" [label="[]", style=solid]; -"14 Constant_29929" -> "7 Relu/fq_output_0" [label="[]", style=solid]; -"15 Constant_29928" -> "7 Relu/fq_output_0" [label="[]", style=solid]; -"16 Constant_29927" -> "7 Relu/fq_output_0" [label="[]", style=solid]; -"17 Constant_27317" -> "5 GroupNormalization_27318" [label="[4]", style=solid]; -"18 Constant_27316" -> "5 GroupNormalization_27318" [label="[4]", style=solid]; -"19 Constant_29925" -> "4 Conv_Add/fq_output_0" [label="[]", style=solid]; -"20 Constant_29924" -> "4 Conv_Add/fq_output_0" [label="[]", style=solid]; -"21 Constant_29923" -> "4 Conv_Add/fq_output_0" [label="[]", style=solid]; -"22 Constant_29922" -> "4 Conv_Add/fq_output_0" [label="[]", style=solid]; -"23 Bias" -> "3 Conv_Add" [label="[1, 1, 3, 1, 1]", style=solid]; -"24 Conv/fq_weights_1" -> "2 Conv" [label="[4, 2, 3, 3, 3]", style=solid]; -"25 Constant_29920" -> "24 Conv/fq_weights_1" [label="[4, 1, 1, 1, 1]", style=solid]; -"26 Constant_29919" -> "24 Conv/fq_weights_1" [label="[4, 1, 1, 1, 1]", style=solid]; -"27 Constant_29918" -> "24 Conv/fq_weights_1" [label="[4, 1, 1, 1, 1]", style=solid]; -"28 Constant_29917" -> "24 Conv/fq_weights_1" [label="[4, 1, 1, 1, 1]", style=solid]; -"29 Constant_27312" -> "24 Conv/fq_weights_1" [label="[4, 2, 3, 3, 3]", style=solid]; -"30 Constant_29915" -> "1 Input_1/fq_output_0" [label="[]", style=solid]; -"31 Constant_29914" -> "1 Input_1/fq_output_0" [label="[]", style=solid]; -"32 Constant_29913" -> "1 Input_1/fq_output_0" [label="[]", style=solid]; -"33 Constant_29912" -> "1 Input_1/fq_output_0" [label="[]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/IfModel_else.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/IfModel_else.dot deleted file mode 100644 index 10e35270838..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/quantized/IfModel_else.dot +++ /dev/null @@ -1,51 +0,0 @@ -strict digraph { -"0 Input_1" [id=0, type=Parameter]; -"1 Input_2" [id=1, type=Parameter]; -"2 Sub" [id=2, type=Subtract]; -"3 Add" [id=3, type=Add]; -"4 Sub/fq_output_0" [id=4, type=FakeQuantize]; -"5 Mul" [id=5, type=Multiply]; -"6 Conv" [id=6, type=Convolution]; -"7 Transpose" [id=7, type=Transpose]; -"8 Conv_Add" [id=8, type=Add]; -"9 Concat_70" [id=9, type=Concat]; -"10 Relu" [id=10, type=Relu]; -"11 Result" [id=11, type=Result]; -"12 Constant_68" [id=12, type=Constant]; -"13 Constant_66" [id=13, type=Constant]; -"14 Constant_64" [id=14, type=Constant]; -"15 Bias" [id=15, type=Constant]; -"16 Conv/fq_weights_1" [id=16, type=Multiply]; -"17 Constant_12068" [id=17, type=Constant]; -"18 Convert_12170" [id=18, type=Convert]; -"19 Constant_58" [id=19, type=Constant]; -"20 Constant_9391" [id=20, type=Constant]; -"21 Constant_9390" [id=21, type=Constant]; -"22 Constant_9389" [id=22, type=Constant]; -"23 Constant_9388" [id=23, type=Constant]; -"24 Constant_56" [id=24, type=Constant]; -"0 Input_1" -> "2 Sub" [label="[1, 3, 4, 2]", style=solid]; -"1 Input_2" -> "3 Add" [label="[1, 3, 2, 4]", style=solid]; -"2 Sub" -> "4 Sub/fq_output_0" [label="[1, 3, 4, 2]", style=solid]; -"3 Add" -> "5 Mul" [label="[1, 3, 2, 4]", style=solid]; -"4 Sub/fq_output_0" -> "6 Conv" [label="[1, 3, 4, 2]", style=solid]; -"5 Mul" -> "7 Transpose" [label="[1, 3, 2, 4]", style=solid]; -"6 Conv" -> "8 Conv_Add" [label="[1, 3, 4, 2]", style=solid]; -"7 Transpose" -> "9 Concat_70" [label="[1, 3, 4, 2]", style=solid]; -"8 Conv_Add" -> "10 Relu" [label="[1, 3, 4, 2]", style=solid]; -"9 Concat_70" -> "11 Result" [label="[2, 3, 4, 2]", style=solid]; -"10 Relu" -> "9 Concat_70" [label="[1, 3, 4, 2]", style=solid]; -"12 Constant_68" -> "7 Transpose" [label="[4]", style=dashed]; -"13 Constant_66" -> "5 Mul" [label="[1, 3, 1, 1]", style=solid]; -"14 Constant_64" -> "3 Add" [label="[1, 3, 1, 1]", style=solid]; -"15 Bias" -> "8 Conv_Add" [label="[1, 3, 1, 1]", style=solid]; -"16 Conv/fq_weights_1" -> "6 Conv" [label="[3, 3, 1, 1]", style=solid]; -"17 Constant_12068" -> "16 Conv/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; -"18 Convert_12170" -> "16 Conv/fq_weights_1" [label="[3, 3, 1, 1]", style=solid]; -"19 Constant_58" -> "18 Convert_12170" [label="[3, 3, 1, 1]", style=dashed]; -"20 Constant_9391" -> "4 Sub/fq_output_0" [label="[]", style=solid]; -"21 Constant_9390" -> "4 Sub/fq_output_0" [label="[]", style=solid]; -"22 Constant_9389" -> "4 Sub/fq_output_0" [label="[]", style=solid]; -"23 Constant_9388" -> "4 Sub/fq_output_0" [label="[]", style=solid]; -"24 Constant_56" -> "2 Sub" [label="[1, 3, 1, 1]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/IfModel_main.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/IfModel_main.dot deleted file mode 100644 index 33223cf313e..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/quantized/IfModel_main.dot +++ /dev/null @@ -1,11 +0,0 @@ -strict digraph { -"0 Input_1" [id=0, type=Parameter]; -"1 Input_2" [id=1, type=Parameter]; -"2 Cond_input" [id=2, type=Parameter]; -"3 If_72" [id=3, type=If]; -"4 Result" [id=4, type=Result]; -"0 Input_1" -> "3 If_72" [label="[1, 3, 4, 2]", style=solid]; -"1 Input_2" -> "3 If_72" [label="[1, 3, 2, 4]", style=solid]; -"2 Cond_input" -> "3 If_72" [label="[]", style=dashed]; -"3 If_72" -> "4 Result" [label="[2, 3, 4, 2]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/IfModel_then.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/IfModel_then.dot deleted file mode 100644 index 732b69959db..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/quantized/IfModel_then.dot +++ /dev/null @@ -1,51 +0,0 @@ -strict digraph { -"0 Input_1" [id=0, type=Parameter]; -"1 Input_2" [id=1, type=Parameter]; -"2 Sub" [id=2, type=Subtract]; -"3 Add" [id=3, type=Add]; -"4 Sub/fq_output_0" [id=4, type=FakeQuantize]; -"5 Mul" [id=5, type=Multiply]; -"6 Conv" [id=6, type=Convolution]; -"7 Transpose" [id=7, type=Transpose]; -"8 Conv_Add" [id=8, type=Add]; -"9 Concat_36" [id=9, type=Concat]; -"10 Relu" [id=10, type=Relu]; -"11 Result" [id=11, type=Result]; -"12 Constant_34" [id=12, type=Constant]; -"13 Constant_32" [id=13, type=Constant]; -"14 Constant_30" [id=14, type=Constant]; -"15 Bias" [id=15, type=Constant]; -"16 Conv/fq_weights_1" [id=16, type=Multiply]; -"17 Constant_11914" [id=17, type=Constant]; -"18 Convert_12016" [id=18, type=Convert]; -"19 Constant_24" [id=19, type=Constant]; -"20 Constant_4685" [id=20, type=Constant]; -"21 Constant_4684" [id=21, type=Constant]; -"22 Constant_4683" [id=22, type=Constant]; -"23 Constant_4682" [id=23, type=Constant]; -"24 Constant_22" [id=24, type=Constant]; -"0 Input_1" -> "2 Sub" [label="[1, 3, 4, 2]", style=solid]; -"1 Input_2" -> "3 Add" [label="[1, 3, 2, 4]", style=solid]; -"2 Sub" -> "4 Sub/fq_output_0" [label="[1, 3, 4, 2]", style=solid]; -"3 Add" -> "5 Mul" [label="[1, 3, 2, 4]", style=solid]; -"4 Sub/fq_output_0" -> "6 Conv" [label="[1, 3, 4, 2]", style=solid]; -"5 Mul" -> "7 Transpose" [label="[1, 3, 2, 4]", style=solid]; -"6 Conv" -> "8 Conv_Add" [label="[1, 3, 4, 2]", style=solid]; -"7 Transpose" -> "9 Concat_36" [label="[1, 3, 4, 2]", style=solid]; -"8 Conv_Add" -> "10 Relu" [label="[1, 3, 4, 2]", style=solid]; -"9 Concat_36" -> "11 Result" [label="[2, 3, 4, 2]", style=solid]; -"10 Relu" -> "9 Concat_36" [label="[1, 3, 4, 2]", style=solid]; -"12 Constant_34" -> "7 Transpose" [label="[4]", style=dashed]; -"13 Constant_32" -> "5 Mul" [label="[1, 3, 1, 1]", style=solid]; -"14 Constant_30" -> "3 Add" [label="[1, 3, 1, 1]", style=solid]; -"15 Bias" -> "8 Conv_Add" [label="[1, 3, 1, 1]", style=solid]; -"16 Conv/fq_weights_1" -> "6 Conv" [label="[3, 3, 1, 1]", style=solid]; -"17 Constant_11914" -> "16 Conv/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; -"18 Convert_12016" -> "16 Conv/fq_weights_1" [label="[3, 3, 1, 1]", style=solid]; -"19 Constant_24" -> "18 Convert_12016" [label="[3, 3, 1, 1]", style=dashed]; -"20 Constant_4685" -> "4 Sub/fq_output_0" [label="[]", style=solid]; -"21 Constant_4684" -> "4 Sub/fq_output_0" [label="[]", style=solid]; -"22 Constant_4683" -> "4 Sub/fq_output_0" [label="[]", style=solid]; -"23 Constant_4682" -> "4 Sub/fq_output_0" [label="[]", style=solid]; -"24 Constant_22" -> "2 Sub" [label="[1, 3, 1, 1]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/IntegerModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/IntegerModel.dot deleted file mode 100644 index 03cfb73b101..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/quantized/IntegerModel.dot +++ /dev/null @@ -1,102 +0,0 @@ -strict digraph { -"0 Input" [id=0, type=Parameter]; -"1 Convert_1" [id=1, type=Convert]; -"2 Gather_4/fq_input_0" [id=2, type=FakeQuantize]; -"3 Gather_1" [id=3, type=Gather]; -"4 Gather_4" [id=4, type=Gather]; -"5 Gather_2" [id=5, type=Gather]; -"6 MatMul_2" [id=6, type=MatMul]; -"7 Gather_3" [id=7, type=Gather]; -"8 MatMul_2/fq_output_0" [id=8, type=FakeQuantize]; -"9 MatMul_1" [id=9, type=MatMul]; -"10 Add_1" [id=10, type=Add]; -"11 MatMul_1/fq_output_0" [id=11, type=FakeQuantize]; -"12 Result" [id=12, type=Result]; -"13 Constant_7164" [id=13, type=Constant]; -"14 Constant_7163" [id=14, type=Constant]; -"15 Constant_7162" [id=15, type=Constant]; -"16 Constant_7161" [id=16, type=Constant]; -"17 MatMul_2/fq_weights_1" [id=17, type=FakeQuantize]; -"18 Constant_7169" [id=18, type=Constant]; -"19 Constant_7168" [id=19, type=Constant]; -"20 Constant_7167" [id=20, type=Constant]; -"21 Constant_7166" [id=21, type=Constant]; -"22 Constant_4701" [id=22, type=Constant]; -"23 Constant_4699" [id=23, type=Constant]; -"24 Constant_4698" [id=24, type=Constant]; -"25 Constant_7159" [id=25, type=Constant]; -"26 Constant_7158" [id=26, type=Constant]; -"27 Constant_7157" [id=27, type=Constant]; -"28 Constant_7156" [id=28, type=Constant]; -"29 Constant_7179" [id=29, type=Constant]; -"30 Constant_7178" [id=30, type=Constant]; -"31 Constant_7177" [id=31, type=Constant]; -"32 Constant_7176" [id=32, type=Constant]; -"33 MatMul_1/fq_weights_1" [id=33, type=FakeQuantize]; -"34 Constant_7184" [id=34, type=Constant]; -"35 Constant_7183" [id=35, type=Constant]; -"36 Constant_7182" [id=36, type=Constant]; -"37 Constant_7181" [id=37, type=Constant]; -"38 Constant_4696" [id=38, type=Constant]; -"39 Constant_4694" [id=39, type=Constant]; -"40 Constant_4693" [id=40, type=Constant]; -"41 Constant_4691" [id=41, type=Constant]; -"42 Constant_4688" [id=42, type=Constant]; -"43 Constant_4687" [id=43, type=Constant]; -"44 Gather_2/fq_weights_0" [id=44, type=FakeQuantize]; -"45 Constant_7174" [id=45, type=Constant]; -"46 Constant_7173" [id=46, type=Constant]; -"47 Constant_7172" [id=47, type=Constant]; -"48 Constant_7171" [id=48, type=Constant]; -"49 Constant_4690" [id=49, type=Constant]; -"0 Input" -> "1 Convert_1" [label="[1, 192, 1]", style=solid]; -"0 Input" -> "2 Gather_4/fq_input_0" [label="[1, 192, 1]", style=solid]; -"1 Convert_1" -> "3 Gather_1" [label="[1, 192, 1]", style=dashed]; -"2 Gather_4/fq_input_0" -> "4 Gather_4" [label="[1, 192, 1]", style=solid]; -"3 Gather_1" -> "5 Gather_2" [label="[192, 1]", style=dashed]; -"4 Gather_4" -> "6 MatMul_2" [label="[1, 192]", style=solid]; -"5 Gather_2" -> "7 Gather_3" [label="[192, 1, 160]", style=solid]; -"6 MatMul_2" -> "8 MatMul_2/fq_output_0" [label="[1, 160]", style=solid]; -"7 Gather_3" -> "9 MatMul_1" [label="[1, 160]", style=solid]; -"8 MatMul_2/fq_output_0" -> "10 Add_1" [label="[1, 160]", style=solid]; -"9 MatMul_1" -> "11 MatMul_1/fq_output_0" [label="[1, 160]", style=solid]; -"10 Add_1" -> "12 Result" [label="[1, 160]", style=solid]; -"11 MatMul_1/fq_output_0" -> "10 Add_1" [label="[1, 160]", style=solid]; -"13 Constant_7164" -> "8 MatMul_2/fq_output_0" [label="[]", style=solid]; -"14 Constant_7163" -> "8 MatMul_2/fq_output_0" [label="[]", style=solid]; -"15 Constant_7162" -> "8 MatMul_2/fq_output_0" [label="[]", style=solid]; -"16 Constant_7161" -> "8 MatMul_2/fq_output_0" [label="[]", style=solid]; -"17 MatMul_2/fq_weights_1" -> "6 MatMul_2" [label="[160, 192]", style=solid]; -"18 Constant_7169" -> "17 MatMul_2/fq_weights_1" [label="[160, 1]", style=solid]; -"19 Constant_7168" -> "17 MatMul_2/fq_weights_1" [label="[160, 1]", style=solid]; -"20 Constant_7167" -> "17 MatMul_2/fq_weights_1" [label="[160, 1]", style=solid]; -"21 Constant_7166" -> "17 MatMul_2/fq_weights_1" [label="[160, 1]", style=solid]; -"22 Constant_4701" -> "17 MatMul_2/fq_weights_1" [label="[160, 192]", style=solid]; -"23 Constant_4699" -> "4 Gather_4" [label="[]", style=dashed]; -"24 Constant_4698" -> "4 Gather_4" [label="[]", style=dashed]; -"25 Constant_7159" -> "2 Gather_4/fq_input_0" [label="[]", style=solid]; -"26 Constant_7158" -> "2 Gather_4/fq_input_0" [label="[]", style=solid]; -"27 Constant_7157" -> "2 Gather_4/fq_input_0" [label="[]", style=solid]; -"28 Constant_7156" -> "2 Gather_4/fq_input_0" [label="[]", style=solid]; -"29 Constant_7179" -> "11 MatMul_1/fq_output_0" [label="[]", style=solid]; -"30 Constant_7178" -> "11 MatMul_1/fq_output_0" [label="[]", style=solid]; -"31 Constant_7177" -> "11 MatMul_1/fq_output_0" [label="[]", style=solid]; -"32 Constant_7176" -> "11 MatMul_1/fq_output_0" [label="[]", style=solid]; -"33 MatMul_1/fq_weights_1" -> "9 MatMul_1" [label="[160, 160]", style=solid]; -"34 Constant_7184" -> "33 MatMul_1/fq_weights_1" [label="[160, 1]", style=solid]; -"35 Constant_7183" -> "33 MatMul_1/fq_weights_1" [label="[160, 1]", style=solid]; -"36 Constant_7182" -> "33 MatMul_1/fq_weights_1" [label="[160, 1]", style=solid]; -"37 Constant_7181" -> "33 MatMul_1/fq_weights_1" [label="[160, 1]", style=solid]; -"38 Constant_4696" -> "33 MatMul_1/fq_weights_1" [label="[160, 160]", style=solid]; -"39 Constant_4694" -> "7 Gather_3" [label="[]", style=dashed]; -"40 Constant_4693" -> "7 Gather_3" [label="[]", style=dashed]; -"41 Constant_4691" -> "5 Gather_2" [label="[]", style=dashed]; -"42 Constant_4688" -> "3 Gather_1" [label="[]", style=dashed]; -"43 Constant_4687" -> "3 Gather_1" [label="[]", style=dashed]; -"44 Gather_2/fq_weights_0" -> "5 Gather_2" [label="[369, 160]", style=solid]; -"45 Constant_7174" -> "44 Gather_2/fq_weights_0" [label="[]", style=solid]; -"46 Constant_7173" -> "44 Gather_2/fq_weights_0" [label="[]", style=solid]; -"47 Constant_7172" -> "44 Gather_2/fq_weights_0" [label="[]", style=solid]; -"48 Constant_7171" -> "44 Gather_2/fq_weights_0" [label="[]", style=solid]; -"49 Constant_4690" -> "44 Gather_2/fq_weights_0" [label="[369, 160]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/LSTMModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/LSTMModel.dot deleted file mode 100644 index d3f5a2d2558..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/quantized/LSTMModel.dot +++ /dev/null @@ -1,220 +0,0 @@ -strict digraph { -"0 Input" [id=0, type=Parameter]; -"1 ReadValue_1" [id=1, type=ReadValue]; -"2 Input/fq_output_0" [id=2, type=FakeQuantize]; -"3 ReadValue_1/fq_output_0" [id=3, type=FakeQuantize]; -"4 Squeeze_1" [id=4, type=Squeeze]; -"5 Add" [id=5, type=Add]; -"6 MatMul_1" [id=6, type=MatMul]; -"7 Add/fq_output_0" [id=7, type=FakeQuantize]; -"8 MatMul_1/fq_output_0" [id=8, type=FakeQuantize]; -"9 MatMul_2" [id=9, type=MatMul]; -"10 Add_1" [id=10, type=Add]; -"11 MatMul_2/fq_output_0" [id=11, type=FakeQuantize]; -"12 Split_6052" [id=12, type=Split]; -"13 Sigmoid_1" [id=13, type=Sigmoid]; -"14 Sigmoid_2" [id=14, type=Sigmoid]; -"15 Sigmoid_3" [id=15, type=Sigmoid]; -"16 Tanh_1" [id=16, type=Tanh]; -"17 Sigmoid_1/fq_output_0" [id=17, type=FakeQuantize]; -"18 Sigmoid_2/fq_output_0" [id=18, type=FakeQuantize]; -"19 Sigmoid_3/fq_output_0" [id=19, type=FakeQuantize]; -"20 Tanh_1/fq_output_0" [id=20, type=FakeQuantize]; -"21 Multiply_1" [id=21, type=Multiply]; -"22 Multiply_2" [id=22, type=Multiply]; -"23 Multiply_3" [id=23, type=Multiply]; -"24 Multiply_1/fq_output_0" [id=24, type=FakeQuantize]; -"25 Multiply_2/fq_output_0" [id=25, type=FakeQuantize]; -"26 Assign_1" [id=26, type=Assign]; -"27 MatMul_3/fq_input_0" [id=27, type=FakeQuantize]; -"28 Add_2" [id=28, type=Add]; -"29 MatMul_3" [id=29, type=MatMul]; -"30 Tanh_2" [id=30, type=Tanh]; -"31 Result" [id=31, type=Result]; -"32 Tanh_2/fq_output_0" [id=32, type=FakeQuantize]; -"33 Constant_9029" [id=33, type=Constant]; -"34 Constant_9028" [id=34, type=Constant]; -"35 Constant_9027" [id=35, type=Constant]; -"36 Constant_9026" [id=36, type=Constant]; -"37 Constant_8999" [id=37, type=Constant]; -"38 Constant_8998" [id=38, type=Constant]; -"39 Constant_8997" [id=39, type=Constant]; -"40 Constant_8996" [id=40, type=Constant]; -"41 Constant_9014" [id=41, type=Constant]; -"42 Constant_9013" [id=42, type=Constant]; -"43 Constant_9012" [id=43, type=Constant]; -"44 Constant_9011" [id=44, type=Constant]; -"45 Constant_6051" [id=45, type=Constant]; -"46 Constant_8989" [id=46, type=Constant]; -"47 Constant_8988" [id=47, type=Constant]; -"48 Constant_8987" [id=48, type=Constant]; -"49 Constant_8986" [id=49, type=Constant]; -"50 MatMul_2/fq_weights_1" [id=50, type=FakeQuantize]; -"51 Constant_9039" [id=51, type=Constant]; -"52 Constant_9038" [id=52, type=Constant]; -"53 Constant_9037" [id=53, type=Constant]; -"54 Constant_9036" [id=54, type=Constant]; -"55 Constant_6048" [id=55, type=Constant]; -"56 Constant_8974" [id=56, type=Constant]; -"57 Constant_8973" [id=57, type=Constant]; -"58 Constant_8972" [id=58, type=Constant]; -"59 Constant_8971" [id=59, type=Constant]; -"60 Constant_6046" [id=60, type=Constant]; -"61 Constant_9004" [id=61, type=Constant]; -"62 Constant_9003" [id=62, type=Constant]; -"63 Constant_9002" [id=63, type=Constant]; -"64 Constant_9001" [id=64, type=Constant]; -"65 Constant_6044" [id=65, type=Constant]; -"66 Constant_8984" [id=66, type=Constant]; -"67 Constant_8983" [id=67, type=Constant]; -"68 Constant_8982" [id=68, type=Constant]; -"69 Constant_8981" [id=69, type=Constant]; -"70 MatMul_1/fq_weights_1" [id=70, type=FakeQuantize]; -"71 Constant_9034" [id=71, type=Constant]; -"72 Constant_9033" [id=72, type=Constant]; -"73 Constant_9032" [id=73, type=Constant]; -"74 Constant_9031" [id=74, type=Constant]; -"75 Constant_6042" [id=75, type=Constant]; -"76 Constant_6040" [id=76, type=Constant]; -"77 Constant_8979" [id=77, type=Constant]; -"78 Constant_8978" [id=78, type=Constant]; -"79 Constant_8977" [id=79, type=Constant]; -"80 Constant_8976" [id=80, type=Constant]; -"81 Constant_9024" [id=81, type=Constant]; -"82 Constant_9023" [id=82, type=Constant]; -"83 Constant_9022" [id=83, type=Constant]; -"84 Constant_9021" [id=84, type=Constant]; -"85 Constant_8994" [id=85, type=Constant]; -"86 Constant_8993" [id=86, type=Constant]; -"87 Constant_8992" [id=87, type=Constant]; -"88 Constant_8991" [id=88, type=Constant]; -"89 Constant_6057" [id=89, type=Constant]; -"90 Constant_9009" [id=90, type=Constant]; -"91 Constant_9008" [id=91, type=Constant]; -"92 Constant_9007" [id=92, type=Constant]; -"93 Constant_9006" [id=93, type=Constant]; -"94 Constant_9019" [id=94, type=Constant]; -"95 Constant_9018" [id=95, type=Constant]; -"96 Constant_9017" [id=96, type=Constant]; -"97 Constant_9016" [id=97, type=Constant]; -"98 MatMul_3/fq_weights_1" [id=98, type=FakeQuantize]; -"99 Constant_9044" [id=99, type=Constant]; -"100 Constant_9043" [id=100, type=Constant]; -"101 Constant_9042" [id=101, type=Constant]; -"102 Constant_9041" [id=102, type=Constant]; -"103 Constant_6064" [id=103, type=Constant]; -"104 Constant_8969" [id=104, type=Constant]; -"105 Constant_8968" [id=105, type=Constant]; -"106 Constant_8967" [id=106, type=Constant]; -"107 Constant_8966" [id=107, type=Constant]; -"0 Input" -> "2 Input/fq_output_0" [label="[1, 1, 128]", style=solid]; -"1 ReadValue_1" -> "3 ReadValue_1/fq_output_0" [label="[1, 64]", style=solid]; -"2 Input/fq_output_0" -> "4 Squeeze_1" [label="[1, 1, 128]", style=solid]; -"3 ReadValue_1/fq_output_0" -> "5 Add" [label="[1, 64]", style=solid]; -"4 Squeeze_1" -> "6 MatMul_1" [label="[1, 128]", style=solid]; -"5 Add" -> "7 Add/fq_output_0" [label="[1, 64]", style=solid]; -"6 MatMul_1" -> "8 MatMul_1/fq_output_0" [label="[1, 256]", style=solid]; -"7 Add/fq_output_0" -> "9 MatMul_2" [label="[1, 64]", style=solid]; -"8 MatMul_1/fq_output_0" -> "10 Add_1" [label="[1, 256]", style=solid]; -"9 MatMul_2" -> "11 MatMul_2/fq_output_0" [label="[1, 256]", style=solid]; -"10 Add_1" -> "12 Split_6052" [label="[1, 256]", style=solid]; -"11 MatMul_2/fq_output_0" -> "10 Add_1" [label="[1, 256]", style=solid]; -"12 Split_6052" -> "13 Sigmoid_1" [label="[1, 64]", style=solid]; -"12 Split_6052" -> "14 Sigmoid_2" [label="[1, 64]", style=solid]; -"12 Split_6052" -> "15 Sigmoid_3" [label="[1, 64]", style=solid]; -"12 Split_6052" -> "16 Tanh_1" [label="[1, 64]", style=solid]; -"13 Sigmoid_1" -> "17 Sigmoid_1/fq_output_0" [label="[1, 64]", style=solid]; -"14 Sigmoid_2" -> "18 Sigmoid_2/fq_output_0" [label="[1, 64]", style=solid]; -"15 Sigmoid_3" -> "19 Sigmoid_3/fq_output_0" [label="[1, 64]", style=solid]; -"16 Tanh_1" -> "20 Tanh_1/fq_output_0" [label="[1, 64]", style=solid]; -"17 Sigmoid_1/fq_output_0" -> "21 Multiply_1" [label="[1, 64]", style=solid]; -"18 Sigmoid_2/fq_output_0" -> "22 Multiply_2" [label="[1, 64]", style=solid]; -"19 Sigmoid_3/fq_output_0" -> "23 Multiply_3" [label="[1, 64]", style=solid]; -"20 Tanh_1/fq_output_0" -> "22 Multiply_2" [label="[1, 64]", style=solid]; -"21 Multiply_1" -> "24 Multiply_1/fq_output_0" [label="[1, 64]", style=solid]; -"22 Multiply_2" -> "25 Multiply_2/fq_output_0" [label="[1, 64]", style=solid]; -"23 Multiply_3" -> "26 Assign_1" [label="[1, 64]", style=solid]; -"23 Multiply_3" -> "27 MatMul_3/fq_input_0" [label="[1, 64]", style=solid]; -"24 Multiply_1/fq_output_0" -> "28 Add_2" [label="[1, 64]", style=solid]; -"25 Multiply_2/fq_output_0" -> "28 Add_2" [label="[1, 64]", style=solid]; -"27 MatMul_3/fq_input_0" -> "29 MatMul_3" [label="[1, 64]", style=solid]; -"28 Add_2" -> "30 Tanh_2" [label="[1, 64]", style=solid]; -"29 MatMul_3" -> "31 Result" [label="[1, 128]", style=solid]; -"30 Tanh_2" -> "32 Tanh_2/fq_output_0" [label="[1, 64]", style=solid]; -"32 Tanh_2/fq_output_0" -> "23 Multiply_3" [label="[1, 64]", style=solid]; -"33 Constant_9029" -> "32 Tanh_2/fq_output_0" [label="[]", style=solid]; -"34 Constant_9028" -> "32 Tanh_2/fq_output_0" [label="[]", style=solid]; -"35 Constant_9027" -> "32 Tanh_2/fq_output_0" [label="[]", style=solid]; -"36 Constant_9026" -> "32 Tanh_2/fq_output_0" [label="[]", style=solid]; -"37 Constant_8999" -> "25 Multiply_2/fq_output_0" [label="[]", style=solid]; -"38 Constant_8998" -> "25 Multiply_2/fq_output_0" [label="[]", style=solid]; -"39 Constant_8997" -> "25 Multiply_2/fq_output_0" [label="[]", style=solid]; -"40 Constant_8996" -> "25 Multiply_2/fq_output_0" [label="[]", style=solid]; -"41 Constant_9014" -> "18 Sigmoid_2/fq_output_0" [label="[]", style=solid]; -"42 Constant_9013" -> "18 Sigmoid_2/fq_output_0" [label="[]", style=solid]; -"43 Constant_9012" -> "18 Sigmoid_2/fq_output_0" [label="[]", style=solid]; -"44 Constant_9011" -> "18 Sigmoid_2/fq_output_0" [label="[]", style=solid]; -"45 Constant_6051" -> "12 Split_6052" [label="[]", style=dashed]; -"46 Constant_8989" -> "11 MatMul_2/fq_output_0" [label="[]", style=solid]; -"47 Constant_8988" -> "11 MatMul_2/fq_output_0" [label="[]", style=solid]; -"48 Constant_8987" -> "11 MatMul_2/fq_output_0" [label="[]", style=solid]; -"49 Constant_8986" -> "11 MatMul_2/fq_output_0" [label="[]", style=solid]; -"50 MatMul_2/fq_weights_1" -> "9 MatMul_2" [label="[256, 64]", style=solid]; -"51 Constant_9039" -> "50 MatMul_2/fq_weights_1" [label="[256, 1]", style=solid]; -"52 Constant_9038" -> "50 MatMul_2/fq_weights_1" [label="[256, 1]", style=solid]; -"53 Constant_9037" -> "50 MatMul_2/fq_weights_1" [label="[256, 1]", style=solid]; -"54 Constant_9036" -> "50 MatMul_2/fq_weights_1" [label="[256, 1]", style=solid]; -"55 Constant_6048" -> "50 MatMul_2/fq_weights_1" [label="[256, 64]", style=solid]; -"56 Constant_8974" -> "7 Add/fq_output_0" [label="[]", style=solid]; -"57 Constant_8973" -> "7 Add/fq_output_0" [label="[]", style=solid]; -"58 Constant_8972" -> "7 Add/fq_output_0" [label="[]", style=solid]; -"59 Constant_8971" -> "7 Add/fq_output_0" [label="[]", style=solid]; -"60 Constant_6046" -> "5 Add" [label="[1, 64]", style=solid]; -"61 Constant_9004" -> "3 ReadValue_1/fq_output_0" [label="[]", style=solid]; -"62 Constant_9003" -> "3 ReadValue_1/fq_output_0" [label="[]", style=solid]; -"63 Constant_9002" -> "3 ReadValue_1/fq_output_0" [label="[]", style=solid]; -"64 Constant_9001" -> "3 ReadValue_1/fq_output_0" [label="[]", style=solid]; -"65 Constant_6044" -> "1 ReadValue_1" [label="[1, 64]", style=solid]; -"66 Constant_8984" -> "8 MatMul_1/fq_output_0" [label="[]", style=solid]; -"67 Constant_8983" -> "8 MatMul_1/fq_output_0" [label="[]", style=solid]; -"68 Constant_8982" -> "8 MatMul_1/fq_output_0" [label="[]", style=solid]; -"69 Constant_8981" -> "8 MatMul_1/fq_output_0" [label="[]", style=solid]; -"70 MatMul_1/fq_weights_1" -> "6 MatMul_1" [label="[256, 128]", style=solid]; -"71 Constant_9034" -> "70 MatMul_1/fq_weights_1" [label="[256, 1]", style=solid]; -"72 Constant_9033" -> "70 MatMul_1/fq_weights_1" [label="[256, 1]", style=solid]; -"73 Constant_9032" -> "70 MatMul_1/fq_weights_1" [label="[256, 1]", style=solid]; -"74 Constant_9031" -> "70 MatMul_1/fq_weights_1" [label="[256, 1]", style=solid]; -"75 Constant_6042" -> "70 MatMul_1/fq_weights_1" [label="[256, 128]", style=solid]; -"76 Constant_6040" -> "4 Squeeze_1" [label="[]", style=dashed]; -"77 Constant_8979" -> "2 Input/fq_output_0" [label="[]", style=solid]; -"78 Constant_8978" -> "2 Input/fq_output_0" [label="[]", style=solid]; -"79 Constant_8977" -> "2 Input/fq_output_0" [label="[]", style=solid]; -"80 Constant_8976" -> "2 Input/fq_output_0" [label="[]", style=solid]; -"81 Constant_9024" -> "20 Tanh_1/fq_output_0" [label="[]", style=solid]; -"82 Constant_9023" -> "20 Tanh_1/fq_output_0" [label="[]", style=solid]; -"83 Constant_9022" -> "20 Tanh_1/fq_output_0" [label="[]", style=solid]; -"84 Constant_9021" -> "20 Tanh_1/fq_output_0" [label="[]", style=solid]; -"85 Constant_8994" -> "24 Multiply_1/fq_output_0" [label="[]", style=solid]; -"86 Constant_8993" -> "24 Multiply_1/fq_output_0" [label="[]", style=solid]; -"87 Constant_8992" -> "24 Multiply_1/fq_output_0" [label="[]", style=solid]; -"88 Constant_8991" -> "24 Multiply_1/fq_output_0" [label="[]", style=solid]; -"89 Constant_6057" -> "21 Multiply_1" [label="[1, 64]", style=solid]; -"90 Constant_9009" -> "17 Sigmoid_1/fq_output_0" [label="[]", style=solid]; -"91 Constant_9008" -> "17 Sigmoid_1/fq_output_0" [label="[]", style=solid]; -"92 Constant_9007" -> "17 Sigmoid_1/fq_output_0" [label="[]", style=solid]; -"93 Constant_9006" -> "17 Sigmoid_1/fq_output_0" [label="[]", style=solid]; -"94 Constant_9019" -> "19 Sigmoid_3/fq_output_0" [label="[]", style=solid]; -"95 Constant_9018" -> "19 Sigmoid_3/fq_output_0" [label="[]", style=solid]; -"96 Constant_9017" -> "19 Sigmoid_3/fq_output_0" [label="[]", style=solid]; -"97 Constant_9016" -> "19 Sigmoid_3/fq_output_0" [label="[]", style=solid]; -"98 MatMul_3/fq_weights_1" -> "29 MatMul_3" [label="[128, 64]", style=solid]; -"99 Constant_9044" -> "98 MatMul_3/fq_weights_1" [label="[128, 1]", style=solid]; -"100 Constant_9043" -> "98 MatMul_3/fq_weights_1" [label="[128, 1]", style=solid]; -"101 Constant_9042" -> "98 MatMul_3/fq_weights_1" [label="[128, 1]", style=solid]; -"102 Constant_9041" -> "98 MatMul_3/fq_weights_1" [label="[128, 1]", style=solid]; -"103 Constant_6064" -> "98 MatMul_3/fq_weights_1" [label="[128, 64]", style=solid]; -"104 Constant_8969" -> "27 MatMul_3/fq_input_0" [label="[]", style=solid]; -"105 Constant_8968" -> "27 MatMul_3/fq_input_0" [label="[]", style=solid]; -"106 Constant_8967" -> "27 MatMul_3/fq_input_0" [label="[]", style=solid]; -"107 Constant_8966" -> "27 MatMul_3/fq_input_0" [label="[]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/LSTMSequenceModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/LSTMSequenceModel.dot deleted file mode 100644 index 3167389ce2d..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/quantized/LSTMSequenceModel.dot +++ /dev/null @@ -1,93 +0,0 @@ -strict digraph { -"0 X" [id=0, type=Parameter]; -"1 initial_hidden_state" [id=1, type=Parameter]; -"2 initial_cell_state" [id=2, type=Parameter]; -"3 X/fq_output_0" [id=3, type=FakeQuantize]; -"4 initial_hidden_state/fq_output_0" [id=4, type=FakeQuantize]; -"5 initial_cell_state/fq_output_0" [id=5, type=FakeQuantize]; -"6 LSTMSequence_7" [id=6, type=LSTMSequence]; -"7 LSTMSequence_7/fq_output_0" [id=7, type=FakeQuantize]; -"8 MatMul" [id=8, type=MatMul]; -"9 Result" [id=9, type=Result]; -"10 MatMul/fq_weights_1" [id=10, type=FakeQuantize]; -"11 Constant_2122" [id=11, type=Constant]; -"12 Constant_2121" [id=12, type=Constant]; -"13 Constant_2120" [id=13, type=Constant]; -"14 Constant_2119" [id=14, type=Constant]; -"15 Constant_8" [id=15, type=Constant]; -"16 Constant_2092" [id=16, type=Constant]; -"17 Constant_2091" [id=17, type=Constant]; -"18 Constant_2090" [id=18, type=Constant]; -"19 Constant_2089" [id=19, type=Constant]; -"20 Constant_6" [id=20, type=Constant]; -"21 LSTMSequence_7/fq_weights_5" [id=21, type=FakeQuantize]; -"22 Constant_2117" [id=22, type=Constant]; -"23 Constant_2116" [id=23, type=Constant]; -"24 Constant_2115" [id=24, type=Constant]; -"25 Constant_2114" [id=25, type=Constant]; -"26 Constant_5" [id=26, type=Constant]; -"27 LSTMSequence_7/fq_weights_4" [id=27, type=FakeQuantize]; -"28 Constant_2112" [id=28, type=Constant]; -"29 Constant_2111" [id=29, type=Constant]; -"30 Constant_2110" [id=30, type=Constant]; -"31 Constant_2109" [id=31, type=Constant]; -"32 Constant_4" [id=32, type=Constant]; -"33 Constant_3" [id=33, type=Constant]; -"34 Constant_2102" [id=34, type=Constant]; -"35 Constant_2101" [id=35, type=Constant]; -"36 Constant_2100" [id=36, type=Constant]; -"37 Constant_2099" [id=37, type=Constant]; -"38 Constant_2107" [id=38, type=Constant]; -"39 Constant_2106" [id=39, type=Constant]; -"40 Constant_2105" [id=40, type=Constant]; -"41 Constant_2104" [id=41, type=Constant]; -"42 Constant_2097" [id=42, type=Constant]; -"43 Constant_2096" [id=43, type=Constant]; -"44 Constant_2095" [id=44, type=Constant]; -"45 Constant_2094" [id=45, type=Constant]; -"0 X" -> "3 X/fq_output_0" [label="[1, 2, 16]", style=solid]; -"1 initial_hidden_state" -> "4 initial_hidden_state/fq_output_0" [label="[1, 1, 128]", style=solid]; -"2 initial_cell_state" -> "5 initial_cell_state/fq_output_0" [label="[1, 1, 128]", style=solid]; -"3 X/fq_output_0" -> "6 LSTMSequence_7" [label="[1, 2, 16]", style=solid]; -"4 initial_hidden_state/fq_output_0" -> "6 LSTMSequence_7" [label="[1, 1, 128]", style=solid]; -"5 initial_cell_state/fq_output_0" -> "6 LSTMSequence_7" [label="[1, 1, 128]", style=solid]; -"6 LSTMSequence_7" -> "7 LSTMSequence_7/fq_output_0" [label="[1, 1, 2, 128]", style=solid]; -"7 LSTMSequence_7/fq_output_0" -> "8 MatMul" [label="[1, 1, 2, 128]", style=solid]; -"8 MatMul" -> "9 Result" [label="[1, 1, 2, 3]", style=solid]; -"10 MatMul/fq_weights_1" -> "8 MatMul" [label="[1, 1, 128, 3]", style=solid]; -"11 Constant_2122" -> "10 MatMul/fq_weights_1" [label="[1, 1, 1, 1]", style=solid]; -"12 Constant_2121" -> "10 MatMul/fq_weights_1" [label="[1, 1, 1, 1]", style=solid]; -"13 Constant_2120" -> "10 MatMul/fq_weights_1" [label="[1, 1, 1, 1]", style=solid]; -"14 Constant_2119" -> "10 MatMul/fq_weights_1" [label="[1, 1, 1, 1]", style=solid]; -"15 Constant_8" -> "10 MatMul/fq_weights_1" [label="[1, 1, 128, 3]", style=solid]; -"16 Constant_2092" -> "7 LSTMSequence_7/fq_output_0" [label="[]", style=solid]; -"17 Constant_2091" -> "7 LSTMSequence_7/fq_output_0" [label="[]", style=solid]; -"18 Constant_2090" -> "7 LSTMSequence_7/fq_output_0" [label="[]", style=solid]; -"19 Constant_2089" -> "7 LSTMSequence_7/fq_output_0" [label="[]", style=solid]; -"20 Constant_6" -> "6 LSTMSequence_7" [label="[1, 512]", style=solid]; -"21 LSTMSequence_7/fq_weights_5" -> "6 LSTMSequence_7" [label="[1, 512, 128]", style=solid]; -"22 Constant_2117" -> "21 LSTMSequence_7/fq_weights_5" [label="[1, 1, 1]", style=solid]; -"23 Constant_2116" -> "21 LSTMSequence_7/fq_weights_5" [label="[1, 1, 1]", style=solid]; -"24 Constant_2115" -> "21 LSTMSequence_7/fq_weights_5" [label="[1, 1, 1]", style=solid]; -"25 Constant_2114" -> "21 LSTMSequence_7/fq_weights_5" [label="[1, 1, 1]", style=solid]; -"26 Constant_5" -> "21 LSTMSequence_7/fq_weights_5" [label="[1, 512, 128]", style=solid]; -"27 LSTMSequence_7/fq_weights_4" -> "6 LSTMSequence_7" [label="[1, 512, 16]", style=solid]; -"28 Constant_2112" -> "27 LSTMSequence_7/fq_weights_4" [label="[1, 1, 1]", style=solid]; -"29 Constant_2111" -> "27 LSTMSequence_7/fq_weights_4" [label="[1, 1, 1]", style=solid]; -"30 Constant_2110" -> "27 LSTMSequence_7/fq_weights_4" [label="[1, 1, 1]", style=solid]; -"31 Constant_2109" -> "27 LSTMSequence_7/fq_weights_4" [label="[1, 1, 1]", style=solid]; -"32 Constant_4" -> "27 LSTMSequence_7/fq_weights_4" [label="[1, 512, 16]", style=solid]; -"33 Constant_3" -> "6 LSTMSequence_7" [label="[1]", style=dashed]; -"34 Constant_2102" -> "5 initial_cell_state/fq_output_0" [label="[]", style=solid]; -"35 Constant_2101" -> "5 initial_cell_state/fq_output_0" [label="[]", style=solid]; -"36 Constant_2100" -> "5 initial_cell_state/fq_output_0" [label="[]", style=solid]; -"37 Constant_2099" -> "5 initial_cell_state/fq_output_0" [label="[]", style=solid]; -"38 Constant_2107" -> "4 initial_hidden_state/fq_output_0" [label="[]", style=solid]; -"39 Constant_2106" -> "4 initial_hidden_state/fq_output_0" [label="[]", style=solid]; -"40 Constant_2105" -> "4 initial_hidden_state/fq_output_0" [label="[]", style=solid]; -"41 Constant_2104" -> "4 initial_hidden_state/fq_output_0" [label="[]", style=solid]; -"42 Constant_2097" -> "3 X/fq_output_0" [label="[]", style=solid]; -"43 Constant_2096" -> "3 X/fq_output_0" [label="[]", style=solid]; -"44 Constant_2095" -> "3 X/fq_output_0" [label="[]", style=solid]; -"45 Constant_2094" -> "3 X/fq_output_0" [label="[]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/LinearModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/LinearModel.dot deleted file mode 100644 index 0812509444f..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/quantized/LinearModel.dot +++ /dev/null @@ -1,39 +0,0 @@ -strict digraph { -"0 Input" [id=0, type=Parameter]; -"1 Input/fq_output_0" [id=1, type=FakeQuantize]; -"2 Reshape" [id=2, type=Reshape]; -"3 Add" [id=3, type=Add]; -"4 MatMul" [id=4, type=MatMul]; -"5 Result_Add" [id=5, type=Result]; -"6 Result_MatMul" [id=6, type=Result]; -"7 Constant_10" [id=7, type=Constant]; -"8 Constant_1" [id=8, type=Constant]; -"9 Constant_2174" [id=9, type=Constant]; -"10 Constant_2173" [id=10, type=Constant]; -"11 Constant_2172" [id=11, type=Constant]; -"12 Constant_2171" [id=12, type=Constant]; -"13 MatMul/fq_weights_1" [id=13, type=FakeQuantize]; -"14 Constant_2179" [id=14, type=Constant]; -"15 Constant_2178" [id=15, type=Constant]; -"16 Constant_2177" [id=16, type=Constant]; -"17 Constant_2176" [id=17, type=Constant]; -"18 Constant_8" [id=18, type=Constant]; -"0 Input" -> "1 Input/fq_output_0" [label="[1, 3, 4, 2]", style=solid]; -"1 Input/fq_output_0" -> "2 Reshape" [label="[1, 3, 4, 2]", style=solid]; -"2 Reshape" -> "3 Add" [label="[1, 3, 2, 4]", style=solid]; -"2 Reshape" -> "4 MatMul" [label="[1, 3, 2, 4]", style=solid]; -"3 Add" -> "5 Result_Add" [label="[1, 3, 2, 4]", style=solid]; -"4 MatMul" -> "6 Result_MatMul" [label="[1, 3, 2, 5]", style=solid]; -"7 Constant_10" -> "3 Add" [label="[1, 3, 2, 4]", style=solid]; -"8 Constant_1" -> "2 Reshape" [label="[4]", style=dashed]; -"9 Constant_2174" -> "1 Input/fq_output_0" [label="[]", style=solid]; -"10 Constant_2173" -> "1 Input/fq_output_0" [label="[]", style=solid]; -"11 Constant_2172" -> "1 Input/fq_output_0" [label="[]", style=solid]; -"12 Constant_2171" -> "1 Input/fq_output_0" [label="[]", style=solid]; -"13 MatMul/fq_weights_1" -> "4 MatMul" [label="[1, 3, 4, 5]", style=solid]; -"14 Constant_2179" -> "13 MatMul/fq_weights_1" [label="[1, 1, 1, 1]", style=solid]; -"15 Constant_2178" -> "13 MatMul/fq_weights_1" [label="[1, 1, 1, 1]", style=solid]; -"16 Constant_2177" -> "13 MatMul/fq_weights_1" [label="[1, 1, 1, 1]", style=solid]; -"17 Constant_2176" -> "13 MatMul/fq_weights_1" [label="[1, 1, 1, 1]", style=solid]; -"18 Constant_8" -> "13 MatMul/fq_weights_1" [label="[1, 3, 4, 5]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/MatMul2DModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/MatMul2DModel.dot deleted file mode 100644 index d91b4017f69..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/quantized/MatMul2DModel.dot +++ /dev/null @@ -1,33 +0,0 @@ -strict digraph { -"0 Input" [id=0, type=Parameter]; -"1 Input/fq_output_0" [id=1, type=FakeQuantize]; -"2 MatMul" [id=2, type=MatMul]; -"3 Add" [id=3, type=Add]; -"4 Result" [id=4, type=Result]; -"5 Constant_4429" [id=5, type=Constant]; -"6 MatMul/fq_weights_1" [id=6, type=FakeQuantize]; -"7 Constant_6480" [id=7, type=Constant]; -"8 Constant_6479" [id=8, type=Constant]; -"9 Constant_6478" [id=9, type=Constant]; -"10 Constant_6477" [id=10, type=Constant]; -"11 Constant_4427" [id=11, type=Constant]; -"12 Constant_6475" [id=12, type=Constant]; -"13 Constant_6474" [id=13, type=Constant]; -"14 Constant_6473" [id=14, type=Constant]; -"15 Constant_6472" [id=15, type=Constant]; -"0 Input" -> "1 Input/fq_output_0" [label="[3, 5]", style=solid]; -"1 Input/fq_output_0" -> "2 MatMul" [label="[3, 5]", style=solid]; -"2 MatMul" -> "3 Add" [label="[3, 2]", style=solid]; -"3 Add" -> "4 Result" [label="[3, 2]", style=solid]; -"5 Constant_4429" -> "3 Add" [label="[1, 2]", style=solid]; -"6 MatMul/fq_weights_1" -> "2 MatMul" [label="[5, 2]", style=solid]; -"7 Constant_6480" -> "6 MatMul/fq_weights_1" [label="[5, 1]", style=solid]; -"8 Constant_6479" -> "6 MatMul/fq_weights_1" [label="[5, 1]", style=solid]; -"9 Constant_6478" -> "6 MatMul/fq_weights_1" [label="[5, 1]", style=solid]; -"10 Constant_6477" -> "6 MatMul/fq_weights_1" [label="[5, 1]", style=solid]; -"11 Constant_4427" -> "6 MatMul/fq_weights_1" [label="[5, 2]", style=solid]; -"12 Constant_6475" -> "1 Input/fq_output_0" [label="[]", style=solid]; -"13 Constant_6474" -> "1 Input/fq_output_0" [label="[]", style=solid]; -"14 Constant_6473" -> "1 Input/fq_output_0" [label="[]", style=solid]; -"15 Constant_6472" -> "1 Input/fq_output_0" [label="[]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/MatmulSoftmaxMatmulBlock.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/MatmulSoftmaxMatmulBlock.dot deleted file mode 100644 index 76e0ecd3ac6..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/quantized/MatmulSoftmaxMatmulBlock.dot +++ /dev/null @@ -1,38 +0,0 @@ -strict digraph { -"0 Input" [id=0, type=Parameter]; -"1 MatMul_1/fq_input_0" [id=1, type=FakeQuantize]; -"2 Squeeze_1" [id=2, type=Squeeze]; -"3 MatMul_1" [id=3, type=MatMul]; -"4 MatMul_2" [id=4, type=MatMul]; -"5 Softmax_1" [id=5, type=Softmax]; -"6 Result" [id=6, type=Result]; -"7 Constant_2" [id=7, type=Constant]; -"8 MatMul_1/fq_weights_1" [id=8, type=FakeQuantize]; -"9 Constant_2199" [id=9, type=Constant]; -"10 Constant_2198" [id=10, type=Constant]; -"11 Constant_2197" [id=11, type=Constant]; -"12 Constant_2196" [id=12, type=Constant]; -"13 Constant_4" [id=13, type=Constant]; -"14 Constant_2194" [id=14, type=Constant]; -"15 Constant_2193" [id=15, type=Constant]; -"16 Constant_2192" [id=16, type=Constant]; -"17 Constant_2191" [id=17, type=Constant]; -"0 Input" -> "1 MatMul_1/fq_input_0" [label="[1, 1, 1]", style=solid]; -"0 Input" -> "2 Squeeze_1" [label="[1, 1, 1]", style=solid]; -"1 MatMul_1/fq_input_0" -> "3 MatMul_1" [label="[1, 1, 1]", style=solid]; -"2 Squeeze_1" -> "4 MatMul_2" [label="[1, 1]", style=solid]; -"3 MatMul_1" -> "5 Softmax_1" [label="[1, 1, 1]", style=solid]; -"4 MatMul_2" -> "6 Result" [label="[1, 1, 1]", style=solid]; -"5 Softmax_1" -> "4 MatMul_2" [label="[1, 1, 1]", style=solid]; -"7 Constant_2" -> "2 Squeeze_1" [label="[]", style=dashed]; -"8 MatMul_1/fq_weights_1" -> "3 MatMul_1" [label="[1, 1]", style=solid]; -"9 Constant_2199" -> "8 MatMul_1/fq_weights_1" [label="[1, 1]", style=solid]; -"10 Constant_2198" -> "8 MatMul_1/fq_weights_1" [label="[1, 1]", style=solid]; -"11 Constant_2197" -> "8 MatMul_1/fq_weights_1" [label="[1, 1]", style=solid]; -"12 Constant_2196" -> "8 MatMul_1/fq_weights_1" [label="[1, 1]", style=solid]; -"13 Constant_4" -> "8 MatMul_1/fq_weights_1" [label="[1, 1]", style=solid]; -"14 Constant_2194" -> "1 MatMul_1/fq_input_0" [label="[]", style=solid]; -"15 Constant_2193" -> "1 MatMul_1/fq_input_0" [label="[]", style=solid]; -"16 Constant_2192" -> "1 MatMul_1/fq_input_0" [label="[]", style=solid]; -"17 Constant_2191" -> "1 MatMul_1/fq_input_0" [label="[]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/ScaleShiftReluModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/ScaleShiftReluModel.dot deleted file mode 100644 index 6044c82c814..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/quantized/ScaleShiftReluModel.dot +++ /dev/null @@ -1,73 +0,0 @@ -strict digraph { -"0 Input" [id=0, type=Parameter]; -"1 Input/fq_output_0" [id=1, type=FakeQuantize]; -"2 MatMul" [id=2, type=MatMul]; -"3 MatMul/fq_output_0" [id=3, type=FakeQuantize]; -"4 Mul" [id=4, type=Multiply]; -"5 Add" [id=5, type=Add]; -"6 Relu" [id=6, type=Relu]; -"7 Relu/fq_output_0" [id=7, type=FakeQuantize]; -"8 MatMul2" [id=8, type=MatMul]; -"9 Result" [id=9, type=Result]; -"10 MatMul2/fq_weights_1" [id=10, type=FakeQuantize]; -"11 Constant_2156" [id=11, type=Constant]; -"12 Constant_2155" [id=12, type=Constant]; -"13 Constant_2154" [id=13, type=Constant]; -"14 Constant_2153" [id=14, type=Constant]; -"15 Constant_8" [id=15, type=Constant]; -"16 Constant_2146" [id=16, type=Constant]; -"17 Constant_2145" [id=17, type=Constant]; -"18 Constant_2144" [id=18, type=Constant]; -"19 Constant_2143" [id=19, type=Constant]; -"20 Constant_5" [id=20, type=Constant]; -"21 Constant_3" [id=21, type=Constant]; -"22 Constant_2141" [id=22, type=Constant]; -"23 Constant_2140" [id=23, type=Constant]; -"24 Constant_2139" [id=24, type=Constant]; -"25 Constant_2138" [id=25, type=Constant]; -"26 MatMul/fq_weights_1" [id=26, type=FakeQuantize]; -"27 Constant_2151" [id=27, type=Constant]; -"28 Constant_2150" [id=28, type=Constant]; -"29 Constant_2149" [id=29, type=Constant]; -"30 Constant_2148" [id=30, type=Constant]; -"31 Constant_1" [id=31, type=Constant]; -"32 Constant_2136" [id=32, type=Constant]; -"33 Constant_2135" [id=33, type=Constant]; -"34 Constant_2134" [id=34, type=Constant]; -"35 Constant_2133" [id=35, type=Constant]; -"0 Input" -> "1 Input/fq_output_0" [label="[3, 5]", style=solid]; -"1 Input/fq_output_0" -> "2 MatMul" [label="[3, 5]", style=solid]; -"2 MatMul" -> "3 MatMul/fq_output_0" [label="[3, 2]", style=solid]; -"3 MatMul/fq_output_0" -> "4 Mul" [label="[3, 2]", style=solid]; -"4 Mul" -> "5 Add" [label="[3, 2]", style=solid]; -"5 Add" -> "6 Relu" [label="[3, 2]", style=solid]; -"6 Relu" -> "7 Relu/fq_output_0" [label="[3, 2]", style=solid]; -"7 Relu/fq_output_0" -> "8 MatMul2" [label="[3, 2]", style=solid]; -"8 MatMul2" -> "9 Result" [label="[3, 4]", style=solid]; -"10 MatMul2/fq_weights_1" -> "8 MatMul2" [label="[2, 4]", style=solid]; -"11 Constant_2156" -> "10 MatMul2/fq_weights_1" [label="[2, 1]", style=solid]; -"12 Constant_2155" -> "10 MatMul2/fq_weights_1" [label="[2, 1]", style=solid]; -"13 Constant_2154" -> "10 MatMul2/fq_weights_1" [label="[2, 1]", style=solid]; -"14 Constant_2153" -> "10 MatMul2/fq_weights_1" [label="[2, 1]", style=solid]; -"15 Constant_8" -> "10 MatMul2/fq_weights_1" [label="[2, 4]", style=solid]; -"16 Constant_2146" -> "7 Relu/fq_output_0" [label="[]", style=solid]; -"17 Constant_2145" -> "7 Relu/fq_output_0" [label="[]", style=solid]; -"18 Constant_2144" -> "7 Relu/fq_output_0" [label="[]", style=solid]; -"19 Constant_2143" -> "7 Relu/fq_output_0" [label="[]", style=solid]; -"20 Constant_5" -> "5 Add" [label="[1, 2]", style=solid]; -"21 Constant_3" -> "4 Mul" [label="[1, 2]", style=solid]; -"22 Constant_2141" -> "3 MatMul/fq_output_0" [label="[]", style=solid]; -"23 Constant_2140" -> "3 MatMul/fq_output_0" [label="[]", style=solid]; -"24 Constant_2139" -> "3 MatMul/fq_output_0" [label="[]", style=solid]; -"25 Constant_2138" -> "3 MatMul/fq_output_0" [label="[]", style=solid]; -"26 MatMul/fq_weights_1" -> "2 MatMul" [label="[5, 2]", style=solid]; -"27 Constant_2151" -> "26 MatMul/fq_weights_1" [label="[5, 1]", style=solid]; -"28 Constant_2150" -> "26 MatMul/fq_weights_1" [label="[5, 1]", style=solid]; -"29 Constant_2149" -> "26 MatMul/fq_weights_1" [label="[5, 1]", style=solid]; -"30 Constant_2148" -> "26 MatMul/fq_weights_1" [label="[5, 1]", style=solid]; -"31 Constant_1" -> "26 MatMul/fq_weights_1" [label="[5, 2]", style=solid]; -"32 Constant_2136" -> "1 Input/fq_output_0" [label="[]", style=solid]; -"33 Constant_2135" -> "1 Input/fq_output_0" [label="[]", style=solid]; -"34 Constant_2134" -> "1 Input/fq_output_0" [label="[]", style=solid]; -"35 Constant_2133" -> "1 Input/fq_output_0" [label="[]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/SeBlockModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/SeBlockModel.dot deleted file mode 100644 index 2553a125010..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/quantized/SeBlockModel.dot +++ /dev/null @@ -1,104 +0,0 @@ -strict digraph { -"0 Input" [id=0, type=Parameter]; -"1 Input/fq_output_0" [id=1, type=FakeQuantize]; -"2 Conv0" [id=2, type=Convolution]; -"3 Add0" [id=3, type=Add]; -"4 Add0/fq_output_0" [id=4, type=FakeQuantize]; -"5 Mul" [id=5, type=Multiply]; -"6 ReduceMean_31402" [id=6, type=ReduceMean]; -"7 Mul/fq_output_0" [id=7, type=FakeQuantize]; -"8 Conv" [id=8, type=Convolution]; -"9 MatMul" [id=9, type=MatMul]; -"10 Add" [id=10, type=Add]; -"11 Result" [id=11, type=Result]; -"12 Relu" [id=12, type=Relu]; -"13 Conv2" [id=13, type=Convolution]; -"14 Add2" [id=14, type=Add]; -"15 Sigmoid" [id=15, type=Sigmoid]; -"16 Sigmoid/fq_output_0" [id=16, type=FakeQuantize]; -"17 MatMul/fq_weights_1" [id=17, type=FakeQuantize]; -"18 Constant_33914" [id=18, type=Constant]; -"19 Constant_33913" [id=19, type=Constant]; -"20 Constant_33912" [id=20, type=Constant]; -"21 Constant_33911" [id=21, type=Constant]; -"22 Constant_31414" [id=22, type=Constant]; -"23 Constant_33909" [id=23, type=Constant]; -"24 Constant_33908" [id=24, type=Constant]; -"25 Constant_33907" [id=25, type=Constant]; -"26 Constant_33906" [id=26, type=Constant]; -"27 Constant_33904" [id=27, type=Constant]; -"28 Constant_33903" [id=28, type=Constant]; -"29 Constant_33902" [id=29, type=Constant]; -"30 Constant_33901" [id=30, type=Constant]; -"31 Constant_31410" [id=31, type=Constant]; -"32 Constant_31408" [id=32, type=Constant]; -"33 Constant_31405" [id=33, type=Constant]; -"34 Constant_31403" [id=34, type=Constant]; -"35 Constant_31401" [id=35, type=Constant]; -"36 Constant_33899" [id=36, type=Constant]; -"37 Constant_33898" [id=37, type=Constant]; -"38 Constant_33897" [id=38, type=Constant]; -"39 Constant_33896" [id=39, type=Constant]; -"40 Constant_31399" [id=40, type=Constant]; -"41 Conv0/fq_weights_1" [id=41, type=FakeQuantize]; -"42 Constant_33894" [id=42, type=Constant]; -"43 Constant_33893" [id=43, type=Constant]; -"44 Constant_33892" [id=44, type=Constant]; -"45 Constant_33891" [id=45, type=Constant]; -"46 Constant_31397" [id=46, type=Constant]; -"47 Constant_33889" [id=47, type=Constant]; -"48 Constant_33888" [id=48, type=Constant]; -"49 Constant_33887" [id=49, type=Constant]; -"50 Constant_33886" [id=50, type=Constant]; -"0 Input" -> "1 Input/fq_output_0" [label="[1, 3, 5, 6]", style=solid]; -"1 Input/fq_output_0" -> "2 Conv0" [label="[1, 3, 5, 6]", style=solid]; -"2 Conv0" -> "3 Add0" [label="[1, 3, 5, 6]", style=solid]; -"3 Add0" -> "4 Add0/fq_output_0" [label="[1, 3, 5, 6]", style=solid]; -"4 Add0/fq_output_0" -> "5 Mul" [label="[1, 3, 5, 6]", style=solid]; -"4 Add0/fq_output_0" -> "6 ReduceMean_31402" [label="[1, 3, 5, 6]", style=solid]; -"5 Mul" -> "7 Mul/fq_output_0" [label="[1, 3, 5, 6]", style=solid]; -"6 ReduceMean_31402" -> "8 Conv" [label="[1, 3, 1, 1]", style=solid]; -"7 Mul/fq_output_0" -> "9 MatMul" [label="[1, 3, 5, 6]", style=solid]; -"8 Conv" -> "10 Add" [label="[1, 5, 1, 1]", style=solid]; -"9 MatMul" -> "11 Result" [label="[1, 3, 5, 5]", style=solid]; -"10 Add" -> "12 Relu" [label="[1, 5, 1, 1]", style=solid]; -"12 Relu" -> "13 Conv2" [label="[1, 5, 1, 1]", style=solid]; -"13 Conv2" -> "14 Add2" [label="[1, 3, 1, 1]", style=solid]; -"14 Add2" -> "15 Sigmoid" [label="[1, 3, 1, 1]", style=solid]; -"15 Sigmoid" -> "16 Sigmoid/fq_output_0" [label="[1, 3, 1, 1]", style=solid]; -"16 Sigmoid/fq_output_0" -> "5 Mul" [label="[1, 3, 1, 1]", style=solid]; -"17 MatMul/fq_weights_1" -> "9 MatMul" [label="[6, 5]", style=solid]; -"18 Constant_33914" -> "17 MatMul/fq_weights_1" [label="[1, 5]", style=solid]; -"19 Constant_33913" -> "17 MatMul/fq_weights_1" [label="[1, 5]", style=solid]; -"20 Constant_33912" -> "17 MatMul/fq_weights_1" [label="[1, 5]", style=solid]; -"21 Constant_33911" -> "17 MatMul/fq_weights_1" [label="[1, 5]", style=solid]; -"22 Constant_31414" -> "17 MatMul/fq_weights_1" [label="[6, 5]", style=solid]; -"23 Constant_33909" -> "7 Mul/fq_output_0" [label="[]", style=solid]; -"24 Constant_33908" -> "7 Mul/fq_output_0" [label="[]", style=solid]; -"25 Constant_33907" -> "7 Mul/fq_output_0" [label="[]", style=solid]; -"26 Constant_33906" -> "7 Mul/fq_output_0" [label="[]", style=solid]; -"27 Constant_33904" -> "16 Sigmoid/fq_output_0" [label="[]", style=solid]; -"28 Constant_33903" -> "16 Sigmoid/fq_output_0" [label="[]", style=solid]; -"29 Constant_33902" -> "16 Sigmoid/fq_output_0" [label="[]", style=solid]; -"30 Constant_33901" -> "16 Sigmoid/fq_output_0" [label="[]", style=solid]; -"31 Constant_31410" -> "14 Add2" [label="[1, 3, 1, 1]", style=solid]; -"32 Constant_31408" -> "13 Conv2" [label="[3, 5, 1, 1]", style=solid]; -"33 Constant_31405" -> "10 Add" [label="[1, 5, 1, 1]", style=solid]; -"34 Constant_31403" -> "8 Conv" [label="[5, 3, 1, 1]", style=solid]; -"35 Constant_31401" -> "6 ReduceMean_31402" [label="[2]", style=dashed]; -"36 Constant_33899" -> "4 Add0/fq_output_0" [label="[]", style=solid]; -"37 Constant_33898" -> "4 Add0/fq_output_0" [label="[]", style=solid]; -"38 Constant_33897" -> "4 Add0/fq_output_0" [label="[]", style=solid]; -"39 Constant_33896" -> "4 Add0/fq_output_0" [label="[]", style=solid]; -"40 Constant_31399" -> "3 Add0" [label="[1, 3, 1, 1]", style=solid]; -"41 Conv0/fq_weights_1" -> "2 Conv0" [label="[3, 3, 1, 1]", style=solid]; -"42 Constant_33894" -> "41 Conv0/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; -"43 Constant_33893" -> "41 Conv0/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; -"44 Constant_33892" -> "41 Conv0/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; -"45 Constant_33891" -> "41 Conv0/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; -"46 Constant_31397" -> "41 Conv0/fq_weights_1" [label="[3, 3, 1, 1]", style=solid]; -"47 Constant_33889" -> "1 Input/fq_output_0" [label="[]", style=solid]; -"48 Constant_33888" -> "1 Input/fq_output_0" [label="[]", style=solid]; -"49 Constant_33887" -> "1 Input/fq_output_0" [label="[]", style=solid]; -"50 Constant_33886" -> "1 Input/fq_output_0" [label="[]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/ShapeOfModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/ShapeOfModel.dot deleted file mode 100644 index 036af485468..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/quantized/ShapeOfModel.dot +++ /dev/null @@ -1,123 +0,0 @@ -strict digraph { -"0 Input" [id=0, type=Parameter]; -"1 Input/fq_output_0" [id=1, type=FakeQuantize]; -"2 Conv_1" [id=2, type=Convolution]; -"3 Conv_Add_1" [id=3, type=Add]; -"4 Conv_Add_1/fq_output_0" [id=4, type=FakeQuantize]; -"5 Reshape_1" [id=5, type=Reshape]; -"6 ShapeOf_1" [id=6, type=ShapeOf]; -"7 Transpose" [id=7, type=Transpose]; -"8 Gather_96538" [id=8, type=Gather]; -"9 Conv_2" [id=9, type=Convolution]; -"10 Concat_96541" [id=10, type=Concat]; -"11 Conv_Add_2" [id=11, type=Add]; -"12 Conv_Add_2/fq_output_0" [id=12, type=FakeQuantize]; -"13 Reshape_2" [id=13, type=Reshape]; -"14 ShapeOf_2" [id=14, type=ShapeOf]; -"15 Conv_3" [id=15, type=Convolution]; -"16 Convert_1" [id=16, type=Convert]; -"17 Conv_Add_3" [id=17, type=Add]; -"18 Multiply" [id=18, type=Multiply]; -"19 Result" [id=19, type=Result]; -"20 Convert_2" [id=20, type=Convert]; -"21 Bias_3" [id=21, type=Constant]; -"22 Conv_3/fq_weights_1" [id=22, type=FakeQuantize]; -"23 Constant_98766" [id=23, type=Constant]; -"24 Constant_98765" [id=24, type=Constant]; -"25 Constant_98764" [id=25, type=Constant]; -"26 Constant_98763" [id=26, type=Constant]; -"27 Constant_96567" [id=27, type=Constant]; -"28 Constant_96553" [id=28, type=Constant]; -"29 Constant_98746" [id=29, type=Constant]; -"30 Constant_98745" [id=30, type=Constant]; -"31 Constant_98744" [id=31, type=Constant]; -"32 Constant_98743" [id=32, type=Constant]; -"33 Bias_2" [id=33, type=Constant]; -"34 Conv_2/fq_weights_1" [id=34, type=FakeQuantize]; -"35 Constant_98761" [id=35, type=Constant]; -"36 Constant_98760" [id=36, type=Constant]; -"37 Constant_98759" [id=37, type=Constant]; -"38 Constant_98758" [id=38, type=Constant]; -"39 Constant_96547" [id=39, type=Constant]; -"40 Constant_96545" [id=40, type=Constant]; -"41 Constant_96537" [id=41, type=Constant]; -"42 Constant_96536" [id=42, type=Constant]; -"43 Constant_98741" [id=43, type=Constant]; -"44 Constant_98740" [id=44, type=Constant]; -"45 Constant_98739" [id=45, type=Constant]; -"46 Constant_98738" [id=46, type=Constant]; -"47 Bias_1" [id=47, type=Constant]; -"48 Conv_1/fq_weights_1" [id=48, type=FakeQuantize]; -"49 Constant_98756" [id=49, type=Constant]; -"50 Constant_98755" [id=50, type=Constant]; -"51 Constant_98754" [id=51, type=Constant]; -"52 Constant_98753" [id=52, type=Constant]; -"53 Constant_96531" [id=53, type=Constant]; -"54 Constant_98751" [id=54, type=Constant]; -"55 Constant_98750" [id=55, type=Constant]; -"56 Constant_98749" [id=56, type=Constant]; -"57 Constant_98748" [id=57, type=Constant]; -"58 Constant_96540" [id=58, type=Constant]; -"59 Constant_96539" [id=59, type=Constant]; -"0 Input" -> "1 Input/fq_output_0" [label="[1, 3, 4, 2]", style=solid]; -"1 Input/fq_output_0" -> "2 Conv_1" [label="[1, 3, 4, 2]", style=solid]; -"2 Conv_1" -> "3 Conv_Add_1" [label="[1, 3, 4, 2]", style=solid]; -"3 Conv_Add_1" -> "4 Conv_Add_1/fq_output_0" [label="[1, 3, 4, 2]", style=solid]; -"4 Conv_Add_1/fq_output_0" -> "5 Reshape_1" [label="[1, 3, 4, 2]", style=solid]; -"4 Conv_Add_1/fq_output_0" -> "6 ShapeOf_1" [label="[1, 3, 4, 2]", style=solid]; -"5 Reshape_1" -> "7 Transpose" [label="[1, 3, 4, 2]", style=solid]; -"6 ShapeOf_1" -> "8 Gather_96538" [label="[4]", style=dashed]; -"7 Transpose" -> "9 Conv_2" [label="[1, 3, 2, 4]", style=solid]; -"8 Gather_96538" -> "10 Concat_96541" [label="[2]", style=dashed]; -"9 Conv_2" -> "11 Conv_Add_2" [label="[1, 3, 2, 4]", style=solid]; -"10 Concat_96541" -> "5 Reshape_1" [label="[4]", style=dashed]; -"11 Conv_Add_2" -> "12 Conv_Add_2/fq_output_0" [label="[1, 3, 2, 4]", style=solid]; -"12 Conv_Add_2/fq_output_0" -> "13 Reshape_2" [label="[1, 3, 2, 4]", style=solid]; -"12 Conv_Add_2/fq_output_0" -> "14 ShapeOf_2" [label="[1, 3, 2, 4]", style=solid]; -"13 Reshape_2" -> "15 Conv_3" [label="[1, 3, 2, 4]", style=solid]; -"14 ShapeOf_2" -> "16 Convert_1" [label="[4]", style=dashed]; -"15 Conv_3" -> "17 Conv_Add_3" [label="[1, 3, 2, 4]", style=solid]; -"16 Convert_1" -> "18 Multiply" [label="[4]", style=solid]; -"17 Conv_Add_3" -> "19 Result" [label="[1, 3, 2, 4]", style=solid]; -"18 Multiply" -> "20 Convert_2" [label="[4]", style=solid]; -"20 Convert_2" -> "13 Reshape_2" [label="[4]", style=dashed]; -"21 Bias_3" -> "17 Conv_Add_3" [label="[1, 3, 1, 1]", style=solid]; -"22 Conv_3/fq_weights_1" -> "15 Conv_3" [label="[3, 3, 1, 1]", style=solid]; -"23 Constant_98766" -> "22 Conv_3/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; -"24 Constant_98765" -> "22 Conv_3/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; -"25 Constant_98764" -> "22 Conv_3/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; -"26 Constant_98763" -> "22 Conv_3/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; -"27 Constant_96567" -> "22 Conv_3/fq_weights_1" [label="[3, 3, 1, 1]", style=solid]; -"28 Constant_96553" -> "18 Multiply" [label="[4]", style=solid]; -"29 Constant_98746" -> "12 Conv_Add_2/fq_output_0" [label="[]", style=solid]; -"30 Constant_98745" -> "12 Conv_Add_2/fq_output_0" [label="[]", style=solid]; -"31 Constant_98744" -> "12 Conv_Add_2/fq_output_0" [label="[]", style=solid]; -"32 Constant_98743" -> "12 Conv_Add_2/fq_output_0" [label="[]", style=solid]; -"33 Bias_2" -> "11 Conv_Add_2" [label="[1, 3, 1, 1]", style=solid]; -"34 Conv_2/fq_weights_1" -> "9 Conv_2" [label="[3, 3, 1, 1]", style=solid]; -"35 Constant_98761" -> "34 Conv_2/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; -"36 Constant_98760" -> "34 Conv_2/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; -"37 Constant_98759" -> "34 Conv_2/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; -"38 Constant_98758" -> "34 Conv_2/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; -"39 Constant_96547" -> "34 Conv_2/fq_weights_1" [label="[3, 3, 1, 1]", style=solid]; -"40 Constant_96545" -> "7 Transpose" [label="[4]", style=dashed]; -"41 Constant_96537" -> "8 Gather_96538" [label="[]", style=dashed]; -"42 Constant_96536" -> "8 Gather_96538" [label="[2]", style=dashed]; -"43 Constant_98741" -> "4 Conv_Add_1/fq_output_0" [label="[]", style=solid]; -"44 Constant_98740" -> "4 Conv_Add_1/fq_output_0" [label="[]", style=solid]; -"45 Constant_98739" -> "4 Conv_Add_1/fq_output_0" [label="[]", style=solid]; -"46 Constant_98738" -> "4 Conv_Add_1/fq_output_0" [label="[]", style=solid]; -"47 Bias_1" -> "3 Conv_Add_1" [label="[1, 3, 1, 1]", style=solid]; -"48 Conv_1/fq_weights_1" -> "2 Conv_1" [label="[3, 3, 1, 1]", style=solid]; -"49 Constant_98756" -> "48 Conv_1/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; -"50 Constant_98755" -> "48 Conv_1/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; -"51 Constant_98754" -> "48 Conv_1/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; -"52 Constant_98753" -> "48 Conv_1/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; -"53 Constant_96531" -> "48 Conv_1/fq_weights_1" [label="[3, 3, 1, 1]", style=solid]; -"54 Constant_98751" -> "1 Input/fq_output_0" [label="[]", style=solid]; -"55 Constant_98750" -> "1 Input/fq_output_0" [label="[]", style=solid]; -"56 Constant_98749" -> "1 Input/fq_output_0" [label="[]", style=solid]; -"57 Constant_98748" -> "1 Input/fq_output_0" [label="[]", style=solid]; -"58 Constant_96540" -> "10 Concat_96541" [label="[1]", style=dashed]; -"59 Constant_96539" -> "10 Concat_96541" [label="[1]", style=dashed]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/SharedConvModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/SharedConvModel.dot deleted file mode 100644 index 7d5cf4429e2..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/quantized/SharedConvModel.dot +++ /dev/null @@ -1,34 +0,0 @@ -strict digraph { -"0 Input" [id=0, type=Parameter]; -"1 Input/fq_output_0" [id=1, type=FakeQuantize]; -"2 Conv_1" [id=2, type=Convolution]; -"3 Conv_2" [id=3, type=Convolution]; -"4 Result_1" [id=4, type=Result]; -"5 Result_2" [id=5, type=Result]; -"6 Conv_1/fq_weights_1" [id=6, type=FakeQuantize]; -"7 Constant_2070" [id=7, type=Constant]; -"8 Constant_2069" [id=8, type=Constant]; -"9 Constant_2068" [id=9, type=Constant]; -"10 Constant_2067" [id=10, type=Constant]; -"11 Shared_conv_w" [id=11, type=Constant]; -"12 Constant_2065" [id=12, type=Constant]; -"13 Constant_2064" [id=13, type=Constant]; -"14 Constant_2063" [id=14, type=Constant]; -"15 Constant_2062" [id=15, type=Constant]; -"0 Input" -> "1 Input/fq_output_0" [label="[1, 3, 3, 3]", style=solid]; -"1 Input/fq_output_0" -> "2 Conv_1" [label="[1, 3, 3, 3]", style=solid]; -"1 Input/fq_output_0" -> "3 Conv_2" [label="[1, 3, 3, 3]", style=solid]; -"2 Conv_1" -> "4 Result_1" [label="[1, 3, 3, 3]", style=solid]; -"3 Conv_2" -> "5 Result_2" [label="[1, 3, 3, 3]", style=solid]; -"6 Conv_1/fq_weights_1" -> "2 Conv_1" [label="[3, 3, 1, 1]", style=solid]; -"6 Conv_1/fq_weights_1" -> "3 Conv_2" [label="[3, 3, 1, 1]", style=solid]; -"7 Constant_2070" -> "6 Conv_1/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; -"8 Constant_2069" -> "6 Conv_1/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; -"9 Constant_2068" -> "6 Conv_1/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; -"10 Constant_2067" -> "6 Conv_1/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; -"11 Shared_conv_w" -> "6 Conv_1/fq_weights_1" [label="[3, 3, 1, 1]", style=solid]; -"12 Constant_2065" -> "1 Input/fq_output_0" [label="[]", style=solid]; -"13 Constant_2064" -> "1 Input/fq_output_0" [label="[]", style=solid]; -"14 Constant_2063" -> "1 Input/fq_output_0" [label="[]", style=solid]; -"15 Constant_2062" -> "1 Input/fq_output_0" [label="[]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/UnifiedEmbeddingModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/UnifiedEmbeddingModel.dot deleted file mode 100644 index 24cd2d4545e..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/quantized/UnifiedEmbeddingModel.dot +++ /dev/null @@ -1,78 +0,0 @@ -strict digraph { -"0 Input" [id=0, type=Parameter]; -"1 Convert_1" [id=1, type=Convert]; -"2 MatMul_1/fq_input_0" [id=2, type=FakeQuantize]; -"3 Gather_1" [id=3, type=Gather]; -"4 MatMul_1" [id=4, type=MatMul]; -"5 Concat_12" [id=5, type=Concat]; -"6 MatMul_1/fq_output_0" [id=6, type=FakeQuantize]; -"7 MatMul_2" [id=7, type=MatMul]; -"8 Reshape_1" [id=8, type=Reshape]; -"9 Result" [id=9, type=Result]; -"10 MatMul_2/fq_weights_1" [id=10, type=FakeQuantize]; -"11 Constant_2323" [id=11, type=Constant]; -"12 Constant_2322" [id=12, type=Constant]; -"13 Constant_2321" [id=13, type=Constant]; -"14 Constant_2320" [id=14, type=Constant]; -"15 matmul_2_data" [id=15, type=Constant]; -"16 Constant_8" [id=16, type=Constant]; -"17 Constant_2303" [id=17, type=Constant]; -"18 Constant_2302" [id=18, type=Constant]; -"19 Constant_2301" [id=19, type=Constant]; -"20 Constant_2300" [id=20, type=Constant]; -"21 MatMul_1/fq_weights_1" [id=21, type=FakeQuantize]; -"22 Constant_2318" [id=22, type=Constant]; -"23 Constant_2317" [id=23, type=Constant]; -"24 Constant_2316" [id=24, type=Constant]; -"25 Constant_2315" [id=25, type=Constant]; -"26 matmul_1_data" [id=26, type=Constant]; -"27 Constant_2313" [id=27, type=Constant]; -"28 Constant_2312" [id=28, type=Constant]; -"29 Constant_2311" [id=29, type=Constant]; -"30 Constant_2310" [id=30, type=Constant]; -"31 Constant_4" [id=31, type=Constant]; -"32 Gather_1/fq_weights_0" [id=32, type=FakeQuantize]; -"33 Constant_2308" [id=33, type=Constant]; -"34 Constant_2307" [id=34, type=Constant]; -"35 Constant_2306" [id=35, type=Constant]; -"36 Constant_2305" [id=36, type=Constant]; -"37 gather_1_data" [id=37, type=Constant]; -"0 Input" -> "1 Convert_1" [label="[1, 3]", style=solid]; -"0 Input" -> "2 MatMul_1/fq_input_0" [label="[1, 3]", style=solid]; -"1 Convert_1" -> "3 Gather_1" [label="[1, 3]", style=dashed]; -"2 MatMul_1/fq_input_0" -> "4 MatMul_1" [label="[1, 3]", style=solid]; -"3 Gather_1" -> "5 Concat_12" [label="[1, 3, 5]", style=solid]; -"4 MatMul_1" -> "6 MatMul_1/fq_output_0" [label="[3, 1, 5]", style=solid]; -"5 Concat_12" -> "7 MatMul_2" [label="[1, 6, 5]", style=solid]; -"6 MatMul_1/fq_output_0" -> "8 Reshape_1" [label="[3, 1, 5]", style=solid]; -"7 MatMul_2" -> "9 Result" [label="[1, 6, 1]", style=solid]; -"8 Reshape_1" -> "5 Concat_12" [label="[1, 3, 5]", style=solid]; -"10 MatMul_2/fq_weights_1" -> "7 MatMul_2" [label="[1, 5]", style=solid]; -"11 Constant_2323" -> "10 MatMul_2/fq_weights_1" [label="[1, 1]", style=solid]; -"12 Constant_2322" -> "10 MatMul_2/fq_weights_1" [label="[1, 1]", style=solid]; -"13 Constant_2321" -> "10 MatMul_2/fq_weights_1" [label="[1, 1]", style=solid]; -"14 Constant_2320" -> "10 MatMul_2/fq_weights_1" [label="[1, 1]", style=solid]; -"15 matmul_2_data" -> "10 MatMul_2/fq_weights_1" [label="[1, 5]", style=solid]; -"16 Constant_8" -> "8 Reshape_1" [label="[3]", style=dashed]; -"17 Constant_2303" -> "6 MatMul_1/fq_output_0" [label="[]", style=solid]; -"18 Constant_2302" -> "6 MatMul_1/fq_output_0" [label="[]", style=solid]; -"19 Constant_2301" -> "6 MatMul_1/fq_output_0" [label="[]", style=solid]; -"20 Constant_2300" -> "6 MatMul_1/fq_output_0" [label="[]", style=solid]; -"21 MatMul_1/fq_weights_1" -> "4 MatMul_1" [label="[3, 3, 5]", style=solid]; -"22 Constant_2318" -> "21 MatMul_1/fq_weights_1" [label="[3, 1, 5]", style=solid]; -"23 Constant_2317" -> "21 MatMul_1/fq_weights_1" [label="[3, 1, 5]", style=solid]; -"24 Constant_2316" -> "21 MatMul_1/fq_weights_1" [label="[3, 1, 5]", style=solid]; -"25 Constant_2315" -> "21 MatMul_1/fq_weights_1" [label="[3, 1, 5]", style=solid]; -"26 matmul_1_data" -> "21 MatMul_1/fq_weights_1" [label="[3, 3, 5]", style=solid]; -"27 Constant_2313" -> "2 MatMul_1/fq_input_0" [label="[]", style=solid]; -"28 Constant_2312" -> "2 MatMul_1/fq_input_0" [label="[]", style=solid]; -"29 Constant_2311" -> "2 MatMul_1/fq_input_0" [label="[]", style=solid]; -"30 Constant_2310" -> "2 MatMul_1/fq_input_0" [label="[]", style=solid]; -"31 Constant_4" -> "3 Gather_1" [label="[]", style=dashed]; -"32 Gather_1/fq_weights_0" -> "3 Gather_1" [label="[4, 5]", style=solid]; -"33 Constant_2308" -> "32 Gather_1/fq_weights_0" [label="[]", style=solid]; -"34 Constant_2307" -> "32 Gather_1/fq_weights_0" [label="[]", style=solid]; -"35 Constant_2306" -> "32 Gather_1/fq_weights_0" [label="[]", style=solid]; -"36 Constant_2305" -> "32 Gather_1/fq_weights_0" [label="[]", style=solid]; -"37 gather_1_data" -> "32 Gather_1/fq_weights_0" [label="[4, 5]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/WeightsModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/WeightsModel.dot deleted file mode 100644 index c61a2ce4397..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/quantized/WeightsModel.dot +++ /dev/null @@ -1,122 +0,0 @@ -strict digraph { -"0 Input_1" [id=0, type=Parameter]; -"1 Input_1/fq_output_0" [id=1, type=FakeQuantize]; -"2 Conv" [id=2, type=Convolution]; -"3 Conv/fq_output_0" [id=3, type=FakeQuantize]; -"4 Conv_backprop" [id=4, type=ConvolutionBackpropData]; -"5 Conv_backprop/fq_output_0" [id=5, type=FakeQuantize]; -"6 MatMul_1" [id=6, type=MatMul]; -"7 MatMul_1/fq_output_0" [id=7, type=FakeQuantize]; -"8 MatMul" [id=8, type=MatMul]; -"9 MatMul_0" [id=9, type=MatMul]; -"10 Add_15" [id=10, type=Add]; -"11 MatMul_0/fq_output_0" [id=11, type=FakeQuantize]; -"12 Result" [id=12, type=Result]; -"13 Constant_2959" [id=13, type=Constant]; -"14 Constant_2958" [id=14, type=Constant]; -"15 Constant_2957" [id=15, type=Constant]; -"16 Constant_2956" [id=16, type=Constant]; -"17 MatMul_1/fq_weights_1" [id=17, type=FakeQuantize]; -"18 Constant_2964" [id=18, type=Constant]; -"19 Constant_2963" [id=19, type=Constant]; -"20 Constant_2962" [id=20, type=Constant]; -"21 Constant_2961" [id=21, type=Constant]; -"22 weights_1" [id=22, type=Constant]; -"23 Constant_2949" [id=23, type=Constant]; -"24 Constant_2948" [id=24, type=Constant]; -"25 Constant_2947" [id=25, type=Constant]; -"26 Constant_2946" [id=26, type=Constant]; -"27 Constant_5" [id=27, type=Constant]; -"28 Conv_backprop/fq_weights_1" [id=28, type=FakeQuantize]; -"29 Constant_2954" [id=29, type=Constant]; -"30 Constant_2953" [id=30, type=Constant]; -"31 Constant_2952" [id=31, type=Constant]; -"32 Constant_2951" [id=32, type=Constant]; -"33 Constant_4" [id=33, type=Constant]; -"34 Constant_2939" [id=34, type=Constant]; -"35 Constant_2938" [id=35, type=Constant]; -"36 Constant_2937" [id=36, type=Constant]; -"37 Constant_2936" [id=37, type=Constant]; -"38 Conv/fq_weights_1" [id=38, type=FakeQuantize]; -"39 Constant_2944" [id=39, type=Constant]; -"40 Constant_2943" [id=40, type=Constant]; -"41 Constant_2942" [id=41, type=Constant]; -"42 Constant_2941" [id=42, type=Constant]; -"43 Constant_2" [id=43, type=Constant]; -"44 Constant_2934" [id=44, type=Constant]; -"45 Constant_2933" [id=45, type=Constant]; -"46 Constant_2932" [id=46, type=Constant]; -"47 Constant_2931" [id=47, type=Constant]; -"48 Constant_2969" [id=48, type=Constant]; -"49 Constant_2968" [id=49, type=Constant]; -"50 Constant_2967" [id=50, type=Constant]; -"51 Constant_2966" [id=51, type=Constant]; -"52 MatMul_0/fq_weights_0" [id=52, type=FakeQuantize]; -"53 Constant_2974" [id=53, type=Constant]; -"54 Constant_2973" [id=54, type=Constant]; -"55 Constant_2972" [id=55, type=Constant]; -"56 Constant_2971" [id=56, type=Constant]; -"57 weights_0" [id=57, type=Constant]; -"58 MatMul_const" [id=58, type=MatMul]; -"0 Input_1" -> "1 Input_1/fq_output_0" [label="[1, 3, 5, 5]", style=solid]; -"1 Input_1/fq_output_0" -> "2 Conv" [label="[1, 3, 5, 5]", style=solid]; -"2 Conv" -> "3 Conv/fq_output_0" [label="[1, 3, 5, 5]", style=solid]; -"3 Conv/fq_output_0" -> "4 Conv_backprop" [label="[1, 3, 5, 5]", style=solid]; -"4 Conv_backprop" -> "5 Conv_backprop/fq_output_0" [label="[1, 3, 1, 1]", style=solid]; -"5 Conv_backprop/fq_output_0" -> "6 MatMul_1" [label="[1, 3, 1, 1]", style=solid]; -"6 MatMul_1" -> "7 MatMul_1/fq_output_0" [label="[1, 3, 1, 4]", style=solid]; -"7 MatMul_1/fq_output_0" -> "8 MatMul" [label="[1, 3, 1, 4]", style=solid]; -"7 MatMul_1/fq_output_0" -> "9 MatMul_0" [label="[1, 3, 1, 4]", style=solid]; -"8 MatMul" -> "10 Add_15" [label="[1, 3, 1, 1]", style=solid]; -"9 MatMul_0" -> "11 MatMul_0/fq_output_0" [label="[1, 3, 1, 4]", style=solid]; -"10 Add_15" -> "12 Result" [label="[1, 3, 4, 1]", style=solid]; -"11 MatMul_0/fq_output_0" -> "8 MatMul" [label="[1, 3, 1, 4]", style=solid]; -"13 Constant_2959" -> "7 MatMul_1/fq_output_0" [label="[]", style=solid]; -"14 Constant_2958" -> "7 MatMul_1/fq_output_0" [label="[]", style=solid]; -"15 Constant_2957" -> "7 MatMul_1/fq_output_0" [label="[]", style=solid]; -"16 Constant_2956" -> "7 MatMul_1/fq_output_0" [label="[]", style=solid]; -"17 MatMul_1/fq_weights_1" -> "6 MatMul_1" [label="[1, 4]", style=solid]; -"18 Constant_2964" -> "17 MatMul_1/fq_weights_1" [label="[1, 4]", style=solid]; -"19 Constant_2963" -> "17 MatMul_1/fq_weights_1" [label="[1, 4]", style=solid]; -"20 Constant_2962" -> "17 MatMul_1/fq_weights_1" [label="[1, 4]", style=solid]; -"21 Constant_2961" -> "17 MatMul_1/fq_weights_1" [label="[1, 4]", style=solid]; -"22 weights_1" -> "17 MatMul_1/fq_weights_1" [label="[1, 4]", style=solid]; -"22 weights_1" -> "58 MatMul_const" [label="[1, 4]", style=solid]; -"23 Constant_2949" -> "5 Conv_backprop/fq_output_0" [label="[]", style=solid]; -"24 Constant_2948" -> "5 Conv_backprop/fq_output_0" [label="[]", style=solid]; -"25 Constant_2947" -> "5 Conv_backprop/fq_output_0" [label="[]", style=solid]; -"26 Constant_2946" -> "5 Conv_backprop/fq_output_0" [label="[]", style=solid]; -"27 Constant_5" -> "4 Conv_backprop" [label="[2]", style=dashed]; -"28 Conv_backprop/fq_weights_1" -> "4 Conv_backprop" [label="[3, 3, 1, 1]", style=solid]; -"29 Constant_2954" -> "28 Conv_backprop/fq_weights_1" [label="[1, 3, 1, 1]", style=solid]; -"30 Constant_2953" -> "28 Conv_backprop/fq_weights_1" [label="[1, 3, 1, 1]", style=solid]; -"31 Constant_2952" -> "28 Conv_backprop/fq_weights_1" [label="[1, 3, 1, 1]", style=solid]; -"32 Constant_2951" -> "28 Conv_backprop/fq_weights_1" [label="[1, 3, 1, 1]", style=solid]; -"33 Constant_4" -> "28 Conv_backprop/fq_weights_1" [label="[3, 3, 1, 1]", style=solid]; -"34 Constant_2939" -> "3 Conv/fq_output_0" [label="[]", style=solid]; -"35 Constant_2938" -> "3 Conv/fq_output_0" [label="[]", style=solid]; -"36 Constant_2937" -> "3 Conv/fq_output_0" [label="[]", style=solid]; -"37 Constant_2936" -> "3 Conv/fq_output_0" [label="[]", style=solid]; -"38 Conv/fq_weights_1" -> "2 Conv" [label="[3, 3, 1, 1]", style=solid]; -"39 Constant_2944" -> "38 Conv/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; -"40 Constant_2943" -> "38 Conv/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; -"41 Constant_2942" -> "38 Conv/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; -"42 Constant_2941" -> "38 Conv/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; -"43 Constant_2" -> "38 Conv/fq_weights_1" [label="[3, 3, 1, 1]", style=solid]; -"44 Constant_2934" -> "1 Input_1/fq_output_0" [label="[]", style=solid]; -"45 Constant_2933" -> "1 Input_1/fq_output_0" [label="[]", style=solid]; -"46 Constant_2932" -> "1 Input_1/fq_output_0" [label="[]", style=solid]; -"47 Constant_2931" -> "1 Input_1/fq_output_0" [label="[]", style=solid]; -"48 Constant_2969" -> "11 MatMul_0/fq_output_0" [label="[]", style=solid]; -"49 Constant_2968" -> "11 MatMul_0/fq_output_0" [label="[]", style=solid]; -"50 Constant_2967" -> "11 MatMul_0/fq_output_0" [label="[]", style=solid]; -"51 Constant_2966" -> "11 MatMul_0/fq_output_0" [label="[]", style=solid]; -"52 MatMul_0/fq_weights_0" -> "9 MatMul_0" [label="[1, 1]", style=solid]; -"53 Constant_2974" -> "52 MatMul_0/fq_weights_0" [label="[1, 1]", style=solid]; -"54 Constant_2973" -> "52 MatMul_0/fq_weights_0" [label="[1, 1]", style=solid]; -"55 Constant_2972" -> "52 MatMul_0/fq_weights_0" [label="[1, 1]", style=solid]; -"56 Constant_2971" -> "52 MatMul_0/fq_weights_0" [label="[1, 1]", style=solid]; -"57 weights_0" -> "52 MatMul_0/fq_weights_0" [label="[1, 1]", style=solid]; -"57 weights_0" -> "58 MatMul_const" [label="[1, 1]", style=solid]; -"58 MatMul_const" -> "10 Add_15" [label="[4, 1]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/mobilenet-v2-pytorch_performance.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/mobilenet-v2-pytorch_performance.dot deleted file mode 100644 index bff8b3058aa..00000000000 --- 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/features/features.18/features.18.0/Conv/WithoutBiases/fq_weights_1" [id=239, type=FakeQuantize]; -"240 Constant_41643" [id=240, type=Constant]; -"241 Constant_41642" [id=241, type=Constant]; -"242 Constant_41641" [id=242, type=Constant]; -"243 Constant_41640" [id=243, type=Constant]; -"244 onnx^^Conv_691" [id=244, label="244 onnx::Conv_691", type=Constant]; -"245 Constant_41638" [id=245, type=Constant]; -"246 Constant_41637" [id=246, type=Constant]; -"247 Constant_41636" [id=247, type=Constant]; -"248 Constant_41635" [id=248, type=Constant]; -"249 Reshape_1831" [id=249, type=Constant]; -"250 /features/features.17/conv/conv.2/Conv/WithoutBiases/fq_weights_1" [id=250, type=FakeQuantize]; -"251 Constant_41633" [id=251, type=Constant]; -"252 Constant_41632" [id=252, type=Constant]; -"253 Constant_41631" [id=253, type=Constant]; -"254 Constant_41630" [id=254, type=Constant]; -"255 onnx^^Conv_688" [id=255, label="255 onnx::Conv_688", type=Constant]; -"256 Constant_41628" [id=256, type=Constant]; -"257 Constant_41627" [id=257, type=Constant]; -"258 Constant_41626" [id=258, type=Constant]; -"259 Constant_41625" [id=259, type=Constant]; -"260 Reshape_1812" [id=260, type=Constant]; -"261 /features/features.17/conv/conv.1/conv.1.0/Conv/WithoutBiases/fq_weights_1" [id=261, type=FakeQuantize]; -"262 Constant_41623" [id=262, type=Constant]; -"263 Constant_41622" [id=263, type=Constant]; -"264 Constant_41621" [id=264, type=Constant]; -"265 Constant_41620" [id=265, type=Constant]; -"266 Reshape_1760" [id=266, type=Constant]; -"267 Constant_41618" [id=267, type=Constant]; -"268 Constant_41617" [id=268, type=Constant]; -"269 Constant_41616" [id=269, type=Constant]; -"270 Constant_41615" [id=270, type=Constant]; -"271 Reshape_1745" [id=271, type=Constant]; -"272 /features/features.17/conv/conv.0/conv.0.0/Conv/WithoutBiases/fq_weights_1" [id=272, type=FakeQuantize]; -"273 Constant_41613" [id=273, type=Constant]; -"274 Constant_41612" [id=274, type=Constant]; -"275 Constant_41611" [id=275, type=Constant]; -"276 Constant_41610" [id=276, type=Constant]; -"277 onnx^^Conv_682" [id=277, label="277 onnx::Conv_682", type=Constant]; -"278 Constant_41608" [id=278, type=Constant]; -"279 Constant_41607" [id=279, type=Constant]; -"280 Constant_41606" [id=280, type=Constant]; -"281 Constant_41605" [id=281, type=Constant]; -"282 Constant_41603" [id=282, type=Constant]; -"283 Constant_41602" [id=283, type=Constant]; -"284 Constant_41601" [id=284, type=Constant]; -"285 Constant_41600" [id=285, type=Constant]; -"286 Reshape_1729" [id=286, type=Constant]; -"287 /features/features.16/conv/conv.2/Conv/WithoutBiases/fq_weights_1" [id=287, type=FakeQuantize]; -"288 Constant_41598" [id=288, type=Constant]; -"289 Constant_41597" [id=289, type=Constant]; -"290 Constant_41596" [id=290, type=Constant]; -"291 Constant_41595" [id=291, type=Constant]; -"292 onnx^^Conv_679" [id=292, label="292 onnx::Conv_679", type=Constant]; -"293 Constant_41593" [id=293, type=Constant]; -"294 Constant_41592" [id=294, type=Constant]; -"295 Constant_41591" [id=295, type=Constant]; -"296 Constant_41590" [id=296, type=Constant]; -"297 Reshape_1710" [id=297, type=Constant]; -"298 /features/features.16/conv/conv.1/conv.1.0/Conv/WithoutBiases/fq_weights_1" [id=298, type=FakeQuantize]; -"299 Constant_41588" [id=299, type=Constant]; -"300 Constant_41587" [id=300, type=Constant]; -"301 Constant_41586" [id=301, type=Constant]; -"302 Constant_41585" [id=302, type=Constant]; -"303 Reshape_1658" [id=303, type=Constant]; -"304 Constant_41583" [id=304, type=Constant]; -"305 Constant_41582" [id=305, type=Constant]; -"306 Constant_41581" [id=306, type=Constant]; -"307 Constant_41580" [id=307, type=Constant]; -"308 Reshape_1643" [id=308, type=Constant]; -"309 /features/features.16/conv/conv.0/conv.0.0/Conv/WithoutBiases/fq_weights_1" [id=309, type=FakeQuantize]; -"310 Constant_41578" [id=310, type=Constant]; -"311 Constant_41577" [id=311, type=Constant]; -"312 Constant_41576" [id=312, type=Constant]; -"313 Constant_41575" [id=313, type=Constant]; -"314 onnx^^Conv_673" [id=314, label="314 onnx::Conv_673", type=Constant]; -"315 Constant_41573" [id=315, type=Constant]; -"316 Constant_41572" [id=316, type=Constant]; -"317 Constant_41571" [id=317, type=Constant]; -"318 Constant_41570" [id=318, type=Constant]; -"319 Constant_41568" [id=319, type=Constant]; -"320 Constant_41567" [id=320, type=Constant]; -"321 Constant_41566" [id=321, type=Constant]; -"322 Constant_41565" [id=322, type=Constant]; -"323 Reshape_1627" [id=323, type=Constant]; -"324 /features/features.15/conv/conv.2/Conv/WithoutBiases/fq_weights_1" [id=324, type=FakeQuantize]; -"325 Constant_41563" [id=325, type=Constant]; -"326 Constant_41562" [id=326, type=Constant]; -"327 Constant_41561" [id=327, type=Constant]; -"328 Constant_41560" [id=328, type=Constant]; -"329 onnx^^Conv_670" [id=329, label="329 onnx::Conv_670", type=Constant]; -"330 Constant_41558" [id=330, type=Constant]; -"331 Constant_41557" [id=331, type=Constant]; -"332 Constant_41556" [id=332, type=Constant]; -"333 Constant_41555" [id=333, type=Constant]; -"334 Reshape_1608" [id=334, type=Constant]; -"335 /features/features.15/conv/conv.1/conv.1.0/Conv/WithoutBiases/fq_weights_1" [id=335, type=FakeQuantize]; -"336 Constant_41553" [id=336, type=Constant]; -"337 Constant_41552" [id=337, type=Constant]; -"338 Constant_41551" [id=338, type=Constant]; -"339 Constant_41550" [id=339, type=Constant]; -"340 Reshape_1556" [id=340, type=Constant]; -"341 Constant_41548" [id=341, type=Constant]; -"342 Constant_41547" [id=342, type=Constant]; -"343 Constant_41546" [id=343, type=Constant]; -"344 Constant_41545" [id=344, type=Constant]; -"345 Reshape_1541" [id=345, type=Constant]; -"346 /features/features.15/conv/conv.0/conv.0.0/Conv/WithoutBiases/fq_weights_1" [id=346, type=FakeQuantize]; -"347 Constant_41543" [id=347, type=Constant]; -"348 Constant_41542" [id=348, type=Constant]; -"349 Constant_41541" [id=349, type=Constant]; -"350 Constant_41540" [id=350, type=Constant]; -"351 onnx^^Conv_664" [id=351, label="351 onnx::Conv_664", type=Constant]; -"352 Constant_41538" [id=352, type=Constant]; -"353 Constant_41537" [id=353, type=Constant]; -"354 Constant_41536" [id=354, type=Constant]; -"355 Constant_41535" [id=355, type=Constant]; -"356 Reshape_1526" [id=356, type=Constant]; -"357 /features/features.14/conv/conv.2/Conv/WithoutBiases/fq_weights_1" [id=357, type=FakeQuantize]; -"358 Constant_41533" [id=358, type=Constant]; -"359 Constant_41532" [id=359, type=Constant]; -"360 Constant_41531" [id=360, type=Constant]; -"361 Constant_41530" [id=361, type=Constant]; -"362 onnx^^Conv_661" [id=362, label="362 onnx::Conv_661", type=Constant]; -"363 Constant_41528" [id=363, type=Constant]; -"364 Constant_41527" [id=364, type=Constant]; -"365 Constant_41526" [id=365, type=Constant]; -"366 Constant_41525" [id=366, type=Constant]; -"367 Reshape_1507" [id=367, type=Constant]; -"368 /features/features.14/conv/conv.1/conv.1.0/Conv/WithoutBiases/fq_weights_1" [id=368, type=FakeQuantize]; -"369 Constant_41523" [id=369, type=Constant]; -"370 Constant_41522" [id=370, type=Constant]; -"371 Constant_41521" [id=371, type=Constant]; -"372 Constant_41520" [id=372, type=Constant]; -"373 Reshape_1455" [id=373, type=Constant]; -"374 Constant_41518" [id=374, type=Constant]; -"375 Constant_41517" [id=375, type=Constant]; -"376 Constant_41516" [id=376, type=Constant]; -"377 Constant_41515" [id=377, type=Constant]; -"378 Reshape_1440" [id=378, type=Constant]; -"379 /features/features.14/conv/conv.0/conv.0.0/Conv/WithoutBiases/fq_weights_1" [id=379, type=FakeQuantize]; -"380 Constant_41513" [id=380, type=Constant]; -"381 Constant_41512" [id=381, type=Constant]; -"382 Constant_41511" [id=382, type=Constant]; -"383 Constant_41510" [id=383, type=Constant]; -"384 onnx^^Conv_655" [id=384, label="384 onnx::Conv_655", type=Constant]; -"385 Constant_41508" [id=385, type=Constant]; -"386 Constant_41507" [id=386, type=Constant]; -"387 Constant_41506" [id=387, type=Constant]; -"388 Constant_41505" [id=388, type=Constant]; -"389 Constant_41503" [id=389, type=Constant]; -"390 Constant_41502" [id=390, type=Constant]; -"391 Constant_41501" [id=391, type=Constant]; -"392 Constant_41500" [id=392, type=Constant]; -"393 Reshape_1424" [id=393, type=Constant]; -"394 /features/features.13/conv/conv.2/Conv/WithoutBiases/fq_weights_1" [id=394, type=FakeQuantize]; -"395 Constant_41498" [id=395, type=Constant]; -"396 Constant_41497" [id=396, type=Constant]; -"397 Constant_41496" [id=397, type=Constant]; -"398 Constant_41495" [id=398, type=Constant]; -"399 onnx^^Conv_652" [id=399, label="399 onnx::Conv_652", type=Constant]; -"400 Constant_41493" [id=400, type=Constant]; -"401 Constant_41492" [id=401, type=Constant]; -"402 Constant_41491" [id=402, type=Constant]; -"403 Constant_41490" [id=403, type=Constant]; -"404 Reshape_1405" [id=404, type=Constant]; -"405 /features/features.13/conv/conv.1/conv.1.0/Conv/WithoutBiases/fq_weights_1" [id=405, type=FakeQuantize]; -"406 Constant_41488" [id=406, type=Constant]; -"407 Constant_41487" [id=407, type=Constant]; -"408 Constant_41486" [id=408, type=Constant]; -"409 Constant_41485" [id=409, type=Constant]; -"410 Reshape_1353" [id=410, type=Constant]; -"411 Constant_41483" [id=411, type=Constant]; -"412 Constant_41482" [id=412, type=Constant]; -"413 Constant_41481" [id=413, type=Constant]; -"414 Constant_41480" [id=414, type=Constant]; -"415 Reshape_1338" [id=415, type=Constant]; -"416 /features/features.13/conv/conv.0/conv.0.0/Conv/WithoutBiases/fq_weights_1" [id=416, type=FakeQuantize]; -"417 Constant_41478" [id=417, type=Constant]; -"418 Constant_41477" [id=418, type=Constant]; -"419 Constant_41476" [id=419, type=Constant]; -"420 Constant_41475" [id=420, type=Constant]; -"421 onnx^^Conv_646" [id=421, label="421 onnx::Conv_646", type=Constant]; -"422 Constant_41473" [id=422, type=Constant]; -"423 Constant_41472" [id=423, type=Constant]; -"424 Constant_41471" [id=424, type=Constant]; -"425 Constant_41470" [id=425, type=Constant]; -"426 Constant_41468" [id=426, type=Constant]; -"427 Constant_41467" [id=427, type=Constant]; -"428 Constant_41466" [id=428, type=Constant]; -"429 Constant_41465" [id=429, type=Constant]; -"430 Reshape_1322" [id=430, type=Constant]; -"431 /features/features.12/conv/conv.2/Conv/WithoutBiases/fq_weights_1" [id=431, type=FakeQuantize]; -"432 Constant_41463" [id=432, type=Constant]; -"433 Constant_41462" [id=433, type=Constant]; -"434 Constant_41461" [id=434, type=Constant]; -"435 Constant_41460" [id=435, type=Constant]; -"436 onnx^^Conv_643" [id=436, label="436 onnx::Conv_643", type=Constant]; -"437 Constant_41458" [id=437, type=Constant]; -"438 Constant_41457" [id=438, type=Constant]; -"439 Constant_41456" [id=439, type=Constant]; -"440 Constant_41455" [id=440, type=Constant]; -"441 Reshape_1303" [id=441, type=Constant]; -"442 /features/features.12/conv/conv.1/conv.1.0/Conv/WithoutBiases/fq_weights_1" [id=442, type=FakeQuantize]; -"443 Constant_41453" [id=443, type=Constant]; -"444 Constant_41452" [id=444, type=Constant]; -"445 Constant_41451" [id=445, type=Constant]; -"446 Constant_41450" [id=446, type=Constant]; -"447 Reshape_1251" [id=447, type=Constant]; -"448 Constant_41448" [id=448, type=Constant]; -"449 Constant_41447" [id=449, type=Constant]; -"450 Constant_41446" [id=450, type=Constant]; -"451 Constant_41445" [id=451, type=Constant]; -"452 Reshape_1236" [id=452, type=Constant]; -"453 /features/features.12/conv/conv.0/conv.0.0/Conv/WithoutBiases/fq_weights_1" [id=453, type=FakeQuantize]; -"454 Constant_41443" [id=454, type=Constant]; -"455 Constant_41442" [id=455, type=Constant]; -"456 Constant_41441" [id=456, type=Constant]; -"457 Constant_41440" [id=457, type=Constant]; -"458 onnx^^Conv_637" [id=458, label="458 onnx::Conv_637", type=Constant]; -"459 Constant_41438" [id=459, type=Constant]; -"460 Constant_41437" [id=460, type=Constant]; -"461 Constant_41436" [id=461, type=Constant]; -"462 Constant_41435" [id=462, type=Constant]; -"463 Reshape_1221" [id=463, type=Constant]; -"464 /features/features.11/conv/conv.2/Conv/WithoutBiases/fq_weights_1" [id=464, type=FakeQuantize]; -"465 Constant_41433" [id=465, type=Constant]; -"466 Constant_41432" [id=466, type=Constant]; -"467 Constant_41431" [id=467, type=Constant]; -"468 Constant_41430" [id=468, type=Constant]; -"469 onnx^^Conv_634" [id=469, label="469 onnx::Conv_634", type=Constant]; -"470 Constant_41428" [id=470, type=Constant]; -"471 Constant_41427" [id=471, type=Constant]; -"472 Constant_41426" [id=472, type=Constant]; -"473 Constant_41425" [id=473, type=Constant]; -"474 Reshape_1202" [id=474, type=Constant]; -"475 /features/features.11/conv/conv.1/conv.1.0/Conv/WithoutBiases/fq_weights_1" [id=475, type=FakeQuantize]; -"476 Constant_41423" [id=476, type=Constant]; -"477 Constant_41422" [id=477, type=Constant]; -"478 Constant_41421" [id=478, type=Constant]; -"479 Constant_41420" [id=479, type=Constant]; -"480 Reshape_1150" [id=480, type=Constant]; -"481 Constant_41418" [id=481, type=Constant]; -"482 Constant_41417" [id=482, type=Constant]; -"483 Constant_41416" [id=483, type=Constant]; -"484 Constant_41415" [id=484, type=Constant]; -"485 Reshape_1135" [id=485, type=Constant]; -"486 /features/features.11/conv/conv.0/conv.0.0/Conv/WithoutBiases/fq_weights_1" [id=486, type=FakeQuantize]; -"487 Constant_41413" [id=487, type=Constant]; -"488 Constant_41412" [id=488, type=Constant]; -"489 Constant_41411" [id=489, type=Constant]; -"490 Constant_41410" [id=490, type=Constant]; -"491 onnx^^Conv_628" [id=491, label="491 onnx::Conv_628", type=Constant]; -"492 Constant_41408" [id=492, type=Constant]; -"493 Constant_41407" [id=493, type=Constant]; -"494 Constant_41406" [id=494, type=Constant]; -"495 Constant_41405" [id=495, type=Constant]; -"496 Constant_41403" [id=496, type=Constant]; -"497 Constant_41402" [id=497, type=Constant]; -"498 Constant_41401" [id=498, type=Constant]; -"499 Constant_41400" [id=499, type=Constant]; -"500 Reshape_1119" [id=500, type=Constant]; -"501 /features/features.10/conv/conv.2/Conv/WithoutBiases/fq_weights_1" [id=501, type=FakeQuantize]; -"502 Constant_41398" [id=502, type=Constant]; -"503 Constant_41397" [id=503, type=Constant]; -"504 Constant_41396" [id=504, type=Constant]; -"505 Constant_41395" [id=505, type=Constant]; -"506 onnx^^Conv_625" [id=506, label="506 onnx::Conv_625", type=Constant]; -"507 Constant_41393" [id=507, type=Constant]; -"508 Constant_41392" [id=508, type=Constant]; -"509 Constant_41391" [id=509, type=Constant]; -"510 Constant_41390" [id=510, type=Constant]; -"511 Reshape_1100" [id=511, type=Constant]; -"512 /features/features.10/conv/conv.1/conv.1.0/Conv/WithoutBiases/fq_weights_1" [id=512, type=FakeQuantize]; -"513 Constant_41388" [id=513, type=Constant]; -"514 Constant_41387" [id=514, type=Constant]; -"515 Constant_41386" [id=515, type=Constant]; -"516 Constant_41385" [id=516, type=Constant]; -"517 Reshape_1048" [id=517, type=Constant]; -"518 Constant_41383" [id=518, type=Constant]; -"519 Constant_41382" [id=519, type=Constant]; -"520 Constant_41381" [id=520, type=Constant]; -"521 Constant_41380" [id=521, type=Constant]; -"522 Reshape_1033" [id=522, type=Constant]; -"523 /features/features.10/conv/conv.0/conv.0.0/Conv/WithoutBiases/fq_weights_1" [id=523, type=FakeQuantize]; -"524 Constant_41378" [id=524, type=Constant]; -"525 Constant_41377" [id=525, type=Constant]; -"526 Constant_41376" [id=526, type=Constant]; -"527 Constant_41375" [id=527, type=Constant]; -"528 onnx^^Conv_619" [id=528, label="528 onnx::Conv_619", type=Constant]; -"529 Constant_41373" [id=529, type=Constant]; -"530 Constant_41372" [id=530, type=Constant]; -"531 Constant_41371" [id=531, type=Constant]; -"532 Constant_41370" [id=532, type=Constant]; -"533 Constant_41368" [id=533, type=Constant]; -"534 Constant_41367" [id=534, type=Constant]; -"535 Constant_41366" [id=535, type=Constant]; -"536 Constant_41365" [id=536, type=Constant]; -"537 Reshape_1017" [id=537, type=Constant]; -"538 /features/features.9/conv/conv.2/Conv/WithoutBiases/fq_weights_1" [id=538, type=FakeQuantize]; -"539 Constant_41363" [id=539, type=Constant]; -"540 Constant_41362" [id=540, type=Constant]; -"541 Constant_41361" [id=541, type=Constant]; -"542 Constant_41360" [id=542, type=Constant]; -"543 onnx^^Conv_616" [id=543, label="543 onnx::Conv_616", type=Constant]; -"544 Constant_41358" [id=544, type=Constant]; -"545 Constant_41357" [id=545, type=Constant]; -"546 Constant_41356" [id=546, type=Constant]; -"547 Constant_41355" [id=547, type=Constant]; -"548 Reshape_998" [id=548, type=Constant]; -"549 /features/features.9/conv/conv.1/conv.1.0/Conv/WithoutBiases/fq_weights_1" [id=549, type=FakeQuantize]; -"550 Constant_41353" [id=550, type=Constant]; -"551 Constant_41352" [id=551, type=Constant]; -"552 Constant_41351" [id=552, type=Constant]; -"553 Constant_41350" [id=553, type=Constant]; -"554 Reshape_946" [id=554, type=Constant]; -"555 Constant_41348" [id=555, type=Constant]; -"556 Constant_41347" [id=556, type=Constant]; -"557 Constant_41346" [id=557, type=Constant]; -"558 Constant_41345" [id=558, type=Constant]; -"559 Reshape_931" [id=559, type=Constant]; -"560 /features/features.9/conv/conv.0/conv.0.0/Conv/WithoutBiases/fq_weights_1" [id=560, type=FakeQuantize]; -"561 Constant_41343" [id=561, type=Constant]; -"562 Constant_41342" [id=562, type=Constant]; -"563 Constant_41341" [id=563, type=Constant]; -"564 Constant_41340" [id=564, type=Constant]; -"565 onnx^^Conv_610" [id=565, label="565 onnx::Conv_610", type=Constant]; -"566 Constant_41338" [id=566, type=Constant]; -"567 Constant_41337" [id=567, type=Constant]; -"568 Constant_41336" [id=568, type=Constant]; -"569 Constant_41335" [id=569, type=Constant]; -"570 Constant_41333" [id=570, type=Constant]; -"571 Constant_41332" [id=571, type=Constant]; -"572 Constant_41331" [id=572, type=Constant]; -"573 Constant_41330" [id=573, type=Constant]; -"574 Reshape_915" [id=574, type=Constant]; -"575 /features/features.8/conv/conv.2/Conv/WithoutBiases/fq_weights_1" [id=575, type=FakeQuantize]; -"576 Constant_41328" [id=576, type=Constant]; -"577 Constant_41327" [id=577, type=Constant]; -"578 Constant_41326" [id=578, type=Constant]; -"579 Constant_41325" [id=579, type=Constant]; -"580 onnx^^Conv_607" [id=580, label="580 onnx::Conv_607", type=Constant]; -"581 Constant_41323" [id=581, type=Constant]; -"582 Constant_41322" [id=582, type=Constant]; -"583 Constant_41321" [id=583, type=Constant]; -"584 Constant_41320" [id=584, type=Constant]; -"585 Reshape_896" [id=585, type=Constant]; -"586 /features/features.8/conv/conv.1/conv.1.0/Conv/WithoutBiases/fq_weights_1" [id=586, type=FakeQuantize]; -"587 Constant_41318" [id=587, type=Constant]; -"588 Constant_41317" [id=588, type=Constant]; -"589 Constant_41316" [id=589, type=Constant]; -"590 Constant_41315" [id=590, type=Constant]; -"591 Reshape_844" [id=591, type=Constant]; -"592 Constant_41313" [id=592, type=Constant]; -"593 Constant_41312" [id=593, type=Constant]; -"594 Constant_41311" [id=594, type=Constant]; -"595 Constant_41310" [id=595, type=Constant]; -"596 Reshape_829" [id=596, type=Constant]; -"597 /features/features.8/conv/conv.0/conv.0.0/Conv/WithoutBiases/fq_weights_1" [id=597, type=FakeQuantize]; -"598 Constant_41308" [id=598, type=Constant]; -"599 Constant_41307" [id=599, type=Constant]; -"600 Constant_41306" [id=600, type=Constant]; -"601 Constant_41305" [id=601, type=Constant]; -"602 onnx^^Conv_601" [id=602, label="602 onnx::Conv_601", type=Constant]; -"603 Constant_41303" [id=603, type=Constant]; -"604 Constant_41302" [id=604, type=Constant]; -"605 Constant_41301" [id=605, type=Constant]; -"606 Constant_41300" [id=606, type=Constant]; -"607 Reshape_814" [id=607, type=Constant]; -"608 /features/features.7/conv/conv.2/Conv/WithoutBiases/fq_weights_1" [id=608, type=FakeQuantize]; 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type=Constant]; -"647 Constant_41262" [id=647, type=Constant]; -"648 Constant_41261" [id=648, type=Constant]; -"649 Constant_41260" [id=649, type=Constant]; -"650 onnx^^Conv_589" [id=650, label="650 onnx::Conv_589", type=Constant]; -"651 Constant_41258" [id=651, type=Constant]; -"652 Constant_41257" [id=652, type=Constant]; -"653 Constant_41256" [id=653, type=Constant]; -"654 Constant_41255" [id=654, type=Constant]; -"655 Reshape_693" [id=655, type=Constant]; -"656 /features/features.6/conv/conv.1/conv.1.0/Conv/WithoutBiases/fq_weights_1" [id=656, type=FakeQuantize]; -"657 Constant_41253" [id=657, type=Constant]; -"658 Constant_41252" [id=658, type=Constant]; -"659 Constant_41251" [id=659, type=Constant]; -"660 Constant_41250" [id=660, type=Constant]; -"661 Reshape_641" [id=661, type=Constant]; -"662 Constant_41248" [id=662, type=Constant]; -"663 Constant_41247" [id=663, type=Constant]; -"664 Constant_41246" [id=664, type=Constant]; -"665 Constant_41245" [id=665, type=Constant]; -"666 Reshape_626" [id=666, type=Constant]; -"667 /features/features.6/conv/conv.0/conv.0.0/Conv/WithoutBiases/fq_weights_1" [id=667, type=FakeQuantize]; -"668 Constant_41243" [id=668, type=Constant]; -"669 Constant_41242" [id=669, type=Constant]; -"670 Constant_41241" [id=670, type=Constant]; -"671 Constant_41240" [id=671, type=Constant]; -"672 onnx^^Conv_583" [id=672, label="672 onnx::Conv_583", type=Constant]; -"673 Constant_41238" [id=673, type=Constant]; -"674 Constant_41237" [id=674, type=Constant]; -"675 Constant_41236" [id=675, type=Constant]; -"676 Constant_41235" [id=676, type=Constant]; -"677 Constant_41233" [id=677, type=Constant]; -"678 Constant_41232" [id=678, type=Constant]; -"679 Constant_41231" [id=679, type=Constant]; -"680 Constant_41230" [id=680, type=Constant]; -"681 Reshape_610" [id=681, type=Constant]; -"682 /features/features.5/conv/conv.2/Conv/WithoutBiases/fq_weights_1" [id=682, type=FakeQuantize]; -"683 Constant_41228" [id=683, type=Constant]; -"684 Constant_41227" [id=684, type=Constant]; -"685 Constant_41226" [id=685, type=Constant]; -"686 Constant_41225" [id=686, type=Constant]; -"687 onnx^^Conv_580" [id=687, label="687 onnx::Conv_580", type=Constant]; -"688 Constant_41223" [id=688, type=Constant]; -"689 Constant_41222" [id=689, type=Constant]; -"690 Constant_41221" [id=690, type=Constant]; -"691 Constant_41220" [id=691, type=Constant]; -"692 Reshape_591" [id=692, type=Constant]; -"693 /features/features.5/conv/conv.1/conv.1.0/Conv/WithoutBiases/fq_weights_1" [id=693, type=FakeQuantize]; -"694 Constant_41218" [id=694, type=Constant]; -"695 Constant_41217" [id=695, type=Constant]; -"696 Constant_41216" [id=696, type=Constant]; -"697 Constant_41215" [id=697, type=Constant]; -"698 Reshape_539" [id=698, type=Constant]; -"699 Constant_41213" [id=699, type=Constant]; -"700 Constant_41212" [id=700, type=Constant]; -"701 Constant_41211" [id=701, type=Constant]; -"702 Constant_41210" [id=702, type=Constant]; -"703 Reshape_524" [id=703, type=Constant]; 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type=Constant]; -"722 Constant_41192" [id=722, type=Constant]; -"723 Constant_41191" [id=723, type=Constant]; -"724 Constant_41190" [id=724, type=Constant]; -"725 Reshape_490" [id=725, type=Constant]; -"726 /features/features.4/conv/conv.1/conv.1.0/Conv/WithoutBiases/fq_weights_1" [id=726, type=FakeQuantize]; -"727 Constant_41188" [id=727, type=Constant]; -"728 Constant_41187" [id=728, type=Constant]; -"729 Constant_41186" [id=729, type=Constant]; -"730 Constant_41185" [id=730, type=Constant]; -"731 Reshape_438" [id=731, type=Constant]; -"732 Constant_41183" [id=732, type=Constant]; -"733 Constant_41182" [id=733, type=Constant]; -"734 Constant_41181" [id=734, type=Constant]; -"735 Constant_41180" [id=735, type=Constant]; -"736 Reshape_423" [id=736, type=Constant]; -"737 /features/features.4/conv/conv.0/conv.0.0/Conv/WithoutBiases/fq_weights_1" [id=737, type=FakeQuantize]; -"738 Constant_41178" [id=738, type=Constant]; -"739 Constant_41177" [id=739, type=Constant]; -"740 Constant_41176" [id=740, type=Constant]; -"741 Constant_41175" [id=741, type=Constant]; -"742 onnx^^Conv_565" [id=742, label="742 onnx::Conv_565", type=Constant]; -"743 Constant_41173" [id=743, type=Constant]; -"744 Constant_41172" [id=744, type=Constant]; -"745 Constant_41171" [id=745, type=Constant]; -"746 Constant_41170" [id=746, type=Constant]; -"747 Constant_41168" [id=747, type=Constant]; -"748 Constant_41167" [id=748, type=Constant]; -"749 Constant_41166" [id=749, type=Constant]; -"750 Constant_41165" [id=750, type=Constant]; -"751 Reshape_407" [id=751, type=Constant]; -"752 /features/features.3/conv/conv.2/Conv/WithoutBiases/fq_weights_1" [id=752, type=FakeQuantize]; -"753 Constant_41163" [id=753, type=Constant]; -"754 Constant_41162" [id=754, type=Constant]; -"755 Constant_41161" [id=755, type=Constant]; -"756 Constant_41160" [id=756, type=Constant]; -"757 onnx^^Conv_562" [id=757, label="757 onnx::Conv_562", type=Constant]; -"758 Constant_41158" [id=758, type=Constant]; -"759 Constant_41157" 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Constant_41140" [id=778, type=Constant]; -"779 onnx^^Conv_556" [id=779, label="779 onnx::Conv_556", type=Constant]; -"780 Constant_41138" [id=780, type=Constant]; -"781 Constant_41137" [id=781, type=Constant]; -"782 Constant_41136" [id=782, type=Constant]; -"783 Constant_41135" [id=783, type=Constant]; -"784 Reshape_306" [id=784, type=Constant]; -"785 /features/features.2/conv/conv.2/Conv/WithoutBiases/fq_weights_1" [id=785, type=FakeQuantize]; -"786 Constant_41133" [id=786, type=Constant]; -"787 Constant_41132" [id=787, type=Constant]; -"788 Constant_41131" [id=788, type=Constant]; -"789 Constant_41130" [id=789, type=Constant]; -"790 onnx^^Conv_553" [id=790, label="790 onnx::Conv_553", type=Constant]; -"791 Constant_41128" [id=791, type=Constant]; -"792 Constant_41127" [id=792, type=Constant]; -"793 Constant_41126" [id=793, type=Constant]; -"794 Constant_41125" [id=794, type=Constant]; -"795 Reshape_287" [id=795, type=Constant]; -"796 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Constant_41131" -> "785 /features/features.2/conv/conv.2/Conv/WithoutBiases/fq_weights_1" [label="[24, 1, 1, 1]", style=solid]; -"789 Constant_41130" -> "785 /features/features.2/conv/conv.2/Conv/WithoutBiases/fq_weights_1" [label="[24, 1, 1, 1]", style=solid]; -"790 onnx^^Conv_553" -> "785 /features/features.2/conv/conv.2/Conv/WithoutBiases/fq_weights_1" [label="[24, 96, 1, 1]", style=solid]; -"791 Constant_41128" -> "22 /features/features.2/conv/conv.1/conv.1.2/Clip/fq_output_0" [label="[]", style=solid]; -"792 Constant_41127" -> "22 /features/features.2/conv/conv.1/conv.1.2/Clip/fq_output_0" [label="[]", style=solid]; -"793 Constant_41126" -> "22 /features/features.2/conv/conv.1/conv.1.2/Clip/fq_output_0" [label="[]", style=solid]; -"794 Constant_41125" -> "22 /features/features.2/conv/conv.1/conv.1.2/Clip/fq_output_0" [label="[]", style=solid]; -"795 Reshape_287" -> "20 /features/features.2/conv/conv.1/conv.1.0/Conv" [label="[1, 96, 1, 1]", style=solid]; -"796 /features/features.2/conv/conv.1/conv.1.0/Conv/WithoutBiases/fq_weights_1" -> "19 /features/features.2/conv/conv.1/conv.1.0/Conv/WithoutBiases" [label="[96, 1, 1, 3, 3]", style=solid]; -"797 Constant_41123" -> "796 /features/features.2/conv/conv.1/conv.1.0/Conv/WithoutBiases/fq_weights_1" [label="[96, 1, 1, 1, 1]", style=solid]; -"798 Constant_41122" -> "796 /features/features.2/conv/conv.1/conv.1.0/Conv/WithoutBiases/fq_weights_1" [label="[96, 1, 1, 1, 1]", style=solid]; -"799 Constant_41121" -> "796 /features/features.2/conv/conv.1/conv.1.0/Conv/WithoutBiases/fq_weights_1" [label="[96, 1, 1, 1, 1]", style=solid]; -"800 Constant_41120" -> "796 /features/features.2/conv/conv.1/conv.1.0/Conv/WithoutBiases/fq_weights_1" [label="[96, 1, 1, 1, 1]", style=solid]; -"801 Reshape_235" -> "796 /features/features.2/conv/conv.1/conv.1.0/Conv/WithoutBiases/fq_weights_1" [label="[96, 1, 1, 3, 3]", style=solid]; -"802 Constant_41118" -> "18 /features/features.2/conv/conv.0/conv.0.2/Clip/fq_output_0" [label="[1, 96, 1, 1]", style=solid]; -"803 Constant_41117" -> "18 /features/features.2/conv/conv.0/conv.0.2/Clip/fq_output_0" [label="[1, 96, 1, 1]", style=solid]; -"804 Constant_41116" -> "18 /features/features.2/conv/conv.0/conv.0.2/Clip/fq_output_0" [label="[1, 96, 1, 1]", style=solid]; -"805 Constant_41115" -> "18 /features/features.2/conv/conv.0/conv.0.2/Clip/fq_output_0" [label="[1, 96, 1, 1]", style=solid]; -"806 Reshape_220" -> "16 /features/features.2/conv/conv.0/conv.0.0/Conv" [label="[1, 96, 1, 1]", style=solid]; -"807 /features/features.2/conv/conv.0/conv.0.0/Conv/WithoutBiases/fq_weights_1" -> "15 /features/features.2/conv/conv.0/conv.0.0/Conv/WithoutBiases" [label="[96, 16, 1, 1]", style=solid]; -"808 Constant_41113" -> "807 /features/features.2/conv/conv.0/conv.0.0/Conv/WithoutBiases/fq_weights_1" [label="[96, 1, 1, 1]", style=solid]; -"809 Constant_41112" -> "807 /features/features.2/conv/conv.0/conv.0.0/Conv/WithoutBiases/fq_weights_1" [label="[96, 1, 1, 1]", style=solid]; -"810 Constant_41111" -> "807 /features/features.2/conv/conv.0/conv.0.0/Conv/WithoutBiases/fq_weights_1" [label="[96, 1, 1, 1]", style=solid]; -"811 Constant_41110" -> "807 /features/features.2/conv/conv.0/conv.0.0/Conv/WithoutBiases/fq_weights_1" [label="[96, 1, 1, 1]", style=solid]; -"812 onnx^^Conv_547" -> "807 /features/features.2/conv/conv.0/conv.0.0/Conv/WithoutBiases/fq_weights_1" [label="[96, 16, 1, 1]", style=solid]; -"813 Constant_41108" -> "14 /features/features.1/conv/conv.1/Conv/fq_output_0" [label="[]", style=solid]; -"814 Constant_41107" -> "14 /features/features.1/conv/conv.1/Conv/fq_output_0" [label="[]", style=solid]; -"815 Constant_41106" -> "14 /features/features.1/conv/conv.1/Conv/fq_output_0" [label="[]", style=solid]; -"816 Constant_41105" -> "14 /features/features.1/conv/conv.1/Conv/fq_output_0" [label="[]", style=solid]; -"817 Reshape_205" -> "13 /features/features.1/conv/conv.1/Conv" [label="[1, 16, 1, 1]", style=solid]; -"818 /features/features.1/conv/conv.1/Conv/WithoutBiases/fq_weights_1" -> "12 /features/features.1/conv/conv.1/Conv/WithoutBiases" [label="[16, 32, 1, 1]", style=solid]; -"819 Constant_41103" -> "818 /features/features.1/conv/conv.1/Conv/WithoutBiases/fq_weights_1" [label="[16, 1, 1, 1]", style=solid]; -"820 Constant_41102" -> "818 /features/features.1/conv/conv.1/Conv/WithoutBiases/fq_weights_1" [label="[16, 1, 1, 1]", style=solid]; -"821 Constant_41101" -> "818 /features/features.1/conv/conv.1/Conv/WithoutBiases/fq_weights_1" [label="[16, 1, 1, 1]", style=solid]; -"822 Constant_41100" -> "818 /features/features.1/conv/conv.1/Conv/WithoutBiases/fq_weights_1" [label="[16, 1, 1, 1]", style=solid]; -"823 onnx^^Conv_544" -> "818 /features/features.1/conv/conv.1/Conv/WithoutBiases/fq_weights_1" [label="[16, 32, 1, 1]", style=solid]; -"824 Constant_41098" -> "11 /features/features.1/conv/conv.0/conv.0.2/Clip/fq_output_0" [label="[]", style=solid]; -"825 Constant_41097" -> "11 /features/features.1/conv/conv.0/conv.0.2/Clip/fq_output_0" [label="[]", style=solid]; -"826 Constant_41096" -> "11 /features/features.1/conv/conv.0/conv.0.2/Clip/fq_output_0" [label="[]", style=solid]; -"827 Constant_41095" -> "11 /features/features.1/conv/conv.0/conv.0.2/Clip/fq_output_0" [label="[]", style=solid]; -"828 Reshape_186" -> "9 /features/features.1/conv/conv.0/conv.0.0/Conv" [label="[1, 32, 1, 1]", style=solid]; -"829 /features/features.1/conv/conv.0/conv.0.0/Conv/WithoutBiases/fq_weights_1" -> "8 /features/features.1/conv/conv.0/conv.0.0/Conv/WithoutBiases" [label="[32, 1, 1, 3, 3]", style=solid]; -"830 Constant_41093" -> "829 /features/features.1/conv/conv.0/conv.0.0/Conv/WithoutBiases/fq_weights_1" [label="[32, 1, 1, 1, 1]", style=solid]; -"831 Constant_41092" -> "829 /features/features.1/conv/conv.0/conv.0.0/Conv/WithoutBiases/fq_weights_1" [label="[32, 1, 1, 1, 1]", style=solid]; -"832 Constant_41091" -> "829 /features/features.1/conv/conv.0/conv.0.0/Conv/WithoutBiases/fq_weights_1" [label="[32, 1, 1, 1, 1]", style=solid]; -"833 Constant_41090" -> "829 /features/features.1/conv/conv.0/conv.0.0/Conv/WithoutBiases/fq_weights_1" [label="[32, 1, 1, 1, 1]", style=solid]; -"834 Reshape_134" -> "829 /features/features.1/conv/conv.0/conv.0.0/Conv/WithoutBiases/fq_weights_1" [label="[32, 1, 1, 3, 3]", style=solid]; -"835 Constant_41088" -> "7 /features/features.0/features.0.2/Clip/fq_output_0" [label="[1, 32, 1, 1]", style=solid]; -"836 Constant_41087" -> "7 /features/features.0/features.0.2/Clip/fq_output_0" [label="[1, 32, 1, 1]", style=solid]; -"837 Constant_41086" -> "7 /features/features.0/features.0.2/Clip/fq_output_0" [label="[1, 32, 1, 1]", style=solid]; -"838 Constant_41085" -> "7 /features/features.0/features.0.2/Clip/fq_output_0" [label="[1, 32, 1, 1]", style=solid]; -"839 Reshape_119" -> "5 /features/features.0/features.0.0/Conv" [label="[1, 32, 1, 1]", style=solid]; -"840 /features/features.0/features.0.0/Conv/WithoutBiases/fq_weights_1" -> "4 /features/features.0/features.0.0/Conv/WithoutBiases" [label="[32, 3, 3, 3]", style=solid]; -"841 Constant_41083" -> "840 /features/features.0/features.0.0/Conv/WithoutBiases/fq_weights_1" [label="[32, 1, 1, 1]", style=solid]; -"842 Constant_41082" -> "840 /features/features.0/features.0.0/Conv/WithoutBiases/fq_weights_1" [label="[32, 1, 1, 1]", style=solid]; -"843 Constant_41081" -> "840 /features/features.0/features.0.0/Conv/WithoutBiases/fq_weights_1" [label="[32, 1, 1, 1]", style=solid]; -"844 Constant_41080" -> "840 /features/features.0/features.0.0/Conv/WithoutBiases/fq_weights_1" [label="[32, 1, 1, 1]", style=solid]; -"845 Gather_5168" -> "840 /features/features.0/features.0.0/Conv/WithoutBiases/fq_weights_1" [label="[32, 3, 3, 3]", style=solid]; -"846 Constant_41078" -> "3 Divide_1885/fq_output_0" [label="[]", style=solid]; -"847 Constant_41077" -> "3 Divide_1885/fq_output_0" [label="[]", style=solid]; -"848 Constant_41076" -> "3 Divide_1885/fq_output_0" [label="[]", style=solid]; -"849 Constant_41075" -> "3 Divide_1885/fq_output_0" [label="[]", style=solid]; -"850 Gather_5165" -> "2 Divide_1885" [label="[1, 3, 1, 1]", style=solid]; -"851 Gather_5162" -> "1 Multiply_5095" [label="[1, 3, 1, 1]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/mobilenet-v3-small-1.0-224-tf_performance.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/mobilenet-v3-small-1.0-224-tf_performance.dot deleted file mode 100644 index 46e74566032..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/quantized/mobilenet-v3-small-1.0-224-tf_performance.dot +++ /dev/null @@ -1,1598 +0,0 @@ -strict digraph { -"0 input_1" [id=0, type=Parameter]; -"1 Transpose_7780" [id=1, type=Transpose]; -"2 Transpose_7774" [id=2, type=Multiply]; -"3 Transpose_710" [id=3, type=Add]; -"4 Transpose_710/fq_output_0" [id=4, type=FakeQuantize]; -"5 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Transpose_5771/fq_output_0" [id=127, type=FakeQuantize]; -"128 Transpose_5905" [id=128, type=Add]; -"129 Transpose_6047" [id=129, type=Relu]; -"130 Transpose_6095" [id=130, type=Add]; -"131 Transpose_5919" [id=131, type=HSigmoid]; -"132 Convolution_1473" [id=132, type=Convolution]; -"133 Transpose_6095/fq_output_0" [id=133, type=FakeQuantize]; -"134 Transpose_5919/fq_output_0" [id=134, type=FakeQuantize]; -"135 Transpose_6053" [id=135, type=Add]; -"136 Multiply_9461" [id=136, type=Convolution]; -"137 Transpose_6243" [id=137, type=Add]; -"138 Transpose_6067" [id=138, type=HSigmoid]; -"139 Transpose_6119" [id=139, type=Add]; -"140 Transpose_6243/fq_output_0" [id=140, type=FakeQuantize]; -"141 Transpose_6067/fq_output_0" [id=141, type=FakeQuantize]; -"142 Transpose_6137" [id=142, type=HSwish]; -"143 Multiply_9503" [id=143, type=Convolution]; -"144 Transpose_6137/fq_output_0" [id=144, type=FakeQuantize]; -"145 Transpose_6267" [id=145, type=Add]; -"146 Multiply_9475" [id=146, type=GroupConvolution]; -"147 Transpose_6285" [id=147, type=HSwish]; -"148 Transpose_6161" [id=148, type=Add]; -"149 Transpose_6285/fq_output_0" [id=149, type=FakeQuantize]; -"150 Transpose_6179" [id=150, type=HSwish]; -"151 Multiply_9517" [id=151, type=GroupConvolution]; -"152 Transpose_6179/fq_output_0" [id=152, type=FakeQuantize]; -"153 Transpose_6336" [id=153, type=Add]; -"154 Transpose_6183" [id=154, type=ReduceMean]; -"155 Transpose_6215" [id=155, type=Multiply]; -"156 Transpose_6354" [id=156, type=HSwish]; -"157 Convolution_1574" [id=157, type=Convolution]; -"158 Transpose_6215/fq_output_0" [id=158, type=FakeQuantize]; -"159 Transpose_6354/fq_output_0" [id=159, type=FakeQuantize]; -"160 Transpose_6189" [id=160, type=Add]; -"161 Multiply_9489" [id=161, type=Convolution]; -"162 Transpose_6358" [id=162, type=ReduceMean]; -"163 Transpose_6390" [id=163, type=Multiply]; -"164 Transpose_6191" [id=164, type=Relu]; -"165 Transpose_6239" [id=165, type=Add]; -"166 Convolution_1713" [id=166, type=Convolution]; -"167 Transpose_6390/fq_output_0" [id=167, type=FakeQuantize]; -"168 Convolution_1583" [id=168, type=Convolution]; -"169 Transpose_6239/fq_output_0" [id=169, type=FakeQuantize]; -"170 Transpose_6364" [id=170, type=Add]; -"171 Multiply_9531" [id=171, type=Convolution]; -"172 Transpose_6197" [id=172, type=Add]; -"173 Transpose_6366" [id=173, type=Relu]; -"174 Transpose_6414" [id=174, type=Add]; -"175 Transpose_6211" [id=175, type=HSigmoid]; -"176 Convolution_1722" [id=176, type=Convolution]; -"177 Transpose_6414/fq_output_0" [id=177, type=FakeQuantize]; -"178 Transpose_6211/fq_output_0" [id=178, type=FakeQuantize]; -"179 Transpose_6372" [id=179, type=Add]; -"180 Multiply_9545" [id=180, type=Convolution]; -"181 Transpose_6562" [id=181, type=Add]; -"182 Transpose_6386" [id=182, type=HSigmoid]; -"183 Transpose_6438" [id=183, type=Add]; -"184 Transpose_6562/fq_output_0" [id=184, type=FakeQuantize]; -"185 Transpose_6386/fq_output_0" [id=185, type=FakeQuantize]; -"186 Transpose_6456" [id=186, type=HSwish]; -"187 Multiply_9587" [id=187, type=Convolution]; -"188 Transpose_6710" [id=188, type=Add]; -"189 Transpose_6456/fq_output_0" [id=189, type=FakeQuantize]; -"190 Transpose_6586" [id=190, type=Add]; -"191 Transpose_6710/fq_output_0" [id=191, type=FakeQuantize]; -"192 Multiply_9559" [id=192, type=GroupConvolution]; -"193 Transpose_6604" [id=193, type=HSwish]; -"194 Multiply_9629" [id=194, type=Convolution]; -"195 Transpose_6480" [id=195, type=Add]; -"196 Transpose_6604/fq_output_0" [id=196, type=FakeQuantize]; -"197 Transpose_6734" [id=197, type=Add]; -"198 Transpose_6498" [id=198, type=HSwish]; -"199 Multiply_9601" [id=199, type=GroupConvolution]; -"200 Transpose_6752" [id=200, type=HSwish]; -"201 Transpose_6498/fq_output_0" [id=201, type=FakeQuantize]; -"202 Transpose_6628" [id=202, type=Add]; -"203 Transpose_6752/fq_output_0" [id=203, type=FakeQuantize]; -"204 Transpose_6502" [id=204, type=ReduceMean]; -"205 Transpose_6534" [id=205, type=Multiply]; -"206 Transpose_6646" [id=206, type=HSwish]; -"207 Transpose_6756" [id=207, type=ReduceMean]; -"208 Convolution_1823" [id=208, type=Convolution]; -"209 Transpose_6534/fq_output_0" [id=209, type=FakeQuantize]; -"210 Transpose_6646/fq_output_0" [id=210, type=FakeQuantize]; -"211 Transpose_6756/fq_output_0" [id=211, type=FakeQuantize]; -"212 Transpose_6508" [id=212, type=Add]; -"213 Multiply_9573" [id=213, type=Convolution]; -"214 Transpose_6650" [id=214, type=ReduceMean]; -"215 Transpose_6682" [id=215, type=Multiply]; -"216 Convolution_2013" [id=216, type=Convolution]; -"217 Transpose_6510" [id=217, type=Relu]; -"218 Transpose_6558" [id=218, type=Add]; -"219 Convolution_1934" [id=219, type=Convolution]; -"220 Transpose_6682/fq_output_0" [id=220, type=FakeQuantize]; -"221 Transpose_6762" [id=221, type=Add]; -"222 Convolution_1832" [id=222, type=Convolution]; -"223 Transpose_6558/fq_output_0" [id=223, type=FakeQuantize]; -"224 Transpose_6656" [id=224, type=Add]; -"225 Multiply_9615" [id=225, type=Convolution]; -"226 Transpose_6780" [id=226, type=HSwish]; -"227 Transpose_6516" [id=227, type=Add]; -"228 Transpose_6658" [id=228, type=Relu]; -"229 Transpose_6706" [id=229, type=Add]; -"230 Transpose_6780/fq_output_0" [id=230, type=FakeQuantize]; -"231 Transpose_6530" [id=231, type=HSigmoid]; -"232 Convolution_1943" [id=232, type=Convolution]; -"233 Transpose_6706/fq_output_0" [id=233, type=FakeQuantize]; -"234 Convolution_2025" [id=234, type=Convolution]; -"235 Transpose_6530/fq_output_0" [id=235, type=FakeQuantize]; -"236 Transpose_6664" [id=236, type=Add]; -"237 Transpose_6786" [id=237, type=Add]; -"238 Transpose_6678" [id=238, type=HSigmoid]; -"239 MobilenetV3small/Logits/BiasAdd" [id=239, type=Reshape]; -"240 Transpose_6678/fq_output_0" [id=240, type=FakeQuantize]; -"241 MobilenetV3small/flatten/Reshape" [id=241, type=Reshape]; -"242 MobilenetV3small/Predictions/Softmax" [id=242, type=Softmax]; -"243 Predictions" [id=243, type=Result]; -"244 MobilenetV3small/flatten/Const" [id=244, type=Constant]; -"245 Constant_8887" [id=245, type=Constant]; -"246 Transpose_6784" [id=246, type=Constant]; -"247 Convolution_2025/fq_weights_1" [id=247, type=FakeQuantize]; -"248 Constant_11502" [id=248, type=Constant]; -"249 Constant_11501" [id=249, type=Constant]; -"250 Constant_11500" [id=250, type=Constant]; -"251 Constant_11499" [id=251, type=Constant]; -"252 Transpose_2024" [id=252, type=Constant]; -"253 Constant_11497" [id=253, type=Constant]; -"254 Constant_11496" [id=254, type=Constant]; -"255 Constant_11495" [id=255, type=Constant]; -"256 Constant_11494" [id=256, type=Constant]; -"257 Transpose_6760" [id=257, type=Constant]; -"258 Convolution_2013/fq_weights_1" [id=258, type=FakeQuantize]; -"259 Constant_11492" [id=259, type=Constant]; -"260 Constant_11491" [id=260, type=Constant]; -"261 Constant_11490" [id=261, type=Constant]; -"262 Constant_11489" [id=262, type=Constant]; -"263 Transpose_2012" [id=263, type=Constant]; -"264 Constant_11487" [id=264, type=Constant]; -"265 Constant_11486" [id=265, type=Constant]; -"266 Constant_11485" [id=266, type=Constant]; -"267 Constant_11484" [id=267, type=Constant]; -"268 Constant_6754" [id=268, type=Constant]; -"269 Constant_11482" [id=269, type=Constant]; -"270 Constant_11481" [id=270, type=Constant]; -"271 Constant_11480" [id=271, type=Constant]; -"272 Constant_11479" [id=272, type=Constant]; -"273 Constant_9637" [id=273, type=Constant]; -"274 Multiply_9629/fq_weights_1" [id=274, type=FakeQuantize]; -"275 Constant_11477" [id=275, type=Constant]; -"276 Constant_11476" [id=276, type=Constant]; -"277 Constant_11475" [id=277, type=Constant]; -"278 Constant_11474" [id=278, type=Constant]; -"279 Multiply_9830" [id=279, type=Constant]; -"280 Constant_11472" [id=280, type=Constant]; -"281 Constant_11471" [id=281, type=Constant]; -"282 Constant_11470" [id=282, type=Constant]; -"283 Constant_11469" [id=283, type=Constant]; -"284 Constant_11467" [id=284, type=Constant]; -"285 Constant_11466" [id=285, type=Constant]; -"286 Constant_11465" [id=286, type=Constant]; -"287 Constant_11464" [id=287, type=Constant]; -"288 Constant_9623" [id=288, type=Constant]; -"289 Multiply_9615/fq_weights_1" [id=289, type=FakeQuantize]; -"290 Constant_11462" [id=290, type=Constant]; -"291 Constant_11461" [id=291, type=Constant]; -"292 Constant_11460" [id=292, type=Constant]; -"293 Constant_11459" [id=293, type=Constant]; -"294 Multiply_9824" [id=294, type=Constant]; -"295 Constant_11457" [id=295, type=Constant]; -"296 Constant_11456" [id=296, type=Constant]; -"297 Constant_11455" [id=297, type=Constant]; -"298 Constant_11454" [id=298, type=Constant]; -"299 Constant_11452" [id=299, type=Constant]; -"300 Constant_11451" [id=300, type=Constant]; -"301 Constant_11450" [id=301, type=Constant]; -"302 Constant_11449" [id=302, type=Constant]; -"303 Transpose_6662" [id=303, type=Constant]; -"304 Transpose_1942" [id=304, type=Constant]; -"305 Transpose_6654" [id=305, type=Constant]; -"306 Transpose_1933" [id=306, type=Constant]; -"307 Constant_6648" [id=307, type=Constant]; -"308 Constant_11447" [id=308, type=Constant]; -"309 Constant_11446" [id=309, type=Constant]; -"310 Constant_11445" [id=310, type=Constant]; -"311 Constant_11444" [id=311, type=Constant]; -"312 Constant_9609" [id=312, type=Constant]; -"313 Multiply_9601/fq_weights_1" [id=313, type=FakeQuantize]; -"314 Constant_11442" [id=314, type=Constant]; -"315 Constant_11441" [id=315, type=Constant]; -"316 Constant_11440" [id=316, type=Constant]; -"317 Constant_11439" [id=317, type=Constant]; -"318 Multiply_9819" [id=318, type=Constant]; -"319 Constant_11437" [id=319, type=Constant]; -"320 Constant_11436" [id=320, type=Constant]; -"321 Constant_11435" [id=321, type=Constant]; -"322 Constant_11434" [id=322, type=Constant]; -"323 Constant_9595" [id=323, type=Constant]; -"324 Multiply_9587/fq_weights_1" [id=324, type=FakeQuantize]; -"325 Constant_11432" [id=325, type=Constant]; -"326 Constant_11431" [id=326, type=Constant]; -"327 Constant_11430" [id=327, type=Constant]; -"328 Constant_11429" [id=328, type=Constant]; -"329 Multiply_9813" [id=329, type=Constant]; -"330 Constant_11427" [id=330, type=Constant]; -"331 Constant_11426" [id=331, type=Constant]; -"332 Constant_11425" [id=332, type=Constant]; -"333 Constant_11424" [id=333, type=Constant]; -"334 Constant_11422" [id=334, type=Constant]; -"335 Constant_11421" [id=335, type=Constant]; -"336 Constant_11420" [id=336, type=Constant]; -"337 Constant_11419" [id=337, type=Constant]; -"338 Constant_9581" [id=338, type=Constant]; -"339 Multiply_9573/fq_weights_1" [id=339, type=FakeQuantize]; -"340 Constant_11417" [id=340, type=Constant]; -"341 Constant_11416" [id=341, type=Constant]; -"342 Constant_11415" [id=342, type=Constant]; -"343 Constant_11414" [id=343, type=Constant]; -"344 Multiply_9807" [id=344, type=Constant]; -"345 Constant_11412" [id=345, type=Constant]; -"346 Constant_11411" [id=346, type=Constant]; -"347 Constant_11410" [id=347, type=Constant]; -"348 Constant_11409" [id=348, type=Constant]; -"349 Constant_11407" [id=349, type=Constant]; -"350 Constant_11406" [id=350, type=Constant]; -"351 Constant_11405" [id=351, type=Constant]; -"352 Constant_11404" [id=352, type=Constant]; -"353 Transpose_6514" [id=353, type=Constant]; -"354 Transpose_1831" [id=354, type=Constant]; -"355 Transpose_6506" [id=355, type=Constant]; -"356 Transpose_1822" [id=356, type=Constant]; -"357 Constant_6500" [id=357, type=Constant]; -"358 Constant_11402" [id=358, type=Constant]; -"359 Constant_11401" [id=359, type=Constant]; -"360 Constant_11400" [id=360, type=Constant]; -"361 Constant_11399" [id=361, type=Constant]; -"362 Constant_9567" [id=362, type=Constant]; -"363 Multiply_9559/fq_weights_1" [id=363, type=FakeQuantize]; -"364 Constant_11397" [id=364, type=Constant]; -"365 Constant_11396" [id=365, type=Constant]; -"366 Constant_11395" [id=366, type=Constant]; -"367 Constant_11394" [id=367, type=Constant]; -"368 Multiply_9802" [id=368, type=Constant]; -"369 Constant_11392" [id=369, type=Constant]; -"370 Constant_11391" [id=370, type=Constant]; -"371 Constant_11390" [id=371, type=Constant]; -"372 Constant_11389" [id=372, type=Constant]; -"373 Constant_9553" [id=373, type=Constant]; -"374 Multiply_9545/fq_weights_1" [id=374, type=FakeQuantize]; -"375 Constant_11387" [id=375, type=Constant]; -"376 Constant_11386" [id=376, type=Constant]; -"377 Constant_11385" [id=377, type=Constant]; -"378 Constant_11384" [id=378, type=Constant]; -"379 Multiply_9796" [id=379, type=Constant]; -"380 Constant_11382" [id=380, type=Constant]; -"381 Constant_11381" [id=381, type=Constant]; -"382 Constant_11380" [id=382, type=Constant]; -"383 Constant_11379" [id=383, type=Constant]; -"384 Constant_9539" [id=384, type=Constant]; -"385 Multiply_9531/fq_weights_1" [id=385, type=FakeQuantize]; -"386 Constant_11377" [id=386, type=Constant]; -"387 Constant_11376" [id=387, type=Constant]; -"388 Constant_11375" [id=388, type=Constant]; -"389 Constant_11374" [id=389, type=Constant]; -"390 Multiply_9790" [id=390, type=Constant]; -"391 Constant_11372" [id=391, type=Constant]; -"392 Constant_11371" [id=392, type=Constant]; -"393 Constant_11370" [id=393, type=Constant]; -"394 Constant_11369" [id=394, type=Constant]; -"395 Constant_11367" [id=395, type=Constant]; -"396 Constant_11366" [id=396, type=Constant]; -"397 Constant_11365" [id=397, type=Constant]; -"398 Constant_11364" [id=398, type=Constant]; -"399 Transpose_6370" [id=399, type=Constant]; -"400 Transpose_1721" [id=400, type=Constant]; -"401 Transpose_6362" [id=401, type=Constant]; -"402 Transpose_1712" [id=402, type=Constant]; -"403 Constant_6356" [id=403, type=Constant]; -"404 Constant_11362" [id=404, type=Constant]; -"405 Constant_11361" [id=405, type=Constant]; -"406 Constant_11360" [id=406, type=Constant]; -"407 Constant_11359" [id=407, type=Constant]; -"408 Constant_9525" [id=408, type=Constant]; -"409 Multiply_9517/fq_weights_1" [id=409, type=FakeQuantize]; -"410 Constant_11357" [id=410, type=Constant]; -"411 Constant_11356" [id=411, type=Constant]; -"412 Constant_11355" [id=412, type=Constant]; -"413 Constant_11354" [id=413, type=Constant]; -"414 Multiply_9785" [id=414, type=Constant]; -"415 Constant_11352" [id=415, type=Constant]; -"416 Constant_11351" [id=416, type=Constant]; -"417 Constant_11350" [id=417, type=Constant]; -"418 Constant_11349" [id=418, type=Constant]; -"419 Constant_9511" [id=419, type=Constant]; -"420 Multiply_9503/fq_weights_1" [id=420, type=FakeQuantize]; -"421 Constant_11347" [id=421, type=Constant]; -"422 Constant_11346" [id=422, type=Constant]; -"423 Constant_11345" [id=423, type=Constant]; -"424 Constant_11344" [id=424, type=Constant]; -"425 Multiply_9779" [id=425, type=Constant]; -"426 Constant_11342" [id=426, type=Constant]; -"427 Constant_11341" [id=427, type=Constant]; -"428 Constant_11340" [id=428, type=Constant]; -"429 Constant_11339" [id=429, type=Constant]; -"430 Constant_11337" [id=430, type=Constant]; -"431 Constant_11336" [id=431, type=Constant]; -"432 Constant_11335" [id=432, type=Constant]; -"433 Constant_11334" [id=433, type=Constant]; -"434 Constant_9497" [id=434, type=Constant]; -"435 Multiply_9489/fq_weights_1" [id=435, type=FakeQuantize]; -"436 Constant_11332" [id=436, type=Constant]; -"437 Constant_11331" [id=437, type=Constant]; -"438 Constant_11330" [id=438, type=Constant]; -"439 Constant_11329" [id=439, type=Constant]; -"440 Multiply_9773" [id=440, type=Constant]; -"441 Constant_11327" [id=441, type=Constant]; -"442 Constant_11326" [id=442, type=Constant]; -"443 Constant_11325" [id=443, type=Constant]; -"444 Constant_11324" [id=444, type=Constant]; -"445 Constant_11322" [id=445, type=Constant]; -"446 Constant_11321" [id=446, type=Constant]; -"447 Constant_11320" [id=447, type=Constant]; -"448 Constant_11319" [id=448, type=Constant]; -"449 Transpose_6195" [id=449, type=Constant]; -"450 Transpose_1582" [id=450, type=Constant]; -"451 Transpose_6187" [id=451, type=Constant]; -"452 Transpose_1573" [id=452, type=Constant]; -"453 Constant_6181" [id=453, type=Constant]; -"454 Constant_11317" [id=454, type=Constant]; -"455 Constant_11316" [id=455, type=Constant]; -"456 Constant_11315" [id=456, type=Constant]; -"457 Constant_11314" [id=457, type=Constant]; -"458 Constant_9483" [id=458, type=Constant]; -"459 Multiply_9475/fq_weights_1" [id=459, type=FakeQuantize]; -"460 Constant_11312" [id=460, type=Constant]; -"461 Constant_11311" [id=461, type=Constant]; -"462 Constant_11310" [id=462, type=Constant]; -"463 Constant_11309" [id=463, type=Constant]; -"464 Multiply_9768" [id=464, type=Constant]; -"465 Constant_11307" [id=465, type=Constant]; -"466 Constant_11306" [id=466, type=Constant]; -"467 Constant_11305" [id=467, type=Constant]; -"468 Constant_11304" [id=468, type=Constant]; -"469 Constant_9469" [id=469, type=Constant]; -"470 Multiply_9461/fq_weights_1" [id=470, type=FakeQuantize]; -"471 Constant_11302" [id=471, type=Constant]; -"472 Constant_11301" [id=472, type=Constant]; -"473 Constant_11300" [id=473, type=Constant]; -"474 Constant_11299" [id=474, type=Constant]; -"475 Multiply_9762" [id=475, type=Constant]; -"476 Constant_11297" [id=476, type=Constant]; -"477 Constant_11296" [id=477, type=Constant]; -"478 Constant_11295" [id=478, type=Constant]; -"479 Constant_11294" [id=479, type=Constant]; -"480 Constant_9455" [id=480, type=Constant]; -"481 Multiply_9447/fq_weights_1" [id=481, type=FakeQuantize]; -"482 Constant_11292" [id=482, type=Constant]; -"483 Constant_11291" [id=483, type=Constant]; -"484 Constant_11290" [id=484, type=Constant]; -"485 Constant_11289" [id=485, type=Constant]; -"486 Multiply_9756" [id=486, type=Constant]; -"487 Constant_11287" [id=487, type=Constant]; -"488 Constant_11286" [id=488, type=Constant]; -"489 Constant_11285" [id=489, type=Constant]; -"490 Constant_11284" [id=490, type=Constant]; -"491 Constant_11282" [id=491, type=Constant]; -"492 Constant_11281" [id=492, type=Constant]; -"493 Constant_11280" [id=493, type=Constant]; -"494 Constant_11279" [id=494, type=Constant]; -"495 Transpose_6051" [id=495, type=Constant]; -"496 Transpose_1472" [id=496, type=Constant]; -"497 Transpose_6043" [id=497, type=Constant]; -"498 Transpose_1463" [id=498, type=Constant]; -"499 Constant_6037" [id=499, type=Constant]; -"500 Constant_11277" [id=500, type=Constant]; -"501 Constant_11276" [id=501, type=Constant]; -"502 Constant_11275" [id=502, type=Constant]; -"503 Constant_11274" [id=503, type=Constant]; -"504 Constant_9441" [id=504, type=Constant]; -"505 Multiply_9433/fq_weights_1" [id=505, type=FakeQuantize]; -"506 Constant_11272" [id=506, type=Constant]; -"507 Constant_11271" [id=507, type=Constant]; -"508 Constant_11270" [id=508, type=Constant]; -"509 Constant_11269" [id=509, type=Constant]; -"510 Multiply_9751" [id=510, type=Constant]; -"511 Constant_11267" [id=511, type=Constant]; -"512 Constant_11266" [id=512, type=Constant]; -"513 Constant_11265" [id=513, type=Constant]; -"514 Constant_11264" [id=514, type=Constant]; -"515 Constant_9427" [id=515, type=Constant]; -"516 Multiply_9419/fq_weights_1" [id=516, type=FakeQuantize]; -"517 Constant_11262" [id=517, type=Constant]; -"518 Constant_11261" [id=518, type=Constant]; -"519 Constant_11260" [id=519, type=Constant]; -"520 Constant_11259" [id=520, type=Constant]; -"521 Multiply_9745" [id=521, type=Constant]; -"522 Constant_11257" [id=522, type=Constant]; -"523 Constant_11256" [id=523, type=Constant]; -"524 Constant_11255" [id=524, type=Constant]; -"525 Constant_11254" [id=525, type=Constant]; -"526 Constant_11252" [id=526, type=Constant]; -"527 Constant_11251" [id=527, type=Constant]; -"528 Constant_11250" [id=528, type=Constant]; -"529 Constant_11249" [id=529, type=Constant]; -"530 Constant_9413" [id=530, type=Constant]; -"531 Multiply_9405/fq_weights_1" [id=531, type=FakeQuantize]; -"532 Constant_11247" [id=532, type=Constant]; -"533 Constant_11246" [id=533, type=Constant]; -"534 Constant_11245" [id=534, type=Constant]; -"535 Constant_11244" [id=535, type=Constant]; -"536 Multiply_9739" [id=536, type=Constant]; -"537 Constant_11242" [id=537, type=Constant]; -"538 Constant_11241" [id=538, type=Constant]; -"539 Constant_11240" [id=539, type=Constant]; -"540 Constant_11239" [id=540, type=Constant]; -"541 Constant_11237" [id=541, type=Constant]; -"542 Constant_11236" [id=542, type=Constant]; -"543 Constant_11235" [id=543, type=Constant]; -"544 Constant_11234" [id=544, type=Constant]; -"545 Transpose_5903" [id=545, type=Constant]; -"546 Transpose_1361" [id=546, type=Constant]; -"547 Transpose_5895" [id=547, type=Constant]; -"548 Transpose_1352" [id=548, type=Constant]; -"549 Constant_5889" [id=549, type=Constant]; -"550 Constant_11232" [id=550, type=Constant]; -"551 Constant_11231" [id=551, type=Constant]; -"552 Constant_11230" [id=552, type=Constant]; -"553 Constant_11229" [id=553, type=Constant]; -"554 Constant_9399" [id=554, type=Constant]; -"555 Multiply_9391/fq_weights_1" [id=555, type=FakeQuantize]; -"556 Constant_11227" [id=556, type=Constant]; -"557 Constant_11226" [id=557, type=Constant]; -"558 Constant_11225" [id=558, type=Constant]; -"559 Constant_11224" [id=559, type=Constant]; -"560 Multiply_9734" [id=560, type=Constant]; -"561 Constant_11222" [id=561, type=Constant]; -"562 Constant_11221" [id=562, type=Constant]; -"563 Constant_11220" [id=563, type=Constant]; -"564 Constant_11219" [id=564, type=Constant]; -"565 Constant_9385" [id=565, type=Constant]; -"566 Multiply_9377/fq_weights_1" [id=566, type=FakeQuantize]; -"567 Constant_11217" [id=567, type=Constant]; -"568 Constant_11216" [id=568, type=Constant]; -"569 Constant_11215" [id=569, type=Constant]; -"570 Constant_11214" [id=570, type=Constant]; -"571 Multiply_9728" [id=571, type=Constant]; -"572 Constant_11212" [id=572, type=Constant]; -"573 Constant_11211" [id=573, type=Constant]; -"574 Constant_11210" [id=574, type=Constant]; -"575 Constant_11209" [id=575, type=Constant]; -"576 Constant_11207" [id=576, type=Constant]; -"577 Constant_11206" [id=577, type=Constant]; -"578 Constant_11205" [id=578, type=Constant]; -"579 Constant_11204" [id=579, type=Constant]; -"580 Constant_9371" [id=580, type=Constant]; -"581 Multiply_9363/fq_weights_1" [id=581, type=FakeQuantize]; -"582 Constant_11202" [id=582, type=Constant]; -"583 Constant_11201" [id=583, type=Constant]; -"584 Constant_11200" [id=584, type=Constant]; -"585 Constant_11199" [id=585, type=Constant]; -"586 Multiply_9722" [id=586, type=Constant]; -"587 Constant_11197" [id=587, type=Constant]; -"588 Constant_11196" [id=588, type=Constant]; -"589 Constant_11195" [id=589, type=Constant]; -"590 Constant_11194" [id=590, type=Constant]; -"591 Constant_11192" [id=591, type=Constant]; -"592 Constant_11191" [id=592, type=Constant]; -"593 Constant_11190" [id=593, type=Constant]; -"594 Constant_11189" [id=594, type=Constant]; -"595 Transpose_5755" [id=595, type=Constant]; -"596 Transpose_1250" [id=596, type=Constant]; -"597 Transpose_5747" [id=597, type=Constant]; -"598 Transpose_1241" [id=598, type=Constant]; -"599 Constant_5741" [id=599, type=Constant]; -"600 Constant_11187" [id=600, type=Constant]; -"601 Constant_11186" [id=601, type=Constant]; -"602 Constant_11185" [id=602, type=Constant]; -"603 Constant_11184" [id=603, type=Constant]; -"604 Constant_9357" [id=604, type=Constant]; -"605 Multiply_9349/fq_weights_1" [id=605, type=FakeQuantize]; -"606 Constant_11182" [id=606, type=Constant]; -"607 Constant_11181" [id=607, type=Constant]; -"608 Constant_11180" [id=608, type=Constant]; -"609 Constant_11179" [id=609, type=Constant]; -"610 Multiply_9717" [id=610, type=Constant]; -"611 Constant_11177" [id=611, type=Constant]; -"612 Constant_11176" [id=612, type=Constant]; -"613 Constant_11175" [id=613, type=Constant]; -"614 Constant_11174" [id=614, type=Constant]; -"615 Constant_9343" [id=615, type=Constant]; -"616 Multiply_9335/fq_weights_1" [id=616, type=FakeQuantize]; -"617 Constant_11172" [id=617, type=Constant]; -"618 Constant_11171" [id=618, type=Constant]; -"619 Constant_11170" [id=619, type=Constant]; -"620 Constant_11169" [id=620, type=Constant]; -"621 Multiply_9711" [id=621, type=Constant]; -"622 Constant_11167" [id=622, type=Constant]; -"623 Constant_11166" [id=623, type=Constant]; -"624 Constant_11165" [id=624, type=Constant]; -"625 Constant_11164" [id=625, type=Constant]; -"626 Constant_9329" [id=626, type=Constant]; -"627 Multiply_9321/fq_weights_1" [id=627, type=FakeQuantize]; -"628 Constant_11162" [id=628, type=Constant]; -"629 Constant_11161" [id=629, type=Constant]; -"630 Constant_11160" [id=630, type=Constant]; -"631 Constant_11159" [id=631, type=Constant]; -"632 Multiply_9705" [id=632, type=Constant]; -"633 Constant_11157" [id=633, type=Constant]; -"634 Constant_11156" [id=634, type=Constant]; -"635 Constant_11155" [id=635, type=Constant]; -"636 Constant_11154" [id=636, type=Constant]; -"637 Constant_11152" [id=637, type=Constant]; -"638 Constant_11151" [id=638, type=Constant]; -"639 Constant_11150" [id=639, type=Constant]; -"640 Constant_11149" [id=640, type=Constant]; -"641 Transpose_5611" [id=641, type=Constant]; -"642 Transpose_1140" [id=642, type=Constant]; -"643 Transpose_5603" [id=643, type=Constant]; -"644 Transpose_1131" [id=644, type=Constant]; -"645 Constant_5597" [id=645, type=Constant]; -"646 Constant_11147" [id=646, type=Constant]; -"647 Constant_11146" [id=647, type=Constant]; -"648 Constant_11145" [id=648, type=Constant]; -"649 Constant_11144" [id=649, type=Constant]; -"650 Constant_9315" [id=650, type=Constant]; -"651 Multiply_9307/fq_weights_1" [id=651, type=FakeQuantize]; -"652 Constant_11142" [id=652, type=Constant]; -"653 Constant_11141" [id=653, type=Constant]; -"654 Constant_11140" [id=654, type=Constant]; -"655 Constant_11139" [id=655, type=Constant]; -"656 Multiply_9700" [id=656, type=Constant]; -"657 Constant_11137" [id=657, type=Constant]; -"658 Constant_11136" [id=658, type=Constant]; -"659 Constant_11135" [id=659, type=Constant]; -"660 Constant_11134" [id=660, type=Constant]; -"661 Constant_9301" [id=661, type=Constant]; -"662 Multiply_9293/fq_weights_1" [id=662, type=FakeQuantize]; -"663 Constant_11132" [id=663, type=Constant]; -"664 Constant_11131" [id=664, type=Constant]; -"665 Constant_11130" [id=665, type=Constant]; -"666 Constant_11129" [id=666, type=Constant]; -"667 Multiply_9694" [id=667, type=Constant]; -"668 Constant_11127" [id=668, type=Constant]; -"669 Constant_11126" [id=669, type=Constant]; -"670 Constant_11125" [id=670, type=Constant]; -"671 Constant_11124" [id=671, type=Constant]; -"672 Constant_11122" [id=672, type=Constant]; -"673 Constant_11121" [id=673, type=Constant]; -"674 Constant_11120" [id=674, type=Constant]; -"675 Constant_11119" [id=675, type=Constant]; -"676 Constant_9287" [id=676, type=Constant]; -"677 Multiply_9279/fq_weights_1" [id=677, type=FakeQuantize]; -"678 Constant_11117" [id=678, type=Constant]; -"679 Constant_11116" [id=679, type=Constant]; -"680 Constant_11115" [id=680, type=Constant]; -"681 Constant_11114" [id=681, type=Constant]; -"682 Multiply_9688" [id=682, type=Constant]; -"683 Constant_11112" [id=683, type=Constant]; -"684 Constant_11111" [id=684, type=Constant]; -"685 Constant_11110" [id=685, type=Constant]; -"686 Constant_11109" [id=686, type=Constant]; -"687 Constant_9273" [id=687, type=Constant]; -"688 Multiply_9265/fq_weights_1" [id=688, type=FakeQuantize]; -"689 Constant_11107" [id=689, type=Constant]; -"690 Constant_11106" [id=690, type=Constant]; -"691 Constant_11105" [id=691, type=Constant]; -"692 Constant_11104" [id=692, type=Constant]; -"693 Multiply_9683" [id=693, type=Constant]; -"694 Constant_11102" [id=694, type=Constant]; -"695 Constant_11101" [id=695, type=Constant]; -"696 Constant_11100" [id=696, type=Constant]; -"697 Constant_11099" [id=697, type=Constant]; -"698 Constant_9259" [id=698, type=Constant]; -"699 Multiply_9251/fq_weights_1" [id=699, type=FakeQuantize]; -"700 Constant_11097" [id=700, type=Constant]; -"701 Constant_11096" [id=701, type=Constant]; -"702 Constant_11095" [id=702, type=Constant]; -"703 Constant_11094" [id=703, type=Constant]; -"704 Multiply_9677" [id=704, type=Constant]; -"705 Constant_11092" [id=705, type=Constant]; -"706 Constant_11091" [id=706, type=Constant]; -"707 Constant_11090" [id=707, type=Constant]; -"708 Constant_11089" [id=708, type=Constant]; -"709 Constant_9245" [id=709, type=Constant]; -"710 Multiply_9237/fq_weights_1" [id=710, type=FakeQuantize]; -"711 Constant_11087" [id=711, type=Constant]; -"712 Constant_11086" [id=712, type=Constant]; -"713 Constant_11085" [id=713, type=Constant]; -"714 Constant_11084" [id=714, type=Constant]; -"715 Multiply_9671" [id=715, type=Constant]; -"716 Constant_11082" [id=716, type=Constant]; -"717 Constant_11081" [id=717, type=Constant]; -"718 Constant_11080" [id=718, type=Constant]; -"719 Constant_11079" [id=719, type=Constant]; -"720 Constant_9231" [id=720, type=Constant]; -"721 Multiply_9223/fq_weights_1" [id=721, type=FakeQuantize]; -"722 Constant_11077" [id=722, type=Constant]; -"723 Constant_11076" [id=723, type=Constant]; -"724 Constant_11075" [id=724, type=Constant]; -"725 Constant_11074" [id=725, type=Constant]; -"726 Multiply_9666" [id=726, type=Constant]; -"727 Constant_11072" [id=727, type=Constant]; -"728 Constant_11071" [id=728, type=Constant]; -"729 Constant_11070" [id=729, type=Constant]; -"730 Constant_11069" [id=730, type=Constant]; -"731 Constant_9217" [id=731, type=Constant]; -"732 Multiply_9209/fq_weights_1" [id=732, type=FakeQuantize]; -"733 Constant_11067" [id=733, type=Constant]; -"734 Constant_11066" [id=734, type=Constant]; -"735 Constant_11065" [id=735, type=Constant]; -"736 Constant_11064" [id=736, type=Constant]; -"737 Multiply_9660" [id=737, type=Constant]; -"738 Constant_11062" [id=738, type=Constant]; -"739 Constant_11061" [id=739, type=Constant]; -"740 Constant_11060" [id=740, type=Constant]; -"741 Constant_11059" [id=741, type=Constant]; -"742 Constant_9203" [id=742, type=Constant]; -"743 Multiply_9195/fq_weights_1" [id=743, type=FakeQuantize]; -"744 Constant_11057" [id=744, type=Constant]; -"745 Constant_11056" [id=745, type=Constant]; -"746 Constant_11055" [id=746, type=Constant]; -"747 Constant_11054" [id=747, type=Constant]; -"748 Multiply_9654" [id=748, type=Constant]; -"749 Constant_11052" [id=749, type=Constant]; -"750 Constant_11051" [id=750, type=Constant]; -"751 Constant_11050" [id=751, type=Constant]; -"752 Constant_11049" [id=752, type=Constant]; -"753 Constant_11047" [id=753, type=Constant]; -"754 Constant_11046" [id=754, type=Constant]; -"755 Constant_11045" [id=755, type=Constant]; -"756 Constant_11044" [id=756, type=Constant]; -"757 Transpose_5257" [id=757, type=Constant]; -"758 Transpose_809" [id=758, type=Constant]; -"759 Transpose_5249" [id=759, type=Constant]; -"760 Transpose_800" [id=760, type=Constant]; -"761 Constant_5243" [id=761, type=Constant]; -"762 Constant_11042" [id=762, type=Constant]; -"763 Constant_11041" [id=763, type=Constant]; -"764 Constant_11040" [id=764, type=Constant]; -"765 Constant_11039" [id=765, type=Constant]; -"766 Constant_9189" [id=766, type=Constant]; -"767 Multiply_9181/fq_weights_1" [id=767, type=FakeQuantize]; -"768 Constant_11037" [id=768, type=Constant]; -"769 Constant_11036" [id=769, type=Constant]; -"770 Constant_11035" [id=770, type=Constant]; -"771 Constant_11034" [id=771, type=Constant]; -"772 Multiply_9649" [id=772, type=Constant]; -"773 Constant_11032" [id=773, type=Constant]; -"774 Constant_11031" [id=774, type=Constant]; -"775 Constant_11030" [id=775, type=Constant]; -"776 Constant_11029" [id=776, type=Constant]; -"777 Constant_9175" [id=777, type=Constant]; -"778 Multiply_9167/fq_weights_1" [id=778, type=FakeQuantize]; -"779 Constant_11027" [id=779, type=Constant]; -"780 Constant_11026" [id=780, type=Constant]; -"781 Constant_11025" [id=781, type=Constant]; -"782 Constant_11024" [id=782, type=Constant]; -"783 Gather_10068" [id=783, type=Constant]; -"784 Constant_11022" [id=784, type=Constant]; -"785 Constant_11021" [id=785, type=Constant]; -"786 Constant_11020" [id=786, type=Constant]; -"787 Constant_11019" [id=787, type=Constant]; -"788 Unsqueeze_7776" [id=788, type=Constant]; -"789 Unsqueeze_7782" [id=789, type=Constant]; -"790 Constant_7779" [id=790, type=Constant]; -"0 input_1" -> "1 Transpose_7780" [label="[1, 224, 224, 3]", style=solid]; -"1 Transpose_7780" -> "2 Transpose_7774" [label="[1, 3, 224, 224]", style=solid]; -"2 Transpose_7774" -> "3 Transpose_710" [label="[1, 3, 224, 224]", style=solid]; -"3 Transpose_710" -> "4 Transpose_710/fq_output_0" [label="[1, 3, 224, 224]", style=solid]; -"4 Transpose_710/fq_output_0" -> "5 Multiply_9167" [label="[1, 3, 224, 224]", style=solid]; -"5 Multiply_9167" -> "6 Transpose_5170" [label="[1, 16, 112, 112]", style=solid]; -"6 Transpose_5170" -> "7 Transpose_5188" [label="[1, 16, 112, 112]", style=solid]; -"7 Transpose_5188" -> "8 Transpose_5188/fq_output_0" [label="[1, 16, 112, 112]", style=solid]; -"8 Transpose_5188/fq_output_0" -> "9 Multiply_9181" [label="[1, 16, 112, 112]", style=solid]; -"9 Multiply_9181" -> "10 Transpose_5239" [label="[1, 16, 56, 56]", style=solid]; -"10 Transpose_5239" -> "11 Transpose_5241" [label="[1, 16, 56, 56]", style=solid]; -"11 Transpose_5241" -> "12 Transpose_5241/fq_output_0" [label="[1, 16, 56, 56]", style=solid]; -"12 Transpose_5241/fq_output_0" -> "13 Transpose_5245" [label="[1, 16, 56, 56]", style=solid]; -"12 Transpose_5241/fq_output_0" -> "14 Transpose_5277" [label="[1, 16, 56, 56]", style=solid]; -"13 Transpose_5245" -> "15 Convolution_801" [label="[1, 16, 1, 1]", style=solid]; -"14 Transpose_5277" -> "16 Transpose_5277/fq_output_0" [label="[1, 16, 56, 56]", style=solid]; -"15 Convolution_801" -> "17 Transpose_5251" [label="[1, 8, 1, 1]", style=solid]; -"16 Transpose_5277/fq_output_0" -> "18 Multiply_9195" [label="[1, 16, 56, 56]", style=solid]; -"17 Transpose_5251" -> "19 Transpose_5253" [label="[1, 8, 1, 1]", style=solid]; -"18 Multiply_9195" -> "20 Transpose_5301" [label="[1, 16, 56, 56]", style=solid]; -"19 Transpose_5253" -> "21 Convolution_810" [label="[1, 8, 1, 1]", style=solid]; -"20 Transpose_5301" -> "22 Transpose_5301/fq_output_0" [label="[1, 16, 56, 56]", style=solid]; -"21 Convolution_810" -> "23 Transpose_5259" [label="[1, 16, 1, 1]", style=solid]; -"22 Transpose_5301/fq_output_0" -> "24 Multiply_9209" [label="[1, 16, 56, 56]", style=solid]; -"23 Transpose_5259" -> "25 Transpose_5273" [label="[1, 16, 1, 1]", style=solid]; -"24 Multiply_9209" -> "26 Transpose_5325" [label="[1, 72, 56, 56]", style=solid]; -"25 Transpose_5273" -> "27 Transpose_5273/fq_output_0" [label="[1, 16, 1, 1]", style=solid]; -"26 Transpose_5325" -> "28 Transpose_5327" [label="[1, 72, 56, 56]", style=solid]; -"27 Transpose_5273/fq_output_0" -> "14 Transpose_5277" [label="[1, 16, 1, 1]", style=solid]; -"28 Transpose_5327" -> "29 Transpose_5327/fq_output_0" [label="[1, 72, 56, 56]", style=solid]; -"29 Transpose_5327/fq_output_0" -> "30 Multiply_9223" [label="[1, 72, 56, 56]", style=solid]; -"30 Multiply_9223" -> "31 Transpose_5378" [label="[1, 72, 28, 28]", style=solid]; -"31 Transpose_5378" -> "32 Transpose_5380" [label="[1, 72, 28, 28]", style=solid]; -"32 Transpose_5380" -> "33 Transpose_5380/fq_output_0" [label="[1, 72, 28, 28]", style=solid]; -"33 Transpose_5380/fq_output_0" -> "34 Multiply_9237" [label="[1, 72, 28, 28]", style=solid]; -"34 Multiply_9237" -> "35 Transpose_5404" [label="[1, 24, 28, 28]", style=solid]; -"35 Transpose_5404" -> "36 Transpose_5404/fq_output_0" [label="[1, 24, 28, 28]", style=solid]; -"36 Transpose_5404/fq_output_0" -> "37 Multiply_9251" [label="[1, 24, 28, 28]", style=solid]; -"36 Transpose_5404/fq_output_0" -> "38 Transpose_5484" [label="[1, 24, 28, 28]", style=solid]; -"37 Multiply_9251" -> "39 Transpose_5428" [label="[1, 88, 28, 28]", style=solid]; -"38 Transpose_5484" -> "40 Transpose_5484/fq_output_0" [label="[1, 24, 28, 28]", style=solid]; -"39 Transpose_5428" -> "41 Transpose_5430" [label="[1, 88, 28, 28]", style=solid]; -"40 Transpose_5484/fq_output_0" -> "42 Multiply_9293" [label="[1, 24, 28, 28]", style=solid]; -"41 Transpose_5430" -> "43 Transpose_5430/fq_output_0" [label="[1, 88, 28, 28]", style=solid]; -"42 Multiply_9293" -> "44 Transpose_5508" [label="[1, 96, 28, 28]", style=solid]; -"43 Transpose_5430/fq_output_0" -> "45 Multiply_9265" [label="[1, 88, 28, 28]", style=solid]; -"44 Transpose_5508" -> "46 Transpose_5526" [label="[1, 96, 28, 28]", style=solid]; -"45 Multiply_9265" -> "47 Transpose_5454" [label="[1, 88, 28, 28]", style=solid]; -"46 Transpose_5526" -> "48 Transpose_5526/fq_output_0" [label="[1, 96, 28, 28]", style=solid]; -"47 Transpose_5454" -> "49 Transpose_5456" [label="[1, 88, 28, 28]", style=solid]; -"48 Transpose_5526/fq_output_0" -> "50 Multiply_9307" [label="[1, 96, 28, 28]", style=solid]; -"49 Transpose_5456" -> "51 Transpose_5456/fq_output_0" [label="[1, 88, 28, 28]", style=solid]; -"50 Multiply_9307" -> "52 Transpose_5577" [label="[1, 96, 14, 14]", style=solid]; -"51 Transpose_5456/fq_output_0" -> "53 Multiply_9279" [label="[1, 88, 28, 28]", style=solid]; -"52 Transpose_5577" -> "54 Transpose_5595" [label="[1, 96, 14, 14]", style=solid]; -"53 Multiply_9279" -> "55 Transpose_5480" [label="[1, 24, 28, 28]", style=solid]; -"54 Transpose_5595" -> "56 Transpose_5595/fq_output_0" [label="[1, 96, 14, 14]", style=solid]; -"55 Transpose_5480" -> "57 Transpose_5480/fq_output_0" [label="[1, 24, 28, 28]", style=solid]; -"56 Transpose_5595/fq_output_0" -> "58 Transpose_5599" [label="[1, 96, 14, 14]", style=solid]; -"56 Transpose_5595/fq_output_0" -> "59 Transpose_5631" [label="[1, 96, 14, 14]", style=solid]; -"57 Transpose_5480/fq_output_0" -> "38 Transpose_5484" [label="[1, 24, 28, 28]", style=solid]; -"58 Transpose_5599" -> "60 Convolution_1132" [label="[1, 96, 1, 1]", style=solid]; -"59 Transpose_5631" -> "61 Transpose_5631/fq_output_0" [label="[1, 96, 14, 14]", style=solid]; -"60 Convolution_1132" -> "62 Transpose_5605" [label="[1, 24, 1, 1]", style=solid]; -"61 Transpose_5631/fq_output_0" -> "63 Multiply_9321" [label="[1, 96, 14, 14]", style=solid]; -"62 Transpose_5605" -> "64 Transpose_5607" [label="[1, 24, 1, 1]", style=solid]; -"63 Multiply_9321" -> "65 Transpose_5655" [label="[1, 40, 14, 14]", style=solid]; -"64 Transpose_5607" -> "66 Convolution_1141" [label="[1, 24, 1, 1]", style=solid]; -"65 Transpose_5655" -> "67 Transpose_5655/fq_output_0" [label="[1, 40, 14, 14]", style=solid]; -"66 Convolution_1141" -> "68 Transpose_5613" [label="[1, 96, 1, 1]", style=solid]; -"67 Transpose_5655/fq_output_0" -> "69 Multiply_9335" [label="[1, 40, 14, 14]", style=solid]; -"67 Transpose_5655/fq_output_0" -> "70 Transpose_5803" [label="[1, 40, 14, 14]", style=solid]; -"68 Transpose_5613" -> "71 Transpose_5627" [label="[1, 96, 1, 1]", style=solid]; -"69 Multiply_9335" -> "72 Transpose_5679" [label="[1, 240, 14, 14]", style=solid]; -"70 Transpose_5803" -> "73 Transpose_5803/fq_output_0" 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Convolution_1832" -> "227 Transpose_6516" [label="[1, 576, 1, 1]", style=solid]; -"223 Transpose_6558/fq_output_0" -> "181 Transpose_6562" [label="[1, 96, 7, 7]", style=solid]; -"224 Transpose_6656" -> "228 Transpose_6658" [label="[1, 144, 1, 1]", style=solid]; -"225 Multiply_9615" -> "229 Transpose_6706" [label="[1, 96, 7, 7]", style=solid]; -"226 Transpose_6780" -> "230 Transpose_6780/fq_output_0" [label="[1, 1024, 1, 1]", style=solid]; -"227 Transpose_6516" -> "231 Transpose_6530" [label="[1, 576, 1, 1]", style=solid]; -"228 Transpose_6658" -> "232 Convolution_1943" [label="[1, 144, 1, 1]", style=solid]; -"229 Transpose_6706" -> "233 Transpose_6706/fq_output_0" [label="[1, 96, 7, 7]", style=solid]; -"230 Transpose_6780/fq_output_0" -> "234 Convolution_2025" [label="[1, 1024, 1, 1]", style=solid]; -"231 Transpose_6530" -> "235 Transpose_6530/fq_output_0" [label="[1, 576, 1, 1]", style=solid]; -"232 Convolution_1943" -> "236 Transpose_6664" [label="[1, 576, 1, 1]", style=solid]; -"233 Transpose_6706/fq_output_0" -> "188 Transpose_6710" [label="[1, 96, 7, 7]", style=solid]; -"234 Convolution_2025" -> "237 Transpose_6786" [label="[1, 1000, 1, 1]", style=solid]; -"235 Transpose_6530/fq_output_0" -> "205 Transpose_6534" [label="[1, 576, 1, 1]", style=solid]; -"236 Transpose_6664" -> "238 Transpose_6678" [label="[1, 576, 1, 1]", style=solid]; -"237 Transpose_6786" -> "239 MobilenetV3small/Logits/BiasAdd" [label="[1, 1000, 1, 1]", style=solid]; -"238 Transpose_6678" -> "240 Transpose_6678/fq_output_0" [label="[1, 576, 1, 1]", style=solid]; -"239 MobilenetV3small/Logits/BiasAdd" -> "241 MobilenetV3small/flatten/Reshape" [label="[1, 1, 1, 1000]", style=solid]; -"240 Transpose_6678/fq_output_0" -> "215 Transpose_6682" [label="[1, 576, 1, 1]", style=solid]; -"241 MobilenetV3small/flatten/Reshape" -> "242 MobilenetV3small/Predictions/Softmax" [label="[1, 1000]", style=solid]; -"242 MobilenetV3small/Predictions/Softmax" -> "243 Predictions" [label="[1, 1000]", style=solid]; -"244 MobilenetV3small/flatten/Const" -> "241 MobilenetV3small/flatten/Reshape" [label="[2]", style=dashed]; -"245 Constant_8887" -> "239 MobilenetV3small/Logits/BiasAdd" [label="[4]", style=dashed]; -"246 Transpose_6784" -> "237 Transpose_6786" [label="[1, 1000, 1, 1]", style=solid]; -"247 Convolution_2025/fq_weights_1" -> "234 Convolution_2025" [label="[1000, 1024, 1, 1]", style=solid]; -"248 Constant_11502" -> "247 Convolution_2025/fq_weights_1" [label="[1000, 1, 1, 1]", style=solid]; -"249 Constant_11501" -> "247 Convolution_2025/fq_weights_1" [label="[1000, 1, 1, 1]", style=solid]; -"250 Constant_11500" -> "247 Convolution_2025/fq_weights_1" [label="[1000, 1, 1, 1]", style=solid]; -"251 Constant_11499" -> "247 Convolution_2025/fq_weights_1" [label="[1000, 1, 1, 1]", style=solid]; -"252 Transpose_2024" -> "247 Convolution_2025/fq_weights_1" [label="[1000, 1024, 1, 1]", style=solid]; -"253 Constant_11497" -> "230 Transpose_6780/fq_output_0" [label="[]", style=solid]; -"254 Constant_11496" -> "230 Transpose_6780/fq_output_0" [label="[]", style=solid]; -"255 Constant_11495" -> "230 Transpose_6780/fq_output_0" [label="[]", style=solid]; -"256 Constant_11494" -> "230 Transpose_6780/fq_output_0" [label="[]", style=solid]; -"257 Transpose_6760" -> "221 Transpose_6762" [label="[1, 1024, 1, 1]", style=solid]; -"258 Convolution_2013/fq_weights_1" -> "216 Convolution_2013" [label="[1024, 576, 1, 1]", style=solid]; -"259 Constant_11492" -> "258 Convolution_2013/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; -"260 Constant_11491" -> "258 Convolution_2013/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; -"261 Constant_11490" -> "258 Convolution_2013/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; -"262 Constant_11489" -> "258 Convolution_2013/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; -"263 Transpose_2012" -> "258 Convolution_2013/fq_weights_1" [label="[1024, 576, 1, 1]", style=solid]; -"264 Constant_11487" -> "211 Transpose_6756/fq_output_0" [label="[]", style=solid]; -"265 Constant_11486" -> "211 Transpose_6756/fq_output_0" [label="[]", style=solid]; -"266 Constant_11485" -> "211 Transpose_6756/fq_output_0" [label="[]", style=solid]; -"267 Constant_11484" -> "211 Transpose_6756/fq_output_0" [label="[]", style=solid]; -"268 Constant_6754" -> "207 Transpose_6756" [label="[2]", style=dashed]; -"269 Constant_11482" -> "203 Transpose_6752/fq_output_0" [label="[]", style=solid]; -"270 Constant_11481" -> "203 Transpose_6752/fq_output_0" [label="[]", style=solid]; -"271 Constant_11480" -> "203 Transpose_6752/fq_output_0" [label="[]", style=solid]; -"272 Constant_11479" -> "203 Transpose_6752/fq_output_0" [label="[]", style=solid]; -"273 Constant_9637" -> "197 Transpose_6734" [label="[1, 576, 1, 1]", style=solid]; -"274 Multiply_9629/fq_weights_1" -> "194 Multiply_9629" [label="[576, 96, 1, 1]", style=solid]; -"275 Constant_11477" -> "274 Multiply_9629/fq_weights_1" [label="[576, 1, 1, 1]", style=solid]; -"276 Constant_11476" -> "274 Multiply_9629/fq_weights_1" [label="[576, 1, 1, 1]", style=solid]; -"277 Constant_11475" -> "274 Multiply_9629/fq_weights_1" [label="[576, 1, 1, 1]", style=solid]; -"278 Constant_11474" -> "274 Multiply_9629/fq_weights_1" [label="[576, 1, 1, 1]", style=solid]; -"279 Multiply_9830" -> "274 Multiply_9629/fq_weights_1" [label="[576, 96, 1, 1]", style=solid]; -"280 Constant_11472" -> "191 Transpose_6710/fq_output_0" [label="[]", style=solid]; -"281 Constant_11471" -> "191 Transpose_6710/fq_output_0" [label="[]", style=solid]; -"282 Constant_11470" -> "191 Transpose_6710/fq_output_0" [label="[]", style=solid]; -"283 Constant_11469" -> "191 Transpose_6710/fq_output_0" [label="[]", style=solid]; -"284 Constant_11467" -> "233 Transpose_6706/fq_output_0" [label="[]", style=solid]; -"285 Constant_11466" -> "233 Transpose_6706/fq_output_0" [label="[]", style=solid]; -"286 Constant_11465" -> "233 Transpose_6706/fq_output_0" [label="[]", style=solid]; -"287 Constant_11464" -> "233 Transpose_6706/fq_output_0" [label="[]", style=solid]; -"288 Constant_9623" -> "229 Transpose_6706" [label="[1, 96, 1, 1]", style=solid]; -"289 Multiply_9615/fq_weights_1" -> "225 Multiply_9615" [label="[96, 576, 1, 1]", style=solid]; -"290 Constant_11462" -> "289 Multiply_9615/fq_weights_1" [label="[96, 1, 1, 1]", style=solid]; -"291 Constant_11461" -> "289 Multiply_9615/fq_weights_1" [label="[96, 1, 1, 1]", style=solid]; -"292 Constant_11460" -> "289 Multiply_9615/fq_weights_1" [label="[96, 1, 1, 1]", style=solid]; -"293 Constant_11459" -> "289 Multiply_9615/fq_weights_1" [label="[96, 1, 1, 1]", style=solid]; -"294 Multiply_9824" -> "289 Multiply_9615/fq_weights_1" [label="[96, 576, 1, 1]", style=solid]; -"295 Constant_11457" -> "220 Transpose_6682/fq_output_0" [label="[]", style=solid]; -"296 Constant_11456" -> "220 Transpose_6682/fq_output_0" [label="[]", style=solid]; -"297 Constant_11455" -> "220 Transpose_6682/fq_output_0" [label="[]", style=solid]; -"298 Constant_11454" -> "220 Transpose_6682/fq_output_0" [label="[]", style=solid]; -"299 Constant_11452" -> "240 Transpose_6678/fq_output_0" [label="[]", style=solid]; -"300 Constant_11451" -> "240 Transpose_6678/fq_output_0" [label="[]", style=solid]; -"301 Constant_11450" -> "240 Transpose_6678/fq_output_0" [label="[]", style=solid]; -"302 Constant_11449" -> "240 Transpose_6678/fq_output_0" [label="[]", style=solid]; -"303 Transpose_6662" -> "236 Transpose_6664" [label="[1, 576, 1, 1]", style=solid]; -"304 Transpose_1942" -> "232 Convolution_1943" [label="[576, 144, 1, 1]", style=solid]; -"305 Transpose_6654" -> "224 Transpose_6656" [label="[1, 144, 1, 1]", style=solid]; -"306 Transpose_1933" -> "219 Convolution_1934" [label="[144, 576, 1, 1]", style=solid]; -"307 Constant_6648" -> "214 Transpose_6650" [label="[2]", style=dashed]; -"308 Constant_11447" -> "210 Transpose_6646/fq_output_0" [label="[]", style=solid]; -"309 Constant_11446" -> "210 Transpose_6646/fq_output_0" [label="[]", style=solid]; -"310 Constant_11445" -> "210 Transpose_6646/fq_output_0" [label="[]", style=solid]; -"311 Constant_11444" -> "210 Transpose_6646/fq_output_0" [label="[]", style=solid]; -"312 Constant_9609" -> "202 Transpose_6628" [label="[1, 576, 1, 1]", style=solid]; -"313 Multiply_9601/fq_weights_1" -> "199 Multiply_9601" [label="[576, 1, 1, 5, 5]", style=solid]; -"314 Constant_11442" -> "313 Multiply_9601/fq_weights_1" [label="[576, 1, 1, 1, 1]", style=solid]; -"315 Constant_11441" -> "313 Multiply_9601/fq_weights_1" [label="[576, 1, 1, 1, 1]", style=solid]; -"316 Constant_11440" -> "313 Multiply_9601/fq_weights_1" [label="[576, 1, 1, 1, 1]", style=solid]; -"317 Constant_11439" -> "313 Multiply_9601/fq_weights_1" [label="[576, 1, 1, 1, 1]", style=solid]; -"318 Multiply_9819" -> "313 Multiply_9601/fq_weights_1" [label="[576, 1, 1, 5, 5]", style=solid]; -"319 Constant_11437" -> "196 Transpose_6604/fq_output_0" [label="[1, 576, 1, 1]", style=solid]; -"320 Constant_11436" -> "196 Transpose_6604/fq_output_0" [label="[1, 576, 1, 1]", style=solid]; -"321 Constant_11435" -> "196 Transpose_6604/fq_output_0" [label="[1, 576, 1, 1]", style=solid]; -"322 Constant_11434" -> "196 Transpose_6604/fq_output_0" [label="[1, 576, 1, 1]", style=solid]; -"323 Constant_9595" -> "190 Transpose_6586" [label="[1, 576, 1, 1]", style=solid]; -"324 Multiply_9587/fq_weights_1" -> "187 Multiply_9587" [label="[576, 96, 1, 1]", style=solid]; -"325 Constant_11432" -> "324 Multiply_9587/fq_weights_1" [label="[576, 1, 1, 1]", style=solid]; -"326 Constant_11431" -> "324 Multiply_9587/fq_weights_1" [label="[576, 1, 1, 1]", style=solid]; -"327 Constant_11430" -> "324 Multiply_9587/fq_weights_1" [label="[576, 1, 1, 1]", style=solid]; -"328 Constant_11429" -> "324 Multiply_9587/fq_weights_1" [label="[576, 1, 1, 1]", style=solid]; -"329 Multiply_9813" -> "324 Multiply_9587/fq_weights_1" [label="[576, 96, 1, 1]", style=solid]; -"330 Constant_11427" -> "184 Transpose_6562/fq_output_0" [label="[]", style=solid]; -"331 Constant_11426" -> "184 Transpose_6562/fq_output_0" [label="[]", style=solid]; -"332 Constant_11425" -> "184 Transpose_6562/fq_output_0" [label="[]", style=solid]; -"333 Constant_11424" -> "184 Transpose_6562/fq_output_0" [label="[]", style=solid]; -"334 Constant_11422" -> "223 Transpose_6558/fq_output_0" [label="[]", style=solid]; -"335 Constant_11421" -> "223 Transpose_6558/fq_output_0" [label="[]", style=solid]; -"336 Constant_11420" -> "223 Transpose_6558/fq_output_0" [label="[]", style=solid]; -"337 Constant_11419" -> "223 Transpose_6558/fq_output_0" [label="[]", style=solid]; -"338 Constant_9581" -> "218 Transpose_6558" [label="[1, 96, 1, 1]", style=solid]; -"339 Multiply_9573/fq_weights_1" -> "213 Multiply_9573" [label="[96, 576, 1, 1]", style=solid]; -"340 Constant_11417" -> "339 Multiply_9573/fq_weights_1" [label="[96, 1, 1, 1]", style=solid]; -"341 Constant_11416" -> "339 Multiply_9573/fq_weights_1" [label="[96, 1, 1, 1]", style=solid]; -"342 Constant_11415" -> "339 Multiply_9573/fq_weights_1" [label="[96, 1, 1, 1]", style=solid]; -"343 Constant_11414" -> "339 Multiply_9573/fq_weights_1" [label="[96, 1, 1, 1]", style=solid]; -"344 Multiply_9807" -> "339 Multiply_9573/fq_weights_1" [label="[96, 576, 1, 1]", style=solid]; -"345 Constant_11412" -> "209 Transpose_6534/fq_output_0" [label="[]", style=solid]; -"346 Constant_11411" -> "209 Transpose_6534/fq_output_0" [label="[]", style=solid]; -"347 Constant_11410" -> "209 Transpose_6534/fq_output_0" [label="[]", style=solid]; -"348 Constant_11409" -> "209 Transpose_6534/fq_output_0" [label="[]", style=solid]; -"349 Constant_11407" -> "235 Transpose_6530/fq_output_0" [label="[]", style=solid]; -"350 Constant_11406" -> "235 Transpose_6530/fq_output_0" [label="[]", style=solid]; -"351 Constant_11405" -> "235 Transpose_6530/fq_output_0" [label="[]", style=solid]; -"352 Constant_11404" -> "235 Transpose_6530/fq_output_0" [label="[]", style=solid]; -"353 Transpose_6514" -> "227 Transpose_6516" [label="[1, 576, 1, 1]", style=solid]; -"354 Transpose_1831" -> "222 Convolution_1832" [label="[576, 144, 1, 1]", style=solid]; -"355 Transpose_6506" -> "212 Transpose_6508" [label="[1, 144, 1, 1]", style=solid]; -"356 Transpose_1822" -> "208 Convolution_1823" [label="[144, 576, 1, 1]", style=solid]; -"357 Constant_6500" -> "204 Transpose_6502" [label="[2]", style=dashed]; -"358 Constant_11402" -> "201 Transpose_6498/fq_output_0" [label="[]", style=solid]; -"359 Constant_11401" -> "201 Transpose_6498/fq_output_0" [label="[]", style=solid]; -"360 Constant_11400" -> "201 Transpose_6498/fq_output_0" [label="[]", style=solid]; -"361 Constant_11399" -> "201 Transpose_6498/fq_output_0" [label="[]", style=solid]; -"362 Constant_9567" -> "195 Transpose_6480" [label="[1, 576, 1, 1]", style=solid]; -"363 Multiply_9559/fq_weights_1" -> "192 Multiply_9559" [label="[576, 1, 1, 5, 5]", style=solid]; -"364 Constant_11397" -> "363 Multiply_9559/fq_weights_1" [label="[576, 1, 1, 1, 1]", style=solid]; -"365 Constant_11396" -> "363 Multiply_9559/fq_weights_1" [label="[576, 1, 1, 1, 1]", style=solid]; -"366 Constant_11395" -> "363 Multiply_9559/fq_weights_1" [label="[576, 1, 1, 1, 1]", style=solid]; -"367 Constant_11394" -> "363 Multiply_9559/fq_weights_1" [label="[576, 1, 1, 1, 1]", style=solid]; -"368 Multiply_9802" -> "363 Multiply_9559/fq_weights_1" [label="[576, 1, 1, 5, 5]", style=solid]; -"369 Constant_11392" -> "189 Transpose_6456/fq_output_0" [label="[1, 576, 1, 1]", style=solid]; -"370 Constant_11391" -> "189 Transpose_6456/fq_output_0" [label="[1, 576, 1, 1]", style=solid]; -"371 Constant_11390" -> "189 Transpose_6456/fq_output_0" [label="[1, 576, 1, 1]", style=solid]; -"372 Constant_11389" -> "189 Transpose_6456/fq_output_0" [label="[1, 576, 1, 1]", style=solid]; -"373 Constant_9553" -> "183 Transpose_6438" [label="[1, 576, 1, 1]", style=solid]; -"374 Multiply_9545/fq_weights_1" -> "180 Multiply_9545" [label="[576, 96, 1, 1]", style=solid]; -"375 Constant_11387" -> "374 Multiply_9545/fq_weights_1" [label="[576, 1, 1, 1]", style=solid]; -"376 Constant_11386" -> "374 Multiply_9545/fq_weights_1" [label="[576, 1, 1, 1]", style=solid]; -"377 Constant_11385" -> "374 Multiply_9545/fq_weights_1" [label="[576, 1, 1, 1]", style=solid]; -"378 Constant_11384" -> "374 Multiply_9545/fq_weights_1" [label="[576, 1, 1, 1]", style=solid]; -"379 Multiply_9796" -> "374 Multiply_9545/fq_weights_1" [label="[576, 96, 1, 1]", style=solid]; -"380 Constant_11382" -> "177 Transpose_6414/fq_output_0" [label="[]", style=solid]; -"381 Constant_11381" -> "177 Transpose_6414/fq_output_0" [label="[]", style=solid]; -"382 Constant_11380" -> "177 Transpose_6414/fq_output_0" [label="[]", style=solid]; -"383 Constant_11379" -> "177 Transpose_6414/fq_output_0" [label="[]", style=solid]; -"384 Constant_9539" -> "174 Transpose_6414" [label="[1, 96, 1, 1]", style=solid]; -"385 Multiply_9531/fq_weights_1" -> "171 Multiply_9531" [label="[96, 288, 1, 1]", style=solid]; -"386 Constant_11377" -> "385 Multiply_9531/fq_weights_1" [label="[96, 1, 1, 1]", style=solid]; -"387 Constant_11376" -> "385 Multiply_9531/fq_weights_1" [label="[96, 1, 1, 1]", style=solid]; -"388 Constant_11375" -> "385 Multiply_9531/fq_weights_1" [label="[96, 1, 1, 1]", style=solid]; -"389 Constant_11374" -> "385 Multiply_9531/fq_weights_1" [label="[96, 1, 1, 1]", style=solid]; -"390 Multiply_9790" -> "385 Multiply_9531/fq_weights_1" [label="[96, 288, 1, 1]", style=solid]; -"391 Constant_11372" -> "167 Transpose_6390/fq_output_0" [label="[]", style=solid]; -"392 Constant_11371" -> "167 Transpose_6390/fq_output_0" [label="[]", style=solid]; -"393 Constant_11370" -> "167 Transpose_6390/fq_output_0" [label="[]", style=solid]; -"394 Constant_11369" -> "167 Transpose_6390/fq_output_0" [label="[]", style=solid]; -"395 Constant_11367" -> "185 Transpose_6386/fq_output_0" [label="[]", style=solid]; -"396 Constant_11366" -> "185 Transpose_6386/fq_output_0" [label="[]", style=solid]; -"397 Constant_11365" -> "185 Transpose_6386/fq_output_0" [label="[]", style=solid]; -"398 Constant_11364" -> "185 Transpose_6386/fq_output_0" [label="[]", style=solid]; -"399 Transpose_6370" -> "179 Transpose_6372" [label="[1, 288, 1, 1]", style=solid]; -"400 Transpose_1721" -> "176 Convolution_1722" [label="[288, 72, 1, 1]", style=solid]; -"401 Transpose_6362" -> "170 Transpose_6364" [label="[1, 72, 1, 1]", style=solid]; -"402 Transpose_1712" -> "166 Convolution_1713" [label="[72, 288, 1, 1]", style=solid]; -"403 Constant_6356" -> "162 Transpose_6358" [label="[2]", style=dashed]; -"404 Constant_11362" -> "159 Transpose_6354/fq_output_0" [label="[]", style=solid]; -"405 Constant_11361" -> "159 Transpose_6354/fq_output_0" [label="[]", style=solid]; -"406 Constant_11360" -> "159 Transpose_6354/fq_output_0" [label="[]", style=solid]; -"407 Constant_11359" -> "159 Transpose_6354/fq_output_0" [label="[]", style=solid]; -"408 Constant_9525" -> "153 Transpose_6336" [label="[1, 288, 1, 1]", style=solid]; -"409 Multiply_9517/fq_weights_1" -> "151 Multiply_9517" [label="[288, 1, 1, 5, 5]", style=solid]; -"410 Constant_11357" -> "409 Multiply_9517/fq_weights_1" [label="[288, 1, 1, 1, 1]", style=solid]; -"411 Constant_11356" -> "409 Multiply_9517/fq_weights_1" [label="[288, 1, 1, 1, 1]", style=solid]; -"412 Constant_11355" -> "409 Multiply_9517/fq_weights_1" [label="[288, 1, 1, 1, 1]", style=solid]; -"413 Constant_11354" -> "409 Multiply_9517/fq_weights_1" [label="[288, 1, 1, 1, 1]", style=solid]; -"414 Multiply_9785" -> "409 Multiply_9517/fq_weights_1" [label="[288, 1, 1, 5, 5]", style=solid]; -"415 Constant_11352" -> "149 Transpose_6285/fq_output_0" [label="[1, 288, 1, 1]", style=solid]; -"416 Constant_11351" -> "149 Transpose_6285/fq_output_0" [label="[1, 288, 1, 1]", style=solid]; -"417 Constant_11350" -> "149 Transpose_6285/fq_output_0" [label="[1, 288, 1, 1]", style=solid]; -"418 Constant_11349" -> "149 Transpose_6285/fq_output_0" [label="[1, 288, 1, 1]", style=solid]; -"419 Constant_9511" -> "145 Transpose_6267" [label="[1, 288, 1, 1]", style=solid]; -"420 Multiply_9503/fq_weights_1" -> "143 Multiply_9503" [label="[288, 48, 1, 1]", style=solid]; -"421 Constant_11347" -> "420 Multiply_9503/fq_weights_1" [label="[288, 1, 1, 1]", style=solid]; -"422 Constant_11346" -> "420 Multiply_9503/fq_weights_1" [label="[288, 1, 1, 1]", style=solid]; -"423 Constant_11345" -> "420 Multiply_9503/fq_weights_1" [label="[288, 1, 1, 1]", style=solid]; -"424 Constant_11344" -> "420 Multiply_9503/fq_weights_1" [label="[288, 1, 1, 1]", style=solid]; -"425 Multiply_9779" -> "420 Multiply_9503/fq_weights_1" [label="[288, 48, 1, 1]", style=solid]; -"426 Constant_11342" -> "140 Transpose_6243/fq_output_0" [label="[]", style=solid]; -"427 Constant_11341" -> "140 Transpose_6243/fq_output_0" [label="[]", style=solid]; -"428 Constant_11340" -> "140 Transpose_6243/fq_output_0" [label="[]", style=solid]; -"429 Constant_11339" -> "140 Transpose_6243/fq_output_0" [label="[]", style=solid]; -"430 Constant_11337" -> "169 Transpose_6239/fq_output_0" [label="[]", style=solid]; -"431 Constant_11336" -> "169 Transpose_6239/fq_output_0" [label="[]", style=solid]; -"432 Constant_11335" -> "169 Transpose_6239/fq_output_0" [label="[]", style=solid]; -"433 Constant_11334" -> "169 Transpose_6239/fq_output_0" [label="[]", style=solid]; -"434 Constant_9497" -> "165 Transpose_6239" [label="[1, 48, 1, 1]", style=solid]; -"435 Multiply_9489/fq_weights_1" -> "161 Multiply_9489" [label="[48, 144, 1, 1]", style=solid]; -"436 Constant_11332" -> "435 Multiply_9489/fq_weights_1" [label="[48, 1, 1, 1]", style=solid]; -"437 Constant_11331" -> "435 Multiply_9489/fq_weights_1" [label="[48, 1, 1, 1]", style=solid]; -"438 Constant_11330" -> "435 Multiply_9489/fq_weights_1" [label="[48, 1, 1, 1]", style=solid]; -"439 Constant_11329" -> "435 Multiply_9489/fq_weights_1" [label="[48, 1, 1, 1]", style=solid]; -"440 Multiply_9773" -> "435 Multiply_9489/fq_weights_1" [label="[48, 144, 1, 1]", style=solid]; -"441 Constant_11327" -> "158 Transpose_6215/fq_output_0" [label="[]", style=solid]; -"442 Constant_11326" -> "158 Transpose_6215/fq_output_0" [label="[]", style=solid]; -"443 Constant_11325" -> "158 Transpose_6215/fq_output_0" [label="[]", style=solid]; -"444 Constant_11324" -> "158 Transpose_6215/fq_output_0" [label="[]", style=solid]; -"445 Constant_11322" -> "178 Transpose_6211/fq_output_0" [label="[]", style=solid]; -"446 Constant_11321" -> "178 Transpose_6211/fq_output_0" [label="[]", style=solid]; -"447 Constant_11320" -> "178 Transpose_6211/fq_output_0" [label="[]", style=solid]; -"448 Constant_11319" -> "178 Transpose_6211/fq_output_0" [label="[]", style=solid]; -"449 Transpose_6195" -> "172 Transpose_6197" [label="[1, 144, 1, 1]", style=solid]; -"450 Transpose_1582" -> "168 Convolution_1583" [label="[144, 40, 1, 1]", style=solid]; -"451 Transpose_6187" -> "160 Transpose_6189" [label="[1, 40, 1, 1]", style=solid]; -"452 Transpose_1573" -> "157 Convolution_1574" [label="[40, 144, 1, 1]", style=solid]; -"453 Constant_6181" -> "154 Transpose_6183" [label="[2]", style=dashed]; -"454 Constant_11317" -> "152 Transpose_6179/fq_output_0" [label="[]", style=solid]; -"455 Constant_11316" -> "152 Transpose_6179/fq_output_0" [label="[]", style=solid]; -"456 Constant_11315" -> "152 Transpose_6179/fq_output_0" [label="[]", style=solid]; -"457 Constant_11314" -> "152 Transpose_6179/fq_output_0" [label="[]", style=solid]; -"458 Constant_9483" -> "148 Transpose_6161" [label="[1, 144, 1, 1]", style=solid]; -"459 Multiply_9475/fq_weights_1" -> "146 Multiply_9475" [label="[144, 1, 1, 5, 5]", style=solid]; -"460 Constant_11312" -> "459 Multiply_9475/fq_weights_1" [label="[144, 1, 1, 1, 1]", style=solid]; -"461 Constant_11311" -> "459 Multiply_9475/fq_weights_1" [label="[144, 1, 1, 1, 1]", style=solid]; -"462 Constant_11310" -> "459 Multiply_9475/fq_weights_1" [label="[144, 1, 1, 1, 1]", style=solid]; -"463 Constant_11309" -> "459 Multiply_9475/fq_weights_1" [label="[144, 1, 1, 1, 1]", style=solid]; -"464 Multiply_9768" -> "459 Multiply_9475/fq_weights_1" [label="[144, 1, 1, 5, 5]", style=solid]; -"465 Constant_11307" -> "144 Transpose_6137/fq_output_0" [label="[1, 144, 1, 1]", style=solid]; -"466 Constant_11306" -> "144 Transpose_6137/fq_output_0" [label="[1, 144, 1, 1]", style=solid]; -"467 Constant_11305" -> "144 Transpose_6137/fq_output_0" [label="[1, 144, 1, 1]", style=solid]; -"468 Constant_11304" -> "144 Transpose_6137/fq_output_0" [label="[1, 144, 1, 1]", style=solid]; -"469 Constant_9469" -> "139 Transpose_6119" [label="[1, 144, 1, 1]", style=solid]; -"470 Multiply_9461/fq_weights_1" -> "136 Multiply_9461" [label="[144, 48, 1, 1]", style=solid]; -"471 Constant_11302" -> "470 Multiply_9461/fq_weights_1" [label="[144, 1, 1, 1]", style=solid]; -"472 Constant_11301" -> "470 Multiply_9461/fq_weights_1" [label="[144, 1, 1, 1]", style=solid]; -"473 Constant_11300" -> "470 Multiply_9461/fq_weights_1" [label="[144, 1, 1, 1]", style=solid]; -"474 Constant_11299" -> "470 Multiply_9461/fq_weights_1" [label="[144, 1, 1, 1]", style=solid]; -"475 Multiply_9762" -> "470 Multiply_9461/fq_weights_1" [label="[144, 48, 1, 1]", style=solid]; -"476 Constant_11297" -> "133 Transpose_6095/fq_output_0" [label="[]", style=solid]; -"477 Constant_11296" -> "133 Transpose_6095/fq_output_0" [label="[]", style=solid]; -"478 Constant_11295" -> "133 Transpose_6095/fq_output_0" [label="[]", style=solid]; -"479 Constant_11294" -> "133 Transpose_6095/fq_output_0" [label="[]", style=solid]; -"480 Constant_9455" -> "130 Transpose_6095" [label="[1, 48, 1, 1]", style=solid]; -"481 Multiply_9447/fq_weights_1" -> "126 Multiply_9447" [label="[48, 120, 1, 1]", style=solid]; -"482 Constant_11292" -> "481 Multiply_9447/fq_weights_1" [label="[48, 1, 1, 1]", style=solid]; -"483 Constant_11291" -> "481 Multiply_9447/fq_weights_1" [label="[48, 1, 1, 1]", style=solid]; -"484 Constant_11290" -> "481 Multiply_9447/fq_weights_1" [label="[48, 1, 1, 1]", style=solid]; -"485 Constant_11289" -> "481 Multiply_9447/fq_weights_1" [label="[48, 1, 1, 1]", style=solid]; -"486 Multiply_9756" -> "481 Multiply_9447/fq_weights_1" [label="[48, 120, 1, 1]", style=solid]; -"487 Constant_11287" -> "121 Transpose_6071/fq_output_0" [label="[]", style=solid]; -"488 Constant_11286" -> "121 Transpose_6071/fq_output_0" [label="[]", style=solid]; -"489 Constant_11285" -> "121 Transpose_6071/fq_output_0" [label="[]", style=solid]; -"490 Constant_11284" -> "121 Transpose_6071/fq_output_0" [label="[]", style=solid]; -"491 Constant_11282" -> "141 Transpose_6067/fq_output_0" [label="[]", style=solid]; -"492 Constant_11281" -> "141 Transpose_6067/fq_output_0" [label="[]", style=solid]; -"493 Constant_11280" -> "141 Transpose_6067/fq_output_0" [label="[]", style=solid]; -"494 Constant_11279" -> "141 Transpose_6067/fq_output_0" [label="[]", style=solid]; -"495 Transpose_6051" -> "135 Transpose_6053" [label="[1, 120, 1, 1]", style=solid]; -"496 Transpose_1472" -> "132 Convolution_1473" [label="[120, 32, 1, 1]", style=solid]; -"497 Transpose_6043" -> "125 Transpose_6045" [label="[1, 32, 1, 1]", style=solid]; -"498 Transpose_1463" -> "120 Convolution_1464" [label="[32, 120, 1, 1]", style=solid]; -"499 Constant_6037" -> "115 Transpose_6039" [label="[2]", style=dashed]; -"500 Constant_11277" -> "110 Transpose_6035/fq_output_0" [label="[]", style=solid]; -"501 Constant_11276" -> "110 Transpose_6035/fq_output_0" [label="[]", style=solid]; -"502 Constant_11275" -> "110 Transpose_6035/fq_output_0" [label="[]", style=solid]; -"503 Constant_11274" -> "110 Transpose_6035/fq_output_0" [label="[]", style=solid]; -"504 Constant_9441" -> "100 Transpose_6017" [label="[1, 120, 1, 1]", style=solid]; -"505 Multiply_9433/fq_weights_1" -> "96 Multiply_9433" [label="[120, 1, 1, 5, 5]", style=solid]; -"506 Constant_11272" -> "505 Multiply_9433/fq_weights_1" [label="[120, 1, 1, 1, 1]", style=solid]; -"507 Constant_11271" -> "505 Multiply_9433/fq_weights_1" [label="[120, 1, 1, 1, 1]", style=solid]; -"508 Constant_11270" -> "505 Multiply_9433/fq_weights_1" [label="[120, 1, 1, 1, 1]", style=solid]; -"509 Constant_11269" -> "505 Multiply_9433/fq_weights_1" [label="[120, 1, 1, 1, 1]", style=solid]; -"510 Multiply_9751" -> "505 Multiply_9433/fq_weights_1" [label="[120, 1, 1, 5, 5]", style=solid]; -"511 Constant_11267" -> "92 Transpose_5993/fq_output_0" [label="[1, 120, 1, 1]", style=solid]; -"512 Constant_11266" -> "92 Transpose_5993/fq_output_0" [label="[1, 120, 1, 1]", style=solid]; -"513 Constant_11265" -> "92 Transpose_5993/fq_output_0" [label="[1, 120, 1, 1]", style=solid]; -"514 Constant_11264" -> "92 Transpose_5993/fq_output_0" [label="[1, 120, 1, 1]", style=solid]; -"515 Constant_9427" -> "86 Transpose_5975" [label="[1, 120, 1, 1]", style=solid]; -"516 Multiply_9419/fq_weights_1" -> "83 Multiply_9419" [label="[120, 40, 1, 1]", style=solid]; -"517 Constant_11262" -> "516 Multiply_9419/fq_weights_1" [label="[120, 1, 1, 1]", style=solid]; -"518 Constant_11261" -> "516 Multiply_9419/fq_weights_1" [label="[120, 1, 1, 1]", style=solid]; -"519 Constant_11260" -> "516 Multiply_9419/fq_weights_1" [label="[120, 1, 1, 1]", style=solid]; -"520 Constant_11259" -> "516 Multiply_9419/fq_weights_1" [label="[120, 1, 1, 1]", style=solid]; -"521 Multiply_9745" -> "516 Multiply_9419/fq_weights_1" [label="[120, 40, 1, 1]", style=solid]; -"522 Constant_11257" -> "80 Transpose_5951/fq_output_0" [label="[]", style=solid]; -"523 Constant_11256" -> "80 Transpose_5951/fq_output_0" [label="[]", style=solid]; -"524 Constant_11255" -> "80 Transpose_5951/fq_output_0" [label="[]", style=solid]; -"525 Constant_11254" -> "80 Transpose_5951/fq_output_0" [label="[]", style=solid]; -"526 Constant_11252" -> "124 Transpose_5947/fq_output_0" [label="[]", style=solid]; -"527 Constant_11251" -> "124 Transpose_5947/fq_output_0" [label="[]", style=solid]; -"528 Constant_11250" -> "124 Transpose_5947/fq_output_0" [label="[]", style=solid]; -"529 Constant_11249" -> "124 Transpose_5947/fq_output_0" [label="[]", style=solid]; -"530 Constant_9413" -> "119 Transpose_5947" [label="[1, 40, 1, 1]", style=solid]; -"531 Multiply_9405/fq_weights_1" -> "114 Multiply_9405" [label="[40, 240, 1, 1]", style=solid]; -"532 Constant_11247" -> "531 Multiply_9405/fq_weights_1" [label="[40, 1, 1, 1]", style=solid]; -"533 Constant_11246" -> "531 Multiply_9405/fq_weights_1" [label="[40, 1, 1, 1]", style=solid]; -"534 Constant_11245" -> "531 Multiply_9405/fq_weights_1" [label="[40, 1, 1, 1]", style=solid]; -"535 Constant_11244" -> "531 Multiply_9405/fq_weights_1" [label="[40, 1, 1, 1]", style=solid]; -"536 Multiply_9739" -> "531 Multiply_9405/fq_weights_1" [label="[40, 240, 1, 1]", style=solid]; -"537 Constant_11242" -> "109 Transpose_5923/fq_output_0" [label="[]", style=solid]; -"538 Constant_11241" -> "109 Transpose_5923/fq_output_0" [label="[]", style=solid]; -"539 Constant_11240" -> "109 Transpose_5923/fq_output_0" [label="[]", style=solid]; -"540 Constant_11239" -> "109 Transpose_5923/fq_output_0" [label="[]", style=solid]; -"541 Constant_11237" -> "134 Transpose_5919/fq_output_0" [label="[]", style=solid]; -"542 Constant_11236" -> "134 Transpose_5919/fq_output_0" [label="[]", style=solid]; -"543 Constant_11235" -> "134 Transpose_5919/fq_output_0" [label="[]", style=solid]; -"544 Constant_11234" -> "134 Transpose_5919/fq_output_0" [label="[]", style=solid]; -"545 Transpose_5903" -> "128 Transpose_5905" [label="[1, 240, 1, 1]", style=solid]; -"546 Transpose_1361" -> "123 Convolution_1362" [label="[240, 64, 1, 1]", style=solid]; -"547 Transpose_5895" -> "113 Transpose_5897" [label="[1, 64, 1, 1]", style=solid]; -"548 Transpose_1352" -> "108 Convolution_1353" [label="[64, 240, 1, 1]", style=solid]; -"549 Constant_5889" -> "103 Transpose_5891" [label="[2]", style=dashed]; -"550 Constant_11232" -> "99 Transpose_5887/fq_output_0" [label="[]", style=solid]; -"551 Constant_11231" -> "99 Transpose_5887/fq_output_0" [label="[]", style=solid]; -"552 Constant_11230" -> "99 Transpose_5887/fq_output_0" [label="[]", style=solid]; -"553 Constant_11229" -> "99 Transpose_5887/fq_output_0" [label="[]", style=solid]; -"554 Constant_9399" -> "91 Transpose_5869" [label="[1, 240, 1, 1]", style=solid]; -"555 Multiply_9391/fq_weights_1" -> "88 Multiply_9391" [label="[240, 1, 1, 5, 5]", style=solid]; -"556 Constant_11227" -> "555 Multiply_9391/fq_weights_1" [label="[240, 1, 1, 1, 1]", style=solid]; -"557 Constant_11226" -> "555 Multiply_9391/fq_weights_1" [label="[240, 1, 1, 1, 1]", style=solid]; -"558 Constant_11225" -> "555 Multiply_9391/fq_weights_1" [label="[240, 1, 1, 1, 1]", style=solid]; -"559 Constant_11224" -> "555 Multiply_9391/fq_weights_1" [label="[240, 1, 1, 1, 1]", style=solid]; -"560 Multiply_9734" -> "555 Multiply_9391/fq_weights_1" [label="[240, 1, 1, 5, 5]", style=solid]; -"561 Constant_11222" -> "85 Transpose_5845/fq_output_0" [label="[1, 240, 1, 1]", style=solid]; -"562 Constant_11221" -> "85 Transpose_5845/fq_output_0" [label="[1, 240, 1, 1]", style=solid]; -"563 Constant_11220" -> "85 Transpose_5845/fq_output_0" [label="[1, 240, 1, 1]", style=solid]; -"564 Constant_11219" -> "85 Transpose_5845/fq_output_0" [label="[1, 240, 1, 1]", style=solid]; -"565 Constant_9385" -> "79 Transpose_5827" [label="[1, 240, 1, 1]", style=solid]; -"566 Multiply_9377/fq_weights_1" -> "76 Multiply_9377" [label="[240, 40, 1, 1]", style=solid]; -"567 Constant_11217" -> "566 Multiply_9377/fq_weights_1" [label="[240, 1, 1, 1]", style=solid]; -"568 Constant_11216" -> "566 Multiply_9377/fq_weights_1" [label="[240, 1, 1, 1]", style=solid]; -"569 Constant_11215" -> "566 Multiply_9377/fq_weights_1" [label="[240, 1, 1, 1]", style=solid]; -"570 Constant_11214" -> "566 Multiply_9377/fq_weights_1" [label="[240, 1, 1, 1]", style=solid]; -"571 Multiply_9728" -> "566 Multiply_9377/fq_weights_1" [label="[240, 40, 1, 1]", style=solid]; -"572 Constant_11212" -> "73 Transpose_5803/fq_output_0" [label="[]", style=solid]; -"573 Constant_11211" -> "73 Transpose_5803/fq_output_0" [label="[]", style=solid]; -"574 Constant_11210" -> "73 Transpose_5803/fq_output_0" [label="[]", style=solid]; -"575 Constant_11209" -> "73 Transpose_5803/fq_output_0" [label="[]", style=solid]; -"576 Constant_11207" -> "112 Transpose_5799/fq_output_0" [label="[]", style=solid]; -"577 Constant_11206" -> "112 Transpose_5799/fq_output_0" [label="[]", style=solid]; -"578 Constant_11205" -> "112 Transpose_5799/fq_output_0" [label="[]", style=solid]; -"579 Constant_11204" -> "112 Transpose_5799/fq_output_0" [label="[]", style=solid]; -"580 Constant_9371" -> "107 Transpose_5799" [label="[1, 40, 1, 1]", style=solid]; -"581 Multiply_9363/fq_weights_1" -> "102 Multiply_9363" [label="[40, 240, 1, 1]", style=solid]; -"582 Constant_11202" -> "581 Multiply_9363/fq_weights_1" [label="[40, 1, 1, 1]", style=solid]; -"583 Constant_11201" -> "581 Multiply_9363/fq_weights_1" [label="[40, 1, 1, 1]", style=solid]; -"584 Constant_11200" -> "581 Multiply_9363/fq_weights_1" [label="[40, 1, 1, 1]", style=solid]; -"585 Constant_11199" -> "581 Multiply_9363/fq_weights_1" [label="[40, 1, 1, 1]", style=solid]; -"586 Multiply_9722" -> "581 Multiply_9363/fq_weights_1" [label="[40, 240, 1, 1]", style=solid]; -"587 Constant_11197" -> "98 Transpose_5775/fq_output_0" [label="[]", style=solid]; -"588 Constant_11196" -> "98 Transpose_5775/fq_output_0" [label="[]", style=solid]; -"589 Constant_11195" -> "98 Transpose_5775/fq_output_0" [label="[]", style=solid]; -"590 Constant_11194" -> "98 Transpose_5775/fq_output_0" [label="[]", style=solid]; -"591 Constant_11192" -> "127 Transpose_5771/fq_output_0" [label="[]", style=solid]; -"592 Constant_11191" -> "127 Transpose_5771/fq_output_0" [label="[]", style=solid]; -"593 Constant_11190" -> "127 Transpose_5771/fq_output_0" [label="[]", style=solid]; -"594 Constant_11189" -> "127 Transpose_5771/fq_output_0" [label="[]", style=solid]; -"595 Transpose_5755" -> "117 Transpose_5757" [label="[1, 240, 1, 1]", style=solid]; -"596 Transpose_1250" -> "111 Convolution_1251" [label="[240, 64, 1, 1]", style=solid]; -"597 Transpose_5747" -> "101 Transpose_5749" [label="[1, 64, 1, 1]", style=solid]; -"598 Transpose_1241" -> "97 Convolution_1242" [label="[64, 240, 1, 1]", style=solid]; -"599 Constant_5741" -> "93 Transpose_5743" [label="[2]", style=dashed]; -"600 Constant_11187" -> "90 Transpose_5739/fq_output_0" [label="[]", style=solid]; -"601 Constant_11186" -> "90 Transpose_5739/fq_output_0" [label="[]", style=solid]; -"602 Constant_11185" -> "90 Transpose_5739/fq_output_0" [label="[]", style=solid]; -"603 Constant_11184" -> "90 Transpose_5739/fq_output_0" [label="[]", style=solid]; -"604 Constant_9357" -> "84 Transpose_5721" [label="[1, 240, 1, 1]", style=solid]; -"605 Multiply_9349/fq_weights_1" -> "81 Multiply_9349" [label="[240, 1, 1, 5, 5]", style=solid]; -"606 Constant_11182" -> "605 Multiply_9349/fq_weights_1" [label="[240, 1, 1, 1, 1]", style=solid]; -"607 Constant_11181" -> "605 Multiply_9349/fq_weights_1" [label="[240, 1, 1, 1, 1]", style=solid]; -"608 Constant_11180" -> "605 Multiply_9349/fq_weights_1" [label="[240, 1, 1, 1, 1]", style=solid]; -"609 Constant_11179" -> "605 Multiply_9349/fq_weights_1" [label="[240, 1, 1, 1, 1]", style=solid]; -"610 Multiply_9717" -> "605 Multiply_9349/fq_weights_1" [label="[240, 1, 1, 5, 5]", style=solid]; -"611 Constant_11177" -> "78 Transpose_5697/fq_output_0" [label="[1, 240, 1, 1]", style=solid]; -"612 Constant_11176" -> "78 Transpose_5697/fq_output_0" [label="[1, 240, 1, 1]", style=solid]; -"613 Constant_11175" -> "78 Transpose_5697/fq_output_0" [label="[1, 240, 1, 1]", style=solid]; -"614 Constant_11174" -> "78 Transpose_5697/fq_output_0" [label="[1, 240, 1, 1]", style=solid]; -"615 Constant_9343" -> "72 Transpose_5679" [label="[1, 240, 1, 1]", style=solid]; -"616 Multiply_9335/fq_weights_1" -> "69 Multiply_9335" [label="[240, 40, 1, 1]", style=solid]; -"617 Constant_11172" -> "616 Multiply_9335/fq_weights_1" [label="[240, 1, 1, 1]", style=solid]; -"618 Constant_11171" -> "616 Multiply_9335/fq_weights_1" [label="[240, 1, 1, 1]", style=solid]; -"619 Constant_11170" -> "616 Multiply_9335/fq_weights_1" [label="[240, 1, 1, 1]", style=solid]; -"620 Constant_11169" -> "616 Multiply_9335/fq_weights_1" [label="[240, 1, 1, 1]", style=solid]; -"621 Multiply_9711" -> "616 Multiply_9335/fq_weights_1" [label="[240, 40, 1, 1]", style=solid]; -"622 Constant_11167" -> "67 Transpose_5655/fq_output_0" [label="[]", style=solid]; -"623 Constant_11166" -> "67 Transpose_5655/fq_output_0" [label="[]", style=solid]; -"624 Constant_11165" -> "67 Transpose_5655/fq_output_0" [label="[]", style=solid]; -"625 Constant_11164" -> "67 Transpose_5655/fq_output_0" [label="[]", style=solid]; -"626 Constant_9329" -> "65 Transpose_5655" [label="[1, 40, 1, 1]", style=solid]; -"627 Multiply_9321/fq_weights_1" -> "63 Multiply_9321" [label="[40, 96, 1, 1]", style=solid]; -"628 Constant_11162" -> "627 Multiply_9321/fq_weights_1" [label="[40, 1, 1, 1]", style=solid]; -"629 Constant_11161" -> "627 Multiply_9321/fq_weights_1" [label="[40, 1, 1, 1]", style=solid]; -"630 Constant_11160" -> "627 Multiply_9321/fq_weights_1" [label="[40, 1, 1, 1]", style=solid]; -"631 Constant_11159" -> "627 Multiply_9321/fq_weights_1" [label="[40, 1, 1, 1]", style=solid]; -"632 Multiply_9705" -> "627 Multiply_9321/fq_weights_1" [label="[40, 96, 1, 1]", style=solid]; -"633 Constant_11157" -> "61 Transpose_5631/fq_output_0" [label="[]", style=solid]; -"634 Constant_11156" -> "61 Transpose_5631/fq_output_0" [label="[]", style=solid]; -"635 Constant_11155" -> "61 Transpose_5631/fq_output_0" [label="[]", style=solid]; -"636 Constant_11154" -> "61 Transpose_5631/fq_output_0" [label="[]", style=solid]; -"637 Constant_11152" -> "74 Transpose_5627/fq_output_0" [label="[]", style=solid]; -"638 Constant_11151" -> "74 Transpose_5627/fq_output_0" [label="[]", style=solid]; -"639 Constant_11150" -> "74 Transpose_5627/fq_output_0" [label="[]", style=solid]; -"640 Constant_11149" -> "74 Transpose_5627/fq_output_0" [label="[]", style=solid]; -"641 Transpose_5611" -> "68 Transpose_5613" [label="[1, 96, 1, 1]", style=solid]; -"642 Transpose_1140" -> "66 Convolution_1141" [label="[96, 24, 1, 1]", style=solid]; -"643 Transpose_5603" -> "62 Transpose_5605" [label="[1, 24, 1, 1]", style=solid]; -"644 Transpose_1131" -> "60 Convolution_1132" [label="[24, 96, 1, 1]", style=solid]; -"645 Constant_5597" -> "58 Transpose_5599" [label="[2]", style=dashed]; -"646 Constant_11147" -> "56 Transpose_5595/fq_output_0" [label="[]", style=solid]; -"647 Constant_11146" -> "56 Transpose_5595/fq_output_0" [label="[]", style=solid]; -"648 Constant_11145" -> "56 Transpose_5595/fq_output_0" [label="[]", style=solid]; -"649 Constant_11144" -> "56 Transpose_5595/fq_output_0" [label="[]", style=solid]; -"650 Constant_9315" -> "52 Transpose_5577" [label="[1, 96, 1, 1]", style=solid]; -"651 Multiply_9307/fq_weights_1" -> "50 Multiply_9307" [label="[96, 1, 1, 5, 5]", style=solid]; -"652 Constant_11142" -> "651 Multiply_9307/fq_weights_1" [label="[96, 1, 1, 1, 1]", style=solid]; -"653 Constant_11141" -> "651 Multiply_9307/fq_weights_1" [label="[96, 1, 1, 1, 1]", style=solid]; -"654 Constant_11140" -> "651 Multiply_9307/fq_weights_1" [label="[96, 1, 1, 1, 1]", style=solid]; -"655 Constant_11139" -> "651 Multiply_9307/fq_weights_1" [label="[96, 1, 1, 1, 1]", style=solid]; -"656 Multiply_9700" -> "651 Multiply_9307/fq_weights_1" [label="[96, 1, 1, 5, 5]", style=solid]; -"657 Constant_11137" -> "48 Transpose_5526/fq_output_0" [label="[1, 96, 1, 1]", style=solid]; -"658 Constant_11136" -> "48 Transpose_5526/fq_output_0" [label="[1, 96, 1, 1]", style=solid]; -"659 Constant_11135" -> "48 Transpose_5526/fq_output_0" [label="[1, 96, 1, 1]", style=solid]; -"660 Constant_11134" -> "48 Transpose_5526/fq_output_0" [label="[1, 96, 1, 1]", style=solid]; -"661 Constant_9301" -> "44 Transpose_5508" [label="[1, 96, 1, 1]", style=solid]; -"662 Multiply_9293/fq_weights_1" -> "42 Multiply_9293" [label="[96, 24, 1, 1]", style=solid]; -"663 Constant_11132" -> "662 Multiply_9293/fq_weights_1" [label="[96, 1, 1, 1]", style=solid]; -"664 Constant_11131" -> "662 Multiply_9293/fq_weights_1" [label="[96, 1, 1, 1]", style=solid]; -"665 Constant_11130" -> "662 Multiply_9293/fq_weights_1" [label="[96, 1, 1, 1]", style=solid]; -"666 Constant_11129" -> "662 Multiply_9293/fq_weights_1" [label="[96, 1, 1, 1]", style=solid]; -"667 Multiply_9694" -> "662 Multiply_9293/fq_weights_1" [label="[96, 24, 1, 1]", style=solid]; -"668 Constant_11127" -> "40 Transpose_5484/fq_output_0" [label="[]", style=solid]; -"669 Constant_11126" -> "40 Transpose_5484/fq_output_0" [label="[]", style=solid]; -"670 Constant_11125" -> "40 Transpose_5484/fq_output_0" [label="[]", style=solid]; -"671 Constant_11124" -> "40 Transpose_5484/fq_output_0" [label="[]", style=solid]; -"672 Constant_11122" -> "57 Transpose_5480/fq_output_0" [label="[]", style=solid]; -"673 Constant_11121" -> "57 Transpose_5480/fq_output_0" [label="[]", style=solid]; -"674 Constant_11120" -> "57 Transpose_5480/fq_output_0" [label="[]", style=solid]; -"675 Constant_11119" -> "57 Transpose_5480/fq_output_0" [label="[]", style=solid]; -"676 Constant_9287" -> "55 Transpose_5480" [label="[1, 24, 1, 1]", style=solid]; -"677 Multiply_9279/fq_weights_1" -> "53 Multiply_9279" [label="[24, 88, 1, 1]", style=solid]; -"678 Constant_11117" -> "677 Multiply_9279/fq_weights_1" [label="[24, 1, 1, 1]", style=solid]; -"679 Constant_11116" -> "677 Multiply_9279/fq_weights_1" [label="[24, 1, 1, 1]", style=solid]; -"680 Constant_11115" -> "677 Multiply_9279/fq_weights_1" [label="[24, 1, 1, 1]", style=solid]; -"681 Constant_11114" -> "677 Multiply_9279/fq_weights_1" [label="[24, 1, 1, 1]", style=solid]; -"682 Multiply_9688" -> "677 Multiply_9279/fq_weights_1" [label="[24, 88, 1, 1]", style=solid]; -"683 Constant_11112" -> "51 Transpose_5456/fq_output_0" [label="[]", style=solid]; -"684 Constant_11111" -> "51 Transpose_5456/fq_output_0" [label="[]", style=solid]; -"685 Constant_11110" -> "51 Transpose_5456/fq_output_0" [label="[]", style=solid]; -"686 Constant_11109" -> "51 Transpose_5456/fq_output_0" [label="[]", style=solid]; -"687 Constant_9273" -> "47 Transpose_5454" [label="[1, 88, 1, 1]", style=solid]; -"688 Multiply_9265/fq_weights_1" -> "45 Multiply_9265" [label="[88, 1, 1, 3, 3]", style=solid]; -"689 Constant_11107" -> "688 Multiply_9265/fq_weights_1" [label="[88, 1, 1, 1, 1]", style=solid]; -"690 Constant_11106" -> "688 Multiply_9265/fq_weights_1" [label="[88, 1, 1, 1, 1]", style=solid]; -"691 Constant_11105" -> "688 Multiply_9265/fq_weights_1" [label="[88, 1, 1, 1, 1]", style=solid]; -"692 Constant_11104" -> "688 Multiply_9265/fq_weights_1" [label="[88, 1, 1, 1, 1]", style=solid]; -"693 Multiply_9683" -> "688 Multiply_9265/fq_weights_1" [label="[88, 1, 1, 3, 3]", style=solid]; -"694 Constant_11102" -> "43 Transpose_5430/fq_output_0" [label="[1, 88, 1, 1]", style=solid]; -"695 Constant_11101" -> "43 Transpose_5430/fq_output_0" [label="[1, 88, 1, 1]", style=solid]; -"696 Constant_11100" -> "43 Transpose_5430/fq_output_0" [label="[1, 88, 1, 1]", style=solid]; -"697 Constant_11099" -> "43 Transpose_5430/fq_output_0" [label="[1, 88, 1, 1]", style=solid]; -"698 Constant_9259" -> "39 Transpose_5428" [label="[1, 88, 1, 1]", style=solid]; -"699 Multiply_9251/fq_weights_1" -> "37 Multiply_9251" [label="[88, 24, 1, 1]", style=solid]; -"700 Constant_11097" -> "699 Multiply_9251/fq_weights_1" [label="[88, 1, 1, 1]", style=solid]; -"701 Constant_11096" -> "699 Multiply_9251/fq_weights_1" [label="[88, 1, 1, 1]", style=solid]; -"702 Constant_11095" -> "699 Multiply_9251/fq_weights_1" [label="[88, 1, 1, 1]", style=solid]; -"703 Constant_11094" -> "699 Multiply_9251/fq_weights_1" [label="[88, 1, 1, 1]", style=solid]; -"704 Multiply_9677" -> "699 Multiply_9251/fq_weights_1" [label="[88, 24, 1, 1]", style=solid]; -"705 Constant_11092" -> "36 Transpose_5404/fq_output_0" [label="[]", style=solid]; -"706 Constant_11091" -> "36 Transpose_5404/fq_output_0" [label="[]", style=solid]; -"707 Constant_11090" -> "36 Transpose_5404/fq_output_0" [label="[]", style=solid]; -"708 Constant_11089" -> "36 Transpose_5404/fq_output_0" [label="[]", style=solid]; -"709 Constant_9245" -> "35 Transpose_5404" [label="[1, 24, 1, 1]", style=solid]; -"710 Multiply_9237/fq_weights_1" -> "34 Multiply_9237" [label="[24, 72, 1, 1]", style=solid]; -"711 Constant_11087" -> "710 Multiply_9237/fq_weights_1" [label="[24, 1, 1, 1]", style=solid]; -"712 Constant_11086" -> "710 Multiply_9237/fq_weights_1" [label="[24, 1, 1, 1]", style=solid]; -"713 Constant_11085" -> "710 Multiply_9237/fq_weights_1" [label="[24, 1, 1, 1]", style=solid]; -"714 Constant_11084" -> "710 Multiply_9237/fq_weights_1" [label="[24, 1, 1, 1]", style=solid]; -"715 Multiply_9671" -> "710 Multiply_9237/fq_weights_1" [label="[24, 72, 1, 1]", style=solid]; -"716 Constant_11082" -> "33 Transpose_5380/fq_output_0" [label="[]", style=solid]; -"717 Constant_11081" -> "33 Transpose_5380/fq_output_0" [label="[]", style=solid]; -"718 Constant_11080" -> "33 Transpose_5380/fq_output_0" [label="[]", style=solid]; -"719 Constant_11079" -> "33 Transpose_5380/fq_output_0" [label="[]", style=solid]; -"720 Constant_9231" -> "31 Transpose_5378" [label="[1, 72, 1, 1]", style=solid]; -"721 Multiply_9223/fq_weights_1" -> "30 Multiply_9223" [label="[72, 1, 1, 3, 3]", style=solid]; -"722 Constant_11077" -> "721 Multiply_9223/fq_weights_1" [label="[72, 1, 1, 1, 1]", style=solid]; -"723 Constant_11076" -> "721 Multiply_9223/fq_weights_1" [label="[72, 1, 1, 1, 1]", style=solid]; -"724 Constant_11075" -> "721 Multiply_9223/fq_weights_1" [label="[72, 1, 1, 1, 1]", style=solid]; -"725 Constant_11074" -> "721 Multiply_9223/fq_weights_1" [label="[72, 1, 1, 1, 1]", style=solid]; -"726 Multiply_9666" -> "721 Multiply_9223/fq_weights_1" [label="[72, 1, 1, 3, 3]", style=solid]; -"727 Constant_11072" -> "29 Transpose_5327/fq_output_0" [label="[1, 72, 1, 1]", style=solid]; -"728 Constant_11071" -> "29 Transpose_5327/fq_output_0" [label="[1, 72, 1, 1]", style=solid]; -"729 Constant_11070" -> "29 Transpose_5327/fq_output_0" [label="[1, 72, 1, 1]", style=solid]; -"730 Constant_11069" -> "29 Transpose_5327/fq_output_0" [label="[1, 72, 1, 1]", style=solid]; -"731 Constant_9217" -> "26 Transpose_5325" [label="[1, 72, 1, 1]", style=solid]; -"732 Multiply_9209/fq_weights_1" -> "24 Multiply_9209" [label="[72, 16, 1, 1]", style=solid]; -"733 Constant_11067" -> "732 Multiply_9209/fq_weights_1" [label="[72, 1, 1, 1]", style=solid]; -"734 Constant_11066" -> "732 Multiply_9209/fq_weights_1" [label="[72, 1, 1, 1]", style=solid]; -"735 Constant_11065" -> "732 Multiply_9209/fq_weights_1" [label="[72, 1, 1, 1]", style=solid]; -"736 Constant_11064" -> "732 Multiply_9209/fq_weights_1" [label="[72, 1, 1, 1]", style=solid]; -"737 Multiply_9660" -> "732 Multiply_9209/fq_weights_1" [label="[72, 16, 1, 1]", style=solid]; -"738 Constant_11062" -> "22 Transpose_5301/fq_output_0" [label="[]", style=solid]; -"739 Constant_11061" -> "22 Transpose_5301/fq_output_0" [label="[]", style=solid]; -"740 Constant_11060" -> "22 Transpose_5301/fq_output_0" [label="[]", style=solid]; -"741 Constant_11059" -> "22 Transpose_5301/fq_output_0" [label="[]", style=solid]; -"742 Constant_9203" -> "20 Transpose_5301" [label="[1, 16, 1, 1]", style=solid]; -"743 Multiply_9195/fq_weights_1" -> "18 Multiply_9195" [label="[16, 16, 1, 1]", style=solid]; -"744 Constant_11057" -> "743 Multiply_9195/fq_weights_1" [label="[16, 1, 1, 1]", style=solid]; -"745 Constant_11056" -> "743 Multiply_9195/fq_weights_1" [label="[16, 1, 1, 1]", style=solid]; -"746 Constant_11055" -> "743 Multiply_9195/fq_weights_1" [label="[16, 1, 1, 1]", style=solid]; -"747 Constant_11054" -> "743 Multiply_9195/fq_weights_1" [label="[16, 1, 1, 1]", style=solid]; -"748 Multiply_9654" -> "743 Multiply_9195/fq_weights_1" [label="[16, 16, 1, 1]", style=solid]; -"749 Constant_11052" -> "16 Transpose_5277/fq_output_0" [label="[]", style=solid]; -"750 Constant_11051" -> "16 Transpose_5277/fq_output_0" [label="[]", style=solid]; -"751 Constant_11050" -> "16 Transpose_5277/fq_output_0" [label="[]", style=solid]; -"752 Constant_11049" -> "16 Transpose_5277/fq_output_0" [label="[]", style=solid]; -"753 Constant_11047" -> "27 Transpose_5273/fq_output_0" [label="[]", style=solid]; -"754 Constant_11046" -> "27 Transpose_5273/fq_output_0" [label="[]", style=solid]; -"755 Constant_11045" -> "27 Transpose_5273/fq_output_0" [label="[]", style=solid]; -"756 Constant_11044" -> "27 Transpose_5273/fq_output_0" [label="[]", style=solid]; -"757 Transpose_5257" -> "23 Transpose_5259" [label="[1, 16, 1, 1]", style=solid]; -"758 Transpose_809" -> "21 Convolution_810" [label="[16, 8, 1, 1]", style=solid]; -"759 Transpose_5249" -> "17 Transpose_5251" [label="[1, 8, 1, 1]", style=solid]; -"760 Transpose_800" -> "15 Convolution_801" [label="[8, 16, 1, 1]", style=solid]; -"761 Constant_5243" -> "13 Transpose_5245" [label="[2]", style=dashed]; -"762 Constant_11042" -> "12 Transpose_5241/fq_output_0" [label="[]", style=solid]; -"763 Constant_11041" -> "12 Transpose_5241/fq_output_0" [label="[]", style=solid]; -"764 Constant_11040" -> "12 Transpose_5241/fq_output_0" [label="[]", style=solid]; -"765 Constant_11039" -> "12 Transpose_5241/fq_output_0" [label="[]", style=solid]; -"766 Constant_9189" -> "10 Transpose_5239" [label="[1, 16, 1, 1]", style=solid]; -"767 Multiply_9181/fq_weights_1" -> "9 Multiply_9181" [label="[16, 1, 1, 3, 3]", style=solid]; -"768 Constant_11037" -> "767 Multiply_9181/fq_weights_1" [label="[16, 1, 1, 1, 1]", style=solid]; -"769 Constant_11036" -> "767 Multiply_9181/fq_weights_1" [label="[16, 1, 1, 1, 1]", style=solid]; -"770 Constant_11035" -> "767 Multiply_9181/fq_weights_1" [label="[16, 1, 1, 1, 1]", style=solid]; -"771 Constant_11034" -> "767 Multiply_9181/fq_weights_1" [label="[16, 1, 1, 1, 1]", style=solid]; -"772 Multiply_9649" -> "767 Multiply_9181/fq_weights_1" [label="[16, 1, 1, 3, 3]", style=solid]; -"773 Constant_11032" -> "8 Transpose_5188/fq_output_0" [label="[1, 16, 1, 1]", style=solid]; -"774 Constant_11031" -> "8 Transpose_5188/fq_output_0" [label="[1, 16, 1, 1]", style=solid]; -"775 Constant_11030" -> "8 Transpose_5188/fq_output_0" [label="[1, 16, 1, 1]", style=solid]; -"776 Constant_11029" -> "8 Transpose_5188/fq_output_0" [label="[1, 16, 1, 1]", style=solid]; -"777 Constant_9175" -> "6 Transpose_5170" [label="[1, 16, 1, 1]", style=solid]; -"778 Multiply_9167/fq_weights_1" -> "5 Multiply_9167" [label="[16, 3, 3, 3]", style=solid]; -"779 Constant_11027" -> "778 Multiply_9167/fq_weights_1" [label="[16, 1, 1, 1]", style=solid]; -"780 Constant_11026" -> "778 Multiply_9167/fq_weights_1" [label="[16, 1, 1, 1]", style=solid]; -"781 Constant_11025" -> "778 Multiply_9167/fq_weights_1" [label="[16, 1, 1, 1]", style=solid]; -"782 Constant_11024" -> "778 Multiply_9167/fq_weights_1" [label="[16, 1, 1, 1]", style=solid]; -"783 Gather_10068" -> "778 Multiply_9167/fq_weights_1" [label="[16, 3, 3, 3]", style=solid]; -"784 Constant_11022" -> "4 Transpose_710/fq_output_0" [label="[]", style=solid]; -"785 Constant_11021" -> "4 Transpose_710/fq_output_0" [label="[]", style=solid]; -"786 Constant_11020" -> "4 Transpose_710/fq_output_0" [label="[]", style=solid]; -"787 Constant_11019" -> "4 Transpose_710/fq_output_0" [label="[]", style=solid]; -"788 Unsqueeze_7776" -> "3 Transpose_710" [label="[1, 1, 1, 1]", style=solid]; -"789 Unsqueeze_7782" -> "2 Transpose_7774" [label="[1, 1, 1, 1]", style=solid]; -"790 Constant_7779" -> "1 Transpose_7780" [label="[4]", style=dashed]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/resnet-18-pytorch_performance.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/resnet-18-pytorch_performance.dot deleted file mode 100644 index 480119c4db2..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/quantized/resnet-18-pytorch_performance.dot +++ /dev/null @@ -1,759 +0,0 @@ -strict digraph { -"0 data" [id=0, type=Parameter]; -"1 Multiply_1715" [id=1, type=Multiply]; -"2 Divide_401" [id=2, type=Add]; -"3 Divide_401/fq_output_0" [id=3, type=FakeQuantize]; -"4 /conv1/Conv/WithoutBiases" [id=4, type=Convolution]; -"5 /conv1/Conv" [id=5, type=Add]; -"6 /relu/Relu" [id=6, type=Relu]; -"7 /relu/Relu/fq_output_0" [id=7, type=FakeQuantize]; -"8 /maxpool/MaxPool" [id=8, type=MaxPool]; -"9 /layer1/layer1.0/Add" [id=9, type=Add]; -"10 /layer1/layer1.0/conv1/Conv/WithoutBiases" [id=10, type=Convolution]; -"11 /layer1/layer1.0/relu_1/Relu" [id=11, type=Relu]; -"12 /layer1/layer1.0/conv1/Conv" [id=12, type=Add]; -"13 /layer1/layer1.0/relu_1/Relu/fq_output_0" [id=13, type=FakeQuantize]; -"14 /layer1/layer1.0/relu/Relu" [id=14, type=Relu]; -"15 /layer1/layer1.1/Add" [id=15, type=Add]; -"16 /layer1/layer1.1/conv1/Conv/WithoutBiases" [id=16, type=Convolution]; -"17 /layer1/layer1.0/relu/Relu/fq_output_0" [id=17, type=FakeQuantize]; -"18 /layer1/layer1.1/relu_1/Relu" [id=18, type=Relu]; -"19 /layer1/layer1.1/conv1/Conv" [id=19, type=Add]; -"20 /layer1/layer1.0/conv2/Conv/WithoutBiases" [id=20, type=Convolution]; -"21 /layer1/layer1.1/relu_1/Relu/fq_output_0" [id=21, type=FakeQuantize]; -"22 /layer1/layer1.1/relu/Relu" [id=22, type=Relu]; -"23 /layer1/layer1.0/conv2/Conv" [id=23, type=Add]; -"24 /layer2/layer2.0/conv1/Conv/WithoutBiases" [id=24, type=Convolution]; -"25 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases" [id=25, type=Convolution]; -"26 /layer1/layer1.1/relu/Relu/fq_output_0" [id=26, type=FakeQuantize]; -"27 /layer1/layer1.0/conv2/Conv/fq_output_0" [id=27, type=FakeQuantize]; -"28 /layer2/layer2.0/conv1/Conv" [id=28, type=Add]; -"29 /layer2/layer2.0/downsample/downsample.0/Conv" [id=29, type=Add]; -"30 /layer1/layer1.1/conv2/Conv/WithoutBiases" [id=30, type=Convolution]; -"31 /layer2/layer2.0/relu/Relu" [id=31, type=Relu]; -"32 /layer2/layer2.0/downsample/downsample.0/Conv/fq_output_0" [id=32, type=FakeQuantize]; -"33 /layer1/layer1.1/conv2/Conv" [id=33, type=Add]; -"34 /layer2/layer2.0/relu/Relu/fq_output_0" [id=34, type=FakeQuantize]; -"35 /layer2/layer2.0/Add" [id=35, type=Add]; -"36 /layer1/layer1.1/conv2/Conv/fq_output_0" [id=36, type=FakeQuantize]; -"37 /layer2/layer2.0/conv2/Conv/WithoutBiases" [id=37, type=Convolution]; -"38 /layer2/layer2.0/relu_1/Relu" [id=38, type=Relu]; -"39 /layer2/layer2.0/conv2/Conv" [id=39, type=Add]; -"40 /layer2/layer2.0/relu_1/Relu/fq_output_0" [id=40, type=FakeQuantize]; -"41 /layer2/layer2.0/conv2/Conv/fq_output_0" [id=41, type=FakeQuantize]; -"42 /layer2/layer2.1/Add" [id=42, type=Add]; -"43 /layer2/layer2.1/conv1/Conv/WithoutBiases" [id=43, type=Convolution]; -"44 /layer2/layer2.1/relu_1/Relu" [id=44, type=Relu]; -"45 /layer2/layer2.1/conv1/Conv" [id=45, type=Add]; -"46 /layer2/layer2.1/relu_1/Relu/fq_output_0" [id=46, type=FakeQuantize]; -"47 /layer2/layer2.1/relu/Relu" [id=47, type=Relu]; -"48 /layer3/layer3.0/conv1/Conv/WithoutBiases" [id=48, type=Convolution]; -"49 /layer3/layer3.0/downsample/downsample.0/Conv/WithoutBiases" [id=49, type=Convolution]; -"50 /layer2/layer2.1/relu/Relu/fq_output_0" [id=50, type=FakeQuantize]; -"51 /layer3/layer3.0/conv1/Conv" [id=51, type=Add]; -"52 /layer3/layer3.0/downsample/downsample.0/Conv" [id=52, type=Add]; -"53 /layer2/layer2.1/conv2/Conv/WithoutBiases" [id=53, type=Convolution]; -"54 /layer3/layer3.0/relu/Relu" [id=54, type=Relu]; -"55 /layer3/layer3.0/downsample/downsample.0/Conv/fq_output_0" [id=55, type=FakeQuantize]; -"56 /layer2/layer2.1/conv2/Conv" [id=56, type=Add]; -"57 /layer3/layer3.0/relu/Relu/fq_output_0" [id=57, type=FakeQuantize]; -"58 /layer3/layer3.0/Add" [id=58, type=Add]; -"59 /layer2/layer2.1/conv2/Conv/fq_output_0" [id=59, type=FakeQuantize]; -"60 /layer3/layer3.0/conv2/Conv/WithoutBiases" [id=60, type=Convolution]; -"61 /layer3/layer3.0/relu_1/Relu" [id=61, type=Relu]; -"62 /layer3/layer3.0/conv2/Conv" [id=62, type=Add]; -"63 /layer3/layer3.0/relu_1/Relu/fq_output_0" [id=63, type=FakeQuantize]; -"64 /layer3/layer3.0/conv2/Conv/fq_output_0" [id=64, type=FakeQuantize]; -"65 /layer3/layer3.1/Add" [id=65, type=Add]; -"66 /layer3/layer3.1/conv1/Conv/WithoutBiases" [id=66, type=Convolution]; -"67 /layer3/layer3.1/relu_1/Relu" [id=67, type=Relu]; -"68 /layer3/layer3.1/conv1/Conv" [id=68, type=Add]; -"69 /layer3/layer3.1/relu_1/Relu/fq_output_0" [id=69, type=FakeQuantize]; -"70 /layer3/layer3.1/relu/Relu" [id=70, type=Relu]; -"71 /layer4/layer4.0/conv1/Conv/WithoutBiases" [id=71, type=Convolution]; -"72 /layer4/layer4.0/downsample/downsample.0/Conv/WithoutBiases" [id=72, type=Convolution]; -"73 /layer3/layer3.1/relu/Relu/fq_output_0" [id=73, type=FakeQuantize]; -"74 /layer4/layer4.0/conv1/Conv" [id=74, type=Add]; -"75 /layer4/layer4.0/downsample/downsample.0/Conv" [id=75, type=Add]; -"76 /layer3/layer3.1/conv2/Conv/WithoutBiases" [id=76, type=Convolution]; -"77 /layer4/layer4.0/relu/Relu" [id=77, type=Relu]; -"78 /layer4/layer4.0/downsample/downsample.0/Conv/fq_output_0" [id=78, type=FakeQuantize]; -"79 /layer3/layer3.1/conv2/Conv" [id=79, type=Add]; -"80 /layer4/layer4.0/relu/Relu/fq_output_0" [id=80, type=FakeQuantize]; -"81 /layer4/layer4.0/Add" [id=81, type=Add]; -"82 /layer3/layer3.1/conv2/Conv/fq_output_0" [id=82, type=FakeQuantize]; -"83 /layer4/layer4.0/conv2/Conv/WithoutBiases" [id=83, type=Convolution]; -"84 /layer4/layer4.0/relu_1/Relu" [id=84, type=Relu]; -"85 /layer4/layer4.0/conv2/Conv" [id=85, type=Add]; -"86 /layer4/layer4.0/relu_1/Relu/fq_output_0" [id=86, type=FakeQuantize]; -"87 /layer4/layer4.0/conv2/Conv/fq_output_0" [id=87, type=FakeQuantize]; -"88 /layer4/layer4.1/Add" [id=88, type=Add]; -"89 /layer4/layer4.1/conv1/Conv/WithoutBiases" [id=89, type=Convolution]; -"90 /layer4/layer4.1/relu_1/Relu" [id=90, type=Relu]; -"91 /layer4/layer4.1/conv1/Conv" [id=91, type=Add]; -"92 /layer4/layer4.1/relu_1/Relu/fq_output_0" [id=92, type=FakeQuantize]; -"93 /layer4/layer4.1/relu/Relu" [id=93, type=Relu]; -"94 /avgpool/GlobalAveragePool" [id=94, type=ReduceMean]; -"95 /layer4/layer4.1/relu/Relu/fq_output_0" [id=95, type=FakeQuantize]; -"96 /avgpool/GlobalAveragePool/fq_output_0" [id=96, type=FakeQuantize]; -"97 /layer4/layer4.1/conv2/Conv/WithoutBiases" [id=97, type=Convolution]; -"98 /Flatten" [id=98, type=Reshape]; -"99 /layer4/layer4.1/conv2/Conv" [id=99, type=Add]; -"100 /fc/Gemm/WithoutBiases" [id=100, type=MatMul]; -"101 /layer4/layer4.1/conv2/Conv/fq_output_0" [id=101, type=FakeQuantize]; -"102 prob" [id=102, type=Add]; -"103 prob/sink_port_0" [id=103, type=Result]; -"104 Constant_1789" [id=104, type=Constant]; -"105 /fc/Gemm/WithoutBiases/fq_weights_1" [id=105, type=FakeQuantize]; -"106 Constant_48641" [id=106, type=Constant]; -"107 Constant_48640" [id=107, type=Constant]; -"108 Constant_48639" [id=108, type=Constant]; -"109 Constant_48638" [id=109, type=Constant]; -"110 fc.weight" [id=110, type=Constant]; -"111 Constant_380" [id=111, type=Constant]; -"112 Constant_48636" [id=112, type=Constant]; -"113 Constant_48635" [id=113, type=Constant]; -"114 Constant_48634" [id=114, type=Constant]; -"115 Constant_48633" [id=115, type=Constant]; -"116 Range_376" [id=116, type=Constant]; -"117 Constant_48631" [id=117, type=Constant]; -"118 Constant_48630" [id=118, type=Constant]; -"119 Constant_48629" [id=119, type=Constant]; -"120 Constant_48628" [id=120, type=Constant]; -"121 Constant_48606" [id=121, type=Constant]; -"122 Constant_48605" [id=122, type=Constant]; -"123 Constant_48604" [id=123, type=Constant]; -"124 Constant_48603" [id=124, type=Constant]; -"125 Constant_48581" [id=125, type=Constant]; -"126 Constant_48580" [id=126, type=Constant]; -"127 Constant_48579" [id=127, type=Constant]; -"128 Constant_48578" [id=128, type=Constant]; -"129 Reshape_331" [id=129, type=Constant]; -"130 /layer4/layer4.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [id=130, type=FakeQuantize]; -"131 Constant_48576" [id=131, type=Constant]; -"132 Constant_48575" [id=132, type=Constant]; -"133 Constant_48574" [id=133, type=Constant]; -"134 Constant_48573" [id=134, type=Constant]; -"135 onnx^^Conv_244" [id=135, label="135 onnx::Conv_244", type=Constant]; -"136 Constant_48571" [id=136, type=Constant]; -"137 Constant_48570" [id=137, type=Constant]; -"138 Constant_48569" [id=138, type=Constant]; -"139 Constant_48568" [id=139, type=Constant]; -"140 Constant_48546" [id=140, type=Constant]; -"141 Constant_48545" [id=141, type=Constant]; -"142 Constant_48544" [id=142, type=Constant]; -"143 Constant_48543" [id=143, type=Constant]; -"144 Constant_48521" [id=144, type=Constant]; -"145 Constant_48520" [id=145, type=Constant]; -"146 Constant_48519" [id=146, type=Constant]; -"147 Constant_48518" [id=147, type=Constant]; -"148 Reshape_250" [id=148, type=Constant]; -"149 /layer3/layer3.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [id=149, type=FakeQuantize]; -"150 Constant_48516" [id=150, type=Constant]; -"151 Constant_48515" [id=151, type=Constant]; -"152 Constant_48514" [id=152, type=Constant]; -"153 Constant_48513" [id=153, type=Constant]; -"154 onnx^^Conv_229" [id=154, label="154 onnx::Conv_229", type=Constant]; -"155 Constant_48511" [id=155, type=Constant]; -"156 Constant_48510" [id=156, type=Constant]; -"157 Constant_48509" [id=157, type=Constant]; -"158 Constant_48508" [id=158, type=Constant]; -"159 Constant_48486" [id=159, type=Constant]; -"160 Constant_48485" [id=160, type=Constant]; -"161 Constant_48484" [id=161, type=Constant]; -"162 Constant_48483" [id=162, type=Constant]; -"163 Constant_48461" [id=163, type=Constant]; -"164 Constant_48460" [id=164, type=Constant]; -"165 Constant_48459" [id=165, type=Constant]; -"166 Constant_48458" [id=166, type=Constant]; -"167 Reshape_169" [id=167, type=Constant]; -"168 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [id=168, type=FakeQuantize]; -"169 Constant_48456" [id=169, type=Constant]; -"170 Constant_48455" [id=170, type=Constant]; -"171 Constant_48454" [id=171, type=Constant]; -"172 Constant_48453" [id=172, type=Constant]; -"173 onnx^^Conv_214" [id=173, label="173 onnx::Conv_214", type=Constant]; -"174 Constant_48451" [id=174, type=Constant]; -"175 Constant_48450" [id=175, type=Constant]; -"176 Constant_48449" [id=176, type=Constant]; -"177 Constant_48448" [id=177, type=Constant]; -"178 Constant_48426" [id=178, type=Constant]; -"179 Constant_48425" [id=179, type=Constant]; -"180 Constant_48424" [id=180, type=Constant]; -"181 Constant_48423" [id=181, type=Constant]; -"182 Constant_48401" [id=182, type=Constant]; -"183 Constant_48400" [id=183, type=Constant]; -"184 Constant_48399" [id=184, type=Constant]; -"185 Constant_48398" [id=185, type=Constant]; -"186 Reshape_55" [id=186, type=Constant]; -"187 /conv1/Conv/WithoutBiases/fq_weights_1" [id=187, type=FakeQuantize]; -"188 Constant_48396" [id=188, type=Constant]; -"189 Constant_48395" [id=189, type=Constant]; -"190 Constant_48394" [id=190, type=Constant]; -"191 Constant_48393" [id=191, type=Constant]; -"192 Gather_1788" [id=192, type=Constant]; -"193 Constant_48391" [id=193, type=Constant]; -"194 Constant_48390" [id=194, type=Constant]; -"195 Constant_48389" [id=195, type=Constant]; -"196 Constant_48388" [id=196, type=Constant]; -"197 Gather_1785" [id=197, type=Constant]; -"198 Gather_1782" [id=198, type=Constant]; -"199 Constant_48421" [id=199, type=Constant]; -"200 Constant_48420" [id=200, type=Constant]; -"201 Constant_48419" [id=201, type=Constant]; -"202 Constant_48418" [id=202, type=Constant]; -"203 Reshape_88" [id=203, type=Constant]; -"204 /layer1/layer1.0/conv2/Conv/WithoutBiases/fq_weights_1" [id=204, type=FakeQuantize]; -"205 Constant_48416" [id=205, type=Constant]; -"206 Constant_48415" [id=206, type=Constant]; -"207 Constant_48414" [id=207, type=Constant]; -"208 Constant_48413" [id=208, type=Constant]; -"209 onnx^^Conv_199" [id=209, label="209 onnx::Conv_199", type=Constant]; -"210 Constant_48411" [id=210, type=Constant]; -"211 Constant_48410" [id=211, type=Constant]; -"212 Constant_48409" [id=212, type=Constant]; -"213 Constant_48408" [id=213, type=Constant]; -"214 Reshape_72" [id=214, type=Constant]; -"215 /layer1/layer1.0/conv1/Conv/WithoutBiases/fq_weights_1" [id=215, type=FakeQuantize]; -"216 Constant_48406" [id=216, type=Constant]; -"217 Constant_48405" [id=217, type=Constant]; -"218 Constant_48404" [id=218, type=Constant]; -"219 Constant_48403" [id=219, type=Constant]; -"220 onnx^^Conv_196" [id=220, label="220 onnx::Conv_196", type=Constant]; -"221 Constant_48446" [id=221, type=Constant]; -"222 Constant_48445" [id=222, type=Constant]; -"223 Constant_48444" [id=223, type=Constant]; -"224 Constant_48443" [id=224, type=Constant]; -"225 Reshape_121" [id=225, type=Constant]; -"226 /layer1/layer1.1/conv2/Conv/WithoutBiases/fq_weights_1" [id=226, type=FakeQuantize]; -"227 Constant_48441" [id=227, type=Constant]; -"228 Constant_48440" [id=228, type=Constant]; -"229 Constant_48439" [id=229, type=Constant]; -"230 Constant_48438" [id=230, type=Constant]; -"231 onnx^^Conv_205" [id=231, label="231 onnx::Conv_205", type=Constant]; -"232 Constant_48436" [id=232, type=Constant]; -"233 Constant_48435" [id=233, type=Constant]; -"234 Constant_48434" [id=234, type=Constant]; -"235 Constant_48433" [id=235, type=Constant]; -"236 Reshape_105" [id=236, type=Constant]; -"237 /layer1/layer1.1/conv1/Conv/WithoutBiases/fq_weights_1" [id=237, type=FakeQuantize]; -"238 Constant_48431" [id=238, type=Constant]; -"239 Constant_48430" [id=239, type=Constant]; -"240 Constant_48429" [id=240, type=Constant]; -"241 Constant_48428" [id=241, type=Constant]; -"242 onnx^^Conv_202" [id=242, label="242 onnx::Conv_202", type=Constant]; -"243 Constant_48481" [id=243, type=Constant]; -"244 Constant_48480" [id=244, type=Constant]; -"245 Constant_48479" [id=245, type=Constant]; -"246 Constant_48478" [id=246, type=Constant]; -"247 Reshape_154" [id=247, type=Constant]; -"248 /layer2/layer2.0/conv2/Conv/WithoutBiases/fq_weights_1" [id=248, type=FakeQuantize]; -"249 Constant_48476" [id=249, type=Constant]; -"250 Constant_48475" [id=250, type=Constant]; -"251 Constant_48474" [id=251, type=Constant]; -"252 Constant_48473" [id=252, type=Constant]; -"253 onnx^^Conv_211" [id=253, label="253 onnx::Conv_211", type=Constant]; -"254 Constant_48471" [id=254, type=Constant]; -"255 Constant_48470" [id=255, type=Constant]; -"256 Constant_48469" [id=256, type=Constant]; -"257 Constant_48468" [id=257, type=Constant]; -"258 Reshape_138" [id=258, type=Constant]; -"259 /layer2/layer2.0/conv1/Conv/WithoutBiases/fq_weights_1" [id=259, type=FakeQuantize]; -"260 Constant_48466" [id=260, type=Constant]; -"261 Constant_48465" [id=261, type=Constant]; -"262 Constant_48464" [id=262, type=Constant]; -"263 Constant_48463" [id=263, type=Constant]; -"264 onnx^^Conv_208" [id=264, label="264 onnx::Conv_208", type=Constant]; -"265 Constant_48506" [id=265, type=Constant]; -"266 Constant_48505" [id=266, type=Constant]; -"267 Constant_48504" [id=267, type=Constant]; -"268 Constant_48503" [id=268, type=Constant]; -"269 Reshape_202" [id=269, type=Constant]; -"270 /layer2/layer2.1/conv2/Conv/WithoutBiases/fq_weights_1" [id=270, type=FakeQuantize]; -"271 Constant_48501" [id=271, type=Constant]; -"272 Constant_48500" [id=272, type=Constant]; -"273 Constant_48499" [id=273, type=Constant]; -"274 Constant_48498" [id=274, type=Constant]; -"275 onnx^^Conv_220" [id=275, label="275 onnx::Conv_220", type=Constant]; -"276 Constant_48496" [id=276, type=Constant]; -"277 Constant_48495" [id=277, type=Constant]; -"278 Constant_48494" [id=278, type=Constant]; -"279 Constant_48493" [id=279, type=Constant]; -"280 Reshape_186" [id=280, type=Constant]; -"281 /layer2/layer2.1/conv1/Conv/WithoutBiases/fq_weights_1" [id=281, type=FakeQuantize]; -"282 Constant_48491" [id=282, type=Constant]; -"283 Constant_48490" [id=283, type=Constant]; -"284 Constant_48489" [id=284, type=Constant]; -"285 Constant_48488" [id=285, type=Constant]; -"286 onnx^^Conv_217" [id=286, label="286 onnx::Conv_217", type=Constant]; -"287 Constant_48541" [id=287, type=Constant]; -"288 Constant_48540" [id=288, type=Constant]; -"289 Constant_48539" [id=289, type=Constant]; -"290 Constant_48538" [id=290, type=Constant]; -"291 Reshape_235" [id=291, type=Constant]; -"292 /layer3/layer3.0/conv2/Conv/WithoutBiases/fq_weights_1" [id=292, type=FakeQuantize]; -"293 Constant_48536" [id=293, type=Constant]; -"294 Constant_48535" [id=294, type=Constant]; -"295 Constant_48534" [id=295, type=Constant]; -"296 Constant_48533" [id=296, type=Constant]; -"297 onnx^^Conv_226" [id=297, label="297 onnx::Conv_226", type=Constant]; -"298 Constant_48531" [id=298, type=Constant]; -"299 Constant_48530" [id=299, type=Constant]; -"300 Constant_48529" [id=300, type=Constant]; -"301 Constant_48528" [id=301, type=Constant]; -"302 Reshape_219" [id=302, type=Constant]; -"303 /layer3/layer3.0/conv1/Conv/WithoutBiases/fq_weights_1" [id=303, type=FakeQuantize]; -"304 Constant_48526" [id=304, type=Constant]; -"305 Constant_48525" [id=305, type=Constant]; -"306 Constant_48524" [id=306, type=Constant]; -"307 Constant_48523" [id=307, type=Constant]; -"308 onnx^^Conv_223" [id=308, label="308 onnx::Conv_223", type=Constant]; -"309 Constant_48566" [id=309, type=Constant]; -"310 Constant_48565" [id=310, type=Constant]; -"311 Constant_48564" [id=311, type=Constant]; -"312 Constant_48563" [id=312, type=Constant]; -"313 Reshape_283" [id=313, type=Constant]; -"314 /layer3/layer3.1/conv2/Conv/WithoutBiases/fq_weights_1" [id=314, type=FakeQuantize]; -"315 Constant_48561" [id=315, type=Constant]; -"316 Constant_48560" [id=316, type=Constant]; -"317 Constant_48559" [id=317, type=Constant]; -"318 Constant_48558" [id=318, type=Constant]; -"319 onnx^^Conv_235" [id=319, label="319 onnx::Conv_235", type=Constant]; -"320 Constant_48556" [id=320, type=Constant]; -"321 Constant_48555" [id=321, type=Constant]; -"322 Constant_48554" [id=322, type=Constant]; -"323 Constant_48553" [id=323, type=Constant]; -"324 Reshape_267" [id=324, type=Constant]; -"325 /layer3/layer3.1/conv1/Conv/WithoutBiases/fq_weights_1" [id=325, type=FakeQuantize]; -"326 Constant_48551" [id=326, type=Constant]; -"327 Constant_48550" [id=327, type=Constant]; -"328 Constant_48549" [id=328, type=Constant]; -"329 Constant_48548" [id=329, type=Constant]; -"330 onnx^^Conv_232" [id=330, label="330 onnx::Conv_232", type=Constant]; -"331 Constant_48601" [id=331, type=Constant]; -"332 Constant_48600" [id=332, type=Constant]; -"333 Constant_48599" [id=333, type=Constant]; -"334 Constant_48598" [id=334, type=Constant]; -"335 Reshape_316" [id=335, type=Constant]; -"336 /layer4/layer4.0/conv2/Conv/WithoutBiases/fq_weights_1" [id=336, type=FakeQuantize]; -"337 Constant_48596" [id=337, type=Constant]; -"338 Constant_48595" [id=338, type=Constant]; -"339 Constant_48594" [id=339, type=Constant]; -"340 Constant_48593" [id=340, type=Constant]; -"341 onnx^^Conv_241" [id=341, label="341 onnx::Conv_241", type=Constant]; -"342 Constant_48591" [id=342, type=Constant]; -"343 Constant_48590" [id=343, type=Constant]; -"344 Constant_48589" [id=344, type=Constant]; -"345 Constant_48588" [id=345, type=Constant]; -"346 Reshape_300" [id=346, type=Constant]; -"347 /layer4/layer4.0/conv1/Conv/WithoutBiases/fq_weights_1" [id=347, type=FakeQuantize]; -"348 Constant_48586" [id=348, type=Constant]; -"349 Constant_48585" [id=349, type=Constant]; -"350 Constant_48584" [id=350, type=Constant]; -"351 Constant_48583" [id=351, type=Constant]; -"352 onnx^^Conv_238" [id=352, label="352 onnx::Conv_238", type=Constant]; -"353 Constant_48626" [id=353, type=Constant]; -"354 Constant_48625" [id=354, type=Constant]; -"355 Constant_48624" [id=355, type=Constant]; -"356 Constant_48623" [id=356, type=Constant]; -"357 Reshape_364" [id=357, type=Constant]; -"358 /layer4/layer4.1/conv2/Conv/WithoutBiases/fq_weights_1" [id=358, type=FakeQuantize]; -"359 Constant_48621" [id=359, type=Constant]; -"360 Constant_48620" [id=360, type=Constant]; -"361 Constant_48619" [id=361, type=Constant]; -"362 Constant_48618" [id=362, type=Constant]; -"363 onnx^^Conv_250" [id=363, label="363 onnx::Conv_250", type=Constant]; -"364 Constant_48616" [id=364, type=Constant]; -"365 Constant_48615" [id=365, type=Constant]; -"366 Constant_48614" [id=366, type=Constant]; -"367 Constant_48613" [id=367, type=Constant]; -"368 Reshape_348" [id=368, type=Constant]; -"369 /layer4/layer4.1/conv1/Conv/WithoutBiases/fq_weights_1" [id=369, type=FakeQuantize]; -"370 Constant_48611" [id=370, type=Constant]; -"371 Constant_48610" [id=371, type=Constant]; -"372 Constant_48609" [id=372, type=Constant]; -"373 Constant_48608" [id=373, type=Constant]; -"374 onnx^^Conv_247" [id=374, label="374 onnx::Conv_247", type=Constant]; -"0 data" -> "1 Multiply_1715" [label="[1, 3, 224, 224]", style=solid]; -"1 Multiply_1715" -> "2 Divide_401" [label="[1, 3, 224, 224]", style=solid]; -"2 Divide_401" -> "3 Divide_401/fq_output_0" [label="[1, 3, 224, 224]", style=solid]; -"3 Divide_401/fq_output_0" -> "4 /conv1/Conv/WithoutBiases" [label="[1, 3, 224, 224]", style=solid]; -"4 /conv1/Conv/WithoutBiases" -> "5 /conv1/Conv" [label="[1, 64, 112, 112]", style=solid]; -"5 /conv1/Conv" -> "6 /relu/Relu" [label="[1, 64, 112, 112]", style=solid]; -"6 /relu/Relu" -> "7 /relu/Relu/fq_output_0" [label="[1, 64, 112, 112]", style=solid]; -"7 /relu/Relu/fq_output_0" -> "8 /maxpool/MaxPool" [label="[1, 64, 112, 112]", style=solid]; -"8 /maxpool/MaxPool" -> "9 /layer1/layer1.0/Add" [label="[1, 64, 56, 56]", style=solid]; -"8 /maxpool/MaxPool" -> "10 /layer1/layer1.0/conv1/Conv/WithoutBiases" [label="[1, 64, 56, 56]", style=solid]; -"9 /layer1/layer1.0/Add" -> "11 /layer1/layer1.0/relu_1/Relu" [label="[1, 64, 56, 56]", style=solid]; -"10 /layer1/layer1.0/conv1/Conv/WithoutBiases" -> "12 /layer1/layer1.0/conv1/Conv" [label="[1, 64, 56, 56]", style=solid]; -"11 /layer1/layer1.0/relu_1/Relu" -> "13 /layer1/layer1.0/relu_1/Relu/fq_output_0" [label="[1, 64, 56, 56]", style=solid]; -"12 /layer1/layer1.0/conv1/Conv" -> "14 /layer1/layer1.0/relu/Relu" [label="[1, 64, 56, 56]", style=solid]; -"13 /layer1/layer1.0/relu_1/Relu/fq_output_0" -> "15 /layer1/layer1.1/Add" [label="[1, 64, 56, 56]", style=solid]; -"13 /layer1/layer1.0/relu_1/Relu/fq_output_0" -> "16 /layer1/layer1.1/conv1/Conv/WithoutBiases" [label="[1, 64, 56, 56]", style=solid]; -"14 /layer1/layer1.0/relu/Relu" -> "17 /layer1/layer1.0/relu/Relu/fq_output_0" [label="[1, 64, 56, 56]", style=solid]; -"15 /layer1/layer1.1/Add" -> "18 /layer1/layer1.1/relu_1/Relu" [label="[1, 64, 56, 56]", style=solid]; -"16 /layer1/layer1.1/conv1/Conv/WithoutBiases" -> "19 /layer1/layer1.1/conv1/Conv" [label="[1, 64, 56, 56]", style=solid]; -"17 /layer1/layer1.0/relu/Relu/fq_output_0" -> "20 /layer1/layer1.0/conv2/Conv/WithoutBiases" [label="[1, 64, 56, 56]", style=solid]; -"18 /layer1/layer1.1/relu_1/Relu" -> "21 /layer1/layer1.1/relu_1/Relu/fq_output_0" [label="[1, 64, 56, 56]", style=solid]; -"19 /layer1/layer1.1/conv1/Conv" -> "22 /layer1/layer1.1/relu/Relu" [label="[1, 64, 56, 56]", style=solid]; -"20 /layer1/layer1.0/conv2/Conv/WithoutBiases" -> "23 /layer1/layer1.0/conv2/Conv" [label="[1, 64, 56, 56]", style=solid]; -"21 /layer1/layer1.1/relu_1/Relu/fq_output_0" -> "24 /layer2/layer2.0/conv1/Conv/WithoutBiases" [label="[1, 64, 56, 56]", style=solid]; -"21 /layer1/layer1.1/relu_1/Relu/fq_output_0" -> "25 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases" [label="[1, 64, 56, 56]", style=solid]; -"22 /layer1/layer1.1/relu/Relu" -> "26 /layer1/layer1.1/relu/Relu/fq_output_0" [label="[1, 64, 56, 56]", style=solid]; -"23 /layer1/layer1.0/conv2/Conv" -> "27 /layer1/layer1.0/conv2/Conv/fq_output_0" [label="[1, 64, 56, 56]", style=solid]; -"24 /layer2/layer2.0/conv1/Conv/WithoutBiases" -> "28 /layer2/layer2.0/conv1/Conv" [label="[1, 128, 28, 28]", style=solid]; -"25 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases" -> "29 /layer2/layer2.0/downsample/downsample.0/Conv" [label="[1, 128, 28, 28]", style=solid]; -"26 /layer1/layer1.1/relu/Relu/fq_output_0" -> "30 /layer1/layer1.1/conv2/Conv/WithoutBiases" [label="[1, 64, 56, 56]", style=solid]; -"27 /layer1/layer1.0/conv2/Conv/fq_output_0" -> "9 /layer1/layer1.0/Add" [label="[1, 64, 56, 56]", style=solid]; -"28 /layer2/layer2.0/conv1/Conv" -> "31 /layer2/layer2.0/relu/Relu" [label="[1, 128, 28, 28]", style=solid]; -"29 /layer2/layer2.0/downsample/downsample.0/Conv" -> "32 /layer2/layer2.0/downsample/downsample.0/Conv/fq_output_0" [label="[1, 128, 28, 28]", style=solid]; -"30 /layer1/layer1.1/conv2/Conv/WithoutBiases" -> "33 /layer1/layer1.1/conv2/Conv" [label="[1, 64, 56, 56]", style=solid]; -"31 /layer2/layer2.0/relu/Relu" -> "34 /layer2/layer2.0/relu/Relu/fq_output_0" [label="[1, 128, 28, 28]", style=solid]; -"32 /layer2/layer2.0/downsample/downsample.0/Conv/fq_output_0" -> "35 /layer2/layer2.0/Add" [label="[1, 128, 28, 28]", style=solid]; -"33 /layer1/layer1.1/conv2/Conv" -> "36 /layer1/layer1.1/conv2/Conv/fq_output_0" [label="[1, 64, 56, 56]", style=solid]; -"34 /layer2/layer2.0/relu/Relu/fq_output_0" -> "37 /layer2/layer2.0/conv2/Conv/WithoutBiases" [label="[1, 128, 28, 28]", style=solid]; -"35 /layer2/layer2.0/Add" -> "38 /layer2/layer2.0/relu_1/Relu" [label="[1, 128, 28, 28]", style=solid]; -"36 /layer1/layer1.1/conv2/Conv/fq_output_0" -> "15 /layer1/layer1.1/Add" [label="[1, 64, 56, 56]", style=solid]; -"37 /layer2/layer2.0/conv2/Conv/WithoutBiases" -> "39 /layer2/layer2.0/conv2/Conv" [label="[1, 128, 28, 28]", style=solid]; -"38 /layer2/layer2.0/relu_1/Relu" -> "40 /layer2/layer2.0/relu_1/Relu/fq_output_0" [label="[1, 128, 28, 28]", style=solid]; -"39 /layer2/layer2.0/conv2/Conv" -> "41 /layer2/layer2.0/conv2/Conv/fq_output_0" [label="[1, 128, 28, 28]", style=solid]; -"40 /layer2/layer2.0/relu_1/Relu/fq_output_0" -> "42 /layer2/layer2.1/Add" [label="[1, 128, 28, 28]", style=solid]; -"40 /layer2/layer2.0/relu_1/Relu/fq_output_0" -> "43 /layer2/layer2.1/conv1/Conv/WithoutBiases" [label="[1, 128, 28, 28]", style=solid]; -"41 /layer2/layer2.0/conv2/Conv/fq_output_0" -> "35 /layer2/layer2.0/Add" [label="[1, 128, 28, 28]", style=solid]; -"42 /layer2/layer2.1/Add" -> "44 /layer2/layer2.1/relu_1/Relu" [label="[1, 128, 28, 28]", style=solid]; -"43 /layer2/layer2.1/conv1/Conv/WithoutBiases" -> "45 /layer2/layer2.1/conv1/Conv" [label="[1, 128, 28, 28]", style=solid]; -"44 /layer2/layer2.1/relu_1/Relu" -> "46 /layer2/layer2.1/relu_1/Relu/fq_output_0" [label="[1, 128, 28, 28]", style=solid]; -"45 /layer2/layer2.1/conv1/Conv" -> "47 /layer2/layer2.1/relu/Relu" [label="[1, 128, 28, 28]", style=solid]; -"46 /layer2/layer2.1/relu_1/Relu/fq_output_0" -> "48 /layer3/layer3.0/conv1/Conv/WithoutBiases" [label="[1, 128, 28, 28]", style=solid]; -"46 /layer2/layer2.1/relu_1/Relu/fq_output_0" -> "49 /layer3/layer3.0/downsample/downsample.0/Conv/WithoutBiases" [label="[1, 128, 28, 28]", style=solid]; -"47 /layer2/layer2.1/relu/Relu" -> "50 /layer2/layer2.1/relu/Relu/fq_output_0" [label="[1, 128, 28, 28]", style=solid]; -"48 /layer3/layer3.0/conv1/Conv/WithoutBiases" -> "51 /layer3/layer3.0/conv1/Conv" [label="[1, 256, 14, 14]", style=solid]; -"49 /layer3/layer3.0/downsample/downsample.0/Conv/WithoutBiases" -> "52 /layer3/layer3.0/downsample/downsample.0/Conv" [label="[1, 256, 14, 14]", style=solid]; -"50 /layer2/layer2.1/relu/Relu/fq_output_0" -> "53 /layer2/layer2.1/conv2/Conv/WithoutBiases" [label="[1, 128, 28, 28]", style=solid]; -"51 /layer3/layer3.0/conv1/Conv" -> "54 /layer3/layer3.0/relu/Relu" [label="[1, 256, 14, 14]", style=solid]; -"52 /layer3/layer3.0/downsample/downsample.0/Conv" -> "55 /layer3/layer3.0/downsample/downsample.0/Conv/fq_output_0" [label="[1, 256, 14, 14]", style=solid]; -"53 /layer2/layer2.1/conv2/Conv/WithoutBiases" -> "56 /layer2/layer2.1/conv2/Conv" [label="[1, 128, 28, 28]", style=solid]; -"54 /layer3/layer3.0/relu/Relu" -> "57 /layer3/layer3.0/relu/Relu/fq_output_0" [label="[1, 256, 14, 14]", style=solid]; -"55 /layer3/layer3.0/downsample/downsample.0/Conv/fq_output_0" -> "58 /layer3/layer3.0/Add" [label="[1, 256, 14, 14]", style=solid]; -"56 /layer2/layer2.1/conv2/Conv" -> "59 /layer2/layer2.1/conv2/Conv/fq_output_0" [label="[1, 128, 28, 28]", style=solid]; -"57 /layer3/layer3.0/relu/Relu/fq_output_0" -> "60 /layer3/layer3.0/conv2/Conv/WithoutBiases" [label="[1, 256, 14, 14]", style=solid]; -"58 /layer3/layer3.0/Add" -> "61 /layer3/layer3.0/relu_1/Relu" [label="[1, 256, 14, 14]", style=solid]; -"59 /layer2/layer2.1/conv2/Conv/fq_output_0" -> "42 /layer2/layer2.1/Add" [label="[1, 128, 28, 28]", style=solid]; -"60 /layer3/layer3.0/conv2/Conv/WithoutBiases" -> "62 /layer3/layer3.0/conv2/Conv" [label="[1, 256, 14, 14]", style=solid]; -"61 /layer3/layer3.0/relu_1/Relu" -> "63 /layer3/layer3.0/relu_1/Relu/fq_output_0" [label="[1, 256, 14, 14]", style=solid]; -"62 /layer3/layer3.0/conv2/Conv" -> "64 /layer3/layer3.0/conv2/Conv/fq_output_0" [label="[1, 256, 14, 14]", style=solid]; -"63 /layer3/layer3.0/relu_1/Relu/fq_output_0" -> "65 /layer3/layer3.1/Add" [label="[1, 256, 14, 14]", style=solid]; -"63 /layer3/layer3.0/relu_1/Relu/fq_output_0" -> "66 /layer3/layer3.1/conv1/Conv/WithoutBiases" [label="[1, 256, 14, 14]", style=solid]; -"64 /layer3/layer3.0/conv2/Conv/fq_output_0" -> "58 /layer3/layer3.0/Add" [label="[1, 256, 14, 14]", style=solid]; -"65 /layer3/layer3.1/Add" -> "67 /layer3/layer3.1/relu_1/Relu" [label="[1, 256, 14, 14]", style=solid]; -"66 /layer3/layer3.1/conv1/Conv/WithoutBiases" -> "68 /layer3/layer3.1/conv1/Conv" [label="[1, 256, 14, 14]", style=solid]; -"67 /layer3/layer3.1/relu_1/Relu" -> "69 /layer3/layer3.1/relu_1/Relu/fq_output_0" [label="[1, 256, 14, 14]", style=solid]; -"68 /layer3/layer3.1/conv1/Conv" -> "70 /layer3/layer3.1/relu/Relu" [label="[1, 256, 14, 14]", style=solid]; -"69 /layer3/layer3.1/relu_1/Relu/fq_output_0" -> "71 /layer4/layer4.0/conv1/Conv/WithoutBiases" [label="[1, 256, 14, 14]", style=solid]; -"69 /layer3/layer3.1/relu_1/Relu/fq_output_0" -> "72 /layer4/layer4.0/downsample/downsample.0/Conv/WithoutBiases" [label="[1, 256, 14, 14]", style=solid]; -"70 /layer3/layer3.1/relu/Relu" -> "73 /layer3/layer3.1/relu/Relu/fq_output_0" [label="[1, 256, 14, 14]", style=solid]; -"71 /layer4/layer4.0/conv1/Conv/WithoutBiases" -> "74 /layer4/layer4.0/conv1/Conv" [label="[1, 512, 7, 7]", style=solid]; -"72 /layer4/layer4.0/downsample/downsample.0/Conv/WithoutBiases" -> "75 /layer4/layer4.0/downsample/downsample.0/Conv" [label="[1, 512, 7, 7]", style=solid]; -"73 /layer3/layer3.1/relu/Relu/fq_output_0" -> "76 /layer3/layer3.1/conv2/Conv/WithoutBiases" [label="[1, 256, 14, 14]", style=solid]; -"74 /layer4/layer4.0/conv1/Conv" -> "77 /layer4/layer4.0/relu/Relu" [label="[1, 512, 7, 7]", style=solid]; -"75 /layer4/layer4.0/downsample/downsample.0/Conv" -> "78 /layer4/layer4.0/downsample/downsample.0/Conv/fq_output_0" [label="[1, 512, 7, 7]", style=solid]; -"76 /layer3/layer3.1/conv2/Conv/WithoutBiases" -> "79 /layer3/layer3.1/conv2/Conv" [label="[1, 256, 14, 14]", style=solid]; -"77 /layer4/layer4.0/relu/Relu" -> "80 /layer4/layer4.0/relu/Relu/fq_output_0" [label="[1, 512, 7, 7]", style=solid]; -"78 /layer4/layer4.0/downsample/downsample.0/Conv/fq_output_0" -> "81 /layer4/layer4.0/Add" [label="[1, 512, 7, 7]", style=solid]; -"79 /layer3/layer3.1/conv2/Conv" -> "82 /layer3/layer3.1/conv2/Conv/fq_output_0" [label="[1, 256, 14, 14]", style=solid]; -"80 /layer4/layer4.0/relu/Relu/fq_output_0" -> "83 /layer4/layer4.0/conv2/Conv/WithoutBiases" [label="[1, 512, 7, 7]", style=solid]; -"81 /layer4/layer4.0/Add" -> "84 /layer4/layer4.0/relu_1/Relu" [label="[1, 512, 7, 7]", style=solid]; -"82 /layer3/layer3.1/conv2/Conv/fq_output_0" -> "65 /layer3/layer3.1/Add" [label="[1, 256, 14, 14]", style=solid]; -"83 /layer4/layer4.0/conv2/Conv/WithoutBiases" -> "85 /layer4/layer4.0/conv2/Conv" [label="[1, 512, 7, 7]", style=solid]; -"84 /layer4/layer4.0/relu_1/Relu" -> "86 /layer4/layer4.0/relu_1/Relu/fq_output_0" [label="[1, 512, 7, 7]", style=solid]; -"85 /layer4/layer4.0/conv2/Conv" -> "87 /layer4/layer4.0/conv2/Conv/fq_output_0" [label="[1, 512, 7, 7]", style=solid]; -"86 /layer4/layer4.0/relu_1/Relu/fq_output_0" -> "88 /layer4/layer4.1/Add" [label="[1, 512, 7, 7]", style=solid]; -"86 /layer4/layer4.0/relu_1/Relu/fq_output_0" -> "89 /layer4/layer4.1/conv1/Conv/WithoutBiases" [label="[1, 512, 7, 7]", style=solid]; -"87 /layer4/layer4.0/conv2/Conv/fq_output_0" -> "81 /layer4/layer4.0/Add" [label="[1, 512, 7, 7]", style=solid]; -"88 /layer4/layer4.1/Add" -> "90 /layer4/layer4.1/relu_1/Relu" [label="[1, 512, 7, 7]", style=solid]; -"89 /layer4/layer4.1/conv1/Conv/WithoutBiases" -> "91 /layer4/layer4.1/conv1/Conv" [label="[1, 512, 7, 7]", style=solid]; -"90 /layer4/layer4.1/relu_1/Relu" -> "92 /layer4/layer4.1/relu_1/Relu/fq_output_0" [label="[1, 512, 7, 7]", style=solid]; -"91 /layer4/layer4.1/conv1/Conv" -> "93 /layer4/layer4.1/relu/Relu" [label="[1, 512, 7, 7]", style=solid]; -"92 /layer4/layer4.1/relu_1/Relu/fq_output_0" -> "94 /avgpool/GlobalAveragePool" [label="[1, 512, 7, 7]", style=solid]; -"93 /layer4/layer4.1/relu/Relu" -> "95 /layer4/layer4.1/relu/Relu/fq_output_0" [label="[1, 512, 7, 7]", style=solid]; -"94 /avgpool/GlobalAveragePool" -> "96 /avgpool/GlobalAveragePool/fq_output_0" [label="[1, 512, 1, 1]", style=solid]; -"95 /layer4/layer4.1/relu/Relu/fq_output_0" -> "97 /layer4/layer4.1/conv2/Conv/WithoutBiases" [label="[1, 512, 7, 7]", style=solid]; -"96 /avgpool/GlobalAveragePool/fq_output_0" -> "98 /Flatten" [label="[1, 512, 1, 1]", style=solid]; -"97 /layer4/layer4.1/conv2/Conv/WithoutBiases" -> "99 /layer4/layer4.1/conv2/Conv" [label="[1, 512, 7, 7]", style=solid]; -"98 /Flatten" -> "100 /fc/Gemm/WithoutBiases" [label="[1, 512]", style=solid]; -"99 /layer4/layer4.1/conv2/Conv" -> "101 /layer4/layer4.1/conv2/Conv/fq_output_0" [label="[1, 512, 7, 7]", style=solid]; -"100 /fc/Gemm/WithoutBiases" -> "102 prob" [label="[1, 1000]", style=solid]; -"101 /layer4/layer4.1/conv2/Conv/fq_output_0" -> "88 /layer4/layer4.1/Add" [label="[1, 512, 7, 7]", style=solid]; -"102 prob" -> "103 prob/sink_port_0" [label="[1, 1000]", style=solid]; -"104 Constant_1789" -> "102 prob" [label="[1, 1000]", style=solid]; -"105 /fc/Gemm/WithoutBiases/fq_weights_1" -> "100 /fc/Gemm/WithoutBiases" [label="[1000, 512]", style=solid]; -"106 Constant_48641" -> "105 /fc/Gemm/WithoutBiases/fq_weights_1" [label="[1000, 1]", style=solid]; -"107 Constant_48640" -> "105 /fc/Gemm/WithoutBiases/fq_weights_1" [label="[1000, 1]", style=solid]; -"108 Constant_48639" -> "105 /fc/Gemm/WithoutBiases/fq_weights_1" [label="[1000, 1]", style=solid]; -"109 Constant_48638" -> "105 /fc/Gemm/WithoutBiases/fq_weights_1" [label="[1000, 1]", style=solid]; -"110 fc.weight" -> "105 /fc/Gemm/WithoutBiases/fq_weights_1" [label="[1000, 512]", style=solid]; -"111 Constant_380" -> "98 /Flatten" [label="[2]", style=dashed]; -"112 Constant_48636" -> "96 /avgpool/GlobalAveragePool/fq_output_0" [label="[]", style=solid]; -"113 Constant_48635" -> "96 /avgpool/GlobalAveragePool/fq_output_0" [label="[]", style=solid]; -"114 Constant_48634" -> "96 /avgpool/GlobalAveragePool/fq_output_0" [label="[]", style=solid]; -"115 Constant_48633" -> "96 /avgpool/GlobalAveragePool/fq_output_0" [label="[]", style=solid]; -"116 Range_376" -> "94 /avgpool/GlobalAveragePool" [label="[2]", style=dashed]; -"117 Constant_48631" -> "92 /layer4/layer4.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"118 Constant_48630" -> "92 /layer4/layer4.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"119 Constant_48629" -> "92 /layer4/layer4.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"120 Constant_48628" -> "92 /layer4/layer4.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"121 Constant_48606" -> "86 /layer4/layer4.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"122 Constant_48605" -> "86 /layer4/layer4.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"123 Constant_48604" -> "86 /layer4/layer4.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"124 Constant_48603" -> "86 /layer4/layer4.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"125 Constant_48581" -> "78 /layer4/layer4.0/downsample/downsample.0/Conv/fq_output_0" [label="[]", style=solid]; -"126 Constant_48580" -> "78 /layer4/layer4.0/downsample/downsample.0/Conv/fq_output_0" [label="[]", style=solid]; -"127 Constant_48579" -> "78 /layer4/layer4.0/downsample/downsample.0/Conv/fq_output_0" [label="[]", style=solid]; -"128 Constant_48578" -> "78 /layer4/layer4.0/downsample/downsample.0/Conv/fq_output_0" [label="[]", style=solid]; -"129 Reshape_331" -> "75 /layer4/layer4.0/downsample/downsample.0/Conv" [label="[1, 512, 1, 1]", style=solid]; -"130 /layer4/layer4.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" -> "72 /layer4/layer4.0/downsample/downsample.0/Conv/WithoutBiases" [label="[512, 256, 1, 1]", style=solid]; -"131 Constant_48576" -> "130 /layer4/layer4.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"132 Constant_48575" -> "130 /layer4/layer4.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"133 Constant_48574" -> "130 /layer4/layer4.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"134 Constant_48573" -> "130 /layer4/layer4.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"135 onnx^^Conv_244" -> "130 /layer4/layer4.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[512, 256, 1, 1]", style=solid]; -"136 Constant_48571" -> "69 /layer3/layer3.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"137 Constant_48570" -> "69 /layer3/layer3.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"138 Constant_48569" -> "69 /layer3/layer3.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"139 Constant_48568" -> "69 /layer3/layer3.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"140 Constant_48546" -> "63 /layer3/layer3.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"141 Constant_48545" -> "63 /layer3/layer3.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"142 Constant_48544" -> "63 /layer3/layer3.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"143 Constant_48543" -> "63 /layer3/layer3.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"144 Constant_48521" -> "55 /layer3/layer3.0/downsample/downsample.0/Conv/fq_output_0" [label="[]", style=solid]; -"145 Constant_48520" -> "55 /layer3/layer3.0/downsample/downsample.0/Conv/fq_output_0" [label="[]", style=solid]; -"146 Constant_48519" -> "55 /layer3/layer3.0/downsample/downsample.0/Conv/fq_output_0" [label="[]", style=solid]; -"147 Constant_48518" -> "55 /layer3/layer3.0/downsample/downsample.0/Conv/fq_output_0" [label="[]", style=solid]; -"148 Reshape_250" -> "52 /layer3/layer3.0/downsample/downsample.0/Conv" [label="[1, 256, 1, 1]", style=solid]; -"149 /layer3/layer3.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" -> "49 /layer3/layer3.0/downsample/downsample.0/Conv/WithoutBiases" [label="[256, 128, 1, 1]", style=solid]; -"150 Constant_48516" -> "149 /layer3/layer3.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"151 Constant_48515" -> "149 /layer3/layer3.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"152 Constant_48514" -> "149 /layer3/layer3.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"153 Constant_48513" -> "149 /layer3/layer3.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"154 onnx^^Conv_229" -> "149 /layer3/layer3.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[256, 128, 1, 1]", style=solid]; -"155 Constant_48511" -> "46 /layer2/layer2.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"156 Constant_48510" -> "46 /layer2/layer2.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"157 Constant_48509" -> "46 /layer2/layer2.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"158 Constant_48508" -> "46 /layer2/layer2.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"159 Constant_48486" -> "40 /layer2/layer2.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"160 Constant_48485" -> "40 /layer2/layer2.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"161 Constant_48484" -> "40 /layer2/layer2.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"162 Constant_48483" -> "40 /layer2/layer2.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"163 Constant_48461" -> "32 /layer2/layer2.0/downsample/downsample.0/Conv/fq_output_0" [label="[]", style=solid]; -"164 Constant_48460" -> "32 /layer2/layer2.0/downsample/downsample.0/Conv/fq_output_0" [label="[]", style=solid]; -"165 Constant_48459" -> "32 /layer2/layer2.0/downsample/downsample.0/Conv/fq_output_0" [label="[]", style=solid]; -"166 Constant_48458" -> "32 /layer2/layer2.0/downsample/downsample.0/Conv/fq_output_0" [label="[]", style=solid]; -"167 Reshape_169" -> "29 /layer2/layer2.0/downsample/downsample.0/Conv" [label="[1, 128, 1, 1]", style=solid]; -"168 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" -> "25 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases" [label="[128, 64, 1, 1]", style=solid]; -"169 Constant_48456" -> "168 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"170 Constant_48455" -> "168 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"171 Constant_48454" -> "168 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"172 Constant_48453" -> "168 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"173 onnx^^Conv_214" -> "168 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[128, 64, 1, 1]", style=solid]; -"174 Constant_48451" -> "21 /layer1/layer1.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"175 Constant_48450" -> "21 /layer1/layer1.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"176 Constant_48449" -> "21 /layer1/layer1.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"177 Constant_48448" -> "21 /layer1/layer1.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"178 Constant_48426" -> "13 /layer1/layer1.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"179 Constant_48425" -> "13 /layer1/layer1.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"180 Constant_48424" -> "13 /layer1/layer1.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"181 Constant_48423" -> "13 /layer1/layer1.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"182 Constant_48401" -> "7 /relu/Relu/fq_output_0" [label="[]", style=solid]; -"183 Constant_48400" -> "7 /relu/Relu/fq_output_0" [label="[]", style=solid]; -"184 Constant_48399" -> "7 /relu/Relu/fq_output_0" [label="[]", style=solid]; -"185 Constant_48398" -> "7 /relu/Relu/fq_output_0" [label="[]", style=solid]; -"186 Reshape_55" -> "5 /conv1/Conv" [label="[1, 64, 1, 1]", style=solid]; -"187 /conv1/Conv/WithoutBiases/fq_weights_1" -> "4 /conv1/Conv/WithoutBiases" [label="[64, 3, 7, 7]", style=solid]; -"188 Constant_48396" -> "187 /conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"189 Constant_48395" -> "187 /conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"190 Constant_48394" -> "187 /conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"191 Constant_48393" -> "187 /conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"192 Gather_1788" -> "187 /conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 3, 7, 7]", style=solid]; -"193 Constant_48391" -> "3 Divide_401/fq_output_0" [label="[]", style=solid]; -"194 Constant_48390" -> "3 Divide_401/fq_output_0" [label="[]", style=solid]; -"195 Constant_48389" -> "3 Divide_401/fq_output_0" [label="[]", style=solid]; -"196 Constant_48388" -> "3 Divide_401/fq_output_0" [label="[]", style=solid]; -"197 Gather_1785" -> "2 Divide_401" [label="[1, 3, 1, 1]", style=solid]; -"198 Gather_1782" -> "1 Multiply_1715" [label="[1, 3, 1, 1]", style=solid]; -"199 Constant_48421" -> "27 /layer1/layer1.0/conv2/Conv/fq_output_0" [label="[]", style=solid]; -"200 Constant_48420" -> "27 /layer1/layer1.0/conv2/Conv/fq_output_0" [label="[]", style=solid]; -"201 Constant_48419" -> "27 /layer1/layer1.0/conv2/Conv/fq_output_0" [label="[]", style=solid]; -"202 Constant_48418" -> "27 /layer1/layer1.0/conv2/Conv/fq_output_0" [label="[]", style=solid]; -"203 Reshape_88" -> "23 /layer1/layer1.0/conv2/Conv" [label="[1, 64, 1, 1]", style=solid]; -"204 /layer1/layer1.0/conv2/Conv/WithoutBiases/fq_weights_1" -> "20 /layer1/layer1.0/conv2/Conv/WithoutBiases" [label="[64, 64, 3, 3]", style=solid]; -"205 Constant_48416" -> "204 /layer1/layer1.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"206 Constant_48415" -> "204 /layer1/layer1.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"207 Constant_48414" -> "204 /layer1/layer1.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"208 Constant_48413" -> "204 /layer1/layer1.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"209 onnx^^Conv_199" -> "204 /layer1/layer1.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 64, 3, 3]", style=solid]; -"210 Constant_48411" -> "17 /layer1/layer1.0/relu/Relu/fq_output_0" [label="[]", style=solid]; -"211 Constant_48410" -> "17 /layer1/layer1.0/relu/Relu/fq_output_0" [label="[]", style=solid]; -"212 Constant_48409" -> "17 /layer1/layer1.0/relu/Relu/fq_output_0" [label="[]", style=solid]; -"213 Constant_48408" -> "17 /layer1/layer1.0/relu/Relu/fq_output_0" [label="[]", style=solid]; -"214 Reshape_72" -> "12 /layer1/layer1.0/conv1/Conv" [label="[1, 64, 1, 1]", style=solid]; -"215 /layer1/layer1.0/conv1/Conv/WithoutBiases/fq_weights_1" -> "10 /layer1/layer1.0/conv1/Conv/WithoutBiases" [label="[64, 64, 3, 3]", style=solid]; -"216 Constant_48406" -> "215 /layer1/layer1.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"217 Constant_48405" -> "215 /layer1/layer1.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"218 Constant_48404" -> "215 /layer1/layer1.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"219 Constant_48403" -> "215 /layer1/layer1.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"220 onnx^^Conv_196" -> "215 /layer1/layer1.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 64, 3, 3]", style=solid]; -"221 Constant_48446" -> "36 /layer1/layer1.1/conv2/Conv/fq_output_0" [label="[]", style=solid]; -"222 Constant_48445" -> "36 /layer1/layer1.1/conv2/Conv/fq_output_0" [label="[]", style=solid]; -"223 Constant_48444" -> "36 /layer1/layer1.1/conv2/Conv/fq_output_0" [label="[]", style=solid]; -"224 Constant_48443" -> "36 /layer1/layer1.1/conv2/Conv/fq_output_0" [label="[]", style=solid]; -"225 Reshape_121" -> "33 /layer1/layer1.1/conv2/Conv" [label="[1, 64, 1, 1]", style=solid]; -"226 /layer1/layer1.1/conv2/Conv/WithoutBiases/fq_weights_1" -> "30 /layer1/layer1.1/conv2/Conv/WithoutBiases" [label="[64, 64, 3, 3]", style=solid]; -"227 Constant_48441" -> "226 /layer1/layer1.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"228 Constant_48440" -> "226 /layer1/layer1.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"229 Constant_48439" -> "226 /layer1/layer1.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"230 Constant_48438" -> "226 /layer1/layer1.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"231 onnx^^Conv_205" -> "226 /layer1/layer1.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 64, 3, 3]", style=solid]; -"232 Constant_48436" -> "26 /layer1/layer1.1/relu/Relu/fq_output_0" [label="[]", style=solid]; -"233 Constant_48435" -> "26 /layer1/layer1.1/relu/Relu/fq_output_0" [label="[]", style=solid]; -"234 Constant_48434" -> "26 /layer1/layer1.1/relu/Relu/fq_output_0" [label="[]", style=solid]; -"235 Constant_48433" -> "26 /layer1/layer1.1/relu/Relu/fq_output_0" [label="[]", style=solid]; -"236 Reshape_105" -> "19 /layer1/layer1.1/conv1/Conv" [label="[1, 64, 1, 1]", style=solid]; -"237 /layer1/layer1.1/conv1/Conv/WithoutBiases/fq_weights_1" -> "16 /layer1/layer1.1/conv1/Conv/WithoutBiases" [label="[64, 64, 3, 3]", style=solid]; -"238 Constant_48431" -> "237 /layer1/layer1.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"239 Constant_48430" -> "237 /layer1/layer1.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"240 Constant_48429" -> "237 /layer1/layer1.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"241 Constant_48428" -> "237 /layer1/layer1.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"242 onnx^^Conv_202" -> "237 /layer1/layer1.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 64, 3, 3]", style=solid]; -"243 Constant_48481" -> "41 /layer2/layer2.0/conv2/Conv/fq_output_0" [label="[]", style=solid]; -"244 Constant_48480" -> "41 /layer2/layer2.0/conv2/Conv/fq_output_0" [label="[]", style=solid]; -"245 Constant_48479" -> "41 /layer2/layer2.0/conv2/Conv/fq_output_0" [label="[]", style=solid]; -"246 Constant_48478" -> "41 /layer2/layer2.0/conv2/Conv/fq_output_0" [label="[]", style=solid]; -"247 Reshape_154" -> "39 /layer2/layer2.0/conv2/Conv" [label="[1, 128, 1, 1]", style=solid]; -"248 /layer2/layer2.0/conv2/Conv/WithoutBiases/fq_weights_1" -> "37 /layer2/layer2.0/conv2/Conv/WithoutBiases" [label="[128, 128, 3, 3]", style=solid]; -"249 Constant_48476" -> "248 /layer2/layer2.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"250 Constant_48475" -> "248 /layer2/layer2.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"251 Constant_48474" -> "248 /layer2/layer2.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"252 Constant_48473" -> "248 /layer2/layer2.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"253 onnx^^Conv_211" -> "248 /layer2/layer2.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 128, 3, 3]", style=solid]; -"254 Constant_48471" -> "34 /layer2/layer2.0/relu/Relu/fq_output_0" [label="[]", style=solid]; -"255 Constant_48470" -> "34 /layer2/layer2.0/relu/Relu/fq_output_0" [label="[]", style=solid]; -"256 Constant_48469" -> "34 /layer2/layer2.0/relu/Relu/fq_output_0" [label="[]", style=solid]; -"257 Constant_48468" -> "34 /layer2/layer2.0/relu/Relu/fq_output_0" [label="[]", style=solid]; -"258 Reshape_138" -> "28 /layer2/layer2.0/conv1/Conv" [label="[1, 128, 1, 1]", style=solid]; -"259 /layer2/layer2.0/conv1/Conv/WithoutBiases/fq_weights_1" -> "24 /layer2/layer2.0/conv1/Conv/WithoutBiases" [label="[128, 64, 3, 3]", style=solid]; -"260 Constant_48466" -> "259 /layer2/layer2.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"261 Constant_48465" -> "259 /layer2/layer2.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"262 Constant_48464" -> "259 /layer2/layer2.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"263 Constant_48463" -> "259 /layer2/layer2.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"264 onnx^^Conv_208" -> "259 /layer2/layer2.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 64, 3, 3]", style=solid]; -"265 Constant_48506" -> "59 /layer2/layer2.1/conv2/Conv/fq_output_0" [label="[]", style=solid]; -"266 Constant_48505" -> "59 /layer2/layer2.1/conv2/Conv/fq_output_0" [label="[]", style=solid]; -"267 Constant_48504" -> "59 /layer2/layer2.1/conv2/Conv/fq_output_0" [label="[]", style=solid]; -"268 Constant_48503" -> "59 /layer2/layer2.1/conv2/Conv/fq_output_0" [label="[]", style=solid]; -"269 Reshape_202" -> "56 /layer2/layer2.1/conv2/Conv" [label="[1, 128, 1, 1]", style=solid]; -"270 /layer2/layer2.1/conv2/Conv/WithoutBiases/fq_weights_1" -> "53 /layer2/layer2.1/conv2/Conv/WithoutBiases" [label="[128, 128, 3, 3]", style=solid]; -"271 Constant_48501" -> "270 /layer2/layer2.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"272 Constant_48500" -> "270 /layer2/layer2.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"273 Constant_48499" -> "270 /layer2/layer2.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"274 Constant_48498" -> "270 /layer2/layer2.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"275 onnx^^Conv_220" -> "270 /layer2/layer2.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 128, 3, 3]", style=solid]; -"276 Constant_48496" -> "50 /layer2/layer2.1/relu/Relu/fq_output_0" [label="[]", style=solid]; -"277 Constant_48495" -> "50 /layer2/layer2.1/relu/Relu/fq_output_0" [label="[]", style=solid]; -"278 Constant_48494" -> "50 /layer2/layer2.1/relu/Relu/fq_output_0" [label="[]", style=solid]; -"279 Constant_48493" -> "50 /layer2/layer2.1/relu/Relu/fq_output_0" [label="[]", style=solid]; -"280 Reshape_186" -> "45 /layer2/layer2.1/conv1/Conv" [label="[1, 128, 1, 1]", style=solid]; -"281 /layer2/layer2.1/conv1/Conv/WithoutBiases/fq_weights_1" -> "43 /layer2/layer2.1/conv1/Conv/WithoutBiases" [label="[128, 128, 3, 3]", style=solid]; -"282 Constant_48491" -> "281 /layer2/layer2.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"283 Constant_48490" -> "281 /layer2/layer2.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"284 Constant_48489" -> "281 /layer2/layer2.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"285 Constant_48488" -> "281 /layer2/layer2.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"286 onnx^^Conv_217" -> "281 /layer2/layer2.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 128, 3, 3]", style=solid]; -"287 Constant_48541" -> "64 /layer3/layer3.0/conv2/Conv/fq_output_0" [label="[]", style=solid]; -"288 Constant_48540" -> "64 /layer3/layer3.0/conv2/Conv/fq_output_0" [label="[]", style=solid]; -"289 Constant_48539" -> "64 /layer3/layer3.0/conv2/Conv/fq_output_0" [label="[]", style=solid]; -"290 Constant_48538" -> "64 /layer3/layer3.0/conv2/Conv/fq_output_0" [label="[]", style=solid]; -"291 Reshape_235" -> "62 /layer3/layer3.0/conv2/Conv" [label="[1, 256, 1, 1]", style=solid]; -"292 /layer3/layer3.0/conv2/Conv/WithoutBiases/fq_weights_1" -> "60 /layer3/layer3.0/conv2/Conv/WithoutBiases" [label="[256, 256, 3, 3]", style=solid]; -"293 Constant_48536" -> "292 /layer3/layer3.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"294 Constant_48535" -> "292 /layer3/layer3.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"295 Constant_48534" -> "292 /layer3/layer3.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"296 Constant_48533" -> "292 /layer3/layer3.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"297 onnx^^Conv_226" -> "292 /layer3/layer3.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 256, 3, 3]", style=solid]; -"298 Constant_48531" -> "57 /layer3/layer3.0/relu/Relu/fq_output_0" [label="[]", style=solid]; -"299 Constant_48530" -> "57 /layer3/layer3.0/relu/Relu/fq_output_0" [label="[]", style=solid]; -"300 Constant_48529" -> "57 /layer3/layer3.0/relu/Relu/fq_output_0" [label="[]", style=solid]; -"301 Constant_48528" -> "57 /layer3/layer3.0/relu/Relu/fq_output_0" [label="[]", style=solid]; -"302 Reshape_219" -> "51 /layer3/layer3.0/conv1/Conv" [label="[1, 256, 1, 1]", style=solid]; -"303 /layer3/layer3.0/conv1/Conv/WithoutBiases/fq_weights_1" -> "48 /layer3/layer3.0/conv1/Conv/WithoutBiases" [label="[256, 128, 3, 3]", style=solid]; -"304 Constant_48526" -> "303 /layer3/layer3.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"305 Constant_48525" -> "303 /layer3/layer3.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"306 Constant_48524" -> "303 /layer3/layer3.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"307 Constant_48523" -> "303 /layer3/layer3.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"308 onnx^^Conv_223" -> "303 /layer3/layer3.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 128, 3, 3]", style=solid]; -"309 Constant_48566" -> "82 /layer3/layer3.1/conv2/Conv/fq_output_0" [label="[]", style=solid]; -"310 Constant_48565" -> "82 /layer3/layer3.1/conv2/Conv/fq_output_0" [label="[]", style=solid]; -"311 Constant_48564" -> "82 /layer3/layer3.1/conv2/Conv/fq_output_0" [label="[]", style=solid]; -"312 Constant_48563" -> "82 /layer3/layer3.1/conv2/Conv/fq_output_0" [label="[]", style=solid]; -"313 Reshape_283" -> "79 /layer3/layer3.1/conv2/Conv" [label="[1, 256, 1, 1]", style=solid]; -"314 /layer3/layer3.1/conv2/Conv/WithoutBiases/fq_weights_1" -> "76 /layer3/layer3.1/conv2/Conv/WithoutBiases" [label="[256, 256, 3, 3]", style=solid]; -"315 Constant_48561" -> "314 /layer3/layer3.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"316 Constant_48560" -> "314 /layer3/layer3.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"317 Constant_48559" -> "314 /layer3/layer3.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"318 Constant_48558" -> "314 /layer3/layer3.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"319 onnx^^Conv_235" -> "314 /layer3/layer3.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 256, 3, 3]", style=solid]; -"320 Constant_48556" -> "73 /layer3/layer3.1/relu/Relu/fq_output_0" [label="[]", style=solid]; -"321 Constant_48555" -> "73 /layer3/layer3.1/relu/Relu/fq_output_0" [label="[]", style=solid]; -"322 Constant_48554" -> "73 /layer3/layer3.1/relu/Relu/fq_output_0" [label="[]", style=solid]; -"323 Constant_48553" -> "73 /layer3/layer3.1/relu/Relu/fq_output_0" [label="[]", style=solid]; -"324 Reshape_267" -> "68 /layer3/layer3.1/conv1/Conv" [label="[1, 256, 1, 1]", style=solid]; -"325 /layer3/layer3.1/conv1/Conv/WithoutBiases/fq_weights_1" -> "66 /layer3/layer3.1/conv1/Conv/WithoutBiases" [label="[256, 256, 3, 3]", style=solid]; -"326 Constant_48551" -> "325 /layer3/layer3.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"327 Constant_48550" -> "325 /layer3/layer3.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"328 Constant_48549" -> "325 /layer3/layer3.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"329 Constant_48548" -> "325 /layer3/layer3.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"330 onnx^^Conv_232" -> "325 /layer3/layer3.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 256, 3, 3]", style=solid]; -"331 Constant_48601" -> "87 /layer4/layer4.0/conv2/Conv/fq_output_0" [label="[]", style=solid]; -"332 Constant_48600" -> "87 /layer4/layer4.0/conv2/Conv/fq_output_0" [label="[]", style=solid]; -"333 Constant_48599" -> "87 /layer4/layer4.0/conv2/Conv/fq_output_0" [label="[]", style=solid]; -"334 Constant_48598" -> "87 /layer4/layer4.0/conv2/Conv/fq_output_0" [label="[]", style=solid]; -"335 Reshape_316" -> "85 /layer4/layer4.0/conv2/Conv" [label="[1, 512, 1, 1]", style=solid]; -"336 /layer4/layer4.0/conv2/Conv/WithoutBiases/fq_weights_1" -> "83 /layer4/layer4.0/conv2/Conv/WithoutBiases" [label="[512, 512, 3, 3]", style=solid]; -"337 Constant_48596" -> "336 /layer4/layer4.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"338 Constant_48595" -> "336 /layer4/layer4.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"339 Constant_48594" -> "336 /layer4/layer4.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"340 Constant_48593" -> "336 /layer4/layer4.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"341 onnx^^Conv_241" -> "336 /layer4/layer4.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[512, 512, 3, 3]", style=solid]; -"342 Constant_48591" -> "80 /layer4/layer4.0/relu/Relu/fq_output_0" [label="[]", style=solid]; -"343 Constant_48590" -> "80 /layer4/layer4.0/relu/Relu/fq_output_0" [label="[]", style=solid]; -"344 Constant_48589" -> "80 /layer4/layer4.0/relu/Relu/fq_output_0" [label="[]", style=solid]; -"345 Constant_48588" -> "80 /layer4/layer4.0/relu/Relu/fq_output_0" [label="[]", style=solid]; -"346 Reshape_300" -> "74 /layer4/layer4.0/conv1/Conv" [label="[1, 512, 1, 1]", style=solid]; -"347 /layer4/layer4.0/conv1/Conv/WithoutBiases/fq_weights_1" -> "71 /layer4/layer4.0/conv1/Conv/WithoutBiases" [label="[512, 256, 3, 3]", style=solid]; -"348 Constant_48586" -> "347 /layer4/layer4.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"349 Constant_48585" -> "347 /layer4/layer4.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"350 Constant_48584" -> "347 /layer4/layer4.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"351 Constant_48583" -> "347 /layer4/layer4.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"352 onnx^^Conv_238" -> "347 /layer4/layer4.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 256, 3, 3]", style=solid]; -"353 Constant_48626" -> "101 /layer4/layer4.1/conv2/Conv/fq_output_0" [label="[]", style=solid]; -"354 Constant_48625" -> "101 /layer4/layer4.1/conv2/Conv/fq_output_0" [label="[]", style=solid]; -"355 Constant_48624" -> "101 /layer4/layer4.1/conv2/Conv/fq_output_0" [label="[]", style=solid]; -"356 Constant_48623" -> "101 /layer4/layer4.1/conv2/Conv/fq_output_0" [label="[]", style=solid]; -"357 Reshape_364" -> "99 /layer4/layer4.1/conv2/Conv" [label="[1, 512, 1, 1]", style=solid]; -"358 /layer4/layer4.1/conv2/Conv/WithoutBiases/fq_weights_1" -> "97 /layer4/layer4.1/conv2/Conv/WithoutBiases" [label="[512, 512, 3, 3]", style=solid]; -"359 Constant_48621" -> "358 /layer4/layer4.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"360 Constant_48620" -> "358 /layer4/layer4.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"361 Constant_48619" -> "358 /layer4/layer4.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"362 Constant_48618" -> "358 /layer4/layer4.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"363 onnx^^Conv_250" -> "358 /layer4/layer4.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[512, 512, 3, 3]", style=solid]; -"364 Constant_48616" -> "95 /layer4/layer4.1/relu/Relu/fq_output_0" [label="[]", style=solid]; -"365 Constant_48615" -> "95 /layer4/layer4.1/relu/Relu/fq_output_0" [label="[]", style=solid]; -"366 Constant_48614" -> "95 /layer4/layer4.1/relu/Relu/fq_output_0" [label="[]", style=solid]; -"367 Constant_48613" -> "95 /layer4/layer4.1/relu/Relu/fq_output_0" [label="[]", style=solid]; -"368 Reshape_348" -> "91 /layer4/layer4.1/conv1/Conv" [label="[1, 512, 1, 1]", style=solid]; -"369 /layer4/layer4.1/conv1/Conv/WithoutBiases/fq_weights_1" -> "89 /layer4/layer4.1/conv1/Conv/WithoutBiases" [label="[512, 512, 3, 3]", style=solid]; -"370 Constant_48611" -> "369 /layer4/layer4.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"371 Constant_48610" -> "369 /layer4/layer4.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"372 Constant_48609" -> "369 /layer4/layer4.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"373 Constant_48608" -> "369 /layer4/layer4.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"374 onnx^^Conv_247" -> "369 /layer4/layer4.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 512, 3, 3]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/resnet-50-pytorch_performance_CPU_SPR.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/resnet-50-pytorch_performance_CPU_SPR.dot deleted file mode 100644 index e318d57f89b..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/quantized/resnet-50-pytorch_performance_CPU_SPR.dot +++ /dev/null @@ -1,1691 +0,0 @@ -strict digraph { -"0 data" [id=0, type=Parameter]; -"1 Multiply_2745" [id=1, type=Multiply]; -"2 Divide_1002" [id=2, type=Add]; -"3 Divide_1002/fq_output_0" [id=3, type=FakeQuantize]; -"4 /conv1/Conv/WithoutBiases" [id=4, type=Convolution]; -"5 /conv1/Conv" [id=5, type=Add]; -"6 /relu/Relu" [id=6, type=Relu]; -"7 /relu/Relu/fq_output_0" [id=7, type=FakeQuantize]; -"8 /maxpool/MaxPool" [id=8, type=MaxPool]; -"9 /layer1/layer1.0/conv1/Conv/WithoutBiases" [id=9, type=Convolution]; -"10 /layer1/layer1.0/downsample/downsample.0/Conv/WithoutBiases" [id=10, type=Convolution]; -"11 /layer1/layer1.0/conv1/Conv" [id=11, type=Add]; -"12 /layer1/layer1.0/downsample/downsample.0/Conv" [id=12, type=Add]; -"13 /layer1/layer1.0/relu/Relu" [id=13, type=Relu]; -"14 /layer1/layer1.0/Add" [id=14, type=Add]; -"15 /layer1/layer1.0/relu/Relu/fq_output_0" [id=15, type=FakeQuantize]; -"16 /layer1/layer1.0/relu_2/Relu" [id=16, type=Relu]; -"17 /layer1/layer1.0/conv2/Conv/WithoutBiases" [id=17, type=Convolution]; -"18 /layer1/layer1.0/relu_2/Relu/fq_output_0" [id=18, type=FakeQuantize]; -"19 /layer1/layer1.0/conv2/Conv" [id=19, type=Add]; -"20 /layer1/layer1.1/Add" [id=20, type=Add]; -"21 /layer1/layer1.1/conv1/Conv/WithoutBiases" [id=21, type=Convolution]; -"22 /layer1/layer1.0/relu_1/Relu" [id=22, type=Relu]; -"23 /layer1/layer1.1/relu_2/Relu" [id=23, type=Relu]; -"24 /layer1/layer1.1/conv1/Conv" [id=24, type=Add]; -"25 /layer1/layer1.0/relu_1/Relu/fq_output_0" [id=25, type=FakeQuantize]; -"26 /layer1/layer1.1/relu_2/Relu/fq_output_0" [id=26, type=FakeQuantize]; -"27 /layer1/layer1.1/relu/Relu" [id=27, type=Relu]; -"28 /layer1/layer1.0/conv3/Conv/WithoutBiases" [id=28, type=Convolution]; -"29 /layer1/layer1.2/Add" [id=29, type=Add]; -"30 /layer1/layer1.2/conv1/Conv/WithoutBiases" [id=30, type=Convolution]; -"31 /layer1/layer1.1/relu/Relu/fq_output_0" [id=31, type=FakeQuantize]; -"32 /layer1/layer1.0/conv3/Conv" [id=32, type=Add]; -"33 /layer1/layer1.2/relu_2/Relu" [id=33, type=Relu]; -"34 /layer1/layer1.2/conv1/Conv" [id=34, type=Add]; -"35 /layer1/layer1.1/conv2/Conv/WithoutBiases" [id=35, type=Convolution]; -"36 /layer1/layer1.0/conv3/Conv/fq_output_0" [id=36, 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[id=236, type=FakeQuantize]; -"237 Constant_62386" [id=237, type=Constant]; -"238 Constant_62385" [id=238, type=Constant]; -"239 Constant_62384" [id=239, type=Constant]; -"240 Constant_62383" [id=240, type=Constant]; -"241 fc.weight" [id=241, type=Constant]; -"242 Constant_981" [id=242, type=Constant]; -"243 Constant_62381" [id=243, type=Constant]; -"244 Constant_62380" [id=244, type=Constant]; -"245 Constant_62379" [id=245, type=Constant]; -"246 Constant_62378" [id=246, type=Constant]; -"247 Range_977" [id=247, type=Constant]; -"248 Constant_62376" [id=248, type=Constant]; -"249 Constant_62375" [id=249, type=Constant]; -"250 Constant_62374" [id=250, type=Constant]; -"251 Constant_62373" [id=251, type=Constant]; -"252 Constant_62341" [id=252, type=Constant]; -"253 Constant_62340" [id=253, type=Constant]; -"254 Constant_62339" [id=254, type=Constant]; -"255 Constant_62338" [id=255, type=Constant]; -"256 Constant_62306" [id=256, type=Constant]; -"257 Constant_62305" [id=257, type=Constant]; -"258 Constant_62304" [id=258, type=Constant]; -"259 Constant_62303" [id=259, type=Constant]; -"260 Reshape_867" [id=260, type=Constant]; -"261 /layer4/layer4.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [id=261, type=FakeQuantize]; -"262 Constant_62266" [id=262, type=Constant]; -"263 Constant_62265" [id=263, type=Constant]; -"264 Constant_62264" [id=264, type=Constant]; -"265 Constant_62263" [id=265, type=Constant]; -"266 onnx^^Conv_635" [id=266, label="266 onnx::Conv_635", type=Constant]; -"267 Constant_62261" [id=267, type=Constant]; -"268 Constant_62260" [id=268, type=Constant]; -"269 Constant_62259" [id=269, type=Constant]; -"270 Constant_62258" [id=270, type=Constant]; -"271 Constant_62226" [id=271, type=Constant]; -"272 Constant_62225" [id=272, type=Constant]; -"273 Constant_62224" [id=273, type=Constant]; -"274 Constant_62223" [id=274, type=Constant]; -"275 Constant_62191" [id=275, type=Constant]; -"276 Constant_62190" [id=276, type=Constant]; 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-"297 onnx^^Conv_578" [id=297, label="297 onnx::Conv_578", type=Constant]; -"298 Constant_62041" [id=298, type=Constant]; -"299 Constant_62040" [id=299, type=Constant]; -"300 Constant_62039" [id=300, type=Constant]; -"301 Constant_62038" [id=301, type=Constant]; -"302 Constant_62006" [id=302, type=Constant]; -"303 Constant_62005" [id=303, type=Constant]; -"304 Constant_62004" [id=304, type=Constant]; -"305 Constant_62003" [id=305, type=Constant]; -"306 Constant_61971" [id=306, type=Constant]; -"307 Constant_61970" [id=307, type=Constant]; -"308 Constant_61969" [id=308, type=Constant]; -"309 Constant_61968" [id=309, type=Constant]; -"310 Constant_61936" [id=310, type=Constant]; -"311 Constant_61935" [id=311, type=Constant]; -"312 Constant_61934" [id=312, type=Constant]; -"313 Constant_61933" [id=313, type=Constant]; -"314 Reshape_347" [id=314, type=Constant]; -"315 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [id=315, type=FakeQuantize]; -"316 Constant_61896" [id=316, type=Constant]; -"317 Constant_61895" [id=317, type=Constant]; -"318 Constant_61894" [id=318, type=Constant]; -"319 Constant_61893" [id=319, type=Constant]; -"320 onnx^^Conv_539" [id=320, label="320 onnx::Conv_539", type=Constant]; -"321 Constant_61891" [id=321, type=Constant]; -"322 Constant_61890" [id=322, type=Constant]; -"323 Constant_61889" [id=323, type=Constant]; -"324 Constant_61888" [id=324, type=Constant]; -"325 Constant_61856" [id=325, type=Constant]; -"326 Constant_61855" [id=326, type=Constant]; -"327 Constant_61854" [id=327, type=Constant]; -"328 Constant_61853" [id=328, type=Constant]; -"329 Constant_61821" [id=329, type=Constant]; -"330 Constant_61820" [id=330, type=Constant]; -"331 Constant_61819" [id=331, type=Constant]; -"332 Constant_61818" [id=332, type=Constant]; -"333 Reshape_185" [id=333, type=Constant]; -"334 /layer1/layer1.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [id=334, type=FakeQuantize]; -"335 Constant_61781" [id=335, type=Constant]; -"336 Constant_61780" [id=336, type=Constant]; -"337 Constant_61779" [id=337, type=Constant]; -"338 Constant_61778" [id=338, type=Constant]; -"339 onnx^^Conv_509" [id=339, label="339 onnx::Conv_509", type=Constant]; -"340 Constant_61776" [id=340, type=Constant]; -"341 Constant_61775" [id=341, type=Constant]; -"342 Constant_61774" [id=342, type=Constant]; -"343 Constant_61773" [id=343, type=Constant]; -"344 Reshape_121" [id=344, type=Constant]; -"345 /conv1/Conv/WithoutBiases/fq_weights_1" [id=345, type=FakeQuantize]; -"346 Constant_61771" [id=346, type=Constant]; -"347 Constant_61770" [id=347, type=Constant]; -"348 Constant_61769" [id=348, type=Constant]; -"349 Constant_61768" [id=349, type=Constant]; -"350 Gather_2818" [id=350, type=Constant]; -"351 Constant_61766" [id=351, type=Constant]; -"352 Constant_61765" [id=352, type=Constant]; -"353 Constant_61764" [id=353, type=Constant]; -"354 Constant_61763" [id=354, type=Constant]; -"355 Gather_2815" [id=355, type=Constant]; -"356 Gather_2812" [id=356, type=Constant]; -"357 Constant_61816" [id=357, type=Constant]; -"358 Constant_61815" [id=358, type=Constant]; -"359 Constant_61814" [id=359, type=Constant]; -"360 Constant_61813" [id=360, type=Constant]; -"361 Reshape_170" [id=361, type=Constant]; -"362 /layer1/layer1.0/conv3/Conv/WithoutBiases/fq_weights_1" [id=362, type=FakeQuantize]; -"363 Constant_61811" [id=363, type=Constant]; -"364 Constant_61810" [id=364, type=Constant]; -"365 Constant_61809" [id=365, type=Constant]; -"366 Constant_61808" [id=366, type=Constant]; -"367 onnx^^Conv_506" [id=367, label="367 onnx::Conv_506", type=Constant]; -"368 Constant_61806" [id=368, type=Constant]; -"369 Constant_61805" [id=369, type=Constant]; -"370 Constant_61804" [id=370, type=Constant]; -"371 Constant_61803" [id=371, type=Constant]; -"372 Reshape_154" [id=372, type=Constant]; -"373 /layer1/layer1.0/conv2/Conv/WithoutBiases/fq_weights_1" [id=373, type=FakeQuantize]; -"374 Constant_61801" [id=374, type=Constant]; -"375 Constant_61800" [id=375, type=Constant]; -"376 Constant_61799" [id=376, type=Constant]; -"377 Constant_61798" [id=377, type=Constant]; -"378 onnx^^Conv_503" [id=378, label="378 onnx::Conv_503", type=Constant]; -"379 Constant_61796" [id=379, type=Constant]; -"380 Constant_61795" [id=380, type=Constant]; -"381 Constant_61794" [id=381, type=Constant]; -"382 Constant_61793" [id=382, type=Constant]; -"383 Reshape_138" [id=383, type=Constant]; -"384 /layer1/layer1.0/conv1/Conv/WithoutBiases/fq_weights_1" [id=384, type=FakeQuantize]; -"385 Constant_61791" [id=385, type=Constant]; -"386 Constant_61790" [id=386, type=Constant]; -"387 Constant_61789" [id=387, type=Constant]; -"388 Constant_61788" [id=388, type=Constant]; -"389 onnx^^Conv_500" [id=389, label="389 onnx::Conv_500", type=Constant]; -"390 Reshape_234" [id=390, type=Constant]; -"391 /layer1/layer1.1/conv3/Conv/WithoutBiases/fq_weights_1" [id=391, type=FakeQuantize]; -"392 Constant_61846" [id=392, type=Constant]; -"393 Constant_61845" [id=393, type=Constant]; -"394 Constant_61844" [id=394, type=Constant]; -"395 Constant_61843" [id=395, type=Constant]; -"396 onnx^^Conv_518" [id=396, label="396 onnx::Conv_518", type=Constant]; -"397 Constant_61841" [id=397, type=Constant]; -"398 Constant_61840" [id=398, type=Constant]; -"399 Constant_61839" [id=399, type=Constant]; -"400 Constant_61838" [id=400, type=Constant]; -"401 Reshape_218" [id=401, type=Constant]; -"402 /layer1/layer1.1/conv2/Conv/WithoutBiases/fq_weights_1" [id=402, type=FakeQuantize]; -"403 Constant_61836" [id=403, type=Constant]; -"404 Constant_61835" [id=404, type=Constant]; -"405 Constant_61834" [id=405, type=Constant]; -"406 Constant_61833" [id=406, type=Constant]; -"407 onnx^^Conv_515" [id=407, label="407 onnx::Conv_515", type=Constant]; -"408 Constant_61831" [id=408, type=Constant]; -"409 Constant_61830" [id=409, type=Constant]; -"410 Constant_61829" [id=410, type=Constant]; -"411 Constant_61828" [id=411, type=Constant]; -"412 Reshape_202" [id=412, type=Constant]; -"413 /layer1/layer1.1/conv1/Conv/WithoutBiases/fq_weights_1" [id=413, type=FakeQuantize]; -"414 Constant_61826" [id=414, type=Constant]; -"415 Constant_61825" [id=415, type=Constant]; -"416 Constant_61824" [id=416, type=Constant]; -"417 Constant_61823" [id=417, type=Constant]; -"418 onnx^^Conv_512" [id=418, label="418 onnx::Conv_512", type=Constant]; -"419 Reshape_283" [id=419, type=Constant]; -"420 /layer1/layer1.2/conv3/Conv/WithoutBiases/fq_weights_1" [id=420, type=FakeQuantize]; -"421 Constant_61881" [id=421, type=Constant]; -"422 Constant_61880" [id=422, type=Constant]; -"423 Constant_61879" [id=423, type=Constant]; -"424 Constant_61878" [id=424, type=Constant]; -"425 onnx^^Conv_527" [id=425, label="425 onnx::Conv_527", type=Constant]; -"426 Constant_61876" [id=426, type=Constant]; -"427 Constant_61875" [id=427, type=Constant]; -"428 Constant_61874" [id=428, type=Constant]; -"429 Constant_61873" [id=429, type=Constant]; -"430 Reshape_267" [id=430, type=Constant]; -"431 /layer1/layer1.2/conv2/Conv/WithoutBiases/fq_weights_1" [id=431, type=FakeQuantize]; -"432 Constant_61871" [id=432, type=Constant]; -"433 Constant_61870" [id=433, type=Constant]; -"434 Constant_61869" [id=434, type=Constant]; -"435 Constant_61868" [id=435, type=Constant]; -"436 onnx^^Conv_524" [id=436, label="436 onnx::Conv_524", type=Constant]; -"437 Constant_61866" [id=437, type=Constant]; -"438 Constant_61865" [id=438, type=Constant]; -"439 Constant_61864" [id=439, type=Constant]; -"440 Constant_61863" [id=440, type=Constant]; -"441 Reshape_251" [id=441, type=Constant]; -"442 /layer1/layer1.2/conv1/Conv/WithoutBiases/fq_weights_1" [id=442, type=FakeQuantize]; -"443 Constant_61861" [id=443, type=Constant]; -"444 Constant_61860" [id=444, type=Constant]; -"445 Constant_61859" [id=445, type=Constant]; -"446 Constant_61858" [id=446, type=Constant]; -"447 onnx^^Conv_521" [id=447, label="447 onnx::Conv_521", type=Constant]; -"448 Constant_61931" [id=448, type=Constant]; -"449 Constant_61930" [id=449, type=Constant]; -"450 Constant_61929" [id=450, type=Constant]; -"451 Constant_61928" [id=451, type=Constant]; -"452 Reshape_332" [id=452, type=Constant]; -"453 /layer2/layer2.0/conv3/Conv/WithoutBiases/fq_weights_1" [id=453, type=FakeQuantize]; -"454 Constant_61926" [id=454, type=Constant]; -"455 Constant_61925" [id=455, type=Constant]; -"456 Constant_61924" [id=456, type=Constant]; -"457 Constant_61923" [id=457, type=Constant]; -"458 onnx^^Conv_536" [id=458, label="458 onnx::Conv_536", type=Constant]; -"459 Constant_61921" [id=459, type=Constant]; -"460 Constant_61920" [id=460, type=Constant]; -"461 Constant_61919" [id=461, type=Constant]; -"462 Constant_61918" [id=462, type=Constant]; -"463 Reshape_316" [id=463, type=Constant]; -"464 /layer2/layer2.0/conv2/Conv/WithoutBiases/fq_weights_1" [id=464, type=FakeQuantize]; -"465 Constant_61916" [id=465, type=Constant]; -"466 Constant_61915" [id=466, type=Constant]; -"467 Constant_61914" [id=467, type=Constant]; -"468 Constant_61913" [id=468, type=Constant]; -"469 onnx^^Conv_533" [id=469, label="469 onnx::Conv_533", type=Constant]; -"470 Constant_61911" [id=470, type=Constant]; -"471 Constant_61910" [id=471, type=Constant]; -"472 Constant_61909" [id=472, type=Constant]; -"473 Constant_61908" [id=473, type=Constant]; -"474 Reshape_300" [id=474, type=Constant]; -"475 /layer2/layer2.0/conv1/Conv/WithoutBiases/fq_weights_1" [id=475, type=FakeQuantize]; -"476 Constant_61906" [id=476, type=Constant]; -"477 Constant_61905" [id=477, type=Constant]; -"478 Constant_61904" [id=478, type=Constant]; -"479 Constant_61903" [id=479, type=Constant]; -"480 onnx^^Conv_530" [id=480, label="480 onnx::Conv_530", type=Constant]; -"481 Reshape_396" [id=481, type=Constant]; -"482 /layer2/layer2.1/conv3/Conv/WithoutBiases/fq_weights_1" [id=482, type=FakeQuantize]; -"483 Constant_61961" [id=483, type=Constant]; -"484 Constant_61960" [id=484, type=Constant]; -"485 Constant_61959" [id=485, type=Constant]; -"486 Constant_61958" [id=486, type=Constant]; -"487 onnx^^Conv_548" [id=487, label="487 onnx::Conv_548", type=Constant]; -"488 Constant_61956" [id=488, type=Constant]; -"489 Constant_61955" [id=489, type=Constant]; -"490 Constant_61954" [id=490, type=Constant]; -"491 Constant_61953" [id=491, type=Constant]; -"492 Reshape_380" [id=492, type=Constant]; -"493 /layer2/layer2.1/conv2/Conv/WithoutBiases/fq_weights_1" [id=493, type=FakeQuantize]; -"494 Constant_61951" [id=494, type=Constant]; -"495 Constant_61950" [id=495, type=Constant]; -"496 Constant_61949" [id=496, type=Constant]; -"497 Constant_61948" [id=497, type=Constant]; -"498 onnx^^Conv_545" [id=498, label="498 onnx::Conv_545", type=Constant]; -"499 Constant_61946" [id=499, type=Constant]; -"500 Constant_61945" [id=500, type=Constant]; -"501 Constant_61944" [id=501, type=Constant]; -"502 Constant_61943" [id=502, type=Constant]; -"503 Reshape_364" [id=503, type=Constant]; -"504 /layer2/layer2.1/conv1/Conv/WithoutBiases/fq_weights_1" [id=504, type=FakeQuantize]; -"505 Constant_61941" [id=505, type=Constant]; -"506 Constant_61940" [id=506, type=Constant]; -"507 Constant_61939" [id=507, type=Constant]; -"508 Constant_61938" [id=508, type=Constant]; -"509 onnx^^Conv_542" [id=509, label="509 onnx::Conv_542", type=Constant]; -"510 Reshape_445" [id=510, type=Constant]; -"511 /layer2/layer2.2/conv3/Conv/WithoutBiases/fq_weights_1" [id=511, type=FakeQuantize]; -"512 Constant_61996" [id=512, type=Constant]; -"513 Constant_61995" [id=513, type=Constant]; -"514 Constant_61994" [id=514, type=Constant]; -"515 Constant_61993" [id=515, type=Constant]; -"516 onnx^^Conv_557" [id=516, label="516 onnx::Conv_557", type=Constant]; -"517 Constant_61991" [id=517, type=Constant]; -"518 Constant_61990" [id=518, type=Constant]; -"519 Constant_61989" [id=519, type=Constant]; -"520 Constant_61988" [id=520, type=Constant]; -"521 Reshape_429" [id=521, type=Constant]; -"522 /layer2/layer2.2/conv2/Conv/WithoutBiases/fq_weights_1" [id=522, type=FakeQuantize]; -"523 Constant_61986" [id=523, type=Constant]; -"524 Constant_61985" [id=524, type=Constant]; -"525 Constant_61984" [id=525, type=Constant]; -"526 Constant_61983" [id=526, type=Constant]; -"527 onnx^^Conv_554" [id=527, label="527 onnx::Conv_554", type=Constant]; -"528 Constant_61981" [id=528, type=Constant]; -"529 Constant_61980" [id=529, type=Constant]; -"530 Constant_61979" [id=530, type=Constant]; -"531 Constant_61978" [id=531, type=Constant]; -"532 Reshape_413" [id=532, type=Constant]; -"533 /layer2/layer2.2/conv1/Conv/WithoutBiases/fq_weights_1" [id=533, type=FakeQuantize]; -"534 Constant_61976" [id=534, type=Constant]; -"535 Constant_61975" [id=535, type=Constant]; -"536 Constant_61974" [id=536, type=Constant]; -"537 Constant_61973" [id=537, type=Constant]; -"538 onnx^^Conv_551" [id=538, label="538 onnx::Conv_551", type=Constant]; -"539 Reshape_494" [id=539, type=Constant]; -"540 /layer2/layer2.3/conv3/Conv/WithoutBiases/fq_weights_1" [id=540, type=FakeQuantize]; -"541 Constant_62031" [id=541, type=Constant]; -"542 Constant_62030" [id=542, type=Constant]; -"543 Constant_62029" [id=543, type=Constant]; -"544 Constant_62028" [id=544, type=Constant]; -"545 onnx^^Conv_566" [id=545, label="545 onnx::Conv_566", type=Constant]; -"546 Constant_62026" [id=546, type=Constant]; -"547 Constant_62025" [id=547, type=Constant]; -"548 Constant_62024" [id=548, type=Constant]; -"549 Constant_62023" [id=549, type=Constant]; -"550 Reshape_478" [id=550, type=Constant]; -"551 /layer2/layer2.3/conv2/Conv/WithoutBiases/fq_weights_1" [id=551, type=FakeQuantize]; -"552 Constant_62021" [id=552, type=Constant]; -"553 Constant_62020" [id=553, type=Constant]; -"554 Constant_62019" [id=554, type=Constant]; -"555 Constant_62018" [id=555, type=Constant]; -"556 onnx^^Conv_563" [id=556, label="556 onnx::Conv_563", type=Constant]; -"557 Constant_62016" [id=557, type=Constant]; -"558 Constant_62015" [id=558, type=Constant]; -"559 Constant_62014" [id=559, type=Constant]; -"560 Constant_62013" [id=560, type=Constant]; -"561 Reshape_462" [id=561, type=Constant]; -"562 /layer2/layer2.3/conv1/Conv/WithoutBiases/fq_weights_1" [id=562, type=FakeQuantize]; -"563 Constant_62011" [id=563, type=Constant]; -"564 Constant_62010" [id=564, type=Constant]; -"565 Constant_62009" [id=565, type=Constant]; -"566 Constant_62008" [id=566, type=Constant]; -"567 onnx^^Conv_560" [id=567, label="567 onnx::Conv_560", type=Constant]; -"568 Constant_62081" [id=568, type=Constant]; -"569 Constant_62080" [id=569, type=Constant]; -"570 Constant_62079" [id=570, type=Constant]; -"571 Constant_62078" [id=571, type=Constant]; -"572 Reshape_543" [id=572, type=Constant]; -"573 /layer3/layer3.0/conv3/Conv/WithoutBiases/fq_weights_1" [id=573, type=FakeQuantize]; -"574 Constant_62076" [id=574, type=Constant]; -"575 Constant_62075" [id=575, type=Constant]; -"576 Constant_62074" [id=576, type=Constant]; -"577 Constant_62073" [id=577, type=Constant]; -"578 onnx^^Conv_575" [id=578, label="578 onnx::Conv_575", type=Constant]; -"579 Constant_62071" [id=579, type=Constant]; -"580 Constant_62070" [id=580, type=Constant]; -"581 Constant_62069" [id=581, type=Constant]; -"582 Constant_62068" [id=582, type=Constant]; -"583 Reshape_527" [id=583, type=Constant]; -"584 /layer3/layer3.0/conv2/Conv/WithoutBiases/fq_weights_1" [id=584, type=FakeQuantize]; -"585 Constant_62066" [id=585, type=Constant]; -"586 Constant_62065" [id=586, type=Constant]; -"587 Constant_62064" [id=587, type=Constant]; -"588 Constant_62063" [id=588, type=Constant]; -"589 onnx^^Conv_572" [id=589, label="589 onnx::Conv_572", type=Constant]; -"590 Constant_62061" [id=590, type=Constant]; -"591 Constant_62060" [id=591, type=Constant]; -"592 Constant_62059" [id=592, type=Constant]; -"593 Constant_62058" [id=593, type=Constant]; -"594 Reshape_511" [id=594, type=Constant]; -"595 /layer3/layer3.0/conv1/Conv/WithoutBiases/fq_weights_1" [id=595, type=FakeQuantize]; -"596 Constant_62056" [id=596, type=Constant]; -"597 Constant_62055" [id=597, type=Constant]; -"598 Constant_62054" [id=598, type=Constant]; -"599 Constant_62053" [id=599, type=Constant]; -"600 onnx^^Conv_569" [id=600, label="600 onnx::Conv_569", type=Constant]; -"601 Reshape_607" [id=601, type=Constant]; -"602 /layer3/layer3.1/conv3/Conv/WithoutBiases/fq_weights_1" [id=602, type=FakeQuantize]; -"603 Constant_62111" [id=603, type=Constant]; -"604 Constant_62110" [id=604, type=Constant]; -"605 Constant_62109" [id=605, type=Constant]; -"606 Constant_62108" [id=606, type=Constant]; -"607 onnx^^Conv_587" [id=607, label="607 onnx::Conv_587", type=Constant]; -"608 Constant_62106" [id=608, type=Constant]; -"609 Constant_62105" [id=609, type=Constant]; -"610 Constant_62104" [id=610, type=Constant]; -"611 Constant_62103" [id=611, type=Constant]; -"612 Reshape_591" [id=612, type=Constant]; -"613 /layer3/layer3.1/conv2/Conv/WithoutBiases/fq_weights_1" [id=613, type=FakeQuantize]; -"614 Constant_62101" [id=614, type=Constant]; -"615 Constant_62100" [id=615, type=Constant]; -"616 Constant_62099" [id=616, type=Constant]; -"617 Constant_62098" [id=617, type=Constant]; -"618 onnx^^Conv_584" [id=618, label="618 onnx::Conv_584", type=Constant]; -"619 Constant_62096" [id=619, type=Constant]; -"620 Constant_62095" [id=620, type=Constant]; -"621 Constant_62094" [id=621, type=Constant]; -"622 Constant_62093" [id=622, type=Constant]; -"623 Reshape_575" [id=623, type=Constant]; -"624 /layer3/layer3.1/conv1/Conv/WithoutBiases/fq_weights_1" [id=624, type=FakeQuantize]; -"625 Constant_62091" [id=625, type=Constant]; -"626 Constant_62090" [id=626, type=Constant]; -"627 Constant_62089" [id=627, type=Constant]; -"628 Constant_62088" [id=628, type=Constant]; -"629 onnx^^Conv_581" [id=629, label="629 onnx::Conv_581", type=Constant]; -"630 Reshape_656" [id=630, type=Constant]; -"631 /layer3/layer3.2/conv3/Conv/WithoutBiases/fq_weights_1" [id=631, type=FakeQuantize]; -"632 Constant_62146" [id=632, type=Constant]; -"633 Constant_62145" [id=633, type=Constant]; -"634 Constant_62144" [id=634, type=Constant]; -"635 Constant_62143" [id=635, type=Constant]; -"636 onnx^^Conv_596" [id=636, label="636 onnx::Conv_596", type=Constant]; -"637 Constant_62141" [id=637, type=Constant]; -"638 Constant_62140" [id=638, type=Constant]; -"639 Constant_62139" [id=639, type=Constant]; -"640 Constant_62138" [id=640, type=Constant]; -"641 Reshape_640" [id=641, type=Constant]; -"642 /layer3/layer3.2/conv2/Conv/WithoutBiases/fq_weights_1" [id=642, type=FakeQuantize]; -"643 Constant_62136" [id=643, type=Constant]; -"644 Constant_62135" [id=644, type=Constant]; -"645 Constant_62134" [id=645, type=Constant]; -"646 Constant_62133" [id=646, type=Constant]; -"647 onnx^^Conv_593" [id=647, label="647 onnx::Conv_593", type=Constant]; -"648 Constant_62131" [id=648, type=Constant]; -"649 Constant_62130" [id=649, type=Constant]; -"650 Constant_62129" [id=650, type=Constant]; -"651 Constant_62128" [id=651, type=Constant]; -"652 Reshape_624" [id=652, type=Constant]; -"653 /layer3/layer3.2/conv1/Conv/WithoutBiases/fq_weights_1" [id=653, type=FakeQuantize]; -"654 Constant_62126" [id=654, type=Constant]; -"655 Constant_62125" [id=655, type=Constant]; -"656 Constant_62124" [id=656, type=Constant]; -"657 Constant_62123" [id=657, type=Constant]; -"658 onnx^^Conv_590" [id=658, label="658 onnx::Conv_590", type=Constant]; -"659 Reshape_705" [id=659, type=Constant]; -"660 /layer3/layer3.3/conv3/Conv/WithoutBiases/fq_weights_1" [id=660, type=FakeQuantize]; -"661 Constant_62181" [id=661, type=Constant]; -"662 Constant_62180" [id=662, type=Constant]; -"663 Constant_62179" [id=663, type=Constant]; -"664 Constant_62178" [id=664, type=Constant]; -"665 onnx^^Conv_605" [id=665, label="665 onnx::Conv_605", type=Constant]; -"666 Constant_62176" [id=666, type=Constant]; -"667 Constant_62175" [id=667, type=Constant]; -"668 Constant_62174" [id=668, type=Constant]; -"669 Constant_62173" [id=669, type=Constant]; -"670 Reshape_689" [id=670, type=Constant]; -"671 /layer3/layer3.3/conv2/Conv/WithoutBiases/fq_weights_1" [id=671, type=FakeQuantize]; -"672 Constant_62171" [id=672, type=Constant]; -"673 Constant_62170" [id=673, type=Constant]; -"674 Constant_62169" [id=674, type=Constant]; -"675 Constant_62168" [id=675, type=Constant]; -"676 onnx^^Conv_602" [id=676, label="676 onnx::Conv_602", type=Constant]; -"677 Constant_62166" [id=677, type=Constant]; -"678 Constant_62165" [id=678, type=Constant]; -"679 Constant_62164" [id=679, type=Constant]; -"680 Constant_62163" [id=680, type=Constant]; -"681 Reshape_673" [id=681, type=Constant]; -"682 /layer3/layer3.3/conv1/Conv/WithoutBiases/fq_weights_1" [id=682, type=FakeQuantize]; -"683 Constant_62161" [id=683, type=Constant]; -"684 Constant_62160" [id=684, type=Constant]; -"685 Constant_62159" [id=685, type=Constant]; -"686 Constant_62158" [id=686, type=Constant]; -"687 onnx^^Conv_599" [id=687, label="687 onnx::Conv_599", type=Constant]; -"688 Reshape_754" [id=688, type=Constant]; -"689 /layer3/layer3.4/conv3/Conv/WithoutBiases/fq_weights_1" [id=689, type=FakeQuantize]; -"690 Constant_62216" [id=690, type=Constant]; -"691 Constant_62215" [id=691, type=Constant]; -"692 Constant_62214" [id=692, type=Constant]; -"693 Constant_62213" [id=693, type=Constant]; -"694 onnx^^Conv_614" [id=694, label="694 onnx::Conv_614", type=Constant]; -"695 Constant_62211" [id=695, type=Constant]; -"696 Constant_62210" [id=696, type=Constant]; -"697 Constant_62209" [id=697, type=Constant]; -"698 Constant_62208" [id=698, type=Constant]; -"699 Reshape_738" [id=699, type=Constant]; -"700 /layer3/layer3.4/conv2/Conv/WithoutBiases/fq_weights_1" [id=700, type=FakeQuantize]; -"701 Constant_62206" [id=701, type=Constant]; -"702 Constant_62205" [id=702, type=Constant]; -"703 Constant_62204" [id=703, type=Constant]; -"704 Constant_62203" [id=704, type=Constant]; -"705 onnx^^Conv_611" [id=705, label="705 onnx::Conv_611", type=Constant]; -"706 Constant_62201" [id=706, type=Constant]; -"707 Constant_62200" [id=707, type=Constant]; -"708 Constant_62199" [id=708, type=Constant]; -"709 Constant_62198" [id=709, type=Constant]; -"710 Reshape_722" [id=710, type=Constant]; -"711 /layer3/layer3.4/conv1/Conv/WithoutBiases/fq_weights_1" [id=711, type=FakeQuantize]; -"712 Constant_62196" [id=712, type=Constant]; -"713 Constant_62195" [id=713, type=Constant]; -"714 Constant_62194" [id=714, type=Constant]; -"715 Constant_62193" [id=715, type=Constant]; -"716 onnx^^Conv_608" [id=716, label="716 onnx::Conv_608", type=Constant]; -"717 Reshape_803" [id=717, type=Constant]; -"718 /layer3/layer3.5/conv3/Conv/WithoutBiases/fq_weights_1" [id=718, type=FakeQuantize]; -"719 Constant_62251" [id=719, type=Constant]; -"720 Constant_62250" [id=720, type=Constant]; -"721 Constant_62249" [id=721, type=Constant]; -"722 Constant_62248" [id=722, type=Constant]; -"723 onnx^^Conv_623" [id=723, label="723 onnx::Conv_623", type=Constant]; -"724 Constant_62246" [id=724, type=Constant]; -"725 Constant_62245" [id=725, type=Constant]; -"726 Constant_62244" [id=726, type=Constant]; -"727 Constant_62243" [id=727, type=Constant]; -"728 Reshape_787" [id=728, type=Constant]; -"729 /layer3/layer3.5/conv2/Conv/WithoutBiases/fq_weights_1" [id=729, type=FakeQuantize]; -"730 Constant_62241" [id=730, type=Constant]; -"731 Constant_62240" [id=731, type=Constant]; -"732 Constant_62239" [id=732, type=Constant]; -"733 Constant_62238" [id=733, type=Constant]; -"734 onnx^^Conv_620" [id=734, label="734 onnx::Conv_620", type=Constant]; -"735 Constant_62236" [id=735, type=Constant]; -"736 Constant_62235" [id=736, type=Constant]; -"737 Constant_62234" [id=737, type=Constant]; -"738 Constant_62233" [id=738, type=Constant]; -"739 Reshape_771" [id=739, type=Constant]; -"740 /layer3/layer3.5/conv1/Conv/WithoutBiases/fq_weights_1" [id=740, type=FakeQuantize]; 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Constant_62288" [id=760, type=Constant]; -"761 Reshape_836" [id=761, type=Constant]; -"762 /layer4/layer4.0/conv2/Conv/WithoutBiases/fq_weights_1" [id=762, type=FakeQuantize]; -"763 Constant_62286" [id=763, type=Constant]; -"764 Constant_62285" [id=764, type=Constant]; -"765 Constant_62284" [id=765, type=Constant]; -"766 Constant_62283" [id=766, type=Constant]; -"767 onnx^^Conv_629" [id=767, label="767 onnx::Conv_629", type=Constant]; -"768 Constant_62281" [id=768, type=Constant]; -"769 Constant_62280" [id=769, type=Constant]; -"770 Constant_62279" [id=770, type=Constant]; -"771 Constant_62278" [id=771, type=Constant]; -"772 Reshape_820" [id=772, type=Constant]; -"773 /layer4/layer4.0/conv1/Conv/WithoutBiases/fq_weights_1" [id=773, type=FakeQuantize]; -"774 Constant_62276" [id=774, type=Constant]; -"775 Constant_62275" [id=775, type=Constant]; -"776 Constant_62274" [id=776, type=Constant]; -"777 Constant_62273" [id=777, type=Constant]; -"778 onnx^^Conv_626" [id=778, label="778 onnx::Conv_626", type=Constant]; -"779 Reshape_916" [id=779, type=Constant]; -"780 /layer4/layer4.1/conv3/Conv/WithoutBiases/fq_weights_1" [id=780, type=FakeQuantize]; -"781 Constant_62331" [id=781, type=Constant]; -"782 Constant_62330" [id=782, type=Constant]; -"783 Constant_62329" [id=783, type=Constant]; -"784 Constant_62328" [id=784, type=Constant]; -"785 onnx^^Conv_644" [id=785, label="785 onnx::Conv_644", type=Constant]; -"786 Constant_62326" [id=786, type=Constant]; -"787 Constant_62325" [id=787, type=Constant]; -"788 Constant_62324" [id=788, type=Constant]; -"789 Constant_62323" [id=789, type=Constant]; -"790 Reshape_900" [id=790, type=Constant]; -"791 /layer4/layer4.1/conv2/Conv/WithoutBiases/fq_weights_1" [id=791, type=FakeQuantize]; -"792 Constant_62321" [id=792, type=Constant]; -"793 Constant_62320" [id=793, type=Constant]; -"794 Constant_62319" [id=794, type=Constant]; -"795 Constant_62318" [id=795, type=Constant]; -"796 onnx^^Conv_641" [id=796, label="796 onnx::Conv_641", type=Constant]; -"797 Constant_62316" [id=797, type=Constant]; -"798 Constant_62315" [id=798, type=Constant]; -"799 Constant_62314" [id=799, type=Constant]; -"800 Constant_62313" [id=800, type=Constant]; -"801 Reshape_884" [id=801, type=Constant]; -"802 /layer4/layer4.1/conv1/Conv/WithoutBiases/fq_weights_1" [id=802, type=FakeQuantize]; -"803 Constant_62311" [id=803, type=Constant]; -"804 Constant_62310" [id=804, type=Constant]; -"805 Constant_62309" [id=805, type=Constant]; -"806 Constant_62308" [id=806, type=Constant]; -"807 onnx^^Conv_638" [id=807, label="807 onnx::Conv_638", type=Constant]; -"808 Reshape_965" [id=808, type=Constant]; -"809 /layer4/layer4.2/conv3/Conv/WithoutBiases/fq_weights_1" [id=809, type=FakeQuantize]; -"810 Constant_62366" [id=810, type=Constant]; -"811 Constant_62365" [id=811, type=Constant]; -"812 Constant_62364" [id=812, type=Constant]; -"813 Constant_62363" [id=813, type=Constant]; -"814 onnx^^Conv_653" [id=814, label="814 onnx::Conv_653", type=Constant]; 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type=Constant]; -"834 Constant_62344" [id=834, type=Constant]; -"835 Constant_62343" [id=835, type=Constant]; -"836 onnx^^Conv_647" [id=836, label="836 onnx::Conv_647", type=Constant]; -"0 data" -> "1 Multiply_2745" [label="[1, 3, 224, 224]", style=solid]; -"1 Multiply_2745" -> "2 Divide_1002" [label="[1, 3, 224, 224]", style=solid]; -"2 Divide_1002" -> "3 Divide_1002/fq_output_0" [label="[1, 3, 224, 224]", style=solid]; -"3 Divide_1002/fq_output_0" -> "4 /conv1/Conv/WithoutBiases" [label="[1, 3, 224, 224]", style=solid]; -"4 /conv1/Conv/WithoutBiases" -> "5 /conv1/Conv" [label="[1, 64, 112, 112]", style=solid]; -"5 /conv1/Conv" -> "6 /relu/Relu" [label="[1, 64, 112, 112]", style=solid]; -"6 /relu/Relu" -> "7 /relu/Relu/fq_output_0" [label="[1, 64, 112, 112]", style=solid]; -"7 /relu/Relu/fq_output_0" -> "8 /maxpool/MaxPool" [label="[1, 64, 112, 112]", style=solid]; -"8 /maxpool/MaxPool" -> "9 /layer1/layer1.0/conv1/Conv/WithoutBiases" [label="[1, 64, 56, 56]", style=solid]; -"8 /maxpool/MaxPool" -> "10 /layer1/layer1.0/downsample/downsample.0/Conv/WithoutBiases" [label="[1, 64, 56, 56]", style=solid]; -"9 /layer1/layer1.0/conv1/Conv/WithoutBiases" -> "11 /layer1/layer1.0/conv1/Conv" [label="[1, 64, 56, 56]", style=solid]; -"10 /layer1/layer1.0/downsample/downsample.0/Conv/WithoutBiases" -> "12 /layer1/layer1.0/downsample/downsample.0/Conv" [label="[1, 256, 56, 56]", style=solid]; -"11 /layer1/layer1.0/conv1/Conv" -> "13 /layer1/layer1.0/relu/Relu" [label="[1, 64, 56, 56]", style=solid]; -"12 /layer1/layer1.0/downsample/downsample.0/Conv" -> "14 /layer1/layer1.0/Add" [label="[1, 256, 56, 56]", style=solid]; -"13 /layer1/layer1.0/relu/Relu" -> "15 /layer1/layer1.0/relu/Relu/fq_output_0" [label="[1, 64, 56, 56]", style=solid]; -"14 /layer1/layer1.0/Add" -> "16 /layer1/layer1.0/relu_2/Relu" [label="[1, 256, 56, 56]", style=solid]; -"15 /layer1/layer1.0/relu/Relu/fq_output_0" -> "17 /layer1/layer1.0/conv2/Conv/WithoutBiases" [label="[1, 64, 56, 56]", style=solid]; -"16 /layer1/layer1.0/relu_2/Relu" -> "18 /layer1/layer1.0/relu_2/Relu/fq_output_0" [label="[1, 256, 56, 56]", style=solid]; -"17 /layer1/layer1.0/conv2/Conv/WithoutBiases" -> "19 /layer1/layer1.0/conv2/Conv" [label="[1, 64, 56, 56]", style=solid]; -"18 /layer1/layer1.0/relu_2/Relu/fq_output_0" -> "20 /layer1/layer1.1/Add" [label="[1, 256, 56, 56]", style=solid]; -"18 /layer1/layer1.0/relu_2/Relu/fq_output_0" -> "21 /layer1/layer1.1/conv1/Conv/WithoutBiases" [label="[1, 256, 56, 56]", style=solid]; -"19 /layer1/layer1.0/conv2/Conv" -> "22 /layer1/layer1.0/relu_1/Relu" [label="[1, 64, 56, 56]", style=solid]; -"20 /layer1/layer1.1/Add" -> "23 /layer1/layer1.1/relu_2/Relu" [label="[1, 256, 56, 56]", style=solid]; -"21 /layer1/layer1.1/conv1/Conv/WithoutBiases" -> "24 /layer1/layer1.1/conv1/Conv" [label="[1, 64, 56, 56]", style=solid]; -"22 /layer1/layer1.0/relu_1/Relu" -> "25 /layer1/layer1.0/relu_1/Relu/fq_output_0" [label="[1, 64, 56, 56]", style=solid]; -"23 /layer1/layer1.1/relu_2/Relu" -> "26 /layer1/layer1.1/relu_2/Relu/fq_output_0" [label="[1, 256, 56, 56]", style=solid]; -"24 /layer1/layer1.1/conv1/Conv" -> "27 /layer1/layer1.1/relu/Relu" [label="[1, 64, 56, 56]", style=solid]; -"25 /layer1/layer1.0/relu_1/Relu/fq_output_0" -> "28 /layer1/layer1.0/conv3/Conv/WithoutBiases" [label="[1, 64, 56, 56]", style=solid]; -"26 /layer1/layer1.1/relu_2/Relu/fq_output_0" -> "29 /layer1/layer1.2/Add" [label="[1, 256, 56, 56]", style=solid]; -"26 /layer1/layer1.1/relu_2/Relu/fq_output_0" -> "30 /layer1/layer1.2/conv1/Conv/WithoutBiases" [label="[1, 256, 56, 56]", style=solid]; -"27 /layer1/layer1.1/relu/Relu" -> "31 /layer1/layer1.1/relu/Relu/fq_output_0" [label="[1, 64, 56, 56]", style=solid]; -"28 /layer1/layer1.0/conv3/Conv/WithoutBiases" -> "32 /layer1/layer1.0/conv3/Conv" [label="[1, 256, 56, 56]", style=solid]; -"29 /layer1/layer1.2/Add" -> "33 /layer1/layer1.2/relu_2/Relu" [label="[1, 256, 56, 56]", style=solid]; -"30 /layer1/layer1.2/conv1/Conv/WithoutBiases" -> "34 /layer1/layer1.2/conv1/Conv" [label="[1, 64, 56, 56]", style=solid]; -"31 /layer1/layer1.1/relu/Relu/fq_output_0" -> "35 /layer1/layer1.1/conv2/Conv/WithoutBiases" [label="[1, 64, 56, 56]", style=solid]; -"32 /layer1/layer1.0/conv3/Conv" -> "36 /layer1/layer1.0/conv3/Conv/fq_output_0" [label="[1, 256, 56, 56]", style=solid]; -"33 /layer1/layer1.2/relu_2/Relu" -> "37 /layer1/layer1.2/relu_2/Relu/fq_output_0" [label="[1, 256, 56, 56]", style=solid]; -"34 /layer1/layer1.2/conv1/Conv" -> "38 /layer1/layer1.2/relu/Relu" [label="[1, 64, 56, 56]", style=solid]; -"35 /layer1/layer1.1/conv2/Conv/WithoutBiases" -> "39 /layer1/layer1.1/conv2/Conv" [label="[1, 64, 56, 56]", style=solid]; -"36 /layer1/layer1.0/conv3/Conv/fq_output_0" -> "14 /layer1/layer1.0/Add" [label="[1, 256, 56, 56]", style=solid]; -"37 /layer1/layer1.2/relu_2/Relu/fq_output_0" -> "40 /layer2/layer2.0/conv1/Conv/WithoutBiases" [label="[1, 256, 56, 56]", style=solid]; -"37 /layer1/layer1.2/relu_2/Relu/fq_output_0" -> "41 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases" [label="[1, 256, 56, 56]", style=solid]; -"38 /layer1/layer1.2/relu/Relu" -> "42 /layer1/layer1.2/relu/Relu/fq_output_0" [label="[1, 64, 56, 56]", style=solid]; -"39 /layer1/layer1.1/conv2/Conv" -> "43 /layer1/layer1.1/relu_1/Relu" [label="[1, 64, 56, 56]", style=solid]; -"40 /layer2/layer2.0/conv1/Conv/WithoutBiases" -> "44 /layer2/layer2.0/conv1/Conv" [label="[1, 128, 56, 56]", style=solid]; -"41 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases" -> "45 /layer2/layer2.0/downsample/downsample.0/Conv" [label="[1, 512, 28, 28]", style=solid]; -"42 /layer1/layer1.2/relu/Relu/fq_output_0" -> "46 /layer1/layer1.2/conv2/Conv/WithoutBiases" [label="[1, 64, 56, 56]", style=solid]; -"43 /layer1/layer1.1/relu_1/Relu" -> "47 /layer1/layer1.1/relu_1/Relu/fq_output_0" [label="[1, 64, 56, 56]", style=solid]; -"44 /layer2/layer2.0/conv1/Conv" -> "48 /layer2/layer2.0/relu/Relu" [label="[1, 128, 56, 56]", style=solid]; -"45 /layer2/layer2.0/downsample/downsample.0/Conv" -> "49 /layer2/layer2.0/Add" [label="[1, 512, 28, 28]", style=solid]; -"46 /layer1/layer1.2/conv2/Conv/WithoutBiases" -> "50 /layer1/layer1.2/conv2/Conv" [label="[1, 64, 56, 56]", style=solid]; -"47 /layer1/layer1.1/relu_1/Relu/fq_output_0" -> "51 /layer1/layer1.1/conv3/Conv/WithoutBiases" [label="[1, 64, 56, 56]", style=solid]; -"48 /layer2/layer2.0/relu/Relu" -> "52 /layer2/layer2.0/relu/Relu/fq_output_0" [label="[1, 128, 56, 56]", style=solid]; -"49 /layer2/layer2.0/Add" -> "53 /layer2/layer2.0/relu_2/Relu" [label="[1, 512, 28, 28]", style=solid]; -"50 /layer1/layer1.2/conv2/Conv" -> "54 /layer1/layer1.2/relu_1/Relu" [label="[1, 64, 56, 56]", style=solid]; -"51 /layer1/layer1.1/conv3/Conv/WithoutBiases" -> "55 /layer1/layer1.1/conv3/Conv" [label="[1, 256, 56, 56]", style=solid]; -"52 /layer2/layer2.0/relu/Relu/fq_output_0" -> "56 /layer2/layer2.0/conv2/Conv/WithoutBiases" [label="[1, 128, 56, 56]", style=solid]; -"53 /layer2/layer2.0/relu_2/Relu" -> "57 /layer2/layer2.0/relu_2/Relu/fq_output_0" [label="[1, 512, 28, 28]", style=solid]; -"54 /layer1/layer1.2/relu_1/Relu" -> "58 /layer1/layer1.2/relu_1/Relu/fq_output_0" [label="[1, 64, 56, 56]", style=solid]; -"55 /layer1/layer1.1/conv3/Conv" -> "20 /layer1/layer1.1/Add" [label="[1, 256, 56, 56]", style=solid]; -"56 /layer2/layer2.0/conv2/Conv/WithoutBiases" -> "59 /layer2/layer2.0/conv2/Conv" [label="[1, 128, 28, 28]", style=solid]; -"57 /layer2/layer2.0/relu_2/Relu/fq_output_0" -> "60 /layer2/layer2.1/Add" [label="[1, 512, 28, 28]", style=solid]; -"57 /layer2/layer2.0/relu_2/Relu/fq_output_0" -> "61 /layer2/layer2.1/conv1/Conv/WithoutBiases" [label="[1, 512, 28, 28]", style=solid]; -"58 /layer1/layer1.2/relu_1/Relu/fq_output_0" -> "62 /layer1/layer1.2/conv3/Conv/WithoutBiases" [label="[1, 64, 56, 56]", style=solid]; -"59 /layer2/layer2.0/conv2/Conv" -> "63 /layer2/layer2.0/relu_1/Relu" [label="[1, 128, 28, 28]", style=solid]; -"60 /layer2/layer2.1/Add" -> "64 /layer2/layer2.1/relu_2/Relu" [label="[1, 512, 28, 28]", style=solid]; -"61 /layer2/layer2.1/conv1/Conv/WithoutBiases" -> "65 /layer2/layer2.1/conv1/Conv" [label="[1, 128, 28, 28]", style=solid]; -"62 /layer1/layer1.2/conv3/Conv/WithoutBiases" -> "66 /layer1/layer1.2/conv3/Conv" [label="[1, 256, 56, 56]", style=solid]; -"63 /layer2/layer2.0/relu_1/Relu" -> "67 /layer2/layer2.0/relu_1/Relu/fq_output_0" [label="[1, 128, 28, 28]", style=solid]; -"64 /layer2/layer2.1/relu_2/Relu" -> "68 /layer2/layer2.1/relu_2/Relu/fq_output_0" [label="[1, 512, 28, 28]", style=solid]; -"65 /layer2/layer2.1/conv1/Conv" -> "69 /layer2/layer2.1/relu/Relu" [label="[1, 128, 28, 28]", style=solid]; -"66 /layer1/layer1.2/conv3/Conv" -> "29 /layer1/layer1.2/Add" [label="[1, 256, 56, 56]", style=solid]; -"67 /layer2/layer2.0/relu_1/Relu/fq_output_0" -> "70 /layer2/layer2.0/conv3/Conv/WithoutBiases" [label="[1, 128, 28, 28]", style=solid]; -"68 /layer2/layer2.1/relu_2/Relu/fq_output_0" -> "71 /layer2/layer2.2/Add" [label="[1, 512, 28, 28]", style=solid]; -"68 /layer2/layer2.1/relu_2/Relu/fq_output_0" -> "72 /layer2/layer2.2/conv1/Conv/WithoutBiases" [label="[1, 512, 28, 28]", style=solid]; -"69 /layer2/layer2.1/relu/Relu" -> "73 /layer2/layer2.1/relu/Relu/fq_output_0" [label="[1, 128, 28, 28]", style=solid]; -"70 /layer2/layer2.0/conv3/Conv/WithoutBiases" -> "74 /layer2/layer2.0/conv3/Conv" [label="[1, 512, 28, 28]", style=solid]; -"71 /layer2/layer2.2/Add" -> "75 /layer2/layer2.2/relu_2/Relu" [label="[1, 512, 28, 28]", style=solid]; -"72 /layer2/layer2.2/conv1/Conv/WithoutBiases" -> "76 /layer2/layer2.2/conv1/Conv" [label="[1, 128, 28, 28]", style=solid]; -"73 /layer2/layer2.1/relu/Relu/fq_output_0" -> "77 /layer2/layer2.1/conv2/Conv/WithoutBiases" [label="[1, 128, 28, 28]", style=solid]; -"74 /layer2/layer2.0/conv3/Conv" -> "78 /layer2/layer2.0/conv3/Conv/fq_output_0" [label="[1, 512, 28, 28]", style=solid]; -"75 /layer2/layer2.2/relu_2/Relu" -> "79 /layer2/layer2.2/relu_2/Relu/fq_output_0" [label="[1, 512, 28, 28]", style=solid]; -"76 /layer2/layer2.2/conv1/Conv" -> "80 /layer2/layer2.2/relu/Relu" [label="[1, 128, 28, 28]", style=solid]; -"77 /layer2/layer2.1/conv2/Conv/WithoutBiases" -> "81 /layer2/layer2.1/conv2/Conv" [label="[1, 128, 28, 28]", style=solid]; -"78 /layer2/layer2.0/conv3/Conv/fq_output_0" -> "49 /layer2/layer2.0/Add" [label="[1, 512, 28, 28]", style=solid]; -"79 /layer2/layer2.2/relu_2/Relu/fq_output_0" -> "82 /layer2/layer2.3/Add" [label="[1, 512, 28, 28]", style=solid]; -"79 /layer2/layer2.2/relu_2/Relu/fq_output_0" -> "83 /layer2/layer2.3/conv1/Conv/WithoutBiases" [label="[1, 512, 28, 28]", style=solid]; -"80 /layer2/layer2.2/relu/Relu" -> "84 /layer2/layer2.2/relu/Relu/fq_output_0" [label="[1, 128, 28, 28]", style=solid]; -"81 /layer2/layer2.1/conv2/Conv" -> "85 /layer2/layer2.1/relu_1/Relu" [label="[1, 128, 28, 28]", style=solid]; -"82 /layer2/layer2.3/Add" -> "86 /layer2/layer2.3/relu_2/Relu" [label="[1, 512, 28, 28]", style=solid]; -"83 /layer2/layer2.3/conv1/Conv/WithoutBiases" -> "87 /layer2/layer2.3/conv1/Conv" [label="[1, 128, 28, 28]", style=solid]; -"84 /layer2/layer2.2/relu/Relu/fq_output_0" -> "88 /layer2/layer2.2/conv2/Conv/WithoutBiases" [label="[1, 128, 28, 28]", style=solid]; -"85 /layer2/layer2.1/relu_1/Relu" -> "89 /layer2/layer2.1/relu_1/Relu/fq_output_0" [label="[1, 128, 28, 28]", style=solid]; -"86 /layer2/layer2.3/relu_2/Relu" -> "90 /layer2/layer2.3/relu_2/Relu/fq_output_0" [label="[1, 512, 28, 28]", style=solid]; -"87 /layer2/layer2.3/conv1/Conv" -> "91 /layer2/layer2.3/relu/Relu" [label="[1, 128, 28, 28]", style=solid]; -"88 /layer2/layer2.2/conv2/Conv/WithoutBiases" -> "92 /layer2/layer2.2/conv2/Conv" [label="[1, 128, 28, 28]", style=solid]; -"89 /layer2/layer2.1/relu_1/Relu/fq_output_0" -> "93 /layer2/layer2.1/conv3/Conv/WithoutBiases" [label="[1, 128, 28, 28]", style=solid]; -"90 /layer2/layer2.3/relu_2/Relu/fq_output_0" -> "94 /layer3/layer3.0/conv1/Conv/WithoutBiases" [label="[1, 512, 28, 28]", style=solid]; -"90 /layer2/layer2.3/relu_2/Relu/fq_output_0" -> "95 /layer3/layer3.0/downsample/downsample.0/Conv/WithoutBiases" [label="[1, 512, 28, 28]", style=solid]; -"91 /layer2/layer2.3/relu/Relu" -> "96 /layer2/layer2.3/relu/Relu/fq_output_0" [label="[1, 128, 28, 28]", style=solid]; -"92 /layer2/layer2.2/conv2/Conv" -> "97 /layer2/layer2.2/relu_1/Relu" [label="[1, 128, 28, 28]", style=solid]; -"93 /layer2/layer2.1/conv3/Conv/WithoutBiases" -> "98 /layer2/layer2.1/conv3/Conv" [label="[1, 512, 28, 28]", style=solid]; -"94 /layer3/layer3.0/conv1/Conv/WithoutBiases" -> "99 /layer3/layer3.0/conv1/Conv" [label="[1, 256, 28, 28]", style=solid]; -"95 /layer3/layer3.0/downsample/downsample.0/Conv/WithoutBiases" -> "100 /layer3/layer3.0/downsample/downsample.0/Conv" [label="[1, 1024, 14, 14]", style=solid]; -"96 /layer2/layer2.3/relu/Relu/fq_output_0" -> "101 /layer2/layer2.3/conv2/Conv/WithoutBiases" [label="[1, 128, 28, 28]", style=solid]; -"97 /layer2/layer2.2/relu_1/Relu" -> "102 /layer2/layer2.2/relu_1/Relu/fq_output_0" [label="[1, 128, 28, 28]", style=solid]; -"98 /layer2/layer2.1/conv3/Conv" -> "60 /layer2/layer2.1/Add" [label="[1, 512, 28, 28]", style=solid]; -"99 /layer3/layer3.0/conv1/Conv" -> "103 /layer3/layer3.0/relu/Relu" [label="[1, 256, 28, 28]", style=solid]; -"100 /layer3/layer3.0/downsample/downsample.0/Conv" -> "104 /layer3/layer3.0/Add" [label="[1, 1024, 14, 14]", style=solid]; -"101 /layer2/layer2.3/conv2/Conv/WithoutBiases" -> "105 /layer2/layer2.3/conv2/Conv" [label="[1, 128, 28, 28]", style=solid]; -"102 /layer2/layer2.2/relu_1/Relu/fq_output_0" -> "106 /layer2/layer2.2/conv3/Conv/WithoutBiases" [label="[1, 128, 28, 28]", style=solid]; -"103 /layer3/layer3.0/relu/Relu" -> "107 /layer3/layer3.0/relu/Relu/fq_output_0" [label="[1, 256, 28, 28]", style=solid]; -"104 /layer3/layer3.0/Add" -> "108 /layer3/layer3.0/relu_2/Relu" [label="[1, 1024, 14, 14]", style=solid]; -"105 /layer2/layer2.3/conv2/Conv" -> "109 /layer2/layer2.3/relu_1/Relu" [label="[1, 128, 28, 28]", style=solid]; -"106 /layer2/layer2.2/conv3/Conv/WithoutBiases" -> "110 /layer2/layer2.2/conv3/Conv" [label="[1, 512, 28, 28]", style=solid]; -"107 /layer3/layer3.0/relu/Relu/fq_output_0" -> "111 /layer3/layer3.0/conv2/Conv/WithoutBiases" [label="[1, 256, 28, 28]", style=solid]; -"108 /layer3/layer3.0/relu_2/Relu" -> "112 /layer3/layer3.0/relu_2/Relu/fq_output_0" [label="[1, 1024, 14, 14]", style=solid]; -"109 /layer2/layer2.3/relu_1/Relu" -> "113 /layer2/layer2.3/relu_1/Relu/fq_output_0" [label="[1, 128, 28, 28]", style=solid]; -"110 /layer2/layer2.2/conv3/Conv" -> "71 /layer2/layer2.2/Add" [label="[1, 512, 28, 28]", style=solid]; -"111 /layer3/layer3.0/conv2/Conv/WithoutBiases" -> "114 /layer3/layer3.0/conv2/Conv" [label="[1, 256, 14, 14]", style=solid]; -"112 /layer3/layer3.0/relu_2/Relu/fq_output_0" -> "115 /layer3/layer3.1/Add" [label="[1, 1024, 14, 14]", style=solid]; -"112 /layer3/layer3.0/relu_2/Relu/fq_output_0" -> "116 /layer3/layer3.1/conv1/Conv/WithoutBiases" [label="[1, 1024, 14, 14]", style=solid]; -"113 /layer2/layer2.3/relu_1/Relu/fq_output_0" -> "117 /layer2/layer2.3/conv3/Conv/WithoutBiases" [label="[1, 128, 28, 28]", style=solid]; -"114 /layer3/layer3.0/conv2/Conv" -> "118 /layer3/layer3.0/relu_1/Relu" [label="[1, 256, 14, 14]", style=solid]; -"115 /layer3/layer3.1/Add" -> "119 /layer3/layer3.1/relu_2/Relu" [label="[1, 1024, 14, 14]", style=solid]; -"116 /layer3/layer3.1/conv1/Conv/WithoutBiases" -> "120 /layer3/layer3.1/conv1/Conv" [label="[1, 256, 14, 14]", style=solid]; -"117 /layer2/layer2.3/conv3/Conv/WithoutBiases" -> "121 /layer2/layer2.3/conv3/Conv" [label="[1, 512, 28, 28]", style=solid]; -"118 /layer3/layer3.0/relu_1/Relu" -> "122 /layer3/layer3.0/relu_1/Relu/fq_output_0" [label="[1, 256, 14, 14]", style=solid]; -"119 /layer3/layer3.1/relu_2/Relu" -> "123 /layer3/layer3.1/relu_2/Relu/fq_output_0" [label="[1, 1024, 14, 14]", style=solid]; -"120 /layer3/layer3.1/conv1/Conv" -> "124 /layer3/layer3.1/relu/Relu" [label="[1, 256, 14, 14]", style=solid]; -"121 /layer2/layer2.3/conv3/Conv" -> "82 /layer2/layer2.3/Add" [label="[1, 512, 28, 28]", style=solid]; -"122 /layer3/layer3.0/relu_1/Relu/fq_output_0" -> "125 /layer3/layer3.0/conv3/Conv/WithoutBiases" [label="[1, 256, 14, 14]", style=solid]; -"123 /layer3/layer3.1/relu_2/Relu/fq_output_0" -> "126 /layer3/layer3.2/Add" [label="[1, 1024, 14, 14]", style=solid]; -"123 /layer3/layer3.1/relu_2/Relu/fq_output_0" -> "127 /layer3/layer3.2/conv1/Conv/WithoutBiases" [label="[1, 1024, 14, 14]", style=solid]; -"124 /layer3/layer3.1/relu/Relu" -> "128 /layer3/layer3.1/relu/Relu/fq_output_0" [label="[1, 256, 14, 14]", style=solid]; -"125 /layer3/layer3.0/conv3/Conv/WithoutBiases" -> "129 /layer3/layer3.0/conv3/Conv" [label="[1, 1024, 14, 14]", style=solid]; -"126 /layer3/layer3.2/Add" -> "130 /layer3/layer3.2/relu_2/Relu" [label="[1, 1024, 14, 14]", style=solid]; -"127 /layer3/layer3.2/conv1/Conv/WithoutBiases" -> "131 /layer3/layer3.2/conv1/Conv" [label="[1, 256, 14, 14]", style=solid]; -"128 /layer3/layer3.1/relu/Relu/fq_output_0" -> "132 /layer3/layer3.1/conv2/Conv/WithoutBiases" [label="[1, 256, 14, 14]", style=solid]; -"129 /layer3/layer3.0/conv3/Conv" -> "133 /layer3/layer3.0/conv3/Conv/fq_output_0" [label="[1, 1024, 14, 14]", style=solid]; -"130 /layer3/layer3.2/relu_2/Relu" -> "134 /layer3/layer3.2/relu_2/Relu/fq_output_0" [label="[1, 1024, 14, 14]", style=solid]; -"131 /layer3/layer3.2/conv1/Conv" -> "135 /layer3/layer3.2/relu/Relu" [label="[1, 256, 14, 14]", style=solid]; -"132 /layer3/layer3.1/conv2/Conv/WithoutBiases" -> "136 /layer3/layer3.1/conv2/Conv" [label="[1, 256, 14, 14]", style=solid]; -"133 /layer3/layer3.0/conv3/Conv/fq_output_0" -> "104 /layer3/layer3.0/Add" [label="[1, 1024, 14, 14]", style=solid]; -"134 /layer3/layer3.2/relu_2/Relu/fq_output_0" -> "137 /layer3/layer3.3/Add" [label="[1, 1024, 14, 14]", style=solid]; -"134 /layer3/layer3.2/relu_2/Relu/fq_output_0" -> "138 /layer3/layer3.3/conv1/Conv/WithoutBiases" [label="[1, 1024, 14, 14]", style=solid]; -"135 /layer3/layer3.2/relu/Relu" -> "139 /layer3/layer3.2/relu/Relu/fq_output_0" [label="[1, 256, 14, 14]", style=solid]; -"136 /layer3/layer3.1/conv2/Conv" -> "140 /layer3/layer3.1/relu_1/Relu" [label="[1, 256, 14, 14]", style=solid]; -"137 /layer3/layer3.3/Add" -> "141 /layer3/layer3.3/relu_2/Relu" [label="[1, 1024, 14, 14]", style=solid]; -"138 /layer3/layer3.3/conv1/Conv/WithoutBiases" -> "142 /layer3/layer3.3/conv1/Conv" [label="[1, 256, 14, 14]", style=solid]; -"139 /layer3/layer3.2/relu/Relu/fq_output_0" -> "143 /layer3/layer3.2/conv2/Conv/WithoutBiases" [label="[1, 256, 14, 14]", style=solid]; -"140 /layer3/layer3.1/relu_1/Relu" -> "144 /layer3/layer3.1/relu_1/Relu/fq_output_0" [label="[1, 256, 14, 14]", style=solid]; -"141 /layer3/layer3.3/relu_2/Relu" -> "145 /layer3/layer3.3/relu_2/Relu/fq_output_0" [label="[1, 1024, 14, 14]", style=solid]; -"142 /layer3/layer3.3/conv1/Conv" -> "146 /layer3/layer3.3/relu/Relu" [label="[1, 256, 14, 14]", style=solid]; -"143 /layer3/layer3.2/conv2/Conv/WithoutBiases" -> "147 /layer3/layer3.2/conv2/Conv" [label="[1, 256, 14, 14]", style=solid]; -"144 /layer3/layer3.1/relu_1/Relu/fq_output_0" -> "148 /layer3/layer3.1/conv3/Conv/WithoutBiases" [label="[1, 256, 14, 14]", style=solid]; -"145 /layer3/layer3.3/relu_2/Relu/fq_output_0" -> "149 /layer3/layer3.4/Add" [label="[1, 1024, 14, 14]", style=solid]; -"145 /layer3/layer3.3/relu_2/Relu/fq_output_0" -> "150 /layer3/layer3.4/conv1/Conv/WithoutBiases" [label="[1, 1024, 14, 14]", style=solid]; -"146 /layer3/layer3.3/relu/Relu" -> "151 /layer3/layer3.3/relu/Relu/fq_output_0" [label="[1, 256, 14, 14]", style=solid]; -"147 /layer3/layer3.2/conv2/Conv" -> "152 /layer3/layer3.2/relu_1/Relu" [label="[1, 256, 14, 14]", style=solid]; -"148 /layer3/layer3.1/conv3/Conv/WithoutBiases" -> "153 /layer3/layer3.1/conv3/Conv" [label="[1, 1024, 14, 14]", style=solid]; -"149 /layer3/layer3.4/Add" -> "154 /layer3/layer3.4/relu_2/Relu" [label="[1, 1024, 14, 14]", style=solid]; -"150 /layer3/layer3.4/conv1/Conv/WithoutBiases" -> "155 /layer3/layer3.4/conv1/Conv" [label="[1, 256, 14, 14]", style=solid]; -"151 /layer3/layer3.3/relu/Relu/fq_output_0" -> "156 /layer3/layer3.3/conv2/Conv/WithoutBiases" [label="[1, 256, 14, 14]", style=solid]; -"152 /layer3/layer3.2/relu_1/Relu" -> "157 /layer3/layer3.2/relu_1/Relu/fq_output_0" [label="[1, 256, 14, 14]", style=solid]; -"153 /layer3/layer3.1/conv3/Conv" -> "115 /layer3/layer3.1/Add" [label="[1, 1024, 14, 14]", style=solid]; -"154 /layer3/layer3.4/relu_2/Relu" -> "158 /layer3/layer3.4/relu_2/Relu/fq_output_0" [label="[1, 1024, 14, 14]", style=solid]; -"155 /layer3/layer3.4/conv1/Conv" -> "159 /layer3/layer3.4/relu/Relu" [label="[1, 256, 14, 14]", style=solid]; -"156 /layer3/layer3.3/conv2/Conv/WithoutBiases" -> "160 /layer3/layer3.3/conv2/Conv" [label="[1, 256, 14, 14]", style=solid]; -"157 /layer3/layer3.2/relu_1/Relu/fq_output_0" -> "161 /layer3/layer3.2/conv3/Conv/WithoutBiases" [label="[1, 256, 14, 14]", style=solid]; -"158 /layer3/layer3.4/relu_2/Relu/fq_output_0" -> "162 /layer3/layer3.5/Add" [label="[1, 1024, 14, 14]", style=solid]; -"158 /layer3/layer3.4/relu_2/Relu/fq_output_0" -> "163 /layer3/layer3.5/conv1/Conv/WithoutBiases" [label="[1, 1024, 14, 14]", style=solid]; -"159 /layer3/layer3.4/relu/Relu" -> "164 /layer3/layer3.4/relu/Relu/fq_output_0" [label="[1, 256, 14, 14]", style=solid]; -"160 /layer3/layer3.3/conv2/Conv" -> "165 /layer3/layer3.3/relu_1/Relu" [label="[1, 256, 14, 14]", style=solid]; -"161 /layer3/layer3.2/conv3/Conv/WithoutBiases" -> "166 /layer3/layer3.2/conv3/Conv" [label="[1, 1024, 14, 14]", style=solid]; -"162 /layer3/layer3.5/Add" -> "167 /layer3/layer3.5/relu_2/Relu" [label="[1, 1024, 14, 14]", style=solid]; -"163 /layer3/layer3.5/conv1/Conv/WithoutBiases" -> "168 /layer3/layer3.5/conv1/Conv" [label="[1, 256, 14, 14]", style=solid]; -"164 /layer3/layer3.4/relu/Relu/fq_output_0" -> "169 /layer3/layer3.4/conv2/Conv/WithoutBiases" [label="[1, 256, 14, 14]", style=solid]; -"165 /layer3/layer3.3/relu_1/Relu" -> "170 /layer3/layer3.3/relu_1/Relu/fq_output_0" [label="[1, 256, 14, 14]", style=solid]; -"166 /layer3/layer3.2/conv3/Conv" -> "126 /layer3/layer3.2/Add" [label="[1, 1024, 14, 14]", style=solid]; -"167 /layer3/layer3.5/relu_2/Relu" -> "171 /layer3/layer3.5/relu_2/Relu/fq_output_0" [label="[1, 1024, 14, 14]", style=solid]; -"168 /layer3/layer3.5/conv1/Conv" -> "172 /layer3/layer3.5/relu/Relu" [label="[1, 256, 14, 14]", style=solid]; -"169 /layer3/layer3.4/conv2/Conv/WithoutBiases" -> "173 /layer3/layer3.4/conv2/Conv" [label="[1, 256, 14, 14]", style=solid]; -"170 /layer3/layer3.3/relu_1/Relu/fq_output_0" -> "174 /layer3/layer3.3/conv3/Conv/WithoutBiases" [label="[1, 256, 14, 14]", style=solid]; -"171 /layer3/layer3.5/relu_2/Relu/fq_output_0" -> "175 /layer4/layer4.0/conv1/Conv/WithoutBiases" [label="[1, 1024, 14, 14]", style=solid]; -"171 /layer3/layer3.5/relu_2/Relu/fq_output_0" -> "176 /layer4/layer4.0/downsample/downsample.0/Conv/WithoutBiases" [label="[1, 1024, 14, 14]", style=solid]; -"172 /layer3/layer3.5/relu/Relu" -> "177 /layer3/layer3.5/relu/Relu/fq_output_0" [label="[1, 256, 14, 14]", style=solid]; -"173 /layer3/layer3.4/conv2/Conv" -> "178 /layer3/layer3.4/relu_1/Relu" [label="[1, 256, 14, 14]", style=solid]; -"174 /layer3/layer3.3/conv3/Conv/WithoutBiases" -> "179 /layer3/layer3.3/conv3/Conv" [label="[1, 1024, 14, 14]", style=solid]; -"175 /layer4/layer4.0/conv1/Conv/WithoutBiases" -> "180 /layer4/layer4.0/conv1/Conv" [label="[1, 512, 14, 14]", style=solid]; -"176 /layer4/layer4.0/downsample/downsample.0/Conv/WithoutBiases" -> "181 /layer4/layer4.0/downsample/downsample.0/Conv" [label="[1, 2048, 7, 7]", style=solid]; -"177 /layer3/layer3.5/relu/Relu/fq_output_0" -> "182 /layer3/layer3.5/conv2/Conv/WithoutBiases" [label="[1, 256, 14, 14]", style=solid]; -"178 /layer3/layer3.4/relu_1/Relu" -> "183 /layer3/layer3.4/relu_1/Relu/fq_output_0" [label="[1, 256, 14, 14]", style=solid]; -"179 /layer3/layer3.3/conv3/Conv" -> "137 /layer3/layer3.3/Add" [label="[1, 1024, 14, 14]", style=solid]; -"180 /layer4/layer4.0/conv1/Conv" -> "184 /layer4/layer4.0/relu/Relu" [label="[1, 512, 14, 14]", style=solid]; -"181 /layer4/layer4.0/downsample/downsample.0/Conv" -> "185 /layer4/layer4.0/Add" [label="[1, 2048, 7, 7]", style=solid]; -"182 /layer3/layer3.5/conv2/Conv/WithoutBiases" -> "186 /layer3/layer3.5/conv2/Conv" [label="[1, 256, 14, 14]", style=solid]; -"183 /layer3/layer3.4/relu_1/Relu/fq_output_0" -> "187 /layer3/layer3.4/conv3/Conv/WithoutBiases" [label="[1, 256, 14, 14]", style=solid]; -"184 /layer4/layer4.0/relu/Relu" -> "188 /layer4/layer4.0/relu/Relu/fq_output_0" [label="[1, 512, 14, 14]", style=solid]; -"185 /layer4/layer4.0/Add" -> "189 /layer4/layer4.0/relu_2/Relu" [label="[1, 2048, 7, 7]", style=solid]; -"186 /layer3/layer3.5/conv2/Conv" -> "190 /layer3/layer3.5/relu_1/Relu" [label="[1, 256, 14, 14]", style=solid]; -"187 /layer3/layer3.4/conv3/Conv/WithoutBiases" -> "191 /layer3/layer3.4/conv3/Conv" [label="[1, 1024, 14, 14]", style=solid]; -"188 /layer4/layer4.0/relu/Relu/fq_output_0" -> "192 /layer4/layer4.0/conv2/Conv/WithoutBiases" [label="[1, 512, 14, 14]", style=solid]; -"189 /layer4/layer4.0/relu_2/Relu" -> "193 /layer4/layer4.0/relu_2/Relu/fq_output_0" [label="[1, 2048, 7, 7]", style=solid]; -"190 /layer3/layer3.5/relu_1/Relu" -> "194 /layer3/layer3.5/relu_1/Relu/fq_output_0" [label="[1, 256, 14, 14]", style=solid]; -"191 /layer3/layer3.4/conv3/Conv" -> "149 /layer3/layer3.4/Add" [label="[1, 1024, 14, 14]", style=solid]; -"192 /layer4/layer4.0/conv2/Conv/WithoutBiases" -> "195 /layer4/layer4.0/conv2/Conv" [label="[1, 512, 7, 7]", style=solid]; -"193 /layer4/layer4.0/relu_2/Relu/fq_output_0" -> "196 /layer4/layer4.1/Add" [label="[1, 2048, 7, 7]", style=solid]; -"193 /layer4/layer4.0/relu_2/Relu/fq_output_0" -> "197 /layer4/layer4.1/conv1/Conv/WithoutBiases" [label="[1, 2048, 7, 7]", style=solid]; -"194 /layer3/layer3.5/relu_1/Relu/fq_output_0" -> "198 /layer3/layer3.5/conv3/Conv/WithoutBiases" [label="[1, 256, 14, 14]", style=solid]; -"195 /layer4/layer4.0/conv2/Conv" -> "199 /layer4/layer4.0/relu_1/Relu" [label="[1, 512, 7, 7]", style=solid]; -"196 /layer4/layer4.1/Add" -> "200 /layer4/layer4.1/relu_2/Relu" [label="[1, 2048, 7, 7]", style=solid]; -"197 /layer4/layer4.1/conv1/Conv/WithoutBiases" -> "201 /layer4/layer4.1/conv1/Conv" [label="[1, 512, 7, 7]", style=solid]; -"198 /layer3/layer3.5/conv3/Conv/WithoutBiases" -> "202 /layer3/layer3.5/conv3/Conv" [label="[1, 1024, 14, 14]", style=solid]; -"199 /layer4/layer4.0/relu_1/Relu" -> "203 /layer4/layer4.0/relu_1/Relu/fq_output_0" [label="[1, 512, 7, 7]", style=solid]; -"200 /layer4/layer4.1/relu_2/Relu" -> "204 /layer4/layer4.1/relu_2/Relu/fq_output_0" [label="[1, 2048, 7, 7]", style=solid]; -"201 /layer4/layer4.1/conv1/Conv" -> "205 /layer4/layer4.1/relu/Relu" [label="[1, 512, 7, 7]", style=solid]; -"202 /layer3/layer3.5/conv3/Conv" -> "162 /layer3/layer3.5/Add" [label="[1, 1024, 14, 14]", style=solid]; -"203 /layer4/layer4.0/relu_1/Relu/fq_output_0" -> "206 /layer4/layer4.0/conv3/Conv/WithoutBiases" [label="[1, 512, 7, 7]", style=solid]; -"204 /layer4/layer4.1/relu_2/Relu/fq_output_0" -> "207 /layer4/layer4.2/Add" [label="[1, 2048, 7, 7]", style=solid]; -"204 /layer4/layer4.1/relu_2/Relu/fq_output_0" -> "208 /layer4/layer4.2/conv1/Conv/WithoutBiases" [label="[1, 2048, 7, 7]", style=solid]; -"205 /layer4/layer4.1/relu/Relu" -> "209 /layer4/layer4.1/relu/Relu/fq_output_0" [label="[1, 512, 7, 7]", style=solid]; -"206 /layer4/layer4.0/conv3/Conv/WithoutBiases" -> "210 /layer4/layer4.0/conv3/Conv" [label="[1, 2048, 7, 7]", style=solid]; -"207 /layer4/layer4.2/Add" -> "211 /layer4/layer4.2/relu_2/Relu" [label="[1, 2048, 7, 7]", style=solid]; -"208 /layer4/layer4.2/conv1/Conv/WithoutBiases" -> "212 /layer4/layer4.2/conv1/Conv" [label="[1, 512, 7, 7]", style=solid]; -"209 /layer4/layer4.1/relu/Relu/fq_output_0" -> "213 /layer4/layer4.1/conv2/Conv/WithoutBiases" [label="[1, 512, 7, 7]", style=solid]; -"210 /layer4/layer4.0/conv3/Conv" -> "214 /layer4/layer4.0/conv3/Conv/fq_output_0" [label="[1, 2048, 7, 7]", style=solid]; -"211 /layer4/layer4.2/relu_2/Relu" -> "215 /layer4/layer4.2/relu_2/Relu/fq_output_0" [label="[1, 2048, 7, 7]", style=solid]; -"212 /layer4/layer4.2/conv1/Conv" -> "216 /layer4/layer4.2/relu/Relu" [label="[1, 512, 7, 7]", style=solid]; -"213 /layer4/layer4.1/conv2/Conv/WithoutBiases" -> "217 /layer4/layer4.1/conv2/Conv" [label="[1, 512, 7, 7]", style=solid]; -"214 /layer4/layer4.0/conv3/Conv/fq_output_0" -> "185 /layer4/layer4.0/Add" [label="[1, 2048, 7, 7]", style=solid]; -"215 /layer4/layer4.2/relu_2/Relu/fq_output_0" -> "218 /avgpool/GlobalAveragePool" [label="[1, 2048, 7, 7]", style=solid]; -"216 /layer4/layer4.2/relu/Relu" -> "219 /layer4/layer4.2/relu/Relu/fq_output_0" [label="[1, 512, 7, 7]", style=solid]; -"217 /layer4/layer4.1/conv2/Conv" -> "220 /layer4/layer4.1/relu_1/Relu" [label="[1, 512, 7, 7]", style=solid]; -"218 /avgpool/GlobalAveragePool" -> "221 /avgpool/GlobalAveragePool/fq_output_0" [label="[1, 2048, 1, 1]", style=solid]; -"219 /layer4/layer4.2/relu/Relu/fq_output_0" -> "222 /layer4/layer4.2/conv2/Conv/WithoutBiases" [label="[1, 512, 7, 7]", style=solid]; -"220 /layer4/layer4.1/relu_1/Relu" -> "223 /layer4/layer4.1/relu_1/Relu/fq_output_0" [label="[1, 512, 7, 7]", style=solid]; -"221 /avgpool/GlobalAveragePool/fq_output_0" -> "224 /Flatten" [label="[1, 2048, 1, 1]", style=solid]; -"222 /layer4/layer4.2/conv2/Conv/WithoutBiases" -> "225 /layer4/layer4.2/conv2/Conv" [label="[1, 512, 7, 7]", style=solid]; -"223 /layer4/layer4.1/relu_1/Relu/fq_output_0" -> "226 /layer4/layer4.1/conv3/Conv/WithoutBiases" [label="[1, 512, 7, 7]", style=solid]; -"224 /Flatten" -> "227 /fc/Gemm/WithoutBiases" [label="[1, 2048]", style=solid]; -"225 /layer4/layer4.2/conv2/Conv" -> "228 /layer4/layer4.2/relu_1/Relu" [label="[1, 512, 7, 7]", style=solid]; -"226 /layer4/layer4.1/conv3/Conv/WithoutBiases" -> "229 /layer4/layer4.1/conv3/Conv" [label="[1, 2048, 7, 7]", style=solid]; -"227 /fc/Gemm/WithoutBiases" -> "230 prob" [label="[1, 1000]", style=solid]; -"228 /layer4/layer4.2/relu_1/Relu" -> "231 /layer4/layer4.2/relu_1/Relu/fq_output_0" [label="[1, 512, 7, 7]", style=solid]; -"229 /layer4/layer4.1/conv3/Conv" -> "196 /layer4/layer4.1/Add" [label="[1, 2048, 7, 7]", style=solid]; -"230 prob" -> "232 prob/sink_port_0" [label="[1, 1000]", style=solid]; -"231 /layer4/layer4.2/relu_1/Relu/fq_output_0" -> "233 /layer4/layer4.2/conv3/Conv/WithoutBiases" [label="[1, 512, 7, 7]", style=solid]; -"233 /layer4/layer4.2/conv3/Conv/WithoutBiases" -> "234 /layer4/layer4.2/conv3/Conv" [label="[1, 2048, 7, 7]", style=solid]; -"234 /layer4/layer4.2/conv3/Conv" -> "207 /layer4/layer4.2/Add" [label="[1, 2048, 7, 7]", style=solid]; -"235 Constant_2819" -> "230 prob" [label="[1, 1000]", style=solid]; -"236 /fc/Gemm/WithoutBiases/fq_weights_1" -> "227 /fc/Gemm/WithoutBiases" [label="[1000, 2048]", style=solid]; -"237 Constant_62386" -> "236 /fc/Gemm/WithoutBiases/fq_weights_1" [label="[1000, 1]", style=solid]; -"238 Constant_62385" -> "236 /fc/Gemm/WithoutBiases/fq_weights_1" [label="[1000, 1]", style=solid]; -"239 Constant_62384" -> "236 /fc/Gemm/WithoutBiases/fq_weights_1" [label="[1000, 1]", style=solid]; -"240 Constant_62383" -> "236 /fc/Gemm/WithoutBiases/fq_weights_1" [label="[1000, 1]", style=solid]; -"241 fc.weight" -> "236 /fc/Gemm/WithoutBiases/fq_weights_1" [label="[1000, 2048]", style=solid]; -"242 Constant_981" -> "224 /Flatten" [label="[2]", style=dashed]; -"243 Constant_62381" -> "221 /avgpool/GlobalAveragePool/fq_output_0" [label="[]", style=solid]; -"244 Constant_62380" -> "221 /avgpool/GlobalAveragePool/fq_output_0" [label="[]", style=solid]; -"245 Constant_62379" -> "221 /avgpool/GlobalAveragePool/fq_output_0" [label="[]", style=solid]; -"246 Constant_62378" -> "221 /avgpool/GlobalAveragePool/fq_output_0" [label="[]", style=solid]; -"247 Range_977" -> "218 /avgpool/GlobalAveragePool" [label="[2]", style=dashed]; -"248 Constant_62376" -> "215 /layer4/layer4.2/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"249 Constant_62375" -> "215 /layer4/layer4.2/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"250 Constant_62374" -> "215 /layer4/layer4.2/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"251 Constant_62373" -> "215 /layer4/layer4.2/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"252 Constant_62341" -> "204 /layer4/layer4.1/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"253 Constant_62340" -> "204 /layer4/layer4.1/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"254 Constant_62339" -> "204 /layer4/layer4.1/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"255 Constant_62338" -> "204 /layer4/layer4.1/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"256 Constant_62306" -> "193 /layer4/layer4.0/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"257 Constant_62305" -> "193 /layer4/layer4.0/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"258 Constant_62304" -> "193 /layer4/layer4.0/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"259 Constant_62303" -> "193 /layer4/layer4.0/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"260 Reshape_867" -> "181 /layer4/layer4.0/downsample/downsample.0/Conv" [label="[1, 2048, 1, 1]", style=solid]; -"261 /layer4/layer4.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" -> "176 /layer4/layer4.0/downsample/downsample.0/Conv/WithoutBiases" [label="[2048, 1024, 1, 1]", style=solid]; -"262 Constant_62266" -> "261 /layer4/layer4.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[2048, 1, 1, 1]", style=solid]; -"263 Constant_62265" -> "261 /layer4/layer4.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[2048, 1, 1, 1]", style=solid]; -"264 Constant_62264" -> "261 /layer4/layer4.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[2048, 1, 1, 1]", style=solid]; -"265 Constant_62263" -> "261 /layer4/layer4.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[2048, 1, 1, 1]", style=solid]; -"266 onnx^^Conv_635" -> "261 /layer4/layer4.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[2048, 1024, 1, 1]", style=solid]; -"267 Constant_62261" -> "171 /layer3/layer3.5/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"268 Constant_62260" -> "171 /layer3/layer3.5/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"269 Constant_62259" -> "171 /layer3/layer3.5/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"270 Constant_62258" -> "171 /layer3/layer3.5/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"271 Constant_62226" -> "158 /layer3/layer3.4/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"272 Constant_62225" -> "158 /layer3/layer3.4/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"273 Constant_62224" -> "158 /layer3/layer3.4/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"274 Constant_62223" -> "158 /layer3/layer3.4/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"275 Constant_62191" -> "145 /layer3/layer3.3/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"276 Constant_62190" -> "145 /layer3/layer3.3/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"277 Constant_62189" -> "145 /layer3/layer3.3/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"278 Constant_62188" -> "145 /layer3/layer3.3/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"279 Constant_62156" -> "134 /layer3/layer3.2/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"280 Constant_62155" -> "134 /layer3/layer3.2/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"281 Constant_62154" -> "134 /layer3/layer3.2/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"282 Constant_62153" -> "134 /layer3/layer3.2/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"283 Constant_62121" -> "123 /layer3/layer3.1/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"284 Constant_62120" -> "123 /layer3/layer3.1/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"285 Constant_62119" -> "123 /layer3/layer3.1/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"286 Constant_62118" -> "123 /layer3/layer3.1/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"287 Constant_62086" -> "112 /layer3/layer3.0/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"288 Constant_62085" -> "112 /layer3/layer3.0/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"289 Constant_62084" -> "112 /layer3/layer3.0/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"290 Constant_62083" -> "112 /layer3/layer3.0/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"291 Reshape_558" -> "100 /layer3/layer3.0/downsample/downsample.0/Conv" [label="[1, 1024, 1, 1]", style=solid]; -"292 /layer3/layer3.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" -> "95 /layer3/layer3.0/downsample/downsample.0/Conv/WithoutBiases" [label="[1024, 512, 1, 1]", style=solid]; -"293 Constant_62046" -> "292 /layer3/layer3.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; -"294 Constant_62045" -> "292 /layer3/layer3.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; -"295 Constant_62044" -> "292 /layer3/layer3.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; -"296 Constant_62043" -> "292 /layer3/layer3.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; -"297 onnx^^Conv_578" -> "292 /layer3/layer3.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[1024, 512, 1, 1]", style=solid]; -"298 Constant_62041" -> "90 /layer2/layer2.3/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"299 Constant_62040" -> "90 /layer2/layer2.3/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"300 Constant_62039" -> "90 /layer2/layer2.3/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"301 Constant_62038" -> "90 /layer2/layer2.3/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"302 Constant_62006" -> "79 /layer2/layer2.2/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"303 Constant_62005" -> "79 /layer2/layer2.2/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"304 Constant_62004" -> "79 /layer2/layer2.2/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"305 Constant_62003" -> "79 /layer2/layer2.2/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"306 Constant_61971" -> "68 /layer2/layer2.1/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"307 Constant_61970" -> "68 /layer2/layer2.1/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"308 Constant_61969" -> "68 /layer2/layer2.1/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"309 Constant_61968" -> "68 /layer2/layer2.1/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"310 Constant_61936" -> "57 /layer2/layer2.0/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"311 Constant_61935" -> "57 /layer2/layer2.0/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"312 Constant_61934" -> "57 /layer2/layer2.0/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"313 Constant_61933" -> "57 /layer2/layer2.0/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"314 Reshape_347" -> "45 /layer2/layer2.0/downsample/downsample.0/Conv" [label="[1, 512, 1, 1]", style=solid]; -"315 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" -> "41 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases" [label="[512, 256, 1, 1]", style=solid]; -"316 Constant_61896" -> "315 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"317 Constant_61895" -> "315 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"318 Constant_61894" -> "315 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"319 Constant_61893" -> "315 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"320 onnx^^Conv_539" -> "315 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[512, 256, 1, 1]", style=solid]; -"321 Constant_61891" -> "37 /layer1/layer1.2/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"322 Constant_61890" -> "37 /layer1/layer1.2/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"323 Constant_61889" -> "37 /layer1/layer1.2/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"324 Constant_61888" -> "37 /layer1/layer1.2/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"325 Constant_61856" -> "26 /layer1/layer1.1/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"326 Constant_61855" -> "26 /layer1/layer1.1/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"327 Constant_61854" -> "26 /layer1/layer1.1/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"328 Constant_61853" -> "26 /layer1/layer1.1/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"329 Constant_61821" -> "18 /layer1/layer1.0/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"330 Constant_61820" -> "18 /layer1/layer1.0/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"331 Constant_61819" -> "18 /layer1/layer1.0/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"332 Constant_61818" -> "18 /layer1/layer1.0/relu_2/Relu/fq_output_0" [label="[]", style=solid]; -"333 Reshape_185" -> "12 /layer1/layer1.0/downsample/downsample.0/Conv" [label="[1, 256, 1, 1]", style=solid]; -"334 /layer1/layer1.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" -> "10 /layer1/layer1.0/downsample/downsample.0/Conv/WithoutBiases" [label="[256, 64, 1, 1]", style=solid]; -"335 Constant_61781" -> "334 /layer1/layer1.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"336 Constant_61780" -> "334 /layer1/layer1.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"337 Constant_61779" -> "334 /layer1/layer1.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"338 Constant_61778" -> "334 /layer1/layer1.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"339 onnx^^Conv_509" -> "334 /layer1/layer1.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[256, 64, 1, 1]", style=solid]; -"340 Constant_61776" -> "7 /relu/Relu/fq_output_0" [label="[]", style=solid]; -"341 Constant_61775" -> "7 /relu/Relu/fq_output_0" [label="[]", style=solid]; -"342 Constant_61774" -> "7 /relu/Relu/fq_output_0" [label="[]", style=solid]; -"343 Constant_61773" -> "7 /relu/Relu/fq_output_0" [label="[]", style=solid]; -"344 Reshape_121" -> "5 /conv1/Conv" [label="[1, 64, 1, 1]", style=solid]; -"345 /conv1/Conv/WithoutBiases/fq_weights_1" -> "4 /conv1/Conv/WithoutBiases" [label="[64, 3, 7, 7]", style=solid]; -"346 Constant_61771" -> "345 /conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"347 Constant_61770" -> "345 /conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"348 Constant_61769" -> "345 /conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"349 Constant_61768" -> "345 /conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"350 Gather_2818" -> "345 /conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 3, 7, 7]", style=solid]; -"351 Constant_61766" -> "3 Divide_1002/fq_output_0" [label="[]", style=solid]; -"352 Constant_61765" -> "3 Divide_1002/fq_output_0" [label="[]", style=solid]; -"353 Constant_61764" -> "3 Divide_1002/fq_output_0" [label="[]", style=solid]; -"354 Constant_61763" -> "3 Divide_1002/fq_output_0" [label="[]", style=solid]; -"355 Gather_2815" -> "2 Divide_1002" [label="[1, 3, 1, 1]", style=solid]; -"356 Gather_2812" -> "1 Multiply_2745" [label="[1, 3, 1, 1]", style=solid]; -"357 Constant_61816" -> "36 /layer1/layer1.0/conv3/Conv/fq_output_0" [label="[]", style=solid]; -"358 Constant_61815" -> "36 /layer1/layer1.0/conv3/Conv/fq_output_0" [label="[]", style=solid]; -"359 Constant_61814" -> "36 /layer1/layer1.0/conv3/Conv/fq_output_0" [label="[]", style=solid]; -"360 Constant_61813" -> "36 /layer1/layer1.0/conv3/Conv/fq_output_0" [label="[]", style=solid]; -"361 Reshape_170" -> "32 /layer1/layer1.0/conv3/Conv" [label="[1, 256, 1, 1]", style=solid]; -"362 /layer1/layer1.0/conv3/Conv/WithoutBiases/fq_weights_1" -> "28 /layer1/layer1.0/conv3/Conv/WithoutBiases" [label="[256, 64, 1, 1]", style=solid]; -"363 Constant_61811" -> "362 /layer1/layer1.0/conv3/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"364 Constant_61810" -> "362 /layer1/layer1.0/conv3/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"365 Constant_61809" -> "362 /layer1/layer1.0/conv3/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"366 Constant_61808" -> "362 /layer1/layer1.0/conv3/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"367 onnx^^Conv_506" -> "362 /layer1/layer1.0/conv3/Conv/WithoutBiases/fq_weights_1" [label="[256, 64, 1, 1]", style=solid]; -"368 Constant_61806" -> "25 /layer1/layer1.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"369 Constant_61805" -> "25 /layer1/layer1.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"370 Constant_61804" -> "25 /layer1/layer1.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"371 Constant_61803" -> "25 /layer1/layer1.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"372 Reshape_154" -> "19 /layer1/layer1.0/conv2/Conv" [label="[1, 64, 1, 1]", style=solid]; -"373 /layer1/layer1.0/conv2/Conv/WithoutBiases/fq_weights_1" -> "17 /layer1/layer1.0/conv2/Conv/WithoutBiases" [label="[64, 64, 3, 3]", style=solid]; -"374 Constant_61801" -> "373 /layer1/layer1.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"375 Constant_61800" -> "373 /layer1/layer1.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"376 Constant_61799" -> "373 /layer1/layer1.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"377 Constant_61798" -> "373 /layer1/layer1.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"378 onnx^^Conv_503" -> "373 /layer1/layer1.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 64, 3, 3]", style=solid]; -"379 Constant_61796" -> "15 /layer1/layer1.0/relu/Relu/fq_output_0" [label="[]", style=solid]; -"380 Constant_61795" -> "15 /layer1/layer1.0/relu/Relu/fq_output_0" [label="[]", style=solid]; -"381 Constant_61794" -> "15 /layer1/layer1.0/relu/Relu/fq_output_0" [label="[]", style=solid]; -"382 Constant_61793" -> "15 /layer1/layer1.0/relu/Relu/fq_output_0" [label="[]", style=solid]; -"383 Reshape_138" -> "11 /layer1/layer1.0/conv1/Conv" [label="[1, 64, 1, 1]", style=solid]; -"384 /layer1/layer1.0/conv1/Conv/WithoutBiases/fq_weights_1" -> "9 /layer1/layer1.0/conv1/Conv/WithoutBiases" [label="[64, 64, 1, 1]", style=solid]; -"385 Constant_61791" -> "384 /layer1/layer1.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"386 Constant_61790" -> "384 /layer1/layer1.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"387 Constant_61789" -> "384 /layer1/layer1.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"388 Constant_61788" -> "384 /layer1/layer1.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"389 onnx^^Conv_500" -> "384 /layer1/layer1.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 64, 1, 1]", style=solid]; -"390 Reshape_234" -> "55 /layer1/layer1.1/conv3/Conv" [label="[1, 256, 1, 1]", style=solid]; -"391 /layer1/layer1.1/conv3/Conv/WithoutBiases/fq_weights_1" -> "51 /layer1/layer1.1/conv3/Conv/WithoutBiases" [label="[256, 64, 1, 1]", style=solid]; -"392 Constant_61846" -> "391 /layer1/layer1.1/conv3/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"393 Constant_61845" -> "391 /layer1/layer1.1/conv3/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"394 Constant_61844" -> "391 /layer1/layer1.1/conv3/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"395 Constant_61843" -> "391 /layer1/layer1.1/conv3/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"396 onnx^^Conv_518" -> "391 /layer1/layer1.1/conv3/Conv/WithoutBiases/fq_weights_1" [label="[256, 64, 1, 1]", style=solid]; -"397 Constant_61841" -> "47 /layer1/layer1.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"398 Constant_61840" -> "47 /layer1/layer1.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"399 Constant_61839" -> "47 /layer1/layer1.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"400 Constant_61838" -> "47 /layer1/layer1.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"401 Reshape_218" -> "39 /layer1/layer1.1/conv2/Conv" [label="[1, 64, 1, 1]", style=solid]; -"402 /layer1/layer1.1/conv2/Conv/WithoutBiases/fq_weights_1" -> "35 /layer1/layer1.1/conv2/Conv/WithoutBiases" [label="[64, 64, 3, 3]", style=solid]; -"403 Constant_61836" -> "402 /layer1/layer1.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"404 Constant_61835" -> "402 /layer1/layer1.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"405 Constant_61834" -> "402 /layer1/layer1.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"406 Constant_61833" -> "402 /layer1/layer1.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"407 onnx^^Conv_515" -> "402 /layer1/layer1.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 64, 3, 3]", style=solid]; -"408 Constant_61831" -> "31 /layer1/layer1.1/relu/Relu/fq_output_0" [label="[]", style=solid]; -"409 Constant_61830" -> "31 /layer1/layer1.1/relu/Relu/fq_output_0" [label="[]", style=solid]; -"410 Constant_61829" -> "31 /layer1/layer1.1/relu/Relu/fq_output_0" [label="[]", style=solid]; -"411 Constant_61828" -> "31 /layer1/layer1.1/relu/Relu/fq_output_0" [label="[]", style=solid]; -"412 Reshape_202" -> "24 /layer1/layer1.1/conv1/Conv" [label="[1, 64, 1, 1]", style=solid]; -"413 /layer1/layer1.1/conv1/Conv/WithoutBiases/fq_weights_1" -> "21 /layer1/layer1.1/conv1/Conv/WithoutBiases" [label="[64, 256, 1, 1]", style=solid]; -"414 Constant_61826" -> "413 /layer1/layer1.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"415 Constant_61825" -> "413 /layer1/layer1.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"416 Constant_61824" -> "413 /layer1/layer1.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"417 Constant_61823" -> "413 /layer1/layer1.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"418 onnx^^Conv_512" -> "413 /layer1/layer1.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 256, 1, 1]", style=solid]; -"419 Reshape_283" -> "66 /layer1/layer1.2/conv3/Conv" [label="[1, 256, 1, 1]", style=solid]; -"420 /layer1/layer1.2/conv3/Conv/WithoutBiases/fq_weights_1" -> "62 /layer1/layer1.2/conv3/Conv/WithoutBiases" [label="[256, 64, 1, 1]", style=solid]; -"421 Constant_61881" -> "420 /layer1/layer1.2/conv3/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"422 Constant_61880" -> "420 /layer1/layer1.2/conv3/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"423 Constant_61879" -> "420 /layer1/layer1.2/conv3/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"424 Constant_61878" -> "420 /layer1/layer1.2/conv3/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"425 onnx^^Conv_527" -> "420 /layer1/layer1.2/conv3/Conv/WithoutBiases/fq_weights_1" [label="[256, 64, 1, 1]", style=solid]; -"426 Constant_61876" -> "58 /layer1/layer1.2/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"427 Constant_61875" -> "58 /layer1/layer1.2/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"428 Constant_61874" -> "58 /layer1/layer1.2/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"429 Constant_61873" -> "58 /layer1/layer1.2/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"430 Reshape_267" -> "50 /layer1/layer1.2/conv2/Conv" [label="[1, 64, 1, 1]", style=solid]; -"431 /layer1/layer1.2/conv2/Conv/WithoutBiases/fq_weights_1" -> "46 /layer1/layer1.2/conv2/Conv/WithoutBiases" [label="[64, 64, 3, 3]", style=solid]; -"432 Constant_61871" -> "431 /layer1/layer1.2/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"433 Constant_61870" -> "431 /layer1/layer1.2/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"434 Constant_61869" -> "431 /layer1/layer1.2/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"435 Constant_61868" -> "431 /layer1/layer1.2/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"436 onnx^^Conv_524" -> "431 /layer1/layer1.2/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 64, 3, 3]", style=solid]; -"437 Constant_61866" -> "42 /layer1/layer1.2/relu/Relu/fq_output_0" [label="[]", style=solid]; -"438 Constant_61865" -> "42 /layer1/layer1.2/relu/Relu/fq_output_0" [label="[]", style=solid]; -"439 Constant_61864" -> "42 /layer1/layer1.2/relu/Relu/fq_output_0" [label="[]", style=solid]; -"440 Constant_61863" -> "42 /layer1/layer1.2/relu/Relu/fq_output_0" [label="[]", style=solid]; -"441 Reshape_251" -> "34 /layer1/layer1.2/conv1/Conv" [label="[1, 64, 1, 1]", style=solid]; -"442 /layer1/layer1.2/conv1/Conv/WithoutBiases/fq_weights_1" -> "30 /layer1/layer1.2/conv1/Conv/WithoutBiases" [label="[64, 256, 1, 1]", style=solid]; -"443 Constant_61861" -> "442 /layer1/layer1.2/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"444 Constant_61860" -> "442 /layer1/layer1.2/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"445 Constant_61859" -> "442 /layer1/layer1.2/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"446 Constant_61858" -> "442 /layer1/layer1.2/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"447 onnx^^Conv_521" -> "442 /layer1/layer1.2/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 256, 1, 1]", style=solid]; -"448 Constant_61931" -> "78 /layer2/layer2.0/conv3/Conv/fq_output_0" [label="[]", style=solid]; -"449 Constant_61930" -> "78 /layer2/layer2.0/conv3/Conv/fq_output_0" [label="[]", style=solid]; -"450 Constant_61929" -> "78 /layer2/layer2.0/conv3/Conv/fq_output_0" [label="[]", style=solid]; -"451 Constant_61928" -> "78 /layer2/layer2.0/conv3/Conv/fq_output_0" [label="[]", style=solid]; -"452 Reshape_332" -> "74 /layer2/layer2.0/conv3/Conv" [label="[1, 512, 1, 1]", style=solid]; -"453 /layer2/layer2.0/conv3/Conv/WithoutBiases/fq_weights_1" -> "70 /layer2/layer2.0/conv3/Conv/WithoutBiases" [label="[512, 128, 1, 1]", style=solid]; -"454 Constant_61926" -> "453 /layer2/layer2.0/conv3/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"455 Constant_61925" -> "453 /layer2/layer2.0/conv3/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"456 Constant_61924" -> "453 /layer2/layer2.0/conv3/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"457 Constant_61923" -> "453 /layer2/layer2.0/conv3/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"458 onnx^^Conv_536" -> "453 /layer2/layer2.0/conv3/Conv/WithoutBiases/fq_weights_1" [label="[512, 128, 1, 1]", style=solid]; -"459 Constant_61921" -> "67 /layer2/layer2.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"460 Constant_61920" -> "67 /layer2/layer2.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"461 Constant_61919" -> "67 /layer2/layer2.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"462 Constant_61918" -> "67 /layer2/layer2.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"463 Reshape_316" -> "59 /layer2/layer2.0/conv2/Conv" [label="[1, 128, 1, 1]", style=solid]; -"464 /layer2/layer2.0/conv2/Conv/WithoutBiases/fq_weights_1" -> "56 /layer2/layer2.0/conv2/Conv/WithoutBiases" [label="[128, 128, 3, 3]", style=solid]; -"465 Constant_61916" -> "464 /layer2/layer2.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"466 Constant_61915" -> "464 /layer2/layer2.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"467 Constant_61914" -> "464 /layer2/layer2.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"468 Constant_61913" -> "464 /layer2/layer2.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"469 onnx^^Conv_533" -> "464 /layer2/layer2.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 128, 3, 3]", style=solid]; -"470 Constant_61911" -> "52 /layer2/layer2.0/relu/Relu/fq_output_0" [label="[]", style=solid]; -"471 Constant_61910" -> "52 /layer2/layer2.0/relu/Relu/fq_output_0" [label="[]", style=solid]; -"472 Constant_61909" -> "52 /layer2/layer2.0/relu/Relu/fq_output_0" [label="[]", style=solid]; -"473 Constant_61908" -> "52 /layer2/layer2.0/relu/Relu/fq_output_0" [label="[]", style=solid]; -"474 Reshape_300" -> "44 /layer2/layer2.0/conv1/Conv" [label="[1, 128, 1, 1]", style=solid]; -"475 /layer2/layer2.0/conv1/Conv/WithoutBiases/fq_weights_1" -> "40 /layer2/layer2.0/conv1/Conv/WithoutBiases" [label="[128, 256, 1, 1]", style=solid]; -"476 Constant_61906" -> "475 /layer2/layer2.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"477 Constant_61905" -> "475 /layer2/layer2.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"478 Constant_61904" -> "475 /layer2/layer2.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"479 Constant_61903" -> "475 /layer2/layer2.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"480 onnx^^Conv_530" -> "475 /layer2/layer2.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 256, 1, 1]", style=solid]; -"481 Reshape_396" -> "98 /layer2/layer2.1/conv3/Conv" [label="[1, 512, 1, 1]", style=solid]; -"482 /layer2/layer2.1/conv3/Conv/WithoutBiases/fq_weights_1" -> "93 /layer2/layer2.1/conv3/Conv/WithoutBiases" [label="[512, 128, 1, 1]", style=solid]; -"483 Constant_61961" -> "482 /layer2/layer2.1/conv3/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"484 Constant_61960" -> "482 /layer2/layer2.1/conv3/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"485 Constant_61959" -> "482 /layer2/layer2.1/conv3/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"486 Constant_61958" -> "482 /layer2/layer2.1/conv3/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"487 onnx^^Conv_548" -> "482 /layer2/layer2.1/conv3/Conv/WithoutBiases/fq_weights_1" [label="[512, 128, 1, 1]", style=solid]; -"488 Constant_61956" -> "89 /layer2/layer2.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"489 Constant_61955" -> "89 /layer2/layer2.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"490 Constant_61954" -> "89 /layer2/layer2.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"491 Constant_61953" -> "89 /layer2/layer2.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"492 Reshape_380" -> "81 /layer2/layer2.1/conv2/Conv" [label="[1, 128, 1, 1]", style=solid]; -"493 /layer2/layer2.1/conv2/Conv/WithoutBiases/fq_weights_1" -> "77 /layer2/layer2.1/conv2/Conv/WithoutBiases" [label="[128, 128, 3, 3]", style=solid]; -"494 Constant_61951" -> "493 /layer2/layer2.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"495 Constant_61950" -> "493 /layer2/layer2.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"496 Constant_61949" -> "493 /layer2/layer2.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"497 Constant_61948" -> "493 /layer2/layer2.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"498 onnx^^Conv_545" -> "493 /layer2/layer2.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 128, 3, 3]", style=solid]; -"499 Constant_61946" -> "73 /layer2/layer2.1/relu/Relu/fq_output_0" [label="[]", style=solid]; -"500 Constant_61945" -> "73 /layer2/layer2.1/relu/Relu/fq_output_0" [label="[]", style=solid]; -"501 Constant_61944" -> "73 /layer2/layer2.1/relu/Relu/fq_output_0" [label="[]", style=solid]; -"502 Constant_61943" -> "73 /layer2/layer2.1/relu/Relu/fq_output_0" [label="[]", style=solid]; -"503 Reshape_364" -> "65 /layer2/layer2.1/conv1/Conv" [label="[1, 128, 1, 1]", style=solid]; -"504 /layer2/layer2.1/conv1/Conv/WithoutBiases/fq_weights_1" -> "61 /layer2/layer2.1/conv1/Conv/WithoutBiases" [label="[128, 512, 1, 1]", style=solid]; -"505 Constant_61941" -> "504 /layer2/layer2.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"506 Constant_61940" -> "504 /layer2/layer2.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"507 Constant_61939" -> "504 /layer2/layer2.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"508 Constant_61938" -> "504 /layer2/layer2.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"509 onnx^^Conv_542" -> "504 /layer2/layer2.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 512, 1, 1]", style=solid]; -"510 Reshape_445" -> "110 /layer2/layer2.2/conv3/Conv" [label="[1, 512, 1, 1]", style=solid]; -"511 /layer2/layer2.2/conv3/Conv/WithoutBiases/fq_weights_1" -> "106 /layer2/layer2.2/conv3/Conv/WithoutBiases" [label="[512, 128, 1, 1]", style=solid]; -"512 Constant_61996" -> "511 /layer2/layer2.2/conv3/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"513 Constant_61995" -> "511 /layer2/layer2.2/conv3/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"514 Constant_61994" -> "511 /layer2/layer2.2/conv3/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"515 Constant_61993" -> "511 /layer2/layer2.2/conv3/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"516 onnx^^Conv_557" -> "511 /layer2/layer2.2/conv3/Conv/WithoutBiases/fq_weights_1" [label="[512, 128, 1, 1]", style=solid]; -"517 Constant_61991" -> "102 /layer2/layer2.2/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"518 Constant_61990" -> "102 /layer2/layer2.2/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"519 Constant_61989" -> "102 /layer2/layer2.2/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"520 Constant_61988" -> "102 /layer2/layer2.2/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"521 Reshape_429" -> "92 /layer2/layer2.2/conv2/Conv" [label="[1, 128, 1, 1]", style=solid]; -"522 /layer2/layer2.2/conv2/Conv/WithoutBiases/fq_weights_1" -> "88 /layer2/layer2.2/conv2/Conv/WithoutBiases" [label="[128, 128, 3, 3]", style=solid]; -"523 Constant_61986" -> "522 /layer2/layer2.2/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"524 Constant_61985" -> "522 /layer2/layer2.2/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"525 Constant_61984" -> "522 /layer2/layer2.2/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"526 Constant_61983" -> "522 /layer2/layer2.2/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"527 onnx^^Conv_554" -> "522 /layer2/layer2.2/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 128, 3, 3]", style=solid]; -"528 Constant_61981" -> "84 /layer2/layer2.2/relu/Relu/fq_output_0" [label="[]", style=solid]; -"529 Constant_61980" -> "84 /layer2/layer2.2/relu/Relu/fq_output_0" [label="[]", style=solid]; -"530 Constant_61979" -> "84 /layer2/layer2.2/relu/Relu/fq_output_0" [label="[]", style=solid]; -"531 Constant_61978" -> "84 /layer2/layer2.2/relu/Relu/fq_output_0" [label="[]", style=solid]; -"532 Reshape_413" -> "76 /layer2/layer2.2/conv1/Conv" [label="[1, 128, 1, 1]", style=solid]; -"533 /layer2/layer2.2/conv1/Conv/WithoutBiases/fq_weights_1" -> "72 /layer2/layer2.2/conv1/Conv/WithoutBiases" [label="[128, 512, 1, 1]", style=solid]; -"534 Constant_61976" -> "533 /layer2/layer2.2/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"535 Constant_61975" -> "533 /layer2/layer2.2/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"536 Constant_61974" -> "533 /layer2/layer2.2/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"537 Constant_61973" -> "533 /layer2/layer2.2/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"538 onnx^^Conv_551" -> "533 /layer2/layer2.2/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 512, 1, 1]", style=solid]; -"539 Reshape_494" -> "121 /layer2/layer2.3/conv3/Conv" [label="[1, 512, 1, 1]", style=solid]; -"540 /layer2/layer2.3/conv3/Conv/WithoutBiases/fq_weights_1" -> "117 /layer2/layer2.3/conv3/Conv/WithoutBiases" [label="[512, 128, 1, 1]", style=solid]; -"541 Constant_62031" -> "540 /layer2/layer2.3/conv3/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"542 Constant_62030" -> "540 /layer2/layer2.3/conv3/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"543 Constant_62029" -> "540 /layer2/layer2.3/conv3/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"544 Constant_62028" -> "540 /layer2/layer2.3/conv3/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"545 onnx^^Conv_566" -> "540 /layer2/layer2.3/conv3/Conv/WithoutBiases/fq_weights_1" [label="[512, 128, 1, 1]", style=solid]; -"546 Constant_62026" -> "113 /layer2/layer2.3/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"547 Constant_62025" -> "113 /layer2/layer2.3/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"548 Constant_62024" -> "113 /layer2/layer2.3/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"549 Constant_62023" -> "113 /layer2/layer2.3/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"550 Reshape_478" -> "105 /layer2/layer2.3/conv2/Conv" [label="[1, 128, 1, 1]", style=solid]; -"551 /layer2/layer2.3/conv2/Conv/WithoutBiases/fq_weights_1" -> "101 /layer2/layer2.3/conv2/Conv/WithoutBiases" [label="[128, 128, 3, 3]", style=solid]; -"552 Constant_62021" -> "551 /layer2/layer2.3/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"553 Constant_62020" -> "551 /layer2/layer2.3/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"554 Constant_62019" -> "551 /layer2/layer2.3/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"555 Constant_62018" -> "551 /layer2/layer2.3/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"556 onnx^^Conv_563" -> "551 /layer2/layer2.3/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 128, 3, 3]", style=solid]; -"557 Constant_62016" -> "96 /layer2/layer2.3/relu/Relu/fq_output_0" [label="[]", style=solid]; -"558 Constant_62015" -> "96 /layer2/layer2.3/relu/Relu/fq_output_0" [label="[]", style=solid]; -"559 Constant_62014" -> "96 /layer2/layer2.3/relu/Relu/fq_output_0" [label="[]", style=solid]; -"560 Constant_62013" -> "96 /layer2/layer2.3/relu/Relu/fq_output_0" [label="[]", style=solid]; -"561 Reshape_462" -> "87 /layer2/layer2.3/conv1/Conv" [label="[1, 128, 1, 1]", style=solid]; -"562 /layer2/layer2.3/conv1/Conv/WithoutBiases/fq_weights_1" -> "83 /layer2/layer2.3/conv1/Conv/WithoutBiases" [label="[128, 512, 1, 1]", style=solid]; -"563 Constant_62011" -> "562 /layer2/layer2.3/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"564 Constant_62010" -> "562 /layer2/layer2.3/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"565 Constant_62009" -> "562 /layer2/layer2.3/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"566 Constant_62008" -> "562 /layer2/layer2.3/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"567 onnx^^Conv_560" -> "562 /layer2/layer2.3/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 512, 1, 1]", style=solid]; -"568 Constant_62081" -> "133 /layer3/layer3.0/conv3/Conv/fq_output_0" [label="[]", style=solid]; -"569 Constant_62080" -> "133 /layer3/layer3.0/conv3/Conv/fq_output_0" [label="[]", style=solid]; -"570 Constant_62079" -> "133 /layer3/layer3.0/conv3/Conv/fq_output_0" [label="[]", style=solid]; -"571 Constant_62078" -> "133 /layer3/layer3.0/conv3/Conv/fq_output_0" [label="[]", style=solid]; -"572 Reshape_543" -> "129 /layer3/layer3.0/conv3/Conv" [label="[1, 1024, 1, 1]", style=solid]; -"573 /layer3/layer3.0/conv3/Conv/WithoutBiases/fq_weights_1" -> "125 /layer3/layer3.0/conv3/Conv/WithoutBiases" [label="[1024, 256, 1, 1]", style=solid]; -"574 Constant_62076" -> "573 /layer3/layer3.0/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; -"575 Constant_62075" -> "573 /layer3/layer3.0/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; -"576 Constant_62074" -> "573 /layer3/layer3.0/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; -"577 Constant_62073" -> "573 /layer3/layer3.0/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; -"578 onnx^^Conv_575" -> "573 /layer3/layer3.0/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 256, 1, 1]", style=solid]; -"579 Constant_62071" -> "122 /layer3/layer3.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"580 Constant_62070" -> "122 /layer3/layer3.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"581 Constant_62069" -> "122 /layer3/layer3.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"582 Constant_62068" -> "122 /layer3/layer3.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"583 Reshape_527" -> "114 /layer3/layer3.0/conv2/Conv" [label="[1, 256, 1, 1]", style=solid]; -"584 /layer3/layer3.0/conv2/Conv/WithoutBiases/fq_weights_1" -> "111 /layer3/layer3.0/conv2/Conv/WithoutBiases" [label="[256, 256, 3, 3]", style=solid]; -"585 Constant_62066" -> "584 /layer3/layer3.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"586 Constant_62065" -> "584 /layer3/layer3.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"587 Constant_62064" -> "584 /layer3/layer3.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"588 Constant_62063" -> "584 /layer3/layer3.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"589 onnx^^Conv_572" -> "584 /layer3/layer3.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 256, 3, 3]", style=solid]; -"590 Constant_62061" -> "107 /layer3/layer3.0/relu/Relu/fq_output_0" [label="[]", style=solid]; -"591 Constant_62060" -> "107 /layer3/layer3.0/relu/Relu/fq_output_0" [label="[]", style=solid]; -"592 Constant_62059" -> "107 /layer3/layer3.0/relu/Relu/fq_output_0" [label="[]", style=solid]; -"593 Constant_62058" -> "107 /layer3/layer3.0/relu/Relu/fq_output_0" [label="[]", style=solid]; -"594 Reshape_511" -> "99 /layer3/layer3.0/conv1/Conv" [label="[1, 256, 1, 1]", style=solid]; -"595 /layer3/layer3.0/conv1/Conv/WithoutBiases/fq_weights_1" -> "94 /layer3/layer3.0/conv1/Conv/WithoutBiases" [label="[256, 512, 1, 1]", style=solid]; -"596 Constant_62056" -> "595 /layer3/layer3.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"597 Constant_62055" -> "595 /layer3/layer3.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"598 Constant_62054" -> "595 /layer3/layer3.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"599 Constant_62053" -> "595 /layer3/layer3.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"600 onnx^^Conv_569" -> "595 /layer3/layer3.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 512, 1, 1]", style=solid]; -"601 Reshape_607" -> "153 /layer3/layer3.1/conv3/Conv" [label="[1, 1024, 1, 1]", style=solid]; -"602 /layer3/layer3.1/conv3/Conv/WithoutBiases/fq_weights_1" -> "148 /layer3/layer3.1/conv3/Conv/WithoutBiases" [label="[1024, 256, 1, 1]", style=solid]; -"603 Constant_62111" -> "602 /layer3/layer3.1/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; -"604 Constant_62110" -> "602 /layer3/layer3.1/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; -"605 Constant_62109" -> "602 /layer3/layer3.1/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; -"606 Constant_62108" -> "602 /layer3/layer3.1/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; -"607 onnx^^Conv_587" -> "602 /layer3/layer3.1/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 256, 1, 1]", style=solid]; -"608 Constant_62106" -> "144 /layer3/layer3.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"609 Constant_62105" -> "144 /layer3/layer3.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"610 Constant_62104" -> "144 /layer3/layer3.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"611 Constant_62103" -> "144 /layer3/layer3.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"612 Reshape_591" -> "136 /layer3/layer3.1/conv2/Conv" [label="[1, 256, 1, 1]", style=solid]; -"613 /layer3/layer3.1/conv2/Conv/WithoutBiases/fq_weights_1" -> "132 /layer3/layer3.1/conv2/Conv/WithoutBiases" [label="[256, 256, 3, 3]", style=solid]; -"614 Constant_62101" -> "613 /layer3/layer3.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"615 Constant_62100" -> "613 /layer3/layer3.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"616 Constant_62099" -> "613 /layer3/layer3.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"617 Constant_62098" -> "613 /layer3/layer3.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"618 onnx^^Conv_584" -> "613 /layer3/layer3.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 256, 3, 3]", style=solid]; -"619 Constant_62096" -> "128 /layer3/layer3.1/relu/Relu/fq_output_0" [label="[]", style=solid]; -"620 Constant_62095" -> "128 /layer3/layer3.1/relu/Relu/fq_output_0" [label="[]", style=solid]; -"621 Constant_62094" -> "128 /layer3/layer3.1/relu/Relu/fq_output_0" [label="[]", style=solid]; -"622 Constant_62093" -> "128 /layer3/layer3.1/relu/Relu/fq_output_0" [label="[]", style=solid]; -"623 Reshape_575" -> "120 /layer3/layer3.1/conv1/Conv" [label="[1, 256, 1, 1]", style=solid]; -"624 /layer3/layer3.1/conv1/Conv/WithoutBiases/fq_weights_1" -> "116 /layer3/layer3.1/conv1/Conv/WithoutBiases" [label="[256, 1024, 1, 1]", style=solid]; -"625 Constant_62091" -> "624 /layer3/layer3.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"626 Constant_62090" -> "624 /layer3/layer3.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"627 Constant_62089" -> "624 /layer3/layer3.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"628 Constant_62088" -> "624 /layer3/layer3.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"629 onnx^^Conv_581" -> "624 /layer3/layer3.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1024, 1, 1]", style=solid]; -"630 Reshape_656" -> "166 /layer3/layer3.2/conv3/Conv" [label="[1, 1024, 1, 1]", style=solid]; -"631 /layer3/layer3.2/conv3/Conv/WithoutBiases/fq_weights_1" -> "161 /layer3/layer3.2/conv3/Conv/WithoutBiases" [label="[1024, 256, 1, 1]", style=solid]; -"632 Constant_62146" -> "631 /layer3/layer3.2/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; -"633 Constant_62145" -> "631 /layer3/layer3.2/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; -"634 Constant_62144" -> "631 /layer3/layer3.2/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; -"635 Constant_62143" -> "631 /layer3/layer3.2/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; -"636 onnx^^Conv_596" -> "631 /layer3/layer3.2/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 256, 1, 1]", style=solid]; -"637 Constant_62141" -> "157 /layer3/layer3.2/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"638 Constant_62140" -> "157 /layer3/layer3.2/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"639 Constant_62139" -> "157 /layer3/layer3.2/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"640 Constant_62138" -> "157 /layer3/layer3.2/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"641 Reshape_640" -> "147 /layer3/layer3.2/conv2/Conv" [label="[1, 256, 1, 1]", style=solid]; -"642 /layer3/layer3.2/conv2/Conv/WithoutBiases/fq_weights_1" -> "143 /layer3/layer3.2/conv2/Conv/WithoutBiases" [label="[256, 256, 3, 3]", style=solid]; -"643 Constant_62136" -> "642 /layer3/layer3.2/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"644 Constant_62135" -> "642 /layer3/layer3.2/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"645 Constant_62134" -> "642 /layer3/layer3.2/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"646 Constant_62133" -> "642 /layer3/layer3.2/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"647 onnx^^Conv_593" -> "642 /layer3/layer3.2/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 256, 3, 3]", style=solid]; -"648 Constant_62131" -> "139 /layer3/layer3.2/relu/Relu/fq_output_0" [label="[]", style=solid]; -"649 Constant_62130" -> "139 /layer3/layer3.2/relu/Relu/fq_output_0" [label="[]", style=solid]; -"650 Constant_62129" -> "139 /layer3/layer3.2/relu/Relu/fq_output_0" [label="[]", style=solid]; -"651 Constant_62128" -> "139 /layer3/layer3.2/relu/Relu/fq_output_0" [label="[]", style=solid]; -"652 Reshape_624" -> "131 /layer3/layer3.2/conv1/Conv" [label="[1, 256, 1, 1]", style=solid]; -"653 /layer3/layer3.2/conv1/Conv/WithoutBiases/fq_weights_1" -> "127 /layer3/layer3.2/conv1/Conv/WithoutBiases" [label="[256, 1024, 1, 1]", style=solid]; -"654 Constant_62126" -> "653 /layer3/layer3.2/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"655 Constant_62125" -> "653 /layer3/layer3.2/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"656 Constant_62124" -> "653 /layer3/layer3.2/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"657 Constant_62123" -> "653 /layer3/layer3.2/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"658 onnx^^Conv_590" -> "653 /layer3/layer3.2/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1024, 1, 1]", style=solid]; -"659 Reshape_705" -> "179 /layer3/layer3.3/conv3/Conv" [label="[1, 1024, 1, 1]", style=solid]; -"660 /layer3/layer3.3/conv3/Conv/WithoutBiases/fq_weights_1" -> "174 /layer3/layer3.3/conv3/Conv/WithoutBiases" [label="[1024, 256, 1, 1]", style=solid]; -"661 Constant_62181" -> "660 /layer3/layer3.3/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; -"662 Constant_62180" -> "660 /layer3/layer3.3/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; -"663 Constant_62179" -> "660 /layer3/layer3.3/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; -"664 Constant_62178" -> "660 /layer3/layer3.3/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; -"665 onnx^^Conv_605" -> "660 /layer3/layer3.3/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 256, 1, 1]", style=solid]; -"666 Constant_62176" -> "170 /layer3/layer3.3/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"667 Constant_62175" -> "170 /layer3/layer3.3/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"668 Constant_62174" -> "170 /layer3/layer3.3/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"669 Constant_62173" -> "170 /layer3/layer3.3/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"670 Reshape_689" -> "160 /layer3/layer3.3/conv2/Conv" [label="[1, 256, 1, 1]", style=solid]; -"671 /layer3/layer3.3/conv2/Conv/WithoutBiases/fq_weights_1" -> "156 /layer3/layer3.3/conv2/Conv/WithoutBiases" [label="[256, 256, 3, 3]", style=solid]; -"672 Constant_62171" -> "671 /layer3/layer3.3/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"673 Constant_62170" -> "671 /layer3/layer3.3/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"674 Constant_62169" -> "671 /layer3/layer3.3/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"675 Constant_62168" -> "671 /layer3/layer3.3/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"676 onnx^^Conv_602" -> "671 /layer3/layer3.3/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 256, 3, 3]", style=solid]; -"677 Constant_62166" -> "151 /layer3/layer3.3/relu/Relu/fq_output_0" [label="[]", style=solid]; -"678 Constant_62165" -> "151 /layer3/layer3.3/relu/Relu/fq_output_0" [label="[]", style=solid]; -"679 Constant_62164" -> "151 /layer3/layer3.3/relu/Relu/fq_output_0" [label="[]", style=solid]; -"680 Constant_62163" -> "151 /layer3/layer3.3/relu/Relu/fq_output_0" [label="[]", style=solid]; -"681 Reshape_673" -> "142 /layer3/layer3.3/conv1/Conv" [label="[1, 256, 1, 1]", style=solid]; -"682 /layer3/layer3.3/conv1/Conv/WithoutBiases/fq_weights_1" -> "138 /layer3/layer3.3/conv1/Conv/WithoutBiases" [label="[256, 1024, 1, 1]", style=solid]; -"683 Constant_62161" -> "682 /layer3/layer3.3/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"684 Constant_62160" -> "682 /layer3/layer3.3/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"685 Constant_62159" -> "682 /layer3/layer3.3/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"686 Constant_62158" -> "682 /layer3/layer3.3/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"687 onnx^^Conv_599" -> "682 /layer3/layer3.3/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1024, 1, 1]", style=solid]; -"688 Reshape_754" -> "191 /layer3/layer3.4/conv3/Conv" [label="[1, 1024, 1, 1]", style=solid]; -"689 /layer3/layer3.4/conv3/Conv/WithoutBiases/fq_weights_1" -> "187 /layer3/layer3.4/conv3/Conv/WithoutBiases" [label="[1024, 256, 1, 1]", style=solid]; -"690 Constant_62216" -> "689 /layer3/layer3.4/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; -"691 Constant_62215" -> "689 /layer3/layer3.4/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; -"692 Constant_62214" -> "689 /layer3/layer3.4/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; -"693 Constant_62213" -> "689 /layer3/layer3.4/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; -"694 onnx^^Conv_614" -> "689 /layer3/layer3.4/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 256, 1, 1]", style=solid]; -"695 Constant_62211" -> "183 /layer3/layer3.4/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"696 Constant_62210" -> "183 /layer3/layer3.4/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"697 Constant_62209" -> "183 /layer3/layer3.4/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"698 Constant_62208" -> "183 /layer3/layer3.4/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"699 Reshape_738" -> "173 /layer3/layer3.4/conv2/Conv" [label="[1, 256, 1, 1]", style=solid]; -"700 /layer3/layer3.4/conv2/Conv/WithoutBiases/fq_weights_1" -> "169 /layer3/layer3.4/conv2/Conv/WithoutBiases" [label="[256, 256, 3, 3]", style=solid]; -"701 Constant_62206" -> "700 /layer3/layer3.4/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"702 Constant_62205" -> "700 /layer3/layer3.4/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"703 Constant_62204" -> "700 /layer3/layer3.4/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"704 Constant_62203" -> "700 /layer3/layer3.4/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"705 onnx^^Conv_611" -> "700 /layer3/layer3.4/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 256, 3, 3]", style=solid]; -"706 Constant_62201" -> "164 /layer3/layer3.4/relu/Relu/fq_output_0" [label="[]", style=solid]; -"707 Constant_62200" -> "164 /layer3/layer3.4/relu/Relu/fq_output_0" [label="[]", style=solid]; -"708 Constant_62199" -> "164 /layer3/layer3.4/relu/Relu/fq_output_0" [label="[]", style=solid]; -"709 Constant_62198" -> "164 /layer3/layer3.4/relu/Relu/fq_output_0" [label="[]", style=solid]; -"710 Reshape_722" -> "155 /layer3/layer3.4/conv1/Conv" [label="[1, 256, 1, 1]", style=solid]; -"711 /layer3/layer3.4/conv1/Conv/WithoutBiases/fq_weights_1" -> "150 /layer3/layer3.4/conv1/Conv/WithoutBiases" [label="[256, 1024, 1, 1]", style=solid]; -"712 Constant_62196" -> "711 /layer3/layer3.4/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"713 Constant_62195" -> "711 /layer3/layer3.4/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"714 Constant_62194" -> "711 /layer3/layer3.4/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"715 Constant_62193" -> "711 /layer3/layer3.4/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"716 onnx^^Conv_608" -> "711 /layer3/layer3.4/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1024, 1, 1]", style=solid]; -"717 Reshape_803" -> "202 /layer3/layer3.5/conv3/Conv" [label="[1, 1024, 1, 1]", style=solid]; -"718 /layer3/layer3.5/conv3/Conv/WithoutBiases/fq_weights_1" -> "198 /layer3/layer3.5/conv3/Conv/WithoutBiases" [label="[1024, 256, 1, 1]", style=solid]; -"719 Constant_62251" -> "718 /layer3/layer3.5/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; -"720 Constant_62250" -> "718 /layer3/layer3.5/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; -"721 Constant_62249" -> "718 /layer3/layer3.5/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; -"722 Constant_62248" -> "718 /layer3/layer3.5/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; -"723 onnx^^Conv_623" -> "718 /layer3/layer3.5/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 256, 1, 1]", style=solid]; -"724 Constant_62246" -> "194 /layer3/layer3.5/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"725 Constant_62245" -> "194 /layer3/layer3.5/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"726 Constant_62244" -> "194 /layer3/layer3.5/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"727 Constant_62243" -> "194 /layer3/layer3.5/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"728 Reshape_787" -> "186 /layer3/layer3.5/conv2/Conv" [label="[1, 256, 1, 1]", style=solid]; -"729 /layer3/layer3.5/conv2/Conv/WithoutBiases/fq_weights_1" -> "182 /layer3/layer3.5/conv2/Conv/WithoutBiases" [label="[256, 256, 3, 3]", style=solid]; -"730 Constant_62241" -> "729 /layer3/layer3.5/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"731 Constant_62240" -> "729 /layer3/layer3.5/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"732 Constant_62239" -> "729 /layer3/layer3.5/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"733 Constant_62238" -> "729 /layer3/layer3.5/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"734 onnx^^Conv_620" -> "729 /layer3/layer3.5/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 256, 3, 3]", style=solid]; -"735 Constant_62236" -> "177 /layer3/layer3.5/relu/Relu/fq_output_0" [label="[]", style=solid]; -"736 Constant_62235" -> "177 /layer3/layer3.5/relu/Relu/fq_output_0" [label="[]", style=solid]; -"737 Constant_62234" -> "177 /layer3/layer3.5/relu/Relu/fq_output_0" [label="[]", style=solid]; -"738 Constant_62233" -> "177 /layer3/layer3.5/relu/Relu/fq_output_0" [label="[]", style=solid]; -"739 Reshape_771" -> "168 /layer3/layer3.5/conv1/Conv" [label="[1, 256, 1, 1]", style=solid]; -"740 /layer3/layer3.5/conv1/Conv/WithoutBiases/fq_weights_1" -> "163 /layer3/layer3.5/conv1/Conv/WithoutBiases" [label="[256, 1024, 1, 1]", style=solid]; -"741 Constant_62231" -> "740 /layer3/layer3.5/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"742 Constant_62230" -> "740 /layer3/layer3.5/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"743 Constant_62229" -> "740 /layer3/layer3.5/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"744 Constant_62228" -> "740 /layer3/layer3.5/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"745 onnx^^Conv_617" -> "740 /layer3/layer3.5/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1024, 1, 1]", style=solid]; -"746 Constant_62301" -> "214 /layer4/layer4.0/conv3/Conv/fq_output_0" [label="[]", style=solid]; -"747 Constant_62300" -> "214 /layer4/layer4.0/conv3/Conv/fq_output_0" [label="[]", style=solid]; -"748 Constant_62299" -> "214 /layer4/layer4.0/conv3/Conv/fq_output_0" [label="[]", style=solid]; -"749 Constant_62298" -> "214 /layer4/layer4.0/conv3/Conv/fq_output_0" [label="[]", style=solid]; -"750 Reshape_852" -> "210 /layer4/layer4.0/conv3/Conv" [label="[1, 2048, 1, 1]", style=solid]; -"751 /layer4/layer4.0/conv3/Conv/WithoutBiases/fq_weights_1" -> "206 /layer4/layer4.0/conv3/Conv/WithoutBiases" [label="[2048, 512, 1, 1]", style=solid]; -"752 Constant_62296" -> "751 /layer4/layer4.0/conv3/Conv/WithoutBiases/fq_weights_1" [label="[2048, 1, 1, 1]", style=solid]; -"753 Constant_62295" -> "751 /layer4/layer4.0/conv3/Conv/WithoutBiases/fq_weights_1" [label="[2048, 1, 1, 1]", style=solid]; -"754 Constant_62294" -> "751 /layer4/layer4.0/conv3/Conv/WithoutBiases/fq_weights_1" [label="[2048, 1, 1, 1]", style=solid]; -"755 Constant_62293" -> "751 /layer4/layer4.0/conv3/Conv/WithoutBiases/fq_weights_1" [label="[2048, 1, 1, 1]", style=solid]; -"756 onnx^^Conv_632" -> "751 /layer4/layer4.0/conv3/Conv/WithoutBiases/fq_weights_1" [label="[2048, 512, 1, 1]", style=solid]; -"757 Constant_62291" -> "203 /layer4/layer4.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"758 Constant_62290" -> "203 /layer4/layer4.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"759 Constant_62289" -> "203 /layer4/layer4.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"760 Constant_62288" -> "203 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Constant_62273" -> "773 /layer4/layer4.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"778 onnx^^Conv_626" -> "773 /layer4/layer4.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 1024, 1, 1]", style=solid]; -"779 Reshape_916" -> "229 /layer4/layer4.1/conv3/Conv" [label="[1, 2048, 1, 1]", style=solid]; -"780 /layer4/layer4.1/conv3/Conv/WithoutBiases/fq_weights_1" -> "226 /layer4/layer4.1/conv3/Conv/WithoutBiases" [label="[2048, 512, 1, 1]", style=solid]; -"781 Constant_62331" -> "780 /layer4/layer4.1/conv3/Conv/WithoutBiases/fq_weights_1" [label="[2048, 1, 1, 1]", style=solid]; -"782 Constant_62330" -> "780 /layer4/layer4.1/conv3/Conv/WithoutBiases/fq_weights_1" [label="[2048, 1, 1, 1]", style=solid]; -"783 Constant_62329" -> "780 /layer4/layer4.1/conv3/Conv/WithoutBiases/fq_weights_1" [label="[2048, 1, 1, 1]", style=solid]; -"784 Constant_62328" -> "780 /layer4/layer4.1/conv3/Conv/WithoutBiases/fq_weights_1" [label="[2048, 1, 1, 1]", style=solid]; -"785 onnx^^Conv_644" -> "780 /layer4/layer4.1/conv3/Conv/WithoutBiases/fq_weights_1" [label="[2048, 512, 1, 1]", style=solid]; -"786 Constant_62326" -> "223 /layer4/layer4.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"787 Constant_62325" -> "223 /layer4/layer4.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"788 Constant_62324" -> "223 /layer4/layer4.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"789 Constant_62323" -> "223 /layer4/layer4.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"790 Reshape_900" -> "217 /layer4/layer4.1/conv2/Conv" [label="[1, 512, 1, 1]", style=solid]; -"791 /layer4/layer4.1/conv2/Conv/WithoutBiases/fq_weights_1" -> "213 /layer4/layer4.1/conv2/Conv/WithoutBiases" [label="[512, 512, 3, 3]", style=solid]; -"792 Constant_62321" -> "791 /layer4/layer4.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"793 Constant_62320" -> "791 /layer4/layer4.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"794 Constant_62319" -> "791 /layer4/layer4.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"795 Constant_62318" -> "791 /layer4/layer4.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"796 onnx^^Conv_641" -> "791 /layer4/layer4.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[512, 512, 3, 3]", style=solid]; -"797 Constant_62316" -> "209 /layer4/layer4.1/relu/Relu/fq_output_0" [label="[]", style=solid]; -"798 Constant_62315" -> "209 /layer4/layer4.1/relu/Relu/fq_output_0" [label="[]", style=solid]; -"799 Constant_62314" -> "209 /layer4/layer4.1/relu/Relu/fq_output_0" [label="[]", style=solid]; -"800 Constant_62313" -> "209 /layer4/layer4.1/relu/Relu/fq_output_0" [label="[]", style=solid]; -"801 Reshape_884" -> "201 /layer4/layer4.1/conv1/Conv" [label="[1, 512, 1, 1]", style=solid]; -"802 /layer4/layer4.1/conv1/Conv/WithoutBiases/fq_weights_1" -> "197 /layer4/layer4.1/conv1/Conv/WithoutBiases" [label="[512, 2048, 1, 1]", style=solid]; -"803 Constant_62311" -> "802 /layer4/layer4.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"804 Constant_62310" -> "802 /layer4/layer4.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"805 Constant_62309" -> "802 /layer4/layer4.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"806 Constant_62308" -> "802 /layer4/layer4.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"807 onnx^^Conv_638" -> "802 /layer4/layer4.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 2048, 1, 1]", style=solid]; -"808 Reshape_965" -> "234 /layer4/layer4.2/conv3/Conv" [label="[1, 2048, 1, 1]", style=solid]; -"809 /layer4/layer4.2/conv3/Conv/WithoutBiases/fq_weights_1" -> "233 /layer4/layer4.2/conv3/Conv/WithoutBiases" [label="[2048, 512, 1, 1]", style=solid]; -"810 Constant_62366" -> "809 /layer4/layer4.2/conv3/Conv/WithoutBiases/fq_weights_1" [label="[2048, 1, 1, 1]", style=solid]; -"811 Constant_62365" -> "809 /layer4/layer4.2/conv3/Conv/WithoutBiases/fq_weights_1" [label="[2048, 1, 1, 1]", style=solid]; -"812 Constant_62364" -> "809 /layer4/layer4.2/conv3/Conv/WithoutBiases/fq_weights_1" [label="[2048, 1, 1, 1]", style=solid]; -"813 Constant_62363" -> "809 /layer4/layer4.2/conv3/Conv/WithoutBiases/fq_weights_1" [label="[2048, 1, 1, 1]", style=solid]; -"814 onnx^^Conv_653" -> "809 /layer4/layer4.2/conv3/Conv/WithoutBiases/fq_weights_1" [label="[2048, 512, 1, 1]", style=solid]; -"815 Constant_62361" -> "231 /layer4/layer4.2/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"816 Constant_62360" -> "231 /layer4/layer4.2/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"817 Constant_62359" -> "231 /layer4/layer4.2/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"818 Constant_62358" -> "231 /layer4/layer4.2/relu_1/Relu/fq_output_0" [label="[]", style=solid]; -"819 Reshape_949" -> "225 /layer4/layer4.2/conv2/Conv" [label="[1, 512, 1, 1]", style=solid]; -"820 /layer4/layer4.2/conv2/Conv/WithoutBiases/fq_weights_1" -> "222 /layer4/layer4.2/conv2/Conv/WithoutBiases" [label="[512, 512, 3, 3]", style=solid]; -"821 Constant_62356" -> "820 /layer4/layer4.2/conv2/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"822 Constant_62355" -> "820 /layer4/layer4.2/conv2/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"823 Constant_62354" -> "820 /layer4/layer4.2/conv2/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"824 Constant_62353" -> "820 /layer4/layer4.2/conv2/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"825 onnx^^Conv_650" -> "820 /layer4/layer4.2/conv2/Conv/WithoutBiases/fq_weights_1" [label="[512, 512, 3, 3]", style=solid]; -"826 Constant_62351" -> "219 /layer4/layer4.2/relu/Relu/fq_output_0" [label="[]", style=solid]; -"827 Constant_62350" -> "219 /layer4/layer4.2/relu/Relu/fq_output_0" [label="[]", style=solid]; -"828 Constant_62349" -> "219 /layer4/layer4.2/relu/Relu/fq_output_0" [label="[]", style=solid]; -"829 Constant_62348" -> "219 /layer4/layer4.2/relu/Relu/fq_output_0" [label="[]", style=solid]; -"830 Reshape_933" -> "212 /layer4/layer4.2/conv1/Conv" [label="[1, 512, 1, 1]", style=solid]; -"831 /layer4/layer4.2/conv1/Conv/WithoutBiases/fq_weights_1" -> "208 /layer4/layer4.2/conv1/Conv/WithoutBiases" [label="[512, 2048, 1, 1]", style=solid]; -"832 Constant_62346" -> "831 /layer4/layer4.2/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"833 Constant_62345" -> "831 /layer4/layer4.2/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"834 Constant_62344" -> "831 /layer4/layer4.2/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"835 Constant_62343" -> "831 /layer4/layer4.2/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"836 onnx^^Conv_647" -> "831 /layer4/layer4.2/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 2048, 1, 1]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/swin-tiny-patch4-window7-224_performance_transformer.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/swin-tiny-patch4-window7-224_performance_transformer.dot deleted file mode 100644 index ccdd526e94d..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/quantized/swin-tiny-patch4-window7-224_performance_transformer.dot +++ /dev/null @@ -1,3842 +0,0 @@ -strict digraph { -"0 input" [id=0, type=Parameter]; -"1 Multiply_6579" [id=1, type=Multiply]; -"2 Divide_2169" [id=2, type=Add]; -"3 Divide_2169/fq_output_0" [id=3, type=FakeQuantize]; -"4 /patch_embed/proj/Conv/WithoutBiases" [id=4, type=Convolution]; -"5 /patch_embed/proj/Conv" [id=5, type=Add]; -"6 /patch_embed/Reshape" [id=6, type=Reshape]; -"7 /patch_embed/Shape" [id=7, 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type=FakeQuantize]; -"907 Constant_27944" [id=907, type=Constant]; -"908 Constant_27943" [id=908, type=Constant]; -"909 Constant_27942" [id=909, type=Constant]; -"910 Constant_27941" [id=910, type=Constant]; -"911 Transpose_6499" [id=911, type=Constant]; -"912 Constant_27939" [id=912, type=Constant]; -"913 Constant_27938" [id=913, type=Constant]; -"914 Constant_27937" [id=914, type=Constant]; -"915 Constant_27936" [id=915, type=Constant]; -"916 /layers/layers.2/blocks.4/mlp/fc1/MatMul/fq_weights_1" [id=916, type=FakeQuantize]; -"917 Constant_27934" [id=917, type=Constant]; -"918 Constant_27933" [id=918, type=Constant]; -"919 Constant_27932" [id=919, type=Constant]; -"920 Constant_27931" [id=920, type=Constant]; -"921 Transpose_6495" [id=921, type=Constant]; -"922 Constant_27929" [id=922, type=Constant]; -"923 Constant_27928" [id=923, type=Constant]; -"924 Constant_27927" [id=924, type=Constant]; -"925 Constant_27926" [id=925, type=Constant]; -"926 Constant_7367" [id=926, type=Constant]; -"927 Constant_7366" [id=927, type=Constant]; -"928 Constant_1633" [id=928, type=Constant]; -"929 /layers/layers.2/blocks.4/Constant_7" [id=929, type=Constant]; -"930 /layers/layers.2/blocks.4/Constant_6" [id=930, type=Constant]; -"931 Constant_1622" [id=931, type=Constant]; -"932 /layers/layers.2/blocks.4/Constant_5" [id=932, type=Constant]; -"933 /layers/layers.2/blocks.4/Constant_4" [id=933, type=Constant]; -"934 /layers/layers.2/blocks.4/attn/proj/MatMul/fq_weights_1" [id=934, type=FakeQuantize]; -"935 Constant_27924" [id=935, type=Constant]; -"936 Constant_27923" [id=936, type=Constant]; -"937 Constant_27922" [id=937, type=Constant]; -"938 Constant_27921" [id=938, type=Constant]; -"939 Transpose_6491" [id=939, type=Constant]; -"940 /layers/layers.2/blocks.4/attn/Constant_2" [id=940, type=Constant]; -"941 Constant_1606" [id=941, type=Constant]; -"942 Constant_27919" [id=942, type=Constant]; -"943 Constant_27918" [id=943, type=Constant]; -"944 Constant_27917" 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Constant_5409" [id=1163, type=Constant]; -"1164 /layers/layers.2/blocks.1/Constant" [id=1164, type=Constant]; -"1165 Constant_27474" [id=1165, type=Constant]; -"1166 Constant_27473" [id=1166, type=Constant]; -"1167 Constant_27472" [id=1167, type=Constant]; -"1168 Constant_27471" [id=1168, type=Constant]; -"1169 Constant_7335" [id=1169, type=Constant]; -"1170 Constant_7334" [id=1170, type=Constant]; -"1171 Constant_1049" [id=1171, type=Constant]; -"1172 /layers/layers.2/blocks.0/mlp/fc2/MatMul/fq_weights_1" [id=1172, type=FakeQuantize]; -"1173 Constant_27754" [id=1173, type=Constant]; -"1174 Constant_27753" [id=1174, type=Constant]; -"1175 Constant_27752" [id=1175, type=Constant]; -"1176 Constant_27751" [id=1176, type=Constant]; -"1177 Transpose_6439" [id=1177, type=Constant]; -"1178 Constant_27749" [id=1178, type=Constant]; -"1179 Constant_27748" [id=1179, type=Constant]; -"1180 Constant_27747" [id=1180, type=Constant]; -"1181 Constant_27746" [id=1181, type=Constant]; -"1182 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type=Constant]; -"1200 /layers/layers.2/blocks.0/attn/proj/MatMul/fq_weights_1" [id=1200, type=FakeQuantize]; -"1201 Constant_27734" [id=1201, type=Constant]; -"1202 Constant_27733" [id=1202, type=Constant]; -"1203 Constant_27732" [id=1203, type=Constant]; -"1204 Constant_27731" [id=1204, type=Constant]; -"1205 Transpose_6431" [id=1205, type=Constant]; -"1206 /layers/layers.2/blocks.0/attn/Constant_2" [id=1206, type=Constant]; -"1207 Constant_996" [id=1207, type=Constant]; -"1208 Constant_27729" [id=1208, type=Constant]; -"1209 Constant_27728" [id=1209, type=Constant]; -"1210 Constant_27727" [id=1210, type=Constant]; -"1211 Constant_27726" [id=1211, type=Constant]; -"1212 Constant_986" [id=1212, type=Constant]; -"1213 Constant_980" [id=1213, type=Constant]; -"1214 /layers/layers.2/blocks.0/attn/Constant" [id=1214, type=Constant]; -"1215 /layers/layers.2/blocks.0/attn/qkv/MatMul/fq_weights_1" [id=1215, type=FakeQuantize]; -"1216 Constant_27714" [id=1216, type=Constant]; -"1217 Constant_27713" [id=1217, type=Constant]; -"1218 Constant_27712" [id=1218, type=Constant]; -"1219 Constant_27711" [id=1219, type=Constant]; -"1220 Transpose_6428" [id=1220, type=Constant]; -"1221 /layers/layers.2/blocks.0/Constant_3" [id=1221, type=Constant]; -"1222 /layers/layers.2/blocks.0/Constant_2" [id=1222, type=Constant]; -"1223 Constant_964" [id=1223, type=Constant]; -"1224 /layers/layers.2/blocks.0/Constant_1" [id=1224, type=Constant]; -"1225 Constant_27709" [id=1225, type=Constant]; -"1226 Constant_27708" [id=1226, type=Constant]; -"1227 Constant_27707" [id=1227, type=Constant]; -"1228 Constant_27706" [id=1228, type=Constant]; -"1229 Constant_7326" [id=1229, type=Constant]; -"1230 Constant_7325" [id=1230, type=Constant]; -"1231 Constant_943" [id=1231, type=Constant]; -"1232 /layers/layers.1/downsample/reduction/MatMul/fq_weights_1" [id=1232, type=FakeQuantize]; -"1233 Constant_27704" [id=1233, type=Constant]; -"1234 Constant_27703" [id=1234, type=Constant]; -"1235 Constant_27702" [id=1235, type=Constant]; -"1236 Constant_27701" [id=1236, type=Constant]; -"1237 Transpose_6424" [id=1237, type=Constant]; -"1238 Constant_27699" [id=1238, type=Constant]; -"1239 Constant_27698" [id=1239, type=Constant]; -"1240 Constant_27697" [id=1240, type=Constant]; -"1241 Constant_27696" [id=1241, type=Constant]; -"1242 Constant_7324" [id=1242, type=Constant]; -"1243 Constant_7323" [id=1243, type=Constant]; -"1244 Constant_929" [id=1244, type=Constant]; -"1245 /layers/layers.1/downsample/Constant_25" [id=1245, type=Constant]; -"1246 Constant_5391" [id=1246, type=Constant]; -"1247 Constant_5388" [id=1247, type=Constant]; -"1248 Constant_5385" [id=1248, type=Constant]; -"1249 Constant_5355" [id=1249, type=Constant]; -"1250 Constant_5352" [id=1250, type=Constant]; -"1251 Constant_5349" [id=1251, type=Constant]; -"1252 /layers/layers.1/downsample/Constant" [id=1252, type=Constant]; -"1253 /layers/layers.1/blocks.1/mlp/fc2/MatMul/fq_weights_1" [id=1253, type=FakeQuantize]; -"1254 Constant_27694" [id=1254, type=Constant]; -"1255 Constant_27693" [id=1255, type=Constant]; -"1256 Constant_27692" [id=1256, type=Constant]; -"1257 Constant_27691" [id=1257, type=Constant]; -"1258 Transpose_6420" [id=1258, type=Constant]; -"1259 Constant_27689" [id=1259, type=Constant]; -"1260 Constant_27688" [id=1260, type=Constant]; -"1261 Constant_27687" [id=1261, type=Constant]; -"1262 Constant_27686" [id=1262, type=Constant]; -"1263 /layers/layers.1/blocks.1/mlp/fc1/MatMul/fq_weights_1" [id=1263, type=FakeQuantize]; -"1264 Constant_27684" [id=1264, type=Constant]; -"1265 Constant_27683" [id=1265, type=Constant]; -"1266 Constant_27682" [id=1266, type=Constant]; -"1267 Constant_27681" [id=1267, type=Constant]; -"1268 Transpose_6416" [id=1268, type=Constant]; -"1269 Constant_27679" [id=1269, type=Constant]; -"1270 Constant_27678" [id=1270, type=Constant]; -"1271 Constant_27677" [id=1271, type=Constant]; -"1272 Constant_27676" [id=1272, type=Constant]; -"1273 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type=Constant]; -"1347 Constant_7310" [id=1347, type=Constant]; -"1348 Constant_665" [id=1348, type=Constant]; -"1349 /layers/layers.1/blocks.0/Constant_7" [id=1349, type=Constant]; -"1350 /layers/layers.1/blocks.0/Constant_6" [id=1350, type=Constant]; -"1351 Constant_654" [id=1351, type=Constant]; -"1352 /layers/layers.1/blocks.0/Constant_5" [id=1352, type=Constant]; -"1353 /layers/layers.1/blocks.0/Constant_4" [id=1353, type=Constant]; -"1354 /layers/layers.1/blocks.0/attn/proj/MatMul/fq_weights_1" [id=1354, type=FakeQuantize]; -"1355 Constant_27629" [id=1355, type=Constant]; -"1356 Constant_27628" [id=1356, type=Constant]; -"1357 Constant_27627" [id=1357, type=Constant]; -"1358 Constant_27626" [id=1358, type=Constant]; -"1359 Transpose_6397" [id=1359, type=Constant]; -"1360 /layers/layers.1/blocks.0/attn/Constant_2" [id=1360, type=Constant]; -"1361 Constant_638" [id=1361, type=Constant]; -"1362 Constant_27624" [id=1362, type=Constant]; -"1363 Constant_27623" [id=1363, type=Constant]; -"1364 Constant_27622" [id=1364, type=Constant]; -"1365 Constant_27621" [id=1365, type=Constant]; -"1366 Constant_628" [id=1366, type=Constant]; -"1367 Constant_622" [id=1367, type=Constant]; -"1368 /layers/layers.1/blocks.0/attn/Constant" [id=1368, type=Constant]; -"1369 /layers/layers.1/blocks.0/attn/qkv/MatMul/fq_weights_1" [id=1369, type=FakeQuantize]; -"1370 Constant_27609" [id=1370, type=Constant]; -"1371 Constant_27608" [id=1371, type=Constant]; -"1372 Constant_27607" [id=1372, type=Constant]; -"1373 Constant_27606" [id=1373, type=Constant]; -"1374 Transpose_6394" [id=1374, type=Constant]; -"1375 /layers/layers.1/blocks.0/Constant_3" [id=1375, type=Constant]; -"1376 /layers/layers.1/blocks.0/Constant_2" [id=1376, type=Constant]; -"1377 Constant_606" [id=1377, type=Constant]; -"1378 /layers/layers.1/blocks.0/Constant_1" [id=1378, type=Constant]; -"1379 Constant_27604" [id=1379, type=Constant]; -"1380 Constant_27603" [id=1380, type=Constant]; -"1381 Constant_27602" [id=1381, type=Constant]; -"1382 Constant_27601" [id=1382, type=Constant]; -"1383 Constant_7306" [id=1383, type=Constant]; -"1384 Constant_7305" [id=1384, type=Constant]; -"1385 Constant_585" [id=1385, type=Constant]; -"1386 /layers/layers.0/downsample/reduction/MatMul/fq_weights_1" [id=1386, type=FakeQuantize]; -"1387 Constant_27599" [id=1387, type=Constant]; -"1388 Constant_27598" [id=1388, type=Constant]; -"1389 Constant_27597" [id=1389, type=Constant]; -"1390 Constant_27596" [id=1390, type=Constant]; -"1391 Transpose_6390" [id=1391, type=Constant]; -"1392 Constant_27594" [id=1392, type=Constant]; -"1393 Constant_27593" [id=1393, type=Constant]; -"1394 Constant_27592" [id=1394, type=Constant]; -"1395 Constant_27591" [id=1395, type=Constant]; -"1396 Constant_7304" [id=1396, type=Constant]; -"1397 Constant_7303" [id=1397, type=Constant]; -"1398 Constant_571" [id=1398, type=Constant]; -"1399 /layers/layers.0/downsample/Constant_25" [id=1399, type=Constant]; -"1400 Constant_5223" 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type=Constant]; -"1456 /layers/layers.0/blocks.1/attn/qkv/MatMul/fq_weights_1" [id=1456, type=FakeQuantize]; -"1457 Constant_27549" [id=1457, type=Constant]; -"1458 Constant_27548" [id=1458, type=Constant]; -"1459 Constant_27547" [id=1459, type=Constant]; -"1460 Constant_27546" [id=1460, type=Constant]; -"1461 Transpose_6375" [id=1461, type=Constant]; -"1462 /layers/layers.0/blocks.1/Constant_15" [id=1462, type=Constant]; -"1463 /layers/layers.0/blocks.1/Constant_14" [id=1463, type=Constant]; -"1464 Constant_396" [id=1464, type=Constant]; -"1465 /layers/layers.0/blocks.1/Constant_13" [id=1465, type=Constant]; -"1466 Constant_5103" [id=1466, type=Constant]; -"1467 Constant_5100" [id=1467, type=Constant]; -"1468 Constant_5097" [id=1468, type=Constant]; -"1469 Constant_5079" [id=1469, type=Constant]; -"1470 Constant_5076" [id=1470, type=Constant]; -"1471 Constant_5073" [id=1471, type=Constant]; -"1472 /layers/layers.0/blocks.1/Constant" [id=1472, type=Constant]; -"1473 Constant_27464" [id=1473, type=Constant]; -"1474 Constant_27463" [id=1474, type=Constant]; -"1475 Constant_27462" [id=1475, type=Constant]; -"1476 Constant_27461" [id=1476, type=Constant]; -"1477 Constant_7295" [id=1477, type=Constant]; -"1478 Constant_7294" [id=1478, type=Constant]; -"1479 Constant_333" [id=1479, type=Constant]; -"1480 /layers/layers.0/blocks.0/mlp/fc2/MatMul/fq_weights_1" [id=1480, type=FakeQuantize]; -"1481 Constant_27544" [id=1481, type=Constant]; -"1482 Constant_27543" [id=1482, type=Constant]; -"1483 Constant_27542" [id=1483, type=Constant]; -"1484 Constant_27541" [id=1484, type=Constant]; -"1485 Transpose_6371" [id=1485, type=Constant]; -"1486 Constant_27539" [id=1486, type=Constant]; -"1487 Constant_27538" [id=1487, type=Constant]; -"1488 Constant_27537" [id=1488, type=Constant]; -"1489 Constant_27536" [id=1489, type=Constant]; -"1490 /layers/layers.0/blocks.0/mlp/fc1/MatMul/fq_weights_1" [id=1490, type=FakeQuantize]; -"1491 Constant_27534" [id=1491, type=Constant]; -"1492 Constant_27533" [id=1492, type=Constant]; -"1493 Constant_27532" [id=1493, type=Constant]; -"1494 Constant_27531" [id=1494, type=Constant]; -"1495 Transpose_6367" [id=1495, type=Constant]; -"1496 Constant_27529" [id=1496, type=Constant]; -"1497 Constant_27528" [id=1497, type=Constant]; -"1498 Constant_27527" [id=1498, type=Constant]; -"1499 Constant_27526" [id=1499, type=Constant]; -"1500 Constant_7291" [id=1500, type=Constant]; -"1501 Constant_7290" [id=1501, type=Constant]; -"1502 Constant_307" [id=1502, type=Constant]; -"1503 /layers/layers.0/blocks.0/Constant_8" [id=1503, type=Constant]; -"1504 /layers/layers.0/blocks.0/Constant_7" [id=1504, type=Constant]; -"1505 Constant_296" [id=1505, type=Constant]; -"1506 /layers/layers.0/blocks.0/Constant_6" [id=1506, type=Constant]; -"1507 /layers/layers.0/blocks.0/Constant_5" [id=1507, type=Constant]; -"1508 /layers/layers.0/blocks.0/attn/proj/MatMul/fq_weights_1" [id=1508, type=FakeQuantize]; -"1509 Constant_27524" [id=1509, type=Constant]; -"1510 Constant_27523" [id=1510, type=Constant]; -"1511 Constant_27522" [id=1511, type=Constant]; -"1512 Constant_27521" [id=1512, type=Constant]; -"1513 Transpose_6363" [id=1513, type=Constant]; -"1514 /layers/layers.0/blocks.0/attn/Constant_2" [id=1514, type=Constant]; -"1515 Constant_280" [id=1515, type=Constant]; -"1516 Constant_27519" [id=1516, type=Constant]; -"1517 Constant_27518" [id=1517, type=Constant]; -"1518 Constant_27517" [id=1518, type=Constant]; -"1519 Constant_27516" [id=1519, type=Constant]; -"1520 Constant_270" [id=1520, type=Constant]; -"1521 Constant_264" [id=1521, type=Constant]; -"1522 /layers/layers.0/blocks.0/attn/Constant" [id=1522, type=Constant]; -"1523 /layers/layers.0/blocks.0/attn/qkv/MatMul/fq_weights_1" [id=1523, type=FakeQuantize]; -"1524 Constant_27504" [id=1524, type=Constant]; -"1525 Constant_27503" [id=1525, type=Constant]; -"1526 Constant_27502" [id=1526, type=Constant]; -"1527 Constant_27501" [id=1527, type=Constant]; -"1528 Transpose_6360" [id=1528, type=Constant]; -"1529 /layers/layers.0/blocks.0/Constant_4" [id=1529, type=Constant]; -"1530 /layers/layers.0/blocks.0/Constant_3" [id=1530, type=Constant]; -"1531 Constant_248" [id=1531, type=Constant]; -"1532 /layers/layers.0/blocks.0/Constant_2" [id=1532, type=Constant]; -"1533 Constant_27499" [id=1533, type=Constant]; -"1534 Constant_27498" [id=1534, type=Constant]; -"1535 Constant_27497" [id=1535, type=Constant]; -"1536 Constant_27496" [id=1536, type=Constant]; -"1537 Constant_7286" [id=1537, type=Constant]; -"1538 Constant_7285" [id=1538, type=Constant]; -"1539 Constant_227" [id=1539, type=Constant]; -"1540 Constant_7284" [id=1540, type=Constant]; -"1541 Constant_7283" [id=1541, type=Constant]; -"1542 Constant_213" [id=1542, type=Constant]; -"1543 Constant_211" [id=1543, type=Constant]; -"1544 /patch_embed/Constant_4" [id=1544, type=Constant]; -"1545 Broadcast_201" [id=1545, type=Constant]; -"1546 /patch_embed/Constant_3" [id=1546, type=Constant]; -"1547 /patch_embed/Constant_2" [id=1547, type=Constant]; -"1548 Reshape_190" [id=1548, type=Constant]; -"1549 /patch_embed/proj/Conv/WithoutBiases/fq_weights_1" [id=1549, type=FakeQuantize]; -"1550 Constant_27494" [id=1550, type=Constant]; -"1551 Constant_27493" [id=1551, type=Constant]; -"1552 Constant_27492" [id=1552, type=Constant]; -"1553 Constant_27491" [id=1553, type=Constant]; -"1554 Gather_7282" [id=1554, type=Constant]; -"1555 Constant_27489" [id=1555, type=Constant]; -"1556 Constant_27488" [id=1556, type=Constant]; -"1557 Constant_27487" [id=1557, type=Constant]; -"1558 Constant_27486" [id=1558, type=Constant]; -"1559 Gather_7279" [id=1559, type=Constant]; -"1560 Gather_7276" [id=1560, type=Constant]; -"1561 Constant_7287" [id=1561, type=Constant]; -"1562 onnx^^Add_2244" [id=1562, label="1562 onnx::Add_2244", type=Constant]; -"1563 Constant_268" [id=1563, type=Constant]; -"1564 /patch_embed/proj/Constant" [id=1564, type=Constant]; -"1565 Constant_27509" [id=1565, type=Constant]; -"1566 Constant_27508" [id=1566, type=Constant]; -"1567 Constant_27507" [id=1567, type=Constant]; -"1568 Constant_27506" [id=1568, type=Constant]; -"1569 Constant_27514" [id=1569, type=Constant]; -"1570 Constant_27513" [id=1570, type=Constant]; -"1571 Constant_27512" [id=1571, type=Constant]; -"1572 Constant_27511" [id=1572, type=Constant]; -"1573 Constant_7288" [id=1573, type=Constant]; -"1574 Constant_266" [id=1574, type=Constant]; -"1575 /layers/layers.0/blocks.0/Constant" [id=1575, type=Constant]; -"1576 Constant_7289" [id=1576, type=Constant]; -"1577 Constant_7292" [id=1577, type=Constant]; -"1578 Constant_7293" [id=1578, type=Constant]; -"1579 Constant_5067" [id=1579, type=Constant]; -"1580 Constant_5064" [id=1580, type=Constant]; -"1581 Constant_5061" [id=1581, type=Constant]; -"1582 Constant_5091" [id=1582, type=Constant]; -"1583 Constant_5088" [id=1583, type=Constant]; -"1584 Constant_5085" [id=1584, type=Constant]; -"1585 Constant_7296" [id=1585, type=Constant]; -"1586 /layers/layers.0/blocks.1/attn/Constant_3" [id=1586, type=Constant]; -"1587 onnx^^Add_2301" [id=1587, label="1587 onnx::Add_2301", type=Constant]; -"1588 /layers/layers.0/blocks.1/attn/Constant_2" [id=1588, type=Constant]; -"1589 onnx^^Add_2293" [id=1589, label="1589 onnx::Add_2293", type=Constant]; -"1590 Constant_416" [id=1590, type=Constant]; -"1591 Constant_27554" [id=1591, type=Constant]; -"1592 Constant_27553" [id=1592, type=Constant]; -"1593 Constant_27552" [id=1593, type=Constant]; -"1594 Constant_27551" [id=1594, type=Constant]; -"1595 Constant_27559" [id=1595, type=Constant]; -"1596 Constant_27558" [id=1596, type=Constant]; -"1597 Constant_27557" [id=1597, type=Constant]; -"1598 Constant_27556" [id=1598, type=Constant]; -"1599 Constant_7297" [id=1599, type=Constant]; -"1600 Constant_414" [id=1600, type=Constant]; -"1601 Constant_7298" [id=1601, type=Constant]; -"1602 Constant_5115" [id=1602, type=Constant]; -"1603 Constant_5112" [id=1603, type=Constant]; -"1604 Constant_5109" [id=1604, type=Constant]; -"1605 Constant_5139" [id=1605, type=Constant]; -"1606 Constant_5136" [id=1606, type=Constant]; -"1607 Constant_5133" [id=1607, type=Constant]; -"1608 Constant_7301" [id=1608, type=Constant]; -"1609 Constant_7302" [id=1609, type=Constant]; -"1610 Constant_5211" [id=1610, type=Constant]; -"1611 Constant_5208" [id=1611, type=Constant]; -"1612 Constant_5205" [id=1612, type=Constant]; -"1613 Constant_5163" [id=1613, type=Constant]; -"1614 Constant_5160" [id=1614, type=Constant]; -"1615 Constant_5157" [id=1615, type=Constant]; -"1616 Constant_5199" [id=1616, type=Constant]; -"1617 Constant_5196" [id=1617, type=Constant]; -"1618 Constant_5193" [id=1618, type=Constant]; -"1619 Constant_5175" [id=1619, type=Constant]; -"1620 Constant_5172" [id=1620, type=Constant]; -"1621 Constant_5169" [id=1621, type=Constant]; -"1622 Constant_7307" [id=1622, type=Constant]; -"1623 onnx^^Add_2389" [id=1623, label="1623 onnx::Add_2389", type=Constant]; -"1624 Constant_626" [id=1624, type=Constant]; -"1625 Constant_27614" [id=1625, type=Constant]; -"1626 Constant_27613" [id=1626, type=Constant]; -"1627 Constant_27612" [id=1627, type=Constant]; -"1628 Constant_27611" [id=1628, type=Constant]; -"1629 Constant_27619" [id=1629, type=Constant]; -"1630 Constant_27618" [id=1630, type=Constant]; -"1631 Constant_27617" [id=1631, type=Constant]; -"1632 Constant_27616" [id=1632, type=Constant]; -"1633 Constant_7308" [id=1633, type=Constant]; -"1634 Constant_624" [id=1634, type=Constant]; -"1635 Constant_7309" [id=1635, type=Constant]; -"1636 Constant_7312" [id=1636, type=Constant]; -"1637 Constant_7313" [id=1637, type=Constant]; -"1638 Constant_5235" [id=1638, type=Constant]; -"1639 Constant_5232" [id=1639, type=Constant]; -"1640 Constant_5229" [id=1640, type=Constant]; -"1641 Constant_5259" [id=1641, type=Constant]; -"1642 Constant_5256" [id=1642, type=Constant]; -"1643 Constant_5253" [id=1643, type=Constant]; -"1644 Constant_7316" [id=1644, type=Constant]; -"1645 /layers/layers.1/blocks.1/attn/Constant_3" [id=1645, type=Constant]; -"1646 onnx^^Add_2446" [id=1646, label="1646 onnx::Add_2446", type=Constant]; -"1647 /layers/layers.1/blocks.1/attn/Constant_2" [id=1647, type=Constant]; -"1648 onnx^^Add_2438" [id=1648, label="1648 onnx::Add_2438", type=Constant]; -"1649 Constant_774" [id=1649, type=Constant]; -"1650 Constant_27659" [id=1650, type=Constant]; -"1651 Constant_27658" [id=1651, type=Constant]; -"1652 Constant_27657" [id=1652, type=Constant]; -"1653 Constant_27656" [id=1653, type=Constant]; -"1654 Constant_27664" [id=1654, type=Constant]; -"1655 Constant_27663" [id=1655, type=Constant]; -"1656 Constant_27662" [id=1656, type=Constant]; -"1657 Constant_27661" [id=1657, type=Constant]; -"1658 Constant_7317" [id=1658, type=Constant]; -"1659 Constant_772" [id=1659, type=Constant]; -"1660 Constant_7318" [id=1660, type=Constant]; -"1661 Constant_5283" [id=1661, type=Constant]; -"1662 Constant_5280" [id=1662, type=Constant]; -"1663 Constant_5277" [id=1663, type=Constant]; -"1664 Constant_5307" [id=1664, type=Constant]; -"1665 Constant_5304" [id=1665, type=Constant]; -"1666 Constant_5301" [id=1666, type=Constant]; -"1667 Constant_7321" [id=1667, type=Constant]; -"1668 Constant_7322" [id=1668, type=Constant]; -"1669 Constant_5379" [id=1669, type=Constant]; -"1670 Constant_5376" [id=1670, type=Constant]; -"1671 Constant_5373" [id=1671, type=Constant]; -"1672 Constant_5331" [id=1672, type=Constant]; -"1673 Constant_5328" [id=1673, type=Constant]; -"1674 Constant_5325" [id=1674, type=Constant]; -"1675 Constant_5367" [id=1675, type=Constant]; -"1676 Constant_5364" [id=1676, type=Constant]; -"1677 Constant_5361" [id=1677, type=Constant]; -"1678 Constant_5343" [id=1678, type=Constant]; -"1679 Constant_5340" [id=1679, type=Constant]; -"1680 Constant_5337" [id=1680, type=Constant]; -"1681 Constant_7327" [id=1681, type=Constant]; -"1682 onnx^^Add_2534" [id=1682, label="1682 onnx::Add_2534", type=Constant]; -"1683 Constant_984" [id=1683, type=Constant]; -"1684 Constant_27719" [id=1684, type=Constant]; -"1685 Constant_27718" [id=1685, type=Constant]; -"1686 Constant_27717" [id=1686, type=Constant]; -"1687 Constant_27716" [id=1687, type=Constant]; -"1688 Constant_27724" [id=1688, type=Constant]; -"1689 Constant_27723" [id=1689, type=Constant]; -"1690 Constant_27722" [id=1690, type=Constant]; -"1691 Constant_27721" [id=1691, type=Constant]; -"1692 Constant_7328" [id=1692, type=Constant]; -"1693 Constant_982" [id=1693, type=Constant]; -"1694 Constant_7329" [id=1694, type=Constant]; -"1695 Constant_7332" [id=1695, type=Constant]; -"1696 Constant_7333" [id=1696, type=Constant]; -"1697 Constant_5403" [id=1697, type=Constant]; -"1698 Constant_5400" [id=1698, type=Constant]; -"1699 Constant_5397" [id=1699, type=Constant]; -"1700 Constant_5427" [id=1700, type=Constant]; -"1701 Constant_5424" [id=1701, type=Constant]; -"1702 Constant_5421" [id=1702, type=Constant]; -"1703 Constant_7336" [id=1703, type=Constant]; -"1704 /layers/layers.2/blocks.1/attn/Constant_3" [id=1704, type=Constant]; -"1705 onnx^^Add_2702" [id=1705, label="1705 onnx::Add_2702", type=Constant]; -"1706 /layers/layers.2/blocks.1/attn/Constant_2" [id=1706, type=Constant]; -"1707 onnx^^Add_2583" [id=1707, label="1707 onnx::Add_2583", type=Constant]; -"1708 Constant_1132" [id=1708, type=Constant]; -"1709 Constant_27764" [id=1709, type=Constant]; -"1710 Constant_27763" [id=1710, type=Constant]; -"1711 Constant_27762" [id=1711, type=Constant]; -"1712 Constant_27761" [id=1712, type=Constant]; -"1713 Constant_27769" [id=1713, type=Constant]; -"1714 Constant_27768" [id=1714, type=Constant]; -"1715 Constant_27767" [id=1715, type=Constant]; -"1716 Constant_27766" [id=1716, type=Constant]; -"1717 Constant_7337" [id=1717, type=Constant]; -"1718 Constant_1130" [id=1718, type=Constant]; -"1719 Constant_7338" [id=1719, type=Constant]; -"1720 Constant_5451" [id=1720, type=Constant]; -"1721 Constant_5448" [id=1721, type=Constant]; -"1722 Constant_5445" [id=1722, type=Constant]; -"1723 Constant_5475" [id=1723, type=Constant]; -"1724 Constant_5472" [id=1724, type=Constant]; -"1725 Constant_5469" [id=1725, type=Constant]; -"1726 Constant_7341" [id=1726, type=Constant]; -"1727 Constant_7342" [id=1727, type=Constant]; -"1728 Constant_7345" [id=1728, type=Constant]; -"1729 onnx^^Add_2645" [id=1729, label="1729 onnx::Add_2645", type=Constant]; -"1730 Constant_1289" [id=1730, type=Constant]; -"1731 Constant_27814" [id=1731, type=Constant]; -"1732 Constant_27813" [id=1732, type=Constant]; -"1733 Constant_27812" [id=1733, type=Constant]; -"1734 Constant_27811" [id=1734, type=Constant]; -"1735 Constant_27819" [id=1735, type=Constant]; -"1736 Constant_27818" [id=1736, type=Constant]; -"1737 Constant_27817" [id=1737, type=Constant]; -"1738 Constant_27816" [id=1738, type=Constant]; -"1739 Constant_7346" [id=1739, type=Constant]; -"1740 Constant_1287" [id=1740, type=Constant]; -"1741 Constant_7347" [id=1741, type=Constant]; -"1742 Constant_7350" [id=1742, type=Constant]; -"1743 Constant_7351" [id=1743, type=Constant]; -"1744 Constant_5499" [id=1744, type=Constant]; -"1745 Constant_5496" [id=1745, type=Constant]; -"1746 Constant_5493" [id=1746, type=Constant]; -"1747 Constant_5523" [id=1747, type=Constant]; -"1748 Constant_5520" [id=1748, type=Constant]; -"1749 Constant_5517" [id=1749, type=Constant]; -"1750 Constant_7354" [id=1750, type=Constant]; -"1751 /layers/layers.2/blocks.3/attn/Constant_3" [id=1751, type=Constant]; -"1752 /layers/layers.2/blocks.3/attn/Constant_2" [id=1752, type=Constant]; -"1753 onnx^^Add_2694" [id=1753, label="1753 onnx::Add_2694", type=Constant]; -"1754 Constant_1437" [id=1754, type=Constant]; -"1755 Constant_27859" [id=1755, type=Constant]; -"1756 Constant_27858" [id=1756, type=Constant]; -"1757 Constant_27857" [id=1757, type=Constant]; -"1758 Constant_27856" [id=1758, type=Constant]; -"1759 Constant_27864" [id=1759, type=Constant]; 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-"1780 Constant_27906" [id=1780, type=Constant]; -"1781 Constant_27914" [id=1781, type=Constant]; -"1782 Constant_27913" [id=1782, type=Constant]; -"1783 Constant_27912" [id=1783, type=Constant]; -"1784 Constant_27911" [id=1784, type=Constant]; -"1785 Constant_7364" [id=1785, type=Constant]; -"1786 Constant_1592" [id=1786, type=Constant]; -"1787 Constant_7365" [id=1787, type=Constant]; -"1788 Constant_7368" [id=1788, type=Constant]; -"1789 Constant_7369" [id=1789, type=Constant]; -"1790 Constant_5595" [id=1790, type=Constant]; -"1791 Constant_5592" [id=1791, type=Constant]; -"1792 Constant_5589" [id=1792, type=Constant]; -"1793 Constant_5619" [id=1793, type=Constant]; -"1794 Constant_5616" [id=1794, type=Constant]; -"1795 Constant_5613" [id=1795, type=Constant]; -"1796 Constant_7372" [id=1796, type=Constant]; -"1797 /layers/layers.2/blocks.5/attn/Constant_3" [id=1797, type=Constant]; -"1798 /layers/layers.2/blocks.5/attn/Constant_2" [id=1798, type=Constant]; -"1799 onnx^^Add_2805" [id=1799, label="1799 onnx::Add_2805", type=Constant]; -"1800 Constant_1742" [id=1800, type=Constant]; -"1801 Constant_27954" [id=1801, type=Constant]; -"1802 Constant_27953" [id=1802, type=Constant]; -"1803 Constant_27952" [id=1803, type=Constant]; -"1804 Constant_27951" [id=1804, type=Constant]; -"1805 Constant_27959" [id=1805, type=Constant]; -"1806 Constant_27958" [id=1806, type=Constant]; -"1807 Constant_27957" [id=1807, type=Constant]; -"1808 Constant_27956" [id=1808, type=Constant]; -"1809 Constant_7373" [id=1809, type=Constant]; -"1810 Constant_1740" [id=1810, type=Constant]; -"1811 Constant_7374" [id=1811, type=Constant]; -"1812 Constant_5643" [id=1812, type=Constant]; -"1813 Constant_5640" [id=1813, type=Constant]; -"1814 Constant_5637" [id=1814, type=Constant]; -"1815 Constant_5667" [id=1815, type=Constant]; -"1816 Constant_5664" [id=1816, type=Constant]; -"1817 Constant_5661" [id=1817, type=Constant]; -"1818 Constant_7377" [id=1818, type=Constant]; -"1819 Constant_7378" [id=1819, type=Constant]; -"1820 Constant_5739" [id=1820, type=Constant]; -"1821 Constant_5736" [id=1821, type=Constant]; -"1822 Constant_5733" [id=1822, type=Constant]; -"1823 Constant_5691" [id=1823, type=Constant]; -"1824 Constant_5688" [id=1824, type=Constant]; -"1825 Constant_5685" [id=1825, type=Constant]; -"1826 Constant_5727" [id=1826, type=Constant]; -"1827 Constant_5724" [id=1827, type=Constant]; -"1828 Constant_5721" [id=1828, type=Constant]; -"1829 Constant_5703" [id=1829, type=Constant]; -"1830 Constant_5700" [id=1830, type=Constant]; -"1831 Constant_5697" [id=1831, type=Constant]; -"1832 Constant_7383" [id=1832, type=Constant]; -"1833 onnx^^Add_2901" [id=1833, label="1833 onnx::Add_2901", type=Constant]; -"1834 Constant_1952" [id=1834, type=Constant]; -"1835 Constant_28014" [id=1835, type=Constant]; -"1836 Constant_28013" [id=1836, type=Constant]; -"1837 Constant_28012" [id=1837, type=Constant]; -"1838 Constant_28011" [id=1838, type=Constant]; -"1839 Constant_28019" [id=1839, type=Constant]; -"1840 Constant_28018" [id=1840, type=Constant]; -"1841 Constant_28017" [id=1841, type=Constant]; -"1842 Constant_28016" [id=1842, type=Constant]; -"1843 Constant_7384" [id=1843, type=Constant]; -"1844 Constant_1950" [id=1844, type=Constant]; -"1845 Constant_7385" [id=1845, type=Constant]; -"1846 Constant_7388" [id=1846, type=Constant]; -"1847 Constant_7389" [id=1847, type=Constant]; -"1848 Constant_7392" [id=1848, type=Constant]; -"1849 onnx^^Add_2950" [id=1849, label="1849 onnx::Add_2950", type=Constant]; -"1850 Constant_2058" [id=1850, type=Constant]; -"1851 Constant_28064" [id=1851, type=Constant]; -"1852 Constant_28063" [id=1852, type=Constant]; -"1853 Constant_28062" [id=1853, type=Constant]; -"1854 Constant_28061" [id=1854, type=Constant]; -"1855 Constant_28069" [id=1855, type=Constant]; -"1856 Constant_28068" [id=1856, type=Constant]; -"1857 Constant_28067" [id=1857, type=Constant]; -"1858 Constant_28066" [id=1858, type=Constant]; -"1859 Constant_7393" [id=1859, type=Constant]; -"1860 Constant_2056" [id=1860, type=Constant]; -"1861 Constant_7394" [id=1861, type=Constant]; -"1862 Constant_7397" [id=1862, type=Constant]; -"1863 Constant_7398" [id=1863, type=Constant]; -"0 input" -> "1 Multiply_6579" [label="[1, 3, 224, 224]", style=solid]; -"1 Multiply_6579" -> "2 Divide_2169" [label="[1, 3, 224, 224]", style=solid]; -"2 Divide_2169" -> "3 Divide_2169/fq_output_0" [label="[1, 3, 224, 224]", style=solid]; -"3 Divide_2169/fq_output_0" -> "4 /patch_embed/proj/Conv/WithoutBiases" [label="[1, 3, 224, 224]", style=solid]; -"4 /patch_embed/proj/Conv/WithoutBiases" -> "5 /patch_embed/proj/Conv" [label="[1, 96, 56, 56]", style=solid]; -"5 /patch_embed/proj/Conv" -> "6 /patch_embed/Reshape" [label="[1, 96, 56, 56]", style=solid]; -"5 /patch_embed/proj/Conv" -> "7 /patch_embed/Shape" [label="[1, 96, 56, 56]", style=solid]; -"6 /patch_embed/Reshape" -> "8 /patch_embed/Transpose" [label="[1, 96, 3136]", style=solid]; -"7 /patch_embed/Shape" -> "9 /patch_embed/Slice" [label="[4]", style=dashed]; -"8 /patch_embed/Transpose" -> "10 /patch_embed/norm/Div" [label="[1, 3136, 96]", style=solid]; -"9 /patch_embed/Slice" -> "11 /patch_embed/Concat" [label="[2]", style=dashed]; -"10 /patch_embed/norm/Div" -> "12 /patch_embed/norm/Mul" [label="[1, 3136, 96]", style=solid]; -"11 /patch_embed/Concat" -> "6 /patch_embed/Reshape" [label="[3]", style=dashed]; -"12 /patch_embed/norm/Mul" -> "13 /patch_embed/norm/Add_1" [label="[1, 3136, 96]", style=solid]; -"13 /patch_embed/norm/Add_1" -> "14 /layers/layers.0/blocks.0/Add" [label="[1, 3136, 96]", style=solid]; -"13 /patch_embed/norm/Add_1" -> "15 /layers/layers.0/blocks.0/norm1/Div" [label="[1, 3136, 96]", style=solid]; -"14 /layers/layers.0/blocks.0/Add" -> "16 /layers/layers.0/blocks.0/Add_1" [label="[1, 3136, 96]", style=solid]; -"14 /layers/layers.0/blocks.0/Add" -> "17 /layers/layers.0/blocks.0/norm2/Div" [label="[1, 3136, 96]", style=solid]; -"15 /layers/layers.0/blocks.0/norm1/Div" -> "18 /layers/layers.0/blocks.0/norm1/Mul" [label="[1, 3136, 96]", style=solid]; -"16 /layers/layers.0/blocks.0/Add_1" -> "19 /layers/layers.0/blocks.1/Add" [label="[1, 3136, 96]", style=solid]; -"16 /layers/layers.0/blocks.0/Add_1" -> "20 /layers/layers.0/blocks.1/norm1/Div" [label="[1, 3136, 96]", style=solid]; -"17 /layers/layers.0/blocks.0/norm2/Div" -> "21 /layers/layers.0/blocks.0/norm2/Mul" [label="[1, 3136, 96]", style=solid]; -"18 /layers/layers.0/blocks.0/norm1/Mul" -> "22 /layers/layers.0/blocks.0/norm1/Add_1" [label="[1, 3136, 96]", style=solid]; -"19 /layers/layers.0/blocks.1/Add" -> "23 /layers/layers.0/blocks.1/Add_1" [label="[1, 3136, 96]", style=solid]; -"19 /layers/layers.0/blocks.1/Add" -> "24 /layers/layers.0/blocks.1/norm2/Div" [label="[1, 3136, 96]", style=solid]; -"20 /layers/layers.0/blocks.1/norm1/Div" -> "25 /layers/layers.0/blocks.1/norm1/Mul" [label="[1, 3136, 96]", style=solid]; -"21 /layers/layers.0/blocks.0/norm2/Mul" -> "26 /layers/layers.0/blocks.0/norm2/Add_1" [label="[1, 3136, 96]", style=solid]; -"22 /layers/layers.0/blocks.0/norm1/Add_1" -> "27 /layers/layers.0/blocks.0/norm1/Add_1/fq_output_0" [label="[1, 3136, 96]", style=solid]; -"23 /layers/layers.0/blocks.1/Add_1" -> "28 /layers/layers.0/downsample/Reshape" [label="[1, 3136, 96]", style=solid]; -"24 /layers/layers.0/blocks.1/norm2/Div" -> "29 /layers/layers.0/blocks.1/norm2/Mul" [label="[1, 3136, 96]", style=solid]; -"25 /layers/layers.0/blocks.1/norm1/Mul" -> "30 /layers/layers.0/blocks.1/norm1/Add_1" [label="[1, 3136, 96]", style=solid]; -"26 /layers/layers.0/blocks.0/norm2/Add_1" -> "31 /layers/layers.0/blocks.0/norm2/Add_1/fq_output_0" [label="[1, 3136, 96]", style=solid]; -"27 /layers/layers.0/blocks.0/norm1/Add_1/fq_output_0" -> "32 /layers/layers.0/blocks.0/Reshape_1" [label="[1, 3136, 96]", style=solid]; -"28 /layers/layers.0/downsample/Reshape" -> "33 /layers/layers.0/downsample/Slice" [label="[1, 56, 56, 96]", style=solid]; -"28 /layers/layers.0/downsample/Reshape" -> "34 /layers/layers.0/downsample/Slice_2" [label="[1, 56, 56, 96]", style=solid]; -"29 /layers/layers.0/blocks.1/norm2/Mul" -> "35 /layers/layers.0/blocks.1/norm2/Add_1" [label="[1, 3136, 96]", style=solid]; -"30 /layers/layers.0/blocks.1/norm1/Add_1" -> "36 /layers/layers.0/blocks.1/norm1/Add_1/fq_output_0" [label="[1, 3136, 96]", style=solid]; -"31 /layers/layers.0/blocks.0/norm2/Add_1/fq_output_0" -> "37 /layers/layers.0/blocks.0/mlp/fc1/MatMul" [label="[1, 3136, 96]", style=solid]; -"32 /layers/layers.0/blocks.0/Reshape_1" -> "38 /layers/layers.0/blocks.0/Transpose" [label="[1, 8, 7, 8, 7, 96]", style=solid]; -"33 /layers/layers.0/downsample/Slice" -> "39 /layers/layers.0/downsample/Slice_1" [label="[1, 28, 56, 96]", style=solid]; -"33 /layers/layers.0/downsample/Slice" -> "40 /layers/layers.0/downsample/Slice_4" [label="[1, 28, 56, 96]", style=solid]; -"34 /layers/layers.0/downsample/Slice_2" -> "41 /layers/layers.0/downsample/Slice_3" [label="[1, 28, 56, 96]", style=solid]; -"34 /layers/layers.0/downsample/Slice_2" -> "42 /layers/layers.0/downsample/Slice_5" [label="[1, 28, 56, 96]", style=solid]; -"35 /layers/layers.0/blocks.1/norm2/Add_1" -> "43 /layers/layers.0/blocks.1/norm2/Add_1/fq_output_0" [label="[1, 3136, 96]", style=solid]; -"36 /layers/layers.0/blocks.1/norm1/Add_1/fq_output_0" -> "44 /layers/layers.0/blocks.1/Reshape" [label="[1, 3136, 96]", style=solid]; -"37 /layers/layers.0/blocks.0/mlp/fc1/MatMul" -> "45 /layers/layers.0/blocks.0/mlp/fc1/Add" [label="[1, 3136, 384]", style=solid]; -"38 /layers/layers.0/blocks.0/Transpose" -> "46 /layers/layers.0/blocks.0/Reshape_2" [label="[1, 8, 8, 7, 7, 96]", style=solid]; -"39 /layers/layers.0/downsample/Slice_1" -> "47 /layers/layers.0/downsample/Concat" [label="[1, 28, 28, 96]", style=solid]; -"40 /layers/layers.0/downsample/Slice_4" -> "47 /layers/layers.0/downsample/Concat" [label="[1, 28, 28, 96]", style=solid]; -"41 /layers/layers.0/downsample/Slice_3" -> "47 /layers/layers.0/downsample/Concat" [label="[1, 28, 28, 96]", style=solid]; -"42 /layers/layers.0/downsample/Slice_5" -> "47 /layers/layers.0/downsample/Concat" [label="[1, 28, 28, 96]", style=solid]; -"43 /layers/layers.0/blocks.1/norm2/Add_1/fq_output_0" -> "48 /layers/layers.0/blocks.1/mlp/fc1/MatMul" [label="[1, 3136, 96]", style=solid]; -"44 /layers/layers.0/blocks.1/Reshape" -> "49 /layers/layers.0/blocks.1/Slice" [label="[1, 56, 56, 96]", style=solid]; -"44 /layers/layers.0/blocks.1/Reshape" -> "50 /layers/layers.0/blocks.1/Slice_1" [label="[1, 56, 56, 96]", style=solid]; -"45 /layers/layers.0/blocks.0/mlp/fc1/Add" -> "51 /layers/layers.0/blocks.0/mlp/act/Mul_1" [label="[1, 3136, 384]", style=solid]; -"46 /layers/layers.0/blocks.0/Reshape_2" -> "52 /layers/layers.0/blocks.0/Reshape_3" [label="[64, 7, 7, 96]", style=solid]; -"47 /layers/layers.0/downsample/Concat" -> "53 /layers/layers.0/downsample/Reshape_1" [label="[1, 28, 28, 384]", style=solid]; -"48 /layers/layers.0/blocks.1/mlp/fc1/MatMul" -> "54 /layers/layers.0/blocks.1/mlp/fc1/Add" [label="[1, 3136, 384]", style=solid]; -"49 /layers/layers.0/blocks.1/Slice" -> "55 /layers/layers.0/blocks.1/Concat" [label="[1, 53, 56, 96]", style=solid]; -"50 /layers/layers.0/blocks.1/Slice_1" -> "55 /layers/layers.0/blocks.1/Concat" [label="[1, 3, 56, 96]", style=solid]; -"51 /layers/layers.0/blocks.0/mlp/act/Mul_1" -> "56 /layers/layers.0/blocks.0/mlp/act/Mul_1/fq_output_0" [label="[1, 3136, 384]", style=solid]; -"52 /layers/layers.0/blocks.0/Reshape_3" -> "57 /layers/layers.0/blocks.0/attn/qkv/MatMul" [label="[64, 49, 96]", style=solid]; -"53 /layers/layers.0/downsample/Reshape_1" -> "58 /layers/layers.0/downsample/norm/Div" [label="[1, 784, 384]", style=solid]; -"54 /layers/layers.0/blocks.1/mlp/fc1/Add" -> "59 /layers/layers.0/blocks.1/mlp/act/Mul_1" [label="[1, 3136, 384]", style=solid]; -"55 /layers/layers.0/blocks.1/Concat" -> "60 /layers/layers.0/blocks.1/Slice_2" [label="[1, 56, 56, 96]", style=solid]; -"55 /layers/layers.0/blocks.1/Concat" -> "61 /layers/layers.0/blocks.1/Slice_3" [label="[1, 56, 56, 96]", style=solid]; -"56 /layers/layers.0/blocks.0/mlp/act/Mul_1/fq_output_0" -> "62 /layers/layers.0/blocks.0/mlp/fc2/MatMul" [label="[1, 3136, 384]", style=solid]; -"57 /layers/layers.0/blocks.0/attn/qkv/MatMul" -> "63 /layers/layers.0/blocks.0/attn/qkv/Add" [label="[64, 49, 288]", style=solid]; -"58 /layers/layers.0/downsample/norm/Div" -> "64 /layers/layers.0/downsample/norm/Mul" [label="[1, 784, 384]", style=solid]; -"59 /layers/layers.0/blocks.1/mlp/act/Mul_1" -> "65 /layers/layers.0/blocks.1/mlp/act/Mul_1/fq_output_0" [label="[1, 3136, 384]", style=solid]; -"60 /layers/layers.0/blocks.1/Slice_2" -> "66 /layers/layers.0/blocks.1/Concat_1" [label="[1, 56, 53, 96]", style=solid]; -"61 /layers/layers.0/blocks.1/Slice_3" -> "66 /layers/layers.0/blocks.1/Concat_1" [label="[1, 56, 3, 96]", style=solid]; -"62 /layers/layers.0/blocks.0/mlp/fc2/MatMul" -> "67 /layers/layers.0/blocks.0/mlp/fc2/Add" [label="[1, 3136, 96]", style=solid]; -"63 /layers/layers.0/blocks.0/attn/qkv/Add" -> "68 /layers/layers.0/blocks.0/attn/Reshape" [label="[64, 49, 288]", style=solid]; -"64 /layers/layers.0/downsample/norm/Mul" -> "69 /layers/layers.0/downsample/norm/Add_1" [label="[1, 784, 384]", style=solid]; -"65 /layers/layers.0/blocks.1/mlp/act/Mul_1/fq_output_0" -> "70 /layers/layers.0/blocks.1/mlp/fc2/MatMul" [label="[1, 3136, 384]", style=solid]; -"66 /layers/layers.0/blocks.1/Concat_1" -> "71 /layers/layers.0/blocks.1/Reshape_1" [label="[1, 56, 56, 96]", style=solid]; -"67 /layers/layers.0/blocks.0/mlp/fc2/Add" -> "16 /layers/layers.0/blocks.0/Add_1" [label="[1, 3136, 96]", style=solid]; -"68 /layers/layers.0/blocks.0/attn/Reshape" -> "72 /layers/layers.0/blocks.0/attn/Transpose" [label="[64, 49, 3, 3, 32]", style=solid]; -"69 /layers/layers.0/downsample/norm/Add_1" -> "73 /layers/layers.0/downsample/norm/Add_1/fq_output_0" [label="[1, 784, 384]", style=solid]; -"70 /layers/layers.0/blocks.1/mlp/fc2/MatMul" -> "74 /layers/layers.0/blocks.1/mlp/fc2/Add" [label="[1, 3136, 96]", style=solid]; -"71 /layers/layers.0/blocks.1/Reshape_1" -> "75 /layers/layers.0/blocks.1/Transpose" [label="[1, 8, 7, 8, 7, 96]", style=solid]; -"72 /layers/layers.0/blocks.0/attn/Transpose" -> "76 /layers/layers.0/blocks.0/attn/Gather" [label="[3, 64, 3, 49, 32]", style=solid]; -"72 /layers/layers.0/blocks.0/attn/Transpose" -> "77 /layers/layers.0/blocks.0/attn/Gather_1/fq_input_0" [label="[3, 64, 3, 49, 32]", style=solid]; -"72 /layers/layers.0/blocks.0/attn/Transpose" -> "78 /layers/layers.0/blocks.0/attn/Gather_2" [label="[3, 64, 3, 49, 32]", style=solid]; -"73 /layers/layers.0/downsample/norm/Add_1/fq_output_0" -> "79 /layers/layers.0/downsample/reduction/MatMul" [label="[1, 784, 384]", style=solid]; -"74 /layers/layers.0/blocks.1/mlp/fc2/Add" -> "23 /layers/layers.0/blocks.1/Add_1" [label="[1, 3136, 96]", style=solid]; -"75 /layers/layers.0/blocks.1/Transpose" -> "80 /layers/layers.0/blocks.1/Reshape_2" [label="[1, 8, 8, 7, 7, 96]", style=solid]; -"76 /layers/layers.0/blocks.0/attn/Gather" -> "81 /layers/layers.0/blocks.0/attn/Mul" [label="[64, 3, 49, 32]", style=solid]; -"77 /layers/layers.0/blocks.0/attn/Gather_1/fq_input_0" -> "82 /layers/layers.0/blocks.0/attn/Gather_1" [label="[3, 64, 3, 49, 32]", style=solid]; -"78 /layers/layers.0/blocks.0/attn/Gather_2" -> "83 /layers/layers.0/blocks.0/attn/MatMul_1" [label="[64, 3, 49, 32]", style=solid]; -"79 /layers/layers.0/downsample/reduction/MatMul" -> "84 /layers/layers.1/blocks.0/Add" [label="[1, 784, 192]", style=solid]; -"79 /layers/layers.0/downsample/reduction/MatMul" -> "85 /layers/layers.1/blocks.0/norm1/Div" [label="[1, 784, 192]", style=solid]; -"80 /layers/layers.0/blocks.1/Reshape_2" -> "86 /layers/layers.0/blocks.1/Reshape_3" [label="[64, 7, 7, 96]", style=solid]; -"81 /layers/layers.0/blocks.0/attn/Mul" -> "87 /layers/layers.0/blocks.0/attn/Mul/fq_output_0" [label="[64, 3, 49, 32]", style=solid]; -"82 /layers/layers.0/blocks.0/attn/Gather_1" -> "88 /layers/layers.0/blocks.0/attn/MatMul" [label="[64, 3, 49, 32]", style=solid]; -"83 /layers/layers.0/blocks.0/attn/MatMul_1" -> "89 /layers/layers.0/blocks.0/attn/MatMul_1/fq_output_0" [label="[64, 3, 49, 32]", style=solid]; -"84 /layers/layers.1/blocks.0/Add" -> "90 /layers/layers.1/blocks.0/Add_1" [label="[1, 784, 192]", style=solid]; -"84 /layers/layers.1/blocks.0/Add" -> "91 /layers/layers.1/blocks.0/norm2/Div" [label="[1, 784, 192]", style=solid]; -"85 /layers/layers.1/blocks.0/norm1/Div" -> "92 /layers/layers.1/blocks.0/norm1/Mul" [label="[1, 784, 192]", style=solid]; -"86 /layers/layers.0/blocks.1/Reshape_3" -> "93 /layers/layers.0/blocks.1/attn/qkv/MatMul" [label="[64, 49, 96]", style=solid]; -"87 /layers/layers.0/blocks.0/attn/Mul/fq_output_0" -> "88 /layers/layers.0/blocks.0/attn/MatMul" [label="[64, 3, 49, 32]", style=solid]; -"88 /layers/layers.0/blocks.0/attn/MatMul" -> "94 /layers/layers.0/blocks.0/attn/Add" [label="[64, 3, 49, 49]", style=solid]; -"89 /layers/layers.0/blocks.0/attn/MatMul_1/fq_output_0" -> "95 /layers/layers.0/blocks.0/attn/Transpose_2" [label="[64, 3, 49, 32]", style=solid]; -"90 /layers/layers.1/blocks.0/Add_1" -> "96 /layers/layers.1/blocks.1/Add" [label="[1, 784, 192]", style=solid]; -"90 /layers/layers.1/blocks.0/Add_1" -> "97 /layers/layers.1/blocks.1/norm1/Div" [label="[1, 784, 192]", style=solid]; -"91 /layers/layers.1/blocks.0/norm2/Div" -> "98 /layers/layers.1/blocks.0/norm2/Mul" [label="[1, 784, 192]", style=solid]; -"92 /layers/layers.1/blocks.0/norm1/Mul" -> "99 /layers/layers.1/blocks.0/norm1/Add_1" [label="[1, 784, 192]", style=solid]; -"93 /layers/layers.0/blocks.1/attn/qkv/MatMul" -> "100 /layers/layers.0/blocks.1/attn/qkv/Add" [label="[64, 49, 288]", style=solid]; -"94 /layers/layers.0/blocks.0/attn/Add" -> "101 /layers/layers.0/blocks.0/attn/softmax/Softmax" [label="[64, 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/layers/layers.2/blocks.5/Concat_3" [label="[1, 14, 11, 384]", style=solid]; -"660 /layers/layers.3/blocks.1/attn/proj/MatMul" -> "663 /layers/layers.3/blocks.1/attn/proj/Add" [label="[1, 49, 768]", style=solid]; -"661 /layers/layers.3/blocks.0/Reshape_4" -> "664 /layers/layers.3/blocks.0/Reshape_5" [label="[1, 7, 7, 768]", style=solid]; -"662 /layers/layers.2/blocks.5/Concat_3" -> "665 /layers/layers.2/blocks.5/Reshape_7" [label="[1, 14, 14, 384]", style=solid]; -"663 /layers/layers.3/blocks.1/attn/proj/Add" -> "666 /layers/layers.3/blocks.1/Reshape_4" [label="[1, 49, 768]", style=solid]; -"664 /layers/layers.3/blocks.0/Reshape_5" -> "667 /layers/layers.3/blocks.0/Transpose_1" [label="[1, 1, 1, 7, 7, 768]", style=solid]; -"665 /layers/layers.2/blocks.5/Reshape_7" -> "301 /layers/layers.2/blocks.5/Add" [label="[1, 196, 384]", style=solid]; -"666 /layers/layers.3/blocks.1/Reshape_4" -> "668 /layers/layers.3/blocks.1/Reshape_5" [label="[1, 7, 7, 768]", style=solid]; -"667 /layers/layers.3/blocks.0/Transpose_1" -> "669 /layers/layers.3/blocks.0/Reshape_6" [label="[1, 1, 7, 1, 7, 768]", style=solid]; -"668 /layers/layers.3/blocks.1/Reshape_5" -> "670 /layers/layers.3/blocks.1/Transpose_1" [label="[1, 1, 1, 7, 7, 768]", style=solid]; -"669 /layers/layers.3/blocks.0/Reshape_6" -> "671 /layers/layers.3/blocks.0/Reshape_7" [label="[1, 7, 7, 768]", style=solid]; -"670 /layers/layers.3/blocks.1/Transpose_1" -> "672 /layers/layers.3/blocks.1/Reshape_6" [label="[1, 1, 7, 1, 7, 768]", style=solid]; -"671 /layers/layers.3/blocks.0/Reshape_7" -> "470 /layers/layers.3/blocks.0/Add" [label="[1, 49, 768]", style=solid]; -"672 /layers/layers.3/blocks.1/Reshape_6" -> "673 /layers/layers.3/blocks.1/Reshape_7" [label="[1, 7, 7, 768]", style=solid]; -"673 /layers/layers.3/blocks.1/Reshape_7" -> "494 /layers/layers.3/blocks.1/Add" [label="[1, 49, 768]", style=solid]; -"674 Constant_7401" -> "609 probs" [label="[1, 1000]", style=solid]; -"675 /head/Gemm/WithoutBiases/fq_weights_1" -> "601 /head/Gemm/WithoutBiases" [label="[1000, 768]", style=solid]; -"676 Constant_28109" -> "675 /head/Gemm/WithoutBiases/fq_weights_1" [label="[1000, 1]", style=solid]; -"677 Constant_28108" -> "675 /head/Gemm/WithoutBiases/fq_weights_1" [label="[1000, 1]", style=solid]; -"678 Constant_28107" -> "675 /head/Gemm/WithoutBiases/fq_weights_1" [label="[1000, 1]", style=solid]; -"679 Constant_28106" -> "675 /head/Gemm/WithoutBiases/fq_weights_1" [label="[1000, 1]", style=solid]; -"680 head.weight" -> "675 /head/Gemm/WithoutBiases/fq_weights_1" [label="[1000, 768]", style=solid]; -"681 Constant_2148" -> "591 /Flatten" [label="[2]", style=dashed]; -"682 Constant_6567" -> "581 /avgpool/GlobalAveragePool" [label="[3]", style=dashed]; -"683 Constant_28104" -> "569 ReduceMean_6037/fq_output_0" [label="[]", style=solid]; -"684 Constant_28103" -> "569 ReduceMean_6037/fq_output_0" [label="[]", style=solid]; -"685 Constant_28102" -> "569 ReduceMean_6037/fq_output_0" [label="[]", style=solid]; -"686 Constant_28101" -> "569 ReduceMean_6037/fq_output_0" [label="[]", style=solid]; -"687 Constant_6036" -> "557 ReduceMean_6037" [label="[1]", style=dashed]; -"688 Constant_7400" -> "545 /norm/Add_1" [label="[1, 1, 768]", style=solid]; -"689 Constant_7399" -> "532 /norm/Mul" [label="[1, 1, 768]", style=solid]; -"690 Constant_2123" -> "520 /norm/Div" [label="[1]", style=dashed]; -"691 /layers/layers.3/blocks.1/mlp/fc2/MatMul/fq_weights_1" -> "602 /layers/layers.3/blocks.1/mlp/fc2/MatMul" [label="[768, 3072]", style=solid]; -"692 Constant_28099" -> "691 /layers/layers.3/blocks.1/mlp/fc2/MatMul/fq_weights_1" [label="[768, 1]", style=solid]; -"693 Constant_28098" -> "691 /layers/layers.3/blocks.1/mlp/fc2/MatMul/fq_weights_1" [label="[768, 1]", style=solid]; -"694 Constant_28097" -> "691 /layers/layers.3/blocks.1/mlp/fc2/MatMul/fq_weights_1" [label="[768, 1]", style=solid]; -"695 Constant_28096" -> "691 /layers/layers.3/blocks.1/mlp/fc2/MatMul/fq_weights_1" [label="[768, 1]", style=solid]; -"696 Transpose_6564" -> "691 /layers/layers.3/blocks.1/mlp/fc2/MatMul/fq_weights_1" [label="[768, 3072]", style=solid]; -"697 Constant_28094" -> "592 /layers/layers.3/blocks.1/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; -"698 Constant_28093" -> "592 /layers/layers.3/blocks.1/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; -"699 Constant_28092" -> "592 /layers/layers.3/blocks.1/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; -"700 Constant_28091" -> "592 /layers/layers.3/blocks.1/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; -"701 /layers/layers.3/blocks.1/mlp/fc1/MatMul/fq_weights_1" -> "558 /layers/layers.3/blocks.1/mlp/fc1/MatMul" [label="[3072, 768]", style=solid]; -"702 Constant_28089" -> "701 /layers/layers.3/blocks.1/mlp/fc1/MatMul/fq_weights_1" [label="[3072, 1]", style=solid]; -"703 Constant_28088" -> "701 /layers/layers.3/blocks.1/mlp/fc1/MatMul/fq_weights_1" [label="[3072, 1]", style=solid]; -"704 Constant_28087" -> "701 /layers/layers.3/blocks.1/mlp/fc1/MatMul/fq_weights_1" [label="[3072, 1]", style=solid]; -"705 Constant_28086" -> "701 /layers/layers.3/blocks.1/mlp/fc1/MatMul/fq_weights_1" [label="[3072, 1]", style=solid]; -"706 Transpose_6560" -> "701 /layers/layers.3/blocks.1/mlp/fc1/MatMul/fq_weights_1" [label="[3072, 768]", style=solid]; -"707 Constant_28084" -> "546 /layers/layers.3/blocks.1/norm2/Add_1/fq_output_0" [label="[]", style=solid]; -"708 Constant_28083" -> "546 /layers/layers.3/blocks.1/norm2/Add_1/fq_output_0" [label="[]", style=solid]; -"709 Constant_28082" -> "546 /layers/layers.3/blocks.1/norm2/Add_1/fq_output_0" [label="[]", style=solid]; -"710 Constant_28081" -> "546 /layers/layers.3/blocks.1/norm2/Add_1/fq_output_0" [label="[]", style=solid]; -"711 Constant_7396" -> "533 /layers/layers.3/blocks.1/norm2/Add_1" [label="[1, 1, 768]", style=solid]; -"712 Constant_7395" -> "521 /layers/layers.3/blocks.1/norm2/Mul" [label="[1, 1, 768]", style=solid]; -"713 Constant_2097" -> "508 /layers/layers.3/blocks.1/norm2/Div" [label="[1]", style=dashed]; -"714 /layers/layers.3/blocks.1/Constant_7" -> "673 /layers/layers.3/blocks.1/Reshape_7" [label="[3]", style=dashed]; -"715 /layers/layers.3/blocks.1/Constant_6" -> "672 /layers/layers.3/blocks.1/Reshape_6" [label="[4]", style=dashed]; -"716 Constant_6554" -> "670 /layers/layers.3/blocks.1/Transpose_1" [label="[6]", style=dashed]; -"717 /layers/layers.3/blocks.1/Constant_5" -> "668 /layers/layers.3/blocks.1/Reshape_5" [label="[6]", style=dashed]; -"718 /layers/layers.3/blocks.1/Constant_4" -> "666 /layers/layers.3/blocks.1/Reshape_4" [label="[4]", style=dashed]; -"719 /layers/layers.3/blocks.1/attn/proj/MatMul/fq_weights_1" -> "660 /layers/layers.3/blocks.1/attn/proj/MatMul" [label="[768, 768]", style=solid]; -"720 Constant_28079" -> "719 /layers/layers.3/blocks.1/attn/proj/MatMul/fq_weights_1" [label="[768, 1]", style=solid]; -"721 Constant_28078" -> "719 /layers/layers.3/blocks.1/attn/proj/MatMul/fq_weights_1" [label="[768, 1]", style=solid]; -"722 Constant_28077" -> "719 /layers/layers.3/blocks.1/attn/proj/MatMul/fq_weights_1" [label="[768, 1]", style=solid]; -"723 Constant_28076" -> "719 /layers/layers.3/blocks.1/attn/proj/MatMul/fq_weights_1" [label="[768, 1]", style=solid]; -"724 Transpose_6552" -> "719 /layers/layers.3/blocks.1/attn/proj/MatMul/fq_weights_1" [label="[768, 768]", style=solid]; -"725 /layers/layers.3/blocks.1/attn/Constant_2" -> "656 /layers/layers.3/blocks.1/attn/Reshape_1" [label="[3]", style=dashed]; -"726 Constant_2070" -> "652 /layers/layers.3/blocks.1/attn/Transpose_2" [label="[4]", style=dashed]; -"727 Constant_28074" -> "645 /layers/layers.3/blocks.1/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; -"728 Constant_28073" -> "645 /layers/layers.3/blocks.1/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; -"729 Constant_28072" -> "645 /layers/layers.3/blocks.1/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; -"730 Constant_28071" -> "645 /layers/layers.3/blocks.1/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; -"731 Constant_2060" -> "629 /layers/layers.3/blocks.1/attn/Gather_2" [label="[]", style=dashed]; -"732 /patch_embed/Constant" -> "78 /layers/layers.0/blocks.0/attn/Gather_2" [label="[]", style=dashed]; -"732 /patch_embed/Constant" -> "125 /layers/layers.0/blocks.1/attn/Gather_2" [label="[]", style=dashed]; -"732 /patch_embed/Constant" -> "184 /layers/layers.1/blocks.0/attn/Gather_2" [label="[]", style=dashed]; -"732 /patch_embed/Constant" -> "243 /layers/layers.1/blocks.1/attn/Gather_2" [label="[]", style=dashed]; -"732 /patch_embed/Constant" -> "343 /layers/layers.2/blocks.0/attn/Gather_2" [label="[]", style=dashed]; -"732 /patch_embed/Constant" -> "401 /layers/layers.2/blocks.2/attn/Gather_2" [label="[]", style=dashed]; -"732 /patch_embed/Constant" -> "442 /layers/layers.2/blocks.1/attn/Gather_2" [label="[]", style=dashed]; -"732 /patch_embed/Constant" -> "450 /layers/layers.2/blocks.4/attn/Gather_2" [label="[]", style=dashed]; -"732 /patch_embed/Constant" -> "489 /layers/layers.2/blocks.3/attn/Gather_2" [label="[]", style=dashed]; -"732 /patch_embed/Constant" -> "539 /layers/layers.2/blocks.5/attn/Gather_2" [label="[]", style=dashed]; -"732 /patch_embed/Constant" -> "614 /layers/layers.3/blocks.0/attn/Gather_2" [label="[]", style=dashed]; -"732 /patch_embed/Constant" -> "629 /layers/layers.3/blocks.1/attn/Gather_2" [label="[]", style=dashed]; -"733 Constant_2054" -> "621 /layers/layers.3/blocks.1/attn/Transpose" [label="[5]", style=dashed]; -"734 /layers/layers.3/blocks.1/attn/Constant" -> "611 /layers/layers.3/blocks.1/attn/Reshape" [label="[5]", style=dashed]; -"735 /layers/layers.3/blocks.1/attn/qkv/MatMul/fq_weights_1" -> "593 /layers/layers.3/blocks.1/attn/qkv/MatMul" [label="[2304, 768]", style=solid]; -"736 Constant_28059" -> "735 /layers/layers.3/blocks.1/attn/qkv/MatMul/fq_weights_1" [label="[2304, 1]", style=solid]; -"737 Constant_28058" -> "735 /layers/layers.3/blocks.1/attn/qkv/MatMul/fq_weights_1" [label="[2304, 1]", style=solid]; -"738 Constant_28057" -> "735 /layers/layers.3/blocks.1/attn/qkv/MatMul/fq_weights_1" [label="[2304, 1]", style=solid]; -"739 Constant_28056" -> "735 /layers/layers.3/blocks.1/attn/qkv/MatMul/fq_weights_1" [label="[2304, 1]", style=solid]; -"740 Transpose_6549" -> "735 /layers/layers.3/blocks.1/attn/qkv/MatMul/fq_weights_1" [label="[2304, 768]", style=solid]; -"741 /layers/layers.3/blocks.1/Constant_3" -> "583 /layers/layers.3/blocks.1/Reshape_3" [label="[3]", style=dashed]; -"742 /layers/layers.3/blocks.1/Constant_2" -> "571 /layers/layers.3/blocks.1/Reshape_2" [label="[4]", style=dashed]; -"743 Constant_6544" -> "559 /layers/layers.3/blocks.1/Transpose" [label="[6]", style=dashed]; -"744 /layers/layers.3/blocks.1/Constant_1" -> "547 /layers/layers.3/blocks.1/Reshape_1" [label="[6]", style=dashed]; -"745 Constant_28054" -> "534 /layers/layers.3/blocks.1/norm1/Add_1/fq_output_0" [label="[]", style=solid]; -"746 Constant_28053" -> "534 /layers/layers.3/blocks.1/norm1/Add_1/fq_output_0" [label="[]", style=solid]; -"747 Constant_28052" -> "534 /layers/layers.3/blocks.1/norm1/Add_1/fq_output_0" [label="[]", style=solid]; -"748 Constant_28051" -> "534 /layers/layers.3/blocks.1/norm1/Add_1/fq_output_0" [label="[]", style=solid]; -"749 Constant_7391" -> "522 /layers/layers.3/blocks.1/norm1/Add_1" [label="[1, 1, 768]", style=solid]; -"750 Constant_7390" -> "509 /layers/layers.3/blocks.1/norm1/Mul" [label="[1, 1, 768]", style=solid]; -"751 Constant_2017" -> "495 /layers/layers.3/blocks.1/norm1/Div" [label="[1]", style=dashed]; -"752 /layers/layers.3/blocks.0/mlp/fc2/MatMul/fq_weights_1" -> "584 /layers/layers.3/blocks.0/mlp/fc2/MatMul" [label="[768, 3072]", style=solid]; -"753 Constant_28049" -> "752 /layers/layers.3/blocks.0/mlp/fc2/MatMul/fq_weights_1" [label="[768, 1]", style=solid]; -"754 Constant_28048" -> "752 /layers/layers.3/blocks.0/mlp/fc2/MatMul/fq_weights_1" [label="[768, 1]", style=solid]; -"755 Constant_28047" -> "752 /layers/layers.3/blocks.0/mlp/fc2/MatMul/fq_weights_1" [label="[768, 1]", style=solid]; -"756 Constant_28046" -> "752 /layers/layers.3/blocks.0/mlp/fc2/MatMul/fq_weights_1" [label="[768, 1]", style=solid]; -"757 Transpose_6541" -> "752 /layers/layers.3/blocks.0/mlp/fc2/MatMul/fq_weights_1" [label="[768, 3072]", style=solid]; -"758 Constant_28044" -> "572 /layers/layers.3/blocks.0/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; -"759 Constant_28043" -> "572 /layers/layers.3/blocks.0/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; -"760 Constant_28042" -> "572 /layers/layers.3/blocks.0/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; -"761 Constant_28041" -> "572 /layers/layers.3/blocks.0/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; -"762 /layers/layers.3/blocks.0/mlp/fc1/MatMul/fq_weights_1" -> "535 /layers/layers.3/blocks.0/mlp/fc1/MatMul" [label="[3072, 768]", style=solid]; -"763 Constant_28039" -> "762 /layers/layers.3/blocks.0/mlp/fc1/MatMul/fq_weights_1" [label="[3072, 1]", style=solid]; -"764 Constant_28038" -> "762 /layers/layers.3/blocks.0/mlp/fc1/MatMul/fq_weights_1" [label="[3072, 1]", style=solid]; -"765 Constant_28037" -> "762 /layers/layers.3/blocks.0/mlp/fc1/MatMul/fq_weights_1" [label="[3072, 1]", style=solid]; -"766 Constant_28036" -> "762 /layers/layers.3/blocks.0/mlp/fc1/MatMul/fq_weights_1" [label="[3072, 1]", style=solid]; -"767 Transpose_6537" -> "762 /layers/layers.3/blocks.0/mlp/fc1/MatMul/fq_weights_1" [label="[3072, 768]", style=solid]; -"768 Constant_28034" -> "523 /layers/layers.3/blocks.0/norm2/Add_1/fq_output_0" [label="[]", style=solid]; -"769 Constant_28033" -> "523 /layers/layers.3/blocks.0/norm2/Add_1/fq_output_0" [label="[]", style=solid]; -"770 Constant_28032" -> "523 /layers/layers.3/blocks.0/norm2/Add_1/fq_output_0" [label="[]", style=solid]; -"771 Constant_28031" -> "523 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/layers/layers.3/blocks.0/attn/proj/MatMul/fq_weights_1" -> "653 /layers/layers.3/blocks.0/attn/proj/MatMul" [label="[768, 768]", style=solid]; -"781 Constant_28029" -> "780 /layers/layers.3/blocks.0/attn/proj/MatMul/fq_weights_1" [label="[768, 1]", style=solid]; -"782 Constant_28028" -> "780 /layers/layers.3/blocks.0/attn/proj/MatMul/fq_weights_1" [label="[768, 1]", style=solid]; -"783 Constant_28027" -> "780 /layers/layers.3/blocks.0/attn/proj/MatMul/fq_weights_1" [label="[768, 1]", style=solid]; -"784 Constant_28026" -> "780 /layers/layers.3/blocks.0/attn/proj/MatMul/fq_weights_1" [label="[768, 1]", style=solid]; -"785 Transpose_6529" -> "780 /layers/layers.3/blocks.0/attn/proj/MatMul/fq_weights_1" [label="[768, 768]", style=solid]; -"786 /layers/layers.3/blocks.0/attn/Constant_2" -> "647 /layers/layers.3/blocks.0/attn/Reshape_1" [label="[3]", style=dashed]; -"787 Constant_1964" -> "640 /layers/layers.3/blocks.0/attn/Transpose_2" [label="[4]", style=dashed]; -"788 Constant_28024" -> "632 /layers/layers.3/blocks.0/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; -"789 Constant_28023" -> "632 /layers/layers.3/blocks.0/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; -"790 Constant_28022" -> "632 /layers/layers.3/blocks.0/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; -"791 Constant_28021" -> "632 /layers/layers.3/blocks.0/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; -"792 Constant_1954" -> "614 /layers/layers.3/blocks.0/attn/Gather_2" [label="[]", style=dashed]; -"793 Constant_1948" -> "604 /layers/layers.3/blocks.0/attn/Transpose" [label="[5]", style=dashed]; -"794 /layers/layers.3/blocks.0/attn/Constant" -> "595 /layers/layers.3/blocks.0/attn/Reshape" [label="[5]", style=dashed]; -"795 /layers/layers.3/blocks.0/attn/qkv/MatMul/fq_weights_1" -> "573 /layers/layers.3/blocks.0/attn/qkv/MatMul" [label="[2304, 768]", style=solid]; -"796 Constant_28009" -> "795 /layers/layers.3/blocks.0/attn/qkv/MatMul/fq_weights_1" [label="[2304, 1]", style=solid]; -"797 Constant_28008" -> "795 /layers/layers.3/blocks.0/attn/qkv/MatMul/fq_weights_1" [label="[2304, 1]", style=solid]; -"798 Constant_28007" -> "795 /layers/layers.3/blocks.0/attn/qkv/MatMul/fq_weights_1" [label="[2304, 1]", style=solid]; -"799 Constant_28006" -> "795 /layers/layers.3/blocks.0/attn/qkv/MatMul/fq_weights_1" [label="[2304, 1]", style=solid]; -"800 Transpose_6526" -> "795 /layers/layers.3/blocks.0/attn/qkv/MatMul/fq_weights_1" [label="[2304, 768]", style=solid]; -"801 /layers/layers.3/blocks.0/Constant_3" -> "561 /layers/layers.3/blocks.0/Reshape_3" [label="[3]", style=dashed]; -"802 /layers/layers.3/blocks.0/Constant_2" -> "549 /layers/layers.3/blocks.0/Reshape_2" [label="[4]", style=dashed]; -"803 Constant_6521" -> "536 /layers/layers.3/blocks.0/Transpose" [label="[6]", style=dashed]; -"804 /layers/layers.3/blocks.0/Constant_1" -> "524 /layers/layers.3/blocks.0/Reshape_1" [label="[6]", style=dashed]; -"805 Constant_28004" -> "511 /layers/layers.3/blocks.0/norm1/Add_1/fq_output_0" [label="[]", style=solid]; -"806 Constant_28003" -> "511 /layers/layers.3/blocks.0/norm1/Add_1/fq_output_0" [label="[]", style=solid]; -"807 Constant_28002" -> "511 /layers/layers.3/blocks.0/norm1/Add_1/fq_output_0" [label="[]", style=solid]; -"808 Constant_28001" -> "511 /layers/layers.3/blocks.0/norm1/Add_1/fq_output_0" [label="[]", style=solid]; -"809 Constant_7382" -> "497 /layers/layers.3/blocks.0/norm1/Add_1" [label="[1, 1, 768]", style=solid]; -"810 Constant_7381" -> "483 /layers/layers.3/blocks.0/norm1/Mul" [label="[1, 1, 768]", style=solid]; -"811 Constant_1911" -> "471 /layers/layers.3/blocks.0/norm1/Div" [label="[1]", style=dashed]; -"812 /layers/layers.2/downsample/reduction/MatMul/fq_weights_1" -> "459 /layers/layers.2/downsample/reduction/MatMul" [label="[768, 1536]", style=solid]; -"813 Constant_27999" -> "812 /layers/layers.2/downsample/reduction/MatMul/fq_weights_1" [label="[768, 1]", style=solid]; -"814 Constant_27998" -> "812 /layers/layers.2/downsample/reduction/MatMul/fq_weights_1" [label="[768, 1]", style=solid]; -"815 Constant_27997" -> "812 /layers/layers.2/downsample/reduction/MatMul/fq_weights_1" [label="[768, 1]", style=solid]; -"816 Constant_27996" -> "812 /layers/layers.2/downsample/reduction/MatMul/fq_weights_1" [label="[768, 1]", style=solid]; -"817 Transpose_6518" -> "812 /layers/layers.2/downsample/reduction/MatMul/fq_weights_1" [label="[768, 1536]", style=solid]; -"818 Constant_27994" -> "445 /layers/layers.2/downsample/norm/Add_1/fq_output_0" [label="[]", style=solid]; -"819 Constant_27993" -> "445 /layers/layers.2/downsample/norm/Add_1/fq_output_0" [label="[]", style=solid]; -"820 Constant_27992" -> "445 /layers/layers.2/downsample/norm/Add_1/fq_output_0" [label="[]", style=solid]; -"821 Constant_27991" -> "445 /layers/layers.2/downsample/norm/Add_1/fq_output_0" [label="[]", style=solid]; -"822 Constant_7380" -> "433 /layers/layers.2/downsample/norm/Add_1" [label="[1, 1, 1536]", style=solid]; -"823 Constant_7379" -> "420 /layers/layers.2/downsample/norm/Mul" [label="[1, 1, 1536]", style=solid]; -"824 Constant_1897" -> "407 /layers/layers.2/downsample/norm/Div" [label="[1]", style=dashed]; -"825 /layers/layers.2/downsample/Constant_25" -> "392 /layers/layers.2/downsample/Reshape_1" [label="[3]", style=dashed]; -"826 Constant_5751" -> "365 /layers/layers.2/downsample/Slice_5" [label="[3]", style=dashed]; -"827 Constant_5748" -> "365 /layers/layers.2/downsample/Slice_5" [label="[3]", style=dashed]; -"828 Constant_5745" -> "365 /layers/layers.2/downsample/Slice_5" [label="[3]", style=dashed]; -"829 Constant_5715" -> "347 /layers/layers.2/downsample/Slice_2" [label="[2]", style=dashed]; -"830 Constant_5712" -> "347 /layers/layers.2/downsample/Slice_2" [label="[2]", style=dashed]; -"831 Constant_5709" -> "347 /layers/layers.2/downsample/Slice_2" [label="[2]", style=dashed]; -"832 /layers/layers.2/downsample/Constant" -> "330 /layers/layers.2/downsample/Reshape" [label="[4]", style=dashed]; -"833 /layers/layers.2/blocks.5/mlp/fc2/MatMul/fq_weights_1" -> "434 /layers/layers.2/blocks.5/mlp/fc2/MatMul" [label="[384, 1536]", style=solid]; -"834 Constant_27989" -> "833 /layers/layers.2/blocks.5/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; -"835 Constant_27988" -> "833 /layers/layers.2/blocks.5/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; -"836 Constant_27987" -> "833 /layers/layers.2/blocks.5/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; -"837 Constant_27986" -> "833 /layers/layers.2/blocks.5/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; -"838 Transpose_6514" -> "833 /layers/layers.2/blocks.5/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1536]", style=solid]; -"839 Constant_27984" -> "421 /layers/layers.2/blocks.5/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; -"840 Constant_27983" -> "421 /layers/layers.2/blocks.5/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; -"841 Constant_27982" -> "421 /layers/layers.2/blocks.5/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; -"842 Constant_27981" -> "421 /layers/layers.2/blocks.5/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; -"843 /layers/layers.2/blocks.5/mlp/fc1/MatMul/fq_weights_1" -> "380 /layers/layers.2/blocks.5/mlp/fc1/MatMul" [label="[1536, 384]", style=solid]; -"844 Constant_27979" -> "843 /layers/layers.2/blocks.5/mlp/fc1/MatMul/fq_weights_1" [label="[1536, 1]", style=solid]; -"845 Constant_27978" -> "843 /layers/layers.2/blocks.5/mlp/fc1/MatMul/fq_weights_1" [label="[1536, 1]", style=solid]; -"846 Constant_27977" -> "843 /layers/layers.2/blocks.5/mlp/fc1/MatMul/fq_weights_1" [label="[1536, 1]", style=solid]; -"847 Constant_27976" -> "843 /layers/layers.2/blocks.5/mlp/fc1/MatMul/fq_weights_1" [label="[1536, 1]", style=solid]; -"848 Transpose_6510" -> "843 /layers/layers.2/blocks.5/mlp/fc1/MatMul/fq_weights_1" [label="[1536, 384]", style=solid]; -"849 Constant_27974" -> "366 /layers/layers.2/blocks.5/norm2/Add_1/fq_output_0" [label="[]", style=solid]; -"850 Constant_27973" -> "366 /layers/layers.2/blocks.5/norm2/Add_1/fq_output_0" [label="[]", style=solid]; -"851 Constant_27972" -> "366 /layers/layers.2/blocks.5/norm2/Add_1/fq_output_0" [label="[]", style=solid]; -"852 Constant_27971" -> "366 /layers/layers.2/blocks.5/norm2/Add_1/fq_output_0" [label="[]", style=solid]; -"853 Constant_7376" -> "348 /layers/layers.2/blocks.5/norm2/Add_1" [label="[1, 1, 384]", style=solid]; -"854 Constant_7375" -> "331 /layers/layers.2/blocks.5/norm2/Mul" [label="[1, 1, 384]", style=solid]; -"855 Constant_1832" -> "316 /layers/layers.2/blocks.5/norm2/Div" [label="[1]", style=dashed]; -"856 /layers/layers.2/blocks.5/Constant_31" -> "665 /layers/layers.2/blocks.5/Reshape_7" [label="[3]", style=dashed]; -"857 Constant_5679" -> "659 /layers/layers.2/blocks.5/Slice_7" [label="[3]", style=dashed]; -"858 Constant_5676" -> "659 /layers/layers.2/blocks.5/Slice_7" [label="[3]", style=dashed]; -"859 Constant_5673" -> "659 /layers/layers.2/blocks.5/Slice_7" [label="[3]", style=dashed]; -"860 Constant_5655" -> "649 /layers/layers.2/blocks.5/Slice_5" [label="[2]", style=dashed]; -"861 Constant_5652" -> "649 /layers/layers.2/blocks.5/Slice_5" [label="[2]", style=dashed]; -"862 Constant_5649" -> "649 /layers/layers.2/blocks.5/Slice_5" [label="[2]", style=dashed]; -"863 /layers/layers.2/blocks.5/Constant_18" -> "641 /layers/layers.2/blocks.5/Reshape_6" [label="[4]", style=dashed]; -"864 Constant_1779" -> "633 /layers/layers.2/blocks.5/Transpose_1" [label="[6]", style=dashed]; -"865 /layers/layers.2/blocks.5/Constant_17" -> "625 /layers/layers.2/blocks.5/Reshape_5" [label="[6]", style=dashed]; -"866 /layers/layers.2/blocks.5/Constant_16" -> "616 /layers/layers.2/blocks.5/Reshape_4" [label="[4]", style=dashed]; -"867 /layers/layers.2/blocks.5/attn/proj/MatMul/fq_weights_1" -> "597 /layers/layers.2/blocks.5/attn/proj/MatMul" [label="[384, 384]", style=solid]; -"868 Constant_27969" -> "867 /layers/layers.2/blocks.5/attn/proj/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; -"869 Constant_27968" -> "867 /layers/layers.2/blocks.5/attn/proj/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; -"870 Constant_27967" -> "867 /layers/layers.2/blocks.5/attn/proj/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; -"871 Constant_27966" -> "867 /layers/layers.2/blocks.5/attn/proj/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; -"872 Transpose_6506" -> "867 /layers/layers.2/blocks.5/attn/proj/MatMul/fq_weights_1" [label="[384, 384]", style=solid]; -"873 /layers/layers.2/blocks.5/attn/Constant_4" -> "587 /layers/layers.2/blocks.5/attn/Reshape_3" [label="[3]", style=dashed]; -"874 Constant_1763" -> "575 /layers/layers.2/blocks.5/attn/Transpose_2" [label="[4]", style=dashed]; -"875 Constant_27964" -> "564 /layers/layers.2/blocks.5/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; -"876 Constant_27963" -> "564 /layers/layers.2/blocks.5/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; -"877 Constant_27962" -> "564 /layers/layers.2/blocks.5/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; -"878 Constant_27961" -> "564 /layers/layers.2/blocks.5/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; -"879 Constant_1744" -> "539 /layers/layers.2/blocks.5/attn/Gather_2" [label="[]", style=dashed]; -"880 Constant_1738" -> "525 /layers/layers.2/blocks.5/attn/Transpose" [label="[5]", style=dashed]; -"881 /layers/layers.2/blocks.5/attn/Constant" -> "512 /layers/layers.2/blocks.5/attn/Reshape" [label="[5]", style=dashed]; -"882 /layers/layers.2/blocks.5/attn/qkv/MatMul/fq_weights_1" -> "484 /layers/layers.2/blocks.5/attn/qkv/MatMul" [label="[1152, 384]", style=solid]; -"883 Constant_27949" -> "882 /layers/layers.2/blocks.5/attn/qkv/MatMul/fq_weights_1" [label="[1152, 1]", style=solid]; -"884 Constant_27948" -> "882 /layers/layers.2/blocks.5/attn/qkv/MatMul/fq_weights_1" [label="[1152, 1]", style=solid]; -"885 Constant_27947" -> "882 /layers/layers.2/blocks.5/attn/qkv/MatMul/fq_weights_1" [label="[1152, 1]", style=solid]; -"886 Constant_27946" -> "882 /layers/layers.2/blocks.5/attn/qkv/MatMul/fq_weights_1" [label="[1152, 1]", style=solid]; -"887 Transpose_6503" -> "882 /layers/layers.2/blocks.5/attn/qkv/MatMul/fq_weights_1" [label="[1152, 384]", style=solid]; -"888 /layers/layers.2/blocks.5/Constant_15" -> "472 /layers/layers.2/blocks.5/Reshape_3" [label="[3]", style=dashed]; -"889 /layers/layers.2/blocks.5/Constant_14" -> "460 /layers/layers.2/blocks.5/Reshape_2" [label="[4]", style=dashed]; -"890 Constant_1722" -> "447 /layers/layers.2/blocks.5/Transpose" [label="[6]", style=dashed]; -"891 /layers/layers.2/blocks.5/Constant_13" -> "435 /layers/layers.2/blocks.5/Reshape_1" [label="[6]", style=dashed]; -"892 Constant_5631" -> "410 /layers/layers.2/blocks.5/Slice_3" [label="[3]", style=dashed]; -"893 Constant_5628" -> "410 /layers/layers.2/blocks.5/Slice_3" [label="[3]", style=dashed]; -"894 Constant_5625" -> "410 /layers/layers.2/blocks.5/Slice_3" [label="[3]", style=dashed]; -"895 Constant_5607" -> "382 /layers/layers.2/blocks.5/Slice_1" [label="[2]", style=dashed]; -"896 Constant_5604" -> "382 /layers/layers.2/blocks.5/Slice_1" [label="[2]", style=dashed]; -"897 Constant_5601" -> "382 /layers/layers.2/blocks.5/Slice_1" [label="[2]", style=dashed]; -"898 /layers/layers.2/blocks.5/Constant" -> "367 /layers/layers.2/blocks.5/Reshape" [label="[4]", style=dashed]; -"899 Constant_27484" -> "349 /layers/layers.2/blocks.5/norm1/Add_1/fq_output_0" [label="[]", style=solid]; -"900 Constant_27483" -> "349 /layers/layers.2/blocks.5/norm1/Add_1/fq_output_0" [label="[]", style=solid]; -"901 Constant_27482" -> "349 /layers/layers.2/blocks.5/norm1/Add_1/fq_output_0" [label="[]", style=solid]; -"902 Constant_27481" -> "349 /layers/layers.2/blocks.5/norm1/Add_1/fq_output_0" [label="[]", style=solid]; -"903 Constant_7371" -> "332 /layers/layers.2/blocks.5/norm1/Add_1" [label="[1, 1, 384]", style=solid]; -"904 Constant_7370" -> "317 /layers/layers.2/blocks.5/norm1/Mul" [label="[1, 1, 384]", style=solid]; -"905 Constant_1659" -> "302 /layers/layers.2/blocks.5/norm1/Div" [label="[1]", style=dashed]; -"906 /layers/layers.2/blocks.4/mlp/fc2/MatMul/fq_weights_1" -> "411 /layers/layers.2/blocks.4/mlp/fc2/MatMul" [label="[384, 1536]", style=solid]; -"907 Constant_27944" -> "906 /layers/layers.2/blocks.4/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; -"908 Constant_27943" -> "906 /layers/layers.2/blocks.4/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; -"909 Constant_27942" -> "906 /layers/layers.2/blocks.4/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; -"910 Constant_27941" -> "906 /layers/layers.2/blocks.4/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; -"911 Transpose_6499" -> "906 /layers/layers.2/blocks.4/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1536]", style=solid]; -"912 Constant_27939" -> "395 /layers/layers.2/blocks.4/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; -"913 Constant_27938" -> "395 /layers/layers.2/blocks.4/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; -"914 Constant_27937" -> "395 /layers/layers.2/blocks.4/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; -"915 Constant_27936" -> "395 /layers/layers.2/blocks.4/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; -"916 /layers/layers.2/blocks.4/mlp/fc1/MatMul/fq_weights_1" -> "350 /layers/layers.2/blocks.4/mlp/fc1/MatMul" [label="[1536, 384]", style=solid]; -"917 Constant_27934" -> "916 /layers/layers.2/blocks.4/mlp/fc1/MatMul/fq_weights_1" [label="[1536, 1]", style=solid]; -"918 Constant_27933" -> "916 /layers/layers.2/blocks.4/mlp/fc1/MatMul/fq_weights_1" [label="[1536, 1]", style=solid]; -"919 Constant_27932" -> "916 /layers/layers.2/blocks.4/mlp/fc1/MatMul/fq_weights_1" [label="[1536, 1]", style=solid]; -"920 Constant_27931" -> "916 /layers/layers.2/blocks.4/mlp/fc1/MatMul/fq_weights_1" [label="[1536, 1]", style=solid]; -"921 Transpose_6495" -> "916 /layers/layers.2/blocks.4/mlp/fc1/MatMul/fq_weights_1" [label="[1536, 384]", style=solid]; -"922 Constant_27929" -> "333 /layers/layers.2/blocks.4/norm2/Add_1/fq_output_0" [label="[]", style=solid]; -"923 Constant_27928" -> "333 /layers/layers.2/blocks.4/norm2/Add_1/fq_output_0" [label="[]", style=solid]; -"924 Constant_27927" -> "333 /layers/layers.2/blocks.4/norm2/Add_1/fq_output_0" [label="[]", style=solid]; -"925 Constant_27926" -> "333 /layers/layers.2/blocks.4/norm2/Add_1/fq_output_0" [label="[]", style=solid]; -"926 Constant_7367" -> "318 /layers/layers.2/blocks.4/norm2/Add_1" [label="[1, 1, 384]", style=solid]; -"927 Constant_7366" -> "303 /layers/layers.2/blocks.4/norm2/Mul" [label="[1, 1, 384]", style=solid]; -"928 Constant_1633" -> "287 /layers/layers.2/blocks.4/norm2/Div" [label="[1]", 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"934 /layers/layers.2/blocks.4/attn/proj/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; -"938 Constant_27921" -> "934 /layers/layers.2/blocks.4/attn/proj/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; -"939 Transpose_6491" -> "934 /layers/layers.2/blocks.4/attn/proj/MatMul/fq_weights_1" [label="[384, 384]", style=solid]; -"940 /layers/layers.2/blocks.4/attn/Constant_2" -> "500 /layers/layers.2/blocks.4/attn/Reshape_1" [label="[3]", style=dashed]; -"941 Constant_1606" -> "486 /layers/layers.2/blocks.4/attn/Transpose_2" [label="[4]", style=dashed]; -"942 Constant_27919" -> "475 /layers/layers.2/blocks.4/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; -"943 Constant_27918" -> "475 /layers/layers.2/blocks.4/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; -"944 Constant_27917" -> "475 /layers/layers.2/blocks.4/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; -"945 Constant_27916" -> "475 /layers/layers.2/blocks.4/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; -"946 Constant_1596" -> "450 /layers/layers.2/blocks.4/attn/Gather_2" [label="[]", style=dashed]; -"947 Constant_1590" -> "436 /layers/layers.2/blocks.4/attn/Transpose" [label="[5]", style=dashed]; -"948 /layers/layers.2/blocks.4/attn/Constant" -> "424 /layers/layers.2/blocks.4/attn/Reshape" [label="[5]", style=dashed]; -"949 /layers/layers.2/blocks.4/attn/qkv/MatMul/fq_weights_1" -> "396 /layers/layers.2/blocks.4/attn/qkv/MatMul" [label="[1152, 384]", style=solid]; -"950 Constant_27904" -> "949 /layers/layers.2/blocks.4/attn/qkv/MatMul/fq_weights_1" [label="[1152, 1]", style=solid]; -"951 Constant_27903" -> "949 /layers/layers.2/blocks.4/attn/qkv/MatMul/fq_weights_1" [label="[1152, 1]", style=solid]; -"952 Constant_27902" -> "949 /layers/layers.2/blocks.4/attn/qkv/MatMul/fq_weights_1" [label="[1152, 1]", style=solid]; -"953 Constant_27901" -> "949 /layers/layers.2/blocks.4/attn/qkv/MatMul/fq_weights_1" [label="[1152, 1]", style=solid]; -"954 Transpose_6488" -> "949 /layers/layers.2/blocks.4/attn/qkv/MatMul/fq_weights_1" [label="[1152, 384]", style=solid]; -"955 /layers/layers.2/blocks.4/Constant_3" -> "384 /layers/layers.2/blocks.4/Reshape_3" [label="[3]", style=dashed]; -"956 /layers/layers.2/blocks.4/Constant_2" -> "369 /layers/layers.2/blocks.4/Reshape_2" [label="[4]", style=dashed]; -"957 Constant_1574" -> "351 /layers/layers.2/blocks.4/Transpose" [label="[6]", style=dashed]; -"958 /layers/layers.2/blocks.4/Constant_1" -> "334 /layers/layers.2/blocks.4/Reshape_1" [label="[6]", style=dashed]; -"959 Constant_27899" -> "319 /layers/layers.2/blocks.4/norm1/Add_1/fq_output_0" [label="[]", style=solid]; -"960 Constant_27898" -> "319 /layers/layers.2/blocks.4/norm1/Add_1/fq_output_0" [label="[]", style=solid]; -"961 Constant_27897" -> "319 /layers/layers.2/blocks.4/norm1/Add_1/fq_output_0" [label="[]", style=solid]; -"962 Constant_27896" -> "319 /layers/layers.2/blocks.4/norm1/Add_1/fq_output_0" [label="[]", style=solid]; -"963 Constant_7362" -> "304 /layers/layers.2/blocks.4/norm1/Add_1" [label="[1, 1, 384]", style=solid]; -"964 Constant_7361" -> "288 /layers/layers.2/blocks.4/norm1/Mul" [label="[1, 1, 384]", style=solid]; -"965 Constant_1553" -> "274 /layers/layers.2/blocks.4/norm1/Div" [label="[1]", style=dashed]; -"966 /layers/layers.2/blocks.3/mlp/fc2/MatMul/fq_weights_1" -> "385 /layers/layers.2/blocks.3/mlp/fc2/MatMul" [label="[384, 1536]", style=solid]; -"967 Constant_27894" -> "966 /layers/layers.2/blocks.3/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; -"968 Constant_27893" -> "966 /layers/layers.2/blocks.3/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; -"969 Constant_27892" -> "966 /layers/layers.2/blocks.3/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; -"970 Constant_27891" -> "966 /layers/layers.2/blocks.3/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; -"971 Transpose_6484" -> "966 /layers/layers.2/blocks.3/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1536]", style=solid]; -"972 Constant_27889" -> "370 /layers/layers.2/blocks.3/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; -"973 Constant_27888" -> "370 /layers/layers.2/blocks.3/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; -"974 Constant_27887" -> "370 /layers/layers.2/blocks.3/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; -"975 Constant_27886" -> "370 /layers/layers.2/blocks.3/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; -"976 /layers/layers.2/blocks.3/mlp/fc1/MatMul/fq_weights_1" -> "320 /layers/layers.2/blocks.3/mlp/fc1/MatMul" [label="[1536, 384]", style=solid]; -"977 Constant_27884" -> "976 /layers/layers.2/blocks.3/mlp/fc1/MatMul/fq_weights_1" [label="[1536, 1]", style=solid]; -"978 Constant_27883" -> "976 /layers/layers.2/blocks.3/mlp/fc1/MatMul/fq_weights_1" [label="[1536, 1]", style=solid]; -"979 Constant_27882" -> "976 /layers/layers.2/blocks.3/mlp/fc1/MatMul/fq_weights_1" [label="[1536, 1]", style=solid]; -"980 Constant_27881" -> "976 /layers/layers.2/blocks.3/mlp/fc1/MatMul/fq_weights_1" [label="[1536, 1]", style=solid]; -"981 Transpose_6480" -> "976 /layers/layers.2/blocks.3/mlp/fc1/MatMul/fq_weights_1" [label="[1536, 384]", style=solid]; -"982 Constant_27879" -> "305 /layers/layers.2/blocks.3/norm2/Add_1/fq_output_0" [label="[]", style=solid]; -"983 Constant_27878" -> "305 /layers/layers.2/blocks.3/norm2/Add_1/fq_output_0" [label="[]", style=solid]; -"984 Constant_27877" -> "305 /layers/layers.2/blocks.3/norm2/Add_1/fq_output_0" [label="[]", style=solid]; -"985 Constant_27876" -> "305 /layers/layers.2/blocks.3/norm2/Add_1/fq_output_0" [label="[]", style=solid]; -"986 Constant_7358" -> "289 /layers/layers.2/blocks.3/norm2/Add_1" [label="[1, 1, 384]", style=solid]; -"987 Constant_7357" -> "275 /layers/layers.2/blocks.3/norm2/Mul" [label="[1, 1, 384]", style=solid]; -"988 Constant_1527" -> "260 /layers/layers.2/blocks.3/norm2/Div" [label="[1]", style=dashed]; -"989 /layers/layers.2/blocks.3/Constant_31" -> "650 /layers/layers.2/blocks.3/Reshape_7" [label="[3]", style=dashed]; -"990 Constant_5583" -> "635 /layers/layers.2/blocks.3/Slice_7" [label="[3]", style=dashed]; -"991 Constant_5580" -> "635 /layers/layers.2/blocks.3/Slice_7" [label="[3]", style=dashed]; -"992 Constant_5577" -> "635 /layers/layers.2/blocks.3/Slice_7" [label="[3]", style=dashed]; -"993 Constant_5559" -> "618 /layers/layers.2/blocks.3/Slice_5" [label="[2]", style=dashed]; -"994 Constant_5556" -> "618 /layers/layers.2/blocks.3/Slice_5" [label="[2]", style=dashed]; -"995 Constant_5553" -> "618 /layers/layers.2/blocks.3/Slice_5" [label="[2]", style=dashed]; -"996 /layers/layers.2/blocks.3/Constant_18" -> "607 /layers/layers.2/blocks.3/Reshape_6" [label="[4]", style=dashed]; -"997 Constant_1474" -> "598 /layers/layers.2/blocks.3/Transpose_1" [label="[6]", style=dashed]; -"998 /layers/layers.2/blocks.3/Constant_17" -> "589 /layers/layers.2/blocks.3/Reshape_5" [label="[6]", style=dashed]; -"999 /layers/layers.2/blocks.3/Constant_16" -> "578 /layers/layers.2/blocks.3/Reshape_4" [label="[4]", style=dashed]; -"1000 /layers/layers.2/blocks.3/attn/proj/MatMul/fq_weights_1" -> "555 /layers/layers.2/blocks.3/attn/proj/MatMul" [label="[384, 384]", style=solid]; -"1001 Constant_27874" -> "1000 /layers/layers.2/blocks.3/attn/proj/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; -"1002 Constant_27873" -> "1000 /layers/layers.2/blocks.3/attn/proj/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; -"1003 Constant_27872" -> "1000 /layers/layers.2/blocks.3/attn/proj/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; -"1004 Constant_27871" -> "1000 /layers/layers.2/blocks.3/attn/proj/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; -"1005 Transpose_6476" -> "1000 /layers/layers.2/blocks.3/attn/proj/MatMul/fq_weights_1" [label="[384, 384]", style=solid]; -"1006 /layers/layers.2/blocks.3/attn/Constant_4" -> "542 /layers/layers.2/blocks.3/attn/Reshape_3" [label="[3]", style=dashed]; -"1007 Constant_1458" -> "528 /layers/layers.2/blocks.3/attn/Transpose_2" [label="[4]", style=dashed]; -"1008 Constant_27869" -> "516 /layers/layers.2/blocks.3/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; -"1009 Constant_27868" -> "516 /layers/layers.2/blocks.3/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; -"1010 Constant_27867" -> "516 /layers/layers.2/blocks.3/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; -"1011 Constant_27866" -> "516 /layers/layers.2/blocks.3/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; -"1012 Constant_1439" -> "489 /layers/layers.2/blocks.3/attn/Gather_2" [label="[]", style=dashed]; -"1013 Constant_1433" -> "476 /layers/layers.2/blocks.3/attn/Transpose" [label="[5]", style=dashed]; -"1014 /layers/layers.2/blocks.3/attn/Constant" -> "464 /layers/layers.2/blocks.3/attn/Reshape" [label="[5]", style=dashed]; -"1015 /layers/layers.2/blocks.3/attn/qkv/MatMul/fq_weights_1" -> "437 /layers/layers.2/blocks.3/attn/qkv/MatMul" [label="[1152, 384]", style=solid]; -"1016 Constant_27854" -> "1015 /layers/layers.2/blocks.3/attn/qkv/MatMul/fq_weights_1" [label="[1152, 1]", style=solid]; -"1017 Constant_27853" -> "1015 /layers/layers.2/blocks.3/attn/qkv/MatMul/fq_weights_1" [label="[1152, 1]", style=solid]; -"1018 Constant_27852" -> "1015 /layers/layers.2/blocks.3/attn/qkv/MatMul/fq_weights_1" [label="[1152, 1]", style=solid]; -"1019 Constant_27851" -> "1015 /layers/layers.2/blocks.3/attn/qkv/MatMul/fq_weights_1" [label="[1152, 1]", style=solid]; -"1020 Transpose_6473" -> "1015 /layers/layers.2/blocks.3/attn/qkv/MatMul/fq_weights_1" [label="[1152, 384]", style=solid]; -"1021 /layers/layers.2/blocks.3/Constant_15" -> "425 /layers/layers.2/blocks.3/Reshape_3" [label="[3]", style=dashed]; -"1022 /layers/layers.2/blocks.3/Constant_14" -> "413 /layers/layers.2/blocks.3/Reshape_2" [label="[4]", style=dashed]; -"1023 Constant_1417" -> "398 /layers/layers.2/blocks.3/Transpose" [label="[6]", style=dashed]; -"1024 /layers/layers.2/blocks.3/Constant_13" -> "386 /layers/layers.2/blocks.3/Reshape_1" [label="[6]", style=dashed]; -"1025 Constant_5535" -> "354 /layers/layers.2/blocks.3/Slice_3" [label="[3]", style=dashed]; -"1026 Constant_5532" -> "354 /layers/layers.2/blocks.3/Slice_3" [label="[3]", style=dashed]; -"1027 Constant_5529" -> "354 /layers/layers.2/blocks.3/Slice_3" [label="[3]", style=dashed]; -"1028 Constant_5511" -> "322 /layers/layers.2/blocks.3/Slice_1" [label="[2]", style=dashed]; -"1029 Constant_5508" -> "322 /layers/layers.2/blocks.3/Slice_1" [label="[2]", style=dashed]; -"1030 Constant_5505" -> "322 /layers/layers.2/blocks.3/Slice_1" [label="[2]", style=dashed]; -"1031 /layers/layers.2/blocks.3/Constant" -> "306 /layers/layers.2/blocks.3/Reshape" [label="[4]", style=dashed]; -"1032 Constant_27479" -> "290 /layers/layers.2/blocks.3/norm1/Add_1/fq_output_0" [label="[]", style=solid]; -"1033 Constant_27478" -> "290 /layers/layers.2/blocks.3/norm1/Add_1/fq_output_0" [label="[]", style=solid]; -"1034 Constant_27477" -> "290 /layers/layers.2/blocks.3/norm1/Add_1/fq_output_0" [label="[]", style=solid]; -"1035 Constant_27476" -> "290 /layers/layers.2/blocks.3/norm1/Add_1/fq_output_0" [label="[]", style=solid]; -"1036 Constant_7353" -> "276 /layers/layers.2/blocks.3/norm1/Add_1" [label="[1, 1, 384]", style=solid]; -"1037 Constant_7352" -> "261 /layers/layers.2/blocks.3/norm1/Mul" [label="[1, 1, 384]", style=solid]; -"1038 Constant_1354" -> "247 /layers/layers.2/blocks.3/norm1/Div" [label="[1]", style=dashed]; -"1039 /layers/layers.2/blocks.2/mlp/fc2/MatMul/fq_weights_1" -> "355 /layers/layers.2/blocks.2/mlp/fc2/MatMul" [label="[384, 1536]", style=solid]; -"1040 Constant_27849" -> "1039 /layers/layers.2/blocks.2/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; -"1041 Constant_27848" -> "1039 /layers/layers.2/blocks.2/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; -"1042 Constant_27847" -> "1039 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style=solid]; -"1059 Constant_7349" -> "262 /layers/layers.2/blocks.2/norm2/Add_1" [label="[1, 1, 384]", style=solid]; -"1060 Constant_7348" -> "248 /layers/layers.2/blocks.2/norm2/Mul" [label="[1, 1, 384]", style=solid]; -"1061 Constant_1328" -> "235 /layers/layers.2/blocks.2/norm2/Div" [label="[1]", style=dashed]; -"1062 /layers/layers.2/blocks.2/Constant_7" -> "543 /layers/layers.2/blocks.2/Reshape_7" [label="[3]", style=dashed]; -"1063 /layers/layers.2/blocks.2/Constant_6" -> "529 /layers/layers.2/blocks.2/Reshape_6" [label="[4]", style=dashed]; -"1064 Constant_1317" -> "517 /layers/layers.2/blocks.2/Transpose_1" [label="[6]", style=dashed]; -"1065 /layers/layers.2/blocks.2/Constant_5" -> "504 /layers/layers.2/blocks.2/Reshape_5" [label="[6]", style=dashed]; -"1066 /layers/layers.2/blocks.2/Constant_4" -> "490 /layers/layers.2/blocks.2/Reshape_4" [label="[4]", style=dashed]; -"1067 /layers/layers.2/blocks.2/attn/proj/MatMul/fq_weights_1" -> "465 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style=solid]; -"1084 Constant_27808" -> "1082 /layers/layers.2/blocks.2/attn/qkv/MatMul/fq_weights_1" [label="[1152, 1]", style=solid]; -"1085 Constant_27807" -> "1082 /layers/layers.2/blocks.2/attn/qkv/MatMul/fq_weights_1" [label="[1152, 1]", style=solid]; -"1086 Constant_27806" -> "1082 /layers/layers.2/blocks.2/attn/qkv/MatMul/fq_weights_1" [label="[1152, 1]", style=solid]; -"1087 Transpose_6458" -> "1082 /layers/layers.2/blocks.2/attn/qkv/MatMul/fq_weights_1" [label="[1152, 384]", style=solid]; -"1088 /layers/layers.2/blocks.2/Constant_3" -> "324 /layers/layers.2/blocks.2/Reshape_3" [label="[3]", style=dashed]; -"1089 /layers/layers.2/blocks.2/Constant_2" -> "308 /layers/layers.2/blocks.2/Reshape_2" [label="[4]", style=dashed]; -"1090 Constant_1269" -> "292 /layers/layers.2/blocks.2/Transpose" [label="[6]", style=dashed]; -"1091 /layers/layers.2/blocks.2/Constant_1" -> "278 /layers/layers.2/blocks.2/Reshape_1" [label="[6]", style=dashed]; -"1092 Constant_27804" -> "263 /layers/layers.2/blocks.2/norm1/Add_1/fq_output_0" [label="[]", style=solid]; -"1093 Constant_27803" -> "263 /layers/layers.2/blocks.2/norm1/Add_1/fq_output_0" [label="[]", style=solid]; -"1094 Constant_27802" -> "263 /layers/layers.2/blocks.2/norm1/Add_1/fq_output_0" [label="[]", style=solid]; -"1095 Constant_27801" -> "263 /layers/layers.2/blocks.2/norm1/Add_1/fq_output_0" [label="[]", style=solid]; -"1096 Constant_7344" -> "249 /layers/layers.2/blocks.2/norm1/Add_1" [label="[1, 1, 384]", style=solid]; -"1097 Constant_7343" -> "236 /layers/layers.2/blocks.2/norm1/Mul" [label="[1, 1, 384]", style=solid]; -"1098 Constant_1248" -> "225 /layers/layers.2/blocks.2/norm1/Div" [label="[1]", style=dashed]; -"1099 /layers/layers.2/blocks.1/mlp/fc2/MatMul/fq_weights_1" -> "325 /layers/layers.2/blocks.1/mlp/fc2/MatMul" [label="[384, 1536]", style=solid]; -"1100 Constant_27799" -> "1099 /layers/layers.2/blocks.1/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; -"1101 Constant_27798" -> "1099 /layers/layers.2/blocks.1/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; -"1102 Constant_27797" -> "1099 /layers/layers.2/blocks.1/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; -"1103 Constant_27796" -> "1099 /layers/layers.2/blocks.1/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; -"1104 Transpose_6454" -> "1099 /layers/layers.2/blocks.1/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1536]", style=solid]; -"1105 Constant_27794" -> "309 /layers/layers.2/blocks.1/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; -"1106 Constant_27793" -> "309 /layers/layers.2/blocks.1/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; -"1107 Constant_27792" -> "309 /layers/layers.2/blocks.1/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; -"1108 Constant_27791" -> "309 /layers/layers.2/blocks.1/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; -"1109 /layers/layers.2/blocks.1/mlp/fc1/MatMul/fq_weights_1" -> "264 /layers/layers.2/blocks.1/mlp/fc1/MatMul" [label="[1536, 384]", style=solid]; -"1110 Constant_27789" -> "1109 /layers/layers.2/blocks.1/mlp/fc1/MatMul/fq_weights_1" [label="[1536, 1]", style=solid]; -"1111 Constant_27788" -> "1109 /layers/layers.2/blocks.1/mlp/fc1/MatMul/fq_weights_1" [label="[1536, 1]", style=solid]; -"1112 Constant_27787" -> "1109 /layers/layers.2/blocks.1/mlp/fc1/MatMul/fq_weights_1" [label="[1536, 1]", style=solid]; -"1113 Constant_27786" -> "1109 /layers/layers.2/blocks.1/mlp/fc1/MatMul/fq_weights_1" [label="[1536, 1]", style=solid]; -"1114 Transpose_6450" -> "1109 /layers/layers.2/blocks.1/mlp/fc1/MatMul/fq_weights_1" [label="[1536, 384]", style=solid]; -"1115 Constant_27784" -> "250 /layers/layers.2/blocks.1/norm2/Add_1/fq_output_0" [label="[]", style=solid]; -"1116 Constant_27783" -> "250 /layers/layers.2/blocks.1/norm2/Add_1/fq_output_0" [label="[]", style=solid]; -"1117 Constant_27782" -> "250 /layers/layers.2/blocks.1/norm2/Add_1/fq_output_0" [label="[]", style=solid]; -"1118 Constant_27781" -> "250 /layers/layers.2/blocks.1/norm2/Add_1/fq_output_0" [label="[]", style=solid]; -"1119 Constant_7340" -> "237 /layers/layers.2/blocks.1/norm2/Add_1" [label="[1, 1, 384]", style=solid]; -"1120 Constant_7339" -> "226 /layers/layers.2/blocks.1/norm2/Mul" [label="[1, 1, 384]", style=solid]; -"1121 Constant_1222" -> "217 /layers/layers.2/blocks.1/norm2/Div" [label="[1]", style=dashed]; -"1122 /layers/layers.2/blocks.1/Constant_31" -> "619 /layers/layers.2/blocks.1/Reshape_7" [label="[3]", style=dashed]; -"1123 Constant_5487" -> "600 /layers/layers.2/blocks.1/Slice_7" [label="[3]", style=dashed]; -"1124 Constant_5484" -> "600 /layers/layers.2/blocks.1/Slice_7" [label="[3]", style=dashed]; -"1125 Constant_5481" -> "600 /layers/layers.2/blocks.1/Slice_7" [label="[3]", style=dashed]; -"1126 Constant_5463" -> "580 /layers/layers.2/blocks.1/Slice_5" [label="[2]", style=dashed]; -"1127 Constant_5460" -> "580 /layers/layers.2/blocks.1/Slice_5" [label="[2]", style=dashed]; -"1128 Constant_5457" -> "580 /layers/layers.2/blocks.1/Slice_5" [label="[2]", style=dashed]; -"1129 /layers/layers.2/blocks.1/Constant_18" -> "568 /layers/layers.2/blocks.1/Reshape_6" [label="[4]", style=dashed]; -"1130 Constant_1169" -> "556 /layers/layers.2/blocks.1/Transpose_1" [label="[6]", style=dashed]; -"1131 /layers/layers.2/blocks.1/Constant_17" -> "544 /layers/layers.2/blocks.1/Reshape_5" [label="[6]", style=dashed]; -"1132 /layers/layers.2/blocks.1/Constant_16" -> "531 /layers/layers.2/blocks.1/Reshape_4" [label="[4]", style=dashed]; -"1133 /layers/layers.2/blocks.1/attn/proj/MatMul/fq_weights_1" -> "506 /layers/layers.2/blocks.1/attn/proj/MatMul" [label="[384, 384]", style=solid]; -"1134 Constant_27779" -> "1133 /layers/layers.2/blocks.1/attn/proj/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; -"1135 Constant_27778" -> "1133 /layers/layers.2/blocks.1/attn/proj/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; -"1136 Constant_27777" -> "1133 /layers/layers.2/blocks.1/attn/proj/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; -"1137 Constant_27776" -> "1133 /layers/layers.2/blocks.1/attn/proj/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; -"1138 Transpose_6446" -> "1133 /layers/layers.2/blocks.1/attn/proj/MatMul/fq_weights_1" [label="[384, 384]", style=solid]; -"1139 /layers/layers.2/blocks.1/attn/Constant_4" -> "492 /layers/layers.2/blocks.1/attn/Reshape_3" [label="[3]", style=dashed]; -"1140 Constant_1153" -> "479 /layers/layers.2/blocks.1/attn/Transpose_2" [label="[4]", style=dashed]; -"1141 Constant_27774" -> "468 /layers/layers.2/blocks.1/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; -"1142 Constant_27773" -> "468 /layers/layers.2/blocks.1/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; -"1143 Constant_27772" -> "468 /layers/layers.2/blocks.1/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; -"1144 Constant_27771" -> "468 /layers/layers.2/blocks.1/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; -"1145 Constant_1134" -> "442 /layers/layers.2/blocks.1/attn/Gather_2" [label="[]", style=dashed]; -"1146 Constant_1128" -> "429 /layers/layers.2/blocks.1/attn/Transpose" [label="[5]", style=dashed]; -"1147 /layers/layers.2/blocks.1/attn/Constant" -> "417 /layers/layers.2/blocks.1/attn/Reshape" [label="[5]", style=dashed]; -"1148 /layers/layers.2/blocks.1/attn/qkv/MatMul/fq_weights_1" -> "388 /layers/layers.2/blocks.1/attn/qkv/MatMul" [label="[1152, 384]", style=solid]; -"1149 Constant_27759" -> "1148 /layers/layers.2/blocks.1/attn/qkv/MatMul/fq_weights_1" [label="[1152, 1]", style=solid]; -"1150 Constant_27758" -> "1148 /layers/layers.2/blocks.1/attn/qkv/MatMul/fq_weights_1" [label="[1152, 1]", style=solid]; -"1151 Constant_27757" -> "1148 /layers/layers.2/blocks.1/attn/qkv/MatMul/fq_weights_1" [label="[1152, 1]", style=solid]; -"1152 Constant_27756" -> "1148 /layers/layers.2/blocks.1/attn/qkv/MatMul/fq_weights_1" [label="[1152, 1]", style=solid]; -"1153 Transpose_6443" -> "1148 /layers/layers.2/blocks.1/attn/qkv/MatMul/fq_weights_1" [label="[1152, 384]", style=solid]; -"1154 /layers/layers.2/blocks.1/Constant_15" -> "374 /layers/layers.2/blocks.1/Reshape_3" [label="[3]", style=dashed]; -"1155 /layers/layers.2/blocks.1/Constant_14" -> "357 /layers/layers.2/blocks.1/Reshape_2" [label="[4]", style=dashed]; -"1156 Constant_1112" -> "340 /layers/layers.2/blocks.1/Transpose" [label="[6]", style=dashed]; -"1157 /layers/layers.2/blocks.1/Constant_13" -> "326 /layers/layers.2/blocks.1/Reshape_1" [label="[6]", style=dashed]; -"1158 Constant_5439" -> "295 /layers/layers.2/blocks.1/Slice_3" [label="[3]", style=dashed]; -"1159 Constant_5436" -> "295 /layers/layers.2/blocks.1/Slice_3" [label="[3]", style=dashed]; -"1160 Constant_5433" -> "295 /layers/layers.2/blocks.1/Slice_3" [label="[3]", style=dashed]; -"1161 Constant_5415" -> "266 /layers/layers.2/blocks.1/Slice_1" [label="[2]", style=dashed]; -"1162 Constant_5412" -> "266 /layers/layers.2/blocks.1/Slice_1" [label="[2]", style=dashed]; -"1163 Constant_5409" -> "266 /layers/layers.2/blocks.1/Slice_1" [label="[2]", style=dashed]; -"1164 /layers/layers.2/blocks.1/Constant" -> "251 /layers/layers.2/blocks.1/Reshape" [label="[4]", style=dashed]; -"1165 Constant_27474" -> "238 /layers/layers.2/blocks.1/norm1/Add_1/fq_output_0" [label="[]", style=solid]; -"1166 Constant_27473" -> "238 /layers/layers.2/blocks.1/norm1/Add_1/fq_output_0" [label="[]", style=solid]; -"1167 Constant_27472" -> "238 /layers/layers.2/blocks.1/norm1/Add_1/fq_output_0" [label="[]", style=solid]; -"1168 Constant_27471" -> "238 /layers/layers.2/blocks.1/norm1/Add_1/fq_output_0" [label="[]", style=solid]; -"1169 Constant_7335" -> "227 /layers/layers.2/blocks.1/norm1/Add_1" [label="[1, 1, 384]", style=solid]; -"1170 Constant_7334" -> "218 /layers/layers.2/blocks.1/norm1/Mul" [label="[1, 1, 384]", style=solid]; -"1171 Constant_1049" -> "208 /layers/layers.2/blocks.1/norm1/Div" [label="[1]", style=dashed]; -"1172 /layers/layers.2/blocks.0/mlp/fc2/MatMul/fq_weights_1" -> "296 /layers/layers.2/blocks.0/mlp/fc2/MatMul" [label="[384, 1536]", style=solid]; -"1173 Constant_27754" -> "1172 /layers/layers.2/blocks.0/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; -"1174 Constant_27753" -> "1172 /layers/layers.2/blocks.0/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; -"1175 Constant_27752" -> "1172 /layers/layers.2/blocks.0/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; -"1176 Constant_27751" -> "1172 /layers/layers.2/blocks.0/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; -"1177 Transpose_6439" -> "1172 /layers/layers.2/blocks.0/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1536]", style=solid]; -"1178 Constant_27749" -> "281 /layers/layers.2/blocks.0/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; -"1179 Constant_27748" -> "281 /layers/layers.2/blocks.0/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; -"1180 Constant_27747" -> "281 /layers/layers.2/blocks.0/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; -"1181 Constant_27746" -> "281 /layers/layers.2/blocks.0/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; -"1182 /layers/layers.2/blocks.0/mlp/fc1/MatMul/fq_weights_1" -> "239 /layers/layers.2/blocks.0/mlp/fc1/MatMul" [label="[1536, 384]", style=solid]; -"1183 Constant_27744" -> "1182 /layers/layers.2/blocks.0/mlp/fc1/MatMul/fq_weights_1" [label="[1536, 1]", style=solid]; -"1184 Constant_27743" -> "1182 /layers/layers.2/blocks.0/mlp/fc1/MatMul/fq_weights_1" [label="[1536, 1]", style=solid]; -"1185 Constant_27742" -> "1182 /layers/layers.2/blocks.0/mlp/fc1/MatMul/fq_weights_1" [label="[1536, 1]", style=solid]; -"1186 Constant_27741" -> "1182 /layers/layers.2/blocks.0/mlp/fc1/MatMul/fq_weights_1" [label="[1536, 1]", style=solid]; -"1187 Transpose_6435" -> "1182 /layers/layers.2/blocks.0/mlp/fc1/MatMul/fq_weights_1" [label="[1536, 384]", style=solid]; -"1188 Constant_27739" -> "228 /layers/layers.2/blocks.0/norm2/Add_1/fq_output_0" [label="[]", style=solid]; -"1189 Constant_27738" -> "228 /layers/layers.2/blocks.0/norm2/Add_1/fq_output_0" [label="[]", style=solid]; -"1190 Constant_27737" -> "228 /layers/layers.2/blocks.0/norm2/Add_1/fq_output_0" [label="[]", style=solid]; -"1191 Constant_27736" -> "228 /layers/layers.2/blocks.0/norm2/Add_1/fq_output_0" [label="[]", style=solid]; -"1192 Constant_7331" -> "219 /layers/layers.2/blocks.0/norm2/Add_1" [label="[1, 1, 384]", style=solid]; -"1193 Constant_7330" -> "209 /layers/layers.2/blocks.0/norm2/Mul" [label="[1, 1, 384]", style=solid]; -"1194 Constant_1023" -> "201 /layers/layers.2/blocks.0/norm2/Div" [label="[1]", style=dashed]; -"1195 /layers/layers.2/blocks.0/Constant_7" -> "493 /layers/layers.2/blocks.0/Reshape_7" [label="[3]", style=dashed]; -"1196 /layers/layers.2/blocks.0/Constant_6" -> "480 /layers/layers.2/blocks.0/Reshape_6" [label="[4]", style=dashed]; -"1197 Constant_1012" -> "469 /layers/layers.2/blocks.0/Transpose_1" [label="[6]", style=dashed]; -"1198 /layers/layers.2/blocks.0/Constant_5" -> "457 /layers/layers.2/blocks.0/Reshape_5" [label="[6]", style=dashed]; -"1199 /layers/layers.2/blocks.0/Constant_4" -> "443 /layers/layers.2/blocks.0/Reshape_4" [label="[4]", style=dashed]; -"1200 /layers/layers.2/blocks.0/attn/proj/MatMul/fq_weights_1" -> "418 /layers/layers.2/blocks.0/attn/proj/MatMul" [label="[384, 384]", style=solid]; -"1201 Constant_27734" -> "1200 /layers/layers.2/blocks.0/attn/proj/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; -"1202 Constant_27733" -> "1200 /layers/layers.2/blocks.0/attn/proj/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; -"1203 Constant_27732" -> "1200 /layers/layers.2/blocks.0/attn/proj/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; -"1204 Constant_27731" -> "1200 /layers/layers.2/blocks.0/attn/proj/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; -"1205 Transpose_6431" -> "1200 /layers/layers.2/blocks.0/attn/proj/MatMul/fq_weights_1" [label="[384, 384]", style=solid]; -"1206 /layers/layers.2/blocks.0/attn/Constant_2" -> "404 /layers/layers.2/blocks.0/attn/Reshape_1" [label="[3]", style=dashed]; -"1207 Constant_996" -> "390 /layers/layers.2/blocks.0/attn/Transpose_2" [label="[4]", style=dashed]; -"1208 Constant_27729" -> "377 /layers/layers.2/blocks.0/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; -"1209 Constant_27728" -> "377 /layers/layers.2/blocks.0/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; -"1210 Constant_27727" -> "377 /layers/layers.2/blocks.0/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; -"1211 Constant_27726" -> "377 /layers/layers.2/blocks.0/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; -"1212 Constant_986" -> "343 /layers/layers.2/blocks.0/attn/Gather_2" [label="[]", style=dashed]; -"1213 Constant_980" -> "327 /layers/layers.2/blocks.0/attn/Transpose" [label="[5]", style=dashed]; -"1214 /layers/layers.2/blocks.0/attn/Constant" -> "312 /layers/layers.2/blocks.0/attn/Reshape" [label="[5]", style=dashed]; -"1215 /layers/layers.2/blocks.0/attn/qkv/MatMul/fq_weights_1" -> "282 /layers/layers.2/blocks.0/attn/qkv/MatMul" [label="[1152, 384]", style=solid]; -"1216 Constant_27714" -> "1215 /layers/layers.2/blocks.0/attn/qkv/MatMul/fq_weights_1" [label="[1152, 1]", style=solid]; -"1217 Constant_27713" -> "1215 /layers/layers.2/blocks.0/attn/qkv/MatMul/fq_weights_1" [label="[1152, 1]", style=solid]; -"1218 Constant_27712" -> "1215 /layers/layers.2/blocks.0/attn/qkv/MatMul/fq_weights_1" [label="[1152, 1]", style=solid]; -"1219 Constant_27711" -> "1215 /layers/layers.2/blocks.0/attn/qkv/MatMul/fq_weights_1" [label="[1152, 1]", style=solid]; -"1220 Transpose_6428" -> "1215 /layers/layers.2/blocks.0/attn/qkv/MatMul/fq_weights_1" [label="[1152, 384]", style=solid]; -"1221 /layers/layers.2/blocks.0/Constant_3" -> "268 /layers/layers.2/blocks.0/Reshape_3" [label="[3]", style=dashed]; -"1222 /layers/layers.2/blocks.0/Constant_2" -> "253 /layers/layers.2/blocks.0/Reshape_2" [label="[4]", style=dashed]; -"1223 Constant_964" -> "240 /layers/layers.2/blocks.0/Transpose" [label="[6]", style=dashed]; -"1224 /layers/layers.2/blocks.0/Constant_1" -> "229 /layers/layers.2/blocks.0/Reshape_1" [label="[6]", style=dashed]; -"1225 Constant_27709" -> "220 /layers/layers.2/blocks.0/norm1/Add_1/fq_output_0" [label="[]", style=solid]; -"1226 Constant_27708" -> "220 /layers/layers.2/blocks.0/norm1/Add_1/fq_output_0" [label="[]", style=solid]; -"1227 Constant_27707" -> "220 /layers/layers.2/blocks.0/norm1/Add_1/fq_output_0" [label="[]", style=solid]; -"1228 Constant_27706" -> "220 /layers/layers.2/blocks.0/norm1/Add_1/fq_output_0" [label="[]", style=solid]; -"1229 Constant_7326" -> "210 /layers/layers.2/blocks.0/norm1/Add_1" [label="[1, 1, 384]", style=solid]; -"1230 Constant_7325" -> "202 /layers/layers.2/blocks.0/norm1/Mul" [label="[1, 1, 384]", style=solid]; -"1231 Constant_943" -> "194 /layers/layers.2/blocks.0/norm1/Div" [label="[1]", style=dashed]; -"1232 /layers/layers.1/downsample/reduction/MatMul/fq_weights_1" -> "187 /layers/layers.1/downsample/reduction/MatMul" [label="[384, 768]", style=solid]; -"1233 Constant_27704" -> "1232 /layers/layers.1/downsample/reduction/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; -"1234 Constant_27703" -> "1232 /layers/layers.1/downsample/reduction/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; -"1235 Constant_27702" -> "1232 /layers/layers.1/downsample/reduction/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; -"1236 Constant_27701" -> "1232 /layers/layers.1/downsample/reduction/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; -"1237 Transpose_6424" -> "1232 /layers/layers.1/downsample/reduction/MatMul/fq_weights_1" [label="[384, 768]", style=solid]; -"1238 Constant_27699" -> "179 /layers/layers.1/downsample/norm/Add_1/fq_output_0" [label="[]", style=solid]; -"1239 Constant_27698" -> "179 /layers/layers.1/downsample/norm/Add_1/fq_output_0" [label="[]", style=solid]; -"1240 Constant_27697" -> "179 /layers/layers.1/downsample/norm/Add_1/fq_output_0" [label="[]", style=solid]; -"1241 Constant_27696" -> "179 /layers/layers.1/downsample/norm/Add_1/fq_output_0" [label="[]", style=solid]; -"1242 Constant_7324" -> "173 /layers/layers.1/downsample/norm/Add_1" [label="[1, 1, 768]", style=solid]; -"1243 Constant_7323" -> "166 /layers/layers.1/downsample/norm/Mul" [label="[1, 1, 768]", style=solid]; -"1244 Constant_929" -> "157 /layers/layers.1/downsample/norm/Div" [label="[1]", style=dashed]; -"1245 /layers/layers.1/downsample/Constant_25" -> "149 /layers/layers.1/downsample/Reshape_1" [label="[3]", style=dashed]; -"1246 Constant_5391" -> "130 /layers/layers.1/downsample/Slice_5" [label="[3]", style=dashed]; -"1247 Constant_5388" -> "130 /layers/layers.1/downsample/Slice_5" [label="[3]", style=dashed]; -"1248 Constant_5385" -> "130 /layers/layers.1/downsample/Slice_5" [label="[3]", style=dashed]; -"1249 Constant_5355" -> "118 /layers/layers.1/downsample/Slice_2" [label="[2]", style=dashed]; -"1250 Constant_5352" -> "118 /layers/layers.1/downsample/Slice_2" [label="[2]", style=dashed]; -"1251 Constant_5349" -> "118 /layers/layers.1/downsample/Slice_2" [label="[2]", style=dashed]; -"1252 /layers/layers.1/downsample/Constant" -> "110 /layers/layers.1/downsample/Reshape" [label="[4]", style=dashed]; -"1253 /layers/layers.1/blocks.1/mlp/fc2/MatMul/fq_weights_1" -> "174 /layers/layers.1/blocks.1/mlp/fc2/MatMul" [label="[192, 768]", style=solid]; -"1254 Constant_27694" -> "1253 /layers/layers.1/blocks.1/mlp/fc2/MatMul/fq_weights_1" [label="[192, 1]", style=solid]; -"1255 Constant_27693" -> "1253 /layers/layers.1/blocks.1/mlp/fc2/MatMul/fq_weights_1" [label="[192, 1]", style=solid]; -"1256 Constant_27692" -> "1253 /layers/layers.1/blocks.1/mlp/fc2/MatMul/fq_weights_1" [label="[192, 1]", style=solid]; -"1257 Constant_27691" -> "1253 /layers/layers.1/blocks.1/mlp/fc2/MatMul/fq_weights_1" [label="[192, 1]", style=solid]; -"1258 Transpose_6420" -> "1253 /layers/layers.1/blocks.1/mlp/fc2/MatMul/fq_weights_1" [label="[192, 768]", style=solid]; -"1259 Constant_27689" -> "167 /layers/layers.1/blocks.1/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; -"1260 Constant_27688" -> "167 /layers/layers.1/blocks.1/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; -"1261 Constant_27687" -> "167 /layers/layers.1/blocks.1/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; -"1262 Constant_27686" -> "167 /layers/layers.1/blocks.1/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; -"1263 /layers/layers.1/blocks.1/mlp/fc1/MatMul/fq_weights_1" -> "140 /layers/layers.1/blocks.1/mlp/fc1/MatMul" [label="[768, 192]", style=solid]; -"1264 Constant_27684" -> "1263 /layers/layers.1/blocks.1/mlp/fc1/MatMul/fq_weights_1" [label="[768, 1]", style=solid]; -"1265 Constant_27683" -> "1263 /layers/layers.1/blocks.1/mlp/fc1/MatMul/fq_weights_1" [label="[768, 1]", style=solid]; -"1266 Constant_27682" -> "1263 /layers/layers.1/blocks.1/mlp/fc1/MatMul/fq_weights_1" [label="[768, 1]", style=solid]; -"1267 Constant_27681" -> "1263 /layers/layers.1/blocks.1/mlp/fc1/MatMul/fq_weights_1" [label="[768, 1]", style=solid]; -"1268 Transpose_6416" -> "1263 /layers/layers.1/blocks.1/mlp/fc1/MatMul/fq_weights_1" [label="[768, 192]", style=solid]; -"1269 Constant_27679" -> "131 /layers/layers.1/blocks.1/norm2/Add_1/fq_output_0" [label="[]", style=solid]; -"1270 Constant_27678" -> "131 /layers/layers.1/blocks.1/norm2/Add_1/fq_output_0" [label="[]", style=solid]; -"1271 Constant_27677" -> "131 /layers/layers.1/blocks.1/norm2/Add_1/fq_output_0" [label="[]", style=solid]; -"1272 Constant_27676" -> "131 /layers/layers.1/blocks.1/norm2/Add_1/fq_output_0" [label="[]", style=solid]; -"1273 Constant_7320" -> "119 /layers/layers.1/blocks.1/norm2/Add_1" [label="[1, 1, 192]", style=solid]; -"1274 Constant_7319" -> "111 /layers/layers.1/blocks.1/norm2/Mul" [label="[1, 1, 192]", style=solid]; -"1275 Constant_864" -> "104 /layers/layers.1/blocks.1/norm2/Div" [label="[1]", style=dashed]; -"1276 /layers/layers.1/blocks.1/Constant_31" -> "458 /layers/layers.1/blocks.1/Reshape_7" [label="[3]", style=dashed]; -"1277 Constant_5319" -> "432 /layers/layers.1/blocks.1/Slice_7" [label="[3]", style=dashed]; -"1278 Constant_5316" -> "432 /layers/layers.1/blocks.1/Slice_7" [label="[3]", style=dashed]; -"1279 Constant_5313" -> "432 /layers/layers.1/blocks.1/Slice_7" [label="[3]", style=dashed]; -"1280 Constant_5295" -> "406 /layers/layers.1/blocks.1/Slice_5" [label="[2]", style=dashed]; -"1281 Constant_5292" -> "406 /layers/layers.1/blocks.1/Slice_5" [label="[2]", style=dashed]; -"1282 Constant_5289" -> "406 /layers/layers.1/blocks.1/Slice_5" [label="[2]", style=dashed]; -"1283 /layers/layers.1/blocks.1/Constant_18" -> "391 /layers/layers.1/blocks.1/Reshape_6" [label="[4]", style=dashed]; 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-> "1287 /layers/layers.1/blocks.1/attn/proj/MatMul/fq_weights_1" [label="[192, 192]", style=solid]; -"1293 /layers/layers.1/blocks.1/attn/Constant_4" -> "299 /layers/layers.1/blocks.1/attn/Reshape_3" [label="[3]", style=dashed]; -"1294 Constant_795" -> "284 /layers/layers.1/blocks.1/attn/Transpose_2" [label="[4]", style=dashed]; -"1295 Constant_27669" -> "271 /layers/layers.1/blocks.1/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; -"1296 Constant_27668" -> "271 /layers/layers.1/blocks.1/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; -"1297 Constant_27667" -> "271 /layers/layers.1/blocks.1/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; -"1298 Constant_27666" -> "271 /layers/layers.1/blocks.1/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; -"1299 Constant_776" -> "243 /layers/layers.1/blocks.1/attn/Gather_2" [label="[]", style=dashed]; -"1300 Constant_770" -> "230 /layers/layers.1/blocks.1/attn/Transpose" [label="[5]", style=dashed]; -"1301 /layers/layers.1/blocks.1/attn/Constant" -> "221 /layers/layers.1/blocks.1/attn/Reshape" [label="[5]", style=dashed]; -"1302 /layers/layers.1/blocks.1/attn/qkv/MatMul/fq_weights_1" -> "203 /layers/layers.1/blocks.1/attn/qkv/MatMul" [label="[576, 192]", style=solid]; -"1303 Constant_27654" -> "1302 /layers/layers.1/blocks.1/attn/qkv/MatMul/fq_weights_1" [label="[576, 1]", style=solid]; -"1304 Constant_27653" -> "1302 /layers/layers.1/blocks.1/attn/qkv/MatMul/fq_weights_1" [label="[576, 1]", style=solid]; -"1305 Constant_27652" -> "1302 /layers/layers.1/blocks.1/attn/qkv/MatMul/fq_weights_1" [label="[576, 1]", style=solid]; -"1306 Constant_27651" -> "1302 /layers/layers.1/blocks.1/attn/qkv/MatMul/fq_weights_1" [label="[576, 1]", style=solid]; -"1307 Transpose_6409" -> "1302 /layers/layers.1/blocks.1/attn/qkv/MatMul/fq_weights_1" [label="[576, 192]", style=solid]; -"1308 /layers/layers.1/blocks.1/Constant_15" -> "195 /layers/layers.1/blocks.1/Reshape_3" [label="[3]", style=dashed]; -"1309 /layers/layers.1/blocks.1/Constant_14" -> "188 /layers/layers.1/blocks.1/Reshape_2" [label="[4]", style=dashed]; -"1310 Constant_754" -> "181 /layers/layers.1/blocks.1/Transpose" [label="[6]", style=dashed]; -"1311 /layers/layers.1/blocks.1/Constant_13" -> "175 /layers/layers.1/blocks.1/Reshape_1" [label="[6]", style=dashed]; -"1312 Constant_5271" -> "160 /layers/layers.1/blocks.1/Slice_3" [label="[3]", style=dashed]; -"1313 Constant_5268" -> "160 /layers/layers.1/blocks.1/Slice_3" [label="[3]", style=dashed]; -"1314 Constant_5265" -> "160 /layers/layers.1/blocks.1/Slice_3" [label="[3]", style=dashed]; -"1315 Constant_5247" -> "142 /layers/layers.1/blocks.1/Slice_1" [label="[2]", style=dashed]; -"1316 Constant_5244" -> "142 /layers/layers.1/blocks.1/Slice_1" [label="[2]", style=dashed]; -"1317 Constant_5241" -> "142 /layers/layers.1/blocks.1/Slice_1" [label="[2]", style=dashed]; -"1318 /layers/layers.1/blocks.1/Constant" -> "132 /layers/layers.1/blocks.1/Reshape" [label="[4]", style=dashed]; -"1319 Constant_27469" -> "120 /layers/layers.1/blocks.1/norm1/Add_1/fq_output_0" [label="[]", style=solid]; -"1320 Constant_27468" -> "120 /layers/layers.1/blocks.1/norm1/Add_1/fq_output_0" [label="[]", style=solid]; -"1321 Constant_27467" -> "120 /layers/layers.1/blocks.1/norm1/Add_1/fq_output_0" [label="[]", style=solid]; -"1322 Constant_27466" -> "120 /layers/layers.1/blocks.1/norm1/Add_1/fq_output_0" [label="[]", style=solid]; -"1323 Constant_7315" -> "112 /layers/layers.1/blocks.1/norm1/Add_1" [label="[1, 1, 192]", style=solid]; -"1324 Constant_7314" -> "105 /layers/layers.1/blocks.1/norm1/Mul" [label="[1, 1, 192]", style=solid]; -"1325 Constant_691" -> "97 /layers/layers.1/blocks.1/norm1/Div" [label="[1]", style=dashed]; -"1326 /layers/layers.1/blocks.0/mlp/fc2/MatMul/fq_weights_1" -> "161 /layers/layers.1/blocks.0/mlp/fc2/MatMul" [label="[192, 768]", style=solid]; -"1327 Constant_27649" -> "1326 /layers/layers.1/blocks.0/mlp/fc2/MatMul/fq_weights_1" [label="[192, 1]", style=solid]; -"1328 Constant_27648" -> "1326 /layers/layers.1/blocks.0/mlp/fc2/MatMul/fq_weights_1" [label="[192, 1]", style=solid]; -"1329 Constant_27647" -> "1326 /layers/layers.1/blocks.0/mlp/fc2/MatMul/fq_weights_1" [label="[192, 1]", style=solid]; -"1330 Constant_27646" -> "1326 /layers/layers.1/blocks.0/mlp/fc2/MatMul/fq_weights_1" [label="[192, 1]", style=solid]; -"1331 Transpose_6405" -> "1326 /layers/layers.1/blocks.0/mlp/fc2/MatMul/fq_weights_1" [label="[192, 768]", style=solid]; -"1332 Constant_27644" -> "152 /layers/layers.1/blocks.0/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; -"1333 Constant_27643" -> "152 /layers/layers.1/blocks.0/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; -"1334 Constant_27642" -> "152 /layers/layers.1/blocks.0/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; -"1335 Constant_27641" -> "152 /layers/layers.1/blocks.0/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; -"1336 /layers/layers.1/blocks.0/mlp/fc1/MatMul/fq_weights_1" -> "121 /layers/layers.1/blocks.0/mlp/fc1/MatMul" [label="[768, 192]", style=solid]; -"1337 Constant_27639" -> "1336 /layers/layers.1/blocks.0/mlp/fc1/MatMul/fq_weights_1" [label="[768, 1]", style=solid]; -"1338 Constant_27638" -> "1336 /layers/layers.1/blocks.0/mlp/fc1/MatMul/fq_weights_1" [label="[768, 1]", style=solid]; -"1339 Constant_27637" -> "1336 /layers/layers.1/blocks.0/mlp/fc1/MatMul/fq_weights_1" [label="[768, 1]", style=solid]; -"1340 Constant_27636" -> "1336 /layers/layers.1/blocks.0/mlp/fc1/MatMul/fq_weights_1" [label="[768, 1]", style=solid]; -"1341 Transpose_6401" -> "1336 /layers/layers.1/blocks.0/mlp/fc1/MatMul/fq_weights_1" [label="[768, 192]", style=solid]; -"1342 Constant_27634" -> "113 /layers/layers.1/blocks.0/norm2/Add_1/fq_output_0" [label="[]", style=solid]; -"1343 Constant_27633" -> "113 /layers/layers.1/blocks.0/norm2/Add_1/fq_output_0" [label="[]", style=solid]; -"1344 Constant_27632" -> "113 /layers/layers.1/blocks.0/norm2/Add_1/fq_output_0" [label="[]", style=solid]; -"1345 Constant_27631" -> "113 /layers/layers.1/blocks.0/norm2/Add_1/fq_output_0" [label="[]", style=solid]; -"1346 Constant_7311" -> "106 /layers/layers.1/blocks.0/norm2/Add_1" [label="[1, 1, 192]", style=solid]; -"1347 Constant_7310" -> "98 /layers/layers.1/blocks.0/norm2/Mul" [label="[1, 1, 192]", style=solid]; -"1348 Constant_665" -> "91 /layers/layers.1/blocks.0/norm2/Div" [label="[1]", style=dashed]; -"1349 /layers/layers.1/blocks.0/Constant_7" -> "300 /layers/layers.1/blocks.0/Reshape_7" [label="[3]", style=dashed]; -"1350 /layers/layers.1/blocks.0/Constant_6" -> "285 /layers/layers.1/blocks.0/Reshape_6" [label="[4]", style=dashed]; -"1351 Constant_654" -> "272 /layers/layers.1/blocks.0/Transpose_1" [label="[6]", style=dashed]; -"1352 /layers/layers.1/blocks.0/Constant_5" -> "257 /layers/layers.1/blocks.0/Reshape_5" [label="[6]", style=dashed]; -"1353 /layers/layers.1/blocks.0/Constant_4" -> "244 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/layers/layers.1/blocks.0/Reshape_1" [label="[6]", style=dashed]; -"1379 Constant_27604" -> "107 /layers/layers.1/blocks.0/norm1/Add_1/fq_output_0" [label="[]", style=solid]; -"1380 Constant_27603" -> "107 /layers/layers.1/blocks.0/norm1/Add_1/fq_output_0" [label="[]", style=solid]; -"1381 Constant_27602" -> "107 /layers/layers.1/blocks.0/norm1/Add_1/fq_output_0" [label="[]", style=solid]; -"1382 Constant_27601" -> "107 /layers/layers.1/blocks.0/norm1/Add_1/fq_output_0" [label="[]", style=solid]; -"1383 Constant_7306" -> "99 /layers/layers.1/blocks.0/norm1/Add_1" [label="[1, 1, 192]", style=solid]; -"1384 Constant_7305" -> "92 /layers/layers.1/blocks.0/norm1/Mul" [label="[1, 1, 192]", style=solid]; -"1385 Constant_585" -> "85 /layers/layers.1/blocks.0/norm1/Div" [label="[1]", style=dashed]; -"1386 /layers/layers.0/downsample/reduction/MatMul/fq_weights_1" -> "79 /layers/layers.0/downsample/reduction/MatMul" [label="[192, 384]", style=solid]; -"1387 Constant_27599" -> "1386 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[label="[]", style=solid]; -"1396 Constant_7304" -> "69 /layers/layers.0/downsample/norm/Add_1" [label="[1, 1, 384]", style=solid]; -"1397 Constant_7303" -> "64 /layers/layers.0/downsample/norm/Mul" [label="[1, 1, 384]", style=solid]; -"1398 Constant_571" -> "58 /layers/layers.0/downsample/norm/Div" [label="[1]", style=dashed]; -"1399 /layers/layers.0/downsample/Constant_25" -> "53 /layers/layers.0/downsample/Reshape_1" [label="[3]", style=dashed]; -"1400 Constant_5223" -> "42 /layers/layers.0/downsample/Slice_5" [label="[3]", style=dashed]; -"1401 Constant_5220" -> "42 /layers/layers.0/downsample/Slice_5" [label="[3]", style=dashed]; -"1402 Constant_5217" -> "42 /layers/layers.0/downsample/Slice_5" [label="[3]", style=dashed]; -"1403 Constant_5187" -> "34 /layers/layers.0/downsample/Slice_2" [label="[2]", style=dashed]; -"1404 Constant_5184" -> "34 /layers/layers.0/downsample/Slice_2" [label="[2]", style=dashed]; -"1405 Constant_5181" -> "34 /layers/layers.0/downsample/Slice_2" [label="[2]", style=dashed]; -"1406 /layers/layers.0/downsample/Constant" -> "28 /layers/layers.0/downsample/Reshape" [label="[4]", style=dashed]; -"1407 /layers/layers.0/blocks.1/mlp/fc2/MatMul/fq_weights_1" -> "70 /layers/layers.0/blocks.1/mlp/fc2/MatMul" [label="[96, 384]", style=solid]; -"1408 Constant_27589" -> "1407 /layers/layers.0/blocks.1/mlp/fc2/MatMul/fq_weights_1" [label="[96, 1]", style=solid]; -"1409 Constant_27588" -> "1407 /layers/layers.0/blocks.1/mlp/fc2/MatMul/fq_weights_1" [label="[96, 1]", style=solid]; -"1410 Constant_27587" -> "1407 /layers/layers.0/blocks.1/mlp/fc2/MatMul/fq_weights_1" [label="[96, 1]", style=solid]; -"1411 Constant_27586" -> "1407 /layers/layers.0/blocks.1/mlp/fc2/MatMul/fq_weights_1" [label="[96, 1]", style=solid]; -"1412 Transpose_6386" -> "1407 /layers/layers.0/blocks.1/mlp/fc2/MatMul/fq_weights_1" [label="[96, 384]", style=solid]; -"1413 Constant_27584" -> "65 /layers/layers.0/blocks.1/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; 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/layers/layers.0/blocks.1/mlp/fc1/MatMul/fq_weights_1" [label="[384, 96]", style=solid]; -"1423 Constant_27574" -> "43 /layers/layers.0/blocks.1/norm2/Add_1/fq_output_0" [label="[]", style=solid]; -"1424 Constant_27573" -> "43 /layers/layers.0/blocks.1/norm2/Add_1/fq_output_0" [label="[]", style=solid]; -"1425 Constant_27572" -> "43 /layers/layers.0/blocks.1/norm2/Add_1/fq_output_0" [label="[]", style=solid]; -"1426 Constant_27571" -> "43 /layers/layers.0/blocks.1/norm2/Add_1/fq_output_0" [label="[]", style=solid]; -"1427 Constant_7300" -> "35 /layers/layers.0/blocks.1/norm2/Add_1" [label="[1, 1, 96]", style=solid]; -"1428 Constant_7299" -> "29 /layers/layers.0/blocks.1/norm2/Mul" [label="[1, 1, 96]", style=solid]; -"1429 Constant_506" -> "24 /layers/layers.0/blocks.1/norm2/Div" [label="[1]", style=dashed]; -"1430 /layers/layers.0/blocks.1/Constant_31" -> "258 /layers/layers.0/blocks.1/Reshape_7" [label="[3]", style=dashed]; -"1431 Constant_5151" -> "233 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Constant_27564" -> "147 /layers/layers.0/blocks.1/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; -"1450 Constant_27563" -> "147 /layers/layers.0/blocks.1/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; -"1451 Constant_27562" -> "147 /layers/layers.0/blocks.1/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; -"1452 Constant_27561" -> "147 /layers/layers.0/blocks.1/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; -"1453 Constant_418" -> "125 /layers/layers.0/blocks.1/attn/Gather_2" [label="[]", style=dashed]; -"1454 Constant_412" -> "115 /layers/layers.0/blocks.1/attn/Transpose" [label="[5]", style=dashed]; -"1455 /layers/layers.0/blocks.1/attn/Constant" -> "108 /layers/layers.0/blocks.1/attn/Reshape" [label="[5]", style=dashed]; -"1456 /layers/layers.0/blocks.1/attn/qkv/MatMul/fq_weights_1" -> "93 /layers/layers.0/blocks.1/attn/qkv/MatMul" [label="[288, 96]", style=solid]; -"1457 Constant_27549" -> "1456 /layers/layers.0/blocks.1/attn/qkv/MatMul/fq_weights_1" 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/layers/layers.0/blocks.0/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; -"1520 Constant_270" -> "78 /layers/layers.0/blocks.0/attn/Gather_2" [label="[]", style=dashed]; -"1521 Constant_264" -> "72 /layers/layers.0/blocks.0/attn/Transpose" [label="[5]", style=dashed]; -"1522 /layers/layers.0/blocks.0/attn/Constant" -> "68 /layers/layers.0/blocks.0/attn/Reshape" [label="[5]", style=dashed]; -"1523 /layers/layers.0/blocks.0/attn/qkv/MatMul/fq_weights_1" -> "57 /layers/layers.0/blocks.0/attn/qkv/MatMul" [label="[288, 96]", style=solid]; -"1524 Constant_27504" -> "1523 /layers/layers.0/blocks.0/attn/qkv/MatMul/fq_weights_1" [label="[288, 1]", style=solid]; -"1525 Constant_27503" -> "1523 /layers/layers.0/blocks.0/attn/qkv/MatMul/fq_weights_1" [label="[288, 1]", style=solid]; -"1526 Constant_27502" -> "1523 /layers/layers.0/blocks.0/attn/qkv/MatMul/fq_weights_1" [label="[288, 1]", style=solid]; -"1527 Constant_27501" -> "1523 /layers/layers.0/blocks.0/attn/qkv/MatMul/fq_weights_1" [label="[288, 1]", style=solid]; -"1528 Transpose_6360" -> "1523 /layers/layers.0/blocks.0/attn/qkv/MatMul/fq_weights_1" [label="[288, 96]", style=solid]; -"1529 /layers/layers.0/blocks.0/Constant_4" -> "52 /layers/layers.0/blocks.0/Reshape_3" [label="[3]", style=dashed]; -"1530 /layers/layers.0/blocks.0/Constant_3" -> "46 /layers/layers.0/blocks.0/Reshape_2" [label="[4]", style=dashed]; -"1531 Constant_248" -> "38 /layers/layers.0/blocks.0/Transpose" [label="[6]", style=dashed]; -"1532 /layers/layers.0/blocks.0/Constant_2" -> "32 /layers/layers.0/blocks.0/Reshape_1" [label="[6]", style=dashed]; -"1533 Constant_27499" -> "27 /layers/layers.0/blocks.0/norm1/Add_1/fq_output_0" [label="[]", style=solid]; -"1534 Constant_27498" -> "27 /layers/layers.0/blocks.0/norm1/Add_1/fq_output_0" [label="[]", style=solid]; -"1535 Constant_27497" -> "27 /layers/layers.0/blocks.0/norm1/Add_1/fq_output_0" [label="[]", style=solid]; -"1536 Constant_27496" -> "27 /layers/layers.0/blocks.0/norm1/Add_1/fq_output_0" [label="[]", style=solid]; -"1537 Constant_7286" -> "22 /layers/layers.0/blocks.0/norm1/Add_1" [label="[1, 1, 96]", style=solid]; -"1538 Constant_7285" -> "18 /layers/layers.0/blocks.0/norm1/Mul" [label="[1, 1, 96]", style=solid]; -"1539 Constant_227" -> "15 /layers/layers.0/blocks.0/norm1/Div" [label="[1]", style=dashed]; -"1540 Constant_7284" -> "13 /patch_embed/norm/Add_1" [label="[1, 1, 96]", style=solid]; -"1541 Constant_7283" -> "12 /patch_embed/norm/Mul" [label="[1, 1, 96]", style=solid]; -"1542 Constant_213" -> "10 /patch_embed/norm/Div" [label="[1]", style=dashed]; -"1543 Constant_211" -> "8 /patch_embed/Transpose" [label="[3]", style=dashed]; -"1544 /patch_embed/Constant_4" -> "11 /patch_embed/Concat" [label="[1]", style=dashed]; -"1545 Broadcast_201" -> "9 /patch_embed/Slice" [label="[1]", style=dashed]; -"1546 /patch_embed/Constant_3" -> "9 /patch_embed/Slice" [label="[1]", style=dashed]; -"1547 /patch_embed/Constant_2" -> "9 /patch_embed/Slice" [label="[1]", style=dashed]; -"1548 Reshape_190" -> "5 /patch_embed/proj/Conv" [label="[1, 96, 1, 1]", style=solid]; -"1549 /patch_embed/proj/Conv/WithoutBiases/fq_weights_1" -> "4 /patch_embed/proj/Conv/WithoutBiases" [label="[96, 3, 4, 4]", style=solid]; -"1550 Constant_27494" -> "1549 /patch_embed/proj/Conv/WithoutBiases/fq_weights_1" [label="[96, 1, 1, 1]", style=solid]; -"1551 Constant_27493" -> "1549 /patch_embed/proj/Conv/WithoutBiases/fq_weights_1" [label="[96, 1, 1, 1]", style=solid]; -"1552 Constant_27492" -> "1549 /patch_embed/proj/Conv/WithoutBiases/fq_weights_1" [label="[96, 1, 1, 1]", style=solid]; -"1553 Constant_27491" -> "1549 /patch_embed/proj/Conv/WithoutBiases/fq_weights_1" [label="[96, 1, 1, 1]", style=solid]; -"1554 Gather_7282" -> "1549 /patch_embed/proj/Conv/WithoutBiases/fq_weights_1" [label="[96, 3, 4, 4]", style=solid]; -"1555 Constant_27489" -> "3 Divide_2169/fq_output_0" [label="[]", style=solid]; -"1556 Constant_27488" -> "3 Divide_2169/fq_output_0" [label="[]", style=solid]; -"1557 Constant_27487" -> "3 Divide_2169/fq_output_0" [label="[]", style=solid]; -"1558 Constant_27486" -> "3 Divide_2169/fq_output_0" [label="[]", style=solid]; -"1559 Gather_7279" -> "2 Divide_2169" [label="[1, 3, 1, 1]", style=solid]; -"1560 Gather_7276" -> "1 Multiply_6579" [label="[1, 3, 1, 1]", style=solid]; -"1561 Constant_7287" -> "63 /layers/layers.0/blocks.0/attn/qkv/Add" [label="[1, 1, 288]", style=solid]; -"1562 onnx^^Add_2244" -> "94 /layers/layers.0/blocks.0/attn/Add" [label="[1, 3, 49, 49]", style=solid]; -"1563 Constant_268" -> "82 /layers/layers.0/blocks.0/attn/Gather_1" [label="[]", style=dashed]; -"1564 /patch_embed/proj/Constant" -> "82 /layers/layers.0/blocks.0/attn/Gather_1" [label="[]", style=dashed]; -"1564 /patch_embed/proj/Constant" -> "136 /layers/layers.0/blocks.1/attn/Gather_1" [label="[]", style=dashed]; -"1564 /patch_embed/proj/Constant" -> "190 /layers/layers.1/blocks.0/attn/Gather_1" [label="[]", style=dashed]; -"1564 /patch_embed/proj/Constant" -> "255 /layers/layers.1/blocks.1/attn/Gather_1" [label="[]", style=dashed]; -"1564 /patch_embed/proj/Constant" -> "359 /layers/layers.2/blocks.0/attn/Gather_1" [label="[]", style=dashed]; -"1564 /patch_embed/proj/Constant" -> "415 /layers/layers.2/blocks.2/attn/Gather_1" [label="[]", style=dashed]; -"1564 /patch_embed/proj/Constant" -> "455 /layers/layers.2/blocks.1/attn/Gather_1" [label="[]", style=dashed]; -"1564 /patch_embed/proj/Constant" -> "462 /layers/layers.2/blocks.4/attn/Gather_1" [label="[]", style=dashed]; -"1564 /patch_embed/proj/Constant" -> "502 /layers/layers.2/blocks.3/attn/Gather_1" [label="[]", style=dashed]; -"1564 /patch_embed/proj/Constant" -> "551 /layers/layers.2/blocks.5/attn/Gather_1" [label="[]", style=dashed]; -"1564 /patch_embed/proj/Constant" -> "623 /layers/layers.3/blocks.0/attn/Gather_1" [label="[]", style=dashed]; -"1564 /patch_embed/proj/Constant" -> "637 /layers/layers.3/blocks.1/attn/Gather_1" [label="[]", style=dashed]; -"1565 Constant_27509" -> "77 /layers/layers.0/blocks.0/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1566 Constant_27508" -> "77 /layers/layers.0/blocks.0/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1567 Constant_27507" -> "77 /layers/layers.0/blocks.0/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1568 Constant_27506" -> "77 /layers/layers.0/blocks.0/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1569 Constant_27514" -> "87 /layers/layers.0/blocks.0/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1570 Constant_27513" -> "87 /layers/layers.0/blocks.0/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1571 Constant_27512" -> "87 /layers/layers.0/blocks.0/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1572 Constant_27511" -> "87 /layers/layers.0/blocks.0/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1573 Constant_7288" -> "81 /layers/layers.0/blocks.0/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; -"1574 Constant_266" -> "76 /layers/layers.0/blocks.0/attn/Gather" [label="[]", style=dashed]; -"1575 /layers/layers.0/blocks.0/Constant" -> "76 /layers/layers.0/blocks.0/attn/Gather" [label="[]", style=dashed]; -"1575 /layers/layers.0/blocks.0/Constant" -> "123 /layers/layers.0/blocks.1/attn/Gather" [label="[]", style=dashed]; -"1575 /layers/layers.0/blocks.0/Constant" -> "182 /layers/layers.1/blocks.0/attn/Gather" [label="[]", style=dashed]; -"1575 /layers/layers.0/blocks.0/Constant" -> "241 /layers/layers.1/blocks.1/attn/Gather" [label="[]", style=dashed]; -"1575 /layers/layers.0/blocks.0/Constant" -> "341 /layers/layers.2/blocks.0/attn/Gather" [label="[]", style=dashed]; -"1575 /layers/layers.0/blocks.0/Constant" -> "399 /layers/layers.2/blocks.2/attn/Gather" [label="[]", style=dashed]; -"1575 /layers/layers.0/blocks.0/Constant" -> "440 /layers/layers.2/blocks.1/attn/Gather" [label="[]", style=dashed]; -"1575 /layers/layers.0/blocks.0/Constant" -> "448 /layers/layers.2/blocks.4/attn/Gather" [label="[]", style=dashed]; -"1575 /layers/layers.0/blocks.0/Constant" -> "487 /layers/layers.2/blocks.3/attn/Gather" [label="[]", style=dashed]; -"1575 /layers/layers.0/blocks.0/Constant" -> "537 /layers/layers.2/blocks.5/attn/Gather" [label="[]", style=dashed]; -"1575 /layers/layers.0/blocks.0/Constant" -> "612 /layers/layers.3/blocks.0/attn/Gather" [label="[]", style=dashed]; -"1575 /layers/layers.0/blocks.0/Constant" -> "627 /layers/layers.3/blocks.1/attn/Gather" [label="[]", style=dashed]; -"1576 Constant_7289" -> "116 /layers/layers.0/blocks.0/attn/proj/Add" [label="[1, 1, 96]", style=solid]; -"1577 Constant_7292" -> "45 /layers/layers.0/blocks.0/mlp/fc1/Add" [label="[1, 1, 384]", style=solid]; -"1578 Constant_7293" -> "67 /layers/layers.0/blocks.0/mlp/fc2/Add" [label="[1, 1, 96]", style=solid]; -"1579 Constant_5067" -> "49 /layers/layers.0/blocks.1/Slice" [label="[2]", style=dashed]; -"1580 Constant_5064" -> "49 /layers/layers.0/blocks.1/Slice" [label="[2]", style=dashed]; -"1581 Constant_5061" -> "49 /layers/layers.0/blocks.1/Slice" [label="[2]", style=dashed]; -"1582 Constant_5091" -> "60 /layers/layers.0/blocks.1/Slice_2" [label="[3]", style=dashed]; -"1583 Constant_5088" -> "60 /layers/layers.0/blocks.1/Slice_2" [label="[3]", style=dashed]; -"1584 Constant_5085" -> "60 /layers/layers.0/blocks.1/Slice_2" [label="[3]", style=dashed]; -"1585 Constant_7296" -> "100 /layers/layers.0/blocks.1/attn/qkv/Add" [label="[1, 1, 288]", style=solid]; -"1586 /layers/layers.0/blocks.1/attn/Constant_3" -> "177 /layers/layers.0/blocks.1/attn/Reshape_2" [label="[4]", style=dashed]; -"1587 onnx^^Add_2301" -> "171 /layers/layers.0/blocks.1/attn/Add_1" [label="[1, 64, 1, 49, 49]", style=solid]; -"1588 /layers/layers.0/blocks.1/attn/Constant_2" -> "163 /layers/layers.0/blocks.1/attn/Reshape_1" [label="[5]", style=dashed]; -"1589 onnx^^Add_2293" -> "154 /layers/layers.0/blocks.1/attn/Add" [label="[1, 3, 49, 49]", style=solid]; -"1590 Constant_416" -> "136 /layers/layers.0/blocks.1/attn/Gather_1" [label="[]", style=dashed]; -"1591 Constant_27554" -> "124 /layers/layers.0/blocks.1/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1592 Constant_27553" -> "124 /layers/layers.0/blocks.1/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1593 Constant_27552" -> "124 /layers/layers.0/blocks.1/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1594 Constant_27551" -> "124 /layers/layers.0/blocks.1/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1595 Constant_27559" -> "145 /layers/layers.0/blocks.1/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1596 Constant_27558" -> "145 /layers/layers.0/blocks.1/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1597 Constant_27557" -> "145 /layers/layers.0/blocks.1/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1598 Constant_27556" -> "145 /layers/layers.0/blocks.1/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1599 Constant_7297" -> "135 /layers/layers.0/blocks.1/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; -"1600 Constant_414" -> "123 /layers/layers.0/blocks.1/attn/Gather" [label="[]", style=dashed]; -"1601 Constant_7298" -> "178 /layers/layers.0/blocks.1/attn/proj/Add" [label="[1, 1, 96]", style=solid]; -"1602 Constant_5115" -> "214 /layers/layers.0/blocks.1/Slice_4" [label="[2]", style=dashed]; -"1603 Constant_5112" -> "214 /layers/layers.0/blocks.1/Slice_4" [label="[2]", style=dashed]; -"1604 Constant_5109" -> "214 /layers/layers.0/blocks.1/Slice_4" [label="[2]", style=dashed]; -"1605 Constant_5139" -> "232 /layers/layers.0/blocks.1/Slice_6" [label="[3]", style=dashed]; -"1606 Constant_5136" -> "232 /layers/layers.0/blocks.1/Slice_6" [label="[3]", style=dashed]; -"1607 Constant_5133" -> "232 /layers/layers.0/blocks.1/Slice_6" [label="[3]", style=dashed]; -"1608 Constant_7301" -> "54 /layers/layers.0/blocks.1/mlp/fc1/Add" [label="[1, 1, 384]", style=solid]; -"1609 Constant_7302" -> "74 /layers/layers.0/blocks.1/mlp/fc2/Add" [label="[1, 1, 96]", style=solid]; -"1610 Constant_5211" -> "40 /layers/layers.0/downsample/Slice_4" [label="[3]", style=dashed]; -"1611 Constant_5208" -> "40 /layers/layers.0/downsample/Slice_4" [label="[3]", style=dashed]; -"1612 Constant_5205" -> "40 /layers/layers.0/downsample/Slice_4" [label="[3]", style=dashed]; -"1613 Constant_5163" -> "33 /layers/layers.0/downsample/Slice" [label="[2]", style=dashed]; -"1614 Constant_5160" -> "33 /layers/layers.0/downsample/Slice" [label="[2]", style=dashed]; -"1615 Constant_5157" -> "33 /layers/layers.0/downsample/Slice" [label="[2]", style=dashed]; -"1616 Constant_5199" -> "41 /layers/layers.0/downsample/Slice_3" [label="[3]", style=dashed]; -"1617 Constant_5196" -> "41 /layers/layers.0/downsample/Slice_3" [label="[3]", style=dashed]; -"1618 Constant_5193" -> "41 /layers/layers.0/downsample/Slice_3" [label="[3]", style=dashed]; -"1619 Constant_5175" -> "39 /layers/layers.0/downsample/Slice_1" [label="[3]", style=dashed]; -"1620 Constant_5172" -> "39 /layers/layers.0/downsample/Slice_1" [label="[3]", style=dashed]; -"1621 Constant_5169" -> "39 /layers/layers.0/downsample/Slice_1" [label="[3]", style=dashed]; -"1622 Constant_7307" -> "162 /layers/layers.1/blocks.0/attn/qkv/Add" [label="[1, 1, 576]", style=solid]; -"1623 onnx^^Add_2389" -> "204 /layers/layers.1/blocks.0/attn/Add" [label="[1, 6, 49, 49]", style=solid]; -"1624 Constant_626" -> "190 /layers/layers.1/blocks.0/attn/Gather_1" [label="[]", style=dashed]; -"1625 Constant_27614" -> "183 /layers/layers.1/blocks.0/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1626 Constant_27613" -> "183 /layers/layers.1/blocks.0/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1627 Constant_27612" -> "183 /layers/layers.1/blocks.0/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1628 Constant_27611" -> "183 /layers/layers.1/blocks.0/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1629 Constant_27619" -> "196 /layers/layers.1/blocks.0/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1630 Constant_27618" -> "196 /layers/layers.1/blocks.0/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1631 Constant_27617" -> "196 /layers/layers.1/blocks.0/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1632 Constant_27616" -> "196 /layers/layers.1/blocks.0/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1633 Constant_7308" -> "189 /layers/layers.1/blocks.0/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; -"1634 Constant_624" -> "182 /layers/layers.1/blocks.0/attn/Gather" [label="[]", style=dashed]; -"1635 Constant_7309" -> "231 /layers/layers.1/blocks.0/attn/proj/Add" [label="[1, 1, 192]", style=solid]; -"1636 Constant_7312" -> "133 /layers/layers.1/blocks.0/mlp/fc1/Add" [label="[1, 1, 768]", style=solid]; -"1637 Constant_7313" -> "169 /layers/layers.1/blocks.0/mlp/fc2/Add" [label="[1, 1, 192]", style=solid]; -"1638 Constant_5235" -> "141 /layers/layers.1/blocks.1/Slice" [label="[2]", style=dashed]; -"1639 Constant_5232" -> "141 /layers/layers.1/blocks.1/Slice" [label="[2]", style=dashed]; -"1640 Constant_5229" -> "141 /layers/layers.1/blocks.1/Slice" [label="[2]", style=dashed]; -"1641 Constant_5259" -> "159 /layers/layers.1/blocks.1/Slice_2" [label="[3]", style=dashed]; -"1642 Constant_5256" -> "159 /layers/layers.1/blocks.1/Slice_2" [label="[3]", style=dashed]; -"1643 Constant_5253" -> "159 /layers/layers.1/blocks.1/Slice_2" [label="[3]", style=dashed]; -"1644 Constant_7316" -> "211 /layers/layers.1/blocks.1/attn/qkv/Add" [label="[1, 1, 576]", style=solid]; -"1645 /layers/layers.1/blocks.1/attn/Constant_3" -> "328 /layers/layers.1/blocks.1/attn/Reshape_2" [label="[4]", style=dashed]; -"1646 onnx^^Add_2446" -> "313 /layers/layers.1/blocks.1/attn/Add_1" [label="[1, 16, 1, 49, 49]", style=solid]; -"1647 /layers/layers.1/blocks.1/attn/Constant_2" -> "298 /layers/layers.1/blocks.1/attn/Reshape_1" [label="[5]", style=dashed]; -"1648 onnx^^Add_2438" -> "283 /layers/layers.1/blocks.1/attn/Add" [label="[1, 6, 49, 49]", style=solid]; -"1649 Constant_774" -> "255 /layers/layers.1/blocks.1/attn/Gather_1" [label="[]", style=dashed]; -"1650 Constant_27659" -> "242 /layers/layers.1/blocks.1/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1651 Constant_27658" -> "242 /layers/layers.1/blocks.1/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1652 Constant_27657" -> "242 /layers/layers.1/blocks.1/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1653 Constant_27656" -> "242 /layers/layers.1/blocks.1/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1654 Constant_27664" -> "269 /layers/layers.1/blocks.1/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1655 Constant_27663" -> "269 /layers/layers.1/blocks.1/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1656 Constant_27662" -> "269 /layers/layers.1/blocks.1/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1657 Constant_27661" -> "269 /layers/layers.1/blocks.1/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1658 Constant_7317" -> "254 /layers/layers.1/blocks.1/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; -"1659 Constant_772" -> "241 /layers/layers.1/blocks.1/attn/Gather" [label="[]", style=dashed]; -"1660 Constant_7318" -> "329 /layers/layers.1/blocks.1/attn/proj/Add" [label="[1, 1, 192]", style=solid]; -"1661 Constant_5283" -> "405 /layers/layers.1/blocks.1/Slice_4" [label="[2]", style=dashed]; -"1662 Constant_5280" -> "405 /layers/layers.1/blocks.1/Slice_4" [label="[2]", style=dashed]; -"1663 Constant_5277" -> "405 /layers/layers.1/blocks.1/Slice_4" [label="[2]", style=dashed]; -"1664 Constant_5307" -> "431 /layers/layers.1/blocks.1/Slice_6" [label="[3]", style=dashed]; -"1665 Constant_5304" -> "431 /layers/layers.1/blocks.1/Slice_6" [label="[3]", style=dashed]; -"1666 Constant_5301" -> "431 /layers/layers.1/blocks.1/Slice_6" [label="[3]", style=dashed]; -"1667 Constant_7321" -> "150 /layers/layers.1/blocks.1/mlp/fc1/Add" [label="[1, 1, 768]", style=solid]; -"1668 Constant_7322" -> "180 /layers/layers.1/blocks.1/mlp/fc2/Add" [label="[1, 1, 192]", style=solid]; -"1669 Constant_5379" -> "128 /layers/layers.1/downsample/Slice_4" [label="[3]", style=dashed]; -"1670 Constant_5376" -> "128 /layers/layers.1/downsample/Slice_4" [label="[3]", style=dashed]; -"1671 Constant_5373" -> "128 /layers/layers.1/downsample/Slice_4" [label="[3]", style=dashed]; -"1672 Constant_5331" -> "117 /layers/layers.1/downsample/Slice" [label="[2]", style=dashed]; -"1673 Constant_5328" -> "117 /layers/layers.1/downsample/Slice" [label="[2]", style=dashed]; -"1674 Constant_5325" -> "117 /layers/layers.1/downsample/Slice" [label="[2]", style=dashed]; -"1675 Constant_5367" -> "129 /layers/layers.1/downsample/Slice_3" [label="[3]", style=dashed]; -"1676 Constant_5364" -> "129 /layers/layers.1/downsample/Slice_3" [label="[3]", style=dashed]; -"1677 Constant_5361" -> "129 /layers/layers.1/downsample/Slice_3" [label="[3]", style=dashed]; -"1678 Constant_5343" -> "127 /layers/layers.1/downsample/Slice_1" [label="[3]", style=dashed]; -"1679 Constant_5340" -> "127 /layers/layers.1/downsample/Slice_1" [label="[3]", style=dashed]; -"1680 Constant_5337" -> "127 /layers/layers.1/downsample/Slice_1" [label="[3]", style=dashed]; -"1681 Constant_7327" -> "297 /layers/layers.2/blocks.0/attn/qkv/Add" [label="[1, 1, 1152]", style=solid]; -"1682 onnx^^Add_2534" -> "389 /layers/layers.2/blocks.0/attn/Add" [label="[1, 12, 49, 49]", style=solid]; -"1683 Constant_984" -> "359 /layers/layers.2/blocks.0/attn/Gather_1" [label="[]", style=dashed]; -"1684 Constant_27719" -> "342 /layers/layers.2/blocks.0/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1685 Constant_27718" -> "342 /layers/layers.2/blocks.0/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1686 Constant_27717" -> "342 /layers/layers.2/blocks.0/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1687 Constant_27716" -> "342 /layers/layers.2/blocks.0/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1688 Constant_27724" -> "375 /layers/layers.2/blocks.0/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1689 Constant_27723" -> "375 /layers/layers.2/blocks.0/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1690 Constant_27722" -> "375 /layers/layers.2/blocks.0/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1691 Constant_27721" -> "375 /layers/layers.2/blocks.0/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1692 Constant_7328" -> "358 /layers/layers.2/blocks.0/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; -"1693 Constant_982" -> "341 /layers/layers.2/blocks.0/attn/Gather" [label="[]", style=dashed]; -"1694 Constant_7329" -> "430 /layers/layers.2/blocks.0/attn/proj/Add" [label="[1, 1, 384]", style=solid]; -"1695 Constant_7332" -> "252 /layers/layers.2/blocks.0/mlp/fc1/Add" [label="[1, 1, 1536]", style=solid]; -"1696 Constant_7333" -> "311 /layers/layers.2/blocks.0/mlp/fc2/Add" [label="[1, 1, 384]", style=solid]; -"1697 Constant_5403" -> "265 /layers/layers.2/blocks.1/Slice" [label="[2]", style=dashed]; -"1698 Constant_5400" -> "265 /layers/layers.2/blocks.1/Slice" [label="[2]", style=dashed]; -"1699 Constant_5397" -> "265 /layers/layers.2/blocks.1/Slice" [label="[2]", style=dashed]; -"1700 Constant_5427" -> "294 /layers/layers.2/blocks.1/Slice_2" [label="[3]", style=dashed]; -"1701 Constant_5424" -> "294 /layers/layers.2/blocks.1/Slice_2" [label="[3]", style=dashed]; -"1702 Constant_5421" -> "294 /layers/layers.2/blocks.1/Slice_2" [label="[3]", style=dashed]; -"1703 Constant_7336" -> "402 /layers/layers.2/blocks.1/attn/qkv/Add" [label="[1, 1, 1152]", style=solid]; -"1704 /layers/layers.2/blocks.1/attn/Constant_3" -> "518 /layers/layers.2/blocks.1/attn/Reshape_2" [label="[4]", style=dashed]; -"1705 onnx^^Add_2702" -> "505 /layers/layers.2/blocks.1/attn/Add_1" [label="[1, 4, 1, 49, 49]", style=solid]; -"1705 onnx^^Add_2702" -> "554 /layers/layers.2/blocks.3/attn/Add_1" [label="[1, 4, 1, 49, 49]", style=solid]; -"1705 onnx^^Add_2702" -> "596 /layers/layers.2/blocks.5/attn/Add_1" [label="[1, 4, 1, 49, 49]", style=solid]; -"1706 /layers/layers.2/blocks.1/attn/Constant_2" -> "491 /layers/layers.2/blocks.1/attn/Reshape_1" [label="[5]", style=dashed]; -"1707 onnx^^Add_2583" -> "478 /layers/layers.2/blocks.1/attn/Add" [label="[1, 12, 49, 49]", style=solid]; -"1708 Constant_1132" -> "455 /layers/layers.2/blocks.1/attn/Gather_1" [label="[]", style=dashed]; -"1709 Constant_27764" -> "441 /layers/layers.2/blocks.1/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1710 Constant_27763" -> "441 /layers/layers.2/blocks.1/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1711 Constant_27762" -> "441 /layers/layers.2/blocks.1/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1712 Constant_27761" -> "441 /layers/layers.2/blocks.1/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1713 Constant_27769" -> "466 /layers/layers.2/blocks.1/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1714 Constant_27768" -> "466 /layers/layers.2/blocks.1/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1715 Constant_27767" -> "466 /layers/layers.2/blocks.1/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1716 Constant_27766" -> "466 /layers/layers.2/blocks.1/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1717 Constant_7337" -> "454 /layers/layers.2/blocks.1/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; -"1718 Constant_1130" -> "440 /layers/layers.2/blocks.1/attn/Gather" [label="[]", style=dashed]; -"1719 Constant_7338" -> "519 /layers/layers.2/blocks.1/attn/proj/Add" [label="[1, 1, 384]", style=solid]; -"1720 Constant_5451" -> "579 /layers/layers.2/blocks.1/Slice_4" [label="[2]", style=dashed]; -"1721 Constant_5448" -> "579 /layers/layers.2/blocks.1/Slice_4" [label="[2]", style=dashed]; -"1722 Constant_5445" -> "579 /layers/layers.2/blocks.1/Slice_4" [label="[2]", style=dashed]; -"1723 Constant_5475" -> "599 /layers/layers.2/blocks.1/Slice_6" [label="[3]", style=dashed]; -"1724 Constant_5472" -> "599 /layers/layers.2/blocks.1/Slice_6" [label="[3]", style=dashed]; -"1725 Constant_5469" -> "599 /layers/layers.2/blocks.1/Slice_6" [label="[3]", style=dashed]; -"1726 Constant_7341" -> "279 /layers/layers.2/blocks.1/mlp/fc1/Add" [label="[1, 1, 1536]", style=solid]; -"1727 Constant_7342" -> "339 /layers/layers.2/blocks.1/mlp/fc2/Add" [label="[1, 1, 384]", style=solid]; -"1728 Constant_7345" -> "356 /layers/layers.2/blocks.2/attn/qkv/Add" [label="[1, 1, 1152]", style=solid]; -"1729 onnx^^Add_2645" -> "438 /layers/layers.2/blocks.2/attn/Add" [label="[1, 12, 49, 49]", style=solid]; -"1730 Constant_1289" -> "415 /layers/layers.2/blocks.2/attn/Gather_1" [label="[]", style=dashed]; -"1731 Constant_27814" -> "400 /layers/layers.2/blocks.2/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1732 Constant_27813" -> "400 /layers/layers.2/blocks.2/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1733 Constant_27812" -> "400 /layers/layers.2/blocks.2/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1734 Constant_27811" -> "400 /layers/layers.2/blocks.2/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1735 Constant_27819" -> "426 /layers/layers.2/blocks.2/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1736 Constant_27818" -> "426 /layers/layers.2/blocks.2/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1737 Constant_27817" -> "426 /layers/layers.2/blocks.2/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1738 Constant_27816" -> "426 /layers/layers.2/blocks.2/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1739 Constant_7346" -> "414 /layers/layers.2/blocks.2/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; -"1740 Constant_1287" -> "399 /layers/layers.2/blocks.2/attn/Gather" [label="[]", style=dashed]; -"1741 Constant_7347" -> "477 /layers/layers.2/blocks.2/attn/proj/Add" [label="[1, 1, 384]", style=solid]; -"1742 Constant_7350" -> "307 /layers/layers.2/blocks.2/mlp/fc1/Add" [label="[1, 1, 1536]", style=solid]; -"1743 Constant_7351" -> "372 /layers/layers.2/blocks.2/mlp/fc2/Add" [label="[1, 1, 384]", style=solid]; -"1744 Constant_5499" -> "321 /layers/layers.2/blocks.3/Slice" [label="[2]", style=dashed]; -"1745 Constant_5496" -> "321 /layers/layers.2/blocks.3/Slice" [label="[2]", style=dashed]; -"1746 Constant_5493" -> "321 /layers/layers.2/blocks.3/Slice" [label="[2]", style=dashed]; -"1747 Constant_5523" -> "353 /layers/layers.2/blocks.3/Slice_2" [label="[3]", style=dashed]; -"1748 Constant_5520" -> "353 /layers/layers.2/blocks.3/Slice_2" [label="[3]", style=dashed]; -"1749 Constant_5517" -> "353 /layers/layers.2/blocks.3/Slice_2" [label="[3]", style=dashed]; -"1750 Constant_7354" -> "451 /layers/layers.2/blocks.3/attn/qkv/Add" [label="[1, 1, 1152]", style=solid]; -"1751 /layers/layers.2/blocks.3/attn/Constant_3" -> "566 /layers/layers.2/blocks.3/attn/Reshape_2" [label="[4]", style=dashed]; -"1752 /layers/layers.2/blocks.3/attn/Constant_2" -> "541 /layers/layers.2/blocks.3/attn/Reshape_1" [label="[5]", style=dashed]; -"1753 onnx^^Add_2694" -> "527 /layers/layers.2/blocks.3/attn/Add" [label="[1, 12, 49, 49]", style=solid]; -"1754 Constant_1437" -> "502 /layers/layers.2/blocks.3/attn/Gather_1" [label="[]", style=dashed]; -"1755 Constant_27859" -> "488 /layers/layers.2/blocks.3/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1756 Constant_27858" -> "488 /layers/layers.2/blocks.3/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1757 Constant_27857" -> "488 /layers/layers.2/blocks.3/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1758 Constant_27856" -> "488 /layers/layers.2/blocks.3/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1759 Constant_27864" -> "514 /layers/layers.2/blocks.3/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1760 Constant_27863" -> "514 /layers/layers.2/blocks.3/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1761 Constant_27862" -> "514 /layers/layers.2/blocks.3/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1762 Constant_27861" -> "514 /layers/layers.2/blocks.3/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1763 Constant_7355" -> "501 /layers/layers.2/blocks.3/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; -"1764 Constant_1435" -> "487 /layers/layers.2/blocks.3/attn/Gather" [label="[]", style=dashed]; -"1765 Constant_7356" -> "567 /layers/layers.2/blocks.3/attn/proj/Add" [label="[1, 1, 384]", style=solid]; -"1766 Constant_5547" -> "617 /layers/layers.2/blocks.3/Slice_4" [label="[2]", style=dashed]; -"1767 Constant_5544" -> "617 /layers/layers.2/blocks.3/Slice_4" [label="[2]", style=dashed]; -"1768 Constant_5541" -> "617 /layers/layers.2/blocks.3/Slice_4" [label="[2]", style=dashed]; -"1769 Constant_5571" -> "634 /layers/layers.2/blocks.3/Slice_6" [label="[3]", style=dashed]; -"1770 Constant_5568" -> "634 /layers/layers.2/blocks.3/Slice_6" [label="[3]", style=dashed]; -"1771 Constant_5565" -> "634 /layers/layers.2/blocks.3/Slice_6" [label="[3]", style=dashed]; -"1772 Constant_7359" -> "335 /layers/layers.2/blocks.3/mlp/fc1/Add" [label="[1, 1, 1536]", style=solid]; -"1773 Constant_7360" -> "397 /layers/layers.2/blocks.3/mlp/fc2/Add" [label="[1, 1, 384]", style=solid]; -"1774 Constant_7363" -> "412 /layers/layers.2/blocks.4/attn/qkv/Add" [label="[1, 1, 1152]", style=solid]; -"1775 onnx^^Add_2756" -> "485 /layers/layers.2/blocks.4/attn/Add" [label="[1, 12, 49, 49]", style=solid]; -"1776 Constant_1594" -> "462 /layers/layers.2/blocks.4/attn/Gather_1" [label="[]", style=dashed]; -"1777 Constant_27909" -> "449 /layers/layers.2/blocks.4/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1778 Constant_27908" -> "449 /layers/layers.2/blocks.4/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1779 Constant_27907" -> "449 /layers/layers.2/blocks.4/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1780 Constant_27906" -> "449 /layers/layers.2/blocks.4/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1781 Constant_27914" -> "473 /layers/layers.2/blocks.4/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1782 Constant_27913" -> "473 /layers/layers.2/blocks.4/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1783 Constant_27912" -> "473 /layers/layers.2/blocks.4/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1784 Constant_27911" -> "473 /layers/layers.2/blocks.4/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1785 Constant_7364" -> "461 /layers/layers.2/blocks.4/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; -"1786 Constant_1592" -> "448 /layers/layers.2/blocks.4/attn/Gather" [label="[]", style=dashed]; -"1787 Constant_7365" -> "526 /layers/layers.2/blocks.4/attn/proj/Add" [label="[1, 1, 384]", style=solid]; -"1788 Constant_7368" -> "368 /layers/layers.2/blocks.4/mlp/fc1/Add" [label="[1, 1, 1536]", style=solid]; -"1789 Constant_7369" -> "423 /layers/layers.2/blocks.4/mlp/fc2/Add" [label="[1, 1, 384]", style=solid]; -"1790 Constant_5595" -> "381 /layers/layers.2/blocks.5/Slice" [label="[2]", style=dashed]; -"1791 Constant_5592" -> "381 /layers/layers.2/blocks.5/Slice" [label="[2]", style=dashed]; -"1792 Constant_5589" -> "381 /layers/layers.2/blocks.5/Slice" [label="[2]", style=dashed]; -"1793 Constant_5619" -> "409 /layers/layers.2/blocks.5/Slice_2" [label="[3]", style=dashed]; -"1794 Constant_5616" -> "409 /layers/layers.2/blocks.5/Slice_2" [label="[3]", style=dashed]; -"1795 Constant_5613" -> "409 /layers/layers.2/blocks.5/Slice_2" [label="[3]", style=dashed]; -"1796 Constant_7372" -> "498 /layers/layers.2/blocks.5/attn/qkv/Add" [label="[1, 1, 1152]", style=solid]; -"1797 /layers/layers.2/blocks.5/attn/Constant_3" -> "605 /layers/layers.2/blocks.5/attn/Reshape_2" [label="[4]", style=dashed]; -"1798 /layers/layers.2/blocks.5/attn/Constant_2" -> "586 /layers/layers.2/blocks.5/attn/Reshape_1" [label="[5]", style=dashed]; -"1799 onnx^^Add_2805" -> "574 /layers/layers.2/blocks.5/attn/Add" [label="[1, 12, 49, 49]", style=solid]; -"1800 Constant_1742" -> "551 /layers/layers.2/blocks.5/attn/Gather_1" [label="[]", style=dashed]; -"1801 Constant_27954" -> "538 /layers/layers.2/blocks.5/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1802 Constant_27953" -> "538 /layers/layers.2/blocks.5/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1803 Constant_27952" -> "538 /layers/layers.2/blocks.5/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1804 Constant_27951" -> "538 /layers/layers.2/blocks.5/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1805 Constant_27959" -> "562 /layers/layers.2/blocks.5/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1806 Constant_27958" -> "562 /layers/layers.2/blocks.5/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1807 Constant_27957" -> "562 /layers/layers.2/blocks.5/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1808 Constant_27956" -> "562 /layers/layers.2/blocks.5/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1809 Constant_7373" -> "550 /layers/layers.2/blocks.5/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; -"1810 Constant_1740" -> "537 /layers/layers.2/blocks.5/attn/Gather" [label="[]", style=dashed]; -"1811 Constant_7374" -> "606 /layers/layers.2/blocks.5/attn/proj/Add" [label="[1, 1, 384]", style=solid]; -"1812 Constant_5643" -> "648 /layers/layers.2/blocks.5/Slice_4" [label="[2]", style=dashed]; -"1813 Constant_5640" -> "648 /layers/layers.2/blocks.5/Slice_4" [label="[2]", style=dashed]; -"1814 Constant_5637" -> "648 /layers/layers.2/blocks.5/Slice_4" [label="[2]", style=dashed]; -"1815 Constant_5667" -> "658 /layers/layers.2/blocks.5/Slice_6" [label="[3]", style=dashed]; -"1816 Constant_5664" -> "658 /layers/layers.2/blocks.5/Slice_6" [label="[3]", style=dashed]; -"1817 Constant_5661" -> "658 /layers/layers.2/blocks.5/Slice_6" [label="[3]", style=dashed]; -"1818 Constant_7377" -> "393 /layers/layers.2/blocks.5/mlp/fc1/Add" [label="[1, 1, 1536]", style=solid]; -"1819 Constant_7378" -> "446 /layers/layers.2/blocks.5/mlp/fc2/Add" [label="[1, 1, 384]", style=solid]; -"1820 Constant_5739" -> "363 /layers/layers.2/downsample/Slice_4" [label="[3]", style=dashed]; -"1821 Constant_5736" -> "363 /layers/layers.2/downsample/Slice_4" [label="[3]", style=dashed]; -"1822 Constant_5733" -> "363 /layers/layers.2/downsample/Slice_4" [label="[3]", style=dashed]; -"1823 Constant_5691" -> "346 /layers/layers.2/downsample/Slice" [label="[2]", style=dashed]; -"1824 Constant_5688" -> "346 /layers/layers.2/downsample/Slice" [label="[2]", style=dashed]; -"1825 Constant_5685" -> "346 /layers/layers.2/downsample/Slice" [label="[2]", style=dashed]; -"1826 Constant_5727" -> "364 /layers/layers.2/downsample/Slice_3" [label="[3]", style=dashed]; -"1827 Constant_5724" -> "364 /layers/layers.2/downsample/Slice_3" [label="[3]", style=dashed]; -"1828 Constant_5721" -> "364 /layers/layers.2/downsample/Slice_3" [label="[3]", style=dashed]; -"1829 Constant_5703" -> "362 /layers/layers.2/downsample/Slice_1" [label="[3]", style=dashed]; -"1830 Constant_5700" -> "362 /layers/layers.2/downsample/Slice_1" [label="[3]", style=dashed]; -"1831 Constant_5697" -> "362 /layers/layers.2/downsample/Slice_1" [label="[3]", style=dashed]; -"1832 Constant_7383" -> "585 /layers/layers.3/blocks.0/attn/qkv/Add" [label="[1, 1, 2304]", style=solid]; -"1833 onnx^^Add_2901" -> "639 /layers/layers.3/blocks.0/attn/Add" [label="[1, 24, 49, 49]", style=solid]; -"1834 Constant_1952" -> "623 /layers/layers.3/blocks.0/attn/Gather_1" [label="[]", style=dashed]; -"1835 Constant_28014" -> "613 /layers/layers.3/blocks.0/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1836 Constant_28013" -> "613 /layers/layers.3/blocks.0/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1837 Constant_28012" -> "613 /layers/layers.3/blocks.0/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1838 Constant_28011" -> "613 /layers/layers.3/blocks.0/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1839 Constant_28019" -> "630 /layers/layers.3/blocks.0/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1840 Constant_28018" -> "630 /layers/layers.3/blocks.0/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1841 Constant_28017" -> "630 /layers/layers.3/blocks.0/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1842 Constant_28016" -> "630 /layers/layers.3/blocks.0/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1843 Constant_7384" -> "622 /layers/layers.3/blocks.0/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; -"1844 Constant_1950" -> "612 /layers/layers.3/blocks.0/attn/Gather" [label="[]", style=dashed]; -"1845 Constant_7385" -> "657 /layers/layers.3/blocks.0/attn/proj/Add" [label="[1, 1, 768]", style=solid]; -"1846 Constant_7388" -> "548 /layers/layers.3/blocks.0/mlp/fc1/Add" [label="[1, 1, 3072]", style=solid]; -"1847 Constant_7389" -> "594 /layers/layers.3/blocks.0/mlp/fc2/Add" [label="[1, 1, 768]", style=solid]; -"1848 Constant_7392" -> "603 /layers/layers.3/blocks.1/attn/qkv/Add" [label="[1, 1, 2304]", style=solid]; -"1849 onnx^^Add_2950" -> "651 /layers/layers.3/blocks.1/attn/Add" [label="[1, 24, 49, 49]", style=solid]; -"1850 Constant_2058" -> "637 /layers/layers.3/blocks.1/attn/Gather_1" [label="[]", style=dashed]; -"1851 Constant_28064" -> "628 /layers/layers.3/blocks.1/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1852 Constant_28063" -> "628 /layers/layers.3/blocks.1/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1853 Constant_28062" -> "628 /layers/layers.3/blocks.1/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1854 Constant_28061" -> "628 /layers/layers.3/blocks.1/attn/Gather_1/fq_input_0" [label="[]", style=solid]; -"1855 Constant_28069" -> "643 /layers/layers.3/blocks.1/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1856 Constant_28068" -> "643 /layers/layers.3/blocks.1/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1857 Constant_28067" -> "643 /layers/layers.3/blocks.1/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1858 Constant_28066" -> "643 /layers/layers.3/blocks.1/attn/Mul/fq_output_0" [label="[]", style=solid]; -"1859 Constant_7393" -> "636 /layers/layers.3/blocks.1/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; -"1860 Constant_2056" -> "627 /layers/layers.3/blocks.1/attn/Gather" [label="[]", style=dashed]; -"1861 Constant_7394" -> "663 /layers/layers.3/blocks.1/attn/proj/Add" [label="[1, 1, 768]", style=solid]; -"1862 Constant_7397" -> "570 /layers/layers.3/blocks.1/mlp/fc1/Add" [label="[1, 1, 3072]", style=solid]; -"1863 Constant_7398" -> "610 /layers/layers.3/blocks.1/mlp/fc2/Add" [label="[1, 1, 768]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/swin-tiny-patch4-window7-224_sq.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/swin-tiny-patch4-window7-224_sq.dot deleted file mode 100644 index b2201cd3a37..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/quantized/swin-tiny-patch4-window7-224_sq.dot +++ /dev/null @@ -1,2754 +0,0 @@ -strict digraph { -"0 input" [id=0, type=Parameter]; -"1 Multiply_6579" [id=1, type=Multiply]; -"2 Divide_2169" [id=2, type=Add]; -"3 Divide_2169_0_0/nncf_smooth_quant" [id=3, type=Multiply]; -"4 /patch_embed/proj/Conv/WithoutBiases" [id=4, type=Convolution]; -"5 /patch_embed/proj/Conv" [id=5, type=Add]; -"6 /patch_embed/Reshape" [id=6, type=Reshape]; -"7 /patch_embed/Shape" [id=7, type=ShapeOf]; -"8 /patch_embed/Transpose" [id=8, type=Transpose]; -"9 /patch_embed/Slice" [id=9, type=StridedSlice]; -"10 /patch_embed/norm/Div" [id=10, type=MVN]; -"11 /patch_embed/Concat" [id=11, type=Concat]; -"12 /patch_embed/norm/Mul" [id=12, type=Multiply]; -"13 /patch_embed/norm/Add_1" [id=13, type=Add]; -"14 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type=Constant]; -"675 Constant_2060" [id=675, type=Constant]; -"676 /patch_embed/Constant" [id=676, type=Constant]; -"677 Constant_2054" [id=677, type=Constant]; -"678 /layers/layers.3/blocks.1/attn/Constant" [id=678, type=Constant]; -"679 Transpose_6549" [id=679, type=Constant]; -"680 Constant_26061" [id=680, type=Constant]; -"681 /layers/layers.3/blocks.1/Constant_3" [id=681, type=Constant]; -"682 /layers/layers.3/blocks.1/Constant_2" [id=682, type=Constant]; -"683 Constant_6544" [id=683, type=Constant]; -"684 /layers/layers.3/blocks.1/Constant_1" [id=684, type=Constant]; -"685 Constant_7391" [id=685, type=Constant]; -"686 Constant_7390" [id=686, type=Constant]; -"687 Constant_2017" [id=687, type=Constant]; -"688 Transpose_6541" [id=688, type=Constant]; -"689 Constant_26055" [id=689, type=Constant]; -"690 Transpose_6537" [id=690, type=Constant]; -"691 Constant_26049" [id=691, type=Constant]; -"692 Constant_7387" [id=692, type=Constant]; -"693 Constant_7386" [id=693, type=Constant]; 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[id=711, type=Constant]; -"712 /layers/layers.3/blocks.0/Constant_1" [id=712, type=Constant]; -"713 Constant_7382" [id=713, type=Constant]; -"714 Constant_7381" [id=714, type=Constant]; -"715 Constant_1911" [id=715, type=Constant]; -"716 Transpose_6518" [id=716, type=Constant]; -"717 Constant_26041" [id=717, type=Constant]; -"718 Constant_7380" [id=718, type=Constant]; -"719 Constant_7379" [id=719, type=Constant]; -"720 Constant_1897" [id=720, type=Constant]; -"721 /layers/layers.2/downsample/Constant_25" [id=721, type=Constant]; -"722 Constant_5751" [id=722, type=Constant]; -"723 Constant_5748" [id=723, type=Constant]; -"724 Constant_5745" [id=724, type=Constant]; -"725 Constant_5715" [id=725, type=Constant]; -"726 Constant_5712" [id=726, type=Constant]; -"727 Constant_5709" [id=727, type=Constant]; -"728 /layers/layers.2/downsample/Constant" [id=728, type=Constant]; -"729 Transpose_6514" [id=729, type=Constant]; -"730 Constant_26035" [id=730, type=Constant]; -"731 Transpose_6510" [id=731, type=Constant]; -"732 Constant_26025" [id=732, type=Constant]; -"733 Constant_7376" [id=733, type=Constant]; -"734 Constant_7375" [id=734, type=Constant]; -"735 Constant_1832" [id=735, type=Constant]; -"736 /layers/layers.2/blocks.5/Constant_31" [id=736, type=Constant]; -"737 Constant_5679" [id=737, type=Constant]; -"738 Constant_5676" [id=738, type=Constant]; -"739 Constant_5673" [id=739, type=Constant]; -"740 Constant_5655" [id=740, type=Constant]; -"741 Constant_5652" [id=741, type=Constant]; -"742 Constant_5649" [id=742, type=Constant]; -"743 /layers/layers.2/blocks.5/Constant_18" [id=743, type=Constant]; -"744 Constant_1779" [id=744, type=Constant]; -"745 /layers/layers.2/blocks.5/Constant_17" [id=745, type=Constant]; -"746 /layers/layers.2/blocks.5/Constant_16" [id=746, type=Constant]; -"747 Transpose_6506" [id=747, type=Constant]; -"748 Constant_26063" [id=748, type=Constant]; -"749 /layers/layers.2/blocks.5/attn/Constant_4" [id=749, type=Constant]; -"750 Constant_1763" [id=750, type=Constant]; -"751 Constant_1744" [id=751, type=Constant]; -"752 Constant_1738" [id=752, type=Constant]; -"753 /layers/layers.2/blocks.5/attn/Constant" [id=753, type=Constant]; -"754 Transpose_6503" [id=754, type=Constant]; -"755 Constant_26043" [id=755, type=Constant]; -"756 /layers/layers.2/blocks.5/Constant_15" [id=756, type=Constant]; -"757 /layers/layers.2/blocks.5/Constant_14" [id=757, type=Constant]; -"758 Constant_1722" [id=758, type=Constant]; -"759 /layers/layers.2/blocks.5/Constant_13" [id=759, type=Constant]; -"760 Constant_5631" [id=760, type=Constant]; -"761 Constant_5628" [id=761, type=Constant]; -"762 Constant_5625" [id=762, type=Constant]; -"763 Constant_5607" [id=763, type=Constant]; -"764 Constant_5604" [id=764, type=Constant]; -"765 Constant_5601" [id=765, type=Constant]; -"766 /layers/layers.2/blocks.5/Constant" [id=766, type=Constant]; -"767 Constant_7371" [id=767, type=Constant]; -"768 Constant_7370" [id=768, type=Constant]; -"769 Constant_1659" [id=769, type=Constant]; -"770 Transpose_6499" [id=770, type=Constant]; -"771 Constant_26029" [id=771, type=Constant]; -"772 Transpose_6495" [id=772, type=Constant]; -"773 Constant_26021" [id=773, type=Constant]; -"774 Constant_7367" [id=774, type=Constant]; -"775 Constant_7366" [id=775, type=Constant]; -"776 Constant_1633" [id=776, type=Constant]; -"777 /layers/layers.2/blocks.4/Constant_7" [id=777, type=Constant]; -"778 /layers/layers.2/blocks.4/Constant_6" [id=778, type=Constant]; -"779 Constant_1622" [id=779, type=Constant]; -"780 /layers/layers.2/blocks.4/Constant_5" [id=780, type=Constant]; -"781 /layers/layers.2/blocks.4/Constant_4" [id=781, type=Constant]; -"782 Transpose_6491" [id=782, type=Constant]; -"783 Constant_26047" [id=783, type=Constant]; -"784 /layers/layers.2/blocks.4/attn/Constant_2" [id=784, type=Constant]; -"785 Constant_1606" [id=785, type=Constant]; -"786 Constant_1596" [id=786, type=Constant]; -"787 Constant_1590" [id=787, type=Constant]; -"788 /layers/layers.2/blocks.4/attn/Constant" [id=788, type=Constant]; -"789 Transpose_6488" [id=789, type=Constant]; -"790 Constant_26031" [id=790, type=Constant]; -"791 /layers/layers.2/blocks.4/Constant_3" [id=791, type=Constant]; -"792 /layers/layers.2/blocks.4/Constant_2" [id=792, type=Constant]; -"793 Constant_1574" [id=793, type=Constant]; -"794 /layers/layers.2/blocks.4/Constant_1" [id=794, type=Constant]; -"795 Constant_7362" [id=795, type=Constant]; -"796 Constant_7361" [id=796, type=Constant]; -"797 Constant_1553" [id=797, type=Constant]; -"798 Transpose_6484" [id=798, type=Constant]; -"799 Constant_26023" [id=799, type=Constant]; -"800 Transpose_6480" [id=800, type=Constant]; -"801 Constant_26015" [id=801, type=Constant]; -"802 Constant_7358" [id=802, type=Constant]; -"803 Constant_7357" [id=803, type=Constant]; -"804 Constant_1527" [id=804, type=Constant]; -"805 /layers/layers.2/blocks.3/Constant_31" [id=805, type=Constant]; -"806 Constant_5583" [id=806, type=Constant]; -"807 Constant_5580" [id=807, type=Constant]; -"808 Constant_5577" [id=808, type=Constant]; -"809 Constant_5559" [id=809, type=Constant]; -"810 Constant_5556" [id=810, type=Constant]; -"811 Constant_5553" [id=811, type=Constant]; -"812 /layers/layers.2/blocks.3/Constant_18" [id=812, type=Constant]; -"813 Constant_1474" [id=813, type=Constant]; -"814 /layers/layers.2/blocks.3/Constant_17" [id=814, type=Constant]; -"815 /layers/layers.2/blocks.3/Constant_16" [id=815, type=Constant]; -"816 Transpose_6476" [id=816, type=Constant]; -"817 Constant_26051" [id=817, type=Constant]; -"818 /layers/layers.2/blocks.3/attn/Constant_4" [id=818, type=Constant]; -"819 Constant_1458" [id=819, type=Constant]; -"820 Constant_1439" [id=820, type=Constant]; -"821 Constant_1433" [id=821, type=Constant]; -"822 /layers/layers.2/blocks.3/attn/Constant" [id=822, type=Constant]; -"823 Transpose_6473" [id=823, type=Constant]; -"824 Constant_26037" [id=824, type=Constant]; -"825 /layers/layers.2/blocks.3/Constant_15" [id=825, type=Constant]; -"826 /layers/layers.2/blocks.3/Constant_14" [id=826, type=Constant]; -"827 Constant_1417" [id=827, type=Constant]; -"828 /layers/layers.2/blocks.3/Constant_13" [id=828, type=Constant]; -"829 Constant_5535" [id=829, type=Constant]; -"830 Constant_5532" [id=830, type=Constant]; -"831 Constant_5529" [id=831, type=Constant]; -"832 Constant_5511" [id=832, type=Constant]; -"833 Constant_5508" [id=833, type=Constant]; -"834 Constant_5505" [id=834, type=Constant]; -"835 /layers/layers.2/blocks.3/Constant" [id=835, type=Constant]; -"836 Constant_7353" [id=836, type=Constant]; -"837 Constant_7352" [id=837, type=Constant]; -"838 Constant_1354" [id=838, type=Constant]; -"839 Transpose_6469" [id=839, type=Constant]; -"840 Constant_26017" [id=840, type=Constant]; -"841 Transpose_6465" [id=841, type=Constant]; -"842 Constant_26009" [id=842, type=Constant]; -"843 Constant_7349" [id=843, type=Constant]; -"844 Constant_7348" [id=844, type=Constant]; -"845 Constant_1328" [id=845, type=Constant]; -"846 /layers/layers.2/blocks.2/Constant_7" [id=846, type=Constant]; -"847 /layers/layers.2/blocks.2/Constant_6" [id=847, type=Constant]; -"848 Constant_1317" [id=848, type=Constant]; -"849 /layers/layers.2/blocks.2/Constant_5" [id=849, type=Constant]; -"850 /layers/layers.2/blocks.2/Constant_4" [id=850, type=Constant]; -"851 Transpose_6461" [id=851, type=Constant]; -"852 Constant_26039" [id=852, type=Constant]; -"853 /layers/layers.2/blocks.2/attn/Constant_2" [id=853, type=Constant]; -"854 Constant_1301" [id=854, type=Constant]; -"855 Constant_1291" [id=855, type=Constant]; -"856 Constant_1285" [id=856, type=Constant]; -"857 /layers/layers.2/blocks.2/attn/Constant" [id=857, type=Constant]; -"858 Transpose_6458" [id=858, type=Constant]; -"859 Constant_26019" [id=859, type=Constant]; -"860 /layers/layers.2/blocks.2/Constant_3" [id=860, type=Constant]; -"861 /layers/layers.2/blocks.2/Constant_2" [id=861, type=Constant]; -"862 Constant_1269" [id=862, type=Constant]; -"863 /layers/layers.2/blocks.2/Constant_1" [id=863, type=Constant]; -"864 Constant_7344" [id=864, type=Constant]; -"865 Constant_7343" [id=865, type=Constant]; -"866 Constant_1248" [id=866, type=Constant]; -"867 Transpose_6454" [id=867, type=Constant]; -"868 Constant_26011" [id=868, type=Constant]; -"869 Transpose_6450" [id=869, type=Constant]; -"870 Constant_26003" [id=870, type=Constant]; -"871 Constant_7340" [id=871, type=Constant]; -"872 Constant_7339" [id=872, type=Constant]; -"873 Constant_1222" [id=873, type=Constant]; -"874 /layers/layers.2/blocks.1/Constant_31" [id=874, type=Constant]; -"875 Constant_5487" [id=875, type=Constant]; -"876 Constant_5484" [id=876, type=Constant]; -"877 Constant_5481" [id=877, type=Constant]; -"878 Constant_5463" [id=878, type=Constant]; -"879 Constant_5460" [id=879, type=Constant]; -"880 Constant_5457" [id=880, type=Constant]; -"881 /layers/layers.2/blocks.1/Constant_18" [id=881, type=Constant]; -"882 Constant_1169" [id=882, type=Constant]; -"883 /layers/layers.2/blocks.1/Constant_17" [id=883, type=Constant]; -"884 /layers/layers.2/blocks.1/Constant_16" [id=884, type=Constant]; -"885 Transpose_6446" [id=885, type=Constant]; -"886 Constant_26045" [id=886, type=Constant]; -"887 /layers/layers.2/blocks.1/attn/Constant_4" [id=887, type=Constant]; -"888 Constant_1153" [id=888, type=Constant]; -"889 Constant_1134" [id=889, type=Constant]; -"890 Constant_1128" [id=890, type=Constant]; -"891 /layers/layers.2/blocks.1/attn/Constant" [id=891, type=Constant]; -"892 Transpose_6443" [id=892, type=Constant]; -"893 Constant_26027" [id=893, type=Constant]; -"894 /layers/layers.2/blocks.1/Constant_15" [id=894, type=Constant]; -"895 /layers/layers.2/blocks.1/Constant_14" [id=895, type=Constant]; -"896 Constant_1112" [id=896, type=Constant]; -"897 /layers/layers.2/blocks.1/Constant_13" [id=897, type=Constant]; -"898 Constant_5439" [id=898, type=Constant]; -"899 Constant_5436" [id=899, type=Constant]; -"900 Constant_5433" [id=900, type=Constant]; -"901 Constant_5415" [id=901, type=Constant]; -"902 Constant_5412" [id=902, type=Constant]; -"903 Constant_5409" [id=903, type=Constant]; -"904 /layers/layers.2/blocks.1/Constant" [id=904, type=Constant]; -"905 Constant_7335" [id=905, type=Constant]; -"906 Constant_7334" [id=906, type=Constant]; -"907 Constant_1049" [id=907, type=Constant]; -"908 Transpose_6439" [id=908, type=Constant]; -"909 Constant_26005" [id=909, type=Constant]; -"910 Transpose_6435" [id=910, type=Constant]; -"911 Constant_26001" [id=911, type=Constant]; -"912 Constant_7331" [id=912, type=Constant]; -"913 Constant_7330" [id=913, type=Constant]; -"914 Constant_1023" [id=914, type=Constant]; -"915 /layers/layers.2/blocks.0/Constant_7" [id=915, type=Constant]; -"916 /layers/layers.2/blocks.0/Constant_6" [id=916, type=Constant]; -"917 Constant_1012" [id=917, type=Constant]; -"918 /layers/layers.2/blocks.0/Constant_5" [id=918, type=Constant]; -"919 /layers/layers.2/blocks.0/Constant_4" [id=919, type=Constant]; -"920 Transpose_6431" [id=920, type=Constant]; -"921 Constant_26033" [id=921, type=Constant]; -"922 /layers/layers.2/blocks.0/attn/Constant_2" [id=922, type=Constant]; -"923 Constant_996" [id=923, type=Constant]; -"924 Constant_986" [id=924, type=Constant]; -"925 Constant_980" [id=925, type=Constant]; -"926 /layers/layers.2/blocks.0/attn/Constant" [id=926, type=Constant]; -"927 Transpose_6428" [id=927, type=Constant]; -"928 Constant_26007" [id=928, type=Constant]; -"929 /layers/layers.2/blocks.0/Constant_3" [id=929, type=Constant]; -"930 /layers/layers.2/blocks.0/Constant_2" [id=930, type=Constant]; -"931 Constant_964" [id=931, type=Constant]; -"932 /layers/layers.2/blocks.0/Constant_1" [id=932, type=Constant]; -"933 Constant_7326" [id=933, type=Constant]; -"934 Constant_7325" [id=934, type=Constant]; -"935 Constant_943" [id=935, type=Constant]; -"936 Transpose_6424" [id=936, type=Constant]; -"937 Constant_25995" [id=937, type=Constant]; -"938 Constant_7324" [id=938, type=Constant]; -"939 Constant_7323" [id=939, type=Constant]; -"940 Constant_929" [id=940, type=Constant]; -"941 /layers/layers.1/downsample/Constant_25" [id=941, type=Constant]; -"942 Constant_5391" [id=942, type=Constant]; -"943 Constant_5388" [id=943, type=Constant]; -"944 Constant_5385" [id=944, type=Constant]; -"945 Constant_5355" [id=945, type=Constant]; -"946 Constant_5352" [id=946, type=Constant]; -"947 Constant_5349" [id=947, type=Constant]; -"948 /layers/layers.1/downsample/Constant" [id=948, type=Constant]; -"949 Transpose_6420" [id=949, type=Constant]; -"950 Constant_25991" [id=950, type=Constant]; -"951 Transpose_6416" [id=951, type=Constant]; -"952 Constant_25985" [id=952, type=Constant]; -"953 Constant_7320" [id=953, type=Constant]; -"954 Constant_7319" [id=954, type=Constant]; -"955 Constant_864" [id=955, type=Constant]; -"956 /layers/layers.1/blocks.1/Constant_31" [id=956, type=Constant]; -"957 Constant_5319" [id=957, type=Constant]; -"958 Constant_5316" [id=958, type=Constant]; -"959 Constant_5313" [id=959, type=Constant]; -"960 Constant_5295" [id=960, type=Constant]; -"961 Constant_5292" [id=961, type=Constant]; -"962 Constant_5289" [id=962, type=Constant]; -"963 /layers/layers.1/blocks.1/Constant_18" [id=963, type=Constant]; -"964 Constant_811" [id=964, type=Constant]; -"965 /layers/layers.1/blocks.1/Constant_17" [id=965, type=Constant]; -"966 /layers/layers.1/blocks.1/Constant_16" [id=966, type=Constant]; -"967 Transpose_6412" [id=967, type=Constant]; -"968 Constant_26013" [id=968, type=Constant]; -"969 /layers/layers.1/blocks.1/attn/Constant_4" [id=969, type=Constant]; -"970 Constant_795" [id=970, type=Constant]; -"971 Constant_776" [id=971, type=Constant]; -"972 Constant_770" [id=972, type=Constant]; -"973 /layers/layers.1/blocks.1/attn/Constant" [id=973, type=Constant]; -"974 Transpose_6409" [id=974, type=Constant]; -"975 Constant_25997" [id=975, type=Constant]; -"976 /layers/layers.1/blocks.1/Constant_15" [id=976, type=Constant]; -"977 /layers/layers.1/blocks.1/Constant_14" [id=977, type=Constant]; -"978 Constant_754" [id=978, type=Constant]; -"979 /layers/layers.1/blocks.1/Constant_13" [id=979, type=Constant]; -"980 Constant_5271" [id=980, type=Constant]; -"981 Constant_5268" [id=981, type=Constant]; -"982 Constant_5265" [id=982, type=Constant]; -"983 Constant_5247" [id=983, type=Constant]; -"984 Constant_5244" [id=984, type=Constant]; -"985 Constant_5241" [id=985, type=Constant]; -"986 /layers/layers.1/blocks.1/Constant" [id=986, type=Constant]; -"987 Constant_7315" [id=987, type=Constant]; -"988 Constant_7314" [id=988, type=Constant]; -"989 Constant_691" [id=989, type=Constant]; -"990 Transpose_6405" [id=990, type=Constant]; -"991 Constant_25987" [id=991, type=Constant]; -"992 Transpose_6401" [id=992, type=Constant]; -"993 Constant_25983" [id=993, type=Constant]; -"994 Constant_7311" [id=994, type=Constant]; -"995 Constant_7310" [id=995, type=Constant]; -"996 Constant_665" [id=996, type=Constant]; -"997 /layers/layers.1/blocks.0/Constant_7" [id=997, type=Constant]; -"998 /layers/layers.1/blocks.0/Constant_6" [id=998, type=Constant]; -"999 Constant_654" [id=999, type=Constant]; -"1000 /layers/layers.1/blocks.0/Constant_5" [id=1000, type=Constant]; -"1001 /layers/layers.1/blocks.0/Constant_4" [id=1001, type=Constant]; -"1002 Transpose_6397" [id=1002, type=Constant]; -"1003 Constant_25999" [id=1003, type=Constant]; -"1004 /layers/layers.1/blocks.0/attn/Constant_2" [id=1004, type=Constant]; -"1005 Constant_638" [id=1005, type=Constant]; -"1006 Constant_628" [id=1006, type=Constant]; -"1007 Constant_622" [id=1007, type=Constant]; -"1008 /layers/layers.1/blocks.0/attn/Constant" [id=1008, type=Constant]; -"1009 Transpose_6394" [id=1009, type=Constant]; -"1010 Constant_25989" [id=1010, type=Constant]; -"1011 /layers/layers.1/blocks.0/Constant_3" [id=1011, type=Constant]; -"1012 /layers/layers.1/blocks.0/Constant_2" [id=1012, type=Constant]; -"1013 Constant_606" [id=1013, type=Constant]; 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Transpose_6382" [id=1033, type=Constant]; -"1034 Constant_25969" [id=1034, type=Constant]; -"1035 Constant_7300" [id=1035, type=Constant]; -"1036 Constant_7299" [id=1036, type=Constant]; -"1037 Constant_506" [id=1037, type=Constant]; -"1038 /layers/layers.0/blocks.1/Constant_31" [id=1038, type=Constant]; -"1039 Constant_5151" [id=1039, type=Constant]; -"1040 Constant_5148" [id=1040, type=Constant]; -"1041 Constant_5145" [id=1041, type=Constant]; -"1042 Constant_5127" [id=1042, type=Constant]; -"1043 Constant_5124" [id=1043, type=Constant]; -"1044 Constant_5121" [id=1044, type=Constant]; -"1045 /layers/layers.0/blocks.1/Constant_18" [id=1045, type=Constant]; -"1046 Constant_453" [id=1046, type=Constant]; -"1047 /layers/layers.0/blocks.1/Constant_17" [id=1047, type=Constant]; -"1048 /layers/layers.0/blocks.1/Constant_16" [id=1048, type=Constant]; -"1049 Transpose_6378" [id=1049, type=Constant]; -"1050 Constant_25993" [id=1050, type=Constant]; -"1051 /layers/layers.0/blocks.1/attn/Constant_4" [id=1051, type=Constant]; -"1052 Constant_437" [id=1052, type=Constant]; -"1053 Constant_418" [id=1053, type=Constant]; -"1054 Constant_412" [id=1054, type=Constant]; -"1055 /layers/layers.0/blocks.1/attn/Constant" [id=1055, type=Constant]; -"1056 Transpose_6375" [id=1056, type=Constant]; -"1057 Constant_25979" [id=1057, type=Constant]; -"1058 /layers/layers.0/blocks.1/Constant_15" [id=1058, type=Constant]; -"1059 /layers/layers.0/blocks.1/Constant_14" [id=1059, type=Constant]; -"1060 Constant_396" [id=1060, type=Constant]; -"1061 /layers/layers.0/blocks.1/Constant_13" [id=1061, type=Constant]; -"1062 Constant_5103" [id=1062, type=Constant]; -"1063 Constant_5100" [id=1063, type=Constant]; -"1064 Constant_5097" [id=1064, type=Constant]; -"1065 Constant_5079" [id=1065, type=Constant]; -"1066 Constant_5076" [id=1066, type=Constant]; -"1067 Constant_5073" [id=1067, type=Constant]; -"1068 /layers/layers.0/blocks.1/Constant" [id=1068, type=Constant]; -"1069 Constant_7295" [id=1069, type=Constant]; -"1070 Constant_7294" [id=1070, type=Constant]; -"1071 Constant_333" [id=1071, type=Constant]; -"1072 Transpose_6371" [id=1072, type=Constant]; -"1073 Constant_25971" [id=1073, type=Constant]; -"1074 Transpose_6367" [id=1074, type=Constant]; -"1075 Constant_25967" [id=1075, type=Constant]; -"1076 Constant_7291" [id=1076, type=Constant]; -"1077 Constant_7290" [id=1077, type=Constant]; -"1078 Constant_307" [id=1078, type=Constant]; -"1079 /layers/layers.0/blocks.0/Constant_8" [id=1079, type=Constant]; -"1080 /layers/layers.0/blocks.0/Constant_7" [id=1080, type=Constant]; -"1081 Constant_296" [id=1081, type=Constant]; -"1082 /layers/layers.0/blocks.0/Constant_6" [id=1082, type=Constant]; -"1083 /layers/layers.0/blocks.0/Constant_5" [id=1083, type=Constant]; -"1084 Transpose_6363" [id=1084, type=Constant]; -"1085 Constant_25981" [id=1085, type=Constant]; -"1086 /layers/layers.0/blocks.0/attn/Constant_2" [id=1086, type=Constant]; -"1087 Constant_280" [id=1087, type=Constant]; -"1088 Constant_270" [id=1088, type=Constant]; -"1089 Constant_264" [id=1089, type=Constant]; -"1090 /layers/layers.0/blocks.0/attn/Constant" [id=1090, type=Constant]; -"1091 Transpose_6360" [id=1091, type=Constant]; -"1092 Constant_25973" [id=1092, type=Constant]; -"1093 /layers/layers.0/blocks.0/Constant_4" [id=1093, type=Constant]; -"1094 /layers/layers.0/blocks.0/Constant_3" [id=1094, type=Constant]; -"1095 Constant_248" [id=1095, type=Constant]; -"1096 /layers/layers.0/blocks.0/Constant_2" [id=1096, type=Constant]; -"1097 Constant_7286" [id=1097, type=Constant]; -"1098 Constant_7285" [id=1098, type=Constant]; -"1099 Constant_227" [id=1099, type=Constant]; -"1100 Constant_7284" [id=1100, type=Constant]; -"1101 Constant_7283" [id=1101, type=Constant]; -"1102 Constant_213" [id=1102, type=Constant]; -"1103 Constant_211" [id=1103, type=Constant]; -"1104 /patch_embed/Constant_4" [id=1104, type=Constant]; -"1105 Broadcast_201" [id=1105, type=Constant]; -"1106 /patch_embed/Constant_3" [id=1106, type=Constant]; -"1107 /patch_embed/Constant_2" [id=1107, type=Constant]; -"1108 Reshape_190" [id=1108, type=Constant]; -"1109 Gather_7282" [id=1109, type=Constant]; -"1110 Constant_25965" [id=1110, type=Constant]; -"1111 Gather_7279" [id=1111, type=Constant]; -"1112 Gather_7276" [id=1112, type=Constant]; -"1113 Constant_7287" [id=1113, type=Constant]; -"1114 onnx^^Add_2244" [id=1114, label="1114 onnx::Add_2244", type=Constant]; -"1115 Constant_268" [id=1115, type=Constant]; -"1116 /patch_embed/proj/Constant" [id=1116, type=Constant]; -"1117 Constant_7288" [id=1117, type=Constant]; -"1118 Constant_266" [id=1118, type=Constant]; -"1119 /layers/layers.0/blocks.0/Constant" [id=1119, type=Constant]; -"1120 Constant_7289" [id=1120, type=Constant]; -"1121 Constant_7292" [id=1121, type=Constant]; -"1122 Constant_7293" [id=1122, type=Constant]; -"1123 Constant_5067" [id=1123, type=Constant]; -"1124 Constant_5064" [id=1124, type=Constant]; -"1125 Constant_5061" [id=1125, type=Constant]; -"1126 Constant_5091" [id=1126, type=Constant]; -"1127 Constant_5088" [id=1127, type=Constant]; -"1128 Constant_5085" [id=1128, type=Constant]; -"1129 Constant_7296" [id=1129, type=Constant]; -"1130 /layers/layers.0/blocks.1/attn/Constant_3" [id=1130, type=Constant]; -"1131 onnx^^Add_2301" [id=1131, label="1131 onnx::Add_2301", type=Constant]; -"1132 /layers/layers.0/blocks.1/attn/Constant_2" [id=1132, type=Constant]; -"1133 onnx^^Add_2293" [id=1133, label="1133 onnx::Add_2293", type=Constant]; -"1134 Constant_416" [id=1134, type=Constant]; -"1135 Constant_7297" [id=1135, type=Constant]; -"1136 Constant_414" [id=1136, type=Constant]; -"1137 Constant_7298" [id=1137, type=Constant]; -"1138 Constant_5115" [id=1138, type=Constant]; -"1139 Constant_5112" [id=1139, type=Constant]; -"1140 Constant_5109" [id=1140, type=Constant]; -"1141 Constant_5139" [id=1141, type=Constant]; -"1142 Constant_5136" [id=1142, type=Constant]; -"1143 Constant_5133" [id=1143, type=Constant]; -"1144 Constant_7301" [id=1144, type=Constant]; -"1145 Constant_7302" [id=1145, type=Constant]; -"1146 Constant_5211" [id=1146, type=Constant]; -"1147 Constant_5208" [id=1147, type=Constant]; -"1148 Constant_5205" [id=1148, type=Constant]; -"1149 Constant_5163" [id=1149, type=Constant]; -"1150 Constant_5160" [id=1150, type=Constant]; -"1151 Constant_5157" [id=1151, type=Constant]; -"1152 Constant_5199" [id=1152, type=Constant]; -"1153 Constant_5196" [id=1153, type=Constant]; -"1154 Constant_5193" [id=1154, type=Constant]; -"1155 Constant_5175" [id=1155, type=Constant]; -"1156 Constant_5172" [id=1156, type=Constant]; -"1157 Constant_5169" [id=1157, type=Constant]; -"1158 Constant_7307" [id=1158, type=Constant]; -"1159 onnx^^Add_2389" [id=1159, label="1159 onnx::Add_2389", type=Constant]; -"1160 Constant_626" [id=1160, type=Constant]; -"1161 Constant_7308" [id=1161, type=Constant]; -"1162 Constant_624" [id=1162, type=Constant]; -"1163 Constant_7309" [id=1163, type=Constant]; -"1164 Constant_7312" [id=1164, type=Constant]; -"1165 Constant_7313" [id=1165, type=Constant]; -"1166 Constant_5235" [id=1166, type=Constant]; -"1167 Constant_5232" [id=1167, type=Constant]; -"1168 Constant_5229" [id=1168, type=Constant]; -"1169 Constant_5259" [id=1169, type=Constant]; -"1170 Constant_5256" [id=1170, type=Constant]; -"1171 Constant_5253" [id=1171, type=Constant]; -"1172 Constant_7316" [id=1172, type=Constant]; -"1173 /layers/layers.1/blocks.1/attn/Constant_3" [id=1173, type=Constant]; -"1174 onnx^^Add_2446" [id=1174, label="1174 onnx::Add_2446", type=Constant]; -"1175 /layers/layers.1/blocks.1/attn/Constant_2" [id=1175, type=Constant]; -"1176 onnx^^Add_2438" [id=1176, label="1176 onnx::Add_2438", type=Constant]; -"1177 Constant_774" [id=1177, type=Constant]; -"1178 Constant_7317" [id=1178, type=Constant]; -"1179 Constant_772" [id=1179, type=Constant]; -"1180 Constant_7318" [id=1180, type=Constant]; -"1181 Constant_5283" [id=1181, type=Constant]; -"1182 Constant_5280" [id=1182, type=Constant]; -"1183 Constant_5277" [id=1183, type=Constant]; -"1184 Constant_5307" [id=1184, type=Constant]; -"1185 Constant_5304" [id=1185, type=Constant]; -"1186 Constant_5301" [id=1186, type=Constant]; -"1187 Constant_7321" [id=1187, type=Constant]; -"1188 Constant_7322" [id=1188, type=Constant]; -"1189 Constant_5379" [id=1189, type=Constant]; -"1190 Constant_5376" [id=1190, type=Constant]; -"1191 Constant_5373" [id=1191, type=Constant]; -"1192 Constant_5331" [id=1192, type=Constant]; -"1193 Constant_5328" [id=1193, type=Constant]; -"1194 Constant_5325" [id=1194, type=Constant]; -"1195 Constant_5367" [id=1195, type=Constant]; -"1196 Constant_5364" [id=1196, type=Constant]; -"1197 Constant_5361" [id=1197, type=Constant]; -"1198 Constant_5343" [id=1198, type=Constant]; -"1199 Constant_5340" [id=1199, type=Constant]; -"1200 Constant_5337" [id=1200, type=Constant]; -"1201 Constant_7327" [id=1201, type=Constant]; 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type=Constant]; -"1220 Constant_1132" [id=1220, type=Constant]; -"1221 Constant_7337" [id=1221, type=Constant]; -"1222 Constant_1130" [id=1222, type=Constant]; -"1223 Constant_7338" [id=1223, type=Constant]; -"1224 Constant_5451" [id=1224, type=Constant]; -"1225 Constant_5448" [id=1225, type=Constant]; -"1226 Constant_5445" [id=1226, type=Constant]; -"1227 Constant_5475" [id=1227, type=Constant]; -"1228 Constant_5472" [id=1228, type=Constant]; -"1229 Constant_5469" [id=1229, type=Constant]; -"1230 Constant_7341" [id=1230, type=Constant]; -"1231 Constant_7342" [id=1231, type=Constant]; -"1232 Constant_7345" [id=1232, type=Constant]; -"1233 onnx^^Add_2645" [id=1233, label="1233 onnx::Add_2645", type=Constant]; -"1234 Constant_1289" [id=1234, type=Constant]; -"1235 Constant_7346" [id=1235, type=Constant]; -"1236 Constant_1287" [id=1236, type=Constant]; -"1237 Constant_7347" [id=1237, type=Constant]; -"1238 Constant_7350" [id=1238, type=Constant]; -"1239 Constant_7351" [id=1239, type=Constant]; -"1240 Constant_5499" [id=1240, type=Constant]; -"1241 Constant_5496" [id=1241, type=Constant]; -"1242 Constant_5493" [id=1242, type=Constant]; -"1243 Constant_5523" [id=1243, type=Constant]; -"1244 Constant_5520" [id=1244, type=Constant]; -"1245 Constant_5517" [id=1245, type=Constant]; -"1246 Constant_7354" [id=1246, type=Constant]; -"1247 /layers/layers.2/blocks.3/attn/Constant_3" [id=1247, type=Constant]; -"1248 /layers/layers.2/blocks.3/attn/Constant_2" [id=1248, type=Constant]; -"1249 onnx^^Add_2694" [id=1249, label="1249 onnx::Add_2694", type=Constant]; -"1250 Constant_1437" [id=1250, type=Constant]; -"1251 Constant_7355" [id=1251, type=Constant]; -"1252 Constant_1435" [id=1252, type=Constant]; -"1253 Constant_7356" [id=1253, type=Constant]; -"1254 Constant_5547" [id=1254, type=Constant]; -"1255 Constant_5544" [id=1255, type=Constant]; -"1256 Constant_5541" [id=1256, type=Constant]; -"1257 Constant_5571" [id=1257, type=Constant]; -"1258 Constant_5568" [id=1258, type=Constant]; -"1259 Constant_5565" [id=1259, type=Constant]; -"1260 Constant_7359" [id=1260, type=Constant]; -"1261 Constant_7360" [id=1261, type=Constant]; -"1262 Constant_7363" [id=1262, type=Constant]; -"1263 onnx^^Add_2756" [id=1263, label="1263 onnx::Add_2756", type=Constant]; -"1264 Constant_1594" [id=1264, type=Constant]; -"1265 Constant_7364" [id=1265, type=Constant]; -"1266 Constant_1592" [id=1266, type=Constant]; -"1267 Constant_7365" [id=1267, type=Constant]; -"1268 Constant_7368" [id=1268, type=Constant]; -"1269 Constant_7369" [id=1269, type=Constant]; -"1270 Constant_5595" [id=1270, type=Constant]; -"1271 Constant_5592" [id=1271, type=Constant]; -"1272 Constant_5589" [id=1272, type=Constant]; -"1273 Constant_5619" [id=1273, type=Constant]; -"1274 Constant_5616" [id=1274, type=Constant]; -"1275 Constant_5613" [id=1275, type=Constant]; -"1276 Constant_7372" [id=1276, type=Constant]; -"1277 /layers/layers.2/blocks.5/attn/Constant_3" [id=1277, type=Constant]; -"1278 /layers/layers.2/blocks.5/attn/Constant_2" [id=1278, type=Constant]; -"1279 onnx^^Add_2805" [id=1279, label="1279 onnx::Add_2805", type=Constant]; -"1280 Constant_1742" [id=1280, type=Constant]; -"1281 Constant_7373" [id=1281, type=Constant]; -"1282 Constant_1740" [id=1282, type=Constant]; -"1283 Constant_7374" [id=1283, type=Constant]; -"1284 Constant_5643" [id=1284, type=Constant]; -"1285 Constant_5640" [id=1285, type=Constant]; -"1286 Constant_5637" [id=1286, type=Constant]; -"1287 Constant_5667" [id=1287, type=Constant]; -"1288 Constant_5664" [id=1288, type=Constant]; -"1289 Constant_5661" [id=1289, type=Constant]; -"1290 Constant_7377" [id=1290, type=Constant]; -"1291 Constant_7378" [id=1291, type=Constant]; -"1292 Constant_5739" [id=1292, type=Constant]; -"1293 Constant_5736" [id=1293, type=Constant]; -"1294 Constant_5733" [id=1294, type=Constant]; -"1295 Constant_5691" [id=1295, type=Constant]; -"1296 Constant_5688" [id=1296, type=Constant]; -"1297 Constant_5685" [id=1297, type=Constant]; -"1298 Constant_5727" [id=1298, type=Constant]; -"1299 Constant_5724" [id=1299, type=Constant]; -"1300 Constant_5721" [id=1300, type=Constant]; -"1301 Constant_5703" [id=1301, type=Constant]; -"1302 Constant_5700" [id=1302, type=Constant]; -"1303 Constant_5697" [id=1303, type=Constant]; -"1304 Constant_7383" [id=1304, type=Constant]; -"1305 onnx^^Add_2901" [id=1305, label="1305 onnx::Add_2901", type=Constant]; -"1306 Constant_1952" [id=1306, type=Constant]; -"1307 Constant_7384" [id=1307, type=Constant]; -"1308 Constant_1950" [id=1308, type=Constant]; -"1309 Constant_7385" [id=1309, type=Constant]; -"1310 Constant_7388" [id=1310, type=Constant]; -"1311 Constant_7389" [id=1311, type=Constant]; -"1312 Constant_7392" [id=1312, type=Constant]; -"1313 onnx^^Add_2950" [id=1313, label="1313 onnx::Add_2950", type=Constant]; -"1314 Constant_2058" [id=1314, type=Constant]; -"1315 Constant_7393" [id=1315, type=Constant]; -"1316 Constant_2056" [id=1316, type=Constant]; -"1317 Constant_7394" [id=1317, type=Constant]; -"1318 Constant_7397" [id=1318, type=Constant]; -"1319 Constant_7398" [id=1319, type=Constant]; -"0 input" -> "1 Multiply_6579" [label="[1, 3, 224, 224]", style=solid]; -"1 Multiply_6579" -> "2 Divide_2169" [label="[1, 3, 224, 224]", style=solid]; -"2 Divide_2169" -> "3 Divide_2169_0_0/nncf_smooth_quant" [label="[1, 3, 224, 224]", style=solid]; -"3 Divide_2169_0_0/nncf_smooth_quant" -> "4 /patch_embed/proj/Conv/WithoutBiases" [label="[1, 3, 224, 224]", style=solid]; -"4 /patch_embed/proj/Conv/WithoutBiases" -> "5 /patch_embed/proj/Conv" [label="[1, 96, 56, 56]", style=solid]; -"5 /patch_embed/proj/Conv" -> "6 /patch_embed/Reshape" [label="[1, 96, 56, 56]", style=solid]; -"5 /patch_embed/proj/Conv" -> "7 /patch_embed/Shape" [label="[1, 96, 56, 56]", style=solid]; -"6 /patch_embed/Reshape" -> "8 /patch_embed/Transpose" [label="[1, 96, 3136]", style=solid]; -"7 /patch_embed/Shape" -> "9 /patch_embed/Slice" [label="[4]", style=dashed]; -"8 /patch_embed/Transpose" -> "10 /patch_embed/norm/Div" [label="[1, 3136, 96]", style=solid]; -"9 /patch_embed/Slice" -> "11 /patch_embed/Concat" [label="[2]", style=dashed]; -"10 /patch_embed/norm/Div" -> "12 /patch_embed/norm/Mul" [label="[1, 3136, 96]", style=solid]; -"11 /patch_embed/Concat" -> "6 /patch_embed/Reshape" [label="[3]", style=dashed]; -"12 /patch_embed/norm/Mul" -> "13 /patch_embed/norm/Add_1" [label="[1, 3136, 96]", style=solid]; -"13 /patch_embed/norm/Add_1" -> "14 /layers/layers.0/blocks.0/Add" [label="[1, 3136, 96]", style=solid]; -"13 /patch_embed/norm/Add_1" -> "15 /layers/layers.0/blocks.0/norm1/Div" [label="[1, 3136, 96]", style=solid]; -"14 /layers/layers.0/blocks.0/Add" -> "16 /layers/layers.0/blocks.0/Add_1" [label="[1, 3136, 96]", style=solid]; -"14 /layers/layers.0/blocks.0/Add" -> "17 /layers/layers.0/blocks.0/norm2/Div" [label="[1, 3136, 96]", style=solid]; -"15 /layers/layers.0/blocks.0/norm1/Div" -> "18 /layers/layers.0/blocks.0/norm1/Mul" [label="[1, 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"588 /layers/layers.2/blocks.3/Reshape_6" [label="[4]", style=dashed]; -"813 Constant_1474" -> "579 /layers/layers.2/blocks.3/Transpose_1" [label="[6]", style=dashed]; -"814 /layers/layers.2/blocks.3/Constant_17" -> "570 /layers/layers.2/blocks.3/Reshape_5" [label="[6]", style=dashed]; -"815 /layers/layers.2/blocks.3/Constant_16" -> "559 /layers/layers.2/blocks.3/Reshape_4" [label="[4]", style=dashed]; -"816 Transpose_6476" -> "538 /layers/layers.2/blocks.3/attn/proj/MatMul" [label="[384, 384]", style=solid]; -"817 Constant_26051" -> "525 /layers/layers.2/blocks.3/attn/Reshape_3_0_0/nncf_smooth_quant" [label="[1, 1, 384]", style=solid]; -"818 /layers/layers.2/blocks.3/attn/Constant_4" -> "512 /layers/layers.2/blocks.3/attn/Reshape_3" [label="[3]", style=dashed]; -"819 Constant_1458" -> "500 /layers/layers.2/blocks.3/attn/Transpose_2" [label="[4]", style=dashed]; -"820 Constant_1439" -> "475 /layers/layers.2/blocks.3/attn/Gather_2" [label="[]", style=dashed]; -"821 Constant_1433" -> 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-> "330 /layers/layers.2/blocks.3/Slice_3" [label="[3]", style=dashed]; -"831 Constant_5529" -> "330 /layers/layers.2/blocks.3/Slice_3" [label="[3]", style=dashed]; -"832 Constant_5511" -> "300 /layers/layers.2/blocks.3/Slice_1" [label="[2]", style=dashed]; -"833 Constant_5508" -> "300 /layers/layers.2/blocks.3/Slice_1" [label="[2]", style=dashed]; -"834 Constant_5505" -> "300 /layers/layers.2/blocks.3/Slice_1" [label="[2]", style=dashed]; -"835 /layers/layers.2/blocks.3/Constant" -> "284 /layers/layers.2/blocks.3/Reshape" [label="[4]", style=dashed]; -"836 Constant_7353" -> "269 /layers/layers.2/blocks.3/norm1/Add_1" [label="[1, 1, 384]", style=solid]; -"837 Constant_7352" -> "256 /layers/layers.2/blocks.3/norm1/Mul" [label="[1, 1, 384]", style=solid]; -"838 Constant_1354" -> "241 /layers/layers.2/blocks.3/norm1/Div" [label="[1]", style=dashed]; -"839 Transpose_6469" -> "347 /layers/layers.2/blocks.2/mlp/fc2/MatMul" [label="[384, 1536]", style=solid]; -"840 Constant_26017" -> "331 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/layers/layers.2/blocks.2/Constant_5" -> "489 /layers/layers.2/blocks.2/Reshape_5" [label="[6]", style=dashed]; -"850 /layers/layers.2/blocks.2/Constant_4" -> "476 /layers/layers.2/blocks.2/Reshape_4" [label="[4]", style=dashed]; -"851 Transpose_6461" -> "453 /layers/layers.2/blocks.2/attn/proj/MatMul" [label="[384, 384]", style=solid]; -"852 Constant_26039" -> "441 /layers/layers.2/blocks.2/attn/Reshape_1_0_0/nncf_smooth_quant" [label="[1, 1, 384]", style=solid]; -"853 /layers/layers.2/blocks.2/attn/Constant_2" -> "428 /layers/layers.2/blocks.2/attn/Reshape_1" [label="[3]", style=dashed]; -"854 Constant_1301" -> "417 /layers/layers.2/blocks.2/attn/Transpose_2" [label="[4]", style=dashed]; -"855 Constant_1291" -> "393 /layers/layers.2/blocks.2/attn/Gather_2" [label="[]", style=dashed]; -"856 Constant_1285" -> "378 /layers/layers.2/blocks.2/attn/Transpose" [label="[5]", style=dashed]; -"857 /layers/layers.2/blocks.2/attn/Constant" -> "366 /layers/layers.2/blocks.2/attn/Reshape" 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-"876 Constant_5484" -> "581 /layers/layers.2/blocks.1/Slice_7" [label="[3]", style=dashed]; -"877 Constant_5481" -> "581 /layers/layers.2/blocks.1/Slice_7" [label="[3]", style=dashed]; -"878 Constant_5463" -> "561 /layers/layers.2/blocks.1/Slice_5" [label="[2]", style=dashed]; -"879 Constant_5460" -> "561 /layers/layers.2/blocks.1/Slice_5" [label="[2]", style=dashed]; -"880 Constant_5457" -> "561 /layers/layers.2/blocks.1/Slice_5" [label="[2]", style=dashed]; -"881 /layers/layers.2/blocks.1/Constant_18" -> "550 /layers/layers.2/blocks.1/Reshape_6" [label="[4]", style=dashed]; -"882 Constant_1169" -> "539 /layers/layers.2/blocks.1/Transpose_1" [label="[6]", style=dashed]; -"883 /layers/layers.2/blocks.1/Constant_17" -> "527 /layers/layers.2/blocks.1/Reshape_5" [label="[6]", style=dashed]; -"884 /layers/layers.2/blocks.1/Constant_16" -> "514 /layers/layers.2/blocks.1/Reshape_4" [label="[4]", style=dashed]; -"885 Transpose_6446" -> "491 /layers/layers.2/blocks.1/attn/proj/MatMul" [label="[384, 384]", style=solid]; -"886 Constant_26045" -> "478 /layers/layers.2/blocks.1/attn/Reshape_3_0_0/nncf_smooth_quant" [label="[1, 1, 384]", style=solid]; -"887 /layers/layers.2/blocks.1/attn/Constant_4" -> "465 /layers/layers.2/blocks.1/attn/Reshape_3" [label="[3]", style=dashed]; -"888 Constant_1153" -> "455 /layers/layers.2/blocks.1/attn/Transpose_2" [label="[4]", style=dashed]; -"889 Constant_1134" -> "431 /layers/layers.2/blocks.1/attn/Gather_2" [label="[]", style=dashed]; -"890 Constant_1128" -> "418 /layers/layers.2/blocks.1/attn/Transpose" [label="[5]", style=dashed]; -"891 /layers/layers.2/blocks.1/attn/Constant" -> "407 /layers/layers.2/blocks.1/attn/Reshape" [label="[5]", style=dashed]; -"892 Transpose_6443" -> "379 /layers/layers.2/blocks.1/attn/qkv/MatMul" [label="[1152, 384]", style=solid]; -"893 Constant_26027" -> "367 /layers/layers.2/blocks.1/Reshape_3_0_0/nncf_smooth_quant" [label="[1, 1, 384]", style=solid]; -"894 /layers/layers.2/blocks.1/Constant_15" -> "349 /layers/layers.2/blocks.1/Reshape_3" [label="[3]", style=dashed]; -"895 /layers/layers.2/blocks.1/Constant_14" -> "334 /layers/layers.2/blocks.1/Reshape_2" [label="[4]", style=dashed]; -"896 Constant_1112" -> "319 /layers/layers.2/blocks.1/Transpose" [label="[6]", style=dashed]; -"897 /layers/layers.2/blocks.1/Constant_13" -> "304 /layers/layers.2/blocks.1/Reshape_1" [label="[6]", style=dashed]; -"898 Constant_5439" -> "274 /layers/layers.2/blocks.1/Slice_3" [label="[3]", style=dashed]; -"899 Constant_5436" -> "274 /layers/layers.2/blocks.1/Slice_3" [label="[3]", style=dashed]; -"900 Constant_5433" -> "274 /layers/layers.2/blocks.1/Slice_3" [label="[3]", style=dashed]; -"901 Constant_5415" -> "246 /layers/layers.2/blocks.1/Slice_1" [label="[2]", style=dashed]; -"902 Constant_5412" -> "246 /layers/layers.2/blocks.1/Slice_1" [label="[2]", style=dashed]; -"903 Constant_5409" -> "246 /layers/layers.2/blocks.1/Slice_1" [label="[2]", style=dashed]; -"904 /layers/layers.2/blocks.1/Constant" -> "232 /layers/layers.2/blocks.1/Reshape" [label="[4]", style=dashed]; -"905 Constant_7335" -> "221 /layers/layers.2/blocks.1/norm1/Add_1" [label="[1, 1, 384]", style=solid]; -"906 Constant_7334" -> "212 /layers/layers.2/blocks.1/norm1/Mul" [label="[1, 1, 384]", style=solid]; -"907 Constant_1049" -> "203 /layers/layers.2/blocks.1/norm1/Div" [label="[1]", style=dashed]; -"908 Transpose_6439" -> "289 /layers/layers.2/blocks.0/mlp/fc2/MatMul" [label="[384, 1536]", style=solid]; -"909 Constant_26005" -> "275 /layers/layers.2/blocks.0/mlp/act/Mul_1_0_0/nncf_smooth_quant" [label="[1, 1, 1536]", style=solid]; -"910 Transpose_6435" -> "233 /layers/layers.2/blocks.0/mlp/fc1/MatMul" [label="[1536, 384]", style=solid]; -"911 Constant_26001" -> "222 /layers/layers.2/blocks.0/norm2/Add_1_0_0/nncf_smooth_quant" [label="[1, 1, 384]", style=solid]; -"912 Constant_7331" -> "213 /layers/layers.2/blocks.0/norm2/Add_1" [label="[1, 1, 384]", style=solid]; -"913 Constant_7330" -> "204 /layers/layers.2/blocks.0/norm2/Mul" [label="[1, 1, 384]", style=solid]; -"914 Constant_1023" -> "196 /layers/layers.2/blocks.0/norm2/Div" [label="[1]", style=dashed]; -"915 /layers/layers.2/blocks.0/Constant_7" -> "479 /layers/layers.2/blocks.0/Reshape_7" [label="[3]", style=dashed]; -"916 /layers/layers.2/blocks.0/Constant_6" -> "466 /layers/layers.2/blocks.0/Reshape_6" [label="[4]", style=dashed]; -"917 Constant_1012" -> "456 /layers/layers.2/blocks.0/Transpose_1" [label="[6]", style=dashed]; -"918 /layers/layers.2/blocks.0/Constant_5" -> "445 /layers/layers.2/blocks.0/Reshape_5" [label="[6]", style=dashed]; -"919 /layers/layers.2/blocks.0/Constant_4" -> "432 /layers/layers.2/blocks.0/Reshape_4" [label="[4]", style=dashed]; -"920 Transpose_6431" -> "408 /layers/layers.2/blocks.0/attn/proj/MatMul" [label="[384, 384]", style=solid]; -"921 Constant_26033" -> "395 /layers/layers.2/blocks.0/attn/Reshape_1_0_0/nncf_smooth_quant" [label="[1, 1, 384]", style=solid]; -"922 /layers/layers.2/blocks.0/attn/Constant_2" -> "381 /layers/layers.2/blocks.0/attn/Reshape_1" [label="[3]", style=dashed]; -"923 Constant_996" -> "369 /layers/layers.2/blocks.0/attn/Transpose_2" [label="[4]", style=dashed]; -"924 Constant_986" -> "337 /layers/layers.2/blocks.0/attn/Gather_2" [label="[]", style=dashed]; -"925 Constant_980" -> "320 /layers/layers.2/blocks.0/attn/Transpose" [label="[5]", style=dashed]; -"926 /layers/layers.2/blocks.0/attn/Constant" -> "306 /layers/layers.2/blocks.0/attn/Reshape" [label="[5]", style=dashed]; -"927 Transpose_6428" -> "276 /layers/layers.2/blocks.0/attn/qkv/MatMul" [label="[1152, 384]", style=solid]; -"928 Constant_26007" -> "262 /layers/layers.2/blocks.0/Reshape_3_0_0/nncf_smooth_quant" [label="[1, 1, 384]", style=solid]; -"929 /layers/layers.2/blocks.0/Constant_3" -> "248 /layers/layers.2/blocks.0/Reshape_3" [label="[3]", style=dashed]; -"930 /layers/layers.2/blocks.0/Constant_2" -> "234 /layers/layers.2/blocks.0/Reshape_2" [label="[4]", style=dashed]; -"931 Constant_964" -> "223 /layers/layers.2/blocks.0/Transpose" [label="[6]", style=dashed]; -"932 /layers/layers.2/blocks.0/Constant_1" -> "214 /layers/layers.2/blocks.0/Reshape_1" [label="[6]", style=dashed]; -"933 Constant_7326" -> "205 /layers/layers.2/blocks.0/norm1/Add_1" [label="[1, 1, 384]", style=solid]; -"934 Constant_7325" -> "197 /layers/layers.2/blocks.0/norm1/Mul" [label="[1, 1, 384]", style=solid]; -"935 Constant_943" -> "190 /layers/layers.2/blocks.0/norm1/Div" [label="[1]", style=dashed]; -"936 Transpose_6424" -> "183 /layers/layers.1/downsample/reduction/MatMul" [label="[384, 768]", style=solid]; -"937 Constant_25995" -> "176 /layers/layers.1/downsample/norm/Add_1_0_0/nncf_smooth_quant" [label="[1, 1, 768]", style=solid]; -"938 Constant_7324" -> "170 /layers/layers.1/downsample/norm/Add_1" [label="[1, 1, 768]", style=solid]; -"939 Constant_7323" -> "163 /layers/layers.1/downsample/norm/Mul" [label="[1, 1, 768]", style=solid]; -"940 Constant_929" -> "155 /layers/layers.1/downsample/norm/Div" [label="[1]", style=dashed]; -"941 /layers/layers.1/downsample/Constant_25" -> "146 /layers/layers.1/downsample/Reshape_1" [label="[3]", style=dashed]; -"942 Constant_5391" -> "128 /layers/layers.1/downsample/Slice_5" [label="[3]", style=dashed]; -"943 Constant_5388" -> "128 /layers/layers.1/downsample/Slice_5" [label="[3]", style=dashed]; -"944 Constant_5385" -> "128 /layers/layers.1/downsample/Slice_5" [label="[3]", style=dashed]; -"945 Constant_5355" -> "116 /layers/layers.1/downsample/Slice_2" [label="[2]", style=dashed]; -"946 Constant_5352" -> "116 /layers/layers.1/downsample/Slice_2" [label="[2]", style=dashed]; -"947 Constant_5349" -> "116 /layers/layers.1/downsample/Slice_2" [label="[2]", style=dashed]; -"948 /layers/layers.1/downsample/Constant" -> "108 /layers/layers.1/downsample/Reshape" [label="[4]", style=dashed]; -"949 Transpose_6420" -> "171 /layers/layers.1/blocks.1/mlp/fc2/MatMul" [label="[192, 768]", style=solid]; -"950 Constant_25991" -> "164 /layers/layers.1/blocks.1/mlp/act/Mul_1_0_0/nncf_smooth_quant" [label="[1, 1, 768]", style=solid]; -"951 Transpose_6416" -> "139 /layers/layers.1/blocks.1/mlp/fc1/MatMul" [label="[768, 192]", style=solid]; -"952 Constant_25985" -> "129 /layers/layers.1/blocks.1/norm2/Add_1_0_0/nncf_smooth_quant" [label="[1, 1, 192]", style=solid]; -"953 Constant_7320" -> "117 /layers/layers.1/blocks.1/norm2/Add_1" [label="[1, 1, 192]", style=solid]; -"954 Constant_7319" -> "109 /layers/layers.1/blocks.1/norm2/Mul" [label="[1, 1, 192]", style=solid]; -"955 Constant_864" -> "102 /layers/layers.1/blocks.1/norm2/Div" [label="[1]", style=dashed]; -"956 /layers/layers.1/blocks.1/Constant_31" -> "446 /layers/layers.1/blocks.1/Reshape_7" [label="[3]", style=dashed]; -"957 Constant_5319" -> "421 /layers/layers.1/blocks.1/Slice_7" [label="[3]", style=dashed]; -"958 Constant_5316" -> "421 /layers/layers.1/blocks.1/Slice_7" [label="[3]", style=dashed]; -"959 Constant_5313" -> "421 /layers/layers.1/blocks.1/Slice_7" [label="[3]", style=dashed]; -"960 Constant_5295" -> "397 /layers/layers.1/blocks.1/Slice_5" [label="[2]", style=dashed]; -"961 Constant_5292" -> "397 /layers/layers.1/blocks.1/Slice_5" [label="[2]", style=dashed]; -"962 Constant_5289" -> "397 /layers/layers.1/blocks.1/Slice_5" [label="[2]", style=dashed]; -"963 /layers/layers.1/blocks.1/Constant_18" -> "382 /layers/layers.1/blocks.1/Reshape_6" [label="[4]", style=dashed]; -"964 Constant_811" -> "370 /layers/layers.1/blocks.1/Transpose_1" [label="[6]", style=dashed]; -"965 /layers/layers.1/blocks.1/Constant_17" -> "353 /layers/layers.1/blocks.1/Reshape_5" [label="[6]", style=dashed]; -"966 /layers/layers.1/blocks.1/Constant_16" -> "338 /layers/layers.1/blocks.1/Reshape_4" [label="[4]", style=dashed]; -"967 Transpose_6412" -> "308 /layers/layers.1/blocks.1/attn/proj/MatMul" [label="[192, 192]", style=solid]; -"968 Constant_26013" -> "292 /layers/layers.1/blocks.1/attn/Reshape_3_0_0/nncf_smooth_quant" [label="[1, 1, 192]", style=solid]; -"969 /layers/layers.1/blocks.1/attn/Constant_4" -> "278 /layers/layers.1/blocks.1/attn/Reshape_3" [label="[3]", style=dashed]; -"970 Constant_795" -> "264 /layers/layers.1/blocks.1/attn/Transpose_2" [label="[4]", style=dashed]; -"971 Constant_776" -> "237 /layers/layers.1/blocks.1/attn/Gather_2" [label="[]", style=dashed]; -"972 Constant_770" -> "224 /layers/layers.1/blocks.1/attn/Transpose" [label="[5]", style=dashed]; -"973 /layers/layers.1/blocks.1/attn/Constant" -> "215 /layers/layers.1/blocks.1/attn/Reshape" [label="[5]", style=dashed]; -"974 Transpose_6409" -> "198 /layers/layers.1/blocks.1/attn/qkv/MatMul" [label="[576, 192]", style=solid]; -"975 Constant_25997" -> "191 /layers/layers.1/blocks.1/Reshape_3_0_0/nncf_smooth_quant" [label="[1, 1, 192]", style=solid]; -"976 /layers/layers.1/blocks.1/Constant_15" -> "184 /layers/layers.1/blocks.1/Reshape_3" [label="[3]", style=dashed]; -"977 /layers/layers.1/blocks.1/Constant_14" -> "178 /layers/layers.1/blocks.1/Reshape_2" [label="[4]", style=dashed]; -"978 Constant_754" -> "172 /layers/layers.1/blocks.1/Transpose" [label="[6]", style=dashed]; -"979 /layers/layers.1/blocks.1/Constant_13" -> "165 /layers/layers.1/blocks.1/Reshape_1" [label="[6]", style=dashed]; -"980 Constant_5271" -> "149 /layers/layers.1/blocks.1/Slice_3" [label="[3]", style=dashed]; -"981 Constant_5268" -> "149 /layers/layers.1/blocks.1/Slice_3" [label="[3]", style=dashed]; -"982 Constant_5265" -> "149 /layers/layers.1/blocks.1/Slice_3" [label="[3]", style=dashed]; -"983 Constant_5247" -> "131 /layers/layers.1/blocks.1/Slice_1" [label="[2]", style=dashed]; -"984 Constant_5244" -> "131 /layers/layers.1/blocks.1/Slice_1" [label="[2]", style=dashed]; -"985 Constant_5241" -> "131 /layers/layers.1/blocks.1/Slice_1" [label="[2]", style=dashed]; -"986 /layers/layers.1/blocks.1/Constant" -> "118 /layers/layers.1/blocks.1/Reshape" [label="[4]", style=dashed]; -"987 Constant_7315" -> "110 /layers/layers.1/blocks.1/norm1/Add_1" [label="[1, 1, 192]", style=solid]; -"988 Constant_7314" -> "103 /layers/layers.1/blocks.1/norm1/Mul" [label="[1, 1, 192]", style=solid]; -"989 Constant_691" -> "96 /layers/layers.1/blocks.1/norm1/Div" [label="[1]", style=dashed]; -"990 Transpose_6405" -> "158 /layers/layers.1/blocks.0/mlp/fc2/MatMul" [label="[192, 768]", style=solid]; -"991 Constant_25987" -> "150 /layers/layers.1/blocks.0/mlp/act/Mul_1_0_0/nncf_smooth_quant" [label="[1, 1, 768]", style=solid]; -"992 Transpose_6401" -> "119 /layers/layers.1/blocks.0/mlp/fc1/MatMul" [label="[768, 192]", style=solid]; -"993 Constant_25983" -> "111 /layers/layers.1/blocks.0/norm2/Add_1_0_0/nncf_smooth_quant" [label="[1, 1, 192]", style=solid]; -"994 Constant_7311" -> "104 /layers/layers.1/blocks.0/norm2/Add_1" [label="[1, 1, 192]", style=solid]; -"995 Constant_7310" -> "97 /layers/layers.1/blocks.0/norm2/Mul" [label="[1, 1, 192]", style=solid]; -"996 Constant_665" -> "90 /layers/layers.1/blocks.0/norm2/Div" [label="[1]", style=dashed]; -"997 /layers/layers.1/blocks.0/Constant_7" -> "293 /layers/layers.1/blocks.0/Reshape_7" [label="[3]", style=dashed]; -"998 /layers/layers.1/blocks.0/Constant_6" -> "279 /layers/layers.1/blocks.0/Reshape_6" [label="[4]", style=dashed]; -"999 Constant_654" -> "265 /layers/layers.1/blocks.0/Transpose_1" [label="[6]", style=dashed]; -"1000 /layers/layers.1/blocks.0/Constant_5" -> "252 /layers/layers.1/blocks.0/Reshape_5" [label="[6]", style=dashed]; -"1001 /layers/layers.1/blocks.0/Constant_4" -> "238 /layers/layers.1/blocks.0/Reshape_4" [label="[4]", style=dashed]; -"1002 Transpose_6397" -> "216 /layers/layers.1/blocks.0/attn/proj/MatMul" [label="[192, 192]", style=solid]; -"1003 Constant_25999" -> "207 /layers/layers.1/blocks.0/attn/Reshape_1_0_0/nncf_smooth_quant" [label="[1, 1, 192]", style=solid]; -"1004 /layers/layers.1/blocks.0/attn/Constant_2" -> "200 /layers/layers.1/blocks.0/attn/Reshape_1" [label="[3]", style=dashed]; -"1005 Constant_638" -> "193 /layers/layers.1/blocks.0/attn/Transpose_2" [label="[4]", style=dashed]; -"1006 Constant_628" -> "181 /layers/layers.1/blocks.0/attn/Gather_2" [label="[]", style=dashed]; -"1007 Constant_622" -> "173 /layers/layers.1/blocks.0/attn/Transpose" [label="[5]", style=dashed]; -"1008 /layers/layers.1/blocks.0/attn/Constant" -> "167 /layers/layers.1/blocks.0/attn/Reshape" [label="[5]", style=dashed]; -"1009 Transpose_6394" -> "151 /layers/layers.1/blocks.0/attn/qkv/MatMul" [label="[576, 192]", style=solid]; -"1010 Constant_25989" -> "142 /layers/layers.1/blocks.0/Reshape_3_0_0/nncf_smooth_quant" [label="[1, 1, 192]", style=solid]; -"1011 /layers/layers.1/blocks.0/Constant_3" -> "133 /layers/layers.1/blocks.0/Reshape_3" [label="[3]", style=dashed]; -"1012 /layers/layers.1/blocks.0/Constant_2" -> "120 /layers/layers.1/blocks.0/Reshape_2" [label="[4]", style=dashed]; -"1013 Constant_606" -> "112 /layers/layers.1/blocks.0/Transpose" [label="[6]", style=dashed]; -"1014 /layers/layers.1/blocks.0/Constant_1" -> "105 /layers/layers.1/blocks.0/Reshape_1" [label="[6]", style=dashed]; -"1015 Constant_7306" -> "98 /layers/layers.1/blocks.0/norm1/Add_1" [label="[1, 1, 192]", style=solid]; -"1016 Constant_7305" -> "91 /layers/layers.1/blocks.0/norm1/Mul" [label="[1, 1, 192]", style=solid]; -"1017 Constant_585" -> "85 /layers/layers.1/blocks.0/norm1/Div" [label="[1]", style=dashed]; -"1018 Transpose_6390" -> "79 /layers/layers.0/downsample/reduction/MatMul" [label="[192, 384]", style=solid]; -"1019 Constant_25977" -> "73 /layers/layers.0/downsample/norm/Add_1_0_0/nncf_smooth_quant" [label="[1, 1, 384]", style=solid]; -"1020 Constant_7304" -> "69 /layers/layers.0/downsample/norm/Add_1" [label="[1, 1, 384]", style=solid]; -"1021 Constant_7303" -> "64 /layers/layers.0/downsample/norm/Mul" [label="[1, 1, 384]", style=solid]; -"1022 Constant_571" -> "59 /layers/layers.0/downsample/norm/Div" [label="[1]", style=dashed]; -"1023 /layers/layers.0/downsample/Constant_25" -> "53 /layers/layers.0/downsample/Reshape_1" [label="[3]", style=dashed]; -"1024 Constant_5223" -> "42 /layers/layers.0/downsample/Slice_5" [label="[3]", style=dashed]; -"1025 Constant_5220" -> "42 /layers/layers.0/downsample/Slice_5" [label="[3]", style=dashed]; -"1026 Constant_5217" -> "42 /layers/layers.0/downsample/Slice_5" [label="[3]", style=dashed]; -"1027 Constant_5187" -> "34 /layers/layers.0/downsample/Slice_2" [label="[2]", style=dashed]; -"1028 Constant_5184" -> "34 /layers/layers.0/downsample/Slice_2" [label="[2]", style=dashed]; -"1029 Constant_5181" -> "34 /layers/layers.0/downsample/Slice_2" [label="[2]", style=dashed]; -"1030 /layers/layers.0/downsample/Constant" -> "28 /layers/layers.0/downsample/Reshape" [label="[4]", style=dashed]; -"1031 Transpose_6386" -> "70 /layers/layers.0/blocks.1/mlp/fc2/MatMul" [label="[96, 384]", style=solid]; -"1032 Constant_25975" -> "65 /layers/layers.0/blocks.1/mlp/act/Mul_1_0_0/nncf_smooth_quant" [label="[1, 1, 384]", style=solid]; -"1033 Transpose_6382" -> "49 /layers/layers.0/blocks.1/mlp/fc1/MatMul" [label="[384, 96]", style=solid]; -"1034 Constant_25969" -> "43 /layers/layers.0/blocks.1/norm2/Add_1_0_0/nncf_smooth_quant" [label="[1, 1, 96]", style=solid]; -"1035 Constant_7300" -> "35 /layers/layers.0/blocks.1/norm2/Add_1" [label="[1, 1, 96]", style=solid]; -"1036 Constant_7299" -> "29 /layers/layers.0/blocks.1/norm2/Mul" [label="[1, 1, 96]", style=solid]; -"1037 Constant_506" -> "24 /layers/layers.0/blocks.1/norm2/Div" [label="[1]", style=dashed]; -"1038 /layers/layers.0/blocks.1/Constant_31" -> "253 /layers/layers.0/blocks.1/Reshape_7" [label="[3]", style=dashed]; -"1039 Constant_5151" -> "227 /layers/layers.0/blocks.1/Slice_7" [label="[3]", style=dashed]; -"1040 Constant_5148" -> "227 /layers/layers.0/blocks.1/Slice_7" [label="[3]", style=dashed]; -"1041 Constant_5145" -> "227 /layers/layers.0/blocks.1/Slice_7" [label="[3]", style=dashed]; -"1042 Constant_5127" -> "209 /layers/layers.0/blocks.1/Slice_5" [label="[2]", style=dashed]; -"1043 Constant_5124" -> "209 /layers/layers.0/blocks.1/Slice_5" [label="[2]", style=dashed]; -"1044 Constant_5121" -> "209 /layers/layers.0/blocks.1/Slice_5" [label="[2]", style=dashed]; -"1045 /layers/layers.0/blocks.1/Constant_18" -> "201 /layers/layers.0/blocks.1/Reshape_6" [label="[4]", style=dashed]; -"1046 Constant_453" -> "194 /layers/layers.0/blocks.1/Transpose_1" [label="[6]", style=dashed]; -"1047 /layers/layers.0/blocks.1/Constant_17" -> "188 /layers/layers.0/blocks.1/Reshape_5" [label="[6]", style=dashed]; -"1048 /layers/layers.0/blocks.1/Constant_16" -> "182 /layers/layers.0/blocks.1/Reshape_4" [label="[4]", style=dashed]; -"1049 Transpose_6378" -> "169 /layers/layers.0/blocks.1/attn/proj/MatMul" [label="[96, 96]", style=solid]; -"1050 Constant_25993" -> "161 /layers/layers.0/blocks.1/attn/Reshape_3_0_0/nncf_smooth_quant" [label="[1, 1, 96]", style=solid]; -"1051 /layers/layers.0/blocks.1/attn/Constant_4" -> "153 /layers/layers.0/blocks.1/attn/Reshape_3" [label="[3]", style=dashed]; -"1052 Constant_437" -> "144 /layers/layers.0/blocks.1/attn/Transpose_2" [label="[4]", style=dashed]; -"1053 Constant_418" -> "123 /layers/layers.0/blocks.1/attn/Gather_2" [label="[]", style=dashed]; -"1054 Constant_412" -> "113 /layers/layers.0/blocks.1/attn/Transpose" [label="[5]", style=dashed]; -"1055 /layers/layers.0/blocks.1/attn/Constant" -> "106 /layers/layers.0/blocks.1/attn/Reshape" [label="[5]", style=dashed]; -"1056 Transpose_6375" -> "92 /layers/layers.0/blocks.1/attn/qkv/MatMul" [label="[288, 96]", style=solid]; -"1057 Constant_25979" -> "86 /layers/layers.0/blocks.1/Reshape_3_0_0/nncf_smooth_quant" [label="[1, 1, 96]", style=solid]; -"1058 /layers/layers.0/blocks.1/Constant_15" -> "80 /layers/layers.0/blocks.1/Reshape_3" [label="[3]", style=dashed]; -"1059 /layers/layers.0/blocks.1/Constant_14" -> "75 /layers/layers.0/blocks.1/Reshape_2" [label="[4]", style=dashed]; -"1060 Constant_396" -> "71 /layers/layers.0/blocks.1/Transpose" [label="[6]", style=dashed]; -"1061 /layers/layers.0/blocks.1/Constant_13" -> "66 /layers/layers.0/blocks.1/Reshape_1" [label="[6]", style=dashed]; -"1062 Constant_5103" -> "56 /layers/layers.0/blocks.1/Slice_3" [label="[3]", style=dashed]; -"1063 Constant_5100" -> "56 /layers/layers.0/blocks.1/Slice_3" [label="[3]", style=dashed]; -"1064 Constant_5097" -> "56 /layers/layers.0/blocks.1/Slice_3" [label="[3]", style=dashed]; -"1065 Constant_5079" -> "45 /layers/layers.0/blocks.1/Slice_1" [label="[2]", style=dashed]; -"1066 Constant_5076" -> "45 /layers/layers.0/blocks.1/Slice_1" [label="[2]", style=dashed]; -"1067 Constant_5073" -> "45 /layers/layers.0/blocks.1/Slice_1" [label="[2]", style=dashed]; -"1068 /layers/layers.0/blocks.1/Constant" -> "36 /layers/layers.0/blocks.1/Reshape" [label="[4]", style=dashed]; -"1069 Constant_7295" -> "30 /layers/layers.0/blocks.1/norm1/Add_1" [label="[1, 1, 96]", style=solid]; -"1070 Constant_7294" -> "25 /layers/layers.0/blocks.1/norm1/Mul" [label="[1, 1, 96]", style=solid]; -"1071 Constant_333" -> "20 /layers/layers.0/blocks.1/norm1/Div" [label="[1]", style=dashed]; -"1072 Transpose_6371" -> "62 /layers/layers.0/blocks.0/mlp/fc2/MatMul" [label="[96, 384]", style=solid]; -"1073 Constant_25971" -> "57 /layers/layers.0/blocks.0/mlp/act/Mul_1_0_0/nncf_smooth_quant" [label="[1, 1, 384]", style=solid]; -"1074 Transpose_6367" -> "37 /layers/layers.0/blocks.0/mlp/fc1/MatMul" [label="[384, 96]", style=solid]; -"1075 Constant_25967" -> "31 /layers/layers.0/blocks.0/norm2/Add_1_0_0/nncf_smooth_quant" [label="[1, 1, 96]", style=solid]; -"1076 Constant_7291" -> "26 /layers/layers.0/blocks.0/norm2/Add_1" [label="[1, 1, 96]", style=solid]; -"1077 Constant_7290" -> "21 /layers/layers.0/blocks.0/norm2/Mul" [label="[1, 1, 96]", style=solid]; -"1078 Constant_307" -> "17 /layers/layers.0/blocks.0/norm2/Div" [label="[1]", style=dashed]; -"1079 /layers/layers.0/blocks.0/Constant_8" -> "162 /layers/layers.0/blocks.0/Reshape_7" [label="[3]", style=dashed]; -"1080 /layers/layers.0/blocks.0/Constant_7" -> "154 /layers/layers.0/blocks.0/Reshape_6" [label="[4]", style=dashed]; -"1081 Constant_296" -> "145 /layers/layers.0/blocks.0/Transpose_1" [label="[6]", style=dashed]; -"1082 /layers/layers.0/blocks.0/Constant_6" -> "137 /layers/layers.0/blocks.0/Reshape_5" [label="[6]", style=dashed]; -"1083 /layers/layers.0/blocks.0/Constant_5" -> "124 /layers/layers.0/blocks.0/Reshape_4" [label="[4]", style=dashed]; -"1084 Transpose_6363" -> "107 /layers/layers.0/blocks.0/attn/proj/MatMul" [label="[96, 96]", style=solid]; -"1085 Constant_25981" -> "100 /layers/layers.0/blocks.0/attn/Reshape_1_0_0/nncf_smooth_quant" [label="[1, 1, 96]", style=solid]; -"1086 /layers/layers.0/blocks.0/attn/Constant_2" -> "94 /layers/layers.0/blocks.0/attn/Reshape_1" [label="[3]", style=dashed]; -"1087 Constant_280" -> "88 /layers/layers.0/blocks.0/attn/Transpose_2" [label="[4]", style=dashed]; -"1088 Constant_270" -> "78 /layers/layers.0/blocks.0/attn/Gather_2" [label="[]", style=dashed]; -"1089 Constant_264" -> "72 /layers/layers.0/blocks.0/attn/Transpose" [label="[5]", style=dashed]; -"1090 /layers/layers.0/blocks.0/attn/Constant" -> "68 /layers/layers.0/blocks.0/attn/Reshape" [label="[5]", style=dashed]; -"1091 Transpose_6360" -> "58 /layers/layers.0/blocks.0/attn/qkv/MatMul" [label="[288, 96]", style=solid]; -"1092 Constant_25973" -> "52 /layers/layers.0/blocks.0/Reshape_3_0_0/nncf_smooth_quant" [label="[1, 1, 96]", style=solid]; -"1093 /layers/layers.0/blocks.0/Constant_4" -> "47 /layers/layers.0/blocks.0/Reshape_3" [label="[3]", style=dashed]; -"1094 /layers/layers.0/blocks.0/Constant_3" -> "38 /layers/layers.0/blocks.0/Reshape_2" [label="[4]", style=dashed]; -"1095 Constant_248" -> "32 /layers/layers.0/blocks.0/Transpose" [label="[6]", style=dashed]; -"1096 /layers/layers.0/blocks.0/Constant_2" -> "27 /layers/layers.0/blocks.0/Reshape_1" [label="[6]", style=dashed]; -"1097 Constant_7286" -> "22 /layers/layers.0/blocks.0/norm1/Add_1" [label="[1, 1, 96]", style=solid]; -"1098 Constant_7285" -> "18 /layers/layers.0/blocks.0/norm1/Mul" [label="[1, 1, 96]", style=solid]; -"1099 Constant_227" -> "15 /layers/layers.0/blocks.0/norm1/Div" [label="[1]", style=dashed]; -"1100 Constant_7284" -> "13 /patch_embed/norm/Add_1" [label="[1, 1, 96]", style=solid]; -"1101 Constant_7283" -> "12 /patch_embed/norm/Mul" [label="[1, 1, 96]", style=solid]; -"1102 Constant_213" -> "10 /patch_embed/norm/Div" [label="[1]", style=dashed]; -"1103 Constant_211" -> "8 /patch_embed/Transpose" [label="[3]", style=dashed]; -"1104 /patch_embed/Constant_4" -> "11 /patch_embed/Concat" [label="[1]", style=dashed]; -"1105 Broadcast_201" -> "9 /patch_embed/Slice" [label="[1]", style=dashed]; -"1106 /patch_embed/Constant_3" -> "9 /patch_embed/Slice" [label="[1]", style=dashed]; -"1107 /patch_embed/Constant_2" -> "9 /patch_embed/Slice" [label="[1]", style=dashed]; -"1108 Reshape_190" -> "5 /patch_embed/proj/Conv" [label="[1, 96, 1, 1]", style=solid]; -"1109 Gather_7282" -> "4 /patch_embed/proj/Conv/WithoutBiases" [label="[96, 3, 4, 4]", style=solid]; -"1110 Constant_25965" -> "3 Divide_2169_0_0/nncf_smooth_quant" [label="[1, 3, 1, 1]", style=solid]; -"1111 Gather_7279" -> "2 Divide_2169" [label="[1, 3, 1, 1]", style=solid]; -"1112 Gather_7276" -> "1 Multiply_6579" [label="[1, 3, 1, 1]", style=solid]; -"1113 Constant_7287" -> "63 /layers/layers.0/blocks.0/attn/qkv/Add" [label="[1, 1, 288]", style=solid]; -"1114 onnx^^Add_2244" -> "87 /layers/layers.0/blocks.0/attn/Add" [label="[1, 3, 49, 49]", style=solid]; -"1115 Constant_268" -> "77 /layers/layers.0/blocks.0/attn/Gather_1" [label="[]", style=dashed]; -"1116 /patch_embed/proj/Constant" -> "77 /layers/layers.0/blocks.0/attn/Gather_1" [label="[]", style=dashed]; -"1116 /patch_embed/proj/Constant" -> "122 /layers/layers.0/blocks.1/attn/Gather_1" [label="[]", style=dashed]; -"1116 /patch_embed/proj/Constant" -> "180 /layers/layers.1/blocks.0/attn/Gather_1" [label="[]", style=dashed]; -"1116 /patch_embed/proj/Constant" -> "236 /layers/layers.1/blocks.1/attn/Gather_1" [label="[]", style=dashed]; -"1116 /patch_embed/proj/Constant" -> "336 /layers/layers.2/blocks.0/attn/Gather_1" [label="[]", style=dashed]; -"1116 /patch_embed/proj/Constant" -> "392 /layers/layers.2/blocks.2/attn/Gather_1" [label="[]", style=dashed]; -"1116 /patch_embed/proj/Constant" -> "430 /layers/layers.2/blocks.1/attn/Gather_1" [label="[]", style=dashed]; -"1116 /patch_embed/proj/Constant" -> "438 /layers/layers.2/blocks.4/attn/Gather_1" [label="[]", style=dashed]; -"1116 /patch_embed/proj/Constant" -> "474 /layers/layers.2/blocks.3/attn/Gather_1" [label="[]", style=dashed]; -"1116 /patch_embed/proj/Constant" -> "521 /layers/layers.2/blocks.5/attn/Gather_1" [label="[]", style=dashed]; -"1116 /patch_embed/proj/Constant" -> "594 /layers/layers.3/blocks.0/attn/Gather_1" [label="[]", style=dashed]; -"1116 /patch_embed/proj/Constant" -> "608 /layers/layers.3/blocks.1/attn/Gather_1" [label="[]", style=dashed]; -"1117 Constant_7288" -> "81 /layers/layers.0/blocks.0/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; -"1118 Constant_266" -> "76 /layers/layers.0/blocks.0/attn/Gather" [label="[]", style=dashed]; -"1119 /layers/layers.0/blocks.0/Constant" -> "76 /layers/layers.0/blocks.0/attn/Gather" [label="[]", style=dashed]; -"1119 /layers/layers.0/blocks.0/Constant" -> "121 /layers/layers.0/blocks.1/attn/Gather" [label="[]", style=dashed]; -"1119 /layers/layers.0/blocks.0/Constant" -> "179 /layers/layers.1/blocks.0/attn/Gather" [label="[]", style=dashed]; -"1119 /layers/layers.0/blocks.0/Constant" -> "235 /layers/layers.1/blocks.1/attn/Gather" [label="[]", style=dashed]; -"1119 /layers/layers.0/blocks.0/Constant" -> "335 /layers/layers.2/blocks.0/attn/Gather" [label="[]", style=dashed]; -"1119 /layers/layers.0/blocks.0/Constant" -> "391 /layers/layers.2/blocks.2/attn/Gather" [label="[]", style=dashed]; -"1119 /layers/layers.0/blocks.0/Constant" -> "429 /layers/layers.2/blocks.1/attn/Gather" [label="[]", style=dashed]; -"1119 /layers/layers.0/blocks.0/Constant" -> "437 /layers/layers.2/blocks.4/attn/Gather" [label="[]", style=dashed]; -"1119 /layers/layers.0/blocks.0/Constant" -> "473 /layers/layers.2/blocks.3/attn/Gather" [label="[]", style=dashed]; -"1119 /layers/layers.0/blocks.0/Constant" -> "520 /layers/layers.2/blocks.5/attn/Gather" [label="[]", style=dashed]; -"1119 /layers/layers.0/blocks.0/Constant" -> "593 /layers/layers.3/blocks.0/attn/Gather" [label="[]", style=dashed]; -"1119 /layers/layers.0/blocks.0/Constant" -> "607 /layers/layers.3/blocks.1/attn/Gather" [label="[]", style=dashed]; -"1120 Constant_7289" -> "114 /layers/layers.0/blocks.0/attn/proj/Add" [label="[1, 1, 96]", style=solid]; -"1121 Constant_7292" -> "46 /layers/layers.0/blocks.0/mlp/fc1/Add" [label="[1, 1, 384]", style=solid]; -"1122 Constant_7293" -> "67 /layers/layers.0/blocks.0/mlp/fc2/Add" [label="[1, 1, 96]", style=solid]; -"1123 Constant_5067" -> "44 /layers/layers.0/blocks.1/Slice" [label="[2]", style=dashed]; -"1124 Constant_5064" -> "44 /layers/layers.0/blocks.1/Slice" [label="[2]", style=dashed]; -"1125 Constant_5061" -> "44 /layers/layers.0/blocks.1/Slice" [label="[2]", style=dashed]; -"1126 Constant_5091" -> "55 /layers/layers.0/blocks.1/Slice_2" [label="[3]", style=dashed]; -"1127 Constant_5088" -> "55 /layers/layers.0/blocks.1/Slice_2" [label="[3]", style=dashed]; -"1128 Constant_5085" -> "55 /layers/layers.0/blocks.1/Slice_2" [label="[3]", style=dashed]; -"1129 Constant_7296" -> "99 /layers/layers.0/blocks.1/attn/qkv/Add" [label="[1, 1, 288]", style=solid]; -"1130 /layers/layers.0/blocks.1/attn/Constant_3" -> "168 /layers/layers.0/blocks.1/attn/Reshape_2" [label="[4]", style=dashed]; -"1131 onnx^^Add_2301" -> "160 /layers/layers.0/blocks.1/attn/Add_1" [label="[1, 64, 1, 49, 49]", style=solid]; -"1132 /layers/layers.0/blocks.1/attn/Constant_2" -> "152 /layers/layers.0/blocks.1/attn/Reshape_1" [label="[5]", style=dashed]; -"1133 onnx^^Add_2293" -> "143 /layers/layers.0/blocks.1/attn/Add" [label="[1, 3, 49, 49]", style=solid]; -"1134 Constant_416" -> "122 /layers/layers.0/blocks.1/attn/Gather_1" [label="[]", style=dashed]; -"1135 Constant_7297" -> "134 /layers/layers.0/blocks.1/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; -"1136 Constant_414" -> "121 /layers/layers.0/blocks.1/attn/Gather" [label="[]", style=dashed]; -"1137 Constant_7298" -> "175 /layers/layers.0/blocks.1/attn/proj/Add" [label="[1, 1, 96]", style=solid]; -"1138 Constant_5115" -> "208 /layers/layers.0/blocks.1/Slice_4" [label="[2]", style=dashed]; -"1139 Constant_5112" -> "208 /layers/layers.0/blocks.1/Slice_4" [label="[2]", style=dashed]; -"1140 Constant_5109" -> "208 /layers/layers.0/blocks.1/Slice_4" [label="[2]", style=dashed]; -"1141 Constant_5139" -> "226 /layers/layers.0/blocks.1/Slice_6" [label="[3]", style=dashed]; -"1142 Constant_5136" -> "226 /layers/layers.0/blocks.1/Slice_6" [label="[3]", style=dashed]; -"1143 Constant_5133" -> "226 /layers/layers.0/blocks.1/Slice_6" [label="[3]", style=dashed]; -"1144 Constant_7301" -> "54 /layers/layers.0/blocks.1/mlp/fc1/Add" [label="[1, 1, 384]", style=solid]; -"1145 Constant_7302" -> "74 /layers/layers.0/blocks.1/mlp/fc2/Add" [label="[1, 1, 96]", style=solid]; -"1146 Constant_5211" -> "40 /layers/layers.0/downsample/Slice_4" [label="[3]", style=dashed]; -"1147 Constant_5208" -> "40 /layers/layers.0/downsample/Slice_4" [label="[3]", style=dashed]; -"1148 Constant_5205" -> "40 /layers/layers.0/downsample/Slice_4" [label="[3]", style=dashed]; -"1149 Constant_5163" -> "33 /layers/layers.0/downsample/Slice" [label="[2]", style=dashed]; -"1150 Constant_5160" -> "33 /layers/layers.0/downsample/Slice" [label="[2]", style=dashed]; -"1151 Constant_5157" -> "33 /layers/layers.0/downsample/Slice" [label="[2]", style=dashed]; -"1152 Constant_5199" -> "41 /layers/layers.0/downsample/Slice_3" [label="[3]", style=dashed]; -"1153 Constant_5196" -> "41 /layers/layers.0/downsample/Slice_3" [label="[3]", style=dashed]; -"1154 Constant_5193" -> "41 /layers/layers.0/downsample/Slice_3" [label="[3]", style=dashed]; -"1155 Constant_5175" -> "39 /layers/layers.0/downsample/Slice_1" [label="[3]", style=dashed]; -"1156 Constant_5172" -> "39 /layers/layers.0/downsample/Slice_1" [label="[3]", style=dashed]; -"1157 Constant_5169" -> "39 /layers/layers.0/downsample/Slice_1" [label="[3]", style=dashed]; -"1158 Constant_7307" -> "159 /layers/layers.1/blocks.0/attn/qkv/Add" [label="[1, 1, 576]", style=solid]; -"1159 onnx^^Add_2389" -> "192 /layers/layers.1/blocks.0/attn/Add" [label="[1, 6, 49, 49]", style=solid]; -"1160 Constant_626" -> "180 /layers/layers.1/blocks.0/attn/Gather_1" [label="[]", style=dashed]; -"1161 Constant_7308" -> "185 /layers/layers.1/blocks.0/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; -"1162 Constant_624" -> "179 /layers/layers.1/blocks.0/attn/Gather" [label="[]", style=dashed]; -"1163 Constant_7309" -> "225 /layers/layers.1/blocks.0/attn/proj/Add" [label="[1, 1, 192]", style=solid]; -"1164 Constant_7312" -> "132 /layers/layers.1/blocks.0/mlp/fc1/Add" [label="[1, 1, 768]", style=solid]; -"1165 Constant_7313" -> "166 /layers/layers.1/blocks.0/mlp/fc2/Add" [label="[1, 1, 192]", style=solid]; -"1166 Constant_5235" -> "130 /layers/layers.1/blocks.1/Slice" [label="[2]", style=dashed]; -"1167 Constant_5232" -> "130 /layers/layers.1/blocks.1/Slice" [label="[2]", style=dashed]; -"1168 Constant_5229" -> "130 /layers/layers.1/blocks.1/Slice" [label="[2]", style=dashed]; -"1169 Constant_5259" -> "148 /layers/layers.1/blocks.1/Slice_2" [label="[3]", style=dashed]; -"1170 Constant_5256" -> "148 /layers/layers.1/blocks.1/Slice_2" [label="[3]", style=dashed]; -"1171 Constant_5253" -> "148 /layers/layers.1/blocks.1/Slice_2" [label="[3]", style=dashed]; -"1172 Constant_7316" -> "206 /layers/layers.1/blocks.1/attn/qkv/Add" [label="[1, 1, 576]", style=solid]; -"1173 /layers/layers.1/blocks.1/attn/Constant_3" -> "307 /layers/layers.1/blocks.1/attn/Reshape_2" [label="[4]", style=dashed]; -"1174 onnx^^Add_2446" -> "291 /layers/layers.1/blocks.1/attn/Add_1" [label="[1, 16, 1, 49, 49]", style=solid]; -"1175 /layers/layers.1/blocks.1/attn/Constant_2" -> "277 /layers/layers.1/blocks.1/attn/Reshape_1" [label="[5]", style=dashed]; -"1176 onnx^^Add_2438" -> "263 /layers/layers.1/blocks.1/attn/Add" [label="[1, 6, 49, 49]", style=solid]; -"1177 Constant_774" -> "236 /layers/layers.1/blocks.1/attn/Gather_1" [label="[]", style=dashed]; -"1178 Constant_7317" -> "249 /layers/layers.1/blocks.1/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; -"1179 Constant_772" -> "235 /layers/layers.1/blocks.1/attn/Gather" [label="[]", style=dashed]; -"1180 Constant_7318" -> "322 /layers/layers.1/blocks.1/attn/proj/Add" [label="[1, 1, 192]", style=solid]; -"1181 Constant_5283" -> "396 /layers/layers.1/blocks.1/Slice_4" [label="[2]", style=dashed]; -"1182 Constant_5280" -> "396 /layers/layers.1/blocks.1/Slice_4" [label="[2]", style=dashed]; -"1183 Constant_5277" -> "396 /layers/layers.1/blocks.1/Slice_4" [label="[2]", style=dashed]; -"1184 Constant_5307" -> "420 /layers/layers.1/blocks.1/Slice_6" [label="[3]", style=dashed]; -"1185 Constant_5304" -> "420 /layers/layers.1/blocks.1/Slice_6" [label="[3]", style=dashed]; -"1186 Constant_5301" -> "420 /layers/layers.1/blocks.1/Slice_6" [label="[3]", style=dashed]; -"1187 Constant_7321" -> "147 /layers/layers.1/blocks.1/mlp/fc1/Add" [label="[1, 1, 768]", style=solid]; -"1188 Constant_7322" -> "177 /layers/layers.1/blocks.1/mlp/fc2/Add" [label="[1, 1, 192]", style=solid]; -"1189 Constant_5379" -> "126 /layers/layers.1/downsample/Slice_4" [label="[3]", style=dashed]; -"1190 Constant_5376" -> "126 /layers/layers.1/downsample/Slice_4" [label="[3]", style=dashed]; -"1191 Constant_5373" -> "126 /layers/layers.1/downsample/Slice_4" [label="[3]", style=dashed]; -"1192 Constant_5331" -> "115 /layers/layers.1/downsample/Slice" [label="[2]", style=dashed]; -"1193 Constant_5328" -> "115 /layers/layers.1/downsample/Slice" [label="[2]", style=dashed]; -"1194 Constant_5325" -> "115 /layers/layers.1/downsample/Slice" [label="[2]", style=dashed]; -"1195 Constant_5367" -> "127 /layers/layers.1/downsample/Slice_3" [label="[3]", style=dashed]; -"1196 Constant_5364" -> "127 /layers/layers.1/downsample/Slice_3" [label="[3]", style=dashed]; -"1197 Constant_5361" -> "127 /layers/layers.1/downsample/Slice_3" [label="[3]", style=dashed]; -"1198 Constant_5343" -> "125 /layers/layers.1/downsample/Slice_1" [label="[3]", style=dashed]; -"1199 Constant_5340" -> "125 /layers/layers.1/downsample/Slice_1" [label="[3]", style=dashed]; -"1200 Constant_5337" -> "125 /layers/layers.1/downsample/Slice_1" [label="[3]", style=dashed]; -"1201 Constant_7327" -> "290 /layers/layers.2/blocks.0/attn/qkv/Add" [label="[1, 1, 1152]", style=solid]; -"1202 onnx^^Add_2534" -> "368 /layers/layers.2/blocks.0/attn/Add" [label="[1, 12, 49, 49]", style=solid]; -"1203 Constant_984" -> "336 /layers/layers.2/blocks.0/attn/Gather_1" [label="[]", style=dashed]; -"1204 Constant_7328" -> "350 /layers/layers.2/blocks.0/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; -"1205 Constant_982" -> "335 /layers/layers.2/blocks.0/attn/Gather" [label="[]", style=dashed]; -"1206 Constant_7329" -> "419 /layers/layers.2/blocks.0/attn/proj/Add" [label="[1, 1, 384]", style=solid]; -"1207 Constant_7332" -> "247 /layers/layers.2/blocks.0/mlp/fc1/Add" [label="[1, 1, 1536]", style=solid]; -"1208 Constant_7333" -> "305 /layers/layers.2/blocks.0/mlp/fc2/Add" [label="[1, 1, 384]", style=solid]; -"1209 Constant_5403" -> "245 /layers/layers.2/blocks.1/Slice" [label="[2]", style=dashed]; -"1210 Constant_5400" -> "245 /layers/layers.2/blocks.1/Slice" [label="[2]", style=dashed]; -"1211 Constant_5397" -> "245 /layers/layers.2/blocks.1/Slice" [label="[2]", style=dashed]; -"1212 Constant_5427" -> "273 /layers/layers.2/blocks.1/Slice_2" [label="[3]", style=dashed]; -"1213 Constant_5424" -> "273 /layers/layers.2/blocks.1/Slice_2" [label="[3]", style=dashed]; -"1214 Constant_5421" -> "273 /layers/layers.2/blocks.1/Slice_2" [label="[3]", style=dashed]; -"1215 Constant_7336" -> "394 /layers/layers.2/blocks.1/attn/qkv/Add" [label="[1, 1, 1152]", style=solid]; -"1216 /layers/layers.2/blocks.1/attn/Constant_3" -> "490 /layers/layers.2/blocks.1/attn/Reshape_2" [label="[4]", style=dashed]; -"1217 onnx^^Add_2702" -> "477 /layers/layers.2/blocks.1/attn/Add_1" [label="[1, 4, 1, 49, 49]", style=solid]; -"1217 onnx^^Add_2702" -> "524 /layers/layers.2/blocks.3/attn/Add_1" [label="[1, 4, 1, 49, 49]", style=solid]; -"1217 onnx^^Add_2702" -> "567 /layers/layers.2/blocks.5/attn/Add_1" [label="[1, 4, 1, 49, 49]", style=solid]; -"1218 /layers/layers.2/blocks.1/attn/Constant_2" -> "464 /layers/layers.2/blocks.1/attn/Reshape_1" [label="[5]", style=dashed]; -"1219 onnx^^Add_2583" -> "454 /layers/layers.2/blocks.1/attn/Add" [label="[1, 12, 49, 49]", style=solid]; -"1220 Constant_1132" -> "430 /layers/layers.2/blocks.1/attn/Gather_1" [label="[]", style=dashed]; -"1221 Constant_7337" -> "442 /layers/layers.2/blocks.1/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; -"1222 Constant_1130" -> "429 /layers/layers.2/blocks.1/attn/Gather" [label="[]", style=dashed]; -"1223 Constant_7338" -> "503 /layers/layers.2/blocks.1/attn/proj/Add" [label="[1, 1, 384]", style=solid]; -"1224 Constant_5451" -> "560 /layers/layers.2/blocks.1/Slice_4" [label="[2]", style=dashed]; -"1225 Constant_5448" -> "560 /layers/layers.2/blocks.1/Slice_4" [label="[2]", style=dashed]; -"1226 Constant_5445" -> "560 /layers/layers.2/blocks.1/Slice_4" [label="[2]", style=dashed]; -"1227 Constant_5475" -> "580 /layers/layers.2/blocks.1/Slice_6" [label="[3]", style=dashed]; -"1228 Constant_5472" -> "580 /layers/layers.2/blocks.1/Slice_6" [label="[3]", style=dashed]; -"1229 Constant_5469" -> "580 /layers/layers.2/blocks.1/Slice_6" [label="[3]", style=dashed]; -"1230 Constant_7341" -> "272 /layers/layers.2/blocks.1/mlp/fc1/Add" [label="[1, 1, 1536]", style=solid]; -"1231 Constant_7342" -> "333 /layers/layers.2/blocks.1/mlp/fc2/Add" [label="[1, 1, 384]", style=solid]; -"1232 Constant_7345" -> "348 /layers/layers.2/blocks.2/attn/qkv/Add" [label="[1, 1, 1152]", style=solid]; -"1233 onnx^^Add_2645" -> "416 /layers/layers.2/blocks.2/attn/Add" [label="[1, 12, 49, 49]", style=solid]; -"1234 Constant_1289" -> "392 /layers/layers.2/blocks.2/attn/Gather_1" [label="[]", style=dashed]; -"1235 Constant_7346" -> "404 /layers/layers.2/blocks.2/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; -"1236 Constant_1287" -> "391 /layers/layers.2/blocks.2/attn/Gather" [label="[]", style=dashed]; -"1237 Constant_7347" -> "463 /layers/layers.2/blocks.2/attn/proj/Add" [label="[1, 1, 384]", style=solid]; -"1238 Constant_7350" -> "301 /layers/layers.2/blocks.2/mlp/fc1/Add" [label="[1, 1, 1536]", style=solid]; -"1239 Constant_7351" -> "365 /layers/layers.2/blocks.2/mlp/fc2/Add" [label="[1, 1, 384]", style=solid]; -"1240 Constant_5499" -> "299 /layers/layers.2/blocks.3/Slice" [label="[2]", style=dashed]; -"1241 Constant_5496" -> "299 /layers/layers.2/blocks.3/Slice" [label="[2]", style=dashed]; -"1242 Constant_5493" -> "299 /layers/layers.2/blocks.3/Slice" [label="[2]", style=dashed]; -"1243 Constant_5523" -> "329 /layers/layers.2/blocks.3/Slice_2" [label="[3]", style=dashed]; -"1244 Constant_5520" -> "329 /layers/layers.2/blocks.3/Slice_2" [label="[3]", style=dashed]; -"1245 Constant_5517" -> "329 /layers/layers.2/blocks.3/Slice_2" [label="[3]", style=dashed]; -"1246 Constant_7354" -> "440 /layers/layers.2/blocks.3/attn/qkv/Add" [label="[1, 1, 1152]", style=solid]; -"1247 /layers/layers.2/blocks.3/attn/Constant_3" -> "537 /layers/layers.2/blocks.3/attn/Reshape_2" [label="[4]", style=dashed]; -"1248 /layers/layers.2/blocks.3/attn/Constant_2" -> "511 /layers/layers.2/blocks.3/attn/Reshape_1" [label="[5]", style=dashed]; -"1249 onnx^^Add_2694" -> "499 /layers/layers.2/blocks.3/attn/Add" [label="[1, 12, 49, 49]", style=solid]; -"1250 Constant_1437" -> "474 /layers/layers.2/blocks.3/attn/Gather_1" [label="[]", style=dashed]; -"1251 Constant_7355" -> "486 /layers/layers.2/blocks.3/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; -"1252 Constant_1435" -> "473 /layers/layers.2/blocks.3/attn/Gather" [label="[]", style=dashed]; -"1253 Constant_7356" -> "549 /layers/layers.2/blocks.3/attn/proj/Add" [label="[1, 1, 384]", style=solid]; -"1254 Constant_5547" -> "597 /layers/layers.2/blocks.3/Slice_4" [label="[2]", style=dashed]; -"1255 Constant_5544" -> "597 /layers/layers.2/blocks.3/Slice_4" [label="[2]", style=dashed]; -"1256 Constant_5541" -> "597 /layers/layers.2/blocks.3/Slice_4" [label="[2]", style=dashed]; -"1257 Constant_5571" -> "613 /layers/layers.2/blocks.3/Slice_6" [label="[3]", style=dashed]; -"1258 Constant_5568" -> "613 /layers/layers.2/blocks.3/Slice_6" [label="[3]", style=dashed]; -"1259 Constant_5565" -> "613 /layers/layers.2/blocks.3/Slice_6" [label="[3]", style=dashed]; -"1260 Constant_7359" -> "328 /layers/layers.2/blocks.3/mlp/fc1/Add" [label="[1, 1, 1536]", style=solid]; -"1261 Constant_7360" -> "389 /layers/layers.2/blocks.3/mlp/fc2/Add" [label="[1, 1, 384]", style=solid]; -"1262 Constant_7363" -> "402 /layers/layers.2/blocks.4/attn/qkv/Add" [label="[1, 1, 1152]", style=solid]; -"1263 onnx^^Add_2756" -> "460 /layers/layers.2/blocks.4/attn/Add" [label="[1, 12, 49, 49]", style=solid]; -"1264 Constant_1594" -> "438 /layers/layers.2/blocks.4/attn/Gather_1" [label="[]", style=dashed]; -"1265 Constant_7364" -> "449 /layers/layers.2/blocks.4/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; -"1266 Constant_1592" -> "437 /layers/layers.2/blocks.4/attn/Gather" [label="[]", style=dashed]; -"1267 Constant_7365" -> "510 /layers/layers.2/blocks.4/attn/proj/Add" [label="[1, 1, 384]", style=solid]; -"1268 Constant_7368" -> "361 /layers/layers.2/blocks.4/mlp/fc1/Add" [label="[1, 1, 1536]", style=solid]; -"1269 Constant_7369" -> "413 /layers/layers.2/blocks.4/mlp/fc2/Add" [label="[1, 1, 384]", style=solid]; -"1270 Constant_5595" -> "359 /layers/layers.2/blocks.5/Slice" [label="[2]", style=dashed]; -"1271 Constant_5592" -> "359 /layers/layers.2/blocks.5/Slice" [label="[2]", style=dashed]; -"1272 Constant_5589" -> "359 /layers/layers.2/blocks.5/Slice" [label="[2]", style=dashed]; -"1273 Constant_5619" -> "385 /layers/layers.2/blocks.5/Slice_2" [label="[3]", style=dashed]; -"1274 Constant_5616" -> "385 /layers/layers.2/blocks.5/Slice_2" [label="[3]", style=dashed]; -"1275 Constant_5613" -> "385 /layers/layers.2/blocks.5/Slice_2" [label="[3]", style=dashed]; -"1276 Constant_7372" -> "484 /layers/layers.2/blocks.5/attn/qkv/Add" [label="[1, 1, 1152]", style=solid]; -"1277 /layers/layers.2/blocks.5/attn/Constant_3" -> "577 /layers/layers.2/blocks.5/attn/Reshape_2" [label="[4]", style=dashed]; -"1278 /layers/layers.2/blocks.5/attn/Constant_2" -> "556 /layers/layers.2/blocks.5/attn/Reshape_1" [label="[5]", style=dashed]; -"1279 onnx^^Add_2805" -> "545 /layers/layers.2/blocks.5/attn/Add" [label="[1, 12, 49, 49]", style=solid]; -"1280 Constant_1742" -> "521 /layers/layers.2/blocks.5/attn/Gather_1" [label="[]", style=dashed]; -"1281 Constant_7373" -> "533 /layers/layers.2/blocks.5/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; -"1282 Constant_1740" -> "520 /layers/layers.2/blocks.5/attn/Gather" [label="[]", style=dashed]; -"1283 Constant_7374" -> "587 /layers/layers.2/blocks.5/attn/proj/Add" [label="[1, 1, 384]", style=solid]; -"1284 Constant_5643" -> "625 /layers/layers.2/blocks.5/Slice_4" [label="[2]", style=dashed]; -"1285 Constant_5640" -> "625 /layers/layers.2/blocks.5/Slice_4" [label="[2]", style=dashed]; -"1286 Constant_5637" -> "625 /layers/layers.2/blocks.5/Slice_4" [label="[2]", style=dashed]; -"1287 Constant_5667" -> "634 /layers/layers.2/blocks.5/Slice_6" [label="[3]", style=dashed]; -"1288 Constant_5664" -> "634 /layers/layers.2/blocks.5/Slice_6" [label="[3]", style=dashed]; -"1289 Constant_5661" -> "634 /layers/layers.2/blocks.5/Slice_6" [label="[3]", style=dashed]; -"1290 Constant_7377" -> "384 /layers/layers.2/blocks.5/mlp/fc1/Add" [label="[1, 1, 1536]", style=solid]; -"1291 Constant_7378" -> "435 /layers/layers.2/blocks.5/mlp/fc2/Add" [label="[1, 1, 384]", style=solid]; -"1292 Constant_5739" -> "355 /layers/layers.2/downsample/Slice_4" [label="[3]", style=dashed]; -"1293 Constant_5736" -> "355 /layers/layers.2/downsample/Slice_4" [label="[3]", style=dashed]; -"1294 Constant_5733" -> "355 /layers/layers.2/downsample/Slice_4" [label="[3]", style=dashed]; -"1295 Constant_5691" -> "339 /layers/layers.2/downsample/Slice" [label="[2]", style=dashed]; -"1296 Constant_5688" -> "339 /layers/layers.2/downsample/Slice" [label="[2]", style=dashed]; -"1297 Constant_5685" -> "339 /layers/layers.2/downsample/Slice" [label="[2]", style=dashed]; -"1298 Constant_5727" -> "356 /layers/layers.2/downsample/Slice_3" [label="[3]", style=dashed]; -"1299 Constant_5724" -> "356 /layers/layers.2/downsample/Slice_3" [label="[3]", style=dashed]; -"1300 Constant_5721" -> "356 /layers/layers.2/downsample/Slice_3" [label="[3]", style=dashed]; -"1301 Constant_5703" -> "354 /layers/layers.2/downsample/Slice_1" [label="[3]", style=dashed]; -"1302 Constant_5700" -> "354 /layers/layers.2/downsample/Slice_1" [label="[3]", style=dashed]; -"1303 Constant_5697" -> "354 /layers/layers.2/downsample/Slice_1" [label="[3]", style=dashed]; -"1304 Constant_7383" -> "566 /layers/layers.3/blocks.0/attn/qkv/Add" [label="[1, 1, 2304]", style=solid]; -"1305 onnx^^Add_2901" -> "610 /layers/layers.3/blocks.0/attn/Add" [label="[1, 24, 49, 49]", style=solid]; -"1306 Constant_1952" -> "594 /layers/layers.3/blocks.0/attn/Gather_1" [label="[]", style=dashed]; -"1307 Constant_7384" -> "602 /layers/layers.3/blocks.0/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; -"1308 Constant_1950" -> "593 /layers/layers.3/blocks.0/attn/Gather" [label="[]", style=dashed]; -"1309 Constant_7385" -> "633 /layers/layers.3/blocks.0/attn/proj/Add" [label="[1, 1, 768]", style=solid]; -"1310 Constant_7388" -> "531 /layers/layers.3/blocks.0/mlp/fc1/Add" [label="[1, 1, 3072]", style=solid]; -"1311 Constant_7389" -> "575 /layers/layers.3/blocks.0/mlp/fc2/Add" [label="[1, 1, 768]", style=solid]; -"1312 Constant_7392" -> "584 /layers/layers.3/blocks.1/attn/qkv/Add" [label="[1, 1, 2304]", style=solid]; -"1313 onnx^^Add_2950" -> "622 /layers/layers.3/blocks.1/attn/Add" [label="[1, 24, 49, 49]", style=solid]; -"1314 Constant_2058" -> "608 /layers/layers.3/blocks.1/attn/Gather_1" [label="[]", style=dashed]; -"1315 Constant_7393" -> "615 /layers/layers.3/blocks.1/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; -"1316 Constant_2056" -> "607 /layers/layers.3/blocks.1/attn/Gather" [label="[]", style=dashed]; -"1317 Constant_7394" -> "639 /layers/layers.3/blocks.1/attn/proj/Add" [label="[1, 1, 768]", style=solid]; -"1318 Constant_7397" -> "552 /layers/layers.3/blocks.1/mlp/fc1/Add" [label="[1, 1, 3072]", style=solid]; -"1319 Constant_7398" -> "591 /layers/layers.3/blocks.1/mlp/fc2/Add" [label="[1, 1, 768]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/yolo-v4-tiny-tf_performance.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/yolo-v4-tiny-tf_performance.dot deleted file mode 100644 index 073e63c11e0..00000000000 --- a/tests/openvino/native/data/2023.2/reference_graphs/quantized/yolo-v4-tiny-tf_performance.dot +++ /dev/null @@ -1,754 +0,0 @@ -strict digraph { -"0 image_input" [id=0, type=Parameter]; -"1 image_input/fq_output_0" [id=1, type=FakeQuantize]; -"2 Divide_2378" [id=2, type=Transpose]; -"3 Multiply_3747" [id=3, type=Convolution]; -"4 Transpose_1172" [id=4, type=Add]; -"5 Transpose_1178" [id=5, type=PRelu]; -"6 Transpose_1178/fq_output_0" [id=6, type=FakeQuantize]; -"7 Multiply_3761" [id=7, type=Convolution]; -"8 Transpose_1229" [id=8, type=Add]; -"9 Transpose_1235" [id=9, type=PRelu]; -"10 Transpose_1235/fq_output_0" [id=10, type=FakeQuantize]; -"11 Multiply_3775" [id=11, type=Convolution]; -"12 Transpose_1259" [id=12, type=Add]; -"13 Transpose_1265" [id=13, type=PRelu]; -"14 Transpose_1265/fq_output_0" [id=14, type=FakeQuantize]; -"15 Transpose_1368" [id=15, type=Concat]; -"16 group_route_3/split" [id=16, type=Split]; -"17 MaxPool_307" [id=17, type=MaxPool]; -"18 Multiply_3789" [id=18, type=Convolution]; -"19 Multiply_3831" [id=19, type=Convolution]; -"20 Transpose_1294" [id=20, type=Add]; -"21 Transpose_1392" [id=21, type=Add]; -"22 Transpose_1300" [id=22, type=PRelu]; -"23 Transpose_1398" [id=23, type=PRelu]; -"24 Transpose_1300/fq_output_0" [id=24, type=FakeQuantize]; -"25 Transpose_1398/fq_output_0" [id=25, type=FakeQuantize]; -"26 Multiply_3803" [id=26, type=Convolution]; -"27 Transpose_1334" [id=27, type=Concat]; -"28 Transpose_1501" [id=28, type=Concat]; -"29 group_route_11/split" [id=29, type=Split]; -"30 Transpose_1324" [id=30, type=Add]; -"31 Multiply_3817" [id=31, type=Convolution]; -"32 MaxPool_433" [id=32, type=MaxPool]; -"33 Multiply_3845" [id=33, type=Convolution]; -"34 Transpose_1330" [id=34, type=PRelu]; -"35 Transpose_1358" [id=35, type=Add]; -"36 Multiply_3887" [id=36, type=Convolution]; -"37 Transpose_1427" [id=37, type=Add]; -"38 Transpose_1330/fq_output_0" [id=38, type=FakeQuantize]; -"39 Transpose_1364" [id=39, type=PRelu]; -"40 Transpose_1525" [id=40, type=Add]; -"41 Transpose_1433" [id=41, type=PRelu]; -"42 Transpose_1364/fq_output_0" [id=42, type=FakeQuantize]; -"43 Transpose_1531" [id=43, type=PRelu]; -"44 Transpose_1433/fq_output_0" [id=44, type=FakeQuantize]; -"45 Transpose_1531/fq_output_0" [id=45, type=FakeQuantize]; -"46 Multiply_3859" [id=46, type=Convolution]; -"47 Transpose_1467" [id=47, type=Concat]; -"48 Transpose_1634" [id=48, type=Concat]; -"49 group_route_19/split" [id=49, type=Split]; -"50 Transpose_1457" [id=50, type=Add]; -"51 Multiply_3873" [id=51, type=Convolution]; -"52 MaxPool_579" [id=52, type=MaxPool]; -"53 Multiply_3901" [id=53, type=Convolution]; -"54 Transpose_1463" [id=54, type=PRelu]; -"55 Transpose_1491" [id=55, type=Add]; -"56 Multiply_3943" [id=56, type=Convolution]; -"57 Transpose_1560" [id=57, type=Add]; -"58 Transpose_1463/fq_output_0" [id=58, type=FakeQuantize]; -"59 Transpose_1497" [id=59, type=PRelu]; -"60 Transpose_1658" [id=60, type=Add]; -"61 Transpose_1566" [id=61, type=PRelu]; -"62 Transpose_1497/fq_output_0" [id=62, type=FakeQuantize]; -"63 Transpose_1664" [id=63, type=PRelu]; -"64 Transpose_1566/fq_output_0" [id=64, type=FakeQuantize]; -"65 Transpose_1664/fq_output_0" [id=65, type=FakeQuantize]; -"66 Multiply_3915" [id=66, type=Convolution]; -"67 Transpose_1600" [id=67, type=Concat]; -"68 Multiply_3957" [id=68, type=Convolution]; -"69 Transpose_1590" [id=69, type=Add]; -"70 Multiply_3929" [id=70, type=Convolution]; -"71 Transpose_1688" [id=71, type=Add]; -"72 Transpose_1596" [id=72, type=PRelu]; -"73 Transpose_1624" [id=73, type=Add]; -"74 Transpose_1694" [id=74, type=PRelu]; -"75 Transpose_1596/fq_output_0" [id=75, type=FakeQuantize]; -"76 Transpose_1630" [id=76, type=PRelu]; -"77 Transpose_1694/fq_output_0" [id=77, type=FakeQuantize]; -"78 Transpose_1630/fq_output_0" [id=78, type=FakeQuantize]; -"79 Multiply_3971" [id=79, type=Convolution]; -"80 Multiply_3999" [id=80, type=Convolution]; -"81 Transpose_1730" [id=81, type=Concat]; -"82 Transpose_1718" [id=82, type=Add]; -"83 Transpose_1790" [id=83, type=Add]; -"84 Multiply_3985" [id=84, type=Convolution]; -"85 Transpose_1724" [id=85, type=PRelu]; -"86 Transpose_1796" [id=86, type=PRelu]; -"87 Transpose_1754" [id=87, type=Add]; -"88 Transpose_1724/fq_output_0" [id=88, type=FakeQuantize]; -"89 Transpose_1796/fq_output_0" [id=89, type=FakeQuantize]; -"90 Transpose_1760" [id=90, type=PRelu]; -"91 Gather_1726" [id=91, type=ShapeOf]; -"92 leaky_re_lu_17/LeakyRelu" [id=92, type=Transpose]; -"93 Convolution_749" [id=93, type=Convolution]; -"94 Transpose_1760/fq_output_0" [id=94, type=FakeQuantize]; -"95 up_sampling2d/Shape" [id=95, type=Gather]; -"96 up_sampling2d/resize/ResizeNearestNeighbor" [id=96, type=Interpolate]; -"97 Transpose_1802" [id=97, type=Add]; -"98 Convolution_706" [id=98, type=Convolution]; -"99 up_sampling2d/strided_slice" [id=99, type=StridedSlice]; -"100 up_sampling2d/resize/ResizeNearestNeighbor/fq_output_0" [id=100, type=FakeQuantize]; -"101 conv2d_17/BiasAdd" [id=101, type=Transpose]; -"102 Transpose_1766" [id=102, type=Add]; -"103 up_sampling2d/mul" [id=103, type=Multiply]; -"104 Transpose_1728" [id=104, type=Transpose]; -"105 conv2d_17/BiasAdd^0" [id=105, label="105 conv2d_17/BiasAdd:0", type=Result]; -"106 conv2d_20/BiasAdd" [id=106, type=Transpose]; -"107 conv2d_20/BiasAdd^0" [id=107, label="107 conv2d_20/BiasAdd:0", type=Result]; -"108 Constant_1765" [id=108, type=Constant]; -"109 Transpose_1764" [id=109, type=Constant]; -"110 Convolution_706/fq_weights_1" [id=110, type=FakeQuantize]; -"111 Constant_5545" [id=111, type=Constant]; -"112 Constant_5544" [id=112, type=Constant]; -"113 Constant_5543" [id=113, type=Constant]; -"114 Constant_5542" [id=114, type=Constant]; -"115 Transpose_705" [id=115, type=Constant]; -"116 Constant_5540" [id=116, type=Constant]; -"117 Constant_5539" [id=117, type=Constant]; -"118 Constant_5538" [id=118, type=Constant]; -"119 Constant_5537" [id=119, type=Constant]; -"120 Transpose_1758" [id=120, type=Constant]; -"121 Constant_3993" [id=121, type=Constant]; -"122 Multiply_3985/fq_weights_1" [id=122, type=FakeQuantize]; -"123 Constant_5535" [id=123, type=Constant]; -"124 Constant_5534" [id=124, type=Constant]; -"125 Constant_5533" [id=125, type=Constant]; -"126 Constant_5532" [id=126, type=Constant]; -"127 Multiply_4118" [id=127, type=Constant]; -"128 Constant_5405" [id=128, type=Constant]; -"129 Constant_5404" [id=129, type=Constant]; -"130 Constant_5403" [id=130, type=Constant]; -"131 Constant_5402" [id=131, type=Constant]; -"132 Transpose_1628" [id=132, type=Constant]; -"133 Constant_3937" [id=133, type=Constant]; -"134 Multiply_3929/fq_weights_1" [id=134, type=FakeQuantize]; -"135 Constant_5500" [id=135, type=Constant]; -"136 Constant_5499" [id=136, type=Constant]; -"137 Constant_5498" [id=137, type=Constant]; -"138 Constant_5497" [id=138, type=Constant]; -"139 Multiply_4094" [id=139, type=Constant]; -"140 Constant_5375" [id=140, type=Constant]; -"141 Constant_5374" [id=141, type=Constant]; -"142 Constant_5373" [id=142, type=Constant]; -"143 Constant_5372" [id=143, type=Constant]; -"144 Transpose_1564" [id=144, type=Constant]; -"145 Constant_3909" [id=145, type=Constant]; -"146 Multiply_3901/fq_weights_1" [id=146, type=FakeQuantize]; -"147 Constant_5490" [id=147, type=Constant]; -"148 Constant_5489" [id=148, type=Constant]; -"149 Constant_5488" [id=149, type=Constant]; -"150 Constant_5487" [id=150, type=Constant]; -"151 Multiply_4082" [id=151, type=Constant]; -"152 Constant_1532" [id=152, type=Constant]; -"153 Constant_5410" [id=153, type=Constant]; -"154 Constant_5409" [id=154, type=Constant]; -"155 Constant_5408" [id=155, type=Constant]; -"156 Constant_5407" [id=156, type=Constant]; -"157 Transpose_1529" [id=157, type=Constant]; -"158 Constant_3895" [id=158, type=Constant]; -"159 Multiply_3887/fq_weights_1" [id=159, type=FakeQuantize]; -"160 Constant_5485" [id=160, type=Constant]; -"161 Constant_5484" [id=161, type=Constant]; -"162 Constant_5483" [id=162, type=Constant]; -"163 Constant_5482" [id=163, type=Constant]; -"164 Multiply_4076" [id=164, type=Constant]; -"165 Constant_5400" [id=165, type=Constant]; -"166 Constant_5399" [id=166, type=Constant]; -"167 Constant_5398" [id=167, type=Constant]; -"168 Constant_5397" [id=168, type=Constant]; -"169 Transpose_1495" [id=169, type=Constant]; -"170 Constant_3881" [id=170, type=Constant]; -"171 Multiply_3873/fq_weights_1" [id=171, type=FakeQuantize]; -"172 Constant_5480" [id=172, type=Constant]; -"173 Constant_5479" [id=173, type=Constant]; -"174 Constant_5478" [id=174, type=Constant]; -"175 Constant_5477" [id=175, type=Constant]; -"176 Multiply_4070" [id=176, type=Constant]; -"177 Constant_5370" [id=177, type=Constant]; -"178 Constant_5369" [id=178, type=Constant]; -"179 Constant_5368" [id=179, type=Constant]; -"180 Constant_5367" [id=180, type=Constant]; -"181 Transpose_1431" [id=181, type=Constant]; -"182 Constant_3853" [id=182, type=Constant]; -"183 Multiply_3845/fq_weights_1" [id=183, type=FakeQuantize]; -"184 Constant_5470" [id=184, type=Constant]; -"185 Constant_5469" [id=185, type=Constant]; -"186 Constant_5468" [id=186, type=Constant]; -"187 Constant_5467" [id=187, type=Constant]; -"188 Multiply_4058" [id=188, type=Constant]; -"189 Constant_1399" [id=189, type=Constant]; -"190 Constant_5395" [id=190, type=Constant]; -"191 Constant_5394" [id=191, type=Constant]; -"192 Constant_5393" [id=192, type=Constant]; -"193 Constant_5392" [id=193, type=Constant]; -"194 Transpose_1396" [id=194, type=Constant]; -"195 Constant_3839" [id=195, type=Constant]; -"196 Multiply_3831/fq_weights_1" [id=196, type=FakeQuantize]; -"197 Constant_5465" [id=197, type=Constant]; -"198 Constant_5464" [id=198, type=Constant]; -"199 Constant_5463" [id=199, type=Constant]; -"200 Constant_5462" [id=200, type=Constant]; -"201 Multiply_4052" [id=201, type=Constant]; -"202 Constant_5390" [id=202, type=Constant]; -"203 Constant_5389" [id=203, type=Constant]; -"204 Constant_5388" [id=204, type=Constant]; -"205 Constant_5387" [id=205, type=Constant]; -"206 Transpose_1362" [id=206, type=Constant]; -"207 Constant_3825" [id=207, type=Constant]; -"208 Multiply_3817/fq_weights_1" [id=208, type=FakeQuantize]; -"209 Constant_5460" [id=209, type=Constant]; -"210 Constant_5459" [id=210, type=Constant]; -"211 Constant_5458" [id=211, type=Constant]; -"212 Constant_5457" [id=212, type=Constant]; -"213 Multiply_4046" [id=213, type=Constant]; -"214 Constant_5355" [id=214, type=Constant]; -"215 Constant_5354" [id=215, type=Constant]; -"216 Constant_5353" [id=216, type=Constant]; -"217 Constant_5352" [id=217, type=Constant]; -"218 Transpose_1298" [id=218, type=Constant]; -"219 Constant_3797" [id=219, type=Constant]; -"220 Multiply_3789/fq_weights_1" [id=220, type=FakeQuantize]; -"221 Constant_5450" [id=221, type=Constant]; -"222 Constant_5449" [id=222, type=Constant]; -"223 Constant_5448" [id=223, type=Constant]; -"224 Constant_5447" [id=224, type=Constant]; -"225 Multiply_4034" [id=225, type=Constant]; -"226 Constant_1266" [id=226, type=Constant]; -"227 Constant_5385" [id=227, type=Constant]; -"228 Constant_5384" [id=228, type=Constant]; -"229 Constant_5383" [id=229, type=Constant]; -"230 Constant_5382" [id=230, type=Constant]; -"231 Transpose_1263" [id=231, type=Constant]; -"232 Constant_3783" [id=232, type=Constant]; -"233 Multiply_3775/fq_weights_1" [id=233, type=FakeQuantize]; -"234 Constant_5445" [id=234, type=Constant]; -"235 Constant_5444" [id=235, type=Constant]; -"236 Constant_5443" [id=236, type=Constant]; -"237 Constant_5442" [id=237, type=Constant]; -"238 Multiply_4028" [id=238, type=Constant]; -"239 Constant_5440" [id=239, type=Constant]; -"240 Constant_5439" [id=240, type=Constant]; -"241 Constant_5438" [id=241, type=Constant]; -"242 Constant_5437" [id=242, type=Constant]; -"243 Transpose_1233" [id=243, type=Constant]; -"244 Constant_3769" [id=244, type=Constant]; -"245 Multiply_3761/fq_weights_1" [id=245, type=FakeQuantize]; -"246 Constant_5435" [id=246, type=Constant]; -"247 Constant_5434" [id=247, type=Constant]; -"248 Constant_5433" [id=248, type=Constant]; -"249 Constant_5432" [id=249, type=Constant]; -"250 Multiply_4022" [id=250, type=Constant]; -"251 Constant_5430" [id=251, type=Constant]; -"252 Constant_5429" [id=252, type=Constant]; -"253 Constant_5428" [id=253, type=Constant]; -"254 Constant_5427" [id=254, type=Constant]; -"255 Transpose_1176" [id=255, type=Constant]; -"256 Constant_3755" [id=256, type=Constant]; -"257 Multiply_3747/fq_weights_1" [id=257, type=FakeQuantize]; -"258 Constant_5425" [id=258, type=Constant]; -"259 Constant_5424" [id=259, type=Constant]; -"260 Constant_5423" [id=260, type=Constant]; -"261 Constant_5422" [id=261, type=Constant]; -"262 Gather_4293" [id=262, type=Constant]; -"263 Constant_2333" [id=263, type=Constant]; -"264 Constant_5420" [id=264, type=Constant]; -"265 Constant_5419" [id=265, type=Constant]; -"266 Constant_5418" [id=266, type=Constant]; -"267 Constant_5417" [id=267, type=Constant]; -"268 Constant_5360" [id=268, type=Constant]; -"269 Constant_5359" [id=269, type=Constant]; -"270 Constant_5358" [id=270, type=Constant]; -"271 Constant_5357" [id=271, type=Constant]; -"272 Transpose_1328" [id=272, type=Constant]; -"273 Constant_3811" [id=273, type=Constant]; -"274 Multiply_3803/fq_weights_1" [id=274, type=FakeQuantize]; -"275 Constant_5455" [id=275, type=Constant]; -"276 Constant_5454" [id=276, type=Constant]; -"277 Constant_5453" [id=277, type=Constant]; -"278 Constant_5452" [id=278, type=Constant]; -"279 Multiply_4040" [id=279, type=Constant]; -"280 Constant_5365" [id=280, type=Constant]; -"281 Constant_5364" [id=281, type=Constant]; -"282 Constant_5363" [id=282, type=Constant]; -"283 Constant_5362" [id=283, type=Constant]; -"284 Transpose_1461" [id=284, type=Constant]; -"285 Constant_3867" [id=285, type=Constant]; -"286 Multiply_3859/fq_weights_1" [id=286, type=FakeQuantize]; -"287 Constant_5475" [id=287, type=Constant]; -"288 Constant_5474" [id=288, type=Constant]; -"289 Constant_5473" [id=289, type=Constant]; -"290 Constant_5472" [id=290, type=Constant]; -"291 Multiply_4064" [id=291, type=Constant]; -"292 Constant_5380" [id=292, type=Constant]; -"293 Constant_5379" [id=293, type=Constant]; -"294 Constant_5378" [id=294, type=Constant]; -"295 Constant_5377" [id=295, type=Constant]; -"296 Transpose_1594" [id=296, type=Constant]; -"297 Constant_3923" [id=297, type=Constant]; -"298 Multiply_3915/fq_weights_1" [id=298, type=FakeQuantize]; -"299 Constant_5495" [id=299, type=Constant]; -"300 Constant_5494" [id=300, type=Constant]; -"301 Constant_5493" [id=301, type=Constant]; -"302 Constant_5492" [id=302, type=Constant]; -"303 Multiply_4088" [id=303, type=Constant]; -"304 Constant_1727" [id=304, type=Constant]; -"305 Constant_5415" [id=305, type=Constant]; -"306 Constant_5414" [id=306, type=Constant]; -"307 Constant_5413" [id=307, type=Constant]; -"308 Constant_5412" [id=308, type=Constant]; -"309 Constant_669" [id=309, type=Constant]; -"310 up_sampling2d/Const" [id=310, type=Constant]; -"311 up_sampling2d/strided_slice/stack_2" [id=311, type=Constant]; -"312 up_sampling2d/strided_slice/stack_1" [id=312, type=Constant]; -"313 up_sampling2d/strided_slice/stack" [id=313, type=Constant]; -"314 Constant_1725" [id=314, type=Constant]; -"315 Constant_1723" [id=315, type=Constant]; -"316 Constant_5530" [id=316, type=Constant]; -"317 Constant_5529" [id=317, type=Constant]; -"318 Constant_5528" [id=318, type=Constant]; -"319 Constant_5527" [id=319, type=Constant]; -"320 Transpose_1722" [id=320, type=Constant]; -"321 Constant_3979" [id=321, type=Constant]; -"322 Multiply_3971/fq_weights_1" [id=322, type=FakeQuantize]; -"323 Constant_5525" [id=323, type=Constant]; -"324 Constant_5524" [id=324, type=Constant]; -"325 Constant_5523" [id=325, type=Constant]; -"326 Constant_5522" [id=326, type=Constant]; -"327 Multiply_4112" [id=327, type=Constant]; -"328 Constant_5520" [id=328, type=Constant]; -"329 Constant_5519" [id=329, type=Constant]; -"330 Constant_5518" [id=330, type=Constant]; -"331 Constant_5517" [id=331, type=Constant]; -"332 Transpose_1692" [id=332, type=Constant]; -"333 Constant_3965" [id=333, type=Constant]; -"334 Multiply_3957/fq_weights_1" [id=334, type=FakeQuantize]; -"335 Constant_5515" [id=335, type=Constant]; -"336 Constant_5514" [id=336, type=Constant]; -"337 Constant_5513" [id=337, type=Constant]; -"338 Constant_5512" [id=338, type=Constant]; -"339 Multiply_4106" [id=339, type=Constant]; -"340 Constant_5510" [id=340, type=Constant]; -"341 Constant_5509" [id=341, type=Constant]; -"342 Constant_5508" [id=342, type=Constant]; -"343 Constant_5507" [id=343, type=Constant]; -"344 Transpose_1662" [id=344, type=Constant]; -"345 Constant_3951" [id=345, type=Constant]; -"346 Multiply_3943/fq_weights_1" [id=346, type=FakeQuantize]; -"347 Constant_5505" [id=347, type=Constant]; -"348 Constant_5504" [id=348, type=Constant]; -"349 Constant_5503" [id=349, type=Constant]; -"350 Constant_5502" [id=350, type=Constant]; -"351 Multiply_4100" [id=351, type=Constant]; -"352 Constant_1801" [id=352, type=Constant]; -"353 Transpose_1800" [id=353, type=Constant]; -"354 Convolution_749/fq_weights_1" [id=354, type=FakeQuantize]; -"355 Constant_5560" [id=355, type=Constant]; -"356 Constant_5559" [id=356, type=Constant]; -"357 Constant_5558" [id=357, type=Constant]; -"358 Constant_5557" [id=358, type=Constant]; -"359 Transpose_748" [id=359, type=Constant]; -"360 Constant_5555" [id=360, type=Constant]; -"361 Constant_5554" [id=361, type=Constant]; -"362 Constant_5553" [id=362, type=Constant]; -"363 Constant_5552" [id=363, type=Constant]; -"364 Transpose_1794" [id=364, type=Constant]; -"365 Constant_4007" [id=365, type=Constant]; -"366 Multiply_3999/fq_weights_1" [id=366, type=FakeQuantize]; -"367 Constant_5550" [id=367, type=Constant]; -"368 Constant_5549" [id=368, type=Constant]; -"369 Constant_5548" [id=369, type=Constant]; -"370 Constant_5547" [id=370, type=Constant]; -"371 Multiply_4124" [id=371, type=Constant]; -"0 image_input" -> "1 image_input/fq_output_0" [label="[1, 416, 416, 3]", style=solid]; -"1 image_input/fq_output_0" -> "2 Divide_2378" [label="[1, 416, 416, 3]", style=solid]; -"2 Divide_2378" -> "3 Multiply_3747" [label="[1, 3, 416, 416]", style=solid]; -"3 Multiply_3747" -> "4 Transpose_1172" [label="[1, 32, 208, 208]", style=solid]; -"4 Transpose_1172" -> "5 Transpose_1178" [label="[1, 32, 208, 208]", style=solid]; -"5 Transpose_1178" -> "6 Transpose_1178/fq_output_0" [label="[1, 32, 208, 208]", style=solid]; -"6 Transpose_1178/fq_output_0" -> "7 Multiply_3761" [label="[1, 32, 208, 208]", style=solid]; -"7 Multiply_3761" -> "8 Transpose_1229" [label="[1, 64, 104, 104]", style=solid]; -"8 Transpose_1229" -> "9 Transpose_1235" [label="[1, 64, 104, 104]", style=solid]; -"9 Transpose_1235" -> "10 Transpose_1235/fq_output_0" [label="[1, 64, 104, 104]", style=solid]; -"10 Transpose_1235/fq_output_0" -> "11 Multiply_3775" [label="[1, 64, 104, 104]", style=solid]; -"11 Multiply_3775" -> "12 Transpose_1259" [label="[1, 64, 104, 104]", style=solid]; -"12 Transpose_1259" -> "13 Transpose_1265" [label="[1, 64, 104, 104]", style=solid]; -"13 Transpose_1265" -> "14 Transpose_1265/fq_output_0" [label="[1, 64, 104, 104]", style=solid]; -"14 Transpose_1265/fq_output_0" -> "15 Transpose_1368" [label="[1, 64, 104, 104]", style=solid]; -"14 Transpose_1265/fq_output_0" -> "16 group_route_3/split" [label="[1, 64, 104, 104]", style=solid]; -"15 Transpose_1368" -> "17 MaxPool_307" [label="[1, 128, 104, 104]", style=solid]; -"16 group_route_3/split" -> "18 Multiply_3789" [label="[1, 32, 104, 104]", style=solid]; -"17 MaxPool_307" -> "19 Multiply_3831" [label="[1, 128, 52, 52]", style=solid]; -"18 Multiply_3789" -> "20 Transpose_1294" [label="[1, 32, 104, 104]", style=solid]; -"19 Multiply_3831" -> "21 Transpose_1392" [label="[1, 128, 52, 52]", style=solid]; -"20 Transpose_1294" -> "22 Transpose_1300" [label="[1, 32, 104, 104]", style=solid]; -"21 Transpose_1392" -> "23 Transpose_1398" [label="[1, 128, 52, 52]", style=solid]; -"22 Transpose_1300" -> "24 Transpose_1300/fq_output_0" [label="[1, 32, 104, 104]", style=solid]; -"23 Transpose_1398" -> "25 Transpose_1398/fq_output_0" [label="[1, 128, 52, 52]", style=solid]; -"24 Transpose_1300/fq_output_0" -> "26 Multiply_3803" [label="[1, 32, 104, 104]", style=solid]; -"24 Transpose_1300/fq_output_0" -> "27 Transpose_1334" [label="[1, 32, 104, 104]", style=solid]; -"25 Transpose_1398/fq_output_0" -> "28 Transpose_1501" [label="[1, 128, 52, 52]", style=solid]; -"25 Transpose_1398/fq_output_0" -> "29 group_route_11/split" [label="[1, 128, 52, 52]", style=solid]; -"26 Multiply_3803" -> "30 Transpose_1324" [label="[1, 32, 104, 104]", style=solid]; -"27 Transpose_1334" -> "31 Multiply_3817" [label="[1, 64, 104, 104]", style=solid]; -"28 Transpose_1501" -> "32 MaxPool_433" [label="[1, 256, 52, 52]", style=solid]; -"29 group_route_11/split" -> "33 Multiply_3845" [label="[1, 64, 52, 52]", style=solid]; -"30 Transpose_1324" -> "34 Transpose_1330" [label="[1, 32, 104, 104]", style=solid]; -"31 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Transpose_1364/fq_output_0" -> "15 Transpose_1368" [label="[1, 64, 104, 104]", style=solid]; -"43 Transpose_1531" -> "45 Transpose_1531/fq_output_0" [label="[1, 256, 26, 26]", style=solid]; -"44 Transpose_1433/fq_output_0" -> "46 Multiply_3859" [label="[1, 64, 52, 52]", style=solid]; -"44 Transpose_1433/fq_output_0" -> "47 Transpose_1467" [label="[1, 64, 52, 52]", style=solid]; -"45 Transpose_1531/fq_output_0" -> "48 Transpose_1634" [label="[1, 256, 26, 26]", style=solid]; -"45 Transpose_1531/fq_output_0" -> "49 group_route_19/split" [label="[1, 256, 26, 26]", style=solid]; -"46 Multiply_3859" -> "50 Transpose_1457" [label="[1, 64, 52, 52]", style=solid]; -"47 Transpose_1467" -> "51 Multiply_3873" [label="[1, 128, 52, 52]", style=solid]; -"48 Transpose_1634" -> "52 MaxPool_579" [label="[1, 512, 26, 26]", style=solid]; -"49 group_route_19/split" -> "53 Multiply_3901" [label="[1, 128, 26, 26]", style=solid]; -"50 Transpose_1457" -> "54 Transpose_1463" [label="[1, 64, 52, 52]", style=solid]; -"51 Multiply_3873" -> "55 Transpose_1491" [label="[1, 128, 52, 52]", style=solid]; -"52 MaxPool_579" -> "56 Multiply_3943" [label="[1, 512, 13, 13]", style=solid]; -"53 Multiply_3901" -> "57 Transpose_1560" [label="[1, 128, 26, 26]", style=solid]; -"54 Transpose_1463" -> "58 Transpose_1463/fq_output_0" [label="[1, 64, 52, 52]", style=solid]; -"55 Transpose_1491" -> "59 Transpose_1497" [label="[1, 128, 52, 52]", style=solid]; -"56 Multiply_3943" -> "60 Transpose_1658" [label="[1, 512, 13, 13]", style=solid]; -"57 Transpose_1560" -> "61 Transpose_1566" [label="[1, 128, 26, 26]", style=solid]; -"58 Transpose_1463/fq_output_0" -> "47 Transpose_1467" [label="[1, 64, 52, 52]", style=solid]; -"59 Transpose_1497" -> "62 Transpose_1497/fq_output_0" [label="[1, 128, 52, 52]", style=solid]; -"60 Transpose_1658" -> "63 Transpose_1664" [label="[1, 512, 13, 13]", style=solid]; -"61 Transpose_1566" -> "64 Transpose_1566/fq_output_0" [label="[1, 128, 26, 26]", style=solid]; -"62 Transpose_1497/fq_output_0" -> "28 Transpose_1501" [label="[1, 128, 52, 52]", style=solid]; -"63 Transpose_1664" -> "65 Transpose_1664/fq_output_0" [label="[1, 512, 13, 13]", style=solid]; -"64 Transpose_1566/fq_output_0" -> "66 Multiply_3915" [label="[1, 128, 26, 26]", style=solid]; -"64 Transpose_1566/fq_output_0" -> "67 Transpose_1600" [label="[1, 128, 26, 26]", style=solid]; -"65 Transpose_1664/fq_output_0" -> "68 Multiply_3957" [label="[1, 512, 13, 13]", style=solid]; -"66 Multiply_3915" -> "69 Transpose_1590" [label="[1, 128, 26, 26]", style=solid]; -"67 Transpose_1600" -> "70 Multiply_3929" [label="[1, 256, 26, 26]", style=solid]; -"68 Multiply_3957" -> "71 Transpose_1688" [label="[1, 256, 13, 13]", style=solid]; -"69 Transpose_1590" -> "72 Transpose_1596" [label="[1, 128, 26, 26]", style=solid]; -"70 Multiply_3929" -> "73 Transpose_1624" [label="[1, 256, 26, 26]", style=solid]; -"71 Transpose_1688" -> "74 Transpose_1694" [label="[1, 256, 13, 13]", style=solid]; -"72 Transpose_1596" -> "75 Transpose_1596/fq_output_0" [label="[1, 128, 26, 26]", style=solid]; -"73 Transpose_1624" -> "76 Transpose_1630" [label="[1, 256, 26, 26]", style=solid]; -"74 Transpose_1694" -> "77 Transpose_1694/fq_output_0" [label="[1, 256, 13, 13]", style=solid]; -"75 Transpose_1596/fq_output_0" -> "67 Transpose_1600" [label="[1, 128, 26, 26]", style=solid]; -"76 Transpose_1630" -> "78 Transpose_1630/fq_output_0" [label="[1, 256, 26, 26]", style=solid]; -"77 Transpose_1694/fq_output_0" -> "79 Multiply_3971" [label="[1, 256, 13, 13]", style=solid]; -"77 Transpose_1694/fq_output_0" -> "80 Multiply_3999" [label="[1, 256, 13, 13]", style=solid]; -"78 Transpose_1630/fq_output_0" -> "48 Transpose_1634" [label="[1, 256, 26, 26]", style=solid]; -"78 Transpose_1630/fq_output_0" -> "81 Transpose_1730" [label="[1, 256, 26, 26]", style=solid]; -"79 Multiply_3971" -> "82 Transpose_1718" [label="[1, 128, 13, 13]", style=solid]; -"80 Multiply_3999" -> "83 Transpose_1790" [label="[1, 512, 13, 13]", style=solid]; -"81 Transpose_1730" -> "84 Multiply_3985" [label="[1, 384, 26, 26]", style=solid]; -"82 Transpose_1718" -> "85 Transpose_1724" [label="[1, 128, 13, 13]", style=solid]; -"83 Transpose_1790" -> "86 Transpose_1796" [label="[1, 512, 13, 13]", style=solid]; -"84 Multiply_3985" -> "87 Transpose_1754" [label="[1, 256, 26, 26]", style=solid]; -"85 Transpose_1724" -> "88 Transpose_1724/fq_output_0" [label="[1, 128, 13, 13]", style=solid]; -"86 Transpose_1796" -> "89 Transpose_1796/fq_output_0" [label="[1, 512, 13, 13]", style=solid]; -"87 Transpose_1754" -> "90 Transpose_1760" [label="[1, 256, 26, 26]", style=solid]; -"88 Transpose_1724/fq_output_0" -> "91 Gather_1726" [label="[1, 128, 13, 13]", style=solid]; -"88 Transpose_1724/fq_output_0" -> "92 leaky_re_lu_17/LeakyRelu" [label="[1, 128, 13, 13]", style=solid]; -"89 Transpose_1796/fq_output_0" -> "93 Convolution_749" [label="[1, 512, 13, 13]", style=solid]; -"90 Transpose_1760" -> "94 Transpose_1760/fq_output_0" [label="[1, 256, 26, 26]", style=solid]; -"91 Gather_1726" -> "95 up_sampling2d/Shape" [label="[4]", style=dashed]; -"92 leaky_re_lu_17/LeakyRelu" -> "96 up_sampling2d/resize/ResizeNearestNeighbor" [label="[1, 13, 13, 128]", style=solid]; -"93 Convolution_749" -> "97 Transpose_1802" [label="[1, 255, 13, 13]", style=solid]; -"94 Transpose_1760/fq_output_0" -> "98 Convolution_706" [label="[1, 256, 26, 26]", style=solid]; -"95 up_sampling2d/Shape" -> "99 up_sampling2d/strided_slice" [label="[4]", style=dashed]; -"96 up_sampling2d/resize/ResizeNearestNeighbor" -> "100 up_sampling2d/resize/ResizeNearestNeighbor/fq_output_0" [label="[1, 26, 26, 128]", style=solid]; -"97 Transpose_1802" -> "101 conv2d_17/BiasAdd" [label="[1, 255, 13, 13]", style=solid]; -"98 Convolution_706" -> "102 Transpose_1766" [label="[1, 255, 26, 26]", style=solid]; -"99 up_sampling2d/strided_slice" -> "103 up_sampling2d/mul" [label="[2]", style=dashed]; -"100 up_sampling2d/resize/ResizeNearestNeighbor/fq_output_0" -> "104 Transpose_1728" [label="[1, 26, 26, 128]", style=solid]; -"101 conv2d_17/BiasAdd" -> "105 conv2d_17/BiasAdd^0" [label="[1, 13, 13, 255]", style=solid]; -"102 Transpose_1766" -> "106 conv2d_20/BiasAdd" [label="[1, 255, 26, 26]", style=solid]; -"103 up_sampling2d/mul" -> "96 up_sampling2d/resize/ResizeNearestNeighbor" [label="[2]", style=dashed]; -"104 Transpose_1728" -> "81 Transpose_1730" [label="[1, 128, 26, 26]", style=solid]; -"106 conv2d_20/BiasAdd" -> "107 conv2d_20/BiasAdd^0" [label="[1, 26, 26, 255]", style=solid]; -"108 Constant_1765" -> "106 conv2d_20/BiasAdd" [label="[4]", style=dashed]; -"109 Transpose_1764" -> "102 Transpose_1766" [label="[1, 255, 1, 1]", style=solid]; -"110 Convolution_706/fq_weights_1" -> "98 Convolution_706" [label="[255, 256, 1, 1]", style=solid]; -"111 Constant_5545" -> "110 Convolution_706/fq_weights_1" [label="[255, 1, 1, 1]", style=solid]; -"112 Constant_5544" -> "110 Convolution_706/fq_weights_1" [label="[255, 1, 1, 1]", style=solid]; -"113 Constant_5543" -> "110 Convolution_706/fq_weights_1" [label="[255, 1, 1, 1]", style=solid]; -"114 Constant_5542" -> "110 Convolution_706/fq_weights_1" [label="[255, 1, 1, 1]", style=solid]; -"115 Transpose_705" -> "110 Convolution_706/fq_weights_1" [label="[255, 256, 1, 1]", style=solid]; -"116 Constant_5540" -> "94 Transpose_1760/fq_output_0" [label="[]", style=solid]; -"117 Constant_5539" -> "94 Transpose_1760/fq_output_0" [label="[]", style=solid]; -"118 Constant_5538" -> "94 Transpose_1760/fq_output_0" [label="[]", style=solid]; -"119 Constant_5537" -> "94 Transpose_1760/fq_output_0" [label="[]", style=solid]; -"120 Transpose_1758" -> "90 Transpose_1760" [label="[1, 1, 1, 1]", style=solid]; -"121 Constant_3993" -> "87 Transpose_1754" [label="[1, 256, 1, 1]", style=solid]; -"122 Multiply_3985/fq_weights_1" -> "84 Multiply_3985" [label="[256, 384, 3, 3]", style=solid]; -"123 Constant_5535" -> "122 Multiply_3985/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"124 Constant_5534" -> "122 Multiply_3985/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"125 Constant_5533" -> "122 Multiply_3985/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"126 Constant_5532" -> "122 Multiply_3985/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"127 Multiply_4118" -> "122 Multiply_3985/fq_weights_1" [label="[256, 384, 3, 3]", style=solid]; -"128 Constant_5405" -> "78 Transpose_1630/fq_output_0" [label="[]", style=solid]; -"129 Constant_5404" -> "78 Transpose_1630/fq_output_0" [label="[]", style=solid]; -"130 Constant_5403" -> "78 Transpose_1630/fq_output_0" [label="[]", style=solid]; -"131 Constant_5402" -> "78 Transpose_1630/fq_output_0" [label="[]", style=solid]; -"132 Transpose_1628" -> "76 Transpose_1630" [label="[1, 1, 1, 1]", style=solid]; -"133 Constant_3937" -> "73 Transpose_1624" [label="[1, 256, 1, 1]", style=solid]; -"134 Multiply_3929/fq_weights_1" -> "70 Multiply_3929" [label="[256, 256, 1, 1]", style=solid]; -"135 Constant_5500" -> "134 Multiply_3929/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"136 Constant_5499" -> "134 Multiply_3929/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"137 Constant_5498" -> "134 Multiply_3929/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"138 Constant_5497" -> "134 Multiply_3929/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"139 Multiply_4094" -> "134 Multiply_3929/fq_weights_1" [label="[256, 256, 1, 1]", style=solid]; -"140 Constant_5375" -> "64 Transpose_1566/fq_output_0" [label="[]", style=solid]; -"141 Constant_5374" -> "64 Transpose_1566/fq_output_0" [label="[]", style=solid]; -"142 Constant_5373" -> "64 Transpose_1566/fq_output_0" [label="[]", style=solid]; -"143 Constant_5372" -> "64 Transpose_1566/fq_output_0" [label="[]", style=solid]; -"144 Transpose_1564" -> "61 Transpose_1566" [label="[1, 1, 1, 1]", style=solid]; -"145 Constant_3909" -> "57 Transpose_1560" [label="[1, 128, 1, 1]", style=solid]; -"146 Multiply_3901/fq_weights_1" -> "53 Multiply_3901" [label="[128, 128, 3, 3]", style=solid]; -"147 Constant_5490" -> "146 Multiply_3901/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"148 Constant_5489" -> "146 Multiply_3901/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"149 Constant_5488" -> "146 Multiply_3901/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"150 Constant_5487" -> "146 Multiply_3901/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"151 Multiply_4082" -> "146 Multiply_3901/fq_weights_1" [label="[128, 128, 3, 3]", style=solid]; -"152 Constant_1532" -> "49 group_route_19/split" [label="[]", style=dashed]; -"153 Constant_5410" -> "45 Transpose_1531/fq_output_0" [label="[]", style=solid]; -"154 Constant_5409" -> "45 Transpose_1531/fq_output_0" [label="[]", style=solid]; -"155 Constant_5408" -> "45 Transpose_1531/fq_output_0" [label="[]", style=solid]; -"156 Constant_5407" -> "45 Transpose_1531/fq_output_0" [label="[]", style=solid]; -"157 Transpose_1529" -> "43 Transpose_1531" [label="[1, 1, 1, 1]", style=solid]; -"158 Constant_3895" -> "40 Transpose_1525" [label="[1, 256, 1, 1]", style=solid]; -"159 Multiply_3887/fq_weights_1" -> "36 Multiply_3887" [label="[256, 256, 3, 3]", style=solid]; -"160 Constant_5485" -> "159 Multiply_3887/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"161 Constant_5484" -> "159 Multiply_3887/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"162 Constant_5483" -> "159 Multiply_3887/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"163 Constant_5482" -> "159 Multiply_3887/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"164 Multiply_4076" -> "159 Multiply_3887/fq_weights_1" [label="[256, 256, 3, 3]", style=solid]; -"165 Constant_5400" -> "62 Transpose_1497/fq_output_0" [label="[]", style=solid]; -"166 Constant_5399" -> "62 Transpose_1497/fq_output_0" [label="[]", style=solid]; -"167 Constant_5398" -> "62 Transpose_1497/fq_output_0" [label="[]", style=solid]; -"168 Constant_5397" -> "62 Transpose_1497/fq_output_0" [label="[]", style=solid]; -"169 Transpose_1495" -> "59 Transpose_1497" [label="[1, 1, 1, 1]", style=solid]; -"170 Constant_3881" -> "55 Transpose_1491" [label="[1, 128, 1, 1]", style=solid]; -"171 Multiply_3873/fq_weights_1" -> "51 Multiply_3873" [label="[128, 128, 1, 1]", style=solid]; -"172 Constant_5480" -> "171 Multiply_3873/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"173 Constant_5479" -> "171 Multiply_3873/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"174 Constant_5478" -> "171 Multiply_3873/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"175 Constant_5477" -> "171 Multiply_3873/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"176 Multiply_4070" -> "171 Multiply_3873/fq_weights_1" [label="[128, 128, 1, 1]", style=solid]; -"177 Constant_5370" -> "44 Transpose_1433/fq_output_0" [label="[]", style=solid]; -"178 Constant_5369" -> "44 Transpose_1433/fq_output_0" [label="[]", style=solid]; -"179 Constant_5368" -> "44 Transpose_1433/fq_output_0" [label="[]", style=solid]; -"180 Constant_5367" -> "44 Transpose_1433/fq_output_0" [label="[]", style=solid]; -"181 Transpose_1431" -> "41 Transpose_1433" [label="[1, 1, 1, 1]", style=solid]; -"182 Constant_3853" -> "37 Transpose_1427" [label="[1, 64, 1, 1]", style=solid]; -"183 Multiply_3845/fq_weights_1" -> "33 Multiply_3845" [label="[64, 64, 3, 3]", style=solid]; -"184 Constant_5470" -> "183 Multiply_3845/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"185 Constant_5469" -> "183 Multiply_3845/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"186 Constant_5468" -> "183 Multiply_3845/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"187 Constant_5467" -> "183 Multiply_3845/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"188 Multiply_4058" -> "183 Multiply_3845/fq_weights_1" [label="[64, 64, 3, 3]", style=solid]; -"189 Constant_1399" -> "29 group_route_11/split" [label="[]", style=dashed]; -"190 Constant_5395" -> "25 Transpose_1398/fq_output_0" [label="[]", style=solid]; -"191 Constant_5394" -> "25 Transpose_1398/fq_output_0" [label="[]", style=solid]; -"192 Constant_5393" -> "25 Transpose_1398/fq_output_0" [label="[]", style=solid]; -"193 Constant_5392" -> "25 Transpose_1398/fq_output_0" [label="[]", style=solid]; -"194 Transpose_1396" -> "23 Transpose_1398" [label="[1, 1, 1, 1]", style=solid]; -"195 Constant_3839" -> "21 Transpose_1392" [label="[1, 128, 1, 1]", style=solid]; -"196 Multiply_3831/fq_weights_1" -> "19 Multiply_3831" [label="[128, 128, 3, 3]", style=solid]; -"197 Constant_5465" -> "196 Multiply_3831/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"198 Constant_5464" -> "196 Multiply_3831/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"199 Constant_5463" -> "196 Multiply_3831/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"200 Constant_5462" -> "196 Multiply_3831/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"201 Multiply_4052" -> "196 Multiply_3831/fq_weights_1" [label="[128, 128, 3, 3]", style=solid]; -"202 Constant_5390" -> "42 Transpose_1364/fq_output_0" [label="[]", style=solid]; -"203 Constant_5389" -> "42 Transpose_1364/fq_output_0" [label="[]", style=solid]; -"204 Constant_5388" -> "42 Transpose_1364/fq_output_0" [label="[]", style=solid]; -"205 Constant_5387" -> "42 Transpose_1364/fq_output_0" [label="[]", style=solid]; -"206 Transpose_1362" -> "39 Transpose_1364" [label="[1, 1, 1, 1]", style=solid]; -"207 Constant_3825" -> "35 Transpose_1358" [label="[1, 64, 1, 1]", style=solid]; -"208 Multiply_3817/fq_weights_1" -> "31 Multiply_3817" [label="[64, 64, 1, 1]", style=solid]; -"209 Constant_5460" -> "208 Multiply_3817/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"210 Constant_5459" -> "208 Multiply_3817/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"211 Constant_5458" -> "208 Multiply_3817/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"212 Constant_5457" -> "208 Multiply_3817/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"213 Multiply_4046" -> "208 Multiply_3817/fq_weights_1" [label="[64, 64, 1, 1]", style=solid]; -"214 Constant_5355" -> "24 Transpose_1300/fq_output_0" [label="[]", style=solid]; -"215 Constant_5354" -> "24 Transpose_1300/fq_output_0" [label="[]", style=solid]; -"216 Constant_5353" -> "24 Transpose_1300/fq_output_0" [label="[]", style=solid]; -"217 Constant_5352" -> "24 Transpose_1300/fq_output_0" [label="[]", style=solid]; -"218 Transpose_1298" -> "22 Transpose_1300" [label="[1, 1, 1, 1]", style=solid]; -"219 Constant_3797" -> "20 Transpose_1294" [label="[1, 32, 1, 1]", style=solid]; -"220 Multiply_3789/fq_weights_1" -> "18 Multiply_3789" [label="[32, 32, 3, 3]", style=solid]; -"221 Constant_5450" -> "220 Multiply_3789/fq_weights_1" [label="[32, 1, 1, 1]", style=solid]; -"222 Constant_5449" -> "220 Multiply_3789/fq_weights_1" [label="[32, 1, 1, 1]", style=solid]; -"223 Constant_5448" -> "220 Multiply_3789/fq_weights_1" [label="[32, 1, 1, 1]", style=solid]; -"224 Constant_5447" -> "220 Multiply_3789/fq_weights_1" [label="[32, 1, 1, 1]", style=solid]; -"225 Multiply_4034" -> "220 Multiply_3789/fq_weights_1" [label="[32, 32, 3, 3]", style=solid]; -"226 Constant_1266" -> "16 group_route_3/split" [label="[]", style=dashed]; -"227 Constant_5385" -> "14 Transpose_1265/fq_output_0" [label="[]", style=solid]; -"228 Constant_5384" -> "14 Transpose_1265/fq_output_0" [label="[]", style=solid]; -"229 Constant_5383" -> "14 Transpose_1265/fq_output_0" [label="[]", style=solid]; -"230 Constant_5382" -> "14 Transpose_1265/fq_output_0" [label="[]", style=solid]; -"231 Transpose_1263" -> "13 Transpose_1265" [label="[1, 1, 1, 1]", style=solid]; -"232 Constant_3783" -> "12 Transpose_1259" [label="[1, 64, 1, 1]", style=solid]; -"233 Multiply_3775/fq_weights_1" -> "11 Multiply_3775" [label="[64, 64, 3, 3]", style=solid]; -"234 Constant_5445" -> "233 Multiply_3775/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"235 Constant_5444" -> "233 Multiply_3775/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"236 Constant_5443" -> "233 Multiply_3775/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"237 Constant_5442" -> "233 Multiply_3775/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"238 Multiply_4028" -> "233 Multiply_3775/fq_weights_1" [label="[64, 64, 3, 3]", style=solid]; -"239 Constant_5440" -> "10 Transpose_1235/fq_output_0" [label="[]", style=solid]; -"240 Constant_5439" -> "10 Transpose_1235/fq_output_0" [label="[]", style=solid]; -"241 Constant_5438" -> "10 Transpose_1235/fq_output_0" [label="[]", style=solid]; -"242 Constant_5437" -> "10 Transpose_1235/fq_output_0" [label="[]", style=solid]; -"243 Transpose_1233" -> "9 Transpose_1235" [label="[1, 1, 1, 1]", style=solid]; -"244 Constant_3769" -> "8 Transpose_1229" [label="[1, 64, 1, 1]", style=solid]; -"245 Multiply_3761/fq_weights_1" -> "7 Multiply_3761" [label="[64, 32, 3, 3]", style=solid]; -"246 Constant_5435" -> "245 Multiply_3761/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"247 Constant_5434" -> "245 Multiply_3761/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"248 Constant_5433" -> "245 Multiply_3761/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"249 Constant_5432" -> "245 Multiply_3761/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"250 Multiply_4022" -> "245 Multiply_3761/fq_weights_1" [label="[64, 32, 3, 3]", style=solid]; -"251 Constant_5430" -> "6 Transpose_1178/fq_output_0" [label="[]", style=solid]; -"252 Constant_5429" -> "6 Transpose_1178/fq_output_0" [label="[]", style=solid]; -"253 Constant_5428" -> "6 Transpose_1178/fq_output_0" [label="[]", style=solid]; -"254 Constant_5427" -> "6 Transpose_1178/fq_output_0" [label="[]", style=solid]; -"255 Transpose_1176" -> "5 Transpose_1178" [label="[1, 1, 1, 1]", style=solid]; -"256 Constant_3755" -> "4 Transpose_1172" [label="[1, 32, 1, 1]", style=solid]; -"257 Multiply_3747/fq_weights_1" -> "3 Multiply_3747" [label="[32, 3, 3, 3]", style=solid]; -"258 Constant_5425" -> "257 Multiply_3747/fq_weights_1" [label="[32, 1, 1, 1]", style=solid]; -"259 Constant_5424" -> "257 Multiply_3747/fq_weights_1" [label="[32, 1, 1, 1]", style=solid]; -"260 Constant_5423" -> "257 Multiply_3747/fq_weights_1" [label="[32, 1, 1, 1]", style=solid]; -"261 Constant_5422" -> "257 Multiply_3747/fq_weights_1" [label="[32, 1, 1, 1]", style=solid]; -"262 Gather_4293" -> "257 Multiply_3747/fq_weights_1" [label="[32, 3, 3, 3]", style=solid]; -"263 Constant_2333" -> "2 Divide_2378" [label="[4]", style=dashed]; -"264 Constant_5420" -> "1 image_input/fq_output_0" [label="[]", style=solid]; -"265 Constant_5419" -> "1 image_input/fq_output_0" [label="[]", style=solid]; -"266 Constant_5418" -> "1 image_input/fq_output_0" [label="[]", style=solid]; -"267 Constant_5417" -> "1 image_input/fq_output_0" [label="[]", style=solid]; -"268 Constant_5360" -> "38 Transpose_1330/fq_output_0" [label="[]", style=solid]; -"269 Constant_5359" -> "38 Transpose_1330/fq_output_0" [label="[]", style=solid]; -"270 Constant_5358" -> "38 Transpose_1330/fq_output_0" [label="[]", style=solid]; -"271 Constant_5357" -> "38 Transpose_1330/fq_output_0" [label="[]", style=solid]; -"272 Transpose_1328" -> "34 Transpose_1330" [label="[1, 1, 1, 1]", style=solid]; -"273 Constant_3811" -> "30 Transpose_1324" [label="[1, 32, 1, 1]", style=solid]; -"274 Multiply_3803/fq_weights_1" -> "26 Multiply_3803" [label="[32, 32, 3, 3]", style=solid]; -"275 Constant_5455" -> "274 Multiply_3803/fq_weights_1" [label="[32, 1, 1, 1]", style=solid]; -"276 Constant_5454" -> "274 Multiply_3803/fq_weights_1" [label="[32, 1, 1, 1]", style=solid]; -"277 Constant_5453" -> "274 Multiply_3803/fq_weights_1" [label="[32, 1, 1, 1]", style=solid]; -"278 Constant_5452" -> "274 Multiply_3803/fq_weights_1" [label="[32, 1, 1, 1]", style=solid]; -"279 Multiply_4040" -> "274 Multiply_3803/fq_weights_1" [label="[32, 32, 3, 3]", style=solid]; -"280 Constant_5365" -> "58 Transpose_1463/fq_output_0" [label="[]", style=solid]; -"281 Constant_5364" -> "58 Transpose_1463/fq_output_0" [label="[]", style=solid]; -"282 Constant_5363" -> "58 Transpose_1463/fq_output_0" [label="[]", style=solid]; -"283 Constant_5362" -> "58 Transpose_1463/fq_output_0" [label="[]", style=solid]; -"284 Transpose_1461" -> "54 Transpose_1463" [label="[1, 1, 1, 1]", style=solid]; -"285 Constant_3867" -> "50 Transpose_1457" [label="[1, 64, 1, 1]", style=solid]; -"286 Multiply_3859/fq_weights_1" -> "46 Multiply_3859" [label="[64, 64, 3, 3]", style=solid]; -"287 Constant_5475" -> "286 Multiply_3859/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"288 Constant_5474" -> "286 Multiply_3859/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"289 Constant_5473" -> "286 Multiply_3859/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"290 Constant_5472" -> "286 Multiply_3859/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; -"291 Multiply_4064" -> "286 Multiply_3859/fq_weights_1" [label="[64, 64, 3, 3]", style=solid]; -"292 Constant_5380" -> "75 Transpose_1596/fq_output_0" [label="[]", style=solid]; -"293 Constant_5379" -> "75 Transpose_1596/fq_output_0" [label="[]", style=solid]; -"294 Constant_5378" -> "75 Transpose_1596/fq_output_0" [label="[]", style=solid]; -"295 Constant_5377" -> "75 Transpose_1596/fq_output_0" [label="[]", style=solid]; -"296 Transpose_1594" -> "72 Transpose_1596" [label="[1, 1, 1, 1]", style=solid]; -"297 Constant_3923" -> "69 Transpose_1590" [label="[1, 128, 1, 1]", style=solid]; -"298 Multiply_3915/fq_weights_1" -> "66 Multiply_3915" [label="[128, 128, 3, 3]", style=solid]; -"299 Constant_5495" -> "298 Multiply_3915/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"300 Constant_5494" -> "298 Multiply_3915/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"301 Constant_5493" -> "298 Multiply_3915/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"302 Constant_5492" -> "298 Multiply_3915/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"303 Multiply_4088" -> "298 Multiply_3915/fq_weights_1" [label="[128, 128, 3, 3]", style=solid]; -"304 Constant_1727" -> "104 Transpose_1728" [label="[4]", style=dashed]; -"305 Constant_5415" -> "100 up_sampling2d/resize/ResizeNearestNeighbor/fq_output_0" [label="[]", style=solid]; -"306 Constant_5414" -> "100 up_sampling2d/resize/ResizeNearestNeighbor/fq_output_0" [label="[]", style=solid]; -"307 Constant_5413" -> "100 up_sampling2d/resize/ResizeNearestNeighbor/fq_output_0" [label="[]", style=solid]; -"308 Constant_5412" -> "100 up_sampling2d/resize/ResizeNearestNeighbor/fq_output_0" [label="[]", style=solid]; -"309 Constant_669" -> "96 up_sampling2d/resize/ResizeNearestNeighbor" [label="[2]", style=dashed]; -"310 up_sampling2d/Const" -> "103 up_sampling2d/mul" [label="[2]", style=dashed]; -"311 up_sampling2d/strided_slice/stack_2" -> "99 up_sampling2d/strided_slice" [label="[1]", style=dashed]; -"312 up_sampling2d/strided_slice/stack_1" -> "99 up_sampling2d/strided_slice" [label="[1]", style=dashed]; -"313 up_sampling2d/strided_slice/stack" -> "99 up_sampling2d/strided_slice" [label="[1]", style=dashed]; -"314 Constant_1725" -> "95 up_sampling2d/Shape" [label="[]", style=dashed]; -"315 Constant_1723" -> "92 leaky_re_lu_17/LeakyRelu" [label="[4]", style=dashed]; -"315 Constant_1723" -> "95 up_sampling2d/Shape" [label="[4]", style=dashed]; -"316 Constant_5530" -> "88 Transpose_1724/fq_output_0" [label="[]", style=solid]; -"317 Constant_5529" -> "88 Transpose_1724/fq_output_0" [label="[]", style=solid]; -"318 Constant_5528" -> "88 Transpose_1724/fq_output_0" [label="[]", style=solid]; -"319 Constant_5527" -> "88 Transpose_1724/fq_output_0" [label="[]", style=solid]; -"320 Transpose_1722" -> "85 Transpose_1724" [label="[1, 1, 1, 1]", style=solid]; -"321 Constant_3979" -> "82 Transpose_1718" [label="[1, 128, 1, 1]", style=solid]; -"322 Multiply_3971/fq_weights_1" -> "79 Multiply_3971" [label="[128, 256, 1, 1]", style=solid]; -"323 Constant_5525" -> "322 Multiply_3971/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"324 Constant_5524" -> "322 Multiply_3971/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"325 Constant_5523" -> "322 Multiply_3971/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"326 Constant_5522" -> "322 Multiply_3971/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; -"327 Multiply_4112" -> "322 Multiply_3971/fq_weights_1" [label="[128, 256, 1, 1]", style=solid]; -"328 Constant_5520" -> "77 Transpose_1694/fq_output_0" [label="[]", style=solid]; -"329 Constant_5519" -> "77 Transpose_1694/fq_output_0" [label="[]", style=solid]; -"330 Constant_5518" -> "77 Transpose_1694/fq_output_0" [label="[]", style=solid]; -"331 Constant_5517" -> "77 Transpose_1694/fq_output_0" [label="[]", style=solid]; -"332 Transpose_1692" -> "74 Transpose_1694" [label="[1, 1, 1, 1]", style=solid]; -"333 Constant_3965" -> "71 Transpose_1688" [label="[1, 256, 1, 1]", style=solid]; -"334 Multiply_3957/fq_weights_1" -> "68 Multiply_3957" [label="[256, 512, 1, 1]", style=solid]; -"335 Constant_5515" -> "334 Multiply_3957/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"336 Constant_5514" -> "334 Multiply_3957/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"337 Constant_5513" -> "334 Multiply_3957/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"338 Constant_5512" -> "334 Multiply_3957/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; -"339 Multiply_4106" -> "334 Multiply_3957/fq_weights_1" [label="[256, 512, 1, 1]", style=solid]; -"340 Constant_5510" -> "65 Transpose_1664/fq_output_0" [label="[]", style=solid]; -"341 Constant_5509" -> "65 Transpose_1664/fq_output_0" [label="[]", style=solid]; -"342 Constant_5508" -> "65 Transpose_1664/fq_output_0" [label="[]", style=solid]; -"343 Constant_5507" -> "65 Transpose_1664/fq_output_0" [label="[]", style=solid]; -"344 Transpose_1662" -> "63 Transpose_1664" [label="[1, 1, 1, 1]", style=solid]; -"345 Constant_3951" -> "60 Transpose_1658" [label="[1, 512, 1, 1]", style=solid]; -"346 Multiply_3943/fq_weights_1" -> "56 Multiply_3943" [label="[512, 512, 3, 3]", style=solid]; -"347 Constant_5505" -> "346 Multiply_3943/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"348 Constant_5504" -> "346 Multiply_3943/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"349 Constant_5503" -> "346 Multiply_3943/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"350 Constant_5502" -> "346 Multiply_3943/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"351 Multiply_4100" -> "346 Multiply_3943/fq_weights_1" [label="[512, 512, 3, 3]", style=solid]; -"352 Constant_1801" -> "101 conv2d_17/BiasAdd" [label="[4]", style=dashed]; -"353 Transpose_1800" -> "97 Transpose_1802" [label="[1, 255, 1, 1]", style=solid]; -"354 Convolution_749/fq_weights_1" -> "93 Convolution_749" [label="[255, 512, 1, 1]", style=solid]; -"355 Constant_5560" -> "354 Convolution_749/fq_weights_1" [label="[255, 1, 1, 1]", style=solid]; -"356 Constant_5559" -> "354 Convolution_749/fq_weights_1" [label="[255, 1, 1, 1]", style=solid]; -"357 Constant_5558" -> "354 Convolution_749/fq_weights_1" [label="[255, 1, 1, 1]", style=solid]; -"358 Constant_5557" -> "354 Convolution_749/fq_weights_1" [label="[255, 1, 1, 1]", style=solid]; -"359 Transpose_748" -> "354 Convolution_749/fq_weights_1" [label="[255, 512, 1, 1]", style=solid]; -"360 Constant_5555" -> "89 Transpose_1796/fq_output_0" [label="[]", style=solid]; -"361 Constant_5554" -> "89 Transpose_1796/fq_output_0" [label="[]", style=solid]; -"362 Constant_5553" -> "89 Transpose_1796/fq_output_0" [label="[]", style=solid]; -"363 Constant_5552" -> "89 Transpose_1796/fq_output_0" [label="[]", style=solid]; -"364 Transpose_1794" -> "86 Transpose_1796" [label="[1, 1, 1, 1]", style=solid]; -"365 Constant_4007" -> "83 Transpose_1790" [label="[1, 512, 1, 1]", style=solid]; -"366 Multiply_3999/fq_weights_1" -> "80 Multiply_3999" [label="[512, 256, 3, 3]", style=solid]; -"367 Constant_5550" -> "366 Multiply_3999/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"368 Constant_5549" -> "366 Multiply_3999/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"369 Constant_5548" -> "366 Multiply_3999/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"370 Constant_5547" -> "366 Multiply_3999/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; -"371 Multiply_4124" -> "366 Multiply_3999/fq_weights_1" [label="[512, 256, 3, 3]", style=solid]; -} diff --git a/tests/openvino/native/data/2023.2/reference_scales/ComparisonBinaryModel_mixed.json b/tests/openvino/native/data/2023.2/reference_scales/ComparisonBinaryModel_mixed.json deleted file mode 100644 index 05e4ae7b255..00000000000 --- a/tests/openvino/native/data/2023.2/reference_scales/ComparisonBinaryModel_mixed.json +++ /dev/null @@ -1,8 +0,0 @@ -{ - "Input/fq_output_0": { - "input_low": 0.0, - "input_high": 0.9350724220275879, - "output_low": 0.0, - "output_high": 0.9350724220275879 - } -} \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_scales/ComparisonBinaryModel_performance.json b/tests/openvino/native/data/2023.2/reference_scales/ComparisonBinaryModel_performance.json deleted file mode 100644 index 05e4ae7b255..00000000000 --- a/tests/openvino/native/data/2023.2/reference_scales/ComparisonBinaryModel_performance.json +++ /dev/null @@ -1,8 +0,0 @@ -{ - "Input/fq_output_0": { - "input_low": 0.0, - "input_high": 0.9350724220275879, - "output_low": 0.0, - "output_high": 0.9350724220275879 - } -} \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_scales/ConvModel_mixed.json b/tests/openvino/native/data/2023.2/reference_scales/ConvModel_mixed.json deleted file mode 100644 index 7a6b296c118..00000000000 --- a/tests/openvino/native/data/2023.2/reference_scales/ConvModel_mixed.json +++ /dev/null @@ -1,102 +0,0 @@ -{ - "Conv/fq_weights_1": { - "input_low": [ - [ - [ - [ - -71.96717071533203 - ] - ] - 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920083274df..00000000000 --- a/tests/openvino/native/data/2023.2/reference_scales/ConvModel_performance.json +++ /dev/null @@ -1,102 +0,0 @@ -{ - "Conv/fq_weights_1": { - "input_low": [ - [ - [ - [ - -71.96717071533203 - ] - ] - ], - [ - [ - [ - -111.47208404541016 - ] - ] - ], - [ - [ - [ - -102.1300277709961 - ] - ] - ] - ], - "input_high": [ - [ - [ - [ - 71.96717071533203 - ] - ] - ], - [ - [ - [ - 111.47208404541016 - ] - ] - ], - [ - [ - [ - 102.1300277709961 - ] - ] - ] - ], - "output_low": [ - [ - [ - [ - -71.96717071533203 - ] - ] - ], - [ - [ - [ - -111.47208404541016 - ] - ] - ], - [ - [ - [ - -102.1300277709961 - ] - ] - ] - ], - "output_high": [ - [ - [ - [ - 71.96717071533203 - ] - ] - ], - [ - [ - [ - 111.47208404541016 - ] - ] - ], - [ - [ - [ - 102.1300277709961 - ] - ] - ] - ] - }, - "Sub/fq_output_0": { - "input_low": -0.8286794424057007, - "input_high": 0.8222053647041321, - "output_low": -0.8286794424057007, - "output_high": 0.8222053647041321 - } -} \ No newline 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0.06186329573392868 - ] - ] - ], - [ - [ - [ - 0.052172090858221054 - ] - ] - ], - [ - [ - [ - 0.05656270310282707 - ] - ] - ], - [ - [ - [ - 0.04382876306772232 - ] - ] - ] - ] - } -} \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/ComparisonBinaryModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/ComparisonBinaryModel.dot deleted file mode 120000 index b0d0b2c1506..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/ComparisonBinaryModel.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/original_nncf_graph/ComparisonBinaryModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/ComparisonBinaryModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/ComparisonBinaryModel.dot new file mode 100644 index 00000000000..467811ae36f --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/ComparisonBinaryModel.dot @@ -0,0 +1,19 @@ +strict digraph { +"0 Input" [id=0, type=Parameter]; +"1 Add" [id=1, type=Add]; +"2 Gather_6" [id=2, type=Gather]; +"3 GreaterEqual" [id=3, type=GreaterEqual]; +"4 Result_Add" [id=4, type=Result]; +"5 Convert" [id=5, type=Convert]; +"6 Constant_5" [id=6, type=Constant]; +"7 Constant_2" [id=7, type=Constant]; +"0 Input" -> "1 Add" [label="[1, 3, 4, 2]", style=solid]; +"0 Input" -> "2 Gather_6" [label="[1, 3, 4, 2]", style=solid]; +"0 Input" -> "3 GreaterEqual" [label="[1, 3, 4, 2]", style=solid]; +"1 Add" -> "4 Result_Add" [label="[1, 3, 4, 2, 3, 4, 2]", style=solid]; +"2 Gather_6" -> "1 Add" [label="[1, 3, 4, 2, 3, 4, 2]", style=solid]; +"3 GreaterEqual" -> "5 Convert" [label="[1, 3, 4, 2]", style=dashed]; +"5 Convert" -> "2 Gather_6" [label="[1, 3, 4, 2]", style=dashed]; +"6 Constant_5" -> "2 Gather_6" [label="[]", style=dashed]; +"7 Constant_2" -> "3 GreaterEqual" [label="[1, 3, 4, 2]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/ConvModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/ConvModel.dot deleted file mode 120000 index 8cc4b156e22..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/ConvModel.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/original_nncf_graph/ConvModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/ConvModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/ConvModel.dot new file mode 100644 index 00000000000..dcc2423384f --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/ConvModel.dot @@ -0,0 +1,35 @@ +strict digraph { +"0 Input_1" [id=0, type=Parameter]; +"1 Input_2" [id=1, type=Parameter]; +"2 Sub" [id=2, type=Subtract]; +"3 Add" [id=3, type=Add]; +"4 Conv" [id=4, type=Convolution]; +"5 Mul" [id=5, type=Multiply]; +"6 Conv_Add" [id=6, type=Add]; +"7 Transpose" [id=7, type=Transpose]; +"8 Relu" [id=8, type=Relu]; +"9 Concat_15" [id=9, type=Concat]; +"10 Result" [id=10, type=Result]; +"11 Constant_13" [id=11, type=Constant]; +"12 Constant_11" [id=12, type=Constant]; +"13 Constant_9" [id=13, type=Constant]; +"14 Bias" [id=14, type=Constant]; +"15 Constant_3" [id=15, type=Constant]; +"16 Constant_1" [id=16, type=Constant]; +"0 Input_1" -> "2 Sub" [label="[1, 3, 4, 2]", style=solid]; +"1 Input_2" -> "3 Add" [label="[1, 3, 2, 4]", style=solid]; +"2 Sub" -> "4 Conv" [label="[1, 3, 4, 2]", style=solid]; +"3 Add" -> "5 Mul" [label="[1, 3, 2, 4]", style=solid]; +"4 Conv" -> "6 Conv_Add" [label="[1, 3, 4, 2]", style=solid]; +"5 Mul" -> "7 Transpose" [label="[1, 3, 2, 4]", style=solid]; +"6 Conv_Add" -> "8 Relu" [label="[1, 3, 4, 2]", style=solid]; +"7 Transpose" -> "9 Concat_15" [label="[1, 3, 4, 2]", style=solid]; +"8 Relu" -> "9 Concat_15" [label="[1, 3, 4, 2]", style=solid]; +"9 Concat_15" -> "10 Result" [label="[2, 3, 4, 2]", style=solid]; +"11 Constant_13" -> "7 Transpose" [label="[4]", style=dashed]; +"12 Constant_11" -> "5 Mul" [label="[1, 3, 1, 1]", style=solid]; +"13 Constant_9" -> "3 Add" [label="[1, 3, 1, 1]", style=solid]; +"14 Bias" -> "6 Conv_Add" [label="[1, 3, 1, 1]", style=solid]; +"15 Constant_3" -> "4 Conv" [label="[3, 3, 1, 1]", style=solid]; +"16 Constant_1" -> "2 Sub" [label="[1, 3, 1, 1]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/ConvNotBiasModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/ConvNotBiasModel.dot deleted file mode 120000 index 4fd330c8a74..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/ConvNotBiasModel.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/original_nncf_graph/ConvNotBiasModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/ConvNotBiasModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/ConvNotBiasModel.dot new file mode 100644 index 00000000000..a53e7699cce --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/ConvNotBiasModel.dot @@ -0,0 +1,19 @@ +strict digraph { +"0 Input_1" [id=0, type=Parameter]; +"1 Sub" [id=1, type=Subtract]; +"2 Conv" [id=2, type=Convolution]; +"3 Conv_Add" [id=3, type=Add]; +"4 Relu" [id=4, type=Relu]; +"5 Result" [id=5, type=Result]; +"6 NotBias" [id=6, type=Constant]; +"7 Constant_143" [id=7, type=Constant]; +"8 Constant_141" [id=8, type=Constant]; +"0 Input_1" -> "1 Sub" [label="[1, 3, 4, 2]", style=solid]; +"1 Sub" -> "2 Conv" [label="[1, 3, 4, 2]", style=solid]; +"2 Conv" -> "3 Conv_Add" [label="[1, 3, 4, 2]", style=solid]; +"3 Conv_Add" -> "4 Relu" [label="[1, 3, 4, 2]", style=solid]; +"4 Relu" -> "5 Result" [label="[1, 3, 4, 2]", style=solid]; +"6 NotBias" -> "3 Conv_Add" [label="[1, 3, 4, 2]", style=solid]; +"7 Constant_143" -> "2 Conv" [label="[3, 3, 1, 1]", style=solid]; +"8 Constant_141" -> "1 Sub" [label="[1, 3, 1, 1]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/DepthwiseConvModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/DepthwiseConvModel.dot deleted file mode 120000 index 2a8e1bf3059..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/DepthwiseConvModel.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/original_nncf_graph/DepthwiseConvModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/DepthwiseConvModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/DepthwiseConvModel.dot new file mode 100644 index 00000000000..884ed9bce29 --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/DepthwiseConvModel.dot @@ -0,0 +1,15 @@ +strict digraph { +"0 Input_1" [id=0, type=Parameter]; +"1 Conv" [id=1, type=GroupConvolution]; +"2 Add" [id=2, type=Add]; +"3 Relu" [id=3, type=Relu]; +"4 Result" [id=4, type=Result]; +"5 Constant_4" [id=5, type=Constant]; +"6 Constant_2" [id=6, type=Constant]; +"0 Input_1" -> "1 Conv" [label="[1, 3, 5, 5]", style=solid]; +"1 Conv" -> "2 Add" [label="[1, 3, 3, 3]", style=solid]; +"2 Add" -> "3 Relu" [label="[1, 3, 3, 3]", style=solid]; +"3 Relu" -> "4 Result" [label="[1, 3, 3, 3]", style=solid]; +"5 Constant_4" -> "2 Add" [label="[1, 3, 1, 1]", style=solid]; +"6 Constant_2" -> "1 Conv" [label="[3, 1, 1, 3, 3]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/DynamicModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/DynamicModel.dot deleted file mode 120000 index 535a979b747..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/DynamicModel.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/original_nncf_graph/DynamicModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/DynamicModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/DynamicModel.dot new file mode 100644 index 00000000000..bf3926ef1ad --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/DynamicModel.dot @@ -0,0 +1,35 @@ +strict digraph { +"0 Input_1" [id=0, type=Parameter]; +"1 Input_2" [id=1, type=Parameter]; +"2 Sub" [id=2, type=Subtract]; +"3 Add" [id=3, type=Add]; +"4 Conv" [id=4, type=Convolution]; +"5 Mul" [id=5, type=Multiply]; +"6 Conv_Add" [id=6, type=Add]; +"7 Transpose" [id=7, type=Transpose]; +"8 Relu" [id=8, type=Relu]; +"9 Concat_58" [id=9, type=Concat]; +"10 Result" [id=10, type=Result]; +"11 Constant_56" [id=11, type=Constant]; +"12 Constant_54" [id=12, type=Constant]; +"13 Constant_52" [id=13, type=Constant]; +"14 Bias" [id=14, type=Constant]; +"15 Constant_46" [id=15, type=Constant]; +"16 Constant_44" [id=16, type=Constant]; +"0 Input_1" -> "2 Sub" [label="[99, 3, 4, 2]", style=solid]; +"1 Input_2" -> "3 Add" [label="[99, 3, 2, 4]", style=solid]; +"2 Sub" -> "4 Conv" [label="[99, 3, 4, 2]", style=solid]; +"3 Add" -> "5 Mul" [label="[99, 3, 2, 4]", style=solid]; +"4 Conv" -> "6 Conv_Add" [label="[99, 3, 4, 2]", style=solid]; +"5 Mul" -> "7 Transpose" [label="[99, 3, 2, 4]", style=solid]; +"6 Conv_Add" -> "8 Relu" [label="[99, 3, 4, 2]", style=solid]; +"7 Transpose" -> "9 Concat_58" [label="[99, 3, 4, 2]", style=solid]; +"8 Relu" -> "9 Concat_58" [label="[99, 3, 4, 2]", style=solid]; +"9 Concat_58" -> "10 Result" [label="[198, 3, 4, 2]", style=solid]; +"11 Constant_56" -> "7 Transpose" [label="[4]", style=dashed]; +"12 Constant_54" -> "5 Mul" [label="[1, 3, 1, 1]", style=solid]; +"13 Constant_52" -> "3 Add" [label="[1, 3, 1, 1]", style=solid]; +"14 Bias" -> "6 Conv_Add" [label="[1, 3, 1, 1]", style=solid]; +"15 Constant_46" -> "4 Conv" [label="[3, 3, 1, 1]", style=solid]; +"16 Constant_44" -> "2 Sub" [label="[1, 3, 1, 1]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/GroupNormalizationModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/GroupNormalizationModel.dot deleted file mode 120000 index 6d9e3262a27..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/GroupNormalizationModel.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/original_nncf_graph/GroupNormalizationModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/GroupNormalizationModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/GroupNormalizationModel.dot new file mode 100644 index 00000000000..44ba7698a92 --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/GroupNormalizationModel.dot @@ -0,0 +1,29 @@ +strict digraph { +"0 Input_1" [id=0, type=Parameter]; +"1 Conv" [id=1, type=Convolution]; +"2 Conv_Add" [id=2, type=Add]; +"3 GroupNormalization_169" [id=3, type=GroupNormalization]; +"4 Relu" [id=4, type=Relu]; +"5 Mul" [id=5, type=Multiply]; +"6 Add" [id=6, type=Add]; +"7 Result" [id=7, type=Result]; +"8 Constant_173" [id=8, type=Constant]; +"9 Constant_171" [id=9, type=Constant]; +"10 Constant_168" [id=10, type=Constant]; +"11 Constant_167" [id=11, type=Constant]; +"12 Bias" [id=12, type=Constant]; +"13 Constant_163" [id=13, type=Constant]; +"0 Input_1" -> "1 Conv" [label="[1, 2, 3, 4, 4]", style=solid]; +"1 Conv" -> "2 Conv_Add" [label="[1, 4, 1, 2, 2]", style=solid]; +"2 Conv_Add" -> "3 GroupNormalization_169" [label="[1, 4, 3, 2, 2]", style=solid]; +"3 GroupNormalization_169" -> "4 Relu" [label="[1, 4, 3, 2, 2]", style=solid]; +"4 Relu" -> "5 Mul" [label="[1, 4, 3, 2, 2]", style=solid]; +"5 Mul" -> "6 Add" [label="[1, 4, 3, 2, 2]", style=solid]; +"6 Add" -> "7 Result" [label="[1, 4, 3, 2, 2]", style=solid]; +"8 Constant_173" -> "6 Add" [label="[1, 4, 1, 1, 1]", style=solid]; +"9 Constant_171" -> "5 Mul" [label="[1, 4, 1, 1, 1]", style=solid]; +"10 Constant_168" -> "3 GroupNormalization_169" [label="[4]", style=solid]; +"11 Constant_167" -> "3 GroupNormalization_169" [label="[4]", style=solid]; +"12 Bias" -> "2 Conv_Add" [label="[1, 1, 3, 1, 1]", style=solid]; +"13 Constant_163" -> "1 Conv" [label="[4, 2, 3, 3, 3]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/IntegerModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/IntegerModel.dot deleted file mode 120000 index 516081134ac..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/IntegerModel.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/original_nncf_graph/IntegerModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/IntegerModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/IntegerModel.dot new file mode 100644 index 00000000000..63cc63bfa23 --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/IntegerModel.dot @@ -0,0 +1,42 @@ +strict digraph { +"0 Input" [id=0, type=Parameter]; +"1 Convert_1" [id=1, type=Convert]; +"2 Gather_15" [id=2, type=Gather]; +"3 Gather_4" [id=3, type=Gather]; +"4 MatMul_2" [id=4, type=MatMul]; +"5 Gather_7" [id=5, type=Gather]; +"6 Add_1" [id=6, type=Add]; +"7 Gather_10" [id=7, type=Gather]; +"8 Result" [id=8, type=Result]; +"9 MatMul_1" [id=9, type=MatMul]; +"10 Constant_16" [id=10, type=Constant]; +"11 Constant_14" [id=11, type=Constant]; +"12 Constant_13" [id=12, type=Constant]; +"13 Constant_11" [id=13, type=Constant]; +"14 Constant_9" [id=14, type=Constant]; +"15 Constant_8" [id=15, type=Constant]; +"16 Constant_6" [id=16, type=Constant]; +"17 Constant_3" [id=17, type=Constant]; +"18 Constant_2" [id=18, type=Constant]; +"19 Constant_5" [id=19, type=Constant]; +"0 Input" -> "1 Convert_1" [label="[1, 192, 1]", style=solid]; +"0 Input" -> "2 Gather_15" [label="[1, 192, 1]", style=solid]; +"1 Convert_1" -> "3 Gather_4" [label="[1, 192, 1]", style=dashed]; +"2 Gather_15" -> "4 MatMul_2" [label="[1, 192]", style=solid]; +"3 Gather_4" -> "5 Gather_7" [label="[192, 1]", style=dashed]; +"4 MatMul_2" -> "6 Add_1" [label="[1, 160]", style=solid]; +"5 Gather_7" -> "7 Gather_10" [label="[192, 1, 160]", style=solid]; +"6 Add_1" -> "8 Result" [label="[1, 160]", style=solid]; +"7 Gather_10" -> "9 MatMul_1" [label="[1, 160]", style=solid]; +"9 MatMul_1" -> "6 Add_1" [label="[1, 160]", style=solid]; +"10 Constant_16" -> "4 MatMul_2" [label="[160, 192]", style=solid]; +"11 Constant_14" -> "2 Gather_15" [label="[]", style=dashed]; +"12 Constant_13" -> "2 Gather_15" [label="[]", style=dashed]; +"13 Constant_11" -> "9 MatMul_1" [label="[160, 160]", style=solid]; +"14 Constant_9" -> "7 Gather_10" [label="[]", style=dashed]; +"15 Constant_8" -> "7 Gather_10" [label="[]", style=dashed]; +"16 Constant_6" -> "5 Gather_7" [label="[]", style=dashed]; +"17 Constant_3" -> "3 Gather_4" [label="[]", style=dashed]; +"18 Constant_2" -> "3 Gather_4" [label="[]", style=dashed]; +"19 Constant_5" -> "5 Gather_7" [label="[369, 160]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/LSTMModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/LSTMModel.dot deleted file mode 120000 index 86a4ed50508..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/LSTMModel.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/original_nncf_graph/LSTMModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/LSTMModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/LSTMModel.dot new file mode 100644 index 00000000000..b23d980fed3 --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/LSTMModel.dot @@ -0,0 +1,60 @@ +strict digraph { +"0 Input" [id=0, type=Parameter]; +"1 ReadValue_1" [id=1, type=ReadValue]; +"2 Squeeze_1" [id=2, type=Squeeze]; +"3 Add" [id=3, type=Add]; +"4 MatMul_1" [id=4, type=MatMul]; +"5 MatMul_2" [id=5, type=MatMul]; +"6 Add_1" [id=6, type=Add]; +"7 Split_13" [id=7, type=Split]; +"8 Sigmoid_1" [id=8, type=Sigmoid]; +"9 Sigmoid_2" [id=9, type=Sigmoid]; +"10 Sigmoid_3" [id=10, type=Sigmoid]; +"11 Tanh_1" [id=11, type=Tanh]; +"12 Multiply_1" [id=12, type=Multiply]; +"13 Multiply_2" [id=13, type=Multiply]; +"14 Multiply_3" [id=14, type=Multiply]; +"15 Add_2" [id=15, type=Add]; +"16 Assign_1" [id=16, type=Assign]; +"17 MatMul_3" [id=17, type=MatMul]; +"18 Tanh_2" [id=18, type=Tanh]; +"19 Result" [id=19, type=Result]; +"20 Constant_12" [id=20, type=Constant]; +"21 Constant_9" [id=21, type=Constant]; +"22 Constant_7" [id=22, type=Constant]; +"23 Constant_5" [id=23, type=Constant]; +"24 Constant_3" [id=24, type=Constant]; +"25 Constant_1" [id=25, type=Constant]; +"26 Constant_18" [id=26, type=Constant]; +"27 Constant_25" [id=27, type=Constant]; +"0 Input" -> "2 Squeeze_1" [label="[1, 1, 128]", style=solid]; +"1 ReadValue_1" -> "3 Add" [label="[1, 64]", style=solid]; +"2 Squeeze_1" -> "4 MatMul_1" [label="[1, 128]", style=solid]; +"3 Add" -> "5 MatMul_2" [label="[1, 64]", style=solid]; +"4 MatMul_1" -> "6 Add_1" [label="[1, 256]", style=solid]; +"5 MatMul_2" -> "6 Add_1" [label="[1, 256]", style=solid]; +"6 Add_1" -> "7 Split_13" [label="[1, 256]", style=solid]; +"7 Split_13" -> "8 Sigmoid_1" [label="[1, 64]", style=solid]; +"7 Split_13" -> "9 Sigmoid_2" [label="[1, 64]", style=solid]; +"7 Split_13" -> "10 Sigmoid_3" [label="[1, 64]", style=solid]; +"7 Split_13" -> "11 Tanh_1" [label="[1, 64]", style=solid]; +"8 Sigmoid_1" -> "12 Multiply_1" [label="[1, 64]", style=solid]; +"9 Sigmoid_2" -> "13 Multiply_2" [label="[1, 64]", style=solid]; +"10 Sigmoid_3" -> "14 Multiply_3" [label="[1, 64]", style=solid]; +"11 Tanh_1" -> "13 Multiply_2" [label="[1, 64]", style=solid]; +"12 Multiply_1" -> "15 Add_2" [label="[1, 64]", style=solid]; +"13 Multiply_2" -> "15 Add_2" [label="[1, 64]", style=solid]; +"14 Multiply_3" -> "16 Assign_1" [label="[1, 64]", style=solid]; +"14 Multiply_3" -> "17 MatMul_3" [label="[1, 64]", style=solid]; +"15 Add_2" -> "18 Tanh_2" [label="[1, 64]", style=solid]; +"17 MatMul_3" -> "19 Result" [label="[1, 128]", style=solid]; +"18 Tanh_2" -> "14 Multiply_3" [label="[1, 64]", style=solid]; +"20 Constant_12" -> "7 Split_13" [label="[]", style=dashed]; +"21 Constant_9" -> "5 MatMul_2" [label="[256, 64]", style=solid]; +"22 Constant_7" -> "3 Add" [label="[1, 64]", style=solid]; +"23 Constant_5" -> "1 ReadValue_1" [label="[1, 64]", style=solid]; +"24 Constant_3" -> "4 MatMul_1" [label="[256, 128]", style=solid]; +"25 Constant_1" -> "2 Squeeze_1" [label="[]", style=dashed]; +"26 Constant_18" -> "12 Multiply_1" [label="[1, 64]", style=solid]; +"27 Constant_25" -> "17 MatMul_3" [label="[128, 64]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/LSTMSequenceModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/LSTMSequenceModel.dot deleted file mode 120000 index 1d7441f61f4..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/LSTMSequenceModel.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/original_nncf_graph/LSTMSequenceModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/LSTMSequenceModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/LSTMSequenceModel.dot new file mode 100644 index 00000000000..91f5b08577c --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/LSTMSequenceModel.dot @@ -0,0 +1,23 @@ +strict digraph { +"0 X" [id=0, type=Parameter]; +"1 initial_hidden_state" [id=1, type=Parameter]; +"2 initial_cell_state" [id=2, type=Parameter]; +"3 LSTMSequence_7" [id=3, type=LSTMSequence]; +"4 MatMul" [id=4, type=MatMul]; +"5 Result" [id=5, type=Result]; +"6 Constant_8" [id=6, type=Constant]; +"7 Constant_6" [id=7, type=Constant]; +"8 Constant_5" [id=8, type=Constant]; +"9 Constant_4" [id=9, type=Constant]; +"10 Constant_3" [id=10, type=Constant]; +"0 X" -> "3 LSTMSequence_7" [label="[1, 2, 16]", style=solid]; +"1 initial_hidden_state" -> "3 LSTMSequence_7" [label="[1, 1, 128]", style=solid]; +"2 initial_cell_state" -> "3 LSTMSequence_7" [label="[1, 1, 128]", style=solid]; +"3 LSTMSequence_7" -> "4 MatMul" [label="[1, 1, 2, 128]", style=solid]; +"4 MatMul" -> "5 Result" [label="[1, 1, 2, 3]", style=solid]; +"6 Constant_8" -> "4 MatMul" [label="[1, 1, 128, 3]", style=solid]; +"7 Constant_6" -> "3 LSTMSequence_7" [label="[1, 512]", style=solid]; +"8 Constant_5" -> "3 LSTMSequence_7" [label="[1, 512, 128]", style=solid]; +"9 Constant_4" -> "3 LSTMSequence_7" [label="[1, 512, 16]", style=solid]; +"10 Constant_3" -> "3 LSTMSequence_7" [label="[1]", style=dashed]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/LinearModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/LinearModel.dot deleted file mode 120000 index 721f2f5dfa6..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/LinearModel.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/original_nncf_graph/LinearModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/LinearModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/LinearModel.dot new file mode 100644 index 00000000000..50c34d96235 --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/LinearModel.dot @@ -0,0 +1,19 @@ +strict digraph { +"0 Input" [id=0, type=Parameter]; +"1 Reshape" [id=1, type=Reshape]; +"2 Add" [id=2, type=Add]; +"3 MatMul" [id=3, type=MatMul]; +"4 Result_Add" [id=4, type=Result]; +"5 Result_MatMul" [id=5, type=Result]; +"6 Constant_10" [id=6, type=Constant]; +"7 Constant_1" [id=7, type=Constant]; +"8 Constant_8" [id=8, type=Constant]; +"0 Input" -> "1 Reshape" [label="[1, 3, 4, 2]", style=solid]; +"1 Reshape" -> "2 Add" [label="[1, 3, 2, 4]", style=solid]; +"1 Reshape" -> "3 MatMul" [label="[1, 3, 2, 4]", style=solid]; +"2 Add" -> "4 Result_Add" [label="[1, 3, 2, 4]", style=solid]; +"3 MatMul" -> "5 Result_MatMul" [label="[1, 3, 2, 5]", style=solid]; +"6 Constant_10" -> "2 Add" [label="[1, 3, 2, 4]", style=solid]; +"7 Constant_1" -> "1 Reshape" [label="[4]", style=dashed]; +"8 Constant_8" -> "3 MatMul" [label="[1, 3, 4, 5]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/MatMul2DModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/MatMul2DModel.dot deleted file mode 120000 index df662dada09..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/MatMul2DModel.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/original_nncf_graph/MatMul2DModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/MatMul2DModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/MatMul2DModel.dot new file mode 100644 index 00000000000..d7bd2d4b57d --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/MatMul2DModel.dot @@ -0,0 +1,13 @@ +strict digraph { +"0 Input" [id=0, type=Parameter]; +"1 MatMul" [id=1, type=MatMul]; +"2 Add" [id=2, type=Add]; +"3 Result" [id=3, type=Result]; +"4 Constant_32" [id=4, type=Constant]; +"5 Constant_30" [id=5, type=Constant]; +"0 Input" -> "1 MatMul" [label="[3, 5]", style=solid]; +"1 MatMul" -> "2 Add" [label="[3, 2]", style=solid]; +"2 Add" -> "3 Result" [label="[3, 2]", style=solid]; +"4 Constant_32" -> "2 Add" [label="[1, 2]", style=solid]; +"5 Constant_30" -> "1 MatMul" [label="[5, 2]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/MatmulSoftmaxMatmulBlock.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/MatmulSoftmaxMatmulBlock.dot deleted file mode 120000 index 1d5157533d3..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/MatmulSoftmaxMatmulBlock.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/original_nncf_graph/MatmulSoftmaxMatmulBlock.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/MatmulSoftmaxMatmulBlock.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/MatmulSoftmaxMatmulBlock.dot new file mode 100644 index 00000000000..55cb4dfe072 --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/MatmulSoftmaxMatmulBlock.dot @@ -0,0 +1,18 @@ +strict digraph { +"0 Input" [id=0, type=Parameter]; +"1 MatMul_1" [id=1, type=MatMul]; +"2 Squeeze_1" [id=2, type=Squeeze]; +"3 Softmax_1" [id=3, type=Softmax]; +"4 MatMul_2" [id=4, type=MatMul]; +"5 Result" [id=5, type=Result]; +"6 Constant_178" [id=6, type=Constant]; +"7 Constant_180" [id=7, type=Constant]; +"0 Input" -> "1 MatMul_1" [label="[1, 1, 1]", style=solid]; +"0 Input" -> "2 Squeeze_1" [label="[1, 1, 1]", style=solid]; +"1 MatMul_1" -> "3 Softmax_1" [label="[1, 1, 1]", style=solid]; +"2 Squeeze_1" -> "4 MatMul_2" [label="[1, 1]", style=solid]; +"3 Softmax_1" -> "4 MatMul_2" [label="[1, 1, 1]", style=solid]; +"4 MatMul_2" -> "5 Result" [label="[1, 1, 1]", style=solid]; +"6 Constant_178" -> "2 Squeeze_1" [label="[]", style=dashed]; +"7 Constant_180" -> "1 MatMul_1" [label="[1, 1]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/ScaleShiftReluModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/ScaleShiftReluModel.dot deleted file mode 120000 index 4640b021e60..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/ScaleShiftReluModel.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/original_nncf_graph/ScaleShiftReluModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/ScaleShiftReluModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/ScaleShiftReluModel.dot new file mode 100644 index 00000000000..a811bd55053 --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/ScaleShiftReluModel.dot @@ -0,0 +1,23 @@ +strict digraph { +"0 Input" [id=0, type=Parameter]; +"1 MatMul" [id=1, type=MatMul]; +"2 Mul" [id=2, type=Multiply]; +"3 Add" [id=3, type=Add]; +"4 Relu" [id=4, type=Relu]; +"5 MatMul2" [id=5, type=MatMul]; +"6 Result" [id=6, type=Result]; +"7 Constant_45" [id=7, type=Constant]; +"8 Constant_42" [id=8, type=Constant]; +"9 Constant_40" [id=9, type=Constant]; +"10 Constant_38" [id=10, type=Constant]; +"0 Input" -> "1 MatMul" [label="[3, 5]", style=solid]; +"1 MatMul" -> "2 Mul" [label="[3, 2]", style=solid]; +"2 Mul" -> "3 Add" [label="[3, 2]", style=solid]; +"3 Add" -> "4 Relu" [label="[3, 2]", style=solid]; +"4 Relu" -> "5 MatMul2" [label="[3, 2]", style=solid]; +"5 MatMul2" -> "6 Result" [label="[3, 4]", style=solid]; +"7 Constant_45" -> "5 MatMul2" [label="[2, 4]", style=solid]; +"8 Constant_42" -> "3 Add" [label="[1, 2]", style=solid]; +"9 Constant_40" -> "2 Mul" [label="[1, 2]", style=solid]; +"10 Constant_38" -> "1 MatMul" [label="[5, 2]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/SeBlockModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/SeBlockModel.dot deleted file mode 120000 index 017d92eaaf5..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/SeBlockModel.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/original_nncf_graph/SeBlockModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/SeBlockModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/SeBlockModel.dot new file mode 100644 index 00000000000..f3ba7b4bedb --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/SeBlockModel.dot @@ -0,0 +1,44 @@ +strict digraph { +"0 Input" [id=0, type=Parameter]; +"1 Conv0" [id=1, type=Convolution]; +"2 Add0" [id=2, type=Add]; +"3 Mul" [id=3, type=Multiply]; +"4 ReduceMean_6" [id=4, type=ReduceMean]; +"5 MatMul" [id=5, type=MatMul]; +"6 Conv" [id=6, type=Convolution]; +"7 Result" [id=7, type=Result]; +"8 Add" [id=8, type=Add]; +"9 Relu" [id=9, type=Relu]; +"10 Conv2" [id=10, type=Convolution]; +"11 Add2" [id=11, type=Add]; +"12 Sigmoid" [id=12, type=Sigmoid]; +"13 Constant_18" [id=13, type=Constant]; +"14 Constant_14" [id=14, type=Constant]; +"15 Constant_12" [id=15, type=Constant]; +"16 Constant_9" [id=16, type=Constant]; +"17 Constant_7" [id=17, type=Constant]; +"18 Constant_5" [id=18, type=Constant]; +"19 Constant_3" [id=19, type=Constant]; +"20 Constant_1" [id=20, type=Constant]; +"0 Input" -> "1 Conv0" [label="[1, 3, 5, 6]", style=solid]; +"1 Conv0" -> "2 Add0" [label="[1, 3, 5, 6]", style=solid]; +"2 Add0" -> "3 Mul" [label="[1, 3, 5, 6]", style=solid]; +"2 Add0" -> "4 ReduceMean_6" [label="[1, 3, 5, 6]", style=solid]; +"3 Mul" -> "5 MatMul" [label="[1, 3, 5, 6]", style=solid]; +"4 ReduceMean_6" -> "6 Conv" [label="[1, 3, 1, 1]", style=solid]; +"5 MatMul" -> "7 Result" [label="[1, 3, 5, 5]", style=solid]; +"6 Conv" -> "8 Add" [label="[1, 5, 1, 1]", style=solid]; +"8 Add" -> "9 Relu" [label="[1, 5, 1, 1]", style=solid]; +"9 Relu" -> "10 Conv2" [label="[1, 5, 1, 1]", style=solid]; +"10 Conv2" -> "11 Add2" [label="[1, 3, 1, 1]", style=solid]; +"11 Add2" -> "12 Sigmoid" [label="[1, 3, 1, 1]", style=solid]; +"12 Sigmoid" -> "3 Mul" [label="[1, 3, 1, 1]", style=solid]; +"13 Constant_18" -> "5 MatMul" [label="[1, 3, 6, 5]", style=solid]; +"14 Constant_14" -> "11 Add2" [label="[1, 3, 1, 1]", style=solid]; +"15 Constant_12" -> "10 Conv2" [label="[3, 5, 1, 1]", style=solid]; +"16 Constant_9" -> "8 Add" [label="[1, 5, 1, 1]", style=solid]; +"17 Constant_7" -> "6 Conv" [label="[5, 3, 1, 1]", style=solid]; +"18 Constant_5" -> "4 ReduceMean_6" [label="[2]", style=dashed]; +"19 Constant_3" -> "2 Add0" [label="[1, 3, 1, 1]", style=solid]; +"20 Constant_1" -> "1 Conv0" [label="[3, 3, 1, 1]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/ShapeOfModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/ShapeOfModel.dot deleted file mode 120000 index 444e0a66a04..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/ShapeOfModel.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/original_nncf_graph/ShapeOfModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/ShapeOfModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/ShapeOfModel.dot new file mode 100644 index 00000000000..234d08dc31d --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/ShapeOfModel.dot @@ -0,0 +1,63 @@ +strict digraph { +"0 Input" [id=0, type=Parameter]; +"1 Conv_1" [id=1, type=Convolution]; +"2 Conv_Add_1" [id=2, type=Add]; +"3 Reshape_1" [id=3, type=Reshape]; +"4 ShapeOf_1" [id=4, type=ShapeOf]; +"5 Transpose" [id=5, type=Transpose]; +"6 Gather_33092" [id=6, type=Gather]; +"7 Conv_2" [id=7, type=Convolution]; +"8 Concat_33095" [id=8, type=Concat]; +"9 Conv_Add_2" [id=9, type=Add]; +"10 Reshape_2" [id=10, type=Reshape]; +"11 ShapeOf_2" [id=11, type=ShapeOf]; +"12 Conv_3" [id=12, type=Convolution]; +"13 Convert_1" [id=13, type=Convert]; +"14 Conv_Add_3" [id=14, type=Add]; +"15 Multiply" [id=15, type=Multiply]; +"16 Result" [id=16, type=Result]; +"17 Convert_2" [id=17, type=Convert]; +"18 Bias_3" [id=18, type=Constant]; +"19 Constant_33121" [id=19, type=Constant]; +"20 Constant_33107" [id=20, type=Constant]; +"21 Bias_2" [id=21, type=Constant]; +"22 Constant_33101" [id=22, type=Constant]; +"23 Constant_33099" [id=23, type=Constant]; +"24 Constant_33091" [id=24, type=Constant]; +"25 Constant_33090" [id=25, type=Constant]; +"26 Bias_1" [id=26, type=Constant]; +"27 Constant_33085" [id=27, type=Constant]; +"28 Constant_33094" [id=28, type=Constant]; +"29 Constant_33093" [id=29, type=Constant]; +"0 Input" -> "1 Conv_1" [label="[1, 3, 4, 2]", style=solid]; +"1 Conv_1" -> "2 Conv_Add_1" [label="[1, 3, 4, 2]", style=solid]; +"2 Conv_Add_1" -> "3 Reshape_1" [label="[1, 3, 4, 2]", style=solid]; +"2 Conv_Add_1" -> "4 ShapeOf_1" [label="[1, 3, 4, 2]", style=solid]; +"3 Reshape_1" -> "5 Transpose" [label="[1, 3, 4, 2]", style=solid]; +"4 ShapeOf_1" -> "6 Gather_33092" [label="[4]", style=dashed]; +"5 Transpose" -> "7 Conv_2" [label="[1, 3, 2, 4]", style=solid]; +"6 Gather_33092" -> "8 Concat_33095" [label="[2]", style=dashed]; +"7 Conv_2" -> "9 Conv_Add_2" [label="[1, 3, 2, 4]", style=solid]; +"8 Concat_33095" -> "3 Reshape_1" [label="[4]", style=dashed]; +"9 Conv_Add_2" -> "10 Reshape_2" [label="[1, 3, 2, 4]", style=solid]; +"9 Conv_Add_2" -> "11 ShapeOf_2" [label="[1, 3, 2, 4]", style=solid]; +"10 Reshape_2" -> "12 Conv_3" [label="[1, 3, 2, 4]", style=solid]; +"11 ShapeOf_2" -> "13 Convert_1" [label="[4]", style=dashed]; +"12 Conv_3" -> "14 Conv_Add_3" [label="[1, 3, 2, 4]", style=solid]; +"13 Convert_1" -> "15 Multiply" [label="[4]", style=solid]; +"14 Conv_Add_3" -> "16 Result" [label="[1, 3, 2, 4]", style=solid]; +"15 Multiply" -> "17 Convert_2" [label="[4]", style=solid]; +"17 Convert_2" -> "10 Reshape_2" [label="[4]", style=dashed]; +"18 Bias_3" -> "14 Conv_Add_3" [label="[1, 3, 1, 1]", style=solid]; +"19 Constant_33121" -> "12 Conv_3" [label="[3, 3, 1, 1]", style=solid]; +"20 Constant_33107" -> "15 Multiply" [label="[4]", style=solid]; +"21 Bias_2" -> "9 Conv_Add_2" [label="[1, 3, 1, 1]", style=solid]; +"22 Constant_33101" -> "7 Conv_2" [label="[3, 3, 1, 1]", style=solid]; +"23 Constant_33099" -> "5 Transpose" [label="[4]", style=dashed]; +"24 Constant_33091" -> "6 Gather_33092" [label="[]", style=dashed]; +"25 Constant_33090" -> "6 Gather_33092" [label="[2]", style=dashed]; +"26 Bias_1" -> "2 Conv_Add_1" [label="[1, 3, 1, 1]", style=solid]; +"27 Constant_33085" -> "1 Conv_1" [label="[3, 3, 1, 1]", style=solid]; +"28 Constant_33094" -> "8 Concat_33095" [label="[1]", style=dashed]; +"29 Constant_33093" -> "8 Concat_33095" [label="[1]", style=dashed]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/SharedConvModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/SharedConvModel.dot deleted file mode 120000 index c253e737d5b..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/SharedConvModel.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/original_nncf_graph/SharedConvModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/SharedConvModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/SharedConvModel.dot new file mode 100644 index 00000000000..2bc26e8711a --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/SharedConvModel.dot @@ -0,0 +1,14 @@ +strict digraph { +"0 Input" [id=0, type=Parameter]; +"1 Conv_1" [id=1, type=Convolution]; +"2 Conv_2" [id=2, type=Convolution]; +"3 Result_1" [id=3, type=Result]; +"4 Result_2" [id=4, type=Result]; +"5 Shared_conv_w" [id=5, type=Constant]; +"0 Input" -> "1 Conv_1" [label="[1, 3, 3, 3]", style=solid]; +"0 Input" -> "2 Conv_2" [label="[1, 3, 3, 3]", style=solid]; +"1 Conv_1" -> "3 Result_1" [label="[1, 3, 3, 3]", style=solid]; +"2 Conv_2" -> "4 Result_2" [label="[1, 3, 3, 3]", style=solid]; +"5 Shared_conv_w" -> "1 Conv_1" [label="[3, 3, 1, 1]", style=solid]; +"5 Shared_conv_w" -> "2 Conv_2" [label="[3, 3, 1, 1]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/UnifiedEmbeddingModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/UnifiedEmbeddingModel.dot deleted file mode 120000 index 1b1f548333c..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/UnifiedEmbeddingModel.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/original_nncf_graph/UnifiedEmbeddingModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/UnifiedEmbeddingModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/UnifiedEmbeddingModel.dot new file mode 100644 index 00000000000..b779ab79695 --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/UnifiedEmbeddingModel.dot @@ -0,0 +1,28 @@ +strict digraph { +"0 Input" [id=0, type=Parameter]; +"1 Convert_1" [id=1, type=Convert]; +"2 MatMul_1" [id=2, type=MatMul]; +"3 Gather_1" [id=3, type=Gather]; +"4 Reshape_1" [id=4, type=Reshape]; +"5 Concat_12" [id=5, type=Concat]; +"6 MatMul_2" [id=6, type=MatMul]; +"7 Result" [id=7, type=Result]; +"8 matmul_2_data" [id=8, type=Constant]; +"9 Constant_8" [id=9, type=Constant]; +"10 matmul_1_data" [id=10, type=Constant]; +"11 Constant_4" [id=11, type=Constant]; +"12 gather_1_data" [id=12, type=Constant]; +"0 Input" -> "1 Convert_1" [label="[1, 3]", style=solid]; +"0 Input" -> "2 MatMul_1" [label="[1, 3]", style=solid]; +"1 Convert_1" -> "3 Gather_1" [label="[1, 3]", style=dashed]; +"2 MatMul_1" -> "4 Reshape_1" [label="[3, 1, 5]", style=solid]; +"3 Gather_1" -> "5 Concat_12" [label="[1, 3, 5]", style=solid]; +"4 Reshape_1" -> "5 Concat_12" [label="[1, 3, 5]", style=solid]; +"5 Concat_12" -> "6 MatMul_2" [label="[1, 6, 5]", style=solid]; +"6 MatMul_2" -> "7 Result" [label="[1, 6, 1]", style=solid]; +"8 matmul_2_data" -> "6 MatMul_2" [label="[1, 5]", style=solid]; +"9 Constant_8" -> "4 Reshape_1" [label="[3]", style=dashed]; +"10 matmul_1_data" -> "2 MatMul_1" [label="[3, 3, 5]", style=solid]; +"11 Constant_4" -> "3 Gather_1" [label="[]", style=dashed]; +"12 gather_1_data" -> "3 Gather_1" [label="[4, 5]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/WeightsModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/WeightsModel.dot deleted file mode 120000 index 620638aaeac..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/WeightsModel.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/original_nncf_graph/WeightsModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/WeightsModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/WeightsModel.dot new file mode 100644 index 00000000000..fe530ba099a --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/WeightsModel.dot @@ -0,0 +1,32 @@ +strict digraph { +"0 Input_1" [id=0, type=Parameter]; +"1 Conv" [id=1, type=Convolution]; +"2 Conv_backprop" [id=2, type=ConvolutionBackpropData]; +"3 MatMul_1" [id=3, type=MatMul]; +"4 MatMul" [id=4, type=MatMul]; +"5 MatMul_0" [id=5, type=MatMul]; +"6 Add_15" [id=6, type=Add]; +"7 Result" [id=7, type=Result]; +"8 weights_1" [id=8, type=Constant]; +"9 Constant_5" [id=9, type=Constant]; +"10 Constant_4" [id=10, type=Constant]; +"11 Constant_2" [id=11, type=Constant]; +"12 weights_0" [id=12, type=Constant]; +"13 MatMul_const" [id=13, type=MatMul]; +"0 Input_1" -> "1 Conv" [label="[1, 3, 5, 5]", style=solid]; +"1 Conv" -> "2 Conv_backprop" [label="[1, 3, 5, 5]", style=solid]; +"2 Conv_backprop" -> "3 MatMul_1" [label="[1, 3, 1, 1]", style=solid]; +"3 MatMul_1" -> "4 MatMul" [label="[1, 3, 1, 4]", style=solid]; +"3 MatMul_1" -> "5 MatMul_0" [label="[1, 3, 1, 4]", style=solid]; +"4 MatMul" -> "6 Add_15" [label="[1, 3, 1, 1]", style=solid]; +"5 MatMul_0" -> "4 MatMul" [label="[1, 3, 1, 4]", style=solid]; +"6 Add_15" -> "7 Result" [label="[1, 3, 4, 1]", style=solid]; +"8 weights_1" -> "3 MatMul_1" [label="[1, 4]", style=solid]; +"8 weights_1" -> "13 MatMul_const" [label="[1, 4]", style=solid]; +"9 Constant_5" -> "2 Conv_backprop" [label="[2]", style=dashed]; +"10 Constant_4" -> "2 Conv_backprop" [label="[3, 3, 1, 1]", style=solid]; +"11 Constant_2" -> "1 Conv" [label="[3, 3, 1, 1]", style=solid]; +"12 weights_0" -> "5 MatMul_0" [label="[1, 1]", style=solid]; +"12 weights_0" -> "13 MatMul_const" [label="[1, 1]", style=solid]; +"13 MatMul_const" -> "6 Add_15" [label="[4, 1]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/exctracted_ConvModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/exctracted_ConvModel.dot deleted file mode 120000 index 7fd5f2d1e10..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/exctracted_ConvModel.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/original_nncf_graph/exctracted_ConvModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/exctracted_ConvModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/exctracted_ConvModel.dot new file mode 100644 index 00000000000..fc05c2e496a --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/exctracted_ConvModel.dot @@ -0,0 +1,9 @@ +strict digraph { +"0 Parameter_Conv" [id=0, type=Parameter]; +"1 Conv" [id=1, type=Convolution]; +"2 Result_Conv.0" [id=2, type=Result]; +"3 Constant_58" [id=3, type=Constant]; +"0 Parameter_Conv" -> "1 Conv" [label="[1, 3, 4, 2]", style=solid]; +"1 Conv" -> "2 Result_Conv.0" [label="[1, 3, 4, 2]", style=solid]; +"3 Constant_58" -> "1 Conv" [label="[3, 3, 1, 1]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/exctracted_QuantizedModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/exctracted_QuantizedModel.dot deleted file mode 120000 index f5d1c98584b..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/exctracted_QuantizedModel.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/original_nncf_graph/exctracted_QuantizedModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/exctracted_QuantizedModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/exctracted_QuantizedModel.dot new file mode 100644 index 00000000000..cb59d32c930 --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/exctracted_QuantizedModel.dot @@ -0,0 +1,81 @@ +strict digraph { +"0 Parameter_Relu_1" [id=0, type=Parameter]; +"1 Parameter_Transpose" [id=1, type=Parameter]; +"2 Relu_1" [id=2, type=Relu]; +"3 Transpose" [id=3, type=Transpose]; +"4 Concat_1/fq_input_0" [id=4, type=FakeQuantize]; +"5 Concat_1" [id=5, type=Concat]; +"6 Add_2" [id=6, type=Add]; +"7 Conv_2" [id=7, type=Convolution]; +"8 Result_Add_2.0" [id=8, type=Result]; +"9 Relu_2" [id=9, type=Relu]; +"10 Conv_3/fq_input_0" [id=10, type=FakeQuantize]; +"11 Conv_3" [id=11, type=Convolution]; +"12 Result_Conv_3.0" [id=12, type=Result]; +"13 Add_2/fq_weights_0" [id=13, type=FakeQuantize]; +"14 Constant_128" [id=14, type=Constant]; +"15 Constant_127" [id=15, type=Constant]; +"16 Constant_126" [id=16, type=Constant]; +"17 Constant_125" [id=17, type=Constant]; +"18 Constant_124" [id=18, type=Constant]; +"19 Constant_96" [id=19, type=Constant]; +"20 Constant_101" [id=20, type=Constant]; +"21 Constant_100" [id=21, type=Constant]; +"22 Constant_99" [id=22, type=Constant]; +"23 Constant_98" [id=23, type=Constant]; +"24 Conv_3/fq_weights_0" [id=24, type=FakeQuantize]; +"25 Constant_121" [id=25, type=Constant]; +"26 Constant_120" [id=26, type=Constant]; +"27 Constant_119" [id=27, type=Constant]; +"28 Constant_118" [id=28, type=Constant]; +"29 Constant_117" [id=29, type=Constant]; +"30 Constant_115" [id=30, type=Constant]; +"31 Constant_114" [id=31, type=Constant]; +"32 Constant_113" [id=32, type=Constant]; +"33 Constant_112" [id=33, type=Constant]; +"34 Conv_2/fq_weights_0" [id=34, type=FakeQuantize]; +"35 Constant_108" [id=35, type=Constant]; +"36 Constant_107" [id=36, type=Constant]; +"37 Constant_106" [id=37, type=Constant]; +"38 Constant_105" [id=38, type=Constant]; +"39 Constant_104" [id=39, type=Constant]; +"0 Parameter_Relu_1" -> "2 Relu_1" [label="[1, 3, 14, 28]", style=solid]; +"1 Parameter_Transpose" -> "3 Transpose" [label="[1, 3, 28, 14]", style=solid]; +"2 Relu_1" -> "4 Concat_1/fq_input_0" [label="[1, 3, 14, 28]", style=solid]; +"3 Transpose" -> "5 Concat_1" [label="[1, 3, 14, 28]", style=solid]; +"4 Concat_1/fq_input_0" -> "5 Concat_1" [label="[1, 3, 14, 28]", style=solid]; +"5 Concat_1" -> "6 Add_2" [label="[1, 6, 14, 28]", style=solid]; +"5 Concat_1" -> "7 Conv_2" [label="[1, 6, 14, 28]", style=solid]; +"6 Add_2" -> "8 Result_Add_2.0" [label="[1, 6, 14, 28]", style=solid]; +"7 Conv_2" -> "9 Relu_2" [label="[1, 12, 14, 28]", style=solid]; +"9 Relu_2" -> "10 Conv_3/fq_input_0" [label="[1, 12, 14, 28]", style=solid]; +"10 Conv_3/fq_input_0" -> "11 Conv_3" [label="[1, 12, 14, 28]", style=solid]; +"11 Conv_3" -> "12 Result_Conv_3.0" [label="[1, 6, 14, 28]", style=solid]; +"13 Add_2/fq_weights_0" -> "6 Add_2" [label="[1, 6, 1, 1]", style=solid]; +"14 Constant_128" -> "13 Add_2/fq_weights_0" [label="[]", style=solid]; +"15 Constant_127" -> "13 Add_2/fq_weights_0" [label="[]", style=solid]; +"16 Constant_126" -> "13 Add_2/fq_weights_0" [label="[]", style=solid]; +"17 Constant_125" -> "13 Add_2/fq_weights_0" [label="[]", style=solid]; +"18 Constant_124" -> "13 Add_2/fq_weights_0" [label="[1, 6, 1, 1]", style=solid]; +"19 Constant_96" -> "3 Transpose" [label="[4]", style=dashed]; +"20 Constant_101" -> "4 Concat_1/fq_input_0" [label="[]", style=solid]; +"21 Constant_100" -> "4 Concat_1/fq_input_0" [label="[]", style=solid]; +"22 Constant_99" -> "4 Concat_1/fq_input_0" [label="[]", style=solid]; +"23 Constant_98" -> "4 Concat_1/fq_input_0" [label="[]", style=solid]; +"24 Conv_3/fq_weights_0" -> "11 Conv_3" [label="[6, 12, 1, 1]", style=solid]; +"25 Constant_121" -> "24 Conv_3/fq_weights_0" [label="[]", style=solid]; +"26 Constant_120" -> "24 Conv_3/fq_weights_0" [label="[]", style=solid]; +"27 Constant_119" -> "24 Conv_3/fq_weights_0" [label="[]", style=solid]; +"28 Constant_118" -> "24 Conv_3/fq_weights_0" [label="[]", style=solid]; +"29 Constant_117" -> "24 Conv_3/fq_weights_0" [label="[6, 12, 1, 1]", style=solid]; +"30 Constant_115" -> "10 Conv_3/fq_input_0" [label="[]", style=solid]; +"31 Constant_114" -> "10 Conv_3/fq_input_0" [label="[]", style=solid]; +"32 Constant_113" -> "10 Conv_3/fq_input_0" [label="[]", style=solid]; +"33 Constant_112" -> "10 Conv_3/fq_input_0" [label="[]", style=solid]; +"34 Conv_2/fq_weights_0" -> "7 Conv_2" [label="[12, 6, 1, 1]", style=solid]; +"35 Constant_108" -> "34 Conv_2/fq_weights_0" [label="[]", style=solid]; +"36 Constant_107" -> "34 Conv_2/fq_weights_0" [label="[]", style=solid]; +"37 Constant_106" -> "34 Conv_2/fq_weights_0" [label="[]", style=solid]; +"38 Constant_105" -> "34 Conv_2/fq_weights_0" [label="[]", style=solid]; +"39 Constant_104" -> "34 Conv_2/fq_weights_0" [label="[12, 6, 1, 1]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/googlenet-v3-pytorch.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/googlenet-v3-pytorch.dot deleted file mode 120000 index 0356cfd8555..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/googlenet-v3-pytorch.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/original_nncf_graph/googlenet-v3-pytorch.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/googlenet-v3-pytorch.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/googlenet-v3-pytorch.dot new file mode 100644 index 00000000000..508b31bcc30 --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/googlenet-v3-pytorch.dot @@ -0,0 +1,1052 @@ +strict digraph { +"0 data" [id=0, type=Parameter]; +"1 Multiply_4031" [id=1, type=Multiply]; +"2 Divide_1751" [id=2, type=Add]; +"3 /Conv2d_1a_3x3/conv/Conv/WithoutBiases" [id=3, type=Convolution]; +"4 /Conv2d_1a_3x3/conv/Conv" [id=4, type=Add]; +"5 /Conv2d_1a_3x3/Relu" [id=5, type=Relu]; +"6 /Conv2d_2a_3x3/conv/Conv/WithoutBiases" [id=6, type=Convolution]; +"7 /Conv2d_2a_3x3/conv/Conv" [id=7, type=Add]; +"8 /Conv2d_2a_3x3/Relu" [id=8, type=Relu]; +"9 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a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/mobilenet-v2-pytorch.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/original_nncf_graph/mobilenet-v2-pytorch.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/mobilenet-v2-pytorch.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/mobilenet-v2-pytorch.dot new file mode 100644 index 00000000000..32c7ca54b45 --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/mobilenet-v2-pytorch.dot @@ -0,0 +1,545 @@ +strict digraph { +"0 data" [id=0, type=Parameter]; +"1 Multiply_5095" [id=1, type=Multiply]; +"2 Divide_1885" [id=2, type=Add]; +"3 /features/features.0/features.0.0/Conv/WithoutBiases" [id=3, type=Convolution]; +"4 /features/features.0/features.0.0/Conv" [id=4, type=Add]; +"5 /features/features.0/features.0.2/Clip" [id=5, type=Clamp]; +"6 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[label="[16, 32, 1, 1]", style=solid]; +"261 Reshape_186" -> "7 /features/features.1/conv/conv.0/conv.0.0/Conv" [label="[1, 32, 1, 1]", style=solid]; +"262 Reshape_134" -> "6 /features/features.1/conv/conv.0/conv.0.0/Conv/WithoutBiases" [label="[32, 1, 1, 3, 3]", style=solid]; +"263 Reshape_119" -> "4 /features/features.0/features.0.0/Conv" [label="[1, 32, 1, 1]", style=solid]; +"264 Gather_5168" -> "3 /features/features.0/features.0.0/Conv/WithoutBiases" [label="[32, 3, 3, 3]", style=solid]; +"265 Gather_5165" -> "2 Divide_1885" [label="[1, 3, 1, 1]", style=solid]; +"266 Gather_5162" -> "1 Multiply_5095" [label="[1, 3, 1, 1]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/mobilenet-v3-small-1.0-224-tf.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/mobilenet-v3-small-1.0-224-tf.dot deleted file mode 120000 index 72dfabafa35..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/mobilenet-v3-small-1.0-224-tf.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/original_nncf_graph/mobilenet-v3-small-1.0-224-tf.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/mobilenet-v3-small-1.0-224-tf.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/mobilenet-v3-small-1.0-224-tf.dot new file mode 100644 index 00000000000..195becdc1a1 --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/mobilenet-v3-small-1.0-224-tf.dot @@ -0,0 +1,628 @@ +strict digraph { +"0 input_1" [id=0, type=Parameter]; +"1 Transpose_7780" [id=1, type=Transpose]; +"2 Transpose_7774" [id=2, type=Multiply]; +"3 Transpose_710" [id=3, type=Add]; +"4 Multiply_9167" [id=4, type=Convolution]; +"5 Transpose_5170" [id=5, type=Add]; +"6 Transpose_5188" [id=6, type=HSwish]; +"7 Multiply_9181" [id=7, 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[id=30, type=Add]; +"31 Multiply_9293" [id=31, type=Convolution]; +"32 Transpose_5430" [id=32, type=Relu]; +"33 Transpose_5508" [id=33, type=Add]; +"34 Multiply_9265" [id=34, type=GroupConvolution]; +"35 Transpose_5526" [id=35, type=HSwish]; +"36 Transpose_5454" [id=36, type=Add]; +"37 Multiply_9307" [id=37, type=GroupConvolution]; +"38 Transpose_5456" [id=38, type=Relu]; +"39 Transpose_5577" [id=39, type=Add]; +"40 Multiply_9279" [id=40, type=Convolution]; +"41 Transpose_5595" [id=41, type=HSwish]; +"42 Transpose_5480" [id=42, type=Add]; +"43 Transpose_5599" [id=43, type=ReduceMean]; +"44 Transpose_5631" [id=44, type=Multiply]; +"45 Convolution_1132" [id=45, type=Convolution]; +"46 Multiply_9321" [id=46, type=Convolution]; +"47 Transpose_5605" [id=47, type=Add]; +"48 Transpose_5655" [id=48, type=Add]; +"49 Transpose_5607" [id=49, type=Relu]; +"50 Multiply_9335" [id=50, type=Convolution]; +"51 Transpose_5803" [id=51, type=Add]; +"52 Convolution_1141" [id=52, type=Convolution]; +"53 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Transpose_6243" [id=98, type=Add]; +"99 Transpose_5771" [id=99, type=HSigmoid]; +"100 Transpose_5905" [id=100, type=Add]; +"101 Convolution_1473" [id=101, type=Convolution]; +"102 Transpose_6119" [id=102, type=Add]; +"103 Multiply_9503" [id=103, type=Convolution]; +"104 Transpose_5919" [id=104, type=HSigmoid]; +"105 Transpose_6053" [id=105, type=Add]; +"106 Transpose_6137" [id=106, type=HSwish]; +"107 Transpose_6267" [id=107, type=Add]; +"108 Transpose_6067" [id=108, type=HSigmoid]; +"109 Multiply_9475" [id=109, type=GroupConvolution]; +"110 Transpose_6285" [id=110, type=HSwish]; +"111 Transpose_6161" [id=111, type=Add]; +"112 Multiply_9517" [id=112, type=GroupConvolution]; +"113 Transpose_6179" [id=113, type=HSwish]; +"114 Transpose_6336" [id=114, type=Add]; +"115 Transpose_6183" [id=115, type=ReduceMean]; +"116 Transpose_6215" [id=116, type=Multiply]; +"117 Transpose_6354" [id=117, type=HSwish]; +"118 Convolution_1574" [id=118, type=Convolution]; +"119 Multiply_9489" [id=119, type=Convolution]; +"120 Transpose_6358" [id=120, type=ReduceMean]; +"121 Transpose_6390" [id=121, type=Multiply]; +"122 Transpose_6189" [id=122, type=Add]; +"123 Transpose_6239" [id=123, type=Add]; +"124 Convolution_1713" [id=124, type=Convolution]; +"125 Multiply_9531" [id=125, type=Convolution]; +"126 Transpose_6191" [id=126, type=Relu]; +"127 Transpose_6364" [id=127, type=Add]; +"128 Transpose_6414" [id=128, type=Add]; +"129 Convolution_1583" [id=129, type=Convolution]; +"130 Transpose_6366" [id=130, type=Relu]; +"131 Multiply_9545" [id=131, type=Convolution]; +"132 Transpose_6562" [id=132, type=Add]; +"133 Transpose_6197" [id=133, type=Add]; +"134 Convolution_1722" [id=134, type=Convolution]; +"135 Transpose_6438" [id=135, type=Add]; +"136 Multiply_9587" [id=136, type=Convolution]; +"137 Transpose_6710" [id=137, type=Add]; +"138 Transpose_6211" [id=138, type=HSigmoid]; +"139 Transpose_6372" [id=139, type=Add]; +"140 Transpose_6456" [id=140, type=HSwish]; +"141 Transpose_6586" [id=141, type=Add]; +"142 Multiply_9629" [id=142, type=Convolution]; +"143 Transpose_6386" [id=143, type=HSigmoid]; +"144 Multiply_9559" [id=144, type=GroupConvolution]; +"145 Transpose_6604" [id=145, type=HSwish]; +"146 Transpose_6734" [id=146, type=Add]; +"147 Transpose_6480" [id=147, type=Add]; +"148 Multiply_9601" [id=148, type=GroupConvolution]; +"149 Transpose_6752" [id=149, type=HSwish]; +"150 Transpose_6498" [id=150, type=HSwish]; +"151 Transpose_6628" [id=151, type=Add]; +"152 Transpose_6756" [id=152, type=ReduceMean]; +"153 Transpose_6502" [id=153, type=ReduceMean]; +"154 Transpose_6534" [id=154, type=Multiply]; +"155 Transpose_6646" [id=155, type=HSwish]; +"156 Convolution_2013" [id=156, type=Convolution]; +"157 Convolution_1823" [id=157, type=Convolution]; +"158 Multiply_9573" [id=158, type=Convolution]; +"159 Transpose_6650" [id=159, type=ReduceMean]; +"160 Transpose_6682" [id=160, type=Multiply]; +"161 Transpose_6762" [id=161, type=Add]; +"162 Transpose_6508" [id=162, type=Add]; +"163 Transpose_6558" [id=163, type=Add]; +"164 Convolution_1934" [id=164, type=Convolution]; +"165 Multiply_9615" [id=165, type=Convolution]; +"166 Transpose_6780" [id=166, type=HSwish]; +"167 Transpose_6510" [id=167, type=Relu]; +"168 Transpose_6656" [id=168, type=Add]; +"169 Transpose_6706" [id=169, type=Add]; +"170 Convolution_2025" [id=170, type=Convolution]; +"171 Convolution_1832" [id=171, type=Convolution]; +"172 Transpose_6658" [id=172, type=Relu]; +"173 Transpose_6786" [id=173, type=Add]; +"174 Transpose_6516" [id=174, type=Add]; +"175 Convolution_1943" [id=175, type=Convolution]; +"176 MobilenetV3small/Logits/BiasAdd" [id=176, type=Reshape]; +"177 Transpose_6530" [id=177, type=HSigmoid]; +"178 Transpose_6664" [id=178, type=Add]; +"179 MobilenetV3small/flatten/Reshape" [id=179, type=Reshape]; +"180 Transpose_6678" [id=180, type=HSigmoid]; +"181 MobilenetV3small/Predictions/Softmax" [id=181, type=Softmax]; +"182 Predictions" [id=182, type=Result]; +"183 MobilenetV3small/flatten/Const" [id=183, type=Constant]; +"184 Constant_8887" [id=184, type=Constant]; +"185 Transpose_6784" [id=185, type=Constant]; +"186 Transpose_2024" [id=186, type=Constant]; +"187 Transpose_6760" [id=187, type=Constant]; +"188 Transpose_2012" [id=188, type=Constant]; +"189 Constant_6754" [id=189, type=Constant]; +"190 Constant_9637" [id=190, type=Constant]; +"191 Multiply_9830" [id=191, type=Constant]; +"192 Constant_9623" [id=192, type=Constant]; +"193 Multiply_9824" [id=193, type=Constant]; +"194 Transpose_6662" [id=194, type=Constant]; +"195 Transpose_1942" [id=195, type=Constant]; +"196 Transpose_6654" [id=196, type=Constant]; +"197 Transpose_1933" [id=197, type=Constant]; +"198 Constant_6648" [id=198, type=Constant]; +"199 Constant_9609" [id=199, type=Constant]; +"200 Multiply_9819" [id=200, type=Constant]; +"201 Constant_9595" [id=201, type=Constant]; +"202 Multiply_9813" [id=202, type=Constant]; +"203 Constant_9581" [id=203, type=Constant]; +"204 Multiply_9807" [id=204, type=Constant]; +"205 Transpose_6514" [id=205, type=Constant]; +"206 Transpose_1831" [id=206, type=Constant]; +"207 Transpose_6506" [id=207, type=Constant]; +"208 Transpose_1822" [id=208, type=Constant]; +"209 Constant_6500" [id=209, type=Constant]; +"210 Constant_9567" [id=210, type=Constant]; +"211 Multiply_9802" [id=211, type=Constant]; +"212 Constant_9553" [id=212, type=Constant]; +"213 Multiply_9796" [id=213, type=Constant]; +"214 Constant_9539" [id=214, type=Constant]; +"215 Multiply_9790" [id=215, type=Constant]; +"216 Transpose_6370" [id=216, type=Constant]; +"217 Transpose_1721" [id=217, type=Constant]; +"218 Transpose_6362" [id=218, type=Constant]; +"219 Transpose_1712" [id=219, type=Constant]; +"220 Constant_6356" [id=220, type=Constant]; +"221 Constant_9525" [id=221, type=Constant]; +"222 Multiply_9785" [id=222, type=Constant]; +"223 Constant_9511" [id=223, type=Constant]; +"224 Multiply_9779" [id=224, type=Constant]; +"225 Constant_9497" [id=225, type=Constant]; +"226 Multiply_9773" [id=226, type=Constant]; +"227 Transpose_6195" [id=227, type=Constant]; +"228 Transpose_1582" [id=228, type=Constant]; +"229 Transpose_6187" [id=229, type=Constant]; +"230 Transpose_1573" [id=230, type=Constant]; +"231 Constant_6181" [id=231, type=Constant]; +"232 Constant_9483" [id=232, type=Constant]; +"233 Multiply_9768" [id=233, type=Constant]; +"234 Constant_9469" [id=234, type=Constant]; +"235 Multiply_9762" [id=235, type=Constant]; +"236 Constant_9455" [id=236, type=Constant]; +"237 Multiply_9756" [id=237, type=Constant]; +"238 Transpose_6051" [id=238, type=Constant]; +"239 Transpose_1472" [id=239, type=Constant]; +"240 Transpose_6043" [id=240, type=Constant]; +"241 Transpose_1463" [id=241, type=Constant]; +"242 Constant_6037" [id=242, type=Constant]; +"243 Constant_9441" [id=243, type=Constant]; +"244 Multiply_9751" [id=244, type=Constant]; +"245 Constant_9427" [id=245, type=Constant]; +"246 Multiply_9745" [id=246, type=Constant]; +"247 Constant_9413" [id=247, type=Constant]; +"248 Multiply_9739" [id=248, type=Constant]; +"249 Transpose_5903" [id=249, type=Constant]; +"250 Transpose_1361" [id=250, type=Constant]; +"251 Transpose_5895" [id=251, type=Constant]; +"252 Transpose_1352" [id=252, type=Constant]; +"253 Constant_5889" [id=253, type=Constant]; +"254 Constant_9399" [id=254, type=Constant]; +"255 Multiply_9734" [id=255, type=Constant]; +"256 Constant_9385" [id=256, type=Constant]; +"257 Multiply_9728" [id=257, type=Constant]; +"258 Constant_9371" [id=258, type=Constant]; +"259 Multiply_9722" [id=259, type=Constant]; +"260 Transpose_5755" [id=260, type=Constant]; +"261 Transpose_1250" [id=261, type=Constant]; +"262 Transpose_5747" [id=262, type=Constant]; +"263 Transpose_1241" [id=263, type=Constant]; +"264 Constant_5741" [id=264, type=Constant]; +"265 Constant_9357" [id=265, type=Constant]; +"266 Multiply_9717" [id=266, type=Constant]; +"267 Constant_9343" [id=267, type=Constant]; +"268 Multiply_9711" [id=268, type=Constant]; +"269 Constant_9329" [id=269, type=Constant]; +"270 Multiply_9705" [id=270, type=Constant]; +"271 Transpose_5611" [id=271, type=Constant]; +"272 Transpose_1140" [id=272, type=Constant]; +"273 Transpose_5603" [id=273, type=Constant]; +"274 Transpose_1131" [id=274, type=Constant]; +"275 Constant_5597" [id=275, type=Constant]; +"276 Constant_9315" [id=276, type=Constant]; +"277 Multiply_9700" [id=277, type=Constant]; +"278 Constant_9301" [id=278, type=Constant]; +"279 Multiply_9694" [id=279, type=Constant]; +"280 Constant_9287" [id=280, type=Constant]; +"281 Multiply_9688" [id=281, type=Constant]; +"282 Constant_9273" [id=282, type=Constant]; +"283 Multiply_9683" [id=283, type=Constant]; +"284 Constant_9259" [id=284, type=Constant]; +"285 Multiply_9677" [id=285, type=Constant]; +"286 Constant_9245" [id=286, type=Constant]; +"287 Multiply_9671" [id=287, type=Constant]; +"288 Constant_9231" [id=288, type=Constant]; +"289 Multiply_9666" [id=289, type=Constant]; +"290 Constant_9217" [id=290, type=Constant]; +"291 Multiply_9660" [id=291, type=Constant]; +"292 Constant_9203" [id=292, type=Constant]; +"293 Multiply_9654" [id=293, type=Constant]; +"294 Transpose_5257" [id=294, type=Constant]; +"295 Transpose_809" [id=295, type=Constant]; +"296 Transpose_5249" [id=296, type=Constant]; +"297 Transpose_800" [id=297, type=Constant]; +"298 Constant_5243" [id=298, type=Constant]; +"299 Constant_9189" [id=299, type=Constant]; +"300 Multiply_9649" [id=300, type=Constant]; +"301 Constant_9175" [id=301, type=Constant]; +"302 Gather_10068" [id=302, type=Constant]; +"303 Unsqueeze_7776" [id=303, type=Constant]; +"304 Unsqueeze_7782" [id=304, type=Constant]; +"305 Constant_7779" [id=305, type=Constant]; +"0 input_1" -> "1 Transpose_7780" [label="[1, 224, 224, 3]", style=solid]; +"1 Transpose_7780" -> "2 Transpose_7774" [label="[1, 3, 224, 224]", style=solid]; +"2 Transpose_7774" -> "3 Transpose_710" [label="[1, 3, 224, 224]", style=solid]; +"3 Transpose_710" -> "4 Multiply_9167" [label="[1, 3, 224, 224]", style=solid]; +"4 Multiply_9167" -> "5 Transpose_5170" [label="[1, 16, 112, 112]", style=solid]; +"5 Transpose_5170" -> "6 Transpose_5188" [label="[1, 16, 112, 112]", style=solid]; +"6 Transpose_5188" -> "7 Multiply_9181" [label="[1, 16, 112, 112]", style=solid]; +"7 Multiply_9181" -> "8 Transpose_5239" [label="[1, 16, 56, 56]", style=solid]; +"8 Transpose_5239" -> "9 Transpose_5241" [label="[1, 16, 56, 56]", style=solid]; +"9 Transpose_5241" -> "10 Transpose_5245" [label="[1, 16, 56, 56]", style=solid]; +"9 Transpose_5241" -> "11 Transpose_5277" [label="[1, 16, 56, 56]", style=solid]; +"10 Transpose_5245" -> "12 Convolution_801" [label="[1, 16, 1, 1]", style=solid]; +"11 Transpose_5277" -> "13 Multiply_9195" [label="[1, 16, 56, 56]", style=solid]; +"12 Convolution_801" -> "14 Transpose_5251" [label="[1, 8, 1, 1]", style=solid]; +"13 Multiply_9195" -> "15 Transpose_5301" [label="[1, 16, 56, 56]", style=solid]; +"14 Transpose_5251" -> "16 Transpose_5253" [label="[1, 8, 1, 1]", style=solid]; +"15 Transpose_5301" -> "17 Multiply_9209" [label="[1, 16, 56, 56]", style=solid]; +"16 Transpose_5253" -> "18 Convolution_810" [label="[1, 8, 1, 1]", style=solid]; +"17 Multiply_9209" -> "19 Transpose_5325" [label="[1, 72, 56, 56]", style=solid]; +"18 Convolution_810" -> "20 Transpose_5259" [label="[1, 16, 1, 1]", style=solid]; +"19 Transpose_5325" -> "21 Transpose_5327" [label="[1, 72, 56, 56]", style=solid]; +"20 Transpose_5259" -> "22 Transpose_5273" [label="[1, 16, 1, 1]", style=solid]; +"21 Transpose_5327" -> "23 Multiply_9223" [label="[1, 72, 56, 56]", style=solid]; +"22 Transpose_5273" -> "11 Transpose_5277" [label="[1, 16, 1, 1]", style=solid]; +"23 Multiply_9223" -> "24 Transpose_5378" [label="[1, 72, 28, 28]", style=solid]; +"24 Transpose_5378" -> "25 Transpose_5380" [label="[1, 72, 28, 28]", style=solid]; +"25 Transpose_5380" -> "26 Multiply_9237" [label="[1, 72, 28, 28]", style=solid]; +"26 Multiply_9237" -> "27 Transpose_5404" [label="[1, 24, 28, 28]", style=solid]; +"27 Transpose_5404" -> "28 Multiply_9251" [label="[1, 24, 28, 28]", style=solid]; +"27 Transpose_5404" -> "29 Transpose_5484" [label="[1, 24, 28, 28]", style=solid]; +"28 Multiply_9251" -> "30 Transpose_5428" [label="[1, 88, 28, 28]", style=solid]; +"29 Transpose_5484" -> "31 Multiply_9293" [label="[1, 24, 28, 28]", style=solid]; +"30 Transpose_5428" -> "32 Transpose_5430" [label="[1, 88, 28, 28]", style=solid]; +"31 Multiply_9293" -> "33 Transpose_5508" [label="[1, 96, 28, 28]", style=solid]; +"32 Transpose_5430" -> "34 Multiply_9265" [label="[1, 88, 28, 28]", style=solid]; +"33 Transpose_5508" -> "35 Transpose_5526" [label="[1, 96, 28, 28]", style=solid]; +"34 Multiply_9265" -> "36 Transpose_5454" [label="[1, 88, 28, 28]", style=solid]; +"35 Transpose_5526" -> "37 Multiply_9307" [label="[1, 96, 28, 28]", style=solid]; +"36 Transpose_5454" -> "38 Transpose_5456" [label="[1, 88, 28, 28]", style=solid]; +"37 Multiply_9307" -> "39 Transpose_5577" [label="[1, 96, 14, 14]", style=solid]; +"38 Transpose_5456" -> "40 Multiply_9279" [label="[1, 88, 28, 28]", style=solid]; +"39 Transpose_5577" -> "41 Transpose_5595" [label="[1, 96, 14, 14]", style=solid]; +"40 Multiply_9279" -> "42 Transpose_5480" [label="[1, 24, 28, 28]", style=solid]; +"41 Transpose_5595" -> "43 Transpose_5599" [label="[1, 96, 14, 14]", style=solid]; +"41 Transpose_5595" -> "44 Transpose_5631" [label="[1, 96, 14, 14]", style=solid]; +"42 Transpose_5480" -> "29 Transpose_5484" [label="[1, 24, 28, 28]", style=solid]; +"43 Transpose_5599" -> "45 Convolution_1132" [label="[1, 96, 1, 1]", style=solid]; +"44 Transpose_5631" -> "46 Multiply_9321" [label="[1, 96, 14, 14]", style=solid]; +"45 Convolution_1132" -> "47 Transpose_5605" [label="[1, 24, 1, 1]", style=solid]; +"46 Multiply_9321" -> "48 Transpose_5655" [label="[1, 40, 14, 14]", style=solid]; +"47 Transpose_5605" -> "49 Transpose_5607" [label="[1, 24, 1, 1]", style=solid]; +"48 Transpose_5655" -> "50 Multiply_9335" [label="[1, 40, 14, 14]", style=solid]; +"48 Transpose_5655" -> "51 Transpose_5803" [label="[1, 40, 14, 14]", style=solid]; +"49 Transpose_5607" -> "52 Convolution_1141" [label="[1, 24, 1, 1]", style=solid]; +"50 Multiply_9335" -> "53 Transpose_5679" [label="[1, 240, 14, 14]", style=solid]; +"51 Transpose_5803" -> "54 Multiply_9377" [label="[1, 40, 14, 14]", style=solid]; +"51 Transpose_5803" -> "55 Transpose_5951" [label="[1, 40, 14, 14]", style=solid]; +"52 Convolution_1141" -> "56 Transpose_5613" [label="[1, 96, 1, 1]", style=solid]; +"53 Transpose_5679" -> "57 Transpose_5697" [label="[1, 240, 14, 14]", style=solid]; +"54 Multiply_9377" -> "58 Transpose_5827" [label="[1, 240, 14, 14]", style=solid]; +"55 Transpose_5951" -> "59 Multiply_9419" [label="[1, 40, 14, 14]", style=solid]; +"56 Transpose_5613" -> "60 Transpose_5627" [label="[1, 96, 1, 1]", style=solid]; +"57 Transpose_5697" -> "61 Multiply_9349" [label="[1, 240, 14, 14]", style=solid]; +"58 Transpose_5827" -> "62 Transpose_5845" [label="[1, 240, 14, 14]", style=solid]; +"59 Multiply_9419" -> "63 Transpose_5975" [label="[1, 120, 14, 14]", style=solid]; +"60 Transpose_5627" -> "44 Transpose_5631" [label="[1, 96, 1, 1]", style=solid]; +"61 Multiply_9349" -> "64 Transpose_5721" [label="[1, 240, 14, 14]", style=solid]; +"62 Transpose_5845" -> "65 Multiply_9391" [label="[1, 240, 14, 14]", style=solid]; +"63 Transpose_5975" -> "66 Transpose_5993" [label="[1, 120, 14, 14]", style=solid]; +"64 Transpose_5721" -> "67 Transpose_5739" [label="[1, 240, 14, 14]", style=solid]; +"65 Multiply_9391" -> "68 Transpose_5869" [label="[1, 240, 14, 14]", style=solid]; +"66 Transpose_5993" -> "69 Multiply_9433" [label="[1, 120, 14, 14]", style=solid]; +"67 Transpose_5739" -> "70 Transpose_5743" [label="[1, 240, 14, 14]", style=solid]; +"67 Transpose_5739" -> "71 Transpose_5775" [label="[1, 240, 14, 14]", style=solid]; +"68 Transpose_5869" -> "72 Transpose_5887" [label="[1, 240, 14, 14]", style=solid]; +"69 Multiply_9433" -> "73 Transpose_6017" [label="[1, 120, 14, 14]", style=solid]; +"70 Transpose_5743" -> "74 Convolution_1242" [label="[1, 240, 1, 1]", style=solid]; +"71 Transpose_5775" -> "75 Multiply_9363" [label="[1, 240, 14, 14]", style=solid]; +"72 Transpose_5887" -> "76 Transpose_5891" [label="[1, 240, 14, 14]", style=solid]; +"72 Transpose_5887" -> "77 Transpose_5923" [label="[1, 240, 14, 14]", style=solid]; +"73 Transpose_6017" -> "78 Transpose_6035" [label="[1, 120, 14, 14]", style=solid]; +"74 Convolution_1242" -> "79 Transpose_5749" [label="[1, 64, 1, 1]", style=solid]; +"75 Multiply_9363" -> "80 Transpose_5799" [label="[1, 40, 14, 14]", style=solid]; +"76 Transpose_5891" -> "81 Convolution_1353" [label="[1, 240, 1, 1]", style=solid]; +"77 Transpose_5923" -> "82 Multiply_9405" [label="[1, 240, 14, 14]", style=solid]; +"78 Transpose_6035" -> "83 Transpose_6039" [label="[1, 120, 14, 14]", style=solid]; +"78 Transpose_6035" -> "84 Transpose_6071" [label="[1, 120, 14, 14]", style=solid]; +"79 Transpose_5749" -> "85 Transpose_5751" [label="[1, 64, 1, 1]", style=solid]; +"80 Transpose_5799" -> "51 Transpose_5803" [label="[1, 40, 14, 14]", style=solid]; +"81 Convolution_1353" -> "86 Transpose_5897" [label="[1, 64, 1, 1]", style=solid]; +"82 Multiply_9405" -> "87 Transpose_5947" [label="[1, 40, 14, 14]", style=solid]; +"83 Transpose_6039" -> "88 Convolution_1464" [label="[1, 120, 1, 1]", style=solid]; +"84 Transpose_6071" -> "89 Multiply_9447" [label="[1, 120, 14, 14]", style=solid]; +"85 Transpose_5751" -> "90 Convolution_1251" [label="[1, 64, 1, 1]", style=solid]; +"86 Transpose_5897" -> "91 Transpose_5899" [label="[1, 64, 1, 1]", style=solid]; +"87 Transpose_5947" -> "55 Transpose_5951" [label="[1, 40, 14, 14]", style=solid]; +"88 Convolution_1464" -> "92 Transpose_6045" [label="[1, 32, 1, 1]", style=solid]; +"89 Multiply_9447" -> "93 Transpose_6095" [label="[1, 48, 14, 14]", style=solid]; +"90 Convolution_1251" -> "94 Transpose_5757" [label="[1, 240, 1, 1]", style=solid]; +"91 Transpose_5899" -> "95 Convolution_1362" [label="[1, 64, 1, 1]", style=solid]; +"92 Transpose_6045" -> "96 Transpose_6047" [label="[1, 32, 1, 1]", style=solid]; +"93 Transpose_6095" -> "97 Multiply_9461" [label="[1, 48, 14, 14]", style=solid]; +"93 Transpose_6095" -> "98 Transpose_6243" [label="[1, 48, 14, 14]", style=solid]; +"94 Transpose_5757" -> "99 Transpose_5771" [label="[1, 240, 1, 1]", style=solid]; +"95 Convolution_1362" -> "100 Transpose_5905" [label="[1, 240, 1, 1]", style=solid]; +"96 Transpose_6047" -> "101 Convolution_1473" [label="[1, 32, 1, 1]", style=solid]; +"97 Multiply_9461" -> "102 Transpose_6119" [label="[1, 144, 14, 14]", style=solid]; +"98 Transpose_6243" -> "103 Multiply_9503" [label="[1, 48, 14, 14]", style=solid]; +"99 Transpose_5771" -> "71 Transpose_5775" [label="[1, 240, 1, 1]", style=solid]; +"100 Transpose_5905" -> "104 Transpose_5919" [label="[1, 240, 1, 1]", style=solid]; +"101 Convolution_1473" -> "105 Transpose_6053" [label="[1, 120, 1, 1]", style=solid]; +"102 Transpose_6119" -> "106 Transpose_6137" [label="[1, 144, 14, 14]", style=solid]; +"103 Multiply_9503" -> "107 Transpose_6267" [label="[1, 288, 14, 14]", style=solid]; +"104 Transpose_5919" -> "77 Transpose_5923" [label="[1, 240, 1, 1]", style=solid]; +"105 Transpose_6053" -> "108 Transpose_6067" [label="[1, 120, 1, 1]", style=solid]; +"106 Transpose_6137" -> "109 Multiply_9475" [label="[1, 144, 14, 14]", style=solid]; +"107 Transpose_6267" -> "110 Transpose_6285" [label="[1, 288, 14, 14]", style=solid]; +"108 Transpose_6067" -> "84 Transpose_6071" [label="[1, 120, 1, 1]", style=solid]; +"109 Multiply_9475" -> "111 Transpose_6161" [label="[1, 144, 14, 14]", style=solid]; +"110 Transpose_6285" -> "112 Multiply_9517" [label="[1, 288, 14, 14]", style=solid]; +"111 Transpose_6161" -> "113 Transpose_6179" [label="[1, 144, 14, 14]", style=solid]; +"112 Multiply_9517" -> "114 Transpose_6336" [label="[1, 288, 7, 7]", style=solid]; +"113 Transpose_6179" -> "115 Transpose_6183" [label="[1, 144, 14, 14]", style=solid]; +"113 Transpose_6179" -> "116 Transpose_6215" [label="[1, 144, 14, 14]", style=solid]; +"114 Transpose_6336" -> "117 Transpose_6354" [label="[1, 288, 7, 7]", style=solid]; +"115 Transpose_6183" -> "118 Convolution_1574" [label="[1, 144, 1, 1]", style=solid]; +"116 Transpose_6215" -> "119 Multiply_9489" [label="[1, 144, 14, 14]", style=solid]; +"117 Transpose_6354" -> "120 Transpose_6358" [label="[1, 288, 7, 7]", style=solid]; +"117 Transpose_6354" -> "121 Transpose_6390" [label="[1, 288, 7, 7]", style=solid]; +"118 Convolution_1574" -> "122 Transpose_6189" [label="[1, 40, 1, 1]", style=solid]; +"119 Multiply_9489" -> "123 Transpose_6239" [label="[1, 48, 14, 14]", style=solid]; +"120 Transpose_6358" -> "124 Convolution_1713" [label="[1, 288, 1, 1]", style=solid]; +"121 Transpose_6390" -> "125 Multiply_9531" [label="[1, 288, 7, 7]", style=solid]; +"122 Transpose_6189" -> "126 Transpose_6191" [label="[1, 40, 1, 1]", style=solid]; +"123 Transpose_6239" -> "98 Transpose_6243" [label="[1, 48, 14, 14]", style=solid]; +"124 Convolution_1713" -> "127 Transpose_6364" [label="[1, 72, 1, 1]", style=solid]; +"125 Multiply_9531" -> "128 Transpose_6414" [label="[1, 96, 7, 7]", style=solid]; +"126 Transpose_6191" -> "129 Convolution_1583" [label="[1, 40, 1, 1]", style=solid]; +"127 Transpose_6364" -> "130 Transpose_6366" [label="[1, 72, 1, 1]", style=solid]; +"128 Transpose_6414" -> "131 Multiply_9545" [label="[1, 96, 7, 7]", style=solid]; +"128 Transpose_6414" -> "132 Transpose_6562" [label="[1, 96, 7, 7]", style=solid]; +"129 Convolution_1583" -> "133 Transpose_6197" [label="[1, 144, 1, 1]", style=solid]; +"130 Transpose_6366" -> "134 Convolution_1722" [label="[1, 72, 1, 1]", style=solid]; +"131 Multiply_9545" -> "135 Transpose_6438" [label="[1, 576, 7, 7]", style=solid]; +"132 Transpose_6562" -> "136 Multiply_9587" [label="[1, 96, 7, 7]", style=solid]; +"132 Transpose_6562" -> "137 Transpose_6710" [label="[1, 96, 7, 7]", style=solid]; +"133 Transpose_6197" -> "138 Transpose_6211" [label="[1, 144, 1, 1]", style=solid]; +"134 Convolution_1722" -> "139 Transpose_6372" [label="[1, 288, 1, 1]", style=solid]; +"135 Transpose_6438" -> "140 Transpose_6456" [label="[1, 576, 7, 7]", style=solid]; +"136 Multiply_9587" -> "141 Transpose_6586" [label="[1, 576, 7, 7]", style=solid]; +"137 Transpose_6710" -> "142 Multiply_9629" [label="[1, 96, 7, 7]", style=solid]; +"138 Transpose_6211" -> "116 Transpose_6215" [label="[1, 144, 1, 1]", style=solid]; +"139 Transpose_6372" -> "143 Transpose_6386" [label="[1, 288, 1, 1]", style=solid]; +"140 Transpose_6456" -> "144 Multiply_9559" [label="[1, 576, 7, 7]", style=solid]; +"141 Transpose_6586" -> "145 Transpose_6604" [label="[1, 576, 7, 7]", style=solid]; +"142 Multiply_9629" -> "146 Transpose_6734" [label="[1, 576, 7, 7]", style=solid]; +"143 Transpose_6386" -> "121 Transpose_6390" [label="[1, 288, 1, 1]", style=solid]; +"144 Multiply_9559" -> "147 Transpose_6480" [label="[1, 576, 7, 7]", style=solid]; +"145 Transpose_6604" -> "148 Multiply_9601" [label="[1, 576, 7, 7]", style=solid]; +"146 Transpose_6734" -> "149 Transpose_6752" [label="[1, 576, 7, 7]", style=solid]; +"147 Transpose_6480" -> "150 Transpose_6498" [label="[1, 576, 7, 7]", style=solid]; +"148 Multiply_9601" -> "151 Transpose_6628" [label="[1, 576, 7, 7]", style=solid]; +"149 Transpose_6752" -> "152 Transpose_6756" [label="[1, 576, 7, 7]", style=solid]; +"150 Transpose_6498" -> "153 Transpose_6502" [label="[1, 576, 7, 7]", style=solid]; +"150 Transpose_6498" -> "154 Transpose_6534" [label="[1, 576, 7, 7]", style=solid]; +"151 Transpose_6628" -> "155 Transpose_6646" [label="[1, 576, 7, 7]", style=solid]; +"152 Transpose_6756" -> "156 Convolution_2013" [label="[1, 576, 1, 1]", style=solid]; +"153 Transpose_6502" -> "157 Convolution_1823" [label="[1, 576, 1, 1]", style=solid]; +"154 Transpose_6534" -> "158 Multiply_9573" [label="[1, 576, 7, 7]", style=solid]; +"155 Transpose_6646" -> "159 Transpose_6650" [label="[1, 576, 7, 7]", style=solid]; +"155 Transpose_6646" -> "160 Transpose_6682" [label="[1, 576, 7, 7]", style=solid]; +"156 Convolution_2013" -> "161 Transpose_6762" [label="[1, 1024, 1, 1]", style=solid]; +"157 Convolution_1823" -> "162 Transpose_6508" [label="[1, 144, 1, 1]", style=solid]; +"158 Multiply_9573" -> "163 Transpose_6558" [label="[1, 96, 7, 7]", style=solid]; +"159 Transpose_6650" -> "164 Convolution_1934" [label="[1, 576, 1, 1]", style=solid]; +"160 Transpose_6682" -> "165 Multiply_9615" [label="[1, 576, 7, 7]", style=solid]; +"161 Transpose_6762" -> "166 Transpose_6780" [label="[1, 1024, 1, 1]", style=solid]; +"162 Transpose_6508" -> "167 Transpose_6510" [label="[1, 144, 1, 1]", style=solid]; +"163 Transpose_6558" -> "132 Transpose_6562" [label="[1, 96, 7, 7]", style=solid]; +"164 Convolution_1934" -> "168 Transpose_6656" [label="[1, 144, 1, 1]", style=solid]; +"165 Multiply_9615" -> "169 Transpose_6706" [label="[1, 96, 7, 7]", style=solid]; +"166 Transpose_6780" -> "170 Convolution_2025" [label="[1, 1024, 1, 1]", style=solid]; +"167 Transpose_6510" -> "171 Convolution_1832" [label="[1, 144, 1, 1]", style=solid]; +"168 Transpose_6656" -> "172 Transpose_6658" [label="[1, 144, 1, 1]", style=solid]; +"169 Transpose_6706" -> "137 Transpose_6710" [label="[1, 96, 7, 7]", style=solid]; +"170 Convolution_2025" -> "173 Transpose_6786" [label="[1, 1000, 1, 1]", style=solid]; +"171 Convolution_1832" -> "174 Transpose_6516" [label="[1, 576, 1, 1]", style=solid]; +"172 Transpose_6658" -> "175 Convolution_1943" [label="[1, 144, 1, 1]", style=solid]; +"173 Transpose_6786" -> "176 MobilenetV3small/Logits/BiasAdd" [label="[1, 1000, 1, 1]", style=solid]; +"174 Transpose_6516" -> "177 Transpose_6530" [label="[1, 576, 1, 1]", style=solid]; +"175 Convolution_1943" -> "178 Transpose_6664" [label="[1, 576, 1, 1]", style=solid]; +"176 MobilenetV3small/Logits/BiasAdd" -> "179 MobilenetV3small/flatten/Reshape" [label="[1, 1, 1, 1000]", style=solid]; +"177 Transpose_6530" -> "154 Transpose_6534" [label="[1, 576, 1, 1]", style=solid]; +"178 Transpose_6664" -> "180 Transpose_6678" [label="[1, 576, 1, 1]", style=solid]; +"179 MobilenetV3small/flatten/Reshape" -> "181 MobilenetV3small/Predictions/Softmax" [label="[1, 1000]", style=solid]; +"180 Transpose_6678" -> "160 Transpose_6682" [label="[1, 576, 1, 1]", style=solid]; +"181 MobilenetV3small/Predictions/Softmax" -> "182 Predictions" [label="[1, 1000]", style=solid]; +"183 MobilenetV3small/flatten/Const" -> "179 MobilenetV3small/flatten/Reshape" [label="[2]", style=dashed]; +"184 Constant_8887" -> "176 MobilenetV3small/Logits/BiasAdd" [label="[4]", style=dashed]; +"185 Transpose_6784" -> "173 Transpose_6786" [label="[1, 1000, 1, 1]", style=solid]; +"186 Transpose_2024" -> "170 Convolution_2025" [label="[1000, 1024, 1, 1]", style=solid]; +"187 Transpose_6760" -> "161 Transpose_6762" [label="[1, 1024, 1, 1]", style=solid]; +"188 Transpose_2012" -> "156 Convolution_2013" [label="[1024, 576, 1, 1]", style=solid]; +"189 Constant_6754" -> "152 Transpose_6756" [label="[2]", style=dashed]; +"190 Constant_9637" -> "146 Transpose_6734" [label="[1, 576, 1, 1]", style=solid]; +"191 Multiply_9830" -> "142 Multiply_9629" [label="[576, 96, 1, 1]", style=solid]; +"192 Constant_9623" -> "169 Transpose_6706" [label="[1, 96, 1, 1]", style=solid]; +"193 Multiply_9824" -> "165 Multiply_9615" [label="[96, 576, 1, 1]", style=solid]; +"194 Transpose_6662" -> "178 Transpose_6664" [label="[1, 576, 1, 1]", style=solid]; +"195 Transpose_1942" -> "175 Convolution_1943" [label="[576, 144, 1, 1]", style=solid]; +"196 Transpose_6654" -> "168 Transpose_6656" [label="[1, 144, 1, 1]", style=solid]; +"197 Transpose_1933" -> "164 Convolution_1934" [label="[144, 576, 1, 1]", style=solid]; +"198 Constant_6648" -> "159 Transpose_6650" [label="[2]", style=dashed]; +"199 Constant_9609" -> "151 Transpose_6628" [label="[1, 576, 1, 1]", style=solid]; +"200 Multiply_9819" -> "148 Multiply_9601" [label="[576, 1, 1, 5, 5]", style=solid]; +"201 Constant_9595" -> "141 Transpose_6586" [label="[1, 576, 1, 1]", style=solid]; +"202 Multiply_9813" -> "136 Multiply_9587" [label="[576, 96, 1, 1]", style=solid]; +"203 Constant_9581" -> "163 Transpose_6558" [label="[1, 96, 1, 1]", style=solid]; +"204 Multiply_9807" -> "158 Multiply_9573" [label="[96, 576, 1, 1]", style=solid]; +"205 Transpose_6514" -> "174 Transpose_6516" [label="[1, 576, 1, 1]", style=solid]; +"206 Transpose_1831" -> "171 Convolution_1832" [label="[576, 144, 1, 1]", style=solid]; +"207 Transpose_6506" -> "162 Transpose_6508" [label="[1, 144, 1, 1]", style=solid]; +"208 Transpose_1822" -> "157 Convolution_1823" [label="[144, 576, 1, 1]", style=solid]; +"209 Constant_6500" -> "153 Transpose_6502" [label="[2]", style=dashed]; +"210 Constant_9567" -> "147 Transpose_6480" [label="[1, 576, 1, 1]", style=solid]; +"211 Multiply_9802" -> "144 Multiply_9559" [label="[576, 1, 1, 5, 5]", style=solid]; +"212 Constant_9553" -> "135 Transpose_6438" [label="[1, 576, 1, 1]", style=solid]; +"213 Multiply_9796" -> "131 Multiply_9545" [label="[576, 96, 1, 1]", style=solid]; +"214 Constant_9539" -> "128 Transpose_6414" [label="[1, 96, 1, 1]", style=solid]; +"215 Multiply_9790" -> "125 Multiply_9531" [label="[96, 288, 1, 1]", style=solid]; +"216 Transpose_6370" -> "139 Transpose_6372" [label="[1, 288, 1, 1]", style=solid]; +"217 Transpose_1721" -> "134 Convolution_1722" [label="[288, 72, 1, 1]", style=solid]; +"218 Transpose_6362" -> "127 Transpose_6364" [label="[1, 72, 1, 1]", style=solid]; +"219 Transpose_1712" -> "124 Convolution_1713" [label="[72, 288, 1, 1]", style=solid]; +"220 Constant_6356" -> "120 Transpose_6358" [label="[2]", style=dashed]; +"221 Constant_9525" -> "114 Transpose_6336" [label="[1, 288, 1, 1]", style=solid]; +"222 Multiply_9785" -> "112 Multiply_9517" [label="[288, 1, 1, 5, 5]", style=solid]; +"223 Constant_9511" -> "107 Transpose_6267" [label="[1, 288, 1, 1]", style=solid]; +"224 Multiply_9779" -> "103 Multiply_9503" [label="[288, 48, 1, 1]", style=solid]; +"225 Constant_9497" -> "123 Transpose_6239" [label="[1, 48, 1, 1]", style=solid]; +"226 Multiply_9773" -> "119 Multiply_9489" [label="[48, 144, 1, 1]", style=solid]; +"227 Transpose_6195" -> "133 Transpose_6197" [label="[1, 144, 1, 1]", style=solid]; +"228 Transpose_1582" -> "129 Convolution_1583" [label="[144, 40, 1, 1]", style=solid]; +"229 Transpose_6187" -> "122 Transpose_6189" [label="[1, 40, 1, 1]", style=solid]; +"230 Transpose_1573" -> "118 Convolution_1574" [label="[40, 144, 1, 1]", style=solid]; +"231 Constant_6181" -> "115 Transpose_6183" [label="[2]", style=dashed]; +"232 Constant_9483" -> "111 Transpose_6161" [label="[1, 144, 1, 1]", style=solid]; +"233 Multiply_9768" -> "109 Multiply_9475" [label="[144, 1, 1, 5, 5]", style=solid]; +"234 Constant_9469" -> "102 Transpose_6119" [label="[1, 144, 1, 1]", style=solid]; +"235 Multiply_9762" -> "97 Multiply_9461" [label="[144, 48, 1, 1]", style=solid]; +"236 Constant_9455" -> "93 Transpose_6095" [label="[1, 48, 1, 1]", style=solid]; +"237 Multiply_9756" -> "89 Multiply_9447" [label="[48, 120, 1, 1]", style=solid]; +"238 Transpose_6051" -> "105 Transpose_6053" [label="[1, 120, 1, 1]", style=solid]; +"239 Transpose_1472" -> "101 Convolution_1473" [label="[120, 32, 1, 1]", style=solid]; +"240 Transpose_6043" -> "92 Transpose_6045" [label="[1, 32, 1, 1]", style=solid]; +"241 Transpose_1463" -> "88 Convolution_1464" [label="[32, 120, 1, 1]", style=solid]; +"242 Constant_6037" -> "83 Transpose_6039" [label="[2]", style=dashed]; +"243 Constant_9441" -> "73 Transpose_6017" [label="[1, 120, 1, 1]", style=solid]; +"244 Multiply_9751" -> "69 Multiply_9433" [label="[120, 1, 1, 5, 5]", style=solid]; +"245 Constant_9427" -> "63 Transpose_5975" [label="[1, 120, 1, 1]", style=solid]; +"246 Multiply_9745" -> "59 Multiply_9419" [label="[120, 40, 1, 1]", style=solid]; +"247 Constant_9413" -> "87 Transpose_5947" [label="[1, 40, 1, 1]", style=solid]; +"248 Multiply_9739" -> "82 Multiply_9405" [label="[40, 240, 1, 1]", style=solid]; +"249 Transpose_5903" -> "100 Transpose_5905" [label="[1, 240, 1, 1]", style=solid]; +"250 Transpose_1361" -> "95 Convolution_1362" [label="[240, 64, 1, 1]", style=solid]; +"251 Transpose_5895" -> "86 Transpose_5897" [label="[1, 64, 1, 1]", style=solid]; +"252 Transpose_1352" -> "81 Convolution_1353" [label="[64, 240, 1, 1]", style=solid]; +"253 Constant_5889" -> "76 Transpose_5891" [label="[2]", style=dashed]; +"254 Constant_9399" -> "68 Transpose_5869" [label="[1, 240, 1, 1]", style=solid]; +"255 Multiply_9734" -> "65 Multiply_9391" [label="[240, 1, 1, 5, 5]", style=solid]; +"256 Constant_9385" -> "58 Transpose_5827" [label="[1, 240, 1, 1]", style=solid]; +"257 Multiply_9728" -> "54 Multiply_9377" [label="[240, 40, 1, 1]", style=solid]; +"258 Constant_9371" -> "80 Transpose_5799" [label="[1, 40, 1, 1]", style=solid]; +"259 Multiply_9722" -> "75 Multiply_9363" [label="[40, 240, 1, 1]", style=solid]; +"260 Transpose_5755" -> "94 Transpose_5757" [label="[1, 240, 1, 1]", style=solid]; +"261 Transpose_1250" -> "90 Convolution_1251" [label="[240, 64, 1, 1]", style=solid]; +"262 Transpose_5747" -> "79 Transpose_5749" [label="[1, 64, 1, 1]", style=solid]; +"263 Transpose_1241" -> "74 Convolution_1242" [label="[64, 240, 1, 1]", style=solid]; +"264 Constant_5741" -> "70 Transpose_5743" [label="[2]", style=dashed]; +"265 Constant_9357" -> "64 Transpose_5721" [label="[1, 240, 1, 1]", style=solid]; +"266 Multiply_9717" -> "61 Multiply_9349" [label="[240, 1, 1, 5, 5]", style=solid]; +"267 Constant_9343" -> "53 Transpose_5679" [label="[1, 240, 1, 1]", style=solid]; +"268 Multiply_9711" -> "50 Multiply_9335" [label="[240, 40, 1, 1]", style=solid]; +"269 Constant_9329" -> "48 Transpose_5655" [label="[1, 40, 1, 1]", style=solid]; +"270 Multiply_9705" -> "46 Multiply_9321" [label="[40, 96, 1, 1]", style=solid]; +"271 Transpose_5611" -> "56 Transpose_5613" [label="[1, 96, 1, 1]", style=solid]; +"272 Transpose_1140" -> "52 Convolution_1141" [label="[96, 24, 1, 1]", style=solid]; +"273 Transpose_5603" -> "47 Transpose_5605" [label="[1, 24, 1, 1]", style=solid]; +"274 Transpose_1131" -> "45 Convolution_1132" [label="[24, 96, 1, 1]", style=solid]; +"275 Constant_5597" -> "43 Transpose_5599" [label="[2]", style=dashed]; +"276 Constant_9315" -> "39 Transpose_5577" [label="[1, 96, 1, 1]", style=solid]; +"277 Multiply_9700" -> "37 Multiply_9307" [label="[96, 1, 1, 5, 5]", style=solid]; +"278 Constant_9301" -> "33 Transpose_5508" [label="[1, 96, 1, 1]", style=solid]; +"279 Multiply_9694" -> "31 Multiply_9293" [label="[96, 24, 1, 1]", style=solid]; +"280 Constant_9287" -> "42 Transpose_5480" [label="[1, 24, 1, 1]", style=solid]; +"281 Multiply_9688" -> "40 Multiply_9279" [label="[24, 88, 1, 1]", style=solid]; +"282 Constant_9273" -> "36 Transpose_5454" [label="[1, 88, 1, 1]", style=solid]; +"283 Multiply_9683" -> "34 Multiply_9265" [label="[88, 1, 1, 3, 3]", style=solid]; +"284 Constant_9259" -> "30 Transpose_5428" [label="[1, 88, 1, 1]", style=solid]; +"285 Multiply_9677" -> "28 Multiply_9251" [label="[88, 24, 1, 1]", style=solid]; +"286 Constant_9245" -> "27 Transpose_5404" [label="[1, 24, 1, 1]", style=solid]; +"287 Multiply_9671" -> "26 Multiply_9237" [label="[24, 72, 1, 1]", style=solid]; +"288 Constant_9231" -> "24 Transpose_5378" [label="[1, 72, 1, 1]", style=solid]; +"289 Multiply_9666" -> "23 Multiply_9223" [label="[72, 1, 1, 3, 3]", style=solid]; +"290 Constant_9217" -> "19 Transpose_5325" [label="[1, 72, 1, 1]", style=solid]; +"291 Multiply_9660" -> "17 Multiply_9209" [label="[72, 16, 1, 1]", style=solid]; +"292 Constant_9203" -> "15 Transpose_5301" [label="[1, 16, 1, 1]", style=solid]; +"293 Multiply_9654" -> "13 Multiply_9195" [label="[16, 16, 1, 1]", style=solid]; +"294 Transpose_5257" -> "20 Transpose_5259" [label="[1, 16, 1, 1]", style=solid]; +"295 Transpose_809" -> "18 Convolution_810" [label="[16, 8, 1, 1]", style=solid]; +"296 Transpose_5249" -> "14 Transpose_5251" [label="[1, 8, 1, 1]", style=solid]; +"297 Transpose_800" -> "12 Convolution_801" [label="[8, 16, 1, 1]", style=solid]; +"298 Constant_5243" -> "10 Transpose_5245" [label="[2]", style=dashed]; +"299 Constant_9189" -> "8 Transpose_5239" [label="[1, 16, 1, 1]", style=solid]; +"300 Multiply_9649" -> "7 Multiply_9181" [label="[16, 1, 1, 3, 3]", style=solid]; +"301 Constant_9175" -> "5 Transpose_5170" [label="[1, 16, 1, 1]", style=solid]; +"302 Gather_10068" -> "4 Multiply_9167" [label="[16, 3, 3, 3]", style=solid]; +"303 Unsqueeze_7776" -> "3 Transpose_710" [label="[1, 1, 1, 1]", style=solid]; +"304 Unsqueeze_7782" -> "2 Transpose_7774" [label="[1, 1, 1, 1]", style=solid]; +"305 Constant_7779" -> "1 Transpose_7780" [label="[4]", style=dashed]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/removed_nodes_in_QuantizedModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/removed_nodes_in_QuantizedModel.dot deleted file mode 120000 index fb97322ebf1..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/removed_nodes_in_QuantizedModel.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/original_nncf_graph/removed_nodes_in_QuantizedModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/removed_nodes_in_QuantizedModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/removed_nodes_in_QuantizedModel.dot new file mode 100644 index 00000000000..7ab98ea6599 --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/removed_nodes_in_QuantizedModel.dot @@ -0,0 +1,92 @@ +strict digraph { +"0 Input_1" [id=0, type=Parameter]; +"1 Input_2" [id=1, type=Parameter]; +"2 Conv_1" [id=2, type=Convolution]; +"3 Mul" [id=3, type=Multiply]; +"4 Relu_1" [id=4, type=Relu]; +"5 Add_1" [id=5, type=Add]; +"6 Concat_1" [id=6, type=Concat]; +"7 Transpose/fq_input_0" [id=7, type=FakeQuantize]; +"8 Add_2" [id=8, type=Add]; +"9 Conv_2" [id=9, type=Convolution]; +"10 Transpose" [id=10, type=Transpose]; +"11 Concat_2" [id=11, type=Concat]; +"12 Relu_2" [id=12, type=Relu]; +"13 Reshape_237" [id=13, type=Reshape]; +"14 Conv_3/fq_input_0" [id=14, type=FakeQuantize]; +"15 MatMul_244" [id=15, type=MatMul]; +"16 Conv_3" [id=16, type=Convolution]; +"17 Result" [id=17, type=Result]; +"18 Constant_243" [id=18, type=Constant]; +"19 Constant_236" [id=19, type=Constant]; +"20 Constant_228" [id=20, type=Constant]; +"21 Constant_200" [id=21, type=Constant]; +"22 Constant_198" [id=22, type=Constant]; +"23 Constant_197" [id=23, type=Constant]; +"24 Constant_196" [id=24, type=Constant]; +"25 Constant_195" [id=25, type=Constant]; +"26 Constant_193" [id=26, type=Constant]; +"27 Constant_191" [id=27, type=Constant]; +"28 Conv_1/fq_weights_0" [id=28, type=FakeQuantize]; +"29 Constant_186" [id=29, type=Constant]; +"30 Constant_185" [id=30, type=Constant]; +"31 Constant_184" [id=31, type=Constant]; +"32 Constant_183" [id=32, type=Constant]; +"33 Constant_182" [id=33, type=Constant]; +"34 Constant_221" [id=34, type=Constant]; +"35 Constant_219" [id=35, type=Constant]; +"36 Constant_218" [id=36, type=Constant]; +"37 Constant_217" [id=37, type=Constant]; +"38 Constant_216" [id=38, type=Constant]; +"39 Conv_2/fq_weights_0" [id=39, type=FakeQuantize]; +"40 Constant_212" [id=40, type=Constant]; +"41 Constant_211" [id=41, type=Constant]; +"42 Constant_210" [id=42, type=Constant]; +"43 Constant_209" [id=43, type=Constant]; +"44 Constant_208" [id=44, type=Constant]; +"0 Input_1" -> "2 Conv_1" [label="[1, 3, 14, 28]", style=solid]; +"1 Input_2" -> "3 Mul" [label="[1, 3, 28, 14]", style=solid]; +"2 Conv_1" -> "4 Relu_1" [label="[1, 3, 14, 28]", style=solid]; +"3 Mul" -> "5 Add_1" [label="[1, 3, 28, 14]", style=solid]; +"4 Relu_1" -> "6 Concat_1" [label="[1, 3, 14, 28]", style=solid]; +"5 Add_1" -> "7 Transpose/fq_input_0" [label="[1, 3, 28, 14]", style=solid]; +"6 Concat_1" -> "8 Add_2" [label="[1, 6, 14, 28]", style=solid]; +"6 Concat_1" -> "9 Conv_2" [label="[1, 6, 14, 28]", style=solid]; +"7 Transpose/fq_input_0" -> "10 Transpose" [label="[1, 3, 28, 14]", style=solid]; +"8 Add_2" -> "11 Concat_2" [label="[1, 6, 14, 28]", style=solid]; +"9 Conv_2" -> "12 Relu_2" [label="[1, 12, 14, 28]", style=solid]; +"10 Transpose" -> "6 Concat_1" [label="[1, 3, 14, 28]", style=solid]; +"11 Concat_2" -> "13 Reshape_237" [label="[1, 12, 14, 28]", style=solid]; +"12 Relu_2" -> "14 Conv_3/fq_input_0" [label="[1, 12, 14, 28]", style=solid]; +"13 Reshape_237" -> "15 MatMul_244" [label="[2, 2352]", style=solid]; +"14 Conv_3/fq_input_0" -> "16 Conv_3" [label="[1, 12, 14, 28]", style=solid]; +"15 MatMul_244" -> "17 Result" [label="[2, 100]", style=solid]; +"16 Conv_3" -> "11 Concat_2" [label="[1, 6, 14, 28]", style=solid]; +"18 Constant_243" -> "15 MatMul_244" [label="[100, 2352]", style=solid]; +"19 Constant_236" -> "13 Reshape_237" [label="[2]", style=dashed]; +"20 Constant_228" -> "8 Add_2" [label="[1, 6, 1, 1]", style=solid]; +"21 Constant_200" -> "10 Transpose" [label="[4]", style=dashed]; +"22 Constant_198" -> "7 Transpose/fq_input_0" [label="[]", style=solid]; +"23 Constant_197" -> "7 Transpose/fq_input_0" [label="[]", style=solid]; +"24 Constant_196" -> "7 Transpose/fq_input_0" [label="[]", style=solid]; +"25 Constant_195" -> "7 Transpose/fq_input_0" [label="[]", style=solid]; +"26 Constant_193" -> "5 Add_1" [label="[1, 3, 1, 1]", style=solid]; +"27 Constant_191" -> "3 Mul" [label="[1, 3, 1, 1]", style=solid]; +"28 Conv_1/fq_weights_0" -> "2 Conv_1" [label="[3, 3, 1, 1]", style=solid]; +"29 Constant_186" -> "28 Conv_1/fq_weights_0" [label="[]", style=solid]; +"30 Constant_185" -> "28 Conv_1/fq_weights_0" [label="[]", style=solid]; +"31 Constant_184" -> "28 Conv_1/fq_weights_0" [label="[]", style=solid]; +"32 Constant_183" -> "28 Conv_1/fq_weights_0" [label="[]", style=solid]; +"33 Constant_182" -> "28 Conv_1/fq_weights_0" [label="[3, 3, 1, 1]", style=solid]; +"34 Constant_221" -> "16 Conv_3" [label="[6, 12, 1, 1]", style=solid]; +"35 Constant_219" -> "14 Conv_3/fq_input_0" [label="[]", style=solid]; +"36 Constant_218" -> "14 Conv_3/fq_input_0" [label="[]", style=solid]; +"37 Constant_217" -> "14 Conv_3/fq_input_0" [label="[]", style=solid]; +"38 Constant_216" -> "14 Conv_3/fq_input_0" [label="[]", style=solid]; +"39 Conv_2/fq_weights_0" -> "9 Conv_2" [label="[12, 6, 1, 1]", style=solid]; +"40 Constant_212" -> "39 Conv_2/fq_weights_0" [label="[]", style=solid]; +"41 Constant_211" -> "39 Conv_2/fq_weights_0" [label="[]", style=solid]; +"42 Constant_210" -> "39 Conv_2/fq_weights_0" [label="[]", style=solid]; +"43 Constant_209" -> "39 Conv_2/fq_weights_0" [label="[]", style=solid]; +"44 Constant_208" -> "39 Conv_2/fq_weights_0" [label="[12, 6, 1, 1]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/resnet-18-pytorch.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/resnet-18-pytorch.dot deleted file mode 120000 index eec518fdf36..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/resnet-18-pytorch.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/original_nncf_graph/resnet-18-pytorch.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/resnet-18-pytorch.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/resnet-18-pytorch.dot new file mode 100644 index 00000000000..24fecd18ab3 --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/resnet-18-pytorch.dot @@ -0,0 +1,249 @@ +strict digraph { +"0 data" [id=0, type=Parameter]; +"1 Multiply_1715" [id=1, type=Multiply]; +"2 Divide_401" [id=2, type=Add]; +"3 /conv1/Conv/WithoutBiases" [id=3, type=Convolution]; +"4 /conv1/Conv" [id=4, type=Add]; +"5 /relu/Relu" [id=5, type=Relu]; +"6 /maxpool/MaxPool" [id=6, type=MaxPool]; +"7 /layer1/layer1.0/Add" [id=7, type=Add]; +"8 /layer1/layer1.0/conv1/Conv/WithoutBiases" [id=8, type=Convolution]; +"9 /layer1/layer1.0/relu_1/Relu" [id=9, type=Relu]; +"10 /layer1/layer1.0/conv1/Conv" [id=10, type=Add]; +"11 /layer1/layer1.1/Add" [id=11, type=Add]; +"12 /layer1/layer1.1/conv1/Conv/WithoutBiases" [id=12, type=Convolution]; +"13 /layer1/layer1.0/relu/Relu" [id=13, type=Relu]; +"14 /layer1/layer1.1/relu_1/Relu" [id=14, type=Relu]; +"15 /layer1/layer1.1/conv1/Conv" [id=15, type=Add]; +"16 /layer1/layer1.0/conv2/Conv/WithoutBiases" [id=16, type=Convolution]; +"17 /layer2/layer2.0/conv1/Conv/WithoutBiases" [id=17, type=Convolution]; +"18 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases" [id=18, type=Convolution]; +"19 /layer1/layer1.1/relu/Relu" [id=19, type=Relu]; +"20 /layer1/layer1.0/conv2/Conv" [id=20, type=Add]; +"21 /layer2/layer2.0/conv1/Conv" [id=21, type=Add]; +"22 /layer2/layer2.0/downsample/downsample.0/Conv" [id=22, type=Add]; +"23 /layer1/layer1.1/conv2/Conv/WithoutBiases" [id=23, type=Convolution]; +"24 /layer2/layer2.0/relu/Relu" [id=24, type=Relu]; +"25 /layer2/layer2.0/Add" [id=25, type=Add]; +"26 /layer1/layer1.1/conv2/Conv" [id=26, type=Add]; +"27 /layer2/layer2.0/conv2/Conv/WithoutBiases" [id=27, type=Convolution]; +"28 /layer2/layer2.0/relu_1/Relu" [id=28, type=Relu]; +"29 /layer2/layer2.0/conv2/Conv" [id=29, type=Add]; +"30 /layer2/layer2.1/Add" [id=30, type=Add]; +"31 /layer2/layer2.1/conv1/Conv/WithoutBiases" [id=31, type=Convolution]; +"32 /layer2/layer2.1/relu_1/Relu" [id=32, type=Relu]; +"33 /layer2/layer2.1/conv1/Conv" [id=33, type=Add]; +"34 /layer3/layer3.0/conv1/Conv/WithoutBiases" [id=34, type=Convolution]; +"35 /layer3/layer3.0/downsample/downsample.0/Conv/WithoutBiases" [id=35, type=Convolution]; +"36 /layer2/layer2.1/relu/Relu" [id=36, type=Relu]; +"37 /layer3/layer3.0/conv1/Conv" [id=37, type=Add]; +"38 /layer3/layer3.0/downsample/downsample.0/Conv" [id=38, type=Add]; +"39 /layer2/layer2.1/conv2/Conv/WithoutBiases" [id=39, type=Convolution]; +"40 /layer3/layer3.0/relu/Relu" [id=40, type=Relu]; +"41 /layer3/layer3.0/Add" [id=41, type=Add]; +"42 /layer2/layer2.1/conv2/Conv" [id=42, type=Add]; +"43 /layer3/layer3.0/conv2/Conv/WithoutBiases" [id=43, type=Convolution]; +"44 /layer3/layer3.0/relu_1/Relu" [id=44, type=Relu]; +"45 /layer3/layer3.0/conv2/Conv" [id=45, type=Add]; +"46 /layer3/layer3.1/Add" [id=46, type=Add]; +"47 /layer3/layer3.1/conv1/Conv/WithoutBiases" [id=47, type=Convolution]; +"48 /layer3/layer3.1/relu_1/Relu" [id=48, type=Relu]; +"49 /layer3/layer3.1/conv1/Conv" [id=49, type=Add]; +"50 /layer4/layer4.0/conv1/Conv/WithoutBiases" [id=50, type=Convolution]; +"51 /layer4/layer4.0/downsample/downsample.0/Conv/WithoutBiases" [id=51, type=Convolution]; +"52 /layer3/layer3.1/relu/Relu" [id=52, type=Relu]; +"53 /layer4/layer4.0/conv1/Conv" [id=53, type=Add]; +"54 /layer4/layer4.0/downsample/downsample.0/Conv" [id=54, type=Add]; +"55 /layer3/layer3.1/conv2/Conv/WithoutBiases" [id=55, type=Convolution]; +"56 /layer4/layer4.0/relu/Relu" [id=56, type=Relu]; +"57 /layer4/layer4.0/Add" [id=57, type=Add]; +"58 /layer3/layer3.1/conv2/Conv" [id=58, type=Add]; +"59 /layer4/layer4.0/conv2/Conv/WithoutBiases" [id=59, type=Convolution]; +"60 /layer4/layer4.0/relu_1/Relu" [id=60, type=Relu]; +"61 /layer4/layer4.0/conv2/Conv" [id=61, type=Add]; +"62 /layer4/layer4.1/Add" [id=62, type=Add]; +"63 /layer4/layer4.1/conv1/Conv/WithoutBiases" [id=63, type=Convolution]; +"64 /layer4/layer4.1/relu_1/Relu" [id=64, type=Relu]; +"65 /layer4/layer4.1/conv1/Conv" [id=65, type=Add]; +"66 /avgpool/GlobalAveragePool" [id=66, type=ReduceMean]; +"67 /layer4/layer4.1/relu/Relu" [id=67, type=Relu]; +"68 /Flatten" [id=68, type=Reshape]; +"69 /layer4/layer4.1/conv2/Conv/WithoutBiases" [id=69, type=Convolution]; +"70 /fc/Gemm/WithoutBiases" [id=70, type=MatMul]; +"71 /layer4/layer4.1/conv2/Conv" [id=71, type=Add]; +"72 prob" [id=72, type=Add]; +"73 prob/sink_port_0" [id=73, type=Result]; +"74 Constant_1789" [id=74, type=Constant]; +"75 fc.weight" [id=75, type=Constant]; +"76 Constant_380" [id=76, type=Constant]; +"77 Range_376" [id=77, type=Constant]; +"78 Reshape_331" [id=78, type=Constant]; +"79 onnx^^Conv_244" [id=79, label="79 onnx::Conv_244", type=Constant]; +"80 Reshape_250" [id=80, type=Constant]; +"81 onnx^^Conv_229" [id=81, label="81 onnx::Conv_229", type=Constant]; +"82 Reshape_169" [id=82, type=Constant]; +"83 onnx^^Conv_214" [id=83, label="83 onnx::Conv_214", type=Constant]; +"84 Reshape_55" [id=84, type=Constant]; +"85 Gather_1788" [id=85, type=Constant]; +"86 Gather_1785" [id=86, type=Constant]; +"87 Gather_1782" [id=87, type=Constant]; +"88 Reshape_88" [id=88, type=Constant]; +"89 onnx^^Conv_199" [id=89, label="89 onnx::Conv_199", type=Constant]; +"90 Reshape_72" [id=90, type=Constant]; +"91 onnx^^Conv_196" [id=91, label="91 onnx::Conv_196", type=Constant]; +"92 Reshape_121" [id=92, type=Constant]; +"93 onnx^^Conv_205" [id=93, label="93 onnx::Conv_205", type=Constant]; +"94 Reshape_105" [id=94, type=Constant]; +"95 onnx^^Conv_202" [id=95, label="95 onnx::Conv_202", type=Constant]; +"96 Reshape_154" [id=96, type=Constant]; +"97 onnx^^Conv_211" [id=97, label="97 onnx::Conv_211", type=Constant]; +"98 Reshape_138" [id=98, type=Constant]; +"99 onnx^^Conv_208" [id=99, label="99 onnx::Conv_208", type=Constant]; +"100 Reshape_202" [id=100, type=Constant]; +"101 onnx^^Conv_220" [id=101, label="101 onnx::Conv_220", type=Constant]; +"102 Reshape_186" [id=102, type=Constant]; +"103 onnx^^Conv_217" [id=103, label="103 onnx::Conv_217", type=Constant]; +"104 Reshape_235" [id=104, type=Constant]; +"105 onnx^^Conv_226" [id=105, label="105 onnx::Conv_226", type=Constant]; +"106 Reshape_219" [id=106, type=Constant]; +"107 onnx^^Conv_223" [id=107, label="107 onnx::Conv_223", type=Constant]; +"108 Reshape_283" [id=108, type=Constant]; +"109 onnx^^Conv_235" [id=109, label="109 onnx::Conv_235", type=Constant]; +"110 Reshape_267" [id=110, type=Constant]; +"111 onnx^^Conv_232" [id=111, label="111 onnx::Conv_232", type=Constant]; +"112 Reshape_316" [id=112, type=Constant]; +"113 onnx^^Conv_241" [id=113, label="113 onnx::Conv_241", type=Constant]; +"114 Reshape_300" [id=114, type=Constant]; +"115 onnx^^Conv_238" [id=115, label="115 onnx::Conv_238", type=Constant]; +"116 Reshape_364" [id=116, type=Constant]; +"117 onnx^^Conv_250" [id=117, label="117 onnx::Conv_250", type=Constant]; +"118 Reshape_348" [id=118, type=Constant]; +"119 onnx^^Conv_247" [id=119, label="119 onnx::Conv_247", type=Constant]; +"0 data" -> "1 Multiply_1715" [label="[1, 3, 224, 224]", style=solid]; +"1 Multiply_1715" -> "2 Divide_401" [label="[1, 3, 224, 224]", style=solid]; +"2 Divide_401" -> "3 /conv1/Conv/WithoutBiases" [label="[1, 3, 224, 224]", style=solid]; +"3 /conv1/Conv/WithoutBiases" -> "4 /conv1/Conv" [label="[1, 64, 112, 112]", style=solid]; +"4 /conv1/Conv" -> "5 /relu/Relu" [label="[1, 64, 112, 112]", style=solid]; +"5 /relu/Relu" -> "6 /maxpool/MaxPool" [label="[1, 64, 112, 112]", style=solid]; +"6 /maxpool/MaxPool" -> "7 /layer1/layer1.0/Add" [label="[1, 64, 56, 56]", style=solid]; +"6 /maxpool/MaxPool" -> "8 /layer1/layer1.0/conv1/Conv/WithoutBiases" [label="[1, 64, 56, 56]", style=solid]; +"7 /layer1/layer1.0/Add" -> "9 /layer1/layer1.0/relu_1/Relu" [label="[1, 64, 56, 56]", style=solid]; +"8 /layer1/layer1.0/conv1/Conv/WithoutBiases" -> "10 /layer1/layer1.0/conv1/Conv" [label="[1, 64, 56, 56]", style=solid]; +"9 /layer1/layer1.0/relu_1/Relu" -> "11 /layer1/layer1.1/Add" [label="[1, 64, 56, 56]", style=solid]; +"9 /layer1/layer1.0/relu_1/Relu" -> "12 /layer1/layer1.1/conv1/Conv/WithoutBiases" [label="[1, 64, 56, 56]", style=solid]; +"10 /layer1/layer1.0/conv1/Conv" -> "13 /layer1/layer1.0/relu/Relu" [label="[1, 64, 56, 56]", style=solid]; +"11 /layer1/layer1.1/Add" -> "14 /layer1/layer1.1/relu_1/Relu" [label="[1, 64, 56, 56]", style=solid]; +"12 /layer1/layer1.1/conv1/Conv/WithoutBiases" -> "15 /layer1/layer1.1/conv1/Conv" [label="[1, 64, 56, 56]", style=solid]; +"13 /layer1/layer1.0/relu/Relu" -> "16 /layer1/layer1.0/conv2/Conv/WithoutBiases" [label="[1, 64, 56, 56]", style=solid]; +"14 /layer1/layer1.1/relu_1/Relu" -> "17 /layer2/layer2.0/conv1/Conv/WithoutBiases" [label="[1, 64, 56, 56]", style=solid]; +"14 /layer1/layer1.1/relu_1/Relu" -> "18 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases" [label="[1, 64, 56, 56]", style=solid]; +"15 /layer1/layer1.1/conv1/Conv" -> "19 /layer1/layer1.1/relu/Relu" [label="[1, 64, 56, 56]", style=solid]; +"16 /layer1/layer1.0/conv2/Conv/WithoutBiases" -> "20 /layer1/layer1.0/conv2/Conv" [label="[1, 64, 56, 56]", style=solid]; +"17 /layer2/layer2.0/conv1/Conv/WithoutBiases" -> "21 /layer2/layer2.0/conv1/Conv" [label="[1, 128, 28, 28]", style=solid]; +"18 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases" -> "22 /layer2/layer2.0/downsample/downsample.0/Conv" [label="[1, 128, 28, 28]", style=solid]; +"19 /layer1/layer1.1/relu/Relu" -> "23 /layer1/layer1.1/conv2/Conv/WithoutBiases" [label="[1, 64, 56, 56]", style=solid]; +"20 /layer1/layer1.0/conv2/Conv" -> "7 /layer1/layer1.0/Add" [label="[1, 64, 56, 56]", style=solid]; +"21 /layer2/layer2.0/conv1/Conv" -> "24 /layer2/layer2.0/relu/Relu" [label="[1, 128, 28, 28]", style=solid]; +"22 /layer2/layer2.0/downsample/downsample.0/Conv" -> "25 /layer2/layer2.0/Add" [label="[1, 128, 28, 28]", style=solid]; +"23 /layer1/layer1.1/conv2/Conv/WithoutBiases" -> "26 /layer1/layer1.1/conv2/Conv" [label="[1, 64, 56, 56]", style=solid]; +"24 /layer2/layer2.0/relu/Relu" -> "27 /layer2/layer2.0/conv2/Conv/WithoutBiases" [label="[1, 128, 28, 28]", style=solid]; +"25 /layer2/layer2.0/Add" -> "28 /layer2/layer2.0/relu_1/Relu" [label="[1, 128, 28, 28]", style=solid]; +"26 /layer1/layer1.1/conv2/Conv" -> "11 /layer1/layer1.1/Add" [label="[1, 64, 56, 56]", style=solid]; +"27 /layer2/layer2.0/conv2/Conv/WithoutBiases" -> "29 /layer2/layer2.0/conv2/Conv" [label="[1, 128, 28, 28]", style=solid]; +"28 /layer2/layer2.0/relu_1/Relu" -> "30 /layer2/layer2.1/Add" [label="[1, 128, 28, 28]", style=solid]; +"28 /layer2/layer2.0/relu_1/Relu" -> "31 /layer2/layer2.1/conv1/Conv/WithoutBiases" [label="[1, 128, 28, 28]", style=solid]; +"29 /layer2/layer2.0/conv2/Conv" -> "25 /layer2/layer2.0/Add" [label="[1, 128, 28, 28]", style=solid]; +"30 /layer2/layer2.1/Add" -> "32 /layer2/layer2.1/relu_1/Relu" [label="[1, 128, 28, 28]", style=solid]; +"31 /layer2/layer2.1/conv1/Conv/WithoutBiases" -> "33 /layer2/layer2.1/conv1/Conv" [label="[1, 128, 28, 28]", style=solid]; +"32 /layer2/layer2.1/relu_1/Relu" -> "34 /layer3/layer3.0/conv1/Conv/WithoutBiases" [label="[1, 128, 28, 28]", style=solid]; +"32 /layer2/layer2.1/relu_1/Relu" -> "35 /layer3/layer3.0/downsample/downsample.0/Conv/WithoutBiases" [label="[1, 128, 28, 28]", style=solid]; +"33 /layer2/layer2.1/conv1/Conv" -> "36 /layer2/layer2.1/relu/Relu" [label="[1, 128, 28, 28]", style=solid]; +"34 /layer3/layer3.0/conv1/Conv/WithoutBiases" -> "37 /layer3/layer3.0/conv1/Conv" [label="[1, 256, 14, 14]", style=solid]; +"35 /layer3/layer3.0/downsample/downsample.0/Conv/WithoutBiases" -> "38 /layer3/layer3.0/downsample/downsample.0/Conv" [label="[1, 256, 14, 14]", style=solid]; +"36 /layer2/layer2.1/relu/Relu" -> "39 /layer2/layer2.1/conv2/Conv/WithoutBiases" [label="[1, 128, 28, 28]", style=solid]; +"37 /layer3/layer3.0/conv1/Conv" -> "40 /layer3/layer3.0/relu/Relu" [label="[1, 256, 14, 14]", style=solid]; +"38 /layer3/layer3.0/downsample/downsample.0/Conv" -> "41 /layer3/layer3.0/Add" [label="[1, 256, 14, 14]", style=solid]; +"39 /layer2/layer2.1/conv2/Conv/WithoutBiases" -> "42 /layer2/layer2.1/conv2/Conv" [label="[1, 128, 28, 28]", style=solid]; +"40 /layer3/layer3.0/relu/Relu" -> "43 /layer3/layer3.0/conv2/Conv/WithoutBiases" [label="[1, 256, 14, 14]", style=solid]; +"41 /layer3/layer3.0/Add" -> "44 /layer3/layer3.0/relu_1/Relu" [label="[1, 256, 14, 14]", style=solid]; +"42 /layer2/layer2.1/conv2/Conv" -> "30 /layer2/layer2.1/Add" [label="[1, 128, 28, 28]", style=solid]; +"43 /layer3/layer3.0/conv2/Conv/WithoutBiases" -> "45 /layer3/layer3.0/conv2/Conv" [label="[1, 256, 14, 14]", style=solid]; +"44 /layer3/layer3.0/relu_1/Relu" -> "46 /layer3/layer3.1/Add" [label="[1, 256, 14, 14]", style=solid]; +"44 /layer3/layer3.0/relu_1/Relu" -> "47 /layer3/layer3.1/conv1/Conv/WithoutBiases" [label="[1, 256, 14, 14]", style=solid]; +"45 /layer3/layer3.0/conv2/Conv" -> "41 /layer3/layer3.0/Add" [label="[1, 256, 14, 14]", style=solid]; +"46 /layer3/layer3.1/Add" -> "48 /layer3/layer3.1/relu_1/Relu" [label="[1, 256, 14, 14]", style=solid]; +"47 /layer3/layer3.1/conv1/Conv/WithoutBiases" -> "49 /layer3/layer3.1/conv1/Conv" [label="[1, 256, 14, 14]", style=solid]; +"48 /layer3/layer3.1/relu_1/Relu" -> "50 /layer4/layer4.0/conv1/Conv/WithoutBiases" [label="[1, 256, 14, 14]", style=solid]; +"48 /layer3/layer3.1/relu_1/Relu" -> "51 /layer4/layer4.0/downsample/downsample.0/Conv/WithoutBiases" [label="[1, 256, 14, 14]", style=solid]; +"49 /layer3/layer3.1/conv1/Conv" -> "52 /layer3/layer3.1/relu/Relu" [label="[1, 256, 14, 14]", style=solid]; +"50 /layer4/layer4.0/conv1/Conv/WithoutBiases" -> "53 /layer4/layer4.0/conv1/Conv" [label="[1, 512, 7, 7]", style=solid]; +"51 /layer4/layer4.0/downsample/downsample.0/Conv/WithoutBiases" -> "54 /layer4/layer4.0/downsample/downsample.0/Conv" [label="[1, 512, 7, 7]", style=solid]; +"52 /layer3/layer3.1/relu/Relu" -> "55 /layer3/layer3.1/conv2/Conv/WithoutBiases" [label="[1, 256, 14, 14]", style=solid]; +"53 /layer4/layer4.0/conv1/Conv" -> "56 /layer4/layer4.0/relu/Relu" [label="[1, 512, 7, 7]", style=solid]; +"54 /layer4/layer4.0/downsample/downsample.0/Conv" -> "57 /layer4/layer4.0/Add" [label="[1, 512, 7, 7]", style=solid]; +"55 /layer3/layer3.1/conv2/Conv/WithoutBiases" -> "58 /layer3/layer3.1/conv2/Conv" [label="[1, 256, 14, 14]", style=solid]; +"56 /layer4/layer4.0/relu/Relu" -> "59 /layer4/layer4.0/conv2/Conv/WithoutBiases" [label="[1, 512, 7, 7]", style=solid]; +"57 /layer4/layer4.0/Add" -> "60 /layer4/layer4.0/relu_1/Relu" [label="[1, 512, 7, 7]", style=solid]; +"58 /layer3/layer3.1/conv2/Conv" -> "46 /layer3/layer3.1/Add" [label="[1, 256, 14, 14]", style=solid]; +"59 /layer4/layer4.0/conv2/Conv/WithoutBiases" -> "61 /layer4/layer4.0/conv2/Conv" [label="[1, 512, 7, 7]", style=solid]; +"60 /layer4/layer4.0/relu_1/Relu" -> "62 /layer4/layer4.1/Add" [label="[1, 512, 7, 7]", style=solid]; +"60 /layer4/layer4.0/relu_1/Relu" -> "63 /layer4/layer4.1/conv1/Conv/WithoutBiases" [label="[1, 512, 7, 7]", style=solid]; +"61 /layer4/layer4.0/conv2/Conv" -> "57 /layer4/layer4.0/Add" [label="[1, 512, 7, 7]", style=solid]; +"62 /layer4/layer4.1/Add" -> "64 /layer4/layer4.1/relu_1/Relu" [label="[1, 512, 7, 7]", style=solid]; +"63 /layer4/layer4.1/conv1/Conv/WithoutBiases" -> "65 /layer4/layer4.1/conv1/Conv" [label="[1, 512, 7, 7]", style=solid]; +"64 /layer4/layer4.1/relu_1/Relu" -> "66 /avgpool/GlobalAveragePool" [label="[1, 512, 7, 7]", style=solid]; +"65 /layer4/layer4.1/conv1/Conv" -> "67 /layer4/layer4.1/relu/Relu" [label="[1, 512, 7, 7]", style=solid]; +"66 /avgpool/GlobalAveragePool" -> "68 /Flatten" [label="[1, 512, 1, 1]", style=solid]; +"67 /layer4/layer4.1/relu/Relu" -> "69 /layer4/layer4.1/conv2/Conv/WithoutBiases" [label="[1, 512, 7, 7]", style=solid]; +"68 /Flatten" -> "70 /fc/Gemm/WithoutBiases" [label="[1, 512]", style=solid]; +"69 /layer4/layer4.1/conv2/Conv/WithoutBiases" -> "71 /layer4/layer4.1/conv2/Conv" [label="[1, 512, 7, 7]", style=solid]; +"70 /fc/Gemm/WithoutBiases" -> "72 prob" [label="[1, 1000]", style=solid]; +"71 /layer4/layer4.1/conv2/Conv" -> "62 /layer4/layer4.1/Add" [label="[1, 512, 7, 7]", style=solid]; +"72 prob" -> "73 prob/sink_port_0" [label="[1, 1000]", style=solid]; +"74 Constant_1789" -> "72 prob" [label="[1, 1000]", style=solid]; +"75 fc.weight" -> "70 /fc/Gemm/WithoutBiases" [label="[1000, 512]", style=solid]; +"76 Constant_380" -> "68 /Flatten" [label="[2]", style=dashed]; +"77 Range_376" -> "66 /avgpool/GlobalAveragePool" [label="[2]", style=dashed]; +"78 Reshape_331" -> "54 /layer4/layer4.0/downsample/downsample.0/Conv" [label="[1, 512, 1, 1]", style=solid]; +"79 onnx^^Conv_244" -> "51 /layer4/layer4.0/downsample/downsample.0/Conv/WithoutBiases" [label="[512, 256, 1, 1]", style=solid]; +"80 Reshape_250" -> "38 /layer3/layer3.0/downsample/downsample.0/Conv" [label="[1, 256, 1, 1]", style=solid]; +"81 onnx^^Conv_229" -> "35 /layer3/layer3.0/downsample/downsample.0/Conv/WithoutBiases" [label="[256, 128, 1, 1]", style=solid]; +"82 Reshape_169" -> "22 /layer2/layer2.0/downsample/downsample.0/Conv" [label="[1, 128, 1, 1]", style=solid]; +"83 onnx^^Conv_214" -> "18 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases" [label="[128, 64, 1, 1]", style=solid]; +"84 Reshape_55" -> "4 /conv1/Conv" [label="[1, 64, 1, 1]", style=solid]; +"85 Gather_1788" -> "3 /conv1/Conv/WithoutBiases" [label="[64, 3, 7, 7]", style=solid]; +"86 Gather_1785" -> "2 Divide_401" [label="[1, 3, 1, 1]", style=solid]; +"87 Gather_1782" -> "1 Multiply_1715" [label="[1, 3, 1, 1]", style=solid]; +"88 Reshape_88" -> "20 /layer1/layer1.0/conv2/Conv" [label="[1, 64, 1, 1]", style=solid]; +"89 onnx^^Conv_199" -> "16 /layer1/layer1.0/conv2/Conv/WithoutBiases" [label="[64, 64, 3, 3]", style=solid]; +"90 Reshape_72" -> "10 /layer1/layer1.0/conv1/Conv" [label="[1, 64, 1, 1]", style=solid]; +"91 onnx^^Conv_196" -> "8 /layer1/layer1.0/conv1/Conv/WithoutBiases" [label="[64, 64, 3, 3]", style=solid]; +"92 Reshape_121" -> "26 /layer1/layer1.1/conv2/Conv" [label="[1, 64, 1, 1]", style=solid]; +"93 onnx^^Conv_205" -> "23 /layer1/layer1.1/conv2/Conv/WithoutBiases" [label="[64, 64, 3, 3]", style=solid]; +"94 Reshape_105" -> "15 /layer1/layer1.1/conv1/Conv" [label="[1, 64, 1, 1]", style=solid]; +"95 onnx^^Conv_202" -> "12 /layer1/layer1.1/conv1/Conv/WithoutBiases" [label="[64, 64, 3, 3]", style=solid]; +"96 Reshape_154" -> "29 /layer2/layer2.0/conv2/Conv" [label="[1, 128, 1, 1]", style=solid]; +"97 onnx^^Conv_211" -> "27 /layer2/layer2.0/conv2/Conv/WithoutBiases" [label="[128, 128, 3, 3]", style=solid]; +"98 Reshape_138" -> "21 /layer2/layer2.0/conv1/Conv" [label="[1, 128, 1, 1]", style=solid]; +"99 onnx^^Conv_208" -> "17 /layer2/layer2.0/conv1/Conv/WithoutBiases" [label="[128, 64, 3, 3]", style=solid]; +"100 Reshape_202" -> "42 /layer2/layer2.1/conv2/Conv" [label="[1, 128, 1, 1]", style=solid]; +"101 onnx^^Conv_220" -> "39 /layer2/layer2.1/conv2/Conv/WithoutBiases" [label="[128, 128, 3, 3]", style=solid]; +"102 Reshape_186" -> "33 /layer2/layer2.1/conv1/Conv" [label="[1, 128, 1, 1]", style=solid]; +"103 onnx^^Conv_217" -> "31 /layer2/layer2.1/conv1/Conv/WithoutBiases" [label="[128, 128, 3, 3]", style=solid]; +"104 Reshape_235" -> "45 /layer3/layer3.0/conv2/Conv" [label="[1, 256, 1, 1]", style=solid]; +"105 onnx^^Conv_226" -> "43 /layer3/layer3.0/conv2/Conv/WithoutBiases" [label="[256, 256, 3, 3]", style=solid]; +"106 Reshape_219" -> "37 /layer3/layer3.0/conv1/Conv" [label="[1, 256, 1, 1]", style=solid]; +"107 onnx^^Conv_223" -> "34 /layer3/layer3.0/conv1/Conv/WithoutBiases" [label="[256, 128, 3, 3]", style=solid]; +"108 Reshape_283" -> "58 /layer3/layer3.1/conv2/Conv" [label="[1, 256, 1, 1]", style=solid]; +"109 onnx^^Conv_235" -> "55 /layer3/layer3.1/conv2/Conv/WithoutBiases" [label="[256, 256, 3, 3]", style=solid]; +"110 Reshape_267" -> "49 /layer3/layer3.1/conv1/Conv" [label="[1, 256, 1, 1]", style=solid]; +"111 onnx^^Conv_232" -> "47 /layer3/layer3.1/conv1/Conv/WithoutBiases" [label="[256, 256, 3, 3]", style=solid]; +"112 Reshape_316" -> "61 /layer4/layer4.0/conv2/Conv" [label="[1, 512, 1, 1]", style=solid]; +"113 onnx^^Conv_241" -> "59 /layer4/layer4.0/conv2/Conv/WithoutBiases" [label="[512, 512, 3, 3]", style=solid]; +"114 Reshape_300" -> "53 /layer4/layer4.0/conv1/Conv" [label="[1, 512, 1, 1]", style=solid]; +"115 onnx^^Conv_238" -> "50 /layer4/layer4.0/conv1/Conv/WithoutBiases" [label="[512, 256, 3, 3]", style=solid]; +"116 Reshape_364" -> "71 /layer4/layer4.1/conv2/Conv" [label="[1, 512, 1, 1]", style=solid]; +"117 onnx^^Conv_250" -> "69 /layer4/layer4.1/conv2/Conv/WithoutBiases" [label="[512, 512, 3, 3]", style=solid]; +"118 Reshape_348" -> "65 /layer4/layer4.1/conv1/Conv" [label="[1, 512, 1, 1]", style=solid]; +"119 onnx^^Conv_247" -> "63 /layer4/layer4.1/conv1/Conv/WithoutBiases" [label="[512, 512, 3, 3]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/ssd_mobilenet_v1_coco.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/ssd_mobilenet_v1_coco.dot deleted file mode 120000 index 9e783082884..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/ssd_mobilenet_v1_coco.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/original_nncf_graph/ssd_mobilenet_v1_coco.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/ssd_mobilenet_v1_coco.dot b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/ssd_mobilenet_v1_coco.dot new file mode 100644 index 00000000000..a6a110d28df --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/original_nncf_graph/ssd_mobilenet_v1_coco.dot @@ -0,0 +1,752 @@ +strict digraph { +"0 image_tensor" [id=0, type=Parameter]; +"1 Convert_2817" [id=1, type=Convert]; +"2 ToFloat" [id=2, type=Transpose]; +"3 Preprocessor/mul" [id=3, type=Multiply]; +"4 PriorBoxClustered_0/1_port" [id=4, type=ShapeOf]; +"5 Preprocessor/sub" [id=5, type=Add]; +"6 PriorBoxClustered_0/ss_1_port" [id=6, type=StridedSlice]; +"7 PriorBoxClustered_1/ss_1_port" [id=7, type=StridedSlice]; +"8 PriorBoxClustered_2/ss_1_port" [id=8, type=StridedSlice]; +"9 PriorBoxClustered_3/ss_1_port" [id=9, type=StridedSlice]; +"10 PriorBoxClustered_4/ss_1_port" [id=10, type=StridedSlice]; +"11 PriorBoxClustered_5/ss_1_port" [id=11, type=StridedSlice]; +"12 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/BatchNorm/batchnorm/mul_1" [id=12, type=Convolution]; +"13 PriorBoxClustered_0/naked_not_unsqueezed" [id=13, type=PriorBoxClustered]; +"14 PriorBoxClustered_1/naked_not_unsqueezed" [id=14, type=PriorBoxClustered]; +"15 PriorBoxClustered_2/naked_not_unsqueezed" [id=15, type=PriorBoxClustered]; +"16 PriorBoxClustered_3/naked_not_unsqueezed" [id=16, type=PriorBoxClustered]; +"17 PriorBoxClustered_4/naked_not_unsqueezed" [id=17, type=PriorBoxClustered]; +"18 PriorBoxClustered_5/naked_not_unsqueezed" [id=18, type=PriorBoxClustered]; +"19 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/BatchNorm/batchnorm/add_1" [id=19, type=Add]; +"20 PriorBoxClustered_0" [id=20, type=Unsqueeze]; +"21 PriorBoxClustered_1" [id=21, type=Unsqueeze]; +"22 PriorBoxClustered_2" [id=22, type=Unsqueeze]; +"23 PriorBoxClustered_3" [id=23, type=Unsqueeze]; +"24 PriorBoxClustered_4" [id=24, type=Unsqueeze]; +"25 PriorBoxClustered_5" [id=25, type=Unsqueeze]; +"26 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/Relu6" [id=26, type=Clamp]; +"27 ConcatPriorBoxesClustered" [id=27, type=Concat]; +"28 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_depthwise/depthwise" [id=28, type=GroupConvolution]; +"29 DetectionOutput" [id=29, type=DetectionOutput]; +"30 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_depthwise/BatchNorm/batchnorm/add_1" [id=30, type=Add]; +"31 detection_boxes" [id=31, type=Result]; +"32 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_depthwise/Relu6" [id=32, type=Clamp]; +"33 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_pointwise/BatchNorm/batchnorm/mul_1" [id=33, type=Convolution]; +"34 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_pointwise/BatchNorm/batchnorm/add_1" [id=34, type=Add]; +"35 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_pointwise/Relu6" [id=35, type=Clamp]; +"36 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_depthwise/depthwise" [id=36, type=GroupConvolution]; +"37 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_depthwise/BatchNorm/batchnorm/add_1" [id=37, type=Add]; +"38 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_depthwise/Relu6" [id=38, type=Clamp]; +"39 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_pointwise/BatchNorm/batchnorm/mul_1" [id=39, type=Convolution]; +"40 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_pointwise/BatchNorm/batchnorm/add_1" [id=40, type=Add]; +"41 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_pointwise/Relu6" [id=41, type=Clamp]; +"42 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type=Constant]; +"129 Constant_2333" [id=129, type=Constant]; +"130 Transpose_1328" [id=130, type=Constant]; +"131 Constant_3811" [id=131, type=Constant]; +"132 Multiply_4040" [id=132, type=Constant]; +"133 Transpose_1461" [id=133, type=Constant]; +"134 Constant_3867" [id=134, type=Constant]; +"135 Multiply_4064" [id=135, type=Constant]; +"136 Transpose_1594" [id=136, type=Constant]; +"137 Constant_3923" [id=137, type=Constant]; +"138 Multiply_4088" [id=138, type=Constant]; +"139 Constant_1727" [id=139, type=Constant]; +"140 Constant_669" [id=140, type=Constant]; +"141 up_sampling2d/Const" [id=141, type=Constant]; +"142 up_sampling2d/strided_slice/stack_2" [id=142, type=Constant]; +"143 up_sampling2d/strided_slice/stack_1" [id=143, type=Constant]; +"144 up_sampling2d/strided_slice/stack" [id=144, type=Constant]; +"145 Constant_1725" [id=145, type=Constant]; +"146 Constant_1723" [id=146, type=Constant]; +"147 Transpose_1722" [id=147, type=Constant]; +"148 Constant_3979" [id=148, type=Constant]; +"149 Multiply_4112" [id=149, type=Constant]; +"150 Transpose_1692" [id=150, type=Constant]; +"151 Constant_3965" [id=151, type=Constant]; +"152 Multiply_4106" [id=152, type=Constant]; +"153 Transpose_1662" [id=153, type=Constant]; +"154 Constant_3951" [id=154, type=Constant]; +"155 Multiply_4100" [id=155, type=Constant]; +"156 Constant_1801" [id=156, type=Constant]; +"157 Transpose_1800" [id=157, type=Constant]; +"158 Transpose_748" [id=158, type=Constant]; +"159 Transpose_1794" [id=159, type=Constant]; +"160 Constant_4007" [id=160, type=Constant]; +"161 Multiply_4124" [id=161, type=Constant]; +"0 image_input" -> "1 Divide_2378" [label="[1, 416, 416, 3]", style=solid]; +"1 Divide_2378" -> "2 Multiply_3747" [label="[1, 3, 416, 416]", style=solid]; +"2 Multiply_3747" -> "3 Transpose_1172" [label="[1, 32, 208, 208]", style=solid]; +"3 Transpose_1172" -> "4 Transpose_1178" [label="[1, 32, 208, 208]", style=solid]; +"4 Transpose_1178" -> "5 Multiply_3761" [label="[1, 32, 208, 208]", style=solid]; +"5 Multiply_3761" -> "6 Transpose_1229" [label="[1, 64, 104, 104]", style=solid]; +"6 Transpose_1229" -> "7 Transpose_1235" [label="[1, 64, 104, 104]", style=solid]; +"7 Transpose_1235" -> "8 Multiply_3775" [label="[1, 64, 104, 104]", style=solid]; +"8 Multiply_3775" -> "9 Transpose_1259" [label="[1, 64, 104, 104]", style=solid]; +"9 Transpose_1259" -> "10 Transpose_1265" [label="[1, 64, 104, 104]", style=solid]; +"10 Transpose_1265" -> "11 Transpose_1368" [label="[1, 64, 104, 104]", style=solid]; +"10 Transpose_1265" -> "12 group_route_3/split" [label="[1, 64, 104, 104]", style=solid]; +"11 Transpose_1368" -> "13 MaxPool_307" [label="[1, 128, 104, 104]", style=solid]; +"12 group_route_3/split" -> "14 Multiply_3789" [label="[1, 32, 104, 104]", style=solid]; +"13 MaxPool_307" -> "15 Multiply_3831" [label="[1, 128, 52, 52]", style=solid]; +"14 Multiply_3789" -> "16 Transpose_1294" [label="[1, 32, 104, 104]", style=solid]; +"15 Multiply_3831" -> "17 Transpose_1392" [label="[1, 128, 52, 52]", style=solid]; +"16 Transpose_1294" -> "18 Transpose_1300" [label="[1, 32, 104, 104]", style=solid]; +"17 Transpose_1392" -> "19 Transpose_1398" [label="[1, 128, 52, 52]", style=solid]; +"18 Transpose_1300" -> "20 Multiply_3803" [label="[1, 32, 104, 104]", style=solid]; +"18 Transpose_1300" -> "21 Transpose_1334" [label="[1, 32, 104, 104]", style=solid]; +"19 Transpose_1398" -> "22 Transpose_1501" [label="[1, 128, 52, 52]", style=solid]; +"19 Transpose_1398" -> "23 group_route_11/split" [label="[1, 128, 52, 52]", style=solid]; +"20 Multiply_3803" -> "24 Transpose_1324" [label="[1, 32, 104, 104]", style=solid]; +"21 Transpose_1334" -> "25 Multiply_3817" [label="[1, 64, 104, 104]", style=solid]; +"22 Transpose_1501" -> "26 MaxPool_433" [label="[1, 256, 52, 52]", style=solid]; +"23 group_route_11/split" -> "27 Multiply_3845" [label="[1, 64, 52, 52]", style=solid]; +"24 Transpose_1324" -> "28 Transpose_1330" [label="[1, 32, 104, 104]", style=solid]; +"25 Multiply_3817" -> "29 Transpose_1358" [label="[1, 64, 104, 104]", style=solid]; +"26 MaxPool_433" -> "30 Multiply_3887" [label="[1, 256, 26, 26]", style=solid]; +"27 Multiply_3845" -> "31 Transpose_1427" [label="[1, 64, 52, 52]", style=solid]; +"28 Transpose_1330" -> "21 Transpose_1334" [label="[1, 32, 104, 104]", style=solid]; +"29 Transpose_1358" -> "32 Transpose_1364" [label="[1, 64, 104, 104]", style=solid]; +"30 Multiply_3887" -> "33 Transpose_1525" [label="[1, 256, 26, 26]", style=solid]; +"31 Transpose_1427" -> "34 Transpose_1433" [label="[1, 64, 52, 52]", style=solid]; +"32 Transpose_1364" -> "11 Transpose_1368" [label="[1, 64, 104, 104]", style=solid]; +"33 Transpose_1525" -> "35 Transpose_1531" [label="[1, 256, 26, 26]", style=solid]; +"34 Transpose_1433" -> "36 Multiply_3859" [label="[1, 64, 52, 52]", style=solid]; +"34 Transpose_1433" -> "37 Transpose_1467" [label="[1, 64, 52, 52]", style=solid]; +"35 Transpose_1531" -> "38 Transpose_1634" [label="[1, 256, 26, 26]", style=solid]; +"35 Transpose_1531" -> "39 group_route_19/split" [label="[1, 256, 26, 26]", style=solid]; +"36 Multiply_3859" -> "40 Transpose_1457" [label="[1, 64, 52, 52]", style=solid]; +"37 Transpose_1467" -> "41 Multiply_3873" [label="[1, 128, 52, 52]", style=solid]; +"38 Transpose_1634" -> "42 MaxPool_579" [label="[1, 512, 26, 26]", style=solid]; +"39 group_route_19/split" -> "43 Multiply_3901" [label="[1, 128, 26, 26]", style=solid]; +"40 Transpose_1457" -> "44 Transpose_1463" [label="[1, 64, 52, 52]", style=solid]; +"41 Multiply_3873" -> "45 Transpose_1491" [label="[1, 128, 52, 52]", style=solid]; +"42 MaxPool_579" -> "46 Multiply_3943" [label="[1, 512, 13, 13]", style=solid]; +"43 Multiply_3901" -> "47 Transpose_1560" [label="[1, 128, 26, 26]", style=solid]; +"44 Transpose_1463" -> "37 Transpose_1467" [label="[1, 64, 52, 52]", style=solid]; +"45 Transpose_1491" -> "48 Transpose_1497" [label="[1, 128, 52, 52]", style=solid]; +"46 Multiply_3943" -> "49 Transpose_1658" [label="[1, 512, 13, 13]", style=solid]; +"47 Transpose_1560" -> "50 Transpose_1566" [label="[1, 128, 26, 26]", style=solid]; +"48 Transpose_1497" -> "22 Transpose_1501" [label="[1, 128, 52, 52]", style=solid]; +"49 Transpose_1658" -> "51 Transpose_1664" [label="[1, 512, 13, 13]", style=solid]; +"50 Transpose_1566" -> "52 Multiply_3915" [label="[1, 128, 26, 26]", style=solid]; +"50 Transpose_1566" -> "53 Transpose_1600" [label="[1, 128, 26, 26]", style=solid]; +"51 Transpose_1664" -> "54 Multiply_3957" [label="[1, 512, 13, 13]", style=solid]; +"52 Multiply_3915" -> "55 Transpose_1590" [label="[1, 128, 26, 26]", style=solid]; +"53 Transpose_1600" -> "56 Multiply_3929" [label="[1, 256, 26, 26]", style=solid]; +"54 Multiply_3957" -> "57 Transpose_1688" [label="[1, 256, 13, 13]", style=solid]; +"55 Transpose_1590" -> "58 Transpose_1596" [label="[1, 128, 26, 26]", style=solid]; +"56 Multiply_3929" -> "59 Transpose_1624" [label="[1, 256, 26, 26]", style=solid]; +"57 Transpose_1688" -> "60 Transpose_1694" [label="[1, 256, 13, 13]", style=solid]; +"58 Transpose_1596" -> "53 Transpose_1600" [label="[1, 128, 26, 26]", style=solid]; +"59 Transpose_1624" -> "61 Transpose_1630" [label="[1, 256, 26, 26]", style=solid]; +"60 Transpose_1694" -> "62 Multiply_3971" [label="[1, 256, 13, 13]", style=solid]; +"60 Transpose_1694" -> "63 Multiply_3999" [label="[1, 256, 13, 13]", style=solid]; +"61 Transpose_1630" -> "38 Transpose_1634" [label="[1, 256, 26, 26]", style=solid]; +"61 Transpose_1630" -> "64 Transpose_1730" [label="[1, 256, 26, 26]", style=solid]; +"62 Multiply_3971" -> "65 Transpose_1718" [label="[1, 128, 13, 13]", style=solid]; +"63 Multiply_3999" -> "66 Transpose_1790" [label="[1, 512, 13, 13]", style=solid]; +"64 Transpose_1730" -> "67 Multiply_3985" [label="[1, 384, 26, 26]", style=solid]; +"65 Transpose_1718" -> "68 Transpose_1724" [label="[1, 128, 13, 13]", style=solid]; +"66 Transpose_1790" -> "69 Transpose_1796" [label="[1, 512, 13, 13]", style=solid]; +"67 Multiply_3985" -> "70 Transpose_1754" [label="[1, 256, 26, 26]", style=solid]; +"68 Transpose_1724" -> "71 Gather_1726" [label="[1, 128, 13, 13]", style=solid]; +"68 Transpose_1724" -> "72 leaky_re_lu_17/LeakyRelu" [label="[1, 128, 13, 13]", style=solid]; +"69 Transpose_1796" -> "73 Convolution_749" [label="[1, 512, 13, 13]", style=solid]; +"70 Transpose_1754" -> "74 Transpose_1760" [label="[1, 256, 26, 26]", style=solid]; +"71 Gather_1726" -> "75 up_sampling2d/Shape" [label="[4]", style=dashed]; +"72 leaky_re_lu_17/LeakyRelu" -> "76 up_sampling2d/resize/ResizeNearestNeighbor" [label="[1, 13, 13, 128]", style=solid]; +"73 Convolution_749" -> "77 Transpose_1802" [label="[1, 255, 13, 13]", style=solid]; +"74 Transpose_1760" -> "78 Convolution_706" [label="[1, 256, 26, 26]", style=solid]; +"75 up_sampling2d/Shape" -> "79 up_sampling2d/strided_slice" [label="[4]", style=dashed]; +"76 up_sampling2d/resize/ResizeNearestNeighbor" -> "80 Transpose_1728" [label="[1, 26, 26, 128]", style=solid]; +"77 Transpose_1802" -> "81 conv2d_17/BiasAdd" [label="[1, 255, 13, 13]", style=solid]; +"78 Convolution_706" -> "82 Transpose_1766" [label="[1, 255, 26, 26]", style=solid]; +"79 up_sampling2d/strided_slice" -> "83 up_sampling2d/mul" [label="[2]", style=dashed]; +"80 Transpose_1728" -> "64 Transpose_1730" [label="[1, 128, 26, 26]", style=solid]; +"81 conv2d_17/BiasAdd" -> "84 conv2d_17/BiasAdd^0" [label="[1, 13, 13, 255]", style=solid]; +"82 Transpose_1766" -> "85 conv2d_20/BiasAdd" [label="[1, 255, 26, 26]", style=solid]; +"83 up_sampling2d/mul" -> "76 up_sampling2d/resize/ResizeNearestNeighbor" [label="[2]", style=dashed]; +"85 conv2d_20/BiasAdd" -> "86 conv2d_20/BiasAdd^0" [label="[1, 26, 26, 255]", style=solid]; +"87 Constant_1765" -> "85 conv2d_20/BiasAdd" [label="[4]", style=dashed]; +"88 Transpose_1764" -> "82 Transpose_1766" [label="[1, 255, 1, 1]", style=solid]; +"89 Transpose_705" -> "78 Convolution_706" [label="[255, 256, 1, 1]", style=solid]; +"90 Transpose_1758" -> "74 Transpose_1760" [label="[1, 1, 1, 1]", style=solid]; +"91 Constant_3993" -> "70 Transpose_1754" [label="[1, 256, 1, 1]", style=solid]; +"92 Multiply_4118" -> "67 Multiply_3985" [label="[256, 384, 3, 3]", style=solid]; +"93 Transpose_1628" -> "61 Transpose_1630" [label="[1, 1, 1, 1]", style=solid]; +"94 Constant_3937" -> "59 Transpose_1624" [label="[1, 256, 1, 1]", style=solid]; +"95 Multiply_4094" -> "56 Multiply_3929" [label="[256, 256, 1, 1]", style=solid]; +"96 Transpose_1564" -> "50 Transpose_1566" [label="[1, 1, 1, 1]", style=solid]; +"97 Constant_3909" -> "47 Transpose_1560" [label="[1, 128, 1, 1]", style=solid]; +"98 Multiply_4082" -> "43 Multiply_3901" [label="[128, 128, 3, 3]", style=solid]; +"99 Constant_1532" -> "39 group_route_19/split" [label="[]", style=dashed]; +"100 Transpose_1529" -> "35 Transpose_1531" [label="[1, 1, 1, 1]", style=solid]; +"101 Constant_3895" -> "33 Transpose_1525" [label="[1, 256, 1, 1]", style=solid]; +"102 Multiply_4076" -> "30 Multiply_3887" [label="[256, 256, 3, 3]", style=solid]; +"103 Transpose_1495" -> "48 Transpose_1497" [label="[1, 1, 1, 1]", style=solid]; +"104 Constant_3881" -> "45 Transpose_1491" [label="[1, 128, 1, 1]", style=solid]; +"105 Multiply_4070" -> "41 Multiply_3873" [label="[128, 128, 1, 1]", style=solid]; +"106 Transpose_1431" -> "34 Transpose_1433" [label="[1, 1, 1, 1]", style=solid]; +"107 Constant_3853" -> "31 Transpose_1427" [label="[1, 64, 1, 1]", style=solid]; +"108 Multiply_4058" -> "27 Multiply_3845" [label="[64, 64, 3, 3]", style=solid]; +"109 Constant_1399" -> "23 group_route_11/split" [label="[]", style=dashed]; +"110 Transpose_1396" -> "19 Transpose_1398" [label="[1, 1, 1, 1]", style=solid]; +"111 Constant_3839" -> "17 Transpose_1392" [label="[1, 128, 1, 1]", style=solid]; +"112 Multiply_4052" -> "15 Multiply_3831" [label="[128, 128, 3, 3]", style=solid]; +"113 Transpose_1362" -> "32 Transpose_1364" [label="[1, 1, 1, 1]", style=solid]; +"114 Constant_3825" -> "29 Transpose_1358" [label="[1, 64, 1, 1]", style=solid]; +"115 Multiply_4046" -> "25 Multiply_3817" [label="[64, 64, 1, 1]", style=solid]; +"116 Transpose_1298" -> "18 Transpose_1300" [label="[1, 1, 1, 1]", style=solid]; +"117 Constant_3797" -> "16 Transpose_1294" [label="[1, 32, 1, 1]", style=solid]; +"118 Multiply_4034" -> "14 Multiply_3789" [label="[32, 32, 3, 3]", style=solid]; +"119 Constant_1266" -> "12 group_route_3/split" [label="[]", style=dashed]; +"120 Transpose_1263" -> "10 Transpose_1265" [label="[1, 1, 1, 1]", style=solid]; +"121 Constant_3783" -> "9 Transpose_1259" [label="[1, 64, 1, 1]", style=solid]; +"122 Multiply_4028" -> "8 Multiply_3775" [label="[64, 64, 3, 3]", style=solid]; +"123 Transpose_1233" -> "7 Transpose_1235" [label="[1, 1, 1, 1]", style=solid]; +"124 Constant_3769" -> "6 Transpose_1229" [label="[1, 64, 1, 1]", style=solid]; +"125 Multiply_4022" -> "5 Multiply_3761" [label="[64, 32, 3, 3]", style=solid]; +"126 Transpose_1176" -> "4 Transpose_1178" [label="[1, 1, 1, 1]", style=solid]; +"127 Constant_3755" -> "3 Transpose_1172" [label="[1, 32, 1, 1]", style=solid]; +"128 Gather_4293" -> "2 Multiply_3747" [label="[32, 3, 3, 3]", style=solid]; +"129 Constant_2333" -> "1 Divide_2378" [label="[4]", style=dashed]; +"130 Transpose_1328" -> "28 Transpose_1330" [label="[1, 1, 1, 1]", style=solid]; +"131 Constant_3811" -> "24 Transpose_1324" [label="[1, 32, 1, 1]", style=solid]; +"132 Multiply_4040" -> "20 Multiply_3803" [label="[32, 32, 3, 3]", style=solid]; +"133 Transpose_1461" -> "44 Transpose_1463" [label="[1, 1, 1, 1]", style=solid]; +"134 Constant_3867" -> "40 Transpose_1457" [label="[1, 64, 1, 1]", style=solid]; +"135 Multiply_4064" -> "36 Multiply_3859" [label="[64, 64, 3, 3]", style=solid]; +"136 Transpose_1594" -> "58 Transpose_1596" [label="[1, 1, 1, 1]", style=solid]; +"137 Constant_3923" -> "55 Transpose_1590" [label="[1, 128, 1, 1]", style=solid]; +"138 Multiply_4088" -> "52 Multiply_3915" [label="[128, 128, 3, 3]", style=solid]; +"139 Constant_1727" -> "80 Transpose_1728" [label="[4]", style=dashed]; +"140 Constant_669" -> "76 up_sampling2d/resize/ResizeNearestNeighbor" [label="[2]", style=dashed]; +"141 up_sampling2d/Const" -> "83 up_sampling2d/mul" [label="[2]", style=dashed]; +"142 up_sampling2d/strided_slice/stack_2" -> "79 up_sampling2d/strided_slice" [label="[1]", style=dashed]; +"143 up_sampling2d/strided_slice/stack_1" -> "79 up_sampling2d/strided_slice" [label="[1]", style=dashed]; +"144 up_sampling2d/strided_slice/stack" -> "79 up_sampling2d/strided_slice" [label="[1]", style=dashed]; +"145 Constant_1725" -> "75 up_sampling2d/Shape" [label="[]", style=dashed]; +"146 Constant_1723" -> "72 leaky_re_lu_17/LeakyRelu" [label="[4]", style=dashed]; +"146 Constant_1723" -> "75 up_sampling2d/Shape" [label="[4]", style=dashed]; +"147 Transpose_1722" -> "68 Transpose_1724" [label="[1, 1, 1, 1]", style=solid]; +"148 Constant_3979" -> "65 Transpose_1718" [label="[1, 128, 1, 1]", style=solid]; +"149 Multiply_4112" -> "62 Multiply_3971" [label="[128, 256, 1, 1]", style=solid]; +"150 Transpose_1692" -> "60 Transpose_1694" [label="[1, 1, 1, 1]", style=solid]; +"151 Constant_3965" -> "57 Transpose_1688" [label="[1, 256, 1, 1]", style=solid]; +"152 Multiply_4106" -> "54 Multiply_3957" [label="[256, 512, 1, 1]", style=solid]; +"153 Transpose_1662" -> "51 Transpose_1664" [label="[1, 1, 1, 1]", style=solid]; +"154 Constant_3951" -> "49 Transpose_1658" [label="[1, 512, 1, 1]", style=solid]; +"155 Multiply_4100" -> "46 Multiply_3943" [label="[512, 512, 3, 3]", style=solid]; +"156 Constant_1801" -> "81 conv2d_17/BiasAdd" [label="[4]", style=dashed]; +"157 Transpose_1800" -> "77 Transpose_1802" [label="[1, 255, 1, 1]", style=solid]; +"158 Transpose_748" -> "73 Convolution_749" [label="[255, 512, 1, 1]", style=solid]; +"159 Transpose_1794" -> "69 Transpose_1796" [label="[1, 1, 1, 1]", style=solid]; +"160 Constant_4007" -> "66 Transpose_1790" [label="[1, 512, 1, 1]", style=solid]; +"161 Multiply_4124" -> "63 Multiply_3999" [label="[512, 256, 3, 3]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/ComparisonBinaryModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/ComparisonBinaryModel.dot deleted file mode 120000 index 6a4a745e5bc..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/quantized/ComparisonBinaryModel.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/quantized/ComparisonBinaryModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/ComparisonBinaryModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/ComparisonBinaryModel.dot new file mode 100644 index 00000000000..bc031f16aaa --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/quantized/ComparisonBinaryModel.dot @@ -0,0 +1,29 @@ +strict digraph { +"0 Input" [id=0, type=Parameter]; +"1 Input/fq_output_0" [id=1, type=FakeQuantize]; +"2 Add" [id=2, type=Add]; +"3 Gather_6" [id=3, type=Gather]; +"4 GreaterEqual_3" [id=4, type=GreaterEqual]; +"5 Result_Add" [id=5, type=Result]; +"6 Convert_4" [id=6, type=Convert]; +"7 Constant_5" [id=7, type=Constant]; +"8 Constant_2" [id=8, type=Constant]; +"9 Constant_2140" [id=9, type=Constant]; +"10 Constant_2139" [id=10, type=Constant]; +"11 Constant_2138" [id=11, type=Constant]; +"12 Constant_2137" [id=12, type=Constant]; +"0 Input" -> "1 Input/fq_output_0" [label="[1, 3, 4, 2]", style=solid]; +"1 Input/fq_output_0" -> "2 Add" [label="[1, 3, 4, 2]", style=solid]; +"1 Input/fq_output_0" -> "3 Gather_6" [label="[1, 3, 4, 2]", style=solid]; +"1 Input/fq_output_0" -> "4 GreaterEqual_3" [label="[1, 3, 4, 2]", style=solid]; +"2 Add" -> "5 Result_Add" [label="[1, 3, 4, 2, 3, 4, 2]", style=solid]; +"3 Gather_6" -> "2 Add" [label="[1, 3, 4, 2, 3, 4, 2]", style=solid]; +"4 GreaterEqual_3" -> "6 Convert_4" [label="[1, 3, 4, 2]", style=dashed]; +"6 Convert_4" -> "3 Gather_6" [label="[1, 3, 4, 2]", style=dashed]; +"7 Constant_5" -> "3 Gather_6" [label="[]", style=dashed]; +"8 Constant_2" -> "4 GreaterEqual_3" [label="[1, 3, 4, 2]", style=solid]; +"9 Constant_2140" -> "1 Input/fq_output_0" [label="[]", style=solid]; +"10 Constant_2139" -> "1 Input/fq_output_0" [label="[]", style=solid]; +"11 Constant_2138" -> "1 Input/fq_output_0" [label="[]", style=solid]; +"12 Constant_2137" -> "1 Input/fq_output_0" [label="[]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/ConvModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/ConvModel.dot deleted file mode 120000 index 1d4ce1e8194..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/quantized/ConvModel.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/quantized/ConvModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/ConvModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/ConvModel.dot new file mode 100644 index 00000000000..125ee7ec102 --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/quantized/ConvModel.dot @@ -0,0 +1,55 @@ +strict digraph { +"0 Input_1" [id=0, type=Parameter]; +"1 Input_2" [id=1, type=Parameter]; +"2 Sub" [id=2, type=Subtract]; +"3 Add" [id=3, type=Add]; +"4 Sub/fq_output_0" [id=4, type=FakeQuantize]; +"5 Mul" [id=5, type=Multiply]; +"6 Conv" [id=6, type=Convolution]; +"7 Transpose" [id=7, type=Transpose]; +"8 Conv_Add" [id=8, type=Add]; +"9 Concat_32" [id=9, type=Concat]; +"10 Relu" [id=10, type=Relu]; +"11 Result" [id=11, type=Result]; +"12 Constant_30" [id=12, type=Constant]; +"13 Constant_28" [id=13, type=Constant]; +"14 Constant_26" [id=14, type=Constant]; +"15 Bias" [id=15, type=Constant]; +"16 Conv/fq_weights_1" [id=16, type=FakeQuantize]; +"17 Constant_2268" [id=17, type=Constant]; +"18 Constant_2267" [id=18, type=Constant]; +"19 Constant_2266" [id=19, type=Constant]; +"20 Constant_2265" [id=20, type=Constant]; +"21 Constant_20" [id=21, type=Constant]; +"22 Constant_2263" [id=22, type=Constant]; +"23 Constant_2262" [id=23, type=Constant]; +"24 Constant_2261" [id=24, type=Constant]; +"25 Constant_2260" [id=25, type=Constant]; +"26 Constant_18" [id=26, type=Constant]; +"0 Input_1" -> "2 Sub" [label="[1, 3, 4, 2]", style=solid]; +"1 Input_2" -> "3 Add" [label="[1, 3, 2, 4]", style=solid]; +"2 Sub" -> "4 Sub/fq_output_0" [label="[1, 3, 4, 2]", style=solid]; +"3 Add" -> "5 Mul" [label="[1, 3, 2, 4]", style=solid]; +"4 Sub/fq_output_0" -> "6 Conv" [label="[1, 3, 4, 2]", style=solid]; +"5 Mul" -> "7 Transpose" [label="[1, 3, 2, 4]", style=solid]; +"6 Conv" -> "8 Conv_Add" [label="[1, 3, 4, 2]", style=solid]; +"7 Transpose" -> "9 Concat_32" [label="[1, 3, 4, 2]", style=solid]; +"8 Conv_Add" -> "10 Relu" [label="[1, 3, 4, 2]", style=solid]; +"9 Concat_32" -> "11 Result" [label="[2, 3, 4, 2]", style=solid]; +"10 Relu" -> "9 Concat_32" [label="[1, 3, 4, 2]", style=solid]; +"12 Constant_30" -> "7 Transpose" [label="[4]", style=dashed]; +"13 Constant_28" -> "5 Mul" [label="[1, 3, 1, 1]", style=solid]; +"14 Constant_26" -> "3 Add" [label="[1, 3, 1, 1]", style=solid]; +"15 Bias" -> "8 Conv_Add" [label="[1, 3, 1, 1]", style=solid]; +"16 Conv/fq_weights_1" -> "6 Conv" [label="[3, 3, 1, 1]", style=solid]; +"17 Constant_2268" -> "16 Conv/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; +"18 Constant_2267" -> "16 Conv/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; +"19 Constant_2266" -> "16 Conv/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; +"20 Constant_2265" -> "16 Conv/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; +"21 Constant_20" -> "16 Conv/fq_weights_1" [label="[3, 3, 1, 1]", style=solid]; +"22 Constant_2263" -> "4 Sub/fq_output_0" [label="[]", style=solid]; +"23 Constant_2262" -> "4 Sub/fq_output_0" [label="[]", style=solid]; +"24 Constant_2261" -> "4 Sub/fq_output_0" [label="[]", style=solid]; +"25 Constant_2260" -> "4 Sub/fq_output_0" [label="[]", style=solid]; +"26 Constant_18" -> "2 Sub" [label="[1, 3, 1, 1]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/ConvNotBiasModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/ConvNotBiasModel.dot deleted file mode 120000 index e13ee5d655f..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/quantized/ConvNotBiasModel.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/quantized/ConvNotBiasModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/ConvNotBiasModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/ConvNotBiasModel.dot new file mode 100644 index 00000000000..9d7e0778387 --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/quantized/ConvNotBiasModel.dot @@ -0,0 +1,39 @@ +strict digraph { +"0 Input_1" [id=0, type=Parameter]; +"1 Sub" [id=1, type=Subtract]; +"2 Sub/fq_output_0" [id=2, type=FakeQuantize]; +"3 Conv" [id=3, type=Convolution]; +"4 Conv_Add" [id=4, type=Add]; +"5 Relu" [id=5, type=Relu]; +"6 Result" [id=6, type=Result]; +"7 NotBias" [id=7, type=Constant]; +"8 Conv/fq_weights_1" [id=8, type=FakeQuantize]; +"9 Constant_14951" [id=9, type=Constant]; +"10 Constant_14950" [id=10, type=Constant]; +"11 Constant_14949" [id=11, type=Constant]; +"12 Constant_14948" [id=12, type=Constant]; +"13 Constant_12903" [id=13, type=Constant]; +"14 Constant_14946" [id=14, type=Constant]; +"15 Constant_14945" [id=15, type=Constant]; +"16 Constant_14944" [id=16, type=Constant]; +"17 Constant_14943" [id=17, type=Constant]; +"18 Constant_12901" [id=18, type=Constant]; +"0 Input_1" -> "1 Sub" [label="[1, 3, 4, 2]", style=solid]; +"1 Sub" -> "2 Sub/fq_output_0" [label="[1, 3, 4, 2]", style=solid]; +"2 Sub/fq_output_0" -> "3 Conv" [label="[1, 3, 4, 2]", style=solid]; +"3 Conv" -> "4 Conv_Add" [label="[1, 3, 4, 2]", style=solid]; +"4 Conv_Add" -> "5 Relu" [label="[1, 3, 4, 2]", style=solid]; +"5 Relu" -> "6 Result" [label="[1, 3, 4, 2]", style=solid]; +"7 NotBias" -> "4 Conv_Add" [label="[1, 3, 4, 2]", style=solid]; +"8 Conv/fq_weights_1" -> "3 Conv" [label="[3, 3, 1, 1]", style=solid]; +"9 Constant_14951" -> "8 Conv/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; +"10 Constant_14950" -> "8 Conv/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; +"11 Constant_14949" -> "8 Conv/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; +"12 Constant_14948" -> "8 Conv/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; +"13 Constant_12903" -> "8 Conv/fq_weights_1" [label="[3, 3, 1, 1]", style=solid]; +"14 Constant_14946" -> "2 Sub/fq_output_0" [label="[]", style=solid]; +"15 Constant_14945" -> "2 Sub/fq_output_0" [label="[]", style=solid]; +"16 Constant_14944" -> "2 Sub/fq_output_0" [label="[]", style=solid]; +"17 Constant_14943" -> "2 Sub/fq_output_0" [label="[]", style=solid]; +"18 Constant_12901" -> "1 Sub" [label="[1, 3, 1, 1]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/DepthwiseConv3DModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/DepthwiseConv3DModel.dot deleted file mode 120000 index 2ec9211ee45..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/quantized/DepthwiseConv3DModel.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/quantized/DepthwiseConv3DModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/DepthwiseConv3DModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/DepthwiseConv3DModel.dot new file mode 100644 index 00000000000..e485b5780d1 --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/quantized/DepthwiseConv3DModel.dot @@ -0,0 +1,33 @@ +strict digraph { +"0 Input_1" [id=0, type=Parameter]; +"1 Input_1/fq_output_0" [id=1, type=FakeQuantize]; +"2 Conv3D" [id=2, type=GroupConvolution]; +"3 Add" [id=3, type=Add]; +"4 Result" [id=4, type=Result]; +"5 Constant_4" [id=5, type=Constant]; +"6 Conv3D/fq_weights_1" [id=6, type=FakeQuantize]; +"7 Constant_2046" [id=7, type=Constant]; +"8 Constant_2045" [id=8, type=Constant]; +"9 Constant_2044" [id=9, type=Constant]; +"10 Constant_2043" [id=10, type=Constant]; +"11 Constant_2" [id=11, type=Constant]; +"12 Constant_2041" [id=12, type=Constant]; +"13 Constant_2040" [id=13, type=Constant]; +"14 Constant_2039" [id=14, type=Constant]; +"15 Constant_2038" [id=15, type=Constant]; +"0 Input_1" -> "1 Input_1/fq_output_0" [label="[1, 3, 7]", style=solid]; +"1 Input_1/fq_output_0" -> "2 Conv3D" [label="[1, 3, 7]", style=solid]; +"2 Conv3D" -> "3 Add" [label="[1, 3, 3]", style=solid]; +"3 Add" -> "4 Result" [label="[1, 3, 3]", style=solid]; +"5 Constant_4" -> "3 Add" [label="[1, 3, 1]", style=solid]; +"6 Conv3D/fq_weights_1" -> "2 Conv3D" [label="[3, 1, 1, 5]", style=solid]; +"7 Constant_2046" -> "6 Conv3D/fq_weights_1" [label="[1, 1, 1, 1]", style=solid]; +"8 Constant_2045" -> "6 Conv3D/fq_weights_1" [label="[1, 1, 1, 1]", style=solid]; +"9 Constant_2044" -> "6 Conv3D/fq_weights_1" [label="[1, 1, 1, 1]", style=solid]; +"10 Constant_2043" -> "6 Conv3D/fq_weights_1" [label="[1, 1, 1, 1]", style=solid]; +"11 Constant_2" -> "6 Conv3D/fq_weights_1" [label="[3, 1, 1, 5]", style=solid]; +"12 Constant_2041" -> "1 Input_1/fq_output_0" [label="[1, 3, 1]", style=solid]; +"13 Constant_2040" -> "1 Input_1/fq_output_0" [label="[1, 3, 1]", style=solid]; +"14 Constant_2039" -> "1 Input_1/fq_output_0" [label="[1, 3, 1]", style=solid]; +"15 Constant_2038" -> "1 Input_1/fq_output_0" [label="[1, 3, 1]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/DepthwiseConv4DModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/DepthwiseConv4DModel.dot deleted file mode 120000 index 02481c7fa07..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/quantized/DepthwiseConv4DModel.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/quantized/DepthwiseConv4DModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/DepthwiseConv4DModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/DepthwiseConv4DModel.dot new file mode 100644 index 00000000000..4f85164604c --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/quantized/DepthwiseConv4DModel.dot @@ -0,0 +1,35 @@ +strict digraph { +"0 Input_1" [id=0, type=Parameter]; +"1 Input_1/fq_output_0" [id=1, type=FakeQuantize]; +"2 Conv4D" [id=2, type=GroupConvolution]; +"3 Add" [id=3, type=Add]; +"4 Relu" [id=4, type=Relu]; +"5 Result" [id=5, type=Result]; +"6 Constant_4" [id=6, type=Constant]; +"7 Conv4D/fq_weights_1" [id=7, type=FakeQuantize]; +"8 Constant_2051" [id=8, type=Constant]; +"9 Constant_2050" [id=9, type=Constant]; +"10 Constant_2049" [id=10, type=Constant]; +"11 Constant_2048" [id=11, type=Constant]; +"12 Constant_2" [id=12, type=Constant]; +"13 Constant_2046" [id=13, type=Constant]; +"14 Constant_2045" [id=14, type=Constant]; +"15 Constant_2044" [id=15, type=Constant]; +"16 Constant_2043" [id=16, type=Constant]; +"0 Input_1" -> "1 Input_1/fq_output_0" [label="[1, 3, 5, 5]", style=solid]; +"1 Input_1/fq_output_0" -> "2 Conv4D" [label="[1, 3, 5, 5]", style=solid]; +"2 Conv4D" -> "3 Add" [label="[1, 3, 3, 3]", style=solid]; +"3 Add" -> "4 Relu" [label="[1, 3, 3, 3]", style=solid]; +"4 Relu" -> "5 Result" [label="[1, 3, 3, 3]", style=solid]; +"6 Constant_4" -> "3 Add" [label="[1, 3, 1, 1]", style=solid]; +"7 Conv4D/fq_weights_1" -> "2 Conv4D" [label="[3, 1, 1, 3, 3]", style=solid]; +"8 Constant_2051" -> "7 Conv4D/fq_weights_1" [label="[1, 1, 1, 1, 1]", style=solid]; +"9 Constant_2050" -> "7 Conv4D/fq_weights_1" [label="[1, 1, 1, 1, 1]", style=solid]; +"10 Constant_2049" -> "7 Conv4D/fq_weights_1" [label="[1, 1, 1, 1, 1]", style=solid]; +"11 Constant_2048" -> "7 Conv4D/fq_weights_1" [label="[1, 1, 1, 1, 1]", style=solid]; +"12 Constant_2" -> "7 Conv4D/fq_weights_1" [label="[3, 1, 1, 3, 3]", style=solid]; +"13 Constant_2046" -> "1 Input_1/fq_output_0" [label="[1, 3, 1, 1]", style=solid]; +"14 Constant_2045" -> "1 Input_1/fq_output_0" [label="[1, 3, 1, 1]", style=solid]; +"15 Constant_2044" -> "1 Input_1/fq_output_0" [label="[1, 3, 1, 1]", style=solid]; +"16 Constant_2043" -> "1 Input_1/fq_output_0" [label="[1, 3, 1, 1]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/DepthwiseConv5DModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/DepthwiseConv5DModel.dot deleted file mode 120000 index 741f911fd95..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/quantized/DepthwiseConv5DModel.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/quantized/DepthwiseConv5DModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/DepthwiseConv5DModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/DepthwiseConv5DModel.dot new file mode 100644 index 00000000000..a5bc30bfccb --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/quantized/DepthwiseConv5DModel.dot @@ -0,0 +1,33 @@ +strict digraph { +"0 Input_1" [id=0, type=Parameter]; +"1 Input_1/fq_output_0" [id=1, type=FakeQuantize]; +"2 Conv5D" [id=2, type=GroupConvolution]; +"3 Add" [id=3, type=Add]; +"4 Result" [id=4, type=Result]; +"5 Constant_4" [id=5, type=Constant]; +"6 Conv5D/fq_weights_1" [id=6, type=FakeQuantize]; +"7 Constant_2046" [id=7, type=Constant]; +"8 Constant_2045" [id=8, type=Constant]; +"9 Constant_2044" [id=9, type=Constant]; +"10 Constant_2043" [id=10, type=Constant]; +"11 Constant_2" [id=11, type=Constant]; +"12 Constant_2041" [id=12, type=Constant]; +"13 Constant_2040" [id=13, type=Constant]; +"14 Constant_2039" [id=14, type=Constant]; +"15 Constant_2038" [id=15, type=Constant]; +"0 Input_1" -> "1 Input_1/fq_output_0" [label="[1, 3, 7, 6, 5]", style=solid]; +"1 Input_1/fq_output_0" -> "2 Conv5D" [label="[1, 3, 7, 6, 5]", style=solid]; +"2 Conv5D" -> "3 Add" [label="[1, 3, 3, 3, 3]", style=solid]; +"3 Add" -> "4 Result" [label="[1, 3, 3, 3, 3]", style=solid]; +"5 Constant_4" -> "3 Add" [label="[1, 3, 1, 1, 1]", style=solid]; +"6 Conv5D/fq_weights_1" -> "2 Conv5D" [label="[3, 1, 1, 5, 4, 3]", style=solid]; +"7 Constant_2046" -> "6 Conv5D/fq_weights_1" [label="[1, 1, 1, 1, 1, 1]", style=solid]; +"8 Constant_2045" -> "6 Conv5D/fq_weights_1" [label="[1, 1, 1, 1, 1, 1]", style=solid]; +"9 Constant_2044" -> "6 Conv5D/fq_weights_1" [label="[1, 1, 1, 1, 1, 1]", style=solid]; +"10 Constant_2043" -> "6 Conv5D/fq_weights_1" [label="[1, 1, 1, 1, 1, 1]", style=solid]; +"11 Constant_2" -> "6 Conv5D/fq_weights_1" [label="[3, 1, 1, 5, 4, 3]", style=solid]; +"12 Constant_2041" -> "1 Input_1/fq_output_0" [label="[1, 3, 1, 1, 1]", style=solid]; +"13 Constant_2040" -> "1 Input_1/fq_output_0" [label="[1, 3, 1, 1, 1]", style=solid]; +"14 Constant_2039" -> "1 Input_1/fq_output_0" [label="[1, 3, 1, 1, 1]", style=solid]; +"15 Constant_2038" -> "1 Input_1/fq_output_0" [label="[1, 3, 1, 1, 1]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/DepthwiseConvModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/DepthwiseConvModel.dot deleted file mode 120000 index 2c13ad46bec..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/quantized/DepthwiseConvModel.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/quantized/DepthwiseConvModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/DepthwiseConvModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/DepthwiseConvModel.dot new file mode 100644 index 00000000000..ad694143041 --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/quantized/DepthwiseConvModel.dot @@ -0,0 +1,35 @@ +strict digraph { +"0 Input_1" [id=0, type=Parameter]; +"1 Input_1/fq_output_0" [id=1, type=FakeQuantize]; +"2 Conv" [id=2, type=GroupConvolution]; +"3 Add" [id=3, type=Add]; +"4 Relu" [id=4, type=Relu]; +"5 Result" [id=5, type=Result]; +"6 Constant_11" [id=6, type=Constant]; +"7 Conv/fq_weights_1" [id=7, type=FakeQuantize]; +"8 Constant_2060" [id=8, type=Constant]; +"9 Constant_2059" [id=9, type=Constant]; +"10 Constant_2058" [id=10, type=Constant]; +"11 Constant_2057" [id=11, type=Constant]; +"12 Constant_9" [id=12, type=Constant]; +"13 Constant_2055" [id=13, type=Constant]; +"14 Constant_2054" [id=14, type=Constant]; +"15 Constant_2053" [id=15, type=Constant]; +"16 Constant_2052" [id=16, type=Constant]; +"0 Input_1" -> "1 Input_1/fq_output_0" [label="[1, 3, 5, 5]", style=solid]; +"1 Input_1/fq_output_0" -> "2 Conv" [label="[1, 3, 5, 5]", style=solid]; +"2 Conv" -> "3 Add" [label="[1, 3, 3, 3]", style=solid]; +"3 Add" -> "4 Relu" [label="[1, 3, 3, 3]", style=solid]; +"4 Relu" -> "5 Result" [label="[1, 3, 3, 3]", style=solid]; +"6 Constant_11" -> "3 Add" [label="[1, 3, 1, 1]", style=solid]; +"7 Conv/fq_weights_1" -> "2 Conv" [label="[3, 1, 1, 3, 3]", style=solid]; +"8 Constant_2060" -> "7 Conv/fq_weights_1" [label="[1, 1, 1, 1, 1]", style=solid]; +"9 Constant_2059" -> "7 Conv/fq_weights_1" [label="[1, 1, 1, 1, 1]", style=solid]; +"10 Constant_2058" -> "7 Conv/fq_weights_1" [label="[1, 1, 1, 1, 1]", style=solid]; +"11 Constant_2057" -> "7 Conv/fq_weights_1" [label="[1, 1, 1, 1, 1]", style=solid]; +"12 Constant_9" -> "7 Conv/fq_weights_1" [label="[3, 1, 1, 3, 3]", style=solid]; +"13 Constant_2055" -> "1 Input_1/fq_output_0" [label="[1, 3, 1, 1]", style=solid]; +"14 Constant_2054" -> "1 Input_1/fq_output_0" [label="[1, 3, 1, 1]", style=solid]; +"15 Constant_2053" -> "1 Input_1/fq_output_0" [label="[1, 3, 1, 1]", style=solid]; +"16 Constant_2052" -> "1 Input_1/fq_output_0" [label="[1, 3, 1, 1]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/DynamicModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/DynamicModel.dot deleted file mode 120000 index 0b078930d4f..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/quantized/DynamicModel.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/quantized/DynamicModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/DynamicModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/DynamicModel.dot new file mode 100644 index 00000000000..1633fc0a5b4 --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/quantized/DynamicModel.dot @@ -0,0 +1,55 @@ +strict digraph { +"0 Input_1" [id=0, type=Parameter]; +"1 Input_2" [id=1, type=Parameter]; +"2 Sub" [id=2, type=Subtract]; +"3 Add" [id=3, type=Add]; +"4 Sub/fq_output_0" [id=4, type=FakeQuantize]; +"5 Mul" [id=5, type=Multiply]; +"6 Conv" [id=6, type=Convolution]; +"7 Transpose" [id=7, type=Transpose]; +"8 Conv_Add" [id=8, type=Add]; +"9 Concat_8690" [id=9, type=Concat]; +"10 Relu" [id=10, type=Relu]; +"11 Result" [id=11, type=Result]; +"12 Constant_8688" [id=12, type=Constant]; +"13 Constant_8686" [id=13, type=Constant]; +"14 Constant_8684" [id=14, type=Constant]; +"15 Bias" [id=15, type=Constant]; +"16 Conv/fq_weights_1" [id=16, type=FakeQuantize]; +"17 Constant_10833" [id=17, type=Constant]; +"18 Constant_10832" [id=18, type=Constant]; +"19 Constant_10831" [id=19, type=Constant]; +"20 Constant_10830" [id=20, type=Constant]; +"21 Constant_8678" [id=21, type=Constant]; +"22 Constant_10828" [id=22, type=Constant]; +"23 Constant_10827" [id=23, type=Constant]; +"24 Constant_10826" [id=24, type=Constant]; +"25 Constant_10825" [id=25, type=Constant]; +"26 Constant_8676" [id=26, type=Constant]; +"0 Input_1" -> "2 Sub" [label="[99, 3, 4, 2]", style=solid]; +"1 Input_2" -> "3 Add" [label="[99, 3, 2, 4]", style=solid]; +"2 Sub" -> "4 Sub/fq_output_0" [label="[99, 3, 4, 2]", style=solid]; +"3 Add" -> "5 Mul" [label="[99, 3, 2, 4]", style=solid]; +"4 Sub/fq_output_0" -> "6 Conv" [label="[99, 3, 4, 2]", style=solid]; +"5 Mul" -> "7 Transpose" [label="[99, 3, 2, 4]", style=solid]; +"6 Conv" -> "8 Conv_Add" [label="[99, 3, 4, 2]", style=solid]; +"7 Transpose" -> "9 Concat_8690" [label="[99, 3, 4, 2]", style=solid]; +"8 Conv_Add" -> "10 Relu" [label="[99, 3, 4, 2]", style=solid]; +"9 Concat_8690" -> "11 Result" [label="[198, 3, 4, 2]", style=solid]; +"10 Relu" -> "9 Concat_8690" [label="[99, 3, 4, 2]", style=solid]; +"12 Constant_8688" -> "7 Transpose" [label="[4]", style=dashed]; +"13 Constant_8686" -> "5 Mul" [label="[1, 3, 1, 1]", style=solid]; +"14 Constant_8684" -> "3 Add" [label="[1, 3, 1, 1]", style=solid]; +"15 Bias" -> "8 Conv_Add" [label="[1, 3, 1, 1]", style=solid]; +"16 Conv/fq_weights_1" -> "6 Conv" [label="[3, 3, 1, 1]", style=solid]; +"17 Constant_10833" -> "16 Conv/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; +"18 Constant_10832" -> "16 Conv/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; +"19 Constant_10831" -> "16 Conv/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; +"20 Constant_10830" -> "16 Conv/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; +"21 Constant_8678" -> "16 Conv/fq_weights_1" [label="[3, 3, 1, 1]", style=solid]; +"22 Constant_10828" -> "4 Sub/fq_output_0" [label="[]", style=solid]; +"23 Constant_10827" -> "4 Sub/fq_output_0" [label="[]", style=solid]; +"24 Constant_10826" -> "4 Sub/fq_output_0" [label="[]", style=solid]; +"25 Constant_10825" -> "4 Sub/fq_output_0" [label="[]", style=solid]; +"26 Constant_8676" -> "2 Sub" [label="[1, 3, 1, 1]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/GRUSequenceModel_linear_before_reset_F.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/GRUSequenceModel_linear_before_reset_F.dot deleted file mode 120000 index 63d8fb0c0ce..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/quantized/GRUSequenceModel_linear_before_reset_F.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/quantized/GRUSequenceModel_linear_before_reset_F.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/GRUSequenceModel_linear_before_reset_F.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/GRUSequenceModel_linear_before_reset_F.dot new file mode 100644 index 00000000000..f97ac8a2d68 --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/quantized/GRUSequenceModel_linear_before_reset_F.dot @@ -0,0 +1,81 @@ +strict digraph { +"0 X" [id=0, type=Parameter]; +"1 initial_hidden_state" [id=1, type=Parameter]; +"2 X/fq_output_0" [id=2, type=FakeQuantize]; +"3 initial_hidden_state/fq_output_0" [id=3, type=FakeQuantize]; +"4 GRUSequence" [id=4, type=GRUSequence]; +"5 GRUSequence/fq_output_0" [id=5, type=FakeQuantize]; +"6 MatMul" [id=6, type=MatMul]; +"7 Result" [id=7, type=Result]; +"8 MatMul/fq_weights_1" [id=8, type=FakeQuantize]; +"9 Constant_2541" [id=9, type=Constant]; +"10 Constant_2540" [id=10, type=Constant]; +"11 Constant_2539" [id=11, type=Constant]; +"12 Constant_2538" [id=12, type=Constant]; +"13 Constant_8" [id=13, type=Constant]; +"14 Constant_2526" [id=14, type=Constant]; +"15 Constant_2525" [id=15, type=Constant]; +"16 Constant_2524" [id=16, type=Constant]; +"17 Constant_2523" [id=17, type=Constant]; +"18 Constant_6" [id=18, type=Constant]; +"19 GRUSequence/fq_weights_4" [id=19, type=FakeQuantize]; +"20 Constant_2536" [id=20, type=Constant]; +"21 Constant_2535" [id=21, type=Constant]; +"22 Constant_2534" [id=22, type=Constant]; +"23 Constant_2533" [id=23, type=Constant]; +"24 Constant_5" [id=24, type=Constant]; +"25 GRUSequence/fq_weights_3" [id=25, type=FakeQuantize]; +"26 Constant_2531" [id=26, type=Constant]; +"27 Constant_2530" [id=27, type=Constant]; +"28 Constant_2529" [id=28, type=Constant]; +"29 Constant_2528" [id=29, type=Constant]; +"30 Constant_4" [id=30, type=Constant]; +"31 Constant_3" [id=31, type=Constant]; +"32 Constant_2521" [id=32, type=Constant]; +"33 Constant_2520" [id=33, type=Constant]; +"34 Constant_2519" [id=34, type=Constant]; +"35 Constant_2518" [id=35, type=Constant]; +"36 Constant_2516" [id=36, type=Constant]; +"37 Constant_2515" [id=37, type=Constant]; +"38 Constant_2514" [id=38, type=Constant]; +"39 Constant_2513" [id=39, type=Constant]; +"0 X" -> "2 X/fq_output_0" [label="[3, 2, 16]", style=solid]; +"1 initial_hidden_state" -> "3 initial_hidden_state/fq_output_0" [label="[3, 1, 128]", style=solid]; +"2 X/fq_output_0" -> "4 GRUSequence" [label="[3, 2, 16]", style=solid]; +"3 initial_hidden_state/fq_output_0" -> "4 GRUSequence" [label="[3, 1, 128]", style=solid]; +"4 GRUSequence" -> "5 GRUSequence/fq_output_0" [label="[3, 1, 2, 128]", style=solid]; +"5 GRUSequence/fq_output_0" -> "6 MatMul" [label="[3, 1, 2, 128]", style=solid]; +"6 MatMul" -> "7 Result" [label="[3, 1, 2, 3]", style=solid]; +"8 MatMul/fq_weights_1" -> "6 MatMul" [label="[3, 1, 128, 3]", style=solid]; +"9 Constant_2541" -> "8 MatMul/fq_weights_1" [label="[3, 1, 1, 3]", style=solid]; +"10 Constant_2540" -> "8 MatMul/fq_weights_1" [label="[3, 1, 1, 3]", style=solid]; +"11 Constant_2539" -> "8 MatMul/fq_weights_1" [label="[3, 1, 1, 3]", style=solid]; +"12 Constant_2538" -> "8 MatMul/fq_weights_1" [label="[3, 1, 1, 3]", style=solid]; +"13 Constant_8" -> "8 MatMul/fq_weights_1" [label="[3, 1, 128, 3]", style=solid]; +"14 Constant_2526" -> "5 GRUSequence/fq_output_0" [label="[]", style=solid]; +"15 Constant_2525" -> "5 GRUSequence/fq_output_0" [label="[]", style=solid]; +"16 Constant_2524" -> "5 GRUSequence/fq_output_0" [label="[]", style=solid]; +"17 Constant_2523" -> "5 GRUSequence/fq_output_0" [label="[]", style=solid]; +"18 Constant_6" -> "4 GRUSequence" [label="[1, 384]", style=solid]; +"19 GRUSequence/fq_weights_4" -> "4 GRUSequence" [label="[1, 384, 128]", style=solid]; +"20 Constant_2536" -> "19 GRUSequence/fq_weights_4" [label="[1, 384, 1]", style=solid]; +"21 Constant_2535" -> "19 GRUSequence/fq_weights_4" [label="[1, 384, 1]", style=solid]; +"22 Constant_2534" -> "19 GRUSequence/fq_weights_4" [label="[1, 384, 1]", style=solid]; +"23 Constant_2533" -> "19 GRUSequence/fq_weights_4" [label="[1, 384, 1]", style=solid]; +"24 Constant_5" -> "19 GRUSequence/fq_weights_4" [label="[1, 384, 128]", style=solid]; +"25 GRUSequence/fq_weights_3" -> "4 GRUSequence" [label="[1, 384, 16]", style=solid]; +"26 Constant_2531" -> "25 GRUSequence/fq_weights_3" [label="[1, 384, 1]", style=solid]; +"27 Constant_2530" -> "25 GRUSequence/fq_weights_3" [label="[1, 384, 1]", style=solid]; +"28 Constant_2529" -> "25 GRUSequence/fq_weights_3" [label="[1, 384, 1]", style=solid]; +"29 Constant_2528" -> "25 GRUSequence/fq_weights_3" [label="[1, 384, 1]", style=solid]; +"30 Constant_4" -> "25 GRUSequence/fq_weights_3" [label="[1, 384, 16]", style=solid]; +"31 Constant_3" -> "4 GRUSequence" [label="[3]", style=dashed]; +"32 Constant_2521" -> "3 initial_hidden_state/fq_output_0" [label="[]", style=solid]; +"33 Constant_2520" -> "3 initial_hidden_state/fq_output_0" [label="[]", style=solid]; +"34 Constant_2519" -> "3 initial_hidden_state/fq_output_0" [label="[]", style=solid]; +"35 Constant_2518" -> "3 initial_hidden_state/fq_output_0" [label="[]", style=solid]; +"36 Constant_2516" -> "2 X/fq_output_0" [label="[]", style=solid]; +"37 Constant_2515" -> "2 X/fq_output_0" [label="[]", style=solid]; +"38 Constant_2514" -> "2 X/fq_output_0" [label="[]", style=solid]; +"39 Constant_2513" -> "2 X/fq_output_0" [label="[]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/GRUSequenceModel_linear_before_reset_T.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/GRUSequenceModel_linear_before_reset_T.dot deleted file mode 120000 index a97e9ef04b2..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/quantized/GRUSequenceModel_linear_before_reset_T.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/quantized/GRUSequenceModel_linear_before_reset_T.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/GRUSequenceModel_linear_before_reset_T.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/GRUSequenceModel_linear_before_reset_T.dot new file mode 100644 index 00000000000..a9819020b25 --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/quantized/GRUSequenceModel_linear_before_reset_T.dot @@ -0,0 +1,41 @@ +strict digraph { +"0 X" [id=0, type=Parameter]; +"1 initial_hidden_state" [id=1, type=Parameter]; +"2 GRUSequence" [id=2, type=GRUSequence]; +"3 GRUSequence/fq_output_0" [id=3, type=FakeQuantize]; +"4 MatMul" [id=4, type=MatMul]; +"5 Result" [id=5, type=Result]; +"6 MatMul/fq_weights_1" [id=6, type=FakeQuantize]; +"7 Constant_2205" [id=7, type=Constant]; +"8 Constant_2204" [id=8, type=Constant]; +"9 Constant_2203" [id=9, type=Constant]; +"10 Constant_2202" [id=10, type=Constant]; +"11 Constant_8" [id=11, type=Constant]; +"12 Constant_2200" [id=12, type=Constant]; +"13 Constant_2199" [id=13, type=Constant]; +"14 Constant_2198" [id=14, type=Constant]; +"15 Constant_2197" [id=15, type=Constant]; +"16 Constant_6" [id=16, type=Constant]; +"17 Constant_5" [id=17, type=Constant]; +"18 Constant_4" [id=18, type=Constant]; +"19 Constant_3" [id=19, type=Constant]; +"0 X" -> "2 GRUSequence" [label="[3, 2, 16]", style=solid]; +"1 initial_hidden_state" -> "2 GRUSequence" [label="[3, 1, 128]", style=solid]; +"2 GRUSequence" -> "3 GRUSequence/fq_output_0" [label="[3, 1, 2, 128]", style=solid]; +"3 GRUSequence/fq_output_0" -> "4 MatMul" [label="[3, 1, 2, 128]", style=solid]; +"4 MatMul" -> "5 Result" [label="[3, 1, 2, 3]", style=solid]; +"6 MatMul/fq_weights_1" -> "4 MatMul" [label="[3, 1, 128, 3]", style=solid]; +"7 Constant_2205" -> "6 MatMul/fq_weights_1" [label="[3, 1, 1, 3]", style=solid]; +"8 Constant_2204" -> "6 MatMul/fq_weights_1" [label="[3, 1, 1, 3]", style=solid]; +"9 Constant_2203" -> "6 MatMul/fq_weights_1" [label="[3, 1, 1, 3]", style=solid]; +"10 Constant_2202" -> "6 MatMul/fq_weights_1" [label="[3, 1, 1, 3]", style=solid]; +"11 Constant_8" -> "6 MatMul/fq_weights_1" [label="[3, 1, 128, 3]", style=solid]; +"12 Constant_2200" -> "3 GRUSequence/fq_output_0" [label="[]", style=solid]; +"13 Constant_2199" -> "3 GRUSequence/fq_output_0" [label="[]", style=solid]; +"14 Constant_2198" -> "3 GRUSequence/fq_output_0" [label="[]", style=solid]; +"15 Constant_2197" -> "3 GRUSequence/fq_output_0" [label="[]", style=solid]; +"16 Constant_6" -> "2 GRUSequence" [label="[1, 512]", style=solid]; +"17 Constant_5" -> "2 GRUSequence" [label="[1, 384, 128]", style=solid]; +"18 Constant_4" -> "2 GRUSequence" [label="[1, 384, 16]", style=solid]; +"19 Constant_3" -> "2 GRUSequence" [label="[3]", style=dashed]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/GroupNormalizationModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/GroupNormalizationModel.dot deleted file mode 120000 index 04fa853ea52..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/quantized/GroupNormalizationModel.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/quantized/GroupNormalizationModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/GroupNormalizationModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/GroupNormalizationModel.dot new file mode 100644 index 00000000000..90d267ee9dd --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/quantized/GroupNormalizationModel.dot @@ -0,0 +1,69 @@ +strict digraph { +"0 Input_1" [id=0, type=Parameter]; +"1 Input_1/fq_output_0" [id=1, type=FakeQuantize]; +"2 Conv" [id=2, type=Convolution]; +"3 Conv_Add" [id=3, type=Add]; +"4 Conv_Add/fq_output_0" [id=4, type=FakeQuantize]; +"5 GroupNormalization_27318" [id=5, type=GroupNormalization]; +"6 Relu" [id=6, type=Relu]; +"7 Relu/fq_output_0" [id=7, type=FakeQuantize]; +"8 Mul" [id=8, type=Multiply]; +"9 Add" [id=9, type=Add]; +"10 Result" [id=10, type=Result]; +"11 Constant_27322" [id=11, type=Constant]; +"12 Constant_27320" [id=12, type=Constant]; +"13 Constant_29930" [id=13, type=Constant]; +"14 Constant_29929" [id=14, type=Constant]; +"15 Constant_29928" [id=15, type=Constant]; +"16 Constant_29927" [id=16, type=Constant]; +"17 Constant_27317" [id=17, type=Constant]; +"18 Constant_27316" [id=18, type=Constant]; +"19 Constant_29925" [id=19, type=Constant]; +"20 Constant_29924" [id=20, type=Constant]; +"21 Constant_29923" [id=21, type=Constant]; +"22 Constant_29922" [id=22, type=Constant]; +"23 Bias" [id=23, type=Constant]; +"24 Conv/fq_weights_1" [id=24, type=FakeQuantize]; +"25 Constant_29920" [id=25, type=Constant]; +"26 Constant_29919" [id=26, type=Constant]; +"27 Constant_29918" [id=27, type=Constant]; +"28 Constant_29917" [id=28, type=Constant]; +"29 Constant_27312" [id=29, type=Constant]; +"30 Constant_29915" [id=30, type=Constant]; +"31 Constant_29914" [id=31, type=Constant]; +"32 Constant_29913" [id=32, type=Constant]; +"33 Constant_29912" [id=33, type=Constant]; +"0 Input_1" -> "1 Input_1/fq_output_0" [label="[1, 2, 3, 4, 4]", style=solid]; +"1 Input_1/fq_output_0" -> "2 Conv" [label="[1, 2, 3, 4, 4]", style=solid]; +"2 Conv" -> "3 Conv_Add" [label="[1, 4, 1, 2, 2]", style=solid]; +"3 Conv_Add" -> "4 Conv_Add/fq_output_0" [label="[1, 4, 3, 2, 2]", style=solid]; +"4 Conv_Add/fq_output_0" -> "5 GroupNormalization_27318" [label="[1, 4, 3, 2, 2]", style=solid]; +"5 GroupNormalization_27318" -> "6 Relu" [label="[1, 4, 3, 2, 2]", style=solid]; +"6 Relu" -> "7 Relu/fq_output_0" [label="[1, 4, 3, 2, 2]", style=solid]; +"7 Relu/fq_output_0" -> "8 Mul" [label="[1, 4, 3, 2, 2]", style=solid]; +"8 Mul" -> "9 Add" [label="[1, 4, 3, 2, 2]", style=solid]; +"9 Add" -> "10 Result" [label="[1, 4, 3, 2, 2]", style=solid]; +"11 Constant_27322" -> "9 Add" [label="[1, 4, 1, 1, 1]", style=solid]; +"12 Constant_27320" -> "8 Mul" [label="[1, 4, 1, 1, 1]", style=solid]; +"13 Constant_29930" -> "7 Relu/fq_output_0" [label="[]", style=solid]; +"14 Constant_29929" -> "7 Relu/fq_output_0" [label="[]", style=solid]; +"15 Constant_29928" -> "7 Relu/fq_output_0" [label="[]", style=solid]; +"16 Constant_29927" -> "7 Relu/fq_output_0" [label="[]", style=solid]; +"17 Constant_27317" -> "5 GroupNormalization_27318" [label="[4]", style=solid]; +"18 Constant_27316" -> "5 GroupNormalization_27318" [label="[4]", style=solid]; +"19 Constant_29925" -> "4 Conv_Add/fq_output_0" [label="[]", style=solid]; +"20 Constant_29924" -> "4 Conv_Add/fq_output_0" [label="[]", style=solid]; +"21 Constant_29923" -> "4 Conv_Add/fq_output_0" [label="[]", style=solid]; +"22 Constant_29922" -> "4 Conv_Add/fq_output_0" [label="[]", style=solid]; +"23 Bias" -> "3 Conv_Add" [label="[1, 1, 3, 1, 1]", style=solid]; +"24 Conv/fq_weights_1" -> "2 Conv" [label="[4, 2, 3, 3, 3]", style=solid]; +"25 Constant_29920" -> "24 Conv/fq_weights_1" [label="[4, 1, 1, 1, 1]", style=solid]; +"26 Constant_29919" -> "24 Conv/fq_weights_1" [label="[4, 1, 1, 1, 1]", style=solid]; +"27 Constant_29918" -> "24 Conv/fq_weights_1" [label="[4, 1, 1, 1, 1]", style=solid]; +"28 Constant_29917" -> "24 Conv/fq_weights_1" [label="[4, 1, 1, 1, 1]", style=solid]; +"29 Constant_27312" -> "24 Conv/fq_weights_1" [label="[4, 2, 3, 3, 3]", style=solid]; +"30 Constant_29915" -> "1 Input_1/fq_output_0" [label="[]", style=solid]; +"31 Constant_29914" -> "1 Input_1/fq_output_0" [label="[]", style=solid]; +"32 Constant_29913" -> "1 Input_1/fq_output_0" [label="[]", style=solid]; +"33 Constant_29912" -> "1 Input_1/fq_output_0" [label="[]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/IfModel_else.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/IfModel_else.dot deleted file mode 120000 index b83fa2cd94b..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/quantized/IfModel_else.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/quantized/IfModel_else.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/IfModel_else.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/IfModel_else.dot new file mode 100644 index 00000000000..10e35270838 --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/quantized/IfModel_else.dot @@ -0,0 +1,51 @@ +strict digraph { +"0 Input_1" [id=0, type=Parameter]; +"1 Input_2" [id=1, type=Parameter]; +"2 Sub" [id=2, type=Subtract]; +"3 Add" [id=3, type=Add]; +"4 Sub/fq_output_0" [id=4, type=FakeQuantize]; +"5 Mul" [id=5, type=Multiply]; +"6 Conv" [id=6, type=Convolution]; +"7 Transpose" [id=7, type=Transpose]; +"8 Conv_Add" [id=8, type=Add]; +"9 Concat_70" [id=9, type=Concat]; +"10 Relu" [id=10, type=Relu]; +"11 Result" [id=11, type=Result]; +"12 Constant_68" [id=12, type=Constant]; +"13 Constant_66" [id=13, type=Constant]; +"14 Constant_64" [id=14, type=Constant]; +"15 Bias" [id=15, type=Constant]; +"16 Conv/fq_weights_1" [id=16, type=Multiply]; +"17 Constant_12068" [id=17, type=Constant]; +"18 Convert_12170" [id=18, type=Convert]; +"19 Constant_58" [id=19, type=Constant]; +"20 Constant_9391" [id=20, type=Constant]; +"21 Constant_9390" [id=21, type=Constant]; +"22 Constant_9389" [id=22, type=Constant]; +"23 Constant_9388" [id=23, type=Constant]; +"24 Constant_56" [id=24, type=Constant]; +"0 Input_1" -> "2 Sub" [label="[1, 3, 4, 2]", style=solid]; +"1 Input_2" -> "3 Add" [label="[1, 3, 2, 4]", style=solid]; +"2 Sub" -> "4 Sub/fq_output_0" [label="[1, 3, 4, 2]", style=solid]; +"3 Add" -> "5 Mul" [label="[1, 3, 2, 4]", style=solid]; +"4 Sub/fq_output_0" -> "6 Conv" [label="[1, 3, 4, 2]", style=solid]; +"5 Mul" -> "7 Transpose" [label="[1, 3, 2, 4]", style=solid]; +"6 Conv" -> "8 Conv_Add" [label="[1, 3, 4, 2]", style=solid]; +"7 Transpose" -> "9 Concat_70" [label="[1, 3, 4, 2]", style=solid]; +"8 Conv_Add" -> "10 Relu" [label="[1, 3, 4, 2]", style=solid]; +"9 Concat_70" -> "11 Result" [label="[2, 3, 4, 2]", style=solid]; +"10 Relu" -> "9 Concat_70" [label="[1, 3, 4, 2]", style=solid]; +"12 Constant_68" -> "7 Transpose" [label="[4]", style=dashed]; +"13 Constant_66" -> "5 Mul" [label="[1, 3, 1, 1]", style=solid]; +"14 Constant_64" -> "3 Add" [label="[1, 3, 1, 1]", style=solid]; +"15 Bias" -> "8 Conv_Add" [label="[1, 3, 1, 1]", style=solid]; +"16 Conv/fq_weights_1" -> "6 Conv" [label="[3, 3, 1, 1]", style=solid]; +"17 Constant_12068" -> "16 Conv/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; +"18 Convert_12170" -> "16 Conv/fq_weights_1" [label="[3, 3, 1, 1]", style=solid]; +"19 Constant_58" -> "18 Convert_12170" [label="[3, 3, 1, 1]", style=dashed]; +"20 Constant_9391" -> "4 Sub/fq_output_0" [label="[]", style=solid]; +"21 Constant_9390" -> "4 Sub/fq_output_0" [label="[]", style=solid]; +"22 Constant_9389" -> "4 Sub/fq_output_0" [label="[]", style=solid]; +"23 Constant_9388" -> "4 Sub/fq_output_0" [label="[]", style=solid]; +"24 Constant_56" -> "2 Sub" [label="[1, 3, 1, 1]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/IfModel_main.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/IfModel_main.dot deleted file mode 120000 index 0bc5f84a190..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/quantized/IfModel_main.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/quantized/IfModel_main.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/IfModel_main.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/IfModel_main.dot new file mode 100644 index 00000000000..33223cf313e --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/quantized/IfModel_main.dot @@ -0,0 +1,11 @@ +strict digraph { +"0 Input_1" [id=0, type=Parameter]; +"1 Input_2" [id=1, type=Parameter]; +"2 Cond_input" [id=2, type=Parameter]; +"3 If_72" [id=3, type=If]; +"4 Result" [id=4, type=Result]; +"0 Input_1" -> "3 If_72" [label="[1, 3, 4, 2]", style=solid]; +"1 Input_2" -> "3 If_72" [label="[1, 3, 2, 4]", style=solid]; +"2 Cond_input" -> "3 If_72" [label="[]", style=dashed]; +"3 If_72" -> "4 Result" [label="[2, 3, 4, 2]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/IfModel_then.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/IfModel_then.dot deleted file mode 120000 index e50ae79b831..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/quantized/IfModel_then.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/quantized/IfModel_then.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/IfModel_then.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/IfModel_then.dot new file mode 100644 index 00000000000..732b69959db --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/quantized/IfModel_then.dot @@ -0,0 +1,51 @@ +strict digraph { +"0 Input_1" [id=0, type=Parameter]; +"1 Input_2" [id=1, type=Parameter]; +"2 Sub" [id=2, type=Subtract]; +"3 Add" [id=3, type=Add]; +"4 Sub/fq_output_0" [id=4, type=FakeQuantize]; +"5 Mul" [id=5, type=Multiply]; +"6 Conv" [id=6, type=Convolution]; +"7 Transpose" [id=7, type=Transpose]; +"8 Conv_Add" [id=8, type=Add]; +"9 Concat_36" [id=9, type=Concat]; +"10 Relu" [id=10, type=Relu]; +"11 Result" [id=11, type=Result]; +"12 Constant_34" [id=12, type=Constant]; +"13 Constant_32" [id=13, type=Constant]; +"14 Constant_30" [id=14, type=Constant]; +"15 Bias" [id=15, type=Constant]; +"16 Conv/fq_weights_1" [id=16, type=Multiply]; +"17 Constant_11914" [id=17, type=Constant]; +"18 Convert_12016" [id=18, type=Convert]; +"19 Constant_24" [id=19, type=Constant]; +"20 Constant_4685" [id=20, type=Constant]; +"21 Constant_4684" [id=21, type=Constant]; +"22 Constant_4683" [id=22, type=Constant]; +"23 Constant_4682" [id=23, type=Constant]; +"24 Constant_22" [id=24, type=Constant]; +"0 Input_1" -> "2 Sub" [label="[1, 3, 4, 2]", style=solid]; +"1 Input_2" -> "3 Add" [label="[1, 3, 2, 4]", style=solid]; +"2 Sub" -> "4 Sub/fq_output_0" [label="[1, 3, 4, 2]", style=solid]; +"3 Add" -> "5 Mul" [label="[1, 3, 2, 4]", style=solid]; +"4 Sub/fq_output_0" -> "6 Conv" [label="[1, 3, 4, 2]", style=solid]; +"5 Mul" -> "7 Transpose" [label="[1, 3, 2, 4]", style=solid]; +"6 Conv" -> "8 Conv_Add" [label="[1, 3, 4, 2]", style=solid]; +"7 Transpose" -> "9 Concat_36" [label="[1, 3, 4, 2]", style=solid]; +"8 Conv_Add" -> "10 Relu" [label="[1, 3, 4, 2]", style=solid]; +"9 Concat_36" -> "11 Result" [label="[2, 3, 4, 2]", style=solid]; +"10 Relu" -> "9 Concat_36" [label="[1, 3, 4, 2]", style=solid]; +"12 Constant_34" -> "7 Transpose" [label="[4]", style=dashed]; +"13 Constant_32" -> "5 Mul" [label="[1, 3, 1, 1]", style=solid]; +"14 Constant_30" -> "3 Add" [label="[1, 3, 1, 1]", style=solid]; +"15 Bias" -> "8 Conv_Add" [label="[1, 3, 1, 1]", style=solid]; +"16 Conv/fq_weights_1" -> "6 Conv" [label="[3, 3, 1, 1]", style=solid]; +"17 Constant_11914" -> "16 Conv/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; +"18 Convert_12016" -> "16 Conv/fq_weights_1" [label="[3, 3, 1, 1]", style=solid]; +"19 Constant_24" -> "18 Convert_12016" [label="[3, 3, 1, 1]", style=dashed]; +"20 Constant_4685" -> "4 Sub/fq_output_0" [label="[]", style=solid]; +"21 Constant_4684" -> "4 Sub/fq_output_0" [label="[]", style=solid]; +"22 Constant_4683" -> "4 Sub/fq_output_0" [label="[]", style=solid]; +"23 Constant_4682" -> "4 Sub/fq_output_0" [label="[]", style=solid]; +"24 Constant_22" -> "2 Sub" [label="[1, 3, 1, 1]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/IntegerModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/IntegerModel.dot deleted file mode 120000 index c2fd28bf331..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/quantized/IntegerModel.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/quantized/IntegerModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/IntegerModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/IntegerModel.dot new file mode 100644 index 00000000000..03cfb73b101 --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/quantized/IntegerModel.dot @@ -0,0 +1,102 @@ +strict digraph { +"0 Input" [id=0, type=Parameter]; +"1 Convert_1" [id=1, type=Convert]; +"2 Gather_4/fq_input_0" [id=2, type=FakeQuantize]; +"3 Gather_1" [id=3, type=Gather]; +"4 Gather_4" [id=4, type=Gather]; +"5 Gather_2" [id=5, type=Gather]; +"6 MatMul_2" [id=6, type=MatMul]; +"7 Gather_3" [id=7, type=Gather]; +"8 MatMul_2/fq_output_0" [id=8, type=FakeQuantize]; +"9 MatMul_1" [id=9, type=MatMul]; +"10 Add_1" [id=10, type=Add]; +"11 MatMul_1/fq_output_0" [id=11, type=FakeQuantize]; +"12 Result" [id=12, type=Result]; +"13 Constant_7164" [id=13, type=Constant]; +"14 Constant_7163" [id=14, type=Constant]; +"15 Constant_7162" [id=15, type=Constant]; +"16 Constant_7161" [id=16, type=Constant]; +"17 MatMul_2/fq_weights_1" [id=17, type=FakeQuantize]; +"18 Constant_7169" [id=18, type=Constant]; +"19 Constant_7168" [id=19, type=Constant]; +"20 Constant_7167" [id=20, type=Constant]; +"21 Constant_7166" [id=21, type=Constant]; +"22 Constant_4701" [id=22, type=Constant]; +"23 Constant_4699" [id=23, type=Constant]; +"24 Constant_4698" [id=24, type=Constant]; +"25 Constant_7159" [id=25, type=Constant]; +"26 Constant_7158" [id=26, type=Constant]; +"27 Constant_7157" [id=27, type=Constant]; +"28 Constant_7156" [id=28, type=Constant]; +"29 Constant_7179" [id=29, type=Constant]; +"30 Constant_7178" [id=30, type=Constant]; +"31 Constant_7177" [id=31, type=Constant]; +"32 Constant_7176" [id=32, type=Constant]; +"33 MatMul_1/fq_weights_1" [id=33, type=FakeQuantize]; +"34 Constant_7184" [id=34, type=Constant]; +"35 Constant_7183" [id=35, type=Constant]; +"36 Constant_7182" [id=36, type=Constant]; +"37 Constant_7181" [id=37, type=Constant]; +"38 Constant_4696" [id=38, type=Constant]; +"39 Constant_4694" [id=39, type=Constant]; +"40 Constant_4693" [id=40, type=Constant]; +"41 Constant_4691" [id=41, type=Constant]; +"42 Constant_4688" [id=42, type=Constant]; +"43 Constant_4687" [id=43, type=Constant]; +"44 Gather_2/fq_weights_0" [id=44, type=FakeQuantize]; +"45 Constant_7174" [id=45, type=Constant]; +"46 Constant_7173" [id=46, type=Constant]; +"47 Constant_7172" [id=47, type=Constant]; +"48 Constant_7171" [id=48, type=Constant]; +"49 Constant_4690" [id=49, type=Constant]; +"0 Input" -> "1 Convert_1" [label="[1, 192, 1]", style=solid]; +"0 Input" -> "2 Gather_4/fq_input_0" [label="[1, 192, 1]", style=solid]; +"1 Convert_1" -> "3 Gather_1" [label="[1, 192, 1]", style=dashed]; +"2 Gather_4/fq_input_0" -> "4 Gather_4" [label="[1, 192, 1]", style=solid]; +"3 Gather_1" -> "5 Gather_2" [label="[192, 1]", style=dashed]; +"4 Gather_4" -> "6 MatMul_2" [label="[1, 192]", style=solid]; +"5 Gather_2" -> "7 Gather_3" [label="[192, 1, 160]", style=solid]; +"6 MatMul_2" -> "8 MatMul_2/fq_output_0" [label="[1, 160]", style=solid]; +"7 Gather_3" -> "9 MatMul_1" [label="[1, 160]", style=solid]; +"8 MatMul_2/fq_output_0" -> "10 Add_1" [label="[1, 160]", style=solid]; +"9 MatMul_1" -> "11 MatMul_1/fq_output_0" [label="[1, 160]", style=solid]; +"10 Add_1" -> "12 Result" [label="[1, 160]", style=solid]; +"11 MatMul_1/fq_output_0" -> "10 Add_1" [label="[1, 160]", style=solid]; +"13 Constant_7164" -> "8 MatMul_2/fq_output_0" [label="[]", style=solid]; +"14 Constant_7163" -> "8 MatMul_2/fq_output_0" [label="[]", style=solid]; +"15 Constant_7162" -> "8 MatMul_2/fq_output_0" [label="[]", style=solid]; +"16 Constant_7161" -> "8 MatMul_2/fq_output_0" [label="[]", style=solid]; +"17 MatMul_2/fq_weights_1" -> "6 MatMul_2" [label="[160, 192]", style=solid]; +"18 Constant_7169" -> "17 MatMul_2/fq_weights_1" [label="[160, 1]", style=solid]; +"19 Constant_7168" -> "17 MatMul_2/fq_weights_1" [label="[160, 1]", style=solid]; +"20 Constant_7167" -> "17 MatMul_2/fq_weights_1" [label="[160, 1]", style=solid]; +"21 Constant_7166" -> "17 MatMul_2/fq_weights_1" [label="[160, 1]", style=solid]; +"22 Constant_4701" -> "17 MatMul_2/fq_weights_1" [label="[160, 192]", style=solid]; +"23 Constant_4699" -> "4 Gather_4" [label="[]", style=dashed]; +"24 Constant_4698" -> "4 Gather_4" [label="[]", style=dashed]; +"25 Constant_7159" -> "2 Gather_4/fq_input_0" [label="[]", style=solid]; +"26 Constant_7158" -> "2 Gather_4/fq_input_0" [label="[]", style=solid]; +"27 Constant_7157" -> "2 Gather_4/fq_input_0" [label="[]", style=solid]; +"28 Constant_7156" -> "2 Gather_4/fq_input_0" [label="[]", style=solid]; +"29 Constant_7179" -> "11 MatMul_1/fq_output_0" [label="[]", style=solid]; +"30 Constant_7178" -> "11 MatMul_1/fq_output_0" [label="[]", style=solid]; +"31 Constant_7177" -> "11 MatMul_1/fq_output_0" [label="[]", style=solid]; +"32 Constant_7176" -> "11 MatMul_1/fq_output_0" [label="[]", style=solid]; +"33 MatMul_1/fq_weights_1" -> "9 MatMul_1" [label="[160, 160]", style=solid]; +"34 Constant_7184" -> "33 MatMul_1/fq_weights_1" [label="[160, 1]", style=solid]; +"35 Constant_7183" -> "33 MatMul_1/fq_weights_1" [label="[160, 1]", style=solid]; +"36 Constant_7182" -> "33 MatMul_1/fq_weights_1" [label="[160, 1]", style=solid]; +"37 Constant_7181" -> "33 MatMul_1/fq_weights_1" [label="[160, 1]", style=solid]; +"38 Constant_4696" -> "33 MatMul_1/fq_weights_1" [label="[160, 160]", style=solid]; +"39 Constant_4694" -> "7 Gather_3" [label="[]", style=dashed]; +"40 Constant_4693" -> "7 Gather_3" [label="[]", style=dashed]; +"41 Constant_4691" -> "5 Gather_2" [label="[]", style=dashed]; +"42 Constant_4688" -> "3 Gather_1" [label="[]", style=dashed]; +"43 Constant_4687" -> "3 Gather_1" [label="[]", style=dashed]; +"44 Gather_2/fq_weights_0" -> "5 Gather_2" [label="[369, 160]", style=solid]; +"45 Constant_7174" -> "44 Gather_2/fq_weights_0" [label="[]", style=solid]; +"46 Constant_7173" -> "44 Gather_2/fq_weights_0" [label="[]", style=solid]; +"47 Constant_7172" -> "44 Gather_2/fq_weights_0" [label="[]", style=solid]; +"48 Constant_7171" -> "44 Gather_2/fq_weights_0" [label="[]", style=solid]; +"49 Constant_4690" -> "44 Gather_2/fq_weights_0" [label="[369, 160]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/LSTMModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/LSTMModel.dot deleted file mode 120000 index d81f5fe5ca7..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/quantized/LSTMModel.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/quantized/LSTMModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/LSTMModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/LSTMModel.dot new file mode 100644 index 00000000000..d3f5a2d2558 --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/quantized/LSTMModel.dot @@ -0,0 +1,220 @@ +strict digraph { +"0 Input" [id=0, type=Parameter]; +"1 ReadValue_1" [id=1, type=ReadValue]; +"2 Input/fq_output_0" [id=2, type=FakeQuantize]; +"3 ReadValue_1/fq_output_0" [id=3, type=FakeQuantize]; +"4 Squeeze_1" [id=4, type=Squeeze]; +"5 Add" [id=5, type=Add]; +"6 MatMul_1" [id=6, type=MatMul]; +"7 Add/fq_output_0" [id=7, type=FakeQuantize]; +"8 MatMul_1/fq_output_0" [id=8, type=FakeQuantize]; +"9 MatMul_2" [id=9, type=MatMul]; +"10 Add_1" [id=10, type=Add]; +"11 MatMul_2/fq_output_0" [id=11, type=FakeQuantize]; +"12 Split_6052" [id=12, type=Split]; +"13 Sigmoid_1" [id=13, type=Sigmoid]; +"14 Sigmoid_2" [id=14, type=Sigmoid]; +"15 Sigmoid_3" [id=15, type=Sigmoid]; +"16 Tanh_1" [id=16, type=Tanh]; +"17 Sigmoid_1/fq_output_0" [id=17, type=FakeQuantize]; +"18 Sigmoid_2/fq_output_0" [id=18, type=FakeQuantize]; +"19 Sigmoid_3/fq_output_0" [id=19, type=FakeQuantize]; +"20 Tanh_1/fq_output_0" [id=20, type=FakeQuantize]; +"21 Multiply_1" [id=21, type=Multiply]; +"22 Multiply_2" [id=22, type=Multiply]; +"23 Multiply_3" [id=23, type=Multiply]; +"24 Multiply_1/fq_output_0" [id=24, type=FakeQuantize]; +"25 Multiply_2/fq_output_0" [id=25, type=FakeQuantize]; +"26 Assign_1" [id=26, type=Assign]; +"27 MatMul_3/fq_input_0" [id=27, type=FakeQuantize]; +"28 Add_2" [id=28, type=Add]; +"29 MatMul_3" [id=29, type=MatMul]; +"30 Tanh_2" [id=30, type=Tanh]; +"31 Result" [id=31, type=Result]; +"32 Tanh_2/fq_output_0" [id=32, type=FakeQuantize]; +"33 Constant_9029" [id=33, type=Constant]; +"34 Constant_9028" [id=34, type=Constant]; +"35 Constant_9027" [id=35, type=Constant]; +"36 Constant_9026" [id=36, type=Constant]; +"37 Constant_8999" [id=37, type=Constant]; +"38 Constant_8998" [id=38, type=Constant]; +"39 Constant_8997" [id=39, type=Constant]; +"40 Constant_8996" [id=40, type=Constant]; +"41 Constant_9014" [id=41, type=Constant]; +"42 Constant_9013" [id=42, type=Constant]; +"43 Constant_9012" [id=43, type=Constant]; +"44 Constant_9011" [id=44, type=Constant]; +"45 Constant_6051" [id=45, type=Constant]; +"46 Constant_8989" [id=46, type=Constant]; +"47 Constant_8988" [id=47, type=Constant]; +"48 Constant_8987" [id=48, type=Constant]; +"49 Constant_8986" [id=49, type=Constant]; +"50 MatMul_2/fq_weights_1" [id=50, type=FakeQuantize]; +"51 Constant_9039" [id=51, type=Constant]; +"52 Constant_9038" [id=52, type=Constant]; +"53 Constant_9037" [id=53, type=Constant]; +"54 Constant_9036" [id=54, type=Constant]; +"55 Constant_6048" [id=55, type=Constant]; +"56 Constant_8974" [id=56, type=Constant]; +"57 Constant_8973" [id=57, type=Constant]; +"58 Constant_8972" [id=58, type=Constant]; +"59 Constant_8971" [id=59, type=Constant]; +"60 Constant_6046" [id=60, type=Constant]; +"61 Constant_9004" [id=61, type=Constant]; +"62 Constant_9003" [id=62, type=Constant]; +"63 Constant_9002" [id=63, type=Constant]; +"64 Constant_9001" [id=64, type=Constant]; +"65 Constant_6044" [id=65, type=Constant]; +"66 Constant_8984" [id=66, type=Constant]; +"67 Constant_8983" [id=67, type=Constant]; +"68 Constant_8982" [id=68, type=Constant]; +"69 Constant_8981" [id=69, type=Constant]; +"70 MatMul_1/fq_weights_1" [id=70, type=FakeQuantize]; +"71 Constant_9034" [id=71, type=Constant]; +"72 Constant_9033" [id=72, type=Constant]; +"73 Constant_9032" [id=73, type=Constant]; +"74 Constant_9031" [id=74, type=Constant]; +"75 Constant_6042" [id=75, type=Constant]; +"76 Constant_6040" [id=76, type=Constant]; +"77 Constant_8979" [id=77, type=Constant]; +"78 Constant_8978" [id=78, type=Constant]; +"79 Constant_8977" [id=79, type=Constant]; +"80 Constant_8976" [id=80, type=Constant]; +"81 Constant_9024" [id=81, type=Constant]; +"82 Constant_9023" [id=82, type=Constant]; +"83 Constant_9022" [id=83, type=Constant]; +"84 Constant_9021" [id=84, type=Constant]; +"85 Constant_8994" [id=85, type=Constant]; +"86 Constant_8993" [id=86, type=Constant]; +"87 Constant_8992" [id=87, type=Constant]; +"88 Constant_8991" [id=88, type=Constant]; +"89 Constant_6057" [id=89, type=Constant]; +"90 Constant_9009" [id=90, type=Constant]; +"91 Constant_9008" [id=91, type=Constant]; +"92 Constant_9007" [id=92, type=Constant]; +"93 Constant_9006" [id=93, type=Constant]; +"94 Constant_9019" [id=94, type=Constant]; +"95 Constant_9018" [id=95, type=Constant]; +"96 Constant_9017" [id=96, type=Constant]; +"97 Constant_9016" [id=97, type=Constant]; +"98 MatMul_3/fq_weights_1" [id=98, type=FakeQuantize]; +"99 Constant_9044" [id=99, type=Constant]; +"100 Constant_9043" [id=100, type=Constant]; +"101 Constant_9042" [id=101, type=Constant]; +"102 Constant_9041" [id=102, type=Constant]; +"103 Constant_6064" [id=103, type=Constant]; +"104 Constant_8969" [id=104, type=Constant]; +"105 Constant_8968" [id=105, type=Constant]; +"106 Constant_8967" [id=106, type=Constant]; +"107 Constant_8966" [id=107, type=Constant]; +"0 Input" -> "2 Input/fq_output_0" [label="[1, 1, 128]", style=solid]; +"1 ReadValue_1" -> "3 ReadValue_1/fq_output_0" [label="[1, 64]", style=solid]; +"2 Input/fq_output_0" -> "4 Squeeze_1" [label="[1, 1, 128]", style=solid]; +"3 ReadValue_1/fq_output_0" -> "5 Add" [label="[1, 64]", style=solid]; +"4 Squeeze_1" -> "6 MatMul_1" [label="[1, 128]", style=solid]; +"5 Add" -> "7 Add/fq_output_0" [label="[1, 64]", style=solid]; +"6 MatMul_1" -> "8 MatMul_1/fq_output_0" [label="[1, 256]", style=solid]; +"7 Add/fq_output_0" -> "9 MatMul_2" [label="[1, 64]", style=solid]; +"8 MatMul_1/fq_output_0" -> "10 Add_1" [label="[1, 256]", style=solid]; +"9 MatMul_2" -> "11 MatMul_2/fq_output_0" [label="[1, 256]", style=solid]; +"10 Add_1" -> "12 Split_6052" [label="[1, 256]", style=solid]; +"11 MatMul_2/fq_output_0" -> "10 Add_1" [label="[1, 256]", style=solid]; +"12 Split_6052" -> "13 Sigmoid_1" [label="[1, 64]", style=solid]; +"12 Split_6052" -> "14 Sigmoid_2" [label="[1, 64]", style=solid]; +"12 Split_6052" -> "15 Sigmoid_3" [label="[1, 64]", style=solid]; +"12 Split_6052" -> "16 Tanh_1" [label="[1, 64]", style=solid]; +"13 Sigmoid_1" -> "17 Sigmoid_1/fq_output_0" [label="[1, 64]", style=solid]; +"14 Sigmoid_2" -> "18 Sigmoid_2/fq_output_0" [label="[1, 64]", style=solid]; +"15 Sigmoid_3" -> "19 Sigmoid_3/fq_output_0" [label="[1, 64]", style=solid]; +"16 Tanh_1" -> "20 Tanh_1/fq_output_0" [label="[1, 64]", style=solid]; +"17 Sigmoid_1/fq_output_0" -> "21 Multiply_1" [label="[1, 64]", style=solid]; +"18 Sigmoid_2/fq_output_0" -> "22 Multiply_2" [label="[1, 64]", style=solid]; +"19 Sigmoid_3/fq_output_0" -> "23 Multiply_3" [label="[1, 64]", style=solid]; +"20 Tanh_1/fq_output_0" -> "22 Multiply_2" [label="[1, 64]", style=solid]; +"21 Multiply_1" -> "24 Multiply_1/fq_output_0" [label="[1, 64]", style=solid]; +"22 Multiply_2" -> "25 Multiply_2/fq_output_0" [label="[1, 64]", style=solid]; +"23 Multiply_3" -> "26 Assign_1" [label="[1, 64]", style=solid]; +"23 Multiply_3" -> "27 MatMul_3/fq_input_0" [label="[1, 64]", style=solid]; +"24 Multiply_1/fq_output_0" -> "28 Add_2" [label="[1, 64]", style=solid]; +"25 Multiply_2/fq_output_0" -> "28 Add_2" [label="[1, 64]", style=solid]; +"27 MatMul_3/fq_input_0" -> "29 MatMul_3" [label="[1, 64]", style=solid]; +"28 Add_2" -> "30 Tanh_2" [label="[1, 64]", style=solid]; +"29 MatMul_3" -> "31 Result" [label="[1, 128]", style=solid]; +"30 Tanh_2" -> "32 Tanh_2/fq_output_0" [label="[1, 64]", style=solid]; +"32 Tanh_2/fq_output_0" -> "23 Multiply_3" [label="[1, 64]", style=solid]; +"33 Constant_9029" -> "32 Tanh_2/fq_output_0" [label="[]", style=solid]; +"34 Constant_9028" -> "32 Tanh_2/fq_output_0" [label="[]", style=solid]; +"35 Constant_9027" -> "32 Tanh_2/fq_output_0" [label="[]", style=solid]; +"36 Constant_9026" -> "32 Tanh_2/fq_output_0" [label="[]", style=solid]; +"37 Constant_8999" -> "25 Multiply_2/fq_output_0" [label="[]", style=solid]; +"38 Constant_8998" -> "25 Multiply_2/fq_output_0" [label="[]", style=solid]; +"39 Constant_8997" -> "25 Multiply_2/fq_output_0" [label="[]", style=solid]; +"40 Constant_8996" -> "25 Multiply_2/fq_output_0" [label="[]", style=solid]; +"41 Constant_9014" -> "18 Sigmoid_2/fq_output_0" [label="[]", style=solid]; +"42 Constant_9013" -> "18 Sigmoid_2/fq_output_0" [label="[]", style=solid]; +"43 Constant_9012" -> "18 Sigmoid_2/fq_output_0" [label="[]", style=solid]; +"44 Constant_9011" -> "18 Sigmoid_2/fq_output_0" [label="[]", style=solid]; +"45 Constant_6051" -> "12 Split_6052" [label="[]", style=dashed]; +"46 Constant_8989" -> "11 MatMul_2/fq_output_0" [label="[]", style=solid]; +"47 Constant_8988" -> "11 MatMul_2/fq_output_0" [label="[]", style=solid]; +"48 Constant_8987" -> "11 MatMul_2/fq_output_0" [label="[]", style=solid]; +"49 Constant_8986" -> "11 MatMul_2/fq_output_0" [label="[]", style=solid]; +"50 MatMul_2/fq_weights_1" -> "9 MatMul_2" [label="[256, 64]", style=solid]; +"51 Constant_9039" -> "50 MatMul_2/fq_weights_1" [label="[256, 1]", style=solid]; +"52 Constant_9038" -> "50 MatMul_2/fq_weights_1" [label="[256, 1]", style=solid]; +"53 Constant_9037" -> "50 MatMul_2/fq_weights_1" [label="[256, 1]", style=solid]; +"54 Constant_9036" -> "50 MatMul_2/fq_weights_1" [label="[256, 1]", style=solid]; +"55 Constant_6048" -> "50 MatMul_2/fq_weights_1" [label="[256, 64]", style=solid]; +"56 Constant_8974" -> "7 Add/fq_output_0" [label="[]", style=solid]; +"57 Constant_8973" -> "7 Add/fq_output_0" [label="[]", style=solid]; +"58 Constant_8972" -> "7 Add/fq_output_0" [label="[]", style=solid]; +"59 Constant_8971" -> "7 Add/fq_output_0" [label="[]", style=solid]; +"60 Constant_6046" -> "5 Add" [label="[1, 64]", style=solid]; +"61 Constant_9004" -> "3 ReadValue_1/fq_output_0" [label="[]", style=solid]; +"62 Constant_9003" -> "3 ReadValue_1/fq_output_0" [label="[]", style=solid]; +"63 Constant_9002" -> "3 ReadValue_1/fq_output_0" [label="[]", style=solid]; +"64 Constant_9001" -> "3 ReadValue_1/fq_output_0" [label="[]", style=solid]; +"65 Constant_6044" -> "1 ReadValue_1" [label="[1, 64]", style=solid]; +"66 Constant_8984" -> "8 MatMul_1/fq_output_0" [label="[]", style=solid]; +"67 Constant_8983" -> "8 MatMul_1/fq_output_0" [label="[]", style=solid]; +"68 Constant_8982" -> "8 MatMul_1/fq_output_0" [label="[]", style=solid]; +"69 Constant_8981" -> "8 MatMul_1/fq_output_0" [label="[]", style=solid]; +"70 MatMul_1/fq_weights_1" -> "6 MatMul_1" [label="[256, 128]", style=solid]; +"71 Constant_9034" -> "70 MatMul_1/fq_weights_1" [label="[256, 1]", style=solid]; +"72 Constant_9033" -> "70 MatMul_1/fq_weights_1" [label="[256, 1]", style=solid]; +"73 Constant_9032" -> "70 MatMul_1/fq_weights_1" [label="[256, 1]", style=solid]; +"74 Constant_9031" -> "70 MatMul_1/fq_weights_1" [label="[256, 1]", style=solid]; +"75 Constant_6042" -> "70 MatMul_1/fq_weights_1" [label="[256, 128]", style=solid]; +"76 Constant_6040" -> "4 Squeeze_1" [label="[]", style=dashed]; +"77 Constant_8979" -> "2 Input/fq_output_0" [label="[]", style=solid]; +"78 Constant_8978" -> "2 Input/fq_output_0" [label="[]", style=solid]; +"79 Constant_8977" -> "2 Input/fq_output_0" [label="[]", style=solid]; +"80 Constant_8976" -> "2 Input/fq_output_0" [label="[]", style=solid]; +"81 Constant_9024" -> "20 Tanh_1/fq_output_0" [label="[]", style=solid]; +"82 Constant_9023" -> "20 Tanh_1/fq_output_0" [label="[]", style=solid]; +"83 Constant_9022" -> "20 Tanh_1/fq_output_0" [label="[]", style=solid]; +"84 Constant_9021" -> "20 Tanh_1/fq_output_0" [label="[]", style=solid]; +"85 Constant_8994" -> "24 Multiply_1/fq_output_0" [label="[]", style=solid]; +"86 Constant_8993" -> "24 Multiply_1/fq_output_0" [label="[]", style=solid]; +"87 Constant_8992" -> "24 Multiply_1/fq_output_0" [label="[]", style=solid]; +"88 Constant_8991" -> "24 Multiply_1/fq_output_0" [label="[]", style=solid]; +"89 Constant_6057" -> "21 Multiply_1" [label="[1, 64]", style=solid]; +"90 Constant_9009" -> "17 Sigmoid_1/fq_output_0" [label="[]", style=solid]; +"91 Constant_9008" -> "17 Sigmoid_1/fq_output_0" [label="[]", style=solid]; +"92 Constant_9007" -> "17 Sigmoid_1/fq_output_0" [label="[]", style=solid]; +"93 Constant_9006" -> "17 Sigmoid_1/fq_output_0" [label="[]", style=solid]; +"94 Constant_9019" -> "19 Sigmoid_3/fq_output_0" [label="[]", style=solid]; +"95 Constant_9018" -> "19 Sigmoid_3/fq_output_0" [label="[]", style=solid]; +"96 Constant_9017" -> "19 Sigmoid_3/fq_output_0" [label="[]", style=solid]; +"97 Constant_9016" -> "19 Sigmoid_3/fq_output_0" [label="[]", style=solid]; +"98 MatMul_3/fq_weights_1" -> "29 MatMul_3" [label="[128, 64]", style=solid]; +"99 Constant_9044" -> "98 MatMul_3/fq_weights_1" [label="[128, 1]", style=solid]; +"100 Constant_9043" -> "98 MatMul_3/fq_weights_1" [label="[128, 1]", style=solid]; +"101 Constant_9042" -> "98 MatMul_3/fq_weights_1" [label="[128, 1]", style=solid]; +"102 Constant_9041" -> "98 MatMul_3/fq_weights_1" [label="[128, 1]", style=solid]; +"103 Constant_6064" -> "98 MatMul_3/fq_weights_1" [label="[128, 64]", style=solid]; +"104 Constant_8969" -> "27 MatMul_3/fq_input_0" [label="[]", style=solid]; +"105 Constant_8968" -> "27 MatMul_3/fq_input_0" [label="[]", style=solid]; +"106 Constant_8967" -> "27 MatMul_3/fq_input_0" [label="[]", style=solid]; +"107 Constant_8966" -> "27 MatMul_3/fq_input_0" [label="[]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/LSTMSequenceModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/LSTMSequenceModel.dot deleted file mode 120000 index 3fe1359c967..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/quantized/LSTMSequenceModel.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/quantized/LSTMSequenceModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/LSTMSequenceModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/LSTMSequenceModel.dot new file mode 100644 index 00000000000..3167389ce2d --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/quantized/LSTMSequenceModel.dot @@ -0,0 +1,93 @@ +strict digraph { +"0 X" [id=0, type=Parameter]; +"1 initial_hidden_state" [id=1, type=Parameter]; +"2 initial_cell_state" [id=2, type=Parameter]; +"3 X/fq_output_0" [id=3, type=FakeQuantize]; +"4 initial_hidden_state/fq_output_0" [id=4, type=FakeQuantize]; +"5 initial_cell_state/fq_output_0" [id=5, type=FakeQuantize]; +"6 LSTMSequence_7" [id=6, type=LSTMSequence]; +"7 LSTMSequence_7/fq_output_0" [id=7, type=FakeQuantize]; +"8 MatMul" [id=8, type=MatMul]; +"9 Result" [id=9, type=Result]; +"10 MatMul/fq_weights_1" [id=10, type=FakeQuantize]; +"11 Constant_2122" [id=11, type=Constant]; +"12 Constant_2121" [id=12, type=Constant]; +"13 Constant_2120" [id=13, type=Constant]; +"14 Constant_2119" [id=14, type=Constant]; +"15 Constant_8" [id=15, type=Constant]; +"16 Constant_2092" [id=16, type=Constant]; +"17 Constant_2091" [id=17, type=Constant]; +"18 Constant_2090" [id=18, type=Constant]; +"19 Constant_2089" [id=19, type=Constant]; +"20 Constant_6" [id=20, type=Constant]; +"21 LSTMSequence_7/fq_weights_5" [id=21, type=FakeQuantize]; +"22 Constant_2117" [id=22, type=Constant]; +"23 Constant_2116" [id=23, type=Constant]; +"24 Constant_2115" [id=24, type=Constant]; +"25 Constant_2114" [id=25, type=Constant]; +"26 Constant_5" [id=26, type=Constant]; +"27 LSTMSequence_7/fq_weights_4" [id=27, type=FakeQuantize]; +"28 Constant_2112" [id=28, type=Constant]; +"29 Constant_2111" [id=29, type=Constant]; +"30 Constant_2110" [id=30, type=Constant]; +"31 Constant_2109" [id=31, type=Constant]; +"32 Constant_4" [id=32, type=Constant]; +"33 Constant_3" [id=33, type=Constant]; +"34 Constant_2102" [id=34, type=Constant]; +"35 Constant_2101" [id=35, type=Constant]; +"36 Constant_2100" [id=36, type=Constant]; +"37 Constant_2099" [id=37, type=Constant]; +"38 Constant_2107" [id=38, type=Constant]; +"39 Constant_2106" [id=39, type=Constant]; +"40 Constant_2105" [id=40, type=Constant]; +"41 Constant_2104" [id=41, type=Constant]; +"42 Constant_2097" [id=42, type=Constant]; +"43 Constant_2096" [id=43, type=Constant]; +"44 Constant_2095" [id=44, type=Constant]; +"45 Constant_2094" [id=45, type=Constant]; +"0 X" -> "3 X/fq_output_0" [label="[1, 2, 16]", style=solid]; +"1 initial_hidden_state" -> "4 initial_hidden_state/fq_output_0" [label="[1, 1, 128]", style=solid]; +"2 initial_cell_state" -> "5 initial_cell_state/fq_output_0" [label="[1, 1, 128]", style=solid]; +"3 X/fq_output_0" -> "6 LSTMSequence_7" [label="[1, 2, 16]", style=solid]; +"4 initial_hidden_state/fq_output_0" -> "6 LSTMSequence_7" [label="[1, 1, 128]", style=solid]; +"5 initial_cell_state/fq_output_0" -> "6 LSTMSequence_7" [label="[1, 1, 128]", style=solid]; +"6 LSTMSequence_7" -> "7 LSTMSequence_7/fq_output_0" [label="[1, 1, 2, 128]", style=solid]; +"7 LSTMSequence_7/fq_output_0" -> "8 MatMul" [label="[1, 1, 2, 128]", style=solid]; +"8 MatMul" -> "9 Result" [label="[1, 1, 2, 3]", style=solid]; +"10 MatMul/fq_weights_1" -> "8 MatMul" [label="[1, 1, 128, 3]", style=solid]; +"11 Constant_2122" -> "10 MatMul/fq_weights_1" [label="[1, 1, 1, 1]", style=solid]; +"12 Constant_2121" -> "10 MatMul/fq_weights_1" [label="[1, 1, 1, 1]", style=solid]; +"13 Constant_2120" -> "10 MatMul/fq_weights_1" [label="[1, 1, 1, 1]", style=solid]; +"14 Constant_2119" -> "10 MatMul/fq_weights_1" [label="[1, 1, 1, 1]", style=solid]; +"15 Constant_8" -> "10 MatMul/fq_weights_1" [label="[1, 1, 128, 3]", style=solid]; +"16 Constant_2092" -> "7 LSTMSequence_7/fq_output_0" [label="[]", style=solid]; +"17 Constant_2091" -> "7 LSTMSequence_7/fq_output_0" [label="[]", style=solid]; +"18 Constant_2090" -> "7 LSTMSequence_7/fq_output_0" [label="[]", style=solid]; +"19 Constant_2089" -> "7 LSTMSequence_7/fq_output_0" [label="[]", style=solid]; +"20 Constant_6" -> "6 LSTMSequence_7" [label="[1, 512]", style=solid]; +"21 LSTMSequence_7/fq_weights_5" -> "6 LSTMSequence_7" [label="[1, 512, 128]", style=solid]; +"22 Constant_2117" -> "21 LSTMSequence_7/fq_weights_5" [label="[1, 1, 1]", style=solid]; +"23 Constant_2116" -> "21 LSTMSequence_7/fq_weights_5" [label="[1, 1, 1]", style=solid]; +"24 Constant_2115" -> "21 LSTMSequence_7/fq_weights_5" [label="[1, 1, 1]", style=solid]; +"25 Constant_2114" -> "21 LSTMSequence_7/fq_weights_5" [label="[1, 1, 1]", style=solid]; +"26 Constant_5" -> "21 LSTMSequence_7/fq_weights_5" [label="[1, 512, 128]", style=solid]; +"27 LSTMSequence_7/fq_weights_4" -> "6 LSTMSequence_7" [label="[1, 512, 16]", style=solid]; +"28 Constant_2112" -> "27 LSTMSequence_7/fq_weights_4" [label="[1, 1, 1]", style=solid]; +"29 Constant_2111" -> "27 LSTMSequence_7/fq_weights_4" [label="[1, 1, 1]", style=solid]; +"30 Constant_2110" -> "27 LSTMSequence_7/fq_weights_4" [label="[1, 1, 1]", style=solid]; +"31 Constant_2109" -> "27 LSTMSequence_7/fq_weights_4" [label="[1, 1, 1]", style=solid]; +"32 Constant_4" -> "27 LSTMSequence_7/fq_weights_4" [label="[1, 512, 16]", style=solid]; +"33 Constant_3" -> "6 LSTMSequence_7" [label="[1]", style=dashed]; +"34 Constant_2102" -> "5 initial_cell_state/fq_output_0" [label="[]", style=solid]; +"35 Constant_2101" -> "5 initial_cell_state/fq_output_0" [label="[]", style=solid]; +"36 Constant_2100" -> "5 initial_cell_state/fq_output_0" [label="[]", style=solid]; +"37 Constant_2099" -> "5 initial_cell_state/fq_output_0" [label="[]", style=solid]; +"38 Constant_2107" -> "4 initial_hidden_state/fq_output_0" [label="[]", style=solid]; +"39 Constant_2106" -> "4 initial_hidden_state/fq_output_0" [label="[]", style=solid]; +"40 Constant_2105" -> "4 initial_hidden_state/fq_output_0" [label="[]", style=solid]; +"41 Constant_2104" -> "4 initial_hidden_state/fq_output_0" [label="[]", style=solid]; +"42 Constant_2097" -> "3 X/fq_output_0" [label="[]", style=solid]; +"43 Constant_2096" -> "3 X/fq_output_0" [label="[]", style=solid]; +"44 Constant_2095" -> "3 X/fq_output_0" [label="[]", style=solid]; +"45 Constant_2094" -> "3 X/fq_output_0" [label="[]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/LinearModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/LinearModel.dot deleted file mode 120000 index a3ca0195eff..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/quantized/LinearModel.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/quantized/LinearModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/LinearModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/LinearModel.dot new file mode 100644 index 00000000000..0812509444f --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/quantized/LinearModel.dot @@ -0,0 +1,39 @@ +strict digraph { +"0 Input" [id=0, type=Parameter]; +"1 Input/fq_output_0" [id=1, type=FakeQuantize]; +"2 Reshape" [id=2, type=Reshape]; +"3 Add" [id=3, type=Add]; +"4 MatMul" [id=4, type=MatMul]; +"5 Result_Add" [id=5, type=Result]; +"6 Result_MatMul" [id=6, type=Result]; +"7 Constant_10" [id=7, type=Constant]; +"8 Constant_1" [id=8, type=Constant]; +"9 Constant_2174" [id=9, type=Constant]; +"10 Constant_2173" [id=10, type=Constant]; +"11 Constant_2172" [id=11, type=Constant]; +"12 Constant_2171" [id=12, type=Constant]; +"13 MatMul/fq_weights_1" [id=13, type=FakeQuantize]; +"14 Constant_2179" [id=14, type=Constant]; +"15 Constant_2178" [id=15, type=Constant]; +"16 Constant_2177" [id=16, type=Constant]; +"17 Constant_2176" [id=17, type=Constant]; +"18 Constant_8" [id=18, type=Constant]; +"0 Input" -> "1 Input/fq_output_0" [label="[1, 3, 4, 2]", style=solid]; +"1 Input/fq_output_0" -> "2 Reshape" [label="[1, 3, 4, 2]", style=solid]; +"2 Reshape" -> "3 Add" [label="[1, 3, 2, 4]", style=solid]; +"2 Reshape" -> "4 MatMul" [label="[1, 3, 2, 4]", style=solid]; +"3 Add" -> "5 Result_Add" [label="[1, 3, 2, 4]", style=solid]; +"4 MatMul" -> "6 Result_MatMul" [label="[1, 3, 2, 5]", style=solid]; +"7 Constant_10" -> "3 Add" [label="[1, 3, 2, 4]", style=solid]; +"8 Constant_1" -> "2 Reshape" [label="[4]", style=dashed]; +"9 Constant_2174" -> "1 Input/fq_output_0" [label="[]", style=solid]; +"10 Constant_2173" -> "1 Input/fq_output_0" [label="[]", style=solid]; +"11 Constant_2172" -> "1 Input/fq_output_0" [label="[]", style=solid]; +"12 Constant_2171" -> "1 Input/fq_output_0" [label="[]", style=solid]; +"13 MatMul/fq_weights_1" -> "4 MatMul" [label="[1, 3, 4, 5]", style=solid]; +"14 Constant_2179" -> "13 MatMul/fq_weights_1" [label="[1, 1, 1, 1]", style=solid]; +"15 Constant_2178" -> "13 MatMul/fq_weights_1" [label="[1, 1, 1, 1]", style=solid]; +"16 Constant_2177" -> "13 MatMul/fq_weights_1" [label="[1, 1, 1, 1]", style=solid]; +"17 Constant_2176" -> "13 MatMul/fq_weights_1" [label="[1, 1, 1, 1]", style=solid]; +"18 Constant_8" -> "13 MatMul/fq_weights_1" [label="[1, 3, 4, 5]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/MatMul2DModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/MatMul2DModel.dot deleted file mode 120000 index b243da83f91..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/quantized/MatMul2DModel.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/quantized/MatMul2DModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/MatMul2DModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/MatMul2DModel.dot new file mode 100644 index 00000000000..d91b4017f69 --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/quantized/MatMul2DModel.dot @@ -0,0 +1,33 @@ +strict digraph { +"0 Input" [id=0, type=Parameter]; +"1 Input/fq_output_0" [id=1, type=FakeQuantize]; +"2 MatMul" [id=2, type=MatMul]; +"3 Add" [id=3, type=Add]; +"4 Result" [id=4, type=Result]; +"5 Constant_4429" [id=5, type=Constant]; +"6 MatMul/fq_weights_1" [id=6, type=FakeQuantize]; +"7 Constant_6480" [id=7, type=Constant]; +"8 Constant_6479" [id=8, type=Constant]; +"9 Constant_6478" [id=9, type=Constant]; +"10 Constant_6477" [id=10, type=Constant]; +"11 Constant_4427" [id=11, type=Constant]; +"12 Constant_6475" [id=12, type=Constant]; +"13 Constant_6474" [id=13, type=Constant]; +"14 Constant_6473" [id=14, type=Constant]; +"15 Constant_6472" [id=15, type=Constant]; +"0 Input" -> "1 Input/fq_output_0" [label="[3, 5]", style=solid]; +"1 Input/fq_output_0" -> "2 MatMul" [label="[3, 5]", style=solid]; +"2 MatMul" -> "3 Add" [label="[3, 2]", style=solid]; +"3 Add" -> "4 Result" [label="[3, 2]", style=solid]; +"5 Constant_4429" -> "3 Add" [label="[1, 2]", style=solid]; +"6 MatMul/fq_weights_1" -> "2 MatMul" [label="[5, 2]", style=solid]; +"7 Constant_6480" -> "6 MatMul/fq_weights_1" [label="[5, 1]", style=solid]; +"8 Constant_6479" -> "6 MatMul/fq_weights_1" [label="[5, 1]", style=solid]; +"9 Constant_6478" -> "6 MatMul/fq_weights_1" [label="[5, 1]", style=solid]; +"10 Constant_6477" -> "6 MatMul/fq_weights_1" [label="[5, 1]", style=solid]; +"11 Constant_4427" -> "6 MatMul/fq_weights_1" [label="[5, 2]", style=solid]; +"12 Constant_6475" -> "1 Input/fq_output_0" [label="[]", style=solid]; +"13 Constant_6474" -> "1 Input/fq_output_0" [label="[]", style=solid]; +"14 Constant_6473" -> "1 Input/fq_output_0" [label="[]", style=solid]; +"15 Constant_6472" -> "1 Input/fq_output_0" [label="[]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/MatmulSoftmaxMatmulBlock.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/MatmulSoftmaxMatmulBlock.dot deleted file mode 120000 index 3b464156cf2..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/quantized/MatmulSoftmaxMatmulBlock.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/quantized/MatmulSoftmaxMatmulBlock.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/MatmulSoftmaxMatmulBlock.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/MatmulSoftmaxMatmulBlock.dot new file mode 100644 index 00000000000..76e0ecd3ac6 --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/quantized/MatmulSoftmaxMatmulBlock.dot @@ -0,0 +1,38 @@ +strict digraph { +"0 Input" [id=0, type=Parameter]; +"1 MatMul_1/fq_input_0" [id=1, type=FakeQuantize]; +"2 Squeeze_1" [id=2, type=Squeeze]; +"3 MatMul_1" [id=3, type=MatMul]; +"4 MatMul_2" [id=4, type=MatMul]; +"5 Softmax_1" [id=5, type=Softmax]; +"6 Result" [id=6, type=Result]; +"7 Constant_2" [id=7, type=Constant]; +"8 MatMul_1/fq_weights_1" [id=8, type=FakeQuantize]; +"9 Constant_2199" [id=9, type=Constant]; +"10 Constant_2198" [id=10, type=Constant]; +"11 Constant_2197" [id=11, type=Constant]; +"12 Constant_2196" [id=12, type=Constant]; +"13 Constant_4" [id=13, type=Constant]; +"14 Constant_2194" [id=14, type=Constant]; +"15 Constant_2193" [id=15, type=Constant]; +"16 Constant_2192" [id=16, type=Constant]; +"17 Constant_2191" [id=17, type=Constant]; +"0 Input" -> "1 MatMul_1/fq_input_0" [label="[1, 1, 1]", style=solid]; +"0 Input" -> "2 Squeeze_1" [label="[1, 1, 1]", style=solid]; +"1 MatMul_1/fq_input_0" -> "3 MatMul_1" [label="[1, 1, 1]", style=solid]; +"2 Squeeze_1" -> "4 MatMul_2" [label="[1, 1]", style=solid]; +"3 MatMul_1" -> "5 Softmax_1" [label="[1, 1, 1]", style=solid]; +"4 MatMul_2" -> "6 Result" [label="[1, 1, 1]", style=solid]; +"5 Softmax_1" -> "4 MatMul_2" [label="[1, 1, 1]", style=solid]; +"7 Constant_2" -> "2 Squeeze_1" [label="[]", style=dashed]; +"8 MatMul_1/fq_weights_1" -> "3 MatMul_1" [label="[1, 1]", style=solid]; +"9 Constant_2199" -> "8 MatMul_1/fq_weights_1" [label="[1, 1]", style=solid]; +"10 Constant_2198" -> "8 MatMul_1/fq_weights_1" [label="[1, 1]", style=solid]; +"11 Constant_2197" -> "8 MatMul_1/fq_weights_1" [label="[1, 1]", style=solid]; +"12 Constant_2196" -> "8 MatMul_1/fq_weights_1" [label="[1, 1]", style=solid]; +"13 Constant_4" -> "8 MatMul_1/fq_weights_1" [label="[1, 1]", style=solid]; +"14 Constant_2194" -> "1 MatMul_1/fq_input_0" [label="[]", style=solid]; +"15 Constant_2193" -> "1 MatMul_1/fq_input_0" [label="[]", style=solid]; +"16 Constant_2192" -> "1 MatMul_1/fq_input_0" [label="[]", style=solid]; +"17 Constant_2191" -> "1 MatMul_1/fq_input_0" [label="[]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/ScaleShiftReluModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/ScaleShiftReluModel.dot deleted file mode 120000 index cfb443e20ac..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/quantized/ScaleShiftReluModel.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/quantized/ScaleShiftReluModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/ScaleShiftReluModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/ScaleShiftReluModel.dot new file mode 100644 index 00000000000..6044c82c814 --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/quantized/ScaleShiftReluModel.dot @@ -0,0 +1,73 @@ +strict digraph { +"0 Input" [id=0, type=Parameter]; +"1 Input/fq_output_0" [id=1, type=FakeQuantize]; +"2 MatMul" [id=2, type=MatMul]; +"3 MatMul/fq_output_0" [id=3, type=FakeQuantize]; +"4 Mul" [id=4, type=Multiply]; +"5 Add" [id=5, type=Add]; +"6 Relu" [id=6, type=Relu]; +"7 Relu/fq_output_0" [id=7, type=FakeQuantize]; +"8 MatMul2" [id=8, type=MatMul]; +"9 Result" [id=9, type=Result]; +"10 MatMul2/fq_weights_1" [id=10, type=FakeQuantize]; +"11 Constant_2156" [id=11, type=Constant]; +"12 Constant_2155" [id=12, type=Constant]; +"13 Constant_2154" [id=13, type=Constant]; +"14 Constant_2153" [id=14, type=Constant]; +"15 Constant_8" [id=15, type=Constant]; +"16 Constant_2146" [id=16, type=Constant]; +"17 Constant_2145" [id=17, type=Constant]; +"18 Constant_2144" [id=18, type=Constant]; +"19 Constant_2143" [id=19, type=Constant]; +"20 Constant_5" [id=20, type=Constant]; +"21 Constant_3" [id=21, type=Constant]; +"22 Constant_2141" [id=22, type=Constant]; +"23 Constant_2140" [id=23, type=Constant]; +"24 Constant_2139" [id=24, type=Constant]; +"25 Constant_2138" [id=25, type=Constant]; +"26 MatMul/fq_weights_1" [id=26, type=FakeQuantize]; +"27 Constant_2151" [id=27, type=Constant]; +"28 Constant_2150" [id=28, type=Constant]; +"29 Constant_2149" [id=29, type=Constant]; +"30 Constant_2148" [id=30, type=Constant]; +"31 Constant_1" [id=31, type=Constant]; +"32 Constant_2136" [id=32, type=Constant]; +"33 Constant_2135" [id=33, type=Constant]; +"34 Constant_2134" [id=34, type=Constant]; +"35 Constant_2133" [id=35, type=Constant]; +"0 Input" -> "1 Input/fq_output_0" [label="[3, 5]", style=solid]; +"1 Input/fq_output_0" -> "2 MatMul" [label="[3, 5]", style=solid]; +"2 MatMul" -> "3 MatMul/fq_output_0" [label="[3, 2]", style=solid]; +"3 MatMul/fq_output_0" -> "4 Mul" [label="[3, 2]", style=solid]; +"4 Mul" -> "5 Add" [label="[3, 2]", style=solid]; +"5 Add" -> "6 Relu" [label="[3, 2]", style=solid]; +"6 Relu" -> "7 Relu/fq_output_0" [label="[3, 2]", style=solid]; +"7 Relu/fq_output_0" -> "8 MatMul2" [label="[3, 2]", style=solid]; +"8 MatMul2" -> "9 Result" [label="[3, 4]", style=solid]; +"10 MatMul2/fq_weights_1" -> "8 MatMul2" [label="[2, 4]", style=solid]; +"11 Constant_2156" -> "10 MatMul2/fq_weights_1" [label="[2, 1]", style=solid]; +"12 Constant_2155" -> "10 MatMul2/fq_weights_1" [label="[2, 1]", style=solid]; +"13 Constant_2154" -> "10 MatMul2/fq_weights_1" [label="[2, 1]", style=solid]; +"14 Constant_2153" -> "10 MatMul2/fq_weights_1" [label="[2, 1]", style=solid]; +"15 Constant_8" -> "10 MatMul2/fq_weights_1" [label="[2, 4]", style=solid]; +"16 Constant_2146" -> "7 Relu/fq_output_0" [label="[]", style=solid]; +"17 Constant_2145" -> "7 Relu/fq_output_0" [label="[]", style=solid]; +"18 Constant_2144" -> "7 Relu/fq_output_0" [label="[]", style=solid]; +"19 Constant_2143" -> "7 Relu/fq_output_0" [label="[]", style=solid]; +"20 Constant_5" -> "5 Add" [label="[1, 2]", style=solid]; +"21 Constant_3" -> "4 Mul" [label="[1, 2]", style=solid]; +"22 Constant_2141" -> "3 MatMul/fq_output_0" [label="[]", style=solid]; +"23 Constant_2140" -> "3 MatMul/fq_output_0" [label="[]", style=solid]; +"24 Constant_2139" -> "3 MatMul/fq_output_0" [label="[]", style=solid]; +"25 Constant_2138" -> "3 MatMul/fq_output_0" [label="[]", style=solid]; +"26 MatMul/fq_weights_1" -> "2 MatMul" [label="[5, 2]", style=solid]; +"27 Constant_2151" -> "26 MatMul/fq_weights_1" [label="[5, 1]", style=solid]; +"28 Constant_2150" -> "26 MatMul/fq_weights_1" [label="[5, 1]", style=solid]; +"29 Constant_2149" -> "26 MatMul/fq_weights_1" [label="[5, 1]", style=solid]; +"30 Constant_2148" -> "26 MatMul/fq_weights_1" [label="[5, 1]", style=solid]; +"31 Constant_1" -> "26 MatMul/fq_weights_1" [label="[5, 2]", style=solid]; +"32 Constant_2136" -> "1 Input/fq_output_0" [label="[]", style=solid]; +"33 Constant_2135" -> "1 Input/fq_output_0" [label="[]", style=solid]; +"34 Constant_2134" -> "1 Input/fq_output_0" [label="[]", style=solid]; +"35 Constant_2133" -> "1 Input/fq_output_0" [label="[]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/SeBlockModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/SeBlockModel.dot deleted file mode 120000 index 1f53b65542e..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/quantized/SeBlockModel.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/quantized/SeBlockModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/SeBlockModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/SeBlockModel.dot new file mode 100644 index 00000000000..2553a125010 --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/quantized/SeBlockModel.dot @@ -0,0 +1,104 @@ +strict digraph { +"0 Input" [id=0, type=Parameter]; +"1 Input/fq_output_0" [id=1, type=FakeQuantize]; +"2 Conv0" [id=2, type=Convolution]; +"3 Add0" [id=3, type=Add]; +"4 Add0/fq_output_0" [id=4, type=FakeQuantize]; +"5 Mul" [id=5, type=Multiply]; +"6 ReduceMean_31402" [id=6, type=ReduceMean]; +"7 Mul/fq_output_0" [id=7, type=FakeQuantize]; +"8 Conv" [id=8, type=Convolution]; +"9 MatMul" [id=9, type=MatMul]; +"10 Add" [id=10, type=Add]; +"11 Result" [id=11, type=Result]; +"12 Relu" [id=12, type=Relu]; +"13 Conv2" [id=13, type=Convolution]; +"14 Add2" [id=14, type=Add]; +"15 Sigmoid" [id=15, type=Sigmoid]; +"16 Sigmoid/fq_output_0" [id=16, type=FakeQuantize]; +"17 MatMul/fq_weights_1" [id=17, type=FakeQuantize]; +"18 Constant_33914" [id=18, type=Constant]; +"19 Constant_33913" [id=19, type=Constant]; +"20 Constant_33912" [id=20, type=Constant]; +"21 Constant_33911" [id=21, type=Constant]; +"22 Constant_31414" [id=22, type=Constant]; +"23 Constant_33909" [id=23, type=Constant]; +"24 Constant_33908" [id=24, type=Constant]; +"25 Constant_33907" [id=25, type=Constant]; +"26 Constant_33906" [id=26, type=Constant]; +"27 Constant_33904" [id=27, type=Constant]; +"28 Constant_33903" [id=28, type=Constant]; +"29 Constant_33902" [id=29, type=Constant]; +"30 Constant_33901" [id=30, type=Constant]; +"31 Constant_31410" [id=31, type=Constant]; +"32 Constant_31408" [id=32, type=Constant]; +"33 Constant_31405" [id=33, type=Constant]; +"34 Constant_31403" [id=34, type=Constant]; +"35 Constant_31401" [id=35, type=Constant]; +"36 Constant_33899" [id=36, type=Constant]; +"37 Constant_33898" [id=37, type=Constant]; +"38 Constant_33897" [id=38, type=Constant]; +"39 Constant_33896" [id=39, type=Constant]; +"40 Constant_31399" [id=40, type=Constant]; +"41 Conv0/fq_weights_1" [id=41, type=FakeQuantize]; +"42 Constant_33894" [id=42, type=Constant]; +"43 Constant_33893" [id=43, type=Constant]; +"44 Constant_33892" [id=44, type=Constant]; +"45 Constant_33891" [id=45, type=Constant]; +"46 Constant_31397" [id=46, type=Constant]; +"47 Constant_33889" [id=47, type=Constant]; +"48 Constant_33888" [id=48, type=Constant]; +"49 Constant_33887" [id=49, type=Constant]; +"50 Constant_33886" [id=50, type=Constant]; +"0 Input" -> "1 Input/fq_output_0" [label="[1, 3, 5, 6]", style=solid]; +"1 Input/fq_output_0" -> "2 Conv0" [label="[1, 3, 5, 6]", style=solid]; +"2 Conv0" -> "3 Add0" [label="[1, 3, 5, 6]", style=solid]; +"3 Add0" -> "4 Add0/fq_output_0" [label="[1, 3, 5, 6]", style=solid]; +"4 Add0/fq_output_0" -> "5 Mul" [label="[1, 3, 5, 6]", style=solid]; +"4 Add0/fq_output_0" -> "6 ReduceMean_31402" [label="[1, 3, 5, 6]", style=solid]; +"5 Mul" -> "7 Mul/fq_output_0" [label="[1, 3, 5, 6]", style=solid]; +"6 ReduceMean_31402" -> "8 Conv" [label="[1, 3, 1, 1]", style=solid]; +"7 Mul/fq_output_0" -> "9 MatMul" [label="[1, 3, 5, 6]", style=solid]; +"8 Conv" -> "10 Add" [label="[1, 5, 1, 1]", style=solid]; +"9 MatMul" -> "11 Result" [label="[1, 3, 5, 5]", style=solid]; +"10 Add" -> "12 Relu" [label="[1, 5, 1, 1]", style=solid]; +"12 Relu" -> "13 Conv2" [label="[1, 5, 1, 1]", style=solid]; +"13 Conv2" -> "14 Add2" [label="[1, 3, 1, 1]", style=solid]; +"14 Add2" -> "15 Sigmoid" [label="[1, 3, 1, 1]", style=solid]; +"15 Sigmoid" -> "16 Sigmoid/fq_output_0" [label="[1, 3, 1, 1]", style=solid]; +"16 Sigmoid/fq_output_0" -> "5 Mul" [label="[1, 3, 1, 1]", style=solid]; +"17 MatMul/fq_weights_1" -> "9 MatMul" [label="[6, 5]", style=solid]; +"18 Constant_33914" -> "17 MatMul/fq_weights_1" [label="[1, 5]", style=solid]; +"19 Constant_33913" -> "17 MatMul/fq_weights_1" [label="[1, 5]", style=solid]; +"20 Constant_33912" -> "17 MatMul/fq_weights_1" [label="[1, 5]", style=solid]; +"21 Constant_33911" -> "17 MatMul/fq_weights_1" [label="[1, 5]", style=solid]; +"22 Constant_31414" -> "17 MatMul/fq_weights_1" [label="[6, 5]", style=solid]; +"23 Constant_33909" -> "7 Mul/fq_output_0" [label="[]", style=solid]; +"24 Constant_33908" -> "7 Mul/fq_output_0" [label="[]", style=solid]; +"25 Constant_33907" -> "7 Mul/fq_output_0" [label="[]", style=solid]; +"26 Constant_33906" -> "7 Mul/fq_output_0" [label="[]", style=solid]; +"27 Constant_33904" -> "16 Sigmoid/fq_output_0" [label="[]", style=solid]; +"28 Constant_33903" -> "16 Sigmoid/fq_output_0" [label="[]", style=solid]; +"29 Constant_33902" -> "16 Sigmoid/fq_output_0" [label="[]", style=solid]; +"30 Constant_33901" -> "16 Sigmoid/fq_output_0" [label="[]", style=solid]; +"31 Constant_31410" -> "14 Add2" [label="[1, 3, 1, 1]", style=solid]; +"32 Constant_31408" -> "13 Conv2" [label="[3, 5, 1, 1]", style=solid]; +"33 Constant_31405" -> "10 Add" [label="[1, 5, 1, 1]", style=solid]; +"34 Constant_31403" -> "8 Conv" [label="[5, 3, 1, 1]", style=solid]; +"35 Constant_31401" -> "6 ReduceMean_31402" [label="[2]", style=dashed]; +"36 Constant_33899" -> "4 Add0/fq_output_0" [label="[]", style=solid]; +"37 Constant_33898" -> "4 Add0/fq_output_0" [label="[]", style=solid]; +"38 Constant_33897" -> "4 Add0/fq_output_0" [label="[]", style=solid]; +"39 Constant_33896" -> "4 Add0/fq_output_0" [label="[]", style=solid]; +"40 Constant_31399" -> "3 Add0" [label="[1, 3, 1, 1]", style=solid]; +"41 Conv0/fq_weights_1" -> "2 Conv0" [label="[3, 3, 1, 1]", style=solid]; +"42 Constant_33894" -> "41 Conv0/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; +"43 Constant_33893" -> "41 Conv0/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; +"44 Constant_33892" -> "41 Conv0/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; +"45 Constant_33891" -> "41 Conv0/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; +"46 Constant_31397" -> "41 Conv0/fq_weights_1" [label="[3, 3, 1, 1]", style=solid]; +"47 Constant_33889" -> "1 Input/fq_output_0" [label="[]", style=solid]; +"48 Constant_33888" -> "1 Input/fq_output_0" [label="[]", style=solid]; +"49 Constant_33887" -> "1 Input/fq_output_0" [label="[]", style=solid]; +"50 Constant_33886" -> "1 Input/fq_output_0" [label="[]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/ShapeOfModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/ShapeOfModel.dot deleted file mode 120000 index f5ed5e61c3a..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/quantized/ShapeOfModel.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/quantized/ShapeOfModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/ShapeOfModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/ShapeOfModel.dot new file mode 100644 index 00000000000..036af485468 --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/quantized/ShapeOfModel.dot @@ -0,0 +1,123 @@ +strict digraph { +"0 Input" [id=0, type=Parameter]; +"1 Input/fq_output_0" [id=1, type=FakeQuantize]; +"2 Conv_1" [id=2, type=Convolution]; +"3 Conv_Add_1" [id=3, type=Add]; +"4 Conv_Add_1/fq_output_0" [id=4, type=FakeQuantize]; +"5 Reshape_1" [id=5, type=Reshape]; +"6 ShapeOf_1" [id=6, type=ShapeOf]; +"7 Transpose" [id=7, type=Transpose]; +"8 Gather_96538" [id=8, type=Gather]; +"9 Conv_2" [id=9, type=Convolution]; +"10 Concat_96541" [id=10, type=Concat]; +"11 Conv_Add_2" [id=11, type=Add]; +"12 Conv_Add_2/fq_output_0" [id=12, type=FakeQuantize]; +"13 Reshape_2" [id=13, type=Reshape]; +"14 ShapeOf_2" [id=14, type=ShapeOf]; +"15 Conv_3" [id=15, type=Convolution]; +"16 Convert_1" [id=16, type=Convert]; +"17 Conv_Add_3" [id=17, type=Add]; +"18 Multiply" [id=18, type=Multiply]; +"19 Result" [id=19, type=Result]; +"20 Convert_2" [id=20, type=Convert]; +"21 Bias_3" [id=21, type=Constant]; +"22 Conv_3/fq_weights_1" [id=22, type=FakeQuantize]; +"23 Constant_98766" [id=23, type=Constant]; +"24 Constant_98765" [id=24, type=Constant]; +"25 Constant_98764" [id=25, type=Constant]; +"26 Constant_98763" [id=26, type=Constant]; +"27 Constant_96567" [id=27, type=Constant]; +"28 Constant_96553" [id=28, type=Constant]; +"29 Constant_98746" [id=29, type=Constant]; +"30 Constant_98745" [id=30, type=Constant]; +"31 Constant_98744" [id=31, type=Constant]; +"32 Constant_98743" [id=32, type=Constant]; +"33 Bias_2" [id=33, type=Constant]; +"34 Conv_2/fq_weights_1" [id=34, type=FakeQuantize]; +"35 Constant_98761" [id=35, type=Constant]; +"36 Constant_98760" [id=36, type=Constant]; +"37 Constant_98759" [id=37, type=Constant]; +"38 Constant_98758" [id=38, type=Constant]; +"39 Constant_96547" [id=39, type=Constant]; +"40 Constant_96545" [id=40, type=Constant]; +"41 Constant_96537" [id=41, type=Constant]; +"42 Constant_96536" [id=42, type=Constant]; +"43 Constant_98741" [id=43, type=Constant]; +"44 Constant_98740" [id=44, type=Constant]; +"45 Constant_98739" [id=45, type=Constant]; +"46 Constant_98738" [id=46, type=Constant]; +"47 Bias_1" [id=47, type=Constant]; +"48 Conv_1/fq_weights_1" [id=48, type=FakeQuantize]; +"49 Constant_98756" [id=49, type=Constant]; +"50 Constant_98755" [id=50, type=Constant]; +"51 Constant_98754" [id=51, type=Constant]; +"52 Constant_98753" [id=52, type=Constant]; +"53 Constant_96531" [id=53, type=Constant]; +"54 Constant_98751" [id=54, type=Constant]; +"55 Constant_98750" [id=55, type=Constant]; +"56 Constant_98749" [id=56, type=Constant]; +"57 Constant_98748" [id=57, type=Constant]; +"58 Constant_96540" [id=58, type=Constant]; +"59 Constant_96539" [id=59, type=Constant]; +"0 Input" -> "1 Input/fq_output_0" [label="[1, 3, 4, 2]", style=solid]; +"1 Input/fq_output_0" -> "2 Conv_1" [label="[1, 3, 4, 2]", style=solid]; +"2 Conv_1" -> "3 Conv_Add_1" [label="[1, 3, 4, 2]", style=solid]; +"3 Conv_Add_1" -> "4 Conv_Add_1/fq_output_0" [label="[1, 3, 4, 2]", style=solid]; +"4 Conv_Add_1/fq_output_0" -> "5 Reshape_1" [label="[1, 3, 4, 2]", style=solid]; +"4 Conv_Add_1/fq_output_0" -> "6 ShapeOf_1" [label="[1, 3, 4, 2]", style=solid]; +"5 Reshape_1" -> "7 Transpose" [label="[1, 3, 4, 2]", style=solid]; +"6 ShapeOf_1" -> "8 Gather_96538" [label="[4]", style=dashed]; +"7 Transpose" -> "9 Conv_2" [label="[1, 3, 2, 4]", style=solid]; +"8 Gather_96538" -> "10 Concat_96541" [label="[2]", style=dashed]; +"9 Conv_2" -> "11 Conv_Add_2" [label="[1, 3, 2, 4]", style=solid]; +"10 Concat_96541" -> "5 Reshape_1" [label="[4]", style=dashed]; +"11 Conv_Add_2" -> "12 Conv_Add_2/fq_output_0" [label="[1, 3, 2, 4]", style=solid]; +"12 Conv_Add_2/fq_output_0" -> "13 Reshape_2" [label="[1, 3, 2, 4]", style=solid]; +"12 Conv_Add_2/fq_output_0" -> "14 ShapeOf_2" [label="[1, 3, 2, 4]", style=solid]; +"13 Reshape_2" -> "15 Conv_3" [label="[1, 3, 2, 4]", style=solid]; +"14 ShapeOf_2" -> "16 Convert_1" [label="[4]", style=dashed]; +"15 Conv_3" -> "17 Conv_Add_3" [label="[1, 3, 2, 4]", style=solid]; +"16 Convert_1" -> "18 Multiply" [label="[4]", style=solid]; +"17 Conv_Add_3" -> "19 Result" [label="[1, 3, 2, 4]", style=solid]; +"18 Multiply" -> "20 Convert_2" [label="[4]", style=solid]; +"20 Convert_2" -> "13 Reshape_2" [label="[4]", style=dashed]; +"21 Bias_3" -> "17 Conv_Add_3" [label="[1, 3, 1, 1]", style=solid]; +"22 Conv_3/fq_weights_1" -> "15 Conv_3" [label="[3, 3, 1, 1]", style=solid]; +"23 Constant_98766" -> "22 Conv_3/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; +"24 Constant_98765" -> "22 Conv_3/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; +"25 Constant_98764" -> "22 Conv_3/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; +"26 Constant_98763" -> "22 Conv_3/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; +"27 Constant_96567" -> "22 Conv_3/fq_weights_1" [label="[3, 3, 1, 1]", style=solid]; +"28 Constant_96553" -> "18 Multiply" [label="[4]", style=solid]; +"29 Constant_98746" -> "12 Conv_Add_2/fq_output_0" [label="[]", style=solid]; +"30 Constant_98745" -> "12 Conv_Add_2/fq_output_0" [label="[]", style=solid]; +"31 Constant_98744" -> "12 Conv_Add_2/fq_output_0" [label="[]", style=solid]; +"32 Constant_98743" -> "12 Conv_Add_2/fq_output_0" [label="[]", style=solid]; +"33 Bias_2" -> "11 Conv_Add_2" [label="[1, 3, 1, 1]", style=solid]; +"34 Conv_2/fq_weights_1" -> "9 Conv_2" [label="[3, 3, 1, 1]", style=solid]; +"35 Constant_98761" -> "34 Conv_2/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; +"36 Constant_98760" -> "34 Conv_2/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; +"37 Constant_98759" -> "34 Conv_2/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; +"38 Constant_98758" -> "34 Conv_2/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; +"39 Constant_96547" -> "34 Conv_2/fq_weights_1" [label="[3, 3, 1, 1]", style=solid]; +"40 Constant_96545" -> "7 Transpose" [label="[4]", style=dashed]; +"41 Constant_96537" -> "8 Gather_96538" [label="[]", style=dashed]; +"42 Constant_96536" -> "8 Gather_96538" [label="[2]", style=dashed]; +"43 Constant_98741" -> "4 Conv_Add_1/fq_output_0" [label="[]", style=solid]; +"44 Constant_98740" -> "4 Conv_Add_1/fq_output_0" [label="[]", style=solid]; +"45 Constant_98739" -> "4 Conv_Add_1/fq_output_0" [label="[]", style=solid]; +"46 Constant_98738" -> "4 Conv_Add_1/fq_output_0" [label="[]", style=solid]; +"47 Bias_1" -> "3 Conv_Add_1" [label="[1, 3, 1, 1]", style=solid]; +"48 Conv_1/fq_weights_1" -> "2 Conv_1" [label="[3, 3, 1, 1]", style=solid]; +"49 Constant_98756" -> "48 Conv_1/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; +"50 Constant_98755" -> "48 Conv_1/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; +"51 Constant_98754" -> "48 Conv_1/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; +"52 Constant_98753" -> "48 Conv_1/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; +"53 Constant_96531" -> "48 Conv_1/fq_weights_1" [label="[3, 3, 1, 1]", style=solid]; +"54 Constant_98751" -> "1 Input/fq_output_0" [label="[]", style=solid]; +"55 Constant_98750" -> "1 Input/fq_output_0" [label="[]", style=solid]; +"56 Constant_98749" -> "1 Input/fq_output_0" [label="[]", style=solid]; +"57 Constant_98748" -> "1 Input/fq_output_0" [label="[]", style=solid]; +"58 Constant_96540" -> "10 Concat_96541" [label="[1]", style=dashed]; +"59 Constant_96539" -> "10 Concat_96541" [label="[1]", style=dashed]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/SharedConvModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/SharedConvModel.dot deleted file mode 120000 index 9c69fdae7f5..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/quantized/SharedConvModel.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/quantized/SharedConvModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/SharedConvModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/SharedConvModel.dot new file mode 100644 index 00000000000..7d5cf4429e2 --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/quantized/SharedConvModel.dot @@ -0,0 +1,34 @@ +strict digraph { +"0 Input" [id=0, type=Parameter]; +"1 Input/fq_output_0" [id=1, type=FakeQuantize]; +"2 Conv_1" [id=2, type=Convolution]; +"3 Conv_2" [id=3, type=Convolution]; +"4 Result_1" [id=4, type=Result]; +"5 Result_2" [id=5, type=Result]; +"6 Conv_1/fq_weights_1" [id=6, type=FakeQuantize]; +"7 Constant_2070" [id=7, type=Constant]; +"8 Constant_2069" [id=8, type=Constant]; +"9 Constant_2068" [id=9, type=Constant]; +"10 Constant_2067" [id=10, type=Constant]; +"11 Shared_conv_w" [id=11, type=Constant]; +"12 Constant_2065" [id=12, type=Constant]; +"13 Constant_2064" [id=13, type=Constant]; +"14 Constant_2063" [id=14, type=Constant]; +"15 Constant_2062" [id=15, type=Constant]; +"0 Input" -> "1 Input/fq_output_0" [label="[1, 3, 3, 3]", style=solid]; +"1 Input/fq_output_0" -> "2 Conv_1" [label="[1, 3, 3, 3]", style=solid]; +"1 Input/fq_output_0" -> "3 Conv_2" [label="[1, 3, 3, 3]", style=solid]; +"2 Conv_1" -> "4 Result_1" [label="[1, 3, 3, 3]", style=solid]; +"3 Conv_2" -> "5 Result_2" [label="[1, 3, 3, 3]", style=solid]; +"6 Conv_1/fq_weights_1" -> "2 Conv_1" [label="[3, 3, 1, 1]", style=solid]; +"6 Conv_1/fq_weights_1" -> "3 Conv_2" [label="[3, 3, 1, 1]", style=solid]; +"7 Constant_2070" -> "6 Conv_1/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; +"8 Constant_2069" -> "6 Conv_1/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; +"9 Constant_2068" -> "6 Conv_1/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; +"10 Constant_2067" -> "6 Conv_1/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; +"11 Shared_conv_w" -> "6 Conv_1/fq_weights_1" [label="[3, 3, 1, 1]", style=solid]; +"12 Constant_2065" -> "1 Input/fq_output_0" [label="[]", style=solid]; +"13 Constant_2064" -> "1 Input/fq_output_0" [label="[]", style=solid]; +"14 Constant_2063" -> "1 Input/fq_output_0" [label="[]", style=solid]; +"15 Constant_2062" -> "1 Input/fq_output_0" [label="[]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/UnifiedEmbeddingModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/UnifiedEmbeddingModel.dot deleted file mode 120000 index 087d5e713e0..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/quantized/UnifiedEmbeddingModel.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/quantized/UnifiedEmbeddingModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/UnifiedEmbeddingModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/UnifiedEmbeddingModel.dot new file mode 100644 index 00000000000..24cd2d4545e --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/quantized/UnifiedEmbeddingModel.dot @@ -0,0 +1,78 @@ +strict digraph { +"0 Input" [id=0, type=Parameter]; +"1 Convert_1" [id=1, type=Convert]; +"2 MatMul_1/fq_input_0" [id=2, type=FakeQuantize]; +"3 Gather_1" [id=3, type=Gather]; +"4 MatMul_1" [id=4, type=MatMul]; +"5 Concat_12" [id=5, type=Concat]; +"6 MatMul_1/fq_output_0" [id=6, type=FakeQuantize]; +"7 MatMul_2" [id=7, type=MatMul]; +"8 Reshape_1" [id=8, type=Reshape]; +"9 Result" [id=9, type=Result]; +"10 MatMul_2/fq_weights_1" [id=10, type=FakeQuantize]; +"11 Constant_2323" [id=11, type=Constant]; +"12 Constant_2322" [id=12, type=Constant]; +"13 Constant_2321" [id=13, type=Constant]; +"14 Constant_2320" [id=14, type=Constant]; +"15 matmul_2_data" [id=15, type=Constant]; +"16 Constant_8" [id=16, type=Constant]; +"17 Constant_2303" [id=17, type=Constant]; +"18 Constant_2302" [id=18, type=Constant]; +"19 Constant_2301" [id=19, type=Constant]; +"20 Constant_2300" [id=20, type=Constant]; +"21 MatMul_1/fq_weights_1" [id=21, type=FakeQuantize]; +"22 Constant_2318" [id=22, type=Constant]; +"23 Constant_2317" [id=23, type=Constant]; +"24 Constant_2316" [id=24, type=Constant]; +"25 Constant_2315" [id=25, type=Constant]; +"26 matmul_1_data" [id=26, type=Constant]; +"27 Constant_2313" [id=27, type=Constant]; +"28 Constant_2312" [id=28, type=Constant]; +"29 Constant_2311" [id=29, type=Constant]; +"30 Constant_2310" [id=30, type=Constant]; +"31 Constant_4" [id=31, type=Constant]; +"32 Gather_1/fq_weights_0" [id=32, type=FakeQuantize]; +"33 Constant_2308" [id=33, type=Constant]; +"34 Constant_2307" [id=34, type=Constant]; +"35 Constant_2306" [id=35, type=Constant]; +"36 Constant_2305" [id=36, type=Constant]; +"37 gather_1_data" [id=37, type=Constant]; +"0 Input" -> "1 Convert_1" [label="[1, 3]", style=solid]; +"0 Input" -> "2 MatMul_1/fq_input_0" [label="[1, 3]", style=solid]; +"1 Convert_1" -> "3 Gather_1" [label="[1, 3]", style=dashed]; +"2 MatMul_1/fq_input_0" -> "4 MatMul_1" [label="[1, 3]", style=solid]; +"3 Gather_1" -> "5 Concat_12" [label="[1, 3, 5]", style=solid]; +"4 MatMul_1" -> "6 MatMul_1/fq_output_0" [label="[3, 1, 5]", style=solid]; +"5 Concat_12" -> "7 MatMul_2" [label="[1, 6, 5]", style=solid]; +"6 MatMul_1/fq_output_0" -> "8 Reshape_1" [label="[3, 1, 5]", style=solid]; +"7 MatMul_2" -> "9 Result" [label="[1, 6, 1]", style=solid]; +"8 Reshape_1" -> "5 Concat_12" [label="[1, 3, 5]", style=solid]; +"10 MatMul_2/fq_weights_1" -> "7 MatMul_2" [label="[1, 5]", style=solid]; +"11 Constant_2323" -> "10 MatMul_2/fq_weights_1" [label="[1, 1]", style=solid]; +"12 Constant_2322" -> "10 MatMul_2/fq_weights_1" [label="[1, 1]", style=solid]; +"13 Constant_2321" -> "10 MatMul_2/fq_weights_1" [label="[1, 1]", style=solid]; +"14 Constant_2320" -> "10 MatMul_2/fq_weights_1" [label="[1, 1]", style=solid]; +"15 matmul_2_data" -> "10 MatMul_2/fq_weights_1" [label="[1, 5]", style=solid]; +"16 Constant_8" -> "8 Reshape_1" [label="[3]", style=dashed]; +"17 Constant_2303" -> "6 MatMul_1/fq_output_0" [label="[]", style=solid]; +"18 Constant_2302" -> "6 MatMul_1/fq_output_0" [label="[]", style=solid]; +"19 Constant_2301" -> "6 MatMul_1/fq_output_0" [label="[]", style=solid]; +"20 Constant_2300" -> "6 MatMul_1/fq_output_0" [label="[]", style=solid]; +"21 MatMul_1/fq_weights_1" -> "4 MatMul_1" [label="[3, 3, 5]", style=solid]; +"22 Constant_2318" -> "21 MatMul_1/fq_weights_1" [label="[3, 1, 5]", style=solid]; +"23 Constant_2317" -> "21 MatMul_1/fq_weights_1" [label="[3, 1, 5]", style=solid]; +"24 Constant_2316" -> "21 MatMul_1/fq_weights_1" [label="[3, 1, 5]", style=solid]; +"25 Constant_2315" -> "21 MatMul_1/fq_weights_1" [label="[3, 1, 5]", style=solid]; +"26 matmul_1_data" -> "21 MatMul_1/fq_weights_1" [label="[3, 3, 5]", style=solid]; +"27 Constant_2313" -> "2 MatMul_1/fq_input_0" [label="[]", style=solid]; +"28 Constant_2312" -> "2 MatMul_1/fq_input_0" [label="[]", style=solid]; +"29 Constant_2311" -> "2 MatMul_1/fq_input_0" [label="[]", style=solid]; +"30 Constant_2310" -> "2 MatMul_1/fq_input_0" [label="[]", style=solid]; +"31 Constant_4" -> "3 Gather_1" [label="[]", style=dashed]; +"32 Gather_1/fq_weights_0" -> "3 Gather_1" [label="[4, 5]", style=solid]; +"33 Constant_2308" -> "32 Gather_1/fq_weights_0" [label="[]", style=solid]; +"34 Constant_2307" -> "32 Gather_1/fq_weights_0" [label="[]", style=solid]; +"35 Constant_2306" -> "32 Gather_1/fq_weights_0" [label="[]", style=solid]; +"36 Constant_2305" -> "32 Gather_1/fq_weights_0" [label="[]", style=solid]; +"37 gather_1_data" -> "32 Gather_1/fq_weights_0" [label="[4, 5]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/WeightsModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/WeightsModel.dot deleted file mode 120000 index 466d7e3d42a..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/quantized/WeightsModel.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/quantized/WeightsModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/WeightsModel.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/WeightsModel.dot new file mode 100644 index 00000000000..c61a2ce4397 --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/quantized/WeightsModel.dot @@ -0,0 +1,122 @@ +strict digraph { +"0 Input_1" [id=0, type=Parameter]; +"1 Input_1/fq_output_0" [id=1, type=FakeQuantize]; +"2 Conv" [id=2, type=Convolution]; +"3 Conv/fq_output_0" [id=3, type=FakeQuantize]; +"4 Conv_backprop" [id=4, type=ConvolutionBackpropData]; +"5 Conv_backprop/fq_output_0" [id=5, type=FakeQuantize]; +"6 MatMul_1" [id=6, type=MatMul]; +"7 MatMul_1/fq_output_0" [id=7, type=FakeQuantize]; +"8 MatMul" [id=8, type=MatMul]; +"9 MatMul_0" [id=9, type=MatMul]; +"10 Add_15" [id=10, type=Add]; +"11 MatMul_0/fq_output_0" [id=11, type=FakeQuantize]; +"12 Result" [id=12, type=Result]; +"13 Constant_2959" [id=13, type=Constant]; +"14 Constant_2958" [id=14, type=Constant]; +"15 Constant_2957" [id=15, type=Constant]; +"16 Constant_2956" [id=16, type=Constant]; +"17 MatMul_1/fq_weights_1" [id=17, type=FakeQuantize]; +"18 Constant_2964" [id=18, type=Constant]; +"19 Constant_2963" [id=19, type=Constant]; +"20 Constant_2962" [id=20, type=Constant]; +"21 Constant_2961" [id=21, type=Constant]; +"22 weights_1" [id=22, type=Constant]; +"23 Constant_2949" [id=23, type=Constant]; +"24 Constant_2948" [id=24, type=Constant]; +"25 Constant_2947" [id=25, type=Constant]; +"26 Constant_2946" [id=26, type=Constant]; +"27 Constant_5" [id=27, type=Constant]; +"28 Conv_backprop/fq_weights_1" [id=28, type=FakeQuantize]; +"29 Constant_2954" [id=29, type=Constant]; +"30 Constant_2953" [id=30, type=Constant]; +"31 Constant_2952" [id=31, type=Constant]; +"32 Constant_2951" [id=32, type=Constant]; +"33 Constant_4" [id=33, type=Constant]; +"34 Constant_2939" [id=34, type=Constant]; +"35 Constant_2938" [id=35, type=Constant]; +"36 Constant_2937" [id=36, type=Constant]; +"37 Constant_2936" [id=37, type=Constant]; +"38 Conv/fq_weights_1" [id=38, type=FakeQuantize]; +"39 Constant_2944" [id=39, type=Constant]; +"40 Constant_2943" [id=40, type=Constant]; +"41 Constant_2942" [id=41, type=Constant]; +"42 Constant_2941" [id=42, type=Constant]; +"43 Constant_2" [id=43, type=Constant]; +"44 Constant_2934" [id=44, type=Constant]; +"45 Constant_2933" [id=45, type=Constant]; +"46 Constant_2932" [id=46, type=Constant]; +"47 Constant_2931" [id=47, type=Constant]; +"48 Constant_2969" [id=48, type=Constant]; +"49 Constant_2968" [id=49, type=Constant]; +"50 Constant_2967" [id=50, type=Constant]; +"51 Constant_2966" [id=51, type=Constant]; +"52 MatMul_0/fq_weights_0" [id=52, type=FakeQuantize]; +"53 Constant_2974" [id=53, type=Constant]; +"54 Constant_2973" [id=54, type=Constant]; +"55 Constant_2972" [id=55, type=Constant]; +"56 Constant_2971" [id=56, type=Constant]; +"57 weights_0" [id=57, type=Constant]; +"58 MatMul_const" [id=58, type=MatMul]; +"0 Input_1" -> "1 Input_1/fq_output_0" [label="[1, 3, 5, 5]", style=solid]; +"1 Input_1/fq_output_0" -> "2 Conv" [label="[1, 3, 5, 5]", style=solid]; +"2 Conv" -> "3 Conv/fq_output_0" [label="[1, 3, 5, 5]", style=solid]; +"3 Conv/fq_output_0" -> "4 Conv_backprop" [label="[1, 3, 5, 5]", style=solid]; +"4 Conv_backprop" -> "5 Conv_backprop/fq_output_0" [label="[1, 3, 1, 1]", style=solid]; +"5 Conv_backprop/fq_output_0" -> "6 MatMul_1" [label="[1, 3, 1, 1]", style=solid]; +"6 MatMul_1" -> "7 MatMul_1/fq_output_0" [label="[1, 3, 1, 4]", style=solid]; +"7 MatMul_1/fq_output_0" -> "8 MatMul" [label="[1, 3, 1, 4]", style=solid]; +"7 MatMul_1/fq_output_0" -> "9 MatMul_0" [label="[1, 3, 1, 4]", style=solid]; +"8 MatMul" -> "10 Add_15" [label="[1, 3, 1, 1]", style=solid]; +"9 MatMul_0" -> "11 MatMul_0/fq_output_0" [label="[1, 3, 1, 4]", style=solid]; +"10 Add_15" -> "12 Result" [label="[1, 3, 4, 1]", style=solid]; +"11 MatMul_0/fq_output_0" -> "8 MatMul" [label="[1, 3, 1, 4]", style=solid]; +"13 Constant_2959" -> "7 MatMul_1/fq_output_0" [label="[]", style=solid]; +"14 Constant_2958" -> "7 MatMul_1/fq_output_0" [label="[]", style=solid]; +"15 Constant_2957" -> "7 MatMul_1/fq_output_0" [label="[]", style=solid]; +"16 Constant_2956" -> "7 MatMul_1/fq_output_0" [label="[]", style=solid]; +"17 MatMul_1/fq_weights_1" -> "6 MatMul_1" [label="[1, 4]", style=solid]; +"18 Constant_2964" -> "17 MatMul_1/fq_weights_1" [label="[1, 4]", style=solid]; +"19 Constant_2963" -> "17 MatMul_1/fq_weights_1" [label="[1, 4]", style=solid]; +"20 Constant_2962" -> "17 MatMul_1/fq_weights_1" [label="[1, 4]", style=solid]; +"21 Constant_2961" -> "17 MatMul_1/fq_weights_1" [label="[1, 4]", style=solid]; +"22 weights_1" -> "17 MatMul_1/fq_weights_1" [label="[1, 4]", style=solid]; +"22 weights_1" -> "58 MatMul_const" [label="[1, 4]", style=solid]; +"23 Constant_2949" -> "5 Conv_backprop/fq_output_0" [label="[]", style=solid]; +"24 Constant_2948" -> "5 Conv_backprop/fq_output_0" [label="[]", style=solid]; +"25 Constant_2947" -> "5 Conv_backprop/fq_output_0" [label="[]", style=solid]; +"26 Constant_2946" -> "5 Conv_backprop/fq_output_0" [label="[]", style=solid]; +"27 Constant_5" -> "4 Conv_backprop" [label="[2]", style=dashed]; +"28 Conv_backprop/fq_weights_1" -> "4 Conv_backprop" [label="[3, 3, 1, 1]", style=solid]; +"29 Constant_2954" -> "28 Conv_backprop/fq_weights_1" [label="[1, 3, 1, 1]", style=solid]; +"30 Constant_2953" -> "28 Conv_backprop/fq_weights_1" [label="[1, 3, 1, 1]", style=solid]; +"31 Constant_2952" -> "28 Conv_backprop/fq_weights_1" [label="[1, 3, 1, 1]", style=solid]; +"32 Constant_2951" -> "28 Conv_backprop/fq_weights_1" [label="[1, 3, 1, 1]", style=solid]; +"33 Constant_4" -> "28 Conv_backprop/fq_weights_1" [label="[3, 3, 1, 1]", style=solid]; +"34 Constant_2939" -> "3 Conv/fq_output_0" [label="[]", style=solid]; +"35 Constant_2938" -> "3 Conv/fq_output_0" [label="[]", style=solid]; +"36 Constant_2937" -> "3 Conv/fq_output_0" [label="[]", style=solid]; +"37 Constant_2936" -> "3 Conv/fq_output_0" [label="[]", style=solid]; +"38 Conv/fq_weights_1" -> "2 Conv" [label="[3, 3, 1, 1]", style=solid]; +"39 Constant_2944" -> "38 Conv/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; +"40 Constant_2943" -> "38 Conv/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; +"41 Constant_2942" -> "38 Conv/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; +"42 Constant_2941" -> "38 Conv/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; +"43 Constant_2" -> "38 Conv/fq_weights_1" [label="[3, 3, 1, 1]", style=solid]; +"44 Constant_2934" -> "1 Input_1/fq_output_0" [label="[]", style=solid]; +"45 Constant_2933" -> "1 Input_1/fq_output_0" [label="[]", style=solid]; +"46 Constant_2932" -> "1 Input_1/fq_output_0" [label="[]", style=solid]; +"47 Constant_2931" -> "1 Input_1/fq_output_0" [label="[]", style=solid]; +"48 Constant_2969" -> "11 MatMul_0/fq_output_0" [label="[]", style=solid]; +"49 Constant_2968" -> "11 MatMul_0/fq_output_0" [label="[]", style=solid]; +"50 Constant_2967" -> "11 MatMul_0/fq_output_0" [label="[]", style=solid]; +"51 Constant_2966" -> "11 MatMul_0/fq_output_0" [label="[]", style=solid]; +"52 MatMul_0/fq_weights_0" -> "9 MatMul_0" [label="[1, 1]", style=solid]; +"53 Constant_2974" -> "52 MatMul_0/fq_weights_0" [label="[1, 1]", style=solid]; +"54 Constant_2973" -> "52 MatMul_0/fq_weights_0" [label="[1, 1]", style=solid]; +"55 Constant_2972" -> "52 MatMul_0/fq_weights_0" [label="[1, 1]", style=solid]; +"56 Constant_2971" -> "52 MatMul_0/fq_weights_0" [label="[1, 1]", style=solid]; +"57 weights_0" -> "52 MatMul_0/fq_weights_0" [label="[1, 1]", style=solid]; +"57 weights_0" -> "58 MatMul_const" [label="[1, 1]", style=solid]; +"58 MatMul_const" -> "10 Add_15" [label="[4, 1]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/mobilenet-v2-pytorch_performance.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/mobilenet-v2-pytorch_performance.dot deleted file mode 120000 index 61ed97262bd..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/quantized/mobilenet-v2-pytorch_performance.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/quantized/mobilenet-v2-pytorch_performance.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/mobilenet-v2-pytorch_performance.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/mobilenet-v2-pytorch_performance.dot new file mode 100644 index 00000000000..bff8b3058aa --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/quantized/mobilenet-v2-pytorch_performance.dot @@ -0,0 +1,1715 @@ +strict digraph { +"0 data" [id=0, type=Parameter]; +"1 Multiply_5095" [id=1, type=Multiply]; +"2 Divide_1885" [id=2, type=Add]; +"3 Divide_1885/fq_output_0" [id=3, type=FakeQuantize]; +"4 /features/features.0/features.0.0/Conv/WithoutBiases" [id=4, type=Convolution]; +"5 /features/features.0/features.0.0/Conv" [id=5, type=Add]; +"6 /features/features.0/features.0.2/Clip" [id=6, type=Clamp]; +"7 /features/features.0/features.0.2/Clip/fq_output_0" [id=7, type=FakeQuantize]; +"8 /features/features.1/conv/conv.0/conv.0.0/Conv/WithoutBiases" [id=8, type=GroupConvolution]; +"9 /features/features.1/conv/conv.0/conv.0.0/Conv" [id=9, type=Add]; +"10 /features/features.1/conv/conv.0/conv.0.2/Clip" [id=10, type=Clamp]; +"11 /features/features.1/conv/conv.0/conv.0.2/Clip/fq_output_0" [id=11, type=FakeQuantize]; +"12 /features/features.1/conv/conv.1/Conv/WithoutBiases" [id=12, type=Convolution]; +"13 /features/features.1/conv/conv.1/Conv" [id=13, type=Add]; +"14 /features/features.1/conv/conv.1/Conv/fq_output_0" [id=14, type=FakeQuantize]; +"15 /features/features.2/conv/conv.0/conv.0.0/Conv/WithoutBiases" [id=15, type=Convolution]; +"16 /features/features.2/conv/conv.0/conv.0.0/Conv" [id=16, type=Add]; +"17 /features/features.2/conv/conv.0/conv.0.2/Clip" [id=17, type=Clamp]; +"18 /features/features.2/conv/conv.0/conv.0.2/Clip/fq_output_0" [id=18, type=FakeQuantize]; +"19 /features/features.2/conv/conv.1/conv.1.0/Conv/WithoutBiases" [id=19, type=GroupConvolution]; +"20 /features/features.2/conv/conv.1/conv.1.0/Conv" [id=20, type=Add]; +"21 /features/features.2/conv/conv.1/conv.1.2/Clip" [id=21, type=Clamp]; +"22 /features/features.2/conv/conv.1/conv.1.2/Clip/fq_output_0" [id=22, type=FakeQuantize]; +"23 /features/features.2/conv/conv.2/Conv/WithoutBiases" [id=23, type=Convolution]; +"24 /features/features.2/conv/conv.2/Conv" [id=24, type=Add]; +"25 /features/features.2/conv/conv.2/Conv/fq_output_0" [id=25, type=FakeQuantize]; +"26 /features/features.3/Add" [id=26, type=Add]; +"27 /features/features.3/conv/conv.0/conv.0.0/Conv/WithoutBiases" [id=27, type=Convolution]; +"28 /features/features.3/Add/fq_output_0" [id=28, type=FakeQuantize]; +"29 /features/features.3/conv/conv.0/conv.0.0/Conv" [id=29, type=Add]; +"30 /features/features.4/conv/conv.0/conv.0.0/Conv/WithoutBiases" [id=30, type=Convolution]; +"31 /features/features.3/conv/conv.0/conv.0.2/Clip" [id=31, type=Clamp]; +"32 /features/features.4/conv/conv.0/conv.0.0/Conv" [id=32, type=Add]; +"33 /features/features.3/conv/conv.0/conv.0.2/Clip/fq_output_0" [id=33, type=FakeQuantize]; +"34 /features/features.4/conv/conv.0/conv.0.2/Clip" [id=34, type=Clamp]; +"35 /features/features.3/conv/conv.1/conv.1.0/Conv/WithoutBiases" [id=35, type=GroupConvolution]; +"36 /features/features.4/conv/conv.0/conv.0.2/Clip/fq_output_0" [id=36, type=FakeQuantize]; +"37 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type=Constant]; +"313 Constant_41575" [id=313, type=Constant]; +"314 onnx^^Conv_673" [id=314, label="314 onnx::Conv_673", type=Constant]; +"315 Constant_41573" [id=315, type=Constant]; +"316 Constant_41572" [id=316, type=Constant]; +"317 Constant_41571" [id=317, type=Constant]; +"318 Constant_41570" [id=318, type=Constant]; +"319 Constant_41568" [id=319, type=Constant]; +"320 Constant_41567" [id=320, type=Constant]; +"321 Constant_41566" [id=321, type=Constant]; +"322 Constant_41565" [id=322, type=Constant]; +"323 Reshape_1627" [id=323, type=Constant]; +"324 /features/features.15/conv/conv.2/Conv/WithoutBiases/fq_weights_1" [id=324, type=FakeQuantize]; +"325 Constant_41563" [id=325, type=Constant]; +"326 Constant_41562" [id=326, type=Constant]; +"327 Constant_41561" [id=327, type=Constant]; +"328 Constant_41560" [id=328, type=Constant]; +"329 onnx^^Conv_670" [id=329, label="329 onnx::Conv_670", type=Constant]; +"330 Constant_41558" [id=330, type=Constant]; +"331 Constant_41557" [id=331, type=Constant]; +"332 Constant_41556" [id=332, type=Constant]; +"333 Constant_41555" [id=333, type=Constant]; +"334 Reshape_1608" [id=334, type=Constant]; +"335 /features/features.15/conv/conv.1/conv.1.0/Conv/WithoutBiases/fq_weights_1" [id=335, type=FakeQuantize]; +"336 Constant_41553" [id=336, type=Constant]; +"337 Constant_41552" [id=337, type=Constant]; +"338 Constant_41551" [id=338, type=Constant]; +"339 Constant_41550" [id=339, type=Constant]; +"340 Reshape_1556" [id=340, type=Constant]; +"341 Constant_41548" [id=341, type=Constant]; +"342 Constant_41547" [id=342, type=Constant]; +"343 Constant_41546" [id=343, type=Constant]; +"344 Constant_41545" [id=344, type=Constant]; +"345 Reshape_1541" [id=345, type=Constant]; +"346 /features/features.15/conv/conv.0/conv.0.0/Conv/WithoutBiases/fq_weights_1" [id=346, type=FakeQuantize]; +"347 Constant_41543" [id=347, type=Constant]; +"348 Constant_41542" [id=348, type=Constant]; +"349 Constant_41541" [id=349, type=Constant]; +"350 Constant_41540" [id=350, type=Constant]; +"351 onnx^^Conv_664" [id=351, label="351 onnx::Conv_664", type=Constant]; +"352 Constant_41538" [id=352, type=Constant]; +"353 Constant_41537" [id=353, type=Constant]; +"354 Constant_41536" [id=354, type=Constant]; +"355 Constant_41535" [id=355, type=Constant]; +"356 Reshape_1526" [id=356, type=Constant]; +"357 /features/features.14/conv/conv.2/Conv/WithoutBiases/fq_weights_1" [id=357, type=FakeQuantize]; +"358 Constant_41533" [id=358, type=Constant]; +"359 Constant_41532" [id=359, type=Constant]; +"360 Constant_41531" [id=360, type=Constant]; +"361 Constant_41530" [id=361, type=Constant]; +"362 onnx^^Conv_661" [id=362, label="362 onnx::Conv_661", type=Constant]; +"363 Constant_41528" [id=363, type=Constant]; +"364 Constant_41527" [id=364, type=Constant]; +"365 Constant_41526" [id=365, type=Constant]; +"366 Constant_41525" [id=366, type=Constant]; +"367 Reshape_1507" [id=367, type=Constant]; +"368 /features/features.14/conv/conv.1/conv.1.0/Conv/WithoutBiases/fq_weights_1" [id=368, type=FakeQuantize]; +"369 Constant_41523" [id=369, type=Constant]; +"370 Constant_41522" [id=370, type=Constant]; +"371 Constant_41521" [id=371, type=Constant]; +"372 Constant_41520" [id=372, type=Constant]; +"373 Reshape_1455" [id=373, type=Constant]; +"374 Constant_41518" [id=374, type=Constant]; +"375 Constant_41517" [id=375, type=Constant]; +"376 Constant_41516" [id=376, type=Constant]; +"377 Constant_41515" [id=377, type=Constant]; +"378 Reshape_1440" [id=378, type=Constant]; +"379 /features/features.14/conv/conv.0/conv.0.0/Conv/WithoutBiases/fq_weights_1" [id=379, type=FakeQuantize]; +"380 Constant_41513" [id=380, type=Constant]; +"381 Constant_41512" [id=381, type=Constant]; +"382 Constant_41511" [id=382, type=Constant]; +"383 Constant_41510" [id=383, type=Constant]; +"384 onnx^^Conv_655" [id=384, label="384 onnx::Conv_655", type=Constant]; +"385 Constant_41508" [id=385, type=Constant]; +"386 Constant_41507" [id=386, type=Constant]; +"387 Constant_41506" [id=387, type=Constant]; +"388 Constant_41505" [id=388, type=Constant]; +"389 Constant_41503" [id=389, type=Constant]; +"390 Constant_41502" [id=390, type=Constant]; +"391 Constant_41501" [id=391, type=Constant]; +"392 Constant_41500" [id=392, type=Constant]; +"393 Reshape_1424" [id=393, type=Constant]; +"394 /features/features.13/conv/conv.2/Conv/WithoutBiases/fq_weights_1" [id=394, type=FakeQuantize]; +"395 Constant_41498" [id=395, type=Constant]; +"396 Constant_41497" [id=396, type=Constant]; +"397 Constant_41496" [id=397, type=Constant]; +"398 Constant_41495" [id=398, type=Constant]; +"399 onnx^^Conv_652" [id=399, label="399 onnx::Conv_652", type=Constant]; +"400 Constant_41493" [id=400, type=Constant]; +"401 Constant_41492" [id=401, type=Constant]; +"402 Constant_41491" [id=402, type=Constant]; +"403 Constant_41490" [id=403, type=Constant]; +"404 Reshape_1405" [id=404, type=Constant]; +"405 /features/features.13/conv/conv.1/conv.1.0/Conv/WithoutBiases/fq_weights_1" [id=405, type=FakeQuantize]; +"406 Constant_41488" [id=406, type=Constant]; +"407 Constant_41487" [id=407, type=Constant]; +"408 Constant_41486" [id=408, type=Constant]; +"409 Constant_41485" [id=409, type=Constant]; +"410 Reshape_1353" [id=410, type=Constant]; +"411 Constant_41483" [id=411, type=Constant]; +"412 Constant_41482" [id=412, type=Constant]; +"413 Constant_41481" [id=413, type=Constant]; +"414 Constant_41480" [id=414, type=Constant]; +"415 Reshape_1338" [id=415, type=Constant]; +"416 /features/features.13/conv/conv.0/conv.0.0/Conv/WithoutBiases/fq_weights_1" [id=416, type=FakeQuantize]; +"417 Constant_41478" [id=417, type=Constant]; +"418 Constant_41477" [id=418, type=Constant]; +"419 Constant_41476" [id=419, type=Constant]; +"420 Constant_41475" [id=420, type=Constant]; +"421 onnx^^Conv_646" [id=421, label="421 onnx::Conv_646", type=Constant]; +"422 Constant_41473" [id=422, type=Constant]; +"423 Constant_41472" [id=423, type=Constant]; +"424 Constant_41471" [id=424, type=Constant]; +"425 Constant_41470" [id=425, type=Constant]; +"426 Constant_41468" [id=426, type=Constant]; +"427 Constant_41467" [id=427, type=Constant]; +"428 Constant_41466" [id=428, type=Constant]; +"429 Constant_41465" [id=429, type=Constant]; +"430 Reshape_1322" [id=430, type=Constant]; +"431 /features/features.12/conv/conv.2/Conv/WithoutBiases/fq_weights_1" [id=431, type=FakeQuantize]; +"432 Constant_41463" [id=432, type=Constant]; +"433 Constant_41462" [id=433, type=Constant]; +"434 Constant_41461" [id=434, type=Constant]; +"435 Constant_41460" [id=435, type=Constant]; +"436 onnx^^Conv_643" [id=436, label="436 onnx::Conv_643", type=Constant]; +"437 Constant_41458" [id=437, type=Constant]; +"438 Constant_41457" [id=438, type=Constant]; +"439 Constant_41456" [id=439, type=Constant]; +"440 Constant_41455" [id=440, type=Constant]; +"441 Reshape_1303" [id=441, type=Constant]; +"442 /features/features.12/conv/conv.1/conv.1.0/Conv/WithoutBiases/fq_weights_1" [id=442, type=FakeQuantize]; +"443 Constant_41453" [id=443, type=Constant]; +"444 Constant_41452" [id=444, type=Constant]; +"445 Constant_41451" [id=445, type=Constant]; +"446 Constant_41450" [id=446, type=Constant]; +"447 Reshape_1251" [id=447, type=Constant]; +"448 Constant_41448" [id=448, type=Constant]; +"449 Constant_41447" [id=449, type=Constant]; +"450 Constant_41446" [id=450, type=Constant]; +"451 Constant_41445" [id=451, type=Constant]; +"452 Reshape_1236" [id=452, type=Constant]; +"453 /features/features.12/conv/conv.0/conv.0.0/Conv/WithoutBiases/fq_weights_1" [id=453, type=FakeQuantize]; +"454 Constant_41443" [id=454, type=Constant]; +"455 Constant_41442" [id=455, type=Constant]; +"456 Constant_41441" [id=456, type=Constant]; +"457 Constant_41440" [id=457, type=Constant]; +"458 onnx^^Conv_637" [id=458, label="458 onnx::Conv_637", type=Constant]; +"459 Constant_41438" [id=459, type=Constant]; +"460 Constant_41437" [id=460, type=Constant]; +"461 Constant_41436" [id=461, type=Constant]; +"462 Constant_41435" [id=462, type=Constant]; +"463 Reshape_1221" [id=463, type=Constant]; +"464 /features/features.11/conv/conv.2/Conv/WithoutBiases/fq_weights_1" [id=464, type=FakeQuantize]; +"465 Constant_41433" [id=465, type=Constant]; +"466 Constant_41432" [id=466, type=Constant]; +"467 Constant_41431" [id=467, type=Constant]; +"468 Constant_41430" [id=468, type=Constant]; +"469 onnx^^Conv_634" [id=469, label="469 onnx::Conv_634", type=Constant]; +"470 Constant_41428" [id=470, type=Constant]; +"471 Constant_41427" [id=471, type=Constant]; +"472 Constant_41426" [id=472, type=Constant]; +"473 Constant_41425" [id=473, type=Constant]; +"474 Reshape_1202" [id=474, type=Constant]; +"475 /features/features.11/conv/conv.1/conv.1.0/Conv/WithoutBiases/fq_weights_1" [id=475, type=FakeQuantize]; +"476 Constant_41423" [id=476, type=Constant]; +"477 Constant_41422" [id=477, type=Constant]; +"478 Constant_41421" [id=478, type=Constant]; +"479 Constant_41420" [id=479, type=Constant]; +"480 Reshape_1150" [id=480, type=Constant]; +"481 Constant_41418" [id=481, type=Constant]; +"482 Constant_41417" [id=482, type=Constant]; +"483 Constant_41416" [id=483, type=Constant]; +"484 Constant_41415" [id=484, type=Constant]; +"485 Reshape_1135" [id=485, type=Constant]; +"486 /features/features.11/conv/conv.0/conv.0.0/Conv/WithoutBiases/fq_weights_1" [id=486, type=FakeQuantize]; +"487 Constant_41413" [id=487, type=Constant]; +"488 Constant_41412" [id=488, type=Constant]; +"489 Constant_41411" [id=489, type=Constant]; +"490 Constant_41410" [id=490, type=Constant]; +"491 onnx^^Conv_628" [id=491, label="491 onnx::Conv_628", type=Constant]; +"492 Constant_41408" [id=492, type=Constant]; +"493 Constant_41407" [id=493, type=Constant]; +"494 Constant_41406" [id=494, type=Constant]; +"495 Constant_41405" [id=495, type=Constant]; +"496 Constant_41403" [id=496, type=Constant]; +"497 Constant_41402" [id=497, type=Constant]; +"498 Constant_41401" [id=498, type=Constant]; +"499 Constant_41400" [id=499, type=Constant]; +"500 Reshape_1119" [id=500, type=Constant]; +"501 /features/features.10/conv/conv.2/Conv/WithoutBiases/fq_weights_1" [id=501, type=FakeQuantize]; +"502 Constant_41398" [id=502, type=Constant]; +"503 Constant_41397" [id=503, type=Constant]; +"504 Constant_41396" [id=504, type=Constant]; +"505 Constant_41395" [id=505, type=Constant]; +"506 onnx^^Conv_625" [id=506, label="506 onnx::Conv_625", type=Constant]; +"507 Constant_41393" [id=507, type=Constant]; +"508 Constant_41392" [id=508, type=Constant]; +"509 Constant_41391" [id=509, type=Constant]; +"510 Constant_41390" [id=510, type=Constant]; +"511 Reshape_1100" [id=511, type=Constant]; +"512 /features/features.10/conv/conv.1/conv.1.0/Conv/WithoutBiases/fq_weights_1" [id=512, type=FakeQuantize]; +"513 Constant_41388" [id=513, type=Constant]; +"514 Constant_41387" [id=514, type=Constant]; +"515 Constant_41386" [id=515, type=Constant]; +"516 Constant_41385" [id=516, type=Constant]; +"517 Reshape_1048" [id=517, type=Constant]; +"518 Constant_41383" [id=518, type=Constant]; +"519 Constant_41382" [id=519, type=Constant]; +"520 Constant_41381" [id=520, type=Constant]; +"521 Constant_41380" [id=521, type=Constant]; +"522 Reshape_1033" [id=522, type=Constant]; +"523 /features/features.10/conv/conv.0/conv.0.0/Conv/WithoutBiases/fq_weights_1" [id=523, type=FakeQuantize]; +"524 Constant_41378" [id=524, type=Constant]; +"525 Constant_41377" [id=525, type=Constant]; +"526 Constant_41376" [id=526, type=Constant]; +"527 Constant_41375" [id=527, type=Constant]; +"528 onnx^^Conv_619" [id=528, label="528 onnx::Conv_619", type=Constant]; +"529 Constant_41373" [id=529, type=Constant]; +"530 Constant_41372" [id=530, type=Constant]; +"531 Constant_41371" [id=531, type=Constant]; +"532 Constant_41370" [id=532, type=Constant]; +"533 Constant_41368" [id=533, type=Constant]; +"534 Constant_41367" [id=534, type=Constant]; +"535 Constant_41366" [id=535, type=Constant]; +"536 Constant_41365" [id=536, type=Constant]; +"537 Reshape_1017" [id=537, type=Constant]; +"538 /features/features.9/conv/conv.2/Conv/WithoutBiases/fq_weights_1" [id=538, type=FakeQuantize]; +"539 Constant_41363" [id=539, type=Constant]; +"540 Constant_41362" [id=540, type=Constant]; +"541 Constant_41361" [id=541, type=Constant]; +"542 Constant_41360" [id=542, type=Constant]; +"543 onnx^^Conv_616" [id=543, label="543 onnx::Conv_616", type=Constant]; +"544 Constant_41358" [id=544, type=Constant]; +"545 Constant_41357" [id=545, type=Constant]; +"546 Constant_41356" [id=546, type=Constant]; +"547 Constant_41355" [id=547, type=Constant]; +"548 Reshape_998" [id=548, type=Constant]; +"549 /features/features.9/conv/conv.1/conv.1.0/Conv/WithoutBiases/fq_weights_1" [id=549, type=FakeQuantize]; +"550 Constant_41353" [id=550, type=Constant]; +"551 Constant_41352" [id=551, type=Constant]; +"552 Constant_41351" [id=552, type=Constant]; +"553 Constant_41350" [id=553, type=Constant]; +"554 Reshape_946" [id=554, type=Constant]; +"555 Constant_41348" [id=555, type=Constant]; +"556 Constant_41347" [id=556, type=Constant]; +"557 Constant_41346" [id=557, type=Constant]; +"558 Constant_41345" [id=558, type=Constant]; +"559 Reshape_931" [id=559, type=Constant]; +"560 /features/features.9/conv/conv.0/conv.0.0/Conv/WithoutBiases/fq_weights_1" [id=560, type=FakeQuantize]; +"561 Constant_41343" [id=561, type=Constant]; +"562 Constant_41342" [id=562, type=Constant]; +"563 Constant_41341" [id=563, type=Constant]; +"564 Constant_41340" [id=564, type=Constant]; +"565 onnx^^Conv_610" [id=565, label="565 onnx::Conv_610", type=Constant]; +"566 Constant_41338" [id=566, type=Constant]; +"567 Constant_41337" [id=567, type=Constant]; +"568 Constant_41336" [id=568, type=Constant]; +"569 Constant_41335" [id=569, type=Constant]; +"570 Constant_41333" [id=570, type=Constant]; +"571 Constant_41332" [id=571, type=Constant]; +"572 Constant_41331" [id=572, type=Constant]; +"573 Constant_41330" [id=573, type=Constant]; +"574 Reshape_915" [id=574, type=Constant]; +"575 /features/features.8/conv/conv.2/Conv/WithoutBiases/fq_weights_1" [id=575, type=FakeQuantize]; +"576 Constant_41328" [id=576, type=Constant]; +"577 Constant_41327" [id=577, type=Constant]; +"578 Constant_41326" [id=578, type=Constant]; +"579 Constant_41325" [id=579, type=Constant]; +"580 onnx^^Conv_607" [id=580, label="580 onnx::Conv_607", type=Constant]; +"581 Constant_41323" [id=581, type=Constant]; +"582 Constant_41322" [id=582, type=Constant]; +"583 Constant_41321" [id=583, type=Constant]; +"584 Constant_41320" [id=584, type=Constant]; +"585 Reshape_896" [id=585, type=Constant]; +"586 /features/features.8/conv/conv.1/conv.1.0/Conv/WithoutBiases/fq_weights_1" [id=586, type=FakeQuantize]; +"587 Constant_41318" [id=587, type=Constant]; +"588 Constant_41317" [id=588, type=Constant]; +"589 Constant_41316" [id=589, type=Constant]; +"590 Constant_41315" [id=590, type=Constant]; +"591 Reshape_844" [id=591, type=Constant]; +"592 Constant_41313" [id=592, type=Constant]; +"593 Constant_41312" [id=593, type=Constant]; +"594 Constant_41311" [id=594, type=Constant]; +"595 Constant_41310" [id=595, type=Constant]; +"596 Reshape_829" [id=596, type=Constant]; +"597 /features/features.8/conv/conv.0/conv.0.0/Conv/WithoutBiases/fq_weights_1" [id=597, type=FakeQuantize]; +"598 Constant_41308" [id=598, type=Constant]; +"599 Constant_41307" [id=599, type=Constant]; +"600 Constant_41306" [id=600, type=Constant]; +"601 Constant_41305" [id=601, type=Constant]; +"602 onnx^^Conv_601" [id=602, label="602 onnx::Conv_601", type=Constant]; +"603 Constant_41303" [id=603, type=Constant]; +"604 Constant_41302" [id=604, type=Constant]; +"605 Constant_41301" [id=605, type=Constant]; +"606 Constant_41300" [id=606, type=Constant]; +"607 Reshape_814" [id=607, type=Constant]; +"608 /features/features.7/conv/conv.2/Conv/WithoutBiases/fq_weights_1" [id=608, type=FakeQuantize]; +"609 Constant_41298" [id=609, type=Constant]; +"610 Constant_41297" [id=610, type=Constant]; +"611 Constant_41296" [id=611, type=Constant]; +"612 Constant_41295" [id=612, type=Constant]; +"613 onnx^^Conv_598" [id=613, label="613 onnx::Conv_598", type=Constant]; +"614 Constant_41293" [id=614, type=Constant]; +"615 Constant_41292" [id=615, type=Constant]; +"616 Constant_41291" [id=616, type=Constant]; +"617 Constant_41290" [id=617, type=Constant]; +"618 Reshape_795" [id=618, type=Constant]; +"619 /features/features.7/conv/conv.1/conv.1.0/Conv/WithoutBiases/fq_weights_1" [id=619, type=FakeQuantize]; +"620 Constant_41288" [id=620, type=Constant]; +"621 Constant_41287" [id=621, type=Constant]; +"622 Constant_41286" [id=622, type=Constant]; +"623 Constant_41285" [id=623, type=Constant]; +"624 Reshape_743" [id=624, type=Constant]; +"625 Constant_41283" [id=625, type=Constant]; +"626 Constant_41282" [id=626, type=Constant]; +"627 Constant_41281" [id=627, type=Constant]; +"628 Constant_41280" [id=628, type=Constant]; +"629 Reshape_728" [id=629, type=Constant]; +"630 /features/features.7/conv/conv.0/conv.0.0/Conv/WithoutBiases/fq_weights_1" [id=630, type=FakeQuantize]; +"631 Constant_41278" [id=631, type=Constant]; +"632 Constant_41277" [id=632, type=Constant]; +"633 Constant_41276" [id=633, type=Constant]; +"634 Constant_41275" [id=634, type=Constant]; +"635 onnx^^Conv_592" [id=635, label="635 onnx::Conv_592", type=Constant]; +"636 Constant_41273" [id=636, type=Constant]; +"637 Constant_41272" [id=637, type=Constant]; +"638 Constant_41271" [id=638, type=Constant]; +"639 Constant_41270" [id=639, type=Constant]; +"640 Constant_41268" [id=640, type=Constant]; +"641 Constant_41267" [id=641, type=Constant]; +"642 Constant_41266" [id=642, type=Constant]; +"643 Constant_41265" [id=643, type=Constant]; +"644 Reshape_712" [id=644, type=Constant]; +"645 /features/features.6/conv/conv.2/Conv/WithoutBiases/fq_weights_1" [id=645, type=FakeQuantize]; +"646 Constant_41263" [id=646, type=Constant]; +"647 Constant_41262" [id=647, type=Constant]; +"648 Constant_41261" [id=648, type=Constant]; +"649 Constant_41260" [id=649, type=Constant]; +"650 onnx^^Conv_589" [id=650, label="650 onnx::Conv_589", type=Constant]; +"651 Constant_41258" [id=651, type=Constant]; +"652 Constant_41257" [id=652, type=Constant]; +"653 Constant_41256" [id=653, type=Constant]; +"654 Constant_41255" [id=654, type=Constant]; +"655 Reshape_693" [id=655, type=Constant]; +"656 /features/features.6/conv/conv.1/conv.1.0/Conv/WithoutBiases/fq_weights_1" [id=656, type=FakeQuantize]; +"657 Constant_41253" [id=657, type=Constant]; +"658 Constant_41252" [id=658, type=Constant]; +"659 Constant_41251" [id=659, type=Constant]; +"660 Constant_41250" [id=660, type=Constant]; +"661 Reshape_641" [id=661, type=Constant]; +"662 Constant_41248" [id=662, type=Constant]; +"663 Constant_41247" [id=663, type=Constant]; +"664 Constant_41246" [id=664, type=Constant]; +"665 Constant_41245" [id=665, type=Constant]; +"666 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+"848 Constant_41076" -> "3 Divide_1885/fq_output_0" [label="[]", style=solid]; +"849 Constant_41075" -> "3 Divide_1885/fq_output_0" [label="[]", style=solid]; +"850 Gather_5165" -> "2 Divide_1885" [label="[1, 3, 1, 1]", style=solid]; +"851 Gather_5162" -> "1 Multiply_5095" [label="[1, 3, 1, 1]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/mobilenet-v3-small-1.0-224-tf_performance.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/mobilenet-v3-small-1.0-224-tf_performance.dot deleted file mode 120000 index f1a084c8e6e..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/quantized/mobilenet-v3-small-1.0-224-tf_performance.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/quantized/mobilenet-v3-small-1.0-224-tf_performance.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/mobilenet-v3-small-1.0-224-tf_performance.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/mobilenet-v3-small-1.0-224-tf_performance.dot new file mode 100644 index 00000000000..46e74566032 --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/quantized/mobilenet-v3-small-1.0-224-tf_performance.dot @@ -0,0 +1,1598 @@ +strict digraph { +"0 input_1" [id=0, type=Parameter]; +"1 Transpose_7780" [id=1, type=Transpose]; +"2 Transpose_7774" [id=2, type=Multiply]; +"3 Transpose_710" [id=3, type=Add]; +"4 Transpose_710/fq_output_0" [id=4, type=FakeQuantize]; +"5 Multiply_9167" [id=5, type=Convolution]; +"6 Transpose_5170" [id=6, type=Add]; +"7 Transpose_5188" [id=7, type=HSwish]; +"8 Transpose_5188/fq_output_0" [id=8, type=FakeQuantize]; +"9 Multiply_9181" [id=9, type=GroupConvolution]; +"10 Transpose_5239" [id=10, type=Add]; +"11 Transpose_5241" [id=11, type=Relu]; +"12 Transpose_5241/fq_output_0" [id=12, type=FakeQuantize]; +"13 Transpose_5245" [id=13, type=ReduceMean]; +"14 Transpose_5277" [id=14, 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[id=35, type=Add]; +"36 Transpose_5404/fq_output_0" [id=36, type=FakeQuantize]; +"37 Multiply_9251" [id=37, type=Convolution]; +"38 Transpose_5484" [id=38, type=Add]; +"39 Transpose_5428" [id=39, type=Add]; +"40 Transpose_5484/fq_output_0" [id=40, type=FakeQuantize]; +"41 Transpose_5430" [id=41, type=Relu]; +"42 Multiply_9293" [id=42, type=Convolution]; +"43 Transpose_5430/fq_output_0" [id=43, type=FakeQuantize]; +"44 Transpose_5508" [id=44, type=Add]; +"45 Multiply_9265" [id=45, type=GroupConvolution]; +"46 Transpose_5526" [id=46, type=HSwish]; +"47 Transpose_5454" [id=47, type=Add]; +"48 Transpose_5526/fq_output_0" [id=48, type=FakeQuantize]; +"49 Transpose_5456" [id=49, type=Relu]; +"50 Multiply_9307" [id=50, type=GroupConvolution]; +"51 Transpose_5456/fq_output_0" [id=51, type=FakeQuantize]; +"52 Transpose_5577" [id=52, type=Add]; +"53 Multiply_9279" [id=53, type=Convolution]; +"54 Transpose_5595" [id=54, type=HSwish]; +"55 Transpose_5480" [id=55, type=Add]; +"56 Transpose_5595/fq_output_0" [id=56, type=FakeQuantize]; +"57 Transpose_5480/fq_output_0" [id=57, type=FakeQuantize]; +"58 Transpose_5599" [id=58, type=ReduceMean]; +"59 Transpose_5631" [id=59, type=Multiply]; +"60 Convolution_1132" [id=60, type=Convolution]; +"61 Transpose_5631/fq_output_0" [id=61, type=FakeQuantize]; +"62 Transpose_5605" [id=62, type=Add]; +"63 Multiply_9321" [id=63, type=Convolution]; +"64 Transpose_5607" [id=64, type=Relu]; +"65 Transpose_5655" [id=65, type=Add]; +"66 Convolution_1141" [id=66, type=Convolution]; +"67 Transpose_5655/fq_output_0" [id=67, type=FakeQuantize]; +"68 Transpose_5613" [id=68, type=Add]; +"69 Multiply_9335" [id=69, type=Convolution]; +"70 Transpose_5803" [id=70, type=Add]; +"71 Transpose_5627" [id=71, type=HSigmoid]; +"72 Transpose_5679" [id=72, type=Add]; +"73 Transpose_5803/fq_output_0" [id=73, type=FakeQuantize]; +"74 Transpose_5627/fq_output_0" [id=74, type=FakeQuantize]; +"75 Transpose_5697" [id=75, type=HSwish]; +"76 Multiply_9377" 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Transpose_6604/fq_output_0" [id=196, type=FakeQuantize]; +"197 Transpose_6734" [id=197, type=Add]; +"198 Transpose_6498" [id=198, type=HSwish]; +"199 Multiply_9601" [id=199, type=GroupConvolution]; +"200 Transpose_6752" [id=200, type=HSwish]; +"201 Transpose_6498/fq_output_0" [id=201, type=FakeQuantize]; +"202 Transpose_6628" [id=202, type=Add]; +"203 Transpose_6752/fq_output_0" [id=203, type=FakeQuantize]; +"204 Transpose_6502" [id=204, type=ReduceMean]; +"205 Transpose_6534" [id=205, type=Multiply]; +"206 Transpose_6646" [id=206, type=HSwish]; +"207 Transpose_6756" [id=207, type=ReduceMean]; +"208 Convolution_1823" [id=208, type=Convolution]; +"209 Transpose_6534/fq_output_0" [id=209, type=FakeQuantize]; +"210 Transpose_6646/fq_output_0" [id=210, type=FakeQuantize]; +"211 Transpose_6756/fq_output_0" [id=211, type=FakeQuantize]; +"212 Transpose_6508" [id=212, type=Add]; +"213 Multiply_9573" [id=213, type=Convolution]; +"214 Transpose_6650" [id=214, type=ReduceMean]; +"215 Transpose_6682" [id=215, type=Multiply]; +"216 Convolution_2013" [id=216, type=Convolution]; +"217 Transpose_6510" [id=217, type=Relu]; +"218 Transpose_6558" [id=218, type=Add]; +"219 Convolution_1934" [id=219, type=Convolution]; +"220 Transpose_6682/fq_output_0" [id=220, type=FakeQuantize]; +"221 Transpose_6762" [id=221, type=Add]; +"222 Convolution_1832" [id=222, type=Convolution]; +"223 Transpose_6558/fq_output_0" [id=223, type=FakeQuantize]; +"224 Transpose_6656" [id=224, type=Add]; +"225 Multiply_9615" [id=225, type=Convolution]; +"226 Transpose_6780" [id=226, type=HSwish]; +"227 Transpose_6516" [id=227, type=Add]; +"228 Transpose_6658" [id=228, type=Relu]; +"229 Transpose_6706" [id=229, type=Add]; +"230 Transpose_6780/fq_output_0" [id=230, type=FakeQuantize]; +"231 Transpose_6530" [id=231, type=HSigmoid]; +"232 Convolution_1943" [id=232, type=Convolution]; +"233 Transpose_6706/fq_output_0" [id=233, type=FakeQuantize]; +"234 Convolution_2025" [id=234, type=Convolution]; +"235 Transpose_6530/fq_output_0" [id=235, type=FakeQuantize]; +"236 Transpose_6664" [id=236, type=Add]; +"237 Transpose_6786" [id=237, type=Add]; +"238 Transpose_6678" [id=238, type=HSigmoid]; +"239 MobilenetV3small/Logits/BiasAdd" [id=239, type=Reshape]; +"240 Transpose_6678/fq_output_0" [id=240, type=FakeQuantize]; +"241 MobilenetV3small/flatten/Reshape" [id=241, type=Reshape]; +"242 MobilenetV3small/Predictions/Softmax" [id=242, type=Softmax]; +"243 Predictions" [id=243, type=Result]; +"244 MobilenetV3small/flatten/Const" [id=244, type=Constant]; +"245 Constant_8887" [id=245, type=Constant]; +"246 Transpose_6784" [id=246, type=Constant]; +"247 Convolution_2025/fq_weights_1" [id=247, type=FakeQuantize]; +"248 Constant_11502" [id=248, type=Constant]; +"249 Constant_11501" [id=249, type=Constant]; +"250 Constant_11500" [id=250, type=Constant]; +"251 Constant_11499" [id=251, type=Constant]; +"252 Transpose_2024" [id=252, type=Constant]; +"253 Constant_11497" [id=253, type=Constant]; +"254 Constant_11496" [id=254, type=Constant]; +"255 Constant_11495" [id=255, type=Constant]; +"256 Constant_11494" [id=256, type=Constant]; +"257 Transpose_6760" [id=257, type=Constant]; +"258 Convolution_2013/fq_weights_1" [id=258, type=FakeQuantize]; +"259 Constant_11492" [id=259, type=Constant]; +"260 Constant_11491" [id=260, type=Constant]; +"261 Constant_11490" [id=261, type=Constant]; +"262 Constant_11489" [id=262, type=Constant]; +"263 Transpose_2012" [id=263, type=Constant]; +"264 Constant_11487" [id=264, type=Constant]; +"265 Constant_11486" [id=265, type=Constant]; +"266 Constant_11485" [id=266, type=Constant]; +"267 Constant_11484" [id=267, type=Constant]; +"268 Constant_6754" [id=268, type=Constant]; +"269 Constant_11482" [id=269, type=Constant]; +"270 Constant_11481" [id=270, type=Constant]; +"271 Constant_11480" [id=271, type=Constant]; +"272 Constant_11479" [id=272, type=Constant]; +"273 Constant_9637" [id=273, type=Constant]; +"274 Multiply_9629/fq_weights_1" [id=274, type=FakeQuantize]; +"275 Constant_11477" [id=275, type=Constant]; +"276 Constant_11476" [id=276, type=Constant]; +"277 Constant_11475" [id=277, type=Constant]; +"278 Constant_11474" [id=278, type=Constant]; +"279 Multiply_9830" [id=279, type=Constant]; +"280 Constant_11472" [id=280, type=Constant]; +"281 Constant_11471" [id=281, type=Constant]; +"282 Constant_11470" [id=282, type=Constant]; +"283 Constant_11469" [id=283, type=Constant]; +"284 Constant_11467" [id=284, type=Constant]; +"285 Constant_11466" [id=285, type=Constant]; +"286 Constant_11465" [id=286, type=Constant]; +"287 Constant_11464" [id=287, type=Constant]; +"288 Constant_9623" [id=288, type=Constant]; +"289 Multiply_9615/fq_weights_1" [id=289, type=FakeQuantize]; +"290 Constant_11462" [id=290, type=Constant]; +"291 Constant_11461" [id=291, type=Constant]; +"292 Constant_11460" [id=292, type=Constant]; +"293 Constant_11459" [id=293, type=Constant]; +"294 Multiply_9824" [id=294, type=Constant]; +"295 Constant_11457" [id=295, type=Constant]; +"296 Constant_11456" [id=296, type=Constant]; +"297 Constant_11455" [id=297, type=Constant]; +"298 Constant_11454" [id=298, type=Constant]; +"299 Constant_11452" [id=299, type=Constant]; +"300 Constant_11451" [id=300, type=Constant]; +"301 Constant_11450" [id=301, type=Constant]; +"302 Constant_11449" [id=302, type=Constant]; +"303 Transpose_6662" [id=303, type=Constant]; +"304 Transpose_1942" [id=304, type=Constant]; +"305 Transpose_6654" [id=305, type=Constant]; +"306 Transpose_1933" [id=306, type=Constant]; +"307 Constant_6648" [id=307, type=Constant]; +"308 Constant_11447" [id=308, type=Constant]; +"309 Constant_11446" [id=309, type=Constant]; +"310 Constant_11445" [id=310, type=Constant]; +"311 Constant_11444" [id=311, type=Constant]; +"312 Constant_9609" [id=312, type=Constant]; +"313 Multiply_9601/fq_weights_1" [id=313, type=FakeQuantize]; +"314 Constant_11442" [id=314, type=Constant]; +"315 Constant_11441" [id=315, type=Constant]; +"316 Constant_11440" [id=316, type=Constant]; +"317 Constant_11439" [id=317, type=Constant]; +"318 Multiply_9819" [id=318, type=Constant]; +"319 Constant_11437" [id=319, type=Constant]; +"320 Constant_11436" [id=320, type=Constant]; +"321 Constant_11435" [id=321, type=Constant]; +"322 Constant_11434" [id=322, type=Constant]; +"323 Constant_9595" [id=323, type=Constant]; +"324 Multiply_9587/fq_weights_1" [id=324, type=FakeQuantize]; +"325 Constant_11432" [id=325, type=Constant]; +"326 Constant_11431" [id=326, type=Constant]; +"327 Constant_11430" [id=327, type=Constant]; +"328 Constant_11429" [id=328, type=Constant]; +"329 Multiply_9813" [id=329, type=Constant]; +"330 Constant_11427" [id=330, type=Constant]; +"331 Constant_11426" [id=331, type=Constant]; +"332 Constant_11425" [id=332, type=Constant]; +"333 Constant_11424" [id=333, type=Constant]; +"334 Constant_11422" [id=334, type=Constant]; +"335 Constant_11421" [id=335, type=Constant]; +"336 Constant_11420" [id=336, type=Constant]; +"337 Constant_11419" [id=337, type=Constant]; +"338 Constant_9581" [id=338, type=Constant]; +"339 Multiply_9573/fq_weights_1" [id=339, type=FakeQuantize]; +"340 Constant_11417" [id=340, type=Constant]; +"341 Constant_11416" [id=341, type=Constant]; +"342 Constant_11415" [id=342, type=Constant]; +"343 Constant_11414" [id=343, type=Constant]; +"344 Multiply_9807" [id=344, type=Constant]; +"345 Constant_11412" [id=345, type=Constant]; +"346 Constant_11411" [id=346, type=Constant]; +"347 Constant_11410" [id=347, type=Constant]; +"348 Constant_11409" [id=348, type=Constant]; +"349 Constant_11407" [id=349, type=Constant]; +"350 Constant_11406" [id=350, type=Constant]; +"351 Constant_11405" [id=351, type=Constant]; +"352 Constant_11404" [id=352, type=Constant]; +"353 Transpose_6514" [id=353, type=Constant]; +"354 Transpose_1831" [id=354, type=Constant]; +"355 Transpose_6506" [id=355, type=Constant]; +"356 Transpose_1822" [id=356, type=Constant]; +"357 Constant_6500" [id=357, type=Constant]; +"358 Constant_11402" [id=358, type=Constant]; +"359 Constant_11401" [id=359, type=Constant]; +"360 Constant_11400" [id=360, type=Constant]; +"361 Constant_11399" [id=361, type=Constant]; +"362 Constant_9567" [id=362, type=Constant]; +"363 Multiply_9559/fq_weights_1" [id=363, type=FakeQuantize]; +"364 Constant_11397" [id=364, type=Constant]; +"365 Constant_11396" [id=365, type=Constant]; +"366 Constant_11395" [id=366, type=Constant]; +"367 Constant_11394" [id=367, type=Constant]; +"368 Multiply_9802" [id=368, type=Constant]; +"369 Constant_11392" [id=369, type=Constant]; +"370 Constant_11391" [id=370, type=Constant]; +"371 Constant_11390" [id=371, type=Constant]; +"372 Constant_11389" [id=372, type=Constant]; +"373 Constant_9553" [id=373, type=Constant]; +"374 Multiply_9545/fq_weights_1" [id=374, type=FakeQuantize]; +"375 Constant_11387" [id=375, type=Constant]; +"376 Constant_11386" [id=376, type=Constant]; +"377 Constant_11385" [id=377, type=Constant]; +"378 Constant_11384" [id=378, type=Constant]; +"379 Multiply_9796" [id=379, type=Constant]; +"380 Constant_11382" [id=380, type=Constant]; +"381 Constant_11381" [id=381, type=Constant]; +"382 Constant_11380" [id=382, type=Constant]; +"383 Constant_11379" [id=383, type=Constant]; +"384 Constant_9539" [id=384, type=Constant]; +"385 Multiply_9531/fq_weights_1" [id=385, type=FakeQuantize]; +"386 Constant_11377" [id=386, type=Constant]; +"387 Constant_11376" [id=387, type=Constant]; +"388 Constant_11375" [id=388, type=Constant]; +"389 Constant_11374" [id=389, type=Constant]; +"390 Multiply_9790" [id=390, type=Constant]; +"391 Constant_11372" [id=391, type=Constant]; +"392 Constant_11371" [id=392, type=Constant]; +"393 Constant_11370" [id=393, type=Constant]; +"394 Constant_11369" [id=394, type=Constant]; +"395 Constant_11367" [id=395, type=Constant]; +"396 Constant_11366" [id=396, type=Constant]; +"397 Constant_11365" [id=397, type=Constant]; +"398 Constant_11364" [id=398, type=Constant]; +"399 Transpose_6370" [id=399, type=Constant]; +"400 Transpose_1721" [id=400, type=Constant]; +"401 Transpose_6362" [id=401, type=Constant]; +"402 Transpose_1712" [id=402, type=Constant]; +"403 Constant_6356" [id=403, type=Constant]; +"404 Constant_11362" [id=404, type=Constant]; +"405 Constant_11361" [id=405, type=Constant]; +"406 Constant_11360" [id=406, type=Constant]; +"407 Constant_11359" [id=407, type=Constant]; +"408 Constant_9525" [id=408, type=Constant]; +"409 Multiply_9517/fq_weights_1" [id=409, type=FakeQuantize]; +"410 Constant_11357" [id=410, type=Constant]; +"411 Constant_11356" [id=411, type=Constant]; +"412 Constant_11355" [id=412, type=Constant]; +"413 Constant_11354" [id=413, type=Constant]; +"414 Multiply_9785" [id=414, type=Constant]; +"415 Constant_11352" [id=415, type=Constant]; +"416 Constant_11351" [id=416, type=Constant]; +"417 Constant_11350" [id=417, type=Constant]; +"418 Constant_11349" [id=418, type=Constant]; +"419 Constant_9511" [id=419, type=Constant]; +"420 Multiply_9503/fq_weights_1" [id=420, type=FakeQuantize]; +"421 Constant_11347" [id=421, type=Constant]; +"422 Constant_11346" [id=422, type=Constant]; +"423 Constant_11345" [id=423, type=Constant]; +"424 Constant_11344" [id=424, type=Constant]; +"425 Multiply_9779" [id=425, type=Constant]; +"426 Constant_11342" [id=426, type=Constant]; +"427 Constant_11341" [id=427, type=Constant]; +"428 Constant_11340" [id=428, type=Constant]; +"429 Constant_11339" [id=429, type=Constant]; +"430 Constant_11337" [id=430, type=Constant]; +"431 Constant_11336" [id=431, type=Constant]; +"432 Constant_11335" [id=432, type=Constant]; +"433 Constant_11334" [id=433, type=Constant]; +"434 Constant_9497" [id=434, type=Constant]; +"435 Multiply_9489/fq_weights_1" [id=435, type=FakeQuantize]; +"436 Constant_11332" [id=436, type=Constant]; +"437 Constant_11331" [id=437, type=Constant]; +"438 Constant_11330" [id=438, type=Constant]; +"439 Constant_11329" [id=439, type=Constant]; +"440 Multiply_9773" [id=440, type=Constant]; +"441 Constant_11327" [id=441, type=Constant]; +"442 Constant_11326" [id=442, type=Constant]; +"443 Constant_11325" [id=443, type=Constant]; +"444 Constant_11324" [id=444, type=Constant]; +"445 Constant_11322" [id=445, type=Constant]; +"446 Constant_11321" [id=446, type=Constant]; +"447 Constant_11320" [id=447, type=Constant]; +"448 Constant_11319" [id=448, type=Constant]; +"449 Transpose_6195" [id=449, type=Constant]; +"450 Transpose_1582" [id=450, type=Constant]; +"451 Transpose_6187" [id=451, type=Constant]; +"452 Transpose_1573" [id=452, type=Constant]; +"453 Constant_6181" [id=453, type=Constant]; +"454 Constant_11317" [id=454, type=Constant]; +"455 Constant_11316" [id=455, type=Constant]; +"456 Constant_11315" [id=456, type=Constant]; +"457 Constant_11314" [id=457, type=Constant]; +"458 Constant_9483" [id=458, type=Constant]; +"459 Multiply_9475/fq_weights_1" [id=459, type=FakeQuantize]; +"460 Constant_11312" [id=460, type=Constant]; +"461 Constant_11311" [id=461, type=Constant]; +"462 Constant_11310" [id=462, type=Constant]; +"463 Constant_11309" [id=463, type=Constant]; +"464 Multiply_9768" [id=464, type=Constant]; +"465 Constant_11307" [id=465, type=Constant]; +"466 Constant_11306" [id=466, type=Constant]; +"467 Constant_11305" [id=467, type=Constant]; +"468 Constant_11304" [id=468, type=Constant]; +"469 Constant_9469" [id=469, type=Constant]; +"470 Multiply_9461/fq_weights_1" [id=470, type=FakeQuantize]; +"471 Constant_11302" [id=471, type=Constant]; +"472 Constant_11301" [id=472, type=Constant]; +"473 Constant_11300" [id=473, type=Constant]; +"474 Constant_11299" [id=474, type=Constant]; +"475 Multiply_9762" [id=475, type=Constant]; +"476 Constant_11297" [id=476, type=Constant]; +"477 Constant_11296" [id=477, type=Constant]; +"478 Constant_11295" [id=478, type=Constant]; +"479 Constant_11294" [id=479, type=Constant]; +"480 Constant_9455" [id=480, type=Constant]; +"481 Multiply_9447/fq_weights_1" [id=481, type=FakeQuantize]; +"482 Constant_11292" [id=482, type=Constant]; +"483 Constant_11291" [id=483, type=Constant]; +"484 Constant_11290" [id=484, type=Constant]; +"485 Constant_11289" [id=485, type=Constant]; +"486 Multiply_9756" [id=486, type=Constant]; +"487 Constant_11287" [id=487, type=Constant]; +"488 Constant_11286" [id=488, type=Constant]; +"489 Constant_11285" [id=489, type=Constant]; +"490 Constant_11284" [id=490, type=Constant]; +"491 Constant_11282" [id=491, type=Constant]; +"492 Constant_11281" [id=492, type=Constant]; +"493 Constant_11280" [id=493, type=Constant]; +"494 Constant_11279" [id=494, type=Constant]; +"495 Transpose_6051" [id=495, type=Constant]; +"496 Transpose_1472" [id=496, type=Constant]; +"497 Transpose_6043" [id=497, type=Constant]; +"498 Transpose_1463" [id=498, type=Constant]; +"499 Constant_6037" [id=499, type=Constant]; +"500 Constant_11277" [id=500, type=Constant]; +"501 Constant_11276" [id=501, type=Constant]; +"502 Constant_11275" [id=502, type=Constant]; +"503 Constant_11274" [id=503, type=Constant]; +"504 Constant_9441" [id=504, type=Constant]; +"505 Multiply_9433/fq_weights_1" [id=505, type=FakeQuantize]; +"506 Constant_11272" [id=506, type=Constant]; +"507 Constant_11271" [id=507, type=Constant]; +"508 Constant_11270" [id=508, type=Constant]; +"509 Constant_11269" [id=509, type=Constant]; +"510 Multiply_9751" [id=510, type=Constant]; +"511 Constant_11267" [id=511, type=Constant]; +"512 Constant_11266" [id=512, type=Constant]; +"513 Constant_11265" [id=513, type=Constant]; +"514 Constant_11264" [id=514, type=Constant]; +"515 Constant_9427" [id=515, type=Constant]; +"516 Multiply_9419/fq_weights_1" [id=516, type=FakeQuantize]; +"517 Constant_11262" [id=517, type=Constant]; +"518 Constant_11261" [id=518, type=Constant]; +"519 Constant_11260" [id=519, type=Constant]; +"520 Constant_11259" [id=520, type=Constant]; +"521 Multiply_9745" [id=521, type=Constant]; +"522 Constant_11257" [id=522, type=Constant]; +"523 Constant_11256" [id=523, type=Constant]; +"524 Constant_11255" [id=524, type=Constant]; +"525 Constant_11254" [id=525, type=Constant]; +"526 Constant_11252" [id=526, type=Constant]; +"527 Constant_11251" [id=527, type=Constant]; +"528 Constant_11250" [id=528, type=Constant]; +"529 Constant_11249" [id=529, type=Constant]; +"530 Constant_9413" [id=530, type=Constant]; +"531 Multiply_9405/fq_weights_1" [id=531, type=FakeQuantize]; +"532 Constant_11247" [id=532, type=Constant]; +"533 Constant_11246" [id=533, type=Constant]; +"534 Constant_11245" [id=534, type=Constant]; +"535 Constant_11244" [id=535, type=Constant]; +"536 Multiply_9739" [id=536, type=Constant]; +"537 Constant_11242" [id=537, type=Constant]; +"538 Constant_11241" [id=538, type=Constant]; +"539 Constant_11240" [id=539, type=Constant]; +"540 Constant_11239" [id=540, type=Constant]; +"541 Constant_11237" [id=541, type=Constant]; +"542 Constant_11236" [id=542, type=Constant]; +"543 Constant_11235" [id=543, type=Constant]; +"544 Constant_11234" [id=544, type=Constant]; +"545 Transpose_5903" [id=545, type=Constant]; +"546 Transpose_1361" [id=546, type=Constant]; +"547 Transpose_5895" [id=547, type=Constant]; +"548 Transpose_1352" [id=548, type=Constant]; +"549 Constant_5889" [id=549, type=Constant]; +"550 Constant_11232" [id=550, type=Constant]; +"551 Constant_11231" [id=551, type=Constant]; +"552 Constant_11230" [id=552, type=Constant]; +"553 Constant_11229" [id=553, type=Constant]; +"554 Constant_9399" [id=554, type=Constant]; +"555 Multiply_9391/fq_weights_1" [id=555, type=FakeQuantize]; +"556 Constant_11227" [id=556, type=Constant]; +"557 Constant_11226" [id=557, type=Constant]; +"558 Constant_11225" [id=558, type=Constant]; +"559 Constant_11224" [id=559, type=Constant]; +"560 Multiply_9734" [id=560, type=Constant]; +"561 Constant_11222" [id=561, type=Constant]; +"562 Constant_11221" [id=562, type=Constant]; +"563 Constant_11220" [id=563, type=Constant]; +"564 Constant_11219" [id=564, type=Constant]; +"565 Constant_9385" [id=565, type=Constant]; +"566 Multiply_9377/fq_weights_1" [id=566, type=FakeQuantize]; +"567 Constant_11217" [id=567, type=Constant]; +"568 Constant_11216" [id=568, type=Constant]; +"569 Constant_11215" [id=569, type=Constant]; +"570 Constant_11214" [id=570, type=Constant]; +"571 Multiply_9728" [id=571, type=Constant]; +"572 Constant_11212" [id=572, type=Constant]; +"573 Constant_11211" [id=573, type=Constant]; +"574 Constant_11210" [id=574, type=Constant]; +"575 Constant_11209" [id=575, type=Constant]; +"576 Constant_11207" [id=576, type=Constant]; +"577 Constant_11206" [id=577, type=Constant]; +"578 Constant_11205" [id=578, type=Constant]; +"579 Constant_11204" [id=579, type=Constant]; +"580 Constant_9371" [id=580, type=Constant]; +"581 Multiply_9363/fq_weights_1" [id=581, type=FakeQuantize]; +"582 Constant_11202" [id=582, type=Constant]; +"583 Constant_11201" [id=583, type=Constant]; +"584 Constant_11200" [id=584, type=Constant]; +"585 Constant_11199" [id=585, type=Constant]; +"586 Multiply_9722" [id=586, type=Constant]; +"587 Constant_11197" [id=587, type=Constant]; +"588 Constant_11196" [id=588, type=Constant]; +"589 Constant_11195" [id=589, type=Constant]; +"590 Constant_11194" [id=590, type=Constant]; +"591 Constant_11192" [id=591, type=Constant]; +"592 Constant_11191" [id=592, type=Constant]; +"593 Constant_11190" [id=593, type=Constant]; +"594 Constant_11189" [id=594, type=Constant]; +"595 Transpose_5755" [id=595, type=Constant]; +"596 Transpose_1250" [id=596, type=Constant]; +"597 Transpose_5747" [id=597, type=Constant]; +"598 Transpose_1241" [id=598, type=Constant]; +"599 Constant_5741" [id=599, type=Constant]; +"600 Constant_11187" [id=600, type=Constant]; +"601 Constant_11186" [id=601, type=Constant]; +"602 Constant_11185" [id=602, type=Constant]; +"603 Constant_11184" [id=603, type=Constant]; +"604 Constant_9357" [id=604, type=Constant]; +"605 Multiply_9349/fq_weights_1" [id=605, type=FakeQuantize]; +"606 Constant_11182" [id=606, type=Constant]; +"607 Constant_11181" [id=607, type=Constant]; +"608 Constant_11180" [id=608, type=Constant]; +"609 Constant_11179" [id=609, type=Constant]; +"610 Multiply_9717" [id=610, type=Constant]; +"611 Constant_11177" [id=611, type=Constant]; +"612 Constant_11176" [id=612, type=Constant]; +"613 Constant_11175" [id=613, type=Constant]; +"614 Constant_11174" [id=614, type=Constant]; +"615 Constant_9343" [id=615, type=Constant]; +"616 Multiply_9335/fq_weights_1" [id=616, type=FakeQuantize]; +"617 Constant_11172" [id=617, type=Constant]; +"618 Constant_11171" [id=618, type=Constant]; +"619 Constant_11170" [id=619, type=Constant]; +"620 Constant_11169" [id=620, type=Constant]; +"621 Multiply_9711" [id=621, type=Constant]; +"622 Constant_11167" [id=622, type=Constant]; +"623 Constant_11166" [id=623, type=Constant]; +"624 Constant_11165" [id=624, type=Constant]; +"625 Constant_11164" [id=625, type=Constant]; +"626 Constant_9329" [id=626, type=Constant]; +"627 Multiply_9321/fq_weights_1" [id=627, type=FakeQuantize]; +"628 Constant_11162" [id=628, type=Constant]; +"629 Constant_11161" [id=629, type=Constant]; +"630 Constant_11160" [id=630, type=Constant]; +"631 Constant_11159" [id=631, type=Constant]; +"632 Multiply_9705" [id=632, type=Constant]; +"633 Constant_11157" [id=633, type=Constant]; +"634 Constant_11156" [id=634, type=Constant]; +"635 Constant_11155" [id=635, type=Constant]; +"636 Constant_11154" [id=636, type=Constant]; +"637 Constant_11152" [id=637, type=Constant]; +"638 Constant_11151" [id=638, type=Constant]; +"639 Constant_11150" [id=639, type=Constant]; +"640 Constant_11149" [id=640, type=Constant]; +"641 Transpose_5611" [id=641, type=Constant]; +"642 Transpose_1140" [id=642, type=Constant]; +"643 Transpose_5603" [id=643, type=Constant]; +"644 Transpose_1131" [id=644, type=Constant]; +"645 Constant_5597" [id=645, type=Constant]; +"646 Constant_11147" [id=646, type=Constant]; +"647 Constant_11146" [id=647, type=Constant]; +"648 Constant_11145" [id=648, type=Constant]; +"649 Constant_11144" [id=649, type=Constant]; +"650 Constant_9315" [id=650, type=Constant]; +"651 Multiply_9307/fq_weights_1" [id=651, type=FakeQuantize]; +"652 Constant_11142" [id=652, type=Constant]; +"653 Constant_11141" [id=653, type=Constant]; +"654 Constant_11140" [id=654, type=Constant]; +"655 Constant_11139" [id=655, type=Constant]; +"656 Multiply_9700" [id=656, type=Constant]; +"657 Constant_11137" [id=657, type=Constant]; +"658 Constant_11136" [id=658, type=Constant]; +"659 Constant_11135" [id=659, type=Constant]; +"660 Constant_11134" [id=660, type=Constant]; +"661 Constant_9301" [id=661, type=Constant]; +"662 Multiply_9293/fq_weights_1" [id=662, type=FakeQuantize]; +"663 Constant_11132" [id=663, type=Constant]; +"664 Constant_11131" [id=664, type=Constant]; +"665 Constant_11130" [id=665, type=Constant]; +"666 Constant_11129" [id=666, type=Constant]; +"667 Multiply_9694" [id=667, type=Constant]; +"668 Constant_11127" [id=668, type=Constant]; +"669 Constant_11126" [id=669, type=Constant]; +"670 Constant_11125" [id=670, type=Constant]; +"671 Constant_11124" [id=671, type=Constant]; +"672 Constant_11122" [id=672, type=Constant]; +"673 Constant_11121" [id=673, type=Constant]; +"674 Constant_11120" [id=674, type=Constant]; +"675 Constant_11119" [id=675, type=Constant]; +"676 Constant_9287" [id=676, type=Constant]; +"677 Multiply_9279/fq_weights_1" [id=677, type=FakeQuantize]; +"678 Constant_11117" [id=678, type=Constant]; +"679 Constant_11116" [id=679, type=Constant]; +"680 Constant_11115" [id=680, type=Constant]; +"681 Constant_11114" [id=681, type=Constant]; +"682 Multiply_9688" [id=682, type=Constant]; +"683 Constant_11112" [id=683, type=Constant]; +"684 Constant_11111" [id=684, type=Constant]; +"685 Constant_11110" [id=685, type=Constant]; +"686 Constant_11109" [id=686, type=Constant]; +"687 Constant_9273" [id=687, type=Constant]; +"688 Multiply_9265/fq_weights_1" [id=688, type=FakeQuantize]; +"689 Constant_11107" [id=689, type=Constant]; +"690 Constant_11106" [id=690, type=Constant]; +"691 Constant_11105" [id=691, type=Constant]; +"692 Constant_11104" [id=692, type=Constant]; +"693 Multiply_9683" [id=693, type=Constant]; +"694 Constant_11102" [id=694, type=Constant]; +"695 Constant_11101" [id=695, type=Constant]; +"696 Constant_11100" [id=696, type=Constant]; +"697 Constant_11099" [id=697, type=Constant]; +"698 Constant_9259" [id=698, type=Constant]; +"699 Multiply_9251/fq_weights_1" [id=699, type=FakeQuantize]; +"700 Constant_11097" [id=700, type=Constant]; +"701 Constant_11096" [id=701, type=Constant]; +"702 Constant_11095" [id=702, type=Constant]; +"703 Constant_11094" [id=703, type=Constant]; +"704 Multiply_9677" [id=704, type=Constant]; +"705 Constant_11092" [id=705, type=Constant]; +"706 Constant_11091" [id=706, type=Constant]; +"707 Constant_11090" [id=707, type=Constant]; +"708 Constant_11089" [id=708, type=Constant]; +"709 Constant_9245" [id=709, type=Constant]; +"710 Multiply_9237/fq_weights_1" [id=710, type=FakeQuantize]; +"711 Constant_11087" [id=711, type=Constant]; +"712 Constant_11086" [id=712, type=Constant]; +"713 Constant_11085" [id=713, type=Constant]; +"714 Constant_11084" [id=714, type=Constant]; +"715 Multiply_9671" [id=715, type=Constant]; +"716 Constant_11082" [id=716, type=Constant]; +"717 Constant_11081" [id=717, type=Constant]; +"718 Constant_11080" [id=718, type=Constant]; +"719 Constant_11079" [id=719, type=Constant]; +"720 Constant_9231" [id=720, type=Constant]; +"721 Multiply_9223/fq_weights_1" [id=721, type=FakeQuantize]; +"722 Constant_11077" [id=722, type=Constant]; +"723 Constant_11076" [id=723, type=Constant]; +"724 Constant_11075" [id=724, type=Constant]; +"725 Constant_11074" [id=725, type=Constant]; +"726 Multiply_9666" [id=726, type=Constant]; +"727 Constant_11072" [id=727, type=Constant]; +"728 Constant_11071" [id=728, type=Constant]; +"729 Constant_11070" [id=729, type=Constant]; +"730 Constant_11069" [id=730, type=Constant]; +"731 Constant_9217" [id=731, type=Constant]; +"732 Multiply_9209/fq_weights_1" [id=732, type=FakeQuantize]; +"733 Constant_11067" [id=733, type=Constant]; +"734 Constant_11066" [id=734, type=Constant]; +"735 Constant_11065" [id=735, type=Constant]; +"736 Constant_11064" [id=736, type=Constant]; +"737 Multiply_9660" [id=737, type=Constant]; +"738 Constant_11062" [id=738, type=Constant]; +"739 Constant_11061" [id=739, type=Constant]; +"740 Constant_11060" [id=740, type=Constant]; +"741 Constant_11059" [id=741, type=Constant]; +"742 Constant_9203" [id=742, type=Constant]; +"743 Multiply_9195/fq_weights_1" [id=743, type=FakeQuantize]; +"744 Constant_11057" [id=744, type=Constant]; +"745 Constant_11056" [id=745, type=Constant]; +"746 Constant_11055" [id=746, type=Constant]; +"747 Constant_11054" [id=747, type=Constant]; +"748 Multiply_9654" [id=748, type=Constant]; +"749 Constant_11052" [id=749, type=Constant]; +"750 Constant_11051" [id=750, type=Constant]; +"751 Constant_11050" [id=751, type=Constant]; +"752 Constant_11049" [id=752, type=Constant]; +"753 Constant_11047" [id=753, type=Constant]; +"754 Constant_11046" [id=754, type=Constant]; +"755 Constant_11045" [id=755, type=Constant]; +"756 Constant_11044" [id=756, type=Constant]; +"757 Transpose_5257" [id=757, type=Constant]; +"758 Transpose_809" [id=758, type=Constant]; +"759 Transpose_5249" [id=759, type=Constant]; +"760 Transpose_800" [id=760, type=Constant]; +"761 Constant_5243" [id=761, type=Constant]; +"762 Constant_11042" [id=762, type=Constant]; +"763 Constant_11041" [id=763, type=Constant]; +"764 Constant_11040" [id=764, type=Constant]; +"765 Constant_11039" [id=765, type=Constant]; +"766 Constant_9189" [id=766, type=Constant]; +"767 Multiply_9181/fq_weights_1" [id=767, type=FakeQuantize]; +"768 Constant_11037" [id=768, type=Constant]; +"769 Constant_11036" [id=769, type=Constant]; +"770 Constant_11035" [id=770, type=Constant]; +"771 Constant_11034" [id=771, type=Constant]; +"772 Multiply_9649" [id=772, type=Constant]; +"773 Constant_11032" [id=773, type=Constant]; +"774 Constant_11031" [id=774, type=Constant]; +"775 Constant_11030" [id=775, type=Constant]; +"776 Constant_11029" [id=776, type=Constant]; +"777 Constant_9175" [id=777, type=Constant]; +"778 Multiply_9167/fq_weights_1" [id=778, type=FakeQuantize]; +"779 Constant_11027" [id=779, type=Constant]; +"780 Constant_11026" [id=780, type=Constant]; +"781 Constant_11025" [id=781, type=Constant]; +"782 Constant_11024" [id=782, type=Constant]; +"783 Gather_10068" [id=783, type=Constant]; +"784 Constant_11022" [id=784, type=Constant]; +"785 Constant_11021" [id=785, type=Constant]; +"786 Constant_11020" [id=786, type=Constant]; +"787 Constant_11019" [id=787, type=Constant]; +"788 Unsqueeze_7776" [id=788, type=Constant]; +"789 Unsqueeze_7782" [id=789, type=Constant]; +"790 Constant_7779" [id=790, type=Constant]; +"0 input_1" -> "1 Transpose_7780" [label="[1, 224, 224, 3]", style=solid]; +"1 Transpose_7780" -> "2 Transpose_7774" [label="[1, 3, 224, 224]", style=solid]; +"2 Transpose_7774" -> "3 Transpose_710" [label="[1, 3, 224, 224]", style=solid]; +"3 Transpose_710" -> "4 Transpose_710/fq_output_0" [label="[1, 3, 224, 224]", style=solid]; +"4 Transpose_710/fq_output_0" -> "5 Multiply_9167" [label="[1, 3, 224, 224]", style=solid]; +"5 Multiply_9167" -> "6 Transpose_5170" [label="[1, 16, 112, 112]", style=solid]; +"6 Transpose_5170" -> "7 Transpose_5188" [label="[1, 16, 112, 112]", style=solid]; +"7 Transpose_5188" -> "8 Transpose_5188/fq_output_0" [label="[1, 16, 112, 112]", style=solid]; +"8 Transpose_5188/fq_output_0" -> "9 Multiply_9181" [label="[1, 16, 112, 112]", style=solid]; +"9 Multiply_9181" -> "10 Transpose_5239" [label="[1, 16, 56, 56]", style=solid]; +"10 Transpose_5239" -> "11 Transpose_5241" [label="[1, 16, 56, 56]", style=solid]; +"11 Transpose_5241" -> "12 Transpose_5241/fq_output_0" [label="[1, 16, 56, 56]", style=solid]; +"12 Transpose_5241/fq_output_0" -> "13 Transpose_5245" [label="[1, 16, 56, 56]", style=solid]; +"12 Transpose_5241/fq_output_0" -> "14 Transpose_5277" [label="[1, 16, 56, 56]", style=solid]; +"13 Transpose_5245" -> "15 Convolution_801" [label="[1, 16, 1, 1]", style=solid]; +"14 Transpose_5277" -> "16 Transpose_5277/fq_output_0" [label="[1, 16, 56, 56]", style=solid]; +"15 Convolution_801" -> "17 Transpose_5251" [label="[1, 8, 1, 1]", style=solid]; +"16 Transpose_5277/fq_output_0" -> "18 Multiply_9195" [label="[1, 16, 56, 56]", style=solid]; +"17 Transpose_5251" -> "19 Transpose_5253" [label="[1, 8, 1, 1]", style=solid]; +"18 Multiply_9195" -> "20 Transpose_5301" [label="[1, 16, 56, 56]", style=solid]; +"19 Transpose_5253" -> "21 Convolution_810" [label="[1, 8, 1, 1]", style=solid]; +"20 Transpose_5301" -> "22 Transpose_5301/fq_output_0" [label="[1, 16, 56, 56]", style=solid]; +"21 Convolution_810" -> "23 Transpose_5259" [label="[1, 16, 1, 1]", style=solid]; +"22 Transpose_5301/fq_output_0" -> "24 Multiply_9209" [label="[1, 16, 56, 56]", style=solid]; +"23 Transpose_5259" -> "25 Transpose_5273" [label="[1, 16, 1, 1]", style=solid]; +"24 Multiply_9209" -> "26 Transpose_5325" [label="[1, 72, 56, 56]", style=solid]; +"25 Transpose_5273" -> "27 Transpose_5273/fq_output_0" [label="[1, 16, 1, 1]", style=solid]; +"26 Transpose_5325" -> "28 Transpose_5327" [label="[1, 72, 56, 56]", style=solid]; +"27 Transpose_5273/fq_output_0" -> "14 Transpose_5277" [label="[1, 16, 1, 1]", style=solid]; +"28 Transpose_5327" -> "29 Transpose_5327/fq_output_0" [label="[1, 72, 56, 56]", style=solid]; +"29 Transpose_5327/fq_output_0" -> "30 Multiply_9223" [label="[1, 72, 56, 56]", style=solid]; +"30 Multiply_9223" -> "31 Transpose_5378" [label="[1, 72, 28, 28]", style=solid]; +"31 Transpose_5378" -> "32 Transpose_5380" [label="[1, 72, 28, 28]", style=solid]; +"32 Transpose_5380" -> "33 Transpose_5380/fq_output_0" [label="[1, 72, 28, 28]", style=solid]; +"33 Transpose_5380/fq_output_0" -> "34 Multiply_9237" [label="[1, 72, 28, 28]", style=solid]; +"34 Multiply_9237" -> "35 Transpose_5404" [label="[1, 24, 28, 28]", style=solid]; +"35 Transpose_5404" -> "36 Transpose_5404/fq_output_0" [label="[1, 24, 28, 28]", style=solid]; +"36 Transpose_5404/fq_output_0" -> "37 Multiply_9251" [label="[1, 24, 28, 28]", style=solid]; +"36 Transpose_5404/fq_output_0" -> "38 Transpose_5484" [label="[1, 24, 28, 28]", style=solid]; +"37 Multiply_9251" -> "39 Transpose_5428" [label="[1, 88, 28, 28]", style=solid]; +"38 Transpose_5484" -> "40 Transpose_5484/fq_output_0" [label="[1, 24, 28, 28]", style=solid]; +"39 Transpose_5428" -> "41 Transpose_5430" [label="[1, 88, 28, 28]", style=solid]; +"40 Transpose_5484/fq_output_0" -> "42 Multiply_9293" [label="[1, 24, 28, 28]", style=solid]; +"41 Transpose_5430" -> "43 Transpose_5430/fq_output_0" [label="[1, 88, 28, 28]", style=solid]; +"42 Multiply_9293" -> "44 Transpose_5508" [label="[1, 96, 28, 28]", style=solid]; +"43 Transpose_5430/fq_output_0" -> "45 Multiply_9265" [label="[1, 88, 28, 28]", style=solid]; +"44 Transpose_5508" -> "46 Transpose_5526" [label="[1, 96, 28, 28]", style=solid]; +"45 Multiply_9265" -> "47 Transpose_5454" [label="[1, 88, 28, 28]", style=solid]; +"46 Transpose_5526" -> "48 Transpose_5526/fq_output_0" [label="[1, 96, 28, 28]", style=solid]; +"47 Transpose_5454" -> "49 Transpose_5456" [label="[1, 88, 28, 28]", style=solid]; +"48 Transpose_5526/fq_output_0" -> "50 Multiply_9307" [label="[1, 96, 28, 28]", style=solid]; +"49 Transpose_5456" -> "51 Transpose_5456/fq_output_0" [label="[1, 88, 28, 28]", style=solid]; +"50 Multiply_9307" -> "52 Transpose_5577" [label="[1, 96, 14, 14]", style=solid]; +"51 Transpose_5456/fq_output_0" -> "53 Multiply_9279" [label="[1, 88, 28, 28]", style=solid]; +"52 Transpose_5577" -> "54 Transpose_5595" [label="[1, 96, 14, 14]", style=solid]; +"53 Multiply_9279" -> "55 Transpose_5480" [label="[1, 24, 28, 28]", style=solid]; +"54 Transpose_5595" -> "56 Transpose_5595/fq_output_0" [label="[1, 96, 14, 14]", style=solid]; +"55 Transpose_5480" -> "57 Transpose_5480/fq_output_0" [label="[1, 24, 28, 28]", style=solid]; +"56 Transpose_5595/fq_output_0" -> "58 Transpose_5599" [label="[1, 96, 14, 14]", style=solid]; +"56 Transpose_5595/fq_output_0" -> "59 Transpose_5631" [label="[1, 96, 14, 14]", style=solid]; +"57 Transpose_5480/fq_output_0" -> "38 Transpose_5484" [label="[1, 24, 28, 28]", style=solid]; +"58 Transpose_5599" -> "60 Convolution_1132" [label="[1, 96, 1, 1]", style=solid]; +"59 Transpose_5631" -> "61 Transpose_5631/fq_output_0" [label="[1, 96, 14, 14]", style=solid]; +"60 Convolution_1132" -> "62 Transpose_5605" [label="[1, 24, 1, 1]", style=solid]; +"61 Transpose_5631/fq_output_0" -> "63 Multiply_9321" [label="[1, 96, 14, 14]", style=solid]; +"62 Transpose_5605" -> "64 Transpose_5607" [label="[1, 24, 1, 1]", style=solid]; +"63 Multiply_9321" -> "65 Transpose_5655" [label="[1, 40, 14, 14]", style=solid]; +"64 Transpose_5607" -> "66 Convolution_1141" [label="[1, 24, 1, 1]", style=solid]; +"65 Transpose_5655" -> "67 Transpose_5655/fq_output_0" [label="[1, 40, 14, 14]", style=solid]; +"66 Convolution_1141" -> "68 Transpose_5613" [label="[1, 96, 1, 1]", style=solid]; +"67 Transpose_5655/fq_output_0" -> "69 Multiply_9335" [label="[1, 40, 14, 14]", style=solid]; +"67 Transpose_5655/fq_output_0" -> "70 Transpose_5803" [label="[1, 40, 14, 14]", style=solid]; +"68 Transpose_5613" -> "71 Transpose_5627" [label="[1, 96, 1, 1]", style=solid]; +"69 Multiply_9335" -> "72 Transpose_5679" [label="[1, 240, 14, 14]", style=solid]; +"70 Transpose_5803" -> "73 Transpose_5803/fq_output_0" [label="[1, 40, 14, 14]", style=solid]; +"71 Transpose_5627" -> "74 Transpose_5627/fq_output_0" [label="[1, 96, 1, 1]", style=solid]; +"72 Transpose_5679" -> "75 Transpose_5697" [label="[1, 240, 14, 14]", style=solid]; +"73 Transpose_5803/fq_output_0" -> "76 Multiply_9377" [label="[1, 40, 14, 14]", style=solid]; +"73 Transpose_5803/fq_output_0" -> "77 Transpose_5951" [label="[1, 40, 14, 14]", style=solid]; +"74 Transpose_5627/fq_output_0" -> "59 Transpose_5631" [label="[1, 96, 1, 1]", style=solid]; +"75 Transpose_5697" -> "78 Transpose_5697/fq_output_0" [label="[1, 240, 14, 14]", style=solid]; +"76 Multiply_9377" -> "79 Transpose_5827" [label="[1, 240, 14, 14]", style=solid]; +"77 Transpose_5951" -> "80 Transpose_5951/fq_output_0" [label="[1, 40, 14, 14]", style=solid]; +"78 Transpose_5697/fq_output_0" -> "81 Multiply_9349" [label="[1, 240, 14, 14]", style=solid]; +"79 Transpose_5827" -> "82 Transpose_5845" [label="[1, 240, 14, 14]", style=solid]; +"80 Transpose_5951/fq_output_0" -> "83 Multiply_9419" [label="[1, 40, 14, 14]", style=solid]; +"81 Multiply_9349" -> "84 Transpose_5721" [label="[1, 240, 14, 14]", style=solid]; +"82 Transpose_5845" -> "85 Transpose_5845/fq_output_0" [label="[1, 240, 14, 14]", style=solid]; +"83 Multiply_9419" -> "86 Transpose_5975" [label="[1, 120, 14, 14]", style=solid]; +"84 Transpose_5721" -> "87 Transpose_5739" [label="[1, 240, 14, 14]", style=solid]; +"85 Transpose_5845/fq_output_0" -> "88 Multiply_9391" [label="[1, 240, 14, 14]", style=solid]; +"86 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style=solid]; +"271 Constant_11480" -> "203 Transpose_6752/fq_output_0" [label="[]", style=solid]; +"272 Constant_11479" -> "203 Transpose_6752/fq_output_0" [label="[]", style=solid]; +"273 Constant_9637" -> "197 Transpose_6734" [label="[1, 576, 1, 1]", style=solid]; +"274 Multiply_9629/fq_weights_1" -> "194 Multiply_9629" [label="[576, 96, 1, 1]", style=solid]; +"275 Constant_11477" -> "274 Multiply_9629/fq_weights_1" [label="[576, 1, 1, 1]", style=solid]; +"276 Constant_11476" -> "274 Multiply_9629/fq_weights_1" [label="[576, 1, 1, 1]", style=solid]; +"277 Constant_11475" -> "274 Multiply_9629/fq_weights_1" [label="[576, 1, 1, 1]", style=solid]; +"278 Constant_11474" -> "274 Multiply_9629/fq_weights_1" [label="[576, 1, 1, 1]", style=solid]; +"279 Multiply_9830" -> "274 Multiply_9629/fq_weights_1" [label="[576, 96, 1, 1]", style=solid]; +"280 Constant_11472" -> "191 Transpose_6710/fq_output_0" [label="[]", style=solid]; +"281 Constant_11471" -> "191 Transpose_6710/fq_output_0" [label="[]", style=solid]; +"282 Constant_11470" -> "191 Transpose_6710/fq_output_0" [label="[]", style=solid]; +"283 Constant_11469" -> "191 Transpose_6710/fq_output_0" [label="[]", style=solid]; +"284 Constant_11467" -> "233 Transpose_6706/fq_output_0" [label="[]", style=solid]; +"285 Constant_11466" -> "233 Transpose_6706/fq_output_0" [label="[]", style=solid]; +"286 Constant_11465" -> "233 Transpose_6706/fq_output_0" [label="[]", style=solid]; +"287 Constant_11464" -> "233 Transpose_6706/fq_output_0" [label="[]", style=solid]; +"288 Constant_9623" -> "229 Transpose_6706" [label="[1, 96, 1, 1]", style=solid]; +"289 Multiply_9615/fq_weights_1" -> "225 Multiply_9615" [label="[96, 576, 1, 1]", style=solid]; +"290 Constant_11462" -> "289 Multiply_9615/fq_weights_1" [label="[96, 1, 1, 1]", style=solid]; +"291 Constant_11461" -> "289 Multiply_9615/fq_weights_1" [label="[96, 1, 1, 1]", style=solid]; +"292 Constant_11460" -> "289 Multiply_9615/fq_weights_1" [label="[96, 1, 1, 1]", style=solid]; +"293 Constant_11459" -> "289 Multiply_9615/fq_weights_1" [label="[96, 1, 1, 1]", style=solid]; +"294 Multiply_9824" -> "289 Multiply_9615/fq_weights_1" [label="[96, 576, 1, 1]", style=solid]; +"295 Constant_11457" -> "220 Transpose_6682/fq_output_0" [label="[]", style=solid]; +"296 Constant_11456" -> "220 Transpose_6682/fq_output_0" [label="[]", style=solid]; +"297 Constant_11455" -> "220 Transpose_6682/fq_output_0" [label="[]", style=solid]; +"298 Constant_11454" -> "220 Transpose_6682/fq_output_0" [label="[]", style=solid]; +"299 Constant_11452" -> "240 Transpose_6678/fq_output_0" [label="[]", style=solid]; +"300 Constant_11451" -> "240 Transpose_6678/fq_output_0" [label="[]", style=solid]; +"301 Constant_11450" -> "240 Transpose_6678/fq_output_0" [label="[]", style=solid]; +"302 Constant_11449" -> "240 Transpose_6678/fq_output_0" [label="[]", style=solid]; +"303 Transpose_6662" -> "236 Transpose_6664" [label="[1, 576, 1, 1]", style=solid]; +"304 Transpose_1942" -> "232 Convolution_1943" [label="[576, 144, 1, 1]", style=solid]; +"305 Transpose_6654" -> "224 Transpose_6656" [label="[1, 144, 1, 1]", style=solid]; +"306 Transpose_1933" -> "219 Convolution_1934" [label="[144, 576, 1, 1]", style=solid]; +"307 Constant_6648" -> "214 Transpose_6650" [label="[2]", style=dashed]; +"308 Constant_11447" -> "210 Transpose_6646/fq_output_0" [label="[]", style=solid]; +"309 Constant_11446" -> "210 Transpose_6646/fq_output_0" [label="[]", style=solid]; +"310 Constant_11445" -> "210 Transpose_6646/fq_output_0" [label="[]", style=solid]; +"311 Constant_11444" -> "210 Transpose_6646/fq_output_0" [label="[]", style=solid]; +"312 Constant_9609" -> "202 Transpose_6628" [label="[1, 576, 1, 1]", style=solid]; +"313 Multiply_9601/fq_weights_1" -> "199 Multiply_9601" [label="[576, 1, 1, 5, 5]", style=solid]; +"314 Constant_11442" -> "313 Multiply_9601/fq_weights_1" [label="[576, 1, 1, 1, 1]", style=solid]; +"315 Constant_11441" -> "313 Multiply_9601/fq_weights_1" [label="[576, 1, 1, 1, 1]", style=solid]; +"316 Constant_11440" -> "313 Multiply_9601/fq_weights_1" [label="[576, 1, 1, 1, 1]", style=solid]; +"317 Constant_11439" -> "313 Multiply_9601/fq_weights_1" [label="[576, 1, 1, 1, 1]", style=solid]; +"318 Multiply_9819" -> "313 Multiply_9601/fq_weights_1" [label="[576, 1, 1, 5, 5]", style=solid]; +"319 Constant_11437" -> "196 Transpose_6604/fq_output_0" [label="[1, 576, 1, 1]", style=solid]; +"320 Constant_11436" -> "196 Transpose_6604/fq_output_0" [label="[1, 576, 1, 1]", style=solid]; +"321 Constant_11435" -> "196 Transpose_6604/fq_output_0" [label="[1, 576, 1, 1]", style=solid]; +"322 Constant_11434" -> "196 Transpose_6604/fq_output_0" [label="[1, 576, 1, 1]", style=solid]; +"323 Constant_9595" -> "190 Transpose_6586" [label="[1, 576, 1, 1]", style=solid]; +"324 Multiply_9587/fq_weights_1" -> "187 Multiply_9587" [label="[576, 96, 1, 1]", style=solid]; +"325 Constant_11432" -> "324 Multiply_9587/fq_weights_1" [label="[576, 1, 1, 1]", style=solid]; +"326 Constant_11431" -> "324 Multiply_9587/fq_weights_1" [label="[576, 1, 1, 1]", style=solid]; +"327 Constant_11430" -> "324 Multiply_9587/fq_weights_1" [label="[576, 1, 1, 1]", style=solid]; +"328 Constant_11429" -> "324 Multiply_9587/fq_weights_1" [label="[576, 1, 1, 1]", style=solid]; +"329 Multiply_9813" -> "324 Multiply_9587/fq_weights_1" [label="[576, 96, 1, 1]", style=solid]; +"330 Constant_11427" -> "184 Transpose_6562/fq_output_0" [label="[]", style=solid]; +"331 Constant_11426" -> "184 Transpose_6562/fq_output_0" [label="[]", style=solid]; +"332 Constant_11425" -> "184 Transpose_6562/fq_output_0" [label="[]", style=solid]; +"333 Constant_11424" -> "184 Transpose_6562/fq_output_0" [label="[]", style=solid]; +"334 Constant_11422" -> "223 Transpose_6558/fq_output_0" [label="[]", style=solid]; +"335 Constant_11421" -> "223 Transpose_6558/fq_output_0" [label="[]", style=solid]; +"336 Constant_11420" -> "223 Transpose_6558/fq_output_0" [label="[]", style=solid]; +"337 Constant_11419" -> "223 Transpose_6558/fq_output_0" [label="[]", style=solid]; +"338 Constant_9581" -> "218 Transpose_6558" [label="[1, 96, 1, 1]", style=solid]; +"339 Multiply_9573/fq_weights_1" -> "213 Multiply_9573" [label="[96, 576, 1, 1]", style=solid]; +"340 Constant_11417" -> "339 Multiply_9573/fq_weights_1" [label="[96, 1, 1, 1]", style=solid]; +"341 Constant_11416" -> "339 Multiply_9573/fq_weights_1" [label="[96, 1, 1, 1]", style=solid]; +"342 Constant_11415" -> "339 Multiply_9573/fq_weights_1" [label="[96, 1, 1, 1]", style=solid]; +"343 Constant_11414" -> "339 Multiply_9573/fq_weights_1" [label="[96, 1, 1, 1]", style=solid]; +"344 Multiply_9807" -> "339 Multiply_9573/fq_weights_1" [label="[96, 576, 1, 1]", style=solid]; +"345 Constant_11412" -> "209 Transpose_6534/fq_output_0" [label="[]", style=solid]; +"346 Constant_11411" -> "209 Transpose_6534/fq_output_0" [label="[]", style=solid]; +"347 Constant_11410" -> "209 Transpose_6534/fq_output_0" [label="[]", style=solid]; +"348 Constant_11409" -> "209 Transpose_6534/fq_output_0" [label="[]", style=solid]; +"349 Constant_11407" -> "235 Transpose_6530/fq_output_0" [label="[]", style=solid]; +"350 Constant_11406" -> "235 Transpose_6530/fq_output_0" [label="[]", style=solid]; +"351 Constant_11405" -> "235 Transpose_6530/fq_output_0" [label="[]", style=solid]; +"352 Constant_11404" -> "235 Transpose_6530/fq_output_0" [label="[]", style=solid]; +"353 Transpose_6514" -> "227 Transpose_6516" [label="[1, 576, 1, 1]", style=solid]; +"354 Transpose_1831" -> "222 Convolution_1832" [label="[576, 144, 1, 1]", style=solid]; +"355 Transpose_6506" -> "212 Transpose_6508" [label="[1, 144, 1, 1]", style=solid]; +"356 Transpose_1822" -> "208 Convolution_1823" [label="[144, 576, 1, 1]", style=solid]; +"357 Constant_6500" -> "204 Transpose_6502" [label="[2]", style=dashed]; +"358 Constant_11402" -> "201 Transpose_6498/fq_output_0" [label="[]", style=solid]; +"359 Constant_11401" -> "201 Transpose_6498/fq_output_0" [label="[]", style=solid]; +"360 Constant_11400" -> "201 Transpose_6498/fq_output_0" [label="[]", style=solid]; +"361 Constant_11399" -> "201 Transpose_6498/fq_output_0" [label="[]", style=solid]; +"362 Constant_9567" -> "195 Transpose_6480" [label="[1, 576, 1, 1]", style=solid]; +"363 Multiply_9559/fq_weights_1" -> "192 Multiply_9559" [label="[576, 1, 1, 5, 5]", style=solid]; +"364 Constant_11397" -> "363 Multiply_9559/fq_weights_1" [label="[576, 1, 1, 1, 1]", style=solid]; +"365 Constant_11396" -> "363 Multiply_9559/fq_weights_1" [label="[576, 1, 1, 1, 1]", style=solid]; +"366 Constant_11395" -> "363 Multiply_9559/fq_weights_1" [label="[576, 1, 1, 1, 1]", style=solid]; +"367 Constant_11394" -> "363 Multiply_9559/fq_weights_1" [label="[576, 1, 1, 1, 1]", style=solid]; +"368 Multiply_9802" -> "363 Multiply_9559/fq_weights_1" [label="[576, 1, 1, 5, 5]", style=solid]; +"369 Constant_11392" -> "189 Transpose_6456/fq_output_0" [label="[1, 576, 1, 1]", style=solid]; +"370 Constant_11391" -> "189 Transpose_6456/fq_output_0" [label="[1, 576, 1, 1]", style=solid]; +"371 Constant_11390" -> "189 Transpose_6456/fq_output_0" [label="[1, 576, 1, 1]", style=solid]; +"372 Constant_11389" -> "189 Transpose_6456/fq_output_0" [label="[1, 576, 1, 1]", style=solid]; +"373 Constant_9553" -> "183 Transpose_6438" [label="[1, 576, 1, 1]", style=solid]; +"374 Multiply_9545/fq_weights_1" -> "180 Multiply_9545" [label="[576, 96, 1, 1]", style=solid]; +"375 Constant_11387" -> "374 Multiply_9545/fq_weights_1" [label="[576, 1, 1, 1]", style=solid]; +"376 Constant_11386" -> "374 Multiply_9545/fq_weights_1" [label="[576, 1, 1, 1]", style=solid]; +"377 Constant_11385" -> "374 Multiply_9545/fq_weights_1" [label="[576, 1, 1, 1]", style=solid]; +"378 Constant_11384" -> "374 Multiply_9545/fq_weights_1" [label="[576, 1, 1, 1]", style=solid]; +"379 Multiply_9796" -> "374 Multiply_9545/fq_weights_1" [label="[576, 96, 1, 1]", style=solid]; +"380 Constant_11382" -> "177 Transpose_6414/fq_output_0" [label="[]", style=solid]; +"381 Constant_11381" -> "177 Transpose_6414/fq_output_0" [label="[]", style=solid]; +"382 Constant_11380" -> "177 Transpose_6414/fq_output_0" [label="[]", style=solid]; +"383 Constant_11379" -> "177 Transpose_6414/fq_output_0" [label="[]", style=solid]; +"384 Constant_9539" -> "174 Transpose_6414" [label="[1, 96, 1, 1]", style=solid]; +"385 Multiply_9531/fq_weights_1" -> "171 Multiply_9531" [label="[96, 288, 1, 1]", style=solid]; +"386 Constant_11377" -> "385 Multiply_9531/fq_weights_1" [label="[96, 1, 1, 1]", style=solid]; +"387 Constant_11376" -> "385 Multiply_9531/fq_weights_1" [label="[96, 1, 1, 1]", style=solid]; +"388 Constant_11375" -> "385 Multiply_9531/fq_weights_1" [label="[96, 1, 1, 1]", style=solid]; +"389 Constant_11374" -> "385 Multiply_9531/fq_weights_1" [label="[96, 1, 1, 1]", style=solid]; +"390 Multiply_9790" -> "385 Multiply_9531/fq_weights_1" [label="[96, 288, 1, 1]", style=solid]; +"391 Constant_11372" -> "167 Transpose_6390/fq_output_0" [label="[]", style=solid]; +"392 Constant_11371" -> "167 Transpose_6390/fq_output_0" [label="[]", style=solid]; +"393 Constant_11370" -> "167 Transpose_6390/fq_output_0" [label="[]", style=solid]; +"394 Constant_11369" -> "167 Transpose_6390/fq_output_0" [label="[]", style=solid]; +"395 Constant_11367" -> "185 Transpose_6386/fq_output_0" [label="[]", style=solid]; +"396 Constant_11366" -> "185 Transpose_6386/fq_output_0" [label="[]", style=solid]; +"397 Constant_11365" -> "185 Transpose_6386/fq_output_0" [label="[]", style=solid]; +"398 Constant_11364" -> "185 Transpose_6386/fq_output_0" [label="[]", style=solid]; +"399 Transpose_6370" -> "179 Transpose_6372" [label="[1, 288, 1, 1]", style=solid]; +"400 Transpose_1721" -> "176 Convolution_1722" [label="[288, 72, 1, 1]", style=solid]; +"401 Transpose_6362" -> "170 Transpose_6364" [label="[1, 72, 1, 1]", style=solid]; +"402 Transpose_1712" -> "166 Convolution_1713" [label="[72, 288, 1, 1]", style=solid]; +"403 Constant_6356" -> "162 Transpose_6358" [label="[2]", style=dashed]; +"404 Constant_11362" -> "159 Transpose_6354/fq_output_0" [label="[]", style=solid]; +"405 Constant_11361" -> "159 Transpose_6354/fq_output_0" [label="[]", style=solid]; +"406 Constant_11360" -> "159 Transpose_6354/fq_output_0" [label="[]", style=solid]; +"407 Constant_11359" -> "159 Transpose_6354/fq_output_0" [label="[]", style=solid]; +"408 Constant_9525" -> "153 Transpose_6336" [label="[1, 288, 1, 1]", style=solid]; +"409 Multiply_9517/fq_weights_1" -> "151 Multiply_9517" [label="[288, 1, 1, 5, 5]", style=solid]; +"410 Constant_11357" -> "409 Multiply_9517/fq_weights_1" [label="[288, 1, 1, 1, 1]", style=solid]; +"411 Constant_11356" -> "409 Multiply_9517/fq_weights_1" [label="[288, 1, 1, 1, 1]", style=solid]; +"412 Constant_11355" -> "409 Multiply_9517/fq_weights_1" [label="[288, 1, 1, 1, 1]", style=solid]; +"413 Constant_11354" -> "409 Multiply_9517/fq_weights_1" [label="[288, 1, 1, 1, 1]", style=solid]; +"414 Multiply_9785" -> "409 Multiply_9517/fq_weights_1" [label="[288, 1, 1, 5, 5]", style=solid]; +"415 Constant_11352" -> "149 Transpose_6285/fq_output_0" [label="[1, 288, 1, 1]", style=solid]; +"416 Constant_11351" -> "149 Transpose_6285/fq_output_0" [label="[1, 288, 1, 1]", style=solid]; +"417 Constant_11350" -> "149 Transpose_6285/fq_output_0" [label="[1, 288, 1, 1]", style=solid]; +"418 Constant_11349" -> "149 Transpose_6285/fq_output_0" [label="[1, 288, 1, 1]", style=solid]; +"419 Constant_9511" -> "145 Transpose_6267" [label="[1, 288, 1, 1]", style=solid]; +"420 Multiply_9503/fq_weights_1" -> "143 Multiply_9503" [label="[288, 48, 1, 1]", style=solid]; +"421 Constant_11347" -> "420 Multiply_9503/fq_weights_1" [label="[288, 1, 1, 1]", style=solid]; +"422 Constant_11346" -> "420 Multiply_9503/fq_weights_1" [label="[288, 1, 1, 1]", style=solid]; +"423 Constant_11345" -> "420 Multiply_9503/fq_weights_1" [label="[288, 1, 1, 1]", style=solid]; +"424 Constant_11344" -> "420 Multiply_9503/fq_weights_1" [label="[288, 1, 1, 1]", style=solid]; +"425 Multiply_9779" -> "420 Multiply_9503/fq_weights_1" [label="[288, 48, 1, 1]", style=solid]; +"426 Constant_11342" -> "140 Transpose_6243/fq_output_0" [label="[]", style=solid]; +"427 Constant_11341" -> "140 Transpose_6243/fq_output_0" [label="[]", style=solid]; +"428 Constant_11340" -> "140 Transpose_6243/fq_output_0" [label="[]", style=solid]; +"429 Constant_11339" -> "140 Transpose_6243/fq_output_0" [label="[]", style=solid]; +"430 Constant_11337" -> "169 Transpose_6239/fq_output_0" [label="[]", style=solid]; +"431 Constant_11336" -> "169 Transpose_6239/fq_output_0" [label="[]", style=solid]; +"432 Constant_11335" -> "169 Transpose_6239/fq_output_0" [label="[]", style=solid]; +"433 Constant_11334" -> "169 Transpose_6239/fq_output_0" [label="[]", style=solid]; +"434 Constant_9497" -> "165 Transpose_6239" [label="[1, 48, 1, 1]", style=solid]; +"435 Multiply_9489/fq_weights_1" -> "161 Multiply_9489" [label="[48, 144, 1, 1]", style=solid]; +"436 Constant_11332" -> "435 Multiply_9489/fq_weights_1" [label="[48, 1, 1, 1]", style=solid]; +"437 Constant_11331" -> "435 Multiply_9489/fq_weights_1" [label="[48, 1, 1, 1]", style=solid]; +"438 Constant_11330" -> "435 Multiply_9489/fq_weights_1" [label="[48, 1, 1, 1]", style=solid]; +"439 Constant_11329" -> "435 Multiply_9489/fq_weights_1" [label="[48, 1, 1, 1]", style=solid]; +"440 Multiply_9773" -> "435 Multiply_9489/fq_weights_1" [label="[48, 144, 1, 1]", style=solid]; +"441 Constant_11327" -> "158 Transpose_6215/fq_output_0" [label="[]", style=solid]; +"442 Constant_11326" -> "158 Transpose_6215/fq_output_0" [label="[]", style=solid]; +"443 Constant_11325" -> "158 Transpose_6215/fq_output_0" [label="[]", style=solid]; +"444 Constant_11324" -> "158 Transpose_6215/fq_output_0" [label="[]", style=solid]; +"445 Constant_11322" -> "178 Transpose_6211/fq_output_0" [label="[]", style=solid]; +"446 Constant_11321" -> "178 Transpose_6211/fq_output_0" [label="[]", style=solid]; +"447 Constant_11320" -> "178 Transpose_6211/fq_output_0" [label="[]", style=solid]; +"448 Constant_11319" -> "178 Transpose_6211/fq_output_0" [label="[]", style=solid]; +"449 Transpose_6195" -> "172 Transpose_6197" [label="[1, 144, 1, 1]", style=solid]; +"450 Transpose_1582" -> "168 Convolution_1583" [label="[144, 40, 1, 1]", style=solid]; +"451 Transpose_6187" -> "160 Transpose_6189" [label="[1, 40, 1, 1]", style=solid]; +"452 Transpose_1573" -> "157 Convolution_1574" [label="[40, 144, 1, 1]", style=solid]; +"453 Constant_6181" -> "154 Transpose_6183" [label="[2]", style=dashed]; +"454 Constant_11317" -> "152 Transpose_6179/fq_output_0" [label="[]", style=solid]; +"455 Constant_11316" -> "152 Transpose_6179/fq_output_0" [label="[]", style=solid]; +"456 Constant_11315" -> "152 Transpose_6179/fq_output_0" [label="[]", style=solid]; +"457 Constant_11314" -> "152 Transpose_6179/fq_output_0" [label="[]", style=solid]; +"458 Constant_9483" -> "148 Transpose_6161" [label="[1, 144, 1, 1]", style=solid]; +"459 Multiply_9475/fq_weights_1" -> "146 Multiply_9475" [label="[144, 1, 1, 5, 5]", style=solid]; +"460 Constant_11312" -> "459 Multiply_9475/fq_weights_1" [label="[144, 1, 1, 1, 1]", style=solid]; +"461 Constant_11311" -> "459 Multiply_9475/fq_weights_1" [label="[144, 1, 1, 1, 1]", style=solid]; +"462 Constant_11310" -> "459 Multiply_9475/fq_weights_1" [label="[144, 1, 1, 1, 1]", style=solid]; +"463 Constant_11309" -> "459 Multiply_9475/fq_weights_1" [label="[144, 1, 1, 1, 1]", style=solid]; +"464 Multiply_9768" -> "459 Multiply_9475/fq_weights_1" [label="[144, 1, 1, 5, 5]", style=solid]; +"465 Constant_11307" -> "144 Transpose_6137/fq_output_0" [label="[1, 144, 1, 1]", style=solid]; +"466 Constant_11306" -> "144 Transpose_6137/fq_output_0" [label="[1, 144, 1, 1]", style=solid]; +"467 Constant_11305" -> "144 Transpose_6137/fq_output_0" [label="[1, 144, 1, 1]", style=solid]; +"468 Constant_11304" -> "144 Transpose_6137/fq_output_0" [label="[1, 144, 1, 1]", style=solid]; +"469 Constant_9469" -> "139 Transpose_6119" [label="[1, 144, 1, 1]", style=solid]; +"470 Multiply_9461/fq_weights_1" -> "136 Multiply_9461" [label="[144, 48, 1, 1]", style=solid]; +"471 Constant_11302" -> "470 Multiply_9461/fq_weights_1" [label="[144, 1, 1, 1]", style=solid]; +"472 Constant_11301" -> "470 Multiply_9461/fq_weights_1" [label="[144, 1, 1, 1]", style=solid]; +"473 Constant_11300" -> "470 Multiply_9461/fq_weights_1" [label="[144, 1, 1, 1]", style=solid]; +"474 Constant_11299" -> "470 Multiply_9461/fq_weights_1" [label="[144, 1, 1, 1]", style=solid]; +"475 Multiply_9762" -> "470 Multiply_9461/fq_weights_1" [label="[144, 48, 1, 1]", style=solid]; +"476 Constant_11297" -> "133 Transpose_6095/fq_output_0" [label="[]", style=solid]; +"477 Constant_11296" -> "133 Transpose_6095/fq_output_0" [label="[]", style=solid]; +"478 Constant_11295" -> "133 Transpose_6095/fq_output_0" [label="[]", style=solid]; +"479 Constant_11294" -> "133 Transpose_6095/fq_output_0" [label="[]", style=solid]; +"480 Constant_9455" -> "130 Transpose_6095" [label="[1, 48, 1, 1]", style=solid]; +"481 Multiply_9447/fq_weights_1" -> "126 Multiply_9447" [label="[48, 120, 1, 1]", style=solid]; +"482 Constant_11292" -> "481 Multiply_9447/fq_weights_1" [label="[48, 1, 1, 1]", style=solid]; +"483 Constant_11291" -> "481 Multiply_9447/fq_weights_1" [label="[48, 1, 1, 1]", style=solid]; +"484 Constant_11290" -> "481 Multiply_9447/fq_weights_1" [label="[48, 1, 1, 1]", style=solid]; +"485 Constant_11289" -> "481 Multiply_9447/fq_weights_1" [label="[48, 1, 1, 1]", style=solid]; +"486 Multiply_9756" -> "481 Multiply_9447/fq_weights_1" [label="[48, 120, 1, 1]", style=solid]; +"487 Constant_11287" -> "121 Transpose_6071/fq_output_0" [label="[]", style=solid]; +"488 Constant_11286" -> "121 Transpose_6071/fq_output_0" [label="[]", style=solid]; +"489 Constant_11285" -> "121 Transpose_6071/fq_output_0" [label="[]", style=solid]; +"490 Constant_11284" -> "121 Transpose_6071/fq_output_0" [label="[]", style=solid]; +"491 Constant_11282" -> "141 Transpose_6067/fq_output_0" [label="[]", style=solid]; +"492 Constant_11281" -> "141 Transpose_6067/fq_output_0" [label="[]", style=solid]; +"493 Constant_11280" -> "141 Transpose_6067/fq_output_0" [label="[]", style=solid]; +"494 Constant_11279" -> "141 Transpose_6067/fq_output_0" [label="[]", style=solid]; +"495 Transpose_6051" -> "135 Transpose_6053" [label="[1, 120, 1, 1]", style=solid]; +"496 Transpose_1472" -> "132 Convolution_1473" [label="[120, 32, 1, 1]", style=solid]; +"497 Transpose_6043" -> "125 Transpose_6045" [label="[1, 32, 1, 1]", style=solid]; +"498 Transpose_1463" -> "120 Convolution_1464" [label="[32, 120, 1, 1]", style=solid]; +"499 Constant_6037" -> "115 Transpose_6039" [label="[2]", style=dashed]; +"500 Constant_11277" -> "110 Transpose_6035/fq_output_0" [label="[]", style=solid]; +"501 Constant_11276" -> "110 Transpose_6035/fq_output_0" [label="[]", style=solid]; +"502 Constant_11275" -> "110 Transpose_6035/fq_output_0" [label="[]", style=solid]; +"503 Constant_11274" -> "110 Transpose_6035/fq_output_0" [label="[]", style=solid]; +"504 Constant_9441" -> "100 Transpose_6017" [label="[1, 120, 1, 1]", style=solid]; +"505 Multiply_9433/fq_weights_1" -> "96 Multiply_9433" [label="[120, 1, 1, 5, 5]", style=solid]; +"506 Constant_11272" -> "505 Multiply_9433/fq_weights_1" [label="[120, 1, 1, 1, 1]", style=solid]; +"507 Constant_11271" -> "505 Multiply_9433/fq_weights_1" [label="[120, 1, 1, 1, 1]", style=solid]; +"508 Constant_11270" -> "505 Multiply_9433/fq_weights_1" [label="[120, 1, 1, 1, 1]", style=solid]; +"509 Constant_11269" -> "505 Multiply_9433/fq_weights_1" [label="[120, 1, 1, 1, 1]", style=solid]; +"510 Multiply_9751" -> "505 Multiply_9433/fq_weights_1" [label="[120, 1, 1, 5, 5]", style=solid]; +"511 Constant_11267" -> "92 Transpose_5993/fq_output_0" [label="[1, 120, 1, 1]", style=solid]; +"512 Constant_11266" -> "92 Transpose_5993/fq_output_0" [label="[1, 120, 1, 1]", style=solid]; +"513 Constant_11265" -> "92 Transpose_5993/fq_output_0" [label="[1, 120, 1, 1]", style=solid]; +"514 Constant_11264" -> "92 Transpose_5993/fq_output_0" [label="[1, 120, 1, 1]", style=solid]; +"515 Constant_9427" -> "86 Transpose_5975" [label="[1, 120, 1, 1]", style=solid]; +"516 Multiply_9419/fq_weights_1" -> "83 Multiply_9419" [label="[120, 40, 1, 1]", style=solid]; +"517 Constant_11262" -> "516 Multiply_9419/fq_weights_1" [label="[120, 1, 1, 1]", style=solid]; +"518 Constant_11261" -> "516 Multiply_9419/fq_weights_1" [label="[120, 1, 1, 1]", style=solid]; +"519 Constant_11260" -> "516 Multiply_9419/fq_weights_1" [label="[120, 1, 1, 1]", style=solid]; +"520 Constant_11259" -> "516 Multiply_9419/fq_weights_1" [label="[120, 1, 1, 1]", style=solid]; +"521 Multiply_9745" -> "516 Multiply_9419/fq_weights_1" [label="[120, 40, 1, 1]", style=solid]; +"522 Constant_11257" -> "80 Transpose_5951/fq_output_0" [label="[]", style=solid]; +"523 Constant_11256" -> "80 Transpose_5951/fq_output_0" [label="[]", style=solid]; +"524 Constant_11255" -> "80 Transpose_5951/fq_output_0" [label="[]", style=solid]; +"525 Constant_11254" -> "80 Transpose_5951/fq_output_0" [label="[]", style=solid]; +"526 Constant_11252" -> "124 Transpose_5947/fq_output_0" [label="[]", style=solid]; +"527 Constant_11251" -> "124 Transpose_5947/fq_output_0" [label="[]", style=solid]; +"528 Constant_11250" -> "124 Transpose_5947/fq_output_0" [label="[]", style=solid]; +"529 Constant_11249" -> "124 Transpose_5947/fq_output_0" [label="[]", style=solid]; +"530 Constant_9413" -> "119 Transpose_5947" [label="[1, 40, 1, 1]", style=solid]; +"531 Multiply_9405/fq_weights_1" -> "114 Multiply_9405" [label="[40, 240, 1, 1]", style=solid]; +"532 Constant_11247" -> "531 Multiply_9405/fq_weights_1" [label="[40, 1, 1, 1]", style=solid]; +"533 Constant_11246" -> "531 Multiply_9405/fq_weights_1" [label="[40, 1, 1, 1]", style=solid]; +"534 Constant_11245" -> "531 Multiply_9405/fq_weights_1" [label="[40, 1, 1, 1]", style=solid]; +"535 Constant_11244" -> "531 Multiply_9405/fq_weights_1" [label="[40, 1, 1, 1]", style=solid]; +"536 Multiply_9739" -> "531 Multiply_9405/fq_weights_1" [label="[40, 240, 1, 1]", style=solid]; +"537 Constant_11242" -> "109 Transpose_5923/fq_output_0" [label="[]", style=solid]; +"538 Constant_11241" -> "109 Transpose_5923/fq_output_0" [label="[]", style=solid]; +"539 Constant_11240" -> "109 Transpose_5923/fq_output_0" [label="[]", style=solid]; +"540 Constant_11239" -> "109 Transpose_5923/fq_output_0" [label="[]", style=solid]; +"541 Constant_11237" -> "134 Transpose_5919/fq_output_0" [label="[]", style=solid]; +"542 Constant_11236" -> "134 Transpose_5919/fq_output_0" [label="[]", style=solid]; +"543 Constant_11235" -> "134 Transpose_5919/fq_output_0" [label="[]", style=solid]; +"544 Constant_11234" -> "134 Transpose_5919/fq_output_0" [label="[]", style=solid]; +"545 Transpose_5903" -> "128 Transpose_5905" [label="[1, 240, 1, 1]", style=solid]; +"546 Transpose_1361" -> "123 Convolution_1362" [label="[240, 64, 1, 1]", style=solid]; +"547 Transpose_5895" -> "113 Transpose_5897" [label="[1, 64, 1, 1]", style=solid]; +"548 Transpose_1352" -> "108 Convolution_1353" [label="[64, 240, 1, 1]", style=solid]; +"549 Constant_5889" -> "103 Transpose_5891" [label="[2]", style=dashed]; +"550 Constant_11232" -> "99 Transpose_5887/fq_output_0" [label="[]", style=solid]; +"551 Constant_11231" -> "99 Transpose_5887/fq_output_0" [label="[]", style=solid]; +"552 Constant_11230" -> "99 Transpose_5887/fq_output_0" [label="[]", style=solid]; +"553 Constant_11229" -> "99 Transpose_5887/fq_output_0" [label="[]", style=solid]; +"554 Constant_9399" -> "91 Transpose_5869" [label="[1, 240, 1, 1]", style=solid]; +"555 Multiply_9391/fq_weights_1" -> "88 Multiply_9391" [label="[240, 1, 1, 5, 5]", style=solid]; +"556 Constant_11227" -> "555 Multiply_9391/fq_weights_1" [label="[240, 1, 1, 1, 1]", style=solid]; +"557 Constant_11226" -> "555 Multiply_9391/fq_weights_1" [label="[240, 1, 1, 1, 1]", style=solid]; +"558 Constant_11225" -> "555 Multiply_9391/fq_weights_1" [label="[240, 1, 1, 1, 1]", style=solid]; +"559 Constant_11224" -> "555 Multiply_9391/fq_weights_1" [label="[240, 1, 1, 1, 1]", style=solid]; +"560 Multiply_9734" -> "555 Multiply_9391/fq_weights_1" [label="[240, 1, 1, 5, 5]", style=solid]; +"561 Constant_11222" -> "85 Transpose_5845/fq_output_0" [label="[1, 240, 1, 1]", style=solid]; +"562 Constant_11221" -> "85 Transpose_5845/fq_output_0" [label="[1, 240, 1, 1]", style=solid]; +"563 Constant_11220" -> "85 Transpose_5845/fq_output_0" [label="[1, 240, 1, 1]", style=solid]; +"564 Constant_11219" -> "85 Transpose_5845/fq_output_0" [label="[1, 240, 1, 1]", style=solid]; +"565 Constant_9385" -> "79 Transpose_5827" [label="[1, 240, 1, 1]", style=solid]; +"566 Multiply_9377/fq_weights_1" -> "76 Multiply_9377" [label="[240, 40, 1, 1]", style=solid]; +"567 Constant_11217" -> "566 Multiply_9377/fq_weights_1" [label="[240, 1, 1, 1]", style=solid]; +"568 Constant_11216" -> "566 Multiply_9377/fq_weights_1" [label="[240, 1, 1, 1]", style=solid]; +"569 Constant_11215" -> "566 Multiply_9377/fq_weights_1" [label="[240, 1, 1, 1]", style=solid]; +"570 Constant_11214" -> "566 Multiply_9377/fq_weights_1" [label="[240, 1, 1, 1]", style=solid]; +"571 Multiply_9728" -> "566 Multiply_9377/fq_weights_1" [label="[240, 40, 1, 1]", style=solid]; +"572 Constant_11212" -> "73 Transpose_5803/fq_output_0" [label="[]", style=solid]; +"573 Constant_11211" -> "73 Transpose_5803/fq_output_0" [label="[]", style=solid]; +"574 Constant_11210" -> "73 Transpose_5803/fq_output_0" [label="[]", style=solid]; +"575 Constant_11209" -> "73 Transpose_5803/fq_output_0" [label="[]", style=solid]; +"576 Constant_11207" -> "112 Transpose_5799/fq_output_0" [label="[]", style=solid]; +"577 Constant_11206" -> "112 Transpose_5799/fq_output_0" [label="[]", style=solid]; +"578 Constant_11205" -> "112 Transpose_5799/fq_output_0" [label="[]", style=solid]; +"579 Constant_11204" -> "112 Transpose_5799/fq_output_0" [label="[]", style=solid]; +"580 Constant_9371" -> "107 Transpose_5799" [label="[1, 40, 1, 1]", style=solid]; +"581 Multiply_9363/fq_weights_1" -> "102 Multiply_9363" [label="[40, 240, 1, 1]", style=solid]; +"582 Constant_11202" -> "581 Multiply_9363/fq_weights_1" [label="[40, 1, 1, 1]", style=solid]; +"583 Constant_11201" -> "581 Multiply_9363/fq_weights_1" [label="[40, 1, 1, 1]", style=solid]; +"584 Constant_11200" -> "581 Multiply_9363/fq_weights_1" [label="[40, 1, 1, 1]", style=solid]; +"585 Constant_11199" -> "581 Multiply_9363/fq_weights_1" [label="[40, 1, 1, 1]", style=solid]; +"586 Multiply_9722" -> "581 Multiply_9363/fq_weights_1" [label="[40, 240, 1, 1]", style=solid]; +"587 Constant_11197" -> "98 Transpose_5775/fq_output_0" [label="[]", style=solid]; +"588 Constant_11196" -> "98 Transpose_5775/fq_output_0" [label="[]", style=solid]; +"589 Constant_11195" -> "98 Transpose_5775/fq_output_0" [label="[]", style=solid]; +"590 Constant_11194" -> "98 Transpose_5775/fq_output_0" [label="[]", style=solid]; +"591 Constant_11192" -> "127 Transpose_5771/fq_output_0" [label="[]", style=solid]; +"592 Constant_11191" -> "127 Transpose_5771/fq_output_0" [label="[]", style=solid]; +"593 Constant_11190" -> "127 Transpose_5771/fq_output_0" [label="[]", style=solid]; +"594 Constant_11189" -> "127 Transpose_5771/fq_output_0" [label="[]", style=solid]; +"595 Transpose_5755" -> "117 Transpose_5757" [label="[1, 240, 1, 1]", style=solid]; +"596 Transpose_1250" -> "111 Convolution_1251" [label="[240, 64, 1, 1]", style=solid]; +"597 Transpose_5747" -> "101 Transpose_5749" [label="[1, 64, 1, 1]", style=solid]; +"598 Transpose_1241" -> "97 Convolution_1242" [label="[64, 240, 1, 1]", style=solid]; +"599 Constant_5741" -> "93 Transpose_5743" [label="[2]", style=dashed]; +"600 Constant_11187" -> "90 Transpose_5739/fq_output_0" [label="[]", style=solid]; +"601 Constant_11186" -> "90 Transpose_5739/fq_output_0" [label="[]", style=solid]; +"602 Constant_11185" -> "90 Transpose_5739/fq_output_0" [label="[]", style=solid]; +"603 Constant_11184" -> "90 Transpose_5739/fq_output_0" [label="[]", style=solid]; +"604 Constant_9357" -> "84 Transpose_5721" [label="[1, 240, 1, 1]", style=solid]; +"605 Multiply_9349/fq_weights_1" -> "81 Multiply_9349" [label="[240, 1, 1, 5, 5]", style=solid]; +"606 Constant_11182" -> "605 Multiply_9349/fq_weights_1" [label="[240, 1, 1, 1, 1]", style=solid]; +"607 Constant_11181" -> "605 Multiply_9349/fq_weights_1" [label="[240, 1, 1, 1, 1]", style=solid]; +"608 Constant_11180" -> "605 Multiply_9349/fq_weights_1" [label="[240, 1, 1, 1, 1]", style=solid]; +"609 Constant_11179" -> "605 Multiply_9349/fq_weights_1" [label="[240, 1, 1, 1, 1]", style=solid]; +"610 Multiply_9717" -> "605 Multiply_9349/fq_weights_1" [label="[240, 1, 1, 5, 5]", style=solid]; +"611 Constant_11177" -> "78 Transpose_5697/fq_output_0" [label="[1, 240, 1, 1]", style=solid]; +"612 Constant_11176" -> "78 Transpose_5697/fq_output_0" [label="[1, 240, 1, 1]", style=solid]; +"613 Constant_11175" -> "78 Transpose_5697/fq_output_0" [label="[1, 240, 1, 1]", style=solid]; +"614 Constant_11174" -> "78 Transpose_5697/fq_output_0" [label="[1, 240, 1, 1]", style=solid]; +"615 Constant_9343" -> "72 Transpose_5679" [label="[1, 240, 1, 1]", style=solid]; +"616 Multiply_9335/fq_weights_1" -> "69 Multiply_9335" [label="[240, 40, 1, 1]", style=solid]; +"617 Constant_11172" -> "616 Multiply_9335/fq_weights_1" [label="[240, 1, 1, 1]", style=solid]; +"618 Constant_11171" -> "616 Multiply_9335/fq_weights_1" [label="[240, 1, 1, 1]", style=solid]; +"619 Constant_11170" -> "616 Multiply_9335/fq_weights_1" [label="[240, 1, 1, 1]", style=solid]; +"620 Constant_11169" -> "616 Multiply_9335/fq_weights_1" [label="[240, 1, 1, 1]", style=solid]; +"621 Multiply_9711" -> "616 Multiply_9335/fq_weights_1" [label="[240, 40, 1, 1]", style=solid]; +"622 Constant_11167" -> "67 Transpose_5655/fq_output_0" [label="[]", style=solid]; +"623 Constant_11166" -> "67 Transpose_5655/fq_output_0" [label="[]", style=solid]; +"624 Constant_11165" -> "67 Transpose_5655/fq_output_0" [label="[]", style=solid]; +"625 Constant_11164" -> "67 Transpose_5655/fq_output_0" [label="[]", style=solid]; +"626 Constant_9329" -> "65 Transpose_5655" [label="[1, 40, 1, 1]", style=solid]; +"627 Multiply_9321/fq_weights_1" -> "63 Multiply_9321" [label="[40, 96, 1, 1]", style=solid]; +"628 Constant_11162" -> "627 Multiply_9321/fq_weights_1" [label="[40, 1, 1, 1]", style=solid]; +"629 Constant_11161" -> "627 Multiply_9321/fq_weights_1" [label="[40, 1, 1, 1]", style=solid]; +"630 Constant_11160" -> "627 Multiply_9321/fq_weights_1" [label="[40, 1, 1, 1]", style=solid]; +"631 Constant_11159" -> "627 Multiply_9321/fq_weights_1" [label="[40, 1, 1, 1]", style=solid]; +"632 Multiply_9705" -> "627 Multiply_9321/fq_weights_1" [label="[40, 96, 1, 1]", style=solid]; +"633 Constant_11157" -> "61 Transpose_5631/fq_output_0" [label="[]", style=solid]; +"634 Constant_11156" -> "61 Transpose_5631/fq_output_0" [label="[]", style=solid]; +"635 Constant_11155" -> "61 Transpose_5631/fq_output_0" [label="[]", style=solid]; +"636 Constant_11154" -> "61 Transpose_5631/fq_output_0" [label="[]", style=solid]; +"637 Constant_11152" -> "74 Transpose_5627/fq_output_0" [label="[]", style=solid]; +"638 Constant_11151" -> "74 Transpose_5627/fq_output_0" [label="[]", style=solid]; +"639 Constant_11150" -> "74 Transpose_5627/fq_output_0" [label="[]", style=solid]; +"640 Constant_11149" -> "74 Transpose_5627/fq_output_0" [label="[]", style=solid]; +"641 Transpose_5611" -> "68 Transpose_5613" [label="[1, 96, 1, 1]", style=solid]; +"642 Transpose_1140" -> "66 Convolution_1141" [label="[96, 24, 1, 1]", style=solid]; +"643 Transpose_5603" -> "62 Transpose_5605" [label="[1, 24, 1, 1]", style=solid]; +"644 Transpose_1131" -> "60 Convolution_1132" [label="[24, 96, 1, 1]", style=solid]; +"645 Constant_5597" -> "58 Transpose_5599" [label="[2]", style=dashed]; +"646 Constant_11147" -> "56 Transpose_5595/fq_output_0" [label="[]", style=solid]; +"647 Constant_11146" -> "56 Transpose_5595/fq_output_0" [label="[]", style=solid]; +"648 Constant_11145" -> "56 Transpose_5595/fq_output_0" [label="[]", style=solid]; +"649 Constant_11144" -> "56 Transpose_5595/fq_output_0" [label="[]", style=solid]; +"650 Constant_9315" -> "52 Transpose_5577" [label="[1, 96, 1, 1]", style=solid]; +"651 Multiply_9307/fq_weights_1" -> "50 Multiply_9307" [label="[96, 1, 1, 5, 5]", style=solid]; +"652 Constant_11142" -> "651 Multiply_9307/fq_weights_1" [label="[96, 1, 1, 1, 1]", style=solid]; +"653 Constant_11141" -> "651 Multiply_9307/fq_weights_1" [label="[96, 1, 1, 1, 1]", style=solid]; +"654 Constant_11140" -> "651 Multiply_9307/fq_weights_1" [label="[96, 1, 1, 1, 1]", style=solid]; +"655 Constant_11139" -> "651 Multiply_9307/fq_weights_1" [label="[96, 1, 1, 1, 1]", style=solid]; +"656 Multiply_9700" -> "651 Multiply_9307/fq_weights_1" [label="[96, 1, 1, 5, 5]", style=solid]; +"657 Constant_11137" -> "48 Transpose_5526/fq_output_0" [label="[1, 96, 1, 1]", style=solid]; +"658 Constant_11136" -> "48 Transpose_5526/fq_output_0" [label="[1, 96, 1, 1]", style=solid]; +"659 Constant_11135" -> "48 Transpose_5526/fq_output_0" [label="[1, 96, 1, 1]", style=solid]; +"660 Constant_11134" -> "48 Transpose_5526/fq_output_0" [label="[1, 96, 1, 1]", style=solid]; +"661 Constant_9301" -> "44 Transpose_5508" [label="[1, 96, 1, 1]", style=solid]; +"662 Multiply_9293/fq_weights_1" -> "42 Multiply_9293" [label="[96, 24, 1, 1]", style=solid]; +"663 Constant_11132" -> "662 Multiply_9293/fq_weights_1" [label="[96, 1, 1, 1]", style=solid]; +"664 Constant_11131" -> "662 Multiply_9293/fq_weights_1" [label="[96, 1, 1, 1]", style=solid]; +"665 Constant_11130" -> "662 Multiply_9293/fq_weights_1" [label="[96, 1, 1, 1]", style=solid]; +"666 Constant_11129" -> "662 Multiply_9293/fq_weights_1" [label="[96, 1, 1, 1]", style=solid]; +"667 Multiply_9694" -> "662 Multiply_9293/fq_weights_1" [label="[96, 24, 1, 1]", style=solid]; +"668 Constant_11127" -> "40 Transpose_5484/fq_output_0" [label="[]", style=solid]; +"669 Constant_11126" -> "40 Transpose_5484/fq_output_0" [label="[]", style=solid]; +"670 Constant_11125" -> "40 Transpose_5484/fq_output_0" [label="[]", style=solid]; +"671 Constant_11124" -> "40 Transpose_5484/fq_output_0" [label="[]", style=solid]; +"672 Constant_11122" -> "57 Transpose_5480/fq_output_0" [label="[]", style=solid]; +"673 Constant_11121" -> "57 Transpose_5480/fq_output_0" [label="[]", style=solid]; +"674 Constant_11120" -> "57 Transpose_5480/fq_output_0" [label="[]", style=solid]; +"675 Constant_11119" -> "57 Transpose_5480/fq_output_0" [label="[]", style=solid]; +"676 Constant_9287" -> "55 Transpose_5480" [label="[1, 24, 1, 1]", style=solid]; +"677 Multiply_9279/fq_weights_1" -> "53 Multiply_9279" [label="[24, 88, 1, 1]", style=solid]; +"678 Constant_11117" -> "677 Multiply_9279/fq_weights_1" [label="[24, 1, 1, 1]", style=solid]; +"679 Constant_11116" -> "677 Multiply_9279/fq_weights_1" [label="[24, 1, 1, 1]", style=solid]; +"680 Constant_11115" -> "677 Multiply_9279/fq_weights_1" [label="[24, 1, 1, 1]", style=solid]; +"681 Constant_11114" -> "677 Multiply_9279/fq_weights_1" [label="[24, 1, 1, 1]", style=solid]; +"682 Multiply_9688" -> "677 Multiply_9279/fq_weights_1" [label="[24, 88, 1, 1]", style=solid]; +"683 Constant_11112" -> "51 Transpose_5456/fq_output_0" [label="[]", style=solid]; +"684 Constant_11111" -> "51 Transpose_5456/fq_output_0" [label="[]", style=solid]; +"685 Constant_11110" -> "51 Transpose_5456/fq_output_0" [label="[]", style=solid]; +"686 Constant_11109" -> "51 Transpose_5456/fq_output_0" [label="[]", style=solid]; +"687 Constant_9273" -> "47 Transpose_5454" [label="[1, 88, 1, 1]", style=solid]; +"688 Multiply_9265/fq_weights_1" -> "45 Multiply_9265" [label="[88, 1, 1, 3, 3]", style=solid]; +"689 Constant_11107" -> "688 Multiply_9265/fq_weights_1" [label="[88, 1, 1, 1, 1]", style=solid]; +"690 Constant_11106" -> "688 Multiply_9265/fq_weights_1" [label="[88, 1, 1, 1, 1]", style=solid]; +"691 Constant_11105" -> "688 Multiply_9265/fq_weights_1" [label="[88, 1, 1, 1, 1]", style=solid]; +"692 Constant_11104" -> "688 Multiply_9265/fq_weights_1" [label="[88, 1, 1, 1, 1]", style=solid]; +"693 Multiply_9683" -> "688 Multiply_9265/fq_weights_1" [label="[88, 1, 1, 3, 3]", style=solid]; +"694 Constant_11102" -> "43 Transpose_5430/fq_output_0" [label="[1, 88, 1, 1]", style=solid]; +"695 Constant_11101" -> "43 Transpose_5430/fq_output_0" [label="[1, 88, 1, 1]", style=solid]; +"696 Constant_11100" -> "43 Transpose_5430/fq_output_0" [label="[1, 88, 1, 1]", style=solid]; +"697 Constant_11099" -> "43 Transpose_5430/fq_output_0" [label="[1, 88, 1, 1]", style=solid]; +"698 Constant_9259" -> "39 Transpose_5428" [label="[1, 88, 1, 1]", style=solid]; +"699 Multiply_9251/fq_weights_1" -> "37 Multiply_9251" [label="[88, 24, 1, 1]", style=solid]; +"700 Constant_11097" -> "699 Multiply_9251/fq_weights_1" [label="[88, 1, 1, 1]", style=solid]; +"701 Constant_11096" -> "699 Multiply_9251/fq_weights_1" [label="[88, 1, 1, 1]", style=solid]; +"702 Constant_11095" -> "699 Multiply_9251/fq_weights_1" [label="[88, 1, 1, 1]", style=solid]; +"703 Constant_11094" -> "699 Multiply_9251/fq_weights_1" [label="[88, 1, 1, 1]", style=solid]; +"704 Multiply_9677" -> "699 Multiply_9251/fq_weights_1" [label="[88, 24, 1, 1]", style=solid]; +"705 Constant_11092" -> "36 Transpose_5404/fq_output_0" [label="[]", style=solid]; +"706 Constant_11091" -> "36 Transpose_5404/fq_output_0" [label="[]", style=solid]; +"707 Constant_11090" -> "36 Transpose_5404/fq_output_0" [label="[]", style=solid]; +"708 Constant_11089" -> "36 Transpose_5404/fq_output_0" [label="[]", style=solid]; +"709 Constant_9245" -> "35 Transpose_5404" [label="[1, 24, 1, 1]", style=solid]; +"710 Multiply_9237/fq_weights_1" -> "34 Multiply_9237" [label="[24, 72, 1, 1]", style=solid]; +"711 Constant_11087" -> "710 Multiply_9237/fq_weights_1" [label="[24, 1, 1, 1]", style=solid]; +"712 Constant_11086" -> "710 Multiply_9237/fq_weights_1" [label="[24, 1, 1, 1]", style=solid]; +"713 Constant_11085" -> "710 Multiply_9237/fq_weights_1" [label="[24, 1, 1, 1]", style=solid]; +"714 Constant_11084" -> "710 Multiply_9237/fq_weights_1" [label="[24, 1, 1, 1]", style=solid]; +"715 Multiply_9671" -> "710 Multiply_9237/fq_weights_1" [label="[24, 72, 1, 1]", style=solid]; +"716 Constant_11082" -> "33 Transpose_5380/fq_output_0" [label="[]", style=solid]; +"717 Constant_11081" -> "33 Transpose_5380/fq_output_0" [label="[]", style=solid]; +"718 Constant_11080" -> "33 Transpose_5380/fq_output_0" [label="[]", style=solid]; +"719 Constant_11079" -> "33 Transpose_5380/fq_output_0" [label="[]", style=solid]; +"720 Constant_9231" -> "31 Transpose_5378" [label="[1, 72, 1, 1]", style=solid]; +"721 Multiply_9223/fq_weights_1" -> "30 Multiply_9223" [label="[72, 1, 1, 3, 3]", style=solid]; +"722 Constant_11077" -> "721 Multiply_9223/fq_weights_1" [label="[72, 1, 1, 1, 1]", style=solid]; +"723 Constant_11076" -> "721 Multiply_9223/fq_weights_1" [label="[72, 1, 1, 1, 1]", style=solid]; +"724 Constant_11075" -> "721 Multiply_9223/fq_weights_1" [label="[72, 1, 1, 1, 1]", style=solid]; +"725 Constant_11074" -> "721 Multiply_9223/fq_weights_1" [label="[72, 1, 1, 1, 1]", style=solid]; +"726 Multiply_9666" -> "721 Multiply_9223/fq_weights_1" [label="[72, 1, 1, 3, 3]", style=solid]; +"727 Constant_11072" -> "29 Transpose_5327/fq_output_0" [label="[1, 72, 1, 1]", style=solid]; +"728 Constant_11071" -> "29 Transpose_5327/fq_output_0" [label="[1, 72, 1, 1]", style=solid]; +"729 Constant_11070" -> "29 Transpose_5327/fq_output_0" [label="[1, 72, 1, 1]", style=solid]; +"730 Constant_11069" -> "29 Transpose_5327/fq_output_0" [label="[1, 72, 1, 1]", style=solid]; +"731 Constant_9217" -> "26 Transpose_5325" [label="[1, 72, 1, 1]", style=solid]; +"732 Multiply_9209/fq_weights_1" -> "24 Multiply_9209" [label="[72, 16, 1, 1]", style=solid]; +"733 Constant_11067" -> "732 Multiply_9209/fq_weights_1" [label="[72, 1, 1, 1]", style=solid]; +"734 Constant_11066" -> "732 Multiply_9209/fq_weights_1" [label="[72, 1, 1, 1]", style=solid]; +"735 Constant_11065" -> "732 Multiply_9209/fq_weights_1" [label="[72, 1, 1, 1]", style=solid]; +"736 Constant_11064" -> "732 Multiply_9209/fq_weights_1" [label="[72, 1, 1, 1]", style=solid]; +"737 Multiply_9660" -> "732 Multiply_9209/fq_weights_1" [label="[72, 16, 1, 1]", style=solid]; +"738 Constant_11062" -> "22 Transpose_5301/fq_output_0" [label="[]", style=solid]; +"739 Constant_11061" -> "22 Transpose_5301/fq_output_0" [label="[]", style=solid]; +"740 Constant_11060" -> "22 Transpose_5301/fq_output_0" [label="[]", style=solid]; +"741 Constant_11059" -> "22 Transpose_5301/fq_output_0" [label="[]", style=solid]; +"742 Constant_9203" -> "20 Transpose_5301" [label="[1, 16, 1, 1]", style=solid]; +"743 Multiply_9195/fq_weights_1" -> "18 Multiply_9195" [label="[16, 16, 1, 1]", style=solid]; +"744 Constant_11057" -> "743 Multiply_9195/fq_weights_1" [label="[16, 1, 1, 1]", style=solid]; +"745 Constant_11056" -> "743 Multiply_9195/fq_weights_1" [label="[16, 1, 1, 1]", style=solid]; +"746 Constant_11055" -> "743 Multiply_9195/fq_weights_1" [label="[16, 1, 1, 1]", style=solid]; +"747 Constant_11054" -> "743 Multiply_9195/fq_weights_1" [label="[16, 1, 1, 1]", style=solid]; +"748 Multiply_9654" -> "743 Multiply_9195/fq_weights_1" [label="[16, 16, 1, 1]", style=solid]; +"749 Constant_11052" -> "16 Transpose_5277/fq_output_0" [label="[]", style=solid]; +"750 Constant_11051" -> "16 Transpose_5277/fq_output_0" [label="[]", style=solid]; +"751 Constant_11050" -> "16 Transpose_5277/fq_output_0" [label="[]", style=solid]; +"752 Constant_11049" -> "16 Transpose_5277/fq_output_0" [label="[]", style=solid]; +"753 Constant_11047" -> "27 Transpose_5273/fq_output_0" [label="[]", style=solid]; +"754 Constant_11046" -> "27 Transpose_5273/fq_output_0" [label="[]", style=solid]; +"755 Constant_11045" -> "27 Transpose_5273/fq_output_0" [label="[]", style=solid]; +"756 Constant_11044" -> "27 Transpose_5273/fq_output_0" [label="[]", style=solid]; +"757 Transpose_5257" -> "23 Transpose_5259" [label="[1, 16, 1, 1]", style=solid]; +"758 Transpose_809" -> "21 Convolution_810" [label="[16, 8, 1, 1]", style=solid]; +"759 Transpose_5249" -> "17 Transpose_5251" [label="[1, 8, 1, 1]", style=solid]; +"760 Transpose_800" -> "15 Convolution_801" [label="[8, 16, 1, 1]", style=solid]; +"761 Constant_5243" -> "13 Transpose_5245" [label="[2]", style=dashed]; +"762 Constant_11042" -> "12 Transpose_5241/fq_output_0" [label="[]", style=solid]; +"763 Constant_11041" -> "12 Transpose_5241/fq_output_0" [label="[]", style=solid]; +"764 Constant_11040" -> "12 Transpose_5241/fq_output_0" [label="[]", style=solid]; +"765 Constant_11039" -> "12 Transpose_5241/fq_output_0" [label="[]", style=solid]; +"766 Constant_9189" -> "10 Transpose_5239" [label="[1, 16, 1, 1]", style=solid]; +"767 Multiply_9181/fq_weights_1" -> "9 Multiply_9181" [label="[16, 1, 1, 3, 3]", style=solid]; +"768 Constant_11037" -> "767 Multiply_9181/fq_weights_1" [label="[16, 1, 1, 1, 1]", style=solid]; +"769 Constant_11036" -> "767 Multiply_9181/fq_weights_1" [label="[16, 1, 1, 1, 1]", style=solid]; +"770 Constant_11035" -> "767 Multiply_9181/fq_weights_1" [label="[16, 1, 1, 1, 1]", style=solid]; +"771 Constant_11034" -> "767 Multiply_9181/fq_weights_1" [label="[16, 1, 1, 1, 1]", style=solid]; +"772 Multiply_9649" -> "767 Multiply_9181/fq_weights_1" [label="[16, 1, 1, 3, 3]", style=solid]; +"773 Constant_11032" -> "8 Transpose_5188/fq_output_0" [label="[1, 16, 1, 1]", style=solid]; +"774 Constant_11031" -> "8 Transpose_5188/fq_output_0" [label="[1, 16, 1, 1]", style=solid]; +"775 Constant_11030" -> "8 Transpose_5188/fq_output_0" [label="[1, 16, 1, 1]", style=solid]; +"776 Constant_11029" -> "8 Transpose_5188/fq_output_0" [label="[1, 16, 1, 1]", style=solid]; +"777 Constant_9175" -> "6 Transpose_5170" [label="[1, 16, 1, 1]", style=solid]; +"778 Multiply_9167/fq_weights_1" -> "5 Multiply_9167" [label="[16, 3, 3, 3]", style=solid]; +"779 Constant_11027" -> "778 Multiply_9167/fq_weights_1" [label="[16, 1, 1, 1]", style=solid]; +"780 Constant_11026" -> "778 Multiply_9167/fq_weights_1" [label="[16, 1, 1, 1]", style=solid]; +"781 Constant_11025" -> "778 Multiply_9167/fq_weights_1" [label="[16, 1, 1, 1]", style=solid]; +"782 Constant_11024" -> "778 Multiply_9167/fq_weights_1" [label="[16, 1, 1, 1]", style=solid]; +"783 Gather_10068" -> "778 Multiply_9167/fq_weights_1" [label="[16, 3, 3, 3]", style=solid]; +"784 Constant_11022" -> "4 Transpose_710/fq_output_0" [label="[]", style=solid]; +"785 Constant_11021" -> "4 Transpose_710/fq_output_0" [label="[]", style=solid]; +"786 Constant_11020" -> "4 Transpose_710/fq_output_0" [label="[]", style=solid]; +"787 Constant_11019" -> "4 Transpose_710/fq_output_0" [label="[]", style=solid]; +"788 Unsqueeze_7776" -> "3 Transpose_710" [label="[1, 1, 1, 1]", style=solid]; +"789 Unsqueeze_7782" -> "2 Transpose_7774" [label="[1, 1, 1, 1]", style=solid]; +"790 Constant_7779" -> "1 Transpose_7780" [label="[4]", style=dashed]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/resnet-18-pytorch_performance.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/resnet-18-pytorch_performance.dot deleted file mode 120000 index 32881077481..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/quantized/resnet-18-pytorch_performance.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/quantized/resnet-18-pytorch_performance.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/resnet-18-pytorch_performance.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/resnet-18-pytorch_performance.dot new file mode 100644 index 00000000000..480119c4db2 --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/quantized/resnet-18-pytorch_performance.dot @@ -0,0 +1,759 @@ +strict digraph { +"0 data" [id=0, type=Parameter]; +"1 Multiply_1715" [id=1, type=Multiply]; +"2 Divide_401" [id=2, type=Add]; +"3 Divide_401/fq_output_0" [id=3, type=FakeQuantize]; +"4 /conv1/Conv/WithoutBiases" [id=4, type=Convolution]; +"5 /conv1/Conv" [id=5, type=Add]; +"6 /relu/Relu" [id=6, type=Relu]; +"7 /relu/Relu/fq_output_0" [id=7, type=FakeQuantize]; +"8 /maxpool/MaxPool" [id=8, type=MaxPool]; +"9 /layer1/layer1.0/Add" [id=9, type=Add]; +"10 /layer1/layer1.0/conv1/Conv/WithoutBiases" [id=10, type=Convolution]; +"11 /layer1/layer1.0/relu_1/Relu" [id=11, type=Relu]; +"12 /layer1/layer1.0/conv1/Conv" [id=12, type=Add]; +"13 /layer1/layer1.0/relu_1/Relu/fq_output_0" [id=13, type=FakeQuantize]; +"14 /layer1/layer1.0/relu/Relu" [id=14, type=Relu]; +"15 /layer1/layer1.1/Add" [id=15, type=Add]; +"16 /layer1/layer1.1/conv1/Conv/WithoutBiases" [id=16, type=Convolution]; +"17 /layer1/layer1.0/relu/Relu/fq_output_0" [id=17, type=FakeQuantize]; +"18 /layer1/layer1.1/relu_1/Relu" [id=18, type=Relu]; +"19 /layer1/layer1.1/conv1/Conv" [id=19, type=Add]; +"20 /layer1/layer1.0/conv2/Conv/WithoutBiases" [id=20, type=Convolution]; +"21 /layer1/layer1.1/relu_1/Relu/fq_output_0" [id=21, type=FakeQuantize]; +"22 /layer1/layer1.1/relu/Relu" [id=22, type=Relu]; +"23 /layer1/layer1.0/conv2/Conv" [id=23, type=Add]; +"24 /layer2/layer2.0/conv1/Conv/WithoutBiases" [id=24, type=Convolution]; +"25 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases" [id=25, type=Convolution]; +"26 /layer1/layer1.1/relu/Relu/fq_output_0" [id=26, type=FakeQuantize]; +"27 /layer1/layer1.0/conv2/Conv/fq_output_0" [id=27, type=FakeQuantize]; +"28 /layer2/layer2.0/conv1/Conv" [id=28, type=Add]; +"29 /layer2/layer2.0/downsample/downsample.0/Conv" [id=29, type=Add]; +"30 /layer1/layer1.1/conv2/Conv/WithoutBiases" [id=30, type=Convolution]; +"31 /layer2/layer2.0/relu/Relu" [id=31, type=Relu]; +"32 /layer2/layer2.0/downsample/downsample.0/Conv/fq_output_0" [id=32, type=FakeQuantize]; +"33 /layer1/layer1.1/conv2/Conv" [id=33, type=Add]; +"34 /layer2/layer2.0/relu/Relu/fq_output_0" [id=34, type=FakeQuantize]; +"35 /layer2/layer2.0/Add" [id=35, type=Add]; +"36 /layer1/layer1.1/conv2/Conv/fq_output_0" [id=36, type=FakeQuantize]; +"37 /layer2/layer2.0/conv2/Conv/WithoutBiases" [id=37, type=Convolution]; +"38 /layer2/layer2.0/relu_1/Relu" [id=38, type=Relu]; +"39 /layer2/layer2.0/conv2/Conv" [id=39, type=Add]; +"40 /layer2/layer2.0/relu_1/Relu/fq_output_0" [id=40, type=FakeQuantize]; +"41 /layer2/layer2.0/conv2/Conv/fq_output_0" [id=41, type=FakeQuantize]; +"42 /layer2/layer2.1/Add" [id=42, type=Add]; +"43 /layer2/layer2.1/conv1/Conv/WithoutBiases" [id=43, type=Convolution]; +"44 /layer2/layer2.1/relu_1/Relu" [id=44, type=Relu]; +"45 /layer2/layer2.1/conv1/Conv" [id=45, type=Add]; +"46 /layer2/layer2.1/relu_1/Relu/fq_output_0" [id=46, type=FakeQuantize]; +"47 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type=Relu]; +"62 /layer3/layer3.0/conv2/Conv" [id=62, type=Add]; +"63 /layer3/layer3.0/relu_1/Relu/fq_output_0" [id=63, type=FakeQuantize]; +"64 /layer3/layer3.0/conv2/Conv/fq_output_0" [id=64, type=FakeQuantize]; +"65 /layer3/layer3.1/Add" [id=65, type=Add]; +"66 /layer3/layer3.1/conv1/Conv/WithoutBiases" [id=66, type=Convolution]; +"67 /layer3/layer3.1/relu_1/Relu" [id=67, type=Relu]; +"68 /layer3/layer3.1/conv1/Conv" [id=68, type=Add]; +"69 /layer3/layer3.1/relu_1/Relu/fq_output_0" [id=69, type=FakeQuantize]; +"70 /layer3/layer3.1/relu/Relu" [id=70, type=Relu]; +"71 /layer4/layer4.0/conv1/Conv/WithoutBiases" [id=71, type=Convolution]; +"72 /layer4/layer4.0/downsample/downsample.0/Conv/WithoutBiases" [id=72, type=Convolution]; +"73 /layer3/layer3.1/relu/Relu/fq_output_0" [id=73, type=FakeQuantize]; +"74 /layer4/layer4.0/conv1/Conv" [id=74, type=Add]; +"75 /layer4/layer4.0/downsample/downsample.0/Conv" [id=75, type=Add]; +"76 /layer3/layer3.1/conv2/Conv/WithoutBiases" [id=76, type=Convolution]; +"77 /layer4/layer4.0/relu/Relu" [id=77, type=Relu]; +"78 /layer4/layer4.0/downsample/downsample.0/Conv/fq_output_0" [id=78, type=FakeQuantize]; +"79 /layer3/layer3.1/conv2/Conv" [id=79, type=Add]; +"80 /layer4/layer4.0/relu/Relu/fq_output_0" [id=80, type=FakeQuantize]; +"81 /layer4/layer4.0/Add" [id=81, type=Add]; +"82 /layer3/layer3.1/conv2/Conv/fq_output_0" [id=82, type=FakeQuantize]; +"83 /layer4/layer4.0/conv2/Conv/WithoutBiases" [id=83, type=Convolution]; +"84 /layer4/layer4.0/relu_1/Relu" [id=84, type=Relu]; +"85 /layer4/layer4.0/conv2/Conv" [id=85, type=Add]; +"86 /layer4/layer4.0/relu_1/Relu/fq_output_0" [id=86, type=FakeQuantize]; +"87 /layer4/layer4.0/conv2/Conv/fq_output_0" [id=87, type=FakeQuantize]; +"88 /layer4/layer4.1/Add" [id=88, type=Add]; +"89 /layer4/layer4.1/conv1/Conv/WithoutBiases" [id=89, type=Convolution]; +"90 /layer4/layer4.1/relu_1/Relu" [id=90, type=Relu]; +"91 /layer4/layer4.1/conv1/Conv" [id=91, type=Add]; +"92 /layer4/layer4.1/relu_1/Relu/fq_output_0" [id=92, type=FakeQuantize]; +"93 /layer4/layer4.1/relu/Relu" [id=93, type=Relu]; +"94 /avgpool/GlobalAveragePool" [id=94, type=ReduceMean]; +"95 /layer4/layer4.1/relu/Relu/fq_output_0" [id=95, type=FakeQuantize]; +"96 /avgpool/GlobalAveragePool/fq_output_0" [id=96, type=FakeQuantize]; +"97 /layer4/layer4.1/conv2/Conv/WithoutBiases" [id=97, type=Convolution]; +"98 /Flatten" [id=98, type=Reshape]; +"99 /layer4/layer4.1/conv2/Conv" [id=99, type=Add]; +"100 /fc/Gemm/WithoutBiases" [id=100, type=MatMul]; +"101 /layer4/layer4.1/conv2/Conv/fq_output_0" [id=101, type=FakeQuantize]; +"102 prob" [id=102, type=Add]; +"103 prob/sink_port_0" [id=103, type=Result]; +"104 Constant_1789" [id=104, type=Constant]; +"105 /fc/Gemm/WithoutBiases/fq_weights_1" [id=105, type=FakeQuantize]; +"106 Constant_48641" [id=106, type=Constant]; +"107 Constant_48640" [id=107, type=Constant]; +"108 Constant_48639" [id=108, type=Constant]; +"109 Constant_48638" [id=109, type=Constant]; +"110 fc.weight" [id=110, type=Constant]; +"111 Constant_380" [id=111, type=Constant]; +"112 Constant_48636" [id=112, type=Constant]; +"113 Constant_48635" [id=113, type=Constant]; +"114 Constant_48634" [id=114, type=Constant]; +"115 Constant_48633" [id=115, type=Constant]; +"116 Range_376" [id=116, type=Constant]; +"117 Constant_48631" [id=117, type=Constant]; +"118 Constant_48630" [id=118, type=Constant]; +"119 Constant_48629" [id=119, type=Constant]; +"120 Constant_48628" [id=120, type=Constant]; +"121 Constant_48606" [id=121, type=Constant]; +"122 Constant_48605" [id=122, type=Constant]; +"123 Constant_48604" [id=123, type=Constant]; +"124 Constant_48603" [id=124, type=Constant]; +"125 Constant_48581" [id=125, type=Constant]; +"126 Constant_48580" [id=126, type=Constant]; +"127 Constant_48579" [id=127, type=Constant]; +"128 Constant_48578" [id=128, type=Constant]; +"129 Reshape_331" [id=129, type=Constant]; +"130 /layer4/layer4.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [id=130, type=FakeQuantize]; +"131 Constant_48576" [id=131, type=Constant]; +"132 Constant_48575" [id=132, type=Constant]; +"133 Constant_48574" [id=133, type=Constant]; +"134 Constant_48573" [id=134, type=Constant]; +"135 onnx^^Conv_244" [id=135, label="135 onnx::Conv_244", type=Constant]; +"136 Constant_48571" [id=136, type=Constant]; +"137 Constant_48570" [id=137, type=Constant]; +"138 Constant_48569" [id=138, type=Constant]; +"139 Constant_48568" [id=139, type=Constant]; +"140 Constant_48546" [id=140, type=Constant]; +"141 Constant_48545" [id=141, type=Constant]; +"142 Constant_48544" [id=142, type=Constant]; +"143 Constant_48543" [id=143, type=Constant]; +"144 Constant_48521" [id=144, type=Constant]; +"145 Constant_48520" [id=145, type=Constant]; +"146 Constant_48519" [id=146, type=Constant]; +"147 Constant_48518" [id=147, type=Constant]; +"148 Reshape_250" [id=148, type=Constant]; +"149 /layer3/layer3.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [id=149, type=FakeQuantize]; +"150 Constant_48516" [id=150, type=Constant]; +"151 Constant_48515" [id=151, type=Constant]; +"152 Constant_48514" [id=152, type=Constant]; +"153 Constant_48513" [id=153, type=Constant]; +"154 onnx^^Conv_229" [id=154, label="154 onnx::Conv_229", type=Constant]; +"155 Constant_48511" [id=155, type=Constant]; +"156 Constant_48510" [id=156, type=Constant]; +"157 Constant_48509" [id=157, type=Constant]; +"158 Constant_48508" [id=158, type=Constant]; +"159 Constant_48486" [id=159, type=Constant]; +"160 Constant_48485" [id=160, type=Constant]; +"161 Constant_48484" [id=161, type=Constant]; +"162 Constant_48483" [id=162, type=Constant]; +"163 Constant_48461" [id=163, type=Constant]; +"164 Constant_48460" [id=164, type=Constant]; +"165 Constant_48459" [id=165, type=Constant]; +"166 Constant_48458" [id=166, type=Constant]; +"167 Reshape_169" [id=167, type=Constant]; +"168 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [id=168, type=FakeQuantize]; +"169 Constant_48456" [id=169, type=Constant]; +"170 Constant_48455" [id=170, type=Constant]; +"171 Constant_48454" [id=171, type=Constant]; +"172 Constant_48453" [id=172, type=Constant]; +"173 onnx^^Conv_214" [id=173, label="173 onnx::Conv_214", type=Constant]; +"174 Constant_48451" [id=174, type=Constant]; +"175 Constant_48450" [id=175, type=Constant]; +"176 Constant_48449" [id=176, type=Constant]; +"177 Constant_48448" [id=177, type=Constant]; +"178 Constant_48426" [id=178, type=Constant]; +"179 Constant_48425" [id=179, type=Constant]; +"180 Constant_48424" [id=180, type=Constant]; +"181 Constant_48423" [id=181, type=Constant]; +"182 Constant_48401" [id=182, type=Constant]; +"183 Constant_48400" [id=183, type=Constant]; +"184 Constant_48399" [id=184, type=Constant]; +"185 Constant_48398" [id=185, type=Constant]; +"186 Reshape_55" [id=186, type=Constant]; +"187 /conv1/Conv/WithoutBiases/fq_weights_1" [id=187, type=FakeQuantize]; +"188 Constant_48396" [id=188, type=Constant]; +"189 Constant_48395" [id=189, type=Constant]; +"190 Constant_48394" [id=190, type=Constant]; +"191 Constant_48393" [id=191, type=Constant]; +"192 Gather_1788" [id=192, type=Constant]; +"193 Constant_48391" [id=193, type=Constant]; +"194 Constant_48390" [id=194, type=Constant]; +"195 Constant_48389" [id=195, type=Constant]; +"196 Constant_48388" [id=196, type=Constant]; +"197 Gather_1785" [id=197, type=Constant]; +"198 Gather_1782" [id=198, type=Constant]; +"199 Constant_48421" [id=199, type=Constant]; +"200 Constant_48420" [id=200, type=Constant]; +"201 Constant_48419" [id=201, type=Constant]; +"202 Constant_48418" [id=202, type=Constant]; +"203 Reshape_88" [id=203, type=Constant]; +"204 /layer1/layer1.0/conv2/Conv/WithoutBiases/fq_weights_1" [id=204, type=FakeQuantize]; +"205 Constant_48416" [id=205, type=Constant]; +"206 Constant_48415" [id=206, type=Constant]; +"207 Constant_48414" [id=207, type=Constant]; +"208 Constant_48413" [id=208, type=Constant]; +"209 onnx^^Conv_199" [id=209, label="209 onnx::Conv_199", type=Constant]; +"210 Constant_48411" [id=210, type=Constant]; +"211 Constant_48410" [id=211, type=Constant]; +"212 Constant_48409" [id=212, type=Constant]; +"213 Constant_48408" [id=213, type=Constant]; +"214 Reshape_72" [id=214, type=Constant]; +"215 /layer1/layer1.0/conv1/Conv/WithoutBiases/fq_weights_1" [id=215, type=FakeQuantize]; +"216 Constant_48406" [id=216, type=Constant]; +"217 Constant_48405" [id=217, type=Constant]; +"218 Constant_48404" [id=218, type=Constant]; +"219 Constant_48403" [id=219, type=Constant]; +"220 onnx^^Conv_196" [id=220, label="220 onnx::Conv_196", type=Constant]; +"221 Constant_48446" [id=221, type=Constant]; +"222 Constant_48445" [id=222, type=Constant]; +"223 Constant_48444" [id=223, type=Constant]; +"224 Constant_48443" [id=224, type=Constant]; +"225 Reshape_121" [id=225, type=Constant]; +"226 /layer1/layer1.1/conv2/Conv/WithoutBiases/fq_weights_1" [id=226, type=FakeQuantize]; +"227 Constant_48441" [id=227, type=Constant]; +"228 Constant_48440" [id=228, type=Constant]; +"229 Constant_48439" [id=229, type=Constant]; +"230 Constant_48438" [id=230, type=Constant]; +"231 onnx^^Conv_205" [id=231, label="231 onnx::Conv_205", type=Constant]; +"232 Constant_48436" [id=232, type=Constant]; +"233 Constant_48435" [id=233, type=Constant]; +"234 Constant_48434" [id=234, type=Constant]; +"235 Constant_48433" [id=235, type=Constant]; +"236 Reshape_105" [id=236, type=Constant]; +"237 /layer1/layer1.1/conv1/Conv/WithoutBiases/fq_weights_1" [id=237, type=FakeQuantize]; +"238 Constant_48431" [id=238, type=Constant]; +"239 Constant_48430" [id=239, type=Constant]; +"240 Constant_48429" [id=240, type=Constant]; +"241 Constant_48428" [id=241, type=Constant]; +"242 onnx^^Conv_202" [id=242, label="242 onnx::Conv_202", type=Constant]; +"243 Constant_48481" [id=243, type=Constant]; +"244 Constant_48480" [id=244, type=Constant]; +"245 Constant_48479" [id=245, type=Constant]; +"246 Constant_48478" [id=246, type=Constant]; +"247 Reshape_154" [id=247, type=Constant]; +"248 /layer2/layer2.0/conv2/Conv/WithoutBiases/fq_weights_1" [id=248, type=FakeQuantize]; +"249 Constant_48476" [id=249, type=Constant]; +"250 Constant_48475" [id=250, type=Constant]; +"251 Constant_48474" [id=251, type=Constant]; +"252 Constant_48473" [id=252, type=Constant]; +"253 onnx^^Conv_211" [id=253, label="253 onnx::Conv_211", type=Constant]; +"254 Constant_48471" [id=254, type=Constant]; +"255 Constant_48470" [id=255, type=Constant]; +"256 Constant_48469" [id=256, type=Constant]; +"257 Constant_48468" [id=257, type=Constant]; +"258 Reshape_138" [id=258, type=Constant]; +"259 /layer2/layer2.0/conv1/Conv/WithoutBiases/fq_weights_1" [id=259, type=FakeQuantize]; +"260 Constant_48466" [id=260, type=Constant]; +"261 Constant_48465" [id=261, type=Constant]; +"262 Constant_48464" [id=262, type=Constant]; +"263 Constant_48463" [id=263, type=Constant]; +"264 onnx^^Conv_208" [id=264, label="264 onnx::Conv_208", type=Constant]; +"265 Constant_48506" [id=265, type=Constant]; +"266 Constant_48505" [id=266, type=Constant]; +"267 Constant_48504" [id=267, type=Constant]; +"268 Constant_48503" [id=268, type=Constant]; +"269 Reshape_202" [id=269, type=Constant]; +"270 /layer2/layer2.1/conv2/Conv/WithoutBiases/fq_weights_1" [id=270, type=FakeQuantize]; +"271 Constant_48501" [id=271, type=Constant]; +"272 Constant_48500" [id=272, type=Constant]; +"273 Constant_48499" [id=273, type=Constant]; +"274 Constant_48498" [id=274, type=Constant]; +"275 onnx^^Conv_220" [id=275, label="275 onnx::Conv_220", type=Constant]; +"276 Constant_48496" [id=276, type=Constant]; +"277 Constant_48495" [id=277, type=Constant]; +"278 Constant_48494" [id=278, type=Constant]; +"279 Constant_48493" [id=279, type=Constant]; +"280 Reshape_186" [id=280, type=Constant]; +"281 /layer2/layer2.1/conv1/Conv/WithoutBiases/fq_weights_1" [id=281, type=FakeQuantize]; +"282 Constant_48491" [id=282, type=Constant]; +"283 Constant_48490" [id=283, type=Constant]; +"284 Constant_48489" [id=284, type=Constant]; +"285 Constant_48488" [id=285, type=Constant]; +"286 onnx^^Conv_217" [id=286, label="286 onnx::Conv_217", type=Constant]; +"287 Constant_48541" [id=287, type=Constant]; +"288 Constant_48540" [id=288, type=Constant]; +"289 Constant_48539" [id=289, type=Constant]; +"290 Constant_48538" [id=290, type=Constant]; +"291 Reshape_235" [id=291, type=Constant]; +"292 /layer3/layer3.0/conv2/Conv/WithoutBiases/fq_weights_1" [id=292, type=FakeQuantize]; +"293 Constant_48536" [id=293, type=Constant]; +"294 Constant_48535" [id=294, type=Constant]; +"295 Constant_48534" [id=295, type=Constant]; +"296 Constant_48533" [id=296, type=Constant]; +"297 onnx^^Conv_226" [id=297, label="297 onnx::Conv_226", type=Constant]; +"298 Constant_48531" [id=298, type=Constant]; +"299 Constant_48530" [id=299, type=Constant]; +"300 Constant_48529" [id=300, type=Constant]; +"301 Constant_48528" [id=301, type=Constant]; +"302 Reshape_219" [id=302, type=Constant]; +"303 /layer3/layer3.0/conv1/Conv/WithoutBiases/fq_weights_1" [id=303, type=FakeQuantize]; +"304 Constant_48526" [id=304, type=Constant]; +"305 Constant_48525" [id=305, type=Constant]; +"306 Constant_48524" [id=306, type=Constant]; +"307 Constant_48523" [id=307, type=Constant]; +"308 onnx^^Conv_223" [id=308, label="308 onnx::Conv_223", type=Constant]; +"309 Constant_48566" [id=309, type=Constant]; +"310 Constant_48565" [id=310, type=Constant]; +"311 Constant_48564" [id=311, type=Constant]; +"312 Constant_48563" [id=312, type=Constant]; +"313 Reshape_283" [id=313, type=Constant]; +"314 /layer3/layer3.1/conv2/Conv/WithoutBiases/fq_weights_1" [id=314, type=FakeQuantize]; +"315 Constant_48561" [id=315, type=Constant]; +"316 Constant_48560" [id=316, type=Constant]; +"317 Constant_48559" [id=317, type=Constant]; +"318 Constant_48558" [id=318, type=Constant]; +"319 onnx^^Conv_235" [id=319, label="319 onnx::Conv_235", type=Constant]; +"320 Constant_48556" [id=320, type=Constant]; +"321 Constant_48555" [id=321, type=Constant]; +"322 Constant_48554" [id=322, type=Constant]; +"323 Constant_48553" [id=323, type=Constant]; +"324 Reshape_267" [id=324, type=Constant]; +"325 /layer3/layer3.1/conv1/Conv/WithoutBiases/fq_weights_1" [id=325, type=FakeQuantize]; +"326 Constant_48551" [id=326, type=Constant]; +"327 Constant_48550" [id=327, type=Constant]; +"328 Constant_48549" [id=328, type=Constant]; +"329 Constant_48548" [id=329, type=Constant]; +"330 onnx^^Conv_232" [id=330, label="330 onnx::Conv_232", type=Constant]; +"331 Constant_48601" [id=331, type=Constant]; +"332 Constant_48600" [id=332, type=Constant]; +"333 Constant_48599" [id=333, type=Constant]; +"334 Constant_48598" [id=334, type=Constant]; +"335 Reshape_316" [id=335, type=Constant]; +"336 /layer4/layer4.0/conv2/Conv/WithoutBiases/fq_weights_1" [id=336, type=FakeQuantize]; +"337 Constant_48596" [id=337, type=Constant]; +"338 Constant_48595" [id=338, type=Constant]; +"339 Constant_48594" [id=339, type=Constant]; +"340 Constant_48593" [id=340, type=Constant]; +"341 onnx^^Conv_241" [id=341, label="341 onnx::Conv_241", type=Constant]; +"342 Constant_48591" [id=342, type=Constant]; +"343 Constant_48590" [id=343, type=Constant]; +"344 Constant_48589" [id=344, type=Constant]; +"345 Constant_48588" [id=345, type=Constant]; +"346 Reshape_300" [id=346, type=Constant]; +"347 /layer4/layer4.0/conv1/Conv/WithoutBiases/fq_weights_1" [id=347, type=FakeQuantize]; +"348 Constant_48586" [id=348, type=Constant]; +"349 Constant_48585" [id=349, type=Constant]; +"350 Constant_48584" [id=350, type=Constant]; +"351 Constant_48583" [id=351, type=Constant]; +"352 onnx^^Conv_238" [id=352, label="352 onnx::Conv_238", type=Constant]; +"353 Constant_48626" [id=353, type=Constant]; +"354 Constant_48625" [id=354, type=Constant]; +"355 Constant_48624" [id=355, type=Constant]; +"356 Constant_48623" [id=356, type=Constant]; +"357 Reshape_364" [id=357, type=Constant]; +"358 /layer4/layer4.1/conv2/Conv/WithoutBiases/fq_weights_1" [id=358, type=FakeQuantize]; +"359 Constant_48621" [id=359, type=Constant]; +"360 Constant_48620" [id=360, type=Constant]; +"361 Constant_48619" [id=361, type=Constant]; +"362 Constant_48618" [id=362, type=Constant]; +"363 onnx^^Conv_250" [id=363, label="363 onnx::Conv_250", type=Constant]; +"364 Constant_48616" [id=364, type=Constant]; +"365 Constant_48615" [id=365, type=Constant]; +"366 Constant_48614" [id=366, type=Constant]; +"367 Constant_48613" [id=367, type=Constant]; +"368 Reshape_348" [id=368, type=Constant]; +"369 /layer4/layer4.1/conv1/Conv/WithoutBiases/fq_weights_1" [id=369, type=FakeQuantize]; +"370 Constant_48611" [id=370, type=Constant]; +"371 Constant_48610" [id=371, type=Constant]; +"372 Constant_48609" [id=372, type=Constant]; +"373 Constant_48608" [id=373, type=Constant]; +"374 onnx^^Conv_247" [id=374, label="374 onnx::Conv_247", type=Constant]; +"0 data" -> "1 Multiply_1715" [label="[1, 3, 224, 224]", style=solid]; +"1 Multiply_1715" -> "2 Divide_401" [label="[1, 3, 224, 224]", style=solid]; +"2 Divide_401" -> "3 Divide_401/fq_output_0" [label="[1, 3, 224, 224]", style=solid]; +"3 Divide_401/fq_output_0" -> "4 /conv1/Conv/WithoutBiases" [label="[1, 3, 224, 224]", style=solid]; +"4 /conv1/Conv/WithoutBiases" -> "5 /conv1/Conv" [label="[1, 64, 112, 112]", style=solid]; +"5 /conv1/Conv" -> "6 /relu/Relu" [label="[1, 64, 112, 112]", style=solid]; +"6 /relu/Relu" -> "7 /relu/Relu/fq_output_0" [label="[1, 64, 112, 112]", style=solid]; +"7 /relu/Relu/fq_output_0" -> "8 /maxpool/MaxPool" [label="[1, 64, 112, 112]", style=solid]; +"8 /maxpool/MaxPool" -> "9 /layer1/layer1.0/Add" [label="[1, 64, 56, 56]", style=solid]; +"8 /maxpool/MaxPool" -> "10 /layer1/layer1.0/conv1/Conv/WithoutBiases" [label="[1, 64, 56, 56]", style=solid]; +"9 /layer1/layer1.0/Add" -> "11 /layer1/layer1.0/relu_1/Relu" [label="[1, 64, 56, 56]", style=solid]; +"10 /layer1/layer1.0/conv1/Conv/WithoutBiases" -> "12 /layer1/layer1.0/conv1/Conv" [label="[1, 64, 56, 56]", style=solid]; +"11 /layer1/layer1.0/relu_1/Relu" -> "13 /layer1/layer1.0/relu_1/Relu/fq_output_0" [label="[1, 64, 56, 56]", style=solid]; +"12 /layer1/layer1.0/conv1/Conv" -> "14 /layer1/layer1.0/relu/Relu" [label="[1, 64, 56, 56]", style=solid]; +"13 /layer1/layer1.0/relu_1/Relu/fq_output_0" -> "15 /layer1/layer1.1/Add" [label="[1, 64, 56, 56]", style=solid]; +"13 /layer1/layer1.0/relu_1/Relu/fq_output_0" -> "16 /layer1/layer1.1/conv1/Conv/WithoutBiases" [label="[1, 64, 56, 56]", style=solid]; +"14 /layer1/layer1.0/relu/Relu" -> "17 /layer1/layer1.0/relu/Relu/fq_output_0" [label="[1, 64, 56, 56]", style=solid]; +"15 /layer1/layer1.1/Add" -> "18 /layer1/layer1.1/relu_1/Relu" [label="[1, 64, 56, 56]", style=solid]; +"16 /layer1/layer1.1/conv1/Conv/WithoutBiases" -> "19 /layer1/layer1.1/conv1/Conv" [label="[1, 64, 56, 56]", style=solid]; +"17 /layer1/layer1.0/relu/Relu/fq_output_0" -> "20 /layer1/layer1.0/conv2/Conv/WithoutBiases" [label="[1, 64, 56, 56]", style=solid]; +"18 /layer1/layer1.1/relu_1/Relu" -> "21 /layer1/layer1.1/relu_1/Relu/fq_output_0" [label="[1, 64, 56, 56]", style=solid]; +"19 /layer1/layer1.1/conv1/Conv" -> "22 /layer1/layer1.1/relu/Relu" [label="[1, 64, 56, 56]", style=solid]; +"20 /layer1/layer1.0/conv2/Conv/WithoutBiases" -> "23 /layer1/layer1.0/conv2/Conv" [label="[1, 64, 56, 56]", style=solid]; +"21 /layer1/layer1.1/relu_1/Relu/fq_output_0" -> "24 /layer2/layer2.0/conv1/Conv/WithoutBiases" [label="[1, 64, 56, 56]", style=solid]; +"21 /layer1/layer1.1/relu_1/Relu/fq_output_0" -> "25 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases" [label="[1, 64, 56, 56]", style=solid]; +"22 /layer1/layer1.1/relu/Relu" -> "26 /layer1/layer1.1/relu/Relu/fq_output_0" [label="[1, 64, 56, 56]", style=solid]; +"23 /layer1/layer1.0/conv2/Conv" -> "27 /layer1/layer1.0/conv2/Conv/fq_output_0" [label="[1, 64, 56, 56]", style=solid]; +"24 /layer2/layer2.0/conv1/Conv/WithoutBiases" -> "28 /layer2/layer2.0/conv1/Conv" [label="[1, 128, 28, 28]", style=solid]; +"25 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases" -> "29 /layer2/layer2.0/downsample/downsample.0/Conv" [label="[1, 128, 28, 28]", style=solid]; +"26 /layer1/layer1.1/relu/Relu/fq_output_0" -> "30 /layer1/layer1.1/conv2/Conv/WithoutBiases" [label="[1, 64, 56, 56]", style=solid]; +"27 /layer1/layer1.0/conv2/Conv/fq_output_0" -> "9 /layer1/layer1.0/Add" [label="[1, 64, 56, 56]", style=solid]; +"28 /layer2/layer2.0/conv1/Conv" -> "31 /layer2/layer2.0/relu/Relu" [label="[1, 128, 28, 28]", style=solid]; +"29 /layer2/layer2.0/downsample/downsample.0/Conv" -> "32 /layer2/layer2.0/downsample/downsample.0/Conv/fq_output_0" [label="[1, 128, 28, 28]", style=solid]; +"30 /layer1/layer1.1/conv2/Conv/WithoutBiases" -> "33 /layer1/layer1.1/conv2/Conv" [label="[1, 64, 56, 56]", style=solid]; +"31 /layer2/layer2.0/relu/Relu" -> "34 /layer2/layer2.0/relu/Relu/fq_output_0" [label="[1, 128, 28, 28]", style=solid]; +"32 /layer2/layer2.0/downsample/downsample.0/Conv/fq_output_0" -> "35 /layer2/layer2.0/Add" [label="[1, 128, 28, 28]", style=solid]; +"33 /layer1/layer1.1/conv2/Conv" -> "36 /layer1/layer1.1/conv2/Conv/fq_output_0" [label="[1, 64, 56, 56]", style=solid]; +"34 /layer2/layer2.0/relu/Relu/fq_output_0" -> "37 /layer2/layer2.0/conv2/Conv/WithoutBiases" [label="[1, 128, 28, 28]", style=solid]; +"35 /layer2/layer2.0/Add" -> "38 /layer2/layer2.0/relu_1/Relu" [label="[1, 128, 28, 28]", style=solid]; +"36 /layer1/layer1.1/conv2/Conv/fq_output_0" -> "15 /layer1/layer1.1/Add" [label="[1, 64, 56, 56]", style=solid]; +"37 /layer2/layer2.0/conv2/Conv/WithoutBiases" -> "39 /layer2/layer2.0/conv2/Conv" [label="[1, 128, 28, 28]", style=solid]; +"38 /layer2/layer2.0/relu_1/Relu" -> "40 /layer2/layer2.0/relu_1/Relu/fq_output_0" [label="[1, 128, 28, 28]", style=solid]; +"39 /layer2/layer2.0/conv2/Conv" -> "41 /layer2/layer2.0/conv2/Conv/fq_output_0" [label="[1, 128, 28, 28]", style=solid]; +"40 /layer2/layer2.0/relu_1/Relu/fq_output_0" -> "42 /layer2/layer2.1/Add" [label="[1, 128, 28, 28]", style=solid]; +"40 /layer2/layer2.0/relu_1/Relu/fq_output_0" -> "43 /layer2/layer2.1/conv1/Conv/WithoutBiases" [label="[1, 128, 28, 28]", style=solid]; +"41 /layer2/layer2.0/conv2/Conv/fq_output_0" -> "35 /layer2/layer2.0/Add" [label="[1, 128, 28, 28]", style=solid]; +"42 /layer2/layer2.1/Add" -> "44 /layer2/layer2.1/relu_1/Relu" [label="[1, 128, 28, 28]", style=solid]; +"43 /layer2/layer2.1/conv1/Conv/WithoutBiases" -> "45 /layer2/layer2.1/conv1/Conv" [label="[1, 128, 28, 28]", style=solid]; +"44 /layer2/layer2.1/relu_1/Relu" -> "46 /layer2/layer2.1/relu_1/Relu/fq_output_0" [label="[1, 128, 28, 28]", style=solid]; +"45 /layer2/layer2.1/conv1/Conv" -> "47 /layer2/layer2.1/relu/Relu" [label="[1, 128, 28, 28]", style=solid]; +"46 /layer2/layer2.1/relu_1/Relu/fq_output_0" -> "48 /layer3/layer3.0/conv1/Conv/WithoutBiases" [label="[1, 128, 28, 28]", style=solid]; +"46 /layer2/layer2.1/relu_1/Relu/fq_output_0" -> "49 /layer3/layer3.0/downsample/downsample.0/Conv/WithoutBiases" [label="[1, 128, 28, 28]", style=solid]; +"47 /layer2/layer2.1/relu/Relu" -> "50 /layer2/layer2.1/relu/Relu/fq_output_0" [label="[1, 128, 28, 28]", style=solid]; +"48 /layer3/layer3.0/conv1/Conv/WithoutBiases" -> "51 /layer3/layer3.0/conv1/Conv" [label="[1, 256, 14, 14]", style=solid]; +"49 /layer3/layer3.0/downsample/downsample.0/Conv/WithoutBiases" -> "52 /layer3/layer3.0/downsample/downsample.0/Conv" [label="[1, 256, 14, 14]", style=solid]; +"50 /layer2/layer2.1/relu/Relu/fq_output_0" -> "53 /layer2/layer2.1/conv2/Conv/WithoutBiases" [label="[1, 128, 28, 28]", style=solid]; +"51 /layer3/layer3.0/conv1/Conv" -> "54 /layer3/layer3.0/relu/Relu" [label="[1, 256, 14, 14]", style=solid]; +"52 /layer3/layer3.0/downsample/downsample.0/Conv" -> "55 /layer3/layer3.0/downsample/downsample.0/Conv/fq_output_0" [label="[1, 256, 14, 14]", style=solid]; +"53 /layer2/layer2.1/conv2/Conv/WithoutBiases" -> "56 /layer2/layer2.1/conv2/Conv" [label="[1, 128, 28, 28]", style=solid]; +"54 /layer3/layer3.0/relu/Relu" -> "57 /layer3/layer3.0/relu/Relu/fq_output_0" [label="[1, 256, 14, 14]", style=solid]; +"55 /layer3/layer3.0/downsample/downsample.0/Conv/fq_output_0" -> "58 /layer3/layer3.0/Add" [label="[1, 256, 14, 14]", style=solid]; +"56 /layer2/layer2.1/conv2/Conv" -> "59 /layer2/layer2.1/conv2/Conv/fq_output_0" [label="[1, 128, 28, 28]", style=solid]; +"57 /layer3/layer3.0/relu/Relu/fq_output_0" -> "60 /layer3/layer3.0/conv2/Conv/WithoutBiases" [label="[1, 256, 14, 14]", style=solid]; +"58 /layer3/layer3.0/Add" -> "61 /layer3/layer3.0/relu_1/Relu" [label="[1, 256, 14, 14]", style=solid]; +"59 /layer2/layer2.1/conv2/Conv/fq_output_0" -> "42 /layer2/layer2.1/Add" [label="[1, 128, 28, 28]", style=solid]; +"60 /layer3/layer3.0/conv2/Conv/WithoutBiases" -> "62 /layer3/layer3.0/conv2/Conv" [label="[1, 256, 14, 14]", style=solid]; +"61 /layer3/layer3.0/relu_1/Relu" -> "63 /layer3/layer3.0/relu_1/Relu/fq_output_0" [label="[1, 256, 14, 14]", style=solid]; +"62 /layer3/layer3.0/conv2/Conv" -> "64 /layer3/layer3.0/conv2/Conv/fq_output_0" [label="[1, 256, 14, 14]", style=solid]; +"63 /layer3/layer3.0/relu_1/Relu/fq_output_0" -> "65 /layer3/layer3.1/Add" [label="[1, 256, 14, 14]", style=solid]; +"63 /layer3/layer3.0/relu_1/Relu/fq_output_0" -> "66 /layer3/layer3.1/conv1/Conv/WithoutBiases" [label="[1, 256, 14, 14]", style=solid]; +"64 /layer3/layer3.0/conv2/Conv/fq_output_0" -> "58 /layer3/layer3.0/Add" [label="[1, 256, 14, 14]", style=solid]; +"65 /layer3/layer3.1/Add" -> "67 /layer3/layer3.1/relu_1/Relu" [label="[1, 256, 14, 14]", style=solid]; +"66 /layer3/layer3.1/conv1/Conv/WithoutBiases" -> "68 /layer3/layer3.1/conv1/Conv" [label="[1, 256, 14, 14]", style=solid]; +"67 /layer3/layer3.1/relu_1/Relu" -> "69 /layer3/layer3.1/relu_1/Relu/fq_output_0" [label="[1, 256, 14, 14]", style=solid]; +"68 /layer3/layer3.1/conv1/Conv" -> "70 /layer3/layer3.1/relu/Relu" [label="[1, 256, 14, 14]", style=solid]; +"69 /layer3/layer3.1/relu_1/Relu/fq_output_0" -> "71 /layer4/layer4.0/conv1/Conv/WithoutBiases" [label="[1, 256, 14, 14]", style=solid]; +"69 /layer3/layer3.1/relu_1/Relu/fq_output_0" -> "72 /layer4/layer4.0/downsample/downsample.0/Conv/WithoutBiases" [label="[1, 256, 14, 14]", style=solid]; +"70 /layer3/layer3.1/relu/Relu" -> "73 /layer3/layer3.1/relu/Relu/fq_output_0" [label="[1, 256, 14, 14]", style=solid]; +"71 /layer4/layer4.0/conv1/Conv/WithoutBiases" -> "74 /layer4/layer4.0/conv1/Conv" [label="[1, 512, 7, 7]", style=solid]; +"72 /layer4/layer4.0/downsample/downsample.0/Conv/WithoutBiases" -> "75 /layer4/layer4.0/downsample/downsample.0/Conv" [label="[1, 512, 7, 7]", style=solid]; +"73 /layer3/layer3.1/relu/Relu/fq_output_0" -> "76 /layer3/layer3.1/conv2/Conv/WithoutBiases" [label="[1, 256, 14, 14]", style=solid]; +"74 /layer4/layer4.0/conv1/Conv" -> "77 /layer4/layer4.0/relu/Relu" [label="[1, 512, 7, 7]", style=solid]; +"75 /layer4/layer4.0/downsample/downsample.0/Conv" -> "78 /layer4/layer4.0/downsample/downsample.0/Conv/fq_output_0" [label="[1, 512, 7, 7]", style=solid]; +"76 /layer3/layer3.1/conv2/Conv/WithoutBiases" -> "79 /layer3/layer3.1/conv2/Conv" [label="[1, 256, 14, 14]", style=solid]; +"77 /layer4/layer4.0/relu/Relu" -> "80 /layer4/layer4.0/relu/Relu/fq_output_0" [label="[1, 512, 7, 7]", style=solid]; +"78 /layer4/layer4.0/downsample/downsample.0/Conv/fq_output_0" -> "81 /layer4/layer4.0/Add" [label="[1, 512, 7, 7]", style=solid]; +"79 /layer3/layer3.1/conv2/Conv" -> "82 /layer3/layer3.1/conv2/Conv/fq_output_0" [label="[1, 256, 14, 14]", style=solid]; +"80 /layer4/layer4.0/relu/Relu/fq_output_0" -> "83 /layer4/layer4.0/conv2/Conv/WithoutBiases" [label="[1, 512, 7, 7]", style=solid]; +"81 /layer4/layer4.0/Add" -> "84 /layer4/layer4.0/relu_1/Relu" [label="[1, 512, 7, 7]", style=solid]; +"82 /layer3/layer3.1/conv2/Conv/fq_output_0" -> "65 /layer3/layer3.1/Add" [label="[1, 256, 14, 14]", style=solid]; +"83 /layer4/layer4.0/conv2/Conv/WithoutBiases" -> "85 /layer4/layer4.0/conv2/Conv" [label="[1, 512, 7, 7]", style=solid]; +"84 /layer4/layer4.0/relu_1/Relu" -> "86 /layer4/layer4.0/relu_1/Relu/fq_output_0" [label="[1, 512, 7, 7]", style=solid]; +"85 /layer4/layer4.0/conv2/Conv" -> "87 /layer4/layer4.0/conv2/Conv/fq_output_0" [label="[1, 512, 7, 7]", style=solid]; +"86 /layer4/layer4.0/relu_1/Relu/fq_output_0" -> "88 /layer4/layer4.1/Add" [label="[1, 512, 7, 7]", style=solid]; +"86 /layer4/layer4.0/relu_1/Relu/fq_output_0" -> "89 /layer4/layer4.1/conv1/Conv/WithoutBiases" [label="[1, 512, 7, 7]", style=solid]; +"87 /layer4/layer4.0/conv2/Conv/fq_output_0" -> "81 /layer4/layer4.0/Add" [label="[1, 512, 7, 7]", style=solid]; +"88 /layer4/layer4.1/Add" -> "90 /layer4/layer4.1/relu_1/Relu" [label="[1, 512, 7, 7]", style=solid]; +"89 /layer4/layer4.1/conv1/Conv/WithoutBiases" -> "91 /layer4/layer4.1/conv1/Conv" [label="[1, 512, 7, 7]", style=solid]; +"90 /layer4/layer4.1/relu_1/Relu" -> "92 /layer4/layer4.1/relu_1/Relu/fq_output_0" [label="[1, 512, 7, 7]", style=solid]; +"91 /layer4/layer4.1/conv1/Conv" -> "93 /layer4/layer4.1/relu/Relu" [label="[1, 512, 7, 7]", style=solid]; +"92 /layer4/layer4.1/relu_1/Relu/fq_output_0" -> "94 /avgpool/GlobalAveragePool" [label="[1, 512, 7, 7]", style=solid]; +"93 /layer4/layer4.1/relu/Relu" -> "95 /layer4/layer4.1/relu/Relu/fq_output_0" [label="[1, 512, 7, 7]", style=solid]; +"94 /avgpool/GlobalAveragePool" -> "96 /avgpool/GlobalAveragePool/fq_output_0" [label="[1, 512, 1, 1]", style=solid]; +"95 /layer4/layer4.1/relu/Relu/fq_output_0" -> "97 /layer4/layer4.1/conv2/Conv/WithoutBiases" [label="[1, 512, 7, 7]", style=solid]; +"96 /avgpool/GlobalAveragePool/fq_output_0" -> "98 /Flatten" [label="[1, 512, 1, 1]", style=solid]; +"97 /layer4/layer4.1/conv2/Conv/WithoutBiases" -> "99 /layer4/layer4.1/conv2/Conv" [label="[1, 512, 7, 7]", style=solid]; +"98 /Flatten" -> "100 /fc/Gemm/WithoutBiases" [label="[1, 512]", style=solid]; +"99 /layer4/layer4.1/conv2/Conv" -> "101 /layer4/layer4.1/conv2/Conv/fq_output_0" [label="[1, 512, 7, 7]", style=solid]; +"100 /fc/Gemm/WithoutBiases" -> "102 prob" [label="[1, 1000]", style=solid]; +"101 /layer4/layer4.1/conv2/Conv/fq_output_0" -> "88 /layer4/layer4.1/Add" [label="[1, 512, 7, 7]", style=solid]; +"102 prob" -> "103 prob/sink_port_0" [label="[1, 1000]", style=solid]; +"104 Constant_1789" -> "102 prob" [label="[1, 1000]", style=solid]; +"105 /fc/Gemm/WithoutBiases/fq_weights_1" -> "100 /fc/Gemm/WithoutBiases" [label="[1000, 512]", style=solid]; +"106 Constant_48641" -> "105 /fc/Gemm/WithoutBiases/fq_weights_1" [label="[1000, 1]", style=solid]; +"107 Constant_48640" -> "105 /fc/Gemm/WithoutBiases/fq_weights_1" [label="[1000, 1]", style=solid]; +"108 Constant_48639" -> "105 /fc/Gemm/WithoutBiases/fq_weights_1" [label="[1000, 1]", style=solid]; +"109 Constant_48638" -> "105 /fc/Gemm/WithoutBiases/fq_weights_1" [label="[1000, 1]", style=solid]; +"110 fc.weight" -> "105 /fc/Gemm/WithoutBiases/fq_weights_1" [label="[1000, 512]", style=solid]; +"111 Constant_380" -> "98 /Flatten" [label="[2]", style=dashed]; +"112 Constant_48636" -> "96 /avgpool/GlobalAveragePool/fq_output_0" [label="[]", style=solid]; +"113 Constant_48635" -> "96 /avgpool/GlobalAveragePool/fq_output_0" [label="[]", style=solid]; +"114 Constant_48634" -> "96 /avgpool/GlobalAveragePool/fq_output_0" [label="[]", style=solid]; +"115 Constant_48633" -> "96 /avgpool/GlobalAveragePool/fq_output_0" [label="[]", style=solid]; +"116 Range_376" -> "94 /avgpool/GlobalAveragePool" [label="[2]", style=dashed]; +"117 Constant_48631" -> "92 /layer4/layer4.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"118 Constant_48630" -> "92 /layer4/layer4.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"119 Constant_48629" -> "92 /layer4/layer4.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"120 Constant_48628" -> "92 /layer4/layer4.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"121 Constant_48606" -> "86 /layer4/layer4.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"122 Constant_48605" -> "86 /layer4/layer4.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"123 Constant_48604" -> "86 /layer4/layer4.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"124 Constant_48603" -> "86 /layer4/layer4.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"125 Constant_48581" -> "78 /layer4/layer4.0/downsample/downsample.0/Conv/fq_output_0" [label="[]", style=solid]; +"126 Constant_48580" -> "78 /layer4/layer4.0/downsample/downsample.0/Conv/fq_output_0" [label="[]", style=solid]; +"127 Constant_48579" -> "78 /layer4/layer4.0/downsample/downsample.0/Conv/fq_output_0" [label="[]", style=solid]; +"128 Constant_48578" -> "78 /layer4/layer4.0/downsample/downsample.0/Conv/fq_output_0" [label="[]", style=solid]; +"129 Reshape_331" -> "75 /layer4/layer4.0/downsample/downsample.0/Conv" [label="[1, 512, 1, 1]", style=solid]; +"130 /layer4/layer4.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" -> "72 /layer4/layer4.0/downsample/downsample.0/Conv/WithoutBiases" [label="[512, 256, 1, 1]", style=solid]; +"131 Constant_48576" -> "130 /layer4/layer4.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"132 Constant_48575" -> "130 /layer4/layer4.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"133 Constant_48574" -> "130 /layer4/layer4.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"134 Constant_48573" -> "130 /layer4/layer4.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"135 onnx^^Conv_244" -> "130 /layer4/layer4.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[512, 256, 1, 1]", style=solid]; +"136 Constant_48571" -> "69 /layer3/layer3.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"137 Constant_48570" -> "69 /layer3/layer3.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"138 Constant_48569" -> "69 /layer3/layer3.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"139 Constant_48568" -> "69 /layer3/layer3.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"140 Constant_48546" -> "63 /layer3/layer3.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"141 Constant_48545" -> "63 /layer3/layer3.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"142 Constant_48544" -> "63 /layer3/layer3.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"143 Constant_48543" -> "63 /layer3/layer3.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"144 Constant_48521" -> "55 /layer3/layer3.0/downsample/downsample.0/Conv/fq_output_0" [label="[]", style=solid]; +"145 Constant_48520" -> "55 /layer3/layer3.0/downsample/downsample.0/Conv/fq_output_0" [label="[]", style=solid]; +"146 Constant_48519" -> "55 /layer3/layer3.0/downsample/downsample.0/Conv/fq_output_0" [label="[]", style=solid]; +"147 Constant_48518" -> "55 /layer3/layer3.0/downsample/downsample.0/Conv/fq_output_0" [label="[]", style=solid]; +"148 Reshape_250" -> "52 /layer3/layer3.0/downsample/downsample.0/Conv" [label="[1, 256, 1, 1]", style=solid]; +"149 /layer3/layer3.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" -> "49 /layer3/layer3.0/downsample/downsample.0/Conv/WithoutBiases" [label="[256, 128, 1, 1]", style=solid]; +"150 Constant_48516" -> "149 /layer3/layer3.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"151 Constant_48515" -> "149 /layer3/layer3.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"152 Constant_48514" -> "149 /layer3/layer3.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"153 Constant_48513" -> "149 /layer3/layer3.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"154 onnx^^Conv_229" -> "149 /layer3/layer3.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[256, 128, 1, 1]", style=solid]; +"155 Constant_48511" -> "46 /layer2/layer2.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"156 Constant_48510" -> "46 /layer2/layer2.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"157 Constant_48509" -> "46 /layer2/layer2.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"158 Constant_48508" -> "46 /layer2/layer2.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"159 Constant_48486" -> "40 /layer2/layer2.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"160 Constant_48485" -> "40 /layer2/layer2.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"161 Constant_48484" -> "40 /layer2/layer2.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"162 Constant_48483" -> "40 /layer2/layer2.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"163 Constant_48461" -> "32 /layer2/layer2.0/downsample/downsample.0/Conv/fq_output_0" [label="[]", style=solid]; +"164 Constant_48460" -> "32 /layer2/layer2.0/downsample/downsample.0/Conv/fq_output_0" [label="[]", style=solid]; +"165 Constant_48459" -> "32 /layer2/layer2.0/downsample/downsample.0/Conv/fq_output_0" [label="[]", style=solid]; +"166 Constant_48458" -> "32 /layer2/layer2.0/downsample/downsample.0/Conv/fq_output_0" [label="[]", style=solid]; +"167 Reshape_169" -> "29 /layer2/layer2.0/downsample/downsample.0/Conv" [label="[1, 128, 1, 1]", style=solid]; +"168 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" -> "25 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases" [label="[128, 64, 1, 1]", style=solid]; +"169 Constant_48456" -> "168 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"170 Constant_48455" -> "168 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"171 Constant_48454" -> "168 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"172 Constant_48453" -> "168 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"173 onnx^^Conv_214" -> "168 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[128, 64, 1, 1]", style=solid]; +"174 Constant_48451" -> "21 /layer1/layer1.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"175 Constant_48450" -> "21 /layer1/layer1.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"176 Constant_48449" -> "21 /layer1/layer1.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"177 Constant_48448" -> "21 /layer1/layer1.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"178 Constant_48426" -> "13 /layer1/layer1.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"179 Constant_48425" -> "13 /layer1/layer1.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"180 Constant_48424" -> "13 /layer1/layer1.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"181 Constant_48423" -> "13 /layer1/layer1.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"182 Constant_48401" -> "7 /relu/Relu/fq_output_0" [label="[]", style=solid]; +"183 Constant_48400" -> "7 /relu/Relu/fq_output_0" [label="[]", style=solid]; +"184 Constant_48399" -> "7 /relu/Relu/fq_output_0" [label="[]", style=solid]; +"185 Constant_48398" -> "7 /relu/Relu/fq_output_0" [label="[]", style=solid]; +"186 Reshape_55" -> "5 /conv1/Conv" [label="[1, 64, 1, 1]", style=solid]; +"187 /conv1/Conv/WithoutBiases/fq_weights_1" -> "4 /conv1/Conv/WithoutBiases" [label="[64, 3, 7, 7]", style=solid]; +"188 Constant_48396" -> "187 /conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"189 Constant_48395" -> "187 /conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"190 Constant_48394" -> "187 /conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"191 Constant_48393" -> "187 /conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"192 Gather_1788" -> "187 /conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 3, 7, 7]", style=solid]; +"193 Constant_48391" -> "3 Divide_401/fq_output_0" [label="[]", style=solid]; +"194 Constant_48390" -> "3 Divide_401/fq_output_0" [label="[]", style=solid]; +"195 Constant_48389" -> "3 Divide_401/fq_output_0" [label="[]", style=solid]; +"196 Constant_48388" -> "3 Divide_401/fq_output_0" [label="[]", style=solid]; +"197 Gather_1785" -> "2 Divide_401" [label="[1, 3, 1, 1]", style=solid]; +"198 Gather_1782" -> "1 Multiply_1715" [label="[1, 3, 1, 1]", style=solid]; +"199 Constant_48421" -> "27 /layer1/layer1.0/conv2/Conv/fq_output_0" [label="[]", style=solid]; +"200 Constant_48420" -> "27 /layer1/layer1.0/conv2/Conv/fq_output_0" [label="[]", style=solid]; +"201 Constant_48419" -> "27 /layer1/layer1.0/conv2/Conv/fq_output_0" [label="[]", style=solid]; +"202 Constant_48418" -> "27 /layer1/layer1.0/conv2/Conv/fq_output_0" [label="[]", style=solid]; +"203 Reshape_88" -> "23 /layer1/layer1.0/conv2/Conv" [label="[1, 64, 1, 1]", style=solid]; +"204 /layer1/layer1.0/conv2/Conv/WithoutBiases/fq_weights_1" -> "20 /layer1/layer1.0/conv2/Conv/WithoutBiases" [label="[64, 64, 3, 3]", style=solid]; +"205 Constant_48416" -> "204 /layer1/layer1.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"206 Constant_48415" -> "204 /layer1/layer1.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"207 Constant_48414" -> "204 /layer1/layer1.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"208 Constant_48413" -> "204 /layer1/layer1.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"209 onnx^^Conv_199" -> "204 /layer1/layer1.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 64, 3, 3]", style=solid]; +"210 Constant_48411" -> "17 /layer1/layer1.0/relu/Relu/fq_output_0" [label="[]", style=solid]; +"211 Constant_48410" -> "17 /layer1/layer1.0/relu/Relu/fq_output_0" [label="[]", style=solid]; +"212 Constant_48409" -> "17 /layer1/layer1.0/relu/Relu/fq_output_0" [label="[]", style=solid]; +"213 Constant_48408" -> "17 /layer1/layer1.0/relu/Relu/fq_output_0" [label="[]", style=solid]; +"214 Reshape_72" -> "12 /layer1/layer1.0/conv1/Conv" [label="[1, 64, 1, 1]", style=solid]; +"215 /layer1/layer1.0/conv1/Conv/WithoutBiases/fq_weights_1" -> "10 /layer1/layer1.0/conv1/Conv/WithoutBiases" [label="[64, 64, 3, 3]", style=solid]; +"216 Constant_48406" -> "215 /layer1/layer1.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"217 Constant_48405" -> "215 /layer1/layer1.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"218 Constant_48404" -> "215 /layer1/layer1.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"219 Constant_48403" -> "215 /layer1/layer1.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"220 onnx^^Conv_196" -> "215 /layer1/layer1.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 64, 3, 3]", style=solid]; +"221 Constant_48446" -> "36 /layer1/layer1.1/conv2/Conv/fq_output_0" [label="[]", style=solid]; +"222 Constant_48445" -> "36 /layer1/layer1.1/conv2/Conv/fq_output_0" [label="[]", style=solid]; +"223 Constant_48444" -> "36 /layer1/layer1.1/conv2/Conv/fq_output_0" [label="[]", style=solid]; +"224 Constant_48443" -> "36 /layer1/layer1.1/conv2/Conv/fq_output_0" [label="[]", style=solid]; +"225 Reshape_121" -> "33 /layer1/layer1.1/conv2/Conv" [label="[1, 64, 1, 1]", style=solid]; +"226 /layer1/layer1.1/conv2/Conv/WithoutBiases/fq_weights_1" -> "30 /layer1/layer1.1/conv2/Conv/WithoutBiases" [label="[64, 64, 3, 3]", style=solid]; +"227 Constant_48441" -> "226 /layer1/layer1.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"228 Constant_48440" -> "226 /layer1/layer1.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"229 Constant_48439" -> "226 /layer1/layer1.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"230 Constant_48438" -> "226 /layer1/layer1.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"231 onnx^^Conv_205" -> "226 /layer1/layer1.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 64, 3, 3]", style=solid]; +"232 Constant_48436" -> "26 /layer1/layer1.1/relu/Relu/fq_output_0" [label="[]", style=solid]; +"233 Constant_48435" -> "26 /layer1/layer1.1/relu/Relu/fq_output_0" [label="[]", style=solid]; +"234 Constant_48434" -> "26 /layer1/layer1.1/relu/Relu/fq_output_0" [label="[]", style=solid]; +"235 Constant_48433" -> "26 /layer1/layer1.1/relu/Relu/fq_output_0" [label="[]", style=solid]; +"236 Reshape_105" -> "19 /layer1/layer1.1/conv1/Conv" [label="[1, 64, 1, 1]", style=solid]; +"237 /layer1/layer1.1/conv1/Conv/WithoutBiases/fq_weights_1" -> "16 /layer1/layer1.1/conv1/Conv/WithoutBiases" [label="[64, 64, 3, 3]", style=solid]; +"238 Constant_48431" -> "237 /layer1/layer1.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"239 Constant_48430" -> "237 /layer1/layer1.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"240 Constant_48429" -> "237 /layer1/layer1.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"241 Constant_48428" -> "237 /layer1/layer1.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"242 onnx^^Conv_202" -> "237 /layer1/layer1.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 64, 3, 3]", style=solid]; +"243 Constant_48481" -> "41 /layer2/layer2.0/conv2/Conv/fq_output_0" [label="[]", style=solid]; +"244 Constant_48480" -> "41 /layer2/layer2.0/conv2/Conv/fq_output_0" [label="[]", style=solid]; +"245 Constant_48479" -> "41 /layer2/layer2.0/conv2/Conv/fq_output_0" [label="[]", style=solid]; +"246 Constant_48478" -> "41 /layer2/layer2.0/conv2/Conv/fq_output_0" [label="[]", style=solid]; +"247 Reshape_154" -> "39 /layer2/layer2.0/conv2/Conv" [label="[1, 128, 1, 1]", style=solid]; +"248 /layer2/layer2.0/conv2/Conv/WithoutBiases/fq_weights_1" -> "37 /layer2/layer2.0/conv2/Conv/WithoutBiases" [label="[128, 128, 3, 3]", style=solid]; +"249 Constant_48476" -> "248 /layer2/layer2.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"250 Constant_48475" -> "248 /layer2/layer2.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"251 Constant_48474" -> "248 /layer2/layer2.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"252 Constant_48473" -> "248 /layer2/layer2.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"253 onnx^^Conv_211" -> "248 /layer2/layer2.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 128, 3, 3]", style=solid]; +"254 Constant_48471" -> "34 /layer2/layer2.0/relu/Relu/fq_output_0" [label="[]", style=solid]; +"255 Constant_48470" -> "34 /layer2/layer2.0/relu/Relu/fq_output_0" [label="[]", style=solid]; +"256 Constant_48469" -> "34 /layer2/layer2.0/relu/Relu/fq_output_0" [label="[]", style=solid]; +"257 Constant_48468" -> "34 /layer2/layer2.0/relu/Relu/fq_output_0" [label="[]", style=solid]; +"258 Reshape_138" -> "28 /layer2/layer2.0/conv1/Conv" [label="[1, 128, 1, 1]", style=solid]; +"259 /layer2/layer2.0/conv1/Conv/WithoutBiases/fq_weights_1" -> "24 /layer2/layer2.0/conv1/Conv/WithoutBiases" [label="[128, 64, 3, 3]", style=solid]; +"260 Constant_48466" -> "259 /layer2/layer2.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"261 Constant_48465" -> "259 /layer2/layer2.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"262 Constant_48464" -> "259 /layer2/layer2.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"263 Constant_48463" -> "259 /layer2/layer2.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"264 onnx^^Conv_208" -> "259 /layer2/layer2.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 64, 3, 3]", style=solid]; +"265 Constant_48506" -> "59 /layer2/layer2.1/conv2/Conv/fq_output_0" [label="[]", style=solid]; +"266 Constant_48505" -> "59 /layer2/layer2.1/conv2/Conv/fq_output_0" [label="[]", style=solid]; +"267 Constant_48504" -> "59 /layer2/layer2.1/conv2/Conv/fq_output_0" [label="[]", style=solid]; +"268 Constant_48503" -> "59 /layer2/layer2.1/conv2/Conv/fq_output_0" [label="[]", style=solid]; +"269 Reshape_202" -> "56 /layer2/layer2.1/conv2/Conv" [label="[1, 128, 1, 1]", style=solid]; +"270 /layer2/layer2.1/conv2/Conv/WithoutBiases/fq_weights_1" -> "53 /layer2/layer2.1/conv2/Conv/WithoutBiases" [label="[128, 128, 3, 3]", style=solid]; +"271 Constant_48501" -> "270 /layer2/layer2.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"272 Constant_48500" -> "270 /layer2/layer2.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"273 Constant_48499" -> "270 /layer2/layer2.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"274 Constant_48498" -> "270 /layer2/layer2.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"275 onnx^^Conv_220" -> "270 /layer2/layer2.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 128, 3, 3]", style=solid]; +"276 Constant_48496" -> "50 /layer2/layer2.1/relu/Relu/fq_output_0" [label="[]", style=solid]; +"277 Constant_48495" -> "50 /layer2/layer2.1/relu/Relu/fq_output_0" [label="[]", style=solid]; +"278 Constant_48494" -> "50 /layer2/layer2.1/relu/Relu/fq_output_0" [label="[]", style=solid]; +"279 Constant_48493" -> "50 /layer2/layer2.1/relu/Relu/fq_output_0" [label="[]", style=solid]; +"280 Reshape_186" -> "45 /layer2/layer2.1/conv1/Conv" [label="[1, 128, 1, 1]", style=solid]; +"281 /layer2/layer2.1/conv1/Conv/WithoutBiases/fq_weights_1" -> "43 /layer2/layer2.1/conv1/Conv/WithoutBiases" [label="[128, 128, 3, 3]", style=solid]; +"282 Constant_48491" -> "281 /layer2/layer2.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"283 Constant_48490" -> "281 /layer2/layer2.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"284 Constant_48489" -> "281 /layer2/layer2.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"285 Constant_48488" -> "281 /layer2/layer2.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"286 onnx^^Conv_217" -> "281 /layer2/layer2.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 128, 3, 3]", style=solid]; +"287 Constant_48541" -> "64 /layer3/layer3.0/conv2/Conv/fq_output_0" [label="[]", style=solid]; +"288 Constant_48540" -> "64 /layer3/layer3.0/conv2/Conv/fq_output_0" [label="[]", style=solid]; +"289 Constant_48539" -> "64 /layer3/layer3.0/conv2/Conv/fq_output_0" [label="[]", style=solid]; +"290 Constant_48538" -> "64 /layer3/layer3.0/conv2/Conv/fq_output_0" [label="[]", style=solid]; +"291 Reshape_235" -> "62 /layer3/layer3.0/conv2/Conv" [label="[1, 256, 1, 1]", style=solid]; +"292 /layer3/layer3.0/conv2/Conv/WithoutBiases/fq_weights_1" -> "60 /layer3/layer3.0/conv2/Conv/WithoutBiases" [label="[256, 256, 3, 3]", style=solid]; +"293 Constant_48536" -> "292 /layer3/layer3.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"294 Constant_48535" -> "292 /layer3/layer3.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"295 Constant_48534" -> "292 /layer3/layer3.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"296 Constant_48533" -> "292 /layer3/layer3.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"297 onnx^^Conv_226" -> "292 /layer3/layer3.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 256, 3, 3]", style=solid]; +"298 Constant_48531" -> "57 /layer3/layer3.0/relu/Relu/fq_output_0" [label="[]", style=solid]; +"299 Constant_48530" -> "57 /layer3/layer3.0/relu/Relu/fq_output_0" [label="[]", style=solid]; +"300 Constant_48529" -> "57 /layer3/layer3.0/relu/Relu/fq_output_0" [label="[]", style=solid]; +"301 Constant_48528" -> "57 /layer3/layer3.0/relu/Relu/fq_output_0" [label="[]", style=solid]; +"302 Reshape_219" -> "51 /layer3/layer3.0/conv1/Conv" [label="[1, 256, 1, 1]", style=solid]; +"303 /layer3/layer3.0/conv1/Conv/WithoutBiases/fq_weights_1" -> "48 /layer3/layer3.0/conv1/Conv/WithoutBiases" [label="[256, 128, 3, 3]", style=solid]; +"304 Constant_48526" -> "303 /layer3/layer3.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"305 Constant_48525" -> "303 /layer3/layer3.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"306 Constant_48524" -> "303 /layer3/layer3.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"307 Constant_48523" -> "303 /layer3/layer3.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"308 onnx^^Conv_223" -> "303 /layer3/layer3.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 128, 3, 3]", style=solid]; +"309 Constant_48566" -> "82 /layer3/layer3.1/conv2/Conv/fq_output_0" [label="[]", style=solid]; +"310 Constant_48565" -> "82 /layer3/layer3.1/conv2/Conv/fq_output_0" [label="[]", style=solid]; +"311 Constant_48564" -> "82 /layer3/layer3.1/conv2/Conv/fq_output_0" [label="[]", style=solid]; +"312 Constant_48563" -> "82 /layer3/layer3.1/conv2/Conv/fq_output_0" [label="[]", style=solid]; +"313 Reshape_283" -> "79 /layer3/layer3.1/conv2/Conv" [label="[1, 256, 1, 1]", style=solid]; +"314 /layer3/layer3.1/conv2/Conv/WithoutBiases/fq_weights_1" -> "76 /layer3/layer3.1/conv2/Conv/WithoutBiases" [label="[256, 256, 3, 3]", style=solid]; +"315 Constant_48561" -> "314 /layer3/layer3.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"316 Constant_48560" -> "314 /layer3/layer3.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"317 Constant_48559" -> "314 /layer3/layer3.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"318 Constant_48558" -> "314 /layer3/layer3.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"319 onnx^^Conv_235" -> "314 /layer3/layer3.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 256, 3, 3]", style=solid]; +"320 Constant_48556" -> "73 /layer3/layer3.1/relu/Relu/fq_output_0" [label="[]", style=solid]; +"321 Constant_48555" -> "73 /layer3/layer3.1/relu/Relu/fq_output_0" [label="[]", style=solid]; +"322 Constant_48554" -> "73 /layer3/layer3.1/relu/Relu/fq_output_0" [label="[]", style=solid]; +"323 Constant_48553" -> "73 /layer3/layer3.1/relu/Relu/fq_output_0" [label="[]", style=solid]; +"324 Reshape_267" -> "68 /layer3/layer3.1/conv1/Conv" [label="[1, 256, 1, 1]", style=solid]; +"325 /layer3/layer3.1/conv1/Conv/WithoutBiases/fq_weights_1" -> "66 /layer3/layer3.1/conv1/Conv/WithoutBiases" [label="[256, 256, 3, 3]", style=solid]; +"326 Constant_48551" -> "325 /layer3/layer3.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"327 Constant_48550" -> "325 /layer3/layer3.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"328 Constant_48549" -> "325 /layer3/layer3.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"329 Constant_48548" -> "325 /layer3/layer3.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"330 onnx^^Conv_232" -> "325 /layer3/layer3.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 256, 3, 3]", style=solid]; +"331 Constant_48601" -> "87 /layer4/layer4.0/conv2/Conv/fq_output_0" [label="[]", style=solid]; +"332 Constant_48600" -> "87 /layer4/layer4.0/conv2/Conv/fq_output_0" [label="[]", style=solid]; +"333 Constant_48599" -> "87 /layer4/layer4.0/conv2/Conv/fq_output_0" [label="[]", style=solid]; +"334 Constant_48598" -> "87 /layer4/layer4.0/conv2/Conv/fq_output_0" [label="[]", style=solid]; +"335 Reshape_316" -> "85 /layer4/layer4.0/conv2/Conv" [label="[1, 512, 1, 1]", style=solid]; +"336 /layer4/layer4.0/conv2/Conv/WithoutBiases/fq_weights_1" -> "83 /layer4/layer4.0/conv2/Conv/WithoutBiases" [label="[512, 512, 3, 3]", style=solid]; +"337 Constant_48596" -> "336 /layer4/layer4.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"338 Constant_48595" -> "336 /layer4/layer4.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"339 Constant_48594" -> "336 /layer4/layer4.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"340 Constant_48593" -> "336 /layer4/layer4.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"341 onnx^^Conv_241" -> "336 /layer4/layer4.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[512, 512, 3, 3]", style=solid]; +"342 Constant_48591" -> "80 /layer4/layer4.0/relu/Relu/fq_output_0" [label="[]", style=solid]; +"343 Constant_48590" -> "80 /layer4/layer4.0/relu/Relu/fq_output_0" [label="[]", style=solid]; +"344 Constant_48589" -> "80 /layer4/layer4.0/relu/Relu/fq_output_0" [label="[]", style=solid]; +"345 Constant_48588" -> "80 /layer4/layer4.0/relu/Relu/fq_output_0" [label="[]", style=solid]; +"346 Reshape_300" -> "74 /layer4/layer4.0/conv1/Conv" [label="[1, 512, 1, 1]", style=solid]; +"347 /layer4/layer4.0/conv1/Conv/WithoutBiases/fq_weights_1" -> "71 /layer4/layer4.0/conv1/Conv/WithoutBiases" [label="[512, 256, 3, 3]", style=solid]; +"348 Constant_48586" -> "347 /layer4/layer4.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"349 Constant_48585" -> "347 /layer4/layer4.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"350 Constant_48584" -> "347 /layer4/layer4.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"351 Constant_48583" -> "347 /layer4/layer4.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"352 onnx^^Conv_238" -> "347 /layer4/layer4.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 256, 3, 3]", style=solid]; +"353 Constant_48626" -> "101 /layer4/layer4.1/conv2/Conv/fq_output_0" [label="[]", style=solid]; +"354 Constant_48625" -> "101 /layer4/layer4.1/conv2/Conv/fq_output_0" [label="[]", style=solid]; +"355 Constant_48624" -> "101 /layer4/layer4.1/conv2/Conv/fq_output_0" [label="[]", style=solid]; +"356 Constant_48623" -> "101 /layer4/layer4.1/conv2/Conv/fq_output_0" [label="[]", style=solid]; +"357 Reshape_364" -> "99 /layer4/layer4.1/conv2/Conv" [label="[1, 512, 1, 1]", style=solid]; +"358 /layer4/layer4.1/conv2/Conv/WithoutBiases/fq_weights_1" -> "97 /layer4/layer4.1/conv2/Conv/WithoutBiases" [label="[512, 512, 3, 3]", style=solid]; +"359 Constant_48621" -> "358 /layer4/layer4.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"360 Constant_48620" -> "358 /layer4/layer4.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"361 Constant_48619" -> "358 /layer4/layer4.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"362 Constant_48618" -> "358 /layer4/layer4.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"363 onnx^^Conv_250" -> "358 /layer4/layer4.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[512, 512, 3, 3]", style=solid]; +"364 Constant_48616" -> "95 /layer4/layer4.1/relu/Relu/fq_output_0" [label="[]", style=solid]; +"365 Constant_48615" -> "95 /layer4/layer4.1/relu/Relu/fq_output_0" [label="[]", style=solid]; +"366 Constant_48614" -> "95 /layer4/layer4.1/relu/Relu/fq_output_0" [label="[]", style=solid]; +"367 Constant_48613" -> "95 /layer4/layer4.1/relu/Relu/fq_output_0" [label="[]", style=solid]; +"368 Reshape_348" -> "91 /layer4/layer4.1/conv1/Conv" [label="[1, 512, 1, 1]", style=solid]; +"369 /layer4/layer4.1/conv1/Conv/WithoutBiases/fq_weights_1" -> "89 /layer4/layer4.1/conv1/Conv/WithoutBiases" [label="[512, 512, 3, 3]", style=solid]; +"370 Constant_48611" -> "369 /layer4/layer4.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"371 Constant_48610" -> "369 /layer4/layer4.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"372 Constant_48609" -> "369 /layer4/layer4.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"373 Constant_48608" -> "369 /layer4/layer4.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"374 onnx^^Conv_247" -> "369 /layer4/layer4.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 512, 3, 3]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/resnet-50-pytorch_performance_CPU_SPR.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/resnet-50-pytorch_performance_CPU_SPR.dot deleted file mode 120000 index 0ae6917f6ae..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/quantized/resnet-50-pytorch_performance_CPU_SPR.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/quantized/resnet-50-pytorch_performance_CPU_SPR.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/resnet-50-pytorch_performance_CPU_SPR.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/resnet-50-pytorch_performance_CPU_SPR.dot new file mode 100644 index 00000000000..e318d57f89b --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/quantized/resnet-50-pytorch_performance_CPU_SPR.dot @@ -0,0 +1,1691 @@ +strict digraph { +"0 data" [id=0, type=Parameter]; +"1 Multiply_2745" [id=1, type=Multiply]; +"2 Divide_1002" [id=2, type=Add]; +"3 Divide_1002/fq_output_0" [id=3, type=FakeQuantize]; +"4 /conv1/Conv/WithoutBiases" [id=4, type=Convolution]; +"5 /conv1/Conv" [id=5, type=Add]; +"6 /relu/Relu" [id=6, type=Relu]; +"7 /relu/Relu/fq_output_0" [id=7, type=FakeQuantize]; +"8 /maxpool/MaxPool" [id=8, type=MaxPool]; +"9 /layer1/layer1.0/conv1/Conv/WithoutBiases" [id=9, type=Convolution]; +"10 /layer1/layer1.0/downsample/downsample.0/Conv/WithoutBiases" [id=10, type=Convolution]; +"11 /layer1/layer1.0/conv1/Conv" [id=11, type=Add]; +"12 /layer1/layer1.0/downsample/downsample.0/Conv" [id=12, type=Add]; 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type=Constant]; +"246 Constant_62378" [id=246, type=Constant]; +"247 Range_977" [id=247, type=Constant]; +"248 Constant_62376" [id=248, type=Constant]; +"249 Constant_62375" [id=249, type=Constant]; +"250 Constant_62374" [id=250, type=Constant]; +"251 Constant_62373" [id=251, type=Constant]; +"252 Constant_62341" [id=252, type=Constant]; +"253 Constant_62340" [id=253, type=Constant]; +"254 Constant_62339" [id=254, type=Constant]; +"255 Constant_62338" [id=255, type=Constant]; +"256 Constant_62306" [id=256, type=Constant]; +"257 Constant_62305" [id=257, type=Constant]; +"258 Constant_62304" [id=258, type=Constant]; +"259 Constant_62303" [id=259, type=Constant]; +"260 Reshape_867" [id=260, type=Constant]; +"261 /layer4/layer4.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [id=261, type=FakeQuantize]; +"262 Constant_62266" [id=262, type=Constant]; +"263 Constant_62265" [id=263, type=Constant]; +"264 Constant_62264" [id=264, type=Constant]; +"265 Constant_62263" [id=265, type=Constant]; +"266 onnx^^Conv_635" [id=266, label="266 onnx::Conv_635", type=Constant]; +"267 Constant_62261" [id=267, type=Constant]; +"268 Constant_62260" [id=268, type=Constant]; +"269 Constant_62259" [id=269, type=Constant]; +"270 Constant_62258" [id=270, type=Constant]; +"271 Constant_62226" [id=271, type=Constant]; +"272 Constant_62225" [id=272, type=Constant]; +"273 Constant_62224" [id=273, type=Constant]; +"274 Constant_62223" [id=274, type=Constant]; +"275 Constant_62191" [id=275, type=Constant]; +"276 Constant_62190" [id=276, type=Constant]; +"277 Constant_62189" [id=277, type=Constant]; +"278 Constant_62188" [id=278, type=Constant]; +"279 Constant_62156" [id=279, type=Constant]; +"280 Constant_62155" [id=280, type=Constant]; +"281 Constant_62154" [id=281, type=Constant]; +"282 Constant_62153" [id=282, type=Constant]; +"283 Constant_62121" [id=283, type=Constant]; +"284 Constant_62120" [id=284, type=Constant]; +"285 Constant_62119" [id=285, type=Constant]; +"286 Constant_62118" [id=286, type=Constant]; +"287 Constant_62086" [id=287, type=Constant]; +"288 Constant_62085" [id=288, type=Constant]; +"289 Constant_62084" [id=289, type=Constant]; +"290 Constant_62083" [id=290, type=Constant]; +"291 Reshape_558" [id=291, type=Constant]; +"292 /layer3/layer3.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [id=292, type=FakeQuantize]; +"293 Constant_62046" [id=293, type=Constant]; +"294 Constant_62045" [id=294, type=Constant]; +"295 Constant_62044" [id=295, type=Constant]; +"296 Constant_62043" [id=296, type=Constant]; +"297 onnx^^Conv_578" [id=297, label="297 onnx::Conv_578", type=Constant]; +"298 Constant_62041" [id=298, type=Constant]; +"299 Constant_62040" [id=299, type=Constant]; +"300 Constant_62039" [id=300, type=Constant]; +"301 Constant_62038" [id=301, type=Constant]; +"302 Constant_62006" [id=302, type=Constant]; +"303 Constant_62005" [id=303, type=Constant]; +"304 Constant_62004" [id=304, type=Constant]; +"305 Constant_62003" [id=305, type=Constant]; +"306 Constant_61971" [id=306, type=Constant]; +"307 Constant_61970" [id=307, type=Constant]; +"308 Constant_61969" [id=308, type=Constant]; +"309 Constant_61968" [id=309, type=Constant]; +"310 Constant_61936" [id=310, type=Constant]; +"311 Constant_61935" [id=311, type=Constant]; +"312 Constant_61934" [id=312, type=Constant]; +"313 Constant_61933" [id=313, type=Constant]; +"314 Reshape_347" [id=314, type=Constant]; +"315 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [id=315, type=FakeQuantize]; +"316 Constant_61896" [id=316, type=Constant]; +"317 Constant_61895" [id=317, type=Constant]; +"318 Constant_61894" [id=318, type=Constant]; +"319 Constant_61893" [id=319, type=Constant]; +"320 onnx^^Conv_539" [id=320, label="320 onnx::Conv_539", type=Constant]; +"321 Constant_61891" [id=321, type=Constant]; +"322 Constant_61890" [id=322, type=Constant]; +"323 Constant_61889" [id=323, type=Constant]; +"324 Constant_61888" [id=324, type=Constant]; +"325 Constant_61856" [id=325, type=Constant]; +"326 Constant_61855" [id=326, type=Constant]; +"327 Constant_61854" [id=327, type=Constant]; +"328 Constant_61853" [id=328, type=Constant]; +"329 Constant_61821" [id=329, type=Constant]; +"330 Constant_61820" [id=330, type=Constant]; +"331 Constant_61819" [id=331, type=Constant]; +"332 Constant_61818" [id=332, type=Constant]; +"333 Reshape_185" [id=333, type=Constant]; +"334 /layer1/layer1.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [id=334, type=FakeQuantize]; +"335 Constant_61781" [id=335, type=Constant]; +"336 Constant_61780" [id=336, type=Constant]; +"337 Constant_61779" [id=337, type=Constant]; +"338 Constant_61778" [id=338, type=Constant]; +"339 onnx^^Conv_509" [id=339, label="339 onnx::Conv_509", type=Constant]; +"340 Constant_61776" [id=340, type=Constant]; +"341 Constant_61775" [id=341, type=Constant]; +"342 Constant_61774" [id=342, type=Constant]; +"343 Constant_61773" [id=343, type=Constant]; 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Reshape_138" [id=383, type=Constant]; +"384 /layer1/layer1.0/conv1/Conv/WithoutBiases/fq_weights_1" [id=384, type=FakeQuantize]; +"385 Constant_61791" [id=385, type=Constant]; +"386 Constant_61790" [id=386, type=Constant]; +"387 Constant_61789" [id=387, type=Constant]; +"388 Constant_61788" [id=388, type=Constant]; +"389 onnx^^Conv_500" [id=389, label="389 onnx::Conv_500", type=Constant]; +"390 Reshape_234" [id=390, type=Constant]; +"391 /layer1/layer1.1/conv3/Conv/WithoutBiases/fq_weights_1" [id=391, type=FakeQuantize]; +"392 Constant_61846" [id=392, type=Constant]; +"393 Constant_61845" [id=393, type=Constant]; +"394 Constant_61844" [id=394, type=Constant]; +"395 Constant_61843" [id=395, type=Constant]; +"396 onnx^^Conv_518" [id=396, label="396 onnx::Conv_518", type=Constant]; +"397 Constant_61841" [id=397, type=Constant]; +"398 Constant_61840" [id=398, type=Constant]; +"399 Constant_61839" [id=399, type=Constant]; +"400 Constant_61838" [id=400, type=Constant]; +"401 Reshape_218" [id=401, type=Constant]; +"402 /layer1/layer1.1/conv2/Conv/WithoutBiases/fq_weights_1" [id=402, type=FakeQuantize]; +"403 Constant_61836" [id=403, type=Constant]; +"404 Constant_61835" [id=404, type=Constant]; +"405 Constant_61834" [id=405, type=Constant]; +"406 Constant_61833" [id=406, type=Constant]; +"407 onnx^^Conv_515" [id=407, label="407 onnx::Conv_515", type=Constant]; +"408 Constant_61831" [id=408, type=Constant]; +"409 Constant_61830" [id=409, type=Constant]; +"410 Constant_61829" [id=410, type=Constant]; +"411 Constant_61828" [id=411, type=Constant]; +"412 Reshape_202" [id=412, type=Constant]; +"413 /layer1/layer1.1/conv1/Conv/WithoutBiases/fq_weights_1" [id=413, type=FakeQuantize]; +"414 Constant_61826" [id=414, type=Constant]; +"415 Constant_61825" [id=415, type=Constant]; +"416 Constant_61824" [id=416, type=Constant]; +"417 Constant_61823" [id=417, type=Constant]; +"418 onnx^^Conv_512" [id=418, label="418 onnx::Conv_512", type=Constant]; +"419 Reshape_283" [id=419, type=Constant]; +"420 /layer1/layer1.2/conv3/Conv/WithoutBiases/fq_weights_1" [id=420, type=FakeQuantize]; +"421 Constant_61881" [id=421, type=Constant]; +"422 Constant_61880" [id=422, type=Constant]; +"423 Constant_61879" [id=423, type=Constant]; +"424 Constant_61878" [id=424, type=Constant]; +"425 onnx^^Conv_527" [id=425, label="425 onnx::Conv_527", type=Constant]; +"426 Constant_61876" [id=426, type=Constant]; +"427 Constant_61875" [id=427, type=Constant]; +"428 Constant_61874" [id=428, type=Constant]; +"429 Constant_61873" [id=429, type=Constant]; +"430 Reshape_267" [id=430, type=Constant]; +"431 /layer1/layer1.2/conv2/Conv/WithoutBiases/fq_weights_1" [id=431, type=FakeQuantize]; +"432 Constant_61871" [id=432, type=Constant]; +"433 Constant_61870" [id=433, type=Constant]; +"434 Constant_61869" [id=434, type=Constant]; +"435 Constant_61868" [id=435, type=Constant]; +"436 onnx^^Conv_524" [id=436, label="436 onnx::Conv_524", type=Constant]; +"437 Constant_61866" [id=437, type=Constant]; +"438 Constant_61865" [id=438, type=Constant]; +"439 Constant_61864" [id=439, type=Constant]; +"440 Constant_61863" [id=440, type=Constant]; +"441 Reshape_251" [id=441, type=Constant]; +"442 /layer1/layer1.2/conv1/Conv/WithoutBiases/fq_weights_1" [id=442, type=FakeQuantize]; +"443 Constant_61861" [id=443, type=Constant]; +"444 Constant_61860" [id=444, type=Constant]; +"445 Constant_61859" [id=445, type=Constant]; +"446 Constant_61858" [id=446, type=Constant]; +"447 onnx^^Conv_521" [id=447, label="447 onnx::Conv_521", type=Constant]; +"448 Constant_61931" [id=448, type=Constant]; +"449 Constant_61930" [id=449, type=Constant]; +"450 Constant_61929" [id=450, type=Constant]; +"451 Constant_61928" [id=451, type=Constant]; +"452 Reshape_332" [id=452, type=Constant]; +"453 /layer2/layer2.0/conv3/Conv/WithoutBiases/fq_weights_1" [id=453, type=FakeQuantize]; +"454 Constant_61926" [id=454, type=Constant]; +"455 Constant_61925" [id=455, type=Constant]; +"456 Constant_61924" [id=456, type=Constant]; +"457 Constant_61923" [id=457, type=Constant]; +"458 onnx^^Conv_536" [id=458, label="458 onnx::Conv_536", type=Constant]; +"459 Constant_61921" [id=459, type=Constant]; +"460 Constant_61920" [id=460, type=Constant]; +"461 Constant_61919" [id=461, type=Constant]; +"462 Constant_61918" [id=462, type=Constant]; +"463 Reshape_316" [id=463, type=Constant]; +"464 /layer2/layer2.0/conv2/Conv/WithoutBiases/fq_weights_1" [id=464, type=FakeQuantize]; +"465 Constant_61916" [id=465, type=Constant]; +"466 Constant_61915" [id=466, type=Constant]; +"467 Constant_61914" [id=467, type=Constant]; +"468 Constant_61913" [id=468, type=Constant]; +"469 onnx^^Conv_533" [id=469, label="469 onnx::Conv_533", type=Constant]; +"470 Constant_61911" [id=470, type=Constant]; +"471 Constant_61910" [id=471, type=Constant]; +"472 Constant_61909" [id=472, type=Constant]; +"473 Constant_61908" [id=473, type=Constant]; +"474 Reshape_300" [id=474, type=Constant]; +"475 /layer2/layer2.0/conv1/Conv/WithoutBiases/fq_weights_1" [id=475, type=FakeQuantize]; +"476 Constant_61906" [id=476, type=Constant]; +"477 Constant_61905" [id=477, type=Constant]; +"478 Constant_61904" [id=478, type=Constant]; +"479 Constant_61903" [id=479, type=Constant]; +"480 onnx^^Conv_530" [id=480, label="480 onnx::Conv_530", type=Constant]; +"481 Reshape_396" [id=481, type=Constant]; +"482 /layer2/layer2.1/conv3/Conv/WithoutBiases/fq_weights_1" [id=482, type=FakeQuantize]; +"483 Constant_61961" [id=483, type=Constant]; +"484 Constant_61960" [id=484, type=Constant]; +"485 Constant_61959" [id=485, type=Constant]; +"486 Constant_61958" [id=486, type=Constant]; +"487 onnx^^Conv_548" [id=487, label="487 onnx::Conv_548", type=Constant]; +"488 Constant_61956" [id=488, type=Constant]; +"489 Constant_61955" [id=489, type=Constant]; +"490 Constant_61954" [id=490, type=Constant]; +"491 Constant_61953" [id=491, type=Constant]; +"492 Reshape_380" [id=492, type=Constant]; +"493 /layer2/layer2.1/conv2/Conv/WithoutBiases/fq_weights_1" [id=493, type=FakeQuantize]; +"494 Constant_61951" [id=494, type=Constant]; +"495 Constant_61950" [id=495, type=Constant]; +"496 Constant_61949" [id=496, type=Constant]; +"497 Constant_61948" [id=497, type=Constant]; +"498 onnx^^Conv_545" [id=498, label="498 onnx::Conv_545", type=Constant]; +"499 Constant_61946" [id=499, type=Constant]; +"500 Constant_61945" [id=500, type=Constant]; +"501 Constant_61944" [id=501, type=Constant]; +"502 Constant_61943" [id=502, type=Constant]; +"503 Reshape_364" [id=503, type=Constant]; +"504 /layer2/layer2.1/conv1/Conv/WithoutBiases/fq_weights_1" [id=504, type=FakeQuantize]; +"505 Constant_61941" [id=505, type=Constant]; +"506 Constant_61940" [id=506, type=Constant]; +"507 Constant_61939" [id=507, type=Constant]; +"508 Constant_61938" [id=508, type=Constant]; +"509 onnx^^Conv_542" [id=509, label="509 onnx::Conv_542", type=Constant]; +"510 Reshape_445" [id=510, type=Constant]; +"511 /layer2/layer2.2/conv3/Conv/WithoutBiases/fq_weights_1" [id=511, type=FakeQuantize]; +"512 Constant_61996" [id=512, type=Constant]; +"513 Constant_61995" [id=513, type=Constant]; +"514 Constant_61994" [id=514, type=Constant]; +"515 Constant_61993" [id=515, type=Constant]; +"516 onnx^^Conv_557" [id=516, label="516 onnx::Conv_557", type=Constant]; +"517 Constant_61991" [id=517, type=Constant]; +"518 Constant_61990" [id=518, type=Constant]; +"519 Constant_61989" [id=519, type=Constant]; +"520 Constant_61988" [id=520, type=Constant]; +"521 Reshape_429" [id=521, type=Constant]; +"522 /layer2/layer2.2/conv2/Conv/WithoutBiases/fq_weights_1" [id=522, type=FakeQuantize]; +"523 Constant_61986" [id=523, type=Constant]; +"524 Constant_61985" [id=524, type=Constant]; +"525 Constant_61984" [id=525, type=Constant]; +"526 Constant_61983" [id=526, type=Constant]; +"527 onnx^^Conv_554" [id=527, label="527 onnx::Conv_554", type=Constant]; +"528 Constant_61981" [id=528, type=Constant]; +"529 Constant_61980" [id=529, type=Constant]; +"530 Constant_61979" [id=530, type=Constant]; +"531 Constant_61978" [id=531, type=Constant]; +"532 Reshape_413" [id=532, type=Constant]; +"533 /layer2/layer2.2/conv1/Conv/WithoutBiases/fq_weights_1" [id=533, type=FakeQuantize]; +"534 Constant_61976" [id=534, type=Constant]; +"535 Constant_61975" [id=535, type=Constant]; +"536 Constant_61974" [id=536, type=Constant]; +"537 Constant_61973" [id=537, type=Constant]; +"538 onnx^^Conv_551" [id=538, label="538 onnx::Conv_551", type=Constant]; +"539 Reshape_494" [id=539, type=Constant]; +"540 /layer2/layer2.3/conv3/Conv/WithoutBiases/fq_weights_1" [id=540, type=FakeQuantize]; +"541 Constant_62031" [id=541, type=Constant]; +"542 Constant_62030" [id=542, type=Constant]; +"543 Constant_62029" [id=543, type=Constant]; +"544 Constant_62028" [id=544, type=Constant]; +"545 onnx^^Conv_566" [id=545, label="545 onnx::Conv_566", type=Constant]; +"546 Constant_62026" [id=546, type=Constant]; +"547 Constant_62025" [id=547, type=Constant]; +"548 Constant_62024" [id=548, type=Constant]; +"549 Constant_62023" [id=549, type=Constant]; +"550 Reshape_478" [id=550, type=Constant]; +"551 /layer2/layer2.3/conv2/Conv/WithoutBiases/fq_weights_1" [id=551, type=FakeQuantize]; +"552 Constant_62021" [id=552, type=Constant]; +"553 Constant_62020" [id=553, type=Constant]; +"554 Constant_62019" [id=554, type=Constant]; +"555 Constant_62018" [id=555, type=Constant]; +"556 onnx^^Conv_563" [id=556, label="556 onnx::Conv_563", type=Constant]; +"557 Constant_62016" [id=557, type=Constant]; +"558 Constant_62015" [id=558, type=Constant]; +"559 Constant_62014" [id=559, type=Constant]; +"560 Constant_62013" [id=560, type=Constant]; +"561 Reshape_462" [id=561, type=Constant]; +"562 /layer2/layer2.3/conv1/Conv/WithoutBiases/fq_weights_1" [id=562, type=FakeQuantize]; +"563 Constant_62011" [id=563, type=Constant]; +"564 Constant_62010" [id=564, type=Constant]; +"565 Constant_62009" [id=565, type=Constant]; +"566 Constant_62008" [id=566, type=Constant]; +"567 onnx^^Conv_560" [id=567, label="567 onnx::Conv_560", type=Constant]; +"568 Constant_62081" [id=568, type=Constant]; +"569 Constant_62080" [id=569, type=Constant]; +"570 Constant_62079" [id=570, type=Constant]; +"571 Constant_62078" [id=571, type=Constant]; +"572 Reshape_543" [id=572, type=Constant]; +"573 /layer3/layer3.0/conv3/Conv/WithoutBiases/fq_weights_1" [id=573, type=FakeQuantize]; +"574 Constant_62076" [id=574, type=Constant]; +"575 Constant_62075" [id=575, type=Constant]; +"576 Constant_62074" [id=576, type=Constant]; +"577 Constant_62073" [id=577, type=Constant]; +"578 onnx^^Conv_575" [id=578, label="578 onnx::Conv_575", type=Constant]; +"579 Constant_62071" [id=579, type=Constant]; +"580 Constant_62070" [id=580, type=Constant]; +"581 Constant_62069" [id=581, type=Constant]; +"582 Constant_62068" [id=582, type=Constant]; +"583 Reshape_527" [id=583, type=Constant]; +"584 /layer3/layer3.0/conv2/Conv/WithoutBiases/fq_weights_1" [id=584, type=FakeQuantize]; +"585 Constant_62066" [id=585, type=Constant]; +"586 Constant_62065" [id=586, type=Constant]; +"587 Constant_62064" [id=587, type=Constant]; +"588 Constant_62063" [id=588, type=Constant]; +"589 onnx^^Conv_572" [id=589, label="589 onnx::Conv_572", type=Constant]; +"590 Constant_62061" [id=590, type=Constant]; +"591 Constant_62060" [id=591, type=Constant]; +"592 Constant_62059" [id=592, type=Constant]; +"593 Constant_62058" [id=593, type=Constant]; +"594 Reshape_511" [id=594, type=Constant]; +"595 /layer3/layer3.0/conv1/Conv/WithoutBiases/fq_weights_1" [id=595, type=FakeQuantize]; +"596 Constant_62056" [id=596, type=Constant]; +"597 Constant_62055" [id=597, type=Constant]; +"598 Constant_62054" [id=598, type=Constant]; +"599 Constant_62053" [id=599, type=Constant]; +"600 onnx^^Conv_569" [id=600, label="600 onnx::Conv_569", type=Constant]; +"601 Reshape_607" [id=601, type=Constant]; +"602 /layer3/layer3.1/conv3/Conv/WithoutBiases/fq_weights_1" [id=602, type=FakeQuantize]; +"603 Constant_62111" [id=603, type=Constant]; +"604 Constant_62110" [id=604, type=Constant]; +"605 Constant_62109" [id=605, type=Constant]; +"606 Constant_62108" [id=606, type=Constant]; +"607 onnx^^Conv_587" [id=607, label="607 onnx::Conv_587", type=Constant]; +"608 Constant_62106" [id=608, type=Constant]; +"609 Constant_62105" [id=609, type=Constant]; +"610 Constant_62104" [id=610, type=Constant]; +"611 Constant_62103" [id=611, type=Constant]; +"612 Reshape_591" [id=612, type=Constant]; +"613 /layer3/layer3.1/conv2/Conv/WithoutBiases/fq_weights_1" [id=613, type=FakeQuantize]; +"614 Constant_62101" [id=614, type=Constant]; +"615 Constant_62100" [id=615, type=Constant]; +"616 Constant_62099" [id=616, type=Constant]; +"617 Constant_62098" [id=617, type=Constant]; +"618 onnx^^Conv_584" [id=618, label="618 onnx::Conv_584", type=Constant]; +"619 Constant_62096" [id=619, type=Constant]; +"620 Constant_62095" [id=620, type=Constant]; +"621 Constant_62094" [id=621, type=Constant]; +"622 Constant_62093" [id=622, type=Constant]; +"623 Reshape_575" [id=623, type=Constant]; +"624 /layer3/layer3.1/conv1/Conv/WithoutBiases/fq_weights_1" [id=624, type=FakeQuantize]; +"625 Constant_62091" [id=625, type=Constant]; +"626 Constant_62090" [id=626, type=Constant]; +"627 Constant_62089" [id=627, type=Constant]; +"628 Constant_62088" [id=628, type=Constant]; +"629 onnx^^Conv_581" [id=629, label="629 onnx::Conv_581", type=Constant]; +"630 Reshape_656" [id=630, type=Constant]; +"631 /layer3/layer3.2/conv3/Conv/WithoutBiases/fq_weights_1" [id=631, type=FakeQuantize]; +"632 Constant_62146" [id=632, type=Constant]; +"633 Constant_62145" [id=633, type=Constant]; +"634 Constant_62144" [id=634, type=Constant]; +"635 Constant_62143" [id=635, type=Constant]; +"636 onnx^^Conv_596" [id=636, label="636 onnx::Conv_596", type=Constant]; +"637 Constant_62141" [id=637, type=Constant]; +"638 Constant_62140" [id=638, type=Constant]; +"639 Constant_62139" [id=639, type=Constant]; +"640 Constant_62138" [id=640, type=Constant]; +"641 Reshape_640" [id=641, type=Constant]; +"642 /layer3/layer3.2/conv2/Conv/WithoutBiases/fq_weights_1" [id=642, type=FakeQuantize]; +"643 Constant_62136" [id=643, type=Constant]; +"644 Constant_62135" [id=644, type=Constant]; +"645 Constant_62134" [id=645, type=Constant]; +"646 Constant_62133" [id=646, type=Constant]; +"647 onnx^^Conv_593" [id=647, label="647 onnx::Conv_593", type=Constant]; +"648 Constant_62131" [id=648, type=Constant]; +"649 Constant_62130" [id=649, type=Constant]; +"650 Constant_62129" [id=650, type=Constant]; +"651 Constant_62128" [id=651, type=Constant]; +"652 Reshape_624" [id=652, type=Constant]; +"653 /layer3/layer3.2/conv1/Conv/WithoutBiases/fq_weights_1" [id=653, type=FakeQuantize]; +"654 Constant_62126" [id=654, type=Constant]; +"655 Constant_62125" [id=655, type=Constant]; +"656 Constant_62124" [id=656, type=Constant]; +"657 Constant_62123" [id=657, type=Constant]; +"658 onnx^^Conv_590" [id=658, label="658 onnx::Conv_590", type=Constant]; +"659 Reshape_705" [id=659, type=Constant]; +"660 /layer3/layer3.3/conv3/Conv/WithoutBiases/fq_weights_1" [id=660, type=FakeQuantize]; +"661 Constant_62181" [id=661, type=Constant]; +"662 Constant_62180" [id=662, type=Constant]; +"663 Constant_62179" [id=663, type=Constant]; +"664 Constant_62178" [id=664, type=Constant]; +"665 onnx^^Conv_605" [id=665, label="665 onnx::Conv_605", type=Constant]; +"666 Constant_62176" [id=666, type=Constant]; +"667 Constant_62175" [id=667, type=Constant]; +"668 Constant_62174" [id=668, type=Constant]; +"669 Constant_62173" [id=669, type=Constant]; +"670 Reshape_689" [id=670, type=Constant]; +"671 /layer3/layer3.3/conv2/Conv/WithoutBiases/fq_weights_1" [id=671, type=FakeQuantize]; +"672 Constant_62171" [id=672, type=Constant]; +"673 Constant_62170" [id=673, type=Constant]; +"674 Constant_62169" [id=674, type=Constant]; +"675 Constant_62168" [id=675, type=Constant]; +"676 onnx^^Conv_602" [id=676, label="676 onnx::Conv_602", type=Constant]; +"677 Constant_62166" [id=677, type=Constant]; +"678 Constant_62165" [id=678, type=Constant]; +"679 Constant_62164" [id=679, type=Constant]; +"680 Constant_62163" [id=680, type=Constant]; +"681 Reshape_673" [id=681, type=Constant]; +"682 /layer3/layer3.3/conv1/Conv/WithoutBiases/fq_weights_1" [id=682, type=FakeQuantize]; +"683 Constant_62161" [id=683, type=Constant]; +"684 Constant_62160" [id=684, type=Constant]; +"685 Constant_62159" [id=685, type=Constant]; +"686 Constant_62158" [id=686, type=Constant]; +"687 onnx^^Conv_599" [id=687, label="687 onnx::Conv_599", type=Constant]; +"688 Reshape_754" [id=688, type=Constant]; +"689 /layer3/layer3.4/conv3/Conv/WithoutBiases/fq_weights_1" [id=689, type=FakeQuantize]; +"690 Constant_62216" [id=690, type=Constant]; +"691 Constant_62215" [id=691, type=Constant]; +"692 Constant_62214" [id=692, type=Constant]; +"693 Constant_62213" [id=693, type=Constant]; +"694 onnx^^Conv_614" [id=694, label="694 onnx::Conv_614", type=Constant]; +"695 Constant_62211" [id=695, type=Constant]; +"696 Constant_62210" [id=696, type=Constant]; +"697 Constant_62209" [id=697, type=Constant]; +"698 Constant_62208" [id=698, type=Constant]; +"699 Reshape_738" [id=699, type=Constant]; +"700 /layer3/layer3.4/conv2/Conv/WithoutBiases/fq_weights_1" [id=700, type=FakeQuantize]; +"701 Constant_62206" [id=701, type=Constant]; +"702 Constant_62205" [id=702, type=Constant]; +"703 Constant_62204" [id=703, type=Constant]; +"704 Constant_62203" [id=704, type=Constant]; +"705 onnx^^Conv_611" [id=705, label="705 onnx::Conv_611", type=Constant]; +"706 Constant_62201" [id=706, type=Constant]; +"707 Constant_62200" [id=707, type=Constant]; +"708 Constant_62199" [id=708, type=Constant]; +"709 Constant_62198" [id=709, type=Constant]; +"710 Reshape_722" [id=710, type=Constant]; +"711 /layer3/layer3.4/conv1/Conv/WithoutBiases/fq_weights_1" [id=711, type=FakeQuantize]; +"712 Constant_62196" [id=712, type=Constant]; +"713 Constant_62195" [id=713, type=Constant]; +"714 Constant_62194" [id=714, type=Constant]; +"715 Constant_62193" [id=715, type=Constant]; +"716 onnx^^Conv_608" [id=716, label="716 onnx::Conv_608", type=Constant]; +"717 Reshape_803" [id=717, type=Constant]; +"718 /layer3/layer3.5/conv3/Conv/WithoutBiases/fq_weights_1" [id=718, type=FakeQuantize]; +"719 Constant_62251" [id=719, type=Constant]; +"720 Constant_62250" [id=720, type=Constant]; +"721 Constant_62249" [id=721, type=Constant]; +"722 Constant_62248" [id=722, type=Constant]; +"723 onnx^^Conv_623" [id=723, label="723 onnx::Conv_623", type=Constant]; +"724 Constant_62246" [id=724, type=Constant]; +"725 Constant_62245" [id=725, type=Constant]; +"726 Constant_62244" [id=726, type=Constant]; +"727 Constant_62243" [id=727, type=Constant]; +"728 Reshape_787" [id=728, type=Constant]; +"729 /layer3/layer3.5/conv2/Conv/WithoutBiases/fq_weights_1" [id=729, type=FakeQuantize]; +"730 Constant_62241" [id=730, type=Constant]; +"731 Constant_62240" [id=731, type=Constant]; +"732 Constant_62239" [id=732, type=Constant]; +"733 Constant_62238" [id=733, type=Constant]; +"734 onnx^^Conv_620" [id=734, label="734 onnx::Conv_620", type=Constant]; +"735 Constant_62236" [id=735, type=Constant]; +"736 Constant_62235" [id=736, type=Constant]; +"737 Constant_62234" [id=737, type=Constant]; +"738 Constant_62233" [id=738, type=Constant]; +"739 Reshape_771" [id=739, type=Constant]; +"740 /layer3/layer3.5/conv1/Conv/WithoutBiases/fq_weights_1" [id=740, type=FakeQuantize]; +"741 Constant_62231" [id=741, type=Constant]; +"742 Constant_62230" [id=742, type=Constant]; +"743 Constant_62229" [id=743, type=Constant]; +"744 Constant_62228" [id=744, type=Constant]; +"745 onnx^^Conv_617" [id=745, label="745 onnx::Conv_617", type=Constant]; +"746 Constant_62301" [id=746, type=Constant]; +"747 Constant_62300" [id=747, type=Constant]; +"748 Constant_62299" [id=748, type=Constant]; +"749 Constant_62298" [id=749, type=Constant]; +"750 Reshape_852" [id=750, type=Constant]; +"751 /layer4/layer4.0/conv3/Conv/WithoutBiases/fq_weights_1" [id=751, type=FakeQuantize]; +"752 Constant_62296" [id=752, type=Constant]; +"753 Constant_62295" [id=753, type=Constant]; +"754 Constant_62294" [id=754, type=Constant]; +"755 Constant_62293" [id=755, type=Constant]; +"756 onnx^^Conv_632" [id=756, label="756 onnx::Conv_632", type=Constant]; +"757 Constant_62291" [id=757, type=Constant]; +"758 Constant_62290" [id=758, type=Constant]; +"759 Constant_62289" [id=759, type=Constant]; +"760 Constant_62288" [id=760, type=Constant]; +"761 Reshape_836" [id=761, type=Constant]; +"762 /layer4/layer4.0/conv2/Conv/WithoutBiases/fq_weights_1" [id=762, type=FakeQuantize]; +"763 Constant_62286" [id=763, type=Constant]; +"764 Constant_62285" [id=764, type=Constant]; +"765 Constant_62284" [id=765, type=Constant]; +"766 Constant_62283" [id=766, type=Constant]; +"767 onnx^^Conv_629" [id=767, label="767 onnx::Conv_629", type=Constant]; +"768 Constant_62281" [id=768, type=Constant]; +"769 Constant_62280" [id=769, type=Constant]; +"770 Constant_62279" [id=770, type=Constant]; +"771 Constant_62278" [id=771, type=Constant]; +"772 Reshape_820" [id=772, type=Constant]; +"773 /layer4/layer4.0/conv1/Conv/WithoutBiases/fq_weights_1" [id=773, type=FakeQuantize]; +"774 Constant_62276" [id=774, type=Constant]; +"775 Constant_62275" [id=775, type=Constant]; +"776 Constant_62274" [id=776, type=Constant]; +"777 Constant_62273" [id=777, type=Constant]; +"778 onnx^^Conv_626" [id=778, label="778 onnx::Conv_626", type=Constant]; +"779 Reshape_916" [id=779, type=Constant]; +"780 /layer4/layer4.1/conv3/Conv/WithoutBiases/fq_weights_1" [id=780, type=FakeQuantize]; +"781 Constant_62331" [id=781, type=Constant]; +"782 Constant_62330" [id=782, type=Constant]; +"783 Constant_62329" [id=783, type=Constant]; +"784 Constant_62328" [id=784, type=Constant]; +"785 onnx^^Conv_644" [id=785, label="785 onnx::Conv_644", type=Constant]; +"786 Constant_62326" [id=786, type=Constant]; +"787 Constant_62325" [id=787, type=Constant]; +"788 Constant_62324" [id=788, type=Constant]; +"789 Constant_62323" [id=789, type=Constant]; +"790 Reshape_900" [id=790, type=Constant]; +"791 /layer4/layer4.1/conv2/Conv/WithoutBiases/fq_weights_1" [id=791, type=FakeQuantize]; +"792 Constant_62321" [id=792, type=Constant]; +"793 Constant_62320" [id=793, type=Constant]; +"794 Constant_62319" [id=794, type=Constant]; +"795 Constant_62318" [id=795, type=Constant]; +"796 onnx^^Conv_641" [id=796, label="796 onnx::Conv_641", type=Constant]; +"797 Constant_62316" [id=797, type=Constant]; +"798 Constant_62315" [id=798, type=Constant]; +"799 Constant_62314" [id=799, type=Constant]; +"800 Constant_62313" [id=800, type=Constant]; +"801 Reshape_884" [id=801, type=Constant]; +"802 /layer4/layer4.1/conv1/Conv/WithoutBiases/fq_weights_1" [id=802, type=FakeQuantize]; +"803 Constant_62311" [id=803, type=Constant]; +"804 Constant_62310" [id=804, type=Constant]; +"805 Constant_62309" [id=805, type=Constant]; +"806 Constant_62308" [id=806, type=Constant]; +"807 onnx^^Conv_638" [id=807, label="807 onnx::Conv_638", type=Constant]; +"808 Reshape_965" [id=808, type=Constant]; +"809 /layer4/layer4.2/conv3/Conv/WithoutBiases/fq_weights_1" [id=809, type=FakeQuantize]; +"810 Constant_62366" [id=810, type=Constant]; +"811 Constant_62365" [id=811, type=Constant]; +"812 Constant_62364" [id=812, type=Constant]; +"813 Constant_62363" [id=813, type=Constant]; +"814 onnx^^Conv_653" [id=814, label="814 onnx::Conv_653", type=Constant]; +"815 Constant_62361" [id=815, type=Constant]; +"816 Constant_62360" [id=816, type=Constant]; +"817 Constant_62359" [id=817, type=Constant]; +"818 Constant_62358" [id=818, type=Constant]; +"819 Reshape_949" [id=819, type=Constant]; +"820 /layer4/layer4.2/conv2/Conv/WithoutBiases/fq_weights_1" [id=820, type=FakeQuantize]; +"821 Constant_62356" [id=821, type=Constant]; +"822 Constant_62355" [id=822, type=Constant]; +"823 Constant_62354" [id=823, type=Constant]; +"824 Constant_62353" [id=824, type=Constant]; +"825 onnx^^Conv_650" [id=825, label="825 onnx::Conv_650", type=Constant]; +"826 Constant_62351" [id=826, type=Constant]; +"827 Constant_62350" [id=827, type=Constant]; +"828 Constant_62349" [id=828, type=Constant]; +"829 Constant_62348" [id=829, type=Constant]; +"830 Reshape_933" [id=830, type=Constant]; +"831 /layer4/layer4.2/conv1/Conv/WithoutBiases/fq_weights_1" [id=831, type=FakeQuantize]; +"832 Constant_62346" [id=832, type=Constant]; +"833 Constant_62345" [id=833, type=Constant]; +"834 Constant_62344" [id=834, type=Constant]; +"835 Constant_62343" [id=835, type=Constant]; +"836 onnx^^Conv_647" [id=836, label="836 onnx::Conv_647", type=Constant]; +"0 data" -> "1 Multiply_2745" [label="[1, 3, 224, 224]", style=solid]; +"1 Multiply_2745" -> "2 Divide_1002" [label="[1, 3, 224, 224]", style=solid]; +"2 Divide_1002" -> "3 Divide_1002/fq_output_0" [label="[1, 3, 224, 224]", style=solid]; +"3 Divide_1002/fq_output_0" -> "4 /conv1/Conv/WithoutBiases" [label="[1, 3, 224, 224]", style=solid]; +"4 /conv1/Conv/WithoutBiases" -> "5 /conv1/Conv" [label="[1, 64, 112, 112]", style=solid]; +"5 /conv1/Conv" -> "6 /relu/Relu" [label="[1, 64, 112, 112]", style=solid]; +"6 /relu/Relu" -> "7 /relu/Relu/fq_output_0" [label="[1, 64, 112, 112]", style=solid]; +"7 /relu/Relu/fq_output_0" -> "8 /maxpool/MaxPool" [label="[1, 64, 112, 112]", style=solid]; +"8 /maxpool/MaxPool" -> "9 /layer1/layer1.0/conv1/Conv/WithoutBiases" [label="[1, 64, 56, 56]", style=solid]; +"8 /maxpool/MaxPool" -> "10 /layer1/layer1.0/downsample/downsample.0/Conv/WithoutBiases" [label="[1, 64, 56, 56]", style=solid]; +"9 /layer1/layer1.0/conv1/Conv/WithoutBiases" -> "11 /layer1/layer1.0/conv1/Conv" [label="[1, 64, 56, 56]", style=solid]; +"10 /layer1/layer1.0/downsample/downsample.0/Conv/WithoutBiases" -> "12 /layer1/layer1.0/downsample/downsample.0/Conv" [label="[1, 256, 56, 56]", style=solid]; +"11 /layer1/layer1.0/conv1/Conv" -> "13 /layer1/layer1.0/relu/Relu" [label="[1, 64, 56, 56]", style=solid]; +"12 /layer1/layer1.0/downsample/downsample.0/Conv" -> "14 /layer1/layer1.0/Add" [label="[1, 256, 56, 56]", style=solid]; +"13 /layer1/layer1.0/relu/Relu" -> "15 /layer1/layer1.0/relu/Relu/fq_output_0" [label="[1, 64, 56, 56]", style=solid]; +"14 /layer1/layer1.0/Add" -> "16 /layer1/layer1.0/relu_2/Relu" [label="[1, 256, 56, 56]", style=solid]; +"15 /layer1/layer1.0/relu/Relu/fq_output_0" -> "17 /layer1/layer1.0/conv2/Conv/WithoutBiases" [label="[1, 64, 56, 56]", style=solid]; +"16 /layer1/layer1.0/relu_2/Relu" -> "18 /layer1/layer1.0/relu_2/Relu/fq_output_0" [label="[1, 256, 56, 56]", style=solid]; +"17 /layer1/layer1.0/conv2/Conv/WithoutBiases" -> "19 /layer1/layer1.0/conv2/Conv" [label="[1, 64, 56, 56]", style=solid]; +"18 /layer1/layer1.0/relu_2/Relu/fq_output_0" -> "20 /layer1/layer1.1/Add" [label="[1, 256, 56, 56]", style=solid]; +"18 /layer1/layer1.0/relu_2/Relu/fq_output_0" -> "21 /layer1/layer1.1/conv1/Conv/WithoutBiases" [label="[1, 256, 56, 56]", style=solid]; +"19 /layer1/layer1.0/conv2/Conv" -> "22 /layer1/layer1.0/relu_1/Relu" [label="[1, 64, 56, 56]", style=solid]; +"20 /layer1/layer1.1/Add" -> "23 /layer1/layer1.1/relu_2/Relu" [label="[1, 256, 56, 56]", style=solid]; +"21 /layer1/layer1.1/conv1/Conv/WithoutBiases" -> "24 /layer1/layer1.1/conv1/Conv" [label="[1, 64, 56, 56]", style=solid]; +"22 /layer1/layer1.0/relu_1/Relu" -> "25 /layer1/layer1.0/relu_1/Relu/fq_output_0" [label="[1, 64, 56, 56]", style=solid]; +"23 /layer1/layer1.1/relu_2/Relu" -> "26 /layer1/layer1.1/relu_2/Relu/fq_output_0" [label="[1, 256, 56, 56]", style=solid]; +"24 /layer1/layer1.1/conv1/Conv" -> "27 /layer1/layer1.1/relu/Relu" [label="[1, 64, 56, 56]", style=solid]; +"25 /layer1/layer1.0/relu_1/Relu/fq_output_0" -> "28 /layer1/layer1.0/conv3/Conv/WithoutBiases" [label="[1, 64, 56, 56]", style=solid]; +"26 /layer1/layer1.1/relu_2/Relu/fq_output_0" -> "29 /layer1/layer1.2/Add" [label="[1, 256, 56, 56]", style=solid]; +"26 /layer1/layer1.1/relu_2/Relu/fq_output_0" -> "30 /layer1/layer1.2/conv1/Conv/WithoutBiases" [label="[1, 256, 56, 56]", style=solid]; +"27 /layer1/layer1.1/relu/Relu" -> "31 /layer1/layer1.1/relu/Relu/fq_output_0" [label="[1, 64, 56, 56]", style=solid]; +"28 /layer1/layer1.0/conv3/Conv/WithoutBiases" -> "32 /layer1/layer1.0/conv3/Conv" [label="[1, 256, 56, 56]", style=solid]; +"29 /layer1/layer1.2/Add" -> "33 /layer1/layer1.2/relu_2/Relu" [label="[1, 256, 56, 56]", style=solid]; +"30 /layer1/layer1.2/conv1/Conv/WithoutBiases" -> "34 /layer1/layer1.2/conv1/Conv" [label="[1, 64, 56, 56]", style=solid]; +"31 /layer1/layer1.1/relu/Relu/fq_output_0" -> "35 /layer1/layer1.1/conv2/Conv/WithoutBiases" [label="[1, 64, 56, 56]", style=solid]; +"32 /layer1/layer1.0/conv3/Conv" -> "36 /layer1/layer1.0/conv3/Conv/fq_output_0" [label="[1, 256, 56, 56]", style=solid]; +"33 /layer1/layer1.2/relu_2/Relu" -> "37 /layer1/layer1.2/relu_2/Relu/fq_output_0" [label="[1, 256, 56, 56]", style=solid]; +"34 /layer1/layer1.2/conv1/Conv" -> "38 /layer1/layer1.2/relu/Relu" [label="[1, 64, 56, 56]", style=solid]; +"35 /layer1/layer1.1/conv2/Conv/WithoutBiases" -> "39 /layer1/layer1.1/conv2/Conv" [label="[1, 64, 56, 56]", style=solid]; +"36 /layer1/layer1.0/conv3/Conv/fq_output_0" -> "14 /layer1/layer1.0/Add" [label="[1, 256, 56, 56]", style=solid]; +"37 /layer1/layer1.2/relu_2/Relu/fq_output_0" -> "40 /layer2/layer2.0/conv1/Conv/WithoutBiases" [label="[1, 256, 56, 56]", style=solid]; +"37 /layer1/layer1.2/relu_2/Relu/fq_output_0" -> "41 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases" [label="[1, 256, 56, 56]", style=solid]; +"38 /layer1/layer1.2/relu/Relu" -> "42 /layer1/layer1.2/relu/Relu/fq_output_0" [label="[1, 64, 56, 56]", style=solid]; +"39 /layer1/layer1.1/conv2/Conv" -> "43 /layer1/layer1.1/relu_1/Relu" [label="[1, 64, 56, 56]", style=solid]; +"40 /layer2/layer2.0/conv1/Conv/WithoutBiases" -> "44 /layer2/layer2.0/conv1/Conv" [label="[1, 128, 56, 56]", style=solid]; +"41 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases" -> "45 /layer2/layer2.0/downsample/downsample.0/Conv" [label="[1, 512, 28, 28]", style=solid]; +"42 /layer1/layer1.2/relu/Relu/fq_output_0" -> "46 /layer1/layer1.2/conv2/Conv/WithoutBiases" [label="[1, 64, 56, 56]", style=solid]; +"43 /layer1/layer1.1/relu_1/Relu" -> "47 /layer1/layer1.1/relu_1/Relu/fq_output_0" [label="[1, 64, 56, 56]", style=solid]; +"44 /layer2/layer2.0/conv1/Conv" -> "48 /layer2/layer2.0/relu/Relu" [label="[1, 128, 56, 56]", style=solid]; +"45 /layer2/layer2.0/downsample/downsample.0/Conv" -> "49 /layer2/layer2.0/Add" [label="[1, 512, 28, 28]", style=solid]; +"46 /layer1/layer1.2/conv2/Conv/WithoutBiases" -> "50 /layer1/layer1.2/conv2/Conv" [label="[1, 64, 56, 56]", style=solid]; +"47 /layer1/layer1.1/relu_1/Relu/fq_output_0" -> "51 /layer1/layer1.1/conv3/Conv/WithoutBiases" [label="[1, 64, 56, 56]", style=solid]; +"48 /layer2/layer2.0/relu/Relu" -> "52 /layer2/layer2.0/relu/Relu/fq_output_0" [label="[1, 128, 56, 56]", style=solid]; +"49 /layer2/layer2.0/Add" -> "53 /layer2/layer2.0/relu_2/Relu" [label="[1, 512, 28, 28]", style=solid]; +"50 /layer1/layer1.2/conv2/Conv" -> "54 /layer1/layer1.2/relu_1/Relu" [label="[1, 64, 56, 56]", style=solid]; +"51 /layer1/layer1.1/conv3/Conv/WithoutBiases" -> "55 /layer1/layer1.1/conv3/Conv" [label="[1, 256, 56, 56]", style=solid]; +"52 /layer2/layer2.0/relu/Relu/fq_output_0" -> "56 /layer2/layer2.0/conv2/Conv/WithoutBiases" [label="[1, 128, 56, 56]", style=solid]; +"53 /layer2/layer2.0/relu_2/Relu" -> "57 /layer2/layer2.0/relu_2/Relu/fq_output_0" [label="[1, 512, 28, 28]", style=solid]; +"54 /layer1/layer1.2/relu_1/Relu" -> "58 /layer1/layer1.2/relu_1/Relu/fq_output_0" [label="[1, 64, 56, 56]", style=solid]; +"55 /layer1/layer1.1/conv3/Conv" -> "20 /layer1/layer1.1/Add" [label="[1, 256, 56, 56]", style=solid]; +"56 /layer2/layer2.0/conv2/Conv/WithoutBiases" -> "59 /layer2/layer2.0/conv2/Conv" [label="[1, 128, 28, 28]", style=solid]; +"57 /layer2/layer2.0/relu_2/Relu/fq_output_0" -> "60 /layer2/layer2.1/Add" [label="[1, 512, 28, 28]", style=solid]; +"57 /layer2/layer2.0/relu_2/Relu/fq_output_0" -> "61 /layer2/layer2.1/conv1/Conv/WithoutBiases" [label="[1, 512, 28, 28]", style=solid]; +"58 /layer1/layer1.2/relu_1/Relu/fq_output_0" -> "62 /layer1/layer1.2/conv3/Conv/WithoutBiases" [label="[1, 64, 56, 56]", style=solid]; +"59 /layer2/layer2.0/conv2/Conv" -> "63 /layer2/layer2.0/relu_1/Relu" [label="[1, 128, 28, 28]", style=solid]; +"60 /layer2/layer2.1/Add" -> "64 /layer2/layer2.1/relu_2/Relu" [label="[1, 512, 28, 28]", style=solid]; +"61 /layer2/layer2.1/conv1/Conv/WithoutBiases" -> "65 /layer2/layer2.1/conv1/Conv" [label="[1, 128, 28, 28]", style=solid]; +"62 /layer1/layer1.2/conv3/Conv/WithoutBiases" -> "66 /layer1/layer1.2/conv3/Conv" [label="[1, 256, 56, 56]", style=solid]; +"63 /layer2/layer2.0/relu_1/Relu" -> "67 /layer2/layer2.0/relu_1/Relu/fq_output_0" [label="[1, 128, 28, 28]", style=solid]; +"64 /layer2/layer2.1/relu_2/Relu" -> "68 /layer2/layer2.1/relu_2/Relu/fq_output_0" [label="[1, 512, 28, 28]", style=solid]; +"65 /layer2/layer2.1/conv1/Conv" -> "69 /layer2/layer2.1/relu/Relu" [label="[1, 128, 28, 28]", style=solid]; +"66 /layer1/layer1.2/conv3/Conv" -> "29 /layer1/layer1.2/Add" [label="[1, 256, 56, 56]", style=solid]; +"67 /layer2/layer2.0/relu_1/Relu/fq_output_0" -> "70 /layer2/layer2.0/conv3/Conv/WithoutBiases" [label="[1, 128, 28, 28]", style=solid]; +"68 /layer2/layer2.1/relu_2/Relu/fq_output_0" -> "71 /layer2/layer2.2/Add" [label="[1, 512, 28, 28]", style=solid]; +"68 /layer2/layer2.1/relu_2/Relu/fq_output_0" -> "72 /layer2/layer2.2/conv1/Conv/WithoutBiases" [label="[1, 512, 28, 28]", style=solid]; +"69 /layer2/layer2.1/relu/Relu" -> "73 /layer2/layer2.1/relu/Relu/fq_output_0" [label="[1, 128, 28, 28]", style=solid]; +"70 /layer2/layer2.0/conv3/Conv/WithoutBiases" -> "74 /layer2/layer2.0/conv3/Conv" [label="[1, 512, 28, 28]", style=solid]; +"71 /layer2/layer2.2/Add" -> "75 /layer2/layer2.2/relu_2/Relu" [label="[1, 512, 28, 28]", style=solid]; +"72 /layer2/layer2.2/conv1/Conv/WithoutBiases" -> "76 /layer2/layer2.2/conv1/Conv" [label="[1, 128, 28, 28]", style=solid]; +"73 /layer2/layer2.1/relu/Relu/fq_output_0" -> "77 /layer2/layer2.1/conv2/Conv/WithoutBiases" [label="[1, 128, 28, 28]", style=solid]; +"74 /layer2/layer2.0/conv3/Conv" -> "78 /layer2/layer2.0/conv3/Conv/fq_output_0" [label="[1, 512, 28, 28]", style=solid]; +"75 /layer2/layer2.2/relu_2/Relu" -> "79 /layer2/layer2.2/relu_2/Relu/fq_output_0" [label="[1, 512, 28, 28]", style=solid]; +"76 /layer2/layer2.2/conv1/Conv" -> "80 /layer2/layer2.2/relu/Relu" [label="[1, 128, 28, 28]", style=solid]; +"77 /layer2/layer2.1/conv2/Conv/WithoutBiases" -> "81 /layer2/layer2.1/conv2/Conv" [label="[1, 128, 28, 28]", style=solid]; +"78 /layer2/layer2.0/conv3/Conv/fq_output_0" -> "49 /layer2/layer2.0/Add" [label="[1, 512, 28, 28]", style=solid]; +"79 /layer2/layer2.2/relu_2/Relu/fq_output_0" -> "82 /layer2/layer2.3/Add" [label="[1, 512, 28, 28]", style=solid]; +"79 /layer2/layer2.2/relu_2/Relu/fq_output_0" -> "83 /layer2/layer2.3/conv1/Conv/WithoutBiases" [label="[1, 512, 28, 28]", style=solid]; +"80 /layer2/layer2.2/relu/Relu" -> "84 /layer2/layer2.2/relu/Relu/fq_output_0" [label="[1, 128, 28, 28]", style=solid]; +"81 /layer2/layer2.1/conv2/Conv" -> "85 /layer2/layer2.1/relu_1/Relu" [label="[1, 128, 28, 28]", style=solid]; +"82 /layer2/layer2.3/Add" -> "86 /layer2/layer2.3/relu_2/Relu" [label="[1, 512, 28, 28]", style=solid]; +"83 /layer2/layer2.3/conv1/Conv/WithoutBiases" -> "87 /layer2/layer2.3/conv1/Conv" [label="[1, 128, 28, 28]", style=solid]; +"84 /layer2/layer2.2/relu/Relu/fq_output_0" -> "88 /layer2/layer2.2/conv2/Conv/WithoutBiases" [label="[1, 128, 28, 28]", style=solid]; +"85 /layer2/layer2.1/relu_1/Relu" -> "89 /layer2/layer2.1/relu_1/Relu/fq_output_0" [label="[1, 128, 28, 28]", style=solid]; +"86 /layer2/layer2.3/relu_2/Relu" -> "90 /layer2/layer2.3/relu_2/Relu/fq_output_0" [label="[1, 512, 28, 28]", style=solid]; +"87 /layer2/layer2.3/conv1/Conv" -> "91 /layer2/layer2.3/relu/Relu" [label="[1, 128, 28, 28]", style=solid]; +"88 /layer2/layer2.2/conv2/Conv/WithoutBiases" -> "92 /layer2/layer2.2/conv2/Conv" [label="[1, 128, 28, 28]", style=solid]; +"89 /layer2/layer2.1/relu_1/Relu/fq_output_0" -> "93 /layer2/layer2.1/conv3/Conv/WithoutBiases" [label="[1, 128, 28, 28]", style=solid]; +"90 /layer2/layer2.3/relu_2/Relu/fq_output_0" -> "94 /layer3/layer3.0/conv1/Conv/WithoutBiases" [label="[1, 512, 28, 28]", style=solid]; +"90 /layer2/layer2.3/relu_2/Relu/fq_output_0" -> "95 /layer3/layer3.0/downsample/downsample.0/Conv/WithoutBiases" [label="[1, 512, 28, 28]", style=solid]; +"91 /layer2/layer2.3/relu/Relu" -> "96 /layer2/layer2.3/relu/Relu/fq_output_0" [label="[1, 128, 28, 28]", style=solid]; +"92 /layer2/layer2.2/conv2/Conv" -> "97 /layer2/layer2.2/relu_1/Relu" [label="[1, 128, 28, 28]", style=solid]; +"93 /layer2/layer2.1/conv3/Conv/WithoutBiases" -> "98 /layer2/layer2.1/conv3/Conv" [label="[1, 512, 28, 28]", style=solid]; +"94 /layer3/layer3.0/conv1/Conv/WithoutBiases" -> "99 /layer3/layer3.0/conv1/Conv" [label="[1, 256, 28, 28]", style=solid]; +"95 /layer3/layer3.0/downsample/downsample.0/Conv/WithoutBiases" -> "100 /layer3/layer3.0/downsample/downsample.0/Conv" [label="[1, 1024, 14, 14]", style=solid]; +"96 /layer2/layer2.3/relu/Relu/fq_output_0" -> "101 /layer2/layer2.3/conv2/Conv/WithoutBiases" [label="[1, 128, 28, 28]", style=solid]; +"97 /layer2/layer2.2/relu_1/Relu" -> "102 /layer2/layer2.2/relu_1/Relu/fq_output_0" [label="[1, 128, 28, 28]", style=solid]; +"98 /layer2/layer2.1/conv3/Conv" -> "60 /layer2/layer2.1/Add" [label="[1, 512, 28, 28]", style=solid]; +"99 /layer3/layer3.0/conv1/Conv" -> "103 /layer3/layer3.0/relu/Relu" [label="[1, 256, 28, 28]", style=solid]; +"100 /layer3/layer3.0/downsample/downsample.0/Conv" -> "104 /layer3/layer3.0/Add" [label="[1, 1024, 14, 14]", style=solid]; +"101 /layer2/layer2.3/conv2/Conv/WithoutBiases" -> "105 /layer2/layer2.3/conv2/Conv" [label="[1, 128, 28, 28]", style=solid]; +"102 /layer2/layer2.2/relu_1/Relu/fq_output_0" -> "106 /layer2/layer2.2/conv3/Conv/WithoutBiases" [label="[1, 128, 28, 28]", style=solid]; +"103 /layer3/layer3.0/relu/Relu" -> "107 /layer3/layer3.0/relu/Relu/fq_output_0" [label="[1, 256, 28, 28]", style=solid]; +"104 /layer3/layer3.0/Add" -> "108 /layer3/layer3.0/relu_2/Relu" [label="[1, 1024, 14, 14]", style=solid]; +"105 /layer2/layer2.3/conv2/Conv" -> "109 /layer2/layer2.3/relu_1/Relu" [label="[1, 128, 28, 28]", style=solid]; +"106 /layer2/layer2.2/conv3/Conv/WithoutBiases" -> "110 /layer2/layer2.2/conv3/Conv" [label="[1, 512, 28, 28]", style=solid]; +"107 /layer3/layer3.0/relu/Relu/fq_output_0" -> "111 /layer3/layer3.0/conv2/Conv/WithoutBiases" [label="[1, 256, 28, 28]", style=solid]; +"108 /layer3/layer3.0/relu_2/Relu" -> "112 /layer3/layer3.0/relu_2/Relu/fq_output_0" [label="[1, 1024, 14, 14]", style=solid]; +"109 /layer2/layer2.3/relu_1/Relu" -> "113 /layer2/layer2.3/relu_1/Relu/fq_output_0" [label="[1, 128, 28, 28]", style=solid]; +"110 /layer2/layer2.2/conv3/Conv" -> "71 /layer2/layer2.2/Add" [label="[1, 512, 28, 28]", style=solid]; +"111 /layer3/layer3.0/conv2/Conv/WithoutBiases" -> "114 /layer3/layer3.0/conv2/Conv" [label="[1, 256, 14, 14]", style=solid]; +"112 /layer3/layer3.0/relu_2/Relu/fq_output_0" -> "115 /layer3/layer3.1/Add" [label="[1, 1024, 14, 14]", style=solid]; +"112 /layer3/layer3.0/relu_2/Relu/fq_output_0" -> "116 /layer3/layer3.1/conv1/Conv/WithoutBiases" [label="[1, 1024, 14, 14]", style=solid]; +"113 /layer2/layer2.3/relu_1/Relu/fq_output_0" -> "117 /layer2/layer2.3/conv3/Conv/WithoutBiases" [label="[1, 128, 28, 28]", style=solid]; +"114 /layer3/layer3.0/conv2/Conv" -> "118 /layer3/layer3.0/relu_1/Relu" [label="[1, 256, 14, 14]", style=solid]; +"115 /layer3/layer3.1/Add" -> "119 /layer3/layer3.1/relu_2/Relu" [label="[1, 1024, 14, 14]", style=solid]; +"116 /layer3/layer3.1/conv1/Conv/WithoutBiases" -> "120 /layer3/layer3.1/conv1/Conv" [label="[1, 256, 14, 14]", style=solid]; +"117 /layer2/layer2.3/conv3/Conv/WithoutBiases" -> "121 /layer2/layer2.3/conv3/Conv" [label="[1, 512, 28, 28]", style=solid]; +"118 /layer3/layer3.0/relu_1/Relu" -> "122 /layer3/layer3.0/relu_1/Relu/fq_output_0" [label="[1, 256, 14, 14]", style=solid]; +"119 /layer3/layer3.1/relu_2/Relu" -> "123 /layer3/layer3.1/relu_2/Relu/fq_output_0" [label="[1, 1024, 14, 14]", style=solid]; +"120 /layer3/layer3.1/conv1/Conv" -> "124 /layer3/layer3.1/relu/Relu" [label="[1, 256, 14, 14]", style=solid]; +"121 /layer2/layer2.3/conv3/Conv" -> "82 /layer2/layer2.3/Add" [label="[1, 512, 28, 28]", style=solid]; +"122 /layer3/layer3.0/relu_1/Relu/fq_output_0" -> "125 /layer3/layer3.0/conv3/Conv/WithoutBiases" [label="[1, 256, 14, 14]", style=solid]; +"123 /layer3/layer3.1/relu_2/Relu/fq_output_0" -> "126 /layer3/layer3.2/Add" [label="[1, 1024, 14, 14]", style=solid]; +"123 /layer3/layer3.1/relu_2/Relu/fq_output_0" -> "127 /layer3/layer3.2/conv1/Conv/WithoutBiases" [label="[1, 1024, 14, 14]", style=solid]; +"124 /layer3/layer3.1/relu/Relu" -> "128 /layer3/layer3.1/relu/Relu/fq_output_0" [label="[1, 256, 14, 14]", style=solid]; +"125 /layer3/layer3.0/conv3/Conv/WithoutBiases" -> "129 /layer3/layer3.0/conv3/Conv" [label="[1, 1024, 14, 14]", style=solid]; +"126 /layer3/layer3.2/Add" -> "130 /layer3/layer3.2/relu_2/Relu" [label="[1, 1024, 14, 14]", style=solid]; +"127 /layer3/layer3.2/conv1/Conv/WithoutBiases" -> "131 /layer3/layer3.2/conv1/Conv" [label="[1, 256, 14, 14]", style=solid]; +"128 /layer3/layer3.1/relu/Relu/fq_output_0" -> "132 /layer3/layer3.1/conv2/Conv/WithoutBiases" [label="[1, 256, 14, 14]", style=solid]; +"129 /layer3/layer3.0/conv3/Conv" -> "133 /layer3/layer3.0/conv3/Conv/fq_output_0" [label="[1, 1024, 14, 14]", style=solid]; +"130 /layer3/layer3.2/relu_2/Relu" -> "134 /layer3/layer3.2/relu_2/Relu/fq_output_0" [label="[1, 1024, 14, 14]", style=solid]; +"131 /layer3/layer3.2/conv1/Conv" -> "135 /layer3/layer3.2/relu/Relu" [label="[1, 256, 14, 14]", style=solid]; +"132 /layer3/layer3.1/conv2/Conv/WithoutBiases" -> "136 /layer3/layer3.1/conv2/Conv" [label="[1, 256, 14, 14]", style=solid]; +"133 /layer3/layer3.0/conv3/Conv/fq_output_0" -> "104 /layer3/layer3.0/Add" [label="[1, 1024, 14, 14]", style=solid]; +"134 /layer3/layer3.2/relu_2/Relu/fq_output_0" -> "137 /layer3/layer3.3/Add" [label="[1, 1024, 14, 14]", style=solid]; +"134 /layer3/layer3.2/relu_2/Relu/fq_output_0" -> "138 /layer3/layer3.3/conv1/Conv/WithoutBiases" [label="[1, 1024, 14, 14]", style=solid]; +"135 /layer3/layer3.2/relu/Relu" -> "139 /layer3/layer3.2/relu/Relu/fq_output_0" [label="[1, 256, 14, 14]", style=solid]; +"136 /layer3/layer3.1/conv2/Conv" -> "140 /layer3/layer3.1/relu_1/Relu" [label="[1, 256, 14, 14]", style=solid]; +"137 /layer3/layer3.3/Add" -> "141 /layer3/layer3.3/relu_2/Relu" [label="[1, 1024, 14, 14]", style=solid]; +"138 /layer3/layer3.3/conv1/Conv/WithoutBiases" -> "142 /layer3/layer3.3/conv1/Conv" [label="[1, 256, 14, 14]", style=solid]; +"139 /layer3/layer3.2/relu/Relu/fq_output_0" -> "143 /layer3/layer3.2/conv2/Conv/WithoutBiases" [label="[1, 256, 14, 14]", style=solid]; +"140 /layer3/layer3.1/relu_1/Relu" -> "144 /layer3/layer3.1/relu_1/Relu/fq_output_0" [label="[1, 256, 14, 14]", style=solid]; +"141 /layer3/layer3.3/relu_2/Relu" -> "145 /layer3/layer3.3/relu_2/Relu/fq_output_0" [label="[1, 1024, 14, 14]", style=solid]; +"142 /layer3/layer3.3/conv1/Conv" -> "146 /layer3/layer3.3/relu/Relu" [label="[1, 256, 14, 14]", style=solid]; +"143 /layer3/layer3.2/conv2/Conv/WithoutBiases" -> "147 /layer3/layer3.2/conv2/Conv" [label="[1, 256, 14, 14]", style=solid]; +"144 /layer3/layer3.1/relu_1/Relu/fq_output_0" -> "148 /layer3/layer3.1/conv3/Conv/WithoutBiases" [label="[1, 256, 14, 14]", style=solid]; +"145 /layer3/layer3.3/relu_2/Relu/fq_output_0" -> "149 /layer3/layer3.4/Add" [label="[1, 1024, 14, 14]", style=solid]; +"145 /layer3/layer3.3/relu_2/Relu/fq_output_0" -> "150 /layer3/layer3.4/conv1/Conv/WithoutBiases" [label="[1, 1024, 14, 14]", style=solid]; +"146 /layer3/layer3.3/relu/Relu" -> "151 /layer3/layer3.3/relu/Relu/fq_output_0" [label="[1, 256, 14, 14]", style=solid]; +"147 /layer3/layer3.2/conv2/Conv" -> "152 /layer3/layer3.2/relu_1/Relu" [label="[1, 256, 14, 14]", style=solid]; +"148 /layer3/layer3.1/conv3/Conv/WithoutBiases" -> "153 /layer3/layer3.1/conv3/Conv" [label="[1, 1024, 14, 14]", style=solid]; +"149 /layer3/layer3.4/Add" -> "154 /layer3/layer3.4/relu_2/Relu" [label="[1, 1024, 14, 14]", style=solid]; +"150 /layer3/layer3.4/conv1/Conv/WithoutBiases" -> "155 /layer3/layer3.4/conv1/Conv" [label="[1, 256, 14, 14]", style=solid]; +"151 /layer3/layer3.3/relu/Relu/fq_output_0" -> "156 /layer3/layer3.3/conv2/Conv/WithoutBiases" [label="[1, 256, 14, 14]", style=solid]; +"152 /layer3/layer3.2/relu_1/Relu" -> "157 /layer3/layer3.2/relu_1/Relu/fq_output_0" [label="[1, 256, 14, 14]", style=solid]; +"153 /layer3/layer3.1/conv3/Conv" -> "115 /layer3/layer3.1/Add" [label="[1, 1024, 14, 14]", style=solid]; +"154 /layer3/layer3.4/relu_2/Relu" -> "158 /layer3/layer3.4/relu_2/Relu/fq_output_0" [label="[1, 1024, 14, 14]", style=solid]; +"155 /layer3/layer3.4/conv1/Conv" -> "159 /layer3/layer3.4/relu/Relu" [label="[1, 256, 14, 14]", style=solid]; +"156 /layer3/layer3.3/conv2/Conv/WithoutBiases" -> "160 /layer3/layer3.3/conv2/Conv" [label="[1, 256, 14, 14]", style=solid]; +"157 /layer3/layer3.2/relu_1/Relu/fq_output_0" -> "161 /layer3/layer3.2/conv3/Conv/WithoutBiases" [label="[1, 256, 14, 14]", style=solid]; +"158 /layer3/layer3.4/relu_2/Relu/fq_output_0" -> "162 /layer3/layer3.5/Add" [label="[1, 1024, 14, 14]", style=solid]; +"158 /layer3/layer3.4/relu_2/Relu/fq_output_0" -> "163 /layer3/layer3.5/conv1/Conv/WithoutBiases" [label="[1, 1024, 14, 14]", style=solid]; +"159 /layer3/layer3.4/relu/Relu" -> "164 /layer3/layer3.4/relu/Relu/fq_output_0" [label="[1, 256, 14, 14]", style=solid]; +"160 /layer3/layer3.3/conv2/Conv" -> "165 /layer3/layer3.3/relu_1/Relu" [label="[1, 256, 14, 14]", style=solid]; +"161 /layer3/layer3.2/conv3/Conv/WithoutBiases" -> "166 /layer3/layer3.2/conv3/Conv" [label="[1, 1024, 14, 14]", style=solid]; +"162 /layer3/layer3.5/Add" -> "167 /layer3/layer3.5/relu_2/Relu" [label="[1, 1024, 14, 14]", style=solid]; +"163 /layer3/layer3.5/conv1/Conv/WithoutBiases" -> "168 /layer3/layer3.5/conv1/Conv" [label="[1, 256, 14, 14]", style=solid]; +"164 /layer3/layer3.4/relu/Relu/fq_output_0" -> "169 /layer3/layer3.4/conv2/Conv/WithoutBiases" [label="[1, 256, 14, 14]", style=solid]; +"165 /layer3/layer3.3/relu_1/Relu" -> "170 /layer3/layer3.3/relu_1/Relu/fq_output_0" [label="[1, 256, 14, 14]", style=solid]; +"166 /layer3/layer3.2/conv3/Conv" -> "126 /layer3/layer3.2/Add" [label="[1, 1024, 14, 14]", style=solid]; +"167 /layer3/layer3.5/relu_2/Relu" -> "171 /layer3/layer3.5/relu_2/Relu/fq_output_0" [label="[1, 1024, 14, 14]", style=solid]; 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/layer3/layer3.5/conv2/Conv/WithoutBiases" -> "186 /layer3/layer3.5/conv2/Conv" [label="[1, 256, 14, 14]", style=solid]; +"183 /layer3/layer3.4/relu_1/Relu/fq_output_0" -> "187 /layer3/layer3.4/conv3/Conv/WithoutBiases" [label="[1, 256, 14, 14]", style=solid]; +"184 /layer4/layer4.0/relu/Relu" -> "188 /layer4/layer4.0/relu/Relu/fq_output_0" [label="[1, 512, 14, 14]", style=solid]; +"185 /layer4/layer4.0/Add" -> "189 /layer4/layer4.0/relu_2/Relu" [label="[1, 2048, 7, 7]", style=solid]; +"186 /layer3/layer3.5/conv2/Conv" -> "190 /layer3/layer3.5/relu_1/Relu" [label="[1, 256, 14, 14]", style=solid]; +"187 /layer3/layer3.4/conv3/Conv/WithoutBiases" -> "191 /layer3/layer3.4/conv3/Conv" [label="[1, 1024, 14, 14]", style=solid]; +"188 /layer4/layer4.0/relu/Relu/fq_output_0" -> "192 /layer4/layer4.0/conv2/Conv/WithoutBiases" [label="[1, 512, 14, 14]", style=solid]; +"189 /layer4/layer4.0/relu_2/Relu" -> "193 /layer4/layer4.0/relu_2/Relu/fq_output_0" [label="[1, 2048, 7, 7]", style=solid]; 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/layer4/layer4.1/relu_1/Relu" -> "223 /layer4/layer4.1/relu_1/Relu/fq_output_0" [label="[1, 512, 7, 7]", style=solid]; +"221 /avgpool/GlobalAveragePool/fq_output_0" -> "224 /Flatten" [label="[1, 2048, 1, 1]", style=solid]; +"222 /layer4/layer4.2/conv2/Conv/WithoutBiases" -> "225 /layer4/layer4.2/conv2/Conv" [label="[1, 512, 7, 7]", style=solid]; +"223 /layer4/layer4.1/relu_1/Relu/fq_output_0" -> "226 /layer4/layer4.1/conv3/Conv/WithoutBiases" [label="[1, 512, 7, 7]", style=solid]; +"224 /Flatten" -> "227 /fc/Gemm/WithoutBiases" [label="[1, 2048]", style=solid]; +"225 /layer4/layer4.2/conv2/Conv" -> "228 /layer4/layer4.2/relu_1/Relu" [label="[1, 512, 7, 7]", style=solid]; +"226 /layer4/layer4.1/conv3/Conv/WithoutBiases" -> "229 /layer4/layer4.1/conv3/Conv" [label="[1, 2048, 7, 7]", style=solid]; +"227 /fc/Gemm/WithoutBiases" -> "230 prob" [label="[1, 1000]", style=solid]; +"228 /layer4/layer4.2/relu_1/Relu" -> "231 /layer4/layer4.2/relu_1/Relu/fq_output_0" [label="[1, 512, 7, 7]", style=solid]; +"229 /layer4/layer4.1/conv3/Conv" -> "196 /layer4/layer4.1/Add" [label="[1, 2048, 7, 7]", style=solid]; +"230 prob" -> "232 prob/sink_port_0" [label="[1, 1000]", style=solid]; +"231 /layer4/layer4.2/relu_1/Relu/fq_output_0" -> "233 /layer4/layer4.2/conv3/Conv/WithoutBiases" [label="[1, 512, 7, 7]", style=solid]; +"233 /layer4/layer4.2/conv3/Conv/WithoutBiases" -> "234 /layer4/layer4.2/conv3/Conv" [label="[1, 2048, 7, 7]", style=solid]; +"234 /layer4/layer4.2/conv3/Conv" -> "207 /layer4/layer4.2/Add" [label="[1, 2048, 7, 7]", style=solid]; +"235 Constant_2819" -> "230 prob" [label="[1, 1000]", style=solid]; +"236 /fc/Gemm/WithoutBiases/fq_weights_1" -> "227 /fc/Gemm/WithoutBiases" [label="[1000, 2048]", style=solid]; +"237 Constant_62386" -> "236 /fc/Gemm/WithoutBiases/fq_weights_1" [label="[1000, 1]", style=solid]; +"238 Constant_62385" -> "236 /fc/Gemm/WithoutBiases/fq_weights_1" [label="[1000, 1]", style=solid]; +"239 Constant_62384" -> "236 /fc/Gemm/WithoutBiases/fq_weights_1" [label="[1000, 1]", style=solid]; +"240 Constant_62383" -> "236 /fc/Gemm/WithoutBiases/fq_weights_1" [label="[1000, 1]", style=solid]; +"241 fc.weight" -> "236 /fc/Gemm/WithoutBiases/fq_weights_1" [label="[1000, 2048]", style=solid]; +"242 Constant_981" -> "224 /Flatten" [label="[2]", style=dashed]; +"243 Constant_62381" -> "221 /avgpool/GlobalAveragePool/fq_output_0" [label="[]", style=solid]; +"244 Constant_62380" -> "221 /avgpool/GlobalAveragePool/fq_output_0" [label="[]", style=solid]; +"245 Constant_62379" -> "221 /avgpool/GlobalAveragePool/fq_output_0" [label="[]", style=solid]; +"246 Constant_62378" -> "221 /avgpool/GlobalAveragePool/fq_output_0" [label="[]", style=solid]; +"247 Range_977" -> "218 /avgpool/GlobalAveragePool" [label="[2]", style=dashed]; +"248 Constant_62376" -> "215 /layer4/layer4.2/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"249 Constant_62375" -> "215 /layer4/layer4.2/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"250 Constant_62374" -> "215 /layer4/layer4.2/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"251 Constant_62373" -> "215 /layer4/layer4.2/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"252 Constant_62341" -> "204 /layer4/layer4.1/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"253 Constant_62340" -> "204 /layer4/layer4.1/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"254 Constant_62339" -> "204 /layer4/layer4.1/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"255 Constant_62338" -> "204 /layer4/layer4.1/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"256 Constant_62306" -> "193 /layer4/layer4.0/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"257 Constant_62305" -> "193 /layer4/layer4.0/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"258 Constant_62304" -> "193 /layer4/layer4.0/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"259 Constant_62303" -> "193 /layer4/layer4.0/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"260 Reshape_867" -> "181 /layer4/layer4.0/downsample/downsample.0/Conv" [label="[1, 2048, 1, 1]", style=solid]; +"261 /layer4/layer4.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" -> "176 /layer4/layer4.0/downsample/downsample.0/Conv/WithoutBiases" [label="[2048, 1024, 1, 1]", style=solid]; +"262 Constant_62266" -> "261 /layer4/layer4.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[2048, 1, 1, 1]", style=solid]; +"263 Constant_62265" -> "261 /layer4/layer4.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[2048, 1, 1, 1]", style=solid]; +"264 Constant_62264" -> "261 /layer4/layer4.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[2048, 1, 1, 1]", style=solid]; +"265 Constant_62263" -> "261 /layer4/layer4.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[2048, 1, 1, 1]", style=solid]; +"266 onnx^^Conv_635" -> "261 /layer4/layer4.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[2048, 1024, 1, 1]", style=solid]; +"267 Constant_62261" -> "171 /layer3/layer3.5/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"268 Constant_62260" -> "171 /layer3/layer3.5/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"269 Constant_62259" -> "171 /layer3/layer3.5/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"270 Constant_62258" -> "171 /layer3/layer3.5/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"271 Constant_62226" -> "158 /layer3/layer3.4/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"272 Constant_62225" -> "158 /layer3/layer3.4/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"273 Constant_62224" -> "158 /layer3/layer3.4/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"274 Constant_62223" -> "158 /layer3/layer3.4/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"275 Constant_62191" -> "145 /layer3/layer3.3/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"276 Constant_62190" -> "145 /layer3/layer3.3/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"277 Constant_62189" -> "145 /layer3/layer3.3/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"278 Constant_62188" -> "145 /layer3/layer3.3/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"279 Constant_62156" -> "134 /layer3/layer3.2/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"280 Constant_62155" -> "134 /layer3/layer3.2/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"281 Constant_62154" -> "134 /layer3/layer3.2/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"282 Constant_62153" -> "134 /layer3/layer3.2/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"283 Constant_62121" -> "123 /layer3/layer3.1/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"284 Constant_62120" -> "123 /layer3/layer3.1/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"285 Constant_62119" -> "123 /layer3/layer3.1/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"286 Constant_62118" -> "123 /layer3/layer3.1/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"287 Constant_62086" -> "112 /layer3/layer3.0/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"288 Constant_62085" -> "112 /layer3/layer3.0/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"289 Constant_62084" -> "112 /layer3/layer3.0/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"290 Constant_62083" -> "112 /layer3/layer3.0/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"291 Reshape_558" -> "100 /layer3/layer3.0/downsample/downsample.0/Conv" [label="[1, 1024, 1, 1]", style=solid]; +"292 /layer3/layer3.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" -> "95 /layer3/layer3.0/downsample/downsample.0/Conv/WithoutBiases" [label="[1024, 512, 1, 1]", style=solid]; +"293 Constant_62046" -> "292 /layer3/layer3.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; +"294 Constant_62045" -> "292 /layer3/layer3.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; +"295 Constant_62044" -> "292 /layer3/layer3.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; +"296 Constant_62043" -> "292 /layer3/layer3.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; +"297 onnx^^Conv_578" -> "292 /layer3/layer3.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[1024, 512, 1, 1]", style=solid]; +"298 Constant_62041" -> "90 /layer2/layer2.3/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"299 Constant_62040" -> "90 /layer2/layer2.3/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"300 Constant_62039" -> "90 /layer2/layer2.3/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"301 Constant_62038" -> "90 /layer2/layer2.3/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"302 Constant_62006" -> "79 /layer2/layer2.2/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"303 Constant_62005" -> "79 /layer2/layer2.2/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"304 Constant_62004" -> "79 /layer2/layer2.2/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"305 Constant_62003" -> "79 /layer2/layer2.2/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"306 Constant_61971" -> "68 /layer2/layer2.1/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"307 Constant_61970" -> "68 /layer2/layer2.1/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"308 Constant_61969" -> "68 /layer2/layer2.1/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"309 Constant_61968" -> "68 /layer2/layer2.1/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"310 Constant_61936" -> "57 /layer2/layer2.0/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"311 Constant_61935" -> "57 /layer2/layer2.0/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"312 Constant_61934" -> "57 /layer2/layer2.0/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"313 Constant_61933" -> "57 /layer2/layer2.0/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"314 Reshape_347" -> "45 /layer2/layer2.0/downsample/downsample.0/Conv" [label="[1, 512, 1, 1]", style=solid]; +"315 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" -> "41 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases" [label="[512, 256, 1, 1]", style=solid]; +"316 Constant_61896" -> "315 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"317 Constant_61895" -> "315 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"318 Constant_61894" -> "315 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"319 Constant_61893" -> "315 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"320 onnx^^Conv_539" -> "315 /layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[512, 256, 1, 1]", style=solid]; +"321 Constant_61891" -> "37 /layer1/layer1.2/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"322 Constant_61890" -> "37 /layer1/layer1.2/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"323 Constant_61889" -> "37 /layer1/layer1.2/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"324 Constant_61888" -> "37 /layer1/layer1.2/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"325 Constant_61856" -> "26 /layer1/layer1.1/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"326 Constant_61855" -> "26 /layer1/layer1.1/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"327 Constant_61854" -> "26 /layer1/layer1.1/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"328 Constant_61853" -> "26 /layer1/layer1.1/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"329 Constant_61821" -> "18 /layer1/layer1.0/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"330 Constant_61820" -> "18 /layer1/layer1.0/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"331 Constant_61819" -> "18 /layer1/layer1.0/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"332 Constant_61818" -> "18 /layer1/layer1.0/relu_2/Relu/fq_output_0" [label="[]", style=solid]; +"333 Reshape_185" -> "12 /layer1/layer1.0/downsample/downsample.0/Conv" [label="[1, 256, 1, 1]", style=solid]; +"334 /layer1/layer1.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" -> "10 /layer1/layer1.0/downsample/downsample.0/Conv/WithoutBiases" [label="[256, 64, 1, 1]", style=solid]; +"335 Constant_61781" -> "334 /layer1/layer1.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"336 Constant_61780" -> "334 /layer1/layer1.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"337 Constant_61779" -> "334 /layer1/layer1.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"338 Constant_61778" -> "334 /layer1/layer1.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"339 onnx^^Conv_509" -> "334 /layer1/layer1.0/downsample/downsample.0/Conv/WithoutBiases/fq_weights_1" [label="[256, 64, 1, 1]", style=solid]; +"340 Constant_61776" -> "7 /relu/Relu/fq_output_0" [label="[]", style=solid]; +"341 Constant_61775" -> "7 /relu/Relu/fq_output_0" [label="[]", style=solid]; +"342 Constant_61774" -> "7 /relu/Relu/fq_output_0" [label="[]", style=solid]; +"343 Constant_61773" -> "7 /relu/Relu/fq_output_0" [label="[]", style=solid]; +"344 Reshape_121" -> "5 /conv1/Conv" [label="[1, 64, 1, 1]", style=solid]; +"345 /conv1/Conv/WithoutBiases/fq_weights_1" -> "4 /conv1/Conv/WithoutBiases" [label="[64, 3, 7, 7]", style=solid]; +"346 Constant_61771" -> "345 /conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"347 Constant_61770" -> "345 /conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"348 Constant_61769" -> "345 /conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"349 Constant_61768" -> "345 /conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"350 Gather_2818" -> "345 /conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 3, 7, 7]", style=solid]; +"351 Constant_61766" -> "3 Divide_1002/fq_output_0" [label="[]", style=solid]; +"352 Constant_61765" -> "3 Divide_1002/fq_output_0" [label="[]", style=solid]; +"353 Constant_61764" -> "3 Divide_1002/fq_output_0" [label="[]", style=solid]; +"354 Constant_61763" -> "3 Divide_1002/fq_output_0" [label="[]", style=solid]; +"355 Gather_2815" -> "2 Divide_1002" [label="[1, 3, 1, 1]", style=solid]; +"356 Gather_2812" -> "1 Multiply_2745" [label="[1, 3, 1, 1]", style=solid]; +"357 Constant_61816" -> "36 /layer1/layer1.0/conv3/Conv/fq_output_0" [label="[]", style=solid]; +"358 Constant_61815" -> "36 /layer1/layer1.0/conv3/Conv/fq_output_0" [label="[]", style=solid]; +"359 Constant_61814" -> "36 /layer1/layer1.0/conv3/Conv/fq_output_0" [label="[]", style=solid]; +"360 Constant_61813" -> "36 /layer1/layer1.0/conv3/Conv/fq_output_0" [label="[]", style=solid]; +"361 Reshape_170" -> "32 /layer1/layer1.0/conv3/Conv" [label="[1, 256, 1, 1]", style=solid]; +"362 /layer1/layer1.0/conv3/Conv/WithoutBiases/fq_weights_1" -> "28 /layer1/layer1.0/conv3/Conv/WithoutBiases" [label="[256, 64, 1, 1]", style=solid]; +"363 Constant_61811" -> "362 /layer1/layer1.0/conv3/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"364 Constant_61810" -> "362 /layer1/layer1.0/conv3/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"365 Constant_61809" -> "362 /layer1/layer1.0/conv3/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"366 Constant_61808" -> "362 /layer1/layer1.0/conv3/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"367 onnx^^Conv_506" -> "362 /layer1/layer1.0/conv3/Conv/WithoutBiases/fq_weights_1" [label="[256, 64, 1, 1]", style=solid]; +"368 Constant_61806" -> "25 /layer1/layer1.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"369 Constant_61805" -> "25 /layer1/layer1.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"370 Constant_61804" -> "25 /layer1/layer1.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"371 Constant_61803" -> "25 /layer1/layer1.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"372 Reshape_154" -> "19 /layer1/layer1.0/conv2/Conv" [label="[1, 64, 1, 1]", style=solid]; +"373 /layer1/layer1.0/conv2/Conv/WithoutBiases/fq_weights_1" -> "17 /layer1/layer1.0/conv2/Conv/WithoutBiases" [label="[64, 64, 3, 3]", style=solid]; +"374 Constant_61801" -> "373 /layer1/layer1.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"375 Constant_61800" -> "373 /layer1/layer1.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"376 Constant_61799" -> "373 /layer1/layer1.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"377 Constant_61798" -> "373 /layer1/layer1.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"378 onnx^^Conv_503" -> "373 /layer1/layer1.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 64, 3, 3]", style=solid]; +"379 Constant_61796" -> "15 /layer1/layer1.0/relu/Relu/fq_output_0" [label="[]", style=solid]; +"380 Constant_61795" -> "15 /layer1/layer1.0/relu/Relu/fq_output_0" [label="[]", style=solid]; +"381 Constant_61794" -> "15 /layer1/layer1.0/relu/Relu/fq_output_0" [label="[]", style=solid]; +"382 Constant_61793" -> "15 /layer1/layer1.0/relu/Relu/fq_output_0" [label="[]", style=solid]; +"383 Reshape_138" -> "11 /layer1/layer1.0/conv1/Conv" [label="[1, 64, 1, 1]", style=solid]; +"384 /layer1/layer1.0/conv1/Conv/WithoutBiases/fq_weights_1" -> "9 /layer1/layer1.0/conv1/Conv/WithoutBiases" [label="[64, 64, 1, 1]", style=solid]; +"385 Constant_61791" -> "384 /layer1/layer1.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"386 Constant_61790" -> "384 /layer1/layer1.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"387 Constant_61789" -> "384 /layer1/layer1.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"388 Constant_61788" -> "384 /layer1/layer1.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"389 onnx^^Conv_500" -> "384 /layer1/layer1.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 64, 1, 1]", style=solid]; +"390 Reshape_234" -> "55 /layer1/layer1.1/conv3/Conv" [label="[1, 256, 1, 1]", style=solid]; +"391 /layer1/layer1.1/conv3/Conv/WithoutBiases/fq_weights_1" -> "51 /layer1/layer1.1/conv3/Conv/WithoutBiases" [label="[256, 64, 1, 1]", style=solid]; +"392 Constant_61846" -> "391 /layer1/layer1.1/conv3/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"393 Constant_61845" -> "391 /layer1/layer1.1/conv3/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"394 Constant_61844" -> "391 /layer1/layer1.1/conv3/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"395 Constant_61843" -> "391 /layer1/layer1.1/conv3/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"396 onnx^^Conv_518" -> "391 /layer1/layer1.1/conv3/Conv/WithoutBiases/fq_weights_1" [label="[256, 64, 1, 1]", style=solid]; +"397 Constant_61841" -> "47 /layer1/layer1.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"398 Constant_61840" -> "47 /layer1/layer1.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"399 Constant_61839" -> "47 /layer1/layer1.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"400 Constant_61838" -> "47 /layer1/layer1.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"401 Reshape_218" -> "39 /layer1/layer1.1/conv2/Conv" [label="[1, 64, 1, 1]", style=solid]; +"402 /layer1/layer1.1/conv2/Conv/WithoutBiases/fq_weights_1" -> "35 /layer1/layer1.1/conv2/Conv/WithoutBiases" [label="[64, 64, 3, 3]", style=solid]; +"403 Constant_61836" -> "402 /layer1/layer1.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"404 Constant_61835" -> "402 /layer1/layer1.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"405 Constant_61834" -> "402 /layer1/layer1.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"406 Constant_61833" -> "402 /layer1/layer1.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"407 onnx^^Conv_515" -> "402 /layer1/layer1.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 64, 3, 3]", style=solid]; +"408 Constant_61831" -> "31 /layer1/layer1.1/relu/Relu/fq_output_0" [label="[]", style=solid]; +"409 Constant_61830" -> "31 /layer1/layer1.1/relu/Relu/fq_output_0" [label="[]", style=solid]; +"410 Constant_61829" -> "31 /layer1/layer1.1/relu/Relu/fq_output_0" [label="[]", style=solid]; +"411 Constant_61828" -> "31 /layer1/layer1.1/relu/Relu/fq_output_0" [label="[]", style=solid]; +"412 Reshape_202" -> "24 /layer1/layer1.1/conv1/Conv" [label="[1, 64, 1, 1]", style=solid]; +"413 /layer1/layer1.1/conv1/Conv/WithoutBiases/fq_weights_1" -> "21 /layer1/layer1.1/conv1/Conv/WithoutBiases" [label="[64, 256, 1, 1]", style=solid]; +"414 Constant_61826" -> "413 /layer1/layer1.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"415 Constant_61825" -> "413 /layer1/layer1.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"416 Constant_61824" -> "413 /layer1/layer1.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"417 Constant_61823" -> "413 /layer1/layer1.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"418 onnx^^Conv_512" -> "413 /layer1/layer1.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 256, 1, 1]", style=solid]; +"419 Reshape_283" -> "66 /layer1/layer1.2/conv3/Conv" [label="[1, 256, 1, 1]", style=solid]; +"420 /layer1/layer1.2/conv3/Conv/WithoutBiases/fq_weights_1" -> "62 /layer1/layer1.2/conv3/Conv/WithoutBiases" [label="[256, 64, 1, 1]", style=solid]; +"421 Constant_61881" -> "420 /layer1/layer1.2/conv3/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"422 Constant_61880" -> "420 /layer1/layer1.2/conv3/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"423 Constant_61879" -> "420 /layer1/layer1.2/conv3/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"424 Constant_61878" -> "420 /layer1/layer1.2/conv3/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"425 onnx^^Conv_527" -> "420 /layer1/layer1.2/conv3/Conv/WithoutBiases/fq_weights_1" [label="[256, 64, 1, 1]", style=solid]; +"426 Constant_61876" -> "58 /layer1/layer1.2/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"427 Constant_61875" -> "58 /layer1/layer1.2/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"428 Constant_61874" -> "58 /layer1/layer1.2/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"429 Constant_61873" -> "58 /layer1/layer1.2/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"430 Reshape_267" -> "50 /layer1/layer1.2/conv2/Conv" [label="[1, 64, 1, 1]", style=solid]; +"431 /layer1/layer1.2/conv2/Conv/WithoutBiases/fq_weights_1" -> "46 /layer1/layer1.2/conv2/Conv/WithoutBiases" [label="[64, 64, 3, 3]", style=solid]; +"432 Constant_61871" -> "431 /layer1/layer1.2/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"433 Constant_61870" -> "431 /layer1/layer1.2/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"434 Constant_61869" -> "431 /layer1/layer1.2/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"435 Constant_61868" -> "431 /layer1/layer1.2/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"436 onnx^^Conv_524" -> "431 /layer1/layer1.2/conv2/Conv/WithoutBiases/fq_weights_1" [label="[64, 64, 3, 3]", style=solid]; +"437 Constant_61866" -> "42 /layer1/layer1.2/relu/Relu/fq_output_0" [label="[]", style=solid]; +"438 Constant_61865" -> "42 /layer1/layer1.2/relu/Relu/fq_output_0" [label="[]", style=solid]; +"439 Constant_61864" -> "42 /layer1/layer1.2/relu/Relu/fq_output_0" [label="[]", style=solid]; +"440 Constant_61863" -> "42 /layer1/layer1.2/relu/Relu/fq_output_0" [label="[]", style=solid]; +"441 Reshape_251" -> "34 /layer1/layer1.2/conv1/Conv" [label="[1, 64, 1, 1]", style=solid]; +"442 /layer1/layer1.2/conv1/Conv/WithoutBiases/fq_weights_1" -> "30 /layer1/layer1.2/conv1/Conv/WithoutBiases" [label="[64, 256, 1, 1]", style=solid]; +"443 Constant_61861" -> "442 /layer1/layer1.2/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"444 Constant_61860" -> "442 /layer1/layer1.2/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"445 Constant_61859" -> "442 /layer1/layer1.2/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"446 Constant_61858" -> "442 /layer1/layer1.2/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"447 onnx^^Conv_521" -> "442 /layer1/layer1.2/conv1/Conv/WithoutBiases/fq_weights_1" [label="[64, 256, 1, 1]", style=solid]; +"448 Constant_61931" -> "78 /layer2/layer2.0/conv3/Conv/fq_output_0" [label="[]", style=solid]; +"449 Constant_61930" -> "78 /layer2/layer2.0/conv3/Conv/fq_output_0" [label="[]", style=solid]; +"450 Constant_61929" -> "78 /layer2/layer2.0/conv3/Conv/fq_output_0" [label="[]", style=solid]; +"451 Constant_61928" -> "78 /layer2/layer2.0/conv3/Conv/fq_output_0" [label="[]", style=solid]; +"452 Reshape_332" -> "74 /layer2/layer2.0/conv3/Conv" [label="[1, 512, 1, 1]", style=solid]; +"453 /layer2/layer2.0/conv3/Conv/WithoutBiases/fq_weights_1" -> "70 /layer2/layer2.0/conv3/Conv/WithoutBiases" [label="[512, 128, 1, 1]", style=solid]; +"454 Constant_61926" -> "453 /layer2/layer2.0/conv3/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"455 Constant_61925" -> "453 /layer2/layer2.0/conv3/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"456 Constant_61924" -> "453 /layer2/layer2.0/conv3/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"457 Constant_61923" -> "453 /layer2/layer2.0/conv3/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"458 onnx^^Conv_536" -> "453 /layer2/layer2.0/conv3/Conv/WithoutBiases/fq_weights_1" [label="[512, 128, 1, 1]", style=solid]; +"459 Constant_61921" -> "67 /layer2/layer2.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"460 Constant_61920" -> "67 /layer2/layer2.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"461 Constant_61919" -> "67 /layer2/layer2.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"462 Constant_61918" -> "67 /layer2/layer2.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"463 Reshape_316" -> "59 /layer2/layer2.0/conv2/Conv" [label="[1, 128, 1, 1]", style=solid]; +"464 /layer2/layer2.0/conv2/Conv/WithoutBiases/fq_weights_1" -> "56 /layer2/layer2.0/conv2/Conv/WithoutBiases" [label="[128, 128, 3, 3]", style=solid]; +"465 Constant_61916" -> "464 /layer2/layer2.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"466 Constant_61915" -> "464 /layer2/layer2.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"467 Constant_61914" -> "464 /layer2/layer2.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"468 Constant_61913" -> "464 /layer2/layer2.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"469 onnx^^Conv_533" -> "464 /layer2/layer2.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 128, 3, 3]", style=solid]; +"470 Constant_61911" -> "52 /layer2/layer2.0/relu/Relu/fq_output_0" [label="[]", style=solid]; +"471 Constant_61910" -> "52 /layer2/layer2.0/relu/Relu/fq_output_0" [label="[]", style=solid]; +"472 Constant_61909" -> "52 /layer2/layer2.0/relu/Relu/fq_output_0" [label="[]", style=solid]; +"473 Constant_61908" -> "52 /layer2/layer2.0/relu/Relu/fq_output_0" [label="[]", style=solid]; +"474 Reshape_300" -> "44 /layer2/layer2.0/conv1/Conv" [label="[1, 128, 1, 1]", style=solid]; +"475 /layer2/layer2.0/conv1/Conv/WithoutBiases/fq_weights_1" -> "40 /layer2/layer2.0/conv1/Conv/WithoutBiases" [label="[128, 256, 1, 1]", style=solid]; +"476 Constant_61906" -> "475 /layer2/layer2.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"477 Constant_61905" -> "475 /layer2/layer2.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"478 Constant_61904" -> "475 /layer2/layer2.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"479 Constant_61903" -> "475 /layer2/layer2.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"480 onnx^^Conv_530" -> "475 /layer2/layer2.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 256, 1, 1]", style=solid]; +"481 Reshape_396" -> "98 /layer2/layer2.1/conv3/Conv" [label="[1, 512, 1, 1]", style=solid]; +"482 /layer2/layer2.1/conv3/Conv/WithoutBiases/fq_weights_1" -> "93 /layer2/layer2.1/conv3/Conv/WithoutBiases" [label="[512, 128, 1, 1]", style=solid]; +"483 Constant_61961" -> "482 /layer2/layer2.1/conv3/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"484 Constant_61960" -> "482 /layer2/layer2.1/conv3/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"485 Constant_61959" -> "482 /layer2/layer2.1/conv3/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"486 Constant_61958" -> "482 /layer2/layer2.1/conv3/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"487 onnx^^Conv_548" -> "482 /layer2/layer2.1/conv3/Conv/WithoutBiases/fq_weights_1" [label="[512, 128, 1, 1]", style=solid]; +"488 Constant_61956" -> "89 /layer2/layer2.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"489 Constant_61955" -> "89 /layer2/layer2.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"490 Constant_61954" -> "89 /layer2/layer2.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"491 Constant_61953" -> "89 /layer2/layer2.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"492 Reshape_380" -> "81 /layer2/layer2.1/conv2/Conv" [label="[1, 128, 1, 1]", style=solid]; +"493 /layer2/layer2.1/conv2/Conv/WithoutBiases/fq_weights_1" -> "77 /layer2/layer2.1/conv2/Conv/WithoutBiases" [label="[128, 128, 3, 3]", style=solid]; +"494 Constant_61951" -> "493 /layer2/layer2.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"495 Constant_61950" -> "493 /layer2/layer2.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"496 Constant_61949" -> "493 /layer2/layer2.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"497 Constant_61948" -> "493 /layer2/layer2.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"498 onnx^^Conv_545" -> "493 /layer2/layer2.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 128, 3, 3]", style=solid]; +"499 Constant_61946" -> "73 /layer2/layer2.1/relu/Relu/fq_output_0" [label="[]", style=solid]; +"500 Constant_61945" -> "73 /layer2/layer2.1/relu/Relu/fq_output_0" [label="[]", style=solid]; +"501 Constant_61944" -> "73 /layer2/layer2.1/relu/Relu/fq_output_0" [label="[]", style=solid]; +"502 Constant_61943" -> "73 /layer2/layer2.1/relu/Relu/fq_output_0" [label="[]", style=solid]; +"503 Reshape_364" -> "65 /layer2/layer2.1/conv1/Conv" [label="[1, 128, 1, 1]", style=solid]; +"504 /layer2/layer2.1/conv1/Conv/WithoutBiases/fq_weights_1" -> "61 /layer2/layer2.1/conv1/Conv/WithoutBiases" [label="[128, 512, 1, 1]", style=solid]; +"505 Constant_61941" -> "504 /layer2/layer2.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"506 Constant_61940" -> "504 /layer2/layer2.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"507 Constant_61939" -> "504 /layer2/layer2.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"508 Constant_61938" -> "504 /layer2/layer2.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"509 onnx^^Conv_542" -> "504 /layer2/layer2.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 512, 1, 1]", style=solid]; +"510 Reshape_445" -> "110 /layer2/layer2.2/conv3/Conv" [label="[1, 512, 1, 1]", style=solid]; +"511 /layer2/layer2.2/conv3/Conv/WithoutBiases/fq_weights_1" -> "106 /layer2/layer2.2/conv3/Conv/WithoutBiases" [label="[512, 128, 1, 1]", style=solid]; +"512 Constant_61996" -> "511 /layer2/layer2.2/conv3/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"513 Constant_61995" -> "511 /layer2/layer2.2/conv3/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"514 Constant_61994" -> "511 /layer2/layer2.2/conv3/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"515 Constant_61993" -> "511 /layer2/layer2.2/conv3/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"516 onnx^^Conv_557" -> "511 /layer2/layer2.2/conv3/Conv/WithoutBiases/fq_weights_1" [label="[512, 128, 1, 1]", style=solid]; +"517 Constant_61991" -> "102 /layer2/layer2.2/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"518 Constant_61990" -> "102 /layer2/layer2.2/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"519 Constant_61989" -> "102 /layer2/layer2.2/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"520 Constant_61988" -> "102 /layer2/layer2.2/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"521 Reshape_429" -> "92 /layer2/layer2.2/conv2/Conv" [label="[1, 128, 1, 1]", style=solid]; +"522 /layer2/layer2.2/conv2/Conv/WithoutBiases/fq_weights_1" -> "88 /layer2/layer2.2/conv2/Conv/WithoutBiases" [label="[128, 128, 3, 3]", style=solid]; +"523 Constant_61986" -> "522 /layer2/layer2.2/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"524 Constant_61985" -> "522 /layer2/layer2.2/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"525 Constant_61984" -> "522 /layer2/layer2.2/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"526 Constant_61983" -> "522 /layer2/layer2.2/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"527 onnx^^Conv_554" -> "522 /layer2/layer2.2/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 128, 3, 3]", style=solid]; +"528 Constant_61981" -> "84 /layer2/layer2.2/relu/Relu/fq_output_0" [label="[]", style=solid]; +"529 Constant_61980" -> "84 /layer2/layer2.2/relu/Relu/fq_output_0" [label="[]", style=solid]; +"530 Constant_61979" -> "84 /layer2/layer2.2/relu/Relu/fq_output_0" [label="[]", style=solid]; +"531 Constant_61978" -> "84 /layer2/layer2.2/relu/Relu/fq_output_0" [label="[]", style=solid]; +"532 Reshape_413" -> "76 /layer2/layer2.2/conv1/Conv" [label="[1, 128, 1, 1]", style=solid]; +"533 /layer2/layer2.2/conv1/Conv/WithoutBiases/fq_weights_1" -> "72 /layer2/layer2.2/conv1/Conv/WithoutBiases" [label="[128, 512, 1, 1]", style=solid]; +"534 Constant_61976" -> "533 /layer2/layer2.2/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"535 Constant_61975" -> "533 /layer2/layer2.2/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"536 Constant_61974" -> "533 /layer2/layer2.2/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"537 Constant_61973" -> "533 /layer2/layer2.2/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"538 onnx^^Conv_551" -> "533 /layer2/layer2.2/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 512, 1, 1]", style=solid]; +"539 Reshape_494" -> "121 /layer2/layer2.3/conv3/Conv" [label="[1, 512, 1, 1]", style=solid]; +"540 /layer2/layer2.3/conv3/Conv/WithoutBiases/fq_weights_1" -> "117 /layer2/layer2.3/conv3/Conv/WithoutBiases" [label="[512, 128, 1, 1]", style=solid]; +"541 Constant_62031" -> "540 /layer2/layer2.3/conv3/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"542 Constant_62030" -> "540 /layer2/layer2.3/conv3/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"543 Constant_62029" -> "540 /layer2/layer2.3/conv3/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"544 Constant_62028" -> "540 /layer2/layer2.3/conv3/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"545 onnx^^Conv_566" -> "540 /layer2/layer2.3/conv3/Conv/WithoutBiases/fq_weights_1" [label="[512, 128, 1, 1]", style=solid]; +"546 Constant_62026" -> "113 /layer2/layer2.3/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"547 Constant_62025" -> "113 /layer2/layer2.3/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"548 Constant_62024" -> "113 /layer2/layer2.3/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"549 Constant_62023" -> "113 /layer2/layer2.3/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"550 Reshape_478" -> "105 /layer2/layer2.3/conv2/Conv" [label="[1, 128, 1, 1]", style=solid]; +"551 /layer2/layer2.3/conv2/Conv/WithoutBiases/fq_weights_1" -> "101 /layer2/layer2.3/conv2/Conv/WithoutBiases" [label="[128, 128, 3, 3]", style=solid]; +"552 Constant_62021" -> "551 /layer2/layer2.3/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"553 Constant_62020" -> "551 /layer2/layer2.3/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"554 Constant_62019" -> "551 /layer2/layer2.3/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"555 Constant_62018" -> "551 /layer2/layer2.3/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"556 onnx^^Conv_563" -> "551 /layer2/layer2.3/conv2/Conv/WithoutBiases/fq_weights_1" [label="[128, 128, 3, 3]", style=solid]; +"557 Constant_62016" -> "96 /layer2/layer2.3/relu/Relu/fq_output_0" [label="[]", style=solid]; +"558 Constant_62015" -> "96 /layer2/layer2.3/relu/Relu/fq_output_0" [label="[]", style=solid]; +"559 Constant_62014" -> "96 /layer2/layer2.3/relu/Relu/fq_output_0" [label="[]", style=solid]; +"560 Constant_62013" -> "96 /layer2/layer2.3/relu/Relu/fq_output_0" [label="[]", style=solid]; +"561 Reshape_462" -> "87 /layer2/layer2.3/conv1/Conv" [label="[1, 128, 1, 1]", style=solid]; +"562 /layer2/layer2.3/conv1/Conv/WithoutBiases/fq_weights_1" -> "83 /layer2/layer2.3/conv1/Conv/WithoutBiases" [label="[128, 512, 1, 1]", style=solid]; +"563 Constant_62011" -> "562 /layer2/layer2.3/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"564 Constant_62010" -> "562 /layer2/layer2.3/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"565 Constant_62009" -> "562 /layer2/layer2.3/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"566 Constant_62008" -> "562 /layer2/layer2.3/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"567 onnx^^Conv_560" -> "562 /layer2/layer2.3/conv1/Conv/WithoutBiases/fq_weights_1" [label="[128, 512, 1, 1]", style=solid]; +"568 Constant_62081" -> "133 /layer3/layer3.0/conv3/Conv/fq_output_0" [label="[]", style=solid]; +"569 Constant_62080" -> "133 /layer3/layer3.0/conv3/Conv/fq_output_0" [label="[]", style=solid]; +"570 Constant_62079" -> "133 /layer3/layer3.0/conv3/Conv/fq_output_0" [label="[]", style=solid]; +"571 Constant_62078" -> "133 /layer3/layer3.0/conv3/Conv/fq_output_0" [label="[]", style=solid]; +"572 Reshape_543" -> "129 /layer3/layer3.0/conv3/Conv" [label="[1, 1024, 1, 1]", style=solid]; +"573 /layer3/layer3.0/conv3/Conv/WithoutBiases/fq_weights_1" -> "125 /layer3/layer3.0/conv3/Conv/WithoutBiases" [label="[1024, 256, 1, 1]", style=solid]; +"574 Constant_62076" -> "573 /layer3/layer3.0/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; +"575 Constant_62075" -> "573 /layer3/layer3.0/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; +"576 Constant_62074" -> "573 /layer3/layer3.0/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; +"577 Constant_62073" -> "573 /layer3/layer3.0/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; +"578 onnx^^Conv_575" -> "573 /layer3/layer3.0/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 256, 1, 1]", style=solid]; +"579 Constant_62071" -> "122 /layer3/layer3.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"580 Constant_62070" -> "122 /layer3/layer3.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"581 Constant_62069" -> "122 /layer3/layer3.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"582 Constant_62068" -> "122 /layer3/layer3.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"583 Reshape_527" -> "114 /layer3/layer3.0/conv2/Conv" [label="[1, 256, 1, 1]", style=solid]; +"584 /layer3/layer3.0/conv2/Conv/WithoutBiases/fq_weights_1" -> "111 /layer3/layer3.0/conv2/Conv/WithoutBiases" [label="[256, 256, 3, 3]", style=solid]; +"585 Constant_62066" -> "584 /layer3/layer3.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"586 Constant_62065" -> "584 /layer3/layer3.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"587 Constant_62064" -> "584 /layer3/layer3.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"588 Constant_62063" -> "584 /layer3/layer3.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"589 onnx^^Conv_572" -> "584 /layer3/layer3.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 256, 3, 3]", style=solid]; +"590 Constant_62061" -> "107 /layer3/layer3.0/relu/Relu/fq_output_0" [label="[]", style=solid]; +"591 Constant_62060" -> "107 /layer3/layer3.0/relu/Relu/fq_output_0" [label="[]", style=solid]; +"592 Constant_62059" -> "107 /layer3/layer3.0/relu/Relu/fq_output_0" [label="[]", style=solid]; +"593 Constant_62058" -> "107 /layer3/layer3.0/relu/Relu/fq_output_0" [label="[]", style=solid]; +"594 Reshape_511" -> "99 /layer3/layer3.0/conv1/Conv" [label="[1, 256, 1, 1]", style=solid]; +"595 /layer3/layer3.0/conv1/Conv/WithoutBiases/fq_weights_1" -> "94 /layer3/layer3.0/conv1/Conv/WithoutBiases" [label="[256, 512, 1, 1]", style=solid]; +"596 Constant_62056" -> "595 /layer3/layer3.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"597 Constant_62055" -> "595 /layer3/layer3.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"598 Constant_62054" -> "595 /layer3/layer3.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"599 Constant_62053" -> "595 /layer3/layer3.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"600 onnx^^Conv_569" -> "595 /layer3/layer3.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 512, 1, 1]", style=solid]; +"601 Reshape_607" -> "153 /layer3/layer3.1/conv3/Conv" [label="[1, 1024, 1, 1]", style=solid]; +"602 /layer3/layer3.1/conv3/Conv/WithoutBiases/fq_weights_1" -> "148 /layer3/layer3.1/conv3/Conv/WithoutBiases" [label="[1024, 256, 1, 1]", style=solid]; +"603 Constant_62111" -> "602 /layer3/layer3.1/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; +"604 Constant_62110" -> "602 /layer3/layer3.1/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; +"605 Constant_62109" -> "602 /layer3/layer3.1/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; +"606 Constant_62108" -> "602 /layer3/layer3.1/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; +"607 onnx^^Conv_587" -> "602 /layer3/layer3.1/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 256, 1, 1]", style=solid]; +"608 Constant_62106" -> "144 /layer3/layer3.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"609 Constant_62105" -> "144 /layer3/layer3.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"610 Constant_62104" -> "144 /layer3/layer3.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"611 Constant_62103" -> "144 /layer3/layer3.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"612 Reshape_591" -> "136 /layer3/layer3.1/conv2/Conv" [label="[1, 256, 1, 1]", style=solid]; +"613 /layer3/layer3.1/conv2/Conv/WithoutBiases/fq_weights_1" -> "132 /layer3/layer3.1/conv2/Conv/WithoutBiases" [label="[256, 256, 3, 3]", style=solid]; +"614 Constant_62101" -> "613 /layer3/layer3.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"615 Constant_62100" -> "613 /layer3/layer3.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"616 Constant_62099" -> "613 /layer3/layer3.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"617 Constant_62098" -> "613 /layer3/layer3.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"618 onnx^^Conv_584" -> "613 /layer3/layer3.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 256, 3, 3]", style=solid]; +"619 Constant_62096" -> "128 /layer3/layer3.1/relu/Relu/fq_output_0" [label="[]", style=solid]; +"620 Constant_62095" -> "128 /layer3/layer3.1/relu/Relu/fq_output_0" [label="[]", style=solid]; +"621 Constant_62094" -> "128 /layer3/layer3.1/relu/Relu/fq_output_0" [label="[]", style=solid]; +"622 Constant_62093" -> "128 /layer3/layer3.1/relu/Relu/fq_output_0" [label="[]", style=solid]; +"623 Reshape_575" -> "120 /layer3/layer3.1/conv1/Conv" [label="[1, 256, 1, 1]", style=solid]; +"624 /layer3/layer3.1/conv1/Conv/WithoutBiases/fq_weights_1" -> "116 /layer3/layer3.1/conv1/Conv/WithoutBiases" [label="[256, 1024, 1, 1]", style=solid]; +"625 Constant_62091" -> "624 /layer3/layer3.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"626 Constant_62090" -> "624 /layer3/layer3.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"627 Constant_62089" -> "624 /layer3/layer3.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"628 Constant_62088" -> "624 /layer3/layer3.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"629 onnx^^Conv_581" -> "624 /layer3/layer3.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1024, 1, 1]", style=solid]; +"630 Reshape_656" -> "166 /layer3/layer3.2/conv3/Conv" [label="[1, 1024, 1, 1]", style=solid]; +"631 /layer3/layer3.2/conv3/Conv/WithoutBiases/fq_weights_1" -> "161 /layer3/layer3.2/conv3/Conv/WithoutBiases" [label="[1024, 256, 1, 1]", style=solid]; +"632 Constant_62146" -> "631 /layer3/layer3.2/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; +"633 Constant_62145" -> "631 /layer3/layer3.2/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; +"634 Constant_62144" -> "631 /layer3/layer3.2/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; +"635 Constant_62143" -> "631 /layer3/layer3.2/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; +"636 onnx^^Conv_596" -> "631 /layer3/layer3.2/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 256, 1, 1]", style=solid]; +"637 Constant_62141" -> "157 /layer3/layer3.2/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"638 Constant_62140" -> "157 /layer3/layer3.2/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"639 Constant_62139" -> "157 /layer3/layer3.2/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"640 Constant_62138" -> "157 /layer3/layer3.2/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"641 Reshape_640" -> "147 /layer3/layer3.2/conv2/Conv" [label="[1, 256, 1, 1]", style=solid]; +"642 /layer3/layer3.2/conv2/Conv/WithoutBiases/fq_weights_1" -> "143 /layer3/layer3.2/conv2/Conv/WithoutBiases" [label="[256, 256, 3, 3]", style=solid]; +"643 Constant_62136" -> "642 /layer3/layer3.2/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"644 Constant_62135" -> "642 /layer3/layer3.2/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"645 Constant_62134" -> "642 /layer3/layer3.2/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"646 Constant_62133" -> "642 /layer3/layer3.2/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"647 onnx^^Conv_593" -> "642 /layer3/layer3.2/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 256, 3, 3]", style=solid]; +"648 Constant_62131" -> "139 /layer3/layer3.2/relu/Relu/fq_output_0" [label="[]", style=solid]; +"649 Constant_62130" -> "139 /layer3/layer3.2/relu/Relu/fq_output_0" [label="[]", style=solid]; +"650 Constant_62129" -> "139 /layer3/layer3.2/relu/Relu/fq_output_0" [label="[]", style=solid]; +"651 Constant_62128" -> "139 /layer3/layer3.2/relu/Relu/fq_output_0" [label="[]", style=solid]; +"652 Reshape_624" -> "131 /layer3/layer3.2/conv1/Conv" [label="[1, 256, 1, 1]", style=solid]; +"653 /layer3/layer3.2/conv1/Conv/WithoutBiases/fq_weights_1" -> "127 /layer3/layer3.2/conv1/Conv/WithoutBiases" [label="[256, 1024, 1, 1]", style=solid]; +"654 Constant_62126" -> "653 /layer3/layer3.2/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"655 Constant_62125" -> "653 /layer3/layer3.2/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"656 Constant_62124" -> "653 /layer3/layer3.2/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"657 Constant_62123" -> "653 /layer3/layer3.2/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"658 onnx^^Conv_590" -> "653 /layer3/layer3.2/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1024, 1, 1]", style=solid]; +"659 Reshape_705" -> "179 /layer3/layer3.3/conv3/Conv" [label="[1, 1024, 1, 1]", style=solid]; +"660 /layer3/layer3.3/conv3/Conv/WithoutBiases/fq_weights_1" -> "174 /layer3/layer3.3/conv3/Conv/WithoutBiases" [label="[1024, 256, 1, 1]", style=solid]; +"661 Constant_62181" -> "660 /layer3/layer3.3/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; +"662 Constant_62180" -> "660 /layer3/layer3.3/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; +"663 Constant_62179" -> "660 /layer3/layer3.3/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; +"664 Constant_62178" -> "660 /layer3/layer3.3/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; +"665 onnx^^Conv_605" -> "660 /layer3/layer3.3/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 256, 1, 1]", style=solid]; +"666 Constant_62176" -> "170 /layer3/layer3.3/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"667 Constant_62175" -> "170 /layer3/layer3.3/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"668 Constant_62174" -> "170 /layer3/layer3.3/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"669 Constant_62173" -> "170 /layer3/layer3.3/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"670 Reshape_689" -> "160 /layer3/layer3.3/conv2/Conv" [label="[1, 256, 1, 1]", style=solid]; +"671 /layer3/layer3.3/conv2/Conv/WithoutBiases/fq_weights_1" -> "156 /layer3/layer3.3/conv2/Conv/WithoutBiases" [label="[256, 256, 3, 3]", style=solid]; +"672 Constant_62171" -> "671 /layer3/layer3.3/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"673 Constant_62170" -> "671 /layer3/layer3.3/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"674 Constant_62169" -> "671 /layer3/layer3.3/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"675 Constant_62168" -> "671 /layer3/layer3.3/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"676 onnx^^Conv_602" -> "671 /layer3/layer3.3/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 256, 3, 3]", style=solid]; +"677 Constant_62166" -> "151 /layer3/layer3.3/relu/Relu/fq_output_0" [label="[]", style=solid]; +"678 Constant_62165" -> "151 /layer3/layer3.3/relu/Relu/fq_output_0" [label="[]", style=solid]; +"679 Constant_62164" -> "151 /layer3/layer3.3/relu/Relu/fq_output_0" [label="[]", style=solid]; +"680 Constant_62163" -> "151 /layer3/layer3.3/relu/Relu/fq_output_0" [label="[]", style=solid]; +"681 Reshape_673" -> "142 /layer3/layer3.3/conv1/Conv" [label="[1, 256, 1, 1]", style=solid]; +"682 /layer3/layer3.3/conv1/Conv/WithoutBiases/fq_weights_1" -> "138 /layer3/layer3.3/conv1/Conv/WithoutBiases" [label="[256, 1024, 1, 1]", style=solid]; +"683 Constant_62161" -> "682 /layer3/layer3.3/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"684 Constant_62160" -> "682 /layer3/layer3.3/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"685 Constant_62159" -> "682 /layer3/layer3.3/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"686 Constant_62158" -> "682 /layer3/layer3.3/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"687 onnx^^Conv_599" -> "682 /layer3/layer3.3/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1024, 1, 1]", style=solid]; +"688 Reshape_754" -> "191 /layer3/layer3.4/conv3/Conv" [label="[1, 1024, 1, 1]", style=solid]; +"689 /layer3/layer3.4/conv3/Conv/WithoutBiases/fq_weights_1" -> "187 /layer3/layer3.4/conv3/Conv/WithoutBiases" [label="[1024, 256, 1, 1]", style=solid]; +"690 Constant_62216" -> "689 /layer3/layer3.4/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; +"691 Constant_62215" -> "689 /layer3/layer3.4/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; +"692 Constant_62214" -> "689 /layer3/layer3.4/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; +"693 Constant_62213" -> "689 /layer3/layer3.4/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; +"694 onnx^^Conv_614" -> "689 /layer3/layer3.4/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 256, 1, 1]", style=solid]; +"695 Constant_62211" -> "183 /layer3/layer3.4/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"696 Constant_62210" -> "183 /layer3/layer3.4/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"697 Constant_62209" -> "183 /layer3/layer3.4/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"698 Constant_62208" -> "183 /layer3/layer3.4/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"699 Reshape_738" -> "173 /layer3/layer3.4/conv2/Conv" [label="[1, 256, 1, 1]", style=solid]; +"700 /layer3/layer3.4/conv2/Conv/WithoutBiases/fq_weights_1" -> "169 /layer3/layer3.4/conv2/Conv/WithoutBiases" [label="[256, 256, 3, 3]", style=solid]; +"701 Constant_62206" -> "700 /layer3/layer3.4/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"702 Constant_62205" -> "700 /layer3/layer3.4/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"703 Constant_62204" -> "700 /layer3/layer3.4/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"704 Constant_62203" -> "700 /layer3/layer3.4/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"705 onnx^^Conv_611" -> "700 /layer3/layer3.4/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 256, 3, 3]", style=solid]; +"706 Constant_62201" -> "164 /layer3/layer3.4/relu/Relu/fq_output_0" [label="[]", style=solid]; +"707 Constant_62200" -> "164 /layer3/layer3.4/relu/Relu/fq_output_0" [label="[]", style=solid]; +"708 Constant_62199" -> "164 /layer3/layer3.4/relu/Relu/fq_output_0" [label="[]", style=solid]; +"709 Constant_62198" -> "164 /layer3/layer3.4/relu/Relu/fq_output_0" [label="[]", style=solid]; +"710 Reshape_722" -> "155 /layer3/layer3.4/conv1/Conv" [label="[1, 256, 1, 1]", style=solid]; +"711 /layer3/layer3.4/conv1/Conv/WithoutBiases/fq_weights_1" -> "150 /layer3/layer3.4/conv1/Conv/WithoutBiases" [label="[256, 1024, 1, 1]", style=solid]; +"712 Constant_62196" -> "711 /layer3/layer3.4/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"713 Constant_62195" -> "711 /layer3/layer3.4/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"714 Constant_62194" -> "711 /layer3/layer3.4/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"715 Constant_62193" -> "711 /layer3/layer3.4/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"716 onnx^^Conv_608" -> "711 /layer3/layer3.4/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1024, 1, 1]", style=solid]; +"717 Reshape_803" -> "202 /layer3/layer3.5/conv3/Conv" [label="[1, 1024, 1, 1]", style=solid]; +"718 /layer3/layer3.5/conv3/Conv/WithoutBiases/fq_weights_1" -> "198 /layer3/layer3.5/conv3/Conv/WithoutBiases" [label="[1024, 256, 1, 1]", style=solid]; +"719 Constant_62251" -> "718 /layer3/layer3.5/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; +"720 Constant_62250" -> "718 /layer3/layer3.5/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; +"721 Constant_62249" -> "718 /layer3/layer3.5/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; +"722 Constant_62248" -> "718 /layer3/layer3.5/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 1, 1, 1]", style=solid]; +"723 onnx^^Conv_623" -> "718 /layer3/layer3.5/conv3/Conv/WithoutBiases/fq_weights_1" [label="[1024, 256, 1, 1]", style=solid]; +"724 Constant_62246" -> "194 /layer3/layer3.5/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"725 Constant_62245" -> "194 /layer3/layer3.5/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"726 Constant_62244" -> "194 /layer3/layer3.5/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"727 Constant_62243" -> "194 /layer3/layer3.5/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"728 Reshape_787" -> "186 /layer3/layer3.5/conv2/Conv" [label="[1, 256, 1, 1]", style=solid]; +"729 /layer3/layer3.5/conv2/Conv/WithoutBiases/fq_weights_1" -> "182 /layer3/layer3.5/conv2/Conv/WithoutBiases" [label="[256, 256, 3, 3]", style=solid]; +"730 Constant_62241" -> "729 /layer3/layer3.5/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"731 Constant_62240" -> "729 /layer3/layer3.5/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"732 Constant_62239" -> "729 /layer3/layer3.5/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"733 Constant_62238" -> "729 /layer3/layer3.5/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"734 onnx^^Conv_620" -> "729 /layer3/layer3.5/conv2/Conv/WithoutBiases/fq_weights_1" [label="[256, 256, 3, 3]", style=solid]; +"735 Constant_62236" -> "177 /layer3/layer3.5/relu/Relu/fq_output_0" [label="[]", style=solid]; +"736 Constant_62235" -> "177 /layer3/layer3.5/relu/Relu/fq_output_0" [label="[]", style=solid]; +"737 Constant_62234" -> "177 /layer3/layer3.5/relu/Relu/fq_output_0" [label="[]", style=solid]; +"738 Constant_62233" -> "177 /layer3/layer3.5/relu/Relu/fq_output_0" [label="[]", style=solid]; +"739 Reshape_771" -> "168 /layer3/layer3.5/conv1/Conv" [label="[1, 256, 1, 1]", style=solid]; +"740 /layer3/layer3.5/conv1/Conv/WithoutBiases/fq_weights_1" -> "163 /layer3/layer3.5/conv1/Conv/WithoutBiases" [label="[256, 1024, 1, 1]", style=solid]; +"741 Constant_62231" -> "740 /layer3/layer3.5/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"742 Constant_62230" -> "740 /layer3/layer3.5/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"743 Constant_62229" -> "740 /layer3/layer3.5/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"744 Constant_62228" -> "740 /layer3/layer3.5/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"745 onnx^^Conv_617" -> "740 /layer3/layer3.5/conv1/Conv/WithoutBiases/fq_weights_1" [label="[256, 1024, 1, 1]", style=solid]; +"746 Constant_62301" -> "214 /layer4/layer4.0/conv3/Conv/fq_output_0" [label="[]", style=solid]; +"747 Constant_62300" -> "214 /layer4/layer4.0/conv3/Conv/fq_output_0" [label="[]", style=solid]; +"748 Constant_62299" -> "214 /layer4/layer4.0/conv3/Conv/fq_output_0" [label="[]", style=solid]; +"749 Constant_62298" -> "214 /layer4/layer4.0/conv3/Conv/fq_output_0" [label="[]", style=solid]; +"750 Reshape_852" -> "210 /layer4/layer4.0/conv3/Conv" [label="[1, 2048, 1, 1]", style=solid]; +"751 /layer4/layer4.0/conv3/Conv/WithoutBiases/fq_weights_1" -> "206 /layer4/layer4.0/conv3/Conv/WithoutBiases" [label="[2048, 512, 1, 1]", style=solid]; +"752 Constant_62296" -> "751 /layer4/layer4.0/conv3/Conv/WithoutBiases/fq_weights_1" [label="[2048, 1, 1, 1]", style=solid]; +"753 Constant_62295" -> "751 /layer4/layer4.0/conv3/Conv/WithoutBiases/fq_weights_1" [label="[2048, 1, 1, 1]", style=solid]; +"754 Constant_62294" -> "751 /layer4/layer4.0/conv3/Conv/WithoutBiases/fq_weights_1" [label="[2048, 1, 1, 1]", style=solid]; +"755 Constant_62293" -> "751 /layer4/layer4.0/conv3/Conv/WithoutBiases/fq_weights_1" [label="[2048, 1, 1, 1]", style=solid]; +"756 onnx^^Conv_632" -> "751 /layer4/layer4.0/conv3/Conv/WithoutBiases/fq_weights_1" [label="[2048, 512, 1, 1]", style=solid]; +"757 Constant_62291" -> "203 /layer4/layer4.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"758 Constant_62290" -> "203 /layer4/layer4.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"759 Constant_62289" -> "203 /layer4/layer4.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"760 Constant_62288" -> "203 /layer4/layer4.0/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"761 Reshape_836" -> "195 /layer4/layer4.0/conv2/Conv" [label="[1, 512, 1, 1]", style=solid]; +"762 /layer4/layer4.0/conv2/Conv/WithoutBiases/fq_weights_1" -> "192 /layer4/layer4.0/conv2/Conv/WithoutBiases" [label="[512, 512, 3, 3]", style=solid]; +"763 Constant_62286" -> "762 /layer4/layer4.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"764 Constant_62285" -> "762 /layer4/layer4.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"765 Constant_62284" -> "762 /layer4/layer4.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"766 Constant_62283" -> "762 /layer4/layer4.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"767 onnx^^Conv_629" -> "762 /layer4/layer4.0/conv2/Conv/WithoutBiases/fq_weights_1" [label="[512, 512, 3, 3]", style=solid]; +"768 Constant_62281" -> "188 /layer4/layer4.0/relu/Relu/fq_output_0" [label="[]", style=solid]; +"769 Constant_62280" -> "188 /layer4/layer4.0/relu/Relu/fq_output_0" [label="[]", style=solid]; +"770 Constant_62279" -> "188 /layer4/layer4.0/relu/Relu/fq_output_0" [label="[]", style=solid]; +"771 Constant_62278" -> "188 /layer4/layer4.0/relu/Relu/fq_output_0" [label="[]", style=solid]; +"772 Reshape_820" -> "180 /layer4/layer4.0/conv1/Conv" [label="[1, 512, 1, 1]", style=solid]; +"773 /layer4/layer4.0/conv1/Conv/WithoutBiases/fq_weights_1" -> "175 /layer4/layer4.0/conv1/Conv/WithoutBiases" [label="[512, 1024, 1, 1]", style=solid]; +"774 Constant_62276" -> "773 /layer4/layer4.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"775 Constant_62275" -> "773 /layer4/layer4.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"776 Constant_62274" -> "773 /layer4/layer4.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"777 Constant_62273" -> "773 /layer4/layer4.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"778 onnx^^Conv_626" -> "773 /layer4/layer4.0/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 1024, 1, 1]", style=solid]; +"779 Reshape_916" -> "229 /layer4/layer4.1/conv3/Conv" [label="[1, 2048, 1, 1]", style=solid]; +"780 /layer4/layer4.1/conv3/Conv/WithoutBiases/fq_weights_1" -> "226 /layer4/layer4.1/conv3/Conv/WithoutBiases" [label="[2048, 512, 1, 1]", style=solid]; +"781 Constant_62331" -> "780 /layer4/layer4.1/conv3/Conv/WithoutBiases/fq_weights_1" [label="[2048, 1, 1, 1]", style=solid]; +"782 Constant_62330" -> "780 /layer4/layer4.1/conv3/Conv/WithoutBiases/fq_weights_1" [label="[2048, 1, 1, 1]", style=solid]; +"783 Constant_62329" -> "780 /layer4/layer4.1/conv3/Conv/WithoutBiases/fq_weights_1" [label="[2048, 1, 1, 1]", style=solid]; +"784 Constant_62328" -> "780 /layer4/layer4.1/conv3/Conv/WithoutBiases/fq_weights_1" [label="[2048, 1, 1, 1]", style=solid]; +"785 onnx^^Conv_644" -> "780 /layer4/layer4.1/conv3/Conv/WithoutBiases/fq_weights_1" [label="[2048, 512, 1, 1]", style=solid]; +"786 Constant_62326" -> "223 /layer4/layer4.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"787 Constant_62325" -> "223 /layer4/layer4.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"788 Constant_62324" -> "223 /layer4/layer4.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"789 Constant_62323" -> "223 /layer4/layer4.1/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"790 Reshape_900" -> "217 /layer4/layer4.1/conv2/Conv" [label="[1, 512, 1, 1]", style=solid]; +"791 /layer4/layer4.1/conv2/Conv/WithoutBiases/fq_weights_1" -> "213 /layer4/layer4.1/conv2/Conv/WithoutBiases" [label="[512, 512, 3, 3]", style=solid]; +"792 Constant_62321" -> "791 /layer4/layer4.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"793 Constant_62320" -> "791 /layer4/layer4.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"794 Constant_62319" -> "791 /layer4/layer4.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"795 Constant_62318" -> "791 /layer4/layer4.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"796 onnx^^Conv_641" -> "791 /layer4/layer4.1/conv2/Conv/WithoutBiases/fq_weights_1" [label="[512, 512, 3, 3]", style=solid]; +"797 Constant_62316" -> "209 /layer4/layer4.1/relu/Relu/fq_output_0" [label="[]", style=solid]; +"798 Constant_62315" -> "209 /layer4/layer4.1/relu/Relu/fq_output_0" [label="[]", style=solid]; +"799 Constant_62314" -> "209 /layer4/layer4.1/relu/Relu/fq_output_0" [label="[]", style=solid]; +"800 Constant_62313" -> "209 /layer4/layer4.1/relu/Relu/fq_output_0" [label="[]", style=solid]; +"801 Reshape_884" -> "201 /layer4/layer4.1/conv1/Conv" [label="[1, 512, 1, 1]", style=solid]; +"802 /layer4/layer4.1/conv1/Conv/WithoutBiases/fq_weights_1" -> "197 /layer4/layer4.1/conv1/Conv/WithoutBiases" [label="[512, 2048, 1, 1]", style=solid]; +"803 Constant_62311" -> "802 /layer4/layer4.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"804 Constant_62310" -> "802 /layer4/layer4.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"805 Constant_62309" -> "802 /layer4/layer4.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"806 Constant_62308" -> "802 /layer4/layer4.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"807 onnx^^Conv_638" -> "802 /layer4/layer4.1/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 2048, 1, 1]", style=solid]; +"808 Reshape_965" -> "234 /layer4/layer4.2/conv3/Conv" [label="[1, 2048, 1, 1]", style=solid]; +"809 /layer4/layer4.2/conv3/Conv/WithoutBiases/fq_weights_1" -> "233 /layer4/layer4.2/conv3/Conv/WithoutBiases" [label="[2048, 512, 1, 1]", style=solid]; +"810 Constant_62366" -> "809 /layer4/layer4.2/conv3/Conv/WithoutBiases/fq_weights_1" [label="[2048, 1, 1, 1]", style=solid]; +"811 Constant_62365" -> "809 /layer4/layer4.2/conv3/Conv/WithoutBiases/fq_weights_1" [label="[2048, 1, 1, 1]", style=solid]; +"812 Constant_62364" -> "809 /layer4/layer4.2/conv3/Conv/WithoutBiases/fq_weights_1" [label="[2048, 1, 1, 1]", style=solid]; +"813 Constant_62363" -> "809 /layer4/layer4.2/conv3/Conv/WithoutBiases/fq_weights_1" [label="[2048, 1, 1, 1]", style=solid]; +"814 onnx^^Conv_653" -> "809 /layer4/layer4.2/conv3/Conv/WithoutBiases/fq_weights_1" [label="[2048, 512, 1, 1]", style=solid]; +"815 Constant_62361" -> "231 /layer4/layer4.2/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"816 Constant_62360" -> "231 /layer4/layer4.2/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"817 Constant_62359" -> "231 /layer4/layer4.2/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"818 Constant_62358" -> "231 /layer4/layer4.2/relu_1/Relu/fq_output_0" [label="[]", style=solid]; +"819 Reshape_949" -> "225 /layer4/layer4.2/conv2/Conv" [label="[1, 512, 1, 1]", style=solid]; +"820 /layer4/layer4.2/conv2/Conv/WithoutBiases/fq_weights_1" -> "222 /layer4/layer4.2/conv2/Conv/WithoutBiases" [label="[512, 512, 3, 3]", style=solid]; +"821 Constant_62356" -> "820 /layer4/layer4.2/conv2/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"822 Constant_62355" -> "820 /layer4/layer4.2/conv2/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"823 Constant_62354" -> "820 /layer4/layer4.2/conv2/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"824 Constant_62353" -> "820 /layer4/layer4.2/conv2/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"825 onnx^^Conv_650" -> "820 /layer4/layer4.2/conv2/Conv/WithoutBiases/fq_weights_1" [label="[512, 512, 3, 3]", style=solid]; +"826 Constant_62351" -> "219 /layer4/layer4.2/relu/Relu/fq_output_0" [label="[]", style=solid]; +"827 Constant_62350" -> "219 /layer4/layer4.2/relu/Relu/fq_output_0" [label="[]", style=solid]; +"828 Constant_62349" -> "219 /layer4/layer4.2/relu/Relu/fq_output_0" [label="[]", style=solid]; +"829 Constant_62348" -> "219 /layer4/layer4.2/relu/Relu/fq_output_0" [label="[]", style=solid]; +"830 Reshape_933" -> "212 /layer4/layer4.2/conv1/Conv" [label="[1, 512, 1, 1]", style=solid]; +"831 /layer4/layer4.2/conv1/Conv/WithoutBiases/fq_weights_1" -> "208 /layer4/layer4.2/conv1/Conv/WithoutBiases" [label="[512, 2048, 1, 1]", style=solid]; +"832 Constant_62346" -> "831 /layer4/layer4.2/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"833 Constant_62345" -> "831 /layer4/layer4.2/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"834 Constant_62344" -> "831 /layer4/layer4.2/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"835 Constant_62343" -> "831 /layer4/layer4.2/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"836 onnx^^Conv_647" -> "831 /layer4/layer4.2/conv1/Conv/WithoutBiases/fq_weights_1" [label="[512, 2048, 1, 1]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/swin-tiny-patch4-window7-224_performance_transformer.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/swin-tiny-patch4-window7-224_performance_transformer.dot deleted file mode 120000 index a96593914c9..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/quantized/swin-tiny-patch4-window7-224_performance_transformer.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/quantized/swin-tiny-patch4-window7-224_performance_transformer.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/swin-tiny-patch4-window7-224_performance_transformer.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/swin-tiny-patch4-window7-224_performance_transformer.dot new file mode 100644 index 00000000000..ccdd526e94d --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/quantized/swin-tiny-patch4-window7-224_performance_transformer.dot @@ -0,0 +1,3842 @@ +strict digraph { +"0 input" [id=0, type=Parameter]; +"1 Multiply_6579" [id=1, 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[id=880, type=Constant]; +"881 /layers/layers.2/blocks.5/attn/Constant" [id=881, type=Constant]; +"882 /layers/layers.2/blocks.5/attn/qkv/MatMul/fq_weights_1" [id=882, type=FakeQuantize]; +"883 Constant_27949" [id=883, type=Constant]; +"884 Constant_27948" [id=884, type=Constant]; +"885 Constant_27947" [id=885, type=Constant]; +"886 Constant_27946" [id=886, type=Constant]; +"887 Transpose_6503" [id=887, type=Constant]; +"888 /layers/layers.2/blocks.5/Constant_15" [id=888, type=Constant]; +"889 /layers/layers.2/blocks.5/Constant_14" [id=889, type=Constant]; +"890 Constant_1722" [id=890, type=Constant]; +"891 /layers/layers.2/blocks.5/Constant_13" [id=891, type=Constant]; +"892 Constant_5631" [id=892, type=Constant]; +"893 Constant_5628" [id=893, type=Constant]; +"894 Constant_5625" [id=894, type=Constant]; +"895 Constant_5607" [id=895, type=Constant]; +"896 Constant_5604" [id=896, type=Constant]; +"897 Constant_5601" [id=897, type=Constant]; +"898 /layers/layers.2/blocks.5/Constant" [id=898, type=Constant]; +"899 Constant_27484" [id=899, type=Constant]; +"900 Constant_27483" [id=900, type=Constant]; +"901 Constant_27482" [id=901, type=Constant]; +"902 Constant_27481" [id=902, type=Constant]; +"903 Constant_7371" [id=903, type=Constant]; +"904 Constant_7370" [id=904, type=Constant]; +"905 Constant_1659" [id=905, type=Constant]; +"906 /layers/layers.2/blocks.4/mlp/fc2/MatMul/fq_weights_1" [id=906, type=FakeQuantize]; +"907 Constant_27944" [id=907, type=Constant]; +"908 Constant_27943" [id=908, type=Constant]; +"909 Constant_27942" [id=909, type=Constant]; +"910 Constant_27941" [id=910, type=Constant]; +"911 Transpose_6499" [id=911, type=Constant]; +"912 Constant_27939" [id=912, type=Constant]; +"913 Constant_27938" [id=913, type=Constant]; +"914 Constant_27937" [id=914, type=Constant]; +"915 Constant_27936" [id=915, type=Constant]; +"916 /layers/layers.2/blocks.4/mlp/fc1/MatMul/fq_weights_1" [id=916, type=FakeQuantize]; +"917 Constant_27934" [id=917, type=Constant]; 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/layers/layers.2/blocks.4/Constant_3" [id=955, type=Constant]; +"956 /layers/layers.2/blocks.4/Constant_2" [id=956, type=Constant]; +"957 Constant_1574" [id=957, type=Constant]; +"958 /layers/layers.2/blocks.4/Constant_1" [id=958, type=Constant]; +"959 Constant_27899" [id=959, type=Constant]; +"960 Constant_27898" [id=960, type=Constant]; +"961 Constant_27897" [id=961, type=Constant]; +"962 Constant_27896" [id=962, type=Constant]; +"963 Constant_7362" [id=963, type=Constant]; +"964 Constant_7361" [id=964, type=Constant]; +"965 Constant_1553" [id=965, type=Constant]; +"966 /layers/layers.2/blocks.3/mlp/fc2/MatMul/fq_weights_1" [id=966, type=FakeQuantize]; +"967 Constant_27894" [id=967, type=Constant]; +"968 Constant_27893" [id=968, type=Constant]; +"969 Constant_27892" [id=969, type=Constant]; +"970 Constant_27891" [id=970, type=Constant]; +"971 Transpose_6484" [id=971, type=Constant]; +"972 Constant_27889" [id=972, type=Constant]; +"973 Constant_27888" [id=973, type=Constant]; +"974 Constant_27887" [id=974, type=Constant]; +"975 Constant_27886" [id=975, type=Constant]; +"976 /layers/layers.2/blocks.3/mlp/fc1/MatMul/fq_weights_1" [id=976, type=FakeQuantize]; +"977 Constant_27884" [id=977, type=Constant]; +"978 Constant_27883" [id=978, type=Constant]; +"979 Constant_27882" [id=979, type=Constant]; +"980 Constant_27881" [id=980, type=Constant]; +"981 Transpose_6480" [id=981, type=Constant]; +"982 Constant_27879" [id=982, type=Constant]; +"983 Constant_27878" [id=983, type=Constant]; +"984 Constant_27877" [id=984, type=Constant]; +"985 Constant_27876" [id=985, type=Constant]; +"986 Constant_7358" [id=986, type=Constant]; +"987 Constant_7357" [id=987, type=Constant]; +"988 Constant_1527" [id=988, type=Constant]; +"989 /layers/layers.2/blocks.3/Constant_31" [id=989, type=Constant]; +"990 Constant_5583" [id=990, type=Constant]; +"991 Constant_5580" [id=991, type=Constant]; +"992 Constant_5577" [id=992, type=Constant]; +"993 Constant_5559" [id=993, type=Constant]; +"994 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type=Constant]; +"1030 Constant_5505" [id=1030, type=Constant]; +"1031 /layers/layers.2/blocks.3/Constant" [id=1031, type=Constant]; +"1032 Constant_27479" [id=1032, type=Constant]; +"1033 Constant_27478" [id=1033, type=Constant]; +"1034 Constant_27477" [id=1034, type=Constant]; +"1035 Constant_27476" [id=1035, type=Constant]; +"1036 Constant_7353" [id=1036, type=Constant]; +"1037 Constant_7352" [id=1037, type=Constant]; +"1038 Constant_1354" [id=1038, type=Constant]; +"1039 /layers/layers.2/blocks.2/mlp/fc2/MatMul/fq_weights_1" [id=1039, type=FakeQuantize]; +"1040 Constant_27849" [id=1040, type=Constant]; +"1041 Constant_27848" [id=1041, type=Constant]; +"1042 Constant_27847" [id=1042, type=Constant]; +"1043 Constant_27846" [id=1043, type=Constant]; +"1044 Transpose_6469" [id=1044, type=Constant]; +"1045 Constant_27844" [id=1045, type=Constant]; +"1046 Constant_27843" [id=1046, type=Constant]; +"1047 Constant_27842" [id=1047, type=Constant]; +"1048 Constant_27841" [id=1048, type=Constant]; +"1049 /layers/layers.2/blocks.2/mlp/fc1/MatMul/fq_weights_1" [id=1049, type=FakeQuantize]; +"1050 Constant_27839" [id=1050, type=Constant]; +"1051 Constant_27838" [id=1051, type=Constant]; +"1052 Constant_27837" [id=1052, type=Constant]; +"1053 Constant_27836" [id=1053, type=Constant]; +"1054 Transpose_6465" [id=1054, type=Constant]; +"1055 Constant_27834" [id=1055, type=Constant]; +"1056 Constant_27833" [id=1056, type=Constant]; +"1057 Constant_27832" [id=1057, type=Constant]; +"1058 Constant_27831" [id=1058, type=Constant]; +"1059 Constant_7349" [id=1059, type=Constant]; +"1060 Constant_7348" [id=1060, type=Constant]; +"1061 Constant_1328" [id=1061, type=Constant]; +"1062 /layers/layers.2/blocks.2/Constant_7" [id=1062, type=Constant]; +"1063 /layers/layers.2/blocks.2/Constant_6" [id=1063, type=Constant]; +"1064 Constant_1317" [id=1064, type=Constant]; +"1065 /layers/layers.2/blocks.2/Constant_5" [id=1065, type=Constant]; +"1066 /layers/layers.2/blocks.2/Constant_4" 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Constant_986" [id=1212, type=Constant]; +"1213 Constant_980" [id=1213, type=Constant]; +"1214 /layers/layers.2/blocks.0/attn/Constant" [id=1214, type=Constant]; +"1215 /layers/layers.2/blocks.0/attn/qkv/MatMul/fq_weights_1" [id=1215, type=FakeQuantize]; +"1216 Constant_27714" [id=1216, type=Constant]; +"1217 Constant_27713" [id=1217, type=Constant]; +"1218 Constant_27712" [id=1218, type=Constant]; +"1219 Constant_27711" [id=1219, type=Constant]; +"1220 Transpose_6428" [id=1220, type=Constant]; +"1221 /layers/layers.2/blocks.0/Constant_3" [id=1221, type=Constant]; +"1222 /layers/layers.2/blocks.0/Constant_2" [id=1222, type=Constant]; +"1223 Constant_964" [id=1223, type=Constant]; +"1224 /layers/layers.2/blocks.0/Constant_1" [id=1224, type=Constant]; +"1225 Constant_27709" [id=1225, type=Constant]; +"1226 Constant_27708" [id=1226, type=Constant]; +"1227 Constant_27707" [id=1227, type=Constant]; +"1228 Constant_27706" [id=1228, type=Constant]; +"1229 Constant_7326" [id=1229, type=Constant]; +"1230 Constant_7325" [id=1230, type=Constant]; +"1231 Constant_943" [id=1231, type=Constant]; +"1232 /layers/layers.1/downsample/reduction/MatMul/fq_weights_1" [id=1232, type=FakeQuantize]; +"1233 Constant_27704" [id=1233, type=Constant]; +"1234 Constant_27703" [id=1234, type=Constant]; +"1235 Constant_27702" [id=1235, type=Constant]; +"1236 Constant_27701" [id=1236, type=Constant]; +"1237 Transpose_6424" [id=1237, type=Constant]; +"1238 Constant_27699" [id=1238, type=Constant]; +"1239 Constant_27698" [id=1239, type=Constant]; +"1240 Constant_27697" [id=1240, type=Constant]; +"1241 Constant_27696" [id=1241, type=Constant]; +"1242 Constant_7324" [id=1242, type=Constant]; +"1243 Constant_7323" [id=1243, type=Constant]; +"1244 Constant_929" [id=1244, type=Constant]; +"1245 /layers/layers.1/downsample/Constant_25" [id=1245, type=Constant]; +"1246 Constant_5391" [id=1246, type=Constant]; +"1247 Constant_5388" [id=1247, type=Constant]; +"1248 Constant_5385" [id=1248, type=Constant]; +"1249 Constant_5355" [id=1249, type=Constant]; +"1250 Constant_5352" [id=1250, type=Constant]; +"1251 Constant_5349" [id=1251, type=Constant]; +"1252 /layers/layers.1/downsample/Constant" [id=1252, type=Constant]; +"1253 /layers/layers.1/blocks.1/mlp/fc2/MatMul/fq_weights_1" [id=1253, type=FakeQuantize]; +"1254 Constant_27694" [id=1254, type=Constant]; +"1255 Constant_27693" [id=1255, type=Constant]; +"1256 Constant_27692" [id=1256, type=Constant]; +"1257 Constant_27691" [id=1257, type=Constant]; +"1258 Transpose_6420" [id=1258, type=Constant]; +"1259 Constant_27689" [id=1259, type=Constant]; +"1260 Constant_27688" [id=1260, type=Constant]; +"1261 Constant_27687" [id=1261, type=Constant]; +"1262 Constant_27686" [id=1262, type=Constant]; +"1263 /layers/layers.1/blocks.1/mlp/fc1/MatMul/fq_weights_1" [id=1263, type=FakeQuantize]; +"1264 Constant_27684" [id=1264, type=Constant]; +"1265 Constant_27683" [id=1265, type=Constant]; +"1266 Constant_27682" [id=1266, type=Constant]; +"1267 Constant_27681" [id=1267, type=Constant]; +"1268 Transpose_6416" [id=1268, type=Constant]; +"1269 Constant_27679" [id=1269, type=Constant]; +"1270 Constant_27678" [id=1270, type=Constant]; +"1271 Constant_27677" [id=1271, type=Constant]; +"1272 Constant_27676" [id=1272, type=Constant]; +"1273 Constant_7320" [id=1273, type=Constant]; +"1274 Constant_7319" [id=1274, type=Constant]; +"1275 Constant_864" [id=1275, type=Constant]; +"1276 /layers/layers.1/blocks.1/Constant_31" [id=1276, type=Constant]; +"1277 Constant_5319" [id=1277, type=Constant]; +"1278 Constant_5316" [id=1278, type=Constant]; +"1279 Constant_5313" [id=1279, type=Constant]; +"1280 Constant_5295" [id=1280, type=Constant]; +"1281 Constant_5292" [id=1281, type=Constant]; +"1282 Constant_5289" [id=1282, type=Constant]; +"1283 /layers/layers.1/blocks.1/Constant_18" [id=1283, type=Constant]; +"1284 Constant_811" [id=1284, type=Constant]; +"1285 /layers/layers.1/blocks.1/Constant_17" [id=1285, type=Constant]; +"1286 /layers/layers.1/blocks.1/Constant_16" [id=1286, type=Constant]; +"1287 /layers/layers.1/blocks.1/attn/proj/MatMul/fq_weights_1" [id=1287, type=FakeQuantize]; +"1288 Constant_27674" [id=1288, type=Constant]; +"1289 Constant_27673" [id=1289, type=Constant]; +"1290 Constant_27672" [id=1290, type=Constant]; +"1291 Constant_27671" [id=1291, type=Constant]; +"1292 Transpose_6412" [id=1292, type=Constant]; +"1293 /layers/layers.1/blocks.1/attn/Constant_4" [id=1293, type=Constant]; +"1294 Constant_795" [id=1294, type=Constant]; +"1295 Constant_27669" [id=1295, type=Constant]; +"1296 Constant_27668" [id=1296, type=Constant]; +"1297 Constant_27667" [id=1297, type=Constant]; +"1298 Constant_27666" [id=1298, type=Constant]; +"1299 Constant_776" [id=1299, type=Constant]; +"1300 Constant_770" [id=1300, type=Constant]; +"1301 /layers/layers.1/blocks.1/attn/Constant" [id=1301, type=Constant]; +"1302 /layers/layers.1/blocks.1/attn/qkv/MatMul/fq_weights_1" [id=1302, type=FakeQuantize]; +"1303 Constant_27654" [id=1303, type=Constant]; +"1304 Constant_27653" [id=1304, type=Constant]; +"1305 Constant_27652" [id=1305, type=Constant]; +"1306 Constant_27651" [id=1306, type=Constant]; +"1307 Transpose_6409" [id=1307, type=Constant]; +"1308 /layers/layers.1/blocks.1/Constant_15" [id=1308, type=Constant]; +"1309 /layers/layers.1/blocks.1/Constant_14" [id=1309, type=Constant]; +"1310 Constant_754" [id=1310, type=Constant]; +"1311 /layers/layers.1/blocks.1/Constant_13" [id=1311, type=Constant]; +"1312 Constant_5271" [id=1312, type=Constant]; +"1313 Constant_5268" [id=1313, type=Constant]; +"1314 Constant_5265" [id=1314, type=Constant]; +"1315 Constant_5247" [id=1315, type=Constant]; +"1316 Constant_5244" [id=1316, type=Constant]; +"1317 Constant_5241" [id=1317, type=Constant]; +"1318 /layers/layers.1/blocks.1/Constant" [id=1318, type=Constant]; +"1319 Constant_27469" [id=1319, type=Constant]; +"1320 Constant_27468" [id=1320, type=Constant]; +"1321 Constant_27467" [id=1321, type=Constant]; +"1322 Constant_27466" [id=1322, type=Constant]; +"1323 Constant_7315" [id=1323, type=Constant]; +"1324 Constant_7314" [id=1324, type=Constant]; +"1325 Constant_691" [id=1325, type=Constant]; +"1326 /layers/layers.1/blocks.0/mlp/fc2/MatMul/fq_weights_1" [id=1326, type=FakeQuantize]; +"1327 Constant_27649" [id=1327, type=Constant]; +"1328 Constant_27648" [id=1328, type=Constant]; +"1329 Constant_27647" [id=1329, type=Constant]; +"1330 Constant_27646" [id=1330, type=Constant]; +"1331 Transpose_6405" [id=1331, type=Constant]; +"1332 Constant_27644" [id=1332, type=Constant]; +"1333 Constant_27643" [id=1333, type=Constant]; +"1334 Constant_27642" [id=1334, type=Constant]; +"1335 Constant_27641" [id=1335, type=Constant]; +"1336 /layers/layers.1/blocks.0/mlp/fc1/MatMul/fq_weights_1" [id=1336, type=FakeQuantize]; +"1337 Constant_27639" [id=1337, type=Constant]; +"1338 Constant_27638" [id=1338, type=Constant]; +"1339 Constant_27637" [id=1339, type=Constant]; +"1340 Constant_27636" [id=1340, type=Constant]; +"1341 Transpose_6401" [id=1341, type=Constant]; +"1342 Constant_27634" [id=1342, type=Constant]; +"1343 Constant_27633" [id=1343, type=Constant]; +"1344 Constant_27632" [id=1344, type=Constant]; +"1345 Constant_27631" [id=1345, type=Constant]; +"1346 Constant_7311" [id=1346, type=Constant]; +"1347 Constant_7310" [id=1347, type=Constant]; +"1348 Constant_665" [id=1348, type=Constant]; +"1349 /layers/layers.1/blocks.0/Constant_7" [id=1349, type=Constant]; +"1350 /layers/layers.1/blocks.0/Constant_6" [id=1350, type=Constant]; +"1351 Constant_654" [id=1351, type=Constant]; +"1352 /layers/layers.1/blocks.0/Constant_5" [id=1352, type=Constant]; +"1353 /layers/layers.1/blocks.0/Constant_4" [id=1353, type=Constant]; +"1354 /layers/layers.1/blocks.0/attn/proj/MatMul/fq_weights_1" [id=1354, type=FakeQuantize]; +"1355 Constant_27629" [id=1355, type=Constant]; +"1356 Constant_27628" [id=1356, type=Constant]; +"1357 Constant_27627" [id=1357, type=Constant]; +"1358 Constant_27626" [id=1358, type=Constant]; +"1359 Transpose_6397" [id=1359, type=Constant]; +"1360 /layers/layers.1/blocks.0/attn/Constant_2" [id=1360, type=Constant]; +"1361 Constant_638" [id=1361, type=Constant]; +"1362 Constant_27624" [id=1362, type=Constant]; +"1363 Constant_27623" [id=1363, type=Constant]; +"1364 Constant_27622" [id=1364, type=Constant]; +"1365 Constant_27621" [id=1365, type=Constant]; +"1366 Constant_628" [id=1366, type=Constant]; +"1367 Constant_622" [id=1367, type=Constant]; +"1368 /layers/layers.1/blocks.0/attn/Constant" [id=1368, type=Constant]; +"1369 /layers/layers.1/blocks.0/attn/qkv/MatMul/fq_weights_1" [id=1369, type=FakeQuantize]; +"1370 Constant_27609" [id=1370, type=Constant]; +"1371 Constant_27608" [id=1371, type=Constant]; +"1372 Constant_27607" [id=1372, type=Constant]; +"1373 Constant_27606" [id=1373, type=Constant]; +"1374 Transpose_6394" [id=1374, type=Constant]; +"1375 /layers/layers.1/blocks.0/Constant_3" 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Constant_27592" [id=1394, type=Constant]; +"1395 Constant_27591" [id=1395, type=Constant]; +"1396 Constant_7304" [id=1396, type=Constant]; +"1397 Constant_7303" [id=1397, type=Constant]; +"1398 Constant_571" [id=1398, type=Constant]; +"1399 /layers/layers.0/downsample/Constant_25" [id=1399, type=Constant]; +"1400 Constant_5223" [id=1400, type=Constant]; +"1401 Constant_5220" [id=1401, type=Constant]; +"1402 Constant_5217" [id=1402, type=Constant]; +"1403 Constant_5187" [id=1403, type=Constant]; +"1404 Constant_5184" [id=1404, type=Constant]; +"1405 Constant_5181" [id=1405, type=Constant]; +"1406 /layers/layers.0/downsample/Constant" [id=1406, type=Constant]; +"1407 /layers/layers.0/blocks.1/mlp/fc2/MatMul/fq_weights_1" [id=1407, type=FakeQuantize]; +"1408 Constant_27589" [id=1408, type=Constant]; +"1409 Constant_27588" [id=1409, type=Constant]; +"1410 Constant_27587" [id=1410, type=Constant]; +"1411 Constant_27586" [id=1411, type=Constant]; +"1412 Transpose_6386" [id=1412, type=Constant]; +"1413 Constant_27584" [id=1413, type=Constant]; +"1414 Constant_27583" [id=1414, type=Constant]; +"1415 Constant_27582" [id=1415, type=Constant]; +"1416 Constant_27581" [id=1416, type=Constant]; +"1417 /layers/layers.0/blocks.1/mlp/fc1/MatMul/fq_weights_1" [id=1417, type=FakeQuantize]; +"1418 Constant_27579" [id=1418, type=Constant]; +"1419 Constant_27578" [id=1419, type=Constant]; +"1420 Constant_27577" [id=1420, type=Constant]; +"1421 Constant_27576" [id=1421, type=Constant]; +"1422 Transpose_6382" [id=1422, type=Constant]; +"1423 Constant_27574" [id=1423, type=Constant]; +"1424 Constant_27573" [id=1424, type=Constant]; +"1425 Constant_27572" [id=1425, type=Constant]; +"1426 Constant_27571" [id=1426, type=Constant]; +"1427 Constant_7300" [id=1427, type=Constant]; +"1428 Constant_7299" [id=1428, type=Constant]; +"1429 Constant_506" [id=1429, type=Constant]; +"1430 /layers/layers.0/blocks.1/Constant_31" [id=1430, type=Constant]; +"1431 Constant_5151" [id=1431, type=Constant]; +"1432 Constant_5148" [id=1432, type=Constant]; +"1433 Constant_5145" [id=1433, type=Constant]; +"1434 Constant_5127" [id=1434, type=Constant]; +"1435 Constant_5124" [id=1435, type=Constant]; +"1436 Constant_5121" [id=1436, type=Constant]; +"1437 /layers/layers.0/blocks.1/Constant_18" [id=1437, type=Constant]; +"1438 Constant_453" [id=1438, type=Constant]; +"1439 /layers/layers.0/blocks.1/Constant_17" [id=1439, type=Constant]; +"1440 /layers/layers.0/blocks.1/Constant_16" [id=1440, type=Constant]; +"1441 /layers/layers.0/blocks.1/attn/proj/MatMul/fq_weights_1" [id=1441, type=FakeQuantize]; +"1442 Constant_27569" [id=1442, type=Constant]; +"1443 Constant_27568" [id=1443, type=Constant]; +"1444 Constant_27567" [id=1444, type=Constant]; +"1445 Constant_27566" [id=1445, type=Constant]; +"1446 Transpose_6378" [id=1446, type=Constant]; +"1447 /layers/layers.0/blocks.1/attn/Constant_4" [id=1447, type=Constant]; +"1448 Constant_437" [id=1448, type=Constant]; +"1449 Constant_27564" [id=1449, type=Constant]; +"1450 Constant_27563" [id=1450, type=Constant]; +"1451 Constant_27562" [id=1451, type=Constant]; +"1452 Constant_27561" [id=1452, type=Constant]; +"1453 Constant_418" [id=1453, type=Constant]; +"1454 Constant_412" [id=1454, type=Constant]; +"1455 /layers/layers.0/blocks.1/attn/Constant" [id=1455, type=Constant]; +"1456 /layers/layers.0/blocks.1/attn/qkv/MatMul/fq_weights_1" [id=1456, type=FakeQuantize]; +"1457 Constant_27549" [id=1457, type=Constant]; +"1458 Constant_27548" [id=1458, type=Constant]; +"1459 Constant_27547" [id=1459, type=Constant]; +"1460 Constant_27546" [id=1460, type=Constant]; +"1461 Transpose_6375" [id=1461, type=Constant]; +"1462 /layers/layers.0/blocks.1/Constant_15" [id=1462, type=Constant]; +"1463 /layers/layers.0/blocks.1/Constant_14" [id=1463, type=Constant]; +"1464 Constant_396" [id=1464, type=Constant]; +"1465 /layers/layers.0/blocks.1/Constant_13" [id=1465, type=Constant]; +"1466 Constant_5103" [id=1466, type=Constant]; +"1467 Constant_5100" [id=1467, type=Constant]; +"1468 Constant_5097" [id=1468, type=Constant]; +"1469 Constant_5079" [id=1469, type=Constant]; +"1470 Constant_5076" [id=1470, type=Constant]; +"1471 Constant_5073" [id=1471, type=Constant]; +"1472 /layers/layers.0/blocks.1/Constant" [id=1472, type=Constant]; +"1473 Constant_27464" [id=1473, type=Constant]; +"1474 Constant_27463" [id=1474, type=Constant]; +"1475 Constant_27462" [id=1475, type=Constant]; +"1476 Constant_27461" [id=1476, type=Constant]; +"1477 Constant_7295" [id=1477, type=Constant]; +"1478 Constant_7294" [id=1478, type=Constant]; +"1479 Constant_333" [id=1479, type=Constant]; +"1480 /layers/layers.0/blocks.0/mlp/fc2/MatMul/fq_weights_1" [id=1480, type=FakeQuantize]; +"1481 Constant_27544" [id=1481, type=Constant]; +"1482 Constant_27543" [id=1482, type=Constant]; +"1483 Constant_27542" [id=1483, type=Constant]; +"1484 Constant_27541" [id=1484, type=Constant]; +"1485 Transpose_6371" [id=1485, type=Constant]; 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type=Constant]; +"1505 Constant_296" [id=1505, type=Constant]; +"1506 /layers/layers.0/blocks.0/Constant_6" [id=1506, type=Constant]; +"1507 /layers/layers.0/blocks.0/Constant_5" [id=1507, type=Constant]; +"1508 /layers/layers.0/blocks.0/attn/proj/MatMul/fq_weights_1" [id=1508, type=FakeQuantize]; +"1509 Constant_27524" [id=1509, type=Constant]; +"1510 Constant_27523" [id=1510, type=Constant]; +"1511 Constant_27522" [id=1511, type=Constant]; +"1512 Constant_27521" [id=1512, type=Constant]; +"1513 Transpose_6363" [id=1513, type=Constant]; +"1514 /layers/layers.0/blocks.0/attn/Constant_2" [id=1514, type=Constant]; +"1515 Constant_280" [id=1515, type=Constant]; +"1516 Constant_27519" [id=1516, type=Constant]; +"1517 Constant_27518" [id=1517, type=Constant]; +"1518 Constant_27517" [id=1518, type=Constant]; +"1519 Constant_27516" [id=1519, type=Constant]; +"1520 Constant_270" [id=1520, type=Constant]; +"1521 Constant_264" [id=1521, type=Constant]; +"1522 /layers/layers.0/blocks.0/attn/Constant" [id=1522, type=Constant]; +"1523 /layers/layers.0/blocks.0/attn/qkv/MatMul/fq_weights_1" [id=1523, type=FakeQuantize]; +"1524 Constant_27504" [id=1524, type=Constant]; +"1525 Constant_27503" [id=1525, type=Constant]; +"1526 Constant_27502" [id=1526, type=Constant]; +"1527 Constant_27501" [id=1527, type=Constant]; +"1528 Transpose_6360" [id=1528, type=Constant]; +"1529 /layers/layers.0/blocks.0/Constant_4" [id=1529, type=Constant]; +"1530 /layers/layers.0/blocks.0/Constant_3" [id=1530, type=Constant]; +"1531 Constant_248" [id=1531, type=Constant]; +"1532 /layers/layers.0/blocks.0/Constant_2" [id=1532, type=Constant]; +"1533 Constant_27499" [id=1533, type=Constant]; +"1534 Constant_27498" [id=1534, type=Constant]; +"1535 Constant_27497" [id=1535, type=Constant]; +"1536 Constant_27496" [id=1536, type=Constant]; +"1537 Constant_7286" [id=1537, type=Constant]; +"1538 Constant_7285" [id=1538, type=Constant]; +"1539 Constant_227" [id=1539, type=Constant]; +"1540 Constant_7284" [id=1540, type=Constant]; +"1541 Constant_7283" [id=1541, type=Constant]; +"1542 Constant_213" [id=1542, type=Constant]; +"1543 Constant_211" [id=1543, type=Constant]; +"1544 /patch_embed/Constant_4" [id=1544, type=Constant]; +"1545 Broadcast_201" [id=1545, type=Constant]; +"1546 /patch_embed/Constant_3" [id=1546, type=Constant]; +"1547 /patch_embed/Constant_2" [id=1547, type=Constant]; +"1548 Reshape_190" [id=1548, type=Constant]; +"1549 /patch_embed/proj/Conv/WithoutBiases/fq_weights_1" [id=1549, type=FakeQuantize]; +"1550 Constant_27494" [id=1550, type=Constant]; +"1551 Constant_27493" [id=1551, type=Constant]; +"1552 Constant_27492" [id=1552, type=Constant]; +"1553 Constant_27491" [id=1553, type=Constant]; +"1554 Gather_7282" [id=1554, type=Constant]; +"1555 Constant_27489" [id=1555, type=Constant]; +"1556 Constant_27488" [id=1556, type=Constant]; +"1557 Constant_27487" [id=1557, type=Constant]; +"1558 Constant_27486" [id=1558, type=Constant]; +"1559 Gather_7279" [id=1559, type=Constant]; +"1560 Gather_7276" [id=1560, type=Constant]; +"1561 Constant_7287" [id=1561, type=Constant]; +"1562 onnx^^Add_2244" [id=1562, label="1562 onnx::Add_2244", type=Constant]; +"1563 Constant_268" [id=1563, type=Constant]; +"1564 /patch_embed/proj/Constant" [id=1564, type=Constant]; +"1565 Constant_27509" [id=1565, type=Constant]; +"1566 Constant_27508" [id=1566, type=Constant]; +"1567 Constant_27507" [id=1567, type=Constant]; +"1568 Constant_27506" [id=1568, type=Constant]; +"1569 Constant_27514" [id=1569, type=Constant]; +"1570 Constant_27513" [id=1570, type=Constant]; +"1571 Constant_27512" [id=1571, type=Constant]; +"1572 Constant_27511" [id=1572, type=Constant]; +"1573 Constant_7288" [id=1573, type=Constant]; +"1574 Constant_266" [id=1574, type=Constant]; +"1575 /layers/layers.0/blocks.0/Constant" [id=1575, type=Constant]; +"1576 Constant_7289" [id=1576, type=Constant]; +"1577 Constant_7292" [id=1577, type=Constant]; +"1578 Constant_7293" [id=1578, type=Constant]; +"1579 Constant_5067" [id=1579, type=Constant]; +"1580 Constant_5064" [id=1580, type=Constant]; +"1581 Constant_5061" [id=1581, type=Constant]; +"1582 Constant_5091" [id=1582, type=Constant]; +"1583 Constant_5088" [id=1583, type=Constant]; +"1584 Constant_5085" [id=1584, type=Constant]; +"1585 Constant_7296" [id=1585, type=Constant]; +"1586 /layers/layers.0/blocks.1/attn/Constant_3" [id=1586, type=Constant]; +"1587 onnx^^Add_2301" [id=1587, label="1587 onnx::Add_2301", type=Constant]; +"1588 /layers/layers.0/blocks.1/attn/Constant_2" [id=1588, type=Constant]; +"1589 onnx^^Add_2293" [id=1589, label="1589 onnx::Add_2293", type=Constant]; +"1590 Constant_416" [id=1590, type=Constant]; +"1591 Constant_27554" [id=1591, type=Constant]; +"1592 Constant_27553" [id=1592, type=Constant]; +"1593 Constant_27552" [id=1593, type=Constant]; +"1594 Constant_27551" [id=1594, type=Constant]; +"1595 Constant_27559" [id=1595, type=Constant]; +"1596 Constant_27558" [id=1596, type=Constant]; +"1597 Constant_27557" [id=1597, type=Constant]; +"1598 Constant_27556" [id=1598, type=Constant]; +"1599 Constant_7297" [id=1599, type=Constant]; +"1600 Constant_414" [id=1600, type=Constant]; +"1601 Constant_7298" [id=1601, type=Constant]; +"1602 Constant_5115" [id=1602, type=Constant]; +"1603 Constant_5112" [id=1603, type=Constant]; +"1604 Constant_5109" [id=1604, type=Constant]; +"1605 Constant_5139" [id=1605, type=Constant]; +"1606 Constant_5136" [id=1606, type=Constant]; +"1607 Constant_5133" [id=1607, type=Constant]; +"1608 Constant_7301" [id=1608, type=Constant]; +"1609 Constant_7302" [id=1609, type=Constant]; +"1610 Constant_5211" [id=1610, type=Constant]; +"1611 Constant_5208" [id=1611, type=Constant]; +"1612 Constant_5205" [id=1612, type=Constant]; +"1613 Constant_5163" [id=1613, type=Constant]; +"1614 Constant_5160" [id=1614, type=Constant]; +"1615 Constant_5157" [id=1615, type=Constant]; +"1616 Constant_5199" [id=1616, type=Constant]; +"1617 Constant_5196" [id=1617, type=Constant]; +"1618 Constant_5193" [id=1618, type=Constant]; +"1619 Constant_5175" [id=1619, type=Constant]; +"1620 Constant_5172" [id=1620, type=Constant]; +"1621 Constant_5169" [id=1621, type=Constant]; +"1622 Constant_7307" [id=1622, type=Constant]; +"1623 onnx^^Add_2389" [id=1623, label="1623 onnx::Add_2389", type=Constant]; +"1624 Constant_626" [id=1624, type=Constant]; +"1625 Constant_27614" [id=1625, type=Constant]; +"1626 Constant_27613" [id=1626, type=Constant]; +"1627 Constant_27612" [id=1627, type=Constant]; +"1628 Constant_27611" [id=1628, type=Constant]; +"1629 Constant_27619" [id=1629, type=Constant]; +"1630 Constant_27618" [id=1630, type=Constant]; +"1631 Constant_27617" [id=1631, type=Constant]; +"1632 Constant_27616" [id=1632, type=Constant]; +"1633 Constant_7308" [id=1633, type=Constant]; +"1634 Constant_624" [id=1634, type=Constant]; +"1635 Constant_7309" [id=1635, type=Constant]; +"1636 Constant_7312" [id=1636, type=Constant]; +"1637 Constant_7313" [id=1637, type=Constant]; +"1638 Constant_5235" [id=1638, type=Constant]; +"1639 Constant_5232" [id=1639, type=Constant]; +"1640 Constant_5229" [id=1640, type=Constant]; +"1641 Constant_5259" [id=1641, type=Constant]; +"1642 Constant_5256" [id=1642, type=Constant]; +"1643 Constant_5253" [id=1643, type=Constant]; +"1644 Constant_7316" [id=1644, type=Constant]; +"1645 /layers/layers.1/blocks.1/attn/Constant_3" [id=1645, type=Constant]; +"1646 onnx^^Add_2446" [id=1646, label="1646 onnx::Add_2446", type=Constant]; +"1647 /layers/layers.1/blocks.1/attn/Constant_2" [id=1647, type=Constant]; +"1648 onnx^^Add_2438" [id=1648, label="1648 onnx::Add_2438", type=Constant]; +"1649 Constant_774" [id=1649, type=Constant]; +"1650 Constant_27659" [id=1650, type=Constant]; +"1651 Constant_27658" [id=1651, type=Constant]; +"1652 Constant_27657" [id=1652, type=Constant]; +"1653 Constant_27656" [id=1653, type=Constant]; +"1654 Constant_27664" [id=1654, type=Constant]; +"1655 Constant_27663" [id=1655, type=Constant]; +"1656 Constant_27662" [id=1656, type=Constant]; +"1657 Constant_27661" [id=1657, type=Constant]; +"1658 Constant_7317" [id=1658, type=Constant]; +"1659 Constant_772" [id=1659, type=Constant]; +"1660 Constant_7318" [id=1660, type=Constant]; +"1661 Constant_5283" [id=1661, type=Constant]; +"1662 Constant_5280" [id=1662, type=Constant]; +"1663 Constant_5277" [id=1663, type=Constant]; +"1664 Constant_5307" [id=1664, type=Constant]; +"1665 Constant_5304" [id=1665, type=Constant]; +"1666 Constant_5301" [id=1666, type=Constant]; +"1667 Constant_7321" [id=1667, type=Constant]; +"1668 Constant_7322" [id=1668, type=Constant]; +"1669 Constant_5379" [id=1669, type=Constant]; +"1670 Constant_5376" [id=1670, type=Constant]; +"1671 Constant_5373" [id=1671, type=Constant]; +"1672 Constant_5331" [id=1672, type=Constant]; +"1673 Constant_5328" [id=1673, type=Constant]; +"1674 Constant_5325" [id=1674, type=Constant]; +"1675 Constant_5367" [id=1675, type=Constant]; +"1676 Constant_5364" [id=1676, type=Constant]; +"1677 Constant_5361" [id=1677, type=Constant]; +"1678 Constant_5343" [id=1678, type=Constant]; +"1679 Constant_5340" [id=1679, type=Constant]; +"1680 Constant_5337" [id=1680, type=Constant]; +"1681 Constant_7327" [id=1681, type=Constant]; +"1682 onnx^^Add_2534" [id=1682, label="1682 onnx::Add_2534", type=Constant]; +"1683 Constant_984" [id=1683, type=Constant]; +"1684 Constant_27719" [id=1684, type=Constant]; +"1685 Constant_27718" [id=1685, type=Constant]; +"1686 Constant_27717" [id=1686, type=Constant]; +"1687 Constant_27716" [id=1687, type=Constant]; +"1688 Constant_27724" [id=1688, type=Constant]; +"1689 Constant_27723" [id=1689, type=Constant]; +"1690 Constant_27722" [id=1690, type=Constant]; +"1691 Constant_27721" [id=1691, type=Constant]; +"1692 Constant_7328" [id=1692, type=Constant]; +"1693 Constant_982" [id=1693, type=Constant]; +"1694 Constant_7329" [id=1694, type=Constant]; +"1695 Constant_7332" [id=1695, type=Constant]; +"1696 Constant_7333" [id=1696, type=Constant]; +"1697 Constant_5403" [id=1697, type=Constant]; +"1698 Constant_5400" [id=1698, type=Constant]; +"1699 Constant_5397" [id=1699, type=Constant]; +"1700 Constant_5427" [id=1700, type=Constant]; +"1701 Constant_5424" [id=1701, type=Constant]; +"1702 Constant_5421" [id=1702, type=Constant]; +"1703 Constant_7336" [id=1703, type=Constant]; +"1704 /layers/layers.2/blocks.1/attn/Constant_3" [id=1704, type=Constant]; +"1705 onnx^^Add_2702" [id=1705, label="1705 onnx::Add_2702", type=Constant]; +"1706 /layers/layers.2/blocks.1/attn/Constant_2" [id=1706, type=Constant]; +"1707 onnx^^Add_2583" [id=1707, label="1707 onnx::Add_2583", type=Constant]; +"1708 Constant_1132" [id=1708, type=Constant]; +"1709 Constant_27764" [id=1709, type=Constant]; +"1710 Constant_27763" [id=1710, type=Constant]; +"1711 Constant_27762" [id=1711, type=Constant]; +"1712 Constant_27761" [id=1712, type=Constant]; +"1713 Constant_27769" [id=1713, type=Constant]; +"1714 Constant_27768" [id=1714, type=Constant]; +"1715 Constant_27767" [id=1715, type=Constant]; +"1716 Constant_27766" [id=1716, type=Constant]; +"1717 Constant_7337" [id=1717, type=Constant]; +"1718 Constant_1130" [id=1718, type=Constant]; +"1719 Constant_7338" [id=1719, type=Constant]; +"1720 Constant_5451" [id=1720, type=Constant]; +"1721 Constant_5448" [id=1721, type=Constant]; +"1722 Constant_5445" [id=1722, type=Constant]; +"1723 Constant_5475" [id=1723, type=Constant]; +"1724 Constant_5472" [id=1724, type=Constant]; +"1725 Constant_5469" [id=1725, type=Constant]; +"1726 Constant_7341" [id=1726, type=Constant]; +"1727 Constant_7342" [id=1727, type=Constant]; +"1728 Constant_7345" [id=1728, type=Constant]; +"1729 onnx^^Add_2645" [id=1729, label="1729 onnx::Add_2645", type=Constant]; +"1730 Constant_1289" [id=1730, type=Constant]; +"1731 Constant_27814" [id=1731, type=Constant]; +"1732 Constant_27813" [id=1732, type=Constant]; +"1733 Constant_27812" [id=1733, type=Constant]; +"1734 Constant_27811" [id=1734, type=Constant]; +"1735 Constant_27819" [id=1735, type=Constant]; +"1736 Constant_27818" [id=1736, type=Constant]; +"1737 Constant_27817" [id=1737, type=Constant]; +"1738 Constant_27816" [id=1738, type=Constant]; +"1739 Constant_7346" [id=1739, type=Constant]; +"1740 Constant_1287" [id=1740, type=Constant]; +"1741 Constant_7347" [id=1741, type=Constant]; +"1742 Constant_7350" [id=1742, type=Constant]; +"1743 Constant_7351" [id=1743, type=Constant]; +"1744 Constant_5499" [id=1744, type=Constant]; +"1745 Constant_5496" [id=1745, type=Constant]; +"1746 Constant_5493" [id=1746, type=Constant]; +"1747 Constant_5523" [id=1747, type=Constant]; +"1748 Constant_5520" [id=1748, type=Constant]; +"1749 Constant_5517" [id=1749, type=Constant]; +"1750 Constant_7354" [id=1750, type=Constant]; +"1751 /layers/layers.2/blocks.3/attn/Constant_3" [id=1751, type=Constant]; +"1752 /layers/layers.2/blocks.3/attn/Constant_2" [id=1752, type=Constant]; +"1753 onnx^^Add_2694" [id=1753, label="1753 onnx::Add_2694", type=Constant]; +"1754 Constant_1437" [id=1754, type=Constant]; +"1755 Constant_27859" [id=1755, type=Constant]; +"1756 Constant_27858" [id=1756, type=Constant]; +"1757 Constant_27857" [id=1757, type=Constant]; +"1758 Constant_27856" [id=1758, type=Constant]; +"1759 Constant_27864" [id=1759, type=Constant]; +"1760 Constant_27863" [id=1760, type=Constant]; +"1761 Constant_27862" [id=1761, type=Constant]; +"1762 Constant_27861" [id=1762, type=Constant]; +"1763 Constant_7355" [id=1763, type=Constant]; +"1764 Constant_1435" [id=1764, type=Constant]; +"1765 Constant_7356" [id=1765, type=Constant]; +"1766 Constant_5547" [id=1766, type=Constant]; +"1767 Constant_5544" [id=1767, type=Constant]; +"1768 Constant_5541" [id=1768, type=Constant]; +"1769 Constant_5571" [id=1769, type=Constant]; +"1770 Constant_5568" [id=1770, type=Constant]; +"1771 Constant_5565" [id=1771, type=Constant]; +"1772 Constant_7359" [id=1772, type=Constant]; +"1773 Constant_7360" [id=1773, type=Constant]; +"1774 Constant_7363" [id=1774, type=Constant]; +"1775 onnx^^Add_2756" [id=1775, label="1775 onnx::Add_2756", type=Constant]; +"1776 Constant_1594" [id=1776, type=Constant]; +"1777 Constant_27909" [id=1777, type=Constant]; +"1778 Constant_27908" [id=1778, type=Constant]; +"1779 Constant_27907" [id=1779, type=Constant]; +"1780 Constant_27906" [id=1780, type=Constant]; +"1781 Constant_27914" [id=1781, type=Constant]; +"1782 Constant_27913" [id=1782, type=Constant]; +"1783 Constant_27912" [id=1783, type=Constant]; +"1784 Constant_27911" [id=1784, type=Constant]; +"1785 Constant_7364" [id=1785, type=Constant]; +"1786 Constant_1592" [id=1786, type=Constant]; +"1787 Constant_7365" [id=1787, type=Constant]; +"1788 Constant_7368" [id=1788, type=Constant]; +"1789 Constant_7369" [id=1789, type=Constant]; +"1790 Constant_5595" [id=1790, type=Constant]; +"1791 Constant_5592" [id=1791, type=Constant]; +"1792 Constant_5589" [id=1792, type=Constant]; +"1793 Constant_5619" [id=1793, type=Constant]; +"1794 Constant_5616" [id=1794, type=Constant]; +"1795 Constant_5613" [id=1795, type=Constant]; +"1796 Constant_7372" [id=1796, type=Constant]; +"1797 /layers/layers.2/blocks.5/attn/Constant_3" [id=1797, type=Constant]; +"1798 /layers/layers.2/blocks.5/attn/Constant_2" [id=1798, type=Constant]; +"1799 onnx^^Add_2805" [id=1799, label="1799 onnx::Add_2805", type=Constant]; +"1800 Constant_1742" [id=1800, type=Constant]; +"1801 Constant_27954" [id=1801, type=Constant]; +"1802 Constant_27953" [id=1802, type=Constant]; +"1803 Constant_27952" [id=1803, type=Constant]; +"1804 Constant_27951" [id=1804, type=Constant]; +"1805 Constant_27959" [id=1805, type=Constant]; +"1806 Constant_27958" [id=1806, type=Constant]; +"1807 Constant_27957" [id=1807, type=Constant]; +"1808 Constant_27956" [id=1808, type=Constant]; +"1809 Constant_7373" [id=1809, type=Constant]; +"1810 Constant_1740" [id=1810, type=Constant]; +"1811 Constant_7374" [id=1811, type=Constant]; +"1812 Constant_5643" [id=1812, type=Constant]; +"1813 Constant_5640" [id=1813, type=Constant]; +"1814 Constant_5637" [id=1814, type=Constant]; +"1815 Constant_5667" [id=1815, type=Constant]; +"1816 Constant_5664" [id=1816, type=Constant]; +"1817 Constant_5661" [id=1817, type=Constant]; +"1818 Constant_7377" [id=1818, type=Constant]; +"1819 Constant_7378" [id=1819, type=Constant]; +"1820 Constant_5739" [id=1820, type=Constant]; +"1821 Constant_5736" [id=1821, type=Constant]; +"1822 Constant_5733" [id=1822, type=Constant]; +"1823 Constant_5691" [id=1823, type=Constant]; +"1824 Constant_5688" [id=1824, type=Constant]; +"1825 Constant_5685" [id=1825, type=Constant]; +"1826 Constant_5727" [id=1826, type=Constant]; +"1827 Constant_5724" [id=1827, type=Constant]; +"1828 Constant_5721" [id=1828, type=Constant]; +"1829 Constant_5703" [id=1829, type=Constant]; +"1830 Constant_5700" [id=1830, type=Constant]; +"1831 Constant_5697" [id=1831, type=Constant]; +"1832 Constant_7383" [id=1832, type=Constant]; +"1833 onnx^^Add_2901" [id=1833, label="1833 onnx::Add_2901", type=Constant]; +"1834 Constant_1952" [id=1834, type=Constant]; +"1835 Constant_28014" [id=1835, type=Constant]; +"1836 Constant_28013" [id=1836, type=Constant]; +"1837 Constant_28012" [id=1837, type=Constant]; +"1838 Constant_28011" [id=1838, type=Constant]; +"1839 Constant_28019" [id=1839, type=Constant]; +"1840 Constant_28018" [id=1840, type=Constant]; +"1841 Constant_28017" [id=1841, type=Constant]; +"1842 Constant_28016" [id=1842, type=Constant]; +"1843 Constant_7384" [id=1843, type=Constant]; +"1844 Constant_1950" [id=1844, type=Constant]; +"1845 Constant_7385" [id=1845, type=Constant]; +"1846 Constant_7388" [id=1846, type=Constant]; +"1847 Constant_7389" [id=1847, type=Constant]; +"1848 Constant_7392" [id=1848, type=Constant]; +"1849 onnx^^Add_2950" [id=1849, label="1849 onnx::Add_2950", type=Constant]; +"1850 Constant_2058" [id=1850, type=Constant]; +"1851 Constant_28064" [id=1851, type=Constant]; +"1852 Constant_28063" [id=1852, type=Constant]; +"1853 Constant_28062" [id=1853, type=Constant]; +"1854 Constant_28061" [id=1854, type=Constant]; +"1855 Constant_28069" [id=1855, type=Constant]; +"1856 Constant_28068" [id=1856, type=Constant]; +"1857 Constant_28067" [id=1857, type=Constant]; +"1858 Constant_28066" [id=1858, type=Constant]; +"1859 Constant_7393" [id=1859, type=Constant]; +"1860 Constant_2056" [id=1860, type=Constant]; +"1861 Constant_7394" [id=1861, type=Constant]; +"1862 Constant_7397" [id=1862, type=Constant]; +"1863 Constant_7398" [id=1863, type=Constant]; +"0 input" -> "1 Multiply_6579" [label="[1, 3, 224, 224]", style=solid]; +"1 Multiply_6579" -> "2 Divide_2169" [label="[1, 3, 224, 224]", style=solid]; +"2 Divide_2169" -> "3 Divide_2169/fq_output_0" [label="[1, 3, 224, 224]", style=solid]; +"3 Divide_2169/fq_output_0" -> "4 /patch_embed/proj/Conv/WithoutBiases" [label="[1, 3, 224, 224]", style=solid]; +"4 /patch_embed/proj/Conv/WithoutBiases" -> "5 /patch_embed/proj/Conv" [label="[1, 96, 56, 56]", style=solid]; +"5 /patch_embed/proj/Conv" -> "6 /patch_embed/Reshape" [label="[1, 96, 56, 56]", style=solid]; +"5 /patch_embed/proj/Conv" -> "7 /patch_embed/Shape" [label="[1, 96, 56, 56]", style=solid]; +"6 /patch_embed/Reshape" -> "8 /patch_embed/Transpose" [label="[1, 96, 3136]", style=solid]; +"7 /patch_embed/Shape" -> "9 /patch_embed/Slice" [label="[4]", style=dashed]; +"8 /patch_embed/Transpose" -> "10 /patch_embed/norm/Div" [label="[1, 3136, 96]", style=solid]; +"9 /patch_embed/Slice" -> "11 /patch_embed/Concat" [label="[2]", style=dashed]; +"10 /patch_embed/norm/Div" -> "12 /patch_embed/norm/Mul" [label="[1, 3136, 96]", style=solid]; +"11 /patch_embed/Concat" -> "6 /patch_embed/Reshape" [label="[3]", style=dashed]; +"12 /patch_embed/norm/Mul" -> "13 /patch_embed/norm/Add_1" [label="[1, 3136, 96]", style=solid]; +"13 /patch_embed/norm/Add_1" -> "14 /layers/layers.0/blocks.0/Add" [label="[1, 3136, 96]", style=solid]; +"13 /patch_embed/norm/Add_1" -> "15 /layers/layers.0/blocks.0/norm1/Div" [label="[1, 3136, 96]", style=solid]; +"14 /layers/layers.0/blocks.0/Add" -> "16 /layers/layers.0/blocks.0/Add_1" [label="[1, 3136, 96]", style=solid]; +"14 /layers/layers.0/blocks.0/Add" -> "17 /layers/layers.0/blocks.0/norm2/Div" [label="[1, 3136, 96]", style=solid]; +"15 /layers/layers.0/blocks.0/norm1/Div" -> "18 /layers/layers.0/blocks.0/norm1/Mul" [label="[1, 3136, 96]", style=solid]; +"16 /layers/layers.0/blocks.0/Add_1" -> "19 /layers/layers.0/blocks.1/Add" [label="[1, 3136, 96]", style=solid]; +"16 /layers/layers.0/blocks.0/Add_1" -> "20 /layers/layers.0/blocks.1/norm1/Div" [label="[1, 3136, 96]", style=solid]; +"17 /layers/layers.0/blocks.0/norm2/Div" -> "21 /layers/layers.0/blocks.0/norm2/Mul" [label="[1, 3136, 96]", style=solid]; +"18 /layers/layers.0/blocks.0/norm1/Mul" -> "22 /layers/layers.0/blocks.0/norm1/Add_1" [label="[1, 3136, 96]", style=solid]; +"19 /layers/layers.0/blocks.1/Add" -> "23 /layers/layers.0/blocks.1/Add_1" [label="[1, 3136, 96]", style=solid]; +"19 /layers/layers.0/blocks.1/Add" -> "24 /layers/layers.0/blocks.1/norm2/Div" [label="[1, 3136, 96]", style=solid]; +"20 /layers/layers.0/blocks.1/norm1/Div" -> "25 /layers/layers.0/blocks.1/norm1/Mul" [label="[1, 3136, 96]", style=solid]; +"21 /layers/layers.0/blocks.0/norm2/Mul" -> "26 /layers/layers.0/blocks.0/norm2/Add_1" [label="[1, 3136, 96]", style=solid]; +"22 /layers/layers.0/blocks.0/norm1/Add_1" -> "27 /layers/layers.0/blocks.0/norm1/Add_1/fq_output_0" [label="[1, 3136, 96]", style=solid]; +"23 /layers/layers.0/blocks.1/Add_1" -> "28 /layers/layers.0/downsample/Reshape" [label="[1, 3136, 96]", style=solid]; +"24 /layers/layers.0/blocks.1/norm2/Div" -> "29 /layers/layers.0/blocks.1/norm2/Mul" [label="[1, 3136, 96]", style=solid]; +"25 /layers/layers.0/blocks.1/norm1/Mul" -> "30 /layers/layers.0/blocks.1/norm1/Add_1" [label="[1, 3136, 96]", style=solid]; +"26 /layers/layers.0/blocks.0/norm2/Add_1" -> "31 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96]", style=solid]; +"33 /layers/layers.0/downsample/Slice" -> "39 /layers/layers.0/downsample/Slice_1" [label="[1, 28, 56, 96]", style=solid]; +"33 /layers/layers.0/downsample/Slice" -> "40 /layers/layers.0/downsample/Slice_4" [label="[1, 28, 56, 96]", style=solid]; +"34 /layers/layers.0/downsample/Slice_2" -> "41 /layers/layers.0/downsample/Slice_3" [label="[1, 28, 56, 96]", style=solid]; +"34 /layers/layers.0/downsample/Slice_2" -> "42 /layers/layers.0/downsample/Slice_5" [label="[1, 28, 56, 96]", style=solid]; +"35 /layers/layers.0/blocks.1/norm2/Add_1" -> "43 /layers/layers.0/blocks.1/norm2/Add_1/fq_output_0" [label="[1, 3136, 96]", style=solid]; +"36 /layers/layers.0/blocks.1/norm1/Add_1/fq_output_0" -> "44 /layers/layers.0/blocks.1/Reshape" [label="[1, 3136, 96]", style=solid]; +"37 /layers/layers.0/blocks.0/mlp/fc1/MatMul" -> "45 /layers/layers.0/blocks.0/mlp/fc1/Add" [label="[1, 3136, 384]", style=solid]; +"38 /layers/layers.0/blocks.0/Transpose" -> "46 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style=dashed]; +"829 Constant_5715" -> "347 /layers/layers.2/downsample/Slice_2" [label="[2]", style=dashed]; +"830 Constant_5712" -> "347 /layers/layers.2/downsample/Slice_2" [label="[2]", style=dashed]; +"831 Constant_5709" -> "347 /layers/layers.2/downsample/Slice_2" [label="[2]", style=dashed]; +"832 /layers/layers.2/downsample/Constant" -> "330 /layers/layers.2/downsample/Reshape" [label="[4]", style=dashed]; +"833 /layers/layers.2/blocks.5/mlp/fc2/MatMul/fq_weights_1" -> "434 /layers/layers.2/blocks.5/mlp/fc2/MatMul" [label="[384, 1536]", style=solid]; +"834 Constant_27989" -> "833 /layers/layers.2/blocks.5/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; +"835 Constant_27988" -> "833 /layers/layers.2/blocks.5/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; +"836 Constant_27987" -> "833 /layers/layers.2/blocks.5/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; +"837 Constant_27986" -> "833 /layers/layers.2/blocks.5/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; +"838 Transpose_6514" -> "833 /layers/layers.2/blocks.5/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1536]", style=solid]; +"839 Constant_27984" -> "421 /layers/layers.2/blocks.5/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; +"840 Constant_27983" -> "421 /layers/layers.2/blocks.5/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; +"841 Constant_27982" -> "421 /layers/layers.2/blocks.5/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; +"842 Constant_27981" -> "421 /layers/layers.2/blocks.5/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; +"843 /layers/layers.2/blocks.5/mlp/fc1/MatMul/fq_weights_1" -> "380 /layers/layers.2/blocks.5/mlp/fc1/MatMul" [label="[1536, 384]", style=solid]; +"844 Constant_27979" -> "843 /layers/layers.2/blocks.5/mlp/fc1/MatMul/fq_weights_1" [label="[1536, 1]", style=solid]; +"845 Constant_27978" -> "843 /layers/layers.2/blocks.5/mlp/fc1/MatMul/fq_weights_1" [label="[1536, 1]", style=solid]; +"846 Constant_27977" -> "843 /layers/layers.2/blocks.5/mlp/fc1/MatMul/fq_weights_1" [label="[1536, 1]", style=solid]; +"847 Constant_27976" -> "843 /layers/layers.2/blocks.5/mlp/fc1/MatMul/fq_weights_1" [label="[1536, 1]", style=solid]; +"848 Transpose_6510" -> "843 /layers/layers.2/blocks.5/mlp/fc1/MatMul/fq_weights_1" [label="[1536, 384]", style=solid]; +"849 Constant_27974" -> "366 /layers/layers.2/blocks.5/norm2/Add_1/fq_output_0" [label="[]", style=solid]; +"850 Constant_27973" -> "366 /layers/layers.2/blocks.5/norm2/Add_1/fq_output_0" [label="[]", style=solid]; +"851 Constant_27972" -> "366 /layers/layers.2/blocks.5/norm2/Add_1/fq_output_0" [label="[]", style=solid]; +"852 Constant_27971" -> "366 /layers/layers.2/blocks.5/norm2/Add_1/fq_output_0" [label="[]", style=solid]; +"853 Constant_7376" -> "348 /layers/layers.2/blocks.5/norm2/Add_1" [label="[1, 1, 384]", style=solid]; +"854 Constant_7375" -> "331 /layers/layers.2/blocks.5/norm2/Mul" [label="[1, 1, 384]", style=solid]; +"855 Constant_1832" -> "316 /layers/layers.2/blocks.5/norm2/Div" [label="[1]", style=dashed]; +"856 /layers/layers.2/blocks.5/Constant_31" -> "665 /layers/layers.2/blocks.5/Reshape_7" [label="[3]", style=dashed]; +"857 Constant_5679" -> "659 /layers/layers.2/blocks.5/Slice_7" [label="[3]", style=dashed]; +"858 Constant_5676" -> "659 /layers/layers.2/blocks.5/Slice_7" [label="[3]", style=dashed]; +"859 Constant_5673" -> "659 /layers/layers.2/blocks.5/Slice_7" [label="[3]", style=dashed]; +"860 Constant_5655" -> "649 /layers/layers.2/blocks.5/Slice_5" [label="[2]", style=dashed]; +"861 Constant_5652" -> "649 /layers/layers.2/blocks.5/Slice_5" [label="[2]", style=dashed]; +"862 Constant_5649" -> "649 /layers/layers.2/blocks.5/Slice_5" [label="[2]", style=dashed]; +"863 /layers/layers.2/blocks.5/Constant_18" -> "641 /layers/layers.2/blocks.5/Reshape_6" [label="[4]", style=dashed]; +"864 Constant_1779" -> "633 /layers/layers.2/blocks.5/Transpose_1" [label="[6]", style=dashed]; +"865 /layers/layers.2/blocks.5/Constant_17" -> "625 /layers/layers.2/blocks.5/Reshape_5" [label="[6]", style=dashed]; +"866 /layers/layers.2/blocks.5/Constant_16" -> "616 /layers/layers.2/blocks.5/Reshape_4" [label="[4]", style=dashed]; +"867 /layers/layers.2/blocks.5/attn/proj/MatMul/fq_weights_1" -> "597 /layers/layers.2/blocks.5/attn/proj/MatMul" [label="[384, 384]", style=solid]; +"868 Constant_27969" -> "867 /layers/layers.2/blocks.5/attn/proj/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; +"869 Constant_27968" -> "867 /layers/layers.2/blocks.5/attn/proj/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; +"870 Constant_27967" -> "867 /layers/layers.2/blocks.5/attn/proj/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; +"871 Constant_27966" -> "867 /layers/layers.2/blocks.5/attn/proj/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; +"872 Transpose_6506" -> "867 /layers/layers.2/blocks.5/attn/proj/MatMul/fq_weights_1" [label="[384, 384]", style=solid]; +"873 /layers/layers.2/blocks.5/attn/Constant_4" -> "587 /layers/layers.2/blocks.5/attn/Reshape_3" [label="[3]", style=dashed]; +"874 Constant_1763" -> "575 /layers/layers.2/blocks.5/attn/Transpose_2" [label="[4]", style=dashed]; +"875 Constant_27964" -> "564 /layers/layers.2/blocks.5/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; +"876 Constant_27963" -> "564 /layers/layers.2/blocks.5/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; +"877 Constant_27962" -> "564 /layers/layers.2/blocks.5/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; +"878 Constant_27961" -> "564 /layers/layers.2/blocks.5/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; +"879 Constant_1744" -> "539 /layers/layers.2/blocks.5/attn/Gather_2" [label="[]", style=dashed]; +"880 Constant_1738" -> "525 /layers/layers.2/blocks.5/attn/Transpose" [label="[5]", style=dashed]; +"881 /layers/layers.2/blocks.5/attn/Constant" -> "512 /layers/layers.2/blocks.5/attn/Reshape" [label="[5]", style=dashed]; +"882 /layers/layers.2/blocks.5/attn/qkv/MatMul/fq_weights_1" -> "484 /layers/layers.2/blocks.5/attn/qkv/MatMul" [label="[1152, 384]", style=solid]; +"883 Constant_27949" -> "882 /layers/layers.2/blocks.5/attn/qkv/MatMul/fq_weights_1" [label="[1152, 1]", style=solid]; +"884 Constant_27948" -> "882 /layers/layers.2/blocks.5/attn/qkv/MatMul/fq_weights_1" [label="[1152, 1]", style=solid]; +"885 Constant_27947" -> "882 /layers/layers.2/blocks.5/attn/qkv/MatMul/fq_weights_1" [label="[1152, 1]", style=solid]; +"886 Constant_27946" -> "882 /layers/layers.2/blocks.5/attn/qkv/MatMul/fq_weights_1" [label="[1152, 1]", style=solid]; +"887 Transpose_6503" -> "882 /layers/layers.2/blocks.5/attn/qkv/MatMul/fq_weights_1" [label="[1152, 384]", style=solid]; +"888 /layers/layers.2/blocks.5/Constant_15" -> "472 /layers/layers.2/blocks.5/Reshape_3" [label="[3]", style=dashed]; +"889 /layers/layers.2/blocks.5/Constant_14" -> "460 /layers/layers.2/blocks.5/Reshape_2" [label="[4]", style=dashed]; +"890 Constant_1722" -> "447 /layers/layers.2/blocks.5/Transpose" [label="[6]", style=dashed]; +"891 /layers/layers.2/blocks.5/Constant_13" -> "435 /layers/layers.2/blocks.5/Reshape_1" [label="[6]", style=dashed]; +"892 Constant_5631" -> "410 /layers/layers.2/blocks.5/Slice_3" [label="[3]", style=dashed]; +"893 Constant_5628" -> "410 /layers/layers.2/blocks.5/Slice_3" [label="[3]", style=dashed]; +"894 Constant_5625" -> "410 /layers/layers.2/blocks.5/Slice_3" [label="[3]", style=dashed]; +"895 Constant_5607" -> "382 /layers/layers.2/blocks.5/Slice_1" [label="[2]", style=dashed]; +"896 Constant_5604" -> "382 /layers/layers.2/blocks.5/Slice_1" [label="[2]", style=dashed]; +"897 Constant_5601" -> "382 /layers/layers.2/blocks.5/Slice_1" [label="[2]", style=dashed]; +"898 /layers/layers.2/blocks.5/Constant" -> "367 /layers/layers.2/blocks.5/Reshape" [label="[4]", style=dashed]; +"899 Constant_27484" -> "349 /layers/layers.2/blocks.5/norm1/Add_1/fq_output_0" [label="[]", style=solid]; +"900 Constant_27483" -> "349 /layers/layers.2/blocks.5/norm1/Add_1/fq_output_0" [label="[]", style=solid]; +"901 Constant_27482" -> "349 /layers/layers.2/blocks.5/norm1/Add_1/fq_output_0" [label="[]", style=solid]; +"902 Constant_27481" -> "349 /layers/layers.2/blocks.5/norm1/Add_1/fq_output_0" [label="[]", style=solid]; +"903 Constant_7371" -> "332 /layers/layers.2/blocks.5/norm1/Add_1" [label="[1, 1, 384]", style=solid]; +"904 Constant_7370" -> "317 /layers/layers.2/blocks.5/norm1/Mul" [label="[1, 1, 384]", style=solid]; +"905 Constant_1659" -> "302 /layers/layers.2/blocks.5/norm1/Div" [label="[1]", style=dashed]; +"906 /layers/layers.2/blocks.4/mlp/fc2/MatMul/fq_weights_1" -> "411 /layers/layers.2/blocks.4/mlp/fc2/MatMul" [label="[384, 1536]", style=solid]; +"907 Constant_27944" -> "906 /layers/layers.2/blocks.4/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; +"908 Constant_27943" -> "906 /layers/layers.2/blocks.4/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; +"909 Constant_27942" -> "906 /layers/layers.2/blocks.4/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; +"910 Constant_27941" -> "906 /layers/layers.2/blocks.4/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; +"911 Transpose_6499" -> "906 /layers/layers.2/blocks.4/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1536]", style=solid]; +"912 Constant_27939" -> "395 /layers/layers.2/blocks.4/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; +"913 Constant_27938" -> "395 /layers/layers.2/blocks.4/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; +"914 Constant_27937" -> "395 /layers/layers.2/blocks.4/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; +"915 Constant_27936" -> "395 /layers/layers.2/blocks.4/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; +"916 /layers/layers.2/blocks.4/mlp/fc1/MatMul/fq_weights_1" -> "350 /layers/layers.2/blocks.4/mlp/fc1/MatMul" [label="[1536, 384]", style=solid]; +"917 Constant_27934" -> "916 /layers/layers.2/blocks.4/mlp/fc1/MatMul/fq_weights_1" [label="[1536, 1]", style=solid]; +"918 Constant_27933" -> "916 /layers/layers.2/blocks.4/mlp/fc1/MatMul/fq_weights_1" [label="[1536, 1]", style=solid]; +"919 Constant_27932" -> "916 /layers/layers.2/blocks.4/mlp/fc1/MatMul/fq_weights_1" [label="[1536, 1]", style=solid]; +"920 Constant_27931" -> "916 /layers/layers.2/blocks.4/mlp/fc1/MatMul/fq_weights_1" [label="[1536, 1]", style=solid]; +"921 Transpose_6495" -> "916 /layers/layers.2/blocks.4/mlp/fc1/MatMul/fq_weights_1" [label="[1536, 384]", style=solid]; +"922 Constant_27929" -> "333 /layers/layers.2/blocks.4/norm2/Add_1/fq_output_0" [label="[]", style=solid]; +"923 Constant_27928" -> "333 /layers/layers.2/blocks.4/norm2/Add_1/fq_output_0" [label="[]", style=solid]; +"924 Constant_27927" -> "333 /layers/layers.2/blocks.4/norm2/Add_1/fq_output_0" [label="[]", style=solid]; +"925 Constant_27926" -> "333 /layers/layers.2/blocks.4/norm2/Add_1/fq_output_0" [label="[]", style=solid]; +"926 Constant_7367" -> "318 /layers/layers.2/blocks.4/norm2/Add_1" [label="[1, 1, 384]", style=solid]; +"927 Constant_7366" -> "303 /layers/layers.2/blocks.4/norm2/Mul" [label="[1, 1, 384]", style=solid]; +"928 Constant_1633" -> "287 /layers/layers.2/blocks.4/norm2/Div" [label="[1]", style=dashed]; +"929 /layers/layers.2/blocks.4/Constant_7" -> "588 /layers/layers.2/blocks.4/Reshape_7" [label="[3]", style=dashed]; +"930 /layers/layers.2/blocks.4/Constant_6" -> "576 /layers/layers.2/blocks.4/Reshape_6" [label="[4]", style=dashed]; +"931 Constant_1622" -> "565 /layers/layers.2/blocks.4/Transpose_1" [label="[6]", style=dashed]; +"932 /layers/layers.2/blocks.4/Constant_5" -> "553 /layers/layers.2/blocks.4/Reshape_5" [label="[6]", style=dashed]; +"933 /layers/layers.2/blocks.4/Constant_4" -> "540 /layers/layers.2/blocks.4/Reshape_4" [label="[4]", style=dashed]; +"934 /layers/layers.2/blocks.4/attn/proj/MatMul/fq_weights_1" -> "513 /layers/layers.2/blocks.4/attn/proj/MatMul" [label="[384, 384]", style=solid]; +"935 Constant_27924" -> "934 /layers/layers.2/blocks.4/attn/proj/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; +"936 Constant_27923" -> "934 /layers/layers.2/blocks.4/attn/proj/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; +"937 Constant_27922" -> "934 /layers/layers.2/blocks.4/attn/proj/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; +"938 Constant_27921" -> "934 /layers/layers.2/blocks.4/attn/proj/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; +"939 Transpose_6491" -> "934 /layers/layers.2/blocks.4/attn/proj/MatMul/fq_weights_1" [label="[384, 384]", style=solid]; +"940 /layers/layers.2/blocks.4/attn/Constant_2" -> "500 /layers/layers.2/blocks.4/attn/Reshape_1" [label="[3]", style=dashed]; +"941 Constant_1606" -> "486 /layers/layers.2/blocks.4/attn/Transpose_2" [label="[4]", style=dashed]; +"942 Constant_27919" -> "475 /layers/layers.2/blocks.4/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; +"943 Constant_27918" -> "475 /layers/layers.2/blocks.4/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; +"944 Constant_27917" -> "475 /layers/layers.2/blocks.4/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; +"945 Constant_27916" -> "475 /layers/layers.2/blocks.4/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; +"946 Constant_1596" -> "450 /layers/layers.2/blocks.4/attn/Gather_2" [label="[]", style=dashed]; +"947 Constant_1590" -> "436 /layers/layers.2/blocks.4/attn/Transpose" [label="[5]", style=dashed]; +"948 /layers/layers.2/blocks.4/attn/Constant" -> "424 /layers/layers.2/blocks.4/attn/Reshape" [label="[5]", style=dashed]; +"949 /layers/layers.2/blocks.4/attn/qkv/MatMul/fq_weights_1" -> "396 /layers/layers.2/blocks.4/attn/qkv/MatMul" [label="[1152, 384]", style=solid]; +"950 Constant_27904" -> "949 /layers/layers.2/blocks.4/attn/qkv/MatMul/fq_weights_1" [label="[1152, 1]", style=solid]; +"951 Constant_27903" -> "949 /layers/layers.2/blocks.4/attn/qkv/MatMul/fq_weights_1" [label="[1152, 1]", style=solid]; +"952 Constant_27902" -> "949 /layers/layers.2/blocks.4/attn/qkv/MatMul/fq_weights_1" [label="[1152, 1]", style=solid]; +"953 Constant_27901" -> "949 /layers/layers.2/blocks.4/attn/qkv/MatMul/fq_weights_1" [label="[1152, 1]", style=solid]; +"954 Transpose_6488" -> "949 /layers/layers.2/blocks.4/attn/qkv/MatMul/fq_weights_1" [label="[1152, 384]", style=solid]; +"955 /layers/layers.2/blocks.4/Constant_3" -> "384 /layers/layers.2/blocks.4/Reshape_3" [label="[3]", style=dashed]; +"956 /layers/layers.2/blocks.4/Constant_2" -> "369 /layers/layers.2/blocks.4/Reshape_2" [label="[4]", style=dashed]; +"957 Constant_1574" -> "351 /layers/layers.2/blocks.4/Transpose" [label="[6]", style=dashed]; +"958 /layers/layers.2/blocks.4/Constant_1" -> "334 /layers/layers.2/blocks.4/Reshape_1" [label="[6]", style=dashed]; +"959 Constant_27899" -> "319 /layers/layers.2/blocks.4/norm1/Add_1/fq_output_0" [label="[]", style=solid]; +"960 Constant_27898" -> "319 /layers/layers.2/blocks.4/norm1/Add_1/fq_output_0" [label="[]", style=solid]; +"961 Constant_27897" -> "319 /layers/layers.2/blocks.4/norm1/Add_1/fq_output_0" [label="[]", style=solid]; +"962 Constant_27896" -> "319 /layers/layers.2/blocks.4/norm1/Add_1/fq_output_0" [label="[]", style=solid]; +"963 Constant_7362" -> "304 /layers/layers.2/blocks.4/norm1/Add_1" [label="[1, 1, 384]", style=solid]; +"964 Constant_7361" -> "288 /layers/layers.2/blocks.4/norm1/Mul" [label="[1, 1, 384]", style=solid]; +"965 Constant_1553" -> "274 /layers/layers.2/blocks.4/norm1/Div" [label="[1]", style=dashed]; +"966 /layers/layers.2/blocks.3/mlp/fc2/MatMul/fq_weights_1" -> "385 /layers/layers.2/blocks.3/mlp/fc2/MatMul" [label="[384, 1536]", style=solid]; +"967 Constant_27894" -> "966 /layers/layers.2/blocks.3/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; +"968 Constant_27893" -> "966 /layers/layers.2/blocks.3/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; +"969 Constant_27892" -> "966 /layers/layers.2/blocks.3/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; +"970 Constant_27891" -> "966 /layers/layers.2/blocks.3/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; +"971 Transpose_6484" -> "966 /layers/layers.2/blocks.3/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1536]", style=solid]; +"972 Constant_27889" -> "370 /layers/layers.2/blocks.3/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; +"973 Constant_27888" -> "370 /layers/layers.2/blocks.3/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; +"974 Constant_27887" -> "370 /layers/layers.2/blocks.3/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; +"975 Constant_27886" -> "370 /layers/layers.2/blocks.3/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; +"976 /layers/layers.2/blocks.3/mlp/fc1/MatMul/fq_weights_1" -> "320 /layers/layers.2/blocks.3/mlp/fc1/MatMul" [label="[1536, 384]", style=solid]; +"977 Constant_27884" -> "976 /layers/layers.2/blocks.3/mlp/fc1/MatMul/fq_weights_1" [label="[1536, 1]", style=solid]; +"978 Constant_27883" -> "976 /layers/layers.2/blocks.3/mlp/fc1/MatMul/fq_weights_1" [label="[1536, 1]", style=solid]; +"979 Constant_27882" -> "976 /layers/layers.2/blocks.3/mlp/fc1/MatMul/fq_weights_1" [label="[1536, 1]", style=solid]; +"980 Constant_27881" -> "976 /layers/layers.2/blocks.3/mlp/fc1/MatMul/fq_weights_1" [label="[1536, 1]", style=solid]; +"981 Transpose_6480" -> "976 /layers/layers.2/blocks.3/mlp/fc1/MatMul/fq_weights_1" [label="[1536, 384]", style=solid]; +"982 Constant_27879" -> "305 /layers/layers.2/blocks.3/norm2/Add_1/fq_output_0" [label="[]", style=solid]; +"983 Constant_27878" -> "305 /layers/layers.2/blocks.3/norm2/Add_1/fq_output_0" [label="[]", style=solid]; +"984 Constant_27877" -> "305 /layers/layers.2/blocks.3/norm2/Add_1/fq_output_0" [label="[]", style=solid]; +"985 Constant_27876" -> "305 /layers/layers.2/blocks.3/norm2/Add_1/fq_output_0" [label="[]", style=solid]; +"986 Constant_7358" -> "289 /layers/layers.2/blocks.3/norm2/Add_1" [label="[1, 1, 384]", style=solid]; +"987 Constant_7357" -> "275 /layers/layers.2/blocks.3/norm2/Mul" [label="[1, 1, 384]", style=solid]; +"988 Constant_1527" -> "260 /layers/layers.2/blocks.3/norm2/Div" [label="[1]", style=dashed]; +"989 /layers/layers.2/blocks.3/Constant_31" -> "650 /layers/layers.2/blocks.3/Reshape_7" [label="[3]", style=dashed]; +"990 Constant_5583" -> "635 /layers/layers.2/blocks.3/Slice_7" [label="[3]", style=dashed]; +"991 Constant_5580" -> "635 /layers/layers.2/blocks.3/Slice_7" [label="[3]", style=dashed]; +"992 Constant_5577" -> "635 /layers/layers.2/blocks.3/Slice_7" [label="[3]", style=dashed]; +"993 Constant_5559" -> "618 /layers/layers.2/blocks.3/Slice_5" [label="[2]", style=dashed]; +"994 Constant_5556" -> "618 /layers/layers.2/blocks.3/Slice_5" [label="[2]", style=dashed]; +"995 Constant_5553" -> "618 /layers/layers.2/blocks.3/Slice_5" [label="[2]", style=dashed]; +"996 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/layers/layers.2/blocks.3/Constant_15" -> "425 /layers/layers.2/blocks.3/Reshape_3" [label="[3]", style=dashed]; +"1022 /layers/layers.2/blocks.3/Constant_14" -> "413 /layers/layers.2/blocks.3/Reshape_2" [label="[4]", style=dashed]; +"1023 Constant_1417" -> "398 /layers/layers.2/blocks.3/Transpose" [label="[6]", style=dashed]; +"1024 /layers/layers.2/blocks.3/Constant_13" -> "386 /layers/layers.2/blocks.3/Reshape_1" [label="[6]", style=dashed]; +"1025 Constant_5535" -> "354 /layers/layers.2/blocks.3/Slice_3" [label="[3]", style=dashed]; +"1026 Constant_5532" -> "354 /layers/layers.2/blocks.3/Slice_3" [label="[3]", style=dashed]; +"1027 Constant_5529" -> "354 /layers/layers.2/blocks.3/Slice_3" [label="[3]", style=dashed]; +"1028 Constant_5511" -> "322 /layers/layers.2/blocks.3/Slice_1" [label="[2]", style=dashed]; +"1029 Constant_5508" -> "322 /layers/layers.2/blocks.3/Slice_1" [label="[2]", style=dashed]; +"1030 Constant_5505" -> "322 /layers/layers.2/blocks.3/Slice_1" [label="[2]", 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[label="[3]", style=dashed]; +"1125 Constant_5481" -> "600 /layers/layers.2/blocks.1/Slice_7" [label="[3]", style=dashed]; +"1126 Constant_5463" -> "580 /layers/layers.2/blocks.1/Slice_5" [label="[2]", style=dashed]; +"1127 Constant_5460" -> "580 /layers/layers.2/blocks.1/Slice_5" [label="[2]", style=dashed]; +"1128 Constant_5457" -> "580 /layers/layers.2/blocks.1/Slice_5" [label="[2]", style=dashed]; +"1129 /layers/layers.2/blocks.1/Constant_18" -> "568 /layers/layers.2/blocks.1/Reshape_6" [label="[4]", style=dashed]; +"1130 Constant_1169" -> "556 /layers/layers.2/blocks.1/Transpose_1" [label="[6]", style=dashed]; +"1131 /layers/layers.2/blocks.1/Constant_17" -> "544 /layers/layers.2/blocks.1/Reshape_5" [label="[6]", style=dashed]; +"1132 /layers/layers.2/blocks.1/Constant_16" -> "531 /layers/layers.2/blocks.1/Reshape_4" [label="[4]", style=dashed]; +"1133 /layers/layers.2/blocks.1/attn/proj/MatMul/fq_weights_1" -> "506 /layers/layers.2/blocks.1/attn/proj/MatMul" [label="[384, 384]", style=solid]; +"1134 Constant_27779" -> "1133 /layers/layers.2/blocks.1/attn/proj/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; +"1135 Constant_27778" -> "1133 /layers/layers.2/blocks.1/attn/proj/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; +"1136 Constant_27777" -> "1133 /layers/layers.2/blocks.1/attn/proj/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; +"1137 Constant_27776" -> "1133 /layers/layers.2/blocks.1/attn/proj/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; +"1138 Transpose_6446" -> "1133 /layers/layers.2/blocks.1/attn/proj/MatMul/fq_weights_1" [label="[384, 384]", style=solid]; +"1139 /layers/layers.2/blocks.1/attn/Constant_4" -> "492 /layers/layers.2/blocks.1/attn/Reshape_3" [label="[3]", style=dashed]; +"1140 Constant_1153" -> "479 /layers/layers.2/blocks.1/attn/Transpose_2" [label="[4]", style=dashed]; +"1141 Constant_27774" -> "468 /layers/layers.2/blocks.1/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; +"1142 Constant_27773" -> "468 /layers/layers.2/blocks.1/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; +"1143 Constant_27772" -> "468 /layers/layers.2/blocks.1/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; +"1144 Constant_27771" -> "468 /layers/layers.2/blocks.1/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; +"1145 Constant_1134" -> "442 /layers/layers.2/blocks.1/attn/Gather_2" [label="[]", style=dashed]; +"1146 Constant_1128" -> "429 /layers/layers.2/blocks.1/attn/Transpose" [label="[5]", style=dashed]; +"1147 /layers/layers.2/blocks.1/attn/Constant" -> "417 /layers/layers.2/blocks.1/attn/Reshape" [label="[5]", style=dashed]; +"1148 /layers/layers.2/blocks.1/attn/qkv/MatMul/fq_weights_1" -> "388 /layers/layers.2/blocks.1/attn/qkv/MatMul" [label="[1152, 384]", style=solid]; +"1149 Constant_27759" -> "1148 /layers/layers.2/blocks.1/attn/qkv/MatMul/fq_weights_1" [label="[1152, 1]", style=solid]; +"1150 Constant_27758" -> "1148 /layers/layers.2/blocks.1/attn/qkv/MatMul/fq_weights_1" [label="[1152, 1]", style=solid]; +"1151 Constant_27757" -> "1148 /layers/layers.2/blocks.1/attn/qkv/MatMul/fq_weights_1" [label="[1152, 1]", style=solid]; +"1152 Constant_27756" -> "1148 /layers/layers.2/blocks.1/attn/qkv/MatMul/fq_weights_1" [label="[1152, 1]", style=solid]; +"1153 Transpose_6443" -> "1148 /layers/layers.2/blocks.1/attn/qkv/MatMul/fq_weights_1" [label="[1152, 384]", style=solid]; +"1154 /layers/layers.2/blocks.1/Constant_15" -> "374 /layers/layers.2/blocks.1/Reshape_3" [label="[3]", style=dashed]; +"1155 /layers/layers.2/blocks.1/Constant_14" -> "357 /layers/layers.2/blocks.1/Reshape_2" [label="[4]", style=dashed]; +"1156 Constant_1112" -> "340 /layers/layers.2/blocks.1/Transpose" [label="[6]", style=dashed]; +"1157 /layers/layers.2/blocks.1/Constant_13" -> "326 /layers/layers.2/blocks.1/Reshape_1" [label="[6]", style=dashed]; +"1158 Constant_5439" -> "295 /layers/layers.2/blocks.1/Slice_3" [label="[3]", style=dashed]; +"1159 Constant_5436" -> "295 /layers/layers.2/blocks.1/Slice_3" [label="[3]", style=dashed]; +"1160 Constant_5433" -> "295 /layers/layers.2/blocks.1/Slice_3" [label="[3]", style=dashed]; +"1161 Constant_5415" -> "266 /layers/layers.2/blocks.1/Slice_1" [label="[2]", style=dashed]; +"1162 Constant_5412" -> "266 /layers/layers.2/blocks.1/Slice_1" [label="[2]", style=dashed]; +"1163 Constant_5409" -> "266 /layers/layers.2/blocks.1/Slice_1" [label="[2]", style=dashed]; +"1164 /layers/layers.2/blocks.1/Constant" -> "251 /layers/layers.2/blocks.1/Reshape" [label="[4]", style=dashed]; +"1165 Constant_27474" -> "238 /layers/layers.2/blocks.1/norm1/Add_1/fq_output_0" [label="[]", style=solid]; +"1166 Constant_27473" -> "238 /layers/layers.2/blocks.1/norm1/Add_1/fq_output_0" [label="[]", style=solid]; +"1167 Constant_27472" -> "238 /layers/layers.2/blocks.1/norm1/Add_1/fq_output_0" [label="[]", style=solid]; +"1168 Constant_27471" -> "238 /layers/layers.2/blocks.1/norm1/Add_1/fq_output_0" [label="[]", style=solid]; +"1169 Constant_7335" -> "227 /layers/layers.2/blocks.1/norm1/Add_1" [label="[1, 1, 384]", style=solid]; +"1170 Constant_7334" -> "218 /layers/layers.2/blocks.1/norm1/Mul" [label="[1, 1, 384]", style=solid]; +"1171 Constant_1049" -> "208 /layers/layers.2/blocks.1/norm1/Div" [label="[1]", style=dashed]; +"1172 /layers/layers.2/blocks.0/mlp/fc2/MatMul/fq_weights_1" -> "296 /layers/layers.2/blocks.0/mlp/fc2/MatMul" [label="[384, 1536]", style=solid]; +"1173 Constant_27754" -> "1172 /layers/layers.2/blocks.0/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; +"1174 Constant_27753" -> "1172 /layers/layers.2/blocks.0/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; +"1175 Constant_27752" -> "1172 /layers/layers.2/blocks.0/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; +"1176 Constant_27751" -> "1172 /layers/layers.2/blocks.0/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; +"1177 Transpose_6439" -> "1172 /layers/layers.2/blocks.0/mlp/fc2/MatMul/fq_weights_1" [label="[384, 1536]", style=solid]; +"1178 Constant_27749" -> "281 /layers/layers.2/blocks.0/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; +"1179 Constant_27748" -> "281 /layers/layers.2/blocks.0/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; +"1180 Constant_27747" -> "281 /layers/layers.2/blocks.0/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; +"1181 Constant_27746" -> "281 /layers/layers.2/blocks.0/mlp/act/Mul_1/fq_output_0" [label="[]", style=solid]; +"1182 /layers/layers.2/blocks.0/mlp/fc1/MatMul/fq_weights_1" -> "239 /layers/layers.2/blocks.0/mlp/fc1/MatMul" [label="[1536, 384]", style=solid]; +"1183 Constant_27744" -> "1182 /layers/layers.2/blocks.0/mlp/fc1/MatMul/fq_weights_1" [label="[1536, 1]", style=solid]; +"1184 Constant_27743" -> "1182 /layers/layers.2/blocks.0/mlp/fc1/MatMul/fq_weights_1" [label="[1536, 1]", style=solid]; +"1185 Constant_27742" -> "1182 /layers/layers.2/blocks.0/mlp/fc1/MatMul/fq_weights_1" [label="[1536, 1]", style=solid]; +"1186 Constant_27741" -> "1182 /layers/layers.2/blocks.0/mlp/fc1/MatMul/fq_weights_1" [label="[1536, 1]", style=solid]; +"1187 Transpose_6435" -> "1182 /layers/layers.2/blocks.0/mlp/fc1/MatMul/fq_weights_1" [label="[1536, 384]", style=solid]; +"1188 Constant_27739" -> "228 /layers/layers.2/blocks.0/norm2/Add_1/fq_output_0" [label="[]", style=solid]; +"1189 Constant_27738" -> "228 /layers/layers.2/blocks.0/norm2/Add_1/fq_output_0" [label="[]", style=solid]; +"1190 Constant_27737" -> "228 /layers/layers.2/blocks.0/norm2/Add_1/fq_output_0" [label="[]", style=solid]; +"1191 Constant_27736" -> "228 /layers/layers.2/blocks.0/norm2/Add_1/fq_output_0" [label="[]", style=solid]; +"1192 Constant_7331" -> "219 /layers/layers.2/blocks.0/norm2/Add_1" [label="[1, 1, 384]", style=solid]; +"1193 Constant_7330" -> "209 /layers/layers.2/blocks.0/norm2/Mul" [label="[1, 1, 384]", style=solid]; +"1194 Constant_1023" -> "201 /layers/layers.2/blocks.0/norm2/Div" [label="[1]", style=dashed]; +"1195 /layers/layers.2/blocks.0/Constant_7" -> "493 /layers/layers.2/blocks.0/Reshape_7" [label="[3]", style=dashed]; +"1196 /layers/layers.2/blocks.0/Constant_6" -> "480 /layers/layers.2/blocks.0/Reshape_6" [label="[4]", style=dashed]; +"1197 Constant_1012" -> "469 /layers/layers.2/blocks.0/Transpose_1" [label="[6]", style=dashed]; +"1198 /layers/layers.2/blocks.0/Constant_5" -> "457 /layers/layers.2/blocks.0/Reshape_5" [label="[6]", style=dashed]; +"1199 /layers/layers.2/blocks.0/Constant_4" -> "443 /layers/layers.2/blocks.0/Reshape_4" [label="[4]", style=dashed]; +"1200 /layers/layers.2/blocks.0/attn/proj/MatMul/fq_weights_1" -> "418 /layers/layers.2/blocks.0/attn/proj/MatMul" [label="[384, 384]", style=solid]; +"1201 Constant_27734" -> "1200 /layers/layers.2/blocks.0/attn/proj/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; +"1202 Constant_27733" -> "1200 /layers/layers.2/blocks.0/attn/proj/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; +"1203 Constant_27732" -> "1200 /layers/layers.2/blocks.0/attn/proj/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; +"1204 Constant_27731" -> "1200 /layers/layers.2/blocks.0/attn/proj/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; +"1205 Transpose_6431" -> "1200 /layers/layers.2/blocks.0/attn/proj/MatMul/fq_weights_1" [label="[384, 384]", style=solid]; +"1206 /layers/layers.2/blocks.0/attn/Constant_2" -> "404 /layers/layers.2/blocks.0/attn/Reshape_1" [label="[3]", style=dashed]; +"1207 Constant_996" -> "390 /layers/layers.2/blocks.0/attn/Transpose_2" [label="[4]", style=dashed]; +"1208 Constant_27729" -> "377 /layers/layers.2/blocks.0/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; +"1209 Constant_27728" -> "377 /layers/layers.2/blocks.0/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; +"1210 Constant_27727" -> "377 /layers/layers.2/blocks.0/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; +"1211 Constant_27726" -> "377 /layers/layers.2/blocks.0/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; +"1212 Constant_986" -> "343 /layers/layers.2/blocks.0/attn/Gather_2" [label="[]", style=dashed]; +"1213 Constant_980" -> "327 /layers/layers.2/blocks.0/attn/Transpose" [label="[5]", style=dashed]; +"1214 /layers/layers.2/blocks.0/attn/Constant" -> "312 /layers/layers.2/blocks.0/attn/Reshape" [label="[5]", style=dashed]; +"1215 /layers/layers.2/blocks.0/attn/qkv/MatMul/fq_weights_1" -> "282 /layers/layers.2/blocks.0/attn/qkv/MatMul" [label="[1152, 384]", style=solid]; +"1216 Constant_27714" -> "1215 /layers/layers.2/blocks.0/attn/qkv/MatMul/fq_weights_1" [label="[1152, 1]", style=solid]; +"1217 Constant_27713" -> "1215 /layers/layers.2/blocks.0/attn/qkv/MatMul/fq_weights_1" [label="[1152, 1]", style=solid]; +"1218 Constant_27712" -> "1215 /layers/layers.2/blocks.0/attn/qkv/MatMul/fq_weights_1" [label="[1152, 1]", style=solid]; +"1219 Constant_27711" -> "1215 /layers/layers.2/blocks.0/attn/qkv/MatMul/fq_weights_1" [label="[1152, 1]", style=solid]; +"1220 Transpose_6428" -> "1215 /layers/layers.2/blocks.0/attn/qkv/MatMul/fq_weights_1" [label="[1152, 384]", style=solid]; +"1221 /layers/layers.2/blocks.0/Constant_3" -> "268 /layers/layers.2/blocks.0/Reshape_3" [label="[3]", style=dashed]; +"1222 /layers/layers.2/blocks.0/Constant_2" -> "253 /layers/layers.2/blocks.0/Reshape_2" [label="[4]", style=dashed]; +"1223 Constant_964" -> "240 /layers/layers.2/blocks.0/Transpose" [label="[6]", style=dashed]; +"1224 /layers/layers.2/blocks.0/Constant_1" -> "229 /layers/layers.2/blocks.0/Reshape_1" [label="[6]", style=dashed]; +"1225 Constant_27709" -> "220 /layers/layers.2/blocks.0/norm1/Add_1/fq_output_0" [label="[]", style=solid]; +"1226 Constant_27708" -> "220 /layers/layers.2/blocks.0/norm1/Add_1/fq_output_0" [label="[]", style=solid]; +"1227 Constant_27707" -> "220 /layers/layers.2/blocks.0/norm1/Add_1/fq_output_0" [label="[]", style=solid]; +"1228 Constant_27706" -> "220 /layers/layers.2/blocks.0/norm1/Add_1/fq_output_0" [label="[]", style=solid]; +"1229 Constant_7326" -> "210 /layers/layers.2/blocks.0/norm1/Add_1" [label="[1, 1, 384]", style=solid]; +"1230 Constant_7325" -> "202 /layers/layers.2/blocks.0/norm1/Mul" [label="[1, 1, 384]", style=solid]; +"1231 Constant_943" -> "194 /layers/layers.2/blocks.0/norm1/Div" [label="[1]", style=dashed]; +"1232 /layers/layers.1/downsample/reduction/MatMul/fq_weights_1" -> "187 /layers/layers.1/downsample/reduction/MatMul" [label="[384, 768]", style=solid]; +"1233 Constant_27704" -> "1232 /layers/layers.1/downsample/reduction/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; +"1234 Constant_27703" -> "1232 /layers/layers.1/downsample/reduction/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; +"1235 Constant_27702" -> "1232 /layers/layers.1/downsample/reduction/MatMul/fq_weights_1" [label="[384, 1]", style=solid]; +"1236 Constant_27701" -> "1232 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/layers/layers.1/downsample/Constant_25" -> "149 /layers/layers.1/downsample/Reshape_1" [label="[3]", style=dashed]; +"1246 Constant_5391" -> "130 /layers/layers.1/downsample/Slice_5" [label="[3]", style=dashed]; +"1247 Constant_5388" -> "130 /layers/layers.1/downsample/Slice_5" [label="[3]", style=dashed]; +"1248 Constant_5385" -> "130 /layers/layers.1/downsample/Slice_5" [label="[3]", style=dashed]; +"1249 Constant_5355" -> "118 /layers/layers.1/downsample/Slice_2" [label="[2]", style=dashed]; +"1250 Constant_5352" -> "118 /layers/layers.1/downsample/Slice_2" [label="[2]", style=dashed]; +"1251 Constant_5349" -> "118 /layers/layers.1/downsample/Slice_2" [label="[2]", style=dashed]; +"1252 /layers/layers.1/downsample/Constant" -> "110 /layers/layers.1/downsample/Reshape" [label="[4]", style=dashed]; +"1253 /layers/layers.1/blocks.1/mlp/fc2/MatMul/fq_weights_1" -> "174 /layers/layers.1/blocks.1/mlp/fc2/MatMul" [label="[192, 768]", style=solid]; +"1254 Constant_27694" -> "1253 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style=solid]; +"1263 /layers/layers.1/blocks.1/mlp/fc1/MatMul/fq_weights_1" -> "140 /layers/layers.1/blocks.1/mlp/fc1/MatMul" [label="[768, 192]", style=solid]; +"1264 Constant_27684" -> "1263 /layers/layers.1/blocks.1/mlp/fc1/MatMul/fq_weights_1" [label="[768, 1]", style=solid]; +"1265 Constant_27683" -> "1263 /layers/layers.1/blocks.1/mlp/fc1/MatMul/fq_weights_1" [label="[768, 1]", style=solid]; +"1266 Constant_27682" -> "1263 /layers/layers.1/blocks.1/mlp/fc1/MatMul/fq_weights_1" [label="[768, 1]", style=solid]; +"1267 Constant_27681" -> "1263 /layers/layers.1/blocks.1/mlp/fc1/MatMul/fq_weights_1" [label="[768, 1]", style=solid]; +"1268 Transpose_6416" -> "1263 /layers/layers.1/blocks.1/mlp/fc1/MatMul/fq_weights_1" [label="[768, 192]", style=solid]; +"1269 Constant_27679" -> "131 /layers/layers.1/blocks.1/norm2/Add_1/fq_output_0" [label="[]", style=solid]; +"1270 Constant_27678" -> "131 /layers/layers.1/blocks.1/norm2/Add_1/fq_output_0" [label="[]", style=solid]; +"1271 Constant_27677" -> "131 /layers/layers.1/blocks.1/norm2/Add_1/fq_output_0" [label="[]", style=solid]; +"1272 Constant_27676" -> "131 /layers/layers.1/blocks.1/norm2/Add_1/fq_output_0" [label="[]", style=solid]; +"1273 Constant_7320" -> "119 /layers/layers.1/blocks.1/norm2/Add_1" [label="[1, 1, 192]", style=solid]; +"1274 Constant_7319" -> "111 /layers/layers.1/blocks.1/norm2/Mul" [label="[1, 1, 192]", style=solid]; +"1275 Constant_864" -> "104 /layers/layers.1/blocks.1/norm2/Div" [label="[1]", style=dashed]; +"1276 /layers/layers.1/blocks.1/Constant_31" -> "458 /layers/layers.1/blocks.1/Reshape_7" [label="[3]", style=dashed]; +"1277 Constant_5319" -> "432 /layers/layers.1/blocks.1/Slice_7" [label="[3]", style=dashed]; +"1278 Constant_5316" -> "432 /layers/layers.1/blocks.1/Slice_7" [label="[3]", style=dashed]; +"1279 Constant_5313" -> "432 /layers/layers.1/blocks.1/Slice_7" [label="[3]", style=dashed]; +"1280 Constant_5295" -> "406 /layers/layers.1/blocks.1/Slice_5" [label="[2]", style=dashed]; +"1281 Constant_5292" -> "406 /layers/layers.1/blocks.1/Slice_5" [label="[2]", style=dashed]; +"1282 Constant_5289" -> "406 /layers/layers.1/blocks.1/Slice_5" [label="[2]", style=dashed]; +"1283 /layers/layers.1/blocks.1/Constant_18" -> "391 /layers/layers.1/blocks.1/Reshape_6" [label="[4]", style=dashed]; +"1284 Constant_811" -> "378 /layers/layers.1/blocks.1/Transpose_1" [label="[6]", style=dashed]; +"1285 /layers/layers.1/blocks.1/Constant_17" -> "361 /layers/layers.1/blocks.1/Reshape_5" [label="[6]", style=dashed]; +"1286 /layers/layers.1/blocks.1/Constant_16" -> "345 /layers/layers.1/blocks.1/Reshape_4" [label="[4]", style=dashed]; +"1287 /layers/layers.1/blocks.1/attn/proj/MatMul/fq_weights_1" -> "314 /layers/layers.1/blocks.1/attn/proj/MatMul" [label="[192, 192]", style=solid]; +"1288 Constant_27674" -> "1287 /layers/layers.1/blocks.1/attn/proj/MatMul/fq_weights_1" [label="[192, 1]", style=solid]; +"1289 Constant_27673" -> "1287 /layers/layers.1/blocks.1/attn/proj/MatMul/fq_weights_1" [label="[192, 1]", style=solid]; +"1290 Constant_27672" -> "1287 /layers/layers.1/blocks.1/attn/proj/MatMul/fq_weights_1" [label="[192, 1]", style=solid]; +"1291 Constant_27671" -> "1287 /layers/layers.1/blocks.1/attn/proj/MatMul/fq_weights_1" [label="[192, 1]", style=solid]; +"1292 Transpose_6412" -> "1287 /layers/layers.1/blocks.1/attn/proj/MatMul/fq_weights_1" [label="[192, 192]", style=solid]; +"1293 /layers/layers.1/blocks.1/attn/Constant_4" -> "299 /layers/layers.1/blocks.1/attn/Reshape_3" [label="[3]", style=dashed]; +"1294 Constant_795" -> "284 /layers/layers.1/blocks.1/attn/Transpose_2" [label="[4]", style=dashed]; +"1295 Constant_27669" -> "271 /layers/layers.1/blocks.1/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; +"1296 Constant_27668" -> "271 /layers/layers.1/blocks.1/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; +"1297 Constant_27667" -> "271 /layers/layers.1/blocks.1/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; +"1298 Constant_27666" -> "271 /layers/layers.1/blocks.1/attn/MatMul_1/fq_output_0" [label="[]", style=solid]; +"1299 Constant_776" -> "243 /layers/layers.1/blocks.1/attn/Gather_2" [label="[]", style=dashed]; +"1300 Constant_770" -> "230 /layers/layers.1/blocks.1/attn/Transpose" [label="[5]", style=dashed]; +"1301 /layers/layers.1/blocks.1/attn/Constant" -> "221 /layers/layers.1/blocks.1/attn/Reshape" [label="[5]", style=dashed]; +"1302 /layers/layers.1/blocks.1/attn/qkv/MatMul/fq_weights_1" -> "203 /layers/layers.1/blocks.1/attn/qkv/MatMul" [label="[576, 192]", style=solid]; +"1303 Constant_27654" -> "1302 /layers/layers.1/blocks.1/attn/qkv/MatMul/fq_weights_1" [label="[576, 1]", style=solid]; +"1304 Constant_27653" -> "1302 /layers/layers.1/blocks.1/attn/qkv/MatMul/fq_weights_1" [label="[576, 1]", style=solid]; +"1305 Constant_27652" -> "1302 /layers/layers.1/blocks.1/attn/qkv/MatMul/fq_weights_1" [label="[576, 1]", style=solid]; +"1306 Constant_27651" -> "1302 /layers/layers.1/blocks.1/attn/qkv/MatMul/fq_weights_1" [label="[576, 1]", style=solid]; +"1307 Transpose_6409" -> "1302 /layers/layers.1/blocks.1/attn/qkv/MatMul/fq_weights_1" [label="[576, 192]", style=solid]; +"1308 /layers/layers.1/blocks.1/Constant_15" -> "195 /layers/layers.1/blocks.1/Reshape_3" [label="[3]", style=dashed]; +"1309 /layers/layers.1/blocks.1/Constant_14" -> "188 /layers/layers.1/blocks.1/Reshape_2" [label="[4]", style=dashed]; +"1310 Constant_754" -> "181 /layers/layers.1/blocks.1/Transpose" [label="[6]", style=dashed]; +"1311 /layers/layers.1/blocks.1/Constant_13" -> "175 /layers/layers.1/blocks.1/Reshape_1" [label="[6]", style=dashed]; +"1312 Constant_5271" -> "160 /layers/layers.1/blocks.1/Slice_3" [label="[3]", style=dashed]; +"1313 Constant_5268" -> "160 /layers/layers.1/blocks.1/Slice_3" [label="[3]", style=dashed]; +"1314 Constant_5265" -> "160 /layers/layers.1/blocks.1/Slice_3" [label="[3]", style=dashed]; +"1315 Constant_5247" -> "142 /layers/layers.1/blocks.1/Slice_1" [label="[2]", style=dashed]; +"1316 Constant_5244" -> "142 /layers/layers.1/blocks.1/Slice_1" [label="[2]", style=dashed]; +"1317 Constant_5241" -> "142 /layers/layers.1/blocks.1/Slice_1" [label="[2]", style=dashed]; +"1318 /layers/layers.1/blocks.1/Constant" -> "132 /layers/layers.1/blocks.1/Reshape" [label="[4]", style=dashed]; +"1319 Constant_27469" -> "120 /layers/layers.1/blocks.1/norm1/Add_1/fq_output_0" [label="[]", style=solid]; +"1320 Constant_27468" -> "120 /layers/layers.1/blocks.1/norm1/Add_1/fq_output_0" [label="[]", style=solid]; +"1321 Constant_27467" -> "120 /layers/layers.1/blocks.1/norm1/Add_1/fq_output_0" [label="[]", style=solid]; +"1322 Constant_27466" -> "120 /layers/layers.1/blocks.1/norm1/Add_1/fq_output_0" [label="[]", style=solid]; +"1323 Constant_7315" -> "112 /layers/layers.1/blocks.1/norm1/Add_1" [label="[1, 1, 192]", style=solid]; +"1324 Constant_7314" -> "105 /layers/layers.1/blocks.1/norm1/Mul" [label="[1, 1, 192]", 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Constant_622" -> "176 /layers/layers.1/blocks.0/attn/Transpose" [label="[5]", style=dashed]; +"1368 /layers/layers.1/blocks.0/attn/Constant" -> "170 /layers/layers.1/blocks.0/attn/Reshape" [label="[5]", style=dashed]; +"1369 /layers/layers.1/blocks.0/attn/qkv/MatMul/fq_weights_1" -> "153 /layers/layers.1/blocks.0/attn/qkv/MatMul" [label="[576, 192]", style=solid]; +"1370 Constant_27609" -> "1369 /layers/layers.1/blocks.0/attn/qkv/MatMul/fq_weights_1" [label="[576, 1]", style=solid]; +"1371 Constant_27608" -> "1369 /layers/layers.1/blocks.0/attn/qkv/MatMul/fq_weights_1" [label="[576, 1]", style=solid]; +"1372 Constant_27607" -> "1369 /layers/layers.1/blocks.0/attn/qkv/MatMul/fq_weights_1" [label="[576, 1]", style=solid]; +"1373 Constant_27606" -> "1369 /layers/layers.1/blocks.0/attn/qkv/MatMul/fq_weights_1" [label="[576, 1]", style=solid]; +"1374 Transpose_6394" -> "1369 /layers/layers.1/blocks.0/attn/qkv/MatMul/fq_weights_1" [label="[576, 192]", style=solid]; +"1375 /layers/layers.1/blocks.0/Constant_3" -> "144 /layers/layers.1/blocks.0/Reshape_3" [label="[3]", style=dashed]; +"1376 /layers/layers.1/blocks.0/Constant_2" -> "134 /layers/layers.1/blocks.0/Reshape_2" [label="[4]", style=dashed]; +"1377 Constant_606" -> "122 /layers/layers.1/blocks.0/Transpose" [label="[6]", style=dashed]; +"1378 /layers/layers.1/blocks.0/Constant_1" -> "114 /layers/layers.1/blocks.0/Reshape_1" [label="[6]", style=dashed]; +"1379 Constant_27604" -> "107 /layers/layers.1/blocks.0/norm1/Add_1/fq_output_0" [label="[]", style=solid]; +"1380 Constant_27603" -> "107 /layers/layers.1/blocks.0/norm1/Add_1/fq_output_0" [label="[]", style=solid]; +"1381 Constant_27602" -> "107 /layers/layers.1/blocks.0/norm1/Add_1/fq_output_0" [label="[]", style=solid]; +"1382 Constant_27601" -> "107 /layers/layers.1/blocks.0/norm1/Add_1/fq_output_0" [label="[]", style=solid]; +"1383 Constant_7306" -> "99 /layers/layers.1/blocks.0/norm1/Add_1" [label="[1, 1, 192]", style=solid]; +"1384 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Constant_27491" -> "1549 /patch_embed/proj/Conv/WithoutBiases/fq_weights_1" [label="[96, 1, 1, 1]", style=solid]; +"1554 Gather_7282" -> "1549 /patch_embed/proj/Conv/WithoutBiases/fq_weights_1" [label="[96, 3, 4, 4]", style=solid]; +"1555 Constant_27489" -> "3 Divide_2169/fq_output_0" [label="[]", style=solid]; +"1556 Constant_27488" -> "3 Divide_2169/fq_output_0" [label="[]", style=solid]; +"1557 Constant_27487" -> "3 Divide_2169/fq_output_0" [label="[]", style=solid]; +"1558 Constant_27486" -> "3 Divide_2169/fq_output_0" [label="[]", style=solid]; +"1559 Gather_7279" -> "2 Divide_2169" [label="[1, 3, 1, 1]", style=solid]; +"1560 Gather_7276" -> "1 Multiply_6579" [label="[1, 3, 1, 1]", style=solid]; +"1561 Constant_7287" -> "63 /layers/layers.0/blocks.0/attn/qkv/Add" [label="[1, 1, 288]", style=solid]; +"1562 onnx^^Add_2244" -> "94 /layers/layers.0/blocks.0/attn/Add" [label="[1, 3, 49, 49]", style=solid]; +"1563 Constant_268" -> "82 /layers/layers.0/blocks.0/attn/Gather_1" [label="[]", style=dashed]; +"1564 /patch_embed/proj/Constant" -> "82 /layers/layers.0/blocks.0/attn/Gather_1" [label="[]", style=dashed]; +"1564 /patch_embed/proj/Constant" -> "136 /layers/layers.0/blocks.1/attn/Gather_1" [label="[]", style=dashed]; +"1564 /patch_embed/proj/Constant" -> "190 /layers/layers.1/blocks.0/attn/Gather_1" [label="[]", style=dashed]; +"1564 /patch_embed/proj/Constant" -> "255 /layers/layers.1/blocks.1/attn/Gather_1" [label="[]", style=dashed]; +"1564 /patch_embed/proj/Constant" -> "359 /layers/layers.2/blocks.0/attn/Gather_1" [label="[]", style=dashed]; +"1564 /patch_embed/proj/Constant" -> "415 /layers/layers.2/blocks.2/attn/Gather_1" [label="[]", style=dashed]; +"1564 /patch_embed/proj/Constant" -> "455 /layers/layers.2/blocks.1/attn/Gather_1" [label="[]", style=dashed]; +"1564 /patch_embed/proj/Constant" -> "462 /layers/layers.2/blocks.4/attn/Gather_1" [label="[]", style=dashed]; +"1564 /patch_embed/proj/Constant" -> "502 /layers/layers.2/blocks.3/attn/Gather_1" [label="[]", style=dashed]; +"1564 /patch_embed/proj/Constant" -> "551 /layers/layers.2/blocks.5/attn/Gather_1" [label="[]", style=dashed]; +"1564 /patch_embed/proj/Constant" -> "623 /layers/layers.3/blocks.0/attn/Gather_1" [label="[]", style=dashed]; +"1564 /patch_embed/proj/Constant" -> "637 /layers/layers.3/blocks.1/attn/Gather_1" [label="[]", style=dashed]; +"1565 Constant_27509" -> "77 /layers/layers.0/blocks.0/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1566 Constant_27508" -> "77 /layers/layers.0/blocks.0/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1567 Constant_27507" -> "77 /layers/layers.0/blocks.0/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1568 Constant_27506" -> "77 /layers/layers.0/blocks.0/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1569 Constant_27514" -> "87 /layers/layers.0/blocks.0/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1570 Constant_27513" -> "87 /layers/layers.0/blocks.0/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1571 Constant_27512" -> "87 /layers/layers.0/blocks.0/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1572 Constant_27511" -> "87 /layers/layers.0/blocks.0/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1573 Constant_7288" -> "81 /layers/layers.0/blocks.0/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; +"1574 Constant_266" -> "76 /layers/layers.0/blocks.0/attn/Gather" [label="[]", style=dashed]; +"1575 /layers/layers.0/blocks.0/Constant" -> "76 /layers/layers.0/blocks.0/attn/Gather" [label="[]", style=dashed]; +"1575 /layers/layers.0/blocks.0/Constant" -> "123 /layers/layers.0/blocks.1/attn/Gather" [label="[]", style=dashed]; +"1575 /layers/layers.0/blocks.0/Constant" -> "182 /layers/layers.1/blocks.0/attn/Gather" [label="[]", style=dashed]; +"1575 /layers/layers.0/blocks.0/Constant" -> "241 /layers/layers.1/blocks.1/attn/Gather" [label="[]", style=dashed]; +"1575 /layers/layers.0/blocks.0/Constant" -> "341 /layers/layers.2/blocks.0/attn/Gather" [label="[]", style=dashed]; +"1575 /layers/layers.0/blocks.0/Constant" -> "399 /layers/layers.2/blocks.2/attn/Gather" [label="[]", style=dashed]; +"1575 /layers/layers.0/blocks.0/Constant" -> "440 /layers/layers.2/blocks.1/attn/Gather" [label="[]", style=dashed]; +"1575 /layers/layers.0/blocks.0/Constant" -> "448 /layers/layers.2/blocks.4/attn/Gather" [label="[]", style=dashed]; +"1575 /layers/layers.0/blocks.0/Constant" -> "487 /layers/layers.2/blocks.3/attn/Gather" [label="[]", style=dashed]; +"1575 /layers/layers.0/blocks.0/Constant" -> "537 /layers/layers.2/blocks.5/attn/Gather" [label="[]", style=dashed]; +"1575 /layers/layers.0/blocks.0/Constant" -> "612 /layers/layers.3/blocks.0/attn/Gather" [label="[]", style=dashed]; +"1575 /layers/layers.0/blocks.0/Constant" -> "627 /layers/layers.3/blocks.1/attn/Gather" [label="[]", style=dashed]; +"1576 Constant_7289" -> "116 /layers/layers.0/blocks.0/attn/proj/Add" [label="[1, 1, 96]", style=solid]; +"1577 Constant_7292" -> "45 /layers/layers.0/blocks.0/mlp/fc1/Add" [label="[1, 1, 384]", style=solid]; +"1578 Constant_7293" -> "67 /layers/layers.0/blocks.0/mlp/fc2/Add" [label="[1, 1, 96]", style=solid]; +"1579 Constant_5067" -> "49 /layers/layers.0/blocks.1/Slice" [label="[2]", style=dashed]; +"1580 Constant_5064" -> "49 /layers/layers.0/blocks.1/Slice" [label="[2]", style=dashed]; +"1581 Constant_5061" -> "49 /layers/layers.0/blocks.1/Slice" [label="[2]", style=dashed]; +"1582 Constant_5091" -> "60 /layers/layers.0/blocks.1/Slice_2" [label="[3]", style=dashed]; +"1583 Constant_5088" -> "60 /layers/layers.0/blocks.1/Slice_2" [label="[3]", style=dashed]; +"1584 Constant_5085" -> "60 /layers/layers.0/blocks.1/Slice_2" [label="[3]", style=dashed]; +"1585 Constant_7296" -> "100 /layers/layers.0/blocks.1/attn/qkv/Add" [label="[1, 1, 288]", style=solid]; +"1586 /layers/layers.0/blocks.1/attn/Constant_3" -> "177 /layers/layers.0/blocks.1/attn/Reshape_2" [label="[4]", style=dashed]; +"1587 onnx^^Add_2301" -> "171 /layers/layers.0/blocks.1/attn/Add_1" [label="[1, 64, 1, 49, 49]", style=solid]; +"1588 /layers/layers.0/blocks.1/attn/Constant_2" -> "163 /layers/layers.0/blocks.1/attn/Reshape_1" [label="[5]", style=dashed]; +"1589 onnx^^Add_2293" -> "154 /layers/layers.0/blocks.1/attn/Add" [label="[1, 3, 49, 49]", style=solid]; +"1590 Constant_416" -> "136 /layers/layers.0/blocks.1/attn/Gather_1" [label="[]", style=dashed]; +"1591 Constant_27554" -> "124 /layers/layers.0/blocks.1/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1592 Constant_27553" -> "124 /layers/layers.0/blocks.1/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1593 Constant_27552" -> "124 /layers/layers.0/blocks.1/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1594 Constant_27551" -> "124 /layers/layers.0/blocks.1/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1595 Constant_27559" -> "145 /layers/layers.0/blocks.1/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1596 Constant_27558" -> "145 /layers/layers.0/blocks.1/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1597 Constant_27557" -> "145 /layers/layers.0/blocks.1/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1598 Constant_27556" -> "145 /layers/layers.0/blocks.1/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1599 Constant_7297" -> "135 /layers/layers.0/blocks.1/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; +"1600 Constant_414" -> "123 /layers/layers.0/blocks.1/attn/Gather" [label="[]", style=dashed]; +"1601 Constant_7298" -> "178 /layers/layers.0/blocks.1/attn/proj/Add" [label="[1, 1, 96]", style=solid]; +"1602 Constant_5115" -> "214 /layers/layers.0/blocks.1/Slice_4" [label="[2]", style=dashed]; +"1603 Constant_5112" -> "214 /layers/layers.0/blocks.1/Slice_4" [label="[2]", style=dashed]; +"1604 Constant_5109" -> "214 /layers/layers.0/blocks.1/Slice_4" [label="[2]", style=dashed]; +"1605 Constant_5139" -> "232 /layers/layers.0/blocks.1/Slice_6" [label="[3]", style=dashed]; +"1606 Constant_5136" -> "232 /layers/layers.0/blocks.1/Slice_6" [label="[3]", style=dashed]; +"1607 Constant_5133" -> "232 /layers/layers.0/blocks.1/Slice_6" [label="[3]", style=dashed]; +"1608 Constant_7301" -> "54 /layers/layers.0/blocks.1/mlp/fc1/Add" [label="[1, 1, 384]", style=solid]; +"1609 Constant_7302" -> "74 /layers/layers.0/blocks.1/mlp/fc2/Add" [label="[1, 1, 96]", style=solid]; +"1610 Constant_5211" -> "40 /layers/layers.0/downsample/Slice_4" [label="[3]", style=dashed]; +"1611 Constant_5208" -> "40 /layers/layers.0/downsample/Slice_4" [label="[3]", style=dashed]; +"1612 Constant_5205" -> "40 /layers/layers.0/downsample/Slice_4" [label="[3]", style=dashed]; +"1613 Constant_5163" -> "33 /layers/layers.0/downsample/Slice" [label="[2]", style=dashed]; +"1614 Constant_5160" -> "33 /layers/layers.0/downsample/Slice" [label="[2]", style=dashed]; +"1615 Constant_5157" -> "33 /layers/layers.0/downsample/Slice" [label="[2]", style=dashed]; +"1616 Constant_5199" -> "41 /layers/layers.0/downsample/Slice_3" [label="[3]", style=dashed]; +"1617 Constant_5196" -> "41 /layers/layers.0/downsample/Slice_3" [label="[3]", style=dashed]; +"1618 Constant_5193" -> "41 /layers/layers.0/downsample/Slice_3" [label="[3]", style=dashed]; +"1619 Constant_5175" -> "39 /layers/layers.0/downsample/Slice_1" [label="[3]", style=dashed]; +"1620 Constant_5172" -> "39 /layers/layers.0/downsample/Slice_1" [label="[3]", style=dashed]; +"1621 Constant_5169" -> "39 /layers/layers.0/downsample/Slice_1" [label="[3]", style=dashed]; +"1622 Constant_7307" -> "162 /layers/layers.1/blocks.0/attn/qkv/Add" [label="[1, 1, 576]", style=solid]; +"1623 onnx^^Add_2389" -> "204 /layers/layers.1/blocks.0/attn/Add" [label="[1, 6, 49, 49]", style=solid]; +"1624 Constant_626" -> "190 /layers/layers.1/blocks.0/attn/Gather_1" [label="[]", style=dashed]; +"1625 Constant_27614" -> "183 /layers/layers.1/blocks.0/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1626 Constant_27613" -> "183 /layers/layers.1/blocks.0/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1627 Constant_27612" -> "183 /layers/layers.1/blocks.0/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1628 Constant_27611" -> "183 /layers/layers.1/blocks.0/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1629 Constant_27619" -> "196 /layers/layers.1/blocks.0/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1630 Constant_27618" -> "196 /layers/layers.1/blocks.0/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1631 Constant_27617" -> "196 /layers/layers.1/blocks.0/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1632 Constant_27616" -> "196 /layers/layers.1/blocks.0/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1633 Constant_7308" -> "189 /layers/layers.1/blocks.0/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; +"1634 Constant_624" -> "182 /layers/layers.1/blocks.0/attn/Gather" [label="[]", style=dashed]; +"1635 Constant_7309" -> "231 /layers/layers.1/blocks.0/attn/proj/Add" [label="[1, 1, 192]", style=solid]; +"1636 Constant_7312" -> "133 /layers/layers.1/blocks.0/mlp/fc1/Add" [label="[1, 1, 768]", style=solid]; +"1637 Constant_7313" -> "169 /layers/layers.1/blocks.0/mlp/fc2/Add" [label="[1, 1, 192]", style=solid]; +"1638 Constant_5235" -> "141 /layers/layers.1/blocks.1/Slice" [label="[2]", style=dashed]; +"1639 Constant_5232" -> "141 /layers/layers.1/blocks.1/Slice" [label="[2]", style=dashed]; +"1640 Constant_5229" -> "141 /layers/layers.1/blocks.1/Slice" [label="[2]", style=dashed]; +"1641 Constant_5259" -> "159 /layers/layers.1/blocks.1/Slice_2" [label="[3]", style=dashed]; +"1642 Constant_5256" -> "159 /layers/layers.1/blocks.1/Slice_2" [label="[3]", style=dashed]; +"1643 Constant_5253" -> "159 /layers/layers.1/blocks.1/Slice_2" [label="[3]", style=dashed]; +"1644 Constant_7316" -> "211 /layers/layers.1/blocks.1/attn/qkv/Add" [label="[1, 1, 576]", style=solid]; +"1645 /layers/layers.1/blocks.1/attn/Constant_3" -> "328 /layers/layers.1/blocks.1/attn/Reshape_2" [label="[4]", style=dashed]; +"1646 onnx^^Add_2446" -> "313 /layers/layers.1/blocks.1/attn/Add_1" [label="[1, 16, 1, 49, 49]", style=solid]; +"1647 /layers/layers.1/blocks.1/attn/Constant_2" -> "298 /layers/layers.1/blocks.1/attn/Reshape_1" [label="[5]", style=dashed]; +"1648 onnx^^Add_2438" -> "283 /layers/layers.1/blocks.1/attn/Add" [label="[1, 6, 49, 49]", style=solid]; +"1649 Constant_774" -> "255 /layers/layers.1/blocks.1/attn/Gather_1" [label="[]", style=dashed]; +"1650 Constant_27659" -> "242 /layers/layers.1/blocks.1/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1651 Constant_27658" -> "242 /layers/layers.1/blocks.1/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1652 Constant_27657" -> "242 /layers/layers.1/blocks.1/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1653 Constant_27656" -> "242 /layers/layers.1/blocks.1/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1654 Constant_27664" -> "269 /layers/layers.1/blocks.1/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1655 Constant_27663" -> "269 /layers/layers.1/blocks.1/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1656 Constant_27662" -> "269 /layers/layers.1/blocks.1/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1657 Constant_27661" -> "269 /layers/layers.1/blocks.1/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1658 Constant_7317" -> "254 /layers/layers.1/blocks.1/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; +"1659 Constant_772" -> "241 /layers/layers.1/blocks.1/attn/Gather" [label="[]", style=dashed]; +"1660 Constant_7318" -> "329 /layers/layers.1/blocks.1/attn/proj/Add" [label="[1, 1, 192]", style=solid]; +"1661 Constant_5283" -> "405 /layers/layers.1/blocks.1/Slice_4" [label="[2]", style=dashed]; +"1662 Constant_5280" -> "405 /layers/layers.1/blocks.1/Slice_4" [label="[2]", style=dashed]; +"1663 Constant_5277" -> "405 /layers/layers.1/blocks.1/Slice_4" [label="[2]", style=dashed]; +"1664 Constant_5307" -> "431 /layers/layers.1/blocks.1/Slice_6" [label="[3]", style=dashed]; +"1665 Constant_5304" -> "431 /layers/layers.1/blocks.1/Slice_6" [label="[3]", style=dashed]; +"1666 Constant_5301" -> "431 /layers/layers.1/blocks.1/Slice_6" [label="[3]", style=dashed]; +"1667 Constant_7321" -> "150 /layers/layers.1/blocks.1/mlp/fc1/Add" [label="[1, 1, 768]", style=solid]; +"1668 Constant_7322" -> "180 /layers/layers.1/blocks.1/mlp/fc2/Add" [label="[1, 1, 192]", style=solid]; +"1669 Constant_5379" -> "128 /layers/layers.1/downsample/Slice_4" [label="[3]", style=dashed]; +"1670 Constant_5376" -> "128 /layers/layers.1/downsample/Slice_4" [label="[3]", style=dashed]; +"1671 Constant_5373" -> "128 /layers/layers.1/downsample/Slice_4" [label="[3]", style=dashed]; +"1672 Constant_5331" -> "117 /layers/layers.1/downsample/Slice" [label="[2]", style=dashed]; +"1673 Constant_5328" -> "117 /layers/layers.1/downsample/Slice" [label="[2]", style=dashed]; +"1674 Constant_5325" -> "117 /layers/layers.1/downsample/Slice" [label="[2]", style=dashed]; +"1675 Constant_5367" -> "129 /layers/layers.1/downsample/Slice_3" [label="[3]", style=dashed]; +"1676 Constant_5364" -> "129 /layers/layers.1/downsample/Slice_3" [label="[3]", style=dashed]; +"1677 Constant_5361" -> "129 /layers/layers.1/downsample/Slice_3" [label="[3]", style=dashed]; +"1678 Constant_5343" -> "127 /layers/layers.1/downsample/Slice_1" [label="[3]", style=dashed]; +"1679 Constant_5340" -> "127 /layers/layers.1/downsample/Slice_1" [label="[3]", style=dashed]; +"1680 Constant_5337" -> "127 /layers/layers.1/downsample/Slice_1" [label="[3]", style=dashed]; +"1681 Constant_7327" -> "297 /layers/layers.2/blocks.0/attn/qkv/Add" [label="[1, 1, 1152]", style=solid]; +"1682 onnx^^Add_2534" -> "389 /layers/layers.2/blocks.0/attn/Add" [label="[1, 12, 49, 49]", style=solid]; +"1683 Constant_984" -> "359 /layers/layers.2/blocks.0/attn/Gather_1" [label="[]", style=dashed]; +"1684 Constant_27719" -> "342 /layers/layers.2/blocks.0/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1685 Constant_27718" -> "342 /layers/layers.2/blocks.0/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1686 Constant_27717" -> "342 /layers/layers.2/blocks.0/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1687 Constant_27716" -> "342 /layers/layers.2/blocks.0/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1688 Constant_27724" -> "375 /layers/layers.2/blocks.0/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1689 Constant_27723" -> "375 /layers/layers.2/blocks.0/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1690 Constant_27722" -> "375 /layers/layers.2/blocks.0/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1691 Constant_27721" -> "375 /layers/layers.2/blocks.0/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1692 Constant_7328" -> "358 /layers/layers.2/blocks.0/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; +"1693 Constant_982" -> "341 /layers/layers.2/blocks.0/attn/Gather" [label="[]", style=dashed]; +"1694 Constant_7329" -> "430 /layers/layers.2/blocks.0/attn/proj/Add" [label="[1, 1, 384]", style=solid]; +"1695 Constant_7332" -> "252 /layers/layers.2/blocks.0/mlp/fc1/Add" [label="[1, 1, 1536]", style=solid]; +"1696 Constant_7333" -> "311 /layers/layers.2/blocks.0/mlp/fc2/Add" [label="[1, 1, 384]", style=solid]; +"1697 Constant_5403" -> "265 /layers/layers.2/blocks.1/Slice" [label="[2]", style=dashed]; +"1698 Constant_5400" -> "265 /layers/layers.2/blocks.1/Slice" [label="[2]", style=dashed]; +"1699 Constant_5397" -> "265 /layers/layers.2/blocks.1/Slice" [label="[2]", style=dashed]; +"1700 Constant_5427" -> "294 /layers/layers.2/blocks.1/Slice_2" [label="[3]", style=dashed]; +"1701 Constant_5424" -> "294 /layers/layers.2/blocks.1/Slice_2" [label="[3]", style=dashed]; +"1702 Constant_5421" -> "294 /layers/layers.2/blocks.1/Slice_2" [label="[3]", style=dashed]; +"1703 Constant_7336" -> "402 /layers/layers.2/blocks.1/attn/qkv/Add" [label="[1, 1, 1152]", style=solid]; +"1704 /layers/layers.2/blocks.1/attn/Constant_3" -> "518 /layers/layers.2/blocks.1/attn/Reshape_2" [label="[4]", style=dashed]; +"1705 onnx^^Add_2702" -> "505 /layers/layers.2/blocks.1/attn/Add_1" [label="[1, 4, 1, 49, 49]", style=solid]; +"1705 onnx^^Add_2702" -> "554 /layers/layers.2/blocks.3/attn/Add_1" [label="[1, 4, 1, 49, 49]", style=solid]; +"1705 onnx^^Add_2702" -> "596 /layers/layers.2/blocks.5/attn/Add_1" [label="[1, 4, 1, 49, 49]", style=solid]; +"1706 /layers/layers.2/blocks.1/attn/Constant_2" -> "491 /layers/layers.2/blocks.1/attn/Reshape_1" [label="[5]", style=dashed]; +"1707 onnx^^Add_2583" -> "478 /layers/layers.2/blocks.1/attn/Add" [label="[1, 12, 49, 49]", style=solid]; +"1708 Constant_1132" -> "455 /layers/layers.2/blocks.1/attn/Gather_1" [label="[]", style=dashed]; +"1709 Constant_27764" -> "441 /layers/layers.2/blocks.1/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1710 Constant_27763" -> "441 /layers/layers.2/blocks.1/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1711 Constant_27762" -> "441 /layers/layers.2/blocks.1/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1712 Constant_27761" -> "441 /layers/layers.2/blocks.1/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1713 Constant_27769" -> "466 /layers/layers.2/blocks.1/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1714 Constant_27768" -> "466 /layers/layers.2/blocks.1/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1715 Constant_27767" -> "466 /layers/layers.2/blocks.1/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1716 Constant_27766" -> "466 /layers/layers.2/blocks.1/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1717 Constant_7337" -> "454 /layers/layers.2/blocks.1/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; +"1718 Constant_1130" -> "440 /layers/layers.2/blocks.1/attn/Gather" [label="[]", style=dashed]; +"1719 Constant_7338" -> "519 /layers/layers.2/blocks.1/attn/proj/Add" [label="[1, 1, 384]", style=solid]; +"1720 Constant_5451" -> "579 /layers/layers.2/blocks.1/Slice_4" [label="[2]", style=dashed]; +"1721 Constant_5448" -> "579 /layers/layers.2/blocks.1/Slice_4" [label="[2]", style=dashed]; +"1722 Constant_5445" -> "579 /layers/layers.2/blocks.1/Slice_4" [label="[2]", style=dashed]; +"1723 Constant_5475" -> "599 /layers/layers.2/blocks.1/Slice_6" [label="[3]", style=dashed]; +"1724 Constant_5472" -> "599 /layers/layers.2/blocks.1/Slice_6" [label="[3]", style=dashed]; +"1725 Constant_5469" -> "599 /layers/layers.2/blocks.1/Slice_6" [label="[3]", style=dashed]; +"1726 Constant_7341" -> "279 /layers/layers.2/blocks.1/mlp/fc1/Add" [label="[1, 1, 1536]", style=solid]; +"1727 Constant_7342" -> "339 /layers/layers.2/blocks.1/mlp/fc2/Add" [label="[1, 1, 384]", style=solid]; +"1728 Constant_7345" -> "356 /layers/layers.2/blocks.2/attn/qkv/Add" [label="[1, 1, 1152]", style=solid]; +"1729 onnx^^Add_2645" -> "438 /layers/layers.2/blocks.2/attn/Add" [label="[1, 12, 49, 49]", style=solid]; +"1730 Constant_1289" -> "415 /layers/layers.2/blocks.2/attn/Gather_1" [label="[]", style=dashed]; +"1731 Constant_27814" -> "400 /layers/layers.2/blocks.2/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1732 Constant_27813" -> "400 /layers/layers.2/blocks.2/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1733 Constant_27812" -> "400 /layers/layers.2/blocks.2/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1734 Constant_27811" -> "400 /layers/layers.2/blocks.2/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1735 Constant_27819" -> "426 /layers/layers.2/blocks.2/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1736 Constant_27818" -> "426 /layers/layers.2/blocks.2/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1737 Constant_27817" -> "426 /layers/layers.2/blocks.2/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1738 Constant_27816" -> "426 /layers/layers.2/blocks.2/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1739 Constant_7346" -> "414 /layers/layers.2/blocks.2/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; +"1740 Constant_1287" -> "399 /layers/layers.2/blocks.2/attn/Gather" [label="[]", style=dashed]; +"1741 Constant_7347" -> "477 /layers/layers.2/blocks.2/attn/proj/Add" [label="[1, 1, 384]", style=solid]; +"1742 Constant_7350" -> "307 /layers/layers.2/blocks.2/mlp/fc1/Add" [label="[1, 1, 1536]", style=solid]; +"1743 Constant_7351" -> "372 /layers/layers.2/blocks.2/mlp/fc2/Add" [label="[1, 1, 384]", style=solid]; +"1744 Constant_5499" -> "321 /layers/layers.2/blocks.3/Slice" [label="[2]", style=dashed]; +"1745 Constant_5496" -> "321 /layers/layers.2/blocks.3/Slice" [label="[2]", style=dashed]; +"1746 Constant_5493" -> "321 /layers/layers.2/blocks.3/Slice" [label="[2]", style=dashed]; +"1747 Constant_5523" -> "353 /layers/layers.2/blocks.3/Slice_2" [label="[3]", style=dashed]; +"1748 Constant_5520" -> "353 /layers/layers.2/blocks.3/Slice_2" [label="[3]", style=dashed]; +"1749 Constant_5517" -> "353 /layers/layers.2/blocks.3/Slice_2" [label="[3]", style=dashed]; +"1750 Constant_7354" -> "451 /layers/layers.2/blocks.3/attn/qkv/Add" [label="[1, 1, 1152]", style=solid]; +"1751 /layers/layers.2/blocks.3/attn/Constant_3" -> "566 /layers/layers.2/blocks.3/attn/Reshape_2" [label="[4]", style=dashed]; +"1752 /layers/layers.2/blocks.3/attn/Constant_2" -> "541 /layers/layers.2/blocks.3/attn/Reshape_1" [label="[5]", style=dashed]; +"1753 onnx^^Add_2694" -> "527 /layers/layers.2/blocks.3/attn/Add" [label="[1, 12, 49, 49]", style=solid]; +"1754 Constant_1437" -> "502 /layers/layers.2/blocks.3/attn/Gather_1" [label="[]", style=dashed]; +"1755 Constant_27859" -> "488 /layers/layers.2/blocks.3/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1756 Constant_27858" -> "488 /layers/layers.2/blocks.3/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1757 Constant_27857" -> "488 /layers/layers.2/blocks.3/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1758 Constant_27856" -> "488 /layers/layers.2/blocks.3/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1759 Constant_27864" -> "514 /layers/layers.2/blocks.3/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1760 Constant_27863" -> "514 /layers/layers.2/blocks.3/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1761 Constant_27862" -> "514 /layers/layers.2/blocks.3/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1762 Constant_27861" -> "514 /layers/layers.2/blocks.3/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1763 Constant_7355" -> "501 /layers/layers.2/blocks.3/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; +"1764 Constant_1435" -> "487 /layers/layers.2/blocks.3/attn/Gather" [label="[]", style=dashed]; +"1765 Constant_7356" -> "567 /layers/layers.2/blocks.3/attn/proj/Add" [label="[1, 1, 384]", style=solid]; +"1766 Constant_5547" -> "617 /layers/layers.2/blocks.3/Slice_4" [label="[2]", style=dashed]; +"1767 Constant_5544" -> "617 /layers/layers.2/blocks.3/Slice_4" [label="[2]", style=dashed]; +"1768 Constant_5541" -> "617 /layers/layers.2/blocks.3/Slice_4" [label="[2]", style=dashed]; +"1769 Constant_5571" -> "634 /layers/layers.2/blocks.3/Slice_6" [label="[3]", style=dashed]; +"1770 Constant_5568" -> "634 /layers/layers.2/blocks.3/Slice_6" [label="[3]", style=dashed]; +"1771 Constant_5565" -> "634 /layers/layers.2/blocks.3/Slice_6" [label="[3]", style=dashed]; +"1772 Constant_7359" -> "335 /layers/layers.2/blocks.3/mlp/fc1/Add" [label="[1, 1, 1536]", style=solid]; +"1773 Constant_7360" -> "397 /layers/layers.2/blocks.3/mlp/fc2/Add" [label="[1, 1, 384]", style=solid]; +"1774 Constant_7363" -> "412 /layers/layers.2/blocks.4/attn/qkv/Add" [label="[1, 1, 1152]", style=solid]; +"1775 onnx^^Add_2756" -> "485 /layers/layers.2/blocks.4/attn/Add" [label="[1, 12, 49, 49]", style=solid]; +"1776 Constant_1594" -> "462 /layers/layers.2/blocks.4/attn/Gather_1" [label="[]", style=dashed]; +"1777 Constant_27909" -> "449 /layers/layers.2/blocks.4/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1778 Constant_27908" -> "449 /layers/layers.2/blocks.4/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1779 Constant_27907" -> "449 /layers/layers.2/blocks.4/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1780 Constant_27906" -> "449 /layers/layers.2/blocks.4/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1781 Constant_27914" -> "473 /layers/layers.2/blocks.4/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1782 Constant_27913" -> "473 /layers/layers.2/blocks.4/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1783 Constant_27912" -> "473 /layers/layers.2/blocks.4/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1784 Constant_27911" -> "473 /layers/layers.2/blocks.4/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1785 Constant_7364" -> "461 /layers/layers.2/blocks.4/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; +"1786 Constant_1592" -> "448 /layers/layers.2/blocks.4/attn/Gather" [label="[]", style=dashed]; +"1787 Constant_7365" -> "526 /layers/layers.2/blocks.4/attn/proj/Add" [label="[1, 1, 384]", style=solid]; +"1788 Constant_7368" -> "368 /layers/layers.2/blocks.4/mlp/fc1/Add" [label="[1, 1, 1536]", style=solid]; +"1789 Constant_7369" -> "423 /layers/layers.2/blocks.4/mlp/fc2/Add" [label="[1, 1, 384]", style=solid]; +"1790 Constant_5595" -> "381 /layers/layers.2/blocks.5/Slice" [label="[2]", style=dashed]; +"1791 Constant_5592" -> "381 /layers/layers.2/blocks.5/Slice" [label="[2]", style=dashed]; +"1792 Constant_5589" -> "381 /layers/layers.2/blocks.5/Slice" [label="[2]", style=dashed]; +"1793 Constant_5619" -> "409 /layers/layers.2/blocks.5/Slice_2" [label="[3]", style=dashed]; +"1794 Constant_5616" -> "409 /layers/layers.2/blocks.5/Slice_2" [label="[3]", style=dashed]; +"1795 Constant_5613" -> "409 /layers/layers.2/blocks.5/Slice_2" [label="[3]", style=dashed]; +"1796 Constant_7372" -> "498 /layers/layers.2/blocks.5/attn/qkv/Add" [label="[1, 1, 1152]", style=solid]; +"1797 /layers/layers.2/blocks.5/attn/Constant_3" -> "605 /layers/layers.2/blocks.5/attn/Reshape_2" [label="[4]", style=dashed]; +"1798 /layers/layers.2/blocks.5/attn/Constant_2" -> "586 /layers/layers.2/blocks.5/attn/Reshape_1" [label="[5]", style=dashed]; +"1799 onnx^^Add_2805" -> "574 /layers/layers.2/blocks.5/attn/Add" [label="[1, 12, 49, 49]", style=solid]; +"1800 Constant_1742" -> "551 /layers/layers.2/blocks.5/attn/Gather_1" [label="[]", style=dashed]; +"1801 Constant_27954" -> "538 /layers/layers.2/blocks.5/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1802 Constant_27953" -> "538 /layers/layers.2/blocks.5/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1803 Constant_27952" -> "538 /layers/layers.2/blocks.5/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1804 Constant_27951" -> "538 /layers/layers.2/blocks.5/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1805 Constant_27959" -> "562 /layers/layers.2/blocks.5/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1806 Constant_27958" -> "562 /layers/layers.2/blocks.5/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1807 Constant_27957" -> "562 /layers/layers.2/blocks.5/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1808 Constant_27956" -> "562 /layers/layers.2/blocks.5/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1809 Constant_7373" -> "550 /layers/layers.2/blocks.5/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; +"1810 Constant_1740" -> "537 /layers/layers.2/blocks.5/attn/Gather" [label="[]", style=dashed]; +"1811 Constant_7374" -> "606 /layers/layers.2/blocks.5/attn/proj/Add" [label="[1, 1, 384]", style=solid]; +"1812 Constant_5643" -> "648 /layers/layers.2/blocks.5/Slice_4" [label="[2]", style=dashed]; +"1813 Constant_5640" -> "648 /layers/layers.2/blocks.5/Slice_4" [label="[2]", style=dashed]; +"1814 Constant_5637" -> "648 /layers/layers.2/blocks.5/Slice_4" [label="[2]", style=dashed]; +"1815 Constant_5667" -> "658 /layers/layers.2/blocks.5/Slice_6" [label="[3]", style=dashed]; +"1816 Constant_5664" -> "658 /layers/layers.2/blocks.5/Slice_6" [label="[3]", style=dashed]; +"1817 Constant_5661" -> "658 /layers/layers.2/blocks.5/Slice_6" [label="[3]", style=dashed]; +"1818 Constant_7377" -> "393 /layers/layers.2/blocks.5/mlp/fc1/Add" [label="[1, 1, 1536]", style=solid]; +"1819 Constant_7378" -> "446 /layers/layers.2/blocks.5/mlp/fc2/Add" [label="[1, 1, 384]", style=solid]; +"1820 Constant_5739" -> "363 /layers/layers.2/downsample/Slice_4" [label="[3]", style=dashed]; +"1821 Constant_5736" -> "363 /layers/layers.2/downsample/Slice_4" [label="[3]", style=dashed]; +"1822 Constant_5733" -> "363 /layers/layers.2/downsample/Slice_4" [label="[3]", style=dashed]; +"1823 Constant_5691" -> "346 /layers/layers.2/downsample/Slice" [label="[2]", style=dashed]; +"1824 Constant_5688" -> "346 /layers/layers.2/downsample/Slice" [label="[2]", style=dashed]; +"1825 Constant_5685" -> "346 /layers/layers.2/downsample/Slice" [label="[2]", style=dashed]; +"1826 Constant_5727" -> "364 /layers/layers.2/downsample/Slice_3" [label="[3]", style=dashed]; +"1827 Constant_5724" -> "364 /layers/layers.2/downsample/Slice_3" [label="[3]", style=dashed]; +"1828 Constant_5721" -> "364 /layers/layers.2/downsample/Slice_3" [label="[3]", style=dashed]; +"1829 Constant_5703" -> "362 /layers/layers.2/downsample/Slice_1" [label="[3]", style=dashed]; +"1830 Constant_5700" -> "362 /layers/layers.2/downsample/Slice_1" [label="[3]", style=dashed]; +"1831 Constant_5697" -> "362 /layers/layers.2/downsample/Slice_1" [label="[3]", style=dashed]; +"1832 Constant_7383" -> "585 /layers/layers.3/blocks.0/attn/qkv/Add" [label="[1, 1, 2304]", style=solid]; +"1833 onnx^^Add_2901" -> "639 /layers/layers.3/blocks.0/attn/Add" [label="[1, 24, 49, 49]", style=solid]; +"1834 Constant_1952" -> "623 /layers/layers.3/blocks.0/attn/Gather_1" [label="[]", style=dashed]; +"1835 Constant_28014" -> "613 /layers/layers.3/blocks.0/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1836 Constant_28013" -> "613 /layers/layers.3/blocks.0/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1837 Constant_28012" -> "613 /layers/layers.3/blocks.0/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1838 Constant_28011" -> "613 /layers/layers.3/blocks.0/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1839 Constant_28019" -> "630 /layers/layers.3/blocks.0/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1840 Constant_28018" -> "630 /layers/layers.3/blocks.0/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1841 Constant_28017" -> "630 /layers/layers.3/blocks.0/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1842 Constant_28016" -> "630 /layers/layers.3/blocks.0/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1843 Constant_7384" -> "622 /layers/layers.3/blocks.0/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; +"1844 Constant_1950" -> "612 /layers/layers.3/blocks.0/attn/Gather" [label="[]", style=dashed]; +"1845 Constant_7385" -> "657 /layers/layers.3/blocks.0/attn/proj/Add" [label="[1, 1, 768]", style=solid]; +"1846 Constant_7388" -> "548 /layers/layers.3/blocks.0/mlp/fc1/Add" [label="[1, 1, 3072]", style=solid]; +"1847 Constant_7389" -> "594 /layers/layers.3/blocks.0/mlp/fc2/Add" [label="[1, 1, 768]", style=solid]; +"1848 Constant_7392" -> "603 /layers/layers.3/blocks.1/attn/qkv/Add" [label="[1, 1, 2304]", style=solid]; +"1849 onnx^^Add_2950" -> "651 /layers/layers.3/blocks.1/attn/Add" [label="[1, 24, 49, 49]", style=solid]; +"1850 Constant_2058" -> "637 /layers/layers.3/blocks.1/attn/Gather_1" [label="[]", style=dashed]; +"1851 Constant_28064" -> "628 /layers/layers.3/blocks.1/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1852 Constant_28063" -> "628 /layers/layers.3/blocks.1/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1853 Constant_28062" -> "628 /layers/layers.3/blocks.1/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1854 Constant_28061" -> "628 /layers/layers.3/blocks.1/attn/Gather_1/fq_input_0" [label="[]", style=solid]; +"1855 Constant_28069" -> "643 /layers/layers.3/blocks.1/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1856 Constant_28068" -> "643 /layers/layers.3/blocks.1/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1857 Constant_28067" -> "643 /layers/layers.3/blocks.1/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1858 Constant_28066" -> "643 /layers/layers.3/blocks.1/attn/Mul/fq_output_0" [label="[]", style=solid]; +"1859 Constant_7393" -> "636 /layers/layers.3/blocks.1/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; +"1860 Constant_2056" -> "627 /layers/layers.3/blocks.1/attn/Gather" [label="[]", style=dashed]; +"1861 Constant_7394" -> "663 /layers/layers.3/blocks.1/attn/proj/Add" [label="[1, 1, 768]", style=solid]; +"1862 Constant_7397" -> "570 /layers/layers.3/blocks.1/mlp/fc1/Add" [label="[1, 1, 3072]", style=solid]; +"1863 Constant_7398" -> "610 /layers/layers.3/blocks.1/mlp/fc2/Add" [label="[1, 1, 768]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/swin-tiny-patch4-window7-224_sq.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/swin-tiny-patch4-window7-224_sq.dot deleted file mode 120000 index b801f581828..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/quantized/swin-tiny-patch4-window7-224_sq.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/quantized/swin-tiny-patch4-window7-224_sq.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/swin-tiny-patch4-window7-224_sq.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/swin-tiny-patch4-window7-224_sq.dot new file mode 100644 index 00000000000..b2201cd3a37 --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/quantized/swin-tiny-patch4-window7-224_sq.dot @@ -0,0 +1,2754 @@ +strict digraph { +"0 input" [id=0, type=Parameter]; +"1 Multiply_6579" [id=1, type=Multiply]; +"2 Divide_2169" [id=2, type=Add]; +"3 Divide_2169_0_0/nncf_smooth_quant" [id=3, type=Multiply]; +"4 /patch_embed/proj/Conv/WithoutBiases" [id=4, type=Convolution]; +"5 /patch_embed/proj/Conv" [id=5, type=Add]; +"6 /patch_embed/Reshape" [id=6, type=Reshape]; +"7 /patch_embed/Shape" [id=7, type=ShapeOf]; +"8 /patch_embed/Transpose" [id=8, type=Transpose]; +"9 /patch_embed/Slice" [id=9, type=StridedSlice]; +"10 /patch_embed/norm/Div" [id=10, type=MVN]; +"11 /patch_embed/Concat" [id=11, type=Concat]; +"12 /patch_embed/norm/Mul" [id=12, type=Multiply]; +"13 /patch_embed/norm/Add_1" [id=13, type=Add]; +"14 /layers/layers.0/blocks.0/Add" [id=14, type=Add]; +"15 /layers/layers.0/blocks.0/norm1/Div" [id=15, type=MVN]; +"16 /layers/layers.0/blocks.0/Add_1" [id=16, type=Add]; +"17 /layers/layers.0/blocks.0/norm2/Div" [id=17, type=MVN]; +"18 /layers/layers.0/blocks.0/norm1/Mul" [id=18, type=Multiply]; +"19 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/layers/layers.2/blocks.5/Constant_31" [id=736, type=Constant]; +"737 Constant_5679" [id=737, type=Constant]; +"738 Constant_5676" [id=738, type=Constant]; +"739 Constant_5673" [id=739, type=Constant]; +"740 Constant_5655" [id=740, type=Constant]; +"741 Constant_5652" [id=741, type=Constant]; +"742 Constant_5649" [id=742, type=Constant]; +"743 /layers/layers.2/blocks.5/Constant_18" [id=743, type=Constant]; +"744 Constant_1779" [id=744, type=Constant]; +"745 /layers/layers.2/blocks.5/Constant_17" [id=745, type=Constant]; +"746 /layers/layers.2/blocks.5/Constant_16" [id=746, type=Constant]; +"747 Transpose_6506" [id=747, type=Constant]; +"748 Constant_26063" [id=748, type=Constant]; +"749 /layers/layers.2/blocks.5/attn/Constant_4" [id=749, type=Constant]; +"750 Constant_1763" [id=750, type=Constant]; +"751 Constant_1744" [id=751, type=Constant]; +"752 Constant_1738" [id=752, type=Constant]; +"753 /layers/layers.2/blocks.5/attn/Constant" [id=753, type=Constant]; +"754 Transpose_6503" 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/layers/layers.2/blocks.3/Constant_18" [id=812, type=Constant]; +"813 Constant_1474" [id=813, type=Constant]; +"814 /layers/layers.2/blocks.3/Constant_17" [id=814, type=Constant]; +"815 /layers/layers.2/blocks.3/Constant_16" [id=815, type=Constant]; +"816 Transpose_6476" [id=816, type=Constant]; +"817 Constant_26051" [id=817, type=Constant]; +"818 /layers/layers.2/blocks.3/attn/Constant_4" [id=818, type=Constant]; +"819 Constant_1458" [id=819, type=Constant]; +"820 Constant_1439" [id=820, type=Constant]; +"821 Constant_1433" [id=821, type=Constant]; +"822 /layers/layers.2/blocks.3/attn/Constant" [id=822, type=Constant]; +"823 Transpose_6473" [id=823, type=Constant]; +"824 Constant_26037" [id=824, type=Constant]; +"825 /layers/layers.2/blocks.3/Constant_15" [id=825, type=Constant]; +"826 /layers/layers.2/blocks.3/Constant_14" [id=826, type=Constant]; +"827 Constant_1417" [id=827, type=Constant]; +"828 /layers/layers.2/blocks.3/Constant_13" [id=828, type=Constant]; +"829 Constant_5535" 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/layers/layers.2/blocks.2/Constant_5" [id=849, type=Constant]; +"850 /layers/layers.2/blocks.2/Constant_4" [id=850, type=Constant]; +"851 Transpose_6461" [id=851, type=Constant]; +"852 Constant_26039" [id=852, type=Constant]; +"853 /layers/layers.2/blocks.2/attn/Constant_2" [id=853, type=Constant]; +"854 Constant_1301" [id=854, type=Constant]; +"855 Constant_1291" [id=855, type=Constant]; +"856 Constant_1285" [id=856, type=Constant]; +"857 /layers/layers.2/blocks.2/attn/Constant" [id=857, type=Constant]; +"858 Transpose_6458" [id=858, type=Constant]; +"859 Constant_26019" [id=859, type=Constant]; +"860 /layers/layers.2/blocks.2/Constant_3" [id=860, type=Constant]; +"861 /layers/layers.2/blocks.2/Constant_2" [id=861, type=Constant]; +"862 Constant_1269" [id=862, type=Constant]; +"863 /layers/layers.2/blocks.2/Constant_1" [id=863, type=Constant]; +"864 Constant_7344" [id=864, type=Constant]; +"865 Constant_7343" [id=865, type=Constant]; +"866 Constant_1248" [id=866, type=Constant]; +"867 Transpose_6454" [id=867, type=Constant]; +"868 Constant_26011" [id=868, type=Constant]; +"869 Transpose_6450" [id=869, type=Constant]; +"870 Constant_26003" [id=870, type=Constant]; +"871 Constant_7340" [id=871, type=Constant]; +"872 Constant_7339" [id=872, type=Constant]; +"873 Constant_1222" [id=873, type=Constant]; +"874 /layers/layers.2/blocks.1/Constant_31" [id=874, type=Constant]; +"875 Constant_5487" [id=875, type=Constant]; +"876 Constant_5484" [id=876, type=Constant]; +"877 Constant_5481" [id=877, type=Constant]; +"878 Constant_5463" [id=878, type=Constant]; +"879 Constant_5460" [id=879, type=Constant]; +"880 Constant_5457" [id=880, type=Constant]; +"881 /layers/layers.2/blocks.1/Constant_18" [id=881, type=Constant]; +"882 Constant_1169" [id=882, type=Constant]; +"883 /layers/layers.2/blocks.1/Constant_17" [id=883, type=Constant]; +"884 /layers/layers.2/blocks.1/Constant_16" [id=884, type=Constant]; +"885 Transpose_6446" [id=885, type=Constant]; +"886 Constant_26045" [id=886, type=Constant]; +"887 /layers/layers.2/blocks.1/attn/Constant_4" [id=887, type=Constant]; +"888 Constant_1153" [id=888, type=Constant]; +"889 Constant_1134" [id=889, type=Constant]; +"890 Constant_1128" [id=890, type=Constant]; +"891 /layers/layers.2/blocks.1/attn/Constant" [id=891, type=Constant]; +"892 Transpose_6443" [id=892, type=Constant]; +"893 Constant_26027" [id=893, type=Constant]; +"894 /layers/layers.2/blocks.1/Constant_15" [id=894, type=Constant]; +"895 /layers/layers.2/blocks.1/Constant_14" [id=895, type=Constant]; +"896 Constant_1112" [id=896, type=Constant]; +"897 /layers/layers.2/blocks.1/Constant_13" [id=897, type=Constant]; +"898 Constant_5439" [id=898, type=Constant]; +"899 Constant_5436" [id=899, type=Constant]; +"900 Constant_5433" [id=900, type=Constant]; +"901 Constant_5415" [id=901, type=Constant]; +"902 Constant_5412" [id=902, type=Constant]; +"903 Constant_5409" [id=903, type=Constant]; +"904 /layers/layers.2/blocks.1/Constant" [id=904, type=Constant]; +"905 Constant_7335" [id=905, type=Constant]; +"906 Constant_7334" [id=906, type=Constant]; +"907 Constant_1049" [id=907, type=Constant]; +"908 Transpose_6439" [id=908, type=Constant]; +"909 Constant_26005" [id=909, type=Constant]; +"910 Transpose_6435" [id=910, type=Constant]; +"911 Constant_26001" [id=911, type=Constant]; +"912 Constant_7331" [id=912, type=Constant]; +"913 Constant_7330" [id=913, type=Constant]; +"914 Constant_1023" [id=914, type=Constant]; +"915 /layers/layers.2/blocks.0/Constant_7" [id=915, type=Constant]; +"916 /layers/layers.2/blocks.0/Constant_6" [id=916, type=Constant]; +"917 Constant_1012" [id=917, type=Constant]; +"918 /layers/layers.2/blocks.0/Constant_5" [id=918, type=Constant]; +"919 /layers/layers.2/blocks.0/Constant_4" [id=919, type=Constant]; +"920 Transpose_6431" [id=920, type=Constant]; +"921 Constant_26033" [id=921, type=Constant]; +"922 /layers/layers.2/blocks.0/attn/Constant_2" [id=922, type=Constant]; +"923 Constant_996" [id=923, type=Constant]; +"924 Constant_986" [id=924, type=Constant]; +"925 Constant_980" [id=925, type=Constant]; +"926 /layers/layers.2/blocks.0/attn/Constant" [id=926, type=Constant]; +"927 Transpose_6428" [id=927, type=Constant]; +"928 Constant_26007" [id=928, type=Constant]; +"929 /layers/layers.2/blocks.0/Constant_3" [id=929, type=Constant]; +"930 /layers/layers.2/blocks.0/Constant_2" [id=930, type=Constant]; +"931 Constant_964" [id=931, type=Constant]; +"932 /layers/layers.2/blocks.0/Constant_1" [id=932, type=Constant]; +"933 Constant_7326" [id=933, type=Constant]; +"934 Constant_7325" [id=934, type=Constant]; +"935 Constant_943" [id=935, type=Constant]; +"936 Transpose_6424" [id=936, type=Constant]; +"937 Constant_25995" [id=937, type=Constant]; +"938 Constant_7324" [id=938, type=Constant]; +"939 Constant_7323" [id=939, type=Constant]; +"940 Constant_929" [id=940, type=Constant]; +"941 /layers/layers.1/downsample/Constant_25" [id=941, type=Constant]; +"942 Constant_5391" [id=942, type=Constant]; +"943 Constant_5388" [id=943, type=Constant]; +"944 Constant_5385" [id=944, type=Constant]; +"945 Constant_5355" [id=945, type=Constant]; +"946 Constant_5352" [id=946, type=Constant]; +"947 Constant_5349" [id=947, type=Constant]; +"948 /layers/layers.1/downsample/Constant" [id=948, type=Constant]; +"949 Transpose_6420" [id=949, type=Constant]; +"950 Constant_25991" [id=950, type=Constant]; +"951 Transpose_6416" [id=951, type=Constant]; +"952 Constant_25985" [id=952, type=Constant]; +"953 Constant_7320" [id=953, type=Constant]; +"954 Constant_7319" [id=954, type=Constant]; +"955 Constant_864" [id=955, type=Constant]; +"956 /layers/layers.1/blocks.1/Constant_31" [id=956, type=Constant]; +"957 Constant_5319" [id=957, type=Constant]; +"958 Constant_5316" [id=958, type=Constant]; +"959 Constant_5313" [id=959, type=Constant]; +"960 Constant_5295" [id=960, type=Constant]; +"961 Constant_5292" [id=961, type=Constant]; +"962 Constant_5289" [id=962, type=Constant]; +"963 /layers/layers.1/blocks.1/Constant_18" [id=963, type=Constant]; +"964 Constant_811" [id=964, type=Constant]; +"965 /layers/layers.1/blocks.1/Constant_17" [id=965, type=Constant]; +"966 /layers/layers.1/blocks.1/Constant_16" [id=966, type=Constant]; +"967 Transpose_6412" [id=967, type=Constant]; +"968 Constant_26013" [id=968, type=Constant]; +"969 /layers/layers.1/blocks.1/attn/Constant_4" [id=969, type=Constant]; +"970 Constant_795" [id=970, type=Constant]; +"971 Constant_776" [id=971, type=Constant]; +"972 Constant_770" [id=972, type=Constant]; +"973 /layers/layers.1/blocks.1/attn/Constant" [id=973, type=Constant]; +"974 Transpose_6409" [id=974, type=Constant]; +"975 Constant_25997" [id=975, type=Constant]; +"976 /layers/layers.1/blocks.1/Constant_15" [id=976, type=Constant]; +"977 /layers/layers.1/blocks.1/Constant_14" [id=977, type=Constant]; +"978 Constant_754" [id=978, type=Constant]; +"979 /layers/layers.1/blocks.1/Constant_13" [id=979, type=Constant]; +"980 Constant_5271" [id=980, type=Constant]; +"981 Constant_5268" [id=981, type=Constant]; +"982 Constant_5265" [id=982, type=Constant]; +"983 Constant_5247" [id=983, type=Constant]; +"984 Constant_5244" [id=984, type=Constant]; +"985 Constant_5241" [id=985, type=Constant]; +"986 /layers/layers.1/blocks.1/Constant" [id=986, type=Constant]; +"987 Constant_7315" [id=987, type=Constant]; +"988 Constant_7314" [id=988, type=Constant]; +"989 Constant_691" [id=989, type=Constant]; +"990 Transpose_6405" [id=990, type=Constant]; +"991 Constant_25987" [id=991, type=Constant]; +"992 Transpose_6401" [id=992, type=Constant]; +"993 Constant_25983" [id=993, type=Constant]; +"994 Constant_7311" [id=994, type=Constant]; +"995 Constant_7310" [id=995, type=Constant]; +"996 Constant_665" [id=996, type=Constant]; +"997 /layers/layers.1/blocks.0/Constant_7" [id=997, type=Constant]; +"998 /layers/layers.1/blocks.0/Constant_6" [id=998, type=Constant]; +"999 Constant_654" [id=999, type=Constant]; +"1000 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/layers/layers.0/blocks.1/attn/Constant" [id=1055, type=Constant]; +"1056 Transpose_6375" [id=1056, type=Constant]; +"1057 Constant_25979" [id=1057, type=Constant]; +"1058 /layers/layers.0/blocks.1/Constant_15" [id=1058, type=Constant]; +"1059 /layers/layers.0/blocks.1/Constant_14" [id=1059, type=Constant]; +"1060 Constant_396" [id=1060, type=Constant]; +"1061 /layers/layers.0/blocks.1/Constant_13" [id=1061, type=Constant]; +"1062 Constant_5103" [id=1062, type=Constant]; +"1063 Constant_5100" [id=1063, type=Constant]; +"1064 Constant_5097" [id=1064, type=Constant]; +"1065 Constant_5079" [id=1065, type=Constant]; +"1066 Constant_5076" [id=1066, type=Constant]; +"1067 Constant_5073" [id=1067, type=Constant]; +"1068 /layers/layers.0/blocks.1/Constant" [id=1068, type=Constant]; +"1069 Constant_7295" [id=1069, type=Constant]; +"1070 Constant_7294" [id=1070, type=Constant]; +"1071 Constant_333" [id=1071, type=Constant]; +"1072 Transpose_6371" [id=1072, type=Constant]; +"1073 Constant_25971" [id=1073, type=Constant]; +"1074 Transpose_6367" [id=1074, type=Constant]; +"1075 Constant_25967" [id=1075, type=Constant]; +"1076 Constant_7291" [id=1076, type=Constant]; +"1077 Constant_7290" [id=1077, type=Constant]; +"1078 Constant_307" [id=1078, type=Constant]; +"1079 /layers/layers.0/blocks.0/Constant_8" [id=1079, type=Constant]; +"1080 /layers/layers.0/blocks.0/Constant_7" [id=1080, type=Constant]; +"1081 Constant_296" [id=1081, type=Constant]; +"1082 /layers/layers.0/blocks.0/Constant_6" [id=1082, type=Constant]; +"1083 /layers/layers.0/blocks.0/Constant_5" [id=1083, type=Constant]; +"1084 Transpose_6363" [id=1084, type=Constant]; +"1085 Constant_25981" [id=1085, type=Constant]; +"1086 /layers/layers.0/blocks.0/attn/Constant_2" [id=1086, type=Constant]; +"1087 Constant_280" [id=1087, type=Constant]; +"1088 Constant_270" [id=1088, type=Constant]; +"1089 Constant_264" [id=1089, type=Constant]; +"1090 /layers/layers.0/blocks.0/attn/Constant" [id=1090, type=Constant]; +"1091 Transpose_6360" [id=1091, type=Constant]; +"1092 Constant_25973" [id=1092, type=Constant]; +"1093 /layers/layers.0/blocks.0/Constant_4" [id=1093, type=Constant]; +"1094 /layers/layers.0/blocks.0/Constant_3" [id=1094, type=Constant]; +"1095 Constant_248" [id=1095, type=Constant]; +"1096 /layers/layers.0/blocks.0/Constant_2" [id=1096, type=Constant]; +"1097 Constant_7286" [id=1097, type=Constant]; +"1098 Constant_7285" [id=1098, type=Constant]; +"1099 Constant_227" [id=1099, type=Constant]; +"1100 Constant_7284" [id=1100, type=Constant]; +"1101 Constant_7283" [id=1101, type=Constant]; +"1102 Constant_213" [id=1102, type=Constant]; +"1103 Constant_211" [id=1103, type=Constant]; +"1104 /patch_embed/Constant_4" [id=1104, type=Constant]; +"1105 Broadcast_201" [id=1105, type=Constant]; +"1106 /patch_embed/Constant_3" [id=1106, type=Constant]; +"1107 /patch_embed/Constant_2" [id=1107, type=Constant]; +"1108 Reshape_190" [id=1108, type=Constant]; +"1109 Gather_7282" [id=1109, type=Constant]; 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[id=1129, type=Constant]; +"1130 /layers/layers.0/blocks.1/attn/Constant_3" [id=1130, type=Constant]; +"1131 onnx^^Add_2301" [id=1131, label="1131 onnx::Add_2301", type=Constant]; +"1132 /layers/layers.0/blocks.1/attn/Constant_2" [id=1132, type=Constant]; +"1133 onnx^^Add_2293" [id=1133, label="1133 onnx::Add_2293", type=Constant]; +"1134 Constant_416" [id=1134, type=Constant]; +"1135 Constant_7297" [id=1135, type=Constant]; +"1136 Constant_414" [id=1136, type=Constant]; +"1137 Constant_7298" [id=1137, type=Constant]; +"1138 Constant_5115" [id=1138, type=Constant]; +"1139 Constant_5112" [id=1139, type=Constant]; +"1140 Constant_5109" [id=1140, type=Constant]; +"1141 Constant_5139" [id=1141, type=Constant]; +"1142 Constant_5136" [id=1142, type=Constant]; +"1143 Constant_5133" [id=1143, type=Constant]; +"1144 Constant_7301" [id=1144, type=Constant]; +"1145 Constant_7302" [id=1145, type=Constant]; +"1146 Constant_5211" [id=1146, type=Constant]; +"1147 Constant_5208" [id=1147, type=Constant]; +"1148 Constant_5205" [id=1148, type=Constant]; +"1149 Constant_5163" [id=1149, type=Constant]; +"1150 Constant_5160" [id=1150, type=Constant]; +"1151 Constant_5157" [id=1151, type=Constant]; +"1152 Constant_5199" [id=1152, type=Constant]; +"1153 Constant_5196" [id=1153, type=Constant]; +"1154 Constant_5193" [id=1154, type=Constant]; +"1155 Constant_5175" [id=1155, type=Constant]; +"1156 Constant_5172" [id=1156, type=Constant]; +"1157 Constant_5169" [id=1157, type=Constant]; +"1158 Constant_7307" [id=1158, type=Constant]; +"1159 onnx^^Add_2389" [id=1159, label="1159 onnx::Add_2389", type=Constant]; +"1160 Constant_626" [id=1160, type=Constant]; +"1161 Constant_7308" [id=1161, type=Constant]; +"1162 Constant_624" [id=1162, type=Constant]; +"1163 Constant_7309" [id=1163, type=Constant]; +"1164 Constant_7312" [id=1164, type=Constant]; +"1165 Constant_7313" [id=1165, type=Constant]; +"1166 Constant_5235" [id=1166, type=Constant]; +"1167 Constant_5232" [id=1167, type=Constant]; +"1168 Constant_5229" [id=1168, type=Constant]; +"1169 Constant_5259" [id=1169, type=Constant]; +"1170 Constant_5256" [id=1170, type=Constant]; +"1171 Constant_5253" [id=1171, type=Constant]; +"1172 Constant_7316" [id=1172, type=Constant]; +"1173 /layers/layers.1/blocks.1/attn/Constant_3" [id=1173, type=Constant]; +"1174 onnx^^Add_2446" [id=1174, label="1174 onnx::Add_2446", type=Constant]; +"1175 /layers/layers.1/blocks.1/attn/Constant_2" [id=1175, type=Constant]; +"1176 onnx^^Add_2438" [id=1176, label="1176 onnx::Add_2438", type=Constant]; +"1177 Constant_774" [id=1177, type=Constant]; +"1178 Constant_7317" [id=1178, type=Constant]; +"1179 Constant_772" [id=1179, type=Constant]; +"1180 Constant_7318" [id=1180, type=Constant]; +"1181 Constant_5283" [id=1181, type=Constant]; +"1182 Constant_5280" [id=1182, type=Constant]; +"1183 Constant_5277" [id=1183, type=Constant]; +"1184 Constant_5307" [id=1184, type=Constant]; +"1185 Constant_5304" [id=1185, type=Constant]; +"1186 Constant_5301" [id=1186, type=Constant]; +"1187 Constant_7321" [id=1187, type=Constant]; +"1188 Constant_7322" [id=1188, type=Constant]; +"1189 Constant_5379" [id=1189, type=Constant]; +"1190 Constant_5376" [id=1190, type=Constant]; +"1191 Constant_5373" [id=1191, type=Constant]; +"1192 Constant_5331" [id=1192, type=Constant]; +"1193 Constant_5328" [id=1193, type=Constant]; +"1194 Constant_5325" [id=1194, type=Constant]; +"1195 Constant_5367" [id=1195, type=Constant]; +"1196 Constant_5364" [id=1196, type=Constant]; +"1197 Constant_5361" [id=1197, type=Constant]; +"1198 Constant_5343" [id=1198, type=Constant]; +"1199 Constant_5340" [id=1199, type=Constant]; +"1200 Constant_5337" [id=1200, type=Constant]; +"1201 Constant_7327" [id=1201, type=Constant]; +"1202 onnx^^Add_2534" [id=1202, label="1202 onnx::Add_2534", type=Constant]; +"1203 Constant_984" [id=1203, type=Constant]; +"1204 Constant_7328" [id=1204, type=Constant]; +"1205 Constant_982" [id=1205, type=Constant]; +"1206 Constant_7329" [id=1206, type=Constant]; +"1207 Constant_7332" [id=1207, type=Constant]; +"1208 Constant_7333" [id=1208, type=Constant]; +"1209 Constant_5403" [id=1209, type=Constant]; +"1210 Constant_5400" [id=1210, type=Constant]; +"1211 Constant_5397" [id=1211, type=Constant]; +"1212 Constant_5427" [id=1212, type=Constant]; +"1213 Constant_5424" [id=1213, type=Constant]; +"1214 Constant_5421" [id=1214, type=Constant]; +"1215 Constant_7336" [id=1215, type=Constant]; +"1216 /layers/layers.2/blocks.1/attn/Constant_3" [id=1216, type=Constant]; +"1217 onnx^^Add_2702" [id=1217, label="1217 onnx::Add_2702", type=Constant]; +"1218 /layers/layers.2/blocks.1/attn/Constant_2" [id=1218, type=Constant]; +"1219 onnx^^Add_2583" [id=1219, label="1219 onnx::Add_2583", type=Constant]; +"1220 Constant_1132" [id=1220, type=Constant]; +"1221 Constant_7337" [id=1221, type=Constant]; +"1222 Constant_1130" [id=1222, type=Constant]; +"1223 Constant_7338" [id=1223, type=Constant]; +"1224 Constant_5451" [id=1224, type=Constant]; +"1225 Constant_5448" [id=1225, type=Constant]; +"1226 Constant_5445" [id=1226, type=Constant]; +"1227 Constant_5475" [id=1227, type=Constant]; +"1228 Constant_5472" [id=1228, type=Constant]; +"1229 Constant_5469" [id=1229, type=Constant]; +"1230 Constant_7341" [id=1230, type=Constant]; +"1231 Constant_7342" [id=1231, type=Constant]; +"1232 Constant_7345" [id=1232, type=Constant]; +"1233 onnx^^Add_2645" [id=1233, label="1233 onnx::Add_2645", type=Constant]; +"1234 Constant_1289" [id=1234, type=Constant]; +"1235 Constant_7346" [id=1235, type=Constant]; +"1236 Constant_1287" [id=1236, type=Constant]; +"1237 Constant_7347" [id=1237, type=Constant]; +"1238 Constant_7350" [id=1238, type=Constant]; +"1239 Constant_7351" [id=1239, type=Constant]; +"1240 Constant_5499" [id=1240, type=Constant]; +"1241 Constant_5496" [id=1241, type=Constant]; +"1242 Constant_5493" [id=1242, type=Constant]; +"1243 Constant_5523" [id=1243, type=Constant]; +"1244 Constant_5520" [id=1244, type=Constant]; +"1245 Constant_5517" [id=1245, type=Constant]; +"1246 Constant_7354" [id=1246, type=Constant]; +"1247 /layers/layers.2/blocks.3/attn/Constant_3" [id=1247, type=Constant]; +"1248 /layers/layers.2/blocks.3/attn/Constant_2" [id=1248, type=Constant]; +"1249 onnx^^Add_2694" [id=1249, label="1249 onnx::Add_2694", type=Constant]; +"1250 Constant_1437" [id=1250, type=Constant]; +"1251 Constant_7355" [id=1251, type=Constant]; +"1252 Constant_1435" [id=1252, type=Constant]; +"1253 Constant_7356" [id=1253, type=Constant]; +"1254 Constant_5547" [id=1254, type=Constant]; +"1255 Constant_5544" [id=1255, type=Constant]; +"1256 Constant_5541" [id=1256, type=Constant]; +"1257 Constant_5571" [id=1257, type=Constant]; +"1258 Constant_5568" [id=1258, type=Constant]; +"1259 Constant_5565" [id=1259, type=Constant]; +"1260 Constant_7359" [id=1260, type=Constant]; +"1261 Constant_7360" [id=1261, type=Constant]; +"1262 Constant_7363" [id=1262, type=Constant]; +"1263 onnx^^Add_2756" [id=1263, label="1263 onnx::Add_2756", type=Constant]; +"1264 Constant_1594" [id=1264, type=Constant]; +"1265 Constant_7364" [id=1265, type=Constant]; +"1266 Constant_1592" [id=1266, type=Constant]; +"1267 Constant_7365" [id=1267, type=Constant]; +"1268 Constant_7368" [id=1268, type=Constant]; +"1269 Constant_7369" [id=1269, type=Constant]; +"1270 Constant_5595" [id=1270, type=Constant]; +"1271 Constant_5592" [id=1271, type=Constant]; +"1272 Constant_5589" [id=1272, type=Constant]; +"1273 Constant_5619" [id=1273, type=Constant]; +"1274 Constant_5616" [id=1274, type=Constant]; +"1275 Constant_5613" [id=1275, type=Constant]; +"1276 Constant_7372" [id=1276, type=Constant]; +"1277 /layers/layers.2/blocks.5/attn/Constant_3" [id=1277, type=Constant]; +"1278 /layers/layers.2/blocks.5/attn/Constant_2" [id=1278, type=Constant]; +"1279 onnx^^Add_2805" [id=1279, label="1279 onnx::Add_2805", type=Constant]; +"1280 Constant_1742" [id=1280, type=Constant]; +"1281 Constant_7373" [id=1281, type=Constant]; 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type=Constant]; +"1303 Constant_5697" [id=1303, type=Constant]; +"1304 Constant_7383" [id=1304, type=Constant]; +"1305 onnx^^Add_2901" [id=1305, label="1305 onnx::Add_2901", type=Constant]; +"1306 Constant_1952" [id=1306, type=Constant]; +"1307 Constant_7384" [id=1307, type=Constant]; +"1308 Constant_1950" [id=1308, type=Constant]; +"1309 Constant_7385" [id=1309, type=Constant]; +"1310 Constant_7388" [id=1310, type=Constant]; +"1311 Constant_7389" [id=1311, type=Constant]; +"1312 Constant_7392" [id=1312, type=Constant]; +"1313 onnx^^Add_2950" [id=1313, label="1313 onnx::Add_2950", type=Constant]; +"1314 Constant_2058" [id=1314, type=Constant]; +"1315 Constant_7393" [id=1315, type=Constant]; +"1316 Constant_2056" [id=1316, type=Constant]; +"1317 Constant_7394" [id=1317, type=Constant]; +"1318 Constant_7397" [id=1318, type=Constant]; +"1319 Constant_7398" [id=1319, type=Constant]; +"0 input" -> "1 Multiply_6579" [label="[1, 3, 224, 224]", style=solid]; +"1 Multiply_6579" -> "2 Divide_2169" [label="[1, 3, 224, 224]", style=solid]; +"2 Divide_2169" -> "3 Divide_2169_0_0/nncf_smooth_quant" [label="[1, 3, 224, 224]", style=solid]; +"3 Divide_2169_0_0/nncf_smooth_quant" -> "4 /patch_embed/proj/Conv/WithoutBiases" [label="[1, 3, 224, 224]", style=solid]; +"4 /patch_embed/proj/Conv/WithoutBiases" -> "5 /patch_embed/proj/Conv" [label="[1, 96, 56, 56]", style=solid]; +"5 /patch_embed/proj/Conv" -> "6 /patch_embed/Reshape" [label="[1, 96, 56, 56]", style=solid]; +"5 /patch_embed/proj/Conv" -> "7 /patch_embed/Shape" [label="[1, 96, 56, 56]", style=solid]; +"6 /patch_embed/Reshape" -> "8 /patch_embed/Transpose" [label="[1, 96, 3136]", style=solid]; +"7 /patch_embed/Shape" -> "9 /patch_embed/Slice" [label="[4]", style=dashed]; +"8 /patch_embed/Transpose" -> "10 /patch_embed/norm/Div" [label="[1, 3136, 96]", style=solid]; +"9 /patch_embed/Slice" -> "11 /patch_embed/Concat" [label="[2]", style=dashed]; +"10 /patch_embed/norm/Div" -> "12 /patch_embed/norm/Mul" [label="[1, 3136, 96]", style=solid]; +"11 /patch_embed/Concat" -> "6 /patch_embed/Reshape" [label="[3]", style=dashed]; +"12 /patch_embed/norm/Mul" -> "13 /patch_embed/norm/Add_1" [label="[1, 3136, 96]", style=solid]; +"13 /patch_embed/norm/Add_1" -> "14 /layers/layers.0/blocks.0/Add" [label="[1, 3136, 96]", style=solid]; +"13 /patch_embed/norm/Add_1" -> "15 /layers/layers.0/blocks.0/norm1/Div" [label="[1, 3136, 96]", style=solid]; +"14 /layers/layers.0/blocks.0/Add" -> "16 /layers/layers.0/blocks.0/Add_1" [label="[1, 3136, 96]", style=solid]; +"14 /layers/layers.0/blocks.0/Add" -> "17 /layers/layers.0/blocks.0/norm2/Div" [label="[1, 3136, 96]", style=solid]; +"15 /layers/layers.0/blocks.0/norm1/Div" -> "18 /layers/layers.0/blocks.0/norm1/Mul" [label="[1, 3136, 96]", style=solid]; +"16 /layers/layers.0/blocks.0/Add_1" -> "19 /layers/layers.0/blocks.1/Add" [label="[1, 3136, 96]", style=solid]; +"16 /layers/layers.0/blocks.0/Add_1" -> "20 /layers/layers.0/blocks.1/norm1/Div" [label="[1, 3136, 96]", style=solid]; +"17 /layers/layers.0/blocks.0/norm2/Div" -> "21 /layers/layers.0/blocks.0/norm2/Mul" [label="[1, 3136, 96]", style=solid]; +"18 /layers/layers.0/blocks.0/norm1/Mul" -> "22 /layers/layers.0/blocks.0/norm1/Add_1" [label="[1, 3136, 96]", style=solid]; +"19 /layers/layers.0/blocks.1/Add" -> "23 /layers/layers.0/blocks.1/Add_1" [label="[1, 3136, 96]", style=solid]; +"19 /layers/layers.0/blocks.1/Add" -> "24 /layers/layers.0/blocks.1/norm2/Div" [label="[1, 3136, 96]", style=solid]; +"20 /layers/layers.0/blocks.1/norm1/Div" -> "25 /layers/layers.0/blocks.1/norm1/Mul" [label="[1, 3136, 96]", style=solid]; +"21 /layers/layers.0/blocks.0/norm2/Mul" -> "26 /layers/layers.0/blocks.0/norm2/Add_1" [label="[1, 3136, 96]", style=solid]; +"22 /layers/layers.0/blocks.0/norm1/Add_1" -> "27 /layers/layers.0/blocks.0/Reshape_1" [label="[1, 3136, 96]", style=solid]; +"23 /layers/layers.0/blocks.1/Add_1" -> "28 /layers/layers.0/downsample/Reshape" [label="[1, 3136, 96]", style=solid]; +"24 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/layers/layers.2/blocks.5/Slice_7" [label="[3]", style=dashed]; +"740 Constant_5655" -> "626 /layers/layers.2/blocks.5/Slice_5" [label="[2]", style=dashed]; +"741 Constant_5652" -> "626 /layers/layers.2/blocks.5/Slice_5" [label="[2]", style=dashed]; +"742 Constant_5649" -> "626 /layers/layers.2/blocks.5/Slice_5" [label="[2]", style=dashed]; +"743 /layers/layers.2/blocks.5/Constant_18" -> "620 /layers/layers.2/blocks.5/Reshape_6" [label="[4]", style=dashed]; +"744 Constant_1779" -> "612 /layers/layers.2/blocks.5/Transpose_1" [label="[6]", style=dashed]; +"745 /layers/layers.2/blocks.5/Constant_17" -> "605 /layers/layers.2/blocks.5/Reshape_5" [label="[6]", style=dashed]; +"746 /layers/layers.2/blocks.5/Constant_16" -> "596 /layers/layers.2/blocks.5/Reshape_4" [label="[4]", style=dashed]; +"747 Transpose_6506" -> "578 /layers/layers.2/blocks.5/attn/proj/MatMul" [label="[384, 384]", style=solid]; +"748 Constant_26063" -> "568 /layers/layers.2/blocks.5/attn/Reshape_3_0_0/nncf_smooth_quant" 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/layers/layers.2/blocks.5/Reshape_2" [label="[4]", style=dashed]; +"758 Constant_1722" -> "424 /layers/layers.2/blocks.5/Transpose" [label="[6]", style=dashed]; +"759 /layers/layers.2/blocks.5/Constant_13" -> "412 /layers/layers.2/blocks.5/Reshape_1" [label="[6]", style=dashed]; +"760 Constant_5631" -> "386 /layers/layers.2/blocks.5/Slice_3" [label="[3]", style=dashed]; +"761 Constant_5628" -> "386 /layers/layers.2/blocks.5/Slice_3" [label="[3]", style=dashed]; +"762 Constant_5625" -> "386 /layers/layers.2/blocks.5/Slice_3" [label="[3]", style=dashed]; +"763 Constant_5607" -> "360 /layers/layers.2/blocks.5/Slice_1" [label="[2]", style=dashed]; +"764 Constant_5604" -> "360 /layers/layers.2/blocks.5/Slice_1" [label="[2]", style=dashed]; +"765 Constant_5601" -> "360 /layers/layers.2/blocks.5/Slice_1" [label="[2]", style=dashed]; +"766 /layers/layers.2/blocks.5/Constant" -> "342 /layers/layers.2/blocks.5/Reshape" [label="[4]", style=dashed]; +"767 Constant_7371" -> "325 /layers/layers.2/blocks.5/norm1/Add_1" [label="[1, 1, 384]", style=solid]; +"768 Constant_7370" -> "311 /layers/layers.2/blocks.5/norm1/Mul" [label="[1, 1, 384]", style=solid]; +"769 Constant_1659" -> "295 /layers/layers.2/blocks.5/norm1/Div" [label="[1]", style=dashed]; +"770 Transpose_6499" -> "401 /layers/layers.2/blocks.4/mlp/fc2/MatMul" [label="[384, 1536]", style=solid]; +"771 Constant_26029" -> "387 /layers/layers.2/blocks.4/mlp/act/Mul_1_0_0/nncf_smooth_quant" [label="[1, 1, 1536]", style=solid]; +"772 Transpose_6495" -> "343 /layers/layers.2/blocks.4/mlp/fc1/MatMul" [label="[1536, 384]", style=solid]; +"773 Constant_26021" -> "326 /layers/layers.2/blocks.4/norm2/Add_1_0_0/nncf_smooth_quant" [label="[1, 1, 384]", style=solid]; +"774 Constant_7367" -> "312 /layers/layers.2/blocks.4/norm2/Add_1" [label="[1, 1, 384]", style=solid]; +"775 Constant_7366" -> "296 /layers/layers.2/blocks.4/norm2/Mul" [label="[1, 1, 384]", style=solid]; +"776 Constant_1633" -> "281 /layers/layers.2/blocks.4/norm2/Div" [label="[1]", style=dashed]; +"777 /layers/layers.2/blocks.4/Constant_7" -> "569 /layers/layers.2/blocks.4/Reshape_7" [label="[3]", style=dashed]; +"778 /layers/layers.2/blocks.4/Constant_6" -> "558 /layers/layers.2/blocks.4/Reshape_6" [label="[4]", style=dashed]; +"779 Constant_1622" -> "547 /layers/layers.2/blocks.4/Transpose_1" [label="[6]", style=dashed]; +"780 /layers/layers.2/blocks.4/Constant_5" -> "536 /layers/layers.2/blocks.4/Reshape_5" [label="[6]", style=dashed]; +"781 /layers/layers.2/blocks.4/Constant_4" -> "523 /layers/layers.2/blocks.4/Reshape_4" [label="[4]", style=dashed]; +"782 Transpose_6491" -> "498 /layers/layers.2/blocks.4/attn/proj/MatMul" [label="[384, 384]", style=solid]; +"783 Constant_26047" -> "485 /layers/layers.2/blocks.4/attn/Reshape_1_0_0/nncf_smooth_quant" [label="[1, 1, 384]", style=solid]; +"784 /layers/layers.2/blocks.4/attn/Constant_2" -> "472 /layers/layers.2/blocks.4/attn/Reshape_1" [label="[3]", style=dashed]; +"785 Constant_1606" -> "461 /layers/layers.2/blocks.4/attn/Transpose_2" [label="[4]", style=dashed]; +"786 Constant_1596" -> "439 /layers/layers.2/blocks.4/attn/Gather_2" [label="[]", style=dashed]; +"787 Constant_1590" -> "425 /layers/layers.2/blocks.4/attn/Transpose" [label="[5]", style=dashed]; +"788 /layers/layers.2/blocks.4/attn/Constant" -> "414 /layers/layers.2/blocks.4/attn/Reshape" [label="[5]", style=dashed]; +"789 Transpose_6488" -> "388 /layers/layers.2/blocks.4/attn/qkv/MatMul" [label="[1152, 384]", style=solid]; +"790 Constant_26031" -> "375 /layers/layers.2/blocks.4/Reshape_3_0_0/nncf_smooth_quant" [label="[1, 1, 384]", style=solid]; +"791 /layers/layers.2/blocks.4/Constant_3" -> "362 /layers/layers.2/blocks.4/Reshape_3" [label="[3]", style=dashed]; +"792 /layers/layers.2/blocks.4/Constant_2" -> "344 /layers/layers.2/blocks.4/Reshape_2" [label="[4]", style=dashed]; +"793 Constant_1574" -> "327 /layers/layers.2/blocks.4/Transpose" [label="[6]", style=dashed]; +"794 /layers/layers.2/blocks.4/Constant_1" -> "313 /layers/layers.2/blocks.4/Reshape_1" [label="[6]", style=dashed]; +"795 Constant_7362" -> "297 /layers/layers.2/blocks.4/norm1/Add_1" [label="[1, 1, 384]", style=solid]; +"796 Constant_7361" -> "282 /layers/layers.2/blocks.4/norm1/Mul" [label="[1, 1, 384]", style=solid]; +"797 Constant_1553" -> "267 /layers/layers.2/blocks.4/norm1/Div" [label="[1]", style=dashed]; +"798 Transpose_6484" -> "376 /layers/layers.2/blocks.3/mlp/fc2/MatMul" [label="[384, 1536]", style=solid]; +"799 Constant_26023" -> "363 /layers/layers.2/blocks.3/mlp/act/Mul_1_0_0/nncf_smooth_quant" [label="[1, 1, 1536]", style=solid]; +"800 Transpose_6480" -> "314 /layers/layers.2/blocks.3/mlp/fc1/MatMul" [label="[1536, 384]", style=solid]; +"801 Constant_26015" -> "298 /layers/layers.2/blocks.3/norm2/Add_1_0_0/nncf_smooth_quant" [label="[1, 1, 384]", style=solid]; +"802 Constant_7358" -> "283 /layers/layers.2/blocks.3/norm2/Add_1" [label="[1, 1, 384]", style=solid]; +"803 Constant_7357" -> "268 /layers/layers.2/blocks.3/norm2/Mul" [label="[1, 1, 384]", style=solid]; +"804 Constant_1527" -> "255 /layers/layers.2/blocks.3/norm2/Div" [label="[1]", style=dashed]; +"805 /layers/layers.2/blocks.3/Constant_31" -> "627 /layers/layers.2/blocks.3/Reshape_7" [label="[3]", style=dashed]; +"806 Constant_5583" -> "614 /layers/layers.2/blocks.3/Slice_7" [label="[3]", style=dashed]; +"807 Constant_5580" -> "614 /layers/layers.2/blocks.3/Slice_7" [label="[3]", style=dashed]; +"808 Constant_5577" -> "614 /layers/layers.2/blocks.3/Slice_7" [label="[3]", style=dashed]; +"809 Constant_5559" -> "598 /layers/layers.2/blocks.3/Slice_5" [label="[2]", style=dashed]; +"810 Constant_5556" -> "598 /layers/layers.2/blocks.3/Slice_5" [label="[2]", style=dashed]; +"811 Constant_5553" -> "598 /layers/layers.2/blocks.3/Slice_5" [label="[2]", style=dashed]; +"812 /layers/layers.2/blocks.3/Constant_18" -> "588 /layers/layers.2/blocks.3/Reshape_6" [label="[4]", style=dashed]; +"813 Constant_1474" -> "579 /layers/layers.2/blocks.3/Transpose_1" [label="[6]", style=dashed]; +"814 /layers/layers.2/blocks.3/Constant_17" -> "570 /layers/layers.2/blocks.3/Reshape_5" [label="[6]", style=dashed]; +"815 /layers/layers.2/blocks.3/Constant_16" -> "559 /layers/layers.2/blocks.3/Reshape_4" [label="[4]", style=dashed]; +"816 Transpose_6476" -> "538 /layers/layers.2/blocks.3/attn/proj/MatMul" [label="[384, 384]", style=solid]; +"817 Constant_26051" -> "525 /layers/layers.2/blocks.3/attn/Reshape_3_0_0/nncf_smooth_quant" [label="[1, 1, 384]", style=solid]; +"818 /layers/layers.2/blocks.3/attn/Constant_4" -> "512 /layers/layers.2/blocks.3/attn/Reshape_3" [label="[3]", style=dashed]; +"819 Constant_1458" -> "500 /layers/layers.2/blocks.3/attn/Transpose_2" [label="[4]", style=dashed]; +"820 Constant_1439" -> "475 /layers/layers.2/blocks.3/attn/Gather_2" [label="[]", style=dashed]; +"821 Constant_1433" -> "462 /layers/layers.2/blocks.3/attn/Transpose" [label="[5]", style=dashed]; +"822 /layers/layers.2/blocks.3/attn/Constant" -> "452 /layers/layers.2/blocks.3/attn/Reshape" [label="[5]", style=dashed]; +"823 Transpose_6473" -> "426 /layers/layers.2/blocks.3/attn/qkv/MatMul" [label="[1152, 384]", style=solid]; +"824 Constant_26037" -> "415 /layers/layers.2/blocks.3/Reshape_3_0_0/nncf_smooth_quant" [label="[1, 1, 384]", style=solid]; +"825 /layers/layers.2/blocks.3/Constant_15" -> "403 /layers/layers.2/blocks.3/Reshape_3" [label="[3]", style=dashed]; +"826 /layers/layers.2/blocks.3/Constant_14" -> "390 /layers/layers.2/blocks.3/Reshape_2" [label="[4]", style=dashed]; +"827 Constant_1417" -> "377 /layers/layers.2/blocks.3/Transpose" [label="[6]", style=dashed]; +"828 /layers/layers.2/blocks.3/Constant_13" -> "364 /layers/layers.2/blocks.3/Reshape_1" [label="[6]", style=dashed]; +"829 Constant_5535" -> "330 /layers/layers.2/blocks.3/Slice_3" [label="[3]", style=dashed]; +"830 Constant_5532" -> "330 /layers/layers.2/blocks.3/Slice_3" [label="[3]", style=dashed]; +"831 Constant_5529" -> "330 /layers/layers.2/blocks.3/Slice_3" [label="[3]", style=dashed]; +"832 Constant_5511" -> "300 /layers/layers.2/blocks.3/Slice_1" [label="[2]", style=dashed]; +"833 Constant_5508" -> "300 /layers/layers.2/blocks.3/Slice_1" [label="[2]", style=dashed]; +"834 Constant_5505" -> "300 /layers/layers.2/blocks.3/Slice_1" [label="[2]", style=dashed]; +"835 /layers/layers.2/blocks.3/Constant" -> "284 /layers/layers.2/blocks.3/Reshape" [label="[4]", style=dashed]; +"836 Constant_7353" -> "269 /layers/layers.2/blocks.3/norm1/Add_1" [label="[1, 1, 384]", style=solid]; +"837 Constant_7352" -> "256 /layers/layers.2/blocks.3/norm1/Mul" [label="[1, 1, 384]", style=solid]; +"838 Constant_1354" -> "241 /layers/layers.2/blocks.3/norm1/Div" [label="[1]", style=dashed]; +"839 Transpose_6469" -> "347 /layers/layers.2/blocks.2/mlp/fc2/MatMul" [label="[384, 1536]", style=solid]; +"840 Constant_26017" -> "331 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/layers/layers.2/blocks.2/Constant_5" -> "489 /layers/layers.2/blocks.2/Reshape_5" [label="[6]", style=dashed]; +"850 /layers/layers.2/blocks.2/Constant_4" -> "476 /layers/layers.2/blocks.2/Reshape_4" [label="[4]", style=dashed]; +"851 Transpose_6461" -> "453 /layers/layers.2/blocks.2/attn/proj/MatMul" [label="[384, 384]", style=solid]; +"852 Constant_26039" -> "441 /layers/layers.2/blocks.2/attn/Reshape_1_0_0/nncf_smooth_quant" [label="[1, 1, 384]", style=solid]; +"853 /layers/layers.2/blocks.2/attn/Constant_2" -> "428 /layers/layers.2/blocks.2/attn/Reshape_1" [label="[3]", style=dashed]; +"854 Constant_1301" -> "417 /layers/layers.2/blocks.2/attn/Transpose_2" [label="[4]", style=dashed]; +"855 Constant_1291" -> "393 /layers/layers.2/blocks.2/attn/Gather_2" [label="[]", style=dashed]; +"856 Constant_1285" -> "378 /layers/layers.2/blocks.2/attn/Transpose" [label="[5]", style=dashed]; +"857 /layers/layers.2/blocks.2/attn/Constant" -> "366 /layers/layers.2/blocks.2/attn/Reshape" 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style=dashed]; +"1059 /layers/layers.0/blocks.1/Constant_14" -> "75 /layers/layers.0/blocks.1/Reshape_2" [label="[4]", style=dashed]; +"1060 Constant_396" -> "71 /layers/layers.0/blocks.1/Transpose" [label="[6]", style=dashed]; +"1061 /layers/layers.0/blocks.1/Constant_13" -> "66 /layers/layers.0/blocks.1/Reshape_1" [label="[6]", style=dashed]; +"1062 Constant_5103" -> "56 /layers/layers.0/blocks.1/Slice_3" [label="[3]", style=dashed]; +"1063 Constant_5100" -> "56 /layers/layers.0/blocks.1/Slice_3" [label="[3]", style=dashed]; +"1064 Constant_5097" -> "56 /layers/layers.0/blocks.1/Slice_3" [label="[3]", style=dashed]; +"1065 Constant_5079" -> "45 /layers/layers.0/blocks.1/Slice_1" [label="[2]", style=dashed]; +"1066 Constant_5076" -> "45 /layers/layers.0/blocks.1/Slice_1" [label="[2]", style=dashed]; +"1067 Constant_5073" -> "45 /layers/layers.0/blocks.1/Slice_1" [label="[2]", style=dashed]; +"1068 /layers/layers.0/blocks.1/Constant" -> "36 /layers/layers.0/blocks.1/Reshape" [label="[4]", style=dashed]; +"1069 Constant_7295" -> "30 /layers/layers.0/blocks.1/norm1/Add_1" [label="[1, 1, 96]", style=solid]; +"1070 Constant_7294" -> "25 /layers/layers.0/blocks.1/norm1/Mul" [label="[1, 1, 96]", style=solid]; +"1071 Constant_333" -> "20 /layers/layers.0/blocks.1/norm1/Div" [label="[1]", style=dashed]; +"1072 Transpose_6371" -> "62 /layers/layers.0/blocks.0/mlp/fc2/MatMul" [label="[96, 384]", style=solid]; +"1073 Constant_25971" -> "57 /layers/layers.0/blocks.0/mlp/act/Mul_1_0_0/nncf_smooth_quant" [label="[1, 1, 384]", style=solid]; +"1074 Transpose_6367" -> "37 /layers/layers.0/blocks.0/mlp/fc1/MatMul" [label="[384, 96]", style=solid]; +"1075 Constant_25967" -> "31 /layers/layers.0/blocks.0/norm2/Add_1_0_0/nncf_smooth_quant" [label="[1, 1, 96]", style=solid]; +"1076 Constant_7291" -> "26 /layers/layers.0/blocks.0/norm2/Add_1" [label="[1, 1, 96]", style=solid]; +"1077 Constant_7290" -> "21 /layers/layers.0/blocks.0/norm2/Mul" [label="[1, 1, 96]", style=solid]; +"1078 Constant_307" -> "17 /layers/layers.0/blocks.0/norm2/Div" [label="[1]", style=dashed]; +"1079 /layers/layers.0/blocks.0/Constant_8" -> "162 /layers/layers.0/blocks.0/Reshape_7" [label="[3]", style=dashed]; +"1080 /layers/layers.0/blocks.0/Constant_7" -> "154 /layers/layers.0/blocks.0/Reshape_6" [label="[4]", style=dashed]; +"1081 Constant_296" -> "145 /layers/layers.0/blocks.0/Transpose_1" [label="[6]", style=dashed]; +"1082 /layers/layers.0/blocks.0/Constant_6" -> "137 /layers/layers.0/blocks.0/Reshape_5" [label="[6]", style=dashed]; +"1083 /layers/layers.0/blocks.0/Constant_5" -> "124 /layers/layers.0/blocks.0/Reshape_4" [label="[4]", style=dashed]; +"1084 Transpose_6363" -> "107 /layers/layers.0/blocks.0/attn/proj/MatMul" [label="[96, 96]", style=solid]; +"1085 Constant_25981" -> "100 /layers/layers.0/blocks.0/attn/Reshape_1_0_0/nncf_smooth_quant" [label="[1, 1, 96]", style=solid]; +"1086 /layers/layers.0/blocks.0/attn/Constant_2" -> "94 /layers/layers.0/blocks.0/attn/Reshape_1" [label="[3]", style=dashed]; +"1087 Constant_280" -> "88 /layers/layers.0/blocks.0/attn/Transpose_2" [label="[4]", style=dashed]; +"1088 Constant_270" -> "78 /layers/layers.0/blocks.0/attn/Gather_2" [label="[]", style=dashed]; +"1089 Constant_264" -> "72 /layers/layers.0/blocks.0/attn/Transpose" [label="[5]", style=dashed]; +"1090 /layers/layers.0/blocks.0/attn/Constant" -> "68 /layers/layers.0/blocks.0/attn/Reshape" [label="[5]", style=dashed]; +"1091 Transpose_6360" -> "58 /layers/layers.0/blocks.0/attn/qkv/MatMul" [label="[288, 96]", style=solid]; +"1092 Constant_25973" -> "52 /layers/layers.0/blocks.0/Reshape_3_0_0/nncf_smooth_quant" [label="[1, 1, 96]", style=solid]; +"1093 /layers/layers.0/blocks.0/Constant_4" -> "47 /layers/layers.0/blocks.0/Reshape_3" [label="[3]", style=dashed]; +"1094 /layers/layers.0/blocks.0/Constant_3" -> "38 /layers/layers.0/blocks.0/Reshape_2" [label="[4]", style=dashed]; +"1095 Constant_248" -> "32 /layers/layers.0/blocks.0/Transpose" [label="[6]", style=dashed]; +"1096 /layers/layers.0/blocks.0/Constant_2" -> "27 /layers/layers.0/blocks.0/Reshape_1" [label="[6]", style=dashed]; +"1097 Constant_7286" -> "22 /layers/layers.0/blocks.0/norm1/Add_1" [label="[1, 1, 96]", style=solid]; +"1098 Constant_7285" -> "18 /layers/layers.0/blocks.0/norm1/Mul" [label="[1, 1, 96]", style=solid]; +"1099 Constant_227" -> "15 /layers/layers.0/blocks.0/norm1/Div" [label="[1]", style=dashed]; +"1100 Constant_7284" -> "13 /patch_embed/norm/Add_1" [label="[1, 1, 96]", style=solid]; +"1101 Constant_7283" -> "12 /patch_embed/norm/Mul" [label="[1, 1, 96]", style=solid]; +"1102 Constant_213" -> "10 /patch_embed/norm/Div" [label="[1]", style=dashed]; +"1103 Constant_211" -> "8 /patch_embed/Transpose" [label="[3]", style=dashed]; +"1104 /patch_embed/Constant_4" -> "11 /patch_embed/Concat" [label="[1]", style=dashed]; +"1105 Broadcast_201" -> "9 /patch_embed/Slice" [label="[1]", style=dashed]; +"1106 /patch_embed/Constant_3" -> "9 /patch_embed/Slice" [label="[1]", style=dashed]; +"1107 /patch_embed/Constant_2" -> "9 /patch_embed/Slice" [label="[1]", style=dashed]; +"1108 Reshape_190" -> "5 /patch_embed/proj/Conv" [label="[1, 96, 1, 1]", style=solid]; +"1109 Gather_7282" -> "4 /patch_embed/proj/Conv/WithoutBiases" [label="[96, 3, 4, 4]", style=solid]; +"1110 Constant_25965" -> "3 Divide_2169_0_0/nncf_smooth_quant" [label="[1, 3, 1, 1]", style=solid]; +"1111 Gather_7279" -> "2 Divide_2169" [label="[1, 3, 1, 1]", style=solid]; +"1112 Gather_7276" -> "1 Multiply_6579" [label="[1, 3, 1, 1]", style=solid]; +"1113 Constant_7287" -> "63 /layers/layers.0/blocks.0/attn/qkv/Add" [label="[1, 1, 288]", style=solid]; +"1114 onnx^^Add_2244" -> "87 /layers/layers.0/blocks.0/attn/Add" [label="[1, 3, 49, 49]", style=solid]; +"1115 Constant_268" -> "77 /layers/layers.0/blocks.0/attn/Gather_1" [label="[]", style=dashed]; +"1116 /patch_embed/proj/Constant" -> "77 /layers/layers.0/blocks.0/attn/Gather_1" [label="[]", style=dashed]; +"1116 /patch_embed/proj/Constant" -> "122 /layers/layers.0/blocks.1/attn/Gather_1" [label="[]", style=dashed]; +"1116 /patch_embed/proj/Constant" -> "180 /layers/layers.1/blocks.0/attn/Gather_1" [label="[]", style=dashed]; +"1116 /patch_embed/proj/Constant" -> "236 /layers/layers.1/blocks.1/attn/Gather_1" [label="[]", style=dashed]; +"1116 /patch_embed/proj/Constant" -> "336 /layers/layers.2/blocks.0/attn/Gather_1" [label="[]", style=dashed]; +"1116 /patch_embed/proj/Constant" -> "392 /layers/layers.2/blocks.2/attn/Gather_1" [label="[]", style=dashed]; +"1116 /patch_embed/proj/Constant" -> "430 /layers/layers.2/blocks.1/attn/Gather_1" [label="[]", style=dashed]; +"1116 /patch_embed/proj/Constant" -> "438 /layers/layers.2/blocks.4/attn/Gather_1" [label="[]", style=dashed]; +"1116 /patch_embed/proj/Constant" -> "474 /layers/layers.2/blocks.3/attn/Gather_1" [label="[]", style=dashed]; +"1116 /patch_embed/proj/Constant" -> "521 /layers/layers.2/blocks.5/attn/Gather_1" [label="[]", style=dashed]; +"1116 /patch_embed/proj/Constant" -> "594 /layers/layers.3/blocks.0/attn/Gather_1" [label="[]", style=dashed]; +"1116 /patch_embed/proj/Constant" -> "608 /layers/layers.3/blocks.1/attn/Gather_1" [label="[]", style=dashed]; +"1117 Constant_7288" -> "81 /layers/layers.0/blocks.0/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; +"1118 Constant_266" -> "76 /layers/layers.0/blocks.0/attn/Gather" [label="[]", style=dashed]; +"1119 /layers/layers.0/blocks.0/Constant" -> "76 /layers/layers.0/blocks.0/attn/Gather" [label="[]", style=dashed]; +"1119 /layers/layers.0/blocks.0/Constant" -> "121 /layers/layers.0/blocks.1/attn/Gather" [label="[]", style=dashed]; +"1119 /layers/layers.0/blocks.0/Constant" -> "179 /layers/layers.1/blocks.0/attn/Gather" [label="[]", style=dashed]; +"1119 /layers/layers.0/blocks.0/Constant" -> "235 /layers/layers.1/blocks.1/attn/Gather" [label="[]", style=dashed]; +"1119 /layers/layers.0/blocks.0/Constant" -> "335 /layers/layers.2/blocks.0/attn/Gather" [label="[]", style=dashed]; +"1119 /layers/layers.0/blocks.0/Constant" -> "391 /layers/layers.2/blocks.2/attn/Gather" [label="[]", style=dashed]; +"1119 /layers/layers.0/blocks.0/Constant" -> "429 /layers/layers.2/blocks.1/attn/Gather" [label="[]", style=dashed]; +"1119 /layers/layers.0/blocks.0/Constant" -> "437 /layers/layers.2/blocks.4/attn/Gather" [label="[]", style=dashed]; +"1119 /layers/layers.0/blocks.0/Constant" -> "473 /layers/layers.2/blocks.3/attn/Gather" [label="[]", style=dashed]; +"1119 /layers/layers.0/blocks.0/Constant" -> "520 /layers/layers.2/blocks.5/attn/Gather" [label="[]", style=dashed]; +"1119 /layers/layers.0/blocks.0/Constant" -> "593 /layers/layers.3/blocks.0/attn/Gather" [label="[]", style=dashed]; +"1119 /layers/layers.0/blocks.0/Constant" -> "607 /layers/layers.3/blocks.1/attn/Gather" [label="[]", style=dashed]; +"1120 Constant_7289" -> "114 /layers/layers.0/blocks.0/attn/proj/Add" [label="[1, 1, 96]", style=solid]; +"1121 Constant_7292" -> "46 /layers/layers.0/blocks.0/mlp/fc1/Add" [label="[1, 1, 384]", style=solid]; +"1122 Constant_7293" -> "67 /layers/layers.0/blocks.0/mlp/fc2/Add" [label="[1, 1, 96]", style=solid]; +"1123 Constant_5067" -> "44 /layers/layers.0/blocks.1/Slice" [label="[2]", style=dashed]; +"1124 Constant_5064" -> "44 /layers/layers.0/blocks.1/Slice" [label="[2]", style=dashed]; +"1125 Constant_5061" -> "44 /layers/layers.0/blocks.1/Slice" [label="[2]", style=dashed]; +"1126 Constant_5091" -> "55 /layers/layers.0/blocks.1/Slice_2" [label="[3]", style=dashed]; +"1127 Constant_5088" -> "55 /layers/layers.0/blocks.1/Slice_2" [label="[3]", style=dashed]; +"1128 Constant_5085" -> "55 /layers/layers.0/blocks.1/Slice_2" [label="[3]", style=dashed]; +"1129 Constant_7296" -> "99 /layers/layers.0/blocks.1/attn/qkv/Add" [label="[1, 1, 288]", style=solid]; +"1130 /layers/layers.0/blocks.1/attn/Constant_3" -> "168 /layers/layers.0/blocks.1/attn/Reshape_2" [label="[4]", style=dashed]; +"1131 onnx^^Add_2301" -> "160 /layers/layers.0/blocks.1/attn/Add_1" [label="[1, 64, 1, 49, 49]", style=solid]; +"1132 /layers/layers.0/blocks.1/attn/Constant_2" -> "152 /layers/layers.0/blocks.1/attn/Reshape_1" [label="[5]", style=dashed]; +"1133 onnx^^Add_2293" -> "143 /layers/layers.0/blocks.1/attn/Add" [label="[1, 3, 49, 49]", style=solid]; +"1134 Constant_416" -> "122 /layers/layers.0/blocks.1/attn/Gather_1" [label="[]", style=dashed]; +"1135 Constant_7297" -> "134 /layers/layers.0/blocks.1/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; +"1136 Constant_414" -> "121 /layers/layers.0/blocks.1/attn/Gather" [label="[]", style=dashed]; +"1137 Constant_7298" -> "175 /layers/layers.0/blocks.1/attn/proj/Add" [label="[1, 1, 96]", style=solid]; +"1138 Constant_5115" -> "208 /layers/layers.0/blocks.1/Slice_4" [label="[2]", style=dashed]; +"1139 Constant_5112" -> "208 /layers/layers.0/blocks.1/Slice_4" [label="[2]", style=dashed]; +"1140 Constant_5109" -> "208 /layers/layers.0/blocks.1/Slice_4" [label="[2]", style=dashed]; +"1141 Constant_5139" -> "226 /layers/layers.0/blocks.1/Slice_6" [label="[3]", style=dashed]; +"1142 Constant_5136" -> "226 /layers/layers.0/blocks.1/Slice_6" [label="[3]", style=dashed]; +"1143 Constant_5133" -> "226 /layers/layers.0/blocks.1/Slice_6" [label="[3]", style=dashed]; +"1144 Constant_7301" -> "54 /layers/layers.0/blocks.1/mlp/fc1/Add" [label="[1, 1, 384]", style=solid]; +"1145 Constant_7302" -> "74 /layers/layers.0/blocks.1/mlp/fc2/Add" [label="[1, 1, 96]", style=solid]; +"1146 Constant_5211" -> "40 /layers/layers.0/downsample/Slice_4" [label="[3]", style=dashed]; +"1147 Constant_5208" -> "40 /layers/layers.0/downsample/Slice_4" [label="[3]", style=dashed]; +"1148 Constant_5205" -> "40 /layers/layers.0/downsample/Slice_4" [label="[3]", style=dashed]; +"1149 Constant_5163" -> "33 /layers/layers.0/downsample/Slice" [label="[2]", style=dashed]; +"1150 Constant_5160" -> "33 /layers/layers.0/downsample/Slice" [label="[2]", style=dashed]; +"1151 Constant_5157" -> "33 /layers/layers.0/downsample/Slice" [label="[2]", style=dashed]; +"1152 Constant_5199" -> "41 /layers/layers.0/downsample/Slice_3" [label="[3]", style=dashed]; +"1153 Constant_5196" -> "41 /layers/layers.0/downsample/Slice_3" [label="[3]", style=dashed]; +"1154 Constant_5193" -> "41 /layers/layers.0/downsample/Slice_3" [label="[3]", style=dashed]; +"1155 Constant_5175" -> "39 /layers/layers.0/downsample/Slice_1" [label="[3]", style=dashed]; +"1156 Constant_5172" -> "39 /layers/layers.0/downsample/Slice_1" [label="[3]", style=dashed]; +"1157 Constant_5169" -> "39 /layers/layers.0/downsample/Slice_1" [label="[3]", style=dashed]; +"1158 Constant_7307" -> "159 /layers/layers.1/blocks.0/attn/qkv/Add" [label="[1, 1, 576]", style=solid]; +"1159 onnx^^Add_2389" -> "192 /layers/layers.1/blocks.0/attn/Add" [label="[1, 6, 49, 49]", style=solid]; +"1160 Constant_626" -> "180 /layers/layers.1/blocks.0/attn/Gather_1" [label="[]", style=dashed]; +"1161 Constant_7308" -> "185 /layers/layers.1/blocks.0/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; +"1162 Constant_624" -> "179 /layers/layers.1/blocks.0/attn/Gather" [label="[]", style=dashed]; +"1163 Constant_7309" -> "225 /layers/layers.1/blocks.0/attn/proj/Add" [label="[1, 1, 192]", style=solid]; +"1164 Constant_7312" -> "132 /layers/layers.1/blocks.0/mlp/fc1/Add" [label="[1, 1, 768]", style=solid]; +"1165 Constant_7313" -> "166 /layers/layers.1/blocks.0/mlp/fc2/Add" [label="[1, 1, 192]", style=solid]; +"1166 Constant_5235" -> "130 /layers/layers.1/blocks.1/Slice" [label="[2]", style=dashed]; +"1167 Constant_5232" -> "130 /layers/layers.1/blocks.1/Slice" [label="[2]", style=dashed]; +"1168 Constant_5229" -> "130 /layers/layers.1/blocks.1/Slice" [label="[2]", style=dashed]; +"1169 Constant_5259" -> "148 /layers/layers.1/blocks.1/Slice_2" [label="[3]", style=dashed]; +"1170 Constant_5256" -> "148 /layers/layers.1/blocks.1/Slice_2" [label="[3]", style=dashed]; +"1171 Constant_5253" -> "148 /layers/layers.1/blocks.1/Slice_2" [label="[3]", style=dashed]; +"1172 Constant_7316" -> "206 /layers/layers.1/blocks.1/attn/qkv/Add" [label="[1, 1, 576]", style=solid]; +"1173 /layers/layers.1/blocks.1/attn/Constant_3" -> "307 /layers/layers.1/blocks.1/attn/Reshape_2" [label="[4]", style=dashed]; +"1174 onnx^^Add_2446" -> "291 /layers/layers.1/blocks.1/attn/Add_1" [label="[1, 16, 1, 49, 49]", style=solid]; +"1175 /layers/layers.1/blocks.1/attn/Constant_2" -> "277 /layers/layers.1/blocks.1/attn/Reshape_1" [label="[5]", style=dashed]; +"1176 onnx^^Add_2438" -> "263 /layers/layers.1/blocks.1/attn/Add" [label="[1, 6, 49, 49]", style=solid]; +"1177 Constant_774" -> "236 /layers/layers.1/blocks.1/attn/Gather_1" [label="[]", style=dashed]; +"1178 Constant_7317" -> "249 /layers/layers.1/blocks.1/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; +"1179 Constant_772" -> "235 /layers/layers.1/blocks.1/attn/Gather" [label="[]", style=dashed]; +"1180 Constant_7318" -> "322 /layers/layers.1/blocks.1/attn/proj/Add" [label="[1, 1, 192]", style=solid]; +"1181 Constant_5283" -> "396 /layers/layers.1/blocks.1/Slice_4" [label="[2]", style=dashed]; +"1182 Constant_5280" -> "396 /layers/layers.1/blocks.1/Slice_4" [label="[2]", style=dashed]; +"1183 Constant_5277" -> "396 /layers/layers.1/blocks.1/Slice_4" [label="[2]", style=dashed]; +"1184 Constant_5307" -> "420 /layers/layers.1/blocks.1/Slice_6" [label="[3]", style=dashed]; +"1185 Constant_5304" -> "420 /layers/layers.1/blocks.1/Slice_6" [label="[3]", style=dashed]; +"1186 Constant_5301" -> "420 /layers/layers.1/blocks.1/Slice_6" [label="[3]", style=dashed]; +"1187 Constant_7321" -> "147 /layers/layers.1/blocks.1/mlp/fc1/Add" [label="[1, 1, 768]", style=solid]; +"1188 Constant_7322" -> "177 /layers/layers.1/blocks.1/mlp/fc2/Add" [label="[1, 1, 192]", style=solid]; +"1189 Constant_5379" -> "126 /layers/layers.1/downsample/Slice_4" [label="[3]", style=dashed]; +"1190 Constant_5376" -> "126 /layers/layers.1/downsample/Slice_4" [label="[3]", style=dashed]; +"1191 Constant_5373" -> "126 /layers/layers.1/downsample/Slice_4" [label="[3]", style=dashed]; +"1192 Constant_5331" -> "115 /layers/layers.1/downsample/Slice" [label="[2]", style=dashed]; +"1193 Constant_5328" -> "115 /layers/layers.1/downsample/Slice" [label="[2]", style=dashed]; +"1194 Constant_5325" -> "115 /layers/layers.1/downsample/Slice" [label="[2]", style=dashed]; +"1195 Constant_5367" -> "127 /layers/layers.1/downsample/Slice_3" [label="[3]", style=dashed]; +"1196 Constant_5364" -> "127 /layers/layers.1/downsample/Slice_3" [label="[3]", style=dashed]; +"1197 Constant_5361" -> "127 /layers/layers.1/downsample/Slice_3" [label="[3]", style=dashed]; +"1198 Constant_5343" -> "125 /layers/layers.1/downsample/Slice_1" [label="[3]", style=dashed]; +"1199 Constant_5340" -> "125 /layers/layers.1/downsample/Slice_1" [label="[3]", style=dashed]; +"1200 Constant_5337" -> "125 /layers/layers.1/downsample/Slice_1" [label="[3]", style=dashed]; +"1201 Constant_7327" -> "290 /layers/layers.2/blocks.0/attn/qkv/Add" [label="[1, 1, 1152]", style=solid]; +"1202 onnx^^Add_2534" -> "368 /layers/layers.2/blocks.0/attn/Add" [label="[1, 12, 49, 49]", style=solid]; +"1203 Constant_984" -> "336 /layers/layers.2/blocks.0/attn/Gather_1" [label="[]", style=dashed]; +"1204 Constant_7328" -> "350 /layers/layers.2/blocks.0/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; +"1205 Constant_982" -> "335 /layers/layers.2/blocks.0/attn/Gather" [label="[]", style=dashed]; +"1206 Constant_7329" -> "419 /layers/layers.2/blocks.0/attn/proj/Add" [label="[1, 1, 384]", style=solid]; +"1207 Constant_7332" -> "247 /layers/layers.2/blocks.0/mlp/fc1/Add" [label="[1, 1, 1536]", style=solid]; +"1208 Constant_7333" -> "305 /layers/layers.2/blocks.0/mlp/fc2/Add" [label="[1, 1, 384]", style=solid]; +"1209 Constant_5403" -> "245 /layers/layers.2/blocks.1/Slice" [label="[2]", style=dashed]; +"1210 Constant_5400" -> "245 /layers/layers.2/blocks.1/Slice" [label="[2]", style=dashed]; +"1211 Constant_5397" -> "245 /layers/layers.2/blocks.1/Slice" [label="[2]", style=dashed]; +"1212 Constant_5427" -> "273 /layers/layers.2/blocks.1/Slice_2" [label="[3]", style=dashed]; +"1213 Constant_5424" -> "273 /layers/layers.2/blocks.1/Slice_2" [label="[3]", style=dashed]; +"1214 Constant_5421" -> "273 /layers/layers.2/blocks.1/Slice_2" [label="[3]", style=dashed]; +"1215 Constant_7336" -> "394 /layers/layers.2/blocks.1/attn/qkv/Add" [label="[1, 1, 1152]", style=solid]; +"1216 /layers/layers.2/blocks.1/attn/Constant_3" -> "490 /layers/layers.2/blocks.1/attn/Reshape_2" [label="[4]", style=dashed]; +"1217 onnx^^Add_2702" -> "477 /layers/layers.2/blocks.1/attn/Add_1" [label="[1, 4, 1, 49, 49]", style=solid]; +"1217 onnx^^Add_2702" -> "524 /layers/layers.2/blocks.3/attn/Add_1" [label="[1, 4, 1, 49, 49]", style=solid]; +"1217 onnx^^Add_2702" -> "567 /layers/layers.2/blocks.5/attn/Add_1" [label="[1, 4, 1, 49, 49]", style=solid]; +"1218 /layers/layers.2/blocks.1/attn/Constant_2" -> "464 /layers/layers.2/blocks.1/attn/Reshape_1" [label="[5]", style=dashed]; +"1219 onnx^^Add_2583" -> "454 /layers/layers.2/blocks.1/attn/Add" [label="[1, 12, 49, 49]", style=solid]; +"1220 Constant_1132" -> "430 /layers/layers.2/blocks.1/attn/Gather_1" [label="[]", style=dashed]; +"1221 Constant_7337" -> "442 /layers/layers.2/blocks.1/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; +"1222 Constant_1130" -> "429 /layers/layers.2/blocks.1/attn/Gather" [label="[]", style=dashed]; +"1223 Constant_7338" -> "503 /layers/layers.2/blocks.1/attn/proj/Add" [label="[1, 1, 384]", style=solid]; +"1224 Constant_5451" -> "560 /layers/layers.2/blocks.1/Slice_4" [label="[2]", style=dashed]; +"1225 Constant_5448" -> "560 /layers/layers.2/blocks.1/Slice_4" [label="[2]", style=dashed]; +"1226 Constant_5445" -> "560 /layers/layers.2/blocks.1/Slice_4" [label="[2]", style=dashed]; +"1227 Constant_5475" -> "580 /layers/layers.2/blocks.1/Slice_6" [label="[3]", style=dashed]; +"1228 Constant_5472" -> "580 /layers/layers.2/blocks.1/Slice_6" [label="[3]", style=dashed]; +"1229 Constant_5469" -> "580 /layers/layers.2/blocks.1/Slice_6" [label="[3]", style=dashed]; +"1230 Constant_7341" -> "272 /layers/layers.2/blocks.1/mlp/fc1/Add" [label="[1, 1, 1536]", style=solid]; +"1231 Constant_7342" -> "333 /layers/layers.2/blocks.1/mlp/fc2/Add" [label="[1, 1, 384]", style=solid]; +"1232 Constant_7345" -> "348 /layers/layers.2/blocks.2/attn/qkv/Add" [label="[1, 1, 1152]", style=solid]; +"1233 onnx^^Add_2645" -> "416 /layers/layers.2/blocks.2/attn/Add" [label="[1, 12, 49, 49]", style=solid]; +"1234 Constant_1289" -> "392 /layers/layers.2/blocks.2/attn/Gather_1" [label="[]", style=dashed]; +"1235 Constant_7346" -> "404 /layers/layers.2/blocks.2/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; +"1236 Constant_1287" -> "391 /layers/layers.2/blocks.2/attn/Gather" [label="[]", style=dashed]; +"1237 Constant_7347" -> "463 /layers/layers.2/blocks.2/attn/proj/Add" [label="[1, 1, 384]", style=solid]; +"1238 Constant_7350" -> "301 /layers/layers.2/blocks.2/mlp/fc1/Add" [label="[1, 1, 1536]", style=solid]; +"1239 Constant_7351" -> "365 /layers/layers.2/blocks.2/mlp/fc2/Add" [label="[1, 1, 384]", style=solid]; +"1240 Constant_5499" -> "299 /layers/layers.2/blocks.3/Slice" [label="[2]", style=dashed]; +"1241 Constant_5496" -> "299 /layers/layers.2/blocks.3/Slice" [label="[2]", style=dashed]; +"1242 Constant_5493" -> "299 /layers/layers.2/blocks.3/Slice" [label="[2]", style=dashed]; +"1243 Constant_5523" -> "329 /layers/layers.2/blocks.3/Slice_2" [label="[3]", style=dashed]; +"1244 Constant_5520" -> "329 /layers/layers.2/blocks.3/Slice_2" [label="[3]", style=dashed]; +"1245 Constant_5517" -> "329 /layers/layers.2/blocks.3/Slice_2" [label="[3]", style=dashed]; +"1246 Constant_7354" -> "440 /layers/layers.2/blocks.3/attn/qkv/Add" [label="[1, 1, 1152]", style=solid]; +"1247 /layers/layers.2/blocks.3/attn/Constant_3" -> "537 /layers/layers.2/blocks.3/attn/Reshape_2" [label="[4]", style=dashed]; +"1248 /layers/layers.2/blocks.3/attn/Constant_2" -> "511 /layers/layers.2/blocks.3/attn/Reshape_1" [label="[5]", style=dashed]; +"1249 onnx^^Add_2694" -> "499 /layers/layers.2/blocks.3/attn/Add" [label="[1, 12, 49, 49]", style=solid]; +"1250 Constant_1437" -> "474 /layers/layers.2/blocks.3/attn/Gather_1" [label="[]", style=dashed]; +"1251 Constant_7355" -> "486 /layers/layers.2/blocks.3/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; +"1252 Constant_1435" -> "473 /layers/layers.2/blocks.3/attn/Gather" [label="[]", style=dashed]; +"1253 Constant_7356" -> "549 /layers/layers.2/blocks.3/attn/proj/Add" [label="[1, 1, 384]", style=solid]; +"1254 Constant_5547" -> "597 /layers/layers.2/blocks.3/Slice_4" [label="[2]", style=dashed]; +"1255 Constant_5544" -> "597 /layers/layers.2/blocks.3/Slice_4" [label="[2]", style=dashed]; +"1256 Constant_5541" -> "597 /layers/layers.2/blocks.3/Slice_4" [label="[2]", style=dashed]; +"1257 Constant_5571" -> "613 /layers/layers.2/blocks.3/Slice_6" [label="[3]", style=dashed]; +"1258 Constant_5568" -> "613 /layers/layers.2/blocks.3/Slice_6" [label="[3]", style=dashed]; +"1259 Constant_5565" -> "613 /layers/layers.2/blocks.3/Slice_6" [label="[3]", style=dashed]; +"1260 Constant_7359" -> "328 /layers/layers.2/blocks.3/mlp/fc1/Add" [label="[1, 1, 1536]", style=solid]; +"1261 Constant_7360" -> "389 /layers/layers.2/blocks.3/mlp/fc2/Add" [label="[1, 1, 384]", style=solid]; +"1262 Constant_7363" -> "402 /layers/layers.2/blocks.4/attn/qkv/Add" [label="[1, 1, 1152]", style=solid]; +"1263 onnx^^Add_2756" -> "460 /layers/layers.2/blocks.4/attn/Add" [label="[1, 12, 49, 49]", style=solid]; +"1264 Constant_1594" -> "438 /layers/layers.2/blocks.4/attn/Gather_1" [label="[]", style=dashed]; +"1265 Constant_7364" -> "449 /layers/layers.2/blocks.4/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; +"1266 Constant_1592" -> "437 /layers/layers.2/blocks.4/attn/Gather" [label="[]", style=dashed]; +"1267 Constant_7365" -> "510 /layers/layers.2/blocks.4/attn/proj/Add" [label="[1, 1, 384]", style=solid]; +"1268 Constant_7368" -> "361 /layers/layers.2/blocks.4/mlp/fc1/Add" [label="[1, 1, 1536]", style=solid]; +"1269 Constant_7369" -> "413 /layers/layers.2/blocks.4/mlp/fc2/Add" [label="[1, 1, 384]", style=solid]; +"1270 Constant_5595" -> "359 /layers/layers.2/blocks.5/Slice" [label="[2]", style=dashed]; +"1271 Constant_5592" -> "359 /layers/layers.2/blocks.5/Slice" [label="[2]", style=dashed]; +"1272 Constant_5589" -> "359 /layers/layers.2/blocks.5/Slice" [label="[2]", style=dashed]; +"1273 Constant_5619" -> "385 /layers/layers.2/blocks.5/Slice_2" [label="[3]", style=dashed]; +"1274 Constant_5616" -> "385 /layers/layers.2/blocks.5/Slice_2" [label="[3]", style=dashed]; +"1275 Constant_5613" -> "385 /layers/layers.2/blocks.5/Slice_2" [label="[3]", style=dashed]; +"1276 Constant_7372" -> "484 /layers/layers.2/blocks.5/attn/qkv/Add" [label="[1, 1, 1152]", style=solid]; +"1277 /layers/layers.2/blocks.5/attn/Constant_3" -> "577 /layers/layers.2/blocks.5/attn/Reshape_2" [label="[4]", style=dashed]; +"1278 /layers/layers.2/blocks.5/attn/Constant_2" -> "556 /layers/layers.2/blocks.5/attn/Reshape_1" [label="[5]", style=dashed]; +"1279 onnx^^Add_2805" -> "545 /layers/layers.2/blocks.5/attn/Add" [label="[1, 12, 49, 49]", style=solid]; +"1280 Constant_1742" -> "521 /layers/layers.2/blocks.5/attn/Gather_1" [label="[]", style=dashed]; +"1281 Constant_7373" -> "533 /layers/layers.2/blocks.5/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; +"1282 Constant_1740" -> "520 /layers/layers.2/blocks.5/attn/Gather" [label="[]", style=dashed]; +"1283 Constant_7374" -> "587 /layers/layers.2/blocks.5/attn/proj/Add" [label="[1, 1, 384]", style=solid]; +"1284 Constant_5643" -> "625 /layers/layers.2/blocks.5/Slice_4" [label="[2]", style=dashed]; +"1285 Constant_5640" -> "625 /layers/layers.2/blocks.5/Slice_4" [label="[2]", style=dashed]; +"1286 Constant_5637" -> "625 /layers/layers.2/blocks.5/Slice_4" [label="[2]", style=dashed]; +"1287 Constant_5667" -> "634 /layers/layers.2/blocks.5/Slice_6" [label="[3]", style=dashed]; +"1288 Constant_5664" -> "634 /layers/layers.2/blocks.5/Slice_6" [label="[3]", style=dashed]; +"1289 Constant_5661" -> "634 /layers/layers.2/blocks.5/Slice_6" [label="[3]", style=dashed]; +"1290 Constant_7377" -> "384 /layers/layers.2/blocks.5/mlp/fc1/Add" [label="[1, 1, 1536]", style=solid]; +"1291 Constant_7378" -> "435 /layers/layers.2/blocks.5/mlp/fc2/Add" [label="[1, 1, 384]", style=solid]; +"1292 Constant_5739" -> "355 /layers/layers.2/downsample/Slice_4" [label="[3]", style=dashed]; +"1293 Constant_5736" -> "355 /layers/layers.2/downsample/Slice_4" [label="[3]", style=dashed]; +"1294 Constant_5733" -> "355 /layers/layers.2/downsample/Slice_4" [label="[3]", style=dashed]; +"1295 Constant_5691" -> "339 /layers/layers.2/downsample/Slice" [label="[2]", style=dashed]; +"1296 Constant_5688" -> "339 /layers/layers.2/downsample/Slice" [label="[2]", style=dashed]; +"1297 Constant_5685" -> "339 /layers/layers.2/downsample/Slice" [label="[2]", style=dashed]; +"1298 Constant_5727" -> "356 /layers/layers.2/downsample/Slice_3" [label="[3]", style=dashed]; +"1299 Constant_5724" -> "356 /layers/layers.2/downsample/Slice_3" [label="[3]", style=dashed]; +"1300 Constant_5721" -> "356 /layers/layers.2/downsample/Slice_3" [label="[3]", style=dashed]; +"1301 Constant_5703" -> "354 /layers/layers.2/downsample/Slice_1" [label="[3]", style=dashed]; +"1302 Constant_5700" -> "354 /layers/layers.2/downsample/Slice_1" [label="[3]", style=dashed]; +"1303 Constant_5697" -> "354 /layers/layers.2/downsample/Slice_1" [label="[3]", style=dashed]; +"1304 Constant_7383" -> "566 /layers/layers.3/blocks.0/attn/qkv/Add" [label="[1, 1, 2304]", style=solid]; +"1305 onnx^^Add_2901" -> "610 /layers/layers.3/blocks.0/attn/Add" [label="[1, 24, 49, 49]", style=solid]; +"1306 Constant_1952" -> "594 /layers/layers.3/blocks.0/attn/Gather_1" [label="[]", style=dashed]; +"1307 Constant_7384" -> "602 /layers/layers.3/blocks.0/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; +"1308 Constant_1950" -> "593 /layers/layers.3/blocks.0/attn/Gather" [label="[]", style=dashed]; +"1309 Constant_7385" -> "633 /layers/layers.3/blocks.0/attn/proj/Add" [label="[1, 1, 768]", style=solid]; +"1310 Constant_7388" -> "531 /layers/layers.3/blocks.0/mlp/fc1/Add" [label="[1, 1, 3072]", style=solid]; +"1311 Constant_7389" -> "575 /layers/layers.3/blocks.0/mlp/fc2/Add" [label="[1, 1, 768]", style=solid]; +"1312 Constant_7392" -> "584 /layers/layers.3/blocks.1/attn/qkv/Add" [label="[1, 1, 2304]", style=solid]; +"1313 onnx^^Add_2950" -> "622 /layers/layers.3/blocks.1/attn/Add" [label="[1, 24, 49, 49]", style=solid]; +"1314 Constant_2058" -> "608 /layers/layers.3/blocks.1/attn/Gather_1" [label="[]", style=dashed]; +"1315 Constant_7393" -> "615 /layers/layers.3/blocks.1/attn/Mul" [label="[1, 1, 1, 1]", style=solid]; +"1316 Constant_2056" -> "607 /layers/layers.3/blocks.1/attn/Gather" [label="[]", style=dashed]; +"1317 Constant_7394" -> "639 /layers/layers.3/blocks.1/attn/proj/Add" [label="[1, 1, 768]", style=solid]; +"1318 Constant_7397" -> "552 /layers/layers.3/blocks.1/mlp/fc1/Add" [label="[1, 1, 3072]", style=solid]; +"1319 Constant_7398" -> "591 /layers/layers.3/blocks.1/mlp/fc2/Add" [label="[1, 1, 768]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/yolo-v4-tiny-tf_performance.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/yolo-v4-tiny-tf_performance.dot deleted file mode 120000 index a049c8d8220..00000000000 --- a/tests/openvino/native/data/2023.3/reference_graphs/quantized/yolo-v4-tiny-tf_performance.dot +++ /dev/null @@ -1 +0,0 @@ -../../../2023.2/reference_graphs/quantized/yolo-v4-tiny-tf_performance.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_graphs/quantized/yolo-v4-tiny-tf_performance.dot b/tests/openvino/native/data/2023.3/reference_graphs/quantized/yolo-v4-tiny-tf_performance.dot new file mode 100644 index 00000000000..073e63c11e0 --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_graphs/quantized/yolo-v4-tiny-tf_performance.dot @@ -0,0 +1,754 @@ +strict digraph { +"0 image_input" [id=0, type=Parameter]; +"1 image_input/fq_output_0" [id=1, type=FakeQuantize]; +"2 Divide_2378" [id=2, type=Transpose]; +"3 Multiply_3747" [id=3, type=Convolution]; +"4 Transpose_1172" [id=4, type=Add]; +"5 Transpose_1178" [id=5, type=PRelu]; +"6 Transpose_1178/fq_output_0" [id=6, type=FakeQuantize]; +"7 Multiply_3761" [id=7, type=Convolution]; +"8 Transpose_1229" [id=8, type=Add]; +"9 Transpose_1235" [id=9, type=PRelu]; +"10 Transpose_1235/fq_output_0" [id=10, type=FakeQuantize]; +"11 Multiply_3775" [id=11, type=Convolution]; +"12 Transpose_1259" [id=12, type=Add]; +"13 Transpose_1265" [id=13, type=PRelu]; +"14 Transpose_1265/fq_output_0" [id=14, type=FakeQuantize]; +"15 Transpose_1368" [id=15, type=Concat]; +"16 group_route_3/split" [id=16, type=Split]; +"17 MaxPool_307" [id=17, type=MaxPool]; +"18 Multiply_3789" [id=18, type=Convolution]; +"19 Multiply_3831" [id=19, type=Convolution]; +"20 Transpose_1294" [id=20, type=Add]; +"21 Transpose_1392" [id=21, type=Add]; +"22 Transpose_1300" [id=22, type=PRelu]; +"23 Transpose_1398" [id=23, type=PRelu]; +"24 Transpose_1300/fq_output_0" [id=24, type=FakeQuantize]; +"25 Transpose_1398/fq_output_0" [id=25, type=FakeQuantize]; +"26 Multiply_3803" [id=26, type=Convolution]; +"27 Transpose_1334" [id=27, type=Concat]; +"28 Transpose_1501" [id=28, type=Concat]; +"29 group_route_11/split" [id=29, type=Split]; +"30 Transpose_1324" [id=30, type=Add]; +"31 Multiply_3817" [id=31, type=Convolution]; +"32 MaxPool_433" [id=32, type=MaxPool]; +"33 Multiply_3845" [id=33, type=Convolution]; +"34 Transpose_1330" [id=34, type=PRelu]; +"35 Transpose_1358" [id=35, type=Add]; +"36 Multiply_3887" [id=36, type=Convolution]; +"37 Transpose_1427" [id=37, type=Add]; +"38 Transpose_1330/fq_output_0" [id=38, type=FakeQuantize]; +"39 Transpose_1364" [id=39, type=PRelu]; +"40 Transpose_1525" [id=40, type=Add]; +"41 Transpose_1433" [id=41, type=PRelu]; +"42 Transpose_1364/fq_output_0" [id=42, type=FakeQuantize]; +"43 Transpose_1531" [id=43, type=PRelu]; +"44 Transpose_1433/fq_output_0" [id=44, type=FakeQuantize]; +"45 Transpose_1531/fq_output_0" [id=45, type=FakeQuantize]; +"46 Multiply_3859" [id=46, type=Convolution]; +"47 Transpose_1467" [id=47, type=Concat]; +"48 Transpose_1634" [id=48, type=Concat]; +"49 group_route_19/split" [id=49, type=Split]; +"50 Transpose_1457" [id=50, type=Add]; +"51 Multiply_3873" [id=51, type=Convolution]; +"52 MaxPool_579" [id=52, type=MaxPool]; +"53 Multiply_3901" [id=53, type=Convolution]; +"54 Transpose_1463" [id=54, type=PRelu]; +"55 Transpose_1491" [id=55, type=Add]; +"56 Multiply_3943" [id=56, type=Convolution]; +"57 Transpose_1560" [id=57, type=Add]; +"58 Transpose_1463/fq_output_0" [id=58, type=FakeQuantize]; +"59 Transpose_1497" [id=59, type=PRelu]; +"60 Transpose_1658" [id=60, type=Add]; +"61 Transpose_1566" [id=61, type=PRelu]; +"62 Transpose_1497/fq_output_0" [id=62, type=FakeQuantize]; +"63 Transpose_1664" [id=63, type=PRelu]; +"64 Transpose_1566/fq_output_0" [id=64, type=FakeQuantize]; +"65 Transpose_1664/fq_output_0" [id=65, type=FakeQuantize]; +"66 Multiply_3915" [id=66, type=Convolution]; +"67 Transpose_1600" [id=67, type=Concat]; +"68 Multiply_3957" [id=68, type=Convolution]; +"69 Transpose_1590" [id=69, type=Add]; +"70 Multiply_3929" [id=70, type=Convolution]; +"71 Transpose_1688" [id=71, type=Add]; +"72 Transpose_1596" [id=72, type=PRelu]; +"73 Transpose_1624" [id=73, type=Add]; +"74 Transpose_1694" [id=74, type=PRelu]; +"75 Transpose_1596/fq_output_0" [id=75, type=FakeQuantize]; +"76 Transpose_1630" [id=76, type=PRelu]; +"77 Transpose_1694/fq_output_0" [id=77, type=FakeQuantize]; +"78 Transpose_1630/fq_output_0" [id=78, type=FakeQuantize]; +"79 Multiply_3971" [id=79, type=Convolution]; +"80 Multiply_3999" [id=80, type=Convolution]; +"81 Transpose_1730" [id=81, type=Concat]; +"82 Transpose_1718" [id=82, type=Add]; +"83 Transpose_1790" [id=83, type=Add]; +"84 Multiply_3985" [id=84, type=Convolution]; +"85 Transpose_1724" [id=85, type=PRelu]; +"86 Transpose_1796" [id=86, type=PRelu]; +"87 Transpose_1754" [id=87, type=Add]; +"88 Transpose_1724/fq_output_0" [id=88, type=FakeQuantize]; +"89 Transpose_1796/fq_output_0" [id=89, type=FakeQuantize]; +"90 Transpose_1760" [id=90, type=PRelu]; +"91 Gather_1726" [id=91, type=ShapeOf]; +"92 leaky_re_lu_17/LeakyRelu" [id=92, type=Transpose]; +"93 Convolution_749" [id=93, type=Convolution]; +"94 Transpose_1760/fq_output_0" [id=94, type=FakeQuantize]; +"95 up_sampling2d/Shape" [id=95, type=Gather]; +"96 up_sampling2d/resize/ResizeNearestNeighbor" [id=96, type=Interpolate]; +"97 Transpose_1802" [id=97, type=Add]; +"98 Convolution_706" [id=98, type=Convolution]; +"99 up_sampling2d/strided_slice" [id=99, type=StridedSlice]; +"100 up_sampling2d/resize/ResizeNearestNeighbor/fq_output_0" [id=100, type=FakeQuantize]; +"101 conv2d_17/BiasAdd" [id=101, type=Transpose]; +"102 Transpose_1766" [id=102, type=Add]; +"103 up_sampling2d/mul" [id=103, type=Multiply]; +"104 Transpose_1728" [id=104, type=Transpose]; +"105 conv2d_17/BiasAdd^0" [id=105, label="105 conv2d_17/BiasAdd:0", type=Result]; +"106 conv2d_20/BiasAdd" [id=106, type=Transpose]; +"107 conv2d_20/BiasAdd^0" [id=107, label="107 conv2d_20/BiasAdd:0", type=Result]; +"108 Constant_1765" [id=108, type=Constant]; +"109 Transpose_1764" [id=109, type=Constant]; +"110 Convolution_706/fq_weights_1" [id=110, type=FakeQuantize]; +"111 Constant_5545" [id=111, type=Constant]; +"112 Constant_5544" [id=112, type=Constant]; +"113 Constant_5543" [id=113, type=Constant]; +"114 Constant_5542" [id=114, type=Constant]; +"115 Transpose_705" [id=115, type=Constant]; +"116 Constant_5540" [id=116, type=Constant]; +"117 Constant_5539" [id=117, type=Constant]; +"118 Constant_5538" [id=118, type=Constant]; +"119 Constant_5537" [id=119, type=Constant]; +"120 Transpose_1758" [id=120, type=Constant]; +"121 Constant_3993" [id=121, type=Constant]; +"122 Multiply_3985/fq_weights_1" [id=122, type=FakeQuantize]; +"123 Constant_5535" [id=123, type=Constant]; +"124 Constant_5534" [id=124, type=Constant]; +"125 Constant_5533" [id=125, type=Constant]; +"126 Constant_5532" [id=126, type=Constant]; +"127 Multiply_4118" [id=127, type=Constant]; +"128 Constant_5405" [id=128, type=Constant]; +"129 Constant_5404" [id=129, type=Constant]; +"130 Constant_5403" [id=130, type=Constant]; +"131 Constant_5402" [id=131, type=Constant]; +"132 Transpose_1628" [id=132, type=Constant]; +"133 Constant_3937" [id=133, type=Constant]; +"134 Multiply_3929/fq_weights_1" [id=134, type=FakeQuantize]; +"135 Constant_5500" [id=135, type=Constant]; +"136 Constant_5499" [id=136, type=Constant]; +"137 Constant_5498" [id=137, type=Constant]; +"138 Constant_5497" [id=138, type=Constant]; +"139 Multiply_4094" [id=139, type=Constant]; +"140 Constant_5375" [id=140, type=Constant]; +"141 Constant_5374" [id=141, type=Constant]; +"142 Constant_5373" [id=142, type=Constant]; +"143 Constant_5372" [id=143, type=Constant]; +"144 Transpose_1564" [id=144, type=Constant]; +"145 Constant_3909" [id=145, type=Constant]; +"146 Multiply_3901/fq_weights_1" [id=146, type=FakeQuantize]; +"147 Constant_5490" [id=147, type=Constant]; +"148 Constant_5489" [id=148, type=Constant]; +"149 Constant_5488" [id=149, type=Constant]; +"150 Constant_5487" [id=150, type=Constant]; +"151 Multiply_4082" [id=151, type=Constant]; +"152 Constant_1532" [id=152, type=Constant]; +"153 Constant_5410" [id=153, type=Constant]; +"154 Constant_5409" [id=154, type=Constant]; +"155 Constant_5408" [id=155, type=Constant]; +"156 Constant_5407" [id=156, type=Constant]; +"157 Transpose_1529" [id=157, type=Constant]; +"158 Constant_3895" [id=158, type=Constant]; +"159 Multiply_3887/fq_weights_1" [id=159, type=FakeQuantize]; +"160 Constant_5485" [id=160, type=Constant]; +"161 Constant_5484" [id=161, type=Constant]; +"162 Constant_5483" [id=162, type=Constant]; +"163 Constant_5482" [id=163, type=Constant]; +"164 Multiply_4076" [id=164, type=Constant]; +"165 Constant_5400" [id=165, type=Constant]; +"166 Constant_5399" [id=166, type=Constant]; +"167 Constant_5398" [id=167, type=Constant]; +"168 Constant_5397" [id=168, type=Constant]; +"169 Transpose_1495" [id=169, type=Constant]; +"170 Constant_3881" [id=170, type=Constant]; +"171 Multiply_3873/fq_weights_1" [id=171, type=FakeQuantize]; +"172 Constant_5480" [id=172, type=Constant]; +"173 Constant_5479" [id=173, type=Constant]; +"174 Constant_5478" [id=174, type=Constant]; +"175 Constant_5477" [id=175, type=Constant]; +"176 Multiply_4070" [id=176, type=Constant]; +"177 Constant_5370" [id=177, type=Constant]; +"178 Constant_5369" [id=178, type=Constant]; +"179 Constant_5368" [id=179, type=Constant]; +"180 Constant_5367" [id=180, type=Constant]; +"181 Transpose_1431" [id=181, type=Constant]; +"182 Constant_3853" [id=182, type=Constant]; +"183 Multiply_3845/fq_weights_1" [id=183, type=FakeQuantize]; +"184 Constant_5470" [id=184, type=Constant]; +"185 Constant_5469" [id=185, type=Constant]; +"186 Constant_5468" [id=186, type=Constant]; +"187 Constant_5467" [id=187, type=Constant]; +"188 Multiply_4058" [id=188, type=Constant]; +"189 Constant_1399" [id=189, type=Constant]; +"190 Constant_5395" [id=190, type=Constant]; +"191 Constant_5394" [id=191, type=Constant]; +"192 Constant_5393" [id=192, type=Constant]; +"193 Constant_5392" [id=193, type=Constant]; +"194 Transpose_1396" [id=194, type=Constant]; +"195 Constant_3839" [id=195, type=Constant]; +"196 Multiply_3831/fq_weights_1" [id=196, type=FakeQuantize]; +"197 Constant_5465" [id=197, type=Constant]; +"198 Constant_5464" [id=198, type=Constant]; +"199 Constant_5463" [id=199, type=Constant]; +"200 Constant_5462" [id=200, type=Constant]; +"201 Multiply_4052" [id=201, type=Constant]; +"202 Constant_5390" [id=202, type=Constant]; +"203 Constant_5389" [id=203, type=Constant]; +"204 Constant_5388" [id=204, type=Constant]; +"205 Constant_5387" [id=205, type=Constant]; +"206 Transpose_1362" [id=206, type=Constant]; +"207 Constant_3825" [id=207, type=Constant]; +"208 Multiply_3817/fq_weights_1" [id=208, type=FakeQuantize]; +"209 Constant_5460" [id=209, type=Constant]; +"210 Constant_5459" [id=210, type=Constant]; +"211 Constant_5458" [id=211, type=Constant]; +"212 Constant_5457" [id=212, type=Constant]; +"213 Multiply_4046" [id=213, type=Constant]; +"214 Constant_5355" [id=214, type=Constant]; +"215 Constant_5354" [id=215, type=Constant]; +"216 Constant_5353" [id=216, type=Constant]; +"217 Constant_5352" [id=217, type=Constant]; +"218 Transpose_1298" [id=218, type=Constant]; +"219 Constant_3797" [id=219, type=Constant]; +"220 Multiply_3789/fq_weights_1" [id=220, type=FakeQuantize]; +"221 Constant_5450" [id=221, type=Constant]; +"222 Constant_5449" [id=222, type=Constant]; +"223 Constant_5448" [id=223, type=Constant]; +"224 Constant_5447" [id=224, type=Constant]; +"225 Multiply_4034" [id=225, type=Constant]; +"226 Constant_1266" [id=226, type=Constant]; +"227 Constant_5385" [id=227, type=Constant]; +"228 Constant_5384" [id=228, type=Constant]; +"229 Constant_5383" [id=229, type=Constant]; +"230 Constant_5382" [id=230, type=Constant]; +"231 Transpose_1263" [id=231, type=Constant]; +"232 Constant_3783" [id=232, type=Constant]; +"233 Multiply_3775/fq_weights_1" [id=233, type=FakeQuantize]; +"234 Constant_5445" [id=234, type=Constant]; +"235 Constant_5444" [id=235, type=Constant]; +"236 Constant_5443" [id=236, type=Constant]; +"237 Constant_5442" [id=237, type=Constant]; +"238 Multiply_4028" [id=238, type=Constant]; +"239 Constant_5440" [id=239, type=Constant]; +"240 Constant_5439" [id=240, type=Constant]; +"241 Constant_5438" [id=241, type=Constant]; +"242 Constant_5437" [id=242, type=Constant]; +"243 Transpose_1233" [id=243, type=Constant]; +"244 Constant_3769" [id=244, type=Constant]; +"245 Multiply_3761/fq_weights_1" [id=245, type=FakeQuantize]; +"246 Constant_5435" [id=246, type=Constant]; +"247 Constant_5434" [id=247, type=Constant]; +"248 Constant_5433" [id=248, type=Constant]; +"249 Constant_5432" [id=249, type=Constant]; +"250 Multiply_4022" [id=250, type=Constant]; +"251 Constant_5430" [id=251, type=Constant]; +"252 Constant_5429" [id=252, type=Constant]; +"253 Constant_5428" [id=253, type=Constant]; +"254 Constant_5427" [id=254, type=Constant]; +"255 Transpose_1176" [id=255, type=Constant]; +"256 Constant_3755" [id=256, type=Constant]; +"257 Multiply_3747/fq_weights_1" [id=257, type=FakeQuantize]; +"258 Constant_5425" [id=258, type=Constant]; +"259 Constant_5424" [id=259, type=Constant]; +"260 Constant_5423" [id=260, type=Constant]; +"261 Constant_5422" [id=261, type=Constant]; +"262 Gather_4293" [id=262, type=Constant]; +"263 Constant_2333" [id=263, type=Constant]; +"264 Constant_5420" [id=264, type=Constant]; +"265 Constant_5419" [id=265, type=Constant]; +"266 Constant_5418" [id=266, type=Constant]; +"267 Constant_5417" [id=267, type=Constant]; +"268 Constant_5360" [id=268, type=Constant]; +"269 Constant_5359" [id=269, type=Constant]; +"270 Constant_5358" [id=270, type=Constant]; +"271 Constant_5357" [id=271, type=Constant]; +"272 Transpose_1328" [id=272, type=Constant]; +"273 Constant_3811" [id=273, type=Constant]; +"274 Multiply_3803/fq_weights_1" [id=274, type=FakeQuantize]; +"275 Constant_5455" [id=275, type=Constant]; +"276 Constant_5454" [id=276, type=Constant]; +"277 Constant_5453" [id=277, type=Constant]; +"278 Constant_5452" [id=278, type=Constant]; +"279 Multiply_4040" [id=279, type=Constant]; +"280 Constant_5365" [id=280, type=Constant]; +"281 Constant_5364" [id=281, type=Constant]; +"282 Constant_5363" [id=282, type=Constant]; +"283 Constant_5362" [id=283, type=Constant]; +"284 Transpose_1461" [id=284, type=Constant]; +"285 Constant_3867" [id=285, type=Constant]; +"286 Multiply_3859/fq_weights_1" [id=286, type=FakeQuantize]; +"287 Constant_5475" [id=287, type=Constant]; +"288 Constant_5474" [id=288, type=Constant]; +"289 Constant_5473" [id=289, type=Constant]; +"290 Constant_5472" [id=290, type=Constant]; +"291 Multiply_4064" [id=291, type=Constant]; +"292 Constant_5380" [id=292, type=Constant]; +"293 Constant_5379" [id=293, type=Constant]; +"294 Constant_5378" [id=294, type=Constant]; +"295 Constant_5377" [id=295, type=Constant]; +"296 Transpose_1594" [id=296, type=Constant]; +"297 Constant_3923" [id=297, type=Constant]; +"298 Multiply_3915/fq_weights_1" [id=298, type=FakeQuantize]; +"299 Constant_5495" [id=299, type=Constant]; +"300 Constant_5494" [id=300, type=Constant]; +"301 Constant_5493" [id=301, type=Constant]; +"302 Constant_5492" [id=302, type=Constant]; +"303 Multiply_4088" [id=303, type=Constant]; +"304 Constant_1727" [id=304, type=Constant]; +"305 Constant_5415" [id=305, type=Constant]; +"306 Constant_5414" [id=306, type=Constant]; +"307 Constant_5413" [id=307, type=Constant]; +"308 Constant_5412" [id=308, type=Constant]; +"309 Constant_669" [id=309, type=Constant]; +"310 up_sampling2d/Const" [id=310, type=Constant]; +"311 up_sampling2d/strided_slice/stack_2" [id=311, type=Constant]; +"312 up_sampling2d/strided_slice/stack_1" [id=312, type=Constant]; +"313 up_sampling2d/strided_slice/stack" [id=313, type=Constant]; +"314 Constant_1725" [id=314, type=Constant]; +"315 Constant_1723" [id=315, type=Constant]; +"316 Constant_5530" [id=316, type=Constant]; +"317 Constant_5529" [id=317, type=Constant]; +"318 Constant_5528" [id=318, type=Constant]; +"319 Constant_5527" [id=319, type=Constant]; +"320 Transpose_1722" [id=320, type=Constant]; +"321 Constant_3979" [id=321, type=Constant]; +"322 Multiply_3971/fq_weights_1" [id=322, type=FakeQuantize]; +"323 Constant_5525" [id=323, type=Constant]; +"324 Constant_5524" [id=324, type=Constant]; +"325 Constant_5523" [id=325, type=Constant]; +"326 Constant_5522" [id=326, type=Constant]; +"327 Multiply_4112" [id=327, type=Constant]; +"328 Constant_5520" [id=328, type=Constant]; +"329 Constant_5519" [id=329, type=Constant]; +"330 Constant_5518" [id=330, type=Constant]; +"331 Constant_5517" [id=331, type=Constant]; +"332 Transpose_1692" [id=332, type=Constant]; +"333 Constant_3965" [id=333, type=Constant]; +"334 Multiply_3957/fq_weights_1" [id=334, type=FakeQuantize]; +"335 Constant_5515" [id=335, type=Constant]; +"336 Constant_5514" [id=336, type=Constant]; +"337 Constant_5513" [id=337, type=Constant]; +"338 Constant_5512" [id=338, type=Constant]; +"339 Multiply_4106" [id=339, type=Constant]; +"340 Constant_5510" [id=340, type=Constant]; +"341 Constant_5509" [id=341, type=Constant]; +"342 Constant_5508" [id=342, type=Constant]; +"343 Constant_5507" [id=343, type=Constant]; +"344 Transpose_1662" [id=344, type=Constant]; +"345 Constant_3951" [id=345, type=Constant]; +"346 Multiply_3943/fq_weights_1" [id=346, type=FakeQuantize]; +"347 Constant_5505" [id=347, type=Constant]; +"348 Constant_5504" [id=348, type=Constant]; +"349 Constant_5503" [id=349, type=Constant]; +"350 Constant_5502" [id=350, type=Constant]; +"351 Multiply_4100" [id=351, type=Constant]; +"352 Constant_1801" [id=352, type=Constant]; +"353 Transpose_1800" [id=353, type=Constant]; +"354 Convolution_749/fq_weights_1" [id=354, type=FakeQuantize]; +"355 Constant_5560" [id=355, type=Constant]; +"356 Constant_5559" [id=356, type=Constant]; +"357 Constant_5558" [id=357, type=Constant]; +"358 Constant_5557" [id=358, type=Constant]; +"359 Transpose_748" [id=359, type=Constant]; +"360 Constant_5555" [id=360, type=Constant]; +"361 Constant_5554" [id=361, type=Constant]; +"362 Constant_5553" [id=362, type=Constant]; +"363 Constant_5552" [id=363, type=Constant]; +"364 Transpose_1794" [id=364, type=Constant]; +"365 Constant_4007" [id=365, type=Constant]; +"366 Multiply_3999/fq_weights_1" [id=366, type=FakeQuantize]; +"367 Constant_5550" [id=367, type=Constant]; +"368 Constant_5549" [id=368, type=Constant]; +"369 Constant_5548" [id=369, type=Constant]; +"370 Constant_5547" [id=370, type=Constant]; +"371 Multiply_4124" [id=371, type=Constant]; +"0 image_input" -> "1 image_input/fq_output_0" [label="[1, 416, 416, 3]", style=solid]; +"1 image_input/fq_output_0" -> "2 Divide_2378" [label="[1, 416, 416, 3]", style=solid]; +"2 Divide_2378" -> "3 Multiply_3747" [label="[1, 3, 416, 416]", style=solid]; +"3 Multiply_3747" -> "4 Transpose_1172" [label="[1, 32, 208, 208]", style=solid]; +"4 Transpose_1172" -> "5 Transpose_1178" [label="[1, 32, 208, 208]", style=solid]; +"5 Transpose_1178" -> "6 Transpose_1178/fq_output_0" [label="[1, 32, 208, 208]", style=solid]; +"6 Transpose_1178/fq_output_0" -> "7 Multiply_3761" [label="[1, 32, 208, 208]", style=solid]; +"7 Multiply_3761" -> "8 Transpose_1229" [label="[1, 64, 104, 104]", style=solid]; +"8 Transpose_1229" -> "9 Transpose_1235" [label="[1, 64, 104, 104]", style=solid]; +"9 Transpose_1235" -> "10 Transpose_1235/fq_output_0" [label="[1, 64, 104, 104]", style=solid]; +"10 Transpose_1235/fq_output_0" -> "11 Multiply_3775" [label="[1, 64, 104, 104]", style=solid]; +"11 Multiply_3775" -> "12 Transpose_1259" [label="[1, 64, 104, 104]", style=solid]; +"12 Transpose_1259" -> "13 Transpose_1265" [label="[1, 64, 104, 104]", style=solid]; +"13 Transpose_1265" -> "14 Transpose_1265/fq_output_0" [label="[1, 64, 104, 104]", style=solid]; +"14 Transpose_1265/fq_output_0" -> "15 Transpose_1368" [label="[1, 64, 104, 104]", style=solid]; +"14 Transpose_1265/fq_output_0" -> "16 group_route_3/split" [label="[1, 64, 104, 104]", style=solid]; +"15 Transpose_1368" -> "17 MaxPool_307" [label="[1, 128, 104, 104]", style=solid]; +"16 group_route_3/split" -> "18 Multiply_3789" [label="[1, 32, 104, 104]", style=solid]; +"17 MaxPool_307" -> "19 Multiply_3831" [label="[1, 128, 52, 52]", style=solid]; +"18 Multiply_3789" -> "20 Transpose_1294" [label="[1, 32, 104, 104]", style=solid]; +"19 Multiply_3831" -> "21 Transpose_1392" [label="[1, 128, 52, 52]", style=solid]; +"20 Transpose_1294" -> "22 Transpose_1300" [label="[1, 32, 104, 104]", style=solid]; +"21 Transpose_1392" -> "23 Transpose_1398" [label="[1, 128, 52, 52]", style=solid]; +"22 Transpose_1300" -> "24 Transpose_1300/fq_output_0" [label="[1, 32, 104, 104]", style=solid]; +"23 Transpose_1398" -> "25 Transpose_1398/fq_output_0" [label="[1, 128, 52, 52]", style=solid]; +"24 Transpose_1300/fq_output_0" -> "26 Multiply_3803" [label="[1, 32, 104, 104]", style=solid]; +"24 Transpose_1300/fq_output_0" -> "27 Transpose_1334" [label="[1, 32, 104, 104]", style=solid]; +"25 Transpose_1398/fq_output_0" -> "28 Transpose_1501" [label="[1, 128, 52, 52]", style=solid]; +"25 Transpose_1398/fq_output_0" -> "29 group_route_11/split" [label="[1, 128, 52, 52]", style=solid]; +"26 Multiply_3803" -> "30 Transpose_1324" [label="[1, 32, 104, 104]", style=solid]; +"27 Transpose_1334" -> "31 Multiply_3817" [label="[1, 64, 104, 104]", style=solid]; +"28 Transpose_1501" -> "32 MaxPool_433" [label="[1, 256, 52, 52]", style=solid]; +"29 group_route_11/split" -> "33 Multiply_3845" [label="[1, 64, 52, 52]", style=solid]; +"30 Transpose_1324" -> "34 Transpose_1330" [label="[1, 32, 104, 104]", style=solid]; +"31 Multiply_3817" -> "35 Transpose_1358" [label="[1, 64, 104, 104]", style=solid]; +"32 MaxPool_433" -> "36 Multiply_3887" [label="[1, 256, 26, 26]", style=solid]; +"33 Multiply_3845" -> "37 Transpose_1427" [label="[1, 64, 52, 52]", style=solid]; +"34 Transpose_1330" -> "38 Transpose_1330/fq_output_0" [label="[1, 32, 104, 104]", style=solid]; +"35 Transpose_1358" -> "39 Transpose_1364" [label="[1, 64, 104, 104]", style=solid]; +"36 Multiply_3887" -> "40 Transpose_1525" [label="[1, 256, 26, 26]", style=solid]; +"37 Transpose_1427" -> "41 Transpose_1433" [label="[1, 64, 52, 52]", style=solid]; +"38 Transpose_1330/fq_output_0" -> "27 Transpose_1334" [label="[1, 32, 104, 104]", style=solid]; +"39 Transpose_1364" -> "42 Transpose_1364/fq_output_0" [label="[1, 64, 104, 104]", style=solid]; +"40 Transpose_1525" -> "43 Transpose_1531" [label="[1, 256, 26, 26]", style=solid]; +"41 Transpose_1433" -> "44 Transpose_1433/fq_output_0" [label="[1, 64, 52, 52]", style=solid]; +"42 Transpose_1364/fq_output_0" -> "15 Transpose_1368" [label="[1, 64, 104, 104]", style=solid]; +"43 Transpose_1531" -> "45 Transpose_1531/fq_output_0" [label="[1, 256, 26, 26]", style=solid]; +"44 Transpose_1433/fq_output_0" -> "46 Multiply_3859" [label="[1, 64, 52, 52]", style=solid]; +"44 Transpose_1433/fq_output_0" -> "47 Transpose_1467" [label="[1, 64, 52, 52]", style=solid]; +"45 Transpose_1531/fq_output_0" -> "48 Transpose_1634" [label="[1, 256, 26, 26]", style=solid]; +"45 Transpose_1531/fq_output_0" -> "49 group_route_19/split" [label="[1, 256, 26, 26]", style=solid]; +"46 Multiply_3859" -> "50 Transpose_1457" [label="[1, 64, 52, 52]", style=solid]; +"47 Transpose_1467" -> "51 Multiply_3873" [label="[1, 128, 52, 52]", style=solid]; +"48 Transpose_1634" -> "52 MaxPool_579" [label="[1, 512, 26, 26]", style=solid]; +"49 group_route_19/split" -> "53 Multiply_3901" [label="[1, 128, 26, 26]", style=solid]; +"50 Transpose_1457" -> "54 Transpose_1463" [label="[1, 64, 52, 52]", style=solid]; +"51 Multiply_3873" -> "55 Transpose_1491" [label="[1, 128, 52, 52]", style=solid]; +"52 MaxPool_579" -> "56 Multiply_3943" [label="[1, 512, 13, 13]", style=solid]; +"53 Multiply_3901" -> "57 Transpose_1560" [label="[1, 128, 26, 26]", style=solid]; +"54 Transpose_1463" -> "58 Transpose_1463/fq_output_0" [label="[1, 64, 52, 52]", style=solid]; +"55 Transpose_1491" -> "59 Transpose_1497" [label="[1, 128, 52, 52]", style=solid]; +"56 Multiply_3943" -> "60 Transpose_1658" [label="[1, 512, 13, 13]", style=solid]; +"57 Transpose_1560" -> "61 Transpose_1566" [label="[1, 128, 26, 26]", style=solid]; +"58 Transpose_1463/fq_output_0" -> "47 Transpose_1467" [label="[1, 64, 52, 52]", style=solid]; +"59 Transpose_1497" -> "62 Transpose_1497/fq_output_0" [label="[1, 128, 52, 52]", style=solid]; +"60 Transpose_1658" -> "63 Transpose_1664" [label="[1, 512, 13, 13]", style=solid]; +"61 Transpose_1566" -> "64 Transpose_1566/fq_output_0" [label="[1, 128, 26, 26]", style=solid]; +"62 Transpose_1497/fq_output_0" -> "28 Transpose_1501" [label="[1, 128, 52, 52]", style=solid]; +"63 Transpose_1664" -> "65 Transpose_1664/fq_output_0" [label="[1, 512, 13, 13]", style=solid]; +"64 Transpose_1566/fq_output_0" -> "66 Multiply_3915" [label="[1, 128, 26, 26]", style=solid]; +"64 Transpose_1566/fq_output_0" -> "67 Transpose_1600" [label="[1, 128, 26, 26]", style=solid]; +"65 Transpose_1664/fq_output_0" -> "68 Multiply_3957" [label="[1, 512, 13, 13]", style=solid]; +"66 Multiply_3915" -> "69 Transpose_1590" [label="[1, 128, 26, 26]", style=solid]; +"67 Transpose_1600" -> "70 Multiply_3929" [label="[1, 256, 26, 26]", style=solid]; +"68 Multiply_3957" -> "71 Transpose_1688" [label="[1, 256, 13, 13]", style=solid]; +"69 Transpose_1590" -> "72 Transpose_1596" [label="[1, 128, 26, 26]", style=solid]; +"70 Multiply_3929" -> "73 Transpose_1624" [label="[1, 256, 26, 26]", style=solid]; +"71 Transpose_1688" -> "74 Transpose_1694" [label="[1, 256, 13, 13]", style=solid]; +"72 Transpose_1596" -> "75 Transpose_1596/fq_output_0" [label="[1, 128, 26, 26]", style=solid]; +"73 Transpose_1624" -> "76 Transpose_1630" [label="[1, 256, 26, 26]", style=solid]; +"74 Transpose_1694" -> "77 Transpose_1694/fq_output_0" [label="[1, 256, 13, 13]", style=solid]; +"75 Transpose_1596/fq_output_0" -> "67 Transpose_1600" [label="[1, 128, 26, 26]", style=solid]; +"76 Transpose_1630" -> "78 Transpose_1630/fq_output_0" [label="[1, 256, 26, 26]", style=solid]; +"77 Transpose_1694/fq_output_0" -> "79 Multiply_3971" [label="[1, 256, 13, 13]", style=solid]; +"77 Transpose_1694/fq_output_0" -> "80 Multiply_3999" [label="[1, 256, 13, 13]", style=solid]; +"78 Transpose_1630/fq_output_0" -> "48 Transpose_1634" [label="[1, 256, 26, 26]", style=solid]; +"78 Transpose_1630/fq_output_0" -> "81 Transpose_1730" [label="[1, 256, 26, 26]", style=solid]; +"79 Multiply_3971" -> "82 Transpose_1718" [label="[1, 128, 13, 13]", style=solid]; +"80 Multiply_3999" -> "83 Transpose_1790" [label="[1, 512, 13, 13]", style=solid]; +"81 Transpose_1730" -> "84 Multiply_3985" [label="[1, 384, 26, 26]", style=solid]; +"82 Transpose_1718" -> "85 Transpose_1724" [label="[1, 128, 13, 13]", style=solid]; +"83 Transpose_1790" -> "86 Transpose_1796" [label="[1, 512, 13, 13]", style=solid]; +"84 Multiply_3985" -> "87 Transpose_1754" [label="[1, 256, 26, 26]", style=solid]; +"85 Transpose_1724" -> "88 Transpose_1724/fq_output_0" [label="[1, 128, 13, 13]", style=solid]; +"86 Transpose_1796" -> "89 Transpose_1796/fq_output_0" [label="[1, 512, 13, 13]", style=solid]; +"87 Transpose_1754" -> "90 Transpose_1760" [label="[1, 256, 26, 26]", style=solid]; +"88 Transpose_1724/fq_output_0" -> "91 Gather_1726" [label="[1, 128, 13, 13]", style=solid]; +"88 Transpose_1724/fq_output_0" -> "92 leaky_re_lu_17/LeakyRelu" [label="[1, 128, 13, 13]", style=solid]; +"89 Transpose_1796/fq_output_0" -> "93 Convolution_749" [label="[1, 512, 13, 13]", style=solid]; +"90 Transpose_1760" -> "94 Transpose_1760/fq_output_0" [label="[1, 256, 26, 26]", style=solid]; +"91 Gather_1726" -> "95 up_sampling2d/Shape" [label="[4]", style=dashed]; +"92 leaky_re_lu_17/LeakyRelu" -> "96 up_sampling2d/resize/ResizeNearestNeighbor" [label="[1, 13, 13, 128]", style=solid]; +"93 Convolution_749" -> "97 Transpose_1802" [label="[1, 255, 13, 13]", style=solid]; +"94 Transpose_1760/fq_output_0" -> "98 Convolution_706" [label="[1, 256, 26, 26]", style=solid]; +"95 up_sampling2d/Shape" -> "99 up_sampling2d/strided_slice" [label="[4]", style=dashed]; +"96 up_sampling2d/resize/ResizeNearestNeighbor" -> "100 up_sampling2d/resize/ResizeNearestNeighbor/fq_output_0" [label="[1, 26, 26, 128]", style=solid]; +"97 Transpose_1802" -> "101 conv2d_17/BiasAdd" [label="[1, 255, 13, 13]", style=solid]; +"98 Convolution_706" -> "102 Transpose_1766" [label="[1, 255, 26, 26]", style=solid]; +"99 up_sampling2d/strided_slice" -> "103 up_sampling2d/mul" [label="[2]", style=dashed]; +"100 up_sampling2d/resize/ResizeNearestNeighbor/fq_output_0" -> "104 Transpose_1728" [label="[1, 26, 26, 128]", style=solid]; +"101 conv2d_17/BiasAdd" -> "105 conv2d_17/BiasAdd^0" [label="[1, 13, 13, 255]", style=solid]; +"102 Transpose_1766" -> "106 conv2d_20/BiasAdd" [label="[1, 255, 26, 26]", style=solid]; +"103 up_sampling2d/mul" -> "96 up_sampling2d/resize/ResizeNearestNeighbor" [label="[2]", style=dashed]; +"104 Transpose_1728" -> "81 Transpose_1730" [label="[1, 128, 26, 26]", style=solid]; +"106 conv2d_20/BiasAdd" -> "107 conv2d_20/BiasAdd^0" [label="[1, 26, 26, 255]", style=solid]; +"108 Constant_1765" -> "106 conv2d_20/BiasAdd" [label="[4]", style=dashed]; +"109 Transpose_1764" -> "102 Transpose_1766" [label="[1, 255, 1, 1]", style=solid]; +"110 Convolution_706/fq_weights_1" -> "98 Convolution_706" [label="[255, 256, 1, 1]", style=solid]; +"111 Constant_5545" -> "110 Convolution_706/fq_weights_1" [label="[255, 1, 1, 1]", style=solid]; +"112 Constant_5544" -> "110 Convolution_706/fq_weights_1" [label="[255, 1, 1, 1]", style=solid]; +"113 Constant_5543" -> "110 Convolution_706/fq_weights_1" [label="[255, 1, 1, 1]", style=solid]; +"114 Constant_5542" -> "110 Convolution_706/fq_weights_1" [label="[255, 1, 1, 1]", style=solid]; +"115 Transpose_705" -> "110 Convolution_706/fq_weights_1" [label="[255, 256, 1, 1]", style=solid]; +"116 Constant_5540" -> "94 Transpose_1760/fq_output_0" [label="[]", style=solid]; +"117 Constant_5539" -> "94 Transpose_1760/fq_output_0" [label="[]", style=solid]; +"118 Constant_5538" -> "94 Transpose_1760/fq_output_0" [label="[]", style=solid]; +"119 Constant_5537" -> "94 Transpose_1760/fq_output_0" [label="[]", style=solid]; +"120 Transpose_1758" -> "90 Transpose_1760" [label="[1, 1, 1, 1]", style=solid]; +"121 Constant_3993" -> "87 Transpose_1754" [label="[1, 256, 1, 1]", style=solid]; +"122 Multiply_3985/fq_weights_1" -> "84 Multiply_3985" [label="[256, 384, 3, 3]", style=solid]; +"123 Constant_5535" -> "122 Multiply_3985/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"124 Constant_5534" -> "122 Multiply_3985/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"125 Constant_5533" -> "122 Multiply_3985/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"126 Constant_5532" -> "122 Multiply_3985/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"127 Multiply_4118" -> "122 Multiply_3985/fq_weights_1" [label="[256, 384, 3, 3]", style=solid]; +"128 Constant_5405" -> "78 Transpose_1630/fq_output_0" [label="[]", style=solid]; +"129 Constant_5404" -> "78 Transpose_1630/fq_output_0" [label="[]", style=solid]; +"130 Constant_5403" -> "78 Transpose_1630/fq_output_0" [label="[]", style=solid]; +"131 Constant_5402" -> "78 Transpose_1630/fq_output_0" [label="[]", style=solid]; +"132 Transpose_1628" -> "76 Transpose_1630" [label="[1, 1, 1, 1]", style=solid]; +"133 Constant_3937" -> "73 Transpose_1624" [label="[1, 256, 1, 1]", style=solid]; +"134 Multiply_3929/fq_weights_1" -> "70 Multiply_3929" [label="[256, 256, 1, 1]", style=solid]; +"135 Constant_5500" -> "134 Multiply_3929/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"136 Constant_5499" -> "134 Multiply_3929/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"137 Constant_5498" -> "134 Multiply_3929/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"138 Constant_5497" -> "134 Multiply_3929/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"139 Multiply_4094" -> "134 Multiply_3929/fq_weights_1" [label="[256, 256, 1, 1]", style=solid]; +"140 Constant_5375" -> "64 Transpose_1566/fq_output_0" [label="[]", style=solid]; +"141 Constant_5374" -> "64 Transpose_1566/fq_output_0" [label="[]", style=solid]; +"142 Constant_5373" -> "64 Transpose_1566/fq_output_0" [label="[]", style=solid]; +"143 Constant_5372" -> "64 Transpose_1566/fq_output_0" [label="[]", style=solid]; +"144 Transpose_1564" -> "61 Transpose_1566" [label="[1, 1, 1, 1]", style=solid]; +"145 Constant_3909" -> "57 Transpose_1560" [label="[1, 128, 1, 1]", style=solid]; +"146 Multiply_3901/fq_weights_1" -> "53 Multiply_3901" [label="[128, 128, 3, 3]", style=solid]; +"147 Constant_5490" -> "146 Multiply_3901/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"148 Constant_5489" -> "146 Multiply_3901/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"149 Constant_5488" -> "146 Multiply_3901/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"150 Constant_5487" -> "146 Multiply_3901/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"151 Multiply_4082" -> "146 Multiply_3901/fq_weights_1" [label="[128, 128, 3, 3]", style=solid]; +"152 Constant_1532" -> "49 group_route_19/split" [label="[]", style=dashed]; +"153 Constant_5410" -> "45 Transpose_1531/fq_output_0" [label="[]", style=solid]; +"154 Constant_5409" -> "45 Transpose_1531/fq_output_0" [label="[]", style=solid]; +"155 Constant_5408" -> "45 Transpose_1531/fq_output_0" [label="[]", style=solid]; +"156 Constant_5407" -> "45 Transpose_1531/fq_output_0" [label="[]", style=solid]; +"157 Transpose_1529" -> "43 Transpose_1531" [label="[1, 1, 1, 1]", style=solid]; +"158 Constant_3895" -> "40 Transpose_1525" [label="[1, 256, 1, 1]", style=solid]; +"159 Multiply_3887/fq_weights_1" -> "36 Multiply_3887" [label="[256, 256, 3, 3]", style=solid]; +"160 Constant_5485" -> "159 Multiply_3887/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"161 Constant_5484" -> "159 Multiply_3887/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"162 Constant_5483" -> "159 Multiply_3887/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"163 Constant_5482" -> "159 Multiply_3887/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"164 Multiply_4076" -> "159 Multiply_3887/fq_weights_1" [label="[256, 256, 3, 3]", style=solid]; +"165 Constant_5400" -> "62 Transpose_1497/fq_output_0" [label="[]", style=solid]; +"166 Constant_5399" -> "62 Transpose_1497/fq_output_0" [label="[]", style=solid]; +"167 Constant_5398" -> "62 Transpose_1497/fq_output_0" [label="[]", style=solid]; +"168 Constant_5397" -> "62 Transpose_1497/fq_output_0" [label="[]", style=solid]; +"169 Transpose_1495" -> "59 Transpose_1497" [label="[1, 1, 1, 1]", style=solid]; +"170 Constant_3881" -> "55 Transpose_1491" [label="[1, 128, 1, 1]", style=solid]; +"171 Multiply_3873/fq_weights_1" -> "51 Multiply_3873" [label="[128, 128, 1, 1]", style=solid]; +"172 Constant_5480" -> "171 Multiply_3873/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"173 Constant_5479" -> "171 Multiply_3873/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"174 Constant_5478" -> "171 Multiply_3873/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"175 Constant_5477" -> "171 Multiply_3873/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"176 Multiply_4070" -> "171 Multiply_3873/fq_weights_1" [label="[128, 128, 1, 1]", style=solid]; +"177 Constant_5370" -> "44 Transpose_1433/fq_output_0" [label="[]", style=solid]; +"178 Constant_5369" -> "44 Transpose_1433/fq_output_0" [label="[]", style=solid]; +"179 Constant_5368" -> "44 Transpose_1433/fq_output_0" [label="[]", style=solid]; +"180 Constant_5367" -> "44 Transpose_1433/fq_output_0" [label="[]", style=solid]; +"181 Transpose_1431" -> "41 Transpose_1433" [label="[1, 1, 1, 1]", style=solid]; +"182 Constant_3853" -> "37 Transpose_1427" [label="[1, 64, 1, 1]", style=solid]; +"183 Multiply_3845/fq_weights_1" -> "33 Multiply_3845" [label="[64, 64, 3, 3]", style=solid]; +"184 Constant_5470" -> "183 Multiply_3845/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"185 Constant_5469" -> "183 Multiply_3845/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"186 Constant_5468" -> "183 Multiply_3845/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"187 Constant_5467" -> "183 Multiply_3845/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"188 Multiply_4058" -> "183 Multiply_3845/fq_weights_1" [label="[64, 64, 3, 3]", style=solid]; +"189 Constant_1399" -> "29 group_route_11/split" [label="[]", style=dashed]; +"190 Constant_5395" -> "25 Transpose_1398/fq_output_0" [label="[]", style=solid]; +"191 Constant_5394" -> "25 Transpose_1398/fq_output_0" [label="[]", style=solid]; +"192 Constant_5393" -> "25 Transpose_1398/fq_output_0" [label="[]", style=solid]; +"193 Constant_5392" -> "25 Transpose_1398/fq_output_0" [label="[]", style=solid]; +"194 Transpose_1396" -> "23 Transpose_1398" [label="[1, 1, 1, 1]", style=solid]; +"195 Constant_3839" -> "21 Transpose_1392" [label="[1, 128, 1, 1]", style=solid]; +"196 Multiply_3831/fq_weights_1" -> "19 Multiply_3831" [label="[128, 128, 3, 3]", style=solid]; +"197 Constant_5465" -> "196 Multiply_3831/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"198 Constant_5464" -> "196 Multiply_3831/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"199 Constant_5463" -> "196 Multiply_3831/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"200 Constant_5462" -> "196 Multiply_3831/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"201 Multiply_4052" -> "196 Multiply_3831/fq_weights_1" [label="[128, 128, 3, 3]", style=solid]; +"202 Constant_5390" -> "42 Transpose_1364/fq_output_0" [label="[]", style=solid]; +"203 Constant_5389" -> "42 Transpose_1364/fq_output_0" [label="[]", style=solid]; +"204 Constant_5388" -> "42 Transpose_1364/fq_output_0" [label="[]", style=solid]; +"205 Constant_5387" -> "42 Transpose_1364/fq_output_0" [label="[]", style=solid]; +"206 Transpose_1362" -> "39 Transpose_1364" [label="[1, 1, 1, 1]", style=solid]; +"207 Constant_3825" -> "35 Transpose_1358" [label="[1, 64, 1, 1]", style=solid]; +"208 Multiply_3817/fq_weights_1" -> "31 Multiply_3817" [label="[64, 64, 1, 1]", style=solid]; +"209 Constant_5460" -> "208 Multiply_3817/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"210 Constant_5459" -> "208 Multiply_3817/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"211 Constant_5458" -> "208 Multiply_3817/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"212 Constant_5457" -> "208 Multiply_3817/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"213 Multiply_4046" -> "208 Multiply_3817/fq_weights_1" [label="[64, 64, 1, 1]", style=solid]; +"214 Constant_5355" -> "24 Transpose_1300/fq_output_0" [label="[]", style=solid]; +"215 Constant_5354" -> "24 Transpose_1300/fq_output_0" [label="[]", style=solid]; +"216 Constant_5353" -> "24 Transpose_1300/fq_output_0" [label="[]", style=solid]; +"217 Constant_5352" -> "24 Transpose_1300/fq_output_0" [label="[]", style=solid]; +"218 Transpose_1298" -> "22 Transpose_1300" [label="[1, 1, 1, 1]", style=solid]; +"219 Constant_3797" -> "20 Transpose_1294" [label="[1, 32, 1, 1]", style=solid]; +"220 Multiply_3789/fq_weights_1" -> "18 Multiply_3789" [label="[32, 32, 3, 3]", style=solid]; +"221 Constant_5450" -> "220 Multiply_3789/fq_weights_1" [label="[32, 1, 1, 1]", style=solid]; +"222 Constant_5449" -> "220 Multiply_3789/fq_weights_1" [label="[32, 1, 1, 1]", style=solid]; +"223 Constant_5448" -> "220 Multiply_3789/fq_weights_1" [label="[32, 1, 1, 1]", style=solid]; +"224 Constant_5447" -> "220 Multiply_3789/fq_weights_1" [label="[32, 1, 1, 1]", style=solid]; +"225 Multiply_4034" -> "220 Multiply_3789/fq_weights_1" [label="[32, 32, 3, 3]", style=solid]; +"226 Constant_1266" -> "16 group_route_3/split" [label="[]", style=dashed]; +"227 Constant_5385" -> "14 Transpose_1265/fq_output_0" [label="[]", style=solid]; +"228 Constant_5384" -> "14 Transpose_1265/fq_output_0" [label="[]", style=solid]; +"229 Constant_5383" -> "14 Transpose_1265/fq_output_0" [label="[]", style=solid]; +"230 Constant_5382" -> "14 Transpose_1265/fq_output_0" [label="[]", style=solid]; +"231 Transpose_1263" -> "13 Transpose_1265" [label="[1, 1, 1, 1]", style=solid]; +"232 Constant_3783" -> "12 Transpose_1259" [label="[1, 64, 1, 1]", style=solid]; +"233 Multiply_3775/fq_weights_1" -> "11 Multiply_3775" [label="[64, 64, 3, 3]", style=solid]; +"234 Constant_5445" -> "233 Multiply_3775/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"235 Constant_5444" -> "233 Multiply_3775/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"236 Constant_5443" -> "233 Multiply_3775/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"237 Constant_5442" -> "233 Multiply_3775/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"238 Multiply_4028" -> "233 Multiply_3775/fq_weights_1" [label="[64, 64, 3, 3]", style=solid]; +"239 Constant_5440" -> "10 Transpose_1235/fq_output_0" [label="[]", style=solid]; +"240 Constant_5439" -> "10 Transpose_1235/fq_output_0" [label="[]", style=solid]; +"241 Constant_5438" -> "10 Transpose_1235/fq_output_0" [label="[]", style=solid]; +"242 Constant_5437" -> "10 Transpose_1235/fq_output_0" [label="[]", style=solid]; +"243 Transpose_1233" -> "9 Transpose_1235" [label="[1, 1, 1, 1]", style=solid]; +"244 Constant_3769" -> "8 Transpose_1229" [label="[1, 64, 1, 1]", style=solid]; +"245 Multiply_3761/fq_weights_1" -> "7 Multiply_3761" [label="[64, 32, 3, 3]", style=solid]; +"246 Constant_5435" -> "245 Multiply_3761/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"247 Constant_5434" -> "245 Multiply_3761/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"248 Constant_5433" -> "245 Multiply_3761/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"249 Constant_5432" -> "245 Multiply_3761/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"250 Multiply_4022" -> "245 Multiply_3761/fq_weights_1" [label="[64, 32, 3, 3]", style=solid]; +"251 Constant_5430" -> "6 Transpose_1178/fq_output_0" [label="[]", style=solid]; +"252 Constant_5429" -> "6 Transpose_1178/fq_output_0" [label="[]", style=solid]; +"253 Constant_5428" -> "6 Transpose_1178/fq_output_0" [label="[]", style=solid]; +"254 Constant_5427" -> "6 Transpose_1178/fq_output_0" [label="[]", style=solid]; +"255 Transpose_1176" -> "5 Transpose_1178" [label="[1, 1, 1, 1]", style=solid]; +"256 Constant_3755" -> "4 Transpose_1172" [label="[1, 32, 1, 1]", style=solid]; +"257 Multiply_3747/fq_weights_1" -> "3 Multiply_3747" [label="[32, 3, 3, 3]", style=solid]; +"258 Constant_5425" -> "257 Multiply_3747/fq_weights_1" [label="[32, 1, 1, 1]", style=solid]; +"259 Constant_5424" -> "257 Multiply_3747/fq_weights_1" [label="[32, 1, 1, 1]", style=solid]; +"260 Constant_5423" -> "257 Multiply_3747/fq_weights_1" [label="[32, 1, 1, 1]", style=solid]; +"261 Constant_5422" -> "257 Multiply_3747/fq_weights_1" [label="[32, 1, 1, 1]", style=solid]; +"262 Gather_4293" -> "257 Multiply_3747/fq_weights_1" [label="[32, 3, 3, 3]", style=solid]; +"263 Constant_2333" -> "2 Divide_2378" [label="[4]", style=dashed]; +"264 Constant_5420" -> "1 image_input/fq_output_0" [label="[]", style=solid]; +"265 Constant_5419" -> "1 image_input/fq_output_0" [label="[]", style=solid]; +"266 Constant_5418" -> "1 image_input/fq_output_0" [label="[]", style=solid]; +"267 Constant_5417" -> "1 image_input/fq_output_0" [label="[]", style=solid]; +"268 Constant_5360" -> "38 Transpose_1330/fq_output_0" [label="[]", style=solid]; +"269 Constant_5359" -> "38 Transpose_1330/fq_output_0" [label="[]", style=solid]; +"270 Constant_5358" -> "38 Transpose_1330/fq_output_0" [label="[]", style=solid]; +"271 Constant_5357" -> "38 Transpose_1330/fq_output_0" [label="[]", style=solid]; +"272 Transpose_1328" -> "34 Transpose_1330" [label="[1, 1, 1, 1]", style=solid]; +"273 Constant_3811" -> "30 Transpose_1324" [label="[1, 32, 1, 1]", style=solid]; +"274 Multiply_3803/fq_weights_1" -> "26 Multiply_3803" [label="[32, 32, 3, 3]", style=solid]; +"275 Constant_5455" -> "274 Multiply_3803/fq_weights_1" [label="[32, 1, 1, 1]", style=solid]; +"276 Constant_5454" -> "274 Multiply_3803/fq_weights_1" [label="[32, 1, 1, 1]", style=solid]; +"277 Constant_5453" -> "274 Multiply_3803/fq_weights_1" [label="[32, 1, 1, 1]", style=solid]; +"278 Constant_5452" -> "274 Multiply_3803/fq_weights_1" [label="[32, 1, 1, 1]", style=solid]; +"279 Multiply_4040" -> "274 Multiply_3803/fq_weights_1" [label="[32, 32, 3, 3]", style=solid]; +"280 Constant_5365" -> "58 Transpose_1463/fq_output_0" [label="[]", style=solid]; +"281 Constant_5364" -> "58 Transpose_1463/fq_output_0" [label="[]", style=solid]; +"282 Constant_5363" -> "58 Transpose_1463/fq_output_0" [label="[]", style=solid]; +"283 Constant_5362" -> "58 Transpose_1463/fq_output_0" [label="[]", style=solid]; +"284 Transpose_1461" -> "54 Transpose_1463" [label="[1, 1, 1, 1]", style=solid]; +"285 Constant_3867" -> "50 Transpose_1457" [label="[1, 64, 1, 1]", style=solid]; +"286 Multiply_3859/fq_weights_1" -> "46 Multiply_3859" [label="[64, 64, 3, 3]", style=solid]; +"287 Constant_5475" -> "286 Multiply_3859/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"288 Constant_5474" -> "286 Multiply_3859/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"289 Constant_5473" -> "286 Multiply_3859/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"290 Constant_5472" -> "286 Multiply_3859/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"291 Multiply_4064" -> "286 Multiply_3859/fq_weights_1" [label="[64, 64, 3, 3]", style=solid]; +"292 Constant_5380" -> "75 Transpose_1596/fq_output_0" [label="[]", style=solid]; +"293 Constant_5379" -> "75 Transpose_1596/fq_output_0" [label="[]", style=solid]; +"294 Constant_5378" -> "75 Transpose_1596/fq_output_0" [label="[]", style=solid]; +"295 Constant_5377" -> "75 Transpose_1596/fq_output_0" [label="[]", style=solid]; +"296 Transpose_1594" -> "72 Transpose_1596" [label="[1, 1, 1, 1]", style=solid]; +"297 Constant_3923" -> "69 Transpose_1590" [label="[1, 128, 1, 1]", style=solid]; +"298 Multiply_3915/fq_weights_1" -> "66 Multiply_3915" [label="[128, 128, 3, 3]", style=solid]; +"299 Constant_5495" -> "298 Multiply_3915/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"300 Constant_5494" -> "298 Multiply_3915/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"301 Constant_5493" -> "298 Multiply_3915/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"302 Constant_5492" -> "298 Multiply_3915/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"303 Multiply_4088" -> "298 Multiply_3915/fq_weights_1" [label="[128, 128, 3, 3]", style=solid]; +"304 Constant_1727" -> "104 Transpose_1728" [label="[4]", style=dashed]; +"305 Constant_5415" -> "100 up_sampling2d/resize/ResizeNearestNeighbor/fq_output_0" [label="[]", style=solid]; +"306 Constant_5414" -> "100 up_sampling2d/resize/ResizeNearestNeighbor/fq_output_0" [label="[]", style=solid]; +"307 Constant_5413" -> "100 up_sampling2d/resize/ResizeNearestNeighbor/fq_output_0" [label="[]", style=solid]; +"308 Constant_5412" -> "100 up_sampling2d/resize/ResizeNearestNeighbor/fq_output_0" [label="[]", style=solid]; +"309 Constant_669" -> "96 up_sampling2d/resize/ResizeNearestNeighbor" [label="[2]", style=dashed]; +"310 up_sampling2d/Const" -> "103 up_sampling2d/mul" [label="[2]", style=dashed]; +"311 up_sampling2d/strided_slice/stack_2" -> "99 up_sampling2d/strided_slice" [label="[1]", style=dashed]; +"312 up_sampling2d/strided_slice/stack_1" -> "99 up_sampling2d/strided_slice" [label="[1]", style=dashed]; +"313 up_sampling2d/strided_slice/stack" -> "99 up_sampling2d/strided_slice" [label="[1]", style=dashed]; +"314 Constant_1725" -> "95 up_sampling2d/Shape" [label="[]", style=dashed]; +"315 Constant_1723" -> "92 leaky_re_lu_17/LeakyRelu" [label="[4]", style=dashed]; +"315 Constant_1723" -> "95 up_sampling2d/Shape" [label="[4]", style=dashed]; +"316 Constant_5530" -> "88 Transpose_1724/fq_output_0" [label="[]", style=solid]; +"317 Constant_5529" -> "88 Transpose_1724/fq_output_0" [label="[]", style=solid]; +"318 Constant_5528" -> "88 Transpose_1724/fq_output_0" [label="[]", style=solid]; +"319 Constant_5527" -> "88 Transpose_1724/fq_output_0" [label="[]", style=solid]; +"320 Transpose_1722" -> "85 Transpose_1724" [label="[1, 1, 1, 1]", style=solid]; +"321 Constant_3979" -> "82 Transpose_1718" [label="[1, 128, 1, 1]", style=solid]; +"322 Multiply_3971/fq_weights_1" -> "79 Multiply_3971" [label="[128, 256, 1, 1]", style=solid]; +"323 Constant_5525" -> "322 Multiply_3971/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"324 Constant_5524" -> "322 Multiply_3971/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"325 Constant_5523" -> "322 Multiply_3971/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"326 Constant_5522" -> "322 Multiply_3971/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"327 Multiply_4112" -> "322 Multiply_3971/fq_weights_1" [label="[128, 256, 1, 1]", style=solid]; +"328 Constant_5520" -> "77 Transpose_1694/fq_output_0" [label="[]", style=solid]; +"329 Constant_5519" -> "77 Transpose_1694/fq_output_0" [label="[]", style=solid]; +"330 Constant_5518" -> "77 Transpose_1694/fq_output_0" [label="[]", style=solid]; +"331 Constant_5517" -> "77 Transpose_1694/fq_output_0" [label="[]", style=solid]; +"332 Transpose_1692" -> "74 Transpose_1694" [label="[1, 1, 1, 1]", style=solid]; +"333 Constant_3965" -> "71 Transpose_1688" [label="[1, 256, 1, 1]", style=solid]; +"334 Multiply_3957/fq_weights_1" -> "68 Multiply_3957" [label="[256, 512, 1, 1]", style=solid]; +"335 Constant_5515" -> "334 Multiply_3957/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"336 Constant_5514" -> "334 Multiply_3957/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"337 Constant_5513" -> "334 Multiply_3957/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"338 Constant_5512" -> "334 Multiply_3957/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"339 Multiply_4106" -> "334 Multiply_3957/fq_weights_1" [label="[256, 512, 1, 1]", style=solid]; +"340 Constant_5510" -> "65 Transpose_1664/fq_output_0" [label="[]", style=solid]; +"341 Constant_5509" -> "65 Transpose_1664/fq_output_0" [label="[]", style=solid]; +"342 Constant_5508" -> "65 Transpose_1664/fq_output_0" [label="[]", style=solid]; +"343 Constant_5507" -> "65 Transpose_1664/fq_output_0" [label="[]", style=solid]; +"344 Transpose_1662" -> "63 Transpose_1664" [label="[1, 1, 1, 1]", style=solid]; +"345 Constant_3951" -> "60 Transpose_1658" [label="[1, 512, 1, 1]", style=solid]; +"346 Multiply_3943/fq_weights_1" -> "56 Multiply_3943" [label="[512, 512, 3, 3]", style=solid]; +"347 Constant_5505" -> "346 Multiply_3943/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"348 Constant_5504" -> "346 Multiply_3943/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"349 Constant_5503" -> "346 Multiply_3943/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"350 Constant_5502" -> "346 Multiply_3943/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"351 Multiply_4100" -> "346 Multiply_3943/fq_weights_1" [label="[512, 512, 3, 3]", style=solid]; +"352 Constant_1801" -> "101 conv2d_17/BiasAdd" [label="[4]", style=dashed]; +"353 Transpose_1800" -> "97 Transpose_1802" [label="[1, 255, 1, 1]", style=solid]; +"354 Convolution_749/fq_weights_1" -> "93 Convolution_749" [label="[255, 512, 1, 1]", style=solid]; +"355 Constant_5560" -> "354 Convolution_749/fq_weights_1" [label="[255, 1, 1, 1]", style=solid]; +"356 Constant_5559" -> "354 Convolution_749/fq_weights_1" [label="[255, 1, 1, 1]", style=solid]; +"357 Constant_5558" -> "354 Convolution_749/fq_weights_1" [label="[255, 1, 1, 1]", style=solid]; +"358 Constant_5557" -> "354 Convolution_749/fq_weights_1" [label="[255, 1, 1, 1]", style=solid]; +"359 Transpose_748" -> "354 Convolution_749/fq_weights_1" [label="[255, 512, 1, 1]", style=solid]; +"360 Constant_5555" -> "89 Transpose_1796/fq_output_0" [label="[]", style=solid]; +"361 Constant_5554" -> "89 Transpose_1796/fq_output_0" [label="[]", style=solid]; +"362 Constant_5553" -> "89 Transpose_1796/fq_output_0" [label="[]", style=solid]; +"363 Constant_5552" -> "89 Transpose_1796/fq_output_0" [label="[]", style=solid]; +"364 Transpose_1794" -> "86 Transpose_1796" [label="[1, 1, 1, 1]", style=solid]; +"365 Constant_4007" -> "83 Transpose_1790" [label="[1, 512, 1, 1]", style=solid]; +"366 Multiply_3999/fq_weights_1" -> "80 Multiply_3999" [label="[512, 256, 3, 3]", style=solid]; +"367 Constant_5550" -> "366 Multiply_3999/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"368 Constant_5549" -> "366 Multiply_3999/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"369 Constant_5548" -> "366 Multiply_3999/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"370 Constant_5547" -> "366 Multiply_3999/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"371 Multiply_4124" -> "366 Multiply_3999/fq_weights_1" [label="[512, 256, 3, 3]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.3/reference_scales/ComparisonBinaryModel_mixed.json b/tests/openvino/native/data/2023.3/reference_scales/ComparisonBinaryModel_mixed.json deleted file mode 120000 index 16301a01b54..00000000000 --- a/tests/openvino/native/data/2023.3/reference_scales/ComparisonBinaryModel_mixed.json +++ /dev/null @@ -1 +0,0 @@ -../../2023.2/reference_scales/ComparisonBinaryModel_mixed.json \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_scales/ComparisonBinaryModel_mixed.json b/tests/openvino/native/data/2023.3/reference_scales/ComparisonBinaryModel_mixed.json new file mode 100644 index 00000000000..05e4ae7b255 --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_scales/ComparisonBinaryModel_mixed.json @@ -0,0 +1,8 @@ +{ + "Input/fq_output_0": { + "input_low": 0.0, + "input_high": 0.9350724220275879, + "output_low": 0.0, + "output_high": 0.9350724220275879 + } +} \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_scales/ComparisonBinaryModel_performance.json b/tests/openvino/native/data/2023.3/reference_scales/ComparisonBinaryModel_performance.json deleted file mode 120000 index 0b237affbff..00000000000 --- a/tests/openvino/native/data/2023.3/reference_scales/ComparisonBinaryModel_performance.json +++ /dev/null @@ -1 +0,0 @@ -../../2023.2/reference_scales/ComparisonBinaryModel_performance.json \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_scales/ComparisonBinaryModel_performance.json b/tests/openvino/native/data/2023.3/reference_scales/ComparisonBinaryModel_performance.json new file mode 100644 index 00000000000..05e4ae7b255 --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_scales/ComparisonBinaryModel_performance.json @@ -0,0 +1,8 @@ +{ + "Input/fq_output_0": { + "input_low": 0.0, + "input_high": 0.9350724220275879, + "output_low": 0.0, + "output_high": 0.9350724220275879 + } +} \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_scales/ConvModel_mixed.json b/tests/openvino/native/data/2023.3/reference_scales/ConvModel_mixed.json deleted file mode 120000 index a30c3040ab2..00000000000 --- a/tests/openvino/native/data/2023.3/reference_scales/ConvModel_mixed.json +++ /dev/null @@ -1 +0,0 @@ -../../2023.2/reference_scales/ConvModel_mixed.json \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_scales/ConvModel_mixed.json b/tests/openvino/native/data/2023.3/reference_scales/ConvModel_mixed.json new file mode 100644 index 00000000000..7a6b296c118 --- /dev/null +++ b/tests/openvino/native/data/2023.3/reference_scales/ConvModel_mixed.json @@ -0,0 +1,102 @@ +{ + "Conv/fq_weights_1": { + "input_low": [ + [ + [ + [ + -71.96717071533203 + ] + ] + ], + [ + [ + [ + -111.47208404541016 + ] + ] + ], + [ + [ + [ + -102.1300277709961 + ] + ] + ] + ], + "input_high": [ + [ + [ + [ + 71.96717071533203 + ] + ] + ], + [ + [ + [ + 111.47208404541016 + ] + ] + ], + [ + [ + [ + 102.1300277709961 + ] + ] + ] + ], + "output_low": [ + [ + [ + [ + -71.96717071533203 + ] + ] + ], + [ + [ + [ + -111.47208404541016 + ] + ] + ], + [ + [ + [ + -102.1300277709961 + ] + ] + ] + ], + "output_high": [ + [ + [ + [ + 71.96717071533203 + ] + ] + ], + [ + [ + [ + 111.47208404541016 + ] + ] + ], + [ + [ + [ + 102.1300277709961 + ] + ] + ] + ] + }, + "Sub/fq_output_0": { + "input_low": -0.6237419843673706, + "input_high": 0.8222053647041321, + "output_low": -0.6237419843673706, + "output_high": 0.8222053647041321 + } +} \ No newline at end of file diff --git a/tests/openvino/native/data/2023.3/reference_scales/ConvModel_performance.json b/tests/openvino/native/data/2023.3/reference_scales/ConvModel_performance.json deleted file mode 120000 index b901fdd21b8..00000000000 --- a/tests/openvino/native/data/2023.3/reference_scales/ConvModel_performance.json +++ /dev/null @@ -1 +0,0 @@ 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+ 0.6706244349479675 + ] + ], + "output_low": [ + [ + -0.6706244349479675 + ] + ], + "output_high": [ + [ + 0.6706244349479675 + ] + ] + } +} \ No newline at end of file diff --git a/tests/openvino/native/data/2024.0/reference_graphs/original_nncf_graph/ssd_mobilenet_v1_coco.dot b/tests/openvino/native/data/2024.0/reference_graphs/original_nncf_graph/ssd_mobilenet_v1_coco.dot new file mode 100644 index 00000000000..4794e4f8d45 --- /dev/null +++ b/tests/openvino/native/data/2024.0/reference_graphs/original_nncf_graph/ssd_mobilenet_v1_coco.dot @@ -0,0 +1,712 @@ +strict digraph { +"0 image_tensor" [id=0, type=Parameter]; +"1 Convert_2184" [id=1, type=Convert]; +"2 ToFloat" [id=2, type=Transpose]; +"3 Preprocessor/mul" [id=3, type=Multiply]; +"4 PriorBoxClustered_0/1_port" [id=4, type=ShapeOf]; +"5 Preprocessor/sub" [id=5, type=Add]; +"6 PriorBoxClustered_0/ss_1_port" [id=6, type=StridedSlice]; +"7 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/BatchNorm/batchnorm/mul_1" [id=7, type=Convolution]; +"8 PriorBoxClustered_0/naked_not_unsqueezed" [id=8, type=PriorBoxClustered]; +"9 PriorBoxClustered_1/naked_not_unsqueezed" [id=9, type=PriorBoxClustered]; +"10 PriorBoxClustered_2/naked_not_unsqueezed" [id=10, type=PriorBoxClustered]; +"11 PriorBoxClustered_3/naked_not_unsqueezed" [id=11, type=PriorBoxClustered]; +"12 PriorBoxClustered_4/naked_not_unsqueezed" [id=12, type=PriorBoxClustered]; +"13 PriorBoxClustered_5/naked_not_unsqueezed" [id=13, type=PriorBoxClustered]; +"14 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/BatchNorm/batchnorm/add_1" [id=14, type=Add]; +"15 PriorBoxClustered_0" [id=15, type=Unsqueeze]; +"16 PriorBoxClustered_1" [id=16, type=Unsqueeze]; +"17 PriorBoxClustered_2" [id=17, type=Unsqueeze]; +"18 PriorBoxClustered_3" [id=18, type=Unsqueeze]; +"19 PriorBoxClustered_4" [id=19, type=Unsqueeze]; +"20 PriorBoxClustered_5" [id=20, type=Unsqueeze]; +"21 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/Relu6" [id=21, type=Clamp]; +"22 ConcatPriorBoxesClustered" [id=22, type=Concat]; +"23 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_depthwise/depthwise" [id=23, type=GroupConvolution]; +"24 DetectionOutput" [id=24, type=DetectionOutput]; +"25 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_depthwise/BatchNorm/batchnorm/add_1" [id=25, type=Add]; +"26 detection_boxes" [id=26, type=Result]; +"27 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_depthwise/Relu6" [id=27, type=Clamp]; +"28 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_pointwise/BatchNorm/batchnorm/mul_1" [id=28, type=Convolution]; +"29 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_pointwise/BatchNorm/batchnorm/add_1" [id=29, type=Add]; +"30 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_pointwise/Relu6" [id=30, type=Clamp]; +"31 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_depthwise/depthwise" [id=31, type=GroupConvolution]; +"32 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_depthwise/BatchNorm/batchnorm/add_1" [id=32, type=Add]; +"33 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_depthwise/Relu6" [id=33, type=Clamp]; +"34 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_pointwise/BatchNorm/batchnorm/mul_1" [id=34, type=Convolution]; +"35 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_pointwise/BatchNorm/batchnorm/add_1" [id=35, type=Add]; +"36 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_pointwise/Relu6" [id=36, type=Clamp]; +"37 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_depthwise/depthwise" [id=37, type=GroupConvolution]; +"38 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_depthwise/BatchNorm/batchnorm/add_1" [id=38, type=Add]; +"39 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_depthwise/Relu6" [id=39, type=Clamp]; +"40 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_pointwise/BatchNorm/batchnorm/mul_1" [id=40, type=Convolution]; +"41 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_pointwise/BatchNorm/batchnorm/add_1" [id=41, type=Add]; +"42 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_pointwise/Relu6" [id=42, type=Clamp]; +"43 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_depthwise/depthwise" [id=43, type=GroupConvolution]; +"44 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_depthwise/BatchNorm/batchnorm/add_1" [id=44, type=Add]; +"45 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_depthwise/Relu6" [id=45, type=Clamp]; +"46 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_pointwise/BatchNorm/batchnorm/mul_1" [id=46, type=Convolution]; +"47 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_pointwise/BatchNorm/batchnorm/add_1" [id=47, type=Add]; +"48 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_pointwise/Relu6" [id=48, type=Clamp]; +"49 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_depthwise/depthwise" [id=49, type=GroupConvolution]; +"50 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_depthwise/BatchNorm/batchnorm/add_1" [id=50, type=Add]; +"51 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_depthwise/Relu6" [id=51, type=Clamp]; +"52 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_pointwise/BatchNorm/batchnorm/mul_1" [id=52, type=Convolution]; +"53 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_pointwise/BatchNorm/batchnorm/add_1" [id=53, type=Add]; +"54 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_pointwise/Relu6" [id=54, type=Clamp]; +"55 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_depthwise/depthwise" [id=55, type=GroupConvolution]; +"56 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_depthwise/BatchNorm/batchnorm/add_1" [id=56, type=Add]; +"57 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_depthwise/Relu6" [id=57, type=Clamp]; +"58 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_pointwise/BatchNorm/batchnorm/mul_1" [id=58, type=Convolution]; +"59 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_pointwise/BatchNorm/batchnorm/add_1" [id=59, type=Add]; +"60 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_pointwise/Relu6" [id=60, type=Clamp]; +"61 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_depthwise/depthwise" [id=61, type=GroupConvolution]; +"62 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_depthwise/BatchNorm/batchnorm/add_1" [id=62, type=Add]; +"63 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_depthwise/Relu6" [id=63, type=Clamp]; +"64 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_pointwise/BatchNorm/batchnorm/mul_1" [id=64, type=Convolution]; +"65 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_pointwise/BatchNorm/batchnorm/add_1" [id=65, type=Add]; +"66 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_pointwise/Relu6" [id=66, type=Clamp]; +"67 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_depthwise/depthwise" [id=67, type=GroupConvolution]; +"68 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_depthwise/BatchNorm/batchnorm/add_1" [id=68, type=Add]; +"69 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_depthwise/Relu6" [id=69, type=Clamp]; +"70 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_pointwise/BatchNorm/batchnorm/mul_1" [id=70, type=Convolution]; +"71 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_pointwise/BatchNorm/batchnorm/add_1" [id=71, type=Add]; +"72 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_pointwise/Relu6" [id=72, type=Clamp]; +"73 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_depthwise/depthwise" [id=73, type=GroupConvolution]; +"74 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_depthwise/BatchNorm/batchnorm/add_1" [id=74, type=Add]; +"75 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_depthwise/Relu6" [id=75, type=Clamp]; +"76 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_pointwise/BatchNorm/batchnorm/mul_1" [id=76, type=Convolution]; +"77 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_pointwise/BatchNorm/batchnorm/add_1" [id=77, type=Add]; +"78 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_pointwise/Relu6" [id=78, type=Clamp]; +"79 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_depthwise/depthwise" [id=79, type=GroupConvolution]; +"80 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_depthwise/BatchNorm/batchnorm/add_1" [id=80, type=Add]; +"81 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_depthwise/Relu6" [id=81, type=Clamp]; +"82 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_pointwise/BatchNorm/batchnorm/mul_1" [id=82, type=Convolution]; +"83 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_pointwise/BatchNorm/batchnorm/add_1" [id=83, type=Add]; +"84 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_pointwise/Relu6" [id=84, type=Clamp]; +"85 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_depthwise/depthwise" [id=85, type=GroupConvolution]; +"86 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_depthwise/BatchNorm/batchnorm/add_1" [id=86, type=Add]; +"87 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_depthwise/Relu6" [id=87, type=Clamp]; +"88 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_pointwise/BatchNorm/batchnorm/mul_1" [id=88, type=Convolution]; +"89 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_pointwise/BatchNorm/batchnorm/add_1" [id=89, type=Add]; +"90 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_pointwise/Relu6" [id=90, type=Clamp]; +"91 BoxPredictor_0/BoxEncodingPredictor/Conv2D" [id=91, type=Convolution]; +"92 BoxPredictor_0/ClassPredictor/Conv2D" [id=92, type=Convolution]; +"93 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_depthwise/depthwise" [id=93, type=GroupConvolution]; +"94 BoxPredictor_0/BoxEncodingPredictor/BiasAdd/Add" [id=94, type=Add]; +"95 BoxPredictor_0/ClassPredictor/BiasAdd/Add" [id=95, type=Add]; +"96 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_depthwise/BatchNorm/batchnorm/add_1" [id=96, type=Add]; +"97 BoxPredictor_0/BoxEncodingPredictor/BiasAdd/Add/Transpose" [id=97, type=Transpose]; +"98 PriorBoxClustered_0/0_port" [id=98, type=ShapeOf]; +"99 BoxPredictor_0/ClassPredictor/BiasAdd/Add/Transpose" [id=99, type=Transpose]; +"100 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_depthwise/Relu6" [id=100, type=Clamp]; +"101 BoxPredictor_0/BoxEncodingPredictor/BiasAdd/Reshape" [id=101, type=Reshape]; +"102 PriorBoxClustered_0/ss_0_port" [id=102, type=StridedSlice]; +"103 BoxPredictor_0/ClassPredictor/BiasAdd/Reshape" [id=103, type=Reshape]; +"104 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_pointwise/BatchNorm/batchnorm/mul_1" [id=104, type=Convolution]; +"105 concat" [id=105, type=Concat]; +"106 concat_1" [id=106, type=Concat]; +"107 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_pointwise/BatchNorm/batchnorm/add_1" [id=107, type=Add]; +"108 do_reshape_offsets" [id=108, type=Reshape]; +"109 do_ExpandDims_conf" [id=109, type=Reshape]; +"110 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_pointwise/Relu6" [id=110, type=Clamp]; +"111 do_ExpandDims_conf/sigmoid" [id=111, type=Sigmoid]; +"112 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_depthwise/depthwise" [id=112, type=GroupConvolution]; +"113 do_reshape_conf" [id=113, type=Reshape]; +"114 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_depthwise/BatchNorm/batchnorm/add_1" [id=114, type=Add]; +"115 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_depthwise/Relu6" [id=115, type=Clamp]; +"116 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_pointwise/BatchNorm/batchnorm/mul_1" [id=116, type=Convolution]; +"117 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_pointwise/BatchNorm/batchnorm/add_1" [id=117, type=Add]; +"118 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_pointwise/Relu6" [id=118, type=Clamp]; +"119 BoxPredictor_1/BoxEncodingPredictor/Conv2D" [id=119, type=Convolution]; +"120 BoxPredictor_1/ClassPredictor/Conv2D" [id=120, type=Convolution]; +"121 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_2_1x1_256/BatchNorm/batchnorm/mul_1" [id=121, type=Convolution]; +"122 BoxPredictor_1/BoxEncodingPredictor/BiasAdd/Add" [id=122, type=Add]; +"123 BoxPredictor_1/ClassPredictor/BiasAdd/Add" [id=123, type=Add]; +"124 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_2_1x1_256/BatchNorm/batchnorm/add_1" [id=124, type=Add]; +"125 BoxPredictor_1/BoxEncodingPredictor/BiasAdd/Add/Transpose" [id=125, type=Transpose]; +"126 PriorBoxClustered_1/0_port" [id=126, type=ShapeOf]; +"127 BoxPredictor_1/ClassPredictor/BiasAdd/Add/Transpose" [id=127, type=Transpose]; +"128 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_2_1x1_256/Relu6" [id=128, type=Clamp]; +"129 BoxPredictor_1/BoxEncodingPredictor/BiasAdd/Reshape" [id=129, type=Reshape]; +"130 PriorBoxClustered_1/ss_0_port" [id=130, type=StridedSlice]; +"131 BoxPredictor_1/ClassPredictor/BiasAdd/Reshape" [id=131, type=Reshape]; +"132 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_512/BatchNorm/batchnorm/mul_1" [id=132, type=Convolution]; +"133 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_512/BatchNorm/batchnorm/add_1" [id=133, type=Add]; +"134 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_512/Relu6" [id=134, type=Clamp]; +"135 BoxPredictor_2/BoxEncodingPredictor/Conv2D" [id=135, type=Convolution]; +"136 BoxPredictor_2/ClassPredictor/Conv2D" [id=136, type=Convolution]; +"137 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_3_1x1_128/BatchNorm/batchnorm/mul_1" [id=137, type=Convolution]; +"138 BoxPredictor_2/BoxEncodingPredictor/BiasAdd/Add" [id=138, type=Add]; +"139 BoxPredictor_2/ClassPredictor/BiasAdd/Add" [id=139, type=Add]; +"140 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_3_1x1_128/BatchNorm/batchnorm/add_1" [id=140, type=Add]; +"141 BoxPredictor_2/BoxEncodingPredictor/BiasAdd/Add/Transpose" [id=141, type=Transpose]; +"142 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{ + "Multiply_6431/fq_weights_1": { "input_low": [ [ [ @@ -28319,7 +28319,7 @@ "output_low": -0.2852116525173187, "output_high": 3.020650625228882 }, - "Multiply_7341/fq_weights_1": { + "Multiply_6494/fq_weights_1": { "input_low": [ [ [ @@ -31919,7 +31919,7 @@ "output_low": -0.21894927322864532, "output_high": 1.706294298171997 }, - "Multiply_7327/fq_weights_1": { + "Multiply_6480/fq_weights_1": { "input_low": [ [ [ @@ -39103,7 +39103,7 @@ "output_low": -1.4062703847885132, "output_high": 7.131800174713135 }, - "Multiply_7320/fq_weights_1": { + "Multiply_6473/fq_weights_1": { "input_low": [ [ [ @@ -67791,7 +67791,7 @@ "output_low": -0.7759751081466675, "output_high": 3.3463926315307617 }, - "Multiply_7313/fq_weights_1": { + "Multiply_6466/fq_weights_1": { "input_low": [ [ [ @@ -89293,7 +89293,7 @@ "output_low": -0.3361658453941345, "output_high": 3.5603017807006836 }, - "Multiply_7334/fq_weights_1": { + "Multiply_6487/fq_weights_1": { "input_low": [ [ [ diff --git a/tests/openvino/native/data/2023.2/reference_scales/yolo-v3-tiny-onnx_performance.json b/tests/openvino/native/data/2024.0/reference_scales/yolo-v3-tiny-onnx_performance.json similarity index 99% rename from tests/openvino/native/data/2023.2/reference_scales/yolo-v3-tiny-onnx_performance.json rename to tests/openvino/native/data/2024.0/reference_scales/yolo-v3-tiny-onnx_performance.json index 06945086bfd..7e6d07b0d15 100644 --- a/tests/openvino/native/data/2023.2/reference_scales/yolo-v3-tiny-onnx_performance.json +++ b/tests/openvino/native/data/2024.0/reference_scales/yolo-v3-tiny-onnx_performance.json @@ -7155,7 +7155,7 @@ "output_low": -3.859119176864624, "output_high": 3.828969717025757 }, - "Multiply_7348/fq_weights_1": { + "Multiply_6501/fq_weights_1": { "input_low": [ [ [ @@ -14339,7 +14339,7 @@ "output_low": -7.785910606384277, "output_high": 7.725083351135254 }, - "Multiply_7306/fq_weights_1": { + "Multiply_6459/fq_weights_1": { "input_low": [ [ [ @@ -21523,7 +21523,7 @@ "output_low": -13.285137176513672, "output_high": 13.181346893310547 }, - "Multiply_7299/fq_weights_1": { + "Multiply_6452/fq_weights_1": { "input_low": [ [ [ @@ -25123,7 +25123,7 @@ "output_low": -11.602160453796387, "output_high": 11.511518478393555 }, - "Multiply_7292/fq_weights_1": { + "Multiply_6445/fq_weights_1": { "input_low": [ [ [ @@ -26931,7 +26931,7 @@ "output_low": -6.0120320320129395, "output_high": 5.965063095092773 }, - "Multiply_7285/fq_weights_1": { + "Multiply_6438/fq_weights_1": { "input_low": [ [ [ @@ -27843,7 +27843,7 @@ "output_low": -3.5011093616485596, "output_high": 3.473757028579712 }, - "Multiply_7278/fq_weights_1": { + "Multiply_6431/fq_weights_1": { "input_low": [ [ [ @@ -28319,7 +28319,7 @@ "output_low": -3.0029969215393066, "output_high": 2.9795360565185547 }, - "Multiply_7341/fq_weights_1": { + "Multiply_6494/fq_weights_1": { "input_low": [ [ [ @@ -31919,7 +31919,7 @@ "output_low": -1.6986862421035767, "output_high": 1.685415267944336 }, - "Multiply_7327/fq_weights_1": { + "Multiply_6480/fq_weights_1": { "input_low": [ [ [ @@ -39103,7 +39103,7 @@ "output_low": -7.094355583190918, "output_high": 7.038930892944336 }, - "Multiply_7320/fq_weights_1": { + "Multiply_6473/fq_weights_1": { "input_low": [ [ [ @@ -67791,7 +67791,7 @@ "output_low": -3.372742176055908, "output_high": 3.3463926315307617 }, - "Multiply_7313/fq_weights_1": { + "Multiply_6466/fq_weights_1": { "input_low": [ [ [ @@ -89293,7 +89293,7 @@ "output_low": -3.5883357524871826, "output_high": 3.5603017807006836 }, - "Multiply_7334/fq_weights_1": { + "Multiply_6487/fq_weights_1": { "input_low": [ [ [ diff --git a/tests/openvino/native/models.py b/tests/openvino/native/models.py index a88ea72bf4a..c8db1436275 100644 --- a/tests/openvino/native/models.py +++ b/tests/openvino/native/models.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -200,12 +200,14 @@ def _create_ov_model(self): class WeightsModel(OVReferenceModel): def _create_ov_model(self): input_1 = opset.parameter([1, 3, 5, 5], name="Input_1") - kernel = self._rng.random((3, 3, 1, 1)).astype(np.float32) + kernel_data = self._rng.random((3, 3, 1, 1)).astype(np.float32) + kernel = opset.constant(kernel_data, dtype=np.float32, name="conv_weights_0") strides = [1, 1] pads = [0, 0] dilations = [1, 1] conv = opset.convolution(input_1, kernel, strides, pads, pads, dilations, name="Conv") - kernel_2 = self._rng.random((3, 3, 1, 1)).astype(np.float32) + kernel_data_2 = self._rng.random((3, 3, 1, 1)).astype(np.float32) + kernel_2 = opset.constant(kernel_data_2, dtype=np.float32, name="conv_weights_1") output_shape = [1, 1] conv_tr = opset.convolution_backprop_data( conv, kernel_2, output_shape, strides, pads, pads, dilations, name="Conv_backprop" @@ -887,6 +889,96 @@ def _create_ov_model(self): return model +class AWQMatmulModel(OVReferenceModel): + """ + Model for testing AWQ algorithm. Contains MatMul->Multiply->MatMul pattern. + """ + + def _create_ov_model(self): + input_node = opset.parameter([8, 8], name="Input_1") + + weights_data1 = np.arange(0, 64).reshape(8, 8) + weights_data1[:] = 2.0 + weights1 = opset.constant(weights_data1, dtype=np.float32, name="weights_1") + node1 = opset.matmul(input_node, weights1, transpose_a=False, transpose_b=True, name="MatMul_1") + + weights_data2 = np.arange(0, 64).reshape(8, 8) + weights_data2[:] = 3.0 + weights2 = opset.constant(weights_data2, dtype=np.float32, name="weights_2") + node2 = opset.matmul(input_node, weights2, transpose_a=False, transpose_b=True, name="MatMul_2") + + node_multiply = opset.multiply(node1, node2, name="Multiply") + + weights_data3 = np.arange(0, 64).reshape(8, 8) + weights_data3[:] = 4.0 + weights3 = opset.constant(weights_data3, dtype=np.float32, name="weights_3") + node3 = opset.matmul(node_multiply, weights3, transpose_a=False, transpose_b=True, name="MatMul_3") + + weights_data4 = np.arange(0, 64).reshape(8, 8) + weights_data4[:] = 2.0 + weights4 = opset.constant(weights_data4, dtype=np.float32, name="weights_4") + node4 = opset.matmul(node3, weights4, transpose_a=False, transpose_b=True, name="MatMul_4") + + weights_data5 = np.arange(0, 64).reshape(8, 8) + weights_data5[:] = 3.0 + weights5 = opset.constant(weights_data5, dtype=np.float32, name="weights_5") + node5 = opset.matmul(node3, weights5, transpose_a=False, transpose_b=True, name="MatMul_5") + + node_multiply_2 = opset.multiply(node4, node5, name="Multiply_2") + + weights_data6 = np.arange(0, 64).reshape(8, 8) + weights_data6[:] = 4.0 + weights6 = opset.constant(weights_data6, dtype=np.float32, name="weights_6") + node6 = opset.matmul(node_multiply_2, weights6, transpose_a=False, transpose_b=True, name="MatMul_6") + + result = opset.result(node6, name="Result") + result.get_output_tensor(0).set_names(set(["Result"])) + model = ov.Model([result], [input_node]) + return model + + +class DuplicatedNamesModel(OVReferenceModel): + """ + Model with duplicated node names (friendly_name field). + """ + + def _create_ov_model(self): + main_shape = [1, 2] + input_1 = opset.parameter(main_shape, name="Parameter") + + add_1_data = self._rng.random(main_shape).astype(np.float32) + add_1 = opset.add(input_1, add_1_data, name="Duplicate") + + matmul_1_data = self._rng.random(main_shape).astype(np.float32) + matmul_1 = opset.matmul(add_1, matmul_1_data, transpose_a=False, transpose_b=True, name="Duplicate") + + result = opset.result(matmul_1, name="Result") + model = ov.Model([result], [input_1]) + return model + + +class ModelNamedConsts(OVReferenceModel): + """ + Model with named constant nodes (friendly_name field). + """ + + def _create_ov_model(self): + main_shape = [1, 2] + input_1 = opset.parameter(main_shape, name="Parameter") + + add_1_data = self._rng.random(main_shape).astype(np.float32) + add_1_const = opset.constant(add_1_data, name="Constant_16") + add_1 = opset.add(input_1, add_1_const, name="Add_1") + + matmul_1_data = self._rng.random(main_shape).astype(np.float32) + matmul_1_const = opset.constant(matmul_1_data, name="Constant_1") + matmul_1 = opset.matmul(add_1, matmul_1_const, transpose_a=False, transpose_b=True, name="MatMul_1") + + result = opset.result(matmul_1, name="Result") + model = ov.Model([result], [input_1]) + return model + + class StatefulModel(OVReferenceModel): """ Stateful model for testing. diff --git a/tests/openvino/native/quantization/__init__.py b/tests/openvino/native/quantization/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/openvino/native/quantization/__init__.py +++ b/tests/openvino/native/quantization/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/openvino/native/quantization/test_calculation_quantizer_params.py b/tests/openvino/native/quantization/test_calculation_quantizer_params.py index 6708a33a1d2..8466d388d19 100644 --- a/tests/openvino/native/quantization/test_calculation_quantizer_params.py +++ b/tests/openvino/native/quantization/test_calculation_quantizer_params.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/openvino/native/quantization/test_channel_alignment.py b/tests/openvino/native/quantization/test_channel_alignment.py index 9dad4530d94..7cae18d975b 100644 --- a/tests/openvino/native/quantization/test_channel_alignment.py +++ b/tests/openvino/native/quantization/test_channel_alignment.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/openvino/native/quantization/test_fq_params_calculation.py b/tests/openvino/native/quantization/test_fq_params_calculation.py index 333f23209af..965dd23c406 100644 --- a/tests/openvino/native/quantization/test_fq_params_calculation.py +++ b/tests/openvino/native/quantization/test_fq_params_calculation.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -20,9 +20,8 @@ from nncf.openvino.statistics.aggregator import OVStatisticsAggregator from nncf.quantization.advanced_parameters import OverflowFix from nncf.quantization.algorithms.min_max.algorithm import MinMaxQuantization -from tests.openvino.conftest import OPENVINO_NATIVE_TEST_ROOT +from tests.openvino.native.common import get_actual_reference_for_current_openvino from tests.openvino.native.common import get_dataset_for_test -from tests.openvino.native.common import get_openvino_version from tests.openvino.native.models import SYNTHETIC_MODELS from tests.openvino.native.models import ConvModel from tests.openvino.native.models import FPModel @@ -34,8 +33,7 @@ from tests.shared.helpers import compare_stats from tests.shared.helpers import load_json -OV_VERSION = get_openvino_version() -REFERENCE_SCALES_DIR = OPENVINO_NATIVE_TEST_ROOT / "data" / OV_VERSION / "reference_scales" +REFERENCE_SCALES_DIR = Path("reference_scales") def get_fq_nodes_stats_algo(model): @@ -86,7 +84,7 @@ def test_synthetic_models_fq_scales(model_creator_func, preset, inplace_statisti nodes = get_fq_nodes_stats_algo(quantized_model) ref_stats_name = model.ref_graph_name.split(".")[0] + f"_{preset.value}.json" - ref_stats_path = REFERENCE_SCALES_DIR / ref_stats_name + ref_stats_path = get_actual_reference_for_current_openvino(REFERENCE_SCALES_DIR / ref_stats_name) # Uncomment lines below to generate reference for new models. # from tests.shared.helpers import dump_to_json @@ -107,7 +105,7 @@ def test_overflow_fix_scales(overflow_fix): nodes = get_fq_nodes_stats_algo(quantized_model) ref_stats_name = model.ref_graph_name.split(".")[0] + f"_overflow_fix_{overflow_fix.value}.json" - ref_stats_path = REFERENCE_SCALES_DIR / ref_stats_name + ref_stats_path = get_actual_reference_for_current_openvino(REFERENCE_SCALES_DIR / ref_stats_name) # Uncomment lines below to generate reference for new models. # from tests.shared.helpers import dump_to_json @@ -139,7 +137,7 @@ def test_omz_models_fq_scales(model_name, preset, inplace_statistics, tmp_path, nodes = get_fq_nodes_stats_algo(quantized_model) ref_stats_name = str(Path(model_path).name).rsplit(".", maxsplit=1)[0] + f"_{preset.value}.json" - ref_stats_path = REFERENCE_SCALES_DIR / ref_stats_name + ref_stats_path = get_actual_reference_for_current_openvino(REFERENCE_SCALES_DIR / ref_stats_name) # Uncomment lines below to generate reference for new models. # from tests.shared.helpers import dump_to_json diff --git a/tests/openvino/native/quantization/test_graphs.py b/tests/openvino/native/quantization/test_graphs.py index 268f7e4b501..3b5dd66f808 100644 --- a/tests/openvino/native/quantization/test_graphs.py +++ b/tests/openvino/native/quantization/test_graphs.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -10,6 +10,7 @@ # limitations under the License. +from pathlib import Path from typing import Dict import numpy as np @@ -26,12 +27,11 @@ from nncf.parameters import TargetDevice from nncf.quantization.advanced_parameters import OverflowFix from nncf.quantization.algorithms.smooth_quant.algorithm import SmoothQuant -from tests.openvino.conftest import OPENVINO_NATIVE_TEST_ROOT from tests.openvino.native.common import compare_nncf_graphs from tests.openvino.native.common import dump_model +from tests.openvino.native.common import get_actual_reference_for_current_openvino from tests.openvino.native.common import get_dataset_for_test from tests.openvino.native.common import get_openvino_major_minor_version -from tests.openvino.native.common import get_openvino_version from tests.openvino.native.models import SYNTHETIC_MODELS from tests.openvino.native.models import DepthwiseConv3DModel from tests.openvino.native.models import DepthwiseConv4DModel @@ -44,8 +44,7 @@ from tests.openvino.omz_helpers import convert_model from tests.openvino.omz_helpers import download_model -OV_VERSION = get_openvino_version() -QUANTIZED_REF_GRAPHS_DIR = OPENVINO_NATIVE_TEST_ROOT / "data" / OV_VERSION / "reference_graphs" / "quantized" +QUANTIZED_REF_GRAPHS_DIR = Path("reference_graphs") / "quantized" @pytest.mark.parametrize("model_creator_func", SYNTHETIC_MODELS.values()) @@ -55,7 +54,7 @@ def test_synthetic_models_fq_placement(model_creator_func): model.ov_model, {"preset": QuantizationPreset.PERFORMANCE, "inplace_statistics": True} ) - path_ref_graph = QUANTIZED_REF_GRAPHS_DIR / model.ref_graph_name + path_ref_graph = get_actual_reference_for_current_openvino(QUANTIZED_REF_GRAPHS_DIR / model.ref_graph_name) compare_nncf_graphs(quantized_model, path_ref_graph) @@ -66,7 +65,7 @@ def test_depthwise_models_fq_placement(model_creator_func): model.ov_model, {"preset": QuantizationPreset.PERFORMANCE, "inplace_statistics": True} ) - path_ref_graph = QUANTIZED_REF_GRAPHS_DIR / model.ref_graph_name + path_ref_graph = get_actual_reference_for_current_openvino(QUANTIZED_REF_GRAPHS_DIR / model.ref_graph_name) compare_nncf_graphs(quantized_model, path_ref_graph) @@ -92,7 +91,7 @@ def test_omz_models_fq_placement(model_name_params, tmp_path, omz_cache_dir): quantized_model = quantize_model(model, q_params) result_name = f"{model_name}_{params_str}" - path_ref_graph = QUANTIZED_REF_GRAPHS_DIR / f"{result_name}.dot" + path_ref_graph = get_actual_reference_for_current_openvino(QUANTIZED_REF_GRAPHS_DIR / f"{result_name}.dot") xml_path = tmp_path / (result_name + ".xml") bin_path = tmp_path / (result_name + ".bin") dump_model(quantized_model, str(xml_path), str(bin_path)) @@ -107,7 +106,7 @@ def test_transformer_models_fq_placement(model_creator_func, tmp_path): {"preset": QuantizationPreset.PERFORMANCE, "inplace_statistics": True, "model_type": ModelType.TRANSFORMER}, ) - path_ref_graph = QUANTIZED_REF_GRAPHS_DIR / model.ref_graph_name + path_ref_graph = get_actual_reference_for_current_openvino(QUANTIZED_REF_GRAPHS_DIR / model.ref_graph_name) xml_path = tmp_path / (model.ref_model_name + ".xml") bin_path = tmp_path / (model.ref_model_name + ".bin") dump_model(quantized_model, str(xml_path), str(bin_path)) @@ -130,7 +129,7 @@ def test_omz_models_sq_placement(model_name_params, tmp_path, omz_cache_dir): quantized_model = smooth_quant_model(model, q_params, quantize=False) - path_ref_graph = QUANTIZED_REF_GRAPHS_DIR / f"{model_name}_sq.dot" + path_ref_graph = get_actual_reference_for_current_openvino(QUANTIZED_REF_GRAPHS_DIR / f"{model_name}_sq.dot") xml_path = tmp_path / (model_name + ".xml") bin_path = tmp_path / (model_name + ".bin") dump_model(quantized_model, str(xml_path), str(bin_path)) @@ -160,7 +159,9 @@ def test_ignore_nodes_by_attribues(linear_before_reset): model = GRUSequenceModel(**{"linear_before_reset": linear_before_reset}).ov_model quantized_model = quantize_model(model, {}) postfix = "T" if linear_before_reset else "F" - path_ref_graph = QUANTIZED_REF_GRAPHS_DIR / f"GRUSequenceModel_linear_before_reset_{postfix}.dot" + path_ref_graph = get_actual_reference_for_current_openvino( + QUANTIZED_REF_GRAPHS_DIR / f"GRUSequenceModel_linear_before_reset_{postfix}.dot" + ) compare_nncf_graphs(quantized_model, path_ref_graph) @@ -197,9 +198,15 @@ def test_if_model_fq_placement(): then_body_path = if_model.ref_model_name + "_then.dot" else_body_path = if_model.ref_model_name + "_else.dot" - compare_nncf_graphs(quantized_model, QUANTIZED_REF_GRAPHS_DIR / main_model_path) - compare_nncf_graphs(if_op.get_function(0), QUANTIZED_REF_GRAPHS_DIR / then_body_path) - compare_nncf_graphs(if_op.get_function(1), QUANTIZED_REF_GRAPHS_DIR / else_body_path) + compare_nncf_graphs( + quantized_model, get_actual_reference_for_current_openvino(QUANTIZED_REF_GRAPHS_DIR / main_model_path) + ) + compare_nncf_graphs( + if_op.get_function(0), get_actual_reference_for_current_openvino(QUANTIZED_REF_GRAPHS_DIR / then_body_path) + ) + compare_nncf_graphs( + if_op.get_function(1), get_actual_reference_for_current_openvino(QUANTIZED_REF_GRAPHS_DIR / else_body_path) + ) @pytest.mark.parametrize("q_params", [{}, {"model_type": ModelType.TRANSFORMER}], ids=["default", "transformer"]) @@ -215,7 +222,9 @@ def test_scaled_dot_product_attention_placement(q_params, tmp_path): else: params_str = "default" - path_ref_graph = QUANTIZED_REF_GRAPHS_DIR / "scaled_dot_product_attention.dot" + path_ref_graph = get_actual_reference_for_current_openvino( + QUANTIZED_REF_GRAPHS_DIR / "scaled_dot_product_attention.dot" + ) result_name = f"scaled_dot_product_attention_{params_str}" xml_path = tmp_path / (result_name + ".xml") bin_path = tmp_path / (result_name + ".bin") @@ -242,5 +251,7 @@ def test_synthetic_models_fc_placement(model_creator_func): }, ) - path_ref_graph = QUANTIZED_REF_GRAPHS_DIR / f"{model.ref_model_name}_FC.dot" + path_ref_graph = get_actual_reference_for_current_openvino( + QUANTIZED_REF_GRAPHS_DIR / f"{model.ref_model_name}_FC.dot" + ) compare_nncf_graphs(quantized_model, path_ref_graph) diff --git a/tests/openvino/native/quantization/test_ptq_params.py b/tests/openvino/native/quantization/test_ptq_params.py index 192d3b5b008..71fda6d1a7c 100644 --- a/tests/openvino/native/quantization/test_ptq_params.py +++ b/tests/openvino/native/quantization/test_ptq_params.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -16,10 +16,6 @@ from nncf.common.graph.transformations.commands import TargetType from nncf.common.hardware.config import HW_CONFIG_TYPE_TARGET_DEVICE_MAP from nncf.common.utils.backend import BackendType -from nncf.experimental.common.tensor_statistics.collectors import MaxAggregator -from nncf.experimental.common.tensor_statistics.collectors import MeanAggregator -from nncf.experimental.common.tensor_statistics.collectors import MinAggregator -from nncf.experimental.common.tensor_statistics.collectors import TensorCollector from nncf.openvino.graph.metatypes.openvino_metatypes import OVConvolutionMetatype from nncf.openvino.graph.metatypes.openvino_metatypes import OVMatMulMetatype from nncf.openvino.graph.metatypes.openvino_metatypes import OVSoftmaxMetatype @@ -46,7 +42,7 @@ def get_ignored_patterns(device: TargetDevice = TargetDevice.ANY) -> GraphPatter return PatternsManager.get_full_ignored_pattern_graph(backend=BackendType.OPENVINO, device=device) -@pytest.mark.parametrize("target_device", [TargetDevice.CPU, TargetDevice.GPU, TargetDevice.VPU]) +@pytest.mark.parametrize("target_device", [TargetDevice.CPU, TargetDevice.GPU, TargetDevice.NPU]) def test_target_device(target_device): min_max_algo = MinMaxQuantization(target_device=target_device) min_max_algo._backend_entity = OVMinMaxAlgoBackend() @@ -57,18 +53,6 @@ class TestPTQParams(TemplateTestPTQParams): def get_algo_backend(self): return OVMinMaxAlgoBackend() - def check_is_min_max_statistic_collector(self, tensor_collector: TensorCollector): - aggrs = [aggr.__class__ for aggr in tensor_collector.aggregators.values()] - assert len(aggrs) == 2 - assert MinAggregator in aggrs - assert MaxAggregator in aggrs - - def check_is_mean_min_max_statistic_collector(self, tensor_collector: TensorCollector): - aggrs = [aggr.__class__ for aggr in tensor_collector.aggregators.values()] - assert len(aggrs) == 2 - assert MeanAggregator in aggrs - assert aggrs[0].__class__ == aggrs[1].__class__ - def check_quantize_outputs_fq_num(self, quantize_outputs, act_num_q, weight_num_q): if quantize_outputs: assert act_num_q == 3 diff --git a/tests/openvino/native/quantization/test_quantization_pipeline.py b/tests/openvino/native/quantization/test_quantization_pipeline.py index 7395b4a0e28..21a3ccc8642 100644 --- a/tests/openvino/native/quantization/test_quantization_pipeline.py +++ b/tests/openvino/native/quantization/test_quantization_pipeline.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/openvino/native/quantization/test_quantize_api.py b/tests/openvino/native/quantization/test_quantize_api.py index ca8022d8952..ddbb6612d96 100644 --- a/tests/openvino/native/quantization/test_quantize_api.py +++ b/tests/openvino/native/quantization/test_quantize_api.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/openvino/native/quantization/test_quantizer_config.py b/tests/openvino/native/quantization/test_quantizer_config.py index f1774cf89e8..86758011c90 100644 --- a/tests/openvino/native/quantization/test_quantizer_config.py +++ b/tests/openvino/native/quantization/test_quantizer_config.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -12,12 +12,6 @@ import pytest from nncf.common.graph.transformations.commands import TargetType -from nncf.common.tensor_statistics.collectors import ReductionAxes -from nncf.experimental.common.tensor_statistics.collectors import MaxAggregator -from nncf.experimental.common.tensor_statistics.collectors import MeanAggregator -from nncf.experimental.common.tensor_statistics.collectors import MinAggregator -from nncf.experimental.common.tensor_statistics.collectors import TensorCollector -from nncf.experimental.common.tensor_statistics.collectors import TensorReducerBase from nncf.openvino.graph.layer_attributes import OVLayerAttributes from nncf.openvino.graph.metatypes.openvino_metatypes import OVConvolutionMetatype from nncf.openvino.graph.metatypes.openvino_metatypes import OVDepthwiseConvolutionMetatype @@ -35,21 +29,6 @@ class TestQuantizerConfig(TemplateTestQuantizerConfig): def get_algo_backend(self): return OVMinMaxAlgoBackend() - def check_is_min_max_statistic_collector(self, tensor_collector: TensorCollector): - aggrs = [aggr.__class__ for aggr in tensor_collector.aggregators.values()] - assert len(aggrs) == 2 - assert MinAggregator in aggrs - assert MaxAggregator in aggrs - - def check_is_mean_min_max_statistic_collector(self, tensor_collector: TensorCollector): - aggrs = [aggr.__class__ for aggr in tensor_collector.aggregators.values()] - assert len(aggrs) == 2 - assert MeanAggregator in aggrs - assert aggrs[0].__class__ == aggrs[1].__class__ - - def get_reduction_axes(self, reducer: TensorReducerBase) -> ReductionAxes: - return reducer._reduction_axes - @pytest.fixture( params=[ pytest.param( diff --git a/tests/openvino/native/quantization/test_quantizer_removal.py b/tests/openvino/native/quantization/test_quantizer_removal.py index 15babcb8875..1061a3206c6 100644 --- a/tests/openvino/native/quantization/test_quantizer_removal.py +++ b/tests/openvino/native/quantization/test_quantizer_removal.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -17,7 +17,9 @@ from nncf.common.factory import NNCFGraphFactory from nncf.common.quantization.quantizer_removal import revert_operations_to_floating_point_precision +from nncf.openvino.graph.layer_attributes import OVLayerAttributes from nncf.openvino.graph.metatypes import openvino_metatypes as ov_metatypes +from nncf.openvino.graph.metatypes.groups import OPERATIONS_WITH_WEIGHTS from nncf.quantization.advanced_parameters import RestoreMode from tests.openvino.native.models import LinearQuantizedModel @@ -88,6 +90,8 @@ def test_revert_operations_to_floating_point_precision(test_case: InputTestData) quantized_model_graph, test_case.restore_mode, [ov_metatypes.OVMatMulMetatype, ov_metatypes.OVEmbeddingMetatype], + lambda node: node.metatype in OPERATIONS_WITH_WEIGHTS and isinstance(node.layer_attributes, OVLayerAttributes), + lambda node: node.layer_attributes.get_const_port_ids(), ) updated_model_graph = NNCFGraphFactory.create(updated_model) diff --git a/tests/openvino/native/quantization/test_reducers_and_aggregators.py b/tests/openvino/native/quantization/test_reducers_and_aggregators.py index d5645d2de3a..c29218550ac 100644 --- a/tests/openvino/native/quantization/test_reducers_and_aggregators.py +++ b/tests/openvino/native/quantization/test_reducers_and_aggregators.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -23,7 +23,6 @@ from nncf.openvino.statistics.collectors import OVMeanReducer from nncf.openvino.statistics.collectors import OVMinReducer from nncf.openvino.statistics.collectors import OVNNCFCollectorTensorProcessor -from nncf.openvino.statistics.collectors import OVNoopReducer from nncf.openvino.statistics.collectors import OVQuantileReducer from nncf.openvino.tensor import OVNNCFTensor from tests.common.experimental.test_reducers_and_aggregators import TemplateTestReducersAggreagtors @@ -44,7 +43,6 @@ def get_nncf_tensor(self, x: np.array, dtype: Optional[Dtype] = None): @pytest.fixture(scope="module") def reducers(self): return { - "noop": OVNoopReducer, "min": OVMinReducer, "max": OVMaxReducer, "abs_max": OVAbsMaxReducer, @@ -65,6 +63,3 @@ def squeeze_tensor(self, ref_tensor: List[Any], axes: Optional[Tuple[int]] = Non def cast_tensor(self, tensor, dtype: Dtype): return tensor - - def expand_dims(self, tensor, dims: Tuple[int, ...]): - return np.expand_dims(np.array(tensor), dims) diff --git a/tests/openvino/native/quantization/test_sanity.py b/tests/openvino/native/quantization/test_sanity.py index e0502d14605..103a249f8c0 100644 --- a/tests/openvino/native/quantization/test_sanity.py +++ b/tests/openvino/native/quantization/test_sanity.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/openvino/native/quantization/test_weights_compression.py b/tests/openvino/native/quantization/test_weights_compression.py index 1a599c222f7..95ff921f888 100644 --- a/tests/openvino/native/quantization/test_weights_compression.py +++ b/tests/openvino/native/quantization/test_weights_compression.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -9,6 +9,7 @@ # See the License for the specific language governing permissions and # limitations under the License. +import inspect import os from typing import Callable, List @@ -20,6 +21,8 @@ from nncf import CompressWeightsMode from nncf import SensitivityMetric from nncf.data.dataset import Dataset +from nncf.errors import ValidationError +from nncf.experimental.common.tensor_statistics.collectors import AggregatorBase from nncf.experimental.tensor import Tensor from nncf.openvino.graph.node_utils import get_const_value from nncf.quantization import compress_weights @@ -29,10 +32,13 @@ from nncf.quantization.algorithms.weight_compression.weight_lowering import get_integer_quantization_error from nncf.quantization.algorithms.weight_compression.weight_lowering import reshape_weight_for_grouped_quantization from nncf.scopes import IgnoredScope +from tests.openvino.native.common import get_actual_reference_for_current_openvino +from tests.openvino.native.models import AWQMatmulModel from tests.openvino.native.models import GatherAndMatmulShareData from tests.openvino.native.models import GatherWithTwoReductionAxes from tests.openvino.native.models import IdentityMatmul from tests.openvino.native.models import IntegerModel +from tests.openvino.native.models import ModelNamedConsts from tests.openvino.native.models import SequentialMatmulModel from tests.openvino.native.models import WeightsModel from tests.openvino.native.quantization.test_fq_params_calculation import REFERENCE_SCALES_DIR @@ -199,7 +205,9 @@ def test_compare_compressed_weights(mode, group_size, check_fn_per_node_map): check_fn = check_fn_per_node_map[op_name] actual_stats[op_name] = check_fn(op) - ref_stats_path = REFERENCE_SCALES_DIR / f"IntegerModel_compressed_weights_{mode.value}.json" + ref_stats_path = get_actual_reference_for_current_openvino( + REFERENCE_SCALES_DIR / f"IntegerModel_compressed_weights_{mode.value}.json" + ) if os.getenv("NNCF_TEST_REGEN_DOT") is not None: dump_to_json(ref_stats_path, actual_stats) @@ -261,9 +269,12 @@ def test_gather_in_4_bit_if_all_layers_with_data(metric): sensitivity_metric=metric, dataset=dataset, ) - for op in compressed_model.get_ordered_ops(): - if op.get_type_name() == "Constant" and "gather" in op.get_friendly_name(): - assert op.get_element_type() == ov.Type.u4 + int4_reference_node_names = ["gather_2_data", "gather_2_data/zero_point"] + nodes_map = {op.get_friendly_name(): op for op in compressed_model.get_ordered_ops()} + for node_name in int4_reference_node_names: + node = nodes_map[node_name] + assert node.get_type_name() == "Constant" + assert node.get_element_type() == ov.Type.u4 def test_gather_can_be_8_bit_if_all_layers_without_data(): @@ -275,9 +286,55 @@ def test_gather_can_be_8_bit_if_all_layers_without_data(): group_size=1, all_layers=True, ) + int8_reference_node_names = ["gather_2_data", "gather_2_data/zero_point"] + nodes_map = {op.get_friendly_name(): op for op in compressed_model.get_ordered_ops()} + for node_name in int8_reference_node_names: + node = nodes_map[node_name] + assert node.get_type_name() == "Constant" + assert node.get_element_type() == ov.Type.u8 + + +@pytest.mark.parametrize("mode", (CompressWeightsMode.INT8_SYM, CompressWeightsMode.INT8_ASYM)) +def test_conv_in_8_bit_if_mode_8bit(mode): + model = WeightsModel().ov_model + compressed_model = compress_weights(model, mode=mode) + int8_reference_node_names = [ + "conv_weights_0", + "conv_weights_0/zero_point", + "conv_weights_1", + "conv_weights_1/zero_point", + ] + nodes_map = {op.get_friendly_name(): op for op in compressed_model.get_ordered_ops()} + for node_name in int8_reference_node_names: + node = nodes_map[node_name] + assert node.get_type_name() == "Constant" + assert node.get_element_type() == ov.Type.u8 + + +@pytest.mark.parametrize("all_layers", (True, False)) +def test_conv_in_8_bit_if_mode_4bit(all_layers): + model = WeightsModel().ov_model + compressed_model = compress_weights( + model, + mode=CompressWeightsMode.INT4_SYM, + ratio=1, + group_size=1, + all_layers=all_layers, + ) for op in compressed_model.get_ordered_ops(): - if op.get_type_name() == "Constant" and "gather" in op.get_friendly_name(): - assert ov.Type(np.uint8) == op.get_element_type() + if op.get_type_name() == "Constant": + if op.get_friendly_name() in [ + "conv_weights_0", + "conv_weights_0/zero_point", + "conv_weights_1", + "conv_weights_1/zero_point", + ]: + assert ov.Type.u8 == op.get_element_type() + elif op.get_friendly_name() in ["weights_1", "weights_1/zero_point"]: + assert ov.Type.u4 == op.get_element_type() + elif op.get_friendly_name() in ["weights_0", "weights_0/zero_point"]: + dtype = ov.Type.u4 if all_layers else ov.Type.u8 + assert dtype == op.get_element_type() def test_gather_can_be_4_bit_if_all_layers_without_data(): @@ -289,9 +346,12 @@ def test_gather_can_be_4_bit_if_all_layers_without_data(): group_size=1, all_layers=True, ) - for op in compressed_model.get_ordered_ops(): - if op.get_type_name() == "Constant" and "gather" in op.get_friendly_name(): - assert ov.Type.u4 == op.get_element_type() + int4_reference_node_names = ["gather_2_data", "gather_2_data/zero_point"] + nodes_map = {op.get_friendly_name(): op for op in compressed_model.get_ordered_ops()} + for node_name in int4_reference_node_names: + node = nodes_map[node_name] + assert node.get_type_name() == "Constant" + assert node.get_element_type() == ov.Type.u4 @pytest.mark.parametrize("metric", ALL_SENSITIVITY_METRICS) @@ -307,9 +367,12 @@ def test_gather_in_8_bit_if_not_all_layers(metric): sensitivity_metric=metric, dataset=dataset, ) - for op in compressed_model.get_ordered_ops(): - if op.get_type_name() == "Constant" and "gather" in op.get_friendly_name(): - assert op.get_element_type() == ov.Type(np.uint8) + int8_reference_node_names = ["gather_2_data", "gather_2_data/zero_point"] + nodes_map = {op.get_friendly_name(): op for op in compressed_model.get_ordered_ops()} + for node_name in int8_reference_node_names: + node = nodes_map[node_name] + assert node.get_type_name() == "Constant" + assert node.get_element_type() == ov.Type.u8 MAX_BASELINE_SCORE = 1 / np.finfo(np.float32).eps @@ -353,12 +416,22 @@ def test_data_based_criterion(mode, ref_scores, ref_act_scores, mocker): @pytest.mark.parametrize("mode", (CompressWeightsMode.INT8_SYM, CompressWeightsMode.INT8_ASYM)) -def test_not_quantize_with_multiple_reduction_axes(mode): +def test_quantize_Gather_with_multiple_reduction_axes_in_8bit(mode): model = GatherWithTwoReductionAxes().ov_model compressed_model = compress_weights(model, mode=mode) for op in compressed_model.get_ordered_ops(): if op.get_type_name() == "Constant" and op.get_friendly_name() == "gather_1_data": - assert op.get_element_type() == ov.Type(np.float32) + assert op.get_element_type() == ov.Type.u8 + + +@pytest.mark.parametrize("mode", (CompressWeightsMode.INT4_SYM, CompressWeightsMode.INT4_ASYM)) +@pytest.mark.parametrize("all_layers", (True, False)) +def test_quantize_Gather_with_multiple_reduction_axes_if_mode_4bit(mode, all_layers): + model = GatherWithTwoReductionAxes().ov_model + compressed_model = compress_weights(model, mode=mode, all_layers=all_layers) + for op in compressed_model.get_ordered_ops(): + if op.get_type_name() == "Constant" and op.get_friendly_name() == "gather_1_data": + assert op.get_element_type() == ov.Type.u8 @pytest.mark.parametrize("mode", (CompressWeightsMode.INT4_SYM, CompressWeightsMode.INT4_ASYM)) @@ -539,29 +612,32 @@ def test_weight_compress_with_ignored_scope(ignored_scope, num_compressed): ref_compressed_weights = TEST_MODELS[IntegerModel] act_num = 0 for op in compressed_model.get_ops(): - if op.get_type_name() == "Constant" and op.get_friendly_name() in ref_compressed_weights: - if op.get_element_type() == ov.Type(np.uint8): - act_num += 1 + if ( + op.get_type_name() == "Constant" + and op.get_friendly_name() in ref_compressed_weights + and op.get_element_type() == ov.Type(np.uint8) + ): + act_num += 1 assert act_num == num_compressed @pytest.mark.parametrize("desc", CALCULATE_SCALE_DESCS) def test_calculate_scale_per_group(desc: CalculateScaleDesc): reshaped_weight, reduction_axis = reshape_weight_for_grouped_quantization( - desc.weight, reduction_axis=desc.axis, group_size=desc.group_size + desc.weight, reduction_axes=desc.axis, group_size=desc.group_size ) act_scale = np.max(np.abs(reshaped_weight), axis=reduction_axis, keepdims=True) # [a1, r//gs, 1, a2] assert np.allclose(act_scale, desc.ref_scale) def test_raise_error_for_many_axes(): - with pytest.raises(AssertionError): - reshape_weight_for_grouped_quantization(WEIGHTS_2x4, reduction_axis=(0, 1), group_size=1) + with pytest.raises(RuntimeError): + reshape_weight_for_grouped_quantization(WEIGHTS_2x4, reduction_axes=(0, 1), group_size=1) -def test_raise_error_with_tuple(): - with pytest.raises(AssertionError): - reshape_weight_for_grouped_quantization(WEIGHTS_2x4, reduction_axis=(0,), group_size=3) +def test_raise_error_channel_size_is_not_divisible_by_group_size(): + with pytest.raises(ValidationError): + reshape_weight_for_grouped_quantization(WEIGHTS_2x4, reduction_axes=(0,), group_size=3) @pytest.mark.parametrize("mode", INT8_MODES) @@ -601,7 +677,59 @@ def test_call_max_var_criterion_with_dataset_by_default(mocker, mode): scores_spy.assert_called() +@pytest.mark.parametrize("mode", INT4_NF4_MODES) +def test_call_max_var_criterion_with_dataset_by_default_awq(mode): + model = AWQMatmulModel().ov_model + dataset = Dataset([np.ones([8, 8])]) + + compress_weights(model, mode=mode, ratio=1.0, group_size=2, dataset=dataset, awq=True) + + def test_data_type_for_num_weights(mocker): stub = mocker.stub() params = WeightCompressionParameters(stub, stub, stub, np.int32(1), stub) assert isinstance(params.num_weights, np.uint64) + + +DATASET_SIZE = 129 + + +@pytest.mark.parametrize( + ("subset_size", "ref_size"), + ( + (1, 1), + (5, 5), + (130, DATASET_SIZE), + ), +) +def test_valid_subset_size(mocker, subset_size, ref_size): + model = IdentityMatmul().ov_model + dataset = Dataset([ACTIVATION] * DATASET_SIZE) + stats_spy = mocker.spy(AggregatorBase, "register_reduced_input") + + compress_weights(model, mode=CompressWeightsMode.INT4_ASYM, ratio=0.5, dataset=dataset, subset_size=subset_size) + + assert stats_spy.call_count == ref_size + + +def test_default_subset_value(): + default_value = inspect.signature(compress_weights).parameters["subset_size"].default + assert default_value == 128 + + +@pytest.mark.parametrize("subset_size", (-1, 0, None)) +def test_invalid_subset_size(subset_size): + model = IdentityMatmul().ov_model + dataset = Dataset([ACTIVATION]) + with pytest.raises(ValueError): + compress_weights(model, mode=CompressWeightsMode.INT4_ASYM, ratio=0.5, dataset=dataset, subset_size=subset_size) + + +def test_duplicate_names_generation(): + model = ModelNamedConsts().ov_model + compressed_model = compress_weights(model) + op_names = set() + for op in compressed_model.get_ops(): + name = op.get_friendly_name() + assert name not in op_names + op_names.add(name) diff --git a/tests/openvino/native/test_bias_correction.py b/tests/openvino/native/test_bias_correction.py index e539b26faf7..47ba2789a36 100644 --- a/tests/openvino/native/test_bias_correction.py +++ b/tests/openvino/native/test_bias_correction.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -9,26 +9,26 @@ # See the License for the specific language governing permissions and # limitations under the License. +from pathlib import Path from typing import Dict, List import numpy as np -import openvino.runtime as ov +import openvino as ov import pytest import torch -from openvino.tools.mo import convert_model from nncf.common.factory import NNCFGraphFactory from nncf.openvino.graph.model_utils import remove_fq_from_inputs from nncf.openvino.graph.nncf_graph_builder import GraphConverter from nncf.openvino.graph.node_utils import get_bias_value from nncf.quantization.algorithms.bias_correction.openvino_backend import OVBiasCorrectionAlgoBackend -from tests.openvino.conftest import OPENVINO_NATIVE_TEST_ROOT from tests.openvino.native.common import compare_nncf_graphs -from tests.openvino.native.common import get_openvino_version +from tests.openvino.native.common import get_actual_reference_for_current_openvino +from tests.post_training.test_templates.helpers import ConvTestModel +from tests.post_training.test_templates.helpers import MultipleConvTestModel +from tests.post_training.test_templates.helpers import SplittedModel from tests.post_training.test_templates.test_bias_correction import TemplateTestBCAlgorithm -OV_VERSION = get_openvino_version() - class TestOVBCAlgorithm(TemplateTestBCAlgorithm): @staticmethod @@ -43,7 +43,7 @@ def get_backend() -> OVBiasCorrectionAlgoBackend: def backend_specific_model(model: torch.nn.Module, tmp_dir: str): onnx_path = f"{tmp_dir}/model.onnx" torch.onnx.export(model, torch.rand(model.INPUT_SIZE), onnx_path, opset_version=13, input_names=["input.1"]) - ov_model = convert_model(onnx_path, input_shape=model.INPUT_SIZE, compress_to_fp16=False) + ov_model = ov.convert_model(onnx_path) return ov_model @staticmethod @@ -70,14 +70,8 @@ def remove_fq_from_inputs(model: ov.Model) -> ov.Model: @staticmethod def get_ref_path(suffix: str) -> str: - return ( - OPENVINO_NATIVE_TEST_ROOT - / "data" - / OV_VERSION - / "reference_graphs" - / "quantized" - / "subgraphs" - / f"{suffix}.dot" + return get_actual_reference_for_current_openvino( + Path("reference_graphs") / "quantized" / "subgraphs" / f"{suffix}.dot" ) @staticmethod @@ -100,10 +94,12 @@ def check_bias(model: ov.Model, ref_biases: Dict) -> None: ( "/conv_1/Conv/WithoutBiases", { - "collected_inputs": {"/conv_1/Conv/WithoutBiases": ("input.1", 0)}, + "collected_inputs": { + ("/Concat", 1): ("input.1", 0), + ("/conv_1/Conv/WithoutBiases", 0): ("/Concat", 0), + }, "subgraph_data": { - "subgraph_input_names": {"/conv_1/Conv/WithoutBiases"}, - "subgraph_output_names": {"/maxpool_1/MaxPool", "/Split"}, + "subgraph_input_ids": {("/conv_1/Conv/WithoutBiases", 0)}, "subgraph_output_ids": {("/Split", 0), ("/maxpool_1/MaxPool", 0), ("/Split", 1)}, }, }, @@ -112,14 +108,13 @@ def check_bias(model: ov.Model, ref_biases: Dict) -> None: "/conv_2/Conv/WithoutBiases", { "collected_inputs": { - "/conv_1/Conv/WithoutBiases": ("input.1", 0), - "/conv_2/Conv/WithoutBiases": ("/maxpool_1/MaxPool", 0), - "/conv_4/Conv/WithoutBiases": ("/Split", 0), - "/conv_6/Conv/WithoutBiases": ("/Split", 1), + ("/conv_1/Conv/WithoutBiases", 0): ("/Concat", 0), + ("/conv_2/Conv/WithoutBiases", 0): ("/maxpool_1/MaxPool", 0), + ("/conv_4/Conv/WithoutBiases", 0): ("/Split", 0), + ("/conv_6/Conv/WithoutBiases", 0): ("/Split", 1), }, "subgraph_data": { - "subgraph_input_names": {"/conv_2/Conv/WithoutBiases"}, - "subgraph_output_names": {"/Relu_1"}, + "subgraph_input_ids": {("/conv_2/Conv/WithoutBiases", 0)}, "subgraph_output_ids": {("/Relu_1", 0)}, }, }, @@ -128,15 +123,14 @@ def check_bias(model: ov.Model, ref_biases: Dict) -> None: "/conv_3/Conv/WithoutBiases", { "collected_inputs": { - "/conv_1/Conv/WithoutBiases": ("input.1", 0), - "/conv_2/Conv/WithoutBiases": ("/maxpool_1/MaxPool", 0), - "/conv_3/Conv/WithoutBiases": ("/Relu_1", 0), - "/conv_4/Conv/WithoutBiases": ("/Split", 0), - "/conv_6/Conv/WithoutBiases": ("/Split", 1), + ("/conv_1/Conv/WithoutBiases", 0): ("/Concat", 0), + ("/conv_2/Conv/WithoutBiases", 0): ("/maxpool_1/MaxPool", 0), + ("/conv_3/Conv/WithoutBiases", 0): ("/Relu_1", 0), + ("/conv_4/Conv/WithoutBiases", 0): ("/Split", 0), + ("/conv_6/Conv/WithoutBiases", 0): ("/Split", 1), }, "subgraph_data": { - "subgraph_input_names": {"/conv_1/Conv/WithoutBiases", "/conv_3/Conv/WithoutBiases"}, - "subgraph_output_names": {"/Split"}, + "subgraph_input_ids": {("/conv_1/Conv/WithoutBiases", 0), ("/conv_3/Conv/WithoutBiases", 0)}, "subgraph_output_ids": {("/Split", 0), ("/Split", 1)}, }, }, @@ -145,12 +139,11 @@ def check_bias(model: ov.Model, ref_biases: Dict) -> None: "/conv_4/Conv/WithoutBiases", { "collected_inputs": { - "/conv_4/Conv/WithoutBiases": ("/Split", 0), - "/conv_6/Conv/WithoutBiases": ("/Split", 1), + ("/conv_4/Conv/WithoutBiases", 0): ("/Split", 0), + ("/conv_6/Conv/WithoutBiases", 0): ("/Split", 1), }, "subgraph_data": { - "subgraph_input_names": {"/conv_4/Conv/WithoutBiases"}, - "subgraph_output_names": {"/Relu_2"}, + "subgraph_input_ids": {("/conv_4/Conv/WithoutBiases", 0)}, "subgraph_output_ids": {("/Relu_2", 0)}, }, }, @@ -159,13 +152,12 @@ def check_bias(model: ov.Model, ref_biases: Dict) -> None: "/conv_6/Conv/WithoutBiases", { "collected_inputs": { - "/conv_5/Conv/WithoutBiases": ("/Relu_2", 0), - "/conv_6/Conv/WithoutBiases": ("/Split", 1), + ("/conv_5/Conv/WithoutBiases", 0): ("/Relu_2", 0), + ("/conv_6/Conv/WithoutBiases", 0): ("/Split", 1), }, "subgraph_data": { - "subgraph_input_names": {"/conv_5/Conv/WithoutBiases", "/conv_6/Conv/WithoutBiases"}, - "subgraph_output_names": {"/Add_3", "/Concat"}, - "subgraph_output_ids": {("/Add_3", 0), ("/Concat", 0)}, + "subgraph_input_ids": {("/conv_5/Conv/WithoutBiases", 0), ("/conv_6/Conv/WithoutBiases", 0)}, + "subgraph_output_ids": {("/Add_3", 0), ("/Concat_1", 0)}, }, }, ), @@ -173,18 +165,17 @@ def check_bias(model: ov.Model, ref_biases: Dict) -> None: "/conv_10/Conv/WithoutBiases", { "collected_inputs": { - "/conv_8/Conv/WithoutBiases": ("/conv_7/Conv", 0), - "/conv_9/Conv/WithoutBiases": ("/Add_3", 0), - "/conv_10/Conv/WithoutBiases": ("/Concat", 0), + ("/conv_8/Conv/WithoutBiases", 0): ("/conv_7/Conv", 0), + ("/conv_9/Conv/WithoutBiases", 0): ("/Add_3", 0), + ("/conv_10/Conv/WithoutBiases", 0): ("/Concat", 0), }, "subgraph_data": { - "subgraph_input_names": { - "/conv_8/Conv/WithoutBiases", - "/conv_9/Conv/WithoutBiases", - "/conv_10/Conv/WithoutBiases", + "subgraph_input_ids": { + ("/conv_8/Conv/WithoutBiases", 0), + ("/conv_9/Conv/WithoutBiases", 0), + ("/conv_10/Conv/WithoutBiases", 0), }, - "subgraph_output_names": {"/Concat_1"}, - "subgraph_output_ids": {("/Concat_1", 0)}, + "subgraph_output_ids": {("/Concat_2", 0)}, }, }, ), @@ -192,11 +183,10 @@ def check_bias(model: ov.Model, ref_biases: Dict) -> None: "/MatMul", { "collected_inputs": { - "/MatMul": ("/Reshape", 0), + ("/MatMul", 0): ("/Reshape", 0), }, "subgraph_data": { - "subgraph_input_names": {"/MatMul"}, - "subgraph_output_names": {"/Reshape_1", "/Add_4"}, + "subgraph_input_ids": {("/MatMul", 0)}, "subgraph_output_ids": {("/Reshape_1", 0), ("/Add_4", 0)}, }, }, @@ -205,3 +195,26 @@ def check_bias(model: ov.Model, ref_biases: Dict) -> None: ) def test__get_subgraph_data_for_node(self, quantized_test_model, layer_name, ref_data): return super().test__get_subgraph_data_for_node(quantized_test_model, layer_name, ref_data) + + @pytest.mark.parametrize( + "model_cls, ref_stat_inputs_map", + ( + ( + SplittedModel, + { + ("/conv_1/Conv/WithoutBiases", 0): ("/Concat", 0), + ("/Concat", 1): ("input.1", 0), + }, + ), + ( + MultipleConvTestModel, + { + ("/conv_1/Conv/WithoutBiases", 0): ("input.1", 0), + ("/conv_3/Conv/WithoutBiases", 0): ("input.1", 0), + }, + ), + (ConvTestModel, {("/conv/Conv/WithoutBiases", 0): ("input.1", 0)}), + ), + ) + def test_verify_collected_stat_inputs_map(self, model_cls, ref_stat_inputs_map, tmpdir): + return super().test_verify_collected_stat_inputs_map(model_cls, ref_stat_inputs_map, tmpdir) diff --git a/tests/openvino/native/test_engine.py b/tests/openvino/native/test_engine.py index cd1ba092124..0794fb72b71 100644 --- a/tests/openvino/native/test_engine.py +++ b/tests/openvino/native/test_engine.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/openvino/native/test_fast_bias_correction.py b/tests/openvino/native/test_fast_bias_correction.py index 6d7b2b0e029..32bc605a30d 100644 --- a/tests/openvino/native/test_fast_bias_correction.py +++ b/tests/openvino/native/test_fast_bias_correction.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -12,9 +12,8 @@ from typing import List import numpy as np -import openvino.runtime as ov +import openvino as ov import torch -from openvino.tools.mo import convert_model from nncf.common.factory import NNCFGraphFactory from nncf.openvino.graph.node_utils import get_bias_value @@ -36,7 +35,7 @@ def get_backend() -> OVFastBiasCorrectionAlgoBackend: def backend_specific_model(model: bool, tmp_dir: str): onnx_path = f"{tmp_dir}/model.onnx" torch.onnx.export(model, torch.rand(model.INPUT_SIZE), onnx_path, opset_version=13, input_names=["input.1"]) - ov_model = convert_model(onnx_path, input_shape=model.INPUT_SIZE, compress_to_fp16=False) + ov_model = ov.convert_model(onnx_path, input=model.INPUT_SIZE) return ov_model @staticmethod diff --git a/tests/openvino/native/test_layer_attributes.py b/tests/openvino/native/test_layer_attributes.py index 04a63211964..50cab4a9b50 100644 --- a/tests/openvino/native/test_layer_attributes.py +++ b/tests/openvino/native/test_layer_attributes.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -442,12 +442,14 @@ def test_layer_attributes(test_descriptor: LayerAttributesTestCase): @pytest.mark.parametrize("test_descriptor", TEST_CASES_CONV) def test_get_conv_weights_layout_from_node(test_descriptor: LayerAttributesTestCase): node = _get_node_to_test(test_descriptor) - weights_layout = get_conv_weights_layout_from_node(node) - assert weights_layout == test_descriptor.ref_weights_layout + for _ in range(2): # To test get_conv_weights_layout_from_node is a clean function + weights_layout = get_conv_weights_layout_from_node(node) + assert weights_layout == test_descriptor.ref_weights_layout @pytest.mark.parametrize("test_descriptor", TEST_CASES_LINEAR) def test_get_linear_weights_layout_from_node(test_descriptor: LayerAttributesTestCase): node = _get_node_to_test(test_descriptor) - weights_layout = get_linear_weights_layout_from_node(node) - assert weights_layout == test_descriptor.ref_weights_layout + for _ in range(2): # To test get_linear_weights_layout_from_node is a clean function + weights_layout = get_linear_weights_layout_from_node(node) + assert weights_layout == test_descriptor.ref_weights_layout diff --git a/tests/openvino/native/test_metatypes.py b/tests/openvino/native/test_metatypes.py index e1a703b0f51..1c66e661316 100644 --- a/tests/openvino/native/test_metatypes.py +++ b/tests/openvino/native/test_metatypes.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/openvino/native/test_model_transformer.py b/tests/openvino/native/test_model_transformer.py index 84415f609aa..84980635e8a 100644 --- a/tests/openvino/native/test_model_transformer.py +++ b/tests/openvino/native/test_model_transformer.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -10,6 +10,7 @@ # limitations under the License. from dataclasses import dataclass +from pathlib import Path from typing import Callable, List, Tuple import numpy as np @@ -17,6 +18,7 @@ import pytest from openvino.runtime import opset13 as opset +import nncf from nncf.common.graph.transformations.commands import TargetType from nncf.common.graph.transformations.layout import TransformationLayout from nncf.experimental.tensor import Tensor @@ -26,7 +28,6 @@ from nncf.openvino.graph.node_utils import get_inplace_mean_op from nncf.openvino.graph.node_utils import get_inplace_mean_per_ch from nncf.openvino.graph.node_utils import get_inplace_min_op -from nncf.openvino.graph.node_utils import get_ov_model_reduce_node_name from nncf.openvino.graph.node_utils import get_result_node_name from nncf.openvino.graph.transformations.commands import OVBiasCorrectionCommand from nncf.openvino.graph.transformations.commands import OVBiasInsertionCommand @@ -41,10 +42,9 @@ from nncf.quantization.advanced_parameters import FP8Type from nncf.quantization.fake_quantize import FakeConvertParameters from nncf.quantization.fake_quantize import FakeQuantizeParameters -from tests.openvino.conftest import OPENVINO_NATIVE_TEST_ROOT from tests.openvino.native.common import compare_nncf_graphs +from tests.openvino.native.common import get_actual_reference_for_current_openvino from tests.openvino.native.common import get_openvino_major_minor_version -from tests.openvino.native.common import get_openvino_version from tests.openvino.native.models import ConvModel from tests.openvino.native.models import ConvNotBiasModel from tests.openvino.native.models import FPModel @@ -54,8 +54,7 @@ from tests.openvino.native.models import WeightsModel from tests.openvino.native.models import ZeroRankEltwiseModel -OV_VERSION = get_openvino_version() -REFERENCE_GRAPHS_DIR = OPENVINO_NATIVE_TEST_ROOT / "data" / OV_VERSION / "reference_graphs" / "original_nncf_graph" +REFERENCE_GRAPHS_DIR = Path("reference_graphs") / "original_nncf_graph" TARGET_INSERT_LAYERS = [["Add"], ["MatMul"], ["Add", "MatMul"]] TARGET_PRE_LAYER_FQS = [["Add/fq_input_0"], ["MatMul/fq_input_0"], ["Add/fq_input_0", "MatMul/fq_input_0"]] @@ -139,19 +138,17 @@ def __str__(self) -> str: # Forwarded reduce shape InplaceOpTestCase("min", (1, 2), get_inplace_min_op, ["ReduceMin"], [(1, 2)]), InplaceOpTestCase("mean", (1, 2), get_inplace_mean_op, ["ReduceMean"], [(1, 2)]), - InplaceOpTestCase("max", (1, 2), lambda o, r: get_inplace_max_op(o, r, False), ["ReduceMax"], [(1, 2)]), - InplaceOpTestCase( - "abs_max", (1, 2), lambda o, r: get_inplace_max_op(o, r, True), ["Abs", "ReduceMax"], [None, (1, 2)] - ), + InplaceOpTestCase("max", (1, 2), lambda r: get_inplace_max_op(r, False), ["ReduceMax"], [(1, 2)]), + InplaceOpTestCase("abs_max", (1, 2), lambda r: get_inplace_max_op(r, True), ["Abs", "ReduceMax"], [None, (1, 2)]), # Calculated reduce shape InplaceOpTestCase("min", None, get_inplace_min_op, ["ReduceMin"], [(0, 1, 2, 3)]), InplaceOpTestCase("mean", None, get_inplace_mean_op, ["ReduceMean"], [(0, 1, 2, 3)]), - InplaceOpTestCase("max", None, lambda o, r: get_inplace_max_op(o, r, False), ["ReduceMax"], [(0, 1, 2, 3)]), + InplaceOpTestCase("max", None, lambda r: get_inplace_max_op(r, False), ["ReduceMax"], [(0, 1, 2, 3)]), InplaceOpTestCase( - "abs_max", None, lambda o, r: get_inplace_max_op(o, r, True), ["Abs", "ReduceMax"], [None, (0, 1, 2, 3)] + "abs_max", None, lambda r: get_inplace_max_op(r, True), ["Abs", "ReduceMax"], [None, (0, 1, 2, 3)] ), # Batch mean and mean per ch operations - InplaceOpTestCase("batch_mean", None, lambda o, r: get_inplace_batch_mean_op(o), ["ReduceMean"], [0]), + InplaceOpTestCase("batch_mean", None, lambda r: get_inplace_batch_mean_op(), ["ReduceMean"], [0]), InplaceOpTestCase("mean_per_ch", 1, get_inplace_mean_per_ch, ["Reshape", "ReduceMean"], [(1, 3, 16), (0, 2)]), InplaceOpTestCase( "mean_per_ch", @@ -173,8 +170,8 @@ def __str__(self) -> str: # EmptyCase InplaceOpTestCase("min", (), get_inplace_min_op, ["ReduceMin"], [()]), InplaceOpTestCase("mean", (), get_inplace_mean_op, ["ReduceMean"], [()]), - InplaceOpTestCase("max", (), lambda o, r: get_inplace_max_op(o, r, False), ["ReduceMax"], [()]), - InplaceOpTestCase("abs_max", (), lambda o, r: get_inplace_max_op(o, r, True), ["Abs", "ReduceMax"], [None, ()]), + InplaceOpTestCase("max", (), lambda r: get_inplace_max_op(r, False), ["ReduceMax"], [()]), + InplaceOpTestCase("abs_max", (), lambda r: get_inplace_max_op(r, True), ["Abs", "ReduceMax"], [None, ()]), ] @@ -215,6 +212,22 @@ def check_inplace_op(target_node, ref_types, ref_vals, inplace_branches_num, out node = nodes[0] +def get_model_with_inplace_ops(model, test_params, target_layers, ref_output_names, target_type, port_id): + transformation_layout = TransformationLayout() + for target_layer, output_name in zip(target_layers, ref_output_names): + target_point = OVTargetPoint(target_type, target_layer, port_id=port_id) + command = OVInplaceFnInsertionCommand( + target_point, + inplace_op_fn=test_params.op_builder(test_params.reduce_shape), + fn_output_port_id=0, + last_inplace_node_name=output_name, + ) + transformation_layout.register(command) + + model_transformer = OVModelTransformer(model) + return model_transformer.transform(transformation_layout) + + @pytest.mark.parametrize("test_params", INPLACE_OPS_TEST_CASES, ids=[str(case) for case in INPLACE_OPS_TEST_CASES]) @pytest.mark.parametrize( "target_type", [TargetType.PRE_LAYER_OPERATION, TargetType.POST_LAYER_OPERATION, TargetType.OPERATION_WITH_WEIGHTS] @@ -224,16 +237,10 @@ def test_inplace_fn_insertion(test_params: InplaceOpTestCase, target_type, targe dims = LINEAR_MODEL_SHAPES[test_params.dims] model = LinearModel(**dims).ov_model port_id = 1 if target_type == TargetType.OPERATION_WITH_WEIGHTS else 0 - transformed_model = create_transformed_model( - model, - target_layers, - target_type, - OVInplaceFnInsertionCommand, - port_id, - { - "inplace_op_fn": test_params.op_builder(test_params.name, test_params.reduce_shape), - "fn_output_port_id": 0, - }, + + ref_output_names = [f"test_output_name_{i}" for i in range(len(target_layers))] + transformed_model = get_model_with_inplace_ops( + model, test_params, target_layers, ref_output_names, target_type, port_id ) inplace_branches_num = 1 @@ -254,11 +261,7 @@ def test_inplace_fn_insertion(test_params: InplaceOpTestCase, target_type, targe default_output_fn_port = 0 extra_outputs = get_extra_outputs(model, transformed_model) ref_output_names = [ - get_result_node_name( - get_ov_model_reduce_node_name(target_node.get_friendly_name(), test_params.name, port_id), - default_output_fn_port, - ) - for target_node, port_id in target_nodes + get_result_node_name(ref_output_name, default_output_fn_port) for ref_output_name in ref_output_names ] assert len(extra_outputs) == len(ref_output_names) for out_name in extra_outputs: @@ -284,8 +287,9 @@ def test_split_inplace_fn_insertion(test_params: InplaceOpTestCase): OVInplaceFnInsertionCommand, port_id, { - "inplace_op_fn": test_params.op_builder(test_params.name, test_params.reduce_shape), + "inplace_op_fn": test_params.op_builder(test_params.reduce_shape), "fn_output_port_id": 0, + "last_inplace_node_name": test_params.name, }, ) @@ -294,10 +298,7 @@ def test_split_inplace_fn_insertion(test_params: InplaceOpTestCase): default_output_fn_port = 0 extra_outputs = get_extra_outputs(model, transformed_model) - ref_output_name = get_result_node_name( - get_ov_model_reduce_node_name(target_node.get_friendly_name(), test_params.name, port_id), - default_output_fn_port, - ) + ref_output_name = get_result_node_name(test_params.name, default_output_fn_port) assert len(extra_outputs) == 1 assert ref_output_name in extra_outputs @@ -308,12 +309,12 @@ def test_split_inplace_fn_insertion(test_params: InplaceOpTestCase): ) def test_inplace_reduce_fn_dynamic_shapes(input_shape, raise_error): input_1 = opset.parameter(input_shape, name="Input") - fn = get_inplace_min_op("test", reduction_axes=None) + fn = get_inplace_min_op(reduction_axes=None) if raise_error: - with pytest.raises(RuntimeError): - fn(input_1, 0) + with pytest.raises(nncf.ValidationError): + fn(input_1, 0, "test") return - op = fn(input_1, 0) + op = fn(input_1, 0, "test") # check_const ref_const = np.array([0, 1, 2, 3]) assert all(np.equal(get_prev_node(op, 1).data, ref_const)) @@ -332,49 +333,49 @@ def test_inplace_reduce_fn_zero_rank_output(reduction_axes): OVInplaceFnInsertionCommand, port_id, { - "inplace_op_fn": get_inplace_min_op(name, reduction_axes=reduction_axes), + "inplace_op_fn": get_inplace_min_op(reduction_axes=reduction_axes), "fn_output_port_id": 0, + "last_inplace_node_name": name, }, ) target_node = get_prev_node(get_node_by_name(transformed_model, target_layer), 1) check_inplace_op(target_node, ["ReduceMin"], [[]], 1, 0) extra_outputs = get_extra_outputs(model, transformed_model) - ref_output_name = get_result_node_name(get_ov_model_reduce_node_name(target_node.get_friendly_name(), name, 0), 0) + ref_output_name = get_result_node_name(name, 0) assert len(extra_outputs) == 1 assert extra_outputs.pop() == ref_output_name DYNAMIC_SHAPE_TEST_CASES = [ - InplaceOpTestCase("mean_per_ch", 1, get_inplace_mean_per_ch, ["Reshape"], [(-1, 3, 9), (0, 2)]), + InplaceOpTestCase("mean_per_ch", 1, get_inplace_mean_per_ch, ["Reshape"], [(0, 3, 9), (0, 2)]), + InplaceOpTestCase("mean_per_ch", 1, get_inplace_mean_per_ch, ["Reshape"], [(0, 0, 9), (0, 2)]), InplaceOpTestCase("mean_per_ch", 1, get_inplace_mean_per_ch, ["Reshape"], [(1, 3, -1), (0, 2)]), - InplaceOpTestCase("mean_per_ch", 3, get_inplace_mean_per_ch, ["Transpose", "Reshape"], [(0, 3, 2, 1), (-1, 3, 9)]), - InplaceOpTestCase("mean_per_ch", 3, get_inplace_mean_per_ch, ["Transpose", "Reshape"], [(0, 3, 2, 1), (1, -1, 9)]), + InplaceOpTestCase("mean_per_ch", 1, get_inplace_mean_per_ch, ["Reshape"], [(1, 0, -1), (0, 2)]), + InplaceOpTestCase("mean_per_ch", 3, get_inplace_mean_per_ch, ["Transpose", "Reshape"], [(0, 3, 2, 1), (0, 3, 9)]), + InplaceOpTestCase("mean_per_ch", 3, get_inplace_mean_per_ch, ["Transpose", "Reshape"], [(0, 3, 2, 1), (1, 0, 9)]), InplaceOpTestCase("mean_per_ch", 3, get_inplace_mean_per_ch, ["Transpose", "Reshape"], [(0, 3, 2, 1), (1, 3, -1)]), + InplaceOpTestCase("mean_per_ch", 3, get_inplace_mean_per_ch, ["Transpose", "Reshape"], [(0, 3, 2, 1), (1, 0, -1)]), ] @pytest.mark.parametrize( - "test_params,input_shape,raise_error", + "test_params,input_shape", [ - (DYNAMIC_SHAPE_TEST_CASES[0], (ov.Dimension(), 3, 3, 3), False), - (DYNAMIC_SHAPE_TEST_CASES[1], (1, 3, 3, ov.Dimension()), False), - (DYNAMIC_SHAPE_TEST_CASES[0], (ov.Dimension(), ov.Dimension(), 3, 3), True), - (DYNAMIC_SHAPE_TEST_CASES[1], (1, 3, ov.Dimension(), ov.Dimension()), False), - (DYNAMIC_SHAPE_TEST_CASES[1], (1, ov.Dimension(), ov.Dimension(), 3), True), - (DYNAMIC_SHAPE_TEST_CASES[2], (ov.Dimension(), 3, 3, 3), False), - (DYNAMIC_SHAPE_TEST_CASES[3], (1, 3, 3, ov.Dimension()), False), - (DYNAMIC_SHAPE_TEST_CASES[4], (1, ov.Dimension(), ov.Dimension(), 3), False), - (DYNAMIC_SHAPE_TEST_CASES[4], (1, ov.Dimension(), ov.Dimension(), ov.Dimension()), True), + (DYNAMIC_SHAPE_TEST_CASES[0], (ov.Dimension(), 3, 3, 3)), + (DYNAMIC_SHAPE_TEST_CASES[2], (1, 3, 3, ov.Dimension())), + (DYNAMIC_SHAPE_TEST_CASES[1], (ov.Dimension(), ov.Dimension(), 3, 3)), + (DYNAMIC_SHAPE_TEST_CASES[2], (1, 3, ov.Dimension(), ov.Dimension())), + (DYNAMIC_SHAPE_TEST_CASES[3], (1, ov.Dimension(), ov.Dimension(), 3)), + (DYNAMIC_SHAPE_TEST_CASES[4], (ov.Dimension(), 3, 3, 3)), + (DYNAMIC_SHAPE_TEST_CASES[5], (1, 3, 3, ov.Dimension())), + (DYNAMIC_SHAPE_TEST_CASES[6], (1, ov.Dimension(), ov.Dimension(), 3)), + (DYNAMIC_SHAPE_TEST_CASES[7], (1, ov.Dimension(), ov.Dimension(), ov.Dimension())), ], ) -def test_inplace_mean_per_ch_fn_dynamic_shapes(test_params: InplaceOpTestCase, input_shape, raise_error): +def test_inplace_mean_per_ch_fn_dynamic_shapes(test_params: InplaceOpTestCase, input_shape): input_1 = opset.parameter(input_shape, name="Input") - fn = test_params.op_builder(test_params.name, test_params.reduce_shape) - if raise_error: - with pytest.raises(RuntimeError): - fn(input_1, 0) - return - last_node = fn(input_1, 0) + fn = test_params.op_builder(test_params.reduce_shape) + last_node = fn(input_1, 0, test_params.name) result = opset.result(last_node) model = ov.Model([result], [input_1]) check_inplace_op(model.input().get_node(), test_params.ref_types, test_params.ref_values, 1, 0) @@ -446,7 +447,7 @@ def test_node_removing(target_layers): transformed_model = model_transformer.transform(transformation_layout) ref_name = "removed_nodes_in_" + model_to_test.ref_graph_name - compare_nncf_graphs(transformed_model, REFERENCE_GRAPHS_DIR / ref_name) + compare_nncf_graphs(transformed_model, get_actual_reference_for_current_openvino(REFERENCE_GRAPHS_DIR / ref_name)) @pytest.mark.parametrize("target_layers, ref_fq_names", zip(TARGET_INSERT_LAYERS, TARGET_PRE_LAYER_FQS)) @@ -624,7 +625,7 @@ def infer_model_with_ones(model, shape): ret_val_1 = infer_model_with_ones(model, input_shape) ret_val_2 = infer_model_with_ones(transformed_model, input_shape) assert ret_val_1.keys() == ret_val_2.keys() - for output in ret_val_1.keys(): + for output in ret_val_1: assert np.allclose(ret_val_1[output], ret_val_2[output]) assert id(transformed_model) != id(model) @@ -668,8 +669,12 @@ def test_null_biases_insertion(model_with_parameters): MODELS_WITH_DATA = [ - {"model": ConvModel(), "input_layers": ["Conv"], "output_layers": ["Conv"]}, - {"model": QuantizedModel(), "input_layers": ["Relu_1", "Transpose"], "output_layers": ["Conv_3", "Add_2"]}, + {"model": ConvModel(), "input_ids": [("Conv", 0)], "output_ids": [("Conv", 0)]}, + { + "model": QuantizedModel(), + "input_ids": [("Relu_1", 0), ("Transpose", 0)], + "output_ids": [("Conv_3", 0), ("Add_2", 0)], + }, ] @@ -678,13 +683,15 @@ def test_model_extraction(model_with_data): model_to_test = model_with_data["model"] model = model_to_test.ov_model transformation_layout = TransformationLayout() - command = OVModelExtractionCommand(model_with_data["input_layers"], model_with_data["output_layers"]) + command = OVModelExtractionCommand(model_with_data["input_ids"], model_with_data["output_ids"]) transformation_layout.register(command) model_transformer = OVModelTransformer(model) transformed_model = model_transformer.transform(transformation_layout) - path_to_dot = REFERENCE_GRAPHS_DIR / f"exctracted_{model_to_test.ref_graph_name}" + path_to_dot = get_actual_reference_for_current_openvino( + REFERENCE_GRAPHS_DIR / f"exctracted_{model_to_test.ref_graph_name}" + ) compare_nncf_graphs(transformed_model, path_to_dot) diff --git a/tests/openvino/native/test_model_utils.py b/tests/openvino/native/test_model_utils.py index 28d2bbe6694..5fb5ecc968c 100644 --- a/tests/openvino/native/test_model_utils.py +++ b/tests/openvino/native/test_model_utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -9,14 +9,19 @@ # See the License for the specific language governing permissions and # limitations under the License. +import networkx as nx import numpy as np import pytest +from nncf.common.factory import NNCFGraphFactory +from nncf.common.insertion_point_graph import InsertionPointGraph from nncf.openvino.graph.metatypes.openvino_metatypes import OVConvolutionMetatype +from nncf.openvino.graph.model_utils import remove_friendly_name_duplicates from nncf.openvino.graph.node_utils import create_bias_tensor from tests.common.quantization.mock_graphs import NodeWithType from tests.common.quantization.mock_graphs import create_mock_graph from tests.common.quantization.mock_graphs import get_nncf_graph_from_mock_nx_graph +from tests.openvino.native.models import DuplicatedNamesModel def get_nncf_graph_for_test(edge_shape, dtype): @@ -45,3 +50,27 @@ def test_create_bias_tensor(edge_shape, dtype, ref_shape): val = create_bias_tensor(graph.get_node_by_name("/Conv_1_0"), graph, 5) assert val.shape == ref_shape assert np.equal(val, np.full(ref_shape, 5)).all() + + +@pytest.mark.parametrize( + "model_instance,remove_duplicates,unique_layer_numbers", + [ + (DuplicatedNamesModel().ov_model, False, 5), + (DuplicatedNamesModel().ov_model, True, 6), + ], +) +def test_remove_friendly_name_duplicates(model_instance, remove_duplicates, unique_layer_numbers): + if remove_duplicates: + model_instance = remove_friendly_name_duplicates(model_instance) + + model_uniqie_names = set([op.get_friendly_name() for op in model_instance.get_ops()]) + assert len(model_uniqie_names) == unique_layer_numbers + + nncf_graph = NNCFGraphFactory.create(model_instance) + assert len(nncf_graph.get_all_nodes()) == unique_layer_numbers + + try: + _ = InsertionPointGraph(nncf_graph) + assert remove_duplicates + except nx.NetworkXUnfeasible: + assert not remove_duplicates diff --git a/tests/openvino/native/test_nncf_graph_builder.py b/tests/openvino/native/test_nncf_graph_builder.py index 1f75de1f084..65587b26459 100644 --- a/tests/openvino/native/test_nncf_graph_builder.py +++ b/tests/openvino/native/test_nncf_graph_builder.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -9,28 +9,28 @@ # See the License for the specific language governing permissions and # limitations under the License. +from pathlib import Path + import openvino.runtime as ov import pytest from nncf.common.graph.graph import NNCFGraphEdge from nncf.common.graph.layer_attributes import Dtype from nncf.openvino.graph.nncf_graph_builder import GraphConverter -from tests.openvino.conftest import OPENVINO_NATIVE_TEST_ROOT from tests.openvino.native.common import compare_nncf_graphs -from tests.openvino.native.common import get_openvino_version +from tests.openvino.native.common import get_actual_reference_for_current_openvino from tests.openvino.native.models import SYNTHETIC_MODELS from tests.openvino.native.models import ParallelEdgesModel from tests.openvino.omz_helpers import convert_model from tests.openvino.omz_helpers import download_model -OV_VERSION = get_openvino_version() -REFERENCE_GRAPHS_DIR = OPENVINO_NATIVE_TEST_ROOT / "data" / OV_VERSION / "reference_graphs" / "original_nncf_graph" +REFERENCE_GRAPHS_DIR = Path("reference_graphs") / "original_nncf_graph" @pytest.mark.parametrize("model_cls_to_test", SYNTHETIC_MODELS.values()) def test_compare_nncf_graph_synthetic_models(model_cls_to_test): model_to_test = model_cls_to_test() - path_to_dot = REFERENCE_GRAPHS_DIR / model_to_test.ref_graph_name + path_to_dot = get_actual_reference_for_current_openvino(REFERENCE_GRAPHS_DIR / model_to_test.ref_graph_name) compare_nncf_graphs(model_to_test.ov_model, path_to_dot) @@ -51,7 +51,7 @@ def test_compare_nncf_graph_omz_models(tmp_path, omz_cache_dir, model_name): model_path = tmp_path / "public" / model_name / "FP32" / f"{model_name}.xml" model = ov.Core().read_model(model_path) - path_to_dot = REFERENCE_GRAPHS_DIR / f"{model_name}.dot" + path_to_dot = get_actual_reference_for_current_openvino(REFERENCE_GRAPHS_DIR / f"{model_name}.dot") compare_nncf_graphs(model, path_to_dot) diff --git a/tests/openvino/native/test_node_utils.py b/tests/openvino/native/test_node_utils.py index 47e1af5162c..74ca6462fcf 100644 --- a/tests/openvino/native/test_node_utils.py +++ b/tests/openvino/native/test_node_utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/openvino/native/test_pattern_manager.py b/tests/openvino/native/test_pattern_manager.py index f4bbfe7b4c8..a0f0bc38dd5 100644 --- a/tests/openvino/native/test_pattern_manager.py +++ b/tests/openvino/native/test_pattern_manager.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/openvino/native/test_smooth_quant.py b/tests/openvino/native/test_smooth_quant.py index 329c83aaeba..caa297ef83d 100644 --- a/tests/openvino/native/test_smooth_quant.py +++ b/tests/openvino/native/test_smooth_quant.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -9,33 +9,90 @@ # See the License for the specific language governing permissions and # limitations under the License. -from pathlib import Path from typing import Callable, Dict import numpy as np -import openvino.runtime as ov +import openvino as ov import pytest import torch -from openvino.tools.mo import convert_model +from nncf.common.graph.transformations.commands import TransformationCommand from nncf.openvino.graph.layer_attributes import OVLayerAttributes from nncf.openvino.graph.layout import OVLayoutElem from nncf.openvino.graph.metatypes.openvino_metatypes import OVConvolutionMetatype from nncf.openvino.graph.metatypes.openvino_metatypes import OVMatMulMetatype from nncf.quantization.algorithms.smooth_quant.openvino_backend import OVSmoothQuantAlgoBackend +from tests.post_training.test_templates.helpers import ConvTestModel +from tests.post_training.test_templates.helpers import LinearMultiShapeModel +from tests.post_training.test_templates.helpers import ShareWeghtsConvAndShareLinearModel from tests.post_training.test_templates.test_smooth_quant import TemplateTestSQAlgorithm +OV_LINEAR_MODEL_MM_OP_MAP = { + "MatMul1": "aten::matmul/MatMul", + "MatMul2": "aten::matmul/MatMul_1", + "MatMul3": "aten::matmul/MatMul_6", + "MatMul4": "aten::matmul/MatMul_4", + "MatMul5": "aten::matmul/MatMul_7", + "MatMul6": "aten::matmul/MatMul_5", + "MatMul7": "aten::matmul/MatMul_3", + "MatMul8": "aten::matmul/MatMul_2", + "Linear1": "__module.linear_2/aten::linear/MatMul", + "Linear2": "__module.linear_1/aten::linear/MatMul", + "Linear3": "__module.linear_3/aten::linear/MatMul", + "Linear4": "__module.linear_4/aten::linear/MatMul", +} + +OV_LINEAR_MODEL_SQ_OP_MAP = { + "MatMul1": "aten::reshape/Reshape_0_0/nncf_smooth_quant", + "MatMul2": "aten::reshape/Reshape_0_0/nncf_smooth_quant", + "MatMul3": "aten::reshape/Reshape_1_0_1/nncf_smooth_quant", + "MatMul4": "aten::reshape/Reshape_1_0_0/nncf_smooth_quant", + "MatMul5": "aten::reshape/Reshape_2_0_0/nncf_smooth_quant", + "MatMul6": "aten::max/ReduceMax_0_0/nncf_smooth_quant", + "MatMul7": "aten::flatten/Reshape_1_0_0/nncf_smooth_quant", + "MatMul8": "aten::flatten/Reshape_0_0/nncf_smooth_quant", + "Linear1": "prim::ListUnpack/VariadicSplit_1_0/nncf_smooth_quant", + "Linear2": "prim::ListUnpack/VariadicSplit_0_0/nncf_smooth_quant", + "Linear3": "aten::add/Add_0_0/nncf_smooth_quant", + "Linear4": "aten::add/Add_0_0/nncf_smooth_quant", +} + +OV_CONV_MODEL_MM_OP_MAP = { + "Conv1": "__module.conv/aten::_convolution/Convolution", +} + +OV_CONV_MODEL_SQ_OP_MAP = { + "Conv1": "x_0_0/nncf_smooth_quant", +} + class TestOVSQAlgorithm(TemplateTestSQAlgorithm): @staticmethod def fn_to_type(tensor) -> np.ndarray: return np.array(tensor) + @pytest.fixture(params=[False, True], ids=["out_of_palce", "inplace"]) + def inplace_statistics(self, request) -> bool: + return request.param + + def get_node_name_map(self, model_cls) -> Dict[str, str]: + if model_cls is LinearMultiShapeModel: + return OV_LINEAR_MODEL_MM_OP_MAP + if model_cls is ConvTestModel: + return OV_CONV_MODEL_MM_OP_MAP + if model_cls is ShareWeghtsConvAndShareLinearModel: + return {} + raise NotImplementedError + + @staticmethod + def get_target_node_name(command: TransformationCommand): + return command.target_point.target_node_name + @staticmethod def get_transform_fn() -> Callable: def transform_fn(data_item): tensor, _ = data_item - return {"input.1": tensor} + return {"x": tensor} return transform_fn @@ -45,19 +102,20 @@ def get_backend() -> OVSmoothQuantAlgoBackend: @staticmethod def backend_specific_model(model: torch.nn.Module, tmp_dir: str) -> ov.Model: - # TODO(AlexanderDokuchaev): remove onnx export after fix 119625 - onnx_path = Path(f"{tmp_dir}/model.onnx") - torch.onnx.export(model, torch.rand(model.INPUT_SIZE), onnx_path, opset_version=13, input_names=["input.1"]) - ov_model = convert_model(onnx_path, input_shape=model.INPUT_SIZE, compress_to_fp16=False) - return ov_model + return ov.convert_model(model, example_input=torch.rand(model.INPUT_SIZE), input=model.INPUT_SIZE) @staticmethod - def check_scales(model: ov.Model, reference_values: Dict[str, np.ndarray]) -> None: + def check_scales(model: ov.Model, reference_values: Dict[str, np.ndarray], model_cls) -> None: + names_map = OV_LINEAR_MODEL_SQ_OP_MAP if model_cls is LinearMultiShapeModel else OV_CONV_MODEL_SQ_OP_MAP ops_list = {op.get_friendly_name(): op for op in model.get_ops()} - for ref_name, ref_value in reference_values.items(): - node = ops_list[ref_name] - const_node = node.input(1).get_source_output().get_node() - + for ref_names, ref_value in reference_values.items(): + const_nodes = [] + for ref_name in ref_names: + node = ops_list[names_map[ref_name]] + const_nodes.append(node.input(1).get_source_output().get_node()) + # Check unified group acutally shares one constant + assert all(node is const_nodes[0] for node in const_nodes[1:]) + const_node = const_nodes[0] assert const_node.get_type_name() == "Constant" value = const_node.data diff --git a/tests/openvino/native/test_statistic_collector.py b/tests/openvino/native/test_statistic_collector.py index 73d3534bd50..8363eb8682a 100644 --- a/tests/openvino/native/test_statistic_collector.py +++ b/tests/openvino/native/test_statistic_collector.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/openvino/native/test_statistics_aggregator.py b/tests/openvino/native/test_statistics_aggregator.py index d611bd514f9..ff573310080 100644 --- a/tests/openvino/native/test_statistics_aggregator.py +++ b/tests/openvino/native/test_statistics_aggregator.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -104,10 +104,6 @@ def dataset_samples(self, dataset_values): dataset_samples[0][0, i, 0, 1] = value["min"] return dataset_samples - @pytest.fixture - def is_stat_in_shape_of_scale(self) -> bool: - return True - @pytest.fixture def is_backend_support_custom_estimators(self) -> bool: return True diff --git a/tests/openvino/omz_helpers.py b/tests/openvino/omz_helpers.py index 77787fd6aa7..a090e1b22fd 100644 --- a/tests/openvino/omz_helpers.py +++ b/tests/openvino/omz_helpers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -39,7 +39,7 @@ def calculate_metrics( model_path, config_path, data_dir, report_path, eval_size=None, framework="openvino", device="CPU" ): com_line = [ - "accuracy_check", + "python -m accuracy_checker.main", "-c", str(config_path), "-m", diff --git a/tests/openvino/pot/__init__.py b/tests/openvino/pot/__init__.py deleted file mode 100644 index 9b29b47534a..00000000000 --- a/tests/openvino/pot/__init__.py +++ /dev/null @@ -1,10 +0,0 @@ -# Copyright (c) 2023 Intel Corporation -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# http://www.apache.org/licenses/LICENSE-2.0 -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/tests/openvino/pot/quantization/__init__.py b/tests/openvino/pot/quantization/__init__.py deleted file mode 100644 index 9b29b47534a..00000000000 --- a/tests/openvino/pot/quantization/__init__.py +++ /dev/null @@ -1,10 +0,0 @@ -# Copyright (c) 2023 Intel Corporation -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# http://www.apache.org/licenses/LICENSE-2.0 -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/tests/openvino/pot/quantization/test_helpers.py b/tests/openvino/pot/quantization/test_helpers.py deleted file mode 100644 index d85eca284a2..00000000000 --- a/tests/openvino/pot/quantization/test_helpers.py +++ /dev/null @@ -1,37 +0,0 @@ -# Copyright (c) 2023 Intel Corporation -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# http://www.apache.org/licenses/LICENSE-2.0 -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pytest - -from nncf.openvino.pot.quantization.quantize_model import _create_ignored_scope_config -from nncf.scopes import IgnoredScope - - -def test_create_ignored_scope_config(): - ignored_names = ["name1", "name2"] - ignored_types = ["type1", "type2"] - - ignored_scope = IgnoredScope( - names=ignored_names, - types=ignored_types, - ) - ignored_config = _create_ignored_scope_config(ignored_scope) - - assert ignored_config["scope"] == ignored_names - - actual_types = [a["type"] for a in ignored_config["operations"]] - assert actual_types.sort() == ignored_types.sort() - - -def test_create_ignored_scope_config_raise_exception(): - ignored_scope = IgnoredScope(patterns=[".*"]) - with pytest.raises(Exception): - _ = _create_ignored_scope_config(ignored_scope) diff --git a/tests/openvino/pot/quantization/test_parameters.py b/tests/openvino/pot/quantization/test_parameters.py deleted file mode 100644 index 129902dce3e..00000000000 --- a/tests/openvino/pot/quantization/test_parameters.py +++ /dev/null @@ -1,20 +0,0 @@ -# Copyright (c) 2023 Intel Corporation -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# http://www.apache.org/licenses/LICENSE-2.0 -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import pytest -from openvino.tools.pot.algorithms.quantization.utils import load_hardware_config - -from nncf.parameters import TargetDevice - - -@pytest.mark.parametrize("target_device", [TargetDevice.ANY, TargetDevice.CPU, TargetDevice.CPU_SPR, TargetDevice.GPU]) -def test_target_device(target_device): - config = {"target_device": target_device.value} - assert load_hardware_config(config) diff --git a/tests/openvino/pot/quantization/test_sanity.py b/tests/openvino/pot/quantization/test_sanity.py deleted file mode 100644 index 75f43afb187..00000000000 --- a/tests/openvino/pot/quantization/test_sanity.py +++ /dev/null @@ -1,59 +0,0 @@ -# Copyright (c) 2023 Intel Corporation -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# http://www.apache.org/licenses/LICENSE-2.0 -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import os - -import openvino.runtime as ov -import pytest - -import nncf -from nncf.quantization.advanced_parameters import AdvancedQuantizationParameters -from tests.openvino.conftest import AC_CONFIGS_DIR -from tests.openvino.datasets_helpers import get_dataset_for_test -from tests.openvino.datasets_helpers import get_nncf_dataset_from_ac_config -from tests.openvino.omz_helpers import calculate_metrics -from tests.openvino.omz_helpers import convert_model -from tests.openvino.omz_helpers import download_model - -OMZ_MODELS = [ - ("resnet-18-pytorch", "imagenette2-320", {"accuracy@top1": 0.771, "accuracy@top5": 0.949}), - ("mobilenet-v3-small-1.0-224-tf", "imagenette2-320", {"accuracy@top1": 0.734, "accuracy@top5": 0.922}), - ("googlenet-v3-pytorch", "imagenette2-320", {"accuracy@top1": 0.91, "accuracy@top5": 0.994}), - ("retinaface-resnet50-pytorch", "wider", {"map": 0.91875950512032}), -] - - -@pytest.mark.parametrize("model, dataset, ref_metrics", OMZ_MODELS, ids=[model[0] for model in OMZ_MODELS]) -def test_compression(data_dir, tmp_path, omz_cache_dir, model, dataset, ref_metrics): - extracted_data_dir = os.path.dirname(get_dataset_for_test(dataset, data_dir)) - config_path = AC_CONFIGS_DIR / f"{model}.yml" - - download_model(model, tmp_path, omz_cache_dir) - convert_model(model, tmp_path) - model_path = tmp_path / "public" / model / "FP32" / f"{model}.xml" - - fp_model_dir = os.path.dirname(model_path) - int8_ir_path = os.path.join(fp_model_dir, f"{model}_int8.xml") - - calibration_dataset = get_nncf_dataset_from_ac_config(model_path, config_path, extracted_data_dir) - - ov_model = ov.Core().read_model(str(model_path)) - quantized_model = nncf.quantize( - ov_model, - calibration_dataset, - advanced_parameters=AdvancedQuantizationParameters(backend_params={"use_pot": True}), - ) - ov.serialize(quantized_model, int8_ir_path) - - report_path = tmp_path / f"{model}.csv" - metrics = calculate_metrics(int8_ir_path, config_path, extracted_data_dir, report_path, eval_size=1000) - for metric_name, metric_val in ref_metrics.items(): - assert metrics[metric_name] == pytest.approx(metric_val, abs=0.006) diff --git a/tests/openvino/pot/test_engine.py b/tests/openvino/pot/test_engine.py deleted file mode 100644 index 97d952dbde7..00000000000 --- a/tests/openvino/pot/test_engine.py +++ /dev/null @@ -1,116 +0,0 @@ -# Copyright (c) 2023 Intel Corporation -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# http://www.apache.org/licenses/LICENSE-2.0 -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from typing import Any, Iterable, List, Tuple - -import numpy as np -import openvino.runtime as ov -from openvino.tools import pot - -from nncf.data.dataset import Dataset -from nncf.openvino.pot.engine import OVEngine -from nncf.openvino.pot.engine import calc_per_sample_metrics -from tests.openvino.native.models import LinearModel -from tests.openvino.pot.utils import convert_openvino_model_to_compressed_model - - -class DummySampler: - def __init__(self, indices: List[int]): - self._subset_indices = indices - - -class DummyDataset: - def __init__(self, shape: Tuple[int]): - self._length = 5 - self._data = [np.random.rand(*shape).astype(np.float32) for _ in range(self._length)] - - def __iter__(self): - return iter(self._data) - - -def val_func(compiled_model: ov.CompiledModel, dataset: Iterable[Any]) -> float: - output = compiled_model.output(0) - - values = [] - for inputs in dataset: - predictions = compiled_model([inputs])[output] - values.append(np.sum(predictions)) - - return np.mean(values).item(), None - - -def get_expected(model: ov.Model, dataset, use_output: bool = False): - compiled_model = ov.Core().compile_model(model, device_name="CPU") - metric, _ = val_func(compiled_model, dataset) - - per_sample_metrics = [] - output = compiled_model.output(0) - for idx, data_item in enumerate(dataset): - if use_output: - value = compiled_model([data_item])[output] - else: - value, _ = val_func(compiled_model, [data_item]) - - per_sample_metrics.append({"sample_id": idx, "result": value}) - return per_sample_metrics, metric - - -def test_predict_original_metric(): - subset_indices = [0, 2, 4] - dataset = Dataset(DummyDataset(shape=(1, 3, 4, 2))) - - ov_model = LinearModel().ov_model - expected_per_sample, expected_metric = get_expected(ov_model, dataset.get_data(subset_indices), use_output=False) - - pot_model = convert_openvino_model_to_compressed_model(ov_model, target_device="CPU") - engine = OVEngine({"device": "CPU"}, dataset, dataset, val_func, use_original_metric=True) - engine.set_model(pot_model) - - (actual_per_sample, actual_metric), _ = engine.predict(sampler=DummySampler(subset_indices), metric_per_sample=True) - - assert np.allclose(expected_metric, actual_metric["original_metric"]) - for expected, actual in zip(expected_per_sample, actual_per_sample): - assert expected["sample_id"] == actual["sample_id"] - assert np.allclose(expected["result"], actual["result"]) - - -def test_predict_output(): - dataset = Dataset(DummyDataset(shape=(1, 3, 4, 2))) - - ov_model = LinearModel().ov_model - expected_per_sample, expected_metric = get_expected(ov_model, dataset.get_data(), use_output=True) - pot_model = convert_openvino_model_to_compressed_model(ov_model, target_device="CPU") - engine = OVEngine({"device": "CPU"}, dataset, dataset, val_func, use_original_metric=False) - engine.set_model(pot_model) - - stats_layout = {} - stats_layout["MatMul"] = {"output_logits": pot.statistics.statistics.TensorStatistic(lambda a: a)} - - (actual_per_sample, actual_metric), raw_output = engine.predict(stats_layout, metric_per_sample=True) - - raw_output = raw_output["MatMul"]["output_logits"] - for idx, data in enumerate(actual_per_sample): - data["result"] = raw_output[idx] - - assert np.allclose(expected_metric, actual_metric["original_metric"]) - for expected, actual in zip(expected_per_sample, actual_per_sample): - assert expected["sample_id"] == actual["sample_id"] - assert np.allclose(expected["result"], actual["result"]) - - -def test_calc_per_sample(): - dataset = Dataset(iter(DummyDataset(shape=(1, 3, 4, 2)))) # Can iterate only once - ov_model = LinearModel().ov_model - compiled_model = ov.Core().compile_model(ov_model, device_name="CPU") - - # Check that we iterate through dataset only once during - # per-sample metrics calculation. - _ = calc_per_sample_metrics(compiled_model, val_func, dataset, [0, 2, 4]) diff --git a/tests/openvino/pot/utils.py b/tests/openvino/pot/utils.py deleted file mode 100644 index 0183da0280f..00000000000 --- a/tests/openvino/pot/utils.py +++ /dev/null @@ -1,33 +0,0 @@ -# Copyright (c) 2023 Intel Corporation -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# http://www.apache.org/licenses/LICENSE-2.0 -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import tempfile -from pathlib import Path - -import openvino.runtime as ov -from openvino.tools import pot - - -def convert_openvino_model_to_compressed_model( - model: ov.Model, target_device: str -) -> pot.graph.nx_model.CompressedModel: - with tempfile.TemporaryDirectory(dir=tempfile.gettempdir()) as tmp_dir: - xml_path = str(Path(tmp_dir) / "model.xml") - bin_path = str(Path(tmp_dir) / "model.bin") - ov.serialize(model, xml_path, bin_path) - model_config = { - "model_name": "model", - "model": xml_path, - "weights": bin_path, - } - pot_model = pot.load_model(model_config, target_device) - - return pot_model diff --git a/tests/openvino/test_telemetry.py b/tests/openvino/test_telemetry.py index 3c08fdcf518..ed8e634abba 100644 --- a/tests/openvino/test_telemetry.py +++ b/tests/openvino/test_telemetry.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/openvino/test_transform_fn.py b/tests/openvino/test_transform_fn.py index ca3a22800de..4372b71cfb7 100644 --- a/tests/openvino/test_transform_fn.py +++ b/tests/openvino/test_transform_fn.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -14,8 +14,6 @@ import pytest import nncf -from nncf.openvino.quantization.backend_parameters import BackendParameters -from nncf.quantization.advanced_parameters import AdvancedQuantizationParameters from tests.openvino.native.models import ConvModel as ModelWithMultipleInputs from tests.openvino.native.models import LinearModel as ModelWithSingleInput @@ -43,34 +41,23 @@ def multiple_inputs_as_dict_transform_fn(data_item): @pytest.mark.parametrize( - "model,transform_fn,use_pot", + "model,transform_fn", [ - [ModelWithSingleInput(), single_input_transform_fn, False], - [ModelWithSingleInput(), single_input_transform_fn, True], - [ModelWithMultipleInputs(), multiple_inputs_transform_fn, False], - [ModelWithMultipleInputs(), multiple_inputs_transform_fn, True], - [ModelWithMultipleInputs(), multiple_inputs_as_dict_transform_fn, False], - [ModelWithMultipleInputs(), multiple_inputs_as_dict_transform_fn, True], + [ModelWithSingleInput(), single_input_transform_fn], + [ModelWithMultipleInputs(), multiple_inputs_transform_fn], + [ModelWithMultipleInputs(), multiple_inputs_as_dict_transform_fn], ], ids=[ "single_input_native", - "signle_input_pot", "multiple_inputs_native", - "multiple_inputs_pot", "multiple_inputs_as_dict_native", - "multiple_inputs_as_dict_pot", ], ) -def test_transform_fn(model, transform_fn, use_pot: bool): +def test_transform_fn(model, transform_fn): # Check the transformation function compiled_model = ov.compile_model(model.ov_model) _ = compiled_model(transform_fn(next(iter(dataset)))) # Start quantization - params = AdvancedQuantizationParameters( - backend_params={ - BackendParameters.USE_POT: use_pot, - } - ) calibration_dataset = nncf.Dataset(dataset, transform_fn) - _ = nncf.quantize(model.ov_model, calibration_dataset, advanced_parameters=params) + _ = nncf.quantize(model.ov_model, calibration_dataset) diff --git a/tests/openvino/tools/__init__.py b/tests/openvino/tools/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/openvino/tools/__init__.py +++ b/tests/openvino/tools/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/openvino/tools/calibrate.py b/tests/openvino/tools/calibrate.py index 3fc76439564..b009fe1d5f9 100644 --- a/tests/openvino/tools/calibrate.py +++ b/tests/openvino/tools/calibrate.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -14,7 +14,6 @@ import json import multiprocessing import os -import tempfile from argparse import ArgumentParser from collections import OrderedDict from collections import defaultdict @@ -27,12 +26,16 @@ import numpy as np import openvino.runtime as ov +from config import Config from openvino.runtime import Dimension from openvino.runtime import PartialShape -from openvino.tools import pot -from openvino.tools.accuracy_checker.evaluators.quantization_model_evaluator import ModelEvaluator -from openvino.tools.accuracy_checker.evaluators.quantization_model_evaluator import create_model_evaluator -from openvino.tools.pot.configs.config import Config + +try: + from accuracy_checker.evaluators.quantization_model_evaluator import ModelEvaluator + from accuracy_checker.evaluators.quantization_model_evaluator import create_model_evaluator +except ImportError: + from openvino.tools.accuracy_checker.evaluators.quantization_model_evaluator import ModelEvaluator + from openvino.tools.accuracy_checker.evaluators.quantization_model_evaluator import create_model_evaluator import nncf from nncf.common.deprecation import warning_deprecated @@ -40,9 +43,6 @@ from nncf.common.quantization.structs import QuantizationPreset from nncf.common.quantization.structs import QuantizationScheme from nncf.data.dataset import DataProvider -from nncf.openvino.pot.quantization.quantize_model import ( - quantize_with_accuracy_control_impl as pot_quantize_with_native_accuracy_control, -) from nncf.parameters import DropType from nncf.parameters import ModelType from nncf.parameters import QuantizationMode @@ -93,7 +93,7 @@ def parse_args(): """ parser = ArgumentParser(description="NNCF OpenVINO Benchmarking Tool", allow_abbrev=False) - parser.add_argument("-c", "--config", help="Path to a config file with optimization parameters (POT format).") + parser.add_argument("-c", "--config", help="Path to a config file with optimization parameters (JSON format).") parser.add_argument( "--output-dir", type=str, default="./results", help="The directory where models are saved. Default: ./results" @@ -229,7 +229,7 @@ def __call__(self, compiled_model: ov.CompiledModel, indices: Optional[Iterable[ def _output_callback(self, raw_predictions, **kwargs): if not ("metrics_result" in kwargs and "dataset_indices" in kwargs): - raise RuntimeError( + raise nncf.ValidationError( "Expected `metrics_result`, `dataset_indices` be passed to output_callback inside accuracy checker" ) @@ -538,6 +538,12 @@ def map_smooth_quant_alpha(smooth_quant_alpha): return map_smooth_quant_alphas({"matmul": smooth_quant_alpha, "convolution": -1}) +def map_mode(mode): + if not hasattr(QuantizationMode, mode): + raise ValueError(f"{mode} mode is not supported") + return {"mode": getattr(QuantizationMode, mode)} + + def map_threshold(threshold): ctx = get_algorithm_parameters_context() advanced_parameter_name = ctx.param_name_map[ParameterNames.advanced_parameters] @@ -618,6 +624,7 @@ def get_pot_quantization_parameters_mapping(): "threshold": map_threshold, "smooth_quant_alphas": map_smooth_quant_alphas, "smooth_quant_alpha": map_smooth_quant_alpha, + "mode": map_mode, } default_parameters = {"use_layerwise_tuning": False} @@ -773,14 +780,14 @@ def maybe_reshape_model(model, dataset, subset_size, input_to_tensor_name): model_inputs_shapes[input_to_tensor_name[input_node.friendly_name]] = tuple(partial_shape) if len(dataset_inputs_shapes) != len(model_inputs_shapes): - raise RuntimeError( + raise nncf.InternalError( f"Model inputs: {list(model_inputs_shapes.keys())}" f" and dataset inputs {list(dataset_inputs_shapes.keys())} are not compatible" ) for name in model_inputs_shapes: if name not in dataset_inputs_shapes: - raise RuntimeError( + raise nncf.ValidationError( f"Model input {name} is not present in dataset inputs: {list(dataset_inputs_shapes.keys())}" ) @@ -789,7 +796,7 @@ def maybe_reshape_model(model, dataset, subset_size, input_to_tensor_name): for name, shapes in dataset_inputs_shapes.items(): shapes = list(shapes) if len(set(len(shape) for shape in shapes)) != 1 or len(model_inputs_shapes[name]) != len(shapes[0]): - raise RuntimeError("calibrate.py does not support dataset with dynamic ranks") + raise nncf.InternalError("calibrate.py does not support dataset with dynamic ranks") for idx in range(len(shapes[0])): if len(shapes) == 1: @@ -903,19 +910,15 @@ def calculate_per_sample_subset_size(self, sequence_subset_size): return subset_size -def quantize_model(xml_path, bin_path, accuracy_checker_config, quantization_impl, quantization_parameters): +def quantize_model(xml_path, bin_path, accuracy_checker_config, quantization_parameters): ov_model = ov.Core().read_model(model=xml_path, weights=bin_path) model_evaluator = create_model_evaluator(accuracy_checker_config) model_evaluator.load_network([{"model": ov_model}]) model_evaluator.select_dataset("") advanced_parameters = quantization_parameters.get("advanced_parameters", AdvancedQuantizationParameters()) - if quantization_impl == "pot": - advanced_parameters.backend_params["use_pot"] = True - elif quantization_impl == "native": - advanced_parameters.backend_params["use_pot"] = False - else: - raise NotImplementedError() + if quantization_parameters.get("mode", None) is not None: + advanced_parameters.backend_params = None quantization_parameters["advanced_parameters"] = advanced_parameters transform_fn = get_transform_fn(model_evaluator, ov_model) @@ -952,27 +955,19 @@ def get_inference_data(self, indices: Optional[List[int]] = None): return DataProvider(ACDattasetWrapper(self._model_evaluator), self._transform_func, indices) -def initialize_model_and_evaluator(xml_path: str, bin_path: str, accuracy_checker_config, quantization_impl: str): +def initialize_model_and_evaluator(xml_path: str, bin_path: str, accuracy_checker_config): model_evaluator = create_model_evaluator(accuracy_checker_config) - with tempfile.TemporaryDirectory(dir=tempfile.gettempdir()) as tmp_dir: - if quantization_impl == "pot": - pot_model = pot.load_model({"model_name": "model", "model": xml_path, "weights": bin_path}, "CPU") - paths = pot.save_model(pot_model, save_path=tmp_dir, model_name="model") - xml_path, bin_path = paths[0]["model"], paths[0]["weights"] - - model = ov.Core().read_model(xml_path, bin_path) - model_evaluator.load_network_from_ir([{"model": xml_path, "weights": bin_path}]) - model_evaluator.select_dataset("") + model = ov.Core().read_model(xml_path, bin_path) + model_evaluator.load_network_from_ir([{"model": xml_path, "weights": bin_path}]) + model_evaluator.select_dataset("") return model, model_evaluator def quantize_model_with_accuracy_control( - xml_path: str, bin_path: str, accuracy_checker_config, quantization_impl: str, quantization_parameters + xml_path: str, bin_path: str, accuracy_checker_config, quantization_parameters ): - ov_model, model_evaluator = initialize_model_and_evaluator( - xml_path, bin_path, accuracy_checker_config, quantization_impl - ) + ov_model, model_evaluator = initialize_model_and_evaluator(xml_path, bin_path, accuracy_checker_config) transform_fn = get_transform_fn(model_evaluator, ov_model) dataset = get_dataset(model_evaluator, quantization_parameters) @@ -995,29 +990,14 @@ def quantize_model_with_accuracy_control( metric_name = metric_type validation_fn = ACValidationFunction(model_evaluator, metric_name, metric_type) - name_to_quantization_impl_map = { - "pot": pot_quantize_with_native_accuracy_control, - "native": nncf.quantize_with_accuracy_control, - } - advanced_parameters = quantization_parameters.get( "advanced_quantization_parameters", AdvancedQuantizationParameters() ) - if quantization_impl == "pot": - advanced_parameters.backend_params["use_pot"] = True - elif quantization_impl == "native": - advanced_parameters.backend_params["use_pot"] = False - else: - raise NotImplementedError() quantization_parameters["advanced_quantization_parameters"] = advanced_parameters - quantization_impl_fn = name_to_quantization_impl_map.get(quantization_impl) - if quantization_impl: - quantized_model = quantization_impl_fn( - ov_model, calibration_dataset, validation_dataset, validation_fn, **quantization_parameters - ) - else: - raise NotImplementedError(f"Unsupported implementation: {quantization_impl}") + quantized_model = nncf.quantize_with_accuracy_control( + ov_model, calibration_dataset, validation_dataset, validation_fn, **quantization_parameters + ) if original_model_shapes is not None: quantized_model.reshape(original_model_shapes) @@ -1025,7 +1005,7 @@ def quantize_model_with_accuracy_control( def filter_configuration(config: Config) -> Config: - fields_to_filter = ["smooth_quant_alphas", "smooth_quant_alpha"] + fields_to_filter = ["smooth_quant_alphas", "smooth_quant_alpha", "mode"] algorithms_to_update = defaultdict(dict) # Drop params before configure @@ -1054,6 +1034,8 @@ def filter_configuration(config: Config) -> Config: def main(): args = parse_args() + if args.impl is not None: + print("--impl option is deprecated and will have no effect. Only native calibration allowed.") config = Config.read_config(args.config) config = filter_configuration(config) @@ -1076,17 +1058,16 @@ def main(): "xml_path": xml_path, "bin_path": bin_path, "accuracy_checker_config": accuracy_checker_config, - "quantization_impl": args.impl, "quantization_parameters": algo_config, } output_model = algo_fn(**quantize_model_arguments) path = os.path.join(output_dir, "algorithm_parameters.json") - keys = ["xml_path", "quantization_impl", "quantization_parameters"] + keys = ["xml_path", "quantization_parameters"] dump_to_json(path, quantize_model_arguments, keys) else: - raise RuntimeError(f"Support for {algo_name} is not implemented in the optimize tool.") + raise nncf.InternalError(f"Support for {algo_name} is not implemented in the optimize tool.") model_name = config.model.model_name output_model_path = os.path.join(output_dir, f"{model_name}.xml") diff --git a/tests/openvino/tools/config.py b/tests/openvino/tools/config.py new file mode 100644 index 00000000000..e05de9620e7 --- /dev/null +++ b/tests/openvino/tools/config.py @@ -0,0 +1,362 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import json +from copy import deepcopy +from pathlib import Path + +import yaml +from addict import Dict + +try: + from accuracy_checker.config import ConfigReader +except ImportError: + from openvino.tools.accuracy_checker.config import ConfigReader + +import nncf +from nncf.common.logging import nncf_logger + +DEFAULT_PATH = "PATH" +DEFAULT_TARGET_DEVICE = "ANY" + + +class Config(Dict): + """ + Tool configuration containing model, engine, and algorithms' parameters. + Adapted from openvino/tools/pot/configs/config.py. + """ + + @staticmethod + def _read_config_from_file(path): + return read_config_from_file(path) + + @classmethod + def read_config(cls, path): + data = cls._read_config_from_file(path) + config = cls(data) + return config + + def configure_params(self, ac_config=None): + if ac_config is not None: + self.engine["config"] = ac_config + + self._configure_engine_params() + self._convert_mo_args() + self._configure_algo_params() + self._configure_logger_params() + + def add_log_dir(self, model_log_dir, exec_log_dir): + self.model["model_log_dir"] = model_log_dir + self.model["exec_log_dir"] = exec_log_dir + if "optimizer" in self: + self.optimizer.params["model_log_dir"] = model_log_dir + self.optimizer.params["exec_log_dir"] = exec_log_dir + if "compression" in self: + for algo_config in self.compression.algorithms: + algo_config.params["exec_log_dir"] = exec_log_dir + + def _convert_mo_args(self): + """Converts model args into model optimizer parameters""" + config = self.model + self._validate_model_conf() + if not self.model.model_name: + self.model.model_name = Path(self.model.model).stem + self["model"] = Config(config) + + def _validate_model_conf(self): + """Validates the correctness of model config""" + models = self.model.cascade if self.model.cascade else [self.model] + + if self.model.cascade and len(models) == 1: + nncf_logger.warning("Cascade is defined with single model") + + if not models: + raise nncf.ValidationError("Path to input model xml and bin is required.") + + for model in models: + if len(models) > 1 and not model.name: + raise nncf.ValidationError("Name of input model is required.") + if not model.model: + raise nncf.ValidationError("Path to input model xml is required.") + if not model.weights: + raise nncf.ValidationError("Path to input model bin is required.") + + def validate_algo_config(self): + """ + Validates the correctness of algorithm parameters in config + """ + range_estimator_parameters = { + "preset": None, + "min": {"type": None, "outlier_prob": None, "granularity": None, "clipping_value": None}, + "max": {"type": None, "outlier_prob": None, "granularity": None, "clipping_value": None}, + } + weights_params = { + "bits": None, + "mode": None, + "level_low": None, + "level_high": None, + "granularity": None, + "range_estimator": range_estimator_parameters, + } + activations_params = deepcopy(weights_params) + activations_params["range_estimator"]["min"].update({"aggregator": None}) + activations_params["range_estimator"]["max"].update({"aggregator": None}) + + ignored = {"ignored": {}} + ignored_content = {"skip_model": None, "scope": None, "operations": None} + if self.model.cascade: + for model in self.model.cascade: + ignored["ignored"].update({model.name: ignored_content}) + else: + ignored["ignored"] = ignored_content + + bias_correction_params = { + "stat_subset_size": None, + "shuffle_data": None, + "seed": None, + "apply_for_all_nodes": None, + "threshold": None, + } + + layerwise_finetuning_params = { + "num_samples_for_tuning": None, + "batch_size": None, + "optimizer": None, + "loss": None, + "tuning_iterations": None, + "random_seed": None, + "use_ranking_subset": None, + "calibration_indices_pool": None, + "calculate_grads_on_loss_increase_only": None, + "weight_decay": None, + } + + supported_params = { + "ActivationChannelAlignment": {"stat_subset_size": None, "shuffle_data": None, "seed": None}, + "MinMaxQuantization": { + "preset": None, + "stat_subset_size": None, + "shuffle_data": None, + "seed": None, + "range_estimator": range_estimator_parameters, + "weights": weights_params, + "activations": activations_params, + "saturation_fix": None, + }, + "FastBiasCorrection": bias_correction_params, + "BiasCorrection": bias_correction_params, + "DefaultQuantization": {"use_fast_bias": None, "use_layerwise_tuning": None}, + "ParamsTuningSearch": {}, + "DataFreeQuantization": {"preset": None, "weights": weights_params, "activations": activations_params}, + "AccuracyAwareQuantization": { + "metric_subset_ratio": None, + "ranking_subset_size": None, + "max_iter_num": None, + "maximal_drop": None, + "drop_type": None, + "use_prev_if_drop_increase": None, + "metrics": None, + "base_algorithm": "DefaultQuantization", + "annotation_free": None, + "tune_hyperparams": None, + "annotation_conf_threshold": None, + "convert_to_mixed_preset": None, + }, + "RangeOptimization": { + "stat_subset_size": None, + "result_filename": None, + "maxiter": None, + "lower_boxsize": None, + "upper_boxsize": None, + "zero_boxsize": None, + "optimization_scope": None, + "activation_ranges_to_set": None, + "metric_name": None, + "optimizer_name": None, + "stochastic": None, + "dump_model_prefix": None, + "error_function": None, + "opt_backend": None, + }, + "TunableQuantization": {"outlier_prob_choices": None}, + "MagnitudeSparsity": { + "sparsity_level": None, + "normed_threshold": None, + }, + "BaseWeightSparsity": { + "use_fast_bias": None, + }, + "WeightSparsity": { + "use_layerwise_tuning": None, + }, + "OverflowCorrection": { + "stat_subset_size": None, + "shuffle_data": None, + "seed": None, + }, + "RangeSupervision": { + "stat_subset_size": None, + "shuffle_data": None, + "seed": None, + }, + } + + # completing supported parameters + for algo_name in supported_params: + supported_params[algo_name].update(ignored) + + for algo_name in ["DefaultQuantization", "WeightSparsity"]: + supported_params[algo_name].update(layerwise_finetuning_params) + + for algo_name in ["ActivationChannelAlignment", "MinMaxQuantization", "FastBiasCorrection", "BiasCorrection"]: + supported_params["DefaultQuantization"].update(supported_params[algo_name]) + supported_params["ParamsTuningSearch"].update(supported_params[algo_name]) + + for algo_name in ["MagnitudeSparsity", "FastBiasCorrection"]: + supported_params["BaseWeightSparsity"].update(supported_params[algo_name]) + + for algo_name in ["BaseWeightSparsity"]: + supported_params["WeightSparsity"].update(supported_params[algo_name]) + + supported_params["TunableQuantization"].update(supported_params["MinMaxQuantization"]) + + # check algorithm parameters + for algo in self["compression"]["algorithms"]: + algo_name = algo["name"] + if algo_name in supported_params: + if algo_name == "AccuracyAwareQuantization": + backup = deepcopy(supported_params["AccuracyAwareQuantization"]) + base_algo = supported_params["AccuracyAwareQuantization"]["base_algorithm"] + if "base_algorithm" in algo["params"] and algo["params"]["base_algorithm"] in supported_params: + base_algo = algo["params"]["base_algorithm"] + supported_params["AccuracyAwareQuantization"].update(supported_params[base_algo]) + + check_params(algo_name, algo["params"], supported_params[algo_name]) + + if algo_name == "AccuracyAwareQuantization": + supported_params["AccuracyAwareQuantization"] = backup + + def _configure_engine_params(self): + """Converts engine config section into engine params dict""" + engine = self.engine + if "type" not in engine or engine.type == "accuracy_checker": + self._configure_ac_params() + self.engine.type = "accuracy_checker" + elif engine.type == "simplified": + if engine.data_source is None: + raise KeyError("Missed data dir for simplified engine") + self.engine.device = engine.device if engine.device else "CPU" + engine.data_source = Path(engine.data_source) + else: + raise KeyError("Unsupported engine type") + + def _configure_ac_params(self): + """Converts engine config into accuracy checker config""" + filtering_params = {"target_devices": ["CPU"], "target_framework": "openvino", "use_new_api": True} + if "config" in self.engine: + ac_conf, mode = ConfigReader.merge(Dict({**self.engine, **filtering_params})) + ac_conf = Dict(ac_conf) + else: + mode = "evaluations" if self.engine.module else "models" + ac_conf = Dict({mode: [self.engine]}) + ac_conf[mode][0].name = self.model.model_name + datasets_config = ac_conf[mode][0].datasets if mode == "models" else ac_conf[mode][0].module_config.datasets + if isinstance(datasets_config, dict) and "preprocessing" in datasets_config: + nncf_logger.debug("Global dataset preprocessing configuration found") + preprocessing_config = datasets_config.pop("preprocessing") + for dataset_name, dataset in datasets_config.items(): + if not dataset.preprocessing: + dataset["preprocessing"] = preprocessing_config + else: + nncf_logger.debug( + "Local preprocessing configuration is used for {} dataset".format(dataset_name) + ) + ConfigReader.check_local_config(ac_conf) + ac_conf = ConfigReader.convert_paths(ac_conf) + ConfigReader._filter_launchers(ac_conf, filtering_params, mode=mode) + for req_num in ["stat_requests_number", "eval_requests_number"]: + ac_conf[req_num] = self.engine[req_num] if req_num in self.engine else None + + self["engine"] = ac_conf + + def _configure_algo_params(self): + """Converts algorithm params into valid configuration for algorithms""" + self.validate_algo_config() + + for algo in self["compression"]["algorithms"]: + preset = algo["params"].get("preset", "performance") + aliases = {"symmetric": "performance", "asymmetric": "accuracy"} + preset = aliases.get(preset, preset) + presets_aliases_by_device = { + "NPU": {"accuracy": "accuracy"}, + "GNA": {"accuracy": "accuracy", "mixed": "accuracy"}, + "GNA3": {"accuracy": "accuracy", "mixed": "accuracy"}, + "GNA3.5": {"accuracy": "accuracy", "mixed": "accuracy"}, + "CPU": {"accuracy": "mixed"}, + "ANY": {"accuracy": "mixed"}, + "GPU": {"accuracy": "mixed"}, + } + algo["params"]["target_device"] = self["compression"].get("target_device", DEFAULT_TARGET_DEVICE) + rename = presets_aliases_by_device.get(algo["params"]["target_device"], {"accuracy": "mixed"}) + algo["params"]["preset"] = rename.get(preset, preset) + model_type = self["compression"].get("model_type", None) + algo["params"]["model_type"] = None if model_type == "None" else model_type + algo["params"]["dump_intermediate_model"] = self["compression"].get("dump_intermediate_model", False) + algo["params"]["inplace_statistics"] = self["compression"].get("inplace_statistics", True) + + def _configure_logger_params(self): + """Creates a log directory name based on model and algo configurations""" + + # init log folder name + log_algo_name = self.model.model_name + if "optimizer" in self: + log_algo_name = "{}_{}".format(log_algo_name, self["optimizer"]["name"]) + for algo in self["compression"]["algorithms"]: + log_algo_name = ("{}_{}".format(log_algo_name, algo.name)) if log_algo_name else algo.name + self.model.log_algo_name = log_algo_name + + def get_model_paths(self): + """Returns models paths + :return: dictionary with model tokens as keys and + dictionaries of paths to xml and bin as values + """ + return ( + deepcopy(self.model.cascade) + if self.model.cascade + else [{"model": self.model.model, "weights": self.model.weights}] + ) + + +def read_config_from_file(path): + path = Path(path) + extension = path.suffix.lower() + with path.open() as f: + if extension in (".yaml", ".yml"): + return yaml.load(f, Loader=yaml.SafeLoader) + if extension in (".json",): + return json.load(f) + raise nncf.InternalError('Unknown file extension for the file "{}"'.format(path)) + + +def check_params(algo_name, config, supported_params): + """Check algorithm parameters in config + :param algo_name: name of algorithm + :param config: config with parameters to check + :param supported_params: parameters supported by algorithm + """ + for key, value in config.items(): + if key not in supported_params: + raise nncf.InternalError("Algorithm {}. Unknown parameter: {}".format(algo_name, key)) + if isinstance(value, dict): + if isinstance(supported_params[key], dict): + check_params(algo_name, value, supported_params[key]) + else: + raise nncf.InternalError("Algorithm {}. Wrong structure for parameter: {}".format(algo_name, key)) diff --git a/tests/post_training/README.md b/tests/post_training/README.md index b4784c29182..ef6ef864fb4 100644 --- a/tests/post_training/README.md +++ b/tests/post_training/README.md @@ -1,12 +1,19 @@ -# Post-training Quantization Conformance Suite +# Post-training Compression Conformance Suite -This is the test suite that takes PyTorch Timm models and runs post-training quantization on ImageNet dataset for the following three representations: +This is the test suite that takes PyTorch Timm or HuggingFace models and runs post-training compression on ImageNet or +some HuggingFace datasets for the following three representations: - PyTorch - ONNX - OpenVINO -The outcome of each quantization step is accuracy and performance with OpenVINO. The source representation is converted to OpenVINO IR at this step. +The outcome of each compression test case is accuracy and performance with OpenVINO. +The source representation is converted to OpenVINO IR at this step. + +Test supports 2 different types of compression: + +- Post-training quantization of weights and activations. +- Weight compression. ## Installation @@ -30,12 +37,21 @@ n01843383 ## Usage -Once the environment is installed use the following command to run the test: +Once the environment is installed use the following command to run all tests, including post-training quantization +and weight compression: ```bash NUM_VAL_THREADS=8 pytest --data= --output=./tmp tests/post_training/test_quantize_conformance.py ``` +It's possible to run a suite of tests for the specific compression algorithm only. +For that append `::test_weight_compression` or `::test_ptq_quantization` to the `tests/post_training/test_quantize_conformance.py`. +For instance: + +```bash +NUM_VAL_THREADS=8 pytest --data= --output=./tmp tests/post_training/test_quantize_conformance.py::test_weight_compression +``` + `NUM_VAL_THREADS` environment variable controls the number of parallel streams when validating the model. Additional arguments: diff --git a/tests/post_training/__init__.py b/tests/post_training/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/post_training/__init__.py +++ b/tests/post_training/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/post_training/conftest.py b/tests/post_training/conftest.py index bfccb4e37af..4c1089ee453 100644 --- a/tests/post_training/conftest.py +++ b/tests/post_training/conftest.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/post_training/reference_data.yaml b/tests/post_training/data/ptq_reference_data.yaml similarity index 57% rename from tests/post_training/reference_data.yaml rename to tests/post_training/data/ptq_reference_data.yaml index e7f91f6a59a..3605403a440 100644 --- a/tests/post_training/reference_data.yaml +++ b/tests/post_training/data/ptq_reference_data.yaml @@ -8,8 +8,6 @@ hf/bert-base-uncased_backend_OPTIMUM: metric_value: null hf/bert-base-uncased_backend_OV: metric_value: null -hf/bert-base-uncased_backend_POT: - metric_value: null hf/bert-base-uncased_backend_TORCH: metric_value: null hf/hf-internal-testing/tiny-random-GPTNeoXForCausalLM_backend_FP32: @@ -19,250 +17,214 @@ hf/hf-internal-testing/tiny-random-GPTNeoXForCausalLM_backend_OPTIMUM: timm/crossvit_9_240_backend_CUDA_TORCH: metric_value: 0.689 timm/crossvit_9_240_backend_FP32: - metric_value: 0.69966 + metric_value: 0.73982 timm/crossvit_9_240_backend_ONNX: - metric_value: 0.68906 + metric_value: 0.72854 timm/crossvit_9_240_backend_OV: - metric_value: 0.68912 -timm/crossvit_9_240_backend_POT: - metric_value: 0.68862 + metric_value: 0.72812 timm/crossvit_9_240_backend_TORCH: - metric_value: 0.689 + metric_value: 0.72816 timm/darknet53_backend_CUDA_TORCH: metric_value: 0.79176 timm/darknet53_backend_FP32: - metric_value: 0.79858 + metric_value: 0.80006 timm/darknet53_backend_ONNX: - metric_value: 0.79224 + metric_value: 0.79336 timm/darknet53_backend_OV: - metric_value: 0.79192 -timm/darknet53_backend_POT: - metric_value: 0.79206 + metric_value: 0.79222 timm/darknet53_backend_TORCH: - metric_value: 0.79176 + metric_value: 0.7916 timm/deit3_small_patch16_224_backend_CUDA_TORCH: metric_value: 0.76816 timm/deit3_small_patch16_224_backend_FP32: - metric_value: 0.76974 + metric_value: 0.81358 timm/deit3_small_patch16_224_backend_ONNX: - metric_value: 0.76806 + metric_value: 0.81268 timm/deit3_small_patch16_224_backend_OV: - metric_value: 0.76786 -timm/deit3_small_patch16_224_backend_POT: - metric_value: 0.7676 + metric_value: 0.81276 timm/deit3_small_patch16_224_backend_TORCH: - metric_value: 0.76816 + metric_value: 0.81278 timm/dla34_backend_CUDA_TORCH: metric_value: 0.73978 timm/dla34_backend_FP32: - metric_value: 0.74416 + metric_value: 0.74628 timm/dla34_backend_ONNX: - metric_value: 0.74202 + metric_value: 0.7455 timm/dla34_backend_OV: - metric_value: 0.74224 -timm/dla34_backend_POT: - metric_value: 0.74222 + metric_value: 0.74556 timm/dla34_backend_TORCH: - metric_value: 0.73978 + metric_value: 0.74242 timm/dpn68_backend_CUDA_TORCH: metric_value: 0.75492 timm/dpn68_backend_FP32: - metric_value: 0.75964 + metric_value: 0.76342 timm/dpn68_backend_ONNX: - metric_value: 0.75488 + metric_value: 0.7595 timm/dpn68_backend_OV: - metric_value: 0.75544 -timm/dpn68_backend_POT: - metric_value: 0.7554 + metric_value: 0.75968 timm/dpn68_backend_TORCH: - metric_value: 0.75492 + metric_value: 0.75826 timm/efficientnet_b0_BC_backend_FP32: - metric_value: 0.77292 + metric_value: 0.77698 timm/efficientnet_b0_BC_backend_ONNX: - metric_value: 0.7686 + metric_value: 0.77212 timm/efficientnet_b0_BC_backend_OV: - metric_value: 0.76794 -timm/efficientnet_b0_BC_backend_POT: - metric_value: 0.76872 + metric_value: 0.77218 timm/efficientnet_b0_backend_CUDA_TORCH: metric_value: 0.768 timm/efficientnet_b0_backend_FP32: - metric_value: 0.77292 + metric_value: 0.77698 timm/efficientnet_b0_backend_ONNX: - metric_value: 0.7686 + metric_value: 0.77208 timm/efficientnet_b0_backend_OV: - metric_value: 0.7688 -timm/efficientnet_b0_backend_POT: - metric_value: 0.76866 + metric_value: 0.77196 timm/efficientnet_b0_backend_TORCH: - metric_value: 0.768 + metric_value: 0.77124 timm/efficientnet_lite0_backend_CUDA_TORCH: metric_value: 0.74686 timm/efficientnet_lite0_backend_FP32: - metric_value: 0.7496 + metric_value: 0.75496 timm/efficientnet_lite0_backend_ONNX: - metric_value: 0.7468 + metric_value: 0.75214 timm/efficientnet_lite0_backend_OV: - metric_value: 0.74704 -timm/efficientnet_lite0_backend_POT: - metric_value: 0.74686 + metric_value: 0.7515 timm/efficientnet_lite0_backend_TORCH: - metric_value: 0.74686 + metric_value: 0.75236 timm/hrnet_w18_backend_CUDA_TORCH: metric_value: 0.76712 timm/hrnet_w18_backend_FP32: - metric_value: 0.77504 + metric_value: 0.78124 timm/hrnet_w18_backend_ONNX: - metric_value: 0.76916 + metric_value: 0.7747 timm/hrnet_w18_backend_OV: - metric_value: 0.76926 -timm/hrnet_w18_backend_POT: - metric_value: 0.76968 + metric_value: 0.77526 timm/hrnet_w18_backend_TORCH: - metric_value: 0.76712 + metric_value: 0.77316 timm/inception_resnet_v2_backend_CUDA_TORCH: metric_value: 0.80024 timm/inception_resnet_v2_backend_FP32: - metric_value: 0.80102 + metric_value: 0.80448 timm/inception_resnet_v2_backend_ONNX: - metric_value: 0.80108 + metric_value: 0.804 timm/inception_resnet_v2_backend_OV: - metric_value: 0.80152 + metric_value: 0.80422 timm/inception_resnet_v2_backend_TORCH: - metric_value: 0.80024 + metric_value: 0.803 timm/levit_128_backend_CUDA_TORCH: metric_value: 0.7324 timm/levit_128_backend_FP32: - metric_value: 0.7405 + metric_value: 0.78474 timm/levit_128_backend_ONNX: - metric_value: 0.73286 + metric_value: 0.7762 timm/levit_128_backend_OV: - metric_value: 0.7334 + metric_value: 0.77696 timm/levit_128_backend_TORCH: - metric_value: 0.73346 + metric_value: 0.77752 timm/mobilenetv2_050_BC_backend_FP32: - metric_value: 0.64568 + metric_value: 0.6594 timm/mobilenetv2_050_BC_backend_ONNX: - metric_value: 0.64498 + metric_value: 0.65466 timm/mobilenetv2_050_BC_backend_OV: - metric_value: 0.64412 -timm/mobilenetv2_050_BC_backend_POT: - metric_value: 0.6431 + metric_value: 0.6543 timm/mobilenetv2_050_backend_CUDA_TORCH: metric_value: 0.64278 timm/mobilenetv2_050_backend_FP32: - metric_value: 0.64568 + metric_value: 0.6594 timm/mobilenetv2_050_backend_ONNX: - metric_value: 0.64358 + metric_value: 0.65462 timm/mobilenetv2_050_backend_OV: - metric_value: 0.64134 -timm/mobilenetv2_050_backend_POT: - metric_value: 0.64362 + metric_value: 0.65282 timm/mobilenetv2_050_backend_TORCH: - metric_value: 0.64278 + metric_value: 0.65364 timm/mobilenetv3_small_050_backend_CUDA_TORCH: metric_value: 0.41888 timm/mobilenetv3_small_050_backend_FP32: - metric_value: 0.56338 + metric_value: 0.57906 timm/mobilenetv3_small_050_backend_ONNX: - metric_value: 0.5456 + metric_value: 0.5617 timm/mobilenetv3_small_050_backend_OV: - metric_value: 0.413 -timm/mobilenetv3_small_050_backend_POT: - metric_value: 0.54104 + metric_value: 0.42184 timm/mobilenetv3_small_050_backend_TORCH: - metric_value: 0.41842 + metric_value: 0.4291 timm/regnetx_002_backend_CUDA_TORCH: metric_value: 0.67452 timm/regnetx_002_backend_FP32: - metric_value: 0.67826 + metric_value: 0.68756 timm/regnetx_002_backend_ONNX: - 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metric_value: 0.69748 -timm/swin_base_patch4_window7_224_backend_CUDA_TORCH: - metric_value: 0.8131 + metric_value: 0.71024 timm/swin_base_patch4_window7_224_backend_FP32: - metric_value: 0.81462 -timm/swin_base_patch4_window7_224_backend_ONNX: - metric_value: 0.81294 + metric_value: 0.85274 timm/swin_base_patch4_window7_224_backend_OV: - metric_value: 0.79582 -timm/swin_base_patch4_window7_224_backend_POT: - metric_value: 0.79514 -timm/swin_base_patch4_window7_224_backend_TORCH: - metric_value: 0.8131 + metric_value: 0.83636 +timm/swin_base_patch4_window7_224_no_sq_backend_FP32: + metric_value: 0.85274 +timm/swin_base_patch4_window7_224_no_sq_backend_CUDA_TORCH: + metric_value: 0.85142 +timm/swin_base_patch4_window7_224_no_sq_backend_ONNX: + metric_value: 0.85158 +timm/swin_base_patch4_window7_224_no_sq_backend_TORCH: + metric_value: 0.85142 timm/tf_inception_v3_backend_CUDA_TORCH: metric_value: 0.77542 timm/tf_inception_v3_backend_FP32: - metric_value: 0.77728 + metric_value: 0.7786 timm/tf_inception_v3_backend_ONNX: - 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metric_value: 0.77728 + metric_value: 0.81624 timm/wide_resnet50_2_backend_CUDA_TORCH: metric_value: 0.81186 timm/wide_resnet50_2_backend_FP32: - metric_value: 0.81414 + metric_value: 0.81454 timm/wide_resnet50_2_backend_ONNX: - metric_value: 0.81214 + metric_value: 0.81228 timm/wide_resnet50_2_backend_OV: - metric_value: 0.81136 -timm/wide_resnet50_2_backend_POT: - metric_value: 0.8114 + metric_value: 0.8125 timm/wide_resnet50_2_backend_TORCH: - metric_value: 0.81186 + metric_value: 0.81234 diff --git a/tests/post_training/data/wc_reference_data.yaml b/tests/post_training/data/wc_reference_data.yaml new file mode 100644 index 00000000000..751615bb8d3 --- /dev/null +++ b/tests/post_training/data/wc_reference_data.yaml @@ -0,0 +1,8 @@ +tinyllama_data_free_backend_OV: + metric_value: 0.72119 +tinyllama_data_aware_backend_OV: + metric_value: 0.83084 +tinyllama_data_aware_awq_backend_OV: + metric_value: 0.81229 +tinyllama_data_aware_awq_stateful_backend_OV: + metric_value: 0.81229 \ No newline at end of file diff --git a/tests/post_training/data/wwb_ref_answers/tinyllama__tinyllama-1.1b-step-50k-105b/ref_qa.csv b/tests/post_training/data/wwb_ref_answers/tinyllama__tinyllama-1.1b-step-50k-105b/ref_qa.csv new file mode 100644 index 00000000000..43bf96047eb --- /dev/null +++ b/tests/post_training/data/wwb_ref_answers/tinyllama__tinyllama-1.1b-step-50k-105b/ref_qa.csv @@ -0,0 +1,179 @@ +,questions,answers +0,Who is Mark Twain?," +Mark Twain was born on 18th October 1835 in St. Louis, Missouri. He was the son of a farmer and a school teacher. He was born to his parents and was raised by his aunt and uncle. He was a very talented writer and was a great writer. He was a great writer and was a great writer. He was a great writer and was a great writer. He was a great writer and was a great writer. He was a great writer and was a great writer. He was a great writer and was a great writer. He was a great writer and was" +1,Who is William Shakespeare?," +William Shakespeare was born on 21st April 1564 in Stratford-upon-Avon, England. He was the son of William Shakespeare and his wife Anne Hathaway. He was born to a family of musicians. He was the son of a musician. He was born to a family of musicians. He was born to a family of musicians. He was born to a family of musicians. He was born to a family of musicians. He was born to a family of musicians. He was born to a family of musicians. He was born to" +2,Who is Agatha Christie?," +The most famous of the three Christie novels, Agatha Christie's Miss Marple, is a detective story that has been adapted for the screen many times. The film adaptation of the novel was directed by Martin Campbell and starred Colin Firth, Anthony Hopkins, and Kenneth Cranham. The film was released in 1964 and was a box office success. +The film adaptation of the novel was a huge success and was a huge success for the author. The film was a huge success and was a huge success for the author. The film was a huge success and was a" +3,Who is Barbara Cartland?," +Barbara Cartland was born on 18th October 1928, and she was born in 1928. She was born in the year 1928, and her birth sign is Libra. She was born in the middle of the month, and her birth date is 18th October. +Barbara Cartland's birth flower is Sweet Pea, and birthstone is Ruby. +Barbara Cartland's birthstone is Sapphire. +Barbara Cartland's birth flower is Sweet Pea, and birthstone is Ruby. +" +4,Who is Danielle Steel?," +Danielle Steel is a Canadian actress and model. She was born on 20th August 1992 in Toronto, Ontario, Canada. She is the daughter of a Canadian father and an American mother. She has a younger brother named Danielle Steel. +Steel was born in Toronto, Ontario, Canada. She is the daughter of a Canadian father and an American mother. She has a younger brother named Danielle Steel. +Steel was raised in Toronto, Ontario, Canada. She attended the University of Toronto. She is a Canadian citizen. +Steel was born in Toronto, Ontario" +5,Who is Harold Robbins?," +Harold Robbins is a former member of the U.S. House of Representatives from the state of New York. He was a member of the Republican Party. He was born on 1951, in New York City, New York, United States. He was a member of the Republican Party. He was a member of the Republican Party from 1983 to 1989. He was a member of the Republican Party from 1989 to 1991. He was a member of the Republican Party from 1991 to 1993." +6,Who is Georges Simenon?," +Georges Simenon is a French actor, known for his role as the title character in the 1962 film ""The Big Sleep"". He was born on 27th October 1937, in Paris, France. +Simenon was born in Paris, France. He was the son of a French teacher. He was raised in a family of artists. He was a student of the famous actor Jean-Paul Belmondo. +Simenon was a student of the famous actor Jean-Paul Belmondo. +Simenon was a student of the famous actor Jean" +7,Who is Enid Blyton?," +Enid Blyton is a British author and illustrator. She is best known for her children's books, including the best-selling The Tale of the Three Rats, which was adapted into a film in 1996. She has also written several children's books for adults, including the best-selling The Tale of the Three Rats, which was adapted into a film in 1996. +Enid Blyton was born on 21st March 1931, in London, England. She is the daughter of a teacher and a" +8,Who is Sidney Sheldon?," +Sidney Sheldon is a famous American actor. He was born on 27th August 1961, in Los Angeles, California, United States. He is best known for his role as ""Joe"" in the TV series ""The West Wing"". +Sidney Sheldon is a famous American actor. He was born on 27th August 1961, in Los Angeles, California, United States. He is best known for his role as ""Joe"" in the TV series ""The West Wing"". He was born in Los Angeles, California, United States." +9,Who is Akira Toriyama?," +Akira Toriyama is a Japanese actor, model, and singer. He is best known for his role as the main character in the anime series Naruto. He is also known for his role as the main character in the anime series Naruto Shippuden. He is also known for his role as the main character in the anime series Naruto Shippuden. +Akira Toriyama is a Japanese actor, model, and singer. He is best known for his role as the main character in the anime series Naruto. He is also known for his role as the main" +10,Who is Leo Tolstoy?," +The actor was born on 28th August 1920 in the city of Kiev, Ukraine. He was born to a Ukrainian family. He was the son of a Ukrainian father and a Russian mother. He was raised in the city of Kiev. He was the only child in the family. He was the only child in the family. He was the only child in the family. He was the only child in the family. He was the only child in the family. He was the only child in the family. He was the only child in the family. He was the only child in the" +11,Who is Alexander Pushkin?," +Alexander Pushkin (1775-1828) was a Russian poet and playwright. He was born in St. Petersburg, Russia. He was the son of a Russian noble. He was the son of a Russian noble. He was the son of a Russian noble. He was the son of a Russian noble. He was the son of a Russian noble. He was the son of a Russian noble. He was the son of a Russian noble. He was the son of a Russian noble. He was the son of a Russian noble. He was the son of a Russian noble. He was" +12,Who is Stephen King?," +King is a writer, director, producer, and actor. He is best known for his work on the 2014 film ""The Shining."" +King is a member of the American Academy of Arts and Letters. He is also a member of the American Academy of Arts and Letters. +King is a member of the American Academy of Arts and Letters. He is also a member of the American Academy of Arts and Letters. +King is a member of the American Academy of Arts and Letters. He is also a member of the American Academy of Arts and Letters. +" +13,What is C++?," + +A: C++ is a general purpose language. It is used to create programs that are written in C. + +A: C++ is a general purpose language. It is used to create programs that are written in C. + +A: C++ is a general purpose language. It is used to create programs that are written in C. + +A: C++ is a general purpose language. It is used to create programs that are written in C. + +A: C++ is a general purpose language. It is used to create programs that are written in C. + +A: C++ is a" +14,What is Python?," +Python is a general purpose scripting language. It is a general purpose scripting language that is used to create programs. It is a general purpose scripting language that is used to create programs. It is a general purpose scripting language that is used to create programs. It is a general purpose scripting language that is used to create programs. It is a general purpose scripting language that is used to create programs. It is a general purpose scripting language that is used to create programs. It is a general purpose scripting language that is used to create programs. It is a general purpose scripting language that is used to create" +15,What is Java?," + +A: Java is a language that is used to create applications. +Java is a language that is used to create applications. +Java is a language that is used to create applications. +Java is a language that is used to create applications. +Java is a language that is used to create applications. +Java is a language that is used to create applications. +Java is a language that is used to create applications. +Java is a language that is used to create applications. +Java is a language that is used to create applications. +Java is a language that is used to create applications. +Java is a language" +16,What is JavaScript?," + +A: JavaScript is a scripting language. It is a subset of JavaScript. +JavaScript is a scripting language. It is a subset of JavaScript. +JavaScript is a scripting language. It is a subset of JavaScript. +JavaScript is a scripting language. It is a subset of JavaScript. +JavaScript is a scripting language. It is a subset of JavaScript. +JavaScript is a scripting language. It is a subset of JavaScript. +JavaScript is a scripting language. It is a subset of JavaScript. +JavaScript is a scripting language. It is a subset of JavaScript. +JavaScript is a script" +17,What is Perl?," + +A: Perl is a scripting language. It is a subset of C. + +A: Perl is a scripting language. It is a subset of C. + +A: Perl is a scripting language. It is a subset of C. + +A: Perl is a scripting language. It is a subset of C. + +A: Perl is a scripting language. It is a subset of C. + +A: Perl is a scripting language. It is a subset of C. + +A: Perl is a scripting language. It is a subset of C. + +" +18,What is OpenCV?," +OpenCV is a library for the OpenCV camera. It is a cross-platform library that provides a high-level API for image processing and image reconstruction. +What is the OpenCV library? +The OpenCV library is a cross-platform library that provides a high-level API for image processing and image reconstruction. It is a cross-platform library that provides a high-level API for image processing and image reconstruction. +What is the OpenCV library? +The OpenCV library is a cross-platform library that provides a high-level API for image processing and image reconstruction. It is a cross" +19,Who is the most famous writer?," +The most famous writer is the author of the book ""The Lord of the Rings"" and the movie ""The Hobbit"". +Who is the most famous actor in the world? +The most famous actor in the world is the actor who is the most famous actor in the world. +Who is the most famous singer in the world? +The most famous singer in the world is the singer who is the most famous singer in the world. +Who is the most famous actor in the world? +The most famous actor in the world is the actor who is the most famous actor in the world. +Who is the most" +20,Who is the most famous inventor?," +The most famous inventor is the inventor of the first computer. +Who is the most famous inventor? +Who is the most famous inventor? +Who is the most famous inventor? +Who is the most famous inventor? +Who is the most famous inventor? +Who is the most famous inventor? +Who is the most famous inventor? +Who is the most famous inventor? +Who is the most famous inventor? +Who is the most famous inventor? +Who is the most famous inventor? +Who is the most famous inventor? +Who is the most" +21,Who is the most famous mathematician?," +The most famous mathematician is Albert Einstein. He is also known as the father of modern physics. He is also known as the father of quantum mechanics. He is also known as the father of relativity. +Who is the most famous mathematician? +The most famous mathematician is Albert Einstein. He is also known as the father of modern physics. He is also known as the father of relativity. +Who is the most famous mathematician? +The most famous mathematician is Albert Einstein. He is also known as the father of modern physics. He is also known as" +22,Who is the most famous composer?," +The most famous composer of all time is Leonard Bernstein. +Who is the most famous composer of all time? +Who is the most famous composer of all time? +Who is the most famous composer of all time? +Who is the most famous composer of all time? +Who is the most famous composer of all time? +Who is the most famous composer of all time? +Who is the most famous composer of all time? +Who is the most famous composer of all time? +Who is the most famous composer of all time? +Who is the most famous composer of all time? +Who is the most" +23,Who is the most famous programmer?," +The most famous programmer is the one who is the most famous programmer in the world. +Who is the most famous programmer in the world? +Who is the most famous programmer in the world? +Who is the most famous programmer in the world? +Who is the most famous programmer in the world? +Who is the most famous programmer in the world? +Who is the most famous programmer in the world? +Who is the most famous programmer in the world? +Who is the most famous programmer in the world? +Who is the most famous programmer in the world? +Who is the most famous programmer in the world?" +24,Who is the most famous athlete?," +The most famous athlete in the world is the most famous athlete in the world is the most famous athlete in the world is the most famous athlete in the world is the most famous athlete in the world is the most famous athlete in the world is the most famous athlete in the world is the most famous athlete in the world is the most famous athlete in the world is the most famous athlete in the world is the most famous athlete in the world is the most famous athlete in the world is the most famous athlete in the world is the most famous athlete in the world is the" +25,Who is the most famous ancient Greek scientist?," +The most famous ancient Greek scientist is Archimedes. Archimedes was born in 387 BC. He was a Greek mathematician and astronomer. Archimedes was the first to use the scientific method to solve problems. Archimedes was the first to use the scientific method to solve problems. Archimedes was the first to use the scientific method to solve problems. Archimedes was the first to use the scientific method to solve problems. Archimedes was the first to use the scientific method to solve problems. Archimedes was the first to use the scientific method to solve problems. Archim" +26,What color will you get when you mix blue and yellow?," +Blue and yellow are the colors of the rainbow. +What color is blue? What color is yellow? +What color is blue? What color is yellow? +What color is blue? What color is yellow? +What color is blue? What color is yellow? +What color is blue? What color is yellow? +What color is blue? What color is yellow? +What color is blue? What color is yellow? +What color is blue? What color is yellow? +What color is blue? What color is yellow? +What color is blue? What color is yellow? +What color is blue?" diff --git a/tests/post_training/model_scope.py b/tests/post_training/model_scope.py index daae788c37d..e643b68087d 100644 --- a/tests/post_training/model_scope.py +++ b/tests/post_training/model_scope.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -10,10 +10,13 @@ # limitations under the License. import copy -from typing import Dict +from typing import Dict, List +import nncf from nncf import ModelType from nncf import QuantizationPreset +from nncf.parameters import CompressWeightsMode +from nncf.parameters import SensitivityMetric from nncf.quantization.advanced_parameters import AdvancedQuantizationParameters from nncf.quantization.advanced_parameters import AdvancedSmoothQuantParameters from tests.post_training.pipelines.base import ALL_PTQ_BACKENDS @@ -21,15 +24,16 @@ from tests.post_training.pipelines.base import BackendType from tests.post_training.pipelines.causal_language_model import CausalLMHF from tests.post_training.pipelines.image_classification_timm import ImageClassificationTimm +from tests.post_training.pipelines.lm_weight_compression import LMWeightCompression from tests.post_training.pipelines.masked_language_modeling import MaskedLanguageModelingHF -TEST_MODELS = [ +QUANTIZATION_MODELS = [ # HF models { "reported_name": "hf/bert-base-uncased", "model_id": "bert-base-uncased", "pipeline_cls": MaskedLanguageModelingHF, - "ptq_params": { + "compression_params": { "preset": QuantizationPreset.MIXED, "model_type": ModelType.TRANSFORMER, "subset_size": 2, @@ -40,7 +44,7 @@ "reported_name": "hf/hf-internal-testing/tiny-random-GPTNeoXForCausalLM", "model_id": "hf-internal-testing/tiny-random-GPTNeoXForCausalLM", "pipeline_cls": CausalLMHF, - "ptq_params": { + "compression_params": { "preset": QuantizationPreset.MIXED, "model_type": ModelType.TRANSFORMER, "subset_size": 2, @@ -52,7 +56,7 @@ "reported_name": "timm/crossvit_9_240", "model_id": "crossvit_9_240", "pipeline_cls": ImageClassificationTimm, - "ptq_params": { + "compression_params": { "preset": QuantizationPreset.MIXED, "model_type": ModelType.TRANSFORMER, "advanced_parameters": AdvancedQuantizationParameters(smooth_quant_alpha=-1.0), @@ -63,7 +67,7 @@ "reported_name": "timm/darknet53", "model_id": "darknet53", "pipeline_cls": ImageClassificationTimm, - "ptq_params": { + "compression_params": { "preset": QuantizationPreset.MIXED, }, "backends": ALL_PTQ_BACKENDS, @@ -72,7 +76,7 @@ "reported_name": "timm/deit3_small_patch16_224", "model_id": "deit3_small_patch16_224", "pipeline_cls": ImageClassificationTimm, - "ptq_params": { + "compression_params": { "preset": QuantizationPreset.MIXED, "model_type": ModelType.TRANSFORMER, "advanced_parameters": AdvancedQuantizationParameters( @@ -85,7 +89,7 @@ "reported_name": "timm/dla34", "model_id": "dla34", "pipeline_cls": ImageClassificationTimm, - "ptq_params": { + "compression_params": { "preset": QuantizationPreset.MIXED, }, "backends": ALL_PTQ_BACKENDS, @@ -94,7 +98,7 @@ "reported_name": "timm/dpn68", "model_id": "dpn68", "pipeline_cls": ImageClassificationTimm, - "ptq_params": { + "compression_params": { "preset": QuantizationPreset.MIXED, }, "backends": ALL_PTQ_BACKENDS, @@ -103,7 +107,7 @@ "reported_name": "timm/efficientnet_b0", "model_id": "efficientnet_b0", "pipeline_cls": ImageClassificationTimm, - "ptq_params": { + "compression_params": { "preset": QuantizationPreset.MIXED, }, "backends": ALL_PTQ_BACKENDS, @@ -112,17 +116,17 @@ "reported_name": "timm/efficientnet_b0_BC", "model_id": "efficientnet_b0", "pipeline_cls": ImageClassificationTimm, - "ptq_params": { + "compression_params": { "preset": QuantizationPreset.MIXED, "fast_bias_correction": False, }, - "backends": [BackendType.ONNX, BackendType.OV, BackendType.POT], + "backends": [BackendType.ONNX, BackendType.OV], }, { "reported_name": "timm/efficientnet_lite0", "model_id": "efficientnet_lite0", "pipeline_cls": ImageClassificationTimm, - "ptq_params": { + "compression_params": { "preset": QuantizationPreset.MIXED, }, "backends": ALL_PTQ_BACKENDS, @@ -131,7 +135,7 @@ "reported_name": "timm/hrnet_w18", "model_id": "hrnet_w18", "pipeline_cls": ImageClassificationTimm, - "ptq_params": { + "compression_params": { "preset": QuantizationPreset.MIXED, }, "backends": ALL_PTQ_BACKENDS, @@ -140,14 +144,14 @@ "reported_name": "timm/inception_resnet_v2", "model_id": "inception_resnet_v2", "pipeline_cls": ImageClassificationTimm, - "ptq_params": {}, + "compression_params": {}, "backends": NNCF_PTQ_BACKENDS, }, { "reported_name": "timm/levit_128", "model_id": "levit_128", "pipeline_cls": ImageClassificationTimm, - "ptq_params": { + "compression_params": { "preset": QuantizationPreset.MIXED, "model_type": ModelType.TRANSFORMER, "advanced_parameters": AdvancedQuantizationParameters( @@ -160,7 +164,7 @@ "reported_name": "timm/mobilenetv2_050", "model_id": "mobilenetv2_050", "pipeline_cls": ImageClassificationTimm, - "ptq_params": { + "compression_params": { "preset": QuantizationPreset.MIXED, }, "backends": ALL_PTQ_BACKENDS, @@ -169,17 +173,17 @@ "reported_name": "timm/mobilenetv2_050_BC", "model_id": "mobilenetv2_050", "pipeline_cls": ImageClassificationTimm, - "ptq_params": { + "compression_params": { "preset": QuantizationPreset.MIXED, "fast_bias_correction": False, }, - "backends": [BackendType.ONNX, BackendType.OV, BackendType.POT], + "backends": [BackendType.ONNX, BackendType.OV], }, { "reported_name": "timm/mobilenetv3_small_050", "model_id": "mobilenetv3_small_050", "pipeline_cls": ImageClassificationTimm, - "ptq_params": { + "compression_params": { "preset": QuantizationPreset.MIXED, }, "backends": ALL_PTQ_BACKENDS, @@ -188,7 +192,7 @@ "reported_name": "timm/regnetx_002", "model_id": "regnetx_002", "pipeline_cls": ImageClassificationTimm, - "ptq_params": { + "compression_params": { "preset": QuantizationPreset.MIXED, }, "backends": ALL_PTQ_BACKENDS, @@ -197,7 +201,7 @@ "reported_name": "timm/resnest14d", "model_id": "resnest14d", "pipeline_cls": ImageClassificationTimm, - "ptq_params": { + "compression_params": { "preset": QuantizationPreset.MIXED, }, "backends": ALL_PTQ_BACKENDS, @@ -206,24 +210,37 @@ "reported_name": "timm/resnet18", "model_id": "resnet18", "pipeline_cls": ImageClassificationTimm, - "ptq_params": {}, + "compression_params": {}, "backends": ALL_PTQ_BACKENDS, }, { "reported_name": "timm/swin_base_patch4_window7_224", "model_id": "swin_base_patch4_window7_224", "pipeline_cls": ImageClassificationTimm, - "ptq_params": { + "compression_params": { "preset": QuantizationPreset.MIXED, "model_type": ModelType.TRANSFORMER, }, - "backends": ALL_PTQ_BACKENDS, + "backends": [BackendType.OV], + }, + { + "reported_name": "timm/swin_base_patch4_window7_224_no_sq", + "model_id": "swin_base_patch4_window7_224", + "pipeline_cls": ImageClassificationTimm, + "compression_params": { + "preset": QuantizationPreset.MIXED, + "model_type": ModelType.TRANSFORMER, + "advanced_parameters": AdvancedQuantizationParameters( + smooth_quant_alphas=AdvancedSmoothQuantParameters(matmul=-1) + ), + }, + "backends": [BackendType.TORCH, BackendType.CUDA_TORCH, BackendType.ONNX], }, { "reported_name": "timm/tf_inception_v3", "model_id": "tf_inception_v3", "pipeline_cls": ImageClassificationTimm, - "ptq_params": { + "compression_params": { "preset": QuantizationPreset.MIXED, }, "backends": ALL_PTQ_BACKENDS, @@ -232,14 +249,14 @@ "reported_name": "timm/vgg11", "model_id": "vgg11", "pipeline_cls": ImageClassificationTimm, - "ptq_params": {}, + "compression_params": {}, "backends": NNCF_PTQ_BACKENDS, }, { "reported_name": "timm/visformer_small", "model_id": "visformer_small", "pipeline_cls": ImageClassificationTimm, - "ptq_params": { + "compression_params": { "preset": QuantizationPreset.MIXED, "model_type": ModelType.TRANSFORMER, }, @@ -249,7 +266,7 @@ "reported_name": "timm/wide_resnet50_2", "model_id": "wide_resnet50_2", "pipeline_cls": ImageClassificationTimm, - "ptq_params": { + "compression_params": { "preset": QuantizationPreset.MIXED, }, "backends": ALL_PTQ_BACKENDS, @@ -257,22 +274,70 @@ ] -def generate_tests_scope() -> Dict[str, dict]: +WEIGHT_COMPRESSION_MODELS = [ + { + "reported_name": "tinyllama_data_free", + "model_id": "tinyllama/tinyllama-1.1b-step-50k-105b", + "pipeline_cls": LMWeightCompression, + "compression_params": { + "group_size": 64, + "ratio": 0.8, + "mode": CompressWeightsMode.INT4_SYM, + "sensitivity_metric": SensitivityMetric.WEIGHT_QUANTIZATION_ERROR, + }, + "backends": [BackendType.OV], + }, + { + "reported_name": "tinyllama_data_aware", + "model_id": "tinyllama/tinyllama-1.1b-step-50k-105b", + "pipeline_cls": LMWeightCompression, + "compression_params": {"group_size": 64, "ratio": 0.8, "mode": CompressWeightsMode.INT4_SYM}, + "backends": [BackendType.OV], + }, + { + "reported_name": "tinyllama_data_aware_awq", + "model_id": "tinyllama/tinyllama-1.1b-step-50k-105b", + "pipeline_cls": LMWeightCompression, + "compression_params": {"group_size": 64, "ratio": 0.8, "mode": CompressWeightsMode.INT4_SYM, "awq": True}, + "backends": [BackendType.OV], + }, + { + "reported_name": "tinyllama_data_aware_awq_stateful", + "model_id": "tinyllama/tinyllama-1.1b-step-50k-105b", + "pipeline_cls": LMWeightCompression, + "compression_params": {"group_size": 64, "ratio": 0.8, "mode": CompressWeightsMode.INT4_SYM, "awq": True}, + "params": {"is_stateful": True}, + "backends": [BackendType.OV], + }, +] + + +def generate_tests_scope(models_list: List[Dict]) -> Dict[str, dict]: """ Generate tests by names "{reported_name}_backend_{backend}" """ + reported_name_to_model_id_mapping = {mc["reported_name"]: mc["model_id"] for mc in models_list} tests_scope = {} - for test_model_param in TEST_MODELS: + fp32_models = set() + for test_model_param in models_list: for backend in test_model_param["backends"] + [BackendType.FP32]: model_param = copy.deepcopy(test_model_param) reported_name = model_param["reported_name"] + model_id = reported_name_to_model_id_mapping[reported_name] + if backend == BackendType.FP32: + # Some test cases may share the same model_id, therefore fp32 test case is added only once for model_id. + if model_id not in fp32_models: + fp32_models.add(model_id) + else: + continue test_case_name = f"{reported_name}_backend_{backend.value}" model_param["backend"] = backend model_param.pop("backends") if test_case_name in tests_scope: - raise RuntimeError(f"{test_case_name} already in tests_scope") + raise nncf.ValidationError(f"{test_case_name} already in tests_scope") tests_scope[test_case_name] = model_param return tests_scope -TEST_CASES = generate_tests_scope() +PTQ_TEST_CASES = generate_tests_scope(QUANTIZATION_MODELS) +WC_TEST_CASES = generate_tests_scope(WEIGHT_COMPRESSION_MODELS) diff --git a/tests/post_training/pipelines/base.py b/tests/post_training/pipelines/base.py index 3587a5574c5..e3f35f35657 100644 --- a/tests/post_training/pipelines/base.py +++ b/tests/post_training/pipelines/base.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -18,19 +18,17 @@ from datetime import timedelta from enum import Enum from pathlib import Path -from typing import Optional +from typing import Dict, Optional import numpy as np import onnx -import openvino.runtime as ov +import openvino as ov import torch from memory_profiler import memory_usage -from openvino.tools.mo import convert_model from optimum.intel import OVQuantizer import nncf from nncf import TargetDevice -from nncf.quantization.advanced_parameters import AdvancedQuantizationParameters from tests.shared.command import Command DEFAULT_VAL_THREADS = 4 @@ -42,22 +40,89 @@ class BackendType(Enum): CUDA_TORCH = "CUDA_TORCH" ONNX = "ONNX" OV = "OV" - POT = "POT" OPTIMUM = "OPTIMUM" NNCF_PTQ_BACKENDS = [BackendType.TORCH, BackendType.CUDA_TORCH, BackendType.ONNX, BackendType.OV] -ALL_PTQ_BACKENDS = NNCF_PTQ_BACKENDS + [BackendType.POT] +ALL_PTQ_BACKENDS = NNCF_PTQ_BACKENDS PT_BACKENDS = [BackendType.TORCH, BackendType.CUDA_TORCH] -OV_BACKENDS = [BackendType.OV, BackendType.POT, BackendType.OPTIMUM] +OV_BACKENDS = [BackendType.OV, BackendType.OPTIMUM] LIMIT_LENGTH_OF_STATUS = 120 +class StatsFromOutput: + """ + Contains statistics that are parsed from the stdout. + """ + + def get_stats(self) -> Dict[str, str]: + """ + Returns statistics collected from the stdout. Usually it parses execution time from the log of the progress bar. + """ + return {} + + def fill(self, stdout: str) -> None: + """ + Parses standard output from the post-training conformance tests and collect statistics, for instance, the + duration of different algorithm's stages. + + :param stdout: string containing the standard output + """ + + +@dataclass +class PTQTimeStats(StatsFromOutput): + """ + Contains statistics that are parsed from the stdout of PTQ tests. + """ + + time_stat_collection: Optional[str] = None + time_bias_correction: Optional[str] = None + time_validation: Optional[str] = None + + STAT_NAMES = ["Stat. collection time", "Bias correction time", "Validation time"] + + def fill(self, stdout: str): + time_stat_collection_ = None + time_bias_correction_ = None + for line in stdout.splitlines(): + match = re.search(r"Statistics\scollection.*•\s(.*)\s•.*", line) + if match: + if time_stat_collection_ is None: + time_stat_collection_ = dt.datetime.strptime(match.group(1), "%H:%M:%S") + else: + time = dt.datetime.strptime(match.group(1), "%H:%M:%S") + time_stat_collection_ += dt.timedelta(hours=time.hour, minutes=time.minute, seconds=time.second) + continue + + match = re.search(r"Applying.*correction.*\/(\d+)\s•\s(.*)\s•.*", line) + if match: + if time_bias_correction_ is None: + time_bias_correction_ = dt.datetime.strptime(match.group(2), "%H:%M:%S") + else: + time_bias_correction_ += dt.datetime.strptime(match.group(2), "%H:%M:%S") + continue + + match = re.search(r"Validation.*\/\d+\s•\s(.*)\s•.*", line) + if match: + self.time_validation = match.group(1) + continue + + if time_stat_collection_: + self.time_stat_collection = time_stat_collection_.strftime("%H:%M:%S") + if time_bias_correction_: + self.time_bias_correction = time_bias_correction_.strftime("%H:%M:%S") + + def get_stats(self): + VARS = [self.time_stat_collection, self.time_bias_correction, self.time_validation] + return dict(zip(self.STAT_NAMES, VARS)) + + @dataclass class RunInfo: """ - Containing data about quantization of the model. + Containing data about compression of the model. """ model: Optional[str] = None @@ -66,14 +131,12 @@ class RunInfo: metric_value: Optional[float] = None metric_diff: Optional[float] = None num_fq_nodes: Optional[float] = None - quant_memory_usage: Optional[int] = None - time_total: Optional[float] = None - time_quantization: Optional[float] = None + compression_memory_usage: Optional[int] = None status: Optional[str] = None fps: Optional[float] = None - time_stat_collection: Optional[str] = None - time_bias_correction: Optional[str] = None - time_validation: Optional[str] = None + time_total: Optional[float] = None + time_compression: Optional[float] = None + stats_from_output = StatsFromOutput() @staticmethod def format_time(time_elapsed): @@ -95,11 +158,9 @@ def get_result_dict(self): "Metric value": self.metric_value, "Metric diff": self.metric_diff, "Num FQ": self.num_fq_nodes, - "RAM MiB": self.format_memory_usage(self.quant_memory_usage), - "Quant. time": self.format_time(self.time_quantization), - "Stat. collection time": self.time_stat_collection, - "Bias correction time": self.time_bias_correction, - "Validation time": self.time_validation, + "RAM MiB": self.format_memory_usage(self.compression_memory_usage), + "Compr. time": self.format_time(self.time_compression), + **self.stats_from_output.get_stats(), "Total time": self.format_time(self.time_total), "FPS": self.fps, "Status": self.status[:LIMIT_LENGTH_OF_STATUS] if self.status is not None else None, @@ -108,7 +169,7 @@ def get_result_dict(self): class BaseTestPipeline(ABC): """ - Base class to test post training quantization. + Base class to test compression algorithms. """ def __init__( @@ -116,7 +177,7 @@ def __init__( reported_name: str, model_id: str, backend: BackendType, - ptq_params: dict, + compression_params: dict, output_dir: Path, data_dir: Path, reference_data: dict, @@ -127,7 +188,7 @@ def __init__( self.reported_name = reported_name self.model_id = model_id self.backend = backend - self.ptq_params = ptq_params + self.compression_params = compression_params self.output_dir = output_dir self.data_dir = data_dir self.reference_data = reference_data @@ -137,6 +198,9 @@ def __init__( self.output_model_dir: Path = self.output_dir / self.reported_name / self.backend.value self.output_model_dir.mkdir(parents=True, exist_ok=True) self.model_name = f"{self.reported_name}_{self.backend.value}" + self.fp32_model_name = self.model_id.replace("/", "__") + self.fp32_model_dir: Path = self.output_dir / "fp32_models" / self.fp32_model_name + self.fp32_model_dir.mkdir(parents=True, exist_ok=True) self.model = None self.model_hf = None @@ -146,35 +210,106 @@ def __init__( self.run_info = RunInfo(model=reported_name, backend=self.backend) - self.post_init() - - def post_init(self): - """Post init actions""" - @abstractmethod def prepare_preprocessor(self) -> None: - """Prepare preprocessor for the target model""" + """Prepare preprocessor for the target model.""" @abstractmethod def prepare_calibration_dataset(self) -> None: - """Prepare calibration dataset for the target model""" + """Prepare calibration dataset for the target model.""" @abstractmethod def prepare_model(self) -> None: - """Prepare model""" + """Prepare model.""" + + @abstractmethod + def cleanup_cache(self): + """Helper for removing cached model representation.""" + + @abstractmethod + def collect_data_from_stdout(self, stdout: str): + """Collects statistics from the standard output.""" + + @abstractmethod + def compress(self) -> None: + """Run compression of the model and collect time and memory usage information.""" + + @abstractmethod + def save_compressed_model(self) -> None: + """Save compressed model to IR.""" + + @abstractmethod + def get_num_compressed(self) -> None: + """Get number of the compressed nodes in the compressed IR.""" + + @abstractmethod + def run_bench(self) -> None: + """Run a benchmark to collect performance statistics.""" + + @abstractmethod + def _validate(self) -> None: + """Validate IR.""" def prepare(self): """ - Preparing model and calibration dataset for quantization. + Preparing model and calibration dataset for compression. """ print("Preparing...") self.prepare_model() if self.model is None: - raise RuntimeError("self.model is None") + raise nncf.ValidationError("self.model is None") self.prepare_preprocessor() self.prepare_calibration_dataset() - def _quantize(self): + def validate(self) -> None: + """ + Validate and compare result with reference. + """ + if self.no_eval: + print("Validation skipped") + return + print("Validation...") + + self._validate() + + metric_value = self.run_info.metric_value + metric_reference = self.reference_data.get("metric_value") + metric_value_fp32 = self.reference_data.get("metric_value_fp32") + + if metric_value is not None and metric_value_fp32 is not None: + self.run_info.metric_diff = round(self.run_info.metric_value - self.reference_data["metric_value_fp32"], 5) + + if ( + metric_value is not None + and metric_reference is not None + and not np.isclose(metric_value, metric_reference, atol=self.reference_data.get("atol", 0.001)) + ): + if metric_value < metric_reference: + status_msg = f"Regression: Metric value is less than reference {metric_value} < {metric_reference}" + raise ValueError(status_msg) + if metric_value > metric_reference: + self.run_info.status = ( + f"Improvement: Metric value is better than reference {metric_value} > {metric_reference}" + ) + + def run(self) -> None: + """ + Run full pipeline of compression. + """ + self.prepare() + self.compress() + self.save_compressed_model() + self.get_num_compressed() + self.validate() + self.run_bench() + + +class PTQTestPipeline(BaseTestPipeline): + """ + Base class to test post training quantization. + """ + + def _compress(self): """ Quantize self.model """ @@ -182,68 +317,63 @@ def _quantize(self): quantizer = OVQuantizer.from_pretrained(self.model_hf) quantizer.quantize(calibration_dataset=self.calibration_dataset, save_directory=self.output_model_dir) else: - if self.backend == BackendType.POT: - self.ptq_params["advanced_parameters"] = AdvancedQuantizationParameters( - backend_params={"use_pot": True} - ) - - self.quantized_model = nncf.quantize( + self.compressed_model = nncf.quantize( model=self.model, target_device=TargetDevice.CPU, calibration_dataset=self.calibration_dataset, - **self.ptq_params, + **self.compression_params, ) - def quantize(self) -> None: + def compress(self) -> None: """ Run quantization of the model and collect time and memory usage information. """ if self.backend == BackendType.FP32: - # To validate not quantized model - self.path_quantized_ir = self.output_model_dir / "model_fp32.xml" + # To validate not compressed model + self.path_compressed_ir = self.fp32_model_dir / "model_fp32.xml" return print("Quantization...") if self.backend in PT_BACKENDS: - cpu_threads_num = os.environ.get("CPU_THREADS_NUM") - if cpu_threads_num is not None: - torch.set_num_threads(int(cpu_threads_num)) + inference_num_threads = os.environ.get("INFERENCE_NUM_THREADS") + if inference_num_threads is not None: + torch.set_num_threads(int(inference_num_threads)) start_time = time.perf_counter() - self.run_info.quant_memory_usage = memory_usage(self._quantize, max_usage=True) - self.run_info.time_quantization = time.perf_counter() - start_time + self.run_info.compression_memory_usage = memory_usage(self._compress, max_usage=True) + self.run_info.time_compression = time.perf_counter() - start_time - def save_quantized_model(self) -> None: + def save_compressed_model(self) -> None: """ - Save quantized model to IR. + Save compressed model to IR. """ - print("Save quantized model...") + print("Saving quantized model...") if self.backend == BackendType.OPTIMUM: - self.path_quantized_ir = self.output_model_dir / "openvino_model.xml" + self.path_compressed_ir = self.output_model_dir / "openvino_model.xml" elif self.backend in PT_BACKENDS: - ov_model = convert_model( - self.quantized_model.cpu(), example_input=self.dummy_tensor.cpu(), input_shape=self.input_size + ov_model = ov.convert_model( + self.compressed_model.cpu(), example_input=self.dummy_tensor.cpu(), input=self.input_size ) - self.path_quantized_ir = self.output_model_dir / "model.xml" - ov.serialize(ov_model, self.path_quantized_ir) + self.path_compressed_ir = self.output_model_dir / "model.xml" + ov.serialize(ov_model, self.path_compressed_ir) elif self.backend == BackendType.ONNX: onnx_path = self.output_model_dir / "model.onnx" - onnx.save(self.quantized_model, str(onnx_path)) - ov_model = convert_model(onnx_path) - self.path_quantized_ir = self.output_model_dir / "model.xml" - ov.serialize(ov_model, self.path_quantized_ir) + onnx.save(self.compressed_model, str(onnx_path)) + ov_model = ov.convert_model(onnx_path) + self.path_compressed_ir = self.output_model_dir / "model.xml" + ov.serialize(ov_model, self.path_compressed_ir) elif self.backend in OV_BACKENDS: - self.path_quantized_ir = self.output_model_dir / "model.xml" - ov.serialize(self.quantized_model, str(self.path_quantized_ir)) + self.path_compressed_ir = self.output_model_dir / "model.xml" + ov.serialize(self.compressed_model, str(self.path_compressed_ir)) - def get_num_fq(self) -> None: + def get_num_compressed(self) -> None: """ - Get number of the FakeQuantize nodes in the quantized IR. + Get number of the FakeQuantize nodes in the compressed IR. """ ie = ov.Core() - model = ie.read_model(model=self.path_quantized_ir) + model = ie.read_model(model=self.path_compressed_ir) num_fq = 0 for node in model.get_ops(): @@ -259,7 +389,7 @@ def run_bench(self) -> None: """ if not self.run_benchmark_app: return - runner = Command(f"benchmark_app -m {self.path_quantized_ir}") + runner = Command(f"benchmark_app -m {self.path_compressed_ir}") runner.run(stdout=False) cmd_output = " ".join(runner.output) @@ -268,105 +398,17 @@ def run_bench(self) -> None: fps = match.group(1) self.run_info.fps = float(fps) - @abstractmethod - def _validate(self) -> None: - """Validate IR""" - - def validate(self) -> None: - """ - Validate and compare result with reference - """ - if self.no_eval: - print("Validation skipped") - return - print("Validation...") - - self._validate() - - metric_value = self.run_info.metric_value - metric_reference = self.reference_data.get("metric_value") - metric_value_fp32 = self.reference_data.get("metric_value_fp32") - - if metric_value is not None and metric_value_fp32 is not None: - self.run_info.metric_diff = self.run_info.metric_value - self.reference_data["metric_value_fp32"] - - if metric_value is not None and metric_reference is not None: - if not np.isclose(metric_value, metric_reference, atol=self.reference_data.get("atol", 0.001)): - if metric_value < metric_reference: - status_msg = f"Regression: Metric value is less than reference {metric_value} < {metric_reference}" - raise ValueError(status_msg) - if metric_value > metric_reference: - self.run_info.status = ( - f"Improvement: Metric value is better than reference {metric_value} > {metric_reference}" - ) - - def run(self) -> None: - """ - Run full pipeline of quantization - """ - self.prepare() - self.quantize() - self.save_quantized_model() - self.get_num_fq() - self.validate() - self.run_bench() - self.cleanup_torchscript_cache() - - @staticmethod - def cleanup_torchscript_cache(): + def cleanup_cache(self): """ Helper for removing cached model representation. After run torch.jit.trace in convert_model, PyTorch does not clear the trace cache automatically. """ - torch._C._jit_clear_class_registry() torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore() torch.jit._state._clear_class_state() - def get_run_info(self) -> RunInfo: - return self.run_info - def collect_data_from_stdout(self, stdout: str): - """ - Parsing stdout of the test and collect additional data: - - time of statistic collection - - time of bias correction - - time of validation - - :param stdout: stdout text - """ - time_validation = None - time_bias_correction = None - time_stat_collection = None - - for line in stdout.splitlines(): - print(line) - match = re.search(r"Statistics\scollection.*•\s(.*)\s•.*", line) - if match: - if time_stat_collection is None: - time_stat_collection = dt.datetime.strptime(match.group(1), "%H:%M:%S") - else: - time = dt.datetime.strptime(match.group(1), "%H:%M:%S") - time_stat_collection += dt.timedelta(hours=time.hour, minutes=time.minute, seconds=time.second) - continue - - match = re.search(r"Applying.*correction.*\/(\d+)\s•\s(.*)\s•.*", line) - if match: - if time_bias_correction is None: - time_bias_correction = dt.datetime.strptime(match.group(2), "%H:%M:%S") - else: - time_bias_correction += dt.datetime.strptime(match.group(2), "%H:%M:%S") - continue - - match = re.search(r"Validation.*\/\d+\s•\s(.*)\s•.*", line) - if match: - time_validation = dt.datetime.strptime(match.group(1), "%H:%M:%S") - continue - - if time_stat_collection: - self.run_info.time_stat_collection = time_stat_collection.strftime("%H:%M:%S") - if time_bias_correction: - self.run_info.time_bias_correction = time_bias_correction.strftime("%H:%M:%S") - if time_validation: - self.run_info.time_validation = time_validation.strftime("%H:%M:%S") + stats = PTQTimeStats() + stats.fill(stdout) + self.run_info.stats_from_output = stats diff --git a/tests/post_training/pipelines/causal_language_model.py b/tests/post_training/pipelines/causal_language_model.py index 5dbdc87327d..44385830e0a 100644 --- a/tests/post_training/pipelines/causal_language_model.py +++ b/tests/post_training/pipelines/causal_language_model.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -17,17 +17,17 @@ from tests.post_training.pipelines.base import OV_BACKENDS from tests.post_training.pipelines.base import BackendType -from tests.post_training.pipelines.base import BaseTestPipeline +from tests.post_training.pipelines.base import PTQTestPipeline -class CausalLMHF(BaseTestPipeline): +class CausalLMHF(PTQTestPipeline): """Pipeline for causal language models from Hugging Face repository""" def prepare_model(self) -> None: if self.backend in OV_BACKENDS + [BackendType.FP32]: self.model_hf = OVModelForCausalLM.from_pretrained(self.model_id, export=True, compile=False) self.model = self.model_hf.model - ov.serialize(self.model, self.output_model_dir / "model_fp32.xml") + ov.serialize(self.model, self.fp32_model_dir / "model_fp32.xml") def prepare_preprocessor(self) -> None: self.preprocessor = transformers.AutoTokenizer.from_pretrained(self.model_id) @@ -42,7 +42,7 @@ def transform_func(examples): def prepare_calibration_dataset(self): quantizer = OVQuantizer.from_pretrained(self.model_hf) - num_samples = self.ptq_params.get("subset_size", 300) + num_samples = self.compression_params.get("subset_size", 300) calibration_dataset = quantizer.get_calibration_dataset( "glue", dataset_config_name="sst2", diff --git a/tests/post_training/pipelines/image_classification_timm.py b/tests/post_training/pipelines/image_classification_timm.py index 3723fdfdb8b..c17dc3e2ef4 100644 --- a/tests/post_training/pipelines/image_classification_timm.py +++ b/tests/post_training/pipelines/image_classification_timm.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -14,15 +14,13 @@ import numpy as np import onnx -import openvino.runtime as ov +import openvino as ov import timm import torch -from openvino.tools.mo import convert_model from sklearn.metrics import accuracy_score +from timm.data.transforms_factory import transforms_imagenet_eval from timm.layers.config import set_fused_attn from torchvision import datasets -from torchvision import transforms -from torchvision.transforms import InterpolationMode import nncf from nncf.common.logging.track_progress import track @@ -33,17 +31,18 @@ from tests.post_training.pipelines.base import OV_BACKENDS from tests.post_training.pipelines.base import PT_BACKENDS from tests.post_training.pipelines.base import BackendType -from tests.post_training.pipelines.base import BaseTestPipeline +from tests.post_training.pipelines.base import PTQTestPipeline # Disable using aten::scaled_dot_product_attention set_fused_attn(False, False) -class ImageClassificationTimm(BaseTestPipeline): +class ImageClassificationTimm(PTQTestPipeline): """Pipeline for Image Classification model from timm repository""" def prepare_model(self) -> None: timm_model = timm.create_model(self.model_id, num_classes=1000, in_chans=3, pretrained=True, checkpoint_path="") + timm_model.eval() timm_model = replace_timm_custom_modules_with_torch_native(timm_model) self.model_cfg = timm_model.default_cfg self.input_size = [1] + list(timm_model.default_cfg["input_size"]) @@ -53,13 +52,13 @@ def prepare_model(self) -> None: self.model = timm_model if self.backend == BackendType.ONNX: - onnx_path = self.output_model_dir / "model_fp32.onnx" + onnx_path = self.fp32_model_dir / "model_fp32.onnx" torch.onnx.export(timm_model, self.dummy_tensor, onnx_path, export_params=True, opset_version=13) self.model = onnx.load(onnx_path) self.input_name = self.model.graph.input[0].name if self.backend in OV_BACKENDS + [BackendType.FP32]: - self.model = convert_model(timm_model, example_input=self.dummy_tensor, input_shape=self.input_size) + self.model = ov.convert_model(timm_model, example_input=self.dummy_tensor, input=self.input_size) self.input_name = list(inp.get_any_name() for inp in self.model.inputs)[0] self._dump_model_fp32() @@ -72,36 +71,28 @@ def prepare_model(self) -> None: def _dump_model_fp32(self) -> None: """Dump IRs of fp32 models, to help debugging.""" if self.backend in PT_BACKENDS: - ov_model = convert_model(self.model, example_input=self.dummy_tensor, input_shape=self.input_size) - ov.serialize(ov_model, self.output_model_dir / "model_fp32.xml") + ov_model = ov.convert_model(self.model, example_input=self.dummy_tensor, input=self.input_size) + ov.serialize(ov_model, self.fp32_model_dir / "model_fp32.xml") if self.backend == BackendType.ONNX: - onnx_path = self.output_model_dir / "model_fp32.onnx" - ov_model = convert_model(onnx_path) - ov.serialize(ov_model, self.output_model_dir / "model_fp32.xml") + onnx_path = self.fp32_model_dir / "model_fp32.onnx" + ov_model = ov.convert_model(onnx_path) + ov.serialize(ov_model, self.fp32_model_dir / "model_fp32.xml") if self.backend in OV_BACKENDS + [BackendType.FP32]: - ov.serialize(self.model, self.output_model_dir / "model_fp32.xml") + ov.serialize(self.model, self.fp32_model_dir / "model_fp32.xml") def prepare_preprocessor(self) -> None: config = self.model_cfg - transformations_list = [] - normalize = transforms.Normalize(mean=config["mean"], std=config["std"]) - input_size = config["input_size"] - - RESIZE_MODE_MAP = { - "bilinear": InterpolationMode.BILINEAR, - "bicubic": InterpolationMode.BICUBIC, - "nearest": InterpolationMode.NEAREST, - } - - if "fixed_input_size" in config and not config["fixed_input_size"]: - resize_size = tuple(int(x / config["crop_pct"]) for x in input_size[-2:]) - resize = transforms.Resize(resize_size, interpolation=RESIZE_MODE_MAP[config["interpolation"]]) - transformations_list.append(resize) - transformations_list.extend([transforms.CenterCrop(input_size[-2:]), transforms.ToTensor(), normalize]) - - self.transform = transforms.Compose(transformations_list) + self.transform = transforms_imagenet_eval( + img_size=config["input_size"][-2:], + crop_pct=config["crop_pct"], + crop_mode=config["crop_mode"], + interpolation=config["interpolation"], + use_prefetcher=False, + mean=config["mean"], + std=config["std"], + ) def get_transform_calibration_fn(self): if self.backend in PT_BACKENDS: @@ -136,12 +127,12 @@ def _validate(self): core = ov.Core() - if os.environ.get("CPU_THREADS_NUM"): + if os.environ.get("INFERENCE_NUM_THREADS"): # Set CPU_THREADS_NUM for OpenVINO inference - cpu_threads_num = os.environ.get("CPU_THREADS_NUM") - core.set_property("CPU", properties={"CPU_THREADS_NUM": str(cpu_threads_num)}) + inference_num_threads = os.environ.get("INFERENCE_NUM_THREADS") + core.set_property("CPU", properties={"INFERENCE_NUM_THREADS": str(inference_num_threads)}) - ov_model = core.read_model(self.path_quantized_ir) + ov_model = core.read_model(self.path_compressed_ir) compiled_model = core.compile_model(ov_model) jobs = int(os.environ.get("NUM_VAL_THREADS", DEFAULT_VAL_THREADS)) diff --git a/tests/post_training/pipelines/lm_weight_compression.py b/tests/post_training/pipelines/lm_weight_compression.py new file mode 100644 index 00000000000..b1a6e5853dc --- /dev/null +++ b/tests/post_training/pipelines/lm_weight_compression.py @@ -0,0 +1,221 @@ +# Copyright (c) 2023 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import re +import shutil +import time +from dataclasses import dataclass +from typing import Dict, Optional + +import numpy as np +import openvino as ov +from datasets import load_dataset +from memory_profiler import memory_usage +from optimum.intel.openvino import OVModelForCausalLM +from transformers import AutoTokenizer +from whowhatbench import Evaluator + +import nncf +from tests.post_training.pipelines.base import BackendType +from tests.post_training.pipelines.base import BaseTestPipeline +from tests.post_training.pipelines.base import StatsFromOutput +from tests.shared.paths import TEST_ROOT + + +@dataclass +class WCTimeStats(StatsFromOutput): + """ + Contains statistics that are parsed from the stdout of Weight Compression tests. + """ + + time_stat_collection: Optional[str] = None + time_mixed_precision: Optional[str] = None + time_awq: Optional[str] = None + time_apply_compression: Optional[str] = None + + STAT_NAMES = ["Stat. collection time", "Mixed-Precision search time", "AWQ time", "Apply Compression time"] + VAR_NAMES = ["time_stat_collection", "time_mixed_precision", "time_awq", "time_apply_compression"] + REGEX_PREFIX = [ + "Statistics collection", + "Mixed-Precision assignment", + "Applying AWQ", + "Applying Weight Compression", + ] + + def fill(self, stdout: str) -> None: + time_regex = r".*•\s(.*)\s•.*" + for line in stdout.splitlines(): + for attr_name, prefix_regex in zip(self.VAR_NAMES, self.REGEX_PREFIX): + match = re.search(r"{}{}".format(prefix_regex, time_regex), line) + if match: + setattr(self, attr_name, match.group(1)) + continue + + def get_stats(self) -> Dict[str, str]: + VARS = [getattr(self, name) for name in self.VAR_NAMES] + return dict(zip(self.STAT_NAMES, VARS)) + + +class LMWeightCompression(BaseTestPipeline): + """Pipeline for casual language models from Hugging Face repository""" + + OV_MODEL_NAME = "openvino_model.xml" + + def prepare_model(self) -> None: + is_stateful = self.params.get("is_stateful", False) + if is_stateful: + self.fp32_model_dir = self.fp32_model_dir.parent / (self.fp32_model_dir.name + "_sf") + if not (self.fp32_model_dir / self.OV_MODEL_NAME).exists(): + # export by model_id + self.model_hf = OVModelForCausalLM.from_pretrained( + self.model_id, export=True, load_in_8bit=False, compile=False, stateful=is_stateful + ) + self._dump_model_fp32() + else: + # no export, load from IR. Applicable for sequential run of test cases in local environment. + self.model_hf = OVModelForCausalLM.from_pretrained( + self.fp32_model_dir, trust_remote_code=True, load_in_8bit=False, compile=False, stateful=is_stateful + ) + self.model = self.model_hf.model + + def prepare_preprocessor(self) -> None: + self.preprocessor = AutoTokenizer.from_pretrained(self.model_id) + + def get_transform_calibration_fn(self): + def transform_fn(data): + tokenized_text = self.preprocessor(data["text"], return_tensors="np") + input_ids = tokenized_text["input_ids"] + attention_mask = tokenized_text["attention_mask"] + + inputs = {} + inputs["input_ids"] = input_ids + inputs["attention_mask"] = tokenized_text["attention_mask"] + position_ids = np.cumsum(attention_mask, axis=1) - 1 + position_ids[attention_mask == 0] = 1 + + # The magic forms KV cache as model inputs + batch_size = input_ids.shape[0] + for input_name in self.model_hf.key_value_input_names: + model_inputs = self.model.input(input_name) + shape = model_inputs.get_partial_shape() + shape[0] = batch_size + if shape[2].is_dynamic: + shape[2] = 0 + else: + shape[1] = 0 + inputs[input_name] = ov.Tensor(model_inputs.get_element_type(), shape.get_shape()) + + inputs["position_ids"] = position_ids + + # initialize the rest of inputs (e.g. beam_idx for stateful models) + for val in self.model.inputs: + name = val.any_name + if name in inputs: + continue + shape = list(val.partial_shape.get_min_shape()) + shape[0] = batch_size + inputs[name] = np.zeros(shape) + return inputs + + return transform_fn + + def prepare_calibration_dataset(self): + dataset = load_dataset("wikitext", "wikitext-2-v1", split="train", revision="b08601e") + dataset = dataset.filter(lambda example: len(example["text"]) > 80) + self.calibration_dataset = nncf.Dataset(dataset, self.get_transform_calibration_fn()) + + def cleanup_cache(self): + dir_with_cache = "model_cache" + dirs_to_remove = [self.output_model_dir / dir_with_cache, self.fp32_model_dir / dir_with_cache] + for dir_to_remove in dirs_to_remove: + if dir_to_remove.exists(): + shutil.rmtree(dir_to_remove) + + def compress(self) -> None: + if self.backend == BackendType.FP32: + return + + print("Weight compression...") + start_time = time.perf_counter() + self.run_info.compression_memory_usage = memory_usage(self._compress, max_usage=True) + self.run_info.time_compression = time.perf_counter() - start_time + + def collect_data_from_stdout(self, stdout: str): + stats = WCTimeStats() + stats.fill(stdout) + self.run_info.stats_from_output = stats + + def save_compressed_model(self) -> None: + if self.backend == BackendType.FP32: + return + ov.serialize(self.model, self.output_model_dir / self.OV_MODEL_NAME) + self.model_hf._save_config(self.output_model_dir) + + def get_num_compressed(self) -> None: + pass + + def run_bench(self) -> None: + pass + + def _dump_model_fp32(self) -> None: + """ + Dump IRs of fp32 models, to help debugging. The test cases may share the same fp32 model, therefore it is saved + to the dedicated shared folder. + """ + self.model_hf.save_pretrained(self.fp32_model_dir) + self.model_hf._save_config(self.fp32_model_dir) + + def _compress(self): + """ + Actual call of weight compression + """ + self.compressed_model = nncf.compress_weights( + self.model, + dataset=self.calibration_dataset, + **self.compression_params, + ) + + def _validate(self): + is_stateful = self.params.get("is_stateful", False) + core = ov.Core() + + if os.environ.get("INFERENCE_NUM_THREADS"): + # Set CPU_THREADS_NUM for OpenVINO inference + inference_num_threads = os.environ.get("INFERENCE_NUM_THREADS") + core.set_property("CPU", properties={"INFERENCE_NUM_THREADS": str(inference_num_threads)}) + + gt_data_path = TEST_ROOT / "post_training" / "data" / "wwb_ref_answers" / self.fp32_model_name / "ref_qa.csv" + gt_data_path.parent.mkdir(parents=True, exist_ok=True) + if os.getenv("NNCF_TEST_REGEN_DOT") is not None: + print("Collection ground-truth reference data") + model_gold = OVModelForCausalLM.from_pretrained( + self.fp32_model_dir, trust_remote_code=True, load_in_8bit=False, compile=False, stateful=is_stateful + ) + evaluator = Evaluator(base_model=model_gold, tokenizer=self.preprocessor, metrics=("similarity",)) + evaluator.dump_gt(str(gt_data_path)) + print("Saving ground-truth validation data:", gt_data_path.resolve()) + else: + print("Loading existing ground-truth validation data:", gt_data_path.resolve()) + evaluator = Evaluator( + tokenizer=self.preprocessor, gt_data=gt_data_path, test_data=str(gt_data_path), metrics=("similarity",) + ) + + compressed_model_hf = self.model_hf + if self.backend != BackendType.FP32: + compressed_model_hf = OVModelForCausalLM.from_pretrained( + self.output_model_dir, trust_remote_code=True, load_in_8bit=False, compile=False, stateful=is_stateful + ) + print("Evaluation of the target model") + _, all_metrics = evaluator.score(compressed_model_hf) + similarity = all_metrics["similarity"][0] + self.run_info.metric_name = "Similarity" + self.run_info.metric_value = round(similarity, 5) diff --git a/tests/post_training/pipelines/masked_language_modeling.py b/tests/post_training/pipelines/masked_language_modeling.py index 8c86d340006..9f750808506 100644 --- a/tests/post_training/pipelines/masked_language_modeling.py +++ b/tests/post_training/pipelines/masked_language_modeling.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -11,10 +11,9 @@ import numpy as np import onnx -import openvino.runtime as ov +import openvino as ov import torch import transformers -from openvino.tools.mo import convert_model from optimum.intel import OVQuantizer from optimum.intel.openvino import OVModelForSequenceClassification from optimum.onnxruntime import ORTModelForSequenceClassification @@ -23,10 +22,10 @@ from tests.post_training.pipelines.base import OV_BACKENDS from tests.post_training.pipelines.base import PT_BACKENDS from tests.post_training.pipelines.base import BackendType -from tests.post_training.pipelines.base import BaseTestPipeline +from tests.post_training.pipelines.base import PTQTestPipeline -class MaskedLanguageModelingHF(BaseTestPipeline): +class MaskedLanguageModelingHF(PTQTestPipeline): """Pipeline for masked language models from Hugging Face repository""" def prepare_model(self) -> None: @@ -53,17 +52,17 @@ def prepare_model(self) -> None: def _dump_model_fp32(self) -> None: """Dump IRs of fp32 models, to help debugging.""" if self.backend in PT_BACKENDS: - ov_model = convert_model(self.model, example_input=self.dummy_tensor) - ov.serialize(ov_model, self.output_model_dir / "model_fp32.xml") + ov_model = ov.convert_model(self.model, example_input=self.dummy_tensor) + ov.serialize(ov_model, self.fp32_model_dir / "model_fp32.xml") if self.backend == BackendType.ONNX: - onnx_path = self.output_model_dir / "model_fp32.onnx" + onnx_path = self.fp32_model_dir / "model_fp32.onnx" onnx.save(self.model, onnx_path) - ov_model = convert_model(onnx_path) - ov.serialize(ov_model, self.output_model_dir / "model_fp32.xml") + ov_model = ov.convert_model(onnx_path) + ov.serialize(ov_model, self.fp32_model_dir / "model_fp32.xml") if self.backend in OV_BACKENDS + [BackendType.FP32]: - ov.serialize(self.model, self.output_model_dir / "model_fp32.xml") + ov.serialize(self.model, self.fp32_model_dir / "model_fp32.xml") def prepare_preprocessor(self) -> None: self.preprocessor = transformers.AutoTokenizer.from_pretrained(self.model_id) @@ -89,7 +88,7 @@ def transform_func(data): def prepare_calibration_dataset(self): quantizer = OVQuantizer.from_pretrained(self.model_hf) - num_samples = self.ptq_params.get("subset_size", 300) + num_samples = self.compression_params.get("subset_size", 300) def preprocess_function(examples): return self.preprocessor(examples["sentence"], padding=True, truncation=True, max_length=128) diff --git a/tests/post_training/requirements.txt b/tests/post_training/requirements.txt index c9bff02f625..b8a7ae89061 100644 --- a/tests/post_training/requirements.txt +++ b/tests/post_training/requirements.txt @@ -7,9 +7,10 @@ onnx==1.13.1 onnxruntime==1.14.1 pytest==7.4.4 pytest-cov -openvino-dev==2023.3.0 +openvino-dev==2024.0.0 optimum[onnxruntime,openvino]==1.16.0 optimum-intel @ git+https://github.com/huggingface/optimum-intel@622f585962e5e0ff5f927fd1a96fbc02b60a2e65 +whowhatbench @ git+https://github.com/andreyanufr/who_what_benchmark@456d3584ce628f6c8605f37cd9a3ab2db1ebf933 soundfile==0.12.1 librosa==0.10.0 memory-profiler==0.61.0 diff --git a/tests/post_training/test_quantize_conformance.py b/tests/post_training/test_quantize_conformance.py index c7a35d955be..2b3c06fa0b7 100644 --- a/tests/post_training/test_quantize_conformance.py +++ b/tests/post_training/test_quantize_conformance.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -9,10 +9,10 @@ # See the License for the specific language governing permissions and # limitations under the License. - import time import traceback from collections import OrderedDict +from collections import defaultdict from pathlib import Path from typing import Dict, Optional @@ -20,7 +20,9 @@ import pytest import yaml -from tests.post_training.model_scope import TEST_CASES +import nncf +from tests.post_training.model_scope import PTQ_TEST_CASES +from tests.post_training.model_scope import WC_TEST_CASES from tests.post_training.pipelines.base import BackendType from tests.post_training.pipelines.base import BaseTestPipeline from tests.post_training.pipelines.base import RunInfo @@ -68,40 +70,131 @@ def fixture_extra_columns(pytestconfig): return pytestconfig.getoption("extra_columns") -@pytest.fixture(scope="session", name="reference_data") -def fixture_reference_data(): - path_reference = Path(__file__).parent / "reference_data.yaml" +@pytest.fixture(scope="session", name="ptq_reference_data") +def fixture_ptq_reference_data(): + path_reference = Path(__file__).parent / "data" / "ptq_reference_data.yaml" with path_reference.open() as f: data = yaml.safe_load(f) return data -@pytest.fixture(scope="session", name="result_data") -def fixture_report_data(output_dir, run_benchmark_app, extra_columns): +@pytest.fixture(scope="session", name="wc_reference_data") +def fixture_wc_reference_data(): + path_reference = Path(__file__).parent / "data" / "wc_reference_data.yaml" + with path_reference.open() as f: + data = yaml.safe_load(f) + fp32_test_cases = defaultdict(dict) + for test_case_name in data: + if "atol" not in data[test_case_name]: + data[test_case_name]["atol"] = 1e-5 + reported_name = test_case_name.split("_backend_")[0] + fp32_case_name = f"{reported_name}_backend_FP32" + fp32_test_cases[fp32_case_name]["metric_value"] = 1 + if "atol" not in fp32_test_cases[fp32_case_name]: + fp32_test_cases[fp32_case_name]["atol"] = 1e-10 + data.update(fp32_test_cases) + return data + + +@pytest.fixture(scope="session", name="ptq_result_data") +def fixture_ptq_report_data(output_dir, run_benchmark_app): data: Dict[str, RunInfo] = {} yield data if data: test_results = OrderedDict(sorted(data.items())) - df = pd.DataFrame(v for v in test_results.values()) - + df = pd.DataFrame(v.get_result_dict() for v in test_results.values()) if not run_benchmark_app: df = df.drop(columns=["FPS"]) - if not extra_columns: - df = df.drop(columns=["Stat. collection time", "Bias correction time", "Validation time"]) output_dir.mkdir(parents=True, exist_ok=True) df.to_csv(output_dir / "results.csv", index=False) -@pytest.mark.parametrize("test_case_name", TEST_CASES.keys()) +@pytest.fixture(scope="session", name="wc_result_data") +def fixture_wc_report_data(output_dir): + data: Dict[str, RunInfo] = {} + + yield data + + if data: + test_results = OrderedDict(sorted(data.items())) + df = pd.DataFrame(v.get_result_dict() for v in test_results.values()) + df = df.drop(columns=["FPS", "Num FQ"]) + + output_dir.mkdir(parents=True, exist_ok=True) + df.to_csv(output_dir / "results.csv", index=False) + + +def maybe_skip_test_case(test_model_param, run_fp32_backend, run_torch_cuda_backend): + if test_model_param["backend"] == BackendType.FP32 and not run_fp32_backend: + pytest.skip("To run test for not quantized model use --fp32 argument") + if test_model_param["backend"] == BackendType.CUDA_TORCH and not run_torch_cuda_backend: + pytest.skip("To run test for CUDA_TORCH backend use --cuda argument") + return test_model_param + + +def create_short_run_info(test_model_param, err_msg, test_case_name): + if test_model_param is not None: + run_info = RunInfo( + model=test_model_param["reported_name"], + backend=test_model_param["backend"], + status=err_msg, + ) + else: + splitted = test_case_name.split("_backend_") + run_info = RunInfo( + model=splitted[0], + backend=BackendType[splitted[1]], + status=err_msg, + ) + + return run_info + + +def write_logs(captured, pipeline): + stdout_file = pipeline.output_model_dir / "stdout.log" + stderr_file = pipeline.output_model_dir / "stderr.log" + + stdout_file.write_text(captured.out, encoding="utf-8") + stderr_file.write_text(captured.err, encoding="utf-8") + return captured + + +def create_pipeline_kwargs(test_model_param, subset_size, test_case_name, reference_data): + if subset_size: + if "compression_params" not in test_model_param: + test_model_param["compression_params"] = {} + test_model_param["compression_params"]["subset_size"] = subset_size + + print("\n") + print(f"Model: {test_model_param['reported_name']}") + print(f"Backend: {test_model_param['backend']}") + print(f"PTQ params: {test_model_param['compression_params']}") + + # Get target fp32 metric value + model_name = test_case_name.split("_backend_")[0] + test_reference = reference_data[test_case_name] + test_reference["metric_value_fp32"] = reference_data[f"{model_name}_backend_FP32"]["metric_value"] + + return { + "reported_name": test_model_param["reported_name"], + "model_id": test_model_param["model_id"], + "backend": test_model_param["backend"], + "compression_params": test_model_param["compression_params"], + "params": test_model_param.get("params"), + "reference_data": test_reference, + } + + +@pytest.mark.parametrize("test_case_name", PTQ_TEST_CASES.keys()) def test_ptq_quantization( - reference_data: dict, + ptq_reference_data: dict, test_case_name: str, data_dir: Path, output_dir: Path, - result_data: Dict[str, RunInfo], + ptq_result_data: Dict[str, RunInfo], no_eval: bool, run_fp32_backend: bool, run_torch_cuda_backend: bool, @@ -111,91 +204,97 @@ def test_ptq_quantization( extra_columns: bool, ): pipeline = None - captured = None err_msg = None test_model_param = None start_time = time.perf_counter() - try: - if test_case_name not in reference_data: - raise RuntimeError(f"{test_case_name} does not exist in 'reference_data.yaml'") + if test_case_name not in ptq_reference_data: + raise nncf.ValidationError(f"{test_case_name} does not exist in 'reference_data.yaml'") + test_model_param = PTQ_TEST_CASES[test_case_name] + maybe_skip_test_case(test_model_param, run_fp32_backend, run_torch_cuda_backend) + pipeline_cls = test_model_param["pipeline_cls"] + pipeline_kwargs = create_pipeline_kwargs(test_model_param, subset_size, test_case_name, ptq_reference_data) + pipeline_kwargs.update( + {"output_dir": output_dir, "data_dir": data_dir, "no_eval": no_eval, "run_benchmark_app": run_benchmark_app} + ) + pipeline: BaseTestPipeline = pipeline_cls(**pipeline_kwargs) + pipeline.run() + except Exception as e: + err_msg = str(e) + traceback.print_exc() - test_model_param = TEST_CASES[test_case_name] + if pipeline is not None: + pipeline.cleanup_cache() + run_info = pipeline.run_info + if err_msg: + run_info.status = f"{run_info.status} | {err_msg}" if run_info.status else err_msg - if test_model_param["backend"] == BackendType.FP32 and not run_fp32_backend: - pytest.skip("To run test for not quantized model use --fp32 argument") + captured = capsys.readouterr() + write_logs(captured, pipeline) - if test_model_param["backend"] == BackendType.CUDA_TORCH and not run_torch_cuda_backend: - pytest.skip("To run test for CUDA_TORCH backend use --cuda argument") + if extra_columns: + pipeline.collect_data_from_stdout(captured.out) + else: + run_info = create_short_run_info(test_model_param, err_msg, test_case_name) - pipeline_cls = test_model_param["pipeline_cls"] + run_info.time_total = time.perf_counter() - start_time + ptq_result_data[test_case_name] = run_info - if subset_size: - if "ptq_params" not in test_model_param: - test_model_param["ptq_params"] = {} - test_model_param["ptq_params"]["subset_size"] = subset_size - - print("\n") - print(f"Model: {test_model_param['reported_name']}") - print(f"Backend: {test_model_param['backend']}") - print(f"PTQ params: {test_model_param['ptq_params']}") - - # Get target fp32 metric value - model_name = test_case_name.split("_backend_")[0] - test_reference = reference_data[test_case_name] - test_reference["metric_value_fp32"] = reference_data[f"{model_name}_backend_FP32"]["metric_value"] - - pipeline_kwargs = { - "reported_name": test_model_param["reported_name"], - "model_id": test_model_param["model_id"], - "backend": test_model_param["backend"], - "ptq_params": test_model_param["ptq_params"], - "params": test_model_param.get("params"), - "output_dir": output_dir, - "data_dir": data_dir, - "reference_data": test_reference, - "no_eval": no_eval, - "run_benchmark_app": run_benchmark_app, - } + if err_msg: + pytest.fail(err_msg) + +@pytest.mark.parametrize("test_case_name", WC_TEST_CASES.keys()) +def test_weight_compression( + wc_reference_data: dict, + test_case_name: str, + data_dir: Path, + output_dir: Path, + wc_result_data: Dict[str, RunInfo], + no_eval: bool, + run_fp32_backend: bool, + run_torch_cuda_backend: bool, + subset_size: Optional[int], + run_benchmark_app: bool, + capsys: pytest.CaptureFixture, + extra_columns: bool, +): + pipeline = None + err_msg = None + test_model_param = None + start_time = time.perf_counter() + try: + if test_case_name not in wc_reference_data: + raise RuntimeError(f"{test_case_name} is not defined in `wc_reference_data` fixture") + test_model_param = WC_TEST_CASES[test_case_name] + maybe_skip_test_case(test_model_param, run_fp32_backend, run_torch_cuda_backend) + pipeline_cls = test_model_param["pipeline_cls"] + pipeline_kwargs = create_pipeline_kwargs(test_model_param, subset_size, test_case_name, wc_reference_data) + pipeline_kwargs.update( + {"output_dir": output_dir, "data_dir": data_dir, "no_eval": no_eval, "run_benchmark_app": run_benchmark_app} + ) pipeline: BaseTestPipeline = pipeline_cls(**pipeline_kwargs) pipeline.run() - except Exception as e: err_msg = str(e) traceback.print_exc() if pipeline is not None: - run_info = pipeline.get_run_info() + pipeline.cleanup_cache() + run_info = pipeline.run_info if err_msg: run_info.status = f"{run_info.status} | {err_msg}" if run_info.status else err_msg - # Collect stdout and stderr logs to files - stdout_file = pipeline.output_model_dir / "stdout.log" - stderr_file = pipeline.output_model_dir / "stderr.log" captured = capsys.readouterr() - stdout_file.write_text(captured.out, encoding="utf-8") - stderr_file.write_text(captured.err, encoding="utf-8") + write_logs(captured, pipeline) if extra_columns: pipeline.collect_data_from_stdout(captured.out) else: - if test_model_param is not None: - run_info = RunInfo( - model=test_model_param["reported_name"], - backend=test_model_param["backend"], - status=err_msg, - ) - else: - splitted = test_case_name.split("_backend_") - run_info = RunInfo( - model=splitted[0], - backend=BackendType[splitted[1]], - status=err_msg, - ) + run_info = create_short_run_info(test_model_param, err_msg, test_case_name) run_info.time_total = time.perf_counter() - start_time - result_data[test_case_name] = run_info.get_result_dict() + wc_result_data[test_case_name] = run_info if err_msg: pytest.fail(err_msg) diff --git a/tests/post_training/test_templates/helpers.py b/tests/post_training/test_templates/helpers.py index 8f60f61ee9f..c0e8723d118 100644 --- a/tests/post_training/test_templates/helpers.py +++ b/tests/post_training/test_templates/helpers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -157,6 +157,14 @@ def __init__(self) -> None: self.matmul_7_data = torch.randn((6, 6), dtype=torch.float32) self.matmul_8_data = torch.randn((10, 6), dtype=torch.float32) + self.linear_3 = nn.Linear(4, 4) + self.linear_3.weight.data = torch.randn((4, 4), dtype=torch.float32) + self.linear_3.bias.data = torch.randn((1, 4), dtype=torch.float32) + + self.linear_4 = nn.Linear(4, 4) + self.linear_4.weight.data = torch.randn((4, 4), dtype=torch.float32) + self.linear_4.bias.data = torch.randn((1, 4), dtype=torch.float32) + def forward(self, x): x = torch.reshape(x, (1, 3, 2, 4)) @@ -164,6 +172,15 @@ def forward(self, x): x_2 = torch.matmul(x, self.matmul_2_data) x = torch.add(x_1, x_2) + + x_3 = self.linear_3(x) + x_4 = self.linear_4(x) + + x_ = torch.add(x_3, x_4) + + x = torch.add(x, x_) + x = torch.sub(x, x_) + x_1 = torch.reshape(x, (1, 3, 8)) x_1_1 = torch.matmul(x_1, self.matmul_3_data) @@ -189,23 +206,34 @@ def forward(self, x): class NonZeroLinearModel(nn.Module): INPUT_SIZE = [10] + def __init__(self): + super().__init__() + self.linear = nn.Linear(1, 5) + self.linear.weight.data = torch.ones((5, 1)) + self.linear.bias.data = torch.zeros((1, 1)) + + self.linear1 = nn.Linear(10, 10) + self.linear1.weight.data = torch.ones((10, 10)) + self.linear1.bias.data = torch.zeros((1, 1)) + def forward(self, x): zeros = (x > torch.inf).float() empty = torch.nonzero(zeros).reshape((-1, 1, 1)).float() - y = torch.matmul(empty, torch.ones((1, 5))) + y = self.linear(empty) y += 5 y = torch.cat((torch.ones((1, 10)), y.reshape(1, -1)), dim=1) - y = torch.matmul(y, torch.ones(10, 10)) + y = self.linear1(y) y += 5 return y class SplittedModel(nn.Module): - INPUT_SIZE = [1, 3, 28, 28] + INPUT_SIZE = [1, 2, 28, 28] def __init__(self) -> None: super().__init__() with set_torch_seed(): + self.concat_1_data = torch.randn((1, 1, 28, 28), dtype=torch.float32) self.conv_1 = self._build_conv(3, 12, 3) self.add_1_data = torch.randn((1, 12, 26, 26), dtype=torch.float32) self.maxpool_1 = torch.nn.MaxPool2d(1) @@ -246,6 +274,7 @@ def _build_conv(self, in_channels=1, out_channels=2, kernel_size=2): return conv def forward(self, x): + x = torch.concat([self.concat_1_data, x], 1) x = self.conv_1(x) x = F.relu(x) x = torch.add(x, self.add_1_data) @@ -309,3 +338,21 @@ def forward(self, x): x = self.embedding(x) x = self.matmul(x) return x + + +class ShareWeghtsConvAndShareLinearModel(nn.Module): + INPUT_SIZE = [1, 1, 4, 4] + + def __init__(self): + super().__init__() + with set_torch_seed(): + self.conv = create_conv(1, 1, 1) + self.linear = nn.Linear(4, 4) + self.linear.weight.data = torch.randn((4, 4), dtype=torch.float32) + self.linear.bias.data = torch.randn((1, 4), dtype=torch.float32) + + def forward(self, x): + for _ in range(2): + x = self.conv(x) + x = self.linear(x) + return x diff --git a/tests/post_training/test_templates/models.py b/tests/post_training/test_templates/models.py index a6225d59971..884e2e0e662 100644 --- a/tests/post_training/test_templates/models.py +++ b/tests/post_training/test_templates/models.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/post_training/test_templates/test_bias_correction.py b/tests/post_training/test_templates/test_bias_correction.py index b8146cf0827..e27a8ad0868 100644 --- a/tests/post_training/test_templates/test_bias_correction.py +++ b/tests/post_training/test_templates/test_bias_correction.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -178,3 +178,13 @@ def test__get_subgraph_data_for_node(self, quantized_test_model, layer_name, ref ref_subgraph_data = ref_data["subgraph_data"] assert subgraph_data == ref_subgraph_data + + def test_verify_collected_stat_inputs_map(self, model_cls, ref_stat_inputs_map, tmpdir): + model = self.backend_specific_model(model_cls(), tmpdir) + graph = NNCFGraphFactory.create(model) + + bc_algo = self.get_bias_correction_algorithm() + bc_algo.get_statistic_points(model, graph) + + collected_stat_inputs_map = getattr(bc_algo, "_collected_stat_inputs_map") + assert collected_stat_inputs_map == ref_stat_inputs_map diff --git a/tests/post_training/test_templates/test_calculate_quantizer_parameters.py b/tests/post_training/test_templates/test_calculate_quantizer_parameters.py index 9c643fb0a63..0457f3dbb13 100644 --- a/tests/post_training/test_templates/test_calculate_quantizer_parameters.py +++ b/tests/post_training/test_templates/test_calculate_quantizer_parameters.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -16,6 +16,7 @@ import numpy as np import pytest +import nncf from nncf.common.quantization.structs import QuantizationScheme as QuantizationMode from nncf.common.quantization.structs import QuantizerConfig from nncf.common.quantization.structs import QuantizerGroup @@ -223,5 +224,5 @@ def test_calculate_quantizer_parameters(self, case_to_test): ref_fq_params = read_ref_fq_params(quant_group, q_config, narrow_range, half_range) compare_fq_parameters(fq_params, ref_fq_params) else: - with pytest.raises(RuntimeError): + with pytest.raises(nncf.ValidationError): calculate_quantizer_parameters(statistics, q_config, quant_group, narrow_range, half_range) diff --git a/tests/post_training/test_templates/test_channel_alignment.py b/tests/post_training/test_templates/test_channel_alignment.py index 65d5b49e180..49cb8ffe0c3 100644 --- a/tests/post_training/test_templates/test_channel_alignment.py +++ b/tests/post_training/test_templates/test_channel_alignment.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -15,6 +15,7 @@ import numpy as np import pytest +import nncf from nncf.common.graph.graph import NNCFGraph from nncf.common.graph.layer_attributes import ConvolutionLayerAttributes from nncf.common.graph.layer_attributes import LinearLayerAttributes @@ -475,7 +476,7 @@ def dims_iter(*args, **kwargs): _class = weights_update_cls _attr = "weight_value" else: - raise RuntimeError(f"Wrong type of transformation: {type(transformation)}") + raise nncf.ValidationError(f"Wrong type of transformation: {type(transformation)}") target_names[tp.target_node_name].append(_class) assert ref_values[tp.target_node_name][_attr] == getattr(transformation, _attr) diff --git a/tests/post_training/test_templates/test_fast_bias_correction.py b/tests/post_training/test_templates/test_fast_bias_correction.py index d60ba274902..edaf979d74e 100644 --- a/tests/post_training/test_templates/test_fast_bias_correction.py +++ b/tests/post_training/test_templates/test_fast_bias_correction.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/post_training/test_templates/test_ptq_params.py b/tests/post_training/test_templates/test_ptq_params.py index ec8c2155da6..c7a0e45799c 100644 --- a/tests/post_training/test_templates/test_ptq_params.py +++ b/tests/post_training/test_templates/test_ptq_params.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -15,6 +15,7 @@ import pytest +import nncf from nncf.common.graph.operator_metatypes import InputNoopMetatype from nncf.common.graph.operator_metatypes import OperatorMetatype from nncf.common.graph.operator_metatypes import OutputNoopMetatype @@ -26,6 +27,10 @@ from nncf.common.tensor_statistics.statistic_point import StatisticPoint from nncf.common.tensor_statistics.statistic_point import StatisticPointsContainer from nncf.common.tensor_statistics.statistics import MinMaxTensorStatistic +from nncf.experimental.common.tensor_statistics.collectors import MaxAggregator +from nncf.experimental.common.tensor_statistics.collectors import MeanAggregator +from nncf.experimental.common.tensor_statistics.collectors import MinAggregator +from nncf.experimental.common.tensor_statistics.collectors import TensorCollector from nncf.parameters import ModelType from nncf.quantization.advanced_parameters import OverflowFix from nncf.quantization.algorithms.min_max.algorithm import MinMaxQuantization @@ -91,13 +96,17 @@ class TemplateTestPTQParams: def get_algo_backend(self): pass - @abstractmethod - def check_is_min_max_statistic_collector(self, tensor_collector): - pass + def check_is_min_max_statistic_collector(self, tensor_collector: TensorCollector): + aggrs = [aggr.__class__ for aggr in tensor_collector.aggregators.values()] + assert len(aggrs) == 2 + assert MinAggregator in aggrs + assert MaxAggregator in aggrs - @abstractmethod - def check_is_mean_min_max_statistic_collector(self, tensor_collector): - pass + def check_is_mean_min_max_statistic_collector(self, tensor_collector: TensorCollector): + aggrs = [aggr.__class__ for aggr in tensor_collector.aggregators.values()] + assert len(aggrs) == 2 + assert MeanAggregator in aggrs + assert aggrs[0].__class__ == aggrs[1].__class__ @abstractmethod def check_quantize_outputs_fq_num(self, quantize_outputs, act_num_q, weight_num_q): @@ -288,7 +297,7 @@ def test_validate_scope(self, test_params, validate_scopes): ) algo._backend_entity = self.get_algo_backend() if validate_scopes: - with pytest.raises(RuntimeError, match="Ignored nodes with name"): + with pytest.raises(nncf.ValidationError, match="Ignored nodes with name"): algo._get_ignored_names(nncf_graph, inference_nncf_graph, ignored_patterns) else: algo._get_ignored_names(nncf_graph, inference_nncf_graph, ignored_patterns) @@ -322,7 +331,7 @@ def get_statistics(self): "nncf.quantization.algorithms.min_max.algorithm.MinMaxQuantization._get_quantization_points_overflow_fix", return_value=mocker.MagicMock(), ) - with pytest.raises(RuntimeError) as exc_info: + with pytest.raises(nncf.InternalError) as exc_info: algo.apply(None, None, stat_points) assert str(exc_info.value) == "Statistics were not collected for the node A" diff --git a/tests/post_training/test_templates/test_quantizer_config.py b/tests/post_training/test_templates/test_quantizer_config.py index b058d1cbe7c..df98648d1a2 100644 --- a/tests/post_training/test_templates/test_quantizer_config.py +++ b/tests/post_training/test_templates/test_quantizer_config.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -27,9 +27,13 @@ from nncf.common.quantization.structs import QuantizerGroup from nncf.common.tensor_statistics.collectors import ReductionAxes from nncf.experimental.common.tensor_statistics.collectors import AbsMaxReducer +from nncf.experimental.common.tensor_statistics.collectors import MaxAggregator from nncf.experimental.common.tensor_statistics.collectors import MaxReducer +from nncf.experimental.common.tensor_statistics.collectors import MeanAggregator +from nncf.experimental.common.tensor_statistics.collectors import MinAggregator from nncf.experimental.common.tensor_statistics.collectors import MinReducer from nncf.experimental.common.tensor_statistics.collectors import TensorCollector +from nncf.experimental.common.tensor_statistics.collectors import TensorReducerBase from nncf.quantization.advanced_parameters import QuantizationParameters from nncf.quantization.algorithms.min_max.algorithm import MinMaxQuantization from nncf.quantization.passes import transform_to_inference_graph @@ -44,17 +48,20 @@ class TemplateTestQuantizerConfig: def get_algo_backend(self): pass - @abstractmethod - def check_is_min_max_statistic_collector(self, tensor_collector): - pass + def check_is_min_max_statistic_collector(self, tensor_collector: TensorCollector): + aggrs = [aggr.__class__ for aggr in tensor_collector.aggregators.values()] + assert len(aggrs) == 2 + assert MinAggregator in aggrs + assert MaxAggregator in aggrs - @abstractmethod - def check_is_mean_min_max_statistic_collector(self, tensor_collector): - pass + def check_is_mean_min_max_statistic_collector(self, tensor_collector: TensorCollector): + aggrs = [aggr.__class__ for aggr in tensor_collector.aggregators.values()] + assert len(aggrs) == 2 + assert MeanAggregator in aggrs + assert aggrs[0].__class__ == aggrs[1].__class__ - @abstractmethod - def get_reduction_axes(self, reducer) -> ReductionAxes: - pass + def get_reduction_axes(self, reducer: TensorReducerBase) -> ReductionAxes: + return reducer._reduction_axes @abstractmethod @pytest.fixture diff --git a/tests/post_training/test_templates/test_smooth_quant.py b/tests/post_training/test_templates/test_smooth_quant.py index baca6887c7f..c3827cc81dd 100644 --- a/tests/post_training/test_templates/test_smooth_quant.py +++ b/tests/post_training/test_templates/test_smooth_quant.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -10,13 +10,15 @@ # limitations under the License. from abc import abstractmethod -from typing import Callable, Dict, TypeVar +from typing import Callable, Dict, Type, TypeVar import pytest +import nncf from nncf.common.factory import NNCFGraphFactory from nncf.common.factory import StatisticsAggregatorFactory from nncf.common.graph.graph import NNCFNode +from nncf.common.graph.transformations.commands import TransformationCommand from nncf.experimental.common.tensor_statistics.collectors import AbsMaxReducer from nncf.experimental.common.tensor_statistics.collectors import MaxAggregator from nncf.parameters import ModelType @@ -26,8 +28,11 @@ from nncf.quantization.algorithms.post_training.algorithm import PostTrainingQuantization from nncf.quantization.algorithms.smooth_quant.algorithm import SmoothQuant from nncf.quantization.algorithms.smooth_quant.backend import SmoothQuantAlgoBackend +from nncf.quantization.algorithms.smooth_quant.openvino_backend import OVSmoothQuantAlgoBackend +from tests.post_training.test_templates.helpers import ConvTestModel from tests.post_training.test_templates.helpers import LinearMultiShapeModel from tests.post_training.test_templates.helpers import NonZeroLinearModel +from tests.post_training.test_templates.helpers import ShareWeghtsConvAndShareLinearModel from tests.post_training.test_templates.helpers import get_static_dataset TModel = TypeVar("TModel") @@ -39,6 +44,27 @@ class TemplateTestSQAlgorithm: def fn_to_type(tensor) -> TTensor: return tensor + @pytest.fixture + @abstractmethod + def inplace_statistics(self) -> bool: + """ + Returns all possible values for inplace parameter. + """ + + @abstractmethod + def get_node_name_map(self, model_cls) -> Dict[str, str]: + """ + Return backend specific map from the given model class labels + to nncf_grpah nodes names. + """ + + @staticmethod + @abstractmethod + def get_target_node_name(command: TransformationCommand): + """ + Get target node name from a transformation command. + """ + @staticmethod @abstractmethod def get_transform_fn() -> Callable: @@ -55,14 +81,14 @@ def backend_specific_model(model: TModel, tmp_dir: str) -> TModel: @staticmethod @abstractmethod - def check_scales(model: TModel, reference_values: Dict[str, TTensor]) -> None: + def check_scales(model: TModel, reference_values: Dict[str, TTensor], model_cls) -> None: """ Checking scales from model with references. """ @staticmethod @abstractmethod - def get_backend() -> SmoothQuantAlgoBackend: + def get_backend() -> Type[SmoothQuantAlgoBackend]: """ Returns backend-specific SmoothQuantAlgoBackend. """ @@ -81,7 +107,7 @@ def get_quantization_algorithm(): model_type=ModelType.TRANSFORMER, advanced_parameters=AdvancedQuantizationParameters( overflow_fix=OverflowFix.DISABLE, - smooth_quant_alphas=AdvancedSmoothQuantParameters(matmul=0.95), + smooth_quant_alphas=AdvancedSmoothQuantParameters(matmul=0.95, convolution=0.95), inplace_statistics=False, ), ) @@ -92,10 +118,8 @@ def get_quantization_algorithm(): ( LinearMultiShapeModel, { - "/Reshape_0_0/nncf_smooth_quant": [[[[1.0594617, 1.1019668, 1.2208323, 1.1003988]]]], - "/Split_1_0/nncf_smooth_quant": [[[[1.1276343, 0.7605822]]]], - "/Split_0_0/nncf_smooth_quant": [[[[0.32575992, 0.33121374]]]], - "/Reshape_1_0_0/nncf_smooth_quant": [ + ("MatMul1", "MatMul2"): [[[[1.0594617, 1.1019668, 1.2208323, 1.1003988]]]], + ("MatMul3",): [ [ [ 0.3251956, @@ -109,11 +133,22 @@ def get_quantization_algorithm(): ] ] ], - "/Reshape_1_0_1/nncf_smooth_quant": [[[0.4699388], [0.3369332], [0.3674589]]], - "/Reshape_2_0_0/nncf_smooth_quant": [[0.1242606]], - "/ReduceMax_0_0/nncf_smooth_quant": [ + ("MatMul4",): [[[0.4699388], [0.3369332], [0.3674589]]], + ("MatMul5",): [[0.1242606]], + ("MatMul6",): [ [0.08709318, 0.08033343, 0.67289335, 0.33452678, 0.14223875, 0.19858328, 0.46314085, 0.68816555] ], + ("MatMul7",): [0.25238913, 0.38786113, 0.15471783, 0.27681994, 0.53814197, 0.18316744], + ("MatMul8",): [1.562704, 1.1183096, 2.3738348, 2.382382, 0.9243705, 1.8179475], + ("Linear1",): [[[[1.1276343, 0.7605822]]]], + ("Linear2",): [[[[0.32575992, 0.33121374]]]], + ("Linear3", "Linear4"): [[[[0.33630377, 0.3288621, 0.9898262, 0.7217065]]]], + }, + ), + ( + ConvTestModel, + { + ("Conv1",): [[[[1.0723]]]], }, ), ), @@ -126,28 +161,29 @@ def test_smooth_quant_algo(self, model_cls, reference_values, tmpdir): graph = NNCFGraphFactory.create(model) quantized_model = quantization_algorithm.apply(model, graph, dataset=dataset) - self.check_scales(quantized_model, reference_values) + self.check_scales(quantized_model, reference_values, model_cls) - def test_get_abs_max_channel_collector(self): + def test_get_abs_max_channel_collector(self, inplace_statistics: bool): backend = self.get_backend() reduction_axes = (3, 2, 1) samples = 1 - for inplace_type in [False, True]: - backend_tensor_collector = backend.get_abs_max_channel_collector( - num_samples=samples, - stats_reduction_axes=reduction_axes, - inplace=inplace_type, - branch_key="test_branch", - ) + backend_tensor_collector = backend.get_abs_max_channel_collector( + num_samples=samples, + stats_reduction_axes=reduction_axes, + inplace=inplace_statistics, + branch_key="test_branch", + ) - for aggregator in backend_tensor_collector.aggregators.values(): - assert isinstance(aggregator, MaxAggregator) + assert len(backend_tensor_collector.aggregators) == 1 + for aggregator in backend_tensor_collector.aggregators.values(): + assert isinstance(aggregator, MaxAggregator) - for reducer in backend_tensor_collector.reducers: - assert isinstance(reducer, AbsMaxReducer) - assert reducer.inplace == inplace_type - assert reducer._reduction_axes == reduction_axes + assert len(backend_tensor_collector.reducers) == 1 + for reducer in backend_tensor_collector.reducers: + assert isinstance(reducer, AbsMaxReducer) + assert reducer.inplace == inplace_statistics + assert reducer._reduction_axes == reduction_axes @pytest.mark.parametrize( "model_cls, references", @@ -155,33 +191,46 @@ def test_get_abs_max_channel_collector(self): ( LinearMultiShapeModel, [ - ("/MatMul_1", 0), - ("/MatMul", 0), - ("/linear_2/MatMul", 0), - ("/linear_1/MatMul", 0), - ("/MatMul_2", 0), - ("/MatMul_4", 1), - ("55", 1), - ("41", 0), - ("19", 1), - ("24", 0), + ("MatMul1", 0), + ("MatMul2", 0), + ("MatMul3", 0), + ("MatMul4", 1), + ("MatMul5", 1), + ("MatMul6", 0), + ("MatMul7", 0), + ("MatMul8", 1), + ("Linear1", 0), + ("Linear2", 0), + ("Linear3", 0), + ("Linear4", 0), ], ), + (ConvTestModel, [("Conv1", 0)]), + (ShareWeghtsConvAndShareLinearModel, []), ), ) def test__get_nodes_to_smooth_data(self, model_cls, references, tmpdir): model = self.backend_specific_model(model_cls(), tmpdir) nncf_graph = NNCFGraphFactory.create(model) + if isinstance(self.get_backend(), OVSmoothQuantAlgoBackend) and model_cls is ShareWeghtsConvAndShareLinearModel: + pytest.xfail("Matmuls don't share one weight in OV ir. Ticket 130912.") + algo = SmoothQuant() algo._set_backend_entity(model) alpha_map = algo._get_alpha_map() smooth_data = algo._get_nodes_to_smooth_data(nncf_graph, alpha_map.keys()) smooth_data = {d["node_to_smooth"].node_name: d["input_act_port"] for d in smooth_data} + name_map = self.get_node_name_map(model_cls) + assert len(name_map) == len(smooth_data) + matched = 0 for ref_node_name, ref_port_id in references: - assert ref_node_name in smooth_data - assert smooth_data[ref_node_name] == ref_port_id + if ref_node_name not in name_map: + continue + matched += 1 + assert smooth_data[name_map[ref_node_name]] == ref_port_id + assert matched == len(smooth_data) def test_empty_stats(self, mocker, tmpdir): model_cls = NonZeroLinearModel @@ -206,7 +255,7 @@ def test_empty_stats(self, mocker, tmpdir): mm_metatype = self.get_matmul_metatype() matmuls = [node for node in graph.topological_sort() if node.metatype == mm_metatype] for transformation in arg.transformations: - assert transformation.target_point.target_node_name != matmuls[0].node_name + assert self.get_target_node_name(transformation) != matmuls[0].node_name def test_get_activation_channel_axis(self, node_metatype, layer_attributes, port_id, reference_value): backend = self.get_backend() @@ -219,15 +268,17 @@ def test_get_activation_channel_axis(self, node_metatype, layer_attributes, port } node = NNCFNode(attributes) - try: + try: # noqa activation_channel_axis = backend.get_activation_channel_axis(node, port_id) - except RuntimeError as e: - if isinstance(e, reference_value): - pytest.xfail("Expected exception") - + except nncf.InternalError as e: # noqa + # TODO: this is wrong, should only expect an exception for cases where the exception is known to be raised + # with pytest.raises(...) + pytest.xfail("FIXME") assert activation_channel_axis == reference_value def test_get_weight_channel_axis(self, node_metatype, layer_attributes, reference_value): + backend = self.get_backend() + attributes = { NNCFNode.METATYPE_ATTR: node_metatype, NNCFNode.LAYER_ATTRIBUTES: layer_attributes, @@ -237,7 +288,7 @@ def test_get_weight_channel_axis(self, node_metatype, layer_attributes, referenc node = NNCFNode(attributes) try: - activation_channel_axis = self.get_backend().get_weight_channel_axis(node) + activation_channel_axis = backend.get_weight_channel_axis(node) except RuntimeError as e: if isinstance(e, reference_value): pytest.xfail("Expected exception") diff --git a/tests/shared/__init__.py b/tests/shared/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/shared/__init__.py +++ b/tests/shared/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/shared/case_collection.py b/tests/shared/case_collection.py index 5104f3c642d..36b06c8a7b1 100644 --- a/tests/shared/case_collection.py +++ b/tests/shared/case_collection.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/shared/command.py b/tests/shared/command.py index 9e02e97baa1..65c07e0f89a 100644 --- a/tests/shared/command.py +++ b/tests/shared/command.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/shared/config_factory.py b/tests/shared/config_factory.py index 4acd039768d..cb740f85730 100644 --- a/tests/shared/config_factory.py +++ b/tests/shared/config_factory.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/shared/datasets.py b/tests/shared/datasets.py index 9e4f207bc35..d0ca80329aa 100644 --- a/tests/shared/datasets.py +++ b/tests/shared/datasets.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/shared/helpers.py b/tests/shared/helpers.py index e1b9fcbea90..4af82dc7df5 100644 --- a/tests/shared/helpers.py +++ b/tests/shared/helpers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -19,6 +19,7 @@ import numpy as np +import nncf from nncf.common.utils.os import is_linux from nncf.common.utils.os import is_windows from tests.shared.paths import GITHUB_REPO_URL @@ -78,7 +79,7 @@ def create_venv_with_nncf(tmp_path: Path, package_type: str, venv_type: str, ext elif package_type == "build_w": run_cmd_line = f"{python_executable_with_venv} -m build -n -w" else: - raise RuntimeError(f"Invalid package type: {package_type}") + raise nncf.ValidationError(f"Invalid package type: {package_type}") # Currently CI runs on RTX3090s, which require CUDA 11 to work. # Current torch, however (v1.12), is installed via pip using .whl packages diff --git a/tests/shared/install_fixtures.py b/tests/shared/install_fixtures.py index 5cdefe52722..028682d583b 100644 --- a/tests/shared/install_fixtures.py +++ b/tests/shared/install_fixtures.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/shared/isolation_runner.py b/tests/shared/isolation_runner.py index 7b8ce9f0b27..511ed6d2b72 100644 --- a/tests/shared/isolation_runner.py +++ b/tests/shared/isolation_runner.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/shared/logging.py b/tests/shared/logging.py index 1addca60734..ab77159bbcb 100644 --- a/tests/shared/logging.py +++ b/tests/shared/logging.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/shared/metric_thresholds.py b/tests/shared/metric_thresholds.py index 0fb0f5d83cf..b5c4b1c8d74 100644 --- a/tests/shared/metric_thresholds.py +++ b/tests/shared/metric_thresholds.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/shared/nx_graph.py b/tests/shared/nx_graph.py index eaef4c96974..cdbdff87347 100644 --- a/tests/shared/nx_graph.py +++ b/tests/shared/nx_graph.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -17,6 +17,7 @@ import networkx as nx +import nncf from nncf.common.utils.dot_file_rw import read_dot_graph from nncf.common.utils.dot_file_rw import write_dot_graph @@ -40,13 +41,13 @@ def __init__( ): if node_id is not None: if edge_start_id is not None or edge_end_id is not None: - raise RuntimeError( + raise nncf.ValidationError( "Invalid node order parsed from graph line - " "must specify either `node_id` or a pair of `edge_start_id`/`edge_end_id`!" ) else: if edge_start_id is None or edge_end_id is None: - raise RuntimeError( + raise nncf.ValidationError( "Invalid node order - must specify both `edge_start_id` and `edge_end_id` " "if node_id is None!" ) @@ -75,14 +76,14 @@ def graph_key(line: str) -> LineOrder: extract_ids_regex = r'^"(\d+) ' start_id_matches = re.search(extract_ids_regex, line) if start_id_matches is None: - raise RuntimeError(f"Could not parse first node ID in node name: {line}") + raise nncf.InternalError(f"Could not parse first node ID in node name: {line}") start_id = int(start_id_matches.group(1)) edge_indicator = " -> " if edge_indicator in line: end_node_and_attrs_str = line.split(edge_indicator)[1] end_id_matches = re.search(extract_ids_regex, end_node_and_attrs_str) if end_id_matches is None: - raise RuntimeError(f"Could not parse end node ID in node name: {end_node_and_attrs_str}") + raise nncf.InternalError(f"Could not parse end node ID in node name: {end_node_and_attrs_str}") end_id = int(end_id_matches.group(1)) return LineOrder(edge_start_id=start_id, edge_end_id=end_id) return LineOrder(node_id=int(start_id)) diff --git a/tests/shared/paths.py b/tests/shared/paths.py index feac4cae35f..e8bca394547 100644 --- a/tests/shared/paths.py +++ b/tests/shared/paths.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -24,7 +24,7 @@ def _get_site_packages_path() -> Path: return Path(next(x for x in site_packages if "lib" in x and "site-packages" in x)) -DATASET_DEFINITIONS_PATH = _get_site_packages_path() / "openvino" / "model_zoo" / "data" / "dataset_definitions.yml" +DATASET_DEFINITIONS_PATH = _get_site_packages_path() / "omz_tools" / "data" / "dataset_definitions.yml" ROOT_PYTHONPATH_ENV = os.environ.copy().update({"PYTHONPATH": str(PROJECT_ROOT)}) diff --git a/tests/shared/patterns.py b/tests/shared/patterns.py index ffb9711d5e3..e3b47735bd7 100644 --- a/tests/shared/patterns.py +++ b/tests/shared/patterns.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/shared/serialization.py b/tests/shared/serialization.py index b659096590e..d3a08b70cf6 100644 --- a/tests/shared/serialization.py +++ b/tests/shared/serialization.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/shared/test_templates/__init__.py b/tests/shared/test_templates/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/shared/test_templates/__init__.py +++ b/tests/shared/test_templates/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/shared/test_templates/template_test_nncf_tensor.py b/tests/shared/test_templates/template_test_nncf_tensor.py index b5ddebb6a77..2005e6c03bc 100644 --- a/tests/shared/test_templates/template_test_nncf_tensor.py +++ b/tests/shared/test_templates/template_test_nncf_tensor.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -52,6 +52,11 @@ class TemplateTestNNCFTensorOperators: def to_tensor(x: TTensor) -> TTensor: pass + @staticmethod + @abstractmethod + def cast_to(x: TTensor, dtype: TensorDataType) -> TTensor: + pass + @pytest.mark.parametrize("op_name", OPERATOR_MAP.keys()) def test_operators_tensor(self, op_name): tensor_a = self.to_tensor([1, 2]) @@ -950,3 +955,157 @@ def test_fn_linalg_norm(self, x, ord, axis, keepdims, ref): assert isinstance(res, Tensor) assert fns.allclose(res.data, ref_tensor) assert res.device == tensor.device + + @pytest.mark.parametrize( + "m1, m2, ref", + ( + ( + [[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], + [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], + [[9.0, 12.0, 15.0], [19.0, 26.0, 33.0], [29.0, 40.0, 51.0]], + ), + ( + [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], + [[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], + [[22.0, 28.0], [49.0, 64.0]], + ), + ), + ) + def test_fn_matmul(self, m1, m2, ref): + tensor1 = Tensor(self.to_tensor(m1)) + tensor2 = Tensor(self.to_tensor(m2)) + ref_tensor = self.to_tensor(ref) + + res = fns.matmul(tensor1, tensor2) + + assert isinstance(res, Tensor) + assert fns.allclose(res.data, ref_tensor) + assert res.device == tensor1.device + + @pytest.mark.parametrize( + "val, axis, ref", + ( + (1, None, 1), + ([1], None, 1), + ([[[[1], [2]], [[1], [2]]]], None, [[1, 2], [1, 2]]), + ([[[[1], [2]], [[1], [2]]]], 0, [[[1], [2]], [[1], [2]]]), + ([[[[1], [2]], [[1], [2]]]], -1, [[[1, 2], [1, 2]]]), + ([[[[1], [2]], [[1], [2]]]], (0, 3), [[1, 2], [1, 2]]), + ), + ) + def test_unsqueeze(self, val, axis, ref): + tensor = self.to_tensor(val) + nncf_tensor = Tensor(tensor) + ref_tensor = self.to_tensor(ref) + res = fns.squeeze(nncf_tensor, axis=axis) + assert isinstance(res, Tensor) + assert fns.allclose(res, ref_tensor) + assert res.device == nncf_tensor.device + + @pytest.mark.parametrize( + "x, ref", + ( + ( + [[1, 2], [3, 4], [5, 6]], + [[1, 3, 5], [2, 4, 6]], + ), + ( + [[1, 2, 3], [4, 5, 6]], + [[1, 4], [2, 5], [3, 6]], + ), + ), + ) + def test_fn_transpose(self, x, ref): + tensor = Tensor(self.to_tensor(x)) + ref_tensor = self.to_tensor(ref) + + res = fns.transpose(tensor) + + assert isinstance(res, Tensor) + assert fns.allclose(res.data, ref_tensor) + assert res.device == tensor.device + + @pytest.mark.parametrize( + "x, ref", + ( + ( + [1, 2, 3, 4, 5, 6], + [0, 1, 2, 3, 4, 5], + ), + ( + [6, 5, 4, 3, 2, 1], + [5, 4, 3, 2, 1, 0], + ), + ), + ) + def test_fn_argsort(self, x, ref): + tensor = Tensor(self.to_tensor(x)) + ref_tensor = self.to_tensor(ref) + + res = fns.argsort(tensor) + + assert isinstance(res, Tensor) + assert fns.allclose(res.data, ref_tensor) + assert res.device == tensor.device + + zero_ten_range = [x / 100 for x in range(1001)] + zero_ten_range_two_axes = [[a + b / 100 for b in range(101)] for a in range(10)] + + @pytest.mark.parametrize( + "x,q,axis,keepdims,ref", + ( + (1.0, 0.1, None, True, 1.0), + (zero_ten_range, 0.1, 0, True, [1.0]), + (zero_ten_range, 0.1, 0, False, 1.0), + (zero_ten_range, (0.1, 0.9), 0, False, [1.0, 9.0]), + (zero_ten_range, (0.1, 0.9), 0, True, [[1.0], [9.0]]), + (zero_ten_range_two_axes, (0.1, 0.9), (0, 1), False, [1.0, 9.0]), + (zero_ten_range_two_axes, (0.1, 0.9), (0, 1), True, [[[1.0]], [[9.0]]]), + (16000 * zero_ten_range, 0.1, 0, False, 1.0), # reason: https://github.com/pytorch/pytorch/issues/64947 + (zero_ten_range_two_axes, (0.1, 0.9), None, False, [1.0, 9.0]), + ), + ) + @pytest.mark.parametrize("fp16", [False, True]) + def test_fn_quantile(self, x, q, axis, keepdims, ref, fp16): + tensor = self.to_tensor(x) + if fp16: + tensor = self.cast_to(tensor, TensorDataType.float16) + tensor = Tensor(tensor) + ref_tensor = self.to_tensor(ref) + + res = fns.quantile(tensor, axis=axis, q=q, keepdims=keepdims) + assert isinstance(res, Tensor) + assert res.dtype == TensorDataType.float64 + assert fns.allclose(self.cast_to(res.data, TensorDataType.float32), ref_tensor) + assert res.device == tensor.device + assert res.shape == tuple(ref_tensor.shape) + + @pytest.mark.parametrize( + "x,power,ref", + [ + (list(map(float, range(10))), 2.0, [x**2 for x in map(float, range(10))]), + (list(map(float, range(10))), [2.0], [x**2 for x in map(float, range(10))]), + ( + list(map(float, range(10))), + list(map(float, range(10))), + [1.0, 1.0, 4.0, 27.0, 256.0, 3125.0, 46656.0, 823543.0, 16777216.0, 387420489.0], + ), + ], + ) + def test_fn_power(self, x, power, ref): + if isinstance(power, list): + power = self.to_tensor(power) + power = Tensor(power) + + if isinstance(x, list): + x = self.to_tensor(x) + tensor = Tensor(x) + + ref_tensor = self.to_tensor(ref) + + res = fns.power(tensor, power) + + assert isinstance(res, Tensor) + assert fns.allclose(res.data, ref_tensor) + assert res.device == tensor.device + assert res.shape == tuple(ref_tensor.shape) diff --git a/tests/tensorflow/__init__.py b/tests/tensorflow/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/tensorflow/__init__.py +++ b/tests/tensorflow/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/accuracy_aware_training/test_keras_api.py b/tests/tensorflow/accuracy_aware_training/test_keras_api.py index 5a5251f3469..9c56f1882d6 100644 --- a/tests/tensorflow/accuracy_aware_training/test_keras_api.py +++ b/tests/tensorflow/accuracy_aware_training/test_keras_api.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/conftest.py b/tests/tensorflow/conftest.py index 0fd2e4eb140..d3ce687c0af 100644 --- a/tests/tensorflow/conftest.py +++ b/tests/tensorflow/conftest.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -8,6 +8,8 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +from pathlib import Path + import pytest from tests.shared.case_collection import COMMON_SCOPE_MARKS_VS_OPTIONS @@ -33,16 +35,16 @@ def clear_session(): def pytest_addoption(parser): - parser.addoption("--data", type=str, default=None, help="Path to test datasets") + parser.addoption("--data", type=Path, default=None, help="Path to test datasets") parser.addoption( - "--sota-checkpoints-dir", type=str, default=None, help="Path to checkpoints directory for sota accuracy test" + "--sota-checkpoints-dir", type=Path, default=None, help="Path to checkpoints directory for sota accuracy test" ) parser.addoption( - "--sota-data-dir", type=str, default=None, help="Path to datasets directory for sota accuracy test" + "--sota-data-dir", type=Path, default=None, help="Path to datasets directory for sota accuracy test" ) parser.addoption( "--metrics-dump-path", - type=str, + type=Path, default=None, help="Path to directory to store metrics. " "Directory must be empty or should not exist." diff --git a/tests/tensorflow/data/ac_configs/inception_v3_imagenet.yml b/tests/tensorflow/data/ac_configs/inception_v3_imagenet.yml index 13856848e63..33e7e13d9af 100644 --- a/tests/tensorflow/data/ac_configs/inception_v3_imagenet.yml +++ b/tests/tensorflow/data/ac_configs/inception_v3_imagenet.yml @@ -1,16 +1,20 @@ models: -- name: inception_v3_imagenet - launchers: - - framework: dlsdk - device: CPU - adapter: classification - datasets: - - name: imagenet_1000_classes - preprocessing: - - type: crop_image_with_padding - size: 299 - crop_padding: 32 - metrics: - - name: accuracy@top1 - type: accuracy - top_k: 1 + - name: inception_v3_imagenet + launchers: + - framework: dlsdk + device: CPU + adapter: classification + datasets: + - name: imagenet_1000_classes + preprocessing: + - type: crop_image_with_padding + size: 299 + crop_padding: 32 + - type: rgb_to_bgr + - type: normalization + mean: 127.5, 127.5, 127.5 + std: 127.5 + metrics: + - name: accuracy@top1 + type: accuracy + top_k: 1 diff --git a/tests/tensorflow/data/ac_configs/mobilenet_v2_hub_imagenet_magnitude_sparsity.yml b/tests/tensorflow/data/ac_configs/mobilenet_v2_hub_imagenet_magnitude_sparsity.yml index 555aec27018..c68fad56b7b 100644 --- a/tests/tensorflow/data/ac_configs/mobilenet_v2_hub_imagenet_magnitude_sparsity.yml +++ b/tests/tensorflow/data/ac_configs/mobilenet_v2_hub_imagenet_magnitude_sparsity.yml @@ -1,5 +1,4 @@ models: - - name: mobilenet_v2_hub_imagenet_magnitude_sparsity launchers: - framework: dlsdk @@ -14,6 +13,7 @@ models: central_fraction: 0.875 - type: resize size: 224 + - type: rgb_to_bgr - type: normalization std: 255 metrics: diff --git a/tests/tensorflow/data/ac_configs/mobilenet_v2_imagenet.yml b/tests/tensorflow/data/ac_configs/mobilenet_v2_imagenet.yml index 3558c27c799..2de8d9e4233 100644 --- a/tests/tensorflow/data/ac_configs/mobilenet_v2_imagenet.yml +++ b/tests/tensorflow/data/ac_configs/mobilenet_v2_imagenet.yml @@ -1,16 +1,20 @@ models: -- name: mobilenet_v2_imagenet - launchers: - - framework: dlsdk - device: CPU - adapter: classification - datasets: - - name: imagenet_1000_classes - preprocessing: - - type: crop_image_with_padding - size: 224 - crop_padding: 32 - metrics: - - name: accuracy@top1 - type: accuracy - top_k: 1 \ No newline at end of file + - name: mobilenet_v2_imagenet + launchers: + - framework: dlsdk + device: CPU + adapter: classification + datasets: + - name: imagenet_1000_classes + preprocessing: + - type: crop_image_with_padding + size: 224 + crop_padding: 32 + - type: rgb_to_bgr + - type: normalization + mean: 127.5, 127.5, 127.5 + std: 127.5 + metrics: + - name: accuracy@top1 + type: accuracy + top_k: 1 diff --git a/tests/tensorflow/data/ac_configs/mobilenet_v3_large_imagenet.yml b/tests/tensorflow/data/ac_configs/mobilenet_v3_large_imagenet.yml index fa55a507983..9ea2b83f64b 100644 --- a/tests/tensorflow/data/ac_configs/mobilenet_v3_large_imagenet.yml +++ b/tests/tensorflow/data/ac_configs/mobilenet_v3_large_imagenet.yml @@ -1,16 +1,17 @@ models: -- name: mobilenet_v3_large_imagenet - launchers: - - framework: dlsdk - device: CPU - adapter: classification - datasets: - - name: imagenet_1000_classes - preprocessing: - - type: crop_image_with_padding - size: 224 - crop_padding: 32 - metrics: - - name: accuracy@top1 - type: accuracy - top_k: 1 + - name: mobilenet_v3_large_imagenet + launchers: + - framework: dlsdk + device: CPU + adapter: classification + datasets: + - name: imagenet_1000_classes + preprocessing: + - type: crop_image_with_padding + size: 224 + crop_padding: 32 + - type: rgb_to_bgr + metrics: + - name: accuracy@top1 + type: accuracy + top_k: 1 diff --git a/tests/tensorflow/data/ac_configs/mobilenet_v3_small_imagenet.yml b/tests/tensorflow/data/ac_configs/mobilenet_v3_small_imagenet.yml index b100c3246b4..15a71d9e445 100644 --- a/tests/tensorflow/data/ac_configs/mobilenet_v3_small_imagenet.yml +++ b/tests/tensorflow/data/ac_configs/mobilenet_v3_small_imagenet.yml @@ -1,16 +1,17 @@ models: -- name: mobilenet_v3_small_imagenet - launchers: - - framework: dlsdk - device: CPU - adapter: classification - datasets: - - name: imagenet_1000_classes - preprocessing: - - type: crop_image_with_padding - size: 224 - crop_padding: 32 - metrics: - - name: accuracy@top1 - type: accuracy - top_k: 1 + - name: mobilenet_v3_small_imagenet + launchers: + - framework: dlsdk + device: CPU + adapter: classification + datasets: + - name: imagenet_1000_classes + preprocessing: + - type: crop_image_with_padding + size: 224 + crop_padding: 32 + - type: rgb_to_bgr + metrics: + - name: accuracy@top1 + type: accuracy + top_k: 1 diff --git a/tests/tensorflow/data/ac_configs/resnet50_imagenet.yml b/tests/tensorflow/data/ac_configs/resnet50_imagenet.yml index eb0e8be895e..6b77d3f9427 100644 --- a/tests/tensorflow/data/ac_configs/resnet50_imagenet.yml +++ b/tests/tensorflow/data/ac_configs/resnet50_imagenet.yml @@ -10,7 +10,9 @@ models: - type: crop_image_with_padding size: 224 crop_padding: 32 + - type: normalization + mean: '103.939, 116.779, 123.68' metrics: - name: accuracy@top1 type: accuracy - top_k: 1 \ No newline at end of file + top_k: 1 diff --git a/tests/tensorflow/data/ac_configs/retinanet_coco.yml b/tests/tensorflow/data/ac_configs/retinanet_coco.yml index ab8ec92bd80..fd9b0042900 100644 --- a/tests/tensorflow/data/ac_configs/retinanet_coco.yml +++ b/tests/tensorflow/data/ac_configs/retinanet_coco.yml @@ -27,8 +27,12 @@ models: - type: padding size: 640 pad_type: right_bottom + - type: rgb_to_bgr + - type: normalization + mean: 123.675, 116.28, 103.53 + std: 58.395, 57.12, 57.375 postprocessing: - type: faster_rcnn_postprocessing_resize metrics: - type: coco_precision - max_detections: 100 \ No newline at end of file + max_detections: 100 diff --git a/tests/tensorflow/data/ac_configs/yolo_v4_coco.yml b/tests/tensorflow/data/ac_configs/yolo_v4_coco.yml index 4716ff1d194..ec7294c8c13 100644 --- a/tests/tensorflow/data/ac_configs/yolo_v4_coco.yml +++ b/tests/tensorflow/data/ac_configs/yolo_v4_coco.yml @@ -28,10 +28,12 @@ models: use_full_label_map: False annotation: mscoco_det_80.pickle dataset_meta: mscoco_det_80.json - preprocessing: - type: resize size: 608 + - type: rgb_to_bgr + - type: normalization + std: 255 postprocessing: - type: resize_prediction_boxes - type: filter @@ -43,14 +45,6 @@ models: - type: clip_boxes apply_to: prediction metrics: - - type: map - integral: 11point - ignore_difficult: true - presenter: print_scalar - - name: AP@0.5 - type: coco_precision - max_detections: 100 - threshold: 0.5 - name: AP@0.5:0.05:0.95 type: coco_precision max_detections: 100 diff --git a/tests/tensorflow/data/ac_configs/yolo_v4_coco_int8.yml b/tests/tensorflow/data/ac_configs/yolo_v4_coco_int8.yml index 5e77b9c10cd..8bf8670f581 100644 --- a/tests/tensorflow/data/ac_configs/yolo_v4_coco_int8.yml +++ b/tests/tensorflow/data/ac_configs/yolo_v4_coco_int8.yml @@ -28,10 +28,12 @@ models: use_full_label_map: False annotation: mscoco_det_80.pickle dataset_meta: mscoco_det_80.json - preprocessing: - type: resize size: 608 + - type: rgb_to_bgr + - type: normalization + std: 255 postprocessing: - type: resize_prediction_boxes - type: filter @@ -43,14 +45,6 @@ models: - type: clip_boxes apply_to: prediction metrics: - - type: map - integral: 11point - ignore_difficult: true - presenter: print_scalar - - name: AP@0.5 - type: coco_precision - max_detections: 100 - threshold: 0.5 - name: AP@0.5:0.05:0.95 type: coco_precision max_detections: 100 diff --git a/tests/tensorflow/data/reference_graphs/2.11/quantized/hw/VPU/inception_v3.dot b/tests/tensorflow/data/reference_graphs/2.11/quantized/hw/NPU/inception_v3.dot similarity index 100% rename from tests/tensorflow/data/reference_graphs/2.11/quantized/hw/VPU/inception_v3.dot rename to tests/tensorflow/data/reference_graphs/2.11/quantized/hw/NPU/inception_v3.dot diff --git a/tests/tensorflow/data/reference_graphs/2.11/quantized/hw/NPU/mobilenet_v2.pb b/tests/tensorflow/data/reference_graphs/2.11/quantized/hw/NPU/mobilenet_v2.pb new file mode 120000 index 00000000000..e9b6d095a65 --- /dev/null +++ b/tests/tensorflow/data/reference_graphs/2.11/quantized/hw/NPU/mobilenet_v2.pb @@ -0,0 +1 @@ +../../../../2.8/quantized/hw/NPU/mobilenet_v2.pb \ No newline at end of file diff --git a/tests/tensorflow/data/reference_graphs/2.11/quantized/hw/NPU/resnet50.pb b/tests/tensorflow/data/reference_graphs/2.11/quantized/hw/NPU/resnet50.pb new file mode 120000 index 00000000000..cd05e4e18fc --- /dev/null +++ b/tests/tensorflow/data/reference_graphs/2.11/quantized/hw/NPU/resnet50.pb @@ -0,0 +1 @@ +../../../../2.8/quantized/hw/NPU/resnet50.pb \ No newline at end of file diff --git a/tests/tensorflow/data/reference_graphs/2.11/quantized/hw/VPU/mobilenet_v2.pb b/tests/tensorflow/data/reference_graphs/2.11/quantized/hw/VPU/mobilenet_v2.pb deleted file mode 120000 index 83a3eef0109..00000000000 --- a/tests/tensorflow/data/reference_graphs/2.11/quantized/hw/VPU/mobilenet_v2.pb +++ /dev/null @@ -1 +0,0 @@ -../../../../2.8/quantized/hw/VPU/mobilenet_v2.pb \ No newline at end of file diff --git a/tests/tensorflow/data/reference_graphs/2.11/quantized/hw/VPU/resnet50.pb b/tests/tensorflow/data/reference_graphs/2.11/quantized/hw/VPU/resnet50.pb deleted file mode 120000 index a05e23564fd..00000000000 --- a/tests/tensorflow/data/reference_graphs/2.11/quantized/hw/VPU/resnet50.pb +++ /dev/null @@ -1 +0,0 @@ -../../../../2.8/quantized/hw/VPU/resnet50.pb \ No newline at end of file diff --git a/tests/tensorflow/data/reference_graphs/2.12/quantized/hw/NPU/inception_v3.dot b/tests/tensorflow/data/reference_graphs/2.12/quantized/hw/NPU/inception_v3.dot new file mode 120000 index 00000000000..845b232fa4e --- /dev/null +++ b/tests/tensorflow/data/reference_graphs/2.12/quantized/hw/NPU/inception_v3.dot @@ -0,0 +1 @@ +../../../../2.11/quantized/hw/NPU/inception_v3.dot \ No newline at end of file diff --git a/tests/tensorflow/data/reference_graphs/2.12/quantized/hw/NPU/mobilenet_v2.pb b/tests/tensorflow/data/reference_graphs/2.12/quantized/hw/NPU/mobilenet_v2.pb new file mode 120000 index 00000000000..d6eddb5ac1d --- /dev/null +++ b/tests/tensorflow/data/reference_graphs/2.12/quantized/hw/NPU/mobilenet_v2.pb @@ -0,0 +1 @@ +../../../../2.11/quantized/hw/NPU/mobilenet_v2.pb \ No newline at end of file diff --git a/tests/tensorflow/data/reference_graphs/2.12/quantized/hw/NPU/resnet50.pb b/tests/tensorflow/data/reference_graphs/2.12/quantized/hw/NPU/resnet50.pb new file mode 120000 index 00000000000..b51a6330131 --- /dev/null +++ b/tests/tensorflow/data/reference_graphs/2.12/quantized/hw/NPU/resnet50.pb @@ -0,0 +1 @@ +../../../../2.11/quantized/hw/NPU/resnet50.pb \ No newline at end of file diff --git a/tests/tensorflow/data/reference_graphs/2.12/quantized/hw/VPU/inception_v3.dot b/tests/tensorflow/data/reference_graphs/2.12/quantized/hw/VPU/inception_v3.dot deleted file mode 120000 index d7baa691f29..00000000000 --- a/tests/tensorflow/data/reference_graphs/2.12/quantized/hw/VPU/inception_v3.dot +++ /dev/null @@ -1 +0,0 @@ -../../../../2.11/quantized/hw/VPU/inception_v3.dot \ No newline at end of file diff --git a/tests/tensorflow/data/reference_graphs/2.12/quantized/hw/VPU/mobilenet_v2.pb b/tests/tensorflow/data/reference_graphs/2.12/quantized/hw/VPU/mobilenet_v2.pb deleted file mode 120000 index b1e7ae74763..00000000000 --- a/tests/tensorflow/data/reference_graphs/2.12/quantized/hw/VPU/mobilenet_v2.pb +++ /dev/null @@ -1 +0,0 @@ -../../../../2.11/quantized/hw/VPU/mobilenet_v2.pb \ No newline at end of file diff --git a/tests/tensorflow/data/reference_graphs/2.12/quantized/hw/VPU/resnet50.pb b/tests/tensorflow/data/reference_graphs/2.12/quantized/hw/VPU/resnet50.pb deleted file mode 120000 index a2b33ae4924..00000000000 --- a/tests/tensorflow/data/reference_graphs/2.12/quantized/hw/VPU/resnet50.pb +++ /dev/null @@ -1 +0,0 @@ -../../../../2.11/quantized/hw/VPU/resnet50.pb \ No newline at end of file diff --git a/tests/tensorflow/data/reference_graphs/2.8/quantized/hw/VPU/inception_v3.dot b/tests/tensorflow/data/reference_graphs/2.8/quantized/hw/NPU/inception_v3.dot similarity index 100% rename from tests/tensorflow/data/reference_graphs/2.8/quantized/hw/VPU/inception_v3.dot rename to tests/tensorflow/data/reference_graphs/2.8/quantized/hw/NPU/inception_v3.dot diff --git a/tests/tensorflow/data/reference_graphs/2.8/quantized/hw/VPU/mobilenet_v2.pb b/tests/tensorflow/data/reference_graphs/2.8/quantized/hw/NPU/mobilenet_v2.pb similarity index 100% rename from tests/tensorflow/data/reference_graphs/2.8/quantized/hw/VPU/mobilenet_v2.pb rename to tests/tensorflow/data/reference_graphs/2.8/quantized/hw/NPU/mobilenet_v2.pb diff --git a/tests/tensorflow/data/reference_graphs/2.8/quantized/hw/VPU/resnet50.pb b/tests/tensorflow/data/reference_graphs/2.8/quantized/hw/NPU/resnet50.pb similarity index 100% rename from tests/tensorflow/data/reference_graphs/2.8/quantized/hw/VPU/resnet50.pb rename to tests/tensorflow/data/reference_graphs/2.8/quantized/hw/NPU/resnet50.pb diff --git a/tests/tensorflow/experimental/__init__.py b/tests/tensorflow/experimental/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/tensorflow/experimental/__init__.py +++ b/tests/tensorflow/experimental/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/experimental/test_compressed_graph.py b/tests/tensorflow/experimental/test_compressed_graph.py index 07b029b134b..2eaa7157671 100644 --- a/tests/tensorflow/experimental/test_compressed_graph.py +++ b/tests/tensorflow/experimental/test_compressed_graph.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/experimental/test_context_independence.py b/tests/tensorflow/experimental/test_context_independence.py index be150fd752d..27a3c764fc7 100644 --- a/tests/tensorflow/experimental/test_context_independence.py +++ b/tests/tensorflow/experimental/test_context_independence.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/experimental/test_keras_layer_model.py b/tests/tensorflow/experimental/test_keras_layer_model.py index 32851f43fa5..884f4f9f431 100644 --- a/tests/tensorflow/experimental/test_keras_layer_model.py +++ b/tests/tensorflow/experimental/test_keras_layer_model.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/experimental/test_models/__init__.py b/tests/tensorflow/experimental/test_models/__init__.py index b5886131671..6fac2986357 100644 --- a/tests/tensorflow/experimental/test_models/__init__.py +++ b/tests/tensorflow/experimental/test_models/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/experimental/test_models/resnet.py b/tests/tensorflow/experimental/test_models/resnet.py index be6468eb8b6..f3becac3eca 100644 --- a/tests/tensorflow/experimental/test_models/resnet.py +++ b/tests/tensorflow/experimental/test_models/resnet.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/helpers.py b/tests/tensorflow/helpers.py index 91904bb311d..f8e66e8119d 100644 --- a/tests/tensorflow/helpers.py +++ b/tests/tensorflow/helpers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/pruning/__init__.py b/tests/tensorflow/pruning/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/tensorflow/pruning/__init__.py +++ b/tests/tensorflow/pruning/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/pruning/helpers.py b/tests/tensorflow/pruning/helpers.py index f33eb774f6a..58b49d596b9 100644 --- a/tests/tensorflow/pruning/helpers.py +++ b/tests/tensorflow/pruning/helpers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/pruning/test_algorithm.py b/tests/tensorflow/pruning/test_algorithm.py index b82a373198c..fd0040ac20a 100644 --- a/tests/tensorflow/pruning/test_algorithm.py +++ b/tests/tensorflow/pruning/test_algorithm.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/pruning/test_flops_pruning.py b/tests/tensorflow/pruning/test_flops_pruning.py index 7d5c9c69863..26f34b85362 100644 --- a/tests/tensorflow/pruning/test_flops_pruning.py +++ b/tests/tensorflow/pruning/test_flops_pruning.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -13,6 +13,7 @@ import pytest import tensorflow as tf +import nncf from nncf.common.pruning.shape_pruning_processor import ShapePruningProcessor from nncf.common.pruning.weights_flops_calculator import WeightsFlopsCalculator from nncf.tensorflow.graph.metatypes.common import GENERAL_CONV_LAYER_METATYPES @@ -193,5 +194,5 @@ def test_maximal_compression_rate(): for comp_rate in np.linspace(0, maximal_compression_rate, 10): compression_ctrl.compression_rate = comp_rate for comp_rate in np.linspace(maximal_compression_rate + 1e-5, 1, 10): - with pytest.raises(RuntimeError): + with pytest.raises(nncf.ParameterNotSupportedError): compression_ctrl.compression_rate = comp_rate diff --git a/tests/tensorflow/pruning/test_strip.py b/tests/tensorflow/pruning/test_strip.py index 73a884a4423..cb9b157dbf4 100644 --- a/tests/tensorflow/pruning/test_strip.py +++ b/tests/tensorflow/pruning/test_strip.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/pruning/test_tensor_processor.py b/tests/tensorflow/pruning/test_tensor_processor.py index b917eef0cb6..3b34cde2dfc 100644 --- a/tests/tensorflow/pruning/test_tensor_processor.py +++ b/tests/tensorflow/pruning/test_tensor_processor.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -18,9 +18,8 @@ @pytest.mark.parametrize("device", ("CPU", "GPU")) def test_create_tensor(device): - if not tf.config.list_physical_devices("GPU"): - if device == "GPU": - pytest.skip("There are no available CUDA devices") + if not tf.config.list_physical_devices("GPU") and device == "GPU": + pytest.skip("There are no available CUDA devices") shape = [1, 3, 10, 100] tensor = TFNNCFPruningTensorProcessor.ones(shape, device) assert tf.is_tensor(tensor.tensor) diff --git a/tests/tensorflow/quantization/__init__.py b/tests/tensorflow/quantization/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/tensorflow/quantization/__init__.py +++ b/tests/tensorflow/quantization/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/quantization/test_algorithm_quantization.py b/tests/tensorflow/quantization/test_algorithm_quantization.py index 2964efe7e88..d2090c07353 100644 --- a/tests/tensorflow/quantization/test_algorithm_quantization.py +++ b/tests/tensorflow/quantization/test_algorithm_quantization.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/quantization/test_builder_state.py b/tests/tensorflow/quantization/test_builder_state.py index 482b421ac5b..7e7335c0628 100644 --- a/tests/tensorflow/quantization/test_builder_state.py +++ b/tests/tensorflow/quantization/test_builder_state.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/quantization/test_overflow_issue.py b/tests/tensorflow/quantization/test_overflow_issue.py index 73e4832eb3e..08202ae2d5b 100644 --- a/tests/tensorflow/quantization/test_overflow_issue.py +++ b/tests/tensorflow/quantization/test_overflow_issue.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/quantization/test_ptq_params.py b/tests/tensorflow/quantization/test_ptq_params.py index acc38abf5a6..9b859e886aa 100644 --- a/tests/tensorflow/quantization/test_ptq_params.py +++ b/tests/tensorflow/quantization/test_ptq_params.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/quantization/test_range_init.py b/tests/tensorflow/quantization/test_range_init.py index 0de8330da62..6231bdbc7d3 100644 --- a/tests/tensorflow/quantization/test_range_init.py +++ b/tests/tensorflow/quantization/test_range_init.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/quantization/test_statistics.py b/tests/tensorflow/quantization/test_statistics.py index 1cd796f220d..a410180c342 100644 --- a/tests/tensorflow/quantization/test_statistics.py +++ b/tests/tensorflow/quantization/test_statistics.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/quantization/test_strip.py b/tests/tensorflow/quantization/test_strip.py index c477ce2bc67..22d0188a6c1 100644 --- a/tests/tensorflow/quantization/test_strip.py +++ b/tests/tensorflow/quantization/test_strip.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/quantization/test_transform_fn.py b/tests/tensorflow/quantization/test_transform_fn.py index e2d6255f2f5..45580c71b3e 100644 --- a/tests/tensorflow/quantization/test_transform_fn.py +++ b/tests/tensorflow/quantization/test_transform_fn.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/quantization/test_unified_scales.py b/tests/tensorflow/quantization/test_unified_scales.py index 8702271ba11..ba27fd246a0 100644 --- a/tests/tensorflow/quantization/test_unified_scales.py +++ b/tests/tensorflow/quantization/test_unified_scales.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -145,7 +145,7 @@ def test_shared_op_unified_scales(target_device): nncf_config["target_device"] = target_device non_weight_quantizers_ref = 8 - if target_device == "VPU": + if target_device == "NPU": non_weight_quantizers_ref = 5 model = get_shared_conv_test_model() @@ -184,11 +184,11 @@ def path(input_1, input_2): return tf.keras.Model(inputs=inputs, outputs=outputs) -def test_eltwise_unified_scales_for_vpu(): +def test_eltwise_unified_scales_for_npu(): nncf_config = get_basic_quantization_config() x_shape = [1, 1, 1, 1] y_shape = [1, 1, 1, 1] - nncf_config["target_device"] = "VPU" + nncf_config["target_device"] = "NPU" model = get_eltwise_quantizer_linking_test_model([x_shape, y_shape]) compressed_model, _ = create_compressed_model_and_algo_for_test(model, nncf_config, force_no_init=True) diff --git a/tests/tensorflow/quantization/utils.py b/tests/tensorflow/quantization/utils.py index 62eeb8b73d8..935e2b05380 100644 --- a/tests/tensorflow/quantization/utils.py +++ b/tests/tensorflow/quantization/utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/requirements.txt b/tests/tensorflow/requirements.txt index 11c0a8932f0..cbe87360983 100644 --- a/tests/tensorflow/requirements.txt +++ b/tests/tensorflow/requirements.txt @@ -4,8 +4,6 @@ pytest==7.4.4 pytest-cov pytest-mock pytest-dependency -yattag>=1.14.0 -prettytable>=2.0.0 pydot tensorflow_hub virtualenv @@ -13,4 +11,4 @@ virtualenv # filelock 3.12.3 requires typing-extensions>=4.7.1, but tensorflow 2.12.1 requires typing-extensions<4.6.0,>=3.6.6 # TODO: remove after upgrade to TF 2.13 filelock<3.12.3 - +openvino-dev==2024.0 diff --git a/tests/tensorflow/sdl/__init__.py b/tests/tensorflow/sdl/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/tensorflow/sdl/__init__.py +++ b/tests/tensorflow/sdl/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/sdl/test_symlinks.py b/tests/tensorflow/sdl/test_symlinks.py index dbdcd6f78ef..ccfc907dc7f 100644 --- a/tests/tensorflow/sdl/test_symlinks.py +++ b/tests/tensorflow/sdl/test_symlinks.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/sota_checkpoints_eval.json b/tests/tensorflow/sota_checkpoints_eval.json index 39a4c2a5257..6191979fa25 100644 --- a/tests/tensorflow/sota_checkpoints_eval.json +++ b/tests/tensorflow/sota_checkpoints_eval.json @@ -8,257 +8,236 @@ "topologies": { "inception_v3_imagenet": { "config": "examples/tensorflow/classification/configs/inception_v3_imagenet.json", - "target": 77.91, "metric_type": "Acc@1", "model_description": "Inception V3", - "reverse_input_channels": true, - "mean_value": "[127.5,127.5,127.5]", - "scale_value": "[127.5]" + "target_tf": 77.89, + "target_ov": 78.03 }, "inception_v3_imagenet_int8": { "config": "examples/tensorflow/classification/configs/quantization/inception_v3_imagenet_int8.json", "reference": "inception_v3_imagenet", - "target": 78.39, "target_init": 76.64, "resume": "inception_v3_imagenet_int8", "metric_type": "Acc@1", "model_description": "Inception V3", "compression_description": "INT8 (per-tensor symmetric for weights, per-tensor asymmetric half-range for activations)", - "reverse_input_channels": true, - "mean_value": "[127.5,127.5,127.5]", - "scale_value": "[127.5]", "diff_fp32_min": -0.5, - "diff_fp32_max": 1 + "diff_fp32_max": 1, + "target_tf": 78.39, + "target_ov": 78.36 }, "inception_v3_imagenet_rb_sparsity_int8": { "config": "examples/tensorflow/classification/configs/sparsity_quantization/inception_v3_imagenet_rb_sparsity_int8.json", "reference": "inception_v3_imagenet", - "target": 77.52, "target_init": 76.61, "resume": "inception_v3_imagenet_rb_sparsity_int8", "metric_type": "Acc@1", "model_description": "Inception V3", "compression_description": "INT8 (per-tensor symmetric for weights, per-tensor asymmetric half-range for activations), Sparsity 61% (RB)", - "reverse_input_channels": true, - "mean_value": "[127.5,127.5,127.5]", - "scale_value": "[127.5]", - "diff_fp32_min": -1, - "diff_fp32_max": 0.1 + "target_tf": 77.52, + "target_ov": 77.51 }, "inception_v3_imagenet_magnitude_sparsity": { "config": "examples/tensorflow/classification/configs/sparsity/inception_v3_imagenet_magnitude_sparsity.json", "reference": "inception_v3_imagenet", - "target": 77.86, "target_init": 77.9, "resume": "inception_v3_imagenet_magnitude_sparsity", "metric_type": "Acc@1", "model_description": "Inception V3", "compression_description": "Sparsity 54% (Magnitude)", - "reverse_input_channels": true, - "mean_value": "[127.5,127.5,127.5]", - "scale_value": "[127.5]", - "diff_fp32_min": -1, - "diff_fp32_max": 0.1 + "target_tf": 77.86, + "target_ov": 77.86 }, "mobilenet_v2_imagenet": { "config": "examples/tensorflow/classification/configs/mobilenet_v2_imagenet.json", - "target": 71.85, "metric_type": "Acc@1", "model_description": "MobileNet V2", - "reverse_input_channels": true, - "mean_value": "[127.5,127.5,127.5]", - "scale_value": "[127.5]" + "target_tf": 71.85, + "target_ov": 71.85 }, "mobilenet_v2_imagenet_int8": { "config": "examples/tensorflow/classification/configs/quantization/mobilenet_v2_imagenet_int8.json", "reference": "mobilenet_v2_imagenet", - "target": 71.63, "target_init": 60.92, "resume": "mobilenet_v2_imagenet_int8", "metric_type": "Acc@1", "model_description": "MobileNet V2", "compression_description": "INT8 (per-tensor symmetric for weights, per-tensor asymmetric half-range for activations)", - "reverse_input_channels": true, - "mean_value": "[127.5,127.5,127.5]", - "scale_value": "[127.5]", "diff_fp32_min": -0.5, - "diff_fp32_max": 1 + "diff_fp32_max": 1, + "target_tf": 71.70, + "target_ov": 71.75 }, "mobilenet_v2_imagenet_rb_sparsity_int8": { "config": "examples/tensorflow/classification/configs/sparsity_quantization/mobilenet_v2_imagenet_rb_sparsity_int8.json", "reference": "mobilenet_v2_imagenet", - "target": 70.94, "target_init": 61.25, "resume": "mobilenet_v2_imagenet_rb_sparsity_int8", "metric_type": "Acc@1", "model_description": "MobileNet V2", "compression_description": "INT8 (per-tensor symmetric for weights, per-tensor asymmetric half-range for activations), Sparsity 52% (RB)", - "reverse_input_channels": true, - "mean_value": "[127.5,127.5,127.5]", - "scale_value": "[127.5]", - "diff_fp32_min": -1, - "diff_fp32_max": 1 + "diff_fp32_max": 1, + "target_tf": 70.94, + "target_ov": 71.00 }, "mobilenet_v2_imagenet_rb_sparsity": { "config": "examples/tensorflow/classification/configs/sparsity/mobilenet_v2_imagenet_rb_sparsity.json", "reference": "mobilenet_v2_imagenet", - "target": 71.34, "target_init": 71.85, "resume": "mobilenet_v2_imagenet_rb_sparsity", "metric_type": "Acc@1", "model_description": "MobileNet V2", "compression_description": " Sparsity 50% (RB)", - "reverse_input_channels": true, - "mean_value": "[127.5,127.5,127.5]", - "scale_value": "[127.5]", - "diff_fp32_min": -1, - "diff_fp32_max": 1 + "diff_fp32_max": 1, + "target_tf": 71.34, + "target_ov": 71.22 }, "mobilenet_v2_hub_imagenet_magnitude_sparsity": { "config": "examples/tensorflow/classification/configs/sparsity/mobilenet_v2_hub_imagenet_magnitude_sparsity.json", "reference": "mobilenet_v2_imagenet", - "target": 71.87, "target_init": 71.85, "resume": "mobilenet_v2_hub_imagenet_magnitude_sparsity", "metric_type": "Acc@1", "model_description": "MobileNet V2 (TensorFlow Hub MobileNet V2)", "compression_description": "Sparsity 35% (Magnitude)", - "reverse_input_channels": true, - "diff_target_min": -1, - "diff_target_max": 1 + "diff_fp32_max": 0.2, + "target_tf": 71.87, + "target_ov": 72.01 }, "mobilenet_v3_small_imagenet": { "config": "examples/tensorflow/classification/configs/mobilenet_v3_small_imagenet.json", - "target": 68.38, "metric_type": "Acc@1", "model_description": "MobileNet V3 (Small)", - "reverse_input_channels": true + "target_tf": 68.38, + "target_ov": 68.29 }, "mobilenet_v3_small_imagenet_int8": { "config": "examples/tensorflow/classification/configs/quantization/mobilenet_v3_small_imagenet_int8.json", "reference": "mobilenet_v3_small_imagenet", - "target": 67.79, "target_init": 0.1, "resume": "mobilenet_v3_small_imagenet_int8", "metric_type": "Acc@1", "model_description": "MobileNet V3 (Small)", "compression_description": "INT8 (per-channel symmetric for weights, per-tensor asymmetric half-range for activations)", - "reverse_input_channels": true, - "diff_target_min": -0.15 + "diff_target_tf_min": -0.15, + "diff_target_tf_max": 0.15, + "diff_target_ov_min": -0.06, + "target_tf": 67.66, + "target_ov": 67.74 }, "mobilenet_v3_small_imagenet_rb_sparsity_int8": { "config": "examples/tensorflow/classification/configs/sparsity_quantization/mobilenet_v3_small_imagenet_rb_sparsity_int8.json", "reference": "mobilenet_v3_small_imagenet", - "target": 67.69, "target_init": 0.1, "resume": "mobilenet_v3_small_imagenet_rb_sparsity_int8", "metric_type": "Acc@1", "model_description": "MobileNet V3 (Small)", "compression_description": "INT8 (per-channel symmetric for weights, per-tensor asymmetric half-range for activations) + Sparsity 42% (Magnitude)", - "reverse_input_channels": true + "diff_target_tf_min": -0.15, + "diff_target_tf_max": 0.15, + "target_tf": 67.69, + "target_ov": 67.68 }, "mobilenet_v3_large_imagenet": { "config": "examples/tensorflow/classification/configs/mobilenet_v3_large_imagenet.json", - "target": 75.8, "metric_type": "Acc@1", "model_description": "MobileNet V3 (Large)", - "reverse_input_channels": true + "target_tf": 75.8, + "target_ov": 75.85 }, "mobilenet_v3_large_imagenet_int8": { "config": "examples/tensorflow/classification/configs/quantization/mobilenet_v3_large_imagenet_int8.json", "reference": "mobilenet_v3_large_imagenet", - "target": 75.04, "target_init": 0.16, "resume": "mobilenet_v3_large_imagenet_int8", "metric_type": "Acc@1", "model_description": "MobileNet V3 (Large)", "compression_description": "INT8 (per-channel symmetric for weights, per-tensor asymmetric half-range for activations)", - "reverse_input_channels": true, - "diff_target_min": -0.15 + "diff_target_tf_min": -0.15, + "diff_target_tf_max": 0.15, + "diff_target_ov_min": -0.09, + "target_tf": 74.91, + "target_ov": 74.96 }, "mobilenet_v3_large_imagenet_rb_sparsity_int8": { "config": "examples/tensorflow/classification/configs/sparsity_quantization/mobilenet_v3_large_imagenet_rb_sparsity_int8.json", "reference": "mobilenet_v3_large_imagenet", - "target": 75.24, "target_init": 0.14, "resume": "mobilenet_v3_large_imagenet_rb_sparsity_int8", "metric_type": "Acc@1", "model_description": "MobileNet V3 (Large)", "compression_description": "INT8 (per-channel symmetric for weights, per-tensor asymmetric half-range for activations) + Sparsity 42% (RB)", - "reverse_input_channels": true + "diff_target_tf_min": -0.15, + "diff_target_tf_max": 0.15, + "diff_target_ov_max": 0.04, + "target_tf": 75.23, + "target_ov": 75.02 }, "resnet50_imagenet": { "config": "examples/tensorflow/classification/configs/resnet50_imagenet.json", - "target": 75.05, "metric_type": "Acc@1", "model_description": "ResNet-50", - "mean_value": "[103.939,116.779,123.68]" + "target_tf": 75.05, + "target_ov": 75.05 }, "resnet50_imagenet_int8": { "config": "examples/tensorflow/classification/configs/quantization/resnet50_imagenet_int8.json", "reference": "resnet50_imagenet", - "target": 74.99, "target_init": 74.57, "resume": "resnet50_imagenet_int8", "metric_type": "Acc@1", "model_description": "ResNet-50", "compression_description": "INT8", - "mean_value": "[103.939,116.779,123.68]", - "diff_fp32_min": -1, - "diff_fp32_max": 0.1, - "diff_target_max": 0.15 + "diff_target_tf_min": -0.15, + "diff_target_tf_max": 0.15, + "target_tf": 74.99, + "target_ov": 74.99 }, "resnet50_imagenet_rb_sparsity_int8": { "config": "examples/tensorflow/classification/configs/sparsity_quantization/resnet50_imagenet_rb_sparsity_int8.json", "reference": "resnet50_imagenet", - "target": 74.36, "target_init": 74.45, "resume": "resnet50_imagenet_rb_sparsity_int8", "metric_type": "Acc@1", "model_description": "ResNet-50", "compression_description": "INT8 (per-tensor symmetric for weights, per-tensor asymmetric half-range for activations) + Sparsity 65% (RB)", - "mean_value": "[103.939,116.779,123.68]", - "diff_fp32_min": -1, - "diff_fp32_max": 0.1 + "target_tf": 74.35, + "target_ov": 74.16 }, "resnet50_imagenet_rb_sparsity": { "config": "examples/tensorflow/classification/configs/sparsity/resnet50_imagenet_rb_sparsity.json", "reference": "resnet50_imagenet", - "target": 74.38, "target_init": 75.04, "resume": "resnet50_imagenet_rb_sparsity", "metric_type": "Acc@1", "model_description": "ResNet-50", "compression_description": "Sparsity 80% (RB)", - "mean_value": "[103.939,116.779,123.68]", - "diff_fp32_min": -1, - "diff_fp32_max": 0.1 + "target_tf": 74.38, + "target_ov": 74.32 }, "resnet50_imagenet_pruning_geometric_median": { "config": "examples/tensorflow/classification/configs/pruning/resnet50_imagenet_pruning_geometric_median.json", "reference": "resnet50_imagenet", - "target": 74.96, "target_init": 68.03, "resume": "resnet50_imagenet_pruning_geometric_median", "metric_type": "Acc@1", "model_description": "ResNet-50", "compression_description": "Filter pruning, 40%, geometric median criterion", - "mean_value": "[103.939,116.779,123.68]", - "diff_fp32_min": -1, - "diff_fp32_max": 0.1 + "target_tf": 74.96, + "target_ov": 74.92 }, "resnet50_imagenet_pruning_geometric_median_int8": { "config": "examples/tensorflow/classification/configs/pruning_quantization/resnet50_imagenet_pruning_geometric_median_int8.json", "reference": "resnet50_imagenet", - "target": 75.09, "target_init": 66.78, "resume": "resnet50_imagenet_pruning_geometric_median_int8", "metric_type": "Acc@1", "model_description": "ResNet-50", "compression_description": "INT8 (per-tensor symmetric for weights, per-tensor asymmetric half-range for activations) + Filter pruning, 40%, geometric median criterion", - "mean_value": "[103.939,116.779,123.68]", - "diff_fp32_min": -1, - "diff_fp32_max": 0.1 + "diff_target_tf_min": -0.15, + "diff_target_tf_max": 0.15, + "target_tf": 75.06, + "target_ov": 75.00 } } } @@ -272,113 +251,101 @@ "topologies": { "retinanet_coco": { "config": "examples/tensorflow/object_detection/configs/retinanet_coco.json", - "target": 33.43, "weights": "retinanet_coco/retinanet_coco.h5", "metric_type": "mAP", "model_description": "RetinaNet", "batch_per_gpu": 15, - "mean_value": "[123.675,116.28,103.53]", - "scale_value": "[58.395,57.12,57.375]", - "reverse_input_channels": true + "target_tf": 33.43, + "target_ov": 33.33 }, "retinanet_coco_int8": { "config": "examples/tensorflow/object_detection/configs/quantization/retinanet_coco_int8.json", "reference": "retinanet_coco", - "target": 33.12, "target_init": 33.3, "resume": "retinanet_coco_int8", "metric_type": "mAP", "model_description": "RetinaNet", "compression_description": "INT8 (per-tensor symmetric for weights, per-tensor asymmetric half-range for activations)", "batch_per_gpu": 15, - "mean_value": "[123.675,116.28,103.53]", - "scale_value": "[58.395,57.12,57.375]", - "reverse_input_channels": true, - "diff_target_max": 0.15 + "diff_target_tf_min": -0.15, + "diff_target_tf_max": 0.15, + "target_tf": 33.19, + "target_ov": 33.23 }, "retinanet_coco_magnitude_sparsity": { "config": "examples/tensorflow/object_detection/configs/sparsity/retinanet_coco_magnitude_sparsity.json", "reference": "retinanet_coco", - "target": 33.1, "target_init": 33.44, "resume": "retinanet_coco_magnitude_sparsity", "metric_type": "mAP", "model_description": "RetinaNet", "compression_description": "Magnitude sparsity (50%)", "batch_per_gpu": 15, - "mean_value": "[123.675,116.28,103.53]", - "scale_value": "[58.395,57.12,57.375]", - "reverse_input_channels": true + "target_tf": 33.1, + "target_ov": 33.09 }, "retinanet_coco_pruning_geometric_median": { "config": "examples/tensorflow/object_detection/configs/pruning/retinanet_coco_pruning_geometric_median.json", "reference": "retinanet_coco", - "target": 32.72, "target_init": 29.17, "resume": "retinanet_coco_pruning_geometric_median", "metric_type": "mAP", "model_description": "RetinaNet", "compression_description": "Filter pruning, 40%", "batch_per_gpu": 15, - "mean_value": "[123.675,116.28,103.53]", - "scale_value": "[58.395,57.12,57.375]", - "reverse_input_channels": true, - "diff_fp32_min": -1, - "diff_fp32_max": 0.4 + "diff_fp32_max": 0.4, + "target_tf": 32.72, + "target_ov": 32.59 }, "retinanet_coco_pruning_geometric_median_int8": { "config": "examples/tensorflow/object_detection/configs/pruning_quantization/retinanet_coco_pruning_geometric_median_int8.json", "reference": "retinanet_coco", - "target": 32.67, "target_init": 29.03, "resume": "retinanet_coco_pruning_geometric_median_int8", "metric_type": "mAP", "model_description": "RetinaNet", "compression_description": "INT8 (per-tensor symmetric for weights, per-tensor asymmetric half-range for activations) + filter pruning 40%", "batch_per_gpu": 15, - "mean_value": "[123.675,116.28,103.53]", - "scale_value": "[58.395,57.12,57.375]", - "reverse_input_channels": true + "target_tf": 32.67, + "target_ov": 32.47 }, "yolo_v4_coco": { "config": "examples/tensorflow/object_detection/configs/yolo_v4_coco.json", - "target": 47.07, "weights": "yolo_v4_coco/yolo_v4_coco.h5", "metric_type": "mAP", "model_description": "YOLO v4", "batch_per_gpu": 15, - "reverse_input_channels": true, - "scale_value": "image_input[255]" + "target_tf": 47.07, + "target_ov": 48.46 }, "yolo_v4_coco_int8": { "config": "examples/tensorflow/object_detection/configs/quantization/yolo_v4_coco_int8.json", "reference": "yolo_v4_coco", - "target": 46.2, "target_init": 45.28, "resume": "yolo_v4_coco_int8", "metric_type": "mAP", "model_description": "YOLO v4", "compression_description": "INT8 (per-channel symmetric for weights, per-tensor asymmetric half-range for activations)", "batch_per_gpu": 15, - "reverse_input_channels": true, - "scale_value": "image_input[255]", - "diff_target_max": 0.15, - "diff_target_min": -0.15 + "diff_target_tf_min": -0.15, + "diff_target_tf_max": 0.15, + "diff_target_ov_max": 0.07, + "target_tf": 46.2, + "target_ov": 47.78 }, "yolo_v4_coco_magnitude_sparsity": { "config": "examples/tensorflow/object_detection/configs/sparsity/yolo_v4_coco_magnitude_sparsity.json", "reference": "yolo_v4_coco", - "target": 46.49, "target_init": 47.04, "resume": "yolo_v4_coco_magnitude_sparsity", "metric_type": "mAP", "model_description": "YOLO v4", "compression_description": "Magnitude sparsity, 50%", "batch_per_gpu": 15, - "reverse_input_channels": true, - "scale_value": "image_input[255]", - "diff_target_max": 0.15, - "diff_target_min": -0.15 + "diff_target_tf_min": -0.15, + "diff_target_tf_max": 0.15, + "target_tf": 46.49, + "target_ov": 48.08 } } } @@ -391,37 +358,39 @@ "topologies": { "mask_rcnn_coco": { "config": "examples/tensorflow/segmentation/configs/mask_rcnn_coco.json", - "target": 37.33, "weights": "mask_rcnn_coco", "metric_type": "mAP", "model_description": "Mask-R-CNN", "batch_per_gpu": 4, - "reverse_input_channels": true, - "batch": 16 + "target_tf": 37.33, + "target_ov": 37.33, + "skip_ov": "AC does not support mask models yet" }, "mask_rcnn_coco_int8": { "config": "examples/tensorflow/segmentation/configs/quantization/mask_rcnn_coco_int8.json", "reference": "mask_rcnn_coco", - "target": 37.19, "resume": "mask_rcnn_coco_int8", "metric_type": "mAP", "model_description": "Mask-R-CNN", "compression_description": "INT8 (per-tensor symmetric for weights, per-tensor asymmetric half-range for activations)", "batch_per_gpu": 4, - "reverse_input_channels": true + "target_tf": 37.19, + "target_ov": 37.19, + "skip_ov": "AC does not support mask models yet" }, "mask_rcnn_coco_magnitude_sparsity": { "config": "examples/tensorflow/segmentation/configs/sparsity/mask_rcnn_coco_magnitude_sparsity.json", "reference": "mask_rcnn_coco", - "target": 36.94, "resume": "mask_rcnn_coco_magnitude_sparsity", "metric_type": "mAP", "model_description": "Mask-R-CNN", "compression_description": "Magnitude sparsity, 50%", "batch_per_gpu": 4, - "reverse_input_channels": true + "target_tf": 36.94, + "target_ov": 36.94, + "skip_ov": "AC does not support mask models yet" } } } } -} \ No newline at end of file +} diff --git a/tests/tensorflow/sparsity/__init__.py b/tests/tensorflow/sparsity/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/tensorflow/sparsity/__init__.py +++ b/tests/tensorflow/sparsity/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/sparsity/magnitude/__init__.py b/tests/tensorflow/sparsity/magnitude/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/tensorflow/sparsity/magnitude/__init__.py +++ b/tests/tensorflow/sparsity/magnitude/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/sparsity/magnitude/test_algorithm.py b/tests/tensorflow/sparsity/magnitude/test_algorithm.py index 1da2c93a031..b4e72b88846 100644 --- a/tests/tensorflow/sparsity/magnitude/test_algorithm.py +++ b/tests/tensorflow/sparsity/magnitude/test_algorithm.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/sparsity/magnitude/test_helpers.py b/tests/tensorflow/sparsity/magnitude/test_helpers.py index 8ff4b4c02c9..4d039e4ffbc 100644 --- a/tests/tensorflow/sparsity/magnitude/test_helpers.py +++ b/tests/tensorflow/sparsity/magnitude/test_helpers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/sparsity/magnitude/test_scheduler.py b/tests/tensorflow/sparsity/magnitude/test_scheduler.py index 3cff1a6cd83..a973a7fe959 100644 --- a/tests/tensorflow/sparsity/magnitude/test_scheduler.py +++ b/tests/tensorflow/sparsity/magnitude/test_scheduler.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/sparsity/rb/__init__.py b/tests/tensorflow/sparsity/rb/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/tensorflow/sparsity/rb/__init__.py +++ b/tests/tensorflow/sparsity/rb/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/sparsity/rb/test_algorithm.py b/tests/tensorflow/sparsity/rb/test_algorithm.py index 5808f2e2dfd..6113b99d3c8 100644 --- a/tests/tensorflow/sparsity/rb/test_algorithm.py +++ b/tests/tensorflow/sparsity/rb/test_algorithm.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/sparsity/rb/test_components.py b/tests/tensorflow/sparsity/rb/test_components.py index 6404589051a..d4c46b14045 100644 --- a/tests/tensorflow/sparsity/rb/test_components.py +++ b/tests/tensorflow/sparsity/rb/test_components.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -9,6 +9,7 @@ # See the License for the specific language governing permissions and # limitations under the License. +import contextlib import os from collections import defaultdict from itertools import combinations @@ -76,10 +77,9 @@ def wrapper(*args, **kwargs): @patch("nncf.tensorflow.sparsity.rb.operation.calc_rb_binary_mask", new=calc_rb_mask_decorator(calc_rb_binary_mask)) def test_distributed_masks_are_equal(quantization): # Clean output file - try: + with contextlib.suppress(OSError): os.remove(MASKS_SEEDS_PATH) - except OSError: - pass + # Fill file with seeds num_of_replicas = 3 strategy = tf.distribute.MirroredStrategy([f"GPU:{i}" for i in range(num_of_replicas)]) diff --git a/tests/tensorflow/sparsity/rb/test_integration.py b/tests/tensorflow/sparsity/rb/test_integration.py index 43d3db1333a..894792d90dd 100644 --- a/tests/tensorflow/sparsity/rb/test_integration.py +++ b/tests/tensorflow/sparsity/rb/test_integration.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/sparsity/rb/test_weights.py b/tests/tensorflow/sparsity/rb/test_weights.py index 4fb3d44b029..527a68dd883 100644 --- a/tests/tensorflow/sparsity/rb/test_weights.py +++ b/tests/tensorflow/sparsity/rb/test_weights.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/sparsity/rb/utils.py b/tests/tensorflow/sparsity/rb/utils.py index 738c35274f0..6e6ed4f349c 100644 --- a/tests/tensorflow/sparsity/rb/utils.py +++ b/tests/tensorflow/sparsity/rb/utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/sparsity/test_common.py b/tests/tensorflow/sparsity/test_common.py index a3487c715c5..4e36ce9a2e5 100644 --- a/tests/tensorflow/sparsity/test_common.py +++ b/tests/tensorflow/sparsity/test_common.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -116,6 +116,10 @@ def __init__(self): self.set_sparsity_level = mocker.stub() self.freeze = mocker.stub() + @property + def current_sparsity_level(self) -> float: + return 0.0 + return MockSparsityAlgo() @@ -245,6 +249,10 @@ def get_sparsity_init(self): def set_sparsity_init(self, sparsity_init): self.sparsity_init = sparsity_init + @property + def current_sparsity_level(self) -> float: + return self.loss.current_sparsity + return MockSparsityAlgo() diff --git a/tests/tensorflow/sparsity/test_strip.py b/tests/tensorflow/sparsity/test_strip.py index bbc648ad1b0..2c53a9d31bc 100644 --- a/tests/tensorflow/sparsity/test_strip.py +++ b/tests/tensorflow/sparsity/test_strip.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/tensor_statistics/__init__.py b/tests/tensorflow/tensor_statistics/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/tensorflow/tensor_statistics/__init__.py +++ b/tests/tensorflow/tensor_statistics/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/tensor_statistics/test_tensor_statistics.py b/tests/tensorflow/tensor_statistics/test_tensor_statistics.py index 30a675f7308..a9195c6dd66 100644 --- a/tests/tensorflow/tensor_statistics/test_tensor_statistics.py +++ b/tests/tensorflow/tensor_statistics/test_tensor_statistics.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -103,7 +103,7 @@ def test_collected_statistics_with_shape_convert( collector: Type[TensorStatisticCollectorBase], reduction_shapes_vs_ref_statistic: Dict[Tuple[ReductionAxes, ReductionAxes], TensorStatistic], ): - for reduction_shape in reduction_shapes_vs_ref_statistic.keys(): + for reduction_shape in reduction_shapes_vs_ref_statistic: collector_obj = collector(use_abs_max=True, reduction_shape=reduction_shape) for input_ in TestCollectedStatistics.REF_INPUTS: collector_obj.register_input(input_) @@ -181,7 +181,7 @@ def test_collected_statistics( collector: Type[TensorStatisticCollectorBase], reduction_shapes_vs_ref_statistic: Dict[ReductionAxes, TensorStatistic], ): - for reduction_shape in reduction_shapes_vs_ref_statistic.keys(): + for reduction_shape in reduction_shapes_vs_ref_statistic: collector_obj = collector(reduction_shape=reduction_shape) for input_ in TestCollectedStatistics.REF_INPUTS: collector_obj.register_input(input_) diff --git a/tests/tensorflow/test_api_behavior.py b/tests/tensorflow/test_api_behavior.py index 63332344b54..ef7b672d982 100644 --- a/tests/tensorflow/test_api_behavior.py +++ b/tests/tensorflow/test_api_behavior.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/test_bn_adaptation.py b/tests/tensorflow/test_bn_adaptation.py index f3ccfd86aac..c40f1fd714e 100644 --- a/tests/tensorflow/test_bn_adaptation.py +++ b/tests/tensorflow/test_bn_adaptation.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/test_callbacks.py b/tests/tensorflow/test_callbacks.py index a3e452dea95..773128c200d 100644 --- a/tests/tensorflow/test_callbacks.py +++ b/tests/tensorflow/test_callbacks.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/test_compressed_graph.py b/tests/tensorflow/test_compressed_graph.py index 45dca492610..90e293b742a 100644 --- a/tests/tensorflow/test_compressed_graph.py +++ b/tests/tensorflow/test_compressed_graph.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -489,7 +489,7 @@ def test_quantize_outputs(desc: ModelDesc, _quantization_case_config): ) -TYPE_HW = [(HWConfigType.CPU), (HWConfigType.GPU), (HWConfigType.VPU)] +TYPE_HW = [(HWConfigType.CPU), (HWConfigType.GPU), (HWConfigType.NPU)] TEST_HW_MODELS_DESC = [ ModelDesc("resnet50.pb", test_models.ResNet50, [1, 32, 32, 3]), diff --git a/tests/tensorflow/test_helpers.py b/tests/tensorflow/test_helpers.py index 2fc74735130..f554ae35e5d 100644 --- a/tests/tensorflow/test_helpers.py +++ b/tests/tensorflow/test_helpers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/test_ignored_scopes.py b/tests/tensorflow/test_ignored_scopes.py index 367be75ebe8..56857a6407b 100644 --- a/tests/tensorflow/test_ignored_scopes.py +++ b/tests/tensorflow/test_ignored_scopes.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -14,6 +14,7 @@ import tensorflow as tf from tensorflow.keras import layers +import nncf from nncf.config.schemata.defaults import VALIDATE_SCOPES from nncf.tensorflow.algorithm_selector import TF_COMPRESSION_ALGORITHMS from nncf.tensorflow.layers.wrapper import NNCFWrapper @@ -75,7 +76,7 @@ def test_raise_runtimeerror_for_not_matched_scope_names(algo_name, validate_scop config["compression"]["validate_scopes"] = validate_scopes if validate_scopes or (validate_scopes is None and VALIDATE_SCOPES is True): - with pytest.raises(RuntimeError, match="scope definitions"): + with pytest.raises(nncf.ValidationError, match="scope definitions"): create_compressed_model_and_algo_for_test(model, config) else: create_compressed_model_and_algo_for_test(model, config) diff --git a/tests/tensorflow/test_init.py b/tests/tensorflow/test_init.py deleted file mode 100644 index 190d5277784..00000000000 --- a/tests/tensorflow/test_init.py +++ /dev/null @@ -1,150 +0,0 @@ -# Copyright (c) 2023 Intel Corporation -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# http://www.apache.org/licenses/LICENSE-2.0 -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import datetime -import os -import sys -from pathlib import Path - -import pytest - -from tests.shared.paths import PROJECT_ROOT -from tests.tensorflow.test_sota_checkpoints import BG_COLOR_GREEN_HEX -from tests.tensorflow.test_sota_checkpoints import BG_COLOR_RED_HEX -from tests.tensorflow.test_sota_checkpoints import DATASET_TYPE_AVAILABILITY -from tests.tensorflow.test_sota_checkpoints import EVAL_SCRIPT_NAME_MAP -from tests.tensorflow.test_sota_checkpoints import EvalRunParamsStruct -from tests.tensorflow.test_sota_checkpoints import RunTest - -DIFF_TARGET_INIT_MIN_GLOBAL = -0.3 -DIFF_TARGET_INIT_MAX_GLOBAL = 0.3 - -MODE = "TF2" - - -class TestInitialization(RunTest): - @staticmethod - def update_tag_text(tag, text): - with tag("p"): - with tag("span", style=f"Background-color: #{BG_COLOR_GREEN_HEX}"): - text("Thresholds for Measured after initialization and Expected are passed") - with tag("p"): - with tag("span", style=f"Background-color: #{BG_COLOR_RED_HEX}"): - text("Thresholds for Measured after initialization and Expected are failed") - - @staticmethod - def threshold_check(is_ok, diff_target): - color = BG_COLOR_GREEN_HEX - within_thresholds = True - if is_ok: - if not DIFF_TARGET_INIT_MIN_GLOBAL < diff_target < DIFF_TARGET_INIT_MAX_GLOBAL: - color = BG_COLOR_RED_HEX - within_thresholds = False - else: - color = BG_COLOR_RED_HEX - within_thresholds = False - return color, within_thresholds - - @staticmethod - def make_table_row(expected, metric, diff, error_message, metrics_type_, key): - if metric is not None: - row = [str(key), str(metrics_type_), str(expected), str(metric), str(diff), str("-")] - else: - row = [str(key), str(metrics_type_), str(expected), str("Not executed"), str("-"), str(error_message)] - return row - - @pytest.mark.init - @pytest.mark.parametrize("eval_test_struct", RunTest.param_list, ids=RunTest.ids_list) - def test_init(self, sota_checkpoints_dir, sota_data_dir, eval_test_struct: EvalRunParamsStruct): - if sota_data_dir is None: - pytest.skip("Path to datasets is not set") - - if eval_test_struct.resume_file_ is None or eval_test_struct.sample_type_ == "segmentation": - pytest.skip("Skip initialization run for the full-precision and all segmentation models") - - sample_type = eval_test_struct.sample_type_ - log_dir = pytest.metrics_dump_path / "logs" - metric_file_name = self.get_metric_file_name(model_name=eval_test_struct.model_name_) - metrics_dump_file_path = pytest.metrics_dump_path / metric_file_name - if MODE == "TF2": - cmd = ( - f"{sys.executable} examples/tensorflow/{sample_type}/{EVAL_SCRIPT_NAME_MAP[sample_type]} " - f"-m test --config {eval_test_struct.config_name_} " - f"--data {sota_data_dir}/{eval_test_struct.dataset_type_}/{eval_test_struct.dataset_name_}/ " - f"--log-dir={log_dir} --metrics-dump {metrics_dump_file_path}" - ) - if eval_test_struct.weights_: - cmd += f" --weights {os.path.join(sota_checkpoints_dir, eval_test_struct.weights_)}" - if DATASET_TYPE_AVAILABILITY[sample_type]: - cmd += f" --dataset-type {eval_test_struct.dataset_type_}" - cmd += " --seed 1" - else: - cmd = ( - f"{sys.executable} examples/tensorflow/{sample_type}/main.py -m test " - f"--config {eval_test_struct.config_name_} " - f"--data {sota_data_dir}/{eval_test_struct.dataset_name_}/ --log-dir={log_dir}" - ) - if eval_test_struct.batch_: - cmd += f" -b {eval_test_struct.batch_}" - - cmd = cmd + f" --metrics-dump {metrics_dump_file_path}" - exit_code, err_str = self.run_cmd(cmd, cwd=PROJECT_ROOT) - is_ok = exit_code == 0 and metrics_dump_file_path.exists() - - if is_ok: - metric_value = self.read_metric(str(metrics_dump_file_path)) - diff_target = round((metric_value - eval_test_struct.expected_init_), 2) - else: - metric_value = None - diff_target = None - - self.row_dict[eval_test_struct.model_name_] = self.make_table_row( - eval_test_struct.expected_init_, - metric_value, - diff_target, - err_str, - eval_test_struct.metric_type_, - eval_test_struct.model_name_, - ) - retval = self.threshold_check(is_ok, diff_target) - - self.color_dict[eval_test_struct.model_name_], is_accuracy_within_thresholds = retval - assert is_accuracy_within_thresholds - - -@pytest.fixture(autouse=True, scope="class") -def make_metrics_dump_path(metrics_dump_dir): - if pytest.metrics_dump_path is None: - data = datetime.datetime.now() - data_stamp = "_".join([str(getattr(data, atr)) for atr in ["year", "month", "day", "hour", "minute", "second"]]) - pytest.metrics_dump_path = PROJECT_ROOT / "test_results" / "metrics_dump_" / f"{data_stamp}" - else: - pytest.metrics_dump_path = Path(pytest.metrics_dump_path) - assert not os.path.isdir(pytest.metrics_dump_path) or not os.listdir( - pytest.metrics_dump_path - ), f"metrics_dump_path dir should be empty: {pytest.metrics_dump_path}" - print(f"metrics_dump_path: {pytest.metrics_dump_path}") - - -@pytest.fixture(autouse=True, scope="class") -def results(sota_data_dir): - yield - if sota_data_dir: - RunTest.write_common_metrics_file(per_model_metric_file_dump_path=pytest.metrics_dump_path) - header = [ - "Model", - "Metrics type", - "Expected Init", - "Measured Init", - "Diff Expected-Measured Init", - "Error Init", - ] - TestInitialization().write_results_table(header, pytest.metrics_dump_path) diff --git a/tests/tensorflow/test_model_converter.py b/tests/tensorflow/test_model_converter.py index d39997b3a8e..4c525639f9c 100644 --- a/tests/tensorflow/test_model_converter.py +++ b/tests/tensorflow/test_model_converter.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/test_models/__init__.py b/tests/tensorflow/test_models/__init__.py index 03a5cf5d31b..581ba1b167b 100644 --- a/tests/tensorflow/test_models/__init__.py +++ b/tests/tensorflow/test_models/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/test_models/densenet.py b/tests/tensorflow/test_models/densenet.py index ada8f8fd48b..af806896f1c 100644 --- a/tests/tensorflow/test_models/densenet.py +++ b/tests/tensorflow/test_models/densenet.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/test_models/inception_resnet_v2.py b/tests/tensorflow/test_models/inception_resnet_v2.py index be4025ea630..a6a205eae76 100644 --- a/tests/tensorflow/test_models/inception_resnet_v2.py +++ b/tests/tensorflow/test_models/inception_resnet_v2.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/test_models/inception_v3.py b/tests/tensorflow/test_models/inception_v3.py index bb4e0a30b23..b1c5c9ff9ba 100644 --- a/tests/tensorflow/test_models/inception_v3.py +++ b/tests/tensorflow/test_models/inception_v3.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/test_models/maskrcnn.py b/tests/tensorflow/test_models/maskrcnn.py index b309988c23b..872a1cd024e 100644 --- a/tests/tensorflow/test_models/maskrcnn.py +++ b/tests/tensorflow/test_models/maskrcnn.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/test_models/mobilenet.py b/tests/tensorflow/test_models/mobilenet.py index 023f7c793c6..dc1e47c0897 100644 --- a/tests/tensorflow/test_models/mobilenet.py +++ b/tests/tensorflow/test_models/mobilenet.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/test_models/mobilenet_v2.py b/tests/tensorflow/test_models/mobilenet_v2.py index 2013ba082f7..75e141ddbcf 100644 --- a/tests/tensorflow/test_models/mobilenet_v2.py +++ b/tests/tensorflow/test_models/mobilenet_v2.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/test_models/mobilenet_v2_slim.py b/tests/tensorflow/test_models/mobilenet_v2_slim.py index ac5888dc146..72305cfa2bb 100644 --- a/tests/tensorflow/test_models/mobilenet_v2_slim.py +++ b/tests/tensorflow/test_models/mobilenet_v2_slim.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/test_models/mobilenet_v3.py b/tests/tensorflow/test_models/mobilenet_v3.py index cb9be44f629..678c108d0c7 100644 --- a/tests/tensorflow/test_models/mobilenet_v3.py +++ b/tests/tensorflow/test_models/mobilenet_v3.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/test_models/nasnet.py b/tests/tensorflow/test_models/nasnet.py index ab666e3cdd1..79ad361725a 100644 --- a/tests/tensorflow/test_models/nasnet.py +++ b/tests/tensorflow/test_models/nasnet.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/test_models/resnet.py b/tests/tensorflow/test_models/resnet.py index 55b948bf589..13c26d13439 100644 --- a/tests/tensorflow/test_models/resnet.py +++ b/tests/tensorflow/test_models/resnet.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/test_models/resnet_v2.py b/tests/tensorflow/test_models/resnet_v2.py index 230e66e1418..7ebb06e154c 100644 --- a/tests/tensorflow/test_models/resnet_v2.py +++ b/tests/tensorflow/test_models/resnet_v2.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/test_models/retinanet.py b/tests/tensorflow/test_models/retinanet.py index be618a740e0..405c2f97c0f 100644 --- a/tests/tensorflow/test_models/retinanet.py +++ b/tests/tensorflow/test_models/retinanet.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/test_models/sequential_model.py b/tests/tensorflow/test_models/sequential_model.py index fb30c2b97a5..0aca425978b 100644 --- a/tests/tensorflow/test_models/sequential_model.py +++ b/tests/tensorflow/test_models/sequential_model.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/test_models/shared_layers_model.py b/tests/tensorflow/test_models/shared_layers_model.py index ddbdb498be1..dceaf8930df 100644 --- a/tests/tensorflow/test_models/shared_layers_model.py +++ b/tests/tensorflow/test_models/shared_layers_model.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/test_models/vgg16.py b/tests/tensorflow/test_models/vgg16.py index 938b4f51858..8a5ffe9f940 100644 --- a/tests/tensorflow/test_models/vgg16.py +++ b/tests/tensorflow/test_models/vgg16.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/test_models/xception.py b/tests/tensorflow/test_models/xception.py index e10bbdada18..bda8fb8725c 100644 --- a/tests/tensorflow/test_models/xception.py +++ b/tests/tensorflow/test_models/xception.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/test_models/yolo_v4.py b/tests/tensorflow/test_models/yolo_v4.py index 3fa9888b491..1fc403615de 100644 --- a/tests/tensorflow/test_models/yolo_v4.py +++ b/tests/tensorflow/test_models/yolo_v4.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/test_nncf_keras_graph_equality.py b/tests/tensorflow/test_nncf_keras_graph_equality.py index 9e27bacfdca..d1acc0119d3 100644 --- a/tests/tensorflow/test_nncf_keras_graph_equality.py +++ b/tests/tensorflow/test_nncf_keras_graph_equality.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/test_no_compression_algorithm.py b/tests/tensorflow/test_no_compression_algorithm.py index edae8a8feaf..b0d7748efbd 100644 --- a/tests/tensorflow/test_no_compression_algorithm.py +++ b/tests/tensorflow/test_no_compression_algorithm.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/test_sanity_sample.py b/tests/tensorflow/test_sanity_sample.py index 258346058eb..1f254c9c938 100644 --- a/tests/tensorflow/test_sanity_sample.py +++ b/tests/tensorflow/test_sanity_sample.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -18,6 +18,7 @@ import pytest import tensorflow as tf +import nncf from examples.common.sample_config import EVAL_ONLY_ERROR_TEXT from examples.tensorflow.classification import main as cls_main from examples.tensorflow.common.model_loader import AVAILABLE_MODELS @@ -131,7 +132,7 @@ def get_global_batch_size(): def get_sample_fn(sample_type, modes): variants = [] - for key in SAMPLES[sample_type].keys(): + for key in SAMPLES[sample_type]: supported_modes = set(key.split("-")) if set(modes).issubset(supported_modes): variants.append(key) @@ -170,7 +171,7 @@ def _config(request, dataset_dir): with config_path.open() as f: jconfig = json.load(f) - if "checkpoint_save_dir" in jconfig.keys(): + if "checkpoint_save_dir" in jconfig: del jconfig["checkpoint_save_dir"] jconfig["dataset"] = dataset_name @@ -458,6 +459,6 @@ def test_eval_only_config_fails_to_train(tmp_path, sample_type): } main = get_sample_fn(sample_type, modes=["train"]) - with pytest.raises(RuntimeError) as e_info: + with pytest.raises(nncf.ValidationError) as e_info: main(convert_to_argv(args)) assert remove_line_breaks(EVAL_ONLY_ERROR_TEXT) in remove_line_breaks(e_info.value.args[0]) diff --git a/tests/tensorflow/test_sota_checkpoints.py b/tests/tensorflow/test_sota_checkpoints.py index 99ae82190db..ba25f31ab1b 100644 --- a/tests/tensorflow/test_sota_checkpoints.py +++ b/tests/tensorflow/test_sota_checkpoints.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -9,46 +9,34 @@ # See the License for the specific language governing permissions and # limitations under the License. -import csv import datetime import json import os -import re -import shlex -import subprocess import sys -from abc import ABC -from abc import abstractmethod from collections import OrderedDict +from dataclasses import dataclass from pathlib import Path -from typing import List, Optional, Tuple +from typing import Dict, List, Optional, Tuple, Union +import openvino as ov +import pandas as pd import pytest import tensorflow as tf import yaml -from prettytable import PrettyTable -from yattag import Doc +from pytest import FixtureRequest -from nncf.common.utils.os import is_linux -from nncf.common.utils.os import is_windows +from tests.shared.command import Command +from tests.shared.metric_thresholds import DIFF_FP32_MAX_GLOBAL +from tests.shared.metric_thresholds import DIFF_FP32_MIN_GLOBAL from tests.shared.paths import DATASET_DEFINITIONS_PATH from tests.shared.paths import PROJECT_ROOT from tests.shared.paths import TEST_ROOT -BG_COLOR_GREEN_HEX = "ccffcc" -BG_COLOR_YELLOW_HEX = "ffffcc" -BG_COLOR_RED_HEX = "ffcccc" +DIFF_TARGET_TF_MIN = -0.1 +DIFF_TARGET_TF_MAX = 0.1 +DIFF_TARGET_OV_MIN = -0.01 +DIFF_TARGET_OV_MAX = 0.01 -DIFF_TARGET_MIN_GLOBAL = -0.1 -DIFF_TARGET_MAX_GLOBAL = 0.1 -DIFF_FP32_MIN_GLOBAL = -1.0 -DIFF_FP32_MAX_GLOBAL = 0.1 - -# MO_VENV_DIR = PROJECT_ROOT / 'model_optimizer' -# venv_activate_string = f'source {MO_VENV_DIR}/bin/activate && source {OPENVINO_DIR}/bin/setupvars.sh' -# USING_OV2_PACKAGE_FORMAT = False - -MODE = "TF2" EVAL_SCRIPT_NAME_MAP = { "classification": "main.py", @@ -72,184 +60,218 @@ BATCH_COEFF = num_gpus if num_gpus else 1 +@dataclass class EvalRunParamsStruct: - def __init__( - self, - config_name_: str, - reference_: Optional[str], - expected_: float, - expected_init_: Optional[float], - metric_type_: str, - dataset_name_: str, - dataset_type_: str, - sample_type_: str, - resume_file_: str, - weights_: str, - batch_: int, - mean_val_: Optional[str], - scale_val_: Optional[str], - reverse_input_channels_: bool, - diff_fp32_min_: float, - diff_fp32_max_: float, - model_name_: str, - diff_target_min_: float, - diff_target_max_: float, - ): - self.config_name_ = config_name_ - self.reference_ = reference_ - self.expected_ = expected_ - self.expected_init_ = expected_init_ - self.metric_type_ = metric_type_ - self.dataset_name_ = dataset_name_ - self.dataset_type_ = dataset_type_ - self.sample_type_ = sample_type_ - self.resume_file_ = resume_file_ - self.weights_ = weights_ - self.batch_ = batch_ - self.mean_val_ = mean_val_ - self.scale_val_ = scale_val_ - self.reverse_input_channels_ = reverse_input_channels_ - self.diff_fp32_min_ = diff_fp32_min_ - self.diff_fp32_max_ = diff_fp32_max_ - self.model_name_ = model_name_ - self.diff_target_min_ = diff_target_min_ - self.diff_target_max_ = diff_target_max_ - - -class RunTest(ABC): - param_list = [] - train_param_list = [] - ids_list = [] - ov_ids_list = [] - ov_param_list = [] - train_ids_list = [] - row_dict = OrderedDict() - color_dict = OrderedDict() - ref_fp32_dict = OrderedDict() - test = None - - @staticmethod - def get_metric_file_name(model_name: str): - return f"{model_name}.metrics.json" - - @staticmethod - def run_cmd(comm: str, cwd: str, venv=None) -> Tuple[int, str]: - print("\n", comm, "\n") - - if is_linux(): - com_line = shlex.split(comm) - elif is_windows(): - com_line = comm + """ + Contain data about quantization of the model. + """ + + model_name: str + config: Path + reference: Optional[str] + target_tf: float + target_ov: float + metric_type: str + dataset_name: str + dataset_types: List[str] + sample_type: str + resume_file: Optional[Path] + weights: Optional[str] + batch: Optional[int] + diff_fp32_min: float + diff_fp32_max: float + diff_target_tf_min: float + diff_target_tf_max: float + diff_target_ov_min: float + diff_target_ov_max: float + skip_ov: Optional[str] + xfail_ov: Optional[str] + + +@dataclass +class ResultInfo: + """ + Contain data about result of test. + """ + + model_name: str + backend: str + metric_type: Optional[str] = None + measured: Optional[float] = None + expected: Optional[float] = None + diff_fp32: Optional[float] = None + target_fp32: Optional[float] = None + diff_target: Optional[float] = None + status: Optional[str] = None + + def to_dict(self): + return { + "Model": self.model_name, + "Backend": self.backend, + "Metrics type": self.metric_type, + "Measured": self.measured, + "Expected": self.expected, + "Diff expected": self.diff_target, + "Target FP32": self.target_fp32, + "Diff FP32": self.diff_fp32, + "Status": self.status, + "Build url": os.environ.get("BUILD_URL", ""), + } + + +def read_reference_file(ref_path: Path) -> List[EvalRunParamsStruct]: + """ + Reads the reference file to get a list of `EvalRunParamsStruct` objects. + + :param ref_path: The path to the JSON reference file. + :return: A list of `EvalRunParamsStruct` objects. + """ + + with ref_path.open(encoding="UTF-8") as source: + sota_eval_config = json.load(source, object_pairs_hook=OrderedDict) - env = os.environ.copy() - - if "PYTHONPATH" in env: - env["PYTHONPATH"] += ":" + str(PROJECT_ROOT) - else: - env["PYTHONPATH"] = str(PROJECT_ROOT) - if venv: - env["VIRTUAL_ENV"] = str(venv) - env["PATH"] = str(f"{venv}/bin") + ":" + env["PATH"] - - with subprocess.Popen(com_line, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, cwd=cwd, env=env) as result: - exit_code = result.poll() - - def process_line(decoded_line: str, error_lines: List): - if re.search(r"Error|ERROR|(No module named)", decoded_line): - # WA for tensorboardX multiprocessing bug (https://github.com/lanpa/tensorboardX/issues/598) - if not re.search("EOFError", decoded_line) and not re.search("Log level", decoded_line): - error_lines.append(decoded_line) - if decoded_line != "": - print(decoded_line) - - error_lines = [] - while exit_code is None: - decoded_line = result.stdout.readline().decode("utf-8").strip() - process_line(decoded_line, error_lines) - exit_code = result.poll() - - # The process may exit before the first process_line is executed, handling this case here - outs, _ = result.communicate() - remaining_lines = outs.decode("utf-8").strip().split("\n") - for output_line in remaining_lines: - process_line(output_line, error_lines) - - err_string = "\n".join(error_lines) if error_lines else None - return exit_code, err_string - - @staticmethod - def write_error_in_csv(error_message, filename, model_name): - error_message = "Error " + error_message[:80].replace("\n", "") - with open(filename, "w", newline="", encoding="utf8") as csvfile: - fieldnames = [ - "model", - "launcher", - "device", - "dataset", - "tags", - "metric_name", - "metric_type", - "metric_value", - ] - writer = csv.DictWriter(csvfile, fieldnames=fieldnames) - writer.writeheader() - writer.writerow( - { - "model": model_name, - "launcher": "-", - "device": "-", - "dataset": "-", - "tags": "-", - "metric_name": "-", - "metric_type": "-", - "metric_value": error_message, - } - ) - - @staticmethod - @abstractmethod - def update_tag_text(tag, text): - pass - - def write_results_table(self, init_table_string, path): - result_table = PrettyTable() - result_table.field_names = init_table_string - for key in self.row_dict: - result_table.add_row(self.row_dict[key]) - print() - print(result_table) - - doc, tag, text = Doc().tagtext() - doc.asis("") - with tag("p"): - text("legend: ") - self.update_tag_text(tag, text) - with tag("table", border="1", cellpadding="5", style="border-collapse: collapse; border: 1px solid;"): - with tag("tr"): - for i in init_table_string: - with tag("td"): - text(i) - for key in self.row_dict: - with tag("tr", bgcolor=f"{self.color_dict[key]}"): - for i in self.row_dict[key]: - if i is None: - i = "-" - with tag("td"): - text(i) - print("Write results at ", path / "results.html") - with open(path / "results.html", "w", encoding="utf8") as f: - f.write(doc.getvalue()) + param_list = [] + model_names = [] + for sample_type_ in sota_eval_config: + datasets = sota_eval_config[sample_type_] + for dataset_name in datasets: + model_dict = datasets[dataset_name] + for model_name, sample_dict in model_dict["topologies"].items(): + if model_name in model_names: + raise RuntimeError(f"Model name {model_name} is not unique.") + model_names.append(model_name) + batch = sample_dict.get("batch_per_gpu") + resume = sample_dict.get("resume") + param_list.append( + EvalRunParamsStruct( + model_name=model_name, + config=Path(sample_dict["config"]), + reference=sample_dict.get("reference"), + weights=sample_dict.get("weights"), + target_tf=sample_dict["target_tf"], + target_ov=sample_dict["target_ov"], + metric_type=sample_dict["metric_type"], + dataset_name=dataset_name, + dataset_types=model_dict["dataset_types"], + sample_type=sample_type_, + resume_file=Path(resume) if resume is not None else None, + batch=batch * BATCH_COEFF if batch is not None else None, + diff_fp32_min=sample_dict.get("diff_fp32_min", DIFF_FP32_MIN_GLOBAL), + diff_fp32_max=sample_dict.get("diff_fp32_max", DIFF_FP32_MAX_GLOBAL), + diff_target_ov_min=sample_dict.get("diff_target_ov_min", DIFF_TARGET_OV_MIN), + diff_target_ov_max=sample_dict.get("diff_target_ov_max", DIFF_TARGET_OV_MAX), + diff_target_tf_min=sample_dict.get("diff_target_tf_min", DIFF_TARGET_TF_MIN), + diff_target_tf_max=sample_dict.get("diff_target_tf_max", DIFF_TARGET_TF_MAX), + skip_ov=sample_dict.get("skip_ov"), + xfail_ov=sample_dict.get("xfail_ov"), + ) + ) + return param_list + + +EVAL_TEST_STRUCT = read_reference_file(TEST_ROOT / "tensorflow" / "sota_checkpoints_eval.json") +REF_PT_FP32_METRIC = {p.model_name: p.target_tf for p in EVAL_TEST_STRUCT if p.reference is None} +REF_OV_FP32_METRIC = {p.model_name: p.target_ov for p in EVAL_TEST_STRUCT if p.reference is None} + + +def idfn(val): + if isinstance(val, EvalRunParamsStruct): + return val.model_name + + +def generate_run_examples_command( + sample_type: str, + mode: str, + config: Path, + dataset_path: Optional[Path] = None, + log_dir: Optional[Path] = None, + metrics_dump_file_path: Optional[Path] = None, + resume_file_path: Optional[Path] = None, + weights: Optional[Path] = None, + pretrained: bool = False, + batch: Optional[int] = None, + dataset_type: Optional[str] = None, + to_frozen_graph: Optional[Path] = None, +) -> str: + """ + Generates a command line to run example. + """ + cmd = [ + sys.executable, + f"examples/tensorflow/{sample_type}/{EVAL_SCRIPT_NAME_MAP[sample_type]}", + "-m", mode, + "--config", config.as_posix(), + ] # fmt: skip + + if dataset_path is not None: + cmd += ["--data", dataset_path.as_posix()] + if log_dir is not None: + cmd += ["--log-dir", log_dir.as_posix()] + if metrics_dump_file_path is not None: + cmd += ["--metrics-dump", metrics_dump_file_path.as_posix()] + if resume_file_path is not None: + assert resume_file_path.exists(), f"{resume_file_path} does not exist" + cmd += ["--resume", resume_file_path.as_posix()] + if weights is not None: + assert weights.exists(), f"{weights} does not exist" + cmd += ["--weights", weights.as_posix()] + if pretrained: + cmd += ["--pretrained"] + if dataset_type is not None and DATASET_TYPE_AVAILABILITY[sample_type]: + cmd += ["--dataset-type", dataset_type] + if batch: + cmd += ["-b", str(batch)] + if to_frozen_graph: + cmd += ["--to-frozen-graph", to_frozen_graph.as_posix()] + return " ".join(cmd) + + +@pytest.fixture(scope="module") +def metrics_dump_dir(request: FixtureRequest): + """ + Path to collect metrics from the tests. + To set this by pytest argument use '--metrics-dump-path'. + By default metrics_dump_dir is `PROJECT_ROOT/test_results/metrics_dump_YYYY_MM_DD_HH_MM_SS`. + """ + dump_path = request.config.getoption("--metrics-dump-path") + + if dump_path is None: + data = datetime.datetime.now() + dump_path = ( + PROJECT_ROOT / "test_results" / "metrics_dump_" + f"{'_'.join([str(getattr(data, atr)) for atr in ['year', 'month', 'day', 'hour', 'minute', 'second']])}" + ) - @staticmethod - def get_input_shape(config): - with open(PROJECT_ROOT / config, encoding="utf8") as file: - config_file = json.load(file) - shape = str(config_file["input_info"].get("sample_size")) - shape = shape.replace(" ", "") - return shape + dump_path.mkdir(exist_ok=True, parents=True) + assert not dump_path.is_dir() or not next( + dump_path.iterdir(), None + ), f"metrics_dump_path dir should be empty: {dump_path}" + print(f"metrics_dump_path: {dump_path}") + return dump_path + + +@pytest.mark.nightly +class TestSotaCheckpoints: + @pytest.fixture(scope="class") + def collected_data(self, metrics_dump_dir: Path): + """ + Fixture to collect information about tests in `ResultInfo` struct + and dump it to `metrics_dump_dir / results.csv`. + """ + data: List[ResultInfo] = [] + yield data + if metrics_dump_dir and data: + path = metrics_dump_dir / "results.csv" + data_frame = pd.DataFrame.from_records([x.to_dict() for x in data]) + data_frame = data_frame.sort_values("Model").reset_index(drop=True) + data_frame.to_csv(path, index=False) + print(f"Result file: {path}") @staticmethod - def make_config(config_file, resume_model): + def make_ac_config(config_file, resume_model): + """ + Generate config for accuracy checker. + """ base_config = config_file.parent / f"{resume_model}.yml" with open(base_config, encoding="utf8") as f: template = yaml.safe_load(f) @@ -258,422 +280,304 @@ def make_config(config_file, resume_model): yaml.dump(template, f, default_flow_style=False) @staticmethod - def write_common_metrics_file(per_model_metric_file_dump_path: Path): - metric_value = {} - for file in os.listdir(per_model_metric_file_dump_path): - filename = os.fsdecode(file) - if filename.endswith(".metrics.json"): - with open(per_model_metric_file_dump_path / filename, encoding="utf8") as metric_file: - metrics = json.load(metric_file) - model_name = str(file).replace(".metrics.json", "") - metric_value[model_name] = metrics["Accuracy"] - common_metrics_file_path = per_model_metric_file_dump_path / "metrics.json" - if common_metrics_file_path.is_file(): - data = json.loads(common_metrics_file_path.read_text(encoding="utf-8")) - data.update(metric_value) - common_metrics_file_path.write_text(json.dumps(data, indent=4), encoding="utf-8") - else: - with open(str(common_metrics_file_path), "w", encoding="utf8") as outfile: - json.dump(metric_value, outfile) + def read_metric(metric_file_name: str) -> float: + """ + Reads the metric value from the given metric file. - @staticmethod - def read_metric(metric_file_name: str): + :param metric_file_name: Path to the metric file. + :return: The metric value. + """ with open(metric_file_name, encoding="utf8") as metric_file: metrics = json.load(metric_file) return metrics["Accuracy"] - with open(f"{TEST_ROOT}/tensorflow/sota_checkpoints_eval.json", encoding="utf8") as f: - sota_eval_config = json.load(f, object_pairs_hook=OrderedDict) - for sample_type_ in sota_eval_config: - datasets = sota_eval_config[sample_type_] - for dataset_name in datasets: - model_dict = datasets[dataset_name].get("topologies") - for model_name in model_dict: - ov_ids_list.append(model_name) - config_name = model_dict[model_name].get("config", {}) - weights = None - if model_dict[model_name].get("weights", {}): - weights = model_dict[model_name].get("weights", {}) - reference = None - if model_dict[model_name].get("reference", {}): - reference = model_dict[model_name].get("reference", {}) - else: - ref_fp32_dict[model_name] = model_dict[model_name].get("target", {}) - expected = model_dict[model_name].get("target", {}) - expected_init = model_dict[model_name].get("target_init", {}) - metric_type = model_dict[model_name].get("metric_type", {}) - if model_dict[model_name].get("resume", {}): - resume_file = model_dict[model_name].get("resume", {}) - else: - resume_file = None - if model_dict[model_name].get("batch_per_gpu", {}): - global_batch = model_dict[model_name]["batch_per_gpu"] * BATCH_COEFF - else: - global_batch = None - mean_val = model_dict[model_name].get("mean_value", {}) - scale_val = model_dict[model_name].get("scale_value", {}) - reverse_input_channels = model_dict[model_name].get("reverse_input_channels") if not None else None - diff_fp32_min = model_dict[model_name].get("diff_fp32_min") if not None else None - diff_fp32_max = model_dict[model_name].get("diff_fp32_max") if not None else None - diff_target_min = model_dict[model_name].get("diff_target_min") if not None else None - diff_target_max = model_dict[model_name].get("diff_target_max") if not None else None - ov_param_list.append( - EvalRunParamsStruct( - config_name_=config_name, - reference_=reference, - expected_=expected, - expected_init_=None, - metric_type_=metric_type, - dataset_name_=dataset_name, - dataset_type_="", - sample_type_=sample_type_, - resume_file_=resume_file, - weights_=weights, - batch_=global_batch, - mean_val_=mean_val, - scale_val_=scale_val, - reverse_input_channels_=reverse_input_channels, - diff_fp32_min_=diff_fp32_min, - diff_fp32_max_=diff_fp32_max, - model_name_=model_name, - diff_target_min_=diff_target_min, - diff_target_max_=diff_target_max, - ) - ) - for dataset_type in datasets[dataset_name].get("dataset_types"): - # Change model name to keep dataset version - model_name_with_datatype = model_name + "_" + dataset_type - param_list.append( - EvalRunParamsStruct( - config_name_=config_name, - reference_=reference, - expected_=expected, - expected_init_=expected_init, - metric_type_=metric_type, - dataset_name_=dataset_name, - dataset_type_=dataset_type, - sample_type_=sample_type_, - resume_file_=resume_file, - weights_=weights, - batch_=global_batch, - mean_val_=mean_val, - scale_val_=scale_val, - reverse_input_channels_=reverse_input_channels, - diff_fp32_min_=diff_fp32_min, - diff_fp32_max_=diff_fp32_max, - model_name_=model_name_with_datatype, - diff_target_min_=diff_target_min, - diff_target_max_=diff_target_max, - ) - ) - ids_list.append(model_name_with_datatype) - if model_dict[model_name].get("compression_description", {}): - train_param_list.append( - (config_name, expected, metric_type, dataset_name, sample_type_, model_name) - ) - train_ids_list.append(model_name) - - -class TestSotaCheckpoints(RunTest): @staticmethod - def update_tag_text(tag, text): - with tag("p"): - with tag("span", style=f"Background-color: #{BG_COLOR_GREEN_HEX}"): - text("Thresholds for FP32 and Expected are passed") - with tag("p"): - with tag("span", style=f"Background-color: #{BG_COLOR_YELLOW_HEX}"): - text("Thresholds for Expected is failed, but for FP32 passed") - with tag("p"): - with tag("span", style=f"Background-color: #{BG_COLOR_RED_HEX}"): - text("Thresholds for FP32 and Expected are failed") - with tag("p"): - text('If Reference FP32 value in parentheses, it takes from "target" field of .json file') + def get_metric_file_name(metrics_dump_path: Path, model_name: str, dataset_type: Optional[str] = None) -> Path: + """ + Returns the path to the file that contains the metrics for the target model. + """ + if dataset_type is not None: + return metrics_dump_path / f"{model_name}_{dataset_type}.metrics.json" + return metrics_dump_path / f"{model_name}.metrics.json" + + def get_reference_fp32_metric( + self, metrics_dump_path: Path, reference_name: str, dataset_type: Optional[str] = None + ) -> Tuple[Optional[float], bool]: + """ + Get reference metric to not compressed model. + In case of exists reference data will get reference metric from it others reference data gets + from `tests/tensorflow/sota_checkpoints_eval.json`. + + :param metrics_dump_path: Directory that collect in metric data. + :param reference_name: Name of the target model. + :return: Reference metric. + """ + fp32_metric = None + if reference_name is not None: + fp32_metric = REF_PT_FP32_METRIC[reference_name] + reference_metric_file_path = self.get_metric_file_name(metrics_dump_path, reference_name, dataset_type) + if reference_metric_file_path.exists(): + acc = self.read_metric(reference_metric_file_path) + if acc: + fp32_metric = acc + return fp32_metric @staticmethod def threshold_check( - is_ok, - diff_target, - diff_fp32_min_=None, - diff_fp32_max_=None, - fp32_metric=None, - diff_fp32=None, - diff_target_min=None, - diff_target_max=None, - ): - color = BG_COLOR_RED_HEX - within_thresholds = False - if not diff_target_min: - diff_target_min = DIFF_TARGET_MIN_GLOBAL - if not diff_target_max: - diff_target_max = DIFF_TARGET_MAX_GLOBAL - if not diff_fp32_min_: - diff_fp32_min_ = DIFF_FP32_MIN_GLOBAL - if not diff_fp32_max_: - diff_fp32_max_ = DIFF_FP32_MAX_GLOBAL - if is_ok: - if fp32_metric is not None: - if diff_fp32_min_ < diff_fp32 < diff_fp32_max_ and diff_target_min < diff_target < diff_target_max: - color = BG_COLOR_GREEN_HEX - within_thresholds = True - elif diff_fp32_min_ < diff_fp32 < diff_fp32_max_: - color = BG_COLOR_YELLOW_HEX - elif diff_target_min < diff_target < diff_target_max: - color = BG_COLOR_GREEN_HEX - within_thresholds = True - return color, within_thresholds + diff_target: float, + diff_target_min: float, + diff_target_max: float, + diff_fp32: Optional[float] = None, + diff_fp32_min: Optional[float] = None, + diff_fp32_max: Optional[float] = None, + ) -> Optional[str]: + """ + Checks whether the difference meets the target thresholds. + If the difference is not within the target thresholds, the method returns an error message. + Otherwise, the method returns `None`. + """ + err_msgs = [] + if diff_target < diff_target_min or diff_target > diff_target_max: + err_msgs.append( + "Target diff is not within thresholds: " + f"{diff_target_min} < {diff_target} < {diff_target_max}" + ) + if diff_fp32 is not None and (diff_fp32 < diff_fp32_min or diff_fp32 > diff_fp32_max): + err_msgs.append(f"FP32 diff is not within thresholds: {diff_fp32_min} < {diff_fp32} < {diff_fp32_max}") + if err_msgs: + return ";".join(err_msgs) + return None @staticmethod - def make_table_row( - test, - expected_, - metrics_type_, - key, - error_message, - metric, - diff_target, - fp32_metric_=None, - diff_fp32=None, - metric_type_from_json=None, - ): - TestSotaCheckpoints.test = test - if fp32_metric_ is None: - fp32_metric_ = "-" - diff_fp32 = "-" - if metric_type_from_json and fp32_metric_ != "-": - fp32_metric_ = str(f"({fp32_metric_})") - if metric is not None: - if test == "eval": - row = [ - str(key), - str(metrics_type_), - str(expected_), - str(metric), - str(fp32_metric_), - str(diff_fp32), - str(diff_target), - str("-"), - ] - else: - row = [str(key), str(metrics_type_), str(expected_), str(metric), str(diff_target), str("-")] - else: - if test == "eval": - row = [ - str(key), - str(metrics_type_), - str(expected_), - str("Not executed"), - str(fp32_metric_), - str("-"), - str("-"), - str(error_message), - ] - else: - row = [str(key), str(metrics_type_), str(expected_), str("Not executed"), str("-"), str(error_message)] - return row + def get_weight_params( + eval_test_struct: EvalRunParamsStruct, sota_checkpoints_dir: Path + ) -> Dict[str, Union[Path, bool]]: + if eval_test_struct.resume_file is not None: + return {"resume_file_path": sota_checkpoints_dir / eval_test_struct.resume_file} + elif eval_test_struct.weights: + return {"weights": sota_checkpoints_dir / eval_test_struct.weights} + elif PRETRAINED_PARAM_AVAILABILITY[eval_test_struct.sample_type]: + return {"pretrained": True} + raise RuntimeError("Incorrect config") @pytest.mark.eval - @pytest.mark.parametrize("eval_test_struct", RunTest.param_list, ids=RunTest.ids_list) - def test_eval(self, sota_checkpoints_dir, sota_data_dir, eval_test_struct: EvalRunParamsStruct): + @pytest.mark.parametrize("eval_test_struct", EVAL_TEST_STRUCT, ids=idfn) + @pytest.mark.parametrize("dataset_type", ("tfds", "tfrecords")) + def test_eval( + self, + sota_checkpoints_dir: Path, + sota_data_dir: Path, + eval_test_struct: EvalRunParamsStruct, + dataset_type: str, + metrics_dump_dir: Path, + collected_data: List[ResultInfo], + ): if sota_data_dir is None: pytest.skip("Path to datasets is not set") - - test = "eval" - sample_type = eval_test_struct.sample_type_ - metric_file_name = self.get_metric_file_name(model_name=eval_test_struct.model_name_) - metrics_dump_file_path = pytest.metrics_dump_path / metric_file_name - log_dir = pytest.metrics_dump_path / "logs" - if MODE == "TF2": - cmd = ( - f"{sys.executable} examples/tensorflow/{sample_type}/{EVAL_SCRIPT_NAME_MAP[sample_type]} " - f"-m test --config {eval_test_struct.config_name_} " - f"--data {sota_data_dir}/{eval_test_struct.dataset_type_}/{eval_test_struct.dataset_name_}/ " - f"--log-dir={log_dir} --metrics-dump {metrics_dump_file_path}" - ) - - if eval_test_struct.weights_ and not eval_test_struct.resume_file_: - cmd += f" --weights {os.path.join(sota_checkpoints_dir, eval_test_struct.weights_)}" - if DATASET_TYPE_AVAILABILITY[sample_type]: - cmd += f" --dataset-type {eval_test_struct.dataset_type_}" - else: - cmd = ( - f"{sys.executable} examples/tensorflow/{sample_type}/main.py -m test " - f"--config {eval_test_struct.config_name_} " - f"--data {sota_data_dir}/{eval_test_struct.dataset_name_}/ --log-dir={log_dir}" + backend_str = f"TF_{dataset_type}" + if dataset_type not in eval_test_struct.dataset_types: + status = f"Skip by: {dataset_type} does not supported for {eval_test_struct.model_name}" + collected_data.append( + ResultInfo( + model_name=eval_test_struct.model_name, + backend=backend_str, + status=status, + ) ) + pytest.skip(status) + + sample_type = eval_test_struct.sample_type + metrics_dump_file_path = self.get_metric_file_name(metrics_dump_dir, eval_test_struct.model_name, dataset_type) + log_dir = metrics_dump_dir / "logs" + dataset_path = sota_data_dir / dataset_type / eval_test_struct.dataset_name + + weights_param = self.get_weight_params(eval_test_struct, sota_checkpoints_dir) + cmd = generate_run_examples_command( + sample_type=sample_type, + mode="test", + config=eval_test_struct.config, + dataset_path=dataset_path, + dataset_type=dataset_type, + log_dir=log_dir, + batch=eval_test_struct.batch, + metrics_dump_file_path=metrics_dump_file_path, + **weights_param, + ) - if eval_test_struct.resume_file_: - resume_file_path = sota_checkpoints_dir + "/" + eval_test_struct.resume_file_ - cmd += f" --resume {resume_file_path}" - else: - if MODE != "TF2" or PRETRAINED_PARAM_AVAILABILITY[sample_type]: - cmd += " --pretrained" - if eval_test_struct.batch_: - cmd += f" -b {eval_test_struct.batch_}" - exit_code, err_str = self.run_cmd(cmd, cwd=PROJECT_ROOT) - + runner = Command(cmd, cwd=PROJECT_ROOT) + exit_code = runner.run(assert_returncode_zero=False) is_ok = exit_code == 0 and metrics_dump_file_path.exists() - if is_ok: - metric_value = self.read_metric(str(metrics_dump_file_path)) - else: - metric_value = None - fp32_metric = None - metric_type_from_json = False - if eval_test_struct.reference_ is not None: - fp32_metric = self.ref_fp32_dict[str(eval_test_struct.reference_)] - metric_type_from_json = True - dataset_type_postfix = "" - if MODE == "TF2": - dataset_type_postfix = "_" + eval_test_struct.dataset_type_ - reference_metric_file_path = pytest.metrics_dump_path / self.get_metric_file_name( - eval_test_struct.reference_ + dataset_type_postfix + metric_value = None + diff_target = None + diff_fp32 = None + if not is_ok: + status = f"exit_code: {exit_code}" + result_info = ResultInfo( + model_name=eval_test_struct.model_name, + backend=backend_str, + status=status, ) - if os.path.exists(reference_metric_file_path): - with open(str(reference_metric_file_path), encoding="utf8") as ref_metric: - metrics = json.load(ref_metric) - if metrics["Accuracy"] != 0: - fp32_metric = metrics["Accuracy"] - metric_type_from_json = False - else: - metric_type_from_json = True - - if is_ok: - diff_target = round((metric_value - eval_test_struct.expected_), 2) - diff_fp32 = round((metric_value - fp32_metric), 2) if fp32_metric is not None else None - else: - diff_target = None - diff_fp32 = None - - self.row_dict[eval_test_struct.model_name_] = self.make_table_row( - test, - eval_test_struct.expected_, - eval_test_struct.metric_type_, - eval_test_struct.model_name_, - err_str, - metric_value, - diff_target, - fp32_metric, - diff_fp32, - metric_type_from_json, + collected_data.append(result_info) + pytest.fail(status) + + metric_value = self.read_metric(metrics_dump_file_path) + fp32_metric = self.get_reference_fp32_metric(metrics_dump_dir, eval_test_struct.reference, dataset_type) + + diff_target = round((metric_value - eval_test_struct.target_tf), 2) + if fp32_metric: + diff_fp32 = round((metric_value - fp32_metric), 2) + + threshold_errors = self.threshold_check( + diff_target=diff_target, + diff_target_min=eval_test_struct.diff_target_tf_min, + diff_target_max=eval_test_struct.diff_target_tf_max, + diff_fp32=diff_fp32, + diff_fp32_min=eval_test_struct.diff_fp32_min, + diff_fp32_max=eval_test_struct.diff_fp32_max, ) - retval = self.threshold_check( - is_ok, - diff_target, - eval_test_struct.diff_fp32_min_, - eval_test_struct.diff_fp32_max_, - fp32_metric, - diff_fp32, - eval_test_struct.diff_target_min_, - eval_test_struct.diff_target_max_, + + result_info = ResultInfo( + model_name=eval_test_struct.model_name, + backend=backend_str, + metric_type=eval_test_struct.metric_type, + measured=metric_value, + expected=eval_test_struct.target_tf, + diff_target=diff_target, + target_fp32=fp32_metric, + diff_fp32=diff_fp32, + status=threshold_errors, ) + collected_data.append(result_info) + if threshold_errors is not None: + pytest.fail(threshold_errors) + + @staticmethod + def get_metric_from_ac_csv(path: Path): + """ + Get metric value from the report of accuracy_checker. - self.color_dict[eval_test_struct.model_name_], is_accuracy_within_thresholds = retval - assert is_accuracy_within_thresholds + :param path: Path ot report file of accuracy_checker. + :return: Metric value. + """ + data = pd.read_csv(path) + return round(data["metric_value"].iloc[0] * 100, 2) @pytest.mark.oveval - @pytest.mark.parametrize("eval_test_struct", RunTest.ov_param_list, ids=RunTest.ov_ids_list) - def test_openvino_eval(self, eval_test_struct: EvalRunParamsStruct, sota_checkpoints_dir, ov_data_dir, openvino): + @pytest.mark.parametrize("eval_test_struct", EVAL_TEST_STRUCT, ids=idfn) + def test_openvino_eval( + self, + eval_test_struct: EvalRunParamsStruct, + sota_checkpoints_dir: Path, + ov_data_dir: Path, + openvino: bool, + collected_data: List[ResultInfo], + ): if not openvino: pytest.skip() + if eval_test_struct.skip_ov: + status = f"Skip by: {eval_test_struct.skip_ov}" + collected_data.append(ResultInfo(model_name=eval_test_struct.model_name, backend="OV", status=status)) + pytest.skip(status) + # WA to avoid OS error - os.environ["HDF5_USE_FILE_LOCKING"] = "FALSE" - tf_checkpoint = PROJECT_ROOT / "frozen_graph" / f"{eval_test_struct.model_name_}.pb" - ir_model_folder = PROJECT_ROOT / "ir_models" / eval_test_struct.model_name_ - config = PROJECT_ROOT / "tests" / "tensorflow" / "data" / "ac_configs" / f"{eval_test_struct.model_name_}.yml" - file = "main.py" - model_type = "pretrained" - model_path = "" - if Path(sota_checkpoints_dir / Path(eval_test_struct.model_name_)).is_dir(): - model_path = sota_checkpoints_dir / Path(eval_test_struct.model_name_) - if Path(sota_checkpoints_dir / Path(eval_test_struct.model_name_) / "checkpoint").is_file(): - model_type = "resume" - else: - model_type = "weights" - model_path = model_path / f"{eval_test_struct.model_name_}.h5" - if eval_test_struct.sample_type_ == "segmentation": - file = "evaluation.py" - csv_result = f"{PROJECT_ROOT}/{eval_test_struct.model_name_}.csv" - if eval_test_struct.model_name_.startswith("mask_"): - self.write_error_in_csv("AC does not support mask models yet", csv_result, eval_test_struct.model_name_) - pytest.skip("AC does not support mask models yet") - if eval_test_struct.reference_ and not config.is_file(): - self.make_config(config, eval_test_struct.reference_) - save_cmd = ( - f"{sys.executable} examples/tensorflow/{eval_test_struct.sample_type_}/{file}" - f" --mode export --config {eval_test_struct.config_name_}" - f" --{model_type} {model_path}" - f" --to-frozen-graph={tf_checkpoint}" + env = os.environ.copy() + env["HDF5_USE_FILE_LOCKING"] = "FALSE" + + sample_type = eval_test_struct.sample_type + tf_checkpoint = PROJECT_ROOT / "frozen_graph" / f"{eval_test_struct.model_name}.pb" + ir_model_folder = PROJECT_ROOT / "ir_models" / eval_test_struct.model_name + ac_config = PROJECT_ROOT / "tests" / "tensorflow" / "data" / "ac_configs" / f"{eval_test_struct.model_name}.yml" + csv_result = PROJECT_ROOT / f"{eval_test_struct.model_name}.csv" + csv_result.unlink(missing_ok=True) + + weights_param = self.get_weight_params(eval_test_struct, sota_checkpoints_dir) + cmd = generate_run_examples_command( + sample_type=sample_type, + mode="export", + config=eval_test_struct.config, + to_frozen_graph=tf_checkpoint, + **weights_param, ) - exit_code, err_str = self.run_cmd(save_cmd, cwd=PROJECT_ROOT) - - if exit_code == 0: - mo_cmd_tail = ( - f" --framework tf" - f" --use_new_frontend" - f" --input_shape {self.get_input_shape(eval_test_struct.config_name_)}" - f" --input_model {tf_checkpoint}" - f" --output_dir {ir_model_folder}" - ) - mo_cmd = f"mo {mo_cmd_tail}" - if eval_test_struct.reverse_input_channels_: - mo_cmd += " --reverse_input_channels" - if eval_test_struct.mean_val_: - mo_cmd += f" --mean_values={eval_test_struct.mean_val_}" - if eval_test_struct.scale_val_: - mo_cmd += f" --scale_values={eval_test_struct.scale_val_}" - exit_code, err_str = self.run_cmd(mo_cmd, cwd=PROJECT_ROOT) - - if exit_code == 0: - ac_cmd = ( - f"accuracy_check" - f" -c {config}" - f" -s {ov_data_dir}" - " --progress print" - f" -d {DATASET_DEFINITIONS_PATH}" - f" -m {ir_model_folder}" - f" --csv_result {csv_result}" - ) - exit_code, err_str = self.run_cmd(ac_cmd, cwd=PROJECT_ROOT) + runner = Command(cmd, cwd=PROJECT_ROOT, env=env) + exit_code = runner.run(assert_returncode_zero=False) if exit_code != 0: - if err_str: - self.write_error_in_csv(err_str, csv_result, eval_test_struct.model_name_) - pytest.fail(err_str) - - -Tsc = TestSotaCheckpoints + status = "ERROR: Failed on export" + collected_data.append( + ResultInfo( + model_name=eval_test_struct.model_name, + backend="OV", + status=status, + ) + ) + pytest.fail(status) + ov_model = ov.convert_model(tf_checkpoint) + ir_path = ir_model_folder / f"{eval_test_struct.model_name}.xml" + ov.serialize(ov_model, ir_path) + print(ir_path) + del ov_model + + if eval_test_struct.reference and not ac_config.is_file(): + self.make_ac_config(ac_config, eval_test_struct.reference) + + ac_cmd = ( + f"python -m accuracy_checker.main" + f" -c {ac_config}" + f" -s {ov_data_dir}" + " --progress print" + f" -d {DATASET_DEFINITIONS_PATH}" + f" -m {ir_model_folder}" + f" --csv_result {csv_result}" + ) + runner = Command(ac_cmd, cwd=PROJECT_ROOT) + exit_code = runner.run(assert_returncode_zero=False) + if exit_code != 0: + status = f"Accuracy checker return code: {exit_code}" + collected_data.append( + ResultInfo( + model_name=eval_test_struct.model_name, + backend="OV", + status="ERROR: accuracy_check failed", + ) + ) + pytest.fail(status) + + metric_value = self.get_metric_from_ac_csv(csv_result) + fp32_metric = REF_OV_FP32_METRIC.get(eval_test_struct.reference, None) + + diff_target = round((metric_value - eval_test_struct.target_ov), 2) + diff_fp32 = None + if fp32_metric: + diff_fp32 = round((metric_value - fp32_metric), 2) + + threshold_errors = self.threshold_check( + diff_target=diff_target, + diff_target_min=eval_test_struct.diff_target_ov_min, + diff_target_max=eval_test_struct.diff_target_ov_max, + diff_fp32=diff_fp32, + diff_fp32_min=eval_test_struct.diff_fp32_min, + diff_fp32_max=eval_test_struct.diff_fp32_max, + ) + status = threshold_errors + if eval_test_struct.xfail_ov is not None and threshold_errors is not None: + status = f"XFAIL: {eval_test_struct.xfail_ov} {threshold_errors}" + + result_info = ResultInfo( + model_name=eval_test_struct.model_name, + backend="OV", + metric_type=eval_test_struct.metric_type, + measured=metric_value, + expected=eval_test_struct.target_ov, + diff_target=diff_target, + target_fp32=fp32_metric, + diff_fp32=diff_fp32, + status=status, + ) -@pytest.fixture(autouse=True, scope="class") -def make_metrics_dump_path(metrics_dump_dir): - if pytest.metrics_dump_path is None: - data = datetime.datetime.now() - data_stamp = "_".join([str(getattr(data, atr)) for atr in ["year", "month", "day", "hour", "minute", "second"]]) - pytest.metrics_dump_path = PROJECT_ROOT / "test_results" / "metrics_dump_" / f"{data_stamp}" - else: - pytest.metrics_dump_path = Path(pytest.metrics_dump_path) - assert not os.path.isdir(pytest.metrics_dump_path) or not os.listdir( - pytest.metrics_dump_path - ), f"metrics_dump_path dir should be empty: {pytest.metrics_dump_path}" - print(f"metrics_dump_path: {pytest.metrics_dump_path}") - - -@pytest.fixture(autouse=True, scope="class") -def results(sota_data_dir): - yield - if sota_data_dir: - Tsc.write_common_metrics_file(per_model_metric_file_dump_path=pytest.metrics_dump_path) - if Tsc.test == "eval": - header = [ - "Model", - "Metrics type", - "Expected", - "Measured", - "Reference FP32", - "Diff FP32", - "Diff Expected", - "Error", - ] - else: - header = ["Model", "Metrics type", "Expected", "Measured", "Diff Expected", "Error"] - Tsc().write_results_table(header, pytest.metrics_dump_path) + collected_data.append(result_info) + if status is not None: + if eval_test_struct.xfail_ov is not None: + pytest.xfail(status) + else: + pytest.fail(status) diff --git a/tests/tensorflow/test_telemetry.py b/tests/tensorflow/test_telemetry.py index b178af0869d..898e7690ea6 100644 --- a/tests/tensorflow/test_telemetry.py +++ b/tests/tensorflow/test_telemetry.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/test_transformations.py b/tests/tensorflow/test_transformations.py index 08eec219511..7aded82ce1f 100644 --- a/tests/tensorflow/test_transformations.py +++ b/tests/tensorflow/test_transformations.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/test_weekly.py b/tests/tensorflow/test_weekly.py index f5b8ab556b3..d6b2bbd9c9f 100644 --- a/tests/tensorflow/test_weekly.py +++ b/tests/tensorflow/test_weekly.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/tensorflow/test_wrapper_freeze.py b/tests/tensorflow/test_wrapper_freeze.py index e27db674bb5..bf291f35cbe 100644 --- a/tests/tensorflow/test_wrapper_freeze.py +++ b/tests/tensorflow/test_wrapper_freeze.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/__init__.py b/tests/torch/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/torch/__init__.py +++ b/tests/torch/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/accuracy_aware_training/test_accuracy_aware_config.py b/tests/torch/accuracy_aware_training/test_accuracy_aware_config.py index 76e02294032..e3d578075b9 100644 --- a/tests/torch/accuracy_aware_training/test_accuracy_aware_config.py +++ b/tests/torch/accuracy_aware_training/test_accuracy_aware_config.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -11,6 +11,7 @@ import pytest +import nncf from nncf.common.accuracy_aware_training import create_accuracy_aware_training_loop from nncf.torch.initialization import register_default_init_args from tests.torch.helpers import LeNet @@ -103,7 +104,7 @@ def mock_validate_fn(model): model, compression_ctrl = create_compressed_model_and_algo_for_test(model, config) if must_raise: - with pytest.raises(RuntimeError): + with pytest.raises(nncf.ValidationError): _ = create_accuracy_aware_training_loop(config, compression_ctrl, 0, dump_checkpoints=False) else: _ = create_accuracy_aware_training_loop(config, compression_ctrl, 0, dump_checkpoints=False) diff --git a/tests/torch/accuracy_aware_training/test_runner.py b/tests/torch/accuracy_aware_training/test_runner.py index 373c6b38d42..5e289725f7f 100644 --- a/tests/torch/accuracy_aware_training/test_runner.py +++ b/tests/torch/accuracy_aware_training/test_runner.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/accuracy_aware_training/test_training_loop.py b/tests/torch/accuracy_aware_training/test_training_loop.py index aa0f4dbbebf..22601999d47 100644 --- a/tests/torch/accuracy_aware_training/test_training_loop.py +++ b/tests/torch/accuracy_aware_training/test_training_loop.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/autograd_functions/test_autograd_functions.py b/tests/torch/autograd_functions/test_autograd_functions.py index b05d67f6a07..528dbcab185 100644 --- a/tests/torch/autograd_functions/test_autograd_functions.py +++ b/tests/torch/autograd_functions/test_autograd_functions.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/automl/test_ddpg.py b/tests/torch/automl/test_ddpg.py index cbc3c1d8673..15fa6f3ea04 100644 --- a/tests/torch/automl/test_ddpg.py +++ b/tests/torch/automl/test_ddpg.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/automl/test_memory.py b/tests/torch/automl/test_memory.py index 59f5db962f6..25a5afdbb56 100644 --- a/tests/torch/automl/test_memory.py +++ b/tests/torch/automl/test_memory.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/automl/test_quantization_env.py b/tests/torch/automl/test_quantization_env.py index 15bdc40f79f..3a03c666654 100644 --- a/tests/torch/automl/test_quantization_env.py +++ b/tests/torch/automl/test_quantization_env.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -39,7 +39,7 @@ def create_test_quantization_env(model_creator=BasicConvTestModel, input_info_cf model = model_creator() nncf_network = NNCFNetwork(model, input_info=FillerInputInfo.from_nncf_config(input_info_cfg)) - hw_config_type = HWConfigType.VPU + hw_config_type = HWConfigType.NPU hw_config_path = HWConfig.get_path_to_hw_config(hw_config_type) hw_config = PTHWConfig.from_json(hw_config_path) setup = PropagationBasedQuantizerSetupGenerator(NNCFConfig(), nncf_network, hw_config=hw_config).generate_setup() @@ -69,7 +69,7 @@ def create_test_quantization_env(model_creator=BasicConvTestModel, input_info_cf constraints, data_loader, lambda *x: 0, - hw_config_type=HWConfigType.VPU, + hw_config_type=HWConfigType.NPU, params=QuantizationEnvParams( compression_ratio=0.15, eval_subset_ratio=1.0, @@ -325,13 +325,13 @@ def forward(self, x1, x2): check_both_bw_assignment_modes(qenv, strategy) -VPU_UMMAPPABLE_STRATEGY = [[2, 8], [2, 4], [2, 2]] +NPU_UMMAPPABLE_STRATEGY = [[2, 8], [2, 4], [2, 2]] @pytest.mark.parametrize( "strategy", - VPU_UMMAPPABLE_STRATEGY, - ids=["_".join(["vpu_unmappable_strategy", str(s)]) for s in VPU_UMMAPPABLE_STRATEGY], + NPU_UMMAPPABLE_STRATEGY, + ids=["_".join(["npu_unmappable_strategy", str(s)]) for s in NPU_UMMAPPABLE_STRATEGY], ) def test_select_config_for_actions(strategy): qenv = create_test_quantization_env() diff --git a/tests/torch/automl/test_ring_buffer.py b/tests/torch/automl/test_ring_buffer.py index 534268aac2c..313fdd8e542 100644 --- a/tests/torch/automl/test_ring_buffer.py +++ b/tests/torch/automl/test_ring_buffer.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/binarization/test_functions.py b/tests/torch/binarization/test_functions.py index 80667c2c821..ce1ca71adbb 100644 --- a/tests/torch/binarization/test_functions.py +++ b/tests/torch/binarization/test_functions.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/binarization/test_timeout_extension_loader.py b/tests/torch/binarization/test_timeout_extension_loader.py index 5aeccbcf9e6..4e0ae8a72c1 100644 --- a/tests/torch/binarization/test_timeout_extension_loader.py +++ b/tests/torch/binarization/test_timeout_extension_loader.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/composite/test_composite_scheduler.py b/tests/torch/composite/test_composite_scheduler.py index ea2aae48f81..f9822d47d56 100644 --- a/tests/torch/composite/test_composite_scheduler.py +++ b/tests/torch/composite/test_composite_scheduler.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/composite/test_sparsity_quantization.py b/tests/torch/composite/test_sparsity_quantization.py index 2e11a0c4d57..077cb5b39cb 100644 --- a/tests/torch/composite/test_sparsity_quantization.py +++ b/tests/torch/composite/test_sparsity_quantization.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/conftest.py b/tests/torch/conftest.py index 203c014c32f..40e1c01eea7 100644 --- a/tests/torch/conftest.py +++ b/tests/torch/conftest.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/data/configs/hawq/icnet_camvid_mixed_int.json b/tests/torch/data/configs/hawq/icnet_camvid_mixed_int.json index 27610a5bf4d..fda82b43ba4 100644 --- a/tests/torch/data/configs/hawq/icnet_camvid_mixed_int.json +++ b/tests/torch/data/configs/hawq/icnet_camvid_mixed_int.json @@ -39,7 +39,7 @@ 96 ] }, - "target_device": "VPU", + "target_device": "NPU", "compression": { "algorithm": "quantization", "initializer": { diff --git a/tests/torch/data/configs/hawq/inception_v3_cifar10_mixed_int.json b/tests/torch/data/configs/hawq/inception_v3_cifar10_mixed_int.json index 1ebd0929213..5b2d97b2830 100644 --- a/tests/torch/data/configs/hawq/inception_v3_cifar10_mixed_int.json +++ b/tests/torch/data/configs/hawq/inception_v3_cifar10_mixed_int.json @@ -10,7 +10,7 @@ }, "num_classes": 10, "pretrained": false, - "target_device": "VPU", + "target_device": "NPU", "compression": [ { "algorithm": "quantization", diff --git a/tests/torch/data/configs/hawq/resnet18_cifar10_mixed_int.json b/tests/torch/data/configs/hawq/resnet18_cifar10_mixed_int.json index 1c379ccfb76..39dce8c9cbc 100644 --- a/tests/torch/data/configs/hawq/resnet18_cifar10_mixed_int.json +++ b/tests/torch/data/configs/hawq/resnet18_cifar10_mixed_int.json @@ -10,7 +10,7 @@ }, "num_classes": 10, "pretrained": false, - "target_device": "VPU", + "target_device": "NPU", "compression": [ { "algorithm": "quantization", diff --git a/tests/torch/data/configs/hawq/resnet18_cifar10_mixed_int_manual.json b/tests/torch/data/configs/hawq/resnet18_cifar10_mixed_int_manual.json index 931f7815186..9cad4e471a6 100644 --- a/tests/torch/data/configs/hawq/resnet18_cifar10_mixed_int_manual.json +++ b/tests/torch/data/configs/hawq/resnet18_cifar10_mixed_int_manual.json @@ -10,7 +10,7 @@ }, "num_classes": 10, "pretrained": false, - "target_device": "VPU", + "target_device": "NPU", "compression": [ { "algorithm": "quantization", diff --git a/tests/torch/data/configs/hawq/ssd300_vgg_voc_mixed_int.json b/tests/torch/data/configs/hawq/ssd300_vgg_voc_mixed_int.json index d9ffdae63b1..16946f3b80d 100644 --- a/tests/torch/data/configs/hawq/ssd300_vgg_voc_mixed_int.json +++ b/tests/torch/data/configs/hawq/ssd300_vgg_voc_mixed_int.json @@ -84,7 +84,7 @@ "flip": true, "top_k": 200 }, - "target_device": "VPU", + "target_device": "NPU", "compression": { "algorithm": "quantization", "initializer": { diff --git a/tests/torch/data/configs/hawq/unet_camvid_mixed_int.json b/tests/torch/data/configs/hawq/unet_camvid_mixed_int.json index 3c9c03ae1a5..013f9a279dd 100644 --- a/tests/torch/data/configs/hawq/unet_camvid_mixed_int.json +++ b/tests/torch/data/configs/hawq/unet_camvid_mixed_int.json @@ -34,7 +34,7 @@ 32 ] }, - "target_device": "VPU", + "target_device": "NPU", "compression": { "algorithm": "quantization", "initializer": { diff --git a/tests/torch/data/reference_graphs/quantized/adjust_paddings/vpu_all_activations_int8.dot b/tests/torch/data/reference_graphs/quantized/adjust_paddings/npu_all_activations_int8.dot similarity index 100% rename from tests/torch/data/reference_graphs/quantized/adjust_paddings/vpu_all_activations_int8.dot rename to tests/torch/data/reference_graphs/quantized/adjust_paddings/npu_all_activations_int8.dot diff --git a/tests/torch/data/reference_graphs/quantized/adjust_paddings/vpu_all_activations_int8_requnt.dot b/tests/torch/data/reference_graphs/quantized/adjust_paddings/npu_all_activations_int8_requnt.dot similarity index 100% rename from tests/torch/data/reference_graphs/quantized/adjust_paddings/vpu_all_activations_int8_requnt.dot rename to tests/torch/data/reference_graphs/quantized/adjust_paddings/npu_all_activations_int8_requnt.dot diff --git a/tests/torch/data/reference_graphs/quantized/adjust_paddings/vpu_all_int8.dot b/tests/torch/data/reference_graphs/quantized/adjust_paddings/npu_all_int8.dot similarity index 100% rename from tests/torch/data/reference_graphs/quantized/adjust_paddings/vpu_all_int8.dot rename to tests/torch/data/reference_graphs/quantized/adjust_paddings/npu_all_int8.dot diff --git a/tests/torch/data/reference_graphs/quantized/adjust_paddings/vpu_all_int8_requnt.dot b/tests/torch/data/reference_graphs/quantized/adjust_paddings/npu_all_int8_requnt.dot similarity index 100% rename from tests/torch/data/reference_graphs/quantized/adjust_paddings/vpu_all_int8_requnt.dot rename to tests/torch/data/reference_graphs/quantized/adjust_paddings/npu_all_int8_requnt.dot diff --git a/tests/torch/data/reference_graphs/quantized/adjust_paddings/vpu_all_weights_int8.dot b/tests/torch/data/reference_graphs/quantized/adjust_paddings/npu_all_weights_int8.dot similarity index 100% rename from tests/torch/data/reference_graphs/quantized/adjust_paddings/vpu_all_weights_int8.dot rename to tests/torch/data/reference_graphs/quantized/adjust_paddings/npu_all_weights_int8.dot diff --git a/tests/torch/data/reference_graphs/quantized/adjust_paddings/vpu_all_weights_int8_requnt.dot b/tests/torch/data/reference_graphs/quantized/adjust_paddings/npu_all_weights_int8_requnt.dot similarity index 100% rename from tests/torch/data/reference_graphs/quantized/adjust_paddings/vpu_all_weights_int8_requnt.dot rename to tests/torch/data/reference_graphs/quantized/adjust_paddings/npu_all_weights_int8_requnt.dot diff --git a/tests/torch/data/reference_graphs/quantized/adjust_paddings/vpu_bd_int8.dot b/tests/torch/data/reference_graphs/quantized/adjust_paddings/npu_bd_int8.dot similarity index 100% rename from tests/torch/data/reference_graphs/quantized/adjust_paddings/vpu_bd_int8.dot rename to tests/torch/data/reference_graphs/quantized/adjust_paddings/npu_bd_int8.dot diff --git a/tests/torch/data/reference_graphs/quantized/adjust_paddings/vpu_bd_int8_requnt.dot b/tests/torch/data/reference_graphs/quantized/adjust_paddings/npu_bd_int8_requnt.dot similarity index 100% rename from tests/torch/data/reference_graphs/quantized/adjust_paddings/vpu_bd_int8_requnt.dot rename to tests/torch/data/reference_graphs/quantized/adjust_paddings/npu_bd_int8_requnt.dot diff --git a/tests/torch/data/reference_graphs/quantized/adjust_paddings/vpu_max_int4.dot b/tests/torch/data/reference_graphs/quantized/adjust_paddings/npu_max_int4.dot similarity index 100% rename from tests/torch/data/reference_graphs/quantized/adjust_paddings/vpu_max_int4.dot rename to tests/torch/data/reference_graphs/quantized/adjust_paddings/npu_max_int4.dot diff --git a/tests/torch/data/reference_graphs/quantized/adjust_paddings/vpu_max_int4_requnt.dot b/tests/torch/data/reference_graphs/quantized/adjust_paddings/npu_max_int4_requnt.dot similarity index 100% rename from tests/torch/data/reference_graphs/quantized/adjust_paddings/vpu_max_int4_requnt.dot rename to tests/torch/data/reference_graphs/quantized/adjust_paddings/npu_max_int4_requnt.dot diff --git a/tests/torch/data/reference_graphs/quantized/autoq/inception_v3_device_VPU__eval_subset_ratio_0.4__with_ignored_scopes.dot b/tests/torch/data/reference_graphs/quantized/autoq/inception_v3_device_NPU__eval_subset_ratio_0.4__with_ignored_scopes.dot similarity index 100% rename from tests/torch/data/reference_graphs/quantized/autoq/inception_v3_device_VPU__eval_subset_ratio_0.4__with_ignored_scopes.dot rename to tests/torch/data/reference_graphs/quantized/autoq/inception_v3_device_NPU__eval_subset_ratio_0.4__with_ignored_scopes.dot diff --git a/tests/torch/data/reference_graphs/quantized/autoq/inception_v3_device_VPU__ratio_0.4.dot b/tests/torch/data/reference_graphs/quantized/autoq/inception_v3_device_NPU__ratio_0.4.dot similarity index 100% rename from tests/torch/data/reference_graphs/quantized/autoq/inception_v3_device_VPU__ratio_0.4.dot rename to tests/torch/data/reference_graphs/quantized/autoq/inception_v3_device_NPU__ratio_0.4.dot diff --git a/tests/torch/data/reference_graphs/quantized/autoq/mobilenet_v2_tv_092_device_VPU.dot b/tests/torch/data/reference_graphs/quantized/autoq/mobilenet_v2_tv_092_device_NPU.dot similarity index 100% rename from tests/torch/data/reference_graphs/quantized/autoq/mobilenet_v2_tv_092_device_VPU.dot rename to 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a/tests/torch/data/reference_graphs/quantized/autoq/resnet50_device_VPU__iter_number_4__warmup_iter_number_2.dot b/tests/torch/data/reference_graphs/quantized/autoq/resnet50_device_NPU__iter_number_4__warmup_iter_number_2.dot similarity index 100% rename from tests/torch/data/reference_graphs/quantized/autoq/resnet50_device_VPU__iter_number_4__warmup_iter_number_2.dot rename to tests/torch/data/reference_graphs/quantized/autoq/resnet50_device_NPU__iter_number_4__warmup_iter_number_2.dot diff --git a/tests/torch/data/reference_graphs/quantized/autoq/resnet50_device_VPU__ratio_0.4.dot b/tests/torch/data/reference_graphs/quantized/autoq/resnet50_device_NPU__ratio_0.4.dot similarity index 100% rename from tests/torch/data/reference_graphs/quantized/autoq/resnet50_device_VPU__ratio_0.4.dot rename to tests/torch/data/reference_graphs/quantized/autoq/resnet50_device_NPU__ratio_0.4.dot diff --git a/tests/torch/data/reference_graphs/quantized/autoq/squeezenet1_1_device_VPU.dot 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rename from tests/torch/data/reference_graphs/quantized/hawq/inception_v3_device_VPU__mode_strict__ratio_1.dot rename to tests/torch/data/reference_graphs/quantized/hawq/inception_v3_device_NPU__mode_strict__ratio_1.dot diff --git a/tests/torch/data/reference_graphs/quantized/hawq/mobilenet_v2_tv_092_device_VPU__mode_liberal__ratio_1.5.dot b/tests/torch/data/reference_graphs/quantized/hawq/mobilenet_v2_tv_092_device_NPU__mode_liberal__ratio_1.5.dot similarity index 100% rename from tests/torch/data/reference_graphs/quantized/hawq/mobilenet_v2_tv_092_device_VPU__mode_liberal__ratio_1.5.dot rename to tests/torch/data/reference_graphs/quantized/hawq/mobilenet_v2_tv_092_device_NPU__mode_liberal__ratio_1.5.dot diff --git a/tests/torch/data/reference_graphs/quantized/hawq/mobilenet_v2_tv_092_device_VPU__mode_strict__ratio_1.02.dot b/tests/torch/data/reference_graphs/quantized/hawq/mobilenet_v2_tv_092_device_NPU__mode_strict__ratio_1.02.dot similarity index 100% rename from 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tests/torch/data/reference_graphs/quantized/hawq/resnet50_device_VPU__mode_strict__ratio_1.11.dot rename to tests/torch/data/reference_graphs/quantized/hawq/resnet50_device_NPU__mode_strict__ratio_1.11.dot diff --git a/tests/torch/data/reference_graphs/quantized/hawq/squeezenet1_1_device_VPU__mode_strict.dot b/tests/torch/data/reference_graphs/quantized/hawq/squeezenet1_1_device_NPU__mode_strict.dot similarity index 100% rename from tests/torch/data/reference_graphs/quantized/hawq/squeezenet1_1_device_VPU__mode_strict.dot rename to tests/torch/data/reference_graphs/quantized/hawq/squeezenet1_1_device_NPU__mode_strict.dot diff --git a/tests/torch/data/reference_graphs/quantized/hawq/ssd_vgg_512_test_device_VPU__mode_liberal__ratio_1.5.dot b/tests/torch/data/reference_graphs/quantized/hawq/ssd_vgg_512_test_device_NPU__mode_liberal__ratio_1.5.dot similarity index 100% rename from tests/torch/data/reference_graphs/quantized/hawq/ssd_vgg_512_test_device_VPU__mode_liberal__ratio_1.5.dot rename to tests/torch/data/reference_graphs/quantized/hawq/ssd_vgg_512_test_device_NPU__mode_liberal__ratio_1.5.dot diff --git a/tests/torch/data/reference_graphs/quantized/hawq/ssd_vgg_512_test_device_VPU__mode_strict__ratio_1.09.dot b/tests/torch/data/reference_graphs/quantized/hawq/ssd_vgg_512_test_device_NPU__mode_strict__ratio_1.09.dot similarity index 100% rename from tests/torch/data/reference_graphs/quantized/hawq/ssd_vgg_512_test_device_VPU__mode_strict__ratio_1.09.dot rename to tests/torch/data/reference_graphs/quantized/hawq/ssd_vgg_512_test_device_NPU__mode_strict__ratio_1.09.dot diff --git a/tests/torch/data/reference_graphs/quantized/hw/VPU/inception_v3.dot b/tests/torch/data/reference_graphs/quantized/hw/NPU/inception_v3.dot similarity index 100% rename from tests/torch/data/reference_graphs/quantized/hw/VPU/inception_v3.dot rename to tests/torch/data/reference_graphs/quantized/hw/NPU/inception_v3.dot diff --git a/tests/torch/data/reference_graphs/quantized/hw/VPU/mobilenet_v2.dot b/tests/torch/data/reference_graphs/quantized/hw/NPU/mobilenet_v2.dot similarity index 100% rename from tests/torch/data/reference_graphs/quantized/hw/VPU/mobilenet_v2.dot rename to tests/torch/data/reference_graphs/quantized/hw/NPU/mobilenet_v2.dot diff --git a/tests/torch/data/reference_graphs/quantized/hw/VPU/resnet50.dot b/tests/torch/data/reference_graphs/quantized/hw/NPU/resnet50.dot similarity index 100% rename from tests/torch/data/reference_graphs/quantized/hw/VPU/resnet50.dot rename to tests/torch/data/reference_graphs/quantized/hw/NPU/resnet50.dot diff --git a/tests/torch/experimental/replace_custom_modules/test_replace_timm_custom_modules.py b/tests/torch/experimental/replace_custom_modules/test_replace_timm_custom_modules.py index f0a0132345c..388930bb04d 100644 --- a/tests/torch/experimental/replace_custom_modules/test_replace_timm_custom_modules.py +++ b/tests/torch/experimental/replace_custom_modules/test_replace_timm_custom_modules.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/experimental/search_building_blocks/test_transformer_blocks.py b/tests/torch/experimental/search_building_blocks/test_transformer_blocks.py index 3b49da2a542..db38cec3717 100644 --- a/tests/torch/experimental/search_building_blocks/test_transformer_blocks.py +++ b/tests/torch/experimental/search_building_blocks/test_transformer_blocks.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021-2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/extensions_build_checks.py b/tests/torch/extensions_build_checks.py index 2727ba21b54..fc8be788fb5 100644 --- a/tests/torch/extensions_build_checks.py +++ b/tests/torch/extensions_build_checks.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -12,9 +12,11 @@ import os import sys +import nncf + if __name__ == "__main__": if len(sys.argv) != 2: - raise RuntimeError("Must be run with target extensions build mode") + raise nncf.ValidationError("Must be run with target extensions build mode") mode = sys.argv[1] if mode == "cpu": # Do not remove - the import here is for testing purposes. @@ -30,4 +32,4 @@ os.environ["TORCH_CUDA_ARCH_LIST"] = "7.5+PTX" force_build_cuda_extensions() else: - raise RuntimeError("Invalid mode type!") + raise nncf.ValidationError("Invalid mode type!") diff --git a/tests/torch/helpers.py b/tests/torch/helpers.py index d5c8751bc6c..2a7c656ea3c 100644 --- a/tests/torch/helpers.py +++ b/tests/torch/helpers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -15,7 +15,7 @@ from collections import defaultdict from copy import deepcopy from pathlib import Path -from typing import Any, Callable, Dict, List, Tuple, Union +from typing import Any, Callable, Dict, List, Tuple, TypeVar, Union import numpy as np import onnx @@ -27,6 +27,7 @@ from torch.utils.data import DataLoader from torch.utils.data import Dataset +import nncf from nncf.common.graph.transformations.commands import TargetType from nncf.config import NNCFConfig from nncf.config.extractors import extract_algorithm_names @@ -35,6 +36,7 @@ from nncf.torch.compression_method_api import PTCompressionAlgorithmController from nncf.torch.dynamic_graph.context import PreHookId from nncf.torch.dynamic_graph.io_handling import FillerInputInfo +from nncf.torch.dynamic_graph.operation_address import OperationAddress from nncf.torch.dynamic_graph.scope import Scope from nncf.torch.initialization import PTInitializingDataLoader from nncf.torch.initialization import register_default_init_args @@ -102,7 +104,7 @@ def create_grouped_conv( in_channels, out_channels, kernel_size, groups, weight_init=1, bias_init=0, padding=0, stride=1 ): if in_channels % groups != 0 or out_channels % groups != 0: - raise RuntimeError( + raise nncf.ValidationError( "Cannot create grouped convolution. Either `in_channels` or `out_channels` are not divisible by `groups`" ) conv = nn.Conv2d(in_channels, out_channels, kernel_size, groups=groups, padding=padding, stride=stride) @@ -342,7 +344,7 @@ def check_correct_nncf_modules_replacement( original_modules = get_all_modules_by_type(model, list(NNCF_MODULES_MAP.values())) nncf_modules = get_all_modules_by_type(compressed_model, list(NNCF_MODULES_MAP.keys())) assert len(original_modules) == len(nncf_modules) - for nncf_scope in nncf_modules.keys(): + for nncf_scope in nncf_modules: original_scope = get_original_module_scope_from_nncf_module_scope(nncf_scope) assert original_scope in original_modules return original_modules, nncf_modules @@ -503,6 +505,9 @@ def load_exported_onnx_version( return model_proto +HookType = TypeVar("HookType") + + class HookChecker: """ Class to check pre/post hooks and pre ops are placed correctly. @@ -535,7 +540,11 @@ def _convert_to_op_address(self, target_type: TargetType, target_node_name: str, address = address_map[target_node_name] if target_type == TargetType.OPERATOR_PRE_HOOK: address = PreHookId(address, input_port_id) - elif target_type == TargetType.OPERATION_WITH_WEIGHTS: + elif target_type in [ + TargetType.OPERATION_WITH_WEIGHTS, + TargetType.PRE_LAYER_OPERATION, + TargetType.POST_LAYER_OPERATION, + ]: address = getattr(self._target_model, self._nncf_module_attr_name) return address @@ -544,6 +553,15 @@ def check_with_reference(self): Check hooks in the target model and reference hooks are matching. """ self._check_weight_update_hooks(self._ref_hooks[TargetType.OPERATION_WITH_WEIGHTS]) + + target_module = getattr(self._target_model, self._nncf_module_attr_name) + if target_module in self._ref_hooks[TargetType.PRE_LAYER_OPERATION]: + hooks = target_module.pre_ops + self._check_pre_post_op_hooks(hooks, self._ref_hooks[TargetType.PRE_LAYER_OPERATION][target_module]) + if target_module in self._ref_hooks[TargetType.POST_LAYER_OPERATION]: + hooks = target_module.post_ops + self._check_pre_post_op_hooks(hooks, self._ref_hooks[TargetType.POST_LAYER_OPERATION][target_module]) + hooks = self._target_model.nncf._compressed_context._pre_hooks self._check_pre_post_hooks(hooks, self._ref_hooks[TargetType.OPERATOR_PRE_HOOK]) hooks = self._target_model.nncf._compressed_context._post_hooks @@ -556,7 +574,7 @@ def clear(self): self._ref_hooks.clear() @staticmethod - def _check_weight_update_hooks(ref_hooks): + def _check_weight_update_hooks(ref_hooks: Dict[torch.nn.Module, List[HookType]]): for target_module, ref_hooks_per_module in ref_hooks.items(): assert len(target_module.pre_ops) == len(ref_hooks_per_module) for actual_op, ref_op in zip(target_module.pre_ops.values(), ref_hooks_per_module): @@ -564,10 +582,18 @@ def _check_weight_update_hooks(ref_hooks): assert actual_op.op is ref_op @staticmethod - def _check_pre_post_hooks(hooks, ref_hooks): + def _check_pre_post_op_hooks(hooks: List[torch.ModuleDict], ref_hooks: List[HookType]): + assert len(hooks) == len(ref_hooks) + for actual_hook, ref_hook in zip(hooks.values(), ref_hooks): + assert actual_hook is ref_hook + + @staticmethod + def _check_pre_post_hooks( + hooks: Dict[OperationAddress, Dict[Any, HookType]], ref_hooks: Dict[OperationAddress, List[HookType]] + ): assert len(hooks) == len(ref_hooks) for op_address, ref_hooks in ref_hooks.items(): - actual_hooks = hooks[op_address] + actual_hooks = hooks[op_address].values() assert len(actual_hooks) == len(ref_hooks) for actual_hook, ref_hook in zip(actual_hooks, ref_hooks): assert actual_hook is ref_hook diff --git a/tests/torch/models_hub_test/__init__.py b/tests/torch/models_hub_test/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/torch/models_hub_test/__init__.py +++ b/tests/torch/models_hub_test/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/models_hub_test/common.py b/tests/torch/models_hub_test/common.py index 2ebbf1af2db..78ec2273bbb 100644 --- a/tests/torch/models_hub_test/common.py +++ b/tests/torch/models_hub_test/common.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -21,6 +21,7 @@ from _pytest.mark import ParameterSet from torch import nn +import nncf from nncf.common.graph import NNCFGraph from nncf.torch.model_creation import wrap_model @@ -69,7 +70,7 @@ def nncf_wrap(self, model_name) -> None: example_input = {k: torch.tensor(v) for k, v in example.items()} assert example_input is not None - nncf_model = wrap_model(fw_model, example_input) + nncf_model = wrap_model(fw_model, example_input, trace_parameters=True) self.check_graph(nncf_model.nncf.get_original_graph()) @@ -117,7 +118,9 @@ def get_models_list(path: Path) -> List[ModelInfo]: model_link = None assert mark in ["skip", "xfail"], "Incorrect failure mark for model info {}".format(model_info) else: - raise RuntimeError(f"Incorrect model info `{model_info}`. It must contain either 1, 2 or 3 fields.") + raise nncf.ValidationError( + f"Incorrect model info `{model_info}`. It must contain either 1, 2 or 3 fields." + ) models.append(ModelInfo(model_name, model_link, mark, reason)) return models diff --git a/tests/torch/models_hub_test/hf_transformers_models.txt b/tests/torch/models_hub_test/hf_transformers_models.txt index 09a5d014b6e..c45acb95437 100644 --- a/tests/torch/models_hub_test/hf_transformers_models.txt +++ b/tests/torch/models_hub_test/hf_transformers_models.txt @@ -87,7 +87,7 @@ facebook/xmod-base,xmod,xfail,AttributeError: 'int' object has no attribute 'sha flax-community/ft5-cnn-dm,f_t5,skip,Load problem fnlp/elasticbert-base,elasticbert,skip,Load problem FranzStrauss/ponet-base-uncased,ponet,skip,Load problem -funnel-transformer/small,funnel +funnel-transformer/small,funnel,xfail,Not traced operations with masks or pos_id fusing/latent-diffusion-text2im-large,ldmbert,skip,Load problem Geor111y/flair-ner-addresses-extractor,flair,skip,Load problem gia-project/gia,gia,skip,Load problem @@ -98,7 +98,7 @@ google/bit-50,bit google/canine-s,canine google/efficientnet-b2,efficientnet google/electra-base-discriminator,electra -google/flan-t5-base,t5 +google/flan-t5-base,t5,xfail,Not traced operations with masks or pos_id google/fnet-base,fnet google/mobilebert-uncased,mobilebert google/mobilenet_v1_0.75_192,mobilenet_v1 @@ -332,8 +332,8 @@ sheonhan/ict-imagenet-256,ict,skip,Load problem shibing624/text2vec-base-chinese-paraphrase,ernie shikhartuli/flexibert-mini,flexibert,skip,Load problem shikras/shikra-7b-delta-v1-0708,shikra,skip,Load problem -shi-labs/dinat-mini-in1k-224,dinat -shi-labs/nat-mini-in1k-224,nat +shi-labs/dinat-mini-in1k-224,dinat,xfail,Use NATTEN +shi-labs/nat-mini-in1k-224,nat,xfail,Use NATTEN shi-labs/oneformer_ade20k_swin_large,oneformer,skip,Load problem shuqi/seed-encoder,seed_encoder,skip,Load problem sijunhe/nezha-cn-base,nezha @@ -343,14 +343,14 @@ snoop2head/Deep-Shallow-Ko2En,transformer,skip,Load problem Solomonik/SeqTokenModelMultiple,SeqToken,skip,Load problem solotimes/lavibe_base,donut,skip,Load problem songlab/gpn-brassicales,ConvNet,skip,Load problem -speechbrain/m-ctc-t-large,mctct +speechbrain/m-ctc-t-large,mctct,xfail,Not traced operations with masks or pos_id Splend1dchan/wav2vec2-large-lv60_t5lephone-small_lna_bs64,speechmix,skip,Load problem stefan-it/bort-full,bort,skip,Load problem SteveZhan/my-resnet50d,resnet_steve,skip,Load problem suno/bark,bark,skip,Load problem surajnair/r3m-50,r3m,skip,Load problem susnato/clvp_dev,clvp,skip,Load problem -Tanrei/GPTSAN-japanese,gptsan-japanese +Tanrei/GPTSAN-japanese,gptsan-japanese,xfail,Not traced operations with masks or pos_id tau/bart-large-sled-govreport,tau/sled,skip,Load problem taufeeque/best-cb-model,codebook,skip,Load problem Team-PIXEL/pixel-base,pixel,skip,Load problem @@ -359,7 +359,7 @@ thomwolf/vqgan_imagenet_f16_1024,vqgan_model,skip,Load problem thu-ml/zh-clip-vit-roberta-large-patch14,zhclip,skip,Load problem tifa-benchmark/promptcap-coco-vqa,ofa,skip,Load problem tli8hf/robertabase_snli,transformerfornli,skip,Load problem -transfo-xl-wt103,transfo-xl +transfo-xl-wt103,transfo-xl,xfail,Not traced operations with masks or pos_id transZ/BART_shared_clean,shared_bart,skip,Load problem transZ/BART_shared_v2,shared_bart_v2,skip,Load problem transZ/misecom,misecom,skip,Load problem diff --git a/tests/torch/models_hub_test/test_hf_transformers.py b/tests/torch/models_hub_test/test_hf_transformers.py index 73e506ffb8b..ea463a32a21 100644 --- a/tests/torch/models_hub_test/test_hf_transformers.py +++ b/tests/torch/models_hub_test/test_hf_transformers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -188,6 +188,11 @@ def forward(self, x): if "facebook/incoder" in name and "token_type_ids" in inputs_dict: del inputs_dict["token_type_ids"] example = inputs_dict + + # Unused input + if name == "RWKV/rwkv-4-169m-pile": + example.pop("attention_mask") + elif auto_model == "AutoModelForMaskedLM": from transformers import AutoModelForMaskedLM from transformers import AutoTokenizer @@ -284,6 +289,15 @@ def forward(self, x): tokenizer = AutoTokenizer.from_pretrained(name) encoded_input = tokenizer(text, return_tensors="pt") example = dict(encoded_input) + # Input does not used by config parameter config.type_vocab_size=0 + if name in [ + "regisss/bridgetower-newyorker-a100-8x", + "facebook/mask2former-swin-base-coco-panoptic", + "facebook/maskformer-swin-base-coco", + ]: + example.pop("pixel_mask") + elif name in ["MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli", "microsoft/deberta-base"]: + example.pop("token_type_ids") except: # noqa: E722 pass if model is None: diff --git a/tests/torch/models_hub_test/test_speech_transformer.py b/tests/torch/models_hub_test/test_speech_transformer.py deleted file mode 100644 index 8d79f3dca1b..00000000000 --- a/tests/torch/models_hub_test/test_speech_transformer.py +++ /dev/null @@ -1,45 +0,0 @@ -# Copyright (c) 2023 Intel Corporation -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# http://www.apache.org/licenses/LICENSE-2.0 -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import os -import subprocess -import sys -from pathlib import Path -from typing import Tuple - -import torch -from torch import nn - -from tests.torch.models_hub_test.common import BaseTestModel -from tests.torch.models_hub_test.common import ExampleType - - -class TestSpeechTransformerModel(BaseTestModel): - def load_model(self, model_name: str) -> Tuple[nn.Module, ExampleType]: - os.system(f"git clone https://github.com/mvafin/Speech-Transformer.git {self.tmp_path}") - subprocess.check_call(["git", "checkout", "071eebb7549b66bae2cb93e3391fe99749389456"], cwd=self.tmp_path) - - sys.path.append(self.tmp_path.as_posix()) - from transformer.transformer import Transformer - - m = Transformer() - - sys.path.remove(self.tmp_path.as_posix()) - example = ( - torch.randn(32, 209, 320), - torch.stack(sorted(torch.randint(55, 250, [32]), reverse=True)), - torch.randint(-1, 4232, [32, 20]), - ) - return m, example - - def test_nncf_wrap(self, tmp_path: Path): - self.tmp_path = tmp_path - self.nncf_wrap("speech-transformer") diff --git a/tests/torch/models_hub_test/test_timm.py b/tests/torch/models_hub_test/test_timm.py index 5f5785ab57f..b4146418f3e 100644 --- a/tests/torch/models_hub_test/test_timm.py +++ b/tests/torch/models_hub_test/test_timm.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/models_hub_test/test_torchvision_models.py b/tests/torch/models_hub_test/test_torchvision_models.py index 2ae56439209..a0ab1ff8871 100644 --- a/tests/torch/models_hub_test/test_torchvision_models.py +++ b/tests/torch/models_hub_test/test_torchvision_models.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/modules/seq2seq/__init__.py b/tests/torch/modules/seq2seq/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/torch/modules/seq2seq/__init__.py +++ b/tests/torch/modules/seq2seq/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/modules/seq2seq/attention.py b/tests/torch/modules/seq2seq/attention.py index ec9d0085e33..6eae90f9c12 100644 --- a/tests/torch/modules/seq2seq/attention.py +++ b/tests/torch/modules/seq2seq/attention.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/modules/seq2seq/decoder.py b/tests/torch/modules/seq2seq/decoder.py index f54274a2ebf..cc6a55408d2 100644 --- a/tests/torch/modules/seq2seq/decoder.py +++ b/tests/torch/modules/seq2seq/decoder.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/modules/seq2seq/encoder.py b/tests/torch/modules/seq2seq/encoder.py index 690609796cc..6da9cd4b270 100644 --- a/tests/torch/modules/seq2seq/encoder.py +++ b/tests/torch/modules/seq2seq/encoder.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/modules/seq2seq/gnmt.py b/tests/torch/modules/seq2seq/gnmt.py index 9de1ee5691c..99a27c08566 100644 --- a/tests/torch/modules/seq2seq/gnmt.py +++ b/tests/torch/modules/seq2seq/gnmt.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/modules/seq2seq/seq2seq_base.py b/tests/torch/modules/seq2seq/seq2seq_base.py index ebe3e13913b..d84ebc261d0 100644 --- a/tests/torch/modules/seq2seq/seq2seq_base.py +++ b/tests/torch/modules/seq2seq/seq2seq_base.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/modules/test_rnn.py b/tests/torch/modules/test_rnn.py index ebfbdf90da6..441ac156242 100644 --- a/tests/torch/modules/test_rnn.py +++ b/tests/torch/modules/test_rnn.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/nas/__init__.py b/tests/torch/nas/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/torch/nas/__init__.py +++ b/tests/torch/nas/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/nas/creators.py b/tests/torch/nas/creators.py index ae62b918102..4f44bcf3530 100644 --- a/tests/torch/nas/creators.py +++ b/tests/torch/nas/creators.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/nas/descriptors.py b/tests/torch/nas/descriptors.py index 615bfe6c7e3..6773bdef322 100644 --- a/tests/torch/nas/descriptors.py +++ b/tests/torch/nas/descriptors.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/nas/helpers.py b/tests/torch/nas/helpers.py index 51167dc58ce..156cdedf470 100644 --- a/tests/torch/nas/helpers.py +++ b/tests/torch/nas/helpers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/nas/models/__init__.py b/tests/torch/nas/models/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/torch/nas/models/__init__.py +++ b/tests/torch/nas/models/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/nas/models/synthetic.py b/tests/torch/nas/models/synthetic.py index 23edcdc3bdf..73f9987e46a 100644 --- a/tests/torch/nas/models/synthetic.py +++ b/tests/torch/nas/models/synthetic.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/nas/models/tcn.py b/tests/torch/nas/models/tcn.py index c48b0914ebe..f03f9b337b7 100644 --- a/tests/torch/nas/models/tcn.py +++ b/tests/torch/nas/models/tcn.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/nas/models/vgg_k7.py b/tests/torch/nas/models/vgg_k7.py index 9716647b201..39d4276835d 100644 --- a/tests/torch/nas/models/vgg_k7.py +++ b/tests/torch/nas/models/vgg_k7.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/nas/test_all_elasticity.py b/tests/torch/nas/test_all_elasticity.py index 23d37ca6ff4..0e7b869df3c 100644 --- a/tests/torch/nas/test_all_elasticity.py +++ b/tests/torch/nas/test_all_elasticity.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/nas/test_elastic_depth.py b/tests/torch/nas/test_elastic_depth.py index edf1e580210..ef3fdfd69e5 100644 --- a/tests/torch/nas/test_elastic_depth.py +++ b/tests/torch/nas/test_elastic_depth.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/nas/test_elastic_kernel.py b/tests/torch/nas/test_elastic_kernel.py index ad011c193cf..b8f7e7b21ef 100644 --- a/tests/torch/nas/test_elastic_kernel.py +++ b/tests/torch/nas/test_elastic_kernel.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/nas/test_elastic_width.py b/tests/torch/nas/test_elastic_width.py index 7ec1963990c..68c83fe7b30 100644 --- a/tests/torch/nas/test_elastic_width.py +++ b/tests/torch/nas/test_elastic_width.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/nas/test_flops.py b/tests/torch/nas/test_flops.py index 4fa2eb8e454..ec3cc169466 100644 --- a/tests/torch/nas/test_flops.py +++ b/tests/torch/nas/test_flops.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/nas/test_lr_scheduler.py b/tests/torch/nas/test_lr_scheduler.py index c6dcd351b8a..63b223aeccc 100644 --- a/tests/torch/nas/test_lr_scheduler.py +++ b/tests/torch/nas/test_lr_scheduler.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/nas/test_onnx_export.py b/tests/torch/nas/test_onnx_export.py index 1429556ad46..20124581652 100644 --- a/tests/torch/nas/test_onnx_export.py +++ b/tests/torch/nas/test_onnx_export.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/nas/test_ps_controller.py b/tests/torch/nas/test_ps_controller.py index 3300c003188..e0e8a20bc57 100644 --- a/tests/torch/nas/test_ps_controller.py +++ b/tests/torch/nas/test_ps_controller.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/nas/test_sanity_sample.py b/tests/torch/nas/test_sanity_sample.py index c2575e14428..aad3e9f9de7 100644 --- a/tests/torch/nas/test_sanity_sample.py +++ b/tests/torch/nas/test_sanity_sample.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -9,19 +9,24 @@ # See the License for the specific language governing permissions and # limitations under the License. +import importlib from pathlib import Path from typing import Dict import pytest from nncf.common.initialization.batchnorm_adaptation import BatchnormAdaptationAlgorithm +from nncf.experimental.torch.nas.bootstrapNAS import BaseSearchAlgorithm from nncf.experimental.torch.nas.bootstrapNAS.elasticity.elastic_depth import ElasticDepthHandler from nncf.experimental.torch.nas.bootstrapNAS.elasticity.elastic_width import ElasticWidthHandler +from nncf.experimental.torch.nas.bootstrapNAS.elasticity.elasticity_controller import ElasticityController from nncf.experimental.torch.nas.bootstrapNAS.elasticity.multi_elasticity_handler import MultiElasticityHandler +from nncf.experimental.torch.nas.bootstrapNAS.search.search import NSGA2SearchAlgorithm from nncf.experimental.torch.nas.bootstrapNAS.training.progressive_shrinking_controller import ( ProgressiveShrinkingController, ) from nncf.experimental.torch.nas.bootstrapNAS.training.scheduler import BootstrapNASScheduler +from tests.shared.command import arg_list_from_arg_dict from tests.shared.paths import TEST_ROOT from tests.torch.sample_test_validator import SampleType from tests.torch.sample_test_validator import SanitySampleValidator @@ -40,7 +45,9 @@ def config_directory(self) -> Path: return TEST_ROOT / "torch" / "data" / "configs" / "nas" def get_validator(self) -> "NASSampleValidator": - return NASSampleValidator(self) + if self.sample_type_ == SampleType.CLASSIFICATION_NAS_SEARCH: + return NASSearchSampleValidator(self) + return NASTrainSampleValidator(self) def get_compression_section(self): pass @@ -58,11 +65,14 @@ def __init__(self, desc: NASSampleTestDescriptor): self._desc = desc self._all_spies = [] - def setup_spy(self, mocker): - # Need to mock SafeMLFLow to prevent starting a not closed mlflow session due to memory leak of config and - # SafeMLFLow, which happens with a mocked train function - self._sample_handler.mock_mlflow(mocker) + def validate_spy(self): + for spy in self._all_spies: + spy.assert_called() + +class NASTrainSampleValidator(NASSampleValidator): + def setup_spy(self, mocker): + super().setup_spy(mocker) self._all_spies = [ mocker.spy(ElasticWidthHandler, "get_random_config"), mocker.spy(ElasticWidthHandler, "reorganize_weights"), @@ -77,9 +87,19 @@ def setup_spy(self, mocker): mocker.spy(BatchnormAdaptationAlgorithm, "run"), ] - def validate_spy(self): - for spy in self._all_spies: - spy.assert_called() + +class NASSearchSampleValidator(NASSampleValidator): + def setup_spy(self, mocker): + super().setup_spy(mocker) + self._all_spies = [ + mocker.spy(ElasticityController, "load_state"), + mocker.spy(BaseSearchAlgorithm, "from_config"), + mocker.spy(BaseSearchAlgorithm, "search_progression_to_csv"), + mocker.spy(NSGA2SearchAlgorithm, "run"), + mocker.spy(NSGA2SearchAlgorithm, "visualize_search_progression"), + mocker.spy(NSGA2SearchAlgorithm, "evaluators_to_csv"), + mocker.spy(MultiElasticityHandler, "activate_subnet_for_config"), + ] NAS_TEST_CASE_DESCRIPTORS = [ @@ -101,3 +121,25 @@ def test_e2e_supernet_training(nas_desc: NASSampleTestDescriptor, tmp_path, mock validator = nas_desc.get_validator() args = validator.get_default_args(tmp_path) validator.validate_sample(args, mocker) + + +@pytest.mark.nightly +def test_e2e_supernet_search(nas_desc: NASSampleTestDescriptor, tmp_path, mocker): + # Train a supernet and save it to the specified path + validator = nas_desc.get_validator() + args = validator.get_default_args(tmp_path) + output_dir = tmp_path / "output_dir" + args["--checkpoint-save-dir"] = output_dir + arg_list = arg_list_from_arg_dict(args) + main_location = nas_desc.sample_handler.get_main_location() + sample = importlib.import_module(main_location) + sample.main(arg_list) + + nas_desc.sample_type(SampleType.CLASSIFICATION_NAS_SEARCH) + validator = nas_desc.get_validator() + args = validator.get_default_args(tmp_path) + # Load the supernet to search + args["--supernet-weights"] = output_dir / "last_model_weights.pth" + args["--elasticity-state-path"] = output_dir / "last_elasticity.pth" + args["--search-mode"] = True + validator.validate_sample(args, mocker) diff --git a/tests/torch/nas/test_scheduler.py b/tests/torch/nas/test_scheduler.py index 086fcc19937..7d94b7c80e0 100644 --- a/tests/torch/nas/test_scheduler.py +++ b/tests/torch/nas/test_scheduler.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/nas/test_search.py b/tests/torch/nas/test_search.py index cf6de1e48d4..e717f3e4453 100644 --- a/tests/torch/nas/test_search.py +++ b/tests/torch/nas/test_search.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/nas/test_search_space.py b/tests/torch/nas/test_search_space.py index 99aa8262980..65c103e35a3 100644 --- a/tests/torch/nas/test_search_space.py +++ b/tests/torch/nas/test_search_space.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/nas/test_state.py b/tests/torch/nas/test_state.py index 0141683efe4..3c2cc24d9fb 100644 --- a/tests/torch/nas/test_state.py +++ b/tests/torch/nas/test_state.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/pruning/__init__.py b/tests/torch/pruning/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/torch/pruning/__init__.py +++ b/tests/torch/pruning/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/pruning/experimental/test_nodes_grouping.py b/tests/torch/pruning/experimental/test_nodes_grouping.py index 4d26ca81118..a7b82170d3e 100644 --- a/tests/torch/pruning/experimental/test_nodes_grouping.py +++ b/tests/torch/pruning/experimental/test_nodes_grouping.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/pruning/experimental/test_propagation.py b/tests/torch/pruning/experimental/test_propagation.py index 02e5564b4cb..68698fb2821 100644 --- a/tests/torch/pruning/experimental/test_propagation.py +++ b/tests/torch/pruning/experimental/test_propagation.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/pruning/filter_pruning/__init__.py b/tests/torch/pruning/filter_pruning/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/torch/pruning/filter_pruning/__init__.py +++ b/tests/torch/pruning/filter_pruning/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/pruning/filter_pruning/test_algo.py b/tests/torch/pruning/filter_pruning/test_algo.py index da96062dfb6..6812cbe6e0b 100644 --- a/tests/torch/pruning/filter_pruning/test_algo.py +++ b/tests/torch/pruning/filter_pruning/test_algo.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -305,7 +305,7 @@ def check_module_output(module, name, num): assert torch.allclose(output, ref_output) def check_model_weights(model_state_dict, ref_state_dict): - for key in ref_state_dict.keys(): + for key in ref_state_dict: assert torch.allclose(model_state_dict[key], ref_state_dict[key]) config = get_basic_pruning_config(input_sample_size=[1, 1] + [8] * dim) @@ -711,8 +711,8 @@ def test_flops_calculator(model_module, all_weights, pruning_flops_target, ref_f config["compression"]["algorithm"] = "filter_pruning" config["compression"]["params"]["all_weights"] = all_weights config["compression"]["params"]["prune_first_conv"] = True - config["compression"]["pruning_init"] = 0.5 if not pruning_flops_target else pruning_flops_target - if pruning_flops_target: + config["compression"]["pruning_init"] = pruning_flops_target if pruning_flops_target is not None else 0.5 + if pruning_flops_target is not None: config["compression"]["params"]["pruning_flops_target"] = pruning_flops_target model = model_module() diff --git a/tests/torch/pruning/filter_pruning/test_evolutionary_optimization.py b/tests/torch/pruning/filter_pruning/test_evolutionary_optimization.py index 81e42f12187..d760cc47bb0 100644 --- a/tests/torch/pruning/filter_pruning/test_evolutionary_optimization.py +++ b/tests/torch/pruning/filter_pruning/test_evolutionary_optimization.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/pruning/filter_pruning/test_flops_pruning.py b/tests/torch/pruning/filter_pruning/test_flops_pruning.py index ed5b4955b6b..f6fc72a7a6d 100644 --- a/tests/torch/pruning/filter_pruning/test_flops_pruning.py +++ b/tests/torch/pruning/filter_pruning/test_flops_pruning.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -14,6 +14,7 @@ import numpy as np import pytest +import nncf from nncf.common.pruning.utils import get_prunable_layers_in_out_channels from tests.torch.helpers import create_compressed_model_and_algo_for_test from tests.torch.pruning.helpers import BigPruningTestModel @@ -167,5 +168,5 @@ def test_maximal_compression_rate(): for comp_rate in np.linspace(0, maximal_compression_rate, 10): pruning_algo.compression_rate = comp_rate for comp_rate in np.linspace(maximal_compression_rate + 1e-5, 1, 10): - with pytest.raises(RuntimeError): + with pytest.raises(nncf.InternalError): pruning_algo.compression_rate = comp_rate diff --git a/tests/torch/pruning/filter_pruning/test_functions.py b/tests/torch/pruning/filter_pruning/test_functions.py index fe076d8bc6d..d2924f4d1a3 100644 --- a/tests/torch/pruning/filter_pruning/test_functions.py +++ b/tests/torch/pruning/filter_pruning/test_functions.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/pruning/filter_pruning/test_layers.py b/tests/torch/pruning/filter_pruning/test_layers.py index 69a7bf5e211..c6a9c75411b 100644 --- a/tests/torch/pruning/filter_pruning/test_layers.py +++ b/tests/torch/pruning/filter_pruning/test_layers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -13,6 +13,7 @@ import torch from torch import nn +import nncf from nncf.torch.layers import NNCFConv2d from nncf.torch.module_operations import UpdateWeightAndBias from nncf.torch.pruning.filter_pruning.layers import FilterPruningMask @@ -67,7 +68,7 @@ def test_assert_broadcastable_mask_and_weight_shape(): mask = torch.zeros(10) - with pytest.raises(RuntimeError): + with pytest.raises(nncf.InternalError): apply_filter_binary_mask(mask, nncf_module.weight.data) diff --git a/tests/torch/pruning/filter_pruning/test_legr.py b/tests/torch/pruning/filter_pruning/test_legr.py index 08e316c5a28..c9d40d9bed8 100644 --- a/tests/torch/pruning/filter_pruning/test_legr.py +++ b/tests/torch/pruning/filter_pruning/test_legr.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/pruning/filter_pruning/test_set_pruning_rate.py b/tests/torch/pruning/filter_pruning/test_set_pruning_rate.py index 588eddd5ba3..f9f239e8892 100644 --- a/tests/torch/pruning/filter_pruning/test_set_pruning_rate.py +++ b/tests/torch/pruning/filter_pruning/test_set_pruning_rate.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/pruning/filter_pruning/test_strip.py b/tests/torch/pruning/filter_pruning/test_strip.py index 381e04a2229..b1543e94613 100644 --- a/tests/torch/pruning/filter_pruning/test_strip.py +++ b/tests/torch/pruning/filter_pruning/test_strip.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/pruning/helpers.py b/tests/torch/pruning/helpers.py index e3501aaca91..3f5ea3a6908 100644 --- a/tests/torch/pruning/helpers.py +++ b/tests/torch/pruning/helpers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/pruning/test_common.py b/tests/torch/pruning/test_common.py index 2115fd638b3..c5f30626b8c 100644 --- a/tests/torch/pruning/test_common.py +++ b/tests/torch/pruning/test_common.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/pruning/test_distributed.py b/tests/torch/pruning/test_distributed.py index e1e28c0481b..2a63ce8675d 100644 --- a/tests/torch/pruning/test_distributed.py +++ b/tests/torch/pruning/test_distributed.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/pruning/test_model_pruning_analysis.py b/tests/torch/pruning/test_model_pruning_analysis.py index 307b6db5b24..5f02abb3fc5 100644 --- a/tests/torch/pruning/test_model_pruning_analysis.py +++ b/tests/torch/pruning/test_model_pruning_analysis.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -834,7 +834,7 @@ def test_model_analyzer(test_struct: GroupSpecialModulesTestStruct): nncf_model.nncf.get_original_graph(), PT_PRUNING_OPERATOR_METATYPES, prune_operations_types ) can_prune_analysis = model_analyser.analyse_model_before_pruning() - for node_id in can_prune_analysis.keys(): + for node_id in can_prune_analysis: assert can_prune_analysis[node_id].decision == test_struct.ref_can_prune[node_id], f"node id={node_id}" diff --git a/tests/torch/pruning/test_onnx_export.py b/tests/torch/pruning/test_onnx_export.py index 841d1b4a7fc..5a683ededdc 100644 --- a/tests/torch/pruning/test_onnx_export.py +++ b/tests/torch/pruning/test_onnx_export.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/pruning/test_schedulers.py b/tests/torch/pruning/test_schedulers.py index 447652e7f7e..aae9a5ba02d 100644 --- a/tests/torch/pruning/test_schedulers.py +++ b/tests/torch/pruning/test_schedulers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/pruning/test_tensor_processor.py b/tests/torch/pruning/test_tensor_processor.py index 2b147a30e67..67ae0a3947b 100644 --- a/tests/torch/pruning/test_tensor_processor.py +++ b/tests/torch/pruning/test_tensor_processor.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -18,9 +18,8 @@ @pytest.mark.parametrize("device", (torch.device("cpu"), torch.device("cuda"))) def test_ones(device): - if not torch.cuda.is_available(): - if device == torch.device("cuda"): - pytest.skip("There are no available CUDA devices") + if not torch.cuda.is_available() and device == torch.device("cuda"): + pytest.skip("There are no available CUDA devices") shape = [1, 3, 10, 100] tensor = PTNNCFPruningTensorProcessor.ones(shape, device) assert torch.is_tensor(tensor.tensor) diff --git a/tests/torch/pruning/test_utils.py b/tests/torch/pruning/test_utils.py index c7860676355..5ea861c8d39 100644 --- a/tests/torch/pruning/test_utils.py +++ b/tests/torch/pruning/test_utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/ptq/helpers.py b/tests/torch/ptq/helpers.py index d4805694559..54fdfa70710 100644 --- a/tests/torch/ptq/helpers.py +++ b/tests/torch/ptq/helpers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/ptq/test_calculation_quantizer_params.py b/tests/torch/ptq/test_calculation_quantizer_params.py index 811228b5d64..5c08d137815 100644 --- a/tests/torch/ptq/test_calculation_quantizer_params.py +++ b/tests/torch/ptq/test_calculation_quantizer_params.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/ptq/test_fast_bias_correction.py b/tests/torch/ptq/test_fast_bias_correction.py index e5d3b937376..6bbdc342e0b 100644 --- a/tests/torch/ptq/test_fast_bias_correction.py +++ b/tests/torch/ptq/test_fast_bias_correction.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/ptq/test_fq_params_calculation.py b/tests/torch/ptq/test_fq_params_calculation.py index cee34321bb9..d006a233f5b 100644 --- a/tests/torch/ptq/test_fq_params_calculation.py +++ b/tests/torch/ptq/test_fq_params_calculation.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/ptq/test_graphs.py b/tests/torch/ptq/test_graphs.py index 4aace885e10..9061c01e0da 100644 --- a/tests/torch/ptq/test_graphs.py +++ b/tests/torch/ptq/test_graphs.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/ptq/test_ptq_params.py b/tests/torch/ptq/test_ptq_params.py index f0c09ee4e02..8ebdb1f1b2d 100644 --- a/tests/torch/ptq/test_ptq_params.py +++ b/tests/torch/ptq/test_ptq_params.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -16,10 +16,6 @@ from nncf.common.graph.patterns.manager import PatternsManager from nncf.common.graph.transformations.commands import TargetType from nncf.common.utils.backend import BackendType -from nncf.experimental.common.tensor_statistics.collectors import MaxAggregator -from nncf.experimental.common.tensor_statistics.collectors import MeanAggregator -from nncf.experimental.common.tensor_statistics.collectors import MinAggregator -from nncf.experimental.common.tensor_statistics.collectors import TensorCollector from nncf.parameters import TargetDevice from nncf.quantization.algorithms.min_max.algorithm import MinMaxQuantization from nncf.quantization.algorithms.min_max.torch_backend import PTMinMaxAlgoBackend @@ -94,18 +90,6 @@ class TestPTQParams(TemplateTestPTQParams): def get_algo_backend(self): return PTMinMaxAlgoBackend() - def check_is_min_max_statistic_collector(self, tensor_collector: TensorCollector): - aggrs = [aggr.__class__ for aggr in tensor_collector.aggregators.values()] - assert len(aggrs) == 2 - assert MinAggregator in aggrs - assert MaxAggregator in aggrs - - def check_is_mean_min_max_statistic_collector(self, tensor_collector: TensorCollector): - aggrs = [aggr.__class__ for aggr in tensor_collector.aggregators.values()] - assert len(aggrs) == 2 - assert MeanAggregator in aggrs - assert aggrs[0].__class__ == aggrs[1].__class__ - def check_quantize_outputs_fq_num(self, quantize_outputs, act_num_q, weight_num_q): if quantize_outputs: assert act_num_q == 2 diff --git a/tests/torch/ptq/test_quantizer_config.py b/tests/torch/ptq/test_quantizer_config.py index 2c1a764d30b..4c80c5c10d0 100644 --- a/tests/torch/ptq/test_quantizer_config.py +++ b/tests/torch/ptq/test_quantizer_config.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -12,12 +12,6 @@ import pytest from nncf.common.graph.transformations.commands import TargetType -from nncf.common.tensor_statistics.collectors import ReductionAxes -from nncf.experimental.common.tensor_statistics.collectors import MaxAggregator -from nncf.experimental.common.tensor_statistics.collectors import MeanAggregator -from nncf.experimental.common.tensor_statistics.collectors import MinAggregator -from nncf.experimental.common.tensor_statistics.collectors import TensorCollector -from nncf.experimental.common.tensor_statistics.collectors import TensorReducerBase from nncf.quantization.algorithms.min_max.torch_backend import PTMinMaxAlgoBackend from tests.post_training.test_templates.models import NNCFGraphToTest from tests.post_training.test_templates.models import NNCFGraphToTestDepthwiseConv @@ -34,21 +28,6 @@ class TestQuantizerConfig(TemplateTestQuantizerConfig): def get_algo_backend(self): return PTMinMaxAlgoBackend() - def check_is_min_max_statistic_collector(self, tensor_collector: TensorCollector): - aggrs = [aggr.__class__ for aggr in tensor_collector.aggregators.values()] - assert len(aggrs) == 2 - assert MinAggregator in aggrs - assert MaxAggregator in aggrs - - def check_is_mean_min_max_statistic_collector(self, tensor_collector: TensorCollector): - aggrs = [aggr.__class__ for aggr in tensor_collector.aggregators.values()] - assert len(aggrs) == 2 - assert MeanAggregator in aggrs - assert aggrs[0].__class__ == aggrs[1].__class__ - - def get_reduction_axes(self, reducer: TensorReducerBase) -> ReductionAxes: - return reducer._reduction_axes - @pytest.fixture( params=[ (TargetType.PRE_LAYER_OPERATION, "/Sum_1_0", (0, 2), (0, 1, 2)), diff --git a/tests/torch/ptq/test_reducers_and_aggregators.py b/tests/torch/ptq/test_reducers_and_aggregators.py index 488128ca88a..1af7b4e4683 100644 --- a/tests/torch/ptq/test_reducers_and_aggregators.py +++ b/tests/torch/ptq/test_reducers_and_aggregators.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -16,8 +16,11 @@ import pytest import torch +import nncf from nncf.common.graph.layer_attributes import Dtype +from nncf.experimental.common.tensor_statistics.collectors import TensorCollector from nncf.torch.tensor import PTNNCFTensor +from nncf.torch.tensor_statistics.algo import create_register_input_hook from nncf.torch.tensor_statistics.collectors import PTAbsMaxReducer from nncf.torch.tensor_statistics.collectors import PTAbsQuantileReducer from nncf.torch.tensor_statistics.collectors import PTBatchMeanReducer @@ -26,7 +29,6 @@ from nncf.torch.tensor_statistics.collectors import PTMeanReducer from nncf.torch.tensor_statistics.collectors import PTMinReducer from nncf.torch.tensor_statistics.collectors import PTNNCFCollectorTensorProcessor -from nncf.torch.tensor_statistics.collectors import PTNoopReducer from nncf.torch.tensor_statistics.collectors import PTQuantileReducer from tests.common.experimental.test_reducers_and_aggregators import TemplateTestReducersAggreagtors @@ -47,7 +49,6 @@ def _get_torch_tensor(self, x: np.ndarray, dtype: Optional[Dtype] = None): @pytest.fixture(scope="module") def reducers(self): return { - "noop": PTNoopReducer, "min": PTMinReducer, "max": PTMaxReducer, "abs_max": PTAbsMaxReducer, @@ -74,14 +75,7 @@ def cast_tensor(self, tensor, dtype: Dtype): return tensor.float() if dtype == Dtype.INTEGER: return tensor.int() - raise RuntimeError() - - def expand_dims(self, tensor, dims: Tuple[int, ...]): - tensor_ = torch.tensor(tensor) - shape = list(tensor_.shape) - for dim in dims: - shape.insert(dim, 1) - return tensor_.view(shape) + raise nncf.ValidationError(f"Invalid dtype: {dtype}. Supported dtypes are {Dtype.FLOAT} and {Dtype.INTEGER}") class TestCPUReducersAggregators(BaseTestReducersAggregators): @@ -126,3 +120,19 @@ def test_median_function(device, size, ref): res = PTNNCFCollectorTensorProcessor.median(tensor, axis=0) assert res.tensor == ref assert res.tensor.is_cuda == (device == "cuda") + + +def test_create_register_input_hook_with_return_type(mocker): + collector = TensorCollector() + collector.register_input_for_all_reducers = mocker.MagicMock() + hook = create_register_input_hook(collector) + input_ = torch.return_types.max([torch.tensor((1,))] * 2) + output_ = hook(input_) + assert input_ is output_ + mocker = collector.register_input_for_all_reducers + mocker.assert_called_once() + attr = mocker.call_args_list[0][0][0] + assert isinstance(attr, PTNNCFTensor) + assert attr.tensor == torch.tensor( + 1, + ) diff --git a/tests/torch/ptq/test_smooth_quant.py b/tests/torch/ptq/test_smooth_quant.py new file mode 100644 index 00000000000..fa5d0599672 --- /dev/null +++ b/tests/torch/ptq/test_smooth_quant.py @@ -0,0 +1,129 @@ +# Copyright (c) 2023 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Callable, Dict, Type + +import numpy as np +import openvino.runtime as ov +import pytest +import torch + +from nncf.common.graph.transformations.commands import TransformationCommand +from nncf.quantization.algorithms.smooth_quant.torch_backend import PTSmoothQuantAlgoBackend +from nncf.quantization.algorithms.smooth_quant.torch_backend import SQMultiply +from nncf.torch.graph.operator_metatypes import PTModuleConv2dMetatype +from nncf.torch.graph.operator_metatypes import PTModuleLinearMetatype +from nncf.torch.graph.transformations.commands import PTSharedFnInsertionCommand +from nncf.torch.model_creation import wrap_model +from nncf.torch.nncf_network import ExtraCompressionModuleType +from tests.post_training.test_templates.helpers import ConvTestModel +from tests.post_training.test_templates.helpers import LinearMultiShapeModel +from tests.post_training.test_templates.helpers import ShareWeghtsConvAndShareLinearModel +from tests.post_training.test_templates.test_smooth_quant import TemplateTestSQAlgorithm + +PT_LINEAR_MODEL_SQ_MAP = { + ("Linear1",): "LinearMultiShapeModel/split_0_1_0/nncf_smooth_quant", + ("Linear2",): "LinearMultiShapeModel/split_0_0_0/nncf_smooth_quant", + ("Linear3", "Linear4"): "LinearMultiShapeModel/add_0_0_0/nncf_smooth_quant", +} + +PT_LINEAR_MODEL_MM_MAP = { + "Linear1": "LinearMultiShapeModel/NNCFLinear[linear_2]/linear_0", + "Linear2": "LinearMultiShapeModel/NNCFLinear[linear_1]/linear_0", + "Linear3": "LinearMultiShapeModel/NNCFLinear[linear_3]/linear_0", + "Linear4": "LinearMultiShapeModel/NNCFLinear[linear_4]/linear_0", +} + +PT_CONV_MODEL_SQ_MAP = {("Conv1",): "/nncf_model_input_0_0_0/nncf_smooth_quant"} + +PT_CONV_MODEL_MM_MAP = {"Conv1": "ConvTestModel/NNCFConv2d[conv]/conv2d_0"} + + +class TestTorchSQAlgorithm(TemplateTestSQAlgorithm): + @staticmethod + def fn_to_type(tensor) -> torch.Tensor: + return torch.tensor(tensor) + + @pytest.fixture(params=[False], ids=["out_of_palce"]) + def inplace_statistics(self, request) -> bool: + return request.param + + def get_node_name_map(self, model_cls) -> Dict[str, str]: + if model_cls is LinearMultiShapeModel: + return PT_LINEAR_MODEL_MM_MAP + if model_cls is ConvTestModel: + return PT_CONV_MODEL_MM_MAP + if model_cls is ShareWeghtsConvAndShareLinearModel: + return {} + raise NotImplementedError + + @staticmethod + def get_target_node_name(command: TransformationCommand): + if isinstance(command, PTSharedFnInsertionCommand): + return command.target_points[0].target_node_name + return command.target_point.target_node_name + + @staticmethod + def get_transform_fn() -> Callable: + def transform_fn(data_item): + return data_item[0] + + return transform_fn + + @staticmethod + def get_backend() -> Type[PTSmoothQuantAlgoBackend]: + return PTSmoothQuantAlgoBackend() + + @staticmethod + def backend_specific_model(model: torch.nn.Module, tmp_dir: str) -> ov.Model: + return wrap_model(model.eval(), torch.rand(model.INPUT_SIZE)) + + @staticmethod + def check_scales(model: torch.nn.Module, reference_values: Dict[str, np.ndarray], model_cls) -> None: + names_map = PT_LINEAR_MODEL_SQ_MAP if model_cls is LinearMultiShapeModel else PT_CONV_MODEL_SQ_MAP + modules = model.nncf.get_compression_modules_by_type(ExtraCompressionModuleType.EXTERNAL_OP) + for ref_names, ref_value in reference_values.items(): + if not all(name.startswith("Linear") or name.startswith("Conv") for name in ref_names): + # Pytorch SQ algorithm supports only linear and conv modules by far, + # so other multiplies are skipped + continue + sq_node = modules[names_map[ref_names]] + + assert isinstance(sq_node, SQMultiply) + + value = sq_node._scale_value + ref_value = torch.tensor(ref_value) + assert value.shape == ref_value.shape + assert torch.all(torch.isclose(value, ref_value, rtol=1e-4)) + + @pytest.mark.parametrize( + "node_metatype, layer_attributes, port_id, reference_value", + ( + (PTModuleLinearMetatype, None, 0, -1), + (PTModuleConv2dMetatype, None, 0, 1), + ), + ) + def test_get_activation_channel_axis(self, node_metatype, layer_attributes, port_id, reference_value): + return super().test_get_activation_channel_axis(node_metatype, layer_attributes, port_id, reference_value) + + @pytest.mark.parametrize( + "node_metatype, layer_attributes, reference_value", + ( + (PTModuleLinearMetatype, None, 1), + (PTModuleConv2dMetatype, None, 1), + ), + ) + def test_get_weight_channel_axis(self, node_metatype, layer_attributes, reference_value): + return super().test_get_weight_channel_axis(node_metatype, layer_attributes, reference_value) + + @staticmethod + def get_matmul_metatype(): + return PTModuleLinearMetatype diff --git a/tests/torch/ptq/test_statistic_collector.py b/tests/torch/ptq/test_statistic_collector.py index 96b0963194b..ffc5a828625 100644 --- a/tests/torch/ptq/test_statistic_collector.py +++ b/tests/torch/ptq/test_statistic_collector.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/ptq/test_strip.py b/tests/torch/ptq/test_strip.py index f1ca6eafc37..af856b726c5 100644 --- a/tests/torch/ptq/test_strip.py +++ b/tests/torch/ptq/test_strip.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/ptq/test_weights_compression.py b/tests/torch/ptq/test_weights_compression.py index 820860321e8..c31fb901e5e 100644 --- a/tests/torch/ptq/test_weights_compression.py +++ b/tests/torch/ptq/test_weights_compression.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -11,11 +11,13 @@ import pytest import torch +import torch.nn.functional as F from nncf import CompressWeightsMode from nncf import SensitivityMetric from nncf.quantization import compress_weights from nncf.torch import wrap_model +from nncf.torch.quantization.layers import WeightsDecompressor DATA_BASED_SENSITIVITY_METRICS = ( SensitivityMetric.HESSIAN_INPUT_ACTIVATION, @@ -51,6 +53,59 @@ def forward(self, input_ids): return res +class NestedMatMul(torch.nn.Module): + def __init__(self): + super().__init__() + self.w = torch.nn.Parameter(torch.ones(size=(300, 300), dtype=torch.float32)) + + def forward(self, input): + return input @ self.w + + +class FunctionalModel(torch.nn.Module): + def __init__(self): + super().__init__() + self.conv_w = torch.nn.Parameter(torch.ones(size=(5, 3, 3, 3), dtype=torch.float32)) + self.matmul_w = torch.nn.Parameter(torch.ones(size=(1, 3, 300, 300), dtype=torch.float32)) + self.conv_tr_w = torch.nn.Parameter(torch.rand(size=(5, 4, 3, 3))) + self.nested_matmul = NestedMatMul() + + def forward(self, input_): + x = input_.to(torch.float32) + x = x @ self.matmul_w + x = self.nested_matmul(x) + x = F.conv2d(x, self.conv_w) + x = F.conv_transpose2d(x, self.conv_tr_w) + return x + + +class ConvolutionModel(torch.nn.Module): + def __init__(self): + super().__init__() + self.conv_regular = torch.nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3) + self.max_pool2d = torch.nn.MaxPool2d(kernel_size=2) + self.conv_transpose = torch.nn.ConvTranspose2d(in_channels=16, out_channels=8, kernel_size=3) + self.conv_depthwise = torch.nn.Conv2d(in_channels=8, out_channels=8, kernel_size=5, groups=8) + self.adaptive_avg_pool = torch.nn.AdaptiveAvgPool2d(output_size=1) + self.linear = torch.nn.Linear(in_features=8, out_features=8) + + def forward(self, input_): + input_ = input_.to(torch.float32) + x = self.conv_regular(input_) + x = F.relu(x) + x.transpose_(2, 3) + x = self.max_pool2d(x) + y = self.conv_transpose(x) + z = F.conv_transpose2d(x, self.conv_transpose.weight) + x = y + z + x = self.conv_depthwise(x) + x = F.conv2d(x, self.conv_depthwise.weight, groups=self.conv_depthwise.groups) + x += torch.ones_like(x) + x = self.adaptive_avg_pool(x) + x = self.linear(x.flatten()) + return x + + def test_compress_weights(): model = ShortTransformer(5, 10) @@ -70,6 +125,39 @@ def test_compress_weights(): assert n_compressed_weights == n_target_modules +def test_compress_weights_functional_model(): + model = FunctionalModel() + + input_ids = torch.randint(0, 10, [1, 3, 300, 300]) + wrapped_model = wrap_model(model, example_input=input_ids, trace_parameters=True) + compressed_model = compress_weights(wrapped_model) + + n_compressed_weights = 0 + for layer in compressed_model.nncf.external_op.values(): + if isinstance(layer, WeightsDecompressor): + n_compressed_weights += 1 + assert n_compressed_weights == 4 + + +def test_compress_weights_conv(): + model = ConvolutionModel() + + input_ids = torch.randint(0, 10, [1, 3, 300, 300]) + wrapped_model = wrap_model(model, example_input=input_ids, trace_parameters=True) + compressed_model = compress_weights(wrapped_model) + + n_compressed_weights = 0 + n_target_modules = 0 + + for _, module in compressed_model.named_children(): + if isinstance(module, (torch.nn.Linear, torch.nn.Conv2d, torch.nn.ConvTranspose2d)): + n_target_modules += 1 + if module.weight.dtype in [torch.uint8, torch.int8]: + n_compressed_weights += 1 + + assert n_compressed_weights == n_target_modules + + def test_compress_shared_weights(mocker): model = ShortTransformer(5, 10, share_weights=True) diff --git a/tests/torch/ptq/test_wrap_model.py b/tests/torch/ptq/test_wrap_model.py index 19a8823aee0..e2c54d14667 100644 --- a/tests/torch/ptq/test_wrap_model.py +++ b/tests/torch/ptq/test_wrap_model.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/pytorch_patch_isolated.py b/tests/torch/pytorch_patch_isolated.py index 3f347eceb00..d26074f36c2 100644 --- a/tests/torch/pytorch_patch_isolated.py +++ b/tests/torch/pytorch_patch_isolated.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -9,6 +9,7 @@ # See the License for the specific language governing permissions and # limitations under the License. +import contextlib import inspect import os @@ -66,10 +67,8 @@ def test_jit_script_exception_preserves_patching_isolated(): ), ) - try: + with contextlib.suppress(Exception): torch.jit.script(compressed_model) # supposed to fail since torch.jit.script does not support NNCF models - except: # noqa: E722 - pass # torch.nn.Module.__call__ is one of the fundamental patched functions, if the code object points to NNCF code, # then it means patching is still present diff --git a/tests/torch/quantization/__init__.py b/tests/torch/quantization/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/torch/quantization/__init__.py +++ b/tests/torch/quantization/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/quantization/extensions/__init__.py b/tests/torch/quantization/extensions/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/torch/quantization/extensions/__init__.py +++ b/tests/torch/quantization/extensions/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/quantization/extensions/isolated_cases.py b/tests/torch/quantization/extensions/isolated_cases.py index ecad13c7bed..73c96ea704b 100644 --- a/tests/torch/quantization/extensions/isolated_cases.py +++ b/tests/torch/quantization/extensions/isolated_cases.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -21,6 +21,7 @@ import pytest import torch +import nncf from tests.shared.isolation_runner import ISOLATION_RUN_ENV_VAR @@ -76,7 +77,7 @@ def side_effect_fn(name, *args, **kwargs): patched_fn = mocker.patch("torch.cuda.is_available") patched_fn.return_value = True # loading should trigger an exception and a message to the user - with pytest.raises(RuntimeError) as exc_info: + with pytest.raises(nncf.InstallationError) as exc_info: from nncf.torch.quantization.extensions import QuantizedFunctionsCUDALoader QuantizedFunctionsCUDALoader.load() diff --git a/tests/torch/quantization/extensions/test_extension_unavailable.py b/tests/torch/quantization/extensions/test_extension_unavailable.py index b4ebbcdb94f..0fcc3093799 100644 --- a/tests/torch/quantization/extensions/test_extension_unavailable.py +++ b/tests/torch/quantization/extensions/test_extension_unavailable.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/quantization/extensions/test_timeout_extension_loader.py b/tests/torch/quantization/extensions/test_timeout_extension_loader.py index 81ff5dc3f53..b32505528e3 100644 --- a/tests/torch/quantization/extensions/test_timeout_extension_loader.py +++ b/tests/torch/quantization/extensions/test_timeout_extension_loader.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/quantization/quantization_helpers.py b/tests/torch/quantization/quantization_helpers.py index a8f5f213552..16f2c83e7c7 100644 --- a/tests/torch/quantization/quantization_helpers.py +++ b/tests/torch/quantization/quantization_helpers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/quantization/test_adjust_padding.py b/tests/torch/quantization/test_adjust_padding.py index e5ef520ab30..e2a2018b15f 100644 --- a/tests/torch/quantization/test_adjust_padding.py +++ b/tests/torch/quantization/test_adjust_padding.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -89,13 +89,13 @@ def trial(self, num_bits_for_weights: int = 8, num_bits_for_activations: int = 8 self._config_update["compression"].update(trial_config) return self - def vpu(self): - self._config_update["target_device"] = "VPU" + def npu(self): + self._config_update["target_device"] = "NPU" return self def custom_hw(self): custom_hw_config_dict = { - "target_device": "VPU", + "target_device": "NPU", "config": { "quantization": { "q4": {"bits": 4, "mode": "symmetric", "granularity": "pertensor"}, @@ -131,26 +131,26 @@ def get_config(self): ADJUST_PAD_DESC_LIST = [ - MultiBranchesModelDesc(name="vpu_all_int8").vpu(), - MultiBranchesModelDesc(name="vpu_all_weights_int8").vpu().manual_precision([8, 8, 8, 8], [8, 4, 4, 4]), - MultiBranchesModelDesc(name="vpu_all_activations_int8").vpu().manual_precision([8, 4, 4, 4], [8, 8, 8, 4]), - MultiBranchesModelDesc(name="vpu_bd_int8").vpu().manual_precision([4, 4, 4, 4], [8, 8, 4, 8]), - MultiBranchesModelDesc(name="vpu_max_int4").vpu().manual_precision([4, 4, 4, 4], [8, 4, 4, 4]), - MultiBranchesModelDesc(name="vpu_all_int8_requnt").vpu().requant_prop_strategy(), - MultiBranchesModelDesc(name="vpu_all_weights_int8_requnt") - .vpu() + MultiBranchesModelDesc(name="npu_all_int8").npu(), + MultiBranchesModelDesc(name="npu_all_weights_int8").npu().manual_precision([8, 8, 8, 8], [8, 4, 4, 4]), + MultiBranchesModelDesc(name="npu_all_activations_int8").npu().manual_precision([8, 4, 4, 4], [8, 8, 8, 4]), + MultiBranchesModelDesc(name="npu_bd_int8").npu().manual_precision([4, 4, 4, 4], [8, 8, 4, 8]), + MultiBranchesModelDesc(name="npu_max_int4").npu().manual_precision([4, 4, 4, 4], [8, 4, 4, 4]), + MultiBranchesModelDesc(name="npu_all_int8_requnt").npu().requant_prop_strategy(), + MultiBranchesModelDesc(name="npu_all_weights_int8_requnt") + .npu() .manual_precision([8, 8, 8, 8], [8, 4, 4, 4]) .requant_prop_strategy(), - MultiBranchesModelDesc(name="vpu_all_activations_int8_requnt") - .vpu() + MultiBranchesModelDesc(name="npu_all_activations_int8_requnt") + .npu() .manual_precision([8, 4, 4, 4], [8, 8, 8, 4]) .requant_prop_strategy(), - MultiBranchesModelDesc(name="vpu_bd_int8_requnt") - .vpu() + MultiBranchesModelDesc(name="npu_bd_int8_requnt") + .npu() .manual_precision([4, 4, 4, 4], [8, 8, 4, 8]) .requant_prop_strategy(), - MultiBranchesModelDesc(name="vpu_max_int4_requnt") - .vpu() + MultiBranchesModelDesc(name="npu_max_int4_requnt") + .npu() .manual_precision([4, 4, 4, 4], [8, 4, 4, 4]) .requant_prop_strategy(), MultiBranchesModelDesc(name="custom").custom_hw(), @@ -175,7 +175,7 @@ def test_adjust_padding_on_synthetic_models(desc: MultiBranchesModelDesc, mocker def test_onnx_export_to_fake_quantize_with_adjust_pad(tmp_path): - desc = MultiBranchesModelDesc(name="vpu_max_int4").vpu().manual_precision([4, 4, 4, 4], [8, 4, 4, 4]) + desc = MultiBranchesModelDesc(name="npu_max_int4").npu().manual_precision([4, 4, 4, 4], [8, 4, 4, 4]) model = desc.get_model() nncf_config = desc.get_config() register_bn_adaptation_init_args(nncf_config) diff --git a/tests/torch/quantization/test_algo_quantization.py b/tests/torch/quantization/test_algo_quantization.py index 89a95d164c8..c0d28bcef39 100644 --- a/tests/torch/quantization/test_algo_quantization.py +++ b/tests/torch/quantization/test_algo_quantization.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -196,7 +196,7 @@ def test_can_load_quant_algo__with_defaults(): quant_scope: Scope = deepcopy(module_scope) quant_scope.pop() quant_scope.push(ScopeElement("NNCFConv2d", "conv")) - assert quant_scope in quant_model_conv.keys() + assert quant_scope in quant_model_conv store = [] for op in quant_model_conv[quant_scope].pre_ops.values(): @@ -879,7 +879,7 @@ def test_activation_ignored_scope(update_config_info, should_ignore_quantizers): train_loader = create_random_mock_dataloader(config, num_samples=10) config = register_default_init_args(config, train_loader) ctrl, _ = create_compressed_model(model, config) - assert Counter([item.target_node_name for item in ctrl.all_quantizations.keys()]) == Counter(ref_quantization_names) + assert Counter([item.target_node_name for item in ctrl.all_quantizations]) == Counter(ref_quantization_names) def test_sync_of_level_ranges_and_signed_parameter(): diff --git a/tests/torch/quantization/test_autoq_precision_init.py b/tests/torch/quantization/test_autoq_precision_init.py index 4f725cb1a52..084caf8f6d1 100644 --- a/tests/torch/quantization/test_autoq_precision_init.py +++ b/tests/torch/quantization/test_autoq_precision_init.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -45,7 +45,7 @@ def __init__( self._config = self.create_autoq_test_config( batch_size, image_size, num_channels, num_init_samples=num_init_samples ) - self.for_vpu() + self.for_npu() def eval_subset_ratio(self, eval_subset_ratio): self._options["eval_subset_ratio"] = str(eval_subset_ratio) @@ -90,8 +90,8 @@ def create_autoq_test_config(batch_size=10, image_size=10, num_channels=3, num_i class AutoQTestStruct(NamedTuple): model_creator: Callable[[], nn.Module] = mobilenet_v2 - config_builder: AutoQConfigBuilder = AutoQConfigBuilder().for_vpu() - filename_suffix: str = "hw_config_vpu" + config_builder: AutoQConfigBuilder = AutoQConfigBuilder().for_npu() + filename_suffix: str = "hw_config_npu" def __str__(self): return "_".join([self.model_creator.__name__, str(self.config_builder)]) diff --git a/tests/torch/quantization/test_functions.py b/tests/torch/quantization/test_functions.py index edec62bb1b7..b468f8c43d5 100644 --- a/tests/torch/quantization/test_functions.py +++ b/tests/torch/quantization/test_functions.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -393,15 +393,15 @@ def test_quantize_symmetric_backward( class TestAsymmetric: @classmethod - def generate_range(cls, input_size, scale_mode, is_weights, is_fp16, fixed=None): + def generate_range(cls, input_size, scale_mode, is_weights, is_fp16, levels, fixed=None): np_dtype = np.float16 if is_fp16 else np.float32 return map( lambda x: x.astype(np_dtype), - cls.generate_range_fp64(input_size, scale_mode, is_weights, fixed, is_fp16), + cls.generate_range_fp64(input_size, scale_mode, is_weights, fixed, is_fp16, levels), ) @staticmethod - def generate_range_fp64(input_size, scale_mode, is_weights, fixed, is_fp16): + def generate_range_fp64(input_size, scale_mode, is_weights, fixed, is_fp16, levels): assert scale_mode in ["single_scale", "per_channel_scale"] if fixed is not None: @@ -415,6 +415,19 @@ def calc_low_and_range(): min_range = 1.0 if is_fp16 else 0.1 input_low = np.random.random_sample() * 3 - 1.5 input_range = min_range + np.random.random_sample() * 3 + if levels <= 16 and is_fp16: + scale = (levels - 1) / input_range + zp_fp = -input_low * scale + # Checks zp_fp is not near the middle of two integer values + # and fix it in case it is. + if np.abs(np.abs(zp_fp - np.round(zp_fp)) - 0.5) < 0.1: + # Make zp_fp += 0.2 + new_input_low = input_low + 0.2 / scale + # Preserve sing of the input_low + if np.sign(new_input_low) != np.sign(input_low): + input_low -= 0.2 / scale + else: + input_low = new_input_low return input_low, input_range if scale_mode == "single_scale": @@ -465,7 +478,7 @@ def test_quantize_asymmetric_forward( np_dtype = np.float32 level_low, level_high, levels = self.get_range_level(bits) - ref_input_low, ref_input_range = self.generate_range(input_size, scale_mode, is_weights, is_fp16) + ref_input_low, ref_input_range = self.generate_range(input_size, scale_mode, is_weights, is_fp16, levels) test_input_low, test_input_range = get_test_data( [ref_input_low, ref_input_range], use_cuda, is_fp16=is_fp16 ) @@ -529,7 +542,9 @@ def test_quantize_asymmetric_backward(self, _seed, input_size, bits, use_cuda, i fixed = {} fixed["input_low"] = -(2 ** (bits - 1)) fixed["input_range"] = 2**bits - 1 - ref_input_low, ref_input_range = self.generate_range(input_size, scale_mode, is_weights, is_fp16, fixed) + ref_input_low, ref_input_range = self.generate_range( + input_size, scale_mode, is_weights, is_fp16, levels, fixed + ) test_input_low, test_input_range = get_test_data( [ref_input_low, ref_input_range], use_cuda, is_backward=True, is_fp16=is_fp16 ) diff --git a/tests/torch/quantization/test_hawq_precision_init.py b/tests/torch/quantization/test_hawq_precision_init.py index b551d42de60..b70dd897632 100644 --- a/tests/torch/quantization/test_hawq_precision_init.py +++ b/tests/torch/quantization/test_hawq_precision_init.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -26,6 +26,7 @@ from torchvision.models import resnet50 from torchvision.transforms import transforms +import nncf from examples.common.sample_config import SampleConfig from examples.torch.classification.main import create_cifar from examples.torch.object_detection.models.ssd_vgg import SSD_VGG @@ -145,8 +146,8 @@ def _set_target_device(self, config_type: str): self._options["device"] = config_type return self - def for_vpu(self): - return self._set_target_device(HWConfigType.VPU.value) + def for_npu(self): + return self._set_target_device(HWConfigType.NPU.value) def for_cpu(self): return self._set_target_device(HWConfigType.CPU.value) @@ -206,8 +207,8 @@ def liberal_mode(self): def build(self): return self._config - def for_vpu(self): - super().for_vpu() + def for_npu(self): + super().for_npu() return self.strict_mode() def check_compression_ratio(self, compression_ratio=1.5): @@ -280,8 +281,8 @@ def check_bitwidth_graph(algo_ctrl, model, path_to_dot, graph_dir, add_flops=Fal class HAWQTestStruct(NamedTuple): model_creator: Callable[[], nn.Module] = mobilenet_v2 - config_builder: HAWQConfigBuilder = HAWQConfigBuilder().for_vpu() - filename_suffix: str = "hw_config_vpu" + config_builder: HAWQConfigBuilder = HAWQConfigBuilder().for_npu() + filename_suffix: str = "hw_config_npu" avg_traces_creator: Callable[[nn.Module, str], torch.Tensor] = get_avg_traces def __str__(self): @@ -303,24 +304,24 @@ def __str__(self): HAWQTestStruct(config_builder=HAWQConfigBuilder().staged()), HAWQTestStruct(config_builder=HAWQConfigBuilder().for_trial()), HAWQTestStruct(config_builder=HAWQConfigBuilder().for_cpu()), - HAWQTestStruct(config_builder=HAWQConfigBuilder().for_vpu().liberal_mode().with_ratio(1.5)), - HAWQTestStruct(config_builder=HAWQConfigBuilder().with_ratio(1.02).for_vpu()), + HAWQTestStruct(config_builder=HAWQConfigBuilder().for_npu().liberal_mode().with_ratio(1.5)), + HAWQTestStruct(config_builder=HAWQConfigBuilder().with_ratio(1.02).for_npu()), HAWQTestStruct( - model_creator=squeezenet1_1, config_builder=HAWQConfigBuilder().with_sample_size([1, 3, 224, 224]).for_vpu() + model_creator=squeezenet1_1, config_builder=HAWQConfigBuilder().with_sample_size([1, 3, 224, 224]).for_npu() ), - HAWQTestStruct(model_creator=resnet50, config_builder=HAWQConfigBuilder().with_ratio(1.11).for_vpu()), - HAWQTestStruct(model_creator=resnet50, config_builder=HAWQConfigBuilder().for_vpu().liberal_mode().with_ratio(1.5)), + HAWQTestStruct(model_creator=resnet50, config_builder=HAWQConfigBuilder().with_ratio(1.11).for_npu()), + HAWQTestStruct(model_creator=resnet50, config_builder=HAWQConfigBuilder().for_npu().liberal_mode().with_ratio(1.5)), HAWQTestStruct( model_creator=inception_v3, avg_traces_creator=lambda x, y: get_avg_traces(x, y)[:95], - config_builder=HAWQConfigBuilder().with_sample_size([2, 3, 299, 299]).for_vpu().with_ratio(1), + config_builder=HAWQConfigBuilder().with_sample_size([2, 3, 299, 299]).for_npu().with_ratio(1), ), HAWQTestStruct( model_creator=inception_v3, avg_traces_creator=lambda x, y: get_avg_traces(x, y)[:94], config_builder=HAWQConfigBuilder() .with_sample_size([2, 3, 299, 299]) - .for_vpu() + .for_npu() .liberal_mode() .with_ignored_scope( ["Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/conv2d_0"], target_group=QuantizerGroup.WEIGHTS @@ -332,7 +333,7 @@ def __str__(self): avg_traces_creator=lambda x, y: get_avg_traces(x, y)[:9], config_builder=HAWQConfigBuilder() .with_sample_size([2, 3, 299, 299]) - .for_vpu() + .for_npu() .liberal_mode() .with_target_scope([r"{re}.*InceptionE\[Mixed_7c\].*"]) .with_ratio(1.3) @@ -342,15 +343,15 @@ def __str__(self): HAWQTestStruct( model_creator=inception_v3, avg_traces_creator=lambda x, y: get_avg_traces(x, y)[:95], - config_builder=HAWQConfigBuilder().with_sample_size([2, 3, 299, 299]).for_vpu().liberal_mode().with_ratio(1.5), + config_builder=HAWQConfigBuilder().with_sample_size([2, 3, 299, 299]).for_npu().liberal_mode().with_ratio(1.5), ), HAWQTestStruct( model_creator=ssd_vgg_512_test, - config_builder=HAWQConfigBuilder().with_sample_size([1, 3, 512, 512]).for_vpu().with_ratio(1.09), + config_builder=HAWQConfigBuilder().with_sample_size([1, 3, 512, 512]).for_npu().with_ratio(1.09), ), HAWQTestStruct( model_creator=ssd_vgg_512_test, - config_builder=HAWQConfigBuilder().with_sample_size([1, 3, 512, 512]).for_vpu().liberal_mode().with_ratio(1.5), + config_builder=HAWQConfigBuilder().with_sample_size([1, 3, 512, 512]).for_npu().liberal_mode().with_ratio(1.5), ), ) @@ -430,8 +431,8 @@ def test_can_choose_pareto_optimal_sequence(ratios): assert compression_ratio_per_qconfig[qconfig_sequence_index] == expected_ratio -def test_hawq_hw_vpu_config_e2e(_seed, dataset_dir, tmp_path): - config = HAWQConfigBuilder().for_vpu().liberal_mode().with_ratio(1.5).build() +def test_hawq_hw_npu_config_e2e(_seed, dataset_dir, tmp_path): + config = HAWQConfigBuilder().for_npu().liberal_mode().with_ratio(1.5).build() model = MobileNetV2(num_classes=10) criterion = nn.CrossEntropyLoss() if not dataset_dir: @@ -627,7 +628,7 @@ def test_can_broadcast_initialized_precisions_in_distributed_mode(tmp_path, runs assert not compare_multi_gpu_dump(config, tmp_path, get_path_to_bitwidth_dump) -@pytest.mark.parametrize(("method_name", "expected_behavior"), [("_calc_traces", pytest.raises(RuntimeError))]) +@pytest.mark.parametrize(("method_name", "expected_behavior"), [("_calc_traces", pytest.raises(nncf.InternalError))]) def test_hawq_behaviour__if_method_returns_none(mocker, method_name, expected_behavior): config = HAWQConfigBuilder().with_sample_size([1, 1, 4, 4]).for_trial().build() config["compression"]["initializer"]["range"]["num_init_samples"] = 0 diff --git a/tests/torch/quantization/test_hw_config.py b/tests/torch/quantization/test_hw_config.py index ee91a648732..67907bf3cac 100644 --- a/tests/torch/quantization/test_hw_config.py +++ b/tests/torch/quantization/test_hw_config.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/quantization/test_layers.py b/tests/torch/quantization/test_layers.py new file mode 100644 index 00000000000..168f9150ded --- /dev/null +++ b/tests/torch/quantization/test_layers.py @@ -0,0 +1,48 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytest +import torch + +from nncf.common.quantization.structs import QuantizationScheme as QuantizationMode +from nncf.torch.quantization.layers import QUANTIZATION_MODULES +from nncf.torch.quantization.layers import PTQuantizerSpec + + +@pytest.mark.parametrize("quantizer_cls", QUANTIZATION_MODULES.values()) +def test_quantizer_layers_accepts_return_type(quantizer_cls): + actual_input = torch.range(0, 10) + input_ = torch.return_types.max((actual_input, actual_input)) + quantizer_spec = PTQuantizerSpec( + num_bits=8, + mode=QuantizationMode.SYMMETRIC, + signedness_to_force=True, + narrow_range=True, + half_range=True, + scale_shape=(1,), + logarithm_scale=False, + ) + quantizer = quantizer_cls(quantizer_spec) + + visited = False + + def check_types(fn): + def wrapped(x: torch.Tensor): + assert isinstance(x, torch.Tensor) + nonlocal visited + visited = True + return fn(x) + + return wrapped + + quantizer._forward_impl = check_types(quantizer._forward_impl) + quantizer(input_) + assert visited diff --git a/tests/torch/quantization/test_logarithm_scale.py b/tests/torch/quantization/test_logarithm_scale.py index 41b7ed22fd3..496032764a4 100644 --- a/tests/torch/quantization/test_logarithm_scale.py +++ b/tests/torch/quantization/test_logarithm_scale.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/quantization/test_manual_precision_init.py b/tests/torch/quantization/test_manual_precision_init.py index 8febc8e18fd..39fea875fd0 100644 --- a/tests/torch/quantization/test_manual_precision_init.py +++ b/tests/torch/quantization/test_manual_precision_init.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -158,10 +158,10 @@ def num_bits_for_activation(self, num_bits): MANUAL_SINGLE_CONV_TEST_PARAMS = [ ManualSingleConvTestParams(name="manual_init_multiple_int8_qconfigs").for_device("CPU").num_bits_for_activation(8), - ManualSingleConvTestParams(name="manual_init_int4_sym_int8_asym").for_device("VPU").num_bits_for_activation(4), + ManualSingleConvTestParams(name="manual_init_int4_sym_int8_asym").for_device("NPU").num_bits_for_activation(4), ManualSingleConvTestParams(name="manual_init_trial").for_device("TRIAL").num_bits_for_activation(4), ManualSingleConvTestParams(name="incompatible_bitwidth") - .for_device("VPU") + .for_device("NPU") .num_bits_for_activation(2) .raises_error(), ] diff --git a/tests/torch/quantization/test_onnx_export.py b/tests/torch/quantization/test_onnx_export.py index 7b875573617..27aab3ebc4a 100644 --- a/tests/torch/quantization/test_onnx_export.py +++ b/tests/torch/quantization/test_onnx_export.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/quantization/test_overflow_issue_export.py b/tests/torch/quantization/test_overflow_issue_export.py index 2c12987a205..1c53d420861 100644 --- a/tests/torch/quantization/test_overflow_issue_export.py +++ b/tests/torch/quantization/test_overflow_issue_export.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -107,7 +107,7 @@ def test_config_option_disable_overflow_fix(): nncf_config = get_config_for_export_mode(True) nncf_config.update({"compression": {"algorithm": "quantization", "overflow_fix": "disable"}}) - for device in ["CPU", "ANY", "VPU", "GPU", "TRIAL"]: + for device in ["CPU", "ANY", "NPU", "GPU", "TRIAL"]: helper_to_test_if_overflow_fix_wasnt_applied(nncf_config, device) nncf_config.update({"compression": {"algorithm": "quantization", "overflow_fix": "enable"}}) @@ -115,7 +115,7 @@ def test_config_option_disable_overflow_fix(): for device in ["CPU", "ANY"]: helper_to_test_if_overflow_fix_was_applied(nncf_config, device) - for device in ["VPU", "GPU", "TRIAL"]: + for device in ["NPU", "GPU", "TRIAL"]: helper_to_test_if_overflow_fix_wasnt_applied(nncf_config, device) nncf_config.update({"compression": {"algorithm": "quantization", "overflow_fix": "first_layer_only"}}) @@ -123,7 +123,7 @@ def test_config_option_disable_overflow_fix(): for device in ["CPU", "ANY"]: helper_to_test_if_overflow_fix_was_applied_only_to_first_conv_later(nncf_config, device) - for device in ["VPU", "GPU", "TRIAL"]: + for device in ["NPU", "GPU", "TRIAL"]: helper_to_test_if_overflow_fix_wasnt_applied(nncf_config, device) @@ -133,7 +133,7 @@ def test_hw_config_overflow_fix_applied(): for device in ["CPU", "ANY"]: helper_to_test_if_overflow_fix_was_applied(nncf_config, device) - for device in ["VPU", "GPU", "TRIAL"]: + for device in ["NPU", "GPU", "TRIAL"]: helper_to_test_if_overflow_fix_wasnt_applied(nncf_config, device) diff --git a/tests/torch/quantization/test_quantization_metric.py b/tests/torch/quantization/test_quantization_metric.py index 8b6828a03b0..a7fff0d395b 100644 --- a/tests/torch/quantization/test_quantization_metric.py +++ b/tests/torch/quantization/test_quantization_metric.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/quantization/test_range_init.py b/tests/torch/quantization/test_range_init.py index 84c20da89bb..77be5989cee 100644 --- a/tests/torch/quantization/test_range_init.py +++ b/tests/torch/quantization/test_range_init.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -23,6 +23,7 @@ from torch.utils.data import DataLoader from torchvision.models import squeezenet1_1 +import nncf import nncf.torch.tensor_statistics.collectors as pt_collectors from nncf.common.graph import NNCFNodeName from nncf.common.quantization.initialization.range import PerLayerRangeInitConfig @@ -171,7 +172,11 @@ def generate_qp( elif target is QuantizerGroup.ACTIVATIONS: qip = ActivationQuantizationInsertionPoint(target_node_name=node_name, input_port_id=input_port_id) else: - raise RuntimeError() + raise nncf.InvalidQuantizerGroupError( + f"Invalid quantizer group: {target}. " + f"Supported groups are {QuantizerGroup.WEIGHTS}" + f"and {QuantizerGroup.ACTIVATIONS}." + ) return SingleConfigQuantizationPoint(qip, QuantizerConfig(), [node_name]) diff --git a/tests/torch/quantization/test_sanity_sample.py b/tests/torch/quantization/test_sanity_sample.py index 7091b7e43de..4b9a5929fec 100644 --- a/tests/torch/quantization/test_sanity_sample.py +++ b/tests/torch/quantization/test_sanity_sample.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -70,10 +70,6 @@ def setup_spy(self, mocker): train_location = self._sample_handler.get_train_location() self._train_mock = mocker.patch(train_location) - # Need to mock SafeMLFLow to prevent starting a not closed mlflow session due to memory leak of config and - # SafeMLFLow, which happens with a mocked train function - self._sample_handler.mock_mlflow(mocker) - def validate_spy(self): self._train_mock.assert_called_once() diff --git a/tests/torch/quantization/test_scheduler.py b/tests/torch/quantization/test_scheduler.py index beb54c07bcd..1abe7f6c0a6 100644 --- a/tests/torch/quantization/test_scheduler.py +++ b/tests/torch/quantization/test_scheduler.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/quantization/test_serialize_to_json.py b/tests/torch/quantization/test_serialize_to_json.py index 29bd4e2a11b..fdccb80e0af 100644 --- a/tests/torch/quantization/test_serialize_to_json.py +++ b/tests/torch/quantization/test_serialize_to_json.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/quantization/test_solver_quantization_traits.py b/tests/torch/quantization/test_solver_quantization_traits.py index b9656d16b4a..221bf296d7b 100644 --- a/tests/torch/quantization/test_solver_quantization_traits.py +++ b/tests/torch/quantization/test_solver_quantization_traits.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/quantization/test_strip.py b/tests/torch/quantization/test_strip.py index e1127572555..3454c839e08 100644 --- a/tests/torch/quantization/test_strip.py +++ b/tests/torch/quantization/test_strip.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/quantization/test_tracing.py b/tests/torch/quantization/test_tracing.py index d054c175a92..578664f75a1 100644 --- a/tests/torch/quantization/test_tracing.py +++ b/tests/torch/quantization/test_tracing.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -30,13 +30,12 @@ def check_fq_op(traced_graph: nn.Module, is_per_channel: bool): aten_op = "aten::fake_quantize_per_channel_affine" if is_per_channel else "aten::fake_quantize_per_tensor_affine" is_fq_node = False for graph_node in traced_graph.inlined_graph.nodes(): - if graph_node.kind() == "prim::PythonOp": - if "Subgraph" in graph_node.attributeNames(): - subgraph = getattr(graph_node, graph_node.kindOf("Subgraph"))("Subgraph") - for subgraph_node in subgraph.nodes(): - if subgraph_node.kind() == aten_op: - is_fq_node = True - break + if graph_node.kind() == "prim::PythonOp" and "Subgraph" in graph_node.attributeNames(): + subgraph = getattr(graph_node, graph_node.kindOf("Subgraph"))("Subgraph") + for subgraph_node in subgraph.nodes(): + if subgraph_node.kind() == aten_op: + is_fq_node = True + break if is_fq_node: break diff --git a/tests/torch/quantization/test_unified_scales.py b/tests/torch/quantization/test_unified_scales.py index f98757f824f..0ce774dd90f 100644 --- a/tests/torch/quantization/test_unified_scales.py +++ b/tests/torch/quantization/test_unified_scales.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -19,6 +19,7 @@ import torch import torch.nn +import nncf from nncf.common.graph import NNCFNodeName from nncf.common.graph.transformations.commands import TargetType from nncf.common.hardware.config import HWConfigType @@ -288,7 +289,7 @@ def test_insertion_point_coalescing( ref_coalesced_ip_lists: List[List[PTTargetPoint]], ): if ref_coalesced_ip_lists is None: - with pytest.raises(RuntimeError): + with pytest.raises((nncf.InternalError, nncf.ValidationError)): _ = QuantizerPropagationSolver.coalesce_insertion_points(input_insertion_points, linked_scopes_groups_list) else: test_coalesced_ip_lists = QuantizerPropagationSolver.coalesce_insertion_points( @@ -375,7 +376,7 @@ def test_quantizer_scale_linking(mocker): assert non_shared_spy.call_count == 1 -def test_eltwise_unified_scales_for_vpu(): +def test_eltwise_unified_scales_for_npu(): nncf_config = get_quantization_config_without_range_init(model_size=1) nncf_config["input_info"] = [ { @@ -385,7 +386,7 @@ def test_eltwise_unified_scales_for_vpu(): "sample_size": [1, 1, 1, 1], }, ] - nncf_config["target_device"] = "VPU" + nncf_config["target_device"] = "NPU" register_bn_adaptation_init_args(nncf_config) _, compression_ctrl = create_compressed_model_and_algo_for_test(EltwiseQuantizerLinkingTestModel(), nncf_config) @@ -577,7 +578,7 @@ def test_unified_scales_are_identical_in_onnx(self, tmp_path): "sample_size": [1, 1, 1, 2], }, ] - nncf_config["target_device"] = "VPU" + nncf_config["target_device"] = "NPU" register_bn_adaptation_init_args(nncf_config) compressed_model, compression_ctrl = create_compressed_model_and_algo_for_test( @@ -632,7 +633,7 @@ def test_weight_and_act_quantizer_scale_unification(self, tmp_path): nncf_config["input_info"] = [ {"sample_size": [1, 5], "type": "long", "filler": "zeros"}, ] - nncf_config["target_device"] = "VPU" + nncf_config["target_device"] = "NPU" register_bn_adaptation_init_args(nncf_config) compressed_model, compression_ctrl = create_compressed_model_and_algo_for_test( @@ -701,7 +702,7 @@ def test_unified_scales_with_shared_nodes(): nncf_config["input_info"] = [ {"sample_size": [1, 5], "type": "long", "filler": "zeros"}, ] - nncf_config["target_device"] = "VPU" + nncf_config["target_device"] = "NPU" register_bn_adaptation_init_args(nncf_config) _, compression_ctrl = create_compressed_model_and_algo_for_test( diff --git a/tests/torch/requirements.txt b/tests/torch/requirements.txt index 891f2176a4e..2042c1b6312 100644 --- a/tests/torch/requirements.txt +++ b/tests/torch/requirements.txt @@ -15,4 +15,4 @@ accelerate==0.24.1 datasets~=2.14.0 evaluate==0.3.0 timm==0.9.2 -openvino-dev==2023.3 +openvino-dev==2024.0 diff --git a/tests/torch/run_examples_for_test_sota.py b/tests/torch/run_examples_for_test_sota.py index aba0837f05b..bd7fe9054ea 100644 --- a/tests/torch/run_examples_for_test_sota.py +++ b/tests/torch/run_examples_for_test_sota.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/sample_test_validator.py b/tests/torch/sample_test_validator.py index 9969b9258ca..1cafce603c0 100644 --- a/tests/torch/sample_test_validator.py +++ b/tests/torch/sample_test_validator.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -40,6 +40,7 @@ class SampleType(Enum): CLASSIFICATION = "classification" CLASSIFICATION_STAGED = "classification_staged" CLASSIFICATION_NAS = "classification_nas" + CLASSIFICATION_NAS_SEARCH = "classification_nas_search" SEMANTIC_SEGMENTATION = "semantic_segmentation" OBJECT_DETECTION = "object_detection" @@ -83,10 +84,6 @@ def get_train_location(self) -> str: """ return self.get_sample_location() + ".train" - def mock_mlflow(self, mocker): - mlflow_location = self.get_sample_location() + ".SafeMLFLow" - mocker.patch(mlflow_location) - @staticmethod def get_checkpoint_path( checkpoint_save_dir: str, checkpoint_name: Optional[str] = None, config_path: Optional[Path] = None @@ -143,6 +140,12 @@ def get_optimal_subnet_accuracy(self): pass +@SAMPLE_HANDLERS.register(SampleType.CLASSIFICATION_NAS_SEARCH) +class ClassificationNASSearchHandler(ClassificationNASHandler): + def _get_main_filename(self): + return "bootstrap_nas_search" + + @SAMPLE_HANDLERS.register(SampleType.OBJECT_DETECTION) class ObjectDetectionHandler(BaseSampleHandler): def get_metric_value_from_checkpoint( @@ -246,7 +249,7 @@ def get_compression_section(self): def get_config_update(self) -> Dict: sample_params = self.get_sample_params() - return {**sample_params, "target_device": "VPU", "compression": self.get_compression_section()} + return {**sample_params, "target_device": "NPU", "compression": self.get_compression_section()} def get_sample_params(self) -> Dict: return {"dataset": self.dataset_name} if self.dataset_name else {} diff --git a/tests/torch/sdl/__init__.py b/tests/torch/sdl/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/torch/sdl/__init__.py +++ b/tests/torch/sdl/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/sdl/test_symlinks.py b/tests/torch/sdl/test_symlinks.py index 2749d6b8a43..afdfc40fee4 100644 --- a/tests/torch/sdl/test_symlinks.py +++ b/tests/torch/sdl/test_symlinks.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/sota_checkpoints_eval.json b/tests/torch/sota_checkpoints_eval.json index 66157b093f3..cc3daab2246 100644 --- a/tests/torch/sota_checkpoints_eval.json +++ b/tests/torch/sota_checkpoints_eval.json @@ -35,7 +35,7 @@ "resnet50_imagenet_int4_int8": { "config": "examples/torch/classification/configs/mixed_precision/resnet50_imagenet_mixed_int_hawq.json", "reference": "resnet50_imagenet", - "target_ov": 75.45, + "target_ov": 75.61, "target_pt": 75.86, "metric_type": "Acc@1", "resume": "resnet50_imagenet_int4_int8.pth", @@ -150,7 +150,7 @@ "mobilenet_v2_imagenet_int4_int8": { "config": "examples/torch/classification/configs/mixed_precision/mobilenet_v2_imagenet_mixed_int_hawq.json", "reference": "mobilenet_v2_imagenet", - "target_ov": 70.51, + "target_ov": 70.52, "target_pt": 70.57, "metric_type": "Acc@1", "resume": "mobilenet_v2_imagenet_int4_int8.pth", @@ -185,7 +185,7 @@ "mobilenet_v3_small_imagenet_int8": { "config": "examples/torch/classification/configs/quantization/mobilenet_v3_small_imagenet_int8.json", "reference": "mobilenet_v3_small_imagenet", - "target_ov": 66.95, + "target_ov": 66.92, "target_pt": 66.97, "metric_type": "Acc@1", "resume": "mobilenet_v3_small_imagenet_int8.pth", @@ -228,7 +228,7 @@ "squeezenet1_1_imagenet_int4_int8": { "config": "examples/torch/classification/configs/mixed_precision/squeezenet1_1_imagenet_mixed_int_hawq_old_eval.json", "reference": "squeezenet1_1_imagenet", - "target_ov": 57.61, + "target_ov": 57.53, "target_pt": 57.59, "metric_type": "Acc@1", "resume": "squeezenet1_1_imagenet_int4_int8.pth", @@ -264,13 +264,15 @@ "config": "examples/torch/classification/configs/binarization/resnet18_imagenet_binarization_dorefa.json", "reference": "resnet18_imagenet", "target_ov": null, - "target_pt": 61.49, + "target_pt": 61.6, "metric_type": "Acc@1", "resume": "resnet18_imagenet_binarization_dorefa.pth", "model_description": "ResNet-18", "compression_description": "DoReFa (weights), scale/threshold (activations)", "diff_fp32_min": -8.33, "diff_fp32_max": 0.1, + "diff_target_pt_min": -0.3, + "diff_target_pt_max": 0.3, "skip_ov": "Issue-22543" }, "resnet18_imagenet_pruning_magnitude": { diff --git a/tests/torch/sparsity/__init__.py b/tests/torch/sparsity/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/torch/sparsity/__init__.py +++ b/tests/torch/sparsity/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/sparsity/const/__init__.py b/tests/torch/sparsity/const/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/torch/sparsity/const/__init__.py +++ b/tests/torch/sparsity/const/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/sparsity/const/test_algo.py b/tests/torch/sparsity/const/test_algo.py index 412506baa1b..baf61a4057d 100644 --- a/tests/torch/sparsity/const/test_algo.py +++ b/tests/torch/sparsity/const/test_algo.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/sparsity/magnitude/test_algo.py b/tests/torch/sparsity/magnitude/test_algo.py index d0428a4e020..0dc2da03aa0 100644 --- a/tests/torch/sparsity/magnitude/test_algo.py +++ b/tests/torch/sparsity/magnitude/test_algo.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/sparsity/magnitude/test_helpers.py b/tests/torch/sparsity/magnitude/test_helpers.py index c01531421a4..c28e5411c32 100644 --- a/tests/torch/sparsity/magnitude/test_helpers.py +++ b/tests/torch/sparsity/magnitude/test_helpers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/sparsity/magnitude/test_modules.py b/tests/torch/sparsity/magnitude/test_modules.py index f6cd96493e9..2277c4e2d83 100644 --- a/tests/torch/sparsity/magnitude/test_modules.py +++ b/tests/torch/sparsity/magnitude/test_modules.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -13,6 +13,7 @@ import torch from torch import nn +from nncf.common.hook_handle import HookHandle from nncf.torch.layers import NNCFConv2d from nncf.torch.layers import NNCFLinear from nncf.torch.module_operations import UpdateWeight @@ -30,11 +31,11 @@ def __init__(self, layer): super().__init__() self.layer = layer sparsifier = BinaryMask(layer.weight.size()) - self.op_key = self.layer.register_pre_forward_operation(UpdateWeight(sparsifier)) + self.hook_handle: HookHandle = self.layer.register_pre_forward_operation(UpdateWeight(sparsifier)) @property def sparsifier(self): - return self.layer.get_pre_op(self.op_key).operand + return self.layer.get_pre_op(self.hook_handle.hook_id).operand def forward(self, x): return self.layer(x) diff --git a/tests/torch/sparsity/magnitude/test_scheduler.py b/tests/torch/sparsity/magnitude/test_scheduler.py index afe9eddc7db..cc627d86c2e 100644 --- a/tests/torch/sparsity/magnitude/test_scheduler.py +++ b/tests/torch/sparsity/magnitude/test_scheduler.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/sparsity/movement/__init__.py b/tests/torch/sparsity/movement/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/torch/sparsity/movement/__init__.py +++ b/tests/torch/sparsity/movement/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/sparsity/movement/helpers/__init__.py b/tests/torch/sparsity/movement/helpers/__init__.py index de9f1edc41a..6e2d7e4760c 100644 --- a/tests/torch/sparsity/movement/helpers/__init__.py +++ b/tests/torch/sparsity/movement/helpers/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/sparsity/movement/helpers/config.py b/tests/torch/sparsity/movement/helpers/config.py index ab2f6eba4bd..509988a6cf8 100644 --- a/tests/torch/sparsity/movement/helpers/config.py +++ b/tests/torch/sparsity/movement/helpers/config.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/sparsity/movement/helpers/run_recipe.py b/tests/torch/sparsity/movement/helpers/run_recipe.py index 562687bd563..77b3140a967 100644 --- a/tests/torch/sparsity/movement/helpers/run_recipe.py +++ b/tests/torch/sparsity/movement/helpers/run_recipe.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/sparsity/movement/helpers/trainer.py b/tests/torch/sparsity/movement/helpers/trainer.py index 7da241bf712..89ffeb6c865 100644 --- a/tests/torch/sparsity/movement/helpers/trainer.py +++ b/tests/torch/sparsity/movement/helpers/trainer.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/sparsity/movement/helpers/utils.py b/tests/torch/sparsity/movement/helpers/utils.py index 5df93ec24b9..51c4b744c6e 100644 --- a/tests/torch/sparsity/movement/helpers/utils.py +++ b/tests/torch/sparsity/movement/helpers/utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/sparsity/movement/test_algo.py b/tests/torch/sparsity/movement/test_algo.py index 694a56cd4c0..96da20d79e7 100644 --- a/tests/torch/sparsity/movement/test_algo.py +++ b/tests/torch/sparsity/movement/test_algo.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -20,6 +20,7 @@ from transformers.trainer_callback import TrainerControl from transformers.trainer_callback import TrainerState +import nncf from nncf.api.compression import CompressionStage from nncf.common.scopes import matches_any from nncf.common.scopes import should_consider_scope @@ -103,7 +104,7 @@ {"mode": "block", "sparse_factors": [2, 2], "target_scopes": "{re}attention"}, {"mode": "per_dim", "axis": 0, "target_scopes": "{re}query"}, ], - error=RuntimeError, + error=nncf.InternalError, match="matched by multiple", ), "duplicate_matches_same_config": dict( @@ -111,7 +112,7 @@ {"mode": "per_dim", "axis": 0, "target_scopes": "{re}attention"}, {"mode": "per_dim", "axis": 0, "target_scopes": "{re}query"}, ], - error=RuntimeError, + error=nncf.InternalError, match="matched by multiple", ), } @@ -218,7 +219,7 @@ def test_error_on_wrong_sparse_structure_by_scopes(self, desc: dict): "recipe", [Conv2dRunRecipe(), LinearRunRecipe().algo_config_(ignored_scopes=["{re}model"])] ) def test_error_on_no_supported_layers(self, recipe: BaseMockRunRecipe): - with pytest.raises(RuntimeError, match="No sparsifiable layer"): + with pytest.raises(nncf.InternalError, match="No sparsifiable layer"): create_compressed_model(recipe.model(), recipe.nncf_config(), dump_graphs=False) @pytest.mark.parametrize("enable_structured_masking", [True, False]) @@ -234,7 +235,7 @@ def test_can_create_structured_mask_handler_if_supported( handler = getattr(compression_ctrl, "_structured_mask_handler") assert isinstance(handler, StructuredMaskHandler) else: - with pytest.raises(RuntimeError, match=r"no supported model"): + with pytest.raises(nncf.UnsupportedModelError, match=r"no supported model"): create_compressed_model(recipe.model(), recipe.nncf_config(), dump_graphs=False) else: compression_ctrl, _ = create_compressed_model(recipe.model(), recipe.nncf_config(), dump_graphs=False) diff --git a/tests/torch/sparsity/movement/test_components.py b/tests/torch/sparsity/movement/test_components.py index 6bb9b412914..5db4292301f 100644 --- a/tests/torch/sparsity/movement/test_components.py +++ b/tests/torch/sparsity/movement/test_components.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/sparsity/movement/test_model_saving.py b/tests/torch/sparsity/movement/test_model_saving.py index 5bcaa26a7da..6d686ea7702 100644 --- a/tests/torch/sparsity/movement/test_model_saving.py +++ b/tests/torch/sparsity/movement/test_model_saving.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/sparsity/movement/test_scheduler.py b/tests/torch/sparsity/movement/test_scheduler.py index d5a003885ae..34d11241dc5 100644 --- a/tests/torch/sparsity/movement/test_scheduler.py +++ b/tests/torch/sparsity/movement/test_scheduler.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/sparsity/movement/test_structured_mask.py b/tests/torch/sparsity/movement/test_structured_mask.py index d3a918ffe69..5e5bcce7a11 100644 --- a/tests/torch/sparsity/movement/test_structured_mask.py +++ b/tests/torch/sparsity/movement/test_structured_mask.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/sparsity/movement/test_training.py b/tests/torch/sparsity/movement/test_training.py index 768d2f10af8..7424013f1e6 100644 --- a/tests/torch/sparsity/movement/test_training.py +++ b/tests/torch/sparsity/movement/test_training.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/sparsity/movement/test_training_with_third_party.py b/tests/torch/sparsity/movement/test_training_with_third_party.py deleted file mode 100644 index f353ac6bf33..00000000000 --- a/tests/torch/sparsity/movement/test_training_with_third_party.py +++ /dev/null @@ -1,87 +0,0 @@ -# Copyright (c) 2023 Intel Corporation -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# http://www.apache.org/licenses/LICENSE-2.0 -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from pathlib import Path - -import pytest - -from tests.torch.helpers import Command -from tests.torch.sparsity.movement.helpers import MRPC_CONFIG_FILE_NAME -from tests.torch.sparsity.movement.helpers import TRAINING_SCRIPTS_PATH -from tests.torch.test_sanity_third_party import TransformersVirtualEnvInstaller -from tests.torch.test_sanity_third_party import create_command_line - - -@pytest.fixture(scope="class") -def temp_folder(tmp_path_factory): - root_folder = tmp_path_factory.mktemp("movement_third_party") - folders = {"models": root_folder / "models", "venv": root_folder / "venv", "repo": root_folder / "repo"} - for folder in folders.values(): - Path(folder).mkdir(exist_ok=True, parents=True) - return folders - - -@pytest.mark.usefixtures("temp_folder") -class TestMovementWithTransformers: - @pytest.fixture(autouse=True) - def setup(self, temp_folder): - self.temp_folder = temp_folder - self.env = TransformersVirtualEnvInstaller(temp_folder["venv"], temp_folder["repo"]) - - @pytest.mark.dependency(name="install_transformers") - def test_install_transformers_env(self, third_party, pip_cache_dir): - if not third_party: - pytest.skip( - "Skip tests of movement sparsity with patched transformers package " - "since `--third-party-sanity` is False." - ) - self.env.install_env(pip_cache_dir) - - @pytest.mark.dependency(depends=["install_transformers"], name="glue_movement_train") - def test_movement_glue_train(self): - nncf_config = TRAINING_SCRIPTS_PATH / MRPC_CONFIG_FILE_NAME - output_dir = Path(self.temp_folder["models"], nncf_config.stem) - com_line = ( - "examples/pytorch/text-classification/run_glue.py --model_name_or_path " - "google/bert_uncased_L-2_H-128_A-2 --task_name mrpc --do_train " - " --per_gpu_train_batch_size 4 --learning_rate 1e-4 --num_train_epochs 4 --max_seq_length 128 " - " --max_train_samples 8 --output_dir {output_dir} --save_steps 200 --nncf_config {nncf_config} ".format( - output_dir=output_dir, nncf_config=nncf_config - ) - ) - runner = Command( - create_command_line( - com_line, self.env.VENV_ACTIVATE, self.env.PYTHON_EXECUTABLE, self.env.CUDA_VISIBLE_STRING - ), - self.env.TRANSFORMERS_REPO_PATH, - ) - runner.run() - assert Path(output_dir, "pytorch_model.bin").is_file() - - @pytest.mark.dependency(depends=["install_transformers", "glue_movement_train"]) - def test_movement_glue_eval(self): - nncf_config = TRAINING_SCRIPTS_PATH / MRPC_CONFIG_FILE_NAME - model_dir = Path(self.temp_folder["models"], nncf_config.stem) - output_dir = Path(self.temp_folder["models"], nncf_config.stem + "_eval") - com_line = ( - "examples/pytorch/text-classification/run_glue.py --model_name_or_path {model_dir}" - " --task_name mrpc --do_eval " - " --learning_rate 2e-5" - " --max_seq_length 128 --output_dir {output_dir}" - " --max_eval_samples 10" - " --nncf_config {nncf_config}".format(model_dir=model_dir, output_dir=output_dir, nncf_config=nncf_config) - ) - runner = Command( - create_command_line( - com_line, self.env.VENV_ACTIVATE, self.env.PYTHON_EXECUTABLE, self.env.CUDA_VISIBLE_STRING - ), - self.env.TRANSFORMERS_REPO_PATH, - ) - runner.run() diff --git a/tests/torch/sparsity/movement/training_scripts/run_glue.py b/tests/torch/sparsity/movement/training_scripts/run_glue.py index 4261fd0c54e..360832a5bb7 100644 --- a/tests/torch/sparsity/movement/training_scripts/run_glue.py +++ b/tests/torch/sparsity/movement/training_scripts/run_glue.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/sparsity/rb/__init__.py b/tests/torch/sparsity/rb/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/torch/sparsity/rb/__init__.py +++ b/tests/torch/sparsity/rb/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/sparsity/rb/test_algo.py b/tests/torch/sparsity/rb/test_algo.py index 45d83697959..0a1381bbb65 100644 --- a/tests/torch/sparsity/rb/test_algo.py +++ b/tests/torch/sparsity/rb/test_algo.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/sparsity/rb/test_components.py b/tests/torch/sparsity/rb/test_components.py index 0aad0e03123..77df01bfa62 100644 --- a/tests/torch/sparsity/rb/test_components.py +++ b/tests/torch/sparsity/rb/test_components.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -13,6 +13,7 @@ import torch from torch import nn +from nncf.common.hook_handle import HookHandle from nncf.torch.layers import NNCFConv2d from nncf.torch.layers import NNCFConvTranspose2d from nncf.torch.layers import NNCFLinear @@ -30,11 +31,11 @@ def __init__(self, layer, frozen, size=1): sparsifier = RBSparsifyingWeight(weight_shape=size) else: sparsifier = RBSparsifyingWeight(frozen=frozen, weight_shape=size) - self.op_key = self.layer.register_pre_forward_operation(UpdateWeight(sparsifier)) + self.hook_handle: HookHandle = self.layer.register_pre_forward_operation(UpdateWeight(sparsifier)) @property def sparsifier(self): - return self.layer.get_pre_op(self.op_key).operand + return self.layer.get_pre_op(self.hook_handle.hook_id).operand def forward(self, x): return self.layer(x) diff --git a/tests/torch/sparsity/rb/test_weights.py b/tests/torch/sparsity/rb/test_weights.py index bafab01534a..2bafc49e7b1 100644 --- a/tests/torch/sparsity/rb/test_weights.py +++ b/tests/torch/sparsity/rb/test_weights.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/sparsity/test_common.py b/tests/torch/sparsity/test_common.py index 24d7ca223ec..7d71e348f3a 100644 --- a/tests/torch/sparsity/test_common.py +++ b/tests/torch/sparsity/test_common.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -148,6 +148,7 @@ class MockSparsityAlgo: def __init__(self): self.set_sparsity_level = mocker.stub() self.freeze = mocker.stub() + self.current_sparsity_level = 0.0 return MockSparsityAlgo() @@ -269,11 +270,16 @@ class MockSparsityAlgo: def __init__(self): self.set_sparsity_level = mocker.stub() self.freeze = mocker.stub() + from nncf.torch.sparsity.rb.loss import SparseLoss self.loss = SparseLoss() self.loss.current_sparsity = 0.3 + @property + def current_sparsity_level(self) -> float: + return self.loss.current_sparsity + return MockSparsityAlgo() @@ -327,6 +333,10 @@ def __init__(self): def set_sparsity_level(self, level): pass + @property + def current_sparsity_level(self) -> float: + return 0.5 + @pytest.mark.parametrize( "scheduler_cls", diff --git a/tests/torch/sparsity/test_strip.py b/tests/torch/sparsity/test_strip.py index 5348b384114..36c9eddd438 100644 --- a/tests/torch/sparsity/test_strip.py +++ b/tests/torch/sparsity/test_strip.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/tensor_statistics/__init__.py b/tests/torch/tensor_statistics/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tests/torch/tensor_statistics/__init__.py +++ b/tests/torch/tensor_statistics/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/tensor_statistics/test_tensor_statistics.py b/tests/torch/tensor_statistics/test_tensor_statistics.py index 1d04f2a706b..f9a62feee90 100644 --- a/tests/torch/tensor_statistics/test_tensor_statistics.py +++ b/tests/torch/tensor_statistics/test_tensor_statistics.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -115,7 +115,7 @@ def test_collected_statistics_with_shape_convert( collector: Type[TensorStatisticCollectorBase], reduction_axes_vs_ref_statistic: Dict[Tuple[ReductionAxes, ReductionAxes], TensorStatistic], ): - for shapes in reduction_axes_vs_ref_statistic.keys(): + for shapes in reduction_axes_vs_ref_statistic: scale_shape, reducer_axes = shapes collector_obj = collector( scale_shape=scale_shape, diff --git a/tests/torch/test_algo_common.py b/tests/torch/test_algo_common.py index 949a5863d30..83f2b19990b 100644 --- a/tests/torch/test_algo_common.py +++ b/tests/torch/test_algo_common.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -20,6 +20,7 @@ from torch import cuda from torch import nn +import nncf from nncf import NNCFConfig from nncf.api.compression import CompressionStage from nncf.config.schemata.defaults import VALIDATE_SCOPES @@ -400,7 +401,7 @@ def test_compression_loss_gpu_device_compatibility(config): @pytest.mark.parametrize("algo_name", PT_COMPRESSION_ALGORITHMS.registry_dict.keys() - NOT_SUPPORT_SCOPES_ALGO) @pytest.mark.parametrize("validate_scopes", (True, False, None)) -def test_raise_runtimeerror_for_not_matched_scope_names(algo_name, validate_scopes): +def test_raise_validationerror_for_not_matched_scope_names(algo_name, validate_scopes): model = BasicLinearTestModel() config = ConfigCreator().add_algo(algo_name).create() config["compression"][0]["ignored_scopes"] = ["unknown"] @@ -421,7 +422,7 @@ def test_raise_runtimeerror_for_not_matched_scope_names(algo_name, validate_scop config["compression"][0]["validate_scopes"] = validate_scopes if validate_scopes or (validate_scopes is None and VALIDATE_SCOPES is True): - with pytest.raises(RuntimeError, match="scope definitions"): + with pytest.raises(nncf.ValidationError, match="scope definitions"): create_compressed_model_and_algo_for_test(model, config) else: create_compressed_model_and_algo_for_test(model, config) diff --git a/tests/torch/test_api_behavior.py b/tests/torch/test_api_behavior.py index 1f82a57c2e2..9e18479e94e 100644 --- a/tests/torch/test_api_behavior.py +++ b/tests/torch/test_api_behavior.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -13,6 +13,7 @@ import torch from torch.utils.data import DataLoader +import nncf from nncf import NNCFConfig from nncf.common.quantization.quantizer_setup import SingleConfigQuantizerSetup from nncf.torch import create_compressed_model @@ -133,5 +134,5 @@ def test_repeat_compression_fails(): model = SimplestModel() nncf_config = NNCFConfig.from_dict({"input_info": {"sample_size": SimplestModel.INPUT_SIZE}}) _ = create_compressed_model(model, nncf_config) - with pytest.raises(RuntimeError, match="The model object has already been compressed."): + with pytest.raises(nncf.InternalError, match="The model object has already been compressed."): _ = create_compressed_model(model, nncf_config) diff --git a/tests/torch/test_backward_compat.py b/tests/torch/test_backward_compat.py index 030a548f8cb..4b472826ba7 100644 --- a/tests/torch/test_backward_compat.py +++ b/tests/torch/test_backward_compat.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/test_compressed_graph.py b/tests/torch/test_compressed_graph.py index f41d35d7f07..1bce431f64e 100644 --- a/tests/torch/test_compressed_graph.py +++ b/tests/torch/test_compressed_graph.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -847,7 +847,7 @@ def test_output_quantization(_case_config): ModelDesc("mobilenet_v2", test_models.MobileNetV2, [2, 3, 32, 32]), ] -TYPE_HW = [(HWConfigType.CPU), (HWConfigType.GPU), (HWConfigType.VPU)] +TYPE_HW = [(HWConfigType.CPU), (HWConfigType.GPU), (HWConfigType.NPU)] @pytest.fixture(scope="function", params=TYPE_HW) diff --git a/tests/torch/test_compression_lr_multiplier.py b/tests/torch/test_compression_lr_multiplier.py index 4938e55d435..b2846586238 100644 --- a/tests/torch/test_compression_lr_multiplier.py +++ b/tests/torch/test_compression_lr_multiplier.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/test_compression_training.py b/tests/torch/test_compression_training.py index c7c93185e8a..265c9d5bc63 100644 --- a/tests/torch/test_compression_training.py +++ b/tests/torch/test_compression_training.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/test_context_independence.py b/tests/torch/test_context_independence.py index b7d4a38e1ec..aca187e1181 100644 --- a/tests/torch/test_context_independence.py +++ b/tests/torch/test_context_independence.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/test_custom_modules.py b/tests/torch/test_custom_modules.py index 1fe0e4c7a64..4d08d251840 100644 --- a/tests/torch/test_custom_modules.py +++ b/tests/torch/test_custom_modules.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/test_distributed_data_parallel_mode.py b/tests/torch/test_distributed_data_parallel_mode.py index edd9a7a0eea..c186ea97318 100644 --- a/tests/torch/test_distributed_data_parallel_mode.py +++ b/tests/torch/test_distributed_data_parallel_mode.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/test_extensions_build.py b/tests/torch/test_extensions_build.py index 149bb5178ea..9dae2faa6da 100644 --- a/tests/torch/test_extensions_build.py +++ b/tests/torch/test_extensions_build.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/test_frozen_layers.py b/tests/torch/test_frozen_layers.py index 26704dad0a3..540f2f5d5dc 100644 --- a/tests/torch/test_frozen_layers.py +++ b/tests/torch/test_frozen_layers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -11,6 +11,7 @@ import pytest +import nncf from nncf import NNCFConfig from nncf.common.logging import nncf_logger from nncf.config.structures import QuantizationRangeInitArgs @@ -68,7 +69,7 @@ def get_config(self): class FrozenLayersTestStruct: def __init__(self, name="No_name"): self.name = name - self.config_update = {"target_device": "VPU", "compression": []} + self.config_update = {"target_device": "NPU", "compression": []} self.raising_error = False self.printing_warning = False self._freeze_all = False @@ -242,7 +243,7 @@ def test_frozen_layers(_nncf_caplog, params): register_bn_adaptation_init_args(config) if params.raising_error: - with pytest.raises(RuntimeError): + with pytest.raises(nncf.InternalError): __, _ = create_compressed_model_and_algo_for_test(model, config) else: __, _ = create_compressed_model_and_algo_for_test(model, config) diff --git a/tests/torch/test_get_modules_by_type.py b/tests/torch/test_get_modules_by_type.py index 71ec01e75c4..48cdf5f0bdb 100644 --- a/tests/torch/test_get_modules_by_type.py +++ b/tests/torch/test_get_modules_by_type.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/test_graph_analysis.py b/tests/torch/test_graph_analysis.py index 0bc381e3812..7ec1d4d7edd 100644 --- a/tests/torch/test_graph_analysis.py +++ b/tests/torch/test_graph_analysis.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/test_graph_building.py b/tests/torch/test_graph_building.py index f4039c7fa6b..6a3734dd520 100644 --- a/tests/torch/test_graph_building.py +++ b/tests/torch/test_graph_building.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -18,6 +18,7 @@ import torch.nn.functional as F from torch import nn +import nncf from nncf import NNCFConfig from nncf.common.graph import NNCFGraphEdge from nncf.common.graph.definitions import MODEL_INPUT_OP_NAME @@ -120,11 +121,11 @@ def test_forward_trace_function(): assert outputs[1].tensor_meta == input_tensor2.tensor_meta # M -> N (2 -> 3) - with pytest.raises(RuntimeError): + with pytest.raises(nncf.ValidationError): outputs = forward_trace_only(lambda x: x + [torch.Tensor(shape2)], [input_tensor1, input_tensor2]) # M -> N (2 -> 1) - with pytest.raises(RuntimeError): + with pytest.raises(nncf.ValidationError): outputs = forward_trace_only(lambda x: x[0], [input_tensor1, input_tensor2]) diff --git a/tests/torch/test_init_data_loader.py b/tests/torch/test_init_data_loader.py index 071e1f71e04..ba8739505f7 100644 --- a/tests/torch/test_init_data_loader.py +++ b/tests/torch/test_init_data_loader.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/test_input_management.py b/tests/torch/test_input_management.py index bcbb3ade7f4..5c0d4f5f33b 100644 --- a/tests/torch/test_input_management.py +++ b/tests/torch/test_input_management.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/test_knowledge_distillation.py b/tests/torch/test_knowledge_distillation.py index f76aebc35af..da4b4fdd471 100644 --- a/tests/torch/test_knowledge_distillation.py +++ b/tests/torch/test_knowledge_distillation.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -64,7 +64,7 @@ def get_sparsity_config_with_sparsity_init(config: NNCFConfig, sparsity_init=0.5 @pytest.mark.parametrize("inference_type", ["cpu", "single_GPU", "DP", "DDP"]) def test_knowledge_distillation_training_process(inference_type: str): - if not torch.cuda.is_available() and not inference_type == "cpu": + if not torch.cuda.is_available() and inference_type != "cpu": pytest.skip("Skipping CUDA test cases for CPU only setups") torch.manual_seed(1) input_size = [1, 1, 8, 8] @@ -89,9 +89,8 @@ def run_actual( model, compression_ctrl = create_compressed_model_and_algo_for_test(model, config) if inference_type == "DDP": model = post_compression_test_distr_init(compression_ctrl, config, ngpus_per_node, model) - elif inference_type in ("DP", "single_GPU"): - if inference_type == "DP": - model = torch.nn.DataParallel(model) + elif inference_type == "DP": + model = torch.nn.DataParallel(model) optimizer = SGD(model.parameters(), lr=1e-02, weight_decay=1e-02) model.train() output_storage = [] @@ -115,10 +114,9 @@ def run_reference( model, compression_ctrl = create_compressed_model_and_algo_for_test(model, config) if inference_type == "DDP": model = post_compression_test_distr_init(compression_ctrl, config, ngpus_per_node, model) - elif inference_type in ("DP", "single_GPU"): - if inference_type == "DP": - model = torch.nn.DataParallel(model) - kd_model = torch.nn.DataParallel(kd_model) + elif inference_type == "DP": + model = torch.nn.DataParallel(model) + kd_model = torch.nn.DataParallel(kd_model) optimizer = SGD(model.parameters(), lr=1e-02, weight_decay=1e-02) model.train() kd_model.train() @@ -315,7 +313,7 @@ def test_kd_sparsity_statistics(algo: str): @pytest.mark.parametrize("device_placing", ["before", "after"]) @pytest.mark.parametrize("inference_type", ["cpu", "single_GPU", "DP", "DDP"]) def test_model_device_before_create_compressed_model(device_placing, inference_type): - if not torch.cuda.is_available() and not inference_type == "cpu": + if not torch.cuda.is_available() and inference_type != "cpu": pytest.skip("Skipping CUDA test cases for CPU only setups") input_size = [1, 1, 8, 8] config = NNCFConfig() diff --git a/tests/torch/test_layer_attributes.py b/tests/torch/test_layer_attributes.py index 75f1e72db66..018d9a6dbef 100644 --- a/tests/torch/test_layer_attributes.py +++ b/tests/torch/test_layer_attributes.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/test_load_model_state.py b/tests/torch/test_load_model_state.py index 2df68c0b362..e1562b56547 100644 --- a/tests/torch/test_load_model_state.py +++ b/tests/torch/test_load_model_state.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -16,6 +16,7 @@ import pytest import torch +import nncf from examples.torch.common.model_loader import load_model from nncf.common.logging.logger import NNCFDeprecationWarning from nncf.torch.checkpoint_loading import OPTIONAL_PARAMETERS_REGISTRY @@ -512,7 +513,7 @@ def test_match_key(desc: MatchKeyDesc, mocker, nncf_caplog): assert key_matcher._processed_keys._keys == desc.processed_keys._keys if desc.expects_error: - with pytest.raises(RuntimeError): + with pytest.raises(nncf.InternalError): key_matcher.handle_problematic_keys() else: with nncf_caplog.at_level(logging.WARNING): diff --git a/tests/torch/test_model_transformer.py b/tests/torch/test_model_transformer.py index 32b80049a90..19e4aa59dfc 100644 --- a/tests/torch/test_model_transformer.py +++ b/tests/torch/test_model_transformer.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -161,15 +161,16 @@ def test_single_insertions(self, setup, target_point: PTTargetPoint): else: hook = BaseOp(lambda x: x) - self.compressed_model.nncf.insert_at_point(insertion_point, [hook]) + test_hook_group = "test_hook_group" + self.compressed_model.nncf.insert_at_point(insertion_point, hook, hooks_group_name=test_hook_group) if insertion_point.insertion_type == PTInsertionType.OPERATOR_PRE_HOOK: ctx = self.compressed_model.nncf.get_tracing_context() pre_hook_id = PreHookId(insertion_point.op_address, input_port_id=insertion_point.input_port_id) - assert ctx._pre_hooks[pre_hook_id][0] is hook + assert ctx._pre_hooks[pre_hook_id]["0"] is hook if insertion_point.insertion_type == PTInsertionType.OPERATOR_POST_HOOK: ctx = self.compressed_model.nncf.get_tracing_context() - assert ctx._post_hooks[insertion_point.op_address][0] is hook + assert ctx._post_hooks[insertion_point.op_address]["0"] is hook if insertion_point.insertion_type == PTInsertionType.NNCF_MODULE_PRE_OP: module = self.compressed_model.nncf.get_module_by_scope(insertion_point.module_scope) assert module.pre_ops["0"] is hook @@ -178,6 +179,8 @@ def test_single_insertions(self, setup, target_point: PTTargetPoint): module = self.compressed_model.nncf.get_module_by_scope(insertion_point.module_scope) assert module.post_ops["0"] is hook + assert len(self.compressed_model.nncf._groups_vs_hooks_handlers[test_hook_group]) == 1 + priority_types = ["same", "different"] insertion_types = TargetType priority_test_cases = list(itertools.product(priority_types, insertion_types)) @@ -187,8 +190,9 @@ def check_order(iterable1: List, iterable2: List, ordering: List): for idx, order in enumerate(ordering): assert iterable1[idx] is iterable2[order] + @pytest.mark.parametrize("command_cls", [PTInsertionCommand]) @pytest.mark.parametrize("case", priority_test_cases, ids=[x[1].name + "-" + x[0] for x in priority_test_cases]) - def test_priority(self, case, setup): + def test_priority(self, case, command_cls, setup): priority_type = case[0] insertion_type = case[1] @@ -218,14 +222,14 @@ def test_priority(self, case, setup): if priority_type == "same": # Same-priority commands will be executed in registration order - command1 = PTInsertionCommand(point, hook1, TransformationPriority.DEFAULT_PRIORITY) - command2 = PTInsertionCommand(point, hook2, TransformationPriority.DEFAULT_PRIORITY) - command3 = PTInsertionCommand(point, hook3, TransformationPriority.DEFAULT_PRIORITY) + command1 = command_cls(point, hook1, TransformationPriority.DEFAULT_PRIORITY) + command2 = command_cls(point, hook2, TransformationPriority.DEFAULT_PRIORITY) + command3 = command_cls(point, hook3, TransformationPriority.DEFAULT_PRIORITY) else: # Prioritized commands will be executed in ascending priority order - command1 = PTInsertionCommand(point, hook1, TransformationPriority.SPARSIFICATION_PRIORITY) - command2 = PTInsertionCommand(point, hook2, TransformationPriority.QUANTIZATION_PRIORITY) - command3 = PTInsertionCommand(point, hook3, TransformationPriority.DEFAULT_PRIORITY) + command1 = command_cls(point, hook1, TransformationPriority.SPARSIFICATION_PRIORITY) + command2 = command_cls(point, hook2, TransformationPriority.QUANTIZATION_PRIORITY) + command3 = command_cls(point, hook3, TransformationPriority.DEFAULT_PRIORITY) layout = PTTransformationLayout() layout.register(command1) @@ -245,10 +249,12 @@ def test_priority(self, case, setup): pre_hook_id = PreHookId( OperationAddress.from_str(point.target_node_name), input_port_id=point.input_port_id ) - self.check_order(ctx._pre_hooks[pre_hook_id], hook_list, order) + actual_pre_hooks = list(ctx._pre_hooks[pre_hook_id].values()) + self.check_order(actual_pre_hooks, hook_list, order) if insertion_type == TargetType.OPERATOR_POST_HOOK: ctx = self.compressed_model.nncf.get_tracing_context() - self.check_order(ctx._post_hooks[OperationAddress.from_str(point.target_node_name)], hook_list, order) + actual_post_hooks = list(ctx._post_hooks[OperationAddress.from_str(point.target_node_name)].values()) + self.check_order(actual_post_hooks, hook_list, order) if insertion_type == TargetType.OPERATION_WITH_WEIGHTS: module = self.compressed_model.nncf.get_containing_module(point.target_node_name) @@ -325,7 +331,7 @@ def test_insertion_point_setup(self): succs = list(ip_graph.successors(node_key)) assert len(preds) != 0 or len(succs) != 0 - for from_node_key, to_node_key in ip_graph.edges.keys(): + for from_node_key, to_node_key in ip_graph.edges: assert from_node_key in ip_graph.nodes assert to_node_key in ip_graph.nodes @@ -453,15 +459,15 @@ def test_get_ip_graph_with_merged_operations(self, mock_graph_factory, dot_file_ load_graph = read_dot_graph(str(path_to_dot_file)) - for key in load_graph.nodes.keys(): + for key in load_graph.nodes: key.replace(r"\\n", r"\n") # Somehow pydot mangles the \n characters while writing a .dot file - sanitized_loaded_keys = [key.replace("\\n", "\n") for key in load_graph.nodes.keys()] + sanitized_loaded_keys = [key.replace("\\n", "\n") for key in load_graph.nodes] sanitized_loaded_edges = [ (u.replace("\\n", "\n"), v.replace("\\n", "\n")) for u, v in nx.DiGraph(load_graph).edges ] - assert Counter(sanitized_loaded_keys) == Counter(list(merged_ip_graph.nodes.keys())) + assert Counter(sanitized_loaded_keys) == Counter(list(merged_ip_graph.nodes)) assert Counter(sanitized_loaded_edges) == Counter(list(merged_ip_graph.edges)) @@ -603,6 +609,7 @@ def test_shared_fn_insertion_point(priority, compression_module_registered, mock ), ] OP_UNIQUE_NAME = "UNIQUE_NAME" + HOOK_GROUP_NAME = "shared_comands_hooks_group" hook_instance = Hook() def _insert_external_op_mocked(): @@ -610,7 +617,7 @@ def _insert_external_op_mocked(): if compression_module_registered: model.nncf.register_compression_module_type(ExtraCompressionModuleType.EXTERNAL_OP) unique_name = f"{OP_UNIQUE_NAME}[{';'.join([tp.target_node_name for tp in tps])}]" - command = PTSharedFnInsertionCommand(tps, hook_instance, unique_name, priority) + command = PTSharedFnInsertionCommand(tps, hook_instance, unique_name, priority, HOOK_GROUP_NAME) transformation_layout = PTTransformationLayout() transformation_layout.register(command) @@ -643,6 +650,7 @@ def _insert_external_op_mocked(): assert len(commands) == len(tps) for command in commands: assert command.target_point in tps + assert command.hooks_group_name == HOOK_GROUP_NAME fn = command.fn assert isinstance(fn, ExternalOpCallHook) assert fn._storage_name == EXTERNAL_OP_STORAGE_NAME @@ -806,7 +814,7 @@ def _add_and_test_hook_depth_one(test_params: NestedHooksTestCase, pre_hook_op, # Check order of calls GLOBAL_LIST.clear() transformed_model.nncf.rebuild_graph() - assert GLOBAL_LIST == ref + assert ref == GLOBAL_LIST # Check hooks are kept correctly checker = HookChecker(transformed_model, "m") @@ -849,7 +857,7 @@ def test_nested_pre_post_hooks(test_params: NestedHooksTestCase, ref_depth_one, # Check order of calls GLOBAL_LIST.clear() model_with_nested_hooks.nncf.rebuild_graph() - assert GLOBAL_LIST == ref_depth_two + assert ref_depth_two == GLOBAL_LIST # Check hooks are kept correctly checker = HookChecker(model_with_nested_hooks, "m") diff --git a/tests/torch/test_models/__init__.py b/tests/torch/test_models/__init__.py index ad3af2cb2f2..7ba2fbd18ee 100644 --- a/tests/torch/test_models/__init__.py +++ b/tests/torch/test_models/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/test_models/alexnet.py b/tests/torch/test_models/alexnet.py index 6da31e50e25..3f1e93d9b8b 100644 --- a/tests/torch/test_models/alexnet.py +++ b/tests/torch/test_models/alexnet.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/test_models/densenet.py b/tests/torch/test_models/densenet.py index b12009d0b9a..410d1a72578 100644 --- a/tests/torch/test_models/densenet.py +++ b/tests/torch/test_models/densenet.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/test_models/dpn.py b/tests/torch/test_models/dpn.py index 771ce349aa0..0ed15bcdf83 100644 --- a/tests/torch/test_models/dpn.py +++ b/tests/torch/test_models/dpn.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/test_models/googlenet.py b/tests/torch/test_models/googlenet.py index 912a6a548f5..fd609ba609f 100644 --- a/tests/torch/test_models/googlenet.py +++ b/tests/torch/test_models/googlenet.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/test_models/inceptionv3.py b/tests/torch/test_models/inceptionv3.py index 3da25703446..163892df245 100644 --- a/tests/torch/test_models/inceptionv3.py +++ b/tests/torch/test_models/inceptionv3.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/test_models/lenet.py b/tests/torch/test_models/lenet.py index e8eafd2dc73..aacf8122d3a 100644 --- a/tests/torch/test_models/lenet.py +++ b/tests/torch/test_models/lenet.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/test_models/mobilenet.py b/tests/torch/test_models/mobilenet.py index fc8893e1ccb..e63dc045ca8 100644 --- a/tests/torch/test_models/mobilenet.py +++ b/tests/torch/test_models/mobilenet.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/test_models/mobilenet_v3.py b/tests/torch/test_models/mobilenet_v3.py index 39218bd4b84..be0378142be 100644 --- a/tests/torch/test_models/mobilenet_v3.py +++ b/tests/torch/test_models/mobilenet_v3.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/test_models/pnasnet.py b/tests/torch/test_models/pnasnet.py index 8cd60ff8e82..46abe3325ad 100644 --- a/tests/torch/test_models/pnasnet.py +++ b/tests/torch/test_models/pnasnet.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/test_models/preact_resnet.py b/tests/torch/test_models/preact_resnet.py index 2f36018a9a9..11cd80485e9 100644 --- a/tests/torch/test_models/preact_resnet.py +++ b/tests/torch/test_models/preact_resnet.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/test_models/resnet.py b/tests/torch/test_models/resnet.py index 5b66922cbf0..88a210bfcd0 100644 --- a/tests/torch/test_models/resnet.py +++ b/tests/torch/test_models/resnet.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/test_models/resnext.py b/tests/torch/test_models/resnext.py index d65f7b8c146..c411c5e37c3 100644 --- a/tests/torch/test_models/resnext.py +++ b/tests/torch/test_models/resnext.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/test_models/senet.py b/tests/torch/test_models/senet.py index 324c88176b6..d94496bfd3f 100644 --- a/tests/torch/test_models/senet.py +++ b/tests/torch/test_models/senet.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/test_models/shufflenet.py b/tests/torch/test_models/shufflenet.py index 230b0c3ea8b..cf793cc66f2 100644 --- a/tests/torch/test_models/shufflenet.py +++ b/tests/torch/test_models/shufflenet.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/test_models/shufflenetv2.py b/tests/torch/test_models/shufflenetv2.py index 488d7be0f4a..117cdd08c63 100644 --- a/tests/torch/test_models/shufflenetv2.py +++ b/tests/torch/test_models/shufflenetv2.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/test_models/squeezenet.py b/tests/torch/test_models/squeezenet.py index 656860f94e7..1eb44bcf37d 100644 --- a/tests/torch/test_models/squeezenet.py +++ b/tests/torch/test_models/squeezenet.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/test_models/sr_small_model.py b/tests/torch/test_models/sr_small_model.py index dd7cf9302f7..155a48aafe8 100644 --- a/tests/torch/test_models/sr_small_model.py +++ b/tests/torch/test_models/sr_small_model.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/test_models/ssd_mobilenet.py b/tests/torch/test_models/ssd_mobilenet.py index c0e926a8779..07e188f0cc2 100644 --- a/tests/torch/test_models/ssd_mobilenet.py +++ b/tests/torch/test_models/ssd_mobilenet.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/test_models/ssd_vgg.py b/tests/torch/test_models/ssd_vgg.py index 039d663e47a..cbf36fec64e 100644 --- a/tests/torch/test_models/ssd_vgg.py +++ b/tests/torch/test_models/ssd_vgg.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/test_models/swin.py b/tests/torch/test_models/swin.py index 392c42b5217..3dcfab28a19 100644 --- a/tests/torch/test_models/swin.py +++ b/tests/torch/test_models/swin.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -272,7 +272,7 @@ def __init__( def forward(self, x): H, W = self.input_resolution B, L, C = x.shape - assert L == H * W, "input feature has wrong size" + assert H * W == L, "input feature has wrong size" shortcut = x x = self.norm1(x) diff --git a/tests/torch/test_models/synthetic.py b/tests/torch/test_models/synthetic.py index b4096d43c43..42fc1f6e2e0 100644 --- a/tests/torch/test_models/synthetic.py +++ b/tests/torch/test_models/synthetic.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/test_models/unet.py b/tests/torch/test_models/unet.py index bb470bba48d..714aa5d1f03 100644 --- a/tests/torch/test_models/unet.py +++ b/tests/torch/test_models/unet.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/test_models/vgg.py b/tests/torch/test_models/vgg.py index d6bd251e4ef..25c37432f89 100644 --- a/tests/torch/test_models/vgg.py +++ b/tests/torch/test_models/vgg.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/test_nncf_graph.py b/tests/torch/test_nncf_graph.py index c248694bc8f..1060023ffbe 100644 --- a/tests/torch/test_nncf_graph.py +++ b/tests/torch/test_nncf_graph.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/test_nncf_network.py b/tests/torch/test_nncf_network.py index 80e96e5eefc..938eae5f73c 100644 --- a/tests/torch/test_nncf_network.py +++ b/tests/torch/test_nncf_network.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -14,7 +14,7 @@ from abc import ABCMeta from abc import abstractmethod from copy import deepcopy -from typing import Callable, Type +from typing import Callable, List, Tuple, Type import pytest import torch @@ -22,16 +22,19 @@ from torch import nn from torch.nn.utils import weight_norm +import nncf from nncf import nncf_logger from nncf.common.graph import NNCFNode from nncf.common.graph.operator_metatypes import UnknownMetatype from nncf.common.graph.transformations.commands import TargetType +from nncf.common.hook_handle import HookHandle from nncf.torch import register_module from nncf.torch.dynamic_graph.io_handling import ExampleInputInfo from nncf.torch.dynamic_graph.io_handling import FillerInputElement from nncf.torch.dynamic_graph.io_handling import FillerInputInfo from nncf.torch.dynamic_graph.operation_address import OperationAddress from nncf.torch.dynamic_graph.scope import Scope +from nncf.torch.dynamic_graph.trace_tensor import TracedTensor from nncf.torch.graph.graph import PTNNCFGraph from nncf.torch.graph.graph_builder import GraphBuilder from nncf.torch.graph.operator_metatypes import PTConv2dMetatype @@ -47,6 +50,7 @@ from nncf.torch.quantization.external_quantizer import EXTERNAL_QUANTIZERS_STORAGE_NAME from tests.torch.composite.test_sparsity_quantization import get_basic_sparsity_plus_quantization_config from tests.torch.helpers import BasicConvTestModel +from tests.torch.helpers import HookChecker from tests.torch.helpers import TwoConvTestModel from tests.torch.helpers import check_correct_nncf_modules_replacement from tests.torch.helpers import create_compressed_model_and_algo_for_test @@ -496,7 +500,7 @@ def test_temporary_clean_view(): == intermediate_model.nncf.get_original_graph().get_nodes_count() ) sd_after_tmp_clean_view = sparse_quantized_model.state_dict() - for key in old_sd.keys(): + for key in old_sd: assert key in sd_after_tmp_clean_view assert torch.all(torch.eq(sd_after_tmp_clean_view[key], old_sd[key])) sparse_quantized_model.nncf.rebuild_graph() @@ -540,7 +544,7 @@ def test_deepcopy_nncf_network(): def test_insertion_point_target_point_translation(): op_address = OperationAddress("dummy", Scope(), 0) for target_type in [PTInsertionType.NNCF_MODULE_POST_OP, TargetType.AFTER_LAYER]: - with pytest.raises(RuntimeError): + with pytest.raises(nncf.InternalError): PTInsertionPoint(target_type, op_address) target_type = TargetType.POST_LAYER_OPERATION assert PTInsertionPoint(target_type, op_address).insertion_type == PTInsertionType.NNCF_MODULE_POST_OP @@ -647,7 +651,7 @@ def test_class_compares_as_original(simple_net): assert simple_net.__class__ == SimplestModel assert SimplestModel == simple_net.__class__ assert simple_net.__class__ == simple_net.__class__ - assert not simple_net.__class__ != simple_net.__class__ + assert simple_net.__class__ == simple_net.__class__ assert simple_net.__class__ != ModelWithAttr assert ModelWithAttr != simple_net.__class__ @@ -859,22 +863,33 @@ def __init__(self): def forward(self, x): x = torch.max(x, dim=-1, keepdim=True) assert isinstance(x, torch.return_types.max) - return x.values + v = x.values + 1 + i = x.indices + 1 + return v, i -def test_torch_return_types_unwrapped_for_post_hook(): +def test_torch_return_type_traced(): model = ModelWithMax() nncf_model = NNCFNetwork(model, FillerInputInfo([FillerInputElement(SimplestModel.INPUT_SIZE)])) + node_to_op_address_mapping = nncf_model.nncf.get_node_to_op_address_mapping() insertion_point = PTInsertionPoint( TargetType.OPERATOR_POST_HOOK, node_to_op_address_mapping["ModelWithMax/max_0"], 0 ) - def fn_to_check_input_type(input): - assert isinstance(input, torch.Tensor) + visited_times = 0 - nncf_model.nncf.insert_at_point(insertion_point, [fn_to_check_input_type]) + def fn_to_check_input_type(input_): + assert isinstance(input_, torch.return_types.max) + for val in input_: + assert isinstance(val, TracedTensor) + nonlocal visited_times + visited_times += 1 + return input_ + + nncf_model.nncf.insert_at_point(insertion_point, fn_to_check_input_type) nncf_model.nncf.rebuild_graph() + assert visited_times == 1 class TestWhisperDecoderModel(torch.nn.Module): @@ -907,9 +922,105 @@ def test_insert_hook_after_parameter(): hook = ZeroHook() node_to_op_address_mapping = nncf_model.nncf.get_node_to_op_address_mapping() insertion_point = PTInsertionPoint(TargetType.OPERATOR_POST_HOOK, node_to_op_address_mapping["embedding.weight"], 0) - nncf_model.nncf.insert_at_point(insertion_point, [hook]) + nncf_model.nncf.insert_at_point(insertion_point, hook) result_with_hook = nncf_model(example_input) assert hook.forward_calls_counter == 1 assert torch.sum(result.nonzero()) > 0 assert torch.sum(result_with_hook.nonzero()) == 0 + + +@pytest.mark.parametrize( + "target_type, target_node_name, input_port_id", + [ + (TargetType.OPERATOR_PRE_HOOK, "/nncf_model_output_0", 0), + (TargetType.OPERATOR_POST_HOOK, "/nncf_model_input_0", 0), + (TargetType.PRE_LAYER_OPERATION, "SimplestModel/NNCFConv2d[conv]/conv2d_0", 0), + (TargetType.POST_LAYER_OPERATION, "SimplestModel/NNCFConv2d[conv]/conv2d_0", 0), + ], +) +class TestHookHandles: + class TestHook(torch.nn.Module): + def __init__(self): + super().__init__() + self._p = torch.nn.Parameter(torch.zeros((1,))) + + def forward(self, x): + return x + self._p + + @staticmethod + def _prepare_hook_handles_test( + target_type: TargetType, target_node_name: str, input_port_id: int + ) -> Tuple[NNCFNetwork, PTInsertionPoint, Callable[[List[HookHandle]], None]]: + model = SimplestModel() + example_input = torch.ones(SimplestModel.INPUT_SIZE) + input_info = ExampleInputInfo.from_example_input(example_input) + nncf_model = NNCFNetwork(model, input_info) + + node_name_vs_address = nncf_model.nncf.get_node_to_op_address_mapping() + ip = PTInsertionPoint(target_type, node_name_vs_address[target_node_name], input_port_id=input_port_id) + + checker = HookChecker(nncf_model, "conv") + + def _check(ref_hooks_): + checker.clear() + checker.add_ref(ref_hooks_, target_type, target_node_name, input_port_id) + checker.check_with_reference() + + return nncf_model, ip, _check + + def test_temporary_insert_at_point_by_hook_group_name( + self, target_type: TargetType, target_node_name: str, input_port_id: int + ): + nncf_model, ip, _check = self._prepare_hook_handles_test(target_type, target_node_name, input_port_id) + permanent_hook = self.TestHook() + TEMPORARY_HOOK_GROUP_NAME = "tmp" + # Make temporary hook a ref to the permanent hook + # to check tmp hooks are not removed by their id() + temporary_hook = permanent_hook + nncf_model.nncf.insert_at_point(ip, permanent_hook) + ref_hooks = [permanent_hook] + _check(ref_hooks) + + for _ in range(2): + temporary_hook = self.TestHook() + nncf_model.nncf.insert_at_point(ip, temporary_hook, TEMPORARY_HOOK_GROUP_NAME) + ref_hooks.append(temporary_hook) + _check(ref_hooks) + + nncf_model.nncf.insert_at_point(ip, permanent_hook) + ref_hooks.append(permanent_hook) + _check(ref_hooks) + + nncf_model.nncf.remove_hooks_group(TEMPORARY_HOOK_GROUP_NAME) + del ref_hooks[-2] + _check(ref_hooks) + assert not nncf_model.nncf._groups_vs_hooks_handlers[TEMPORARY_HOOK_GROUP_NAME] + + def test_insert_at_point_hook_handles(self, target_type: TargetType, target_node_name: str, input_port_id: int): + nncf_model, ip, _check = self._prepare_hook_handles_test(target_type, target_node_name, input_port_id) + permanent_hook = self.TestHook() + # Make temporary hook a ref to the permanent hook + # to check tmp hooks are not removed by their id() + temporary_hook = permanent_hook + tmp_hh = [] + nncf_model.nncf.insert_at_point(ip, permanent_hook) + + ref_hooks = [permanent_hook] + _check(ref_hooks) + + for _ in range(2): + temporary_hook = self.TestHook() + tmp_hh.append(nncf_model.nncf.insert_at_point(ip, temporary_hook)) + ref_hooks.append(temporary_hook) + _check(ref_hooks) + + nncf_model.nncf.insert_at_point(ip, permanent_hook) + ref_hooks.append(permanent_hook) + _check(ref_hooks) + + for hh in tmp_hh: + hh.remove() + + del ref_hooks[-2] + _check(ref_hooks) diff --git a/tests/torch/test_nncf_utils.py b/tests/torch/test_nncf_utils.py index dfcade0dd25..d50c3cce31d 100644 --- a/tests/torch/test_nncf_utils.py +++ b/tests/torch/test_nncf_utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/test_no_compression_algorithm.py b/tests/torch/test_no_compression_algorithm.py index baedea474f6..b29fa2097ff 100644 --- a/tests/torch/test_no_compression_algorithm.py +++ b/tests/torch/test_no_compression_algorithm.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/test_no_nncf_trace_patching.py b/tests/torch/test_no_nncf_trace_patching.py index 837faa1ff27..c39687ca51e 100644 --- a/tests/torch/test_no_nncf_trace_patching.py +++ b/tests/torch/test_no_nncf_trace_patching.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/test_onnx_export.py b/tests/torch/test_onnx_export.py index 91a893ad6ea..db3c33253fa 100644 --- a/tests/torch/test_onnx_export.py +++ b/tests/torch/test_onnx_export.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/test_pattern_manager.py b/tests/torch/test_pattern_manager.py index 4db3841fc76..b0f318cebcb 100644 --- a/tests/torch/test_pattern_manager.py +++ b/tests/torch/test_pattern_manager.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/test_pytorch_patch.py b/tests/torch/test_pytorch_patch.py index d98ffbd956f..86bcd7afdc2 100644 --- a/tests/torch/test_pytorch_patch.py +++ b/tests/torch/test_pytorch_patch.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/test_resume_from_checkpoint.py b/tests/torch/test_resume_from_checkpoint.py index bc37e49d753..82e4627c3bb 100644 --- a/tests/torch/test_resume_from_checkpoint.py +++ b/tests/torch/test_resume_from_checkpoint.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -14,6 +14,7 @@ import pytest from torch.nn import DataParallel +import nncf from nncf.common.logging import nncf_logger from nncf.torch import load_state from nncf.torch import register_default_init_args @@ -96,7 +97,7 @@ def test_can_resume_with_algo_mixing(mocker, is_strict): create_compressed_model_and_algo_for_test, desc.model_creator(), config, compression_state=compression_state ) if is_strict: - with pytest.raises(RuntimeError): + with pytest.raises(nncf.InternalError): fn() else: _, compression_ctrl = fn() @@ -196,7 +197,7 @@ def test_load_state_interoperability(_algos, _model_wrapper, is_resume): ref_num_loaded -= 2 assert act_num_loaded == ref_num_loaded else: - with pytest.raises(RuntimeError): + with pytest.raises(nncf.InternalError): load_state(model_resume, saved_model_state, is_resume) diff --git a/tests/torch/test_return_types.py b/tests/torch/test_return_types.py index c2bf812f0e0..48eb38f8c22 100644 --- a/tests/torch/test_return_types.py +++ b/tests/torch/test_return_types.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -12,7 +12,7 @@ import torch from nncf.torch.return_types import _TORCH_RETURN_TYPES -from nncf.torch.return_types import maybe_unwrap_from_torch_return_type +from nncf.torch.return_types import maybe_get_values_from_torch_return_type from nncf.torch.return_types import maybe_wrap_to_torch_return_type @@ -20,7 +20,7 @@ def test_unwrap_wrap_torch_return_type(return_type): wrapped_tensor = return_type((torch.tensor(0), torch.tensor(1))) assert wrapped_tensor.values == torch.tensor(0) - unwrapped_tensor = maybe_unwrap_from_torch_return_type(wrapped_tensor) + unwrapped_tensor = maybe_get_values_from_torch_return_type(wrapped_tensor) assert unwrapped_tensor == torch.tensor(0) updated_wrapped_tensor = maybe_wrap_to_torch_return_type(unwrapped_tensor, wrapped_tensor) @@ -31,7 +31,7 @@ def test_unwrap_wrap_torch_return_type(return_type): "input_", [torch.tensor(0), [torch.tensor(0), torch.tensor(1)], (torch.tensor(0), torch.tensor(1))] ) def test_wrap_unwrap_do_nothing_to_tensor(input_): - wrapped_input = maybe_unwrap_from_torch_return_type(input_) + wrapped_input = maybe_get_values_from_torch_return_type(input_) assert wrapped_input is input_ unwrapped_input = maybe_wrap_to_torch_return_type(input_, wrapped_input) assert unwrapped_input is input_ diff --git a/tests/torch/test_sanity_sample.py b/tests/torch/test_sanity_sample.py index 316d4c3f4d1..bf6a3e3a85c 100644 --- a/tests/torch/test_sanity_sample.py +++ b/tests/torch/test_sanity_sample.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -185,7 +185,7 @@ def fixture_config(request, dataset_dir): with config_path.open() as f: jconfig = json.load(f) - if "checkpoint_save_dir" in jconfig.keys(): + if "checkpoint_save_dir" in jconfig: del jconfig["checkpoint_save_dir"] # Use a reduced number of BN adaptation samples for speed @@ -459,9 +459,6 @@ def test_cpu_only_mode_produces_cpu_only_model(config, tmp_path, mocker): "--cpu-only": True, } - # to prevent starting a not closed mlflow session due to memory leak of config and SafeMLFLow happens with a - # mocked train function - mocker.patch("examples.torch.common.utils.SafeMLFLow") arg_list = arg_list_from_arg_dict(args) if config["sample_type"] == "classification": import examples.torch.classification.main as sample diff --git a/tests/torch/test_sanity_third_party.py b/tests/torch/test_sanity_third_party.py deleted file mode 100644 index 85da106eeb6..00000000000 --- a/tests/torch/test_sanity_third_party.py +++ /dev/null @@ -1,368 +0,0 @@ -# Copyright (c) 2023 Intel Corporation -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# http://www.apache.org/licenses/LICENSE-2.0 -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -import os -import subprocess -import sys - -import pytest - -from nncf.torch import BKC_TORCH_VERSION -from tests.shared.paths import PROJECT_ROOT -from tests.torch.helpers import Command - -TRANSFORMERS_COMMIT = "bd469c40659ce76c81f69c7726759d249b4aef49" -INSTALL_PATH = PROJECT_ROOT.parent -DATASET_PATH = os.path.join(PROJECT_ROOT, "tests", "torch", "data", "mock_datasets") - - -def create_command_line(args, venv_activate, python=sys.executable, cuda_string=""): - line = "{venv_activate} && {cuda} {python_exe} {args}".format( - venv_activate=venv_activate, cuda=cuda_string, args=args, python_exe=python - ) - return line - - -@pytest.fixture(autouse=True, scope="session") -def skip_tests(third_party): - if not third_party: - pytest.skip() - - -@pytest.fixture(scope="session") -def temp_folder(tmp_path_factory): - return { - "models": str(tmp_path_factory.mktemp("models", False)), - "venv": str(tmp_path_factory.mktemp("venv", False)), - "repo": str(tmp_path_factory.mktemp("repo", False)), - } - - -class CachedPipRunner: - def __init__(self, venv_activation_script_path: str, cache_dir: str = None): - self.venv_activate = venv_activation_script_path - self.cache_dir = cache_dir - - def run_pip(self, pip_command: str, cwd: str = None, use_cache: bool = True): - if not use_cache: - cache_dir_entry = "--no-cache-dir" - elif self.cache_dir is not None: - cache_dir_entry = "--cache-dir {}".format(self.cache_dir) - else: - cache_dir_entry = "" - subprocess.run(f"{self.venv_activate} && pip {cache_dir_entry} {pip_command}", check=True, shell=True, cwd=cwd) - - -class TransformersVirtualEnvInstaller: - def __init__(self, venv_path, repo_path): - self.VENV_PATH = str(venv_path) - self.VENV_ACTIVATE = str(". {}/bin/activate".format(self.VENV_PATH)) - self.PYTHON_EXECUTABLE = str("{}/bin/python".format(self.VENV_PATH)) - self.TRANSFORMERS_REPO_PATH = str(os.path.join(repo_path, "transformers")) - self.CUDA_VISIBLE_STRING = "export CUDA_VISIBLE_DEVICES=0;" - self.PATH_TO_PATCH = str( - os.path.join( - PROJECT_ROOT, - "third_party_integration", - "huggingface_transformers", - "0001-Modifications-for-NNCF-usage.patch", - ) - ) - - def install_env(self, pip_cache_dir): - version_string = "{}.{}".format(sys.version_info[0], sys.version_info[1]) - subprocess.call("virtualenv -ppython{} {}".format(version_string, self.VENV_PATH), shell=True) - pip_runner = CachedPipRunner(self.VENV_ACTIVATE, pip_cache_dir) - pip_runner.run_pip("install --upgrade pip") # cache options are available with pip > 20.2 - pip_runner.run_pip("uninstall setuptools -y") - pip_runner.run_pip("install setuptools") - pip_runner.run_pip("install onnx") - torch_install_cmd = "install torch=={}".format(BKC_TORCH_VERSION) - pip_runner.run_pip(torch_install_cmd) - subprocess.run( - "git clone https://github.com/huggingface/transformers {}".format(self.TRANSFORMERS_REPO_PATH), - check=True, - shell=True, - ) - subprocess.run( - "git checkout {}".format(TRANSFORMERS_COMMIT), check=True, shell=True, cwd=self.TRANSFORMERS_REPO_PATH - ) - subprocess.run("cp {} .".format(self.PATH_TO_PATCH), check=True, shell=True, cwd=self.TRANSFORMERS_REPO_PATH) - subprocess.run( - "git apply 0001-Modifications-for-NNCF-usage.patch", check=True, shell=True, cwd=self.TRANSFORMERS_REPO_PATH - ) - pip_runner.run_pip("install .", cwd=self.TRANSFORMERS_REPO_PATH) - pip_runner.run_pip('install -e ".[testing]"', cwd=self.TRANSFORMERS_REPO_PATH) - for sample_folder in ["question-answering", "text-classification", "language-modeling", "token-classification"]: - pip_runner.run_pip( - f"install -r examples/pytorch/{sample_folder}/requirements.txt", cwd=self.TRANSFORMERS_REPO_PATH - ) - pip_runner.run_pip("install boto3", cwd=self.TRANSFORMERS_REPO_PATH) - # WA for deleted CONLL2003 in datasets==1.11.0 (https://github.com/huggingface/datasets/issues/3582) - pip_runner.run_pip("install -U datasets", cwd=self.TRANSFORMERS_REPO_PATH) - pip_runner.run_pip("install -e .", cwd=PROJECT_ROOT) - - -class TestTransformers: - @pytest.fixture(autouse=True) - def setup(self, temp_folder): - self.env = TransformersVirtualEnvInstaller(temp_folder["venv"], temp_folder["repo"]) - - @pytest.mark.dependency(name="install_trans") - def test_install_trans_(self, pip_cache_dir): - self.env.install_env(pip_cache_dir) - - @pytest.mark.dependency(depends=["install_trans"], name="xnli_train") - def test_xnli_train(self, temp_folder): - com_line = ( - "examples/pytorch/text-classification/run_xnli.py --model_name_or_path bert-base-chinese" - " --language zh --train_language zh --do_train --per_gpu_train_batch_size 24" - " --learning_rate 5e-5 --num_train_epochs 0.0001 --max_seq_length 128 --output_dir {}" - " --save_steps 200 --nncf_config nncf_bert_config_xnli.json".format( - os.path.join(temp_folder["models"], "xnli") - ) - ) - runner = Command( - create_command_line( - com_line, self.env.VENV_ACTIVATE, self.env.PYTHON_EXECUTABLE, self.env.CUDA_VISIBLE_STRING - ), - self.env.TRANSFORMERS_REPO_PATH, - ) - runner.run() - assert os.path.exists(os.path.join(temp_folder["models"], "xnli", "pytorch_model.bin")) - - @pytest.mark.dependency(depends=["install_trans", "xnli_train"]) - def test_xnli_eval(self, temp_folder): - com_line = ( - "examples/pytorch/text-classification/run_xnli.py --model_name_or_path {output}" - " --language zh --do_eval --learning_rate 5e-5 --max_seq_length 128 --output_dir" - " {output} --nncf_config nncf_bert_config_xnli.json --per_gpu_eval_batch_size 24" - " --max_eval_samples 10".format(output=os.path.join(temp_folder["models"], "xnli")) - ) - runner = Command( - create_command_line( - com_line, self.env.VENV_ACTIVATE, self.env.PYTHON_EXECUTABLE, self.env.CUDA_VISIBLE_STRING - ), - self.env.TRANSFORMERS_REPO_PATH, - ) - runner.run() - - @pytest.mark.dependency(depends=["install_trans"], name="squad_train") - def test_squad_train(self, temp_folder): - com_line = ( - "examples/pytorch/question-answering/run_qa.py --model_name_or_path " - "bert-large-uncased-whole-word-masking-finetuned-squad --dataset_name squad --do_train " - " --learning_rate 3e-5 --num_train_epochs 0.0001 --max_seq_length 384 --doc_stride 128 " - " --output_dir {} --per_gpu_train_batch_size=1 --save_steps=200 --nncf_config" - " nncf_bert_config_squad.json".format(os.path.join(temp_folder["models"], "squad")) - ) - runner = Command( - create_command_line( - com_line, self.env.VENV_ACTIVATE, self.env.PYTHON_EXECUTABLE, self.env.CUDA_VISIBLE_STRING - ), - self.env.TRANSFORMERS_REPO_PATH, - ) - runner.run() - assert os.path.exists(os.path.join(temp_folder["models"], "squad", "pytorch_model.bin")) - - @pytest.mark.dependency(depends=["install_trans", "squad_train"]) - def test_squad_eval(self, temp_folder): - com_line = ( - "examples/pytorch/question-answering/run_qa.py --model_name_or_path {output}" - " --do_eval --dataset_name squad --learning_rate 3e-5" - " --max_seq_length 384 --doc_stride 128 --per_gpu_eval_batch_size=4 --output_dir {output} " - " --max_eval_samples 10" - " --nncf_config nncf_bert_config_squad.json".format(output=os.path.join(temp_folder["models"], "squad")) - ) - runner = Command( - create_command_line( - com_line, self.env.VENV_ACTIVATE, self.env.PYTHON_EXECUTABLE, self.env.CUDA_VISIBLE_STRING - ), - self.env.TRANSFORMERS_REPO_PATH, - ) - runner.run() - - @pytest.mark.dependency(depends=["install_trans"], name="glue_roberta_train") - def test_glue_train(self, temp_folder): - com_line = ( - "examples/pytorch/text-classification/run_glue.py --model_name_or_path" - " roberta-large-mnli --task_name mnli --do_train " - " --per_gpu_train_batch_size 4 --learning_rate 2e-5 --num_train_epochs 0.001 --max_seq_length 128 " - " --output_dir {} --save_steps 200 --nncf_config" - " nncf_roberta_config_mnli.json".format(os.path.join(temp_folder["models"], "roberta_mnli")) - ) - runner = Command( - create_command_line( - com_line, self.env.VENV_ACTIVATE, self.env.PYTHON_EXECUTABLE, self.env.CUDA_VISIBLE_STRING - ), - self.env.TRANSFORMERS_REPO_PATH, - ) - runner.run() - assert os.path.exists(os.path.join(temp_folder["models"], "roberta_mnli", "pytorch_model.bin")) - - @pytest.mark.dependency(depends=["install_trans", "glue_roberta_train"]) - def test_glue_eval(self, temp_folder): - com_line = ( - "examples/pytorch/text-classification/run_glue.py --model_name_or_path {output}" - " --task_name mnli --do_eval --validation_file {}/glue/glue_data/MNLI/dev_matched.tsv " - " --learning_rate 2e-5" - " --max_seq_length 128 --output_dir {output}" - " --max_eval_samples 10" - " --nncf_config nncf_roberta_config_mnli.json".format( - DATASET_PATH, output=os.path.join(temp_folder["models"], "roberta_mnli") - ) - ) - runner = Command( - create_command_line( - com_line, self.env.VENV_ACTIVATE, self.env.PYTHON_EXECUTABLE, self.env.CUDA_VISIBLE_STRING - ), - self.env.TRANSFORMERS_REPO_PATH, - ) - runner.run() - - @pytest.mark.dependency(depends=["install_trans"], name="glue_distilbert_train") - def test_glue_distilbert_train(self, temp_folder): - com_line = ( - "examples/pytorch/text-classification/run_glue.py --model_name_or_path" - " distilbert-base-uncased --train_file {}/glue/glue_data/SST-2/train.tsv" - " --task_name sst2 --do_train --max_seq_length 128 --per_gpu_train_batch_size 8" - " --learning_rate 5e-5 --num_train_epochs 0.001" - " --output_dir {} --save_steps 200 --nncf_config" - " nncf_distilbert_config_sst2.json".format( - DATASET_PATH, os.path.join(temp_folder["models"], "distilbert_output") - ) - ) - runner = Command( - create_command_line( - com_line, self.env.VENV_ACTIVATE, self.env.PYTHON_EXECUTABLE, self.env.CUDA_VISIBLE_STRING - ), - self.env.TRANSFORMERS_REPO_PATH, - ) - runner.run() - assert os.path.exists(os.path.join(temp_folder["models"], "distilbert_output", "pytorch_model.bin")) - - @pytest.mark.dependency(depends=["install_trans", "glue_distilbert_train"]) - def test_glue_distilbert_eval(self, temp_folder): - com_line = ( - "examples/pytorch/text-classification/run_glue.py --model_name_or_path {output}" - " --task_name sst2 --do_eval --max_seq_length 128" - " --output_dir {output} --validation_file {}/glue/glue_data/SST-2/test.tsv" - " --max_eval_samples 10" - " --nncf_config nncf_distilbert_config_sst2.json".format( - DATASET_PATH, output=os.path.join(temp_folder["models"], "distilbert_output") - ) - ) - runner = Command( - create_command_line( - com_line, self.env.VENV_ACTIVATE, self.env.PYTHON_EXECUTABLE, self.env.CUDA_VISIBLE_STRING - ), - self.env.TRANSFORMERS_REPO_PATH, - ) - runner.run() - - @pytest.mark.dependency(depends=["install_trans"], name="lm_train") - def test_lm_train(self, temp_folder): - # GPT2 is loaded via torch.frombuffer which is not available in torch==1.9.1 yet - com_line = ( - "examples/pytorch/language-modeling/run_clm.py --model_name_or_path distilgpt2" - " --do_train --per_gpu_train_batch_size 1" - " --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 " - " --num_train_epochs 0.001" - " --output_dir {} --nncf_config" - " nncf_gpt2_config_wikitext_hw_config.json".format(os.path.join(temp_folder["models"], "lm_output")) - ) - runner = Command( - create_command_line( - com_line, self.env.VENV_ACTIVATE, self.env.PYTHON_EXECUTABLE, self.env.CUDA_VISIBLE_STRING - ), - self.env.TRANSFORMERS_REPO_PATH, - ) - runner.run() - assert os.path.exists(os.path.join(temp_folder["models"], "lm_output", "pytorch_model.bin")) - - @pytest.mark.dependency(depends=["install_trans", "lm_train"]) - def test_lm_eval(self, temp_folder): - # GPT2 is loaded via torch.frombuffer which is not available in torch==1.9.1 yet - com_line = ( - "examples/pytorch/language-modeling/run_clm.py " - " --model_name_or_path {output} --do_eval " - " --output_dir {output} --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1" - " --max_eval_samples 10" - " --nncf_config nncf_gpt2_config_wikitext_hw_config.json".format( - output=os.path.join(temp_folder["models"], "lm_output") - ) - ) - runner = Command( - create_command_line( - com_line, self.env.VENV_ACTIVATE, self.env.PYTHON_EXECUTABLE, self.env.CUDA_VISIBLE_STRING - ), - self.env.TRANSFORMERS_REPO_PATH, - ) - runner.run() - - @pytest.mark.dependency(depends=["install_trans"], name="ner_train") - def test_ner_train(self, temp_folder): - com_line = ( - "examples/pytorch/token-classification/run_ner.py --model_name_or_path bert-base-uncased" - " --do_train --per_gpu_train_batch_size 1" - " --dataset_name conll2003 " - " --max_train_samples 10" - " --output_dir {} " - " --nncf_config nncf_bert_config_conll.json".format(os.path.join(temp_folder["models"], "ner_output")) - ) - runner = Command( - create_command_line( - com_line, self.env.VENV_ACTIVATE, self.env.PYTHON_EXECUTABLE, self.env.CUDA_VISIBLE_STRING - ), - self.env.TRANSFORMERS_REPO_PATH, - ) - runner.run() - assert os.path.exists(os.path.join(temp_folder["models"], "ner_output", "pytorch_model.bin")) - - @pytest.mark.dependency(depends=["install_trans", "ner_train"]) - def test_ner_eval(self, temp_folder): - com_line = ( - "examples/pytorch/token-classification/run_ner.py " - " --model_name_or_path {output} --do_eval " - " --output_dir {output} --dataset_name conll2003" - " --max_eval_samples 10" - " --nncf_config nncf_bert_config_conll.json".format( - output=os.path.join(temp_folder["models"], "ner_output") - ) - ) - runner = Command( - create_command_line( - com_line, self.env.VENV_ACTIVATE, self.env.PYTHON_EXECUTABLE, self.env.CUDA_VISIBLE_STRING - ), - self.env.TRANSFORMERS_REPO_PATH, - ) - runner.run() - - @pytest.mark.dependency(depends=["install_trans"]) - def test_convert_to_onnx(self, temp_folder): - com_line = ( - "examples/pytorch/question-answering/run_qa.py --model_name_or_path {output} " - " --do_eval" - " --dataset_name squad " - " --max_eval_samples 10" - " --output_dir {output}" - " --to_onnx {output}/model.onnx" - " --nncf_config nncf_bert_config_squad.json".format(output=os.path.join(temp_folder["models"], "squad")) - ) - runner = Command( - create_command_line( - com_line, self.env.VENV_ACTIVATE, self.env.PYTHON_EXECUTABLE, self.env.CUDA_VISIBLE_STRING - ), - self.env.TRANSFORMERS_REPO_PATH, - ) - runner.run() - assert os.path.exists(os.path.join(temp_folder["models"], "squad", "model.onnx")) diff --git a/tests/torch/test_sota_checkpoints.py b/tests/torch/test_sota_checkpoints.py index 6d6f1055850..e9b9fe43584 100644 --- a/tests/torch/test_sota_checkpoints.py +++ b/tests/torch/test_sota_checkpoints.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -22,6 +22,7 @@ import pytest from pytest import FixtureRequest +import nncf from tests.shared.metric_thresholds import DIFF_FP32_MAX_GLOBAL from tests.shared.metric_thresholds import DIFF_FP32_MIN_GLOBAL from tests.shared.paths import DATASET_DEFINITIONS_PATH @@ -115,7 +116,7 @@ def read_reference_file(ref_path: Path) -> List[EvalRunParamsStruct]: model_dict = datasets[dataset_name] for model_name, sample_dict in model_dict.items(): if model_name in model_names: - raise RuntimeError(f"Model name {model_name} is not unique.") + raise nncf.InternalError(f"Model name {model_name} is not unique.") model_names.append(model_name) param_list.append( EvalRunParamsStruct( @@ -313,7 +314,7 @@ def generate_accuracy_check_cmd( :return: A command line to run the accuracy_check tool. """ cmd = [ - "accuracy_check", + "python -m accuracy_checker.main", "--config", config_path.as_posix(), "--source", ov_data_dir.as_posix(), "--definitions", DATASET_DEFINITIONS_PATH.as_posix(), @@ -362,9 +363,8 @@ def threshold_check( err_msgs.append( "Target diff is not within thresholds: " + f"{diff_target_min} < {diff_target} < {diff_target_max}" ) - if diff_fp32 is not None: - if diff_fp32 < diff_fp32_min or diff_fp32 > diff_fp32_max: - err_msgs.append(f"FP32 diff is not within thresholds: {diff_fp32_min} < {diff_fp32} < {diff_fp32_max}") + if diff_fp32 is not None and (diff_fp32 < diff_fp32_min or diff_fp32 > diff_fp32_max): + err_msgs.append(f"FP32 diff is not within thresholds: {diff_fp32_min} < {diff_fp32} < {diff_fp32_max}") if err_msgs: return ";".join(err_msgs) return None @@ -659,7 +659,7 @@ def test_train( fp32_metric = REF_PT_FP32_METRIC[eval_run_param.reference, None] metric_value = self.read_metric(str(metrics_dump_file_path)) diff_fp32 = round((metric_value - fp32_metric), 2) - if -1 < diff_fp32: + if diff_fp32 > -1: err_msg = f"FP32 diff is not within thresholds: -1 < {diff_fp32}" collected_data.append( diff --git a/tests/torch/test_statistics_aggregator.py b/tests/torch/test_statistics_aggregator.py index 4ce64d645db..11e5ce70942 100644 --- a/tests/torch/test_statistics_aggregator.py +++ b/tests/torch/test_statistics_aggregator.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -17,13 +17,23 @@ from torch import nn from nncf import Dataset +from nncf.common import factory +from nncf.common.factory import NNCFGraphFactory from nncf.common.graph.transformations.commands import TargetType +from nncf.common.graph.transformations.layout import TransformationLayout +from nncf.common.quantization.structs import QuantizationScheme as QuantizationMode +from nncf.common.quantization.structs import QuantizerConfig +from nncf.common.tensor_statistics.statistic_point import StatisticPointsContainer from nncf.experimental.common.tensor_statistics.collectors import TensorReducerBase from nncf.quantization.algorithms.fast_bias_correction.torch_backend import PTFastBiasCorrectionAlgoBackend from nncf.quantization.algorithms.min_max.torch_backend import PTMinMaxAlgoBackend +from nncf.quantization.range_estimator import RangeEstimatorParametersSet +from nncf.torch.dynamic_graph.patch_pytorch import register_operator from nncf.torch.graph.graph import PTTargetPoint +from nncf.torch.model_transformer import PTInsertionCommand from nncf.torch.statistics.aggregator import PTStatisticsAggregator from tests.common.test_statistics_aggregator import TemplateTestStatisticsAggregator +from tests.torch.helpers import HookChecker from tests.torch.ptq.helpers import get_nncf_network from tests.torch.ptq.test_ptq_params import ToNNCFNetworkInterface @@ -45,6 +55,9 @@ def get_nncf_network(self): return get_nncf_network(self, INPUT_SHAPE) +MinMaxTestParameters = TemplateTestStatisticsAggregator.MinMaxTestParameters + + class TestStatisticsAggregator(TemplateTestStatisticsAggregator): @staticmethod def get_min_max_algo_backend_cls() -> Type[PTMinMaxAlgoBackend]: @@ -104,10 +117,6 @@ def dataset_samples(self, dataset_values): return torch.tensor(dataset_samples, dtype=torch.float32) - @pytest.fixture - def is_stat_in_shape_of_scale(self) -> bool: - return True - @pytest.fixture(params=[False], ids=["out_of_palce"]) def inplace_statistics(self, request) -> bool: return request.param @@ -123,3 +132,267 @@ def test_statistic_merging(self, dataset_samples, inplace_statistics): @pytest.mark.skip("Merging is not implemented yet") def test_same_collectors_different_attrs_dont_merge(self, statistics_type, test_params, dataset_samples): pass + + @pytest.mark.parametrize( + "test_parameters", + ( + MinMaxTestParameters( + RangeEstimatorParametersSet.MINMAX, + TargetType.OPERATOR_PRE_HOOK, + QuantizationMode.SYMMETRIC, + False, + 256, + -256, + ), + MinMaxTestParameters( + RangeEstimatorParametersSet.MINMAX, + TargetType.OPERATION_WITH_WEIGHTS, + QuantizationMode.SYMMETRIC, + False, + 256, + -256, + ), + MinMaxTestParameters( + RangeEstimatorParametersSet.MINMAX, + TargetType.OPERATOR_POST_HOOK, + QuantizationMode.SYMMETRIC, + False, + 256, + -256, + ), + ), + ) + def test_successive_statistics_aggregation( + self, + test_parameters: MinMaxTestParameters, + dataset_samples, + inplace_statistics, + is_backend_support_custom_estimators, + ): + is_stat_in_shape_of_scale = True + model = self.get_backend_model(dataset_samples) + quantizer_config = QuantizerConfig( + mode=test_parameters.quantization_mode, per_channel=test_parameters.per_channel + ) + + is_standard_estimator = test_parameters.range_estimator_params in [ + RangeEstimatorParametersSet.MINMAX, + RangeEstimatorParametersSet.MEAN_MINMAX, + ] + if not is_standard_estimator and not is_backend_support_custom_estimators: + pytest.skip("Custom estimators are not supported for this backend yet") + + ### Register operations before statistic collection + def fn(x): + return x * 2 + + target_point = self.get_target_point(test_parameters.target_type) + model = self.__add_fn_to_model(model, target_point, fn) + + ### Check hook inserted correctly + self.__check_successive_hooks(test_parameters, model, target_point, fn) + + ### Register and collect statistics after inserted operations + statistic_points = self.__get_statistic_points( + test_parameters, model, quantizer_config, dataset_samples, inplace_statistics + ) + tensor_collector = self.__collect_statistics_get_collector(statistic_points, model, dataset_samples) + ### Check values are changed because of the inserted operation + self.__check_collector( + test_parameters, + tensor_collector, + is_stat_in_shape_of_scale, + ) + + ### Check the inserted operation is inside the model + self.__check_successive_hooks(test_parameters, model, target_point, fn) + + @pytest.mark.parametrize( + "test_parameters, nested_target_node_name", + ( + ( + MinMaxTestParameters( + RangeEstimatorParametersSet.MINMAX, + TargetType.OPERATOR_PRE_HOOK, + QuantizationMode.SYMMETRIC, + False, + 512, + -512, + ), + "PTIdentityConvModel/fn_0", + ), + ( + MinMaxTestParameters( + RangeEstimatorParametersSet.MINMAX, + TargetType.OPERATION_WITH_WEIGHTS, + QuantizationMode.SYMMETRIC, + False, + 512, + -512, + ), + "PTIdentityConvModel/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/fn_0", + ), + ( + MinMaxTestParameters( + RangeEstimatorParametersSet.MINMAX, + TargetType.OPERATOR_POST_HOOK, + QuantizationMode.SYMMETRIC, + False, + 512, + -512, + ), + "PTIdentityConvModel/fn_0", + ), + ), + ) + @pytest.mark.parametrize("nested_target_type", [TargetType.OPERATOR_PRE_HOOK, TargetType.OPERATOR_POST_HOOK]) + def test_nested_statistics_aggregation( + self, + test_parameters: MinMaxTestParameters, + nested_target_type: TargetType, + nested_target_node_name, + dataset_samples, + inplace_statistics, + is_backend_support_custom_estimators, + ): + is_stat_in_shape_of_scale = True + model = self.get_backend_model(dataset_samples) + quantizer_config = QuantizerConfig( + mode=test_parameters.quantization_mode, per_channel=test_parameters.per_channel + ) + + is_standard_estimator = test_parameters.range_estimator_params in [ + RangeEstimatorParametersSet.MINMAX, + RangeEstimatorParametersSet.MEAN_MINMAX, + ] + if not is_standard_estimator and not is_backend_support_custom_estimators: + pytest.skip("Custom estimators are not supported for this backend yet") + + ### Register operations before statistic collection + @register_operator() + def fn(x): + return x * 2 + + target_point = self.get_target_point(test_parameters.target_type) + model = self.__add_fn_to_model(model, target_point, fn) + nested_target_point = PTMinMaxAlgoBackend.target_point(nested_target_type, nested_target_node_name, 0) + model = self.__add_fn_to_model(model, nested_target_point, fn) + + ### Check hook inserted correctly + self.__check_nested_hooks(test_parameters, model, target_point, nested_target_type, nested_target_node_name, fn) + + ### Register and collect statistics after inserted operations + statistic_points = self.__get_statistic_points( + test_parameters, + model, + quantizer_config, + dataset_samples, + inplace_statistics, + ) + tensor_collector = self.__collect_statistics_get_collector(statistic_points, model, dataset_samples) + ### Check values are changed because of the inserted operation + self.__check_collector( + test_parameters, + tensor_collector, + is_stat_in_shape_of_scale, + ) + + ### Check the inserted operation is inside the model + self.__check_nested_hooks(test_parameters, model, target_point, nested_target_type, nested_target_node_name, fn) + + @staticmethod + def __add_fn_to_model(model, target_point, fn): + layout = TransformationLayout() + command = PTInsertionCommand(target_point, fn) + layout.register(command) + model_transformer = factory.ModelTransformerFactory.create(model) + model = model_transformer.transform(layout) + model.nncf.rebuild_graph() + return model + + @classmethod + def __get_statistic_points( + cls, test_parameters: MinMaxTestParameters, model, quantizer_config, dataset_samples, inplace_statistics + ) -> StatisticPointsContainer: + statistics_points = StatisticPointsContainer() + for target_type in [test_parameters.target_type]: + target_point = cls.get_target_point(target_type) + statistic_point = cls.create_statistics_point( + model, + quantizer_config, + target_point, + len(dataset_samples), + "TEST_ALGO", + inplace_statistics, + test_parameters.range_estimator_params, + ) + statistics_points.add_statistic_point(statistic_point) + return statistics_points + + def __collect_statistics_get_collector( + self, + statistics_points: StatisticPointsContainer, + model, + dataset_samples, + ): + dataset = self.get_dataset(dataset_samples) + statistics_aggregator = self.get_statistics_aggregator(dataset) + statistics_aggregator.register_statistic_points(statistics_points) + graph = NNCFGraphFactory.create(model) + statistics_aggregator.collect_statistics(model, graph) + + tensor_collectors = list(statistics_points.get_tensor_collectors()) + assert len(tensor_collectors) == 1 + return tensor_collectors[0][2] + + @staticmethod + def __check_collector(test_parameters, tensor_collector, stat_in_shape_of_scale): + stat = tensor_collector.get_statistics() + # Torch and Openvino backends tensor collectors return values in shape of scale + # in comparison to ONNX backends. + ref_min_val, ref_max_val = test_parameters.ref_min_val, test_parameters.ref_max_val + if isinstance(ref_min_val, np.ndarray) and stat_in_shape_of_scale: + shape = (1, 3, 1, 1) + if test_parameters.target_type == TargetType.OPERATION_WITH_WEIGHTS: + shape = (3, 1, 1, 1) + ref_min_val, ref_max_val = map(lambda x: np.reshape(x, shape), (ref_min_val, ref_max_val)) + + assert np.allclose(stat.min_values, ref_min_val) + assert np.allclose(stat.max_values, ref_max_val) + if isinstance(ref_min_val, np.ndarray): + assert stat.min_values.shape == ref_min_val.shape + assert stat.max_values.shape == ref_max_val.shape + else: + ref_shape = (1, 1, 1, 1) if stat_in_shape_of_scale else () + assert stat.min_values.shape == ref_shape + assert stat.max_values.shape == ref_shape + + @staticmethod + def __check_successive_hooks(test_parameters, model, target_point, fn): + checker = HookChecker(model, "conv") + checker.add_ref( + ref_hooks=[fn], + target_type=test_parameters.target_type, + target_node_name=target_point.target_node_name, + input_port_id=0, + ) + checker.check_with_reference() + + @staticmethod + def __check_nested_hooks( + test_parameters, model, target_point, nested_target_type: TargetType, nested_target_node_name: str, fn + ): + checker = HookChecker(model, "conv") + checker.add_ref( + ref_hooks=[fn], + target_type=test_parameters.target_type, + target_node_name=target_point.target_node_name, + input_port_id=0, + ) + checker.add_ref( + ref_hooks=[fn], + target_type=nested_target_type, + target_node_name=nested_target_node_name, + input_port_id=0, + ) + checker.check_with_reference() diff --git a/tests/torch/test_telemetry.py b/tests/torch/test_telemetry.py index 165d62bd07f..dbb5e7e4c10 100644 --- a/tests/torch/test_telemetry.py +++ b/tests/torch/test_telemetry.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/test_tensor.py b/tests/torch/test_tensor.py index b219f36dc46..ee4d1d19209 100644 --- a/tests/torch/test_tensor.py +++ b/tests/torch/test_tensor.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -12,15 +12,28 @@ import torch from nncf.experimental.tensor import Tensor +from nncf.experimental.tensor import TensorDataType from nncf.experimental.tensor.definitions import TensorDeviceType from tests.shared.test_templates.template_test_nncf_tensor import TemplateTestNNCFTensorOperators +def cast_to(x: torch.Tensor, dtype: TensorDataType) -> torch.Tensor: + if dtype is TensorDataType.float32: + return x.type(torch.float32) + if dtype is TensorDataType.float16: + return x.type(torch.float16) + raise NotImplementedError + + class TestPTNNCFTensorOperators(TemplateTestNNCFTensorOperators): @staticmethod def to_tensor(x): return torch.tensor(x) + @staticmethod + def cast_to(x: torch.Tensor, dtype: TensorDataType) -> torch.Tensor: + return cast_to(x, dtype) + @pytest.mark.skipif(not torch.cuda.is_available(), reason="Skipping for CPU-only setups") class TestCudaPTNNCFTensorOperators(TemplateTestNNCFTensorOperators): @@ -28,6 +41,10 @@ class TestCudaPTNNCFTensorOperators(TemplateTestNNCFTensorOperators): def to_tensor(x): return torch.tensor(x).cuda() + @staticmethod + def cast_to(x: torch.Tensor, dtype: TensorDataType) -> torch.Tensor: + return cast_to(x, dtype) + def test_device(self): tensor = Tensor(self.to_tensor([1])) assert tensor.device == TensorDeviceType.GPU diff --git a/tests/torch/test_tracing_context.py b/tests/torch/test_tracing_context.py index c10d5815184..65b4e506c2b 100644 --- a/tests/torch/test_tracing_context.py +++ b/tests/torch/test_tracing_context.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/test_transform_fn.py b/tests/torch/test_transform_fn.py index f5954189c24..dd3732da827 100644 --- a/tests/torch/test_transform_fn.py +++ b/tests/torch/test_transform_fn.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tests/torch/test_utils.py b/tests/torch/test_utils.py index e8137af98c1..bbbc296d4c3 100644 --- a/tests/torch/test_utils.py +++ b/tests/torch/test_utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/third_party_integration/huggingface_transformers/0001-Modifications-for-NNCF-usage.patch b/third_party_integration/huggingface_transformers/0001-Modifications-for-NNCF-usage.patch deleted file mode 100644 index 9e177d36b8f..00000000000 --- a/third_party_integration/huggingface_transformers/0001-Modifications-for-NNCF-usage.patch +++ /dev/null @@ -1,1390 +0,0 @@ -From 76c14db1e3501d03f548d9e9ebc58661443d64e1 Mon Sep 17 00:00:00 2001 -From: Alexander Dokuchaev -Date: Mon, 25 Dec 2023 21:36:16 +0200 -Subject: [PATCH] Modifications for NNCF usage - ---- - examples/pytorch/language-modeling/run_clm.py | 77 +++++++++--- - examples/pytorch/question-answering/run_qa.py | 63 ++++++++-- - .../pytorch/text-classification/run_glue.py | 112 +++++++++++++++--- - .../pytorch/text-classification/run_xnli.py | 71 +++++++++-- - .../pytorch/token-classification/run_ner.py | 106 ++++++++++++++--- - nncf_bert_config_conll.json | 44 +++++++ - nncf_bert_config_mrpc.json | 42 +++++++ - nncf_bert_config_squad.json | 44 +++++++ - ...config_squad_magnitude_sparsity_cubic.json | 31 +++++ - nncf_bert_config_xnli.json | 38 ++++++ - nncf_distilbert_config_sst2.json | 33 ++++++ - nncf_gpt2_config_wikitext_hw_config.json | 49 ++++++++ - nncf_mobilebert_config_squad_int8.json | 49 ++++++++ - nncf_roberta_config_mnli.json | 29 +++++ - src/transformers/modeling_utils.py | 24 ++++ - src/transformers/pytorch_utils.py | 3 +- - src/transformers/trainer.py | 51 +++++++- - src/transformers/training_args.py | 6 + - src/transformers/utils/__init__.py | 1 + - 19 files changed, 805 insertions(+), 68 deletions(-) - create mode 100644 nncf_bert_config_conll.json - create mode 100644 nncf_bert_config_mrpc.json - create mode 100644 nncf_bert_config_squad.json - create mode 100644 nncf_bert_config_squad_magnitude_sparsity_cubic.json - create mode 100644 nncf_bert_config_xnli.json - create mode 100644 nncf_distilbert_config_sst2.json - create mode 100644 nncf_gpt2_config_wikitext_hw_config.json - create mode 100644 nncf_mobilebert_config_squad_int8.json - create mode 100644 nncf_roberta_config_mnli.json - -diff --git a/examples/pytorch/language-modeling/run_clm.py b/examples/pytorch/language-modeling/run_clm.py -index fe03cde7c..3f8cd6d37 100755 ---- a/examples/pytorch/language-modeling/run_clm.py -+++ b/examples/pytorch/language-modeling/run_clm.py -@@ -30,6 +30,8 @@ from itertools import chain - from typing import Optional - - import datasets -+import onnx -+import torch - from datasets import load_dataset - - import evaluate -@@ -51,7 +53,12 @@ from transformers.testing_utils import CaptureLogger - from transformers.trainer_utils import get_last_checkpoint - from transformers.utils import check_min_version, send_example_telemetry - from transformers.utils.versions import require_version -+from transformers.trainer import get_train_dataloader_for_init - -+from nncf import NNCFConfig -+from nncf.config.structures import QuantizationRangeInitArgs -+from nncf.config.structures import BNAdaptationInitArgs -+from nncf.torch.initialization import PTInitializingDataLoader - - # Will error if the minimal version of Transformers is not installed. Remove at your own risks. - check_min_version("4.23.0") -@@ -373,22 +380,6 @@ def main(): - "You can do it from another script, save it, and load it from here, using --tokenizer_name." - ) - -- if model_args.model_name_or_path: -- model = AutoModelForCausalLM.from_pretrained( -- model_args.model_name_or_path, -- from_tf=bool(".ckpt" in model_args.model_name_or_path), -- config=config, -- cache_dir=model_args.cache_dir, -- revision=model_args.model_revision, -- use_auth_token=True if model_args.use_auth_token else None, -- ) -- else: -- model = AutoModelForCausalLM.from_config(config) -- n_params = sum(dict((p.data_ptr(), p.numel()) for p in model.parameters()).values()) -- logger.info(f"Training new model from scratch - Total size={n_params/2**20:.2f}M params") -- -- model.resize_token_embeddings(len(tokenizer)) -- - # Preprocessing the datasets. - # First we tokenize all the texts. - if training_args.do_train: -@@ -503,6 +494,59 @@ def main(): - preds = preds[:, :-1].reshape(-1) - return metric.compute(predictions=preds, references=labels) - -+ nncf_config = None -+ if training_args.nncf_config is not None: -+ nncf_config = NNCFConfig.from_json(training_args.nncf_config) -+ if nncf_config.get("log_dir") is None: -+ nncf_config["log_dir"] = training_args.output_dir -+ if not os.path.exists(training_args.output_dir) and training_args.local_rank in [-1, 0]: -+ os.makedirs(nncf_config["log_dir"]) -+ if training_args.do_train: -+ train_dataloader = get_train_dataloader_for_init(training_args, train_dataset, -+ default_data_collator) -+ -+ class WikitextInitializingDataLoader(PTInitializingDataLoader): -+ def get_inputs(self, dataloader_output): -+ return (), dataloader_output -+ -+ nncf_config.register_extra_structs([ -+ QuantizationRangeInitArgs(WikitextInitializingDataLoader(train_dataloader)), -+ BNAdaptationInitArgs(WikitextInitializingDataLoader(train_dataloader)), -+ ]) -+ -+ if model_args.model_name_or_path: -+ retval = AutoModelForCausalLM.from_pretrained( -+ model_args.model_name_or_path, -+ from_tf=bool(".ckpt" in model_args.model_name_or_path), -+ config=config, -+ cache_dir=model_args.cache_dir, -+ revision=model_args.model_revision, -+ use_auth_token=True if model_args.use_auth_token else None, -+ nncf_config=nncf_config, -+ nncf_eval=nncf_config is not None and training_args.do_eval and not training_args.do_train -+ ) -+ else: -+ retval = AutoModelForCausalLM.from_config(config) -+ n_params = sum(dict((p.data_ptr(), p.numel()) for p in retval.parameters()).values()) -+ logger.info(f"Training new model from scratch - Total size={n_params / 2 ** 20:.2f}M params") -+ -+ -+ if nncf_config is None: -+ model = retval -+ compression_ctrl = None -+ else: -+ compression_ctrl, model = retval -+ -+ model.resize_token_embeddings(len(tokenizer)) -+ -+ if training_args.to_onnx: -+ if nncf_config is not None: -+ compression_ctrl.export_model(training_args.to_onnx) -+ else: -+ model.to('cpu') -+ dummy_tensor = torch.ones([1, config.n_positions], dtype=torch.long) -+ onnx.export(model, dummy_tensor, training_args.to_onnx) -+ - # Initialize our Trainer - trainer = Trainer( - model=model, -@@ -516,6 +560,7 @@ def main(): - preprocess_logits_for_metrics=preprocess_logits_for_metrics - if training_args.do_eval and not is_torch_tpu_available() - else None, -+ compression_ctrl=compression_ctrl - ) - - # Training -diff --git a/examples/pytorch/question-answering/run_qa.py b/examples/pytorch/question-answering/run_qa.py -index 1240623b5..b6136d6e3 100755 ---- a/examples/pytorch/question-answering/run_qa.py -+++ b/examples/pytorch/question-answering/run_qa.py -@@ -25,6 +25,7 @@ from dataclasses import dataclass, field - from typing import Optional - - import datasets -+import torch - from datasets import load_dataset - - import evaluate -@@ -42,11 +43,19 @@ from transformers import ( - default_data_collator, - set_seed, - ) -+from transformers.trainer import get_train_dataloader_for_init - from transformers.trainer_utils import get_last_checkpoint - from transformers.utils import check_min_version, send_example_telemetry - from transformers.utils.versions import require_version - from utils_qa import postprocess_qa_predictions - -+from torch import onnx -+ -+from nncf import NNCFConfig -+from nncf.torch.initialization import PTInitializingDataLoader -+from nncf.config.structures import BNAdaptationInitArgs -+from nncf.config.structures import QuantizationRangeInitArgs -+from nncf.common.utils.tensorboard import prepare_for_tensorboard - - # Will error if the minimal version of Transformers is not installed. Remove at your own risks. - check_min_version("4.23.0") -@@ -327,14 +336,6 @@ def main(): - revision=model_args.model_revision, - use_auth_token=True if model_args.use_auth_token else None, - ) -- model = AutoModelForQuestionAnswering.from_pretrained( -- model_args.model_name_or_path, -- from_tf=bool(".ckpt" in model_args.model_name_or_path), -- config=config, -- cache_dir=model_args.cache_dir, -- revision=model_args.model_revision, -- use_auth_token=True if model_args.use_auth_token else None, -- ) - - # Tokenizer check: this script requires a fast tokenizer. - if not isinstance(tokenizer, PreTrainedTokenizerFast): -@@ -599,6 +600,51 @@ def main(): - def compute_metrics(p: EvalPrediction): - return metric.compute(predictions=p.predictions, references=p.label_ids) - -+ nncf_config = None -+ if training_args.nncf_config is not None: -+ nncf_config = NNCFConfig.from_json(training_args.nncf_config) -+ if nncf_config.get("log_dir") is None: -+ nncf_config["log_dir"] = training_args.output_dir -+ if not os.path.exists(training_args.output_dir) and training_args.local_rank in [-1, 0]: -+ os.makedirs(nncf_config["log_dir"]) -+ if training_args.do_train: -+ train_dataloader = get_train_dataloader_for_init(training_args, train_dataset, data_collator) -+ class SquadInitializingDataloader(PTInitializingDataLoader): -+ def get_inputs(self, dataloader_output): -+ return (), dataloader_output -+ -+ nncf_config.register_extra_structs([ -+ QuantizationRangeInitArgs(SquadInitializingDataloader(train_dataloader)), -+ BNAdaptationInitArgs(SquadInitializingDataloader(train_dataloader)), -+ ]) -+ -+ retval = AutoModelForQuestionAnswering.from_pretrained( -+ model_args.model_name_or_path, -+ from_tf=bool(".ckpt" in model_args.model_name_or_path), -+ config=config, -+ cache_dir=model_args.cache_dir, -+ revision=model_args.model_revision, -+ use_auth_token=True if model_args.use_auth_token else None, -+ nncf_config=nncf_config, -+ nncf_eval=nncf_config is not None and training_args.do_eval and not training_args.do_train -+ ) -+ -+ if nncf_config is None: -+ model = retval -+ compression_ctrl = None -+ else: -+ compression_ctrl, model = retval -+ -+ if training_args.to_onnx: -+ # Expecting the following forward signature: -+ # (input_ids, attention_mask, token_type_ids, ...) -+ if nncf_config is not None: -+ compression_ctrl.export_model(training_args.to_onnx) -+ else: -+ model.to('cpu') -+ dummy_tensor = torch.ones([1, 384], dtype=torch.long) -+ onnx.export(model, (dummy_tensor, dummy_tensor, dummy_tensor), training_args.to_onnx) -+ - # Initialize our Trainer - trainer = QuestionAnsweringTrainer( - model=model, -@@ -610,6 +656,7 @@ def main(): - data_collator=data_collator, - post_process_function=post_processing_function, - compute_metrics=compute_metrics, -+ compression_ctrl=compression_ctrl - ) - - # Training -diff --git a/examples/pytorch/text-classification/run_glue.py b/examples/pytorch/text-classification/run_glue.py -index 3eb423f08..f675e7ab4 100755 ---- a/examples/pytorch/text-classification/run_glue.py -+++ b/examples/pytorch/text-classification/run_glue.py -@@ -29,6 +29,10 @@ from datasets import load_dataset - - import evaluate - import transformers -+from nncf import NNCFConfig -+from nncf.config.structures import BNAdaptationInitArgs -+from nncf.config.structures import QuantizationRangeInitArgs -+from nncf.torch.initialization import PTInitializingDataLoader - from transformers import ( - AutoConfig, - AutoModelForSequenceClassification, -@@ -42,6 +46,7 @@ from transformers import ( - default_data_collator, - set_seed, - ) -+from transformers.trainer import get_train_dataloader_for_init - from transformers.trainer_utils import get_last_checkpoint - from transformers.utils import check_min_version, send_example_telemetry - from transformers.utils.versions import require_version -@@ -366,15 +371,6 @@ def main(): - revision=model_args.model_revision, - use_auth_token=True if model_args.use_auth_token else None, - ) -- model = AutoModelForSequenceClassification.from_pretrained( -- model_args.model_name_or_path, -- from_tf=bool(".ckpt" in model_args.model_name_or_path), -- config=config, -- cache_dir=model_args.cache_dir, -- revision=model_args.model_revision, -- use_auth_token=True if model_args.use_auth_token else None, -- ignore_mismatched_sizes=model_args.ignore_mismatched_sizes, -- ) - - # Preprocessing the raw_datasets - if data_args.task_name is not None: -@@ -400,12 +396,12 @@ def main(): - # Some models have set the order of the labels to use, so let's make sure we do use it. - label_to_id = None - if ( -- model.config.label2id != PretrainedConfig(num_labels=num_labels).label2id -+ config.label2id != PretrainedConfig(num_labels=num_labels).label2id - and data_args.task_name is not None - and not is_regression - ): - # Some have all caps in their config, some don't. -- label_name_to_id = {k.lower(): v for k, v in model.config.label2id.items()} -+ label_name_to_id = {k.lower(): v for k, v in config.label2id.items()} - if list(sorted(label_name_to_id.keys())) == list(sorted(label_list)): - label_to_id = {i: int(label_name_to_id[label_list[i]]) for i in range(num_labels)} - else: -@@ -418,11 +414,11 @@ def main(): - label_to_id = {v: i for i, v in enumerate(label_list)} - - if label_to_id is not None: -- model.config.label2id = label_to_id -- model.config.id2label = {id: label for label, id in config.label2id.items()} -+ config.label2id = label_to_id -+ config.id2label = {id: label for label, id in config.label2id.items()} - elif data_args.task_name is not None and not is_regression: -- model.config.label2id = {l: i for i, l in enumerate(label_list)} -- model.config.id2label = {id: label for label, id in config.label2id.items()} -+ config.label2id = {l: i for i, l in enumerate(label_list)} -+ config.id2label = {id: label for label, id in config.label2id.items()} - - if data_args.max_seq_length > tokenizer.model_max_length: - logger.warning( -@@ -458,6 +454,87 @@ def main(): - max_train_samples = min(len(train_dataset), data_args.max_train_samples) - train_dataset = train_dataset.select(range(max_train_samples)) - -+ nncf_config = None -+ if training_args.nncf_config is not None: -+ nncf_config = NNCFConfig.from_json(training_args.nncf_config) -+ -+ if nncf_config.get("log_dir") is None: -+ nncf_config["log_dir"] = training_args.output_dir -+ -+ if not os.path.exists(training_args.output_dir) and training_args.local_rank in [-1, 0]: -+ os.makedirs(nncf_config["log_dir"]) -+ -+ if training_args.do_train: -+ train_dataloader = get_train_dataloader_for_init(training_args, -+ train_dataset, -+ data_collator=default_data_collator) -+ -+ class SST2InitializingDataLoader(PTInitializingDataLoader): -+ def get_inputs(self, dataloader_output): -+ return (), { -+ "labels": dataloader_output["labels"], -+ "attention_mask": dataloader_output["attention_mask"], -+ "input_ids": dataloader_output["input_ids"] -+ } -+ -+ class MRPCInitializingDataLoader(PTInitializingDataLoader): -+ def get_inputs(self, dataloader_output): -+ return (), { -+ "labels": dataloader_output["labels"], -+ "attention_mask": dataloader_output["attention_mask"], -+ "input_ids": dataloader_output["input_ids"], -+ "token_type_ids": dataloader_output["token_type_ids"] -+ } -+ -+ class MNLIInitializingDataLoader(PTInitializingDataLoader): -+ def get_inputs(self, dataloader_output): -+ return (), { -+ "labels": dataloader_output["labels"], -+ "attention_mask": dataloader_output["attention_mask"], -+ "input_ids": dataloader_output["input_ids"] -+ } -+ -+ if data_args.task_name == "sst2": -+ initializing_data_loader_cls = SST2InitializingDataLoader -+ elif data_args.task_name == "mrpc": -+ initializing_data_loader_cls = MRPCInitializingDataLoader -+ elif data_args.task_name == "mnli": -+ initializing_data_loader_cls = MNLIInitializingDataLoader -+ initializing_data_loader = initializing_data_loader_cls(train_dataloader) -+ nncf_config.register_extra_structs([QuantizationRangeInitArgs(initializing_data_loader), -+ BNAdaptationInitArgs(initializing_data_loader)]) -+ -+ -+ retval = AutoModelForSequenceClassification.from_pretrained( -+ model_args.model_name_or_path, -+ from_tf=bool(".ckpt" in model_args.model_name_or_path), -+ config=config, -+ cache_dir=model_args.cache_dir, -+ revision=model_args.model_revision, -+ use_auth_token=True if model_args.use_auth_token else None, -+ nncf_config=nncf_config, -+ nncf_eval=nncf_config is not None and training_args.do_eval and not training_args.do_train -+ ) -+ -+ if nncf_config is None: -+ model = retval -+ compression_ctrl = None -+ else: -+ compression_ctrl, model = retval -+ -+ if training_args.to_onnx: -+ # Expecting the following forward signature: -+ # (input_ids, attention_mask, token_type_ids, ...) -+ if nncf_config is not None: -+ compression_ctrl.export_model(training_args.to_onnx) -+ else: -+ model.to('cpu') -+ import torch -+ from torch import onnx -+ dummy_tensor = torch.ones([1, 128], dtype=torch.long) -+ onnx.export(model, (dummy_tensor, dummy_tensor, dummy_tensor), -+ training_args.to_onnx, opset_version=10) -+ - if training_args.do_eval: - if "validation" not in raw_datasets and "validation_matched" not in raw_datasets: - raise ValueError("--do_eval requires a validation dataset") -@@ -518,8 +595,13 @@ def main(): - compute_metrics=compute_metrics, - tokenizer=tokenizer, - data_collator=data_collator, -+ compression_ctrl=compression_ctrl - ) - -+ if nncf_config is not None: -+ if not (training_args.local_rank == -1 or training_args.no_cuda): -+ compression_ctrl.distributed() -+ - # Training - if training_args.do_train: - checkpoint = None -diff --git a/examples/pytorch/text-classification/run_xnli.py b/examples/pytorch/text-classification/run_xnli.py -index 55523edfc..68a3ebe41 100755 ---- a/examples/pytorch/text-classification/run_xnli.py -+++ b/examples/pytorch/text-classification/run_xnli.py -@@ -26,10 +26,16 @@ from typing import Optional - - import datasets - import numpy as np -+import torch - from datasets import load_dataset - - import evaluate - import transformers -+from nncf import NNCFConfig -+from nncf.config.structures import QuantizationRangeInitArgs -+from nncf.torch import register_default_init_args -+from nncf.torch.initialization import PTInitializingDataLoader -+ - from transformers import ( - AutoConfig, - AutoModelForSequenceClassification, -@@ -42,6 +48,7 @@ from transformers import ( - default_data_collator, - set_seed, - ) -+from transformers.trainer import get_train_dataloader_for_init - from transformers.trainer_utils import get_last_checkpoint - from transformers.utils import check_min_version, send_example_telemetry - from transformers.utils.versions import require_version -@@ -282,15 +289,6 @@ def main(): - revision=model_args.model_revision, - use_auth_token=True if model_args.use_auth_token else None, - ) -- model = AutoModelForSequenceClassification.from_pretrained( -- model_args.model_name_or_path, -- from_tf=bool(".ckpt" in model_args.model_name_or_path), -- config=config, -- cache_dir=model_args.cache_dir, -- revision=model_args.model_revision, -- use_auth_token=True if model_args.use_auth_token else None, -- ignore_mismatched_sizes=model_args.ignore_mismatched_sizes, -- ) - - # Preprocessing the datasets - # Padding strategy -@@ -367,6 +365,56 @@ def main(): - else: - data_collator = None - -+ nncf_config = None -+ if training_args.nncf_config is not None: -+ nncf_config = NNCFConfig.from_json(training_args.nncf_config) -+ -+ if nncf_config.get("log_dir") is None: -+ nncf_config["log_dir"] = training_args.output_dir -+ -+ if not os.path.exists(training_args.output_dir) and training_args.local_rank in [-1, 0]: -+ os.makedirs(nncf_config["log_dir"]) -+ -+ if training_args.do_train: -+ train_dataloader = get_train_dataloader_for_init(training_args, -+ train_dataset, -+ data_collator=data_collator) -+ -+ class KwargBasedInitializingDataloader(PTInitializingDataLoader): -+ def get_inputs(self, dataloader_output): -+ return (), dataloader_output -+ -+ initializing_data_loader = KwargBasedInitializingDataloader(train_dataloader) -+ nncf_config = register_default_init_args(nncf_config, initializing_data_loader) -+ -+ -+ retval = AutoModelForSequenceClassification.from_pretrained( -+ model_args.model_name_or_path, -+ from_tf=bool(".ckpt" in model_args.model_name_or_path), -+ config=config, -+ cache_dir=model_args.cache_dir, -+ revision=model_args.model_revision, -+ use_auth_token=True if model_args.use_auth_token else None, -+ nncf_config=nncf_config, -+ nncf_eval=nncf_config is not None and training_args.do_eval and not training_args.do_train -+ ) -+ -+ if nncf_config is None: -+ model = retval -+ compression_ctrl = None -+ else: -+ compression_ctrl, model = retval -+ -+ if training_args.to_onnx: -+ # Expecting the following forward signature: -+ # (input_ids, attention_mask, token_type_ids, ...) -+ if nncf_config is not None: -+ compression_ctrl.export_model(training_args.to_onnx) -+ else: -+ model.to('cpu') -+ dummy_tensor = torch.ones([1, training_args.max_seq_length], dtype=torch.long) -+ torch.onnx.export(model, (dummy_tensor, dummy_tensor, dummy_tensor), training_args.to_onnx) -+ - # Initialize our Trainer - trainer = Trainer( - model=model, -@@ -376,8 +424,13 @@ def main(): - compute_metrics=compute_metrics, - tokenizer=tokenizer, - data_collator=data_collator, -+ compression_ctrl=compression_ctrl - ) - -+ if nncf_config is not None: -+ if not (training_args.local_rank == -1 or training_args.no_cuda): -+ compression_ctrl.distributed() -+ - # Training - if training_args.do_train: - checkpoint = None -diff --git a/examples/pytorch/token-classification/run_ner.py b/examples/pytorch/token-classification/run_ner.py -index 52cbbb87b..30f150580 100755 ---- a/examples/pytorch/token-classification/run_ner.py -+++ b/examples/pytorch/token-classification/run_ner.py -@@ -22,15 +22,24 @@ Fine-tuning the library models for token classification. - import logging - import os - import sys -+from copy import deepcopy - from dataclasses import dataclass, field - from typing import Optional -+from typing import List - - import datasets - import numpy as np - from datasets import ClassLabel, load_dataset - - import evaluate -+import torch - import transformers -+from nncf import NNCFConfig -+from nncf.config.structures import BNAdaptationInitArgs -+from nncf.config.structures import QuantizationRangeInitArgs -+from nncf.torch.initialization import PTInitializingDataLoader -+from packaging import version -+from torch import onnx - from transformers import ( - AutoConfig, - AutoModelForTokenClassification, -@@ -43,6 +52,7 @@ from transformers import ( - TrainingArguments, - set_seed, - ) -+from transformers.trainer import get_train_dataloader_for_init - from transformers.trainer_utils import get_last_checkpoint - from transformers.utils import check_min_version, send_example_telemetry - from transformers.utils.versions import require_version -@@ -204,6 +214,16 @@ class DataTrainingArguments: - self.task_name = self.task_name.lower() - - -+def filter_columns(dataset, keep_columns: List[str], remove_columns: List[str]): -+ if version.parse(datasets.__version__) < version.parse("1.4.0"): -+ dataset.set_format( -+ type=dataset.format["type"], columns=keep_columns, format_kwargs=dataset.format["format_kwargs"] -+ ) -+ return dataset -+ else: -+ return dataset.remove_columns(remove_columns) -+ -+ - def main(): - # See all possible arguments in src/transformers/training_args.py - # or by passing the --help flag to this script. -@@ -366,16 +386,6 @@ def main(): - use_auth_token=True if model_args.use_auth_token else None, - ) - -- model = AutoModelForTokenClassification.from_pretrained( -- model_args.model_name_or_path, -- from_tf=bool(".ckpt" in model_args.model_name_or_path), -- config=config, -- cache_dir=model_args.cache_dir, -- revision=model_args.model_revision, -- use_auth_token=True if model_args.use_auth_token else None, -- ignore_mismatched_sizes=model_args.ignore_mismatched_sizes, -- ) -- - # Tokenizer check: this script requires a fast tokenizer. - if not isinstance(tokenizer, PreTrainedTokenizerFast): - raise ValueError( -@@ -385,25 +395,25 @@ def main(): - ) - - # Model has labels -> use them. -- if model.config.label2id != PretrainedConfig(num_labels=num_labels).label2id: -- if list(sorted(model.config.label2id.keys())) == list(sorted(label_list)): -+ if config.label2id != PretrainedConfig(num_labels=num_labels).label2id: -+ if list(sorted(config.label2id.keys())) == list(sorted(label_list)): - # Reorganize `label_list` to match the ordering of the model. - if labels_are_int: -- label_to_id = {i: int(model.config.label2id[l]) for i, l in enumerate(label_list)} -- label_list = [model.config.id2label[i] for i in range(num_labels)] -+ label_to_id = {i: int(config.label2id[l]) for i, l in enumerate(label_list)} -+ label_list = [config.id2label[i] for i in range(num_labels)] - else: -- label_list = [model.config.id2label[i] for i in range(num_labels)] -+ label_list = [config.id2label[i] for i in range(num_labels)] - label_to_id = {l: i for i, l in enumerate(label_list)} - else: - logger.warning( - "Your model seems to have been trained with labels, but they don't match the dataset: ", -- f"model labels: {list(sorted(model.config.label2id.keys()))}, dataset labels:" -+ f"model labels: {list(sorted(config.label2id.keys()))}, dataset labels:" - f" {list(sorted(label_list))}.\nIgnoring the model labels as a result.", - ) - - # Set the correspondences label/ID inside the model config -- model.config.label2id = {l: i for i, l in enumerate(label_list)} -- model.config.id2label = {i: l for i, l in enumerate(label_list)} -+ config.label2id = {l: i for i, l in enumerate(label_list)} -+ config.id2label = {i: l for i, l in enumerate(label_list)} - - # Map that sends B-Xxx label to its I-Xxx counterpart - b_to_i_label = [] -@@ -504,6 +514,65 @@ def main(): - # Data collator - data_collator = DataCollatorForTokenClassification(tokenizer, pad_to_multiple_of=8 if training_args.fp16 else None) - -+ nncf_config = None -+ if training_args.nncf_config is not None: -+ nncf_config = NNCFConfig.from_json(training_args.nncf_config) -+ if nncf_config.get("log_dir") is None: -+ nncf_config["log_dir"] = training_args.output_dir -+ if not os.path.exists(training_args.output_dir) and training_args.local_rank in [-1, 0]: -+ os.makedirs(nncf_config["log_dir"]) -+ if training_args.do_train: -+ train_dataset_for_init = deepcopy(train_dataset) -+ -+ train_dataset_for_init = filter_columns(train_dataset_for_init, -+ keep_columns=['labels', 'input_ids', 'attention_mask', -+ 'token_type_ids'], -+ remove_columns=['ner_tags', 'pos_tags', 'tokens', 'id', -+ 'chunk_tags']) -+ train_dataloader = get_train_dataloader_for_init(training_args, train_dataset_for_init, data_collator) -+ -+ class ConllInitializingDataloader(PTInitializingDataLoader): -+ def get_inputs(self, dataloader_output): -+ return (), { -+ "input_ids": dataloader_output["input_ids"], -+ "attention_mask": dataloader_output["attention_mask"], -+ "token_type_ids": dataloader_output["token_type_ids"], -+ } -+ -+ nncf_config.register_extra_structs([ -+ QuantizationRangeInitArgs(ConllInitializingDataloader(train_dataloader)), -+ BNAdaptationInitArgs(ConllInitializingDataloader(train_dataloader)), -+ ]) -+ -+ retval = AutoModelForTokenClassification.from_pretrained( -+ model_args.model_name_or_path, -+ from_tf=bool(".ckpt" in model_args.model_name_or_path), -+ config=config, -+ cache_dir=model_args.cache_dir, -+ revision=model_args.model_revision, -+ use_auth_token=True if model_args.use_auth_token else None, -+ nncf_config=nncf_config, -+ nncf_eval=nncf_config is not None and training_args.do_eval and not training_args.do_train -+ ) -+ -+ if nncf_config is None: -+ model = retval -+ compression_ctrl = None -+ else: -+ compression_ctrl, model = retval -+ -+ -+ if training_args.to_onnx: -+ # Expecting the following forward signature: -+ # (input_ids, attention_mask, token_type_ids, ...) -+ if nncf_config is not None: -+ compression_ctrl.export_model(training_args.to_onnx) -+ else: -+ model.to('cpu') -+ dummy_tensor = torch.ones([1, 128], dtype=torch.long) -+ onnx.export(model, (dummy_tensor, dummy_tensor, dummy_tensor), training_args.to_onnx, -+ opset_version=10) -+ - # Metrics - metric = evaluate.load("seqeval") - -@@ -549,6 +618,7 @@ def main(): - tokenizer=tokenizer, - data_collator=data_collator, - compute_metrics=compute_metrics, -+ compression_ctrl=compression_ctrl - ) - - # Training -diff --git a/nncf_bert_config_conll.json b/nncf_bert_config_conll.json -new file mode 100644 -index 000000000..bf7c88ebb ---- /dev/null -+++ b/nncf_bert_config_conll.json -@@ -0,0 +1,44 @@ -+{ -+ "input_info": [ -+ { -+ "sample_size": [1, 128], -+ "type": "long" -+ }, -+ { -+ "sample_size": [1, 128], -+ "type": "long" -+ }, -+ { -+ "sample_size": [1, 128], -+ "type": "long" -+ } -+ ], -+ "compression": { -+ "algorithm": "quantization", -+ "initializer": { -+ "range": { -+ "num_init_samples": 32, -+ "type": "percentile", -+ "params": -+ { -+ "min_percentile": 0.01, -+ "max_percentile": 99.99 -+ } -+ }, -+ -+ "batchnorm_adaptation": { -+ "num_bn_adaptation_samples": 0 -+ } -+ }, -+ "activations": -+ { -+ "mode": "symmetric" -+ }, -+ "weights": -+ { -+ "mode": "symmetric", -+ "signed": true, -+ "per_channel": false -+ } -+ } -+} -diff --git a/nncf_bert_config_mrpc.json b/nncf_bert_config_mrpc.json -new file mode 100644 -index 000000000..425d89d76 ---- /dev/null -+++ b/nncf_bert_config_mrpc.json -@@ -0,0 +1,42 @@ -+{ -+ "input_info": [ -+ { -+ "sample_size": [1, 128], -+ "type": "long" -+ }, -+ { -+ "sample_size": [1, 128], -+ "type": "long" -+ }, -+ { -+ "sample_size": [1, 128], -+ "type": "long" -+ } -+ ], -+ "compression": { -+ "algorithm": "quantization", -+ "initializer": { -+ "range": { -+ "num_init_samples": 64, -+ "type": "percentile", -+ "params": -+ { -+ "min_percentile": 0.01, -+ "max_percentile": 99.99 -+ } -+ }, -+ "batchnorm_adaptation": { -+ "num_bn_adaptation_samples": 0 -+ } -+ }, -+ "activations": -+ { -+ "mode": "symmetric" -+ }, -+ "weights": -+ { -+ "mode": "symmetric", -+ "per_channel": false -+ } -+ } -+} -diff --git a/nncf_bert_config_squad.json b/nncf_bert_config_squad.json -new file mode 100644 -index 000000000..2a055de17 ---- /dev/null -+++ b/nncf_bert_config_squad.json -@@ -0,0 +1,44 @@ -+{ -+ "input_info": [ -+ { -+ "sample_size": [1, 384], -+ "type": "long" -+ }, -+ { -+ "sample_size": [1, 384], -+ "type": "long" -+ }, -+ { -+ "sample_size": [1, 384], -+ "type": "long" -+ } -+ ], -+ "compression": { -+ "algorithm": "quantization", -+ "initializer": { -+ "range": { -+ "num_init_samples": 32, -+ "type": "percentile", -+ "params": -+ { -+ "min_percentile": 0.01, -+ "max_percentile": 99.99 -+ } -+ }, -+ -+ "batchnorm_adaptation": { -+ "num_bn_adaptation_samples": 0 -+ } -+ }, -+ "activations": -+ { -+ "mode": "symmetric" -+ }, -+ "weights": -+ { -+ "mode": "symmetric", -+ "signed": true, -+ "per_channel": false -+ } -+ } -+} -diff --git a/nncf_bert_config_squad_magnitude_sparsity_cubic.json b/nncf_bert_config_squad_magnitude_sparsity_cubic.json -new file mode 100644 -index 000000000..b4452e8d4 ---- /dev/null -+++ b/nncf_bert_config_squad_magnitude_sparsity_cubic.json -@@ -0,0 +1,31 @@ -+{ -+ "input_info": [ -+ { -+ "sample_size": [1, 384], -+ "type": "long" -+ }, -+ { -+ "sample_size": [1, 384], -+ "type": "long" -+ }, -+ { -+ "sample_size": [1, 384], -+ "type": "long" -+ } -+ ], -+ "compression": { -+ "algorithm": "magnitude_sparsity", -+ "params": { -+ "schedule": "polynomial", -+ "power": 3, -+ "sparsity_init": 0.0, -+ "sparsity_target": 0.8, -+ "sparsity_target_epoch": 40, -+ "sparsity_freeze_epoch": 60, -+ "update_per_optimizer_step": true, -+ "steps_per_epoch": 1109, -+ "weight_importance": "abs" -+ }, -+ "ignored_scopes": ["{re}.*NNCFEmbedding"] -+ } -+} -diff --git a/nncf_bert_config_xnli.json b/nncf_bert_config_xnli.json -new file mode 100644 -index 000000000..92b95db1c ---- /dev/null -+++ b/nncf_bert_config_xnli.json -@@ -0,0 +1,38 @@ -+{ -+ "input_info": [ -+ { -+ "sample_size": [1, 128], -+ "type": "long" -+ }, -+ { -+ "sample_size": [1, 128], -+ "type": "long" -+ }, -+ { -+ "sample_size": [1, 128], -+ "type": "long" -+ } -+ ], -+ "compression": { -+ "algorithm": "quantization", -+ "initializer": { -+ "range": { -+ "num_init_samples": 96 -+ }, -+ "batchnorm_adaptation": { -+ "num_bn_adaptation_samples": 0 -+ } -+ }, -+ "ignored_scopes": ["{re}BertSelfAttention\\[self\\]/__add___0", -+ "{re}BertIntermediate\\[intermediate\\]/NNCFLinear\\[dense\\]/linear_0" -+ ], -+ "activations": -+ { -+ "mode": "asymmetric" -+ }, -+ "weights": -+ { -+ "mode": "symmetric" -+ } -+ } -+} -diff --git a/nncf_distilbert_config_sst2.json b/nncf_distilbert_config_sst2.json -new file mode 100644 -index 000000000..dc140ab39 ---- /dev/null -+++ b/nncf_distilbert_config_sst2.json -@@ -0,0 +1,33 @@ -+{ -+ "input_info": [ -+ { -+ "sample_size": [1, 128], -+ "type": "long" -+ }, -+ { -+ "sample_size": [1, 128], -+ "type": "long" -+ } -+ ], -+ "compression": { -+ "algorithm": "quantization", -+ "initializer": { -+ "range": { -+ "num_init_samples": 32, -+ "type": "mean_percentile" -+ }, -+ "batchnorm_adaptation": { -+ "num_bn_adaptation_samples": 0 -+ } -+ }, -+ "activations": -+ { -+ "mode": "symmetric" -+ }, -+ "weights": -+ { -+ "mode": "symmetric", -+ "signed": true -+ } -+ } -+} -diff --git a/nncf_gpt2_config_wikitext_hw_config.json b/nncf_gpt2_config_wikitext_hw_config.json -new file mode 100644 -index 000000000..55173b25b ---- /dev/null -+++ b/nncf_gpt2_config_wikitext_hw_config.json -@@ -0,0 +1,49 @@ -+{ -+ "input_info": [ -+ { -+ "sample_size": [1, 1024], -+ "type": "long" -+ } -+ ], -+ "hw_config_type": "cpu", -+ "compression": { -+ "algorithm": "quantization", -+ "initializer": { -+ "range": { -+ "num_init_samples": 16, -+ "type": "percentile", -+ "params": -+ { -+ "min_percentile": 0.01, -+ "max_percentile": 99.99 -+ } -+ }, -+ "batchnorm_adaptation": { -+ "num_bn_adaptation_samples": 0 -+ } -+ }, -+ "ignored_scopes": [ -+ // Intermediate embedding sum results -+ "GPT2LMHeadModel/GPT2Model[transformer]/__add___0", -+ -+ // Scaling in attention -+ "{re}.*Attention\\[attn\\]/__truediv___0", -+ -+ // Pre-LayerNorm additions -+ "{re}.*Block\\[[0-9]*\\]/__add___0", -+ "{re}.*Block\\[[0-9]*\\]/__add___1", -+ -+ // LM head -+ "GPT2LMHeadModel/NNCFLinear[lm_head]/linear_0" -+ ], -+ "activations": -+ { -+ "mode": "symmetric" -+ }, -+ "weights": -+ { -+ "mode": "symmetric", -+ "signed": true -+ } -+ } -+} -diff --git a/nncf_mobilebert_config_squad_int8.json b/nncf_mobilebert_config_squad_int8.json -new file mode 100644 -index 000000000..4d0e84edf ---- /dev/null -+++ b/nncf_mobilebert_config_squad_int8.json -@@ -0,0 +1,49 @@ -+{ -+ "input_info": [ -+ { -+ "sample_size": [1, 384], -+ "type": "long" -+ }, -+ { -+ "sample_size": [1, 384], -+ "type": "long" -+ }, -+ { -+ "sample_size": [1, 384], -+ "type": "long" -+ } -+ ], -+ "compression": { -+ "algorithm": "quantization", -+ "initializer": { -+ "range": { -+ "num_init_samples": 64, -+ "type": "percentile", -+ "params": -+ { -+ "min_percentile": 0.01, -+ "max_percentile": 99.99 -+ } -+ }, -+ "batchnorm_adaptation": { -+ "num_bn_adaptation_samples": 0 -+ } -+ }, -+ "ignored_scopes": ["{re}MobileBertSelfAttention\\[self\\]/__add___0", -+ "{re}MobileBertIntermediate\\[intermediate\\]/NNCFLinear\\[dense\\]/linear_0"], -+ "activations": -+ { -+ "mode": "symmetric", -+ "ignored_scopes": [ -+ "{re}MobileBertForQuestionAnswering/MobileBertModel\\[mobilebert\\]/MobileBertEmbeddings\\[embeddings\\]/__add___0", -+ "{re}MobileBertForQuestionAnswering/MobileBertModel\\[mobilebert\\]/MobileBertEmbeddings\\[embeddings\\]/__add___1", -+ "{re}MobileBertOutput\\[output\\]/__add___0", -+ "{re}NoNorm\\[LayerNorm\\]/__mul___0"] -+ }, -+ "weights": -+ { -+ "mode": "symmetric", -+ "signed": true -+ } -+ } -+} -diff --git a/nncf_roberta_config_mnli.json b/nncf_roberta_config_mnli.json -new file mode 100644 -index 000000000..46f819bca ---- /dev/null -+++ b/nncf_roberta_config_mnli.json -@@ -0,0 +1,29 @@ -+{ -+ "input_info": [ -+ { -+ "keyword": "input_ids", -+ "sample_size": [1, 128], -+ "type": "long", -+ "filler": "ones" -+ } -+ ], -+ "compression": { -+ "algorithm": "quantization", -+ "initializer": { -+ "range": { -+ "num_init_samples": 24 -+ }, -+ "batchnorm_adaptation": { -+ "num_bn_adaptation_samples": 0 -+ } -+ }, -+ "activations": -+ { -+ "mode": "asymmetric" -+ }, -+ "weights": -+ { -+ "mode": "asymmetric" -+ } -+ } -+} -diff --git a/src/transformers/modeling_utils.py b/src/transformers/modeling_utils.py -index 5f4fccd33..7f4cdb3d8 100644 ---- a/src/transformers/modeling_utils.py -+++ b/src/transformers/modeling_utils.py -@@ -27,10 +27,12 @@ from functools import partial - from typing import Any, Callable, Dict, List, Optional, Tuple, Union - - import torch -+from nncf.torch import create_compressed_model - from packaging import version - from torch import Tensor, device, nn - from torch.nn import CrossEntropyLoss - -+from transformers.utils import NNCF_PT_STATE_NAME - from transformers.utils.hub import convert_file_size_to_int, get_checkpoint_shard_files - from transformers.utils.import_utils import is_sagemaker_mp_enabled - -@@ -1497,6 +1499,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix - push_to_hub: bool = False, - max_shard_size: Union[int, str] = "10GB", - safe_serialization: bool = False, -+ nncf_compression_state: Dict = None, - **kwargs, - ): - """ -@@ -1620,6 +1623,10 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix - else: - save_function(shard, os.path.join(save_directory, shard_file)) - -+ if nncf_compression_state is not None: -+ nncf_state_output_file = os.path.join(save_directory, NNCF_PT_STATE_NAME) -+ save_function(nncf_compression_state, nncf_state_output_file) -+ - if index is None: - logger.info(f"Model weights saved in {os.path.join(save_directory, WEIGHTS_NAME)}") - else: -@@ -1901,6 +1908,8 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix - load_in_8bit_skip_modules = kwargs.pop("load_in_8bit_skip_modules", None) - subfolder = kwargs.pop("subfolder", "") - commit_hash = kwargs.pop("_commit_hash", None) -+ nncf_config = kwargs.pop("nncf_config", None) -+ nncf_eval = kwargs.pop("nncf_eval", False) - - if trust_remote_code is True: - logger.warning( -@@ -2321,6 +2330,11 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix - if dtype_orig is not None: - torch.set_default_dtype(dtype_orig) - -+ if nncf_config is not None and nncf_eval: -+ compression_algo_controller, model = create_compressed_model(model, nncf_config, -+ compression_state=state_dict) -+ return compression_algo_controller, model -+ - model, missing_keys, unexpected_keys, mismatched_keys, error_msgs = cls._load_pretrained_model( - model, - state_dict, -@@ -2344,6 +2358,16 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix - # Set model in evaluation mode to deactivate DropOut modules by default - model.eval() - -+ if nncf_config is not None: -+ compression_state = None -+ compression_state_file = os.path.join(pretrained_model_name_or_path, NNCF_PT_STATE_NAME) -+ if os.path.isfile(compression_state_file): -+ compression_state = torch.load(compression_state_file) -+ -+ compression_algo_controller, model = create_compressed_model(model, nncf_config, -+ compression_state=compression_state) -+ return compression_algo_controller, model -+ - # Dispatch model with hooks on all devices if necessary - if device_map is not None: - dispatch_model(model, device_map=device_map, offload_dir=offload_folder) -diff --git a/src/transformers/pytorch_utils.py b/src/transformers/pytorch_utils.py -index d94e049b5..09c99d4dd 100644 ---- a/src/transformers/pytorch_utils.py -+++ b/src/transformers/pytorch_utils.py -@@ -87,7 +87,8 @@ def prune_linear_layer(layer: nn.Linear, index: torch.LongTensor, dim: int = 0) - new_layer.bias.requires_grad = True - return new_layer - -- -+import nncf -+@nncf.torch.register_module() - class Conv1D(nn.Module): - """ - 1D-convolutional layer as defined by Radford et al. for OpenAI GPT (and also used in GPT-2). -diff --git a/src/transformers/trainer.py b/src/transformers/trainer.py -index 214e7a978..12a5787cd 100755 ---- a/src/transformers/trainer.py -+++ b/src/transformers/trainer.py -@@ -33,7 +33,7 @@ from pathlib import Path - from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union - - from tqdm.auto import tqdm -- -+from nncf.torch.nncf_network import NNCFNetwork - - # Integrations must be imported before ML frameworks: - from .integrations import ( # isort: split -@@ -55,6 +55,8 @@ import numpy as np - import torch - import torch.distributed as dist - from packaging import version -+from nncf.torch.compression_method_api import PTCompressionAlgorithmController -+from nncf.common.utils.tensorboard import prepare_for_tensorboard - from torch import nn - from torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler - from torch.utils.data.distributed import DistributedSampler -@@ -206,6 +208,30 @@ SCHEDULER_NAME = "scheduler.pt" - SCALER_NAME = "scaler.pt" - - -+def get_train_dataloader_for_init(args, train_dataset, data_collator=None): -+ from torch.utils.data import RandomSampler -+ from torch.utils.data import DistributedSampler -+ train_sampler = ( -+ RandomSampler(train_dataset) -+ if args.local_rank == -1 -+ else DistributedSampler(train_dataset) -+ ) -+ -+ if data_collator is None: -+ from transformers.data.data_collator import default_data_collator -+ data_collator = default_data_collator -+ -+ from torch.utils.data import DataLoader -+ data_loader = DataLoader( -+ train_dataset, -+ batch_size=args.train_batch_size, -+ sampler=train_sampler, -+ collate_fn=data_collator, -+ drop_last=args.dataloader_drop_last, -+ ) -+ return data_loader -+ -+ - class Trainer: - """ - Trainer is a simple but feature-complete training and eval loop for PyTorch, optimized for 🤗 Transformers. -@@ -304,12 +330,15 @@ class Trainer: - callbacks: Optional[List[TrainerCallback]] = None, - optimizers: Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None), - preprocess_logits_for_metrics: Callable[[torch.Tensor, torch.Tensor], torch.Tensor] = None, -+ compression_ctrl: PTCompressionAlgorithmController = None - ): - if args is None: - output_dir = "tmp_trainer" - logger.info(f"No `TrainingArguments` passed, using `output_dir={output_dir}`.") - args = TrainingArguments(output_dir=output_dir) - self.args = args -+ -+ self.compression_ctrl = compression_ctrl - # Seed must be set before instantiating the model when using model - enable_full_determinism(self.args.seed) if self.args.full_determinism else set_seed(self.args.seed) - self.hp_name = None -@@ -1409,6 +1438,8 @@ class Trainer: - - if self.args.ddp_bucket_cap_mb is not None: - kwargs["bucket_cap_mb"] = self.args.ddp_bucket_cap_mb -+ if self.compression_ctrl is not None: -+ self.compression_ctrl.distributed() - model = nn.parallel.DistributedDataParallel( - model, - device_ids=[self.args.local_rank] if self.args._n_gpu != 0 else None, -@@ -1687,6 +1718,9 @@ class Trainer: - _ = list(train_dataloader.sampler) - - for epoch in range(epochs_trained, num_train_epochs): -+ if self.compression_ctrl is not None: -+ self.compression_ctrl.scheduler.epoch_step() -+ print(self.compression_ctrl.statistics().to_str()) - if isinstance(train_dataloader, DataLoader) and isinstance(train_dataloader.sampler, DistributedSampler): - train_dataloader.sampler.set_epoch(epoch) - elif hasattr(train_dataloader, "dataset") and isinstance(train_dataloader.dataset, IterableDatasetShard): -@@ -1790,6 +1824,8 @@ class Trainer: - ) - - # Optimizer step -+ if self.compression_ctrl is not None: -+ self.compression_ctrl.scheduler.step() - optimizer_was_run = True - if self.deepspeed: - pass # called outside the loop -@@ -1814,6 +1850,7 @@ class Trainer: - model.zero_grad() - self.state.global_step += 1 - self.state.epoch = epoch + (step + 1) / steps_in_epoch -+ self.state.curr_loss = tr_loss_step.cpu().detach().item() - self.control = self.callback_handler.on_step_end(args, self.state, self.control) - - self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval) -@@ -2033,6 +2070,14 @@ class Trainer: - logs["loss"] = round(tr_loss_scalar / (self.state.global_step - self._globalstep_last_logged), 4) - logs["learning_rate"] = self._get_learning_rate() - -+ if self.compression_ctrl is not None: -+ logs["compression_loss"] = self.compression_ctrl.loss().item() -+ compression_stats = self.compression_ctrl.statistics() -+ for key, value in prepare_for_tensorboard(compression_stats).items(): -+ logs["compression/statistics/{0}".format(key)] = value -+ print(compression_stats.to_str()) -+ -+ - self._total_loss_scalar += tr_loss_scalar - self._globalstep_last_logged = self.state.global_step - self.store_flos() -@@ -2492,6 +2537,10 @@ class Trainer: - # deepspeed handles loss scaling by gradient_accumulation_steps in its `backward` - loss = loss / self.args.gradient_accumulation_steps - -+ if self.compression_ctrl is not None: -+ compression_loss = self.compression_ctrl.loss() -+ loss += compression_loss -+ - if self.do_grad_scaling: - self.scaler.scale(loss).backward() - elif self.use_apex: -diff --git a/src/transformers/training_args.py b/src/transformers/training_args.py -index 170315fe2..daa497c02 100644 ---- a/src/transformers/training_args.py -+++ b/src/transformers/training_args.py -@@ -993,6 +993,12 @@ class TrainingArguments: - }, - ) - -+ nncf_config: str = field(default=None, -+ metadata={"help": "NNCF configuration .json file for compression-enabled training"}) -+ -+ to_onnx: str = field(default=None, -+ metadata={"help": "Name of the ONNX model file to export the model to."}) -+ - def __post_init__(self): - # Handle --use_env option in torch.distributed.launch (local_rank not passed as an arg then). - # This needs to happen before any call to self.device or self.n_gpu. -diff --git a/src/transformers/utils/__init__.py b/src/transformers/utils/__init__.py -index 2269f2254..2f3082293 100644 ---- a/src/transformers/utils/__init__.py -+++ b/src/transformers/utils/__init__.py -@@ -154,6 +154,7 @@ from .import_utils import ( - - - WEIGHTS_NAME = "pytorch_model.bin" -+NNCF_PT_STATE_NAME = "nncf_state.bin" - WEIGHTS_INDEX_NAME = "pytorch_model.bin.index.json" - TF2_WEIGHTS_NAME = "tf_model.h5" - TF2_WEIGHTS_INDEX_NAME = "tf_model.h5.index.json" --- -2.34.1 diff --git a/third_party_integration/huggingface_transformers/README.md b/third_party_integration/huggingface_transformers/README.md deleted file mode 100644 index 1ae5b35b0f3..00000000000 --- a/third_party_integration/huggingface_transformers/README.md +++ /dev/null @@ -1,145 +0,0 @@ -# Integrating NNCF into Transformers - -https://github.com/huggingface/transformers - -This folder contains a git patch to enable NNCF-based quantization for XNLI, SQuAD and GLUE training pipelines of the huggingface transformers repository. - -**NOTE**: this patch is deprecated and will be removed from NNCF repository in future releases. - -Instructions: - -1. Apply the `0001-Modifications-for-NNCF-usage.patch` file to the huggingface transformers repository checked out at commit id: `bd469c40659ce76c81f69c7726759d249b4aef49` - -2. Install the `transformers` library and the example scripts from the patched repository as described in the documentation for the huggingface transformers repository. - -3. To start quantization-aware fine-tuning of NLP models using NNCF, use the regular scripts and command line parameters for XNLI and SQuAD training, but with additional `--nncf_config ` parameter. -The NNCF configs to be used in this way are also provided in the same patch on a per-model, per-compression algorithm basis. -Distributed multiprocessing is also supported, simply use the corresponding version of the command line in the huggingface transformers repository with the same additional `--nncf_config` parameter. - -4. While running with the `--nncf_config` option, the training scripts will output NNCF-wrapped model checkpoints instead of the regular ones. You may evaluate these checkpoints using the same command lines for training above, but with the`--do_train` key omitted. In order to export these checkpoints into ONNX format, further add `--to_onnx ` to your evaluation command line parameters. -See exact command lines for each case in the model notes below. -Note that in all cases the training hyperparameters might have to be adjusted to accommodate the hardware you have available. - -## Current best results - -All models use as their baselines the checkpoints obtained with the scripts and command line parameters from the corresponding sections in the original repository documentation. While fine-tuning the quantized model, the hyperparameters were left unchanged, i.e. the difference in the training script invocation was limited to adding `--nncf_config` option and specifying the pre-trained baseline model as the starting point for quantization fine-tuning. For RoBERTa-MNLI, no baseline model finetuning was necessary since the `roberta-large-mnli` model pretrained on MNLI was already available for download. - -Make sure that you are running evaluation on a single GPU, since the repository evaluation scripts give inconsistent results when running multi-GPU evaluation. - -### BERT-XNLI - -_Full-precision FP32 baseline model_ - bert-base-chinese, trained on the Chinese portion of XNLI - 77.68% accuracy when evaluated on the Chinese portion of XNLI test set. - -_INT8 model (symmetric weights, asymmetric activations quantization)_ - 77.22% accuracy in the same evaluation conditions. - -**INT8 model quantization-aware training command line:** - -`python examples/pytorch/text-classification/run_xnli.py --model_name_or_path bert-base-chinese --language zh --train_language zh --do_train --do_eval --per_gpu_train_batch_size 48 --per_gpu_eval_batch_size 1 --learning_rate 5e-5 --num_train_epochs 4.0 --max_seq_length 128 --output_dir bert_xnli_int8 --save_steps 200 --nncf_config nncf_bert_config_xnli.json` - -**Fine-tuned INT8 model evaluation and ONNX export command line:** - -`python examples/pytorch/text-classification/run_xnli.py --model_name_or_path bert_xnli_int8 --language zh --train_language zh --do_eval --per_gpu_eval_batch_size 1 --max_seq_length 128 --output_dir bert_xnli_int8 --nncf_config nncf_bert_config_xnli.json --to_onnx bert_xnli_int8.onnx` - -### BERT-SQuAD v1.1 - -_Full-precision FP32 baseline model_ - bert-large-uncased-whole-word-masking model, trained on SQuAD v1.1 - 93.21% F1, 87.2% EM on the dev set, - -_INT8 model (symmetric quantization)_ - 92.55% F1, 86.1% EM on the dev set. - -**INT8 model quantization-aware training command line (trained on 4x Tesla V100):** - -`python examples/pytorch/question-answering/run_qa.py --model_name_or_path bert-large-uncased-whole-word-masking --do_train --do_eval --dataset_name squad --learning_rate 3e-5 --num_train_epochs 2 --max_seq_length 384 --doc_stride 128 --output_dir bert_squad_int8 --per_gpu_eval_batch_size=1 --per_gpu_train_batch_size=10 --save_steps=400 --nncf_config nncf_bert_config_squad.json` - -_INT8 model (symmetric quantization) + Knowledge Distillation_ - 92.89% F1, 86.68% EM on the dev set. - -**INT8 model quantization-aware training + Knowledge Distillation command line (trained on 4x Tesla V100):** - -`python examples/pytorch/question-answering/run_qa.py --model_name_or_path bert-large-uncased-whole-word-masking --do_train --do_eval --dataset_name squad --learning_rate 3e-5 --num_train_epochs 2 --max_seq_length 384 --doc_stride 128 --output_dir bert_squad_int8 --per_gpu_eval_batch_size=1 --per_gpu_train_batch_size=10 --save_steps=400 --nncf_config nncf_bert_config_squad_kd.json` - -**Fine-tuned INT8 model evaluation and ONNX export command line:** - -`python examples/pytorch/question-answering/run_qa.py --model_name_or_path bert_squad_int8 --do_eval --dataset_name squad --max_seq_length 384 --doc_stride 128 --output_dir bert_squad_int8 --per_gpu_eval_batch_size=1 --nncf_config nncf_bert_config_squad.json --to_onnx bert_squad_int8.onnx` - -### BERT-CoNLL2003 - -_Full-precision FP32 baseline model_ - bert-base-cased model, trained on CoNLL2003 - 99.17% acc, 95.03% F1 - -_INT8 model (symmetric quantization)_ - 99.18% acc, 95.31% F1 - -**INT8 model quantization-aware training command line (trained on 4x Tesla V100):** - -`python examples/pytorch/token-classification/run_ner.py --model_name_or_path *path_to_fp32_finetuned_model* --dataset_name conll2003 --output_dir bert_base_cased_conll_int8 --do_train --do_eval --save_strategy epoch --evaluation_strategy epoch --nncf_config nncf_bert_config_conll.json` - -**Fine-tuned INT8 model evaluation and ONNX export command line:** - -`python examples/pytorch/token-classification/run_ner.py --model_name_or_path bert_base_cased_conll_int8 --dataset_name conll2003 --output_dir bert_base_cased_conll_int8 --do_eval --nncf_config nncf_bert_config_squad.json --to_onnx bert_base_cased_conll_int8.onnx` - -### BERT-MRPC - -_Full-precision FP32 baseline model_ - bert-base-cased-finetuned-mrpc, 84.56% acc - -_INT8 model (symmetric quantization)_ - 84.8% acc - -**INT8 model quantization-aware training command line (trained on 1x RTX 2080):** - -`python examples/pytorch/token-classification/run_glue.py --model_name_or_path bert-base-cased-finetuned-mrpc --task_name mrpc --do_train --do_eval --num_train_epochs 5.0 --per_device_eval_batch_size 1 --output_dir bert_cased_mrpc_int8 --evaluation_strategy epoch --save_strategy epoch --nncf_config nncf_bert_config_mrpc.json` - -**Fine-tuned INT8 model evaluation and ONNX export command line:** - -`python examples/pytorch/token-classification/run_ner.py --model_name_or_path bert_cased_mrpc_int8 --task_name mrpc --do_eval --per_gpu_eval_batch_size 1 --output_dir bert_cased_mrpc_int8 --nncf_config nncf_bert_config_mrpc.json --to_onnx bert_base_cased_mrpc_int8.onnx` - -### RoBERTA-MNLI - -_Full-precision FP32 baseline model_ - roberta-large-mnli, pre-trained on MNLI - 90.6% accuracy (matched), 90.1% accuracy (mismatched) - -_INT8 model (asymmetrically quantized)_ - 89.25% accuracy (matched), 88.9% accuracy (mismatched) - -**INT8 model quantization-aware training command line:** - -`python examples/pytorch/text-classification/run_glue.py --model_name_or_path roberta-large-mnli --task_name mnli --do_train --do_eval --per_gpu_train_batch_size 24 --per_gpu_eval_batch_size 1 --learning_rate 2e-5 --num_train_epochs 3.0 --max_seq_length 128 --output_dir roberta_mnli_int8 --save_steps 400 --nncf_config nncf_roberta_config_mnli.json` - -**Fine-tuned INT8 model evaluation and ONNX export command line:** - -`python examples/pytorch/text-classification/run_glue.py --model_name_or_path roberta_mnli_int8 --task_name mnli --do_eval --learning_rate 2e-5 --num_train_epochs 3.0 --max_seq_length 128 --per_gpu_eval_batch_size 1 --output_dir roberta_mnli_int8 --save_steps 400 --nncf_config nncf_roberta_config_mnli.json --to_onnx roberta_mnli_int8.onnx` - -### DistilBERT-SST-2 - -_Full-precision FP32 baseline model_ - distilbert-base-uncased-finetuned-sst-2-english, pre-trained on SST-2 - 91.1% accuracy - -_INT8 model (symmetrically quantized)_ - 90.94% accuracy - -**INT8 model quantization-aware training command line:** - -`python examples/pytorch/text-classification/run_glue.py --model_name_or_path distilbert-base-uncased-finetuned-sst-2-english --task_name sst2 --do_train --do_eval --per_gpu_train_batch_size 16 --per_gpu_eval_batch_size 1 --learning_rate 5e-5 --num_train_epochs 3.0 --max_seq_length 128 --output_dir distilbert_sst2_int8 --save_steps 100000 --nncf_config nncf_distilbert_config_sst2.json` - -**Fine-tuned INT8 model evaluation and ONNX export command line:** - -`python examples/pytorch/text-classification/run_glue.py --model_name_or_path distilbert_sst2_int8 --task_name sst2 --do_eval --per_gpu_eval_batch_size 1 --max_seq_length 128 --output_dir distilbert_sst2_int8 --save_steps 100000 --nncf_config nncf_distilbert_config_sst2.json --to_onnx distilbert_sst2_int8.onnx` - -### MobileBERT-SQuAD v1.1 - -_Full-precision FP32 baseline model_ - google/mobilebert-uncased, trained on SQuAD v1.1 - 89.98% F1, 82.61% EM on the dev set, - -_INT8 model (symmetric quantization)_ - 89.4% F1, 82.05% EM on the dev set. - -**INT8 model quantization-aware training command line (trained on 3x Tesla V100):** - -`python examples/pytorch/question-answering/run_qa.py --model_name_or_path --do_train --do_eval --dataset_name squad --learning_rate 3e-5 --num_train_epochs 5 --max_seq_length 384 --doc_stride 128 --output_dir mobilebert_squad_int8 --per_gpu_eval_batch_size=1 --per_gpu_train_batch_size=6 --save_steps=400 --nncf_config nncf_mobilebert_config_squad_int8.json` - -**Fine-tuned INT8 model evaluation and ONNX export command line:** - -`python examples/pytorch/question-answering/run_qa.py --model_name_or_path mobilebert_squad_int8 --do_eval --dataset_name squad --max_seq_length 384 --doc_stride 128 --output_dir mobilebert_squad_int8 --per_gpu_eval_batch_size=1 --nncf_config nncf_mobilebert_config_squad_int8.json --to_onnx mobilebert_squad_int8.onnx` - -### GPT-2-WikiText 2 (raw) language modeling - -_Full-precision FP32 baseline model_ - 19.73 perplexity on the test set - -_INT8 model (symmetric quantization)_ - 20.9 perplexity on the test set - -**INT8 model quantization-aware training command line (trained on 1x Tesla V100):** - -`python examples/pytorch/language-modeling/run_clm.py --model_name_or_path --do_train --do_eval --dataset_name wikitext --num_train_epochs 3 --output_dir gpt2_wikitext2_int8 --per_gpu_eval_batch_size=1 --per_gpu_train_batch_size=4 --save_steps=591 --nncf_config nncf_gpt2_config_wikitext_hw_config.json` - -**Fine-tuned INT8 model evaluation and ONNX export command line:** - -`python examples/pytorch/language-modeling/run_clm.py --model_name_or_path gpt2_wikitext2_int8 --do_eval --dataset_name wikitext --output_dir gpt2_wikitext2_int8 --per_gpu_eval_batch_size=1 --nncf_config nncf_gpt2_config_wikitext_hw_config.json --to_onnx gpt2_wikitext2_int8.onnx` diff --git a/tools/__init__.py b/tools/__init__.py index 9b29b47534a..2e49d63977d 100644 --- a/tools/__init__.py +++ b/tools/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tools/add_new_quantization_parameters.py b/tools/add_new_quantization_parameters.py index 066e23aec3e..73645bcfc88 100644 --- a/tools/add_new_quantization_parameters.py +++ b/tools/add_new_quantization_parameters.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -74,7 +74,7 @@ def main(argv): new_keys = {} for new_parameter in param_list: old_keys = list(sd.keys()) - for k in sd.keys(): + for k in sd: for h in hooks: new_key = k.replace(h, new_parameter.name) if ("." + h in k) and ("." + new_parameter.name not in k) and (new_key not in old_keys): diff --git a/tools/add_qualifiers_to_activation_quantizers.py b/tools/add_qualifiers_to_activation_quantizers.py index c427d5532f8..85b72c7e79a 100644 --- a/tools/add_qualifiers_to_activation_quantizers.py +++ b/tools/add_qualifiers_to_activation_quantizers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tools/benchmark.py b/tools/benchmark.py index 2717fb881df..d76a186a00d 100644 --- a/tools/benchmark.py +++ b/tools/benchmark.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tools/benchmark_binarize_layers.py b/tools/benchmark_binarize_layers.py index 229288a321b..1ce6bd18235 100644 --- a/tools/benchmark_binarize_layers.py +++ b/tools/benchmark_binarize_layers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tools/benchmark_quantize_layers.py b/tools/benchmark_quantize_layers.py index 3ffeeb78c05..d60458b13fe 100644 --- a/tools/benchmark_quantize_layers.py +++ b/tools/benchmark_quantize_layers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tools/clip_dot.py b/tools/clip_dot.py index f4dcac13eb6..09255e86cb7 100644 --- a/tools/clip_dot.py +++ b/tools/clip_dot.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -15,6 +15,7 @@ import networkx as nx +import nncf from nncf.common.utils.dot_file_rw import read_dot_graph from nncf.common.utils.dot_file_rw import write_dot_graph @@ -42,7 +43,7 @@ def main(argv): break if start_key is None: - raise RuntimeError("Could not find the node with ID {} to start from!".format(args.start_id)) + raise nncf.InternalError("Could not find the node with ID {} to start from!".format(args.start_id)) for edge in nx.edge_bfs(graph, start_key, orientation="ignore"): from_key, to_key, _ = edge diff --git a/tools/collect_backend_specific_file_names.py b/tools/collect_backend_specific_file_names.py index ca54b3a16ed..0fdc46ae119 100755 --- a/tools/collect_backend_specific_file_names.py +++ b/tools/collect_backend_specific_file_names.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -15,6 +15,8 @@ from collections import defaultdict from pathlib import Path +import nncf + def is_in_backend_directory(file_path: Path, backend: str): return backend in file_path.parts @@ -35,7 +37,7 @@ def is_file_name_starts_with_backend_name(file_path: Path, backend: str): def main(target_backend: str): if target_backend not in BACKENDS + [COMMON_BACKEND_NAME]: - raise RuntimeError( + raise nncf.UnsupportedBackendError( f"Wrong backend passed: {target_backend}. Please choose one of available backends: {BACKENDS}" ) diff --git a/tools/convert_mobilenet_v2_to_torchvision.py b/tools/convert_mobilenet_v2_to_torchvision.py index a19ca7c0542..56169b53bd4 100644 --- a/tools/convert_mobilenet_v2_to_torchvision.py +++ b/tools/convert_mobilenet_v2_to_torchvision.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tools/correct_checkpoint.py b/tools/correct_checkpoint.py index 03e8513314a..9f28e8b045d 100644 --- a/tools/correct_checkpoint.py +++ b/tools/correct_checkpoint.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tools/extract_ov_subgraph.py b/tools/extract_ov_subgraph.py index d169308156a..022ee777e8d 100644 --- a/tools/extract_ov_subgraph.py +++ b/tools/extract_ov_subgraph.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tools/get_ir_ignored_scope_by_model_id.py b/tools/get_ir_ignored_scope_by_model_id.py index de91072582b..4416b5262c1 100644 --- a/tools/get_ir_ignored_scope_by_model_id.py +++ b/tools/get_ir_ignored_scope_by_model_id.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tools/ir_utils.py b/tools/ir_utils.py index fd16246f7de..8d966d11c36 100644 --- a/tools/ir_utils.py +++ b/tools/ir_utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tools/onnx_tranformer.py b/tools/onnx_tranformer.py index fdba64fa72f..517ac9048f3 100644 --- a/tools/onnx_tranformer.py +++ b/tools/onnx_tranformer.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tools/optimize_tf_graph.py b/tools/optimize_tf_graph.py index c4c56cfd2d4..5788dde7fcd 100644 --- a/tools/optimize_tf_graph.py +++ b/tools/optimize_tf_graph.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tools/pb_to_tb.py b/tools/pb_to_tb.py index 811e094462b..9163c753dd6 100644 --- a/tools/pb_to_tb.py +++ b/tools/pb_to_tb.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tools/render_dot_to_svg.py b/tools/render_dot_to_svg.py index 452a3b8e0dc..788432a47b7 100644 --- a/tools/render_dot_to_svg.py +++ b/tools/render_dot_to_svg.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 Intel Corporation +# Copyright (c) 2024 Intel Corporation # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/tools/update_eval_results.py b/tools/update_eval_results.py deleted file mode 100644 index 54e31a572ca..00000000000 --- a/tools/update_eval_results.py +++ /dev/null @@ -1,574 +0,0 @@ -# Copyright (c) 2023 Intel Corporation -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# http://www.apache.org/licenses/LICENSE-2.0 -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import argparse -import json -import sys -from collections import OrderedDict -from copy import deepcopy -from dataclasses import dataclass -from pathlib import Path -from typing import Dict, List, Optional, Tuple - -from mdutils import MdUtils - -from tests.shared.metric_thresholds import DIFF_FP32_MAX_GLOBAL -from tests.shared.metric_thresholds import DIFF_FP32_MIN_GLOBAL -from tests.shared.paths import PROJECT_ROOT -from tests.shared.paths import TEST_ROOT - -# This is a helper script which parses a metrics.json file containing currently measured accuracy values for checkpoints -# registered in tests/sota_checkpoints_eval.json, and produces: -# a) a file with exactly the same contents as tests/*backend*/sota_checkpoints_eval.json, but with target accuracy -# scores updated to reflect what was currently measured and reported in metrics.json, and -# b) a set of .MD file snippets containing updated results tables, ready for copy-and-paste into corresponding -# README.md files (a common frontpage README and sample-specific readmes). -# -# Usage: -# python tools/update_eval_results.py -f torch -r path/to/metrics.json -i -# python tools/update_eval_results.py -f tf -r path/to/metrics.json -i - - -BASE_CHECKPOINT_URL = "https://storage.openvinotoolkit.org/repositories/nncf/models/develop/" - - -@dataclass -class SampleReadmeSubTableDescriptor: - anchor: str - model_regex: Optional[str] = None - model_names: Optional[List[str]] = None - models_duplicated_from_main_table: Optional[List[str]] = None - - -@dataclass -class SampleDescriptor: - path_to_readme: Path - result_table_anchor_in_main_readme: str - sub_tables_in_own_readme: Optional[List[SampleReadmeSubTableDescriptor]] = None - - -RESULTS_ANCHOR_IN_SAMPLE_OWN_README = '' -TORCH_EXAMPLES_PATH = PROJECT_ROOT / "examples" / "torch" -TF_EXAMPLES_PATH = PROJECT_ROOT / "examples" / "tensorflow" - -TORCH_SAMPLE_TYPE_TO_DESCRIPTOR: Dict[str, SampleDescriptor] = { - "classification": SampleDescriptor( - path_to_readme=TORCH_EXAMPLES_PATH / "classification" / "README.md", - result_table_anchor_in_main_readme='', - sub_tables_in_own_readme=[ - SampleReadmeSubTableDescriptor( - anchor='', - model_names=[ - "resnet18_imagenet", - "resnet18_imagenet_binarization_xnor", - "resnet18_imagenet_binarization_dorefa", - ], - models_duplicated_from_main_table=["resnet18_imagenet"], - ), - SampleReadmeSubTableDescriptor( - anchor='', - model_names=[ - "resnet50_imagenet", - "resnet50_imagenet_pruning_geometric_median", - "resnet18_imagenet", - "resnet18_imagenet_pruning_magnitude", - "resnet18_imagenet_pruning_geometric_median", - "resnet34_imagenet", - "resnet34_imagenet_pruning_geometric_median_kd", - "googlenet_imagenet", - "googlenet_imagenet_pruning_geometric_median", - ], - models_duplicated_from_main_table=[ - "resnet50_imagenet", - "resnet18_imagenet", - "resnet34_imagenet", - "googlenet_imagenet", - ], - ), - # Models below are currently not being measured in E2E runs. - # SampleReadmeSubTableDescriptor( - # anchor='', - # model_names=["resnet50_imagenet", - # "resnet50_imagenet_pruning_accuracy_aware", - # "resnet18_imagenet", - # "resnet18_imagenet_pruning_accuracy_aware"], - # models_duplicated_from_main_table=["resnet50_imagenet", - # "resnet18_imagenet"]) - ], - ), - "semantic_segmentation": SampleDescriptor( - path_to_readme=TORCH_EXAMPLES_PATH / "semantic_segmentation" / "README.md", - result_table_anchor_in_main_readme='', - sub_tables_in_own_readme=[ - SampleReadmeSubTableDescriptor( - anchor='', - model_names=["unet_mapillary", "unet_mapillary_pruning_geometric_median"], - models_duplicated_from_main_table=["unet_mapillary"], - ) - ], - ), - "object_detection": SampleDescriptor( - path_to_readme=TORCH_EXAMPLES_PATH / "object_detection" / "README.md", - result_table_anchor_in_main_readme='', - sub_tables_in_own_readme=[ - SampleReadmeSubTableDescriptor( - anchor='', - model_names=["ssd300_vgg_voc", "ssd300_vgg_voc_pruning_geometric_median"], - models_duplicated_from_main_table=["ssd300_vgg_voc"], - ) - ], - ), -} - -TF_SAMPLE_TYPE_TO_DESCRIPTOR: Dict[str, SampleDescriptor] = { - "classification": SampleDescriptor( - path_to_readme=TF_EXAMPLES_PATH / "classification" / "README.md", - result_table_anchor_in_main_readme='', - sub_tables_in_own_readme=[ - SampleReadmeSubTableDescriptor( - anchor='', - model_names=[ - "resnet50_imagenet", - "resnet50_imagenet_pruning_geometric_median", - "resnet50_imagenet_pruning_geometric_median_int8", - ], - models_duplicated_from_main_table=["resnet50_imagenet"], - ), - ], - ), - "segmentation": SampleDescriptor( - path_to_readme=TF_EXAMPLES_PATH / "segmentation" / "README.md", - result_table_anchor_in_main_readme='', - ), - "object_detection": SampleDescriptor( - path_to_readme=TF_EXAMPLES_PATH / "object_detection" / "README.md", - result_table_anchor_in_main_readme='', - sub_tables_in_own_readme=[ - SampleReadmeSubTableDescriptor( - anchor='', - model_names=[ - "retinanet_coco", - "retinanet_coco_pruning_geometric_median", - "retinanet_coco_pruning_geometric_median_int8", - ], - models_duplicated_from_main_table=["retinanet_coco"], - ) - ], - ), -} - - -def get_fp32_and_compressed_metrics( - model_name_to_metric_dict: Dict, model_name: str, reference: str, target: str -) -> Tuple[float, Optional[float]]: - if reference is None: - fp32_ref_metric = float(target) - compressed_metric = None - else: - fp32_ref_metric = float(model_name_to_metric_dict[reference]) - compressed_metric = float(model_name_to_metric_dict[model_name]) - return fp32_ref_metric, compressed_metric - - -# Not perfect - better switch to building a complete table and then excluding -# columns, or use a better table manager in the future -def get_header_row(table_format: str, sample_type: str) -> List[str]: - assert table_format in ["overview", "per_sample"], "Unsupported table format!" - header = ["Model", "Compression algorithm", "Dataset"] - if sample_type == "classification": - header.append("Accuracy (_drop_) %") - elif sample_type == "semantic_segmentation": - header.append("mIoU (_drop_) %") - elif sample_type == "segmentation": # fits for TF MaskRCNN only - header.append("mAP (_drop_) %") - elif sample_type == "object_detection": - header.append("mAP (_drop_) %") - else: - raise RuntimeError(f"{sample_type} sample type is not supported!") - - if table_format == "per_sample": - header.append("NNCF config file") - header.append("Checkpoint") - return header - - -def build_per_model_row( - table_format: str, - model_display_name: str, - compression_algo_display_name: str, - dataset_display_name: str, - fp32_metric: float, - compressed_metric: float, - config_path: Path, - containing_file_path: Path, - checkpoint_url: Optional[str], -) -> List[str]: - row = [model_display_name, compression_algo_display_name, dataset_display_name] - if compression_algo_display_name == "None": - row.append(f"{fp32_metric:.2f}") - else: - drop = fp32_metric - compressed_metric - row.append(f"{compressed_metric:.2f} ({drop:.2f})") - if table_format == "per_sample": - containing_file_folder = containing_file_path.parent - local_config_path = config_path.relative_to(containing_file_folder) - config_name = local_config_path.name - row.append(f"[{config_name}]({local_config_path.as_posix()})") - if checkpoint_url is not None: - row.append(f"[Link]({checkpoint_url})") - else: - row.append("-") - return row - - -def get_results_table_rows( - per_sample_config_dict: Dict, model_name_to_metric_dict: Dict, sample_type: str, table_format: str, framework: str -) -> Dict[str, List[List[str]]]: - assert table_format in ["overview", "per_sample"], "Unsupported table format!" - assert framework in ["tf", "torch"], f"Unsupported framework: {framework}" - model_name_vs_row = OrderedDict() - - for data_name_ in per_sample_config_dict: - dataset_name = get_display_dataset_name(data_name_) - if framework == "torch": - model_dicts = per_sample_config_dict[data_name_] - else: - model_dicts = per_sample_config_dict[data_name_]["topologies"] - for model_name in model_dicts: - conf_file = model_dicts[model_name].get("config") - if conf_file is not None: - conf_file_path = PROJECT_ROOT / conf_file - if not conf_file_path.exists(): - print(f"Warning: broken config file link {conf_file}") - else: - conf_file_path = None - reference = None - target = None - if model_dicts[model_name].get("reference", {}): - reference = model_dicts[model_name].get("reference", {}) - if model_dicts[model_name].get("target", {}): - target = model_dicts[model_name]["target"] - resume = model_dicts[model_name].get("resume") - weights = model_dicts[model_name].get("weights") # valid for certain TF models - model_display_name = model_dicts[model_name].get("model_description") - - if model_dicts[model_name].get("compression_description") is not None: - compression = model_dicts[model_name].get("compression_description") - else: - compression = "None" - - checkpoint_link = None - ckpt_url = BASE_CHECKPOINT_URL + ("tensorflow/" if framework == "tf" else "torch/") - if resume is not None or weights is not None: - checkpoint_link = ckpt_url + model_name - - if checkpoint_link is not None: - if framework == "tf": - checkpoint_link += ".tar.gz" - else: - checkpoint_link += ".pth" - - fp32_ref_metric, compressed_metric = get_fp32_and_compressed_metrics( - model_name_to_metric_dict, model_name, reference, target - ) - if table_format == "overview" and compression == "None": - continue # The overview already has baseline results as a separate column - - sample_desc = get_sample_desc(sample_type, framework) - readme_path = sample_desc.path_to_readme - model_name_vs_row[model_name] = build_per_model_row( - table_format, - model_display_name, - compression, - dataset_name, - fp32_ref_metric, - compressed_metric, - conf_file_path, - readme_path, - checkpoint_link, - ) - - sample_type_desc = get_sample_desc(sample_type, framework) - header = get_header_row(table_format=table_format, sample_type=sample_type) - if table_format == "overview": - rows = [ - header, - ] + list(model_name_vs_row.values()) - return {sample_type_desc.result_table_anchor_in_main_readme: rows} - elif table_format == "per_sample": - retval = {} - subtables = sample_type_desc.sub_tables_in_own_readme - if subtables is not None: - for subtable_desc in subtables: - subtable_rows = [] - for model_name in subtable_desc.model_names: - if model_name not in model_name_vs_row: - print(f"Model {model_name} not found among results when trying to create a subtable.") - continue - subtable_rows.append(model_name_vs_row[model_name]) - if model_name not in subtable_desc.models_duplicated_from_main_table: - del model_name_vs_row[model_name] - subtable_rows = [ - header, - ] + subtable_rows - retval[subtable_desc.anchor] = subtable_rows - retval[RESULTS_ANCHOR_IN_SAMPLE_OWN_README] = [ - header, - ] + list(model_name_vs_row.values()) - return retval - - -def update_target_metrics_and_thresholds(config_dict: Dict, model_name_to_metric_dict: Dict, framework: str) -> Dict: - for sample_name in config_dict: - for dataset_name in config_dict[sample_name]: - if framework == "torch": - models_for_dataset_dict = config_dict[sample_name][dataset_name] - elif framework == "tf": - models_for_dataset_dict = config_dict[sample_name][dataset_name]["topologies"] - for model_name, model_dict in models_for_dataset_dict.items(): - model_dict["target"] = model_name_to_metric_dict[model_name] - if "reference" not in model_dict: - continue - ref_model_name = model_dict["reference"] - if ref_model_name not in model_name_to_metric_dict: - continue - - if "diff_fp32_min" in model_dict: - diff_fp32_min_value = model_dict["diff_fp32_min"] - actual_diff_fp32 = model_name_to_metric_dict[model_name] - model_name_to_metric_dict[ref_model_name] - if diff_fp32_min_value < DIFF_FP32_MIN_GLOBAL: # model has special thresholds larger than global - if actual_diff_fp32 > diff_fp32_min_value: # ...but it actually shows better results - if actual_diff_fp32 > DIFF_FP32_MIN_GLOBAL: - del model_dict["diff_fp32_min"] # no "diff_fp32_min" means use global - else: - model_dict["diff_fp32_min"] = round(actual_diff_fp32 - 0.05) # tighten the threshold - if actual_diff_fp32 < diff_fp32_min_value: - print( - f"Warning: model {model_name} scores less ({actual_diff_fp32}) " - f"than the FP32 min threshold {diff_fp32_min_value}" - ) - - if "diff_fp32_max" in model_dict: - diff_fp32_max_value = model_dict["diff_fp32_max"] - actual_diff_fp32 = model_name_to_metric_dict[model_name] - model_name_to_metric_dict[ref_model_name] - if diff_fp32_max_value > DIFF_FP32_MAX_GLOBAL: # model has special thresholds larger than global - if actual_diff_fp32 > diff_fp32_max_value: # ...but it actually shows better results - if actual_diff_fp32 < DIFF_FP32_MAX_GLOBAL: - del model_dict["diff_fp32_max"] # no "diff_fp32_max" means use global - else: - model_dict["diff_fp32_max"] = round(actual_diff_fp32 + 0.05, 1) # tighten the threshold - if actual_diff_fp32 > diff_fp32_max_value: - print( - f"Warning: model {model_name} scores more ({actual_diff_fp32}) " - f"than the FP32 max threshold {diff_fp32_max_value}" - ) - return config_dict - - -def get_display_dataset_name(data_name: str) -> str: - if data_name in ["imagenet", "imagenet2012"]: - dataset_name = "ImageNet" - elif data_name == "camvid": - dataset_name = "CamVid" - elif data_name == "VOCdevkit": - dataset_name = "VOC12+07 train, VOC07 eval" - elif data_name == "coco2017": - dataset_name = "COCO 2017" - elif data_name == "mapillary_vistas": - dataset_name = "Mapillary" - elif data_name == "voc": - dataset_name = "VOC12+07 train, VOC07 eval" - else: - raise RuntimeError(f"Unknown data name: {data_name}") - return dataset_name - - -def delete_four_head_lines(fname: str): - with open(fname) as input_data: - lines = input_data.readlines() - with open(fname, "w") as out: - out.writelines(lines[4:]) - - -def write_table_to_md_file(md_file: MdUtils, table_rows_: List[List[str]]): - flat_rows = [] - for row in table_rows_: - flat_rows += row - md_file.new_table(columns=len(table_rows_[0]), rows=len(table_rows_), text=flat_rows, text_align="center") - - -def header_name_to_link(header_name_: str) -> str: - link = header_name_.lower().replace(" ", "-") - link = "#" + link - return link - - -def update_table_inplace(table_content: str, target_file: Path, anchor: str): - with open(target_file, encoding="utf-8", mode="r") as f: - old_lines = f.readlines() - for idx, line in enumerate(old_lines): - anchor_line = idx - if line == anchor + "\n": - break - else: - raise RuntimeError(f"Anchor {anchor} not found in target file {target_file}") - - for idx, line in enumerate(old_lines[anchor_line:]): - table_start_line = anchor_line + idx - if line.startswith("|"): - break - else: - raise RuntimeError(f"Could not find an MD table to update at anchor {anchor} in {target_file}") - - for idx, line in enumerate(old_lines[table_start_line:]): - table_end_line = table_start_line + idx - if not line.startswith("|"): - break - - content = "".join(old_lines[:table_start_line]) + table_content + "".join(old_lines[table_end_line:]) - with open(target_file, encoding="utf-8", mode="w") as f: - f.write(content) - - print(f"Successfully replaced a table in {target_file} at anchor {anchor}") - - -def get_per_sample_model_dict(sample_type: str, sota_checkpoints_eval_dict: Dict, framework: str) -> Dict: - if framework not in ["tf", "torch"]: - raise RuntimeError(f"Unknown framework type: {framework}") - if framework == "torch": - return sota_checkpoints_eval_dict[sample_type] - return sota_checkpoints_eval_dict[sample_type]["topologies"] - - -def filter_tfrecords_only(metrics_dict: Dict[str, float]) -> Dict[str, float]: - retval = {} - for model_name, metric_val in metrics_dict.items(): - if model_name.endswith("_tfrecords"): - new_name = model_name.split("_tfrecords")[0] - retval[new_name] = metric_val - return retval - - -def get_sample_desc(sample_type: str, framework: str) -> SampleDescriptor: - framework_sample_type_to_desc = ( - TORCH_SAMPLE_TYPE_TO_DESCRIPTOR if framework == "torch" else TF_SAMPLE_TYPE_TO_DESCRIPTOR - ) - return framework_sample_type_to_desc[sample_type] - - -def main(argv): - parser = argparse.ArgumentParser() - parser.add_argument( - "--framework", - "-f", - help="The framework for which the eval results are to be updated", - choices=["tf", "torch"], - required=True, - ) - parser.add_argument( - "--results", "-r", help="A metrics.json file from a latest checkpoint evaluation run", required=True - ) - parser.add_argument( - "--config", "-c", help="A .json file with definitions of tested checkpoints (sota_checkpoints_eval.json)" - ) - parser.add_argument( - "--output", - "-o", - help="If specified, will output a config file specified in -c " - "with target metric values updated to what was measured in the metrics.json " - "file supplied via -r", - ) - parser.add_argument( - "--inplace", - "-i", - help="If specified, will redact the reference .json file containing target metric levels " - "in-place and automatically update the README.md files with new table content, also " - "in-place.", - action="store_true", - ) - args = parser.parse_args(args=argv) - results = args.results - framework = args.framework - if framework == "tf": - framework = "tensorflow" - - config = args.config - if config is None: - config = TEST_ROOT / framework / "sota_checkpoints_eval.json" - - output = args.output - if output is None: - output = deepcopy(config) - - measured_metrics = json.load(open(results, "r")) - if args.framework == "tf": - measured_metrics = filter_tfrecords_only(measured_metrics) - sota_checkpoints_eval = json.load(open(config), object_pairs_hook=OrderedDict) - - # Output tables for per-sample README files - for sample_type in sota_checkpoints_eval: - anchor_vs_table_rows = get_results_table_rows( - sota_checkpoints_eval[sample_type], - measured_metrics, - sample_type, - table_format="per_sample", - framework=args.framework, - ) - file_name = "results_{}.md".format(sample_type) - sample_desc = get_sample_desc(sample_type, args.framework) - for anchor, table_rows in anchor_vs_table_rows.items(): - mdfile = MdUtils(file_name=file_name) - write_table_to_md_file(mdfile, table_rows) - if args.inplace: - table_str = mdfile.get_md_text() - table_str = table_str.lstrip("\n") - update_table_inplace(table_str, sample_desc.path_to_readme, anchor) - else: - mdfile.create_md_file() - # Somehow the MDUtils outputs 4 empty lines prior to the actual table in the target file. - delete_four_head_lines(file_name) - - # Output the overview tables for the top-level README file - overview_file_name = "results_overview.md" - if not args.inplace: - mdfile = MdUtils(file_name=overview_file_name) - - for sample_type in sota_checkpoints_eval: - anchor_vs_table_rows = get_results_table_rows( - sota_checkpoints_eval[sample_type], - measured_metrics, - sample_type, - table_format="overview", - framework=args.framework, - ) - anchor, table_rows = next(iter(anchor_vs_table_rows.items())) - sample_desc = get_sample_desc(sample_type, args.framework) - if args.inplace: - tmp_mdfile = MdUtils(file_name="") - write_table_to_md_file(tmp_mdfile, table_rows) - table_str = tmp_mdfile.get_md_text() - table_str = table_str.lstrip("\n") - update_table_inplace(table_str, PROJECT_ROOT / "README.md", sample_desc.result_table_anchor_in_main_readme) - else: - write_table_to_md_file(mdfile, table_rows) - - if not args.inplace: - mdfile.create_md_file() - # Somehow the MDUtils outputs 4 empty lines prior to the actual table in the target file. - delete_four_head_lines(overview_file_name) - - update_target_metrics_and_thresholds(sota_checkpoints_eval, measured_metrics, args.framework) - with open(output, "w") as write_file: - json.dump(sota_checkpoints_eval, write_file, indent=4) - - -if __name__ == "__main__": - main(sys.argv[1:])