[MMSIG] Add new configuration files for SlowFast

configs/dgcnn/_base_/default_runtime.py

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+from mmengine.hooks.checkpoint_hook import CheckpointHook
+from mmengine.hooks.iter_timer_hook import IterTimerHook
+from mmengine.hooks.logger_hook import LoggerHook
+from mmengine.hooks.param_scheduler_hook import ParamSchedulerHook
+from mmengine.hooks.runtime_info_hook import RuntimeInfoHook
+from mmengine.hooks.sampler_seed_hook import DistSamplerSeedHook
+from mmengine.hooks.sync_buffer_hook import SyncBuffersHook
+from mmengine.runner.log_processor import LogProcessor
+
+from mmaction.visualization.action_visualizer import ActionVisualizer
+from mmaction.visualization.video_backend import LocalVisBackend
+
+# hooks
+default_hooks = dict(
+    runtime_info=dict(type=RuntimeInfoHook),
+    timer=dict(type=IterTimerHook),
+    logger=dict(type=LoggerHook, interval=20, ignore_last=False),
+    param_scheduler=dict(type=ParamSchedulerHook),
+    checkpoint=dict(type=CheckpointHook, interval=1, save_best='auto'),
+    sampler_seed=dict(type=DistSamplerSeedHook),
+    sync_buffers=dict(type=SyncBuffersHook))
+
+env_cfg = dict(
+    cudnn_benchmark=False,
+    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
+    dist_cfg=dict(backend='nccl'))
+
+log_processor = dict(type=LogProcessor, window_size=20, by_epoch=True)
+
+vis_backends = [dict(type=LocalVisBackend)]
+visualizer = dict(type=ActionVisualizer, vis_backends=vis_backends)
+
+log_level = 'INFO'
+load_from = None
+resume = False

configs/dgcnn/_base_/models/slowfast_r50.py

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+from mmaction.models.backbones.resnet3d_slowfast import ResNet3dSlowFast
+from mmaction.models.data_preprocessors.data_preprocessor import \
+    ActionDataPreprocessor
+from mmaction.models.heads.slowfast_head import SlowFastHead
+from mmaction.models.recognizers.recognizer3d import Recognizer3D
+
+# model settings
+model = dict(
+    type=Recognizer3D,
+    backbone=dict(
+        type=ResNet3dSlowFast,
+        pretrained=None,
+        resample_rate=8,  # tau
+        speed_ratio=8,  # alpha
+        channel_ratio=8,  # beta_inv
+        slow_pathway=dict(
+            type='resnet3d',
+            depth=50,
+            pretrained=None,
+            lateral=True,
+            conv1_kernel=(1, 7, 7),
+            dilations=(1, 1, 1, 1),
+            conv1_stride_t=1,
+            pool1_stride_t=1,
+            inflate=(0, 0, 1, 1),
+            norm_eval=False),
+        fast_pathway=dict(
+            type='resnet3d',
+            depth=50,
+            pretrained=None,
+            lateral=False,
+            base_channels=8,
+            conv1_kernel=(5, 7, 7),
+            conv1_stride_t=1,
+            pool1_stride_t=1,
+            norm_eval=False)),
+    cls_head=dict(
+        type=SlowFastHead,
+        in_channels=2304,  # 2048+256
+        num_classes=400,
+        spatial_type='avg',
+        dropout_ratio=0.5,
+        average_clips='prob'),
+    data_preprocessor=dict(
+        type=ActionDataPreprocessor,
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        format_shape='NCTHW'))

configs/dgcnn/recognition/slowfast/slowfast_r101_8xb8_8x8x1_256e_kinetics400_rgb.py

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+from mmengine.config import read_base
+
+with read_base():
+    from .slowfast_r50_8xb8_8x8x1_256e_kinetics400_rgb import *
+
+model = dict(
+    backbone=dict(slow_pathway=dict(depth=101), fast_pathway=dict(depth=101)))

configs/dgcnn/recognition/slowfast/slowfast_r101_r50_32xb8_4x16x1_256e_kinetics400_rgb.py

@@ -0,0 +1,8 @@
+from mmengine.config import read_base
+
+with read_base():
+    from .slowfast_r50_8xb8_4x16x1_256e_kinetics400_rgb import *
+
+model = dict(backbone=dict(slow_pathway=dict(depth=101)))
+
+optim_wrapper = dict(optimizer=dict(lr=0.1 * 4))

configs/dgcnn/recognition/slowfast/slowfast_r50_8xb8-8x8x1_steplr_256e_kinetics400_rgb.py

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+from mmengine.config import read_base
+
+with read_base():
+    from .slowfast_r50_8xb8_8x8x1_256e_kinetics400_rgb import *
+
+model = dict(backbone=dict(slow_pathway=dict(lateral_norm=True)))
+
+param_scheduler = [
+    dict(
+        type='LinearLR',
+        start_factor=0.1,
+        by_epoch=True,
+        begin=0,
+        end=34,
+        convert_to_iter_based=True),
+    dict(
+        type='MultiStepLR',
+        begin=0,
+        end=256,
+        by_epoch=True,
+        milestones=[94, 154, 196],
+        gamma=0.1)
+]

configs/dgcnn/recognition/slowfast/slowfast_r50_8xb8_4x16x1_256e_kinetics400_rgb.py

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+from mmengine.config import read_base
+from mmengine.dataset import DefaultSampler
+from mmengine.optim import CosineAnnealingLR, LinearLR
+from mmengine.runner import EpochBasedTrainLoop, TestLoop, ValLoop
+from torch.optim import SGD
+
+from mmaction.datasets.transforms.formatting import (FormatShape,
+                                                     PackActionInputs)
+from mmaction.datasets.transforms.loading import (DecordDecode, DecordInit,
+                                                  SampleFrames)
+from mmaction.datasets.transforms.processing import (CenterCrop, Flip,
+                                                     RandomResizedCrop, Resize,
+                                                     ThreeCrop)
+from mmaction.datasets.video_dataset import VideoDataset
+from mmaction.evaluation.metrics.acc_metric import AccMetric
+
+with read_base():
+    from ..._base_.default_runtime import *
+    from ..._base_.models.slowfast_r50 import *
+
+dataset_type = VideoDataset
+data_root = 'data/kinetics400/videos_train'
+data_root_val = 'data/kinetics400/videos_val'
+ann_file_train = 'data/kinetics400/kinetics400_train_list_videos.txt'
+ann_file_val = 'data/kinetics400/kinetics400_val_list_videos.txt'
+ann_file_test = 'data/kinetics400/kinetics400_val_list_videos.txt'
+
+file_client_args = dict(io_backend='disk')
+train_pipeline = [
+    dict(type=DecordInit, **file_client_args),
+    dict(type=SampleFrames, clip_len=32, frame_interval=2, num_clips=1),
+    dict(type=DecordDecode),
+    dict(type=Resize, scale=(-1, 256)),
+    dict(type=RandomResizedCrop),
+    dict(type=Resize, scale=(224, 224), keep_ratio=False),
+    dict(type=Flip, flip_ratio=0.5),
+    dict(type=FormatShape, input_format='NCTHW'),
+    dict(type=PackActionInputs)
+]
+val_pipeline = [
+    dict(type=DecordInit, **file_client_args),
+    dict(
+        type=SampleFrames,
+        clip_len=32,
+        frame_interval=2,
+        num_clips=1,
+        test_mode=True),
+    dict(type=DecordDecode),
+    dict(type=Resize, scale=(-1, 256)),
+    dict(type=CenterCrop, crop_size=224),
+    dict(type=FormatShape, input_format='NCTHW'),
+    dict(type=PackActionInputs)
+]
+test_pipeline = [
+    dict(type=DecordInit, **file_client_args),
+    dict(
+        type=SampleFrames,
+        clip_len=32,
+        frame_interval=2,
+        num_clips=10,
+        test_mode=True),
+    dict(type=DecordDecode),
+    dict(type=Resize, scale=(-1, 256)),
+    dict(type=ThreeCrop, crop_size=256),
+    dict(type=FormatShape, input_format='NCTHW'),
+    dict(type=PackActionInputs)
+]
+train_dataloader = dict(
+    batch_size=8,
+    num_workers=8,
+    persistent_workers=True,
+    sampler=dict(type=DefaultSampler, shuffle=True),
+    dataset=dict(
+        type=dataset_type,
+        ann_file=ann_file_train,
+        data_prefix=dict(video=data_root),
+        pipeline=train_pipeline))
+val_dataloader = dict(
+    batch_size=8,
+    num_workers=8,
+    persistent_workers=True,
+    sampler=dict(type=DefaultSampler, shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        ann_file=ann_file_val,
+        data_prefix=dict(video=data_root_val),
+        pipeline=val_pipeline,
+        test_mode=True))
+test_dataloader = dict(
+    batch_size=1,
+    num_workers=8,
+    persistent_workers=True,
+    sampler=dict(type=DefaultSampler, shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        ann_file=ann_file_test,
+        data_prefix=dict(video=data_root_val),
+        pipeline=test_pipeline,
+        test_mode=True))
+
+val_evaluator = dict(type=AccMetric)
+test_evaluator = val_evaluator
+
+train_cfg = dict(
+    type=EpochBasedTrainLoop, max_epochs=256, val_begin=1, val_interval=5)
+val_cfg = dict(type=ValLoop)
+test_cfg = dict(type=TestLoop)
+
+optim_wrapper = dict(
+    optimizer=dict(type=SGD, lr=0.1, momentum=0.9, weight_decay=1e-4),
+    clip_grad=dict(max_norm=40, norm_type=2))
+
+param_scheduler = [
+    dict(
+        type=LinearLR,
+        start_factor=0.1,
+        by_epoch=True,
+        begin=0,
+        end=34,
+        convert_to_iter_based=True),
+    dict(
+        type=CosineAnnealingLR,
+        T_max=256,
+        eta_min=0,
+        by_epoch=True,
+        begin=0,
+        end=256)
+]
+
+default_hooks.update(
+    dict(
+        checkpoint=dict(interval=4, max_keep_ckpts=3),
+        logger=dict(interval=100)))

configs/dgcnn/recognition/slowfast/slowfast_r50_8xb8_8x8x1_256e_kinetics400_rgb.py

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+from mmengine.config import read_base
+
+with read_base():
+    from .slowfast_r50_8xb8_4x16x1_256e_kinetics400_rgb import *
+
+model = dict(
+    backbone=dict(
+        resample_rate=4,  # tau
+        speed_ratio=4,  # alpha
+        channel_ratio=8,  # beta_inv
+        slow_pathway=dict(fusion_kernel=7)))