A high level framework for general purpose neural networks in Pytorch.
Personally, going from Theano to Pytorch is pretty much like time traveling from 90s to the modern day. However, we feel like despite having a lot of bells and whistles, Pytorch is still missing many elements that are confirmed to never be added to the library. Therefore, this library is written to supplement more features to the current magical Pytorch. All the modules in the package directly subclass the corresponding modules from Pytorch, so everything should still be familiar.
Neuralnet-pytorch mostly preserves the same spirit of native Pytorch but in a (hopefully) less verbose way. The most prominent feature of Neuralnet-pytorch is the ability to handle input and output tensor shapes of operators abstractly (powered by Sympy). For example, the following snippet in Pytorch
>>> from torch import nn
>>> model = nn.Sequential(
... nn.Conv2d(1, 20, 5, padding=0),
... nn.ReLU(),
... nn.Conv2d(20, 64, 5, padding=0),
... nn.ReLU()
... )
>>> print(model)
Sequential(
(0): Conv2d(1, 20, kernel_size=(5, 5), stride=(1, 1))
(1): ReLU()
(2): Conv2d(20, 64, kernel_size=(5, 5), stride=(1, 1))
(3): ReLU()
)
can be rewritten in Neuralnet-pytorch as
>>> import neuralnet_pytorch as nnt
>>> model = nnt.Sequential(
... nnt.Conv2d(1, 20, 5, padding=0, activation='relu'),
... nnt.Conv2d(20, 64, 5, padding=0, activation='relu')
... )
>>> print(model)
Sequential(
(0): Conv2d(1, 20, kernel_size=(5, 5), stride=(1, 1), activation=relu) -> (b0, 20, x0 - 4, x1 - 4)
(1): Conv2d(20, 64, kernel_size=(5, 5), stride=(1, 1), activation=relu) -> (b0, 64, x0 - 8, x1 - 8)
) -> (b0, 64, x0 - 8, x1 - 8)
which is the same as the native Pytorch, or
>>> import neuralnet_pytorch as nnt
>>> model = nnt.Sequential(input_shape=1)
>>> model.conv1 = nnt.Conv2d(model.output_shape, 20, 5, padding=0, activation='relu')
>>> model.conv2 = nnt.Conv2d(model.output_shape, 64, 5, padding=0, activation='relu')
>>> print(model)
Sequential(
(conv1): Conv2d(1, 20, kernel_size=(5, 5), stride=(1, 1), activation=relu) -> (b0, 20, x0 - 4, x1 - 4)
(conv2): Conv2d(20, 64, kernel_size=(5, 5), stride=(1, 1), activation=relu) -> (b0, 64, x0 - 8, x1 - 8)
) -> (b0, 64, x0 - 8, x1 - 8)
which frees you from a lot of memorization and manual calculations when adding one layer on top of another. Theano folks will also find some reminiscence as many functions are highly inspired by Theano.
Pytorch >= 1.0.0
Gin-config (optional)
Pykeops (optional, required for the calculation of Sinkhorn Wasserstein loss.)
Geomloss (optional, required for the calculation of Sinkhorn Wasserstein loss.)
https://neuralnet-pytorch.readthedocs.io
Stable version
pip install --upgrade neuralnet-pytorch
Bleeding-edge version (recommended)
pip install git+git://github.com/justanhduc/neuralnet-pytorch.git@master
To install the package with optional dependencies, try
pip install "neuralnet-pytorch[option] @ git+git://github.com/justanhduc/neuralnet-pytorch.git@master"
in which option can be gin/geom/visdom/slack.
To install the version with some collected Cuda/C++ ops, use
pip install git+git://github.com/justanhduc/neuralnet-pytorch.git@master --global-option="--cuda-ext"
The manual reference is still under development and is available at https://neuralnet-pytorch.readthedocs.io.
- Adding introduction and installation
- Writing documentations
- Adding examples
- Adding benchmarks for examples
This package is a product from my little free time during my PhD, so most but not all the written modules are properly checked. No replacements or refunds for buggy performance. All PRs are welcome.
The CUDA Chamfer distance is taken from the AtlasNet repo.
The AdaBound optimizer is taken from its official repo.
The adapted Gin for Pytorch code is taken from Gin-config.
The monitor scheme is inspired from WGAN.
The EMD CUDA implementation is adapted form here.