pytorch2keras 0.1.6

The deep learning models convertor

pytorch2keras

Pytorch to Keras model convertor. Still beta for now.

Installation

pip install pytorch2keras 

Important notice

At that moment the only PyTorch 0.4.0 is supported.

To use the converter properly, please, make changes in your ~/.keras/keras.json:

...
"backend": "tensorflow",
"image_data_format": "channels_first",
...

Python 3.7

There are some problem related to a new version:

Q. PyTorch 0.4 hadn't released wheel package for Python 3.7

A. You can build it from source:

git clone https://github.com/pytorch/pytorch

cd pytorch

git checkout v0.4.0

NO_CUDA=1 python setup.py install

Q. Tensorflow isn't available for Python 3.7

A. Yes, we're waiting for it.

Tensorflow.js

For the proper convertion to the tensorflow.js format, please use a new flag names='short'.

How to build the latest PyTorch

Please, follow this guide to compile the latest version.

Additional information for Arch Linux users:

• the latest gcc8 is incompatible with actual nvcc version
• the legacy gcc54 can't compile C/C++ modules because of compiler flags

How to use

It's the convertor of pytorch graph to a Keras (Tensorflow backend) graph.

Firstly, we need to load (or create) pytorch model:

class TestConv2d(nn.Module):
    """Module for Conv2d convertion testing
    """

    def __init__(self, inp=10, out=16, kernel_size=3):
        super(TestConv2d, self).__init__()
        self.conv2d = nn.Conv2d(inp, out, stride=(inp % 3 + 1), kernel_size=kernel_size, bias=True)

    def forward(self, x):
        x = self.conv2d(x)
        return x

model = TestConv2d()

# load weights here
# model.load_state_dict(torch.load(path_to_weights.pth))

The next step - create a dummy variable with correct shapes:

input_np = np.random.uniform(0, 1, (1, 10, 32, 32))
input_var = Variable(torch.FloatTensor(input_np))

We're using dummy-variable in order to trace the model.

from converter import pytorch_to_keras
# we should specify shape of the input tensor
k_model = pytorch_to_keras(model, input_var, [(10, 32, 32,)], verbose=True)  

You can also set H and W dimensions to None to make your model shape-agnostic:

from converter import pytorch_to_keras
# we should specify shape of the input tensor
k_model = pytorch_to_keras(model, input_var, [(10, None, None,)], verbose=True)  

That's all! If all is ok, the Keras model is stores into the k_model variable.

Supported layers

Layers:

• Linear
• Conv2d (also with groups)
• DepthwiseConv2d (with limited parameters)
• Conv3d
• ConvTranspose2d
• MaxPool2d
• MaxPool3d
• AvgPool2d
• Global average pooling (as special case of AdaptiveAvgPool2d)
• Embedding
• UpsamplingNearest2d
• BatchNorm2d
• InstanceNorm2d

Reshape:

• View
• Reshape
• Transpose

Activations:

• ReLU
• LeakyReLU
• Tanh
• HardTanh (clamp)
• Softmax
• Sigmoid

Element-wise:

• Addition
• Multiplication
• Subtraction

Misc:

• reduce sum ( .sum() method)

Unsupported parameters

• Pooling: count_include_pad, dilation, ceil_mode

Models converted with pytorch2keras

• ResNet*
• PreResNet*
• SqueezeNet (with ceil_mode=False)
• SqueezeNext
• DenseNet*
• AlexNet
• Inception
• SeNet
• Mobilenet v2

Usage

Look at the tests directory.

License

This software is covered by MIT License.