pytorch2keras 0.1.9

The deep learning models convertor

pytorch2keras

PyTorch to Keras model convertor.

Installation

pip install pytorch2keras 

Important notice

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

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

PyTorch 0.4.1 and greater

There are some problem related to a new version:

To make it work, please, cast all your .view() parameters to int. For example:

class ResNet(torchvision.models.resnet.ResNet):
    def __init__(self, *args, **kwargs):
        super(ResNet, self).__init__(*args, **kwargs)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)
        x = x.view(int(x.size(0)), -1)  #  << Here
        x = self.fc(x)
        return x

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.