Pytorch to Keras model convertor. Very beta for now.

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

...
"image_data_format": "channels_first",
...

Note 1: some layers parameters (like ceiling and etc) isn't supported. Note 2: recurrent layers isn't supported too.

It's a convertor of pytorch graph to a keras (tensorflow backend) graph.

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

For example:

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

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

    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 - iterate model with some data (for gradients computing):

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

We've got some useless output. Now we use this output variable to traverse the graph.

from converter import pytorch_to_keras
k_model = pytorch_to_keras((10, 32, 32,), output)  #we should specify shape of the input tensor

That's all! If all is ok, the Keras model was stored to the k_model variable.

Layers:

• Linear
• Conv2d
• ConvTranspose2d
• MaxPool2d
• AvgPool2d

Reshape:

• View

Activations:

• ReLU
• LeakyReLU
• PReLU
• SELU
• Tanh
• Softmax
• Softplus
• Softsign
• Sigmoid

Element-wise:

• Addition
• Multiplication
• Subtraction

• ResNet18
• ResNet50
• SqueezeNet
• DenseNet
• AlexNet

Look at the tests directory.

This software is covered by MIT License.