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The deep learning models convertor

Project description

pytorch2keras

Build Status GitHub License Python Version Downloads PyPI

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*
  • VGG*
  • PreResNet*
  • SqueezeNet (with ceil_mode=False)
  • SqueezeNext
  • DenseNet*
  • AlexNet
  • Inception
  • SeNet
  • Mobilenet v2
  • DiracNet
  • DARTS
  • DRNC
Model Top1 Top5 Params FLOPs Source weights Remarks
ResNet-10 37.09 15.55 5,418,792 892.62M osmr's repo Success
ResNet-12 35.86 14.46 5,492,776 1,124.23M osmr's repo Success
ResNet-14 32.85 12.41 5,788,200 1,355.64M osmr's repo Success
ResNet-16 30.68 11.10 6,968,872 1,586.95M osmr's repo Success
ResNet-18 x0.25 49.16 24.45 831,096 136.64M osmr's repo Success
ResNet-18 x0.5 36.54 14.96 3,055,880 485.22M osmr's repo Success
ResNet-18 x0.75 33.25 12.54 6,675,352 1,045.75M osmr's repo Success
ResNet-18 29.13 9.94 11,689,512 1,818.21M osmr's repo Success
ResNet-34 25.34 7.92 21,797,672 3,669.16M osmr's repo Success
ResNet-50 23.50 6.87 25,557,032 3,868.96M osmr's repo Success
ResNet-50b 22.92 6.44 25,557,032 4,100.70M osmr's repo Success
ResNet-101 21.66 5.99 44,549,160 7,586.30M osmr's repo Success
ResNet-101b 21.18 5.60 44,549,160 7,818.04M osmr's repo Success
ResNet-152 21.01 5.61 60,192,808 11,304.85M osmr's repo Success
ResNet-152b 20.54 5.37 60,192,808 11,536.58M osmr's repo Success
PreResNet-18 28.72 9.88 11,687,848 1,818.41M osmr's repo Success
PreResNet-34 25.88 8.11 21,796,008 3,669.36M osmr's repo Success
PreResNet-50 23.39 6.68 25,549,480 3,869.16M osmr's repo Success
PreResNet-50b 23.16 6.64 25,549,480 4,100.90M osmr's repo Success
PreResNet-101 21.45 5.75 44,541,608 7,586.50M osmr's repo Success
PreResNet-101b 21.73 5.88 44,541,608 7,818.24M osmr's repo Success
PreResNet-152 20.70 5.32 60,185,256 11,305.05M osmr's repo Success
PreResNet-152b 21.00 5.75 60,185,256 11,536.78M Gluon Model Zoo Success
PreResNet-200b 21.10 5.64 64,666,280 15,040.27M tornadomeet/ResNet Success
DenseNet-121 25.11 7.80 7,978,856 2,852.39M Gluon Model Zoo Success
DenseNet-161 22.40 6.18 28,681,000 7,761.25M Gluon Model Zoo Success
DenseNet-169 23.89 6.89 14,149,480 3,381.48M Gluon Model Zoo Success
DenseNet-201 22.71 6.36 20,013,928 4,318.75M Gluon Model Zoo Success
DarkNet Tiny 40.31 17.46 1,042,104 496.34M osmr's repo Success
DarkNet Ref 38.00 16.68 7,319,416 365.55M osmr's repo Success
SqueezeNet v1.0 40.97 18.96 1,248,424 828.30M osmr's repo Success
SqueezeNet v1.1 39.09 17.39 1,235,496 354.88M osmr's repo Success
MobileNet x0.25 45.78 22.18 470,072 42.30M osmr's repo Success
MobileNet x0.5 36.12 14.81 1,331,592 152.04M osmr's repo Success
MobileNet x0.75 32.71 12.28 2,585,560 329.22M Gluon Model Zoo Success
MobileNet x1.0 29.25 10.03 4,231,976 573.83M Gluon Model Zoo Success
FD-MobileNet x0.25 56.19 31.38 383,160 12.44M osmr's repo Success
FD-MobileNet x0.5 42.62 19.69 993,928 40.93M osmr's repo Success
FD-MobileNet x1.0 35.95 14.72 2,901,288 146.08M clavichord93/FD-MobileNet Success
MobileNetV2 x0.25 48.89 25.24 1,516,392 32.22M Gluon Model Zoo Success
MobileNetV2 x0.5 35.51 14.64 1,964,736 95.62M Gluon Model Zoo Success
MobileNetV2 x0.75 30.82 11.26 2,627,592 191.61M Gluon Model Zoo Success
MobileNetV2 x1.0 28.51 9.90 3,504,960 320.19M Gluon Model Zoo Success
InceptionV3 21.22 5.59 23,834,568 5,746.72M Gluon Model Zoo Success
DiracNetV2-18 31.47 11.70 11,511,784 1,798.43M szagoruyko/diracnets Success
DiracNetV2-34 28.75 9.93 21,616,232 3,649.37M szagoruyko/diracnets Success
DARTS 26.70 8.74 4,718,752 537.64M szagoruyko/diracnets Success

Usage

Look at the tests directory.

License

This software is covered by MIT License.

Project details


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Source Distribution

pytorch2keras-0.1.11.tar.gz (30.0 kB view hashes)

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