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# wide_resnet101_2 |
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Implementation of Wide ResNet proposed in [\"Wide Residual |
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Networks\"](https://arxiv.org/pdf/1605.07146.pdf) |
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Create a default model |
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``` python |
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WideResNet.wide_resnet50_2() |
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WideResNet.wide_resnet101_2() |
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# create a wide_resnet18_4 |
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WideResNet.resnet18(block=WideResNetBottleNeckBlock, width_factor=4) |
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``` |
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Examples: |
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``` python |
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# change activation |
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WideResNet.resnext50_32x4d(activation = nn.SELU) |
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# change number of classes (default is 1000 ) |
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WideResNet.resnext50_32x4d(n_classes=100) |
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# pass a different block |
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WideResNet.resnext50_32x4d(block=SENetBasicBlock) |
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# change the initial convolution |
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model = WideResNet.resnext50_32x4d |
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model.encoder.gate.conv1 = nn.Conv2d(3, 64, kernel_size=3) |
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# store each feature |
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x = torch.rand((1, 3, 224, 224)) |
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model = WideResNet.wide_resnet50_2() |
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features = [] |
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x = model.encoder.gate(x) |
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for block in model.encoder.layers: |
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x = block(x) |
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features.append(x) |
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print([x.shape for x in features]) |
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# [torch.Size([1, 64, 56, 56]), torch.Size([1, 128, 28, 28]), torch.Size([1, 256, 14, 14]), torch.Size([1, 512, 7, 7])] |
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``` |
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