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README.md
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#resnet26d
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Implementation of ResNet proposed in [Deep Residual Learning for Image
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Recognition](https://arxiv.org/abs/1512.03385)
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``` python
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ResNet.resnet18()
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ResNet.resnet26()
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ResNet.resnet34()
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ResNet.resnet50()
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ResNet.resnet101()
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ResNet.resnet152()
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ResNet.resnet200()
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Variants (d) proposed in `Bag of Tricks for Image Classification with Convolutional Neural Networks <https://arxiv.org/pdf/1812.01187.pdf`_
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ResNet.resnet26d()
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ResNet.resnet34d()
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ResNet.resnet50d()
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# You can construct your own one by chaning `stem` and `block`
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resnet101d = ResNet.resnet101(stem=ResNetStemC, block=partial(ResNetBottleneckBlock, shortcut=ResNetShorcutD))
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```
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Examples:
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``` python
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# change activation
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ResNet.resnet18(activation = nn.SELU)
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# change number of classes (default is 1000 )
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ResNet.resnet18(n_classes=100)
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# pass a different block
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ResNet.resnet18(block=SENetBasicBlock)
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# change the steam
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model = ResNet.resnet18(stem=ResNetStemC)
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change shortcut
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model = ResNet.resnet18(block=partial(ResNetBasicBlock, shortcut=ResNetShorcutD))
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# store each feature
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x = torch.rand((1, 3, 224, 224))
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# get features
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model = ResNet.resnet18()
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# first call .features, this will activate the forward hooks and tells the model you'll like to get the features
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model.encoder.features
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model(torch.randn((1,3,224,224)))
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# get the features from the encoder
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features = model.encoder.features
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print([x.shape for x in features])
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#[torch.Size([1, 64, 112, 112]), torch.Size([1, 64, 56, 56]), torch.Size([1, 128, 28, 28]), torch.Size([1, 256, 14, 14])]
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```
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