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