Implementation of ResNetXt proposed in "Aggregated Residual Transformation for Deep Neural Networks"
Create a default model
ResNetXt.resnext50_32x4d() ResNetXt.resnext101_32x8d() # create a resnetxt18_32x4d ResNetXt.resnet18(block=ResNetXtBottleNeckBlock, groups=32, base_width=4)
python # change activation ResNetXt.resnext50_32x4d(activation = nn.SELU) # change number of classes (default is 1000 ) ResNetXt.resnext50_32x4d(n_classes=100) # pass a different block ResNetXt.resnext50_32x4d(block=SENetBasicBlock) # change the initial convolution model = ResNetXt.resnext50_32x4d model.encoder.gate.conv1 = nn.Conv2d(3, 64, kernel_size=3) # store each feature x = torch.rand((1, 3, 224, 224)) model = ResNetXt.resnext50_32x4d() # 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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