readme: # resnext50_32x4d
  Implementation of ResNetXt proposed in [\"Aggregated Residual
  Transformation for Deep Neural
  Networks\"](https://arxiv.org/pdf/1611.05431.pdf)
  
   Create a default model
  
   ``` python
   ResNetXt.resnext50_32x4d()
   ResNetXt.resnext101_32x8d()
   # create a resnetxt18_32x4d
   ResNetXt.resnet18(block=ResNetXtBottleNeckBlock, groups=32, base_width=4)
   ```
  
   Examples:
  
   :   ``` 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])]
       ```
  
   