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