resmakelayer.py

  def _make_layer(self,block,num_res_blocks,int_channels,stride):
    identity_downsample =  None
    layers = []

    if stride!=1 or self.in_channels != int_channels*4:
      identity_downsample = nn.Sequential(nn.Conv2d(self.in_channels,int_channels*4,
                                                    kernel_size=1,stride=stride),
                                          nn.BatchNorm2d(int_channels*4))
      layers.append(ResBlock(self.in_channels,int_channels,identity_downsample,stride))
      #this expansion size will always be 4 for all the types of ResNets
      self.in_channels =  int_channels*4

      for i in range(num_res_blocks-1):
        layers.append(ResBlock(self.in_channels,int_channels))

      return nn.Sequential(*layers)