text: import torch
  import torch.nn as nn
  
  
  class Shufflenet(nn.Module):
  
      def __init__(self, inp, oup, mid_channels, *, ksize, stride):
          super(Shufflenet, self).__init__()
          self.stride = stride
          assert stride in [1, 2]
          assert ksize in [3, 5, 7]
  
          self.base_mid_channel = mid_channels
          self.ksize = ksize
          pad = ksize // 2
          self.pad = pad
          self.inp = inp
  
          outputs = oup - inp
  
          branch_main = [
              # pw
              nn.Conv2d(inp, mid_channels, 1, 1, 0, bias=False),
              nn.BatchNorm2d(mid_channels, affine=False),
              nn.ReLU(inplace=True),
              # dw
              nn.Conv2d(mid_channels, mid_channels, ksize,
                        stride, pad, groups=mid_channels, bias=False),
              nn.BatchNorm2d(mid_channels, affine=False),
              # pw-linear
              nn.Conv2d(mid_channels, outputs, 1, 1, 0, bias=False),
              nn.BatchNorm2d(outputs, affine=False),
              nn.ReLU(inplace=True),
          ]
          self.branch_main = nn.Sequential(*branch_main)
  
          if stride == 2:
              branch_proj = [
                  # dw
                  nn.Conv2d(
                      inp, inp, ksize, stride, pad, groups=inp, bias=False),
                  nn.BatchNorm2d(inp, affine=False),
                  # pw-linear
                  nn.Conv2d(inp, inp, 1, 1, 0, bias=False),
                  nn.BatchNorm2d(inp, affine=False),
                  nn.ReLU(inplace=True),
              ]
              self.branch_proj = nn.Sequential(*branch_proj)
  
      def forward(self, old_x):
          if self.stride == 1:
              x_proj, x = channel_shuffle(old_x)
              return torch.cat((x_proj, self.branch_main(x)), 1)
          elif self.stride == 2:
              x_proj = old_x
              x = old_x
              return torch.cat((self.branch_proj(x_proj), self.branch_main(x)), 1)
  
  
  class Shuffle_Xception(nn.Module):
  
      def __init__(self, inp, oup, mid_channels, *, stride):
          super(Shuffle_Xception, self).__init__()
  
          assert stride in [1, 2]
  
          self.base_mid_channel = mid_channels
          self.stride = stride
          self.ksize = 3
          self.pad = 1
          self.inp = inp
          outputs = oup - inp
  
          branch_main = [
              # dw
              nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False),
              nn.BatchNorm2d(inp, affine=False),
              # pw
              nn.Conv2d(inp, mid_channels, 1, 1, 0, bias=False),
              nn.BatchNorm2d(mid_channels, affine=False),
              nn.ReLU(inplace=True),
              # dw
              nn.Conv2d(mid_channels, mid_channels, 3,
                        1, 1, groups=mid_channels, bias=False),
              nn.BatchNorm2d(mid_channels, affine=False),
              # pw
              nn.Conv2d(mid_channels, mid_channels, 1, 1, 0, bias=False),
              nn.BatchNorm2d(mid_channels, affine=False),
              nn.ReLU(inplace=True),
              # dw
              nn.Conv2d(mid_channels, mid_channels, 3,
                        1, 1, groups=mid_channels, bias=False),
              nn.BatchNorm2d(mid_channels, affine=False),
              # pw
              nn.Conv2d(mid_channels, outputs, 1, 1, 0, bias=False),
              nn.BatchNorm2d(outputs, affine=False),
              nn.ReLU(inplace=True),
          ]
  
          self.branch_main = nn.Sequential(*branch_main)
  
          if self.stride == 2:
              branch_proj = [
                  # dw
                  nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False),
                  nn.BatchNorm2d(inp, affine=False),
                  # pw-linear
                  nn.Conv2d(inp, inp, 1, 1, 0, bias=False),
                  nn.BatchNorm2d(inp, affine=False),
                  nn.ReLU(inplace=True),
              ]
              self.branch_proj = nn.Sequential(*branch_proj)
  
      def forward(self, old_x):
          if self.stride == 1:
              x_proj, x = channel_shuffle(old_x)
              return torch.cat((x_proj, self.branch_main(x)), 1)
          elif self.stride == 2:
              x_proj = old_x
              x = old_x
              return torch.cat((self.branch_proj(x_proj), self.branch_main(x)), 1)
  
  
  def channel_shuffle(x):
      batchsize, num_channels, height, width = x.data.size()
      assert (num_channels % 4 == 0)
      x = x.reshape(batchsize * num_channels // 2, 2, height * width)
      x = x.permute(1, 0, 2)
      x = x.reshape(2, -1, num_channels // 2, height, width)
      return x[0], x[1]
  