text: '''Pre-activation ResNet in PyTorch.
  
  Reference:
  [1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
      Identity Mappings in Deep Residual Networks. arXiv:1603.05027
  '''
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  
  class PreActBlock(nn.Module):
      '''Pre-activation version of the BasicBlock.'''
      expansion = 1
  
      def __init__(self, in_planes, planes, stride=1):
          super(PreActBlock, self).__init__()
          self.bn1 = nn.BatchNorm2d(in_planes)
          self.conv1 = nn.Conv2d(
              in_planes,
              planes,
              kernel_size=3,
              stride=stride,
              padding=1,
              bias=False)
          self.bn2 = nn.BatchNorm2d(planes)
          self.conv2 = nn.Conv2d(
              planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
  
          if stride != 1 or in_planes != self.expansion * planes:
              self.shortcut = nn.Sequential(
                  nn.Conv2d(
                      in_planes,
                      self.expansion * planes,
                      kernel_size=1,
                      stride=stride,
                      bias=False))
  
      def forward(self, x):
          out = F.relu(self.bn1(x))
          shortcut = self.shortcut(out) if hasattr(self, 'shortcut') else x
          out = self.conv1(out)
          out = self.conv2(F.relu(self.bn2(out)))
          out += shortcut
          return out
  
  
  class PreActBottleneck(nn.Module):
      '''Pre-activation version of the original Bottleneck module.'''
      expansion = 4
  
      def __init__(self, in_planes, planes, stride=1):
          super(PreActBottleneck, self).__init__()
          self.bn1 = nn.BatchNorm2d(in_planes)
          self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
          self.bn2 = nn.BatchNorm2d(planes)
          self.conv2 = nn.Conv2d(
              planes,
              planes,
              kernel_size=3,
              stride=stride,
              padding=1,
              bias=False)
          self.bn3 = nn.BatchNorm2d(planes)
          self.conv3 = nn.Conv2d(
              planes, self.expansion * planes, kernel_size=1, bias=False)
  
          if stride != 1 or in_planes != self.expansion * planes:
              self.shortcut = nn.Sequential(
                  nn.Conv2d(
                      in_planes,
                      self.expansion * planes,
                      kernel_size=1,
                      stride=stride,
                      bias=False))
  
      def forward(self, x):
          out = F.relu(self.bn1(x))
          shortcut = self.shortcut(out) if hasattr(self, 'shortcut') else x
          out = self.conv1(out)
          out = self.conv2(F.relu(self.bn2(out)))
          out = self.conv3(F.relu(self.bn3(out)))
          out += shortcut
          return out
  
  
  class PreActResNet(nn.Module):
      def __init__(self, block, num_blocks, num_classes=10):
          super(PreActResNet, self).__init__()
          self.in_planes = 64
  
          self.conv1 = nn.Conv2d(
              3, 64, kernel_size=3, stride=1, padding=1, bias=False)
          self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
          self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
          self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
          self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
          self.linear = nn.Linear(512 * block.expansion, num_classes)
  
      def _make_layer(self, block, planes, num_blocks, stride):
          strides = [stride] + [1] * (num_blocks - 1)
          layers = []
          for stride in strides:
              layers.append(block(self.in_planes, planes, stride))
              self.in_planes = planes * block.expansion
          return nn.Sequential(*layers)
  
      def forward(self, x):
          out = self.conv1(x)
          out = self.layer1(out)
          out = self.layer2(out)
          out = self.layer3(out)
          out = self.layer4(out)
          out = F.avg_pool2d(out, 4)
          out = out.view(out.size(0), -1)
          out = self.linear(out)
          return out
  
  
  def PreActResNet18():
      return PreActResNet(PreActBlock, [2, 2, 2, 2])
  
  
  def PreActResNet34():
      return PreActResNet(PreActBlock, [3, 4, 6, 3])
  
  
  def PreActResNet50():
      return PreActResNet(PreActBottleneck, [3, 4, 6, 3])
  
  
  def PreActResNet101():
      return PreActResNet(PreActBottleneck, [3, 4, 23, 3])
  
  
  def PreActResNet152():
      return PreActResNet(PreActBottleneck, [3, 8, 36, 3])
  
  
  def test():
      net = PreActResNet18()
      y = net((torch.randn(1, 3, 32, 32)))
      print(y.size())
  
  
  if __name__ == '__main__':
      test()