text: # -*- coding: utf-8 -*-
  """
  @author: WZM
  @time: 2021/1/13 14:40
  @function:
  """
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  
  # 用于ResNet18和34的残差块，用的是2个3x3的卷积
  class BasicBlock(nn.Module):
      expansion = 1
  
      def __init__(self, in_planes, planes, stride=1):
          super(BasicBlock, self).__init__()
          self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3,
                                 stride=stride, padding=1, bias=False)
          self.bn1 = nn.BatchNorm2d(planes)
          self.conv2 = nn.Conv2d(planes, planes, kernel_size=3,
                                 stride=1, padding=1, bias=False)
          self.bn2 = nn.BatchNorm2d(planes)
          self.shortcut = nn.Sequential()
          # 经过处理后的x要与x的维度相同(尺寸和深度)
          # 如果不相同，需要添加卷积+BN来变换为同一维度
          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),
                  nn.BatchNorm2d(self.expansion * planes)
              )
  
      def forward(self, x):
          out = F.relu(self.bn1(self.conv1(x)))
          out = self.bn2(self.conv2(out))
          out += self.shortcut(x)
          out = F.relu(out)
          return out
  
  
  # 用于ResNet50,101和152的残差块，用的是1x1+3x3+1x1的卷积
  class Bottleneck(nn.Module):
      # 前面1x1和3x3卷积的filter个数相等，最后1x1卷积是其expansion倍
      expansion = 4
  
      def __init__(self, in_planes, planes, stride=1):
          super(Bottleneck, self).__init__()
          self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
          self.bn1 = nn.BatchNorm2d(planes)
          self.conv2 = nn.Conv2d(planes, planes, kernel_size=3,
                                 stride=stride, padding=1, bias=False)
          self.bn2 = nn.BatchNorm2d(planes)
          self.conv3 = nn.Conv2d(planes, self.expansion * planes,
                                 kernel_size=1, bias=False)
          self.bn3 = nn.BatchNorm2d(self.expansion * planes)
  
          self.shortcut = nn.Sequential()
          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),
                  nn.BatchNorm2d(self.expansion * planes)
              )
  
      def forward(self, x):
          out = F.relu(self.bn1(self.conv1(x)))
          out = F.relu(self.bn2(self.conv2(out)))
          out = self.bn3(self.conv3(out))
          out += self.shortcut(x)
          out = F.relu(out)
          return out
  
  
  class ResNet(nn.Module):
      def __init__(self, block, num_blocks, num_classes=3):
          super(ResNet, self).__init__()
          self.in_planes = 64
  
          self.conv1 = nn.Conv2d(3, 64, kernel_size=3,
                                 stride=1, padding=1, bias=False)
          self.conv1_1 = nn.Conv2d(1, 64, kernel_size=3,
                                 stride=1, padding=1, bias=False)
          self.bn1 = nn.BatchNorm2d(64)
  
          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)
          '''
          图片是32*32的话，就用512，因为前面得到的是512*1*1和2048*1*1
          图片是128*128的话，就用8192，因为前面得到的是512*4*4和2048*4*4
          图片是224*224的话，就用25088，因为前面得到的是512*7*7和2048*7*7
          '''
          self.linear = nn.Linear(131072 * 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, channel=3):
          '''
          x: [16, 3, 128, 128]
          '''
          if channel == 3:
              out = F.relu(self.bn1(self.conv1(x)))  # [16, 64, 128, 128]
          else:
              out = F.relu(self.bn1(self.conv1_1(x)))  # [16, 64, 128, 128]
          out = self.layer1(out)  # [16, 64, 128, 128]
          out = self.layer2(out)  # [16, 128, 64, 64]
          out = self.layer3(out)  # [16, 256, 32, 32]
          out = self.layer4(out)  # [16, 512, 16, 16]
          # out = F.avg_pool2d(out, 4)  # [16, 512, 4, 4]
          # out = out.view(out.size(0), -1)  # [16, 8192]
          # out = self.linear(out)  # [16, 3]
          return out
  
  
  def ResNet18():
      return ResNet(BasicBlock, [2, 2, 2, 2])
  
  
  def ResNet34():
      return ResNet(BasicBlock, [3, 4, 6, 3])
  
  
  def ResNet50():
      return ResNet(Bottleneck, [3, 4, 6, 3])
  
  
  def ResNet101():
      return ResNet(Bottleneck, [3, 4, 23, 3])
  
  
  def ResNet152():
      return ResNet(Bottleneck, [3, 8, 36, 3])
  
  
  if __name__ == '__main__':
      net = ResNet34()
      y = net(torch.randn(1, 3, 64, 64), 3)
      print(y.size())
  
  