text: import torch.nn as nn
  from .SE_module import SELayer
  import torch.nn.functional as F
  from opt import opt
  
  
  class Bottleneck(nn.Module):
      expansion = 4
  
      def __init__(self, inplanes, planes, stride=1, downsample=None, reduction=False):
          super(Bottleneck, self).__init__()
          self.conv1 = nn.Conv2d(inplanes, 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, planes * 4, kernel_size=1, bias=False)
          self.bn3 = nn.BatchNorm2d(planes * 4)
          if reduction:
              self.se = SELayer(planes * 4)
  
          self.reduc = reduction
          self.downsample = downsample
          self.stride = stride
  
      def forward(self, x):
          residual = x
  
          out = F.relu(self.bn1(self.conv1(x)), inplace=True)
          out = F.relu(self.bn2(self.conv2(out)), inplace=True)
  
          out = self.conv3(out)
          out = self.bn3(out)
          if self.reduc:
              out = self.se(out)
  
          if self.downsample is not None:
              residual = self.downsample(x)
  
          out += residual
          out = F.relu(out)
  
          return out
  
  
  class SEResnet(nn.Module):
      """ SEResnet """
  
      def __init__(self, architecture):
          super(SEResnet, self).__init__()
          assert architecture in ["resnet50", "resnet101"]
          self.inplanes = 64
          self.layers = [3, 4, {"resnet50": 6, "resnet101": 23}[architecture], 3]
          self.block = Bottleneck
  
          self.conv1 = nn.Conv2d(3, 64, kernel_size=7,
                                 stride=2, padding=3, bias=False)
          self.bn1 = nn.BatchNorm2d(64, eps=1e-5, momentum=0.01, affine=True)
          self.relu = nn.ReLU(inplace=True)
          self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
  
          self.layer1 = self.make_layer(self.block, 64, self.layers[0])
          self.layer2 = self.make_layer(
              self.block, 128, self.layers[1], stride=2)
          self.layer3 = self.make_layer(
              self.block, 256, self.layers[2], stride=2)
  
          self.layer4 = self.make_layer(
              self.block, 512, self.layers[3], stride=2)
  
      def forward(self, x):
          x = self.maxpool(self.relu(self.bn1(self.conv1(x))))  # 64 * h/4 * w/4
          if opt.loadModel is not None and opt.epoch < 75:
              for param in self.layer1.parameters():
                  param.requires_grad = False
              for param in self.layer2.parameters():
                  param.requires_grad = False
              for param in self.layer3.parameters():
                  param.requires_grad = False
          x = self.layer1(x)  # 256 * h/4 * w/4
          x = self.layer2(x)  # 512 * h/8 * w/8
          x = self.layer3(x)  # 1024 * h/16 * w/16
          x = self.layer4(x)  # 2048 * h/32 * w/32
          return x
  
      def stages(self):
          return [self.layer1, self.layer2, self.layer3, self.layer4]
  
      def make_layer(self, block, planes, blocks, stride=1):
          downsample = None
          if stride != 1 or self.inplanes != planes * block.expansion:
              downsample = nn.Sequential(
                  nn.Conv2d(self.inplanes, planes * block.expansion,
                            kernel_size=1, stride=stride, bias=False),
                  nn.BatchNorm2d(planes * block.expansion),
              )
  
          layers = []
          if downsample is not None:
              layers.append(block(self.inplanes, planes, stride, downsample, reduction=True))
          else:
              layers.append(block(self.inplanes, planes, stride, downsample))
          self.inplanes = planes * block.expansion
          for i in range(1, blocks):
              layers.append(block(self.inplanes, planes))
  
          return nn.Sequential(*layers)
  