text: import torch.nn as nn
  import torch.utils.model_zoo as model_zoo
  
  from backbone.wsgn import customized_func as L
  
  __all__ = ["ResNet", "l_resnet50", "l_resnet101"]
  
  model_urls = {
      "resnet50": "http://cs.jhu.edu/~syqiao/WeightStandardization/R-50-GN-WS.pth.tar",
      "resnet101": "http://cs.jhu.edu/~syqiao/WeightStandardization/R-101-GN-WS.pth.tar",
  }
  
  
  def conv3x3(in_planes, out_planes, stride=1):
      """3x3 convolution with padding"""
      return L.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
  
  
  def conv1x1(in_planes, out_planes, stride=1):
      """1x1 convolution"""
      return L.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
  
  
  class BasicBlock(nn.Module):
      expansion = 1
  
      def __init__(self, inplanes, planes, stride=1, downsample=None):
          super(BasicBlock, self).__init__()
          self.conv1 = conv3x3(inplanes, planes, stride)
          self.bn1 = L.BatchNorm2d(planes)
          self.relu = nn.ReLU(inplace=True)
          self.conv2 = conv3x3(planes, planes)
          self.bn2 = L.BatchNorm2d(planes)
          self.downsample = downsample
          self.stride = stride
  
      def forward(self, x):
          identity = x
  
          out = self.conv1(x)
          out = self.bn1(out)
          out = self.relu(out)
  
          out = self.conv2(out)
          out = self.bn2(out)
  
          if self.downsample is not None:
              identity = self.downsample(x)
  
          out += identity
          out = self.relu(out)
  
          return out
  
  
  class Bottleneck(nn.Module):
      expansion = 4
  
      def __init__(self, inplanes, planes, stride=1, downsample=None):
          super(Bottleneck, self).__init__()
          self.conv1 = conv1x1(inplanes, planes)
          self.bn1 = L.BatchNorm2d(planes)
          self.conv2 = conv3x3(planes, planes, stride)
          self.bn2 = L.BatchNorm2d(planes)
          self.conv3 = conv1x1(planes, planes * self.expansion)
          self.bn3 = L.BatchNorm2d(planes * self.expansion)
          self.relu = nn.ReLU(inplace=True)
          self.downsample = downsample
          self.stride = stride
  
      def forward(self, x):
          identity = x
  
          out = self.conv1(x)
          out = self.bn1(out)
          out = self.relu(out)
  
          out = self.conv2(out)
          out = self.bn2(out)
          out = self.relu(out)
  
          out = self.conv3(out)
          out = self.bn3(out)
  
          if self.downsample is not None:
              identity = self.downsample(x)
  
          out += identity
          out = self.relu(out)
  
          return out
  
  
  class ResNet(nn.Module):
      def __init__(self, block, layers, num_classes=1000, zero_init_residual=False):
          super(ResNet, self).__init__()
          self.inplanes = 64
          self.conv1 = L.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
          self.bn1 = L.BatchNorm2d(64)
          self.relu = nn.ReLU(inplace=True)
          self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
          self.layer1 = self._make_layer(block, 64, layers[0])
          self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
          self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
          self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
          self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
          self.fc = nn.Linear(512 * block.expansion, num_classes)
  
          for m in self.modules():
              if isinstance(m, nn.Conv2d):
                  nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
              elif isinstance(m, nn.BatchNorm2d):
                  nn.init.constant_(m.weight, 1)
                  nn.init.constant_(m.bias, 0)
  
          # Zero-initialize the last BN in each residual branch,
          # so that the residual branch starts with zeros, and each residual block behaves like an identity.
          # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
          if zero_init_residual:
              for m in self.modules():
                  if isinstance(m, Bottleneck):
                      nn.init.constant_(m.bn3.weight, 0)
                  elif isinstance(m, BasicBlock):
                      nn.init.constant_(m.bn2.weight, 0)
  
      def _make_layer(self, block, planes, blocks, stride=1):
          downsample = None
          if stride != 1 or self.inplanes != planes * block.expansion:
              downsample = nn.Sequential(
                  conv1x1(self.inplanes, planes * block.expansion, stride),
                  L.BatchNorm2d(planes * block.expansion),
              )
  
          layers = []
          layers.append(block(self.inplanes, planes, stride, downsample))
          self.inplanes = planes * block.expansion
          for _ in range(1, blocks):
              layers.append(block(self.inplanes, planes))
  
          return nn.Sequential(*layers)
  
      def forward(self, x):
          x = self.conv1(x)
          x = self.bn1(x)
          x = self.relu(x)
          x = self.maxpool(x)
  
          x = self.layer1(x)
          x = self.layer2(x)
          x = self.layer3(x)
          x = self.layer4(x)
  
          x = self.avgpool(x)
          x = x.view(x.size(0), -1)
          x = self.fc(x)
  
          return x
  
  
  def l_resnet50(pretrained=False, **kwargs):
      """Constructs a ResNet-50 model.
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
      if pretrained:
          L.load_pretrained_params(model, model_url=model_urls["resnet50"])
      return model
  
  
  def l_resnet101(pretrained=False, **kwargs):
      """Constructs a ResNet-101 model.
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
      if pretrained:
          L.load_pretrained_params(model, model_url=model_urls["resnet101"])
      return model
  
  
  if __name__ == "__main__":
      l_resnet50(pretrained=True)
  