text: import torch.nn as nn
  import torch.utils.model_zoo as model_zoo
  from libs.net.sync_batchnorm import SynchronizedBatchNorm2d
  
  __all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
             'resnet152']
  
  
  model_urls = {
      'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
      'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
      'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
      'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
      'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
  }
  
  
  def conv3x3(in_planes, out_planes, stride=1):
      """3x3 convolution with padding"""
      return nn.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 nn.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 = SynchronizedBatchNorm2d(planes)
          self.relu = nn.ReLU(inplace=True)
          self.conv2 = conv3x3(planes, planes)
          self.bn2 = SynchronizedBatchNorm2d(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 = SynchronizedBatchNorm2d(planes)
          self.conv2 = conv3x3(planes, planes, stride)
          self.bn2 = SynchronizedBatchNorm2d(planes)
          self.conv3 = conv1x1(planes, planes * self.expansion)
          self.bn3 = SynchronizedBatchNorm2d(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.layers = []
          self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                                 bias=False)
          self.bn1 = SynchronizedBatchNorm2d(64)
          self.relu = nn.ReLU(inplace=True)
          self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
          self.layer0 = nn.Sequential(
              self.conv1,
              self.bn1,
              self.relu,
              self.maxpool,
          )
          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)
  
          self.outdim_16 = 1024
          self.outdim_4 = 256
          for m in self.modules():
              if isinstance(m, nn.Conv2d):
                  nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
              elif isinstance(m, SynchronizedBatchNorm2d):
                  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),
                  SynchronizedBatchNorm2d(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):
          self.layers = []
          x = self.layer0(x)
          x = self.layer1(x)
          self.layers.append(x)
          x = self.layer2(x)
          self.layers.append(x)
          x = self.layer3(x)
          self.layers.append(x)
  
          return x
  
  
  def resnet18(pretrained=False, **kwargs):
      """Constructs a ResNet-18 model.
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
      if pretrained:
          model.load_state_dict(model_zoo.load_url(model_urls['resnet18']),strict=False)
      return model
  
  
  def resnet34(pretrained=False, **kwargs):
      """Constructs a ResNet-34 model.
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
      if pretrained:
          model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
      return model
  
  
  def 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:
          model.load_state_dict(model_zoo.load_url(model_urls['resnet50']),strict=False)
      return model
  
  
  def 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:
          model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
      return model
  
  
  def resnet152(pretrained=False, **kwargs):
      """Constructs a ResNet-152 model.
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
      if pretrained:
          model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
      return model
  #
  # if __name__ == '__main__':
  #     model = ResNet(BasicBlock, [2, 2, 2, 2])
  #     model.load_state_dict(model_zoo.load_url(model_urls['resnet18']), strict=False)