text: import torch.nn as nn
  import math
  import torch.utils.model_zoo as model_zoo
  
  
  __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)
  
  
  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 = nn.BatchNorm2d(planes)
          self.relu = nn.ReLU(inplace=True)
          self.conv2 = conv3x3(planes, planes)
          self.bn2 = nn.BatchNorm2d(planes)
          self.downsample = downsample
          self.stride = stride
  
      def forward(self, x):
          residual = 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:
              residual = self.downsample(x)
  
          out += residual
          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 = 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)
          self.relu = nn.ReLU(inplace=True)
          self.downsample = downsample 
          self.stride = stride
  
      def forward(self, x):
          residual = 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:
              residual = self.downsample(x)
  
          out += residual
          out = self.relu(out)
  
          return out
  
  MODEL = {
      18:{
          'layers': [2, 2, 2, 2],
          'block' : BasicBlock
      },
      34:{
          'layers': [3, 4, 6, 3],
          'block' : BasicBlock
      },
      50:{
          'layers': [3, 4, 6, 3],
          'block' : Bottleneck
      },
      101:{
          'layers': [3, 4, 23, 3],
          'block' : Bottleneck
      },
      152:{
          'layers': [3, 8, 36, 3],
          'block' : Bottleneck
      }
  }
  
  class ResNet(nn.Module):
                          #block, layers, num_classes=1000
      def __init__(self, config):
          self.inplanes = 64
          super(ResNet, self).__init__()
          model_dic = MODEL[config.num_layers_r]
          # model_dic = MODEL[50]
          layers = model_dic['layers']
          block = model_dic['block']
  
          self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                                 bias=False)
          self.bn1 = nn.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)
  
          for m in self.modules():
              if isinstance(m, nn.Conv2d):
                  n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                  m.weight.data.normal_(0, math.sqrt(2. / n))
              elif isinstance(m, nn.BatchNorm2d):
                  m.weight.data.fill_(1)
                  m.bias.data.zero_()
  
      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 = []
          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)
  
      def forward(self, x):
          x = self.conv1(x)
          x = self.bn1(x)
          x = self.relu(x)
          x = self.maxpool(x)
  
          x1 = self.layer1(x)
          x2 = self.layer2(x1)
          x3 = self.layer3(x2)
          x4 = self.layer4(x3)
  
          return [x4, x3, x2, x1]
  
  
  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:
          from collections import OrderedDict
          state_dict = model.state_dict()
          pretrained_state_dict = model_zoo.load_url(model_urls['resnet18'])
          for k, v in pretrained_state_dict.items():
              if k not in state_dict:
                  continue
              state_dict[k] = v
          model.load_state_dict(state_dict)
      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:
          from collections import OrderedDict
          state_dict = model.state_dict()
          pretrained_state_dict = model_zoo.load_url(model_urls['resnet34'])
          for k, v in pretrained_state_dict.items():
              if k not in state_dict:
                  continue
              state_dict[k] = v
          model.load_state_dict(state_dict)
      return model
  
  
  def resnet50( opt):
      """Constructs a ResNet-50 model.
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = ResNet(opt)
      if opt.pretrained:
          print('Initialize with pre-trained ResNet')
          from collections import OrderedDict
          state_dict = model.state_dict()
          pretrained_state_dict = model_zoo.load_url(model_urls['resnet50'])
          for k, v in pretrained_state_dict.items():
              if k not in state_dict:
                  continue
              state_dict[k] = v
          print('successfully load '+str(len(state_dict.keys()))+' keys')
          model.load_state_dict(state_dict)
      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:
          print('Initialize with pre-trained ResNet')
          from collections import OrderedDict
          state_dict = model.state_dict()
          pretrained_state_dict = model_zoo.load_url(model_urls['resnet101'])
          for k, v in pretrained_state_dict.items():
              if k not in state_dict:
                  continue
              state_dict[k] = v
          print('successfully load '+str(len(state_dict.keys()))+' keys')
          model.load_state_dict(state_dict)
      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:
          from collections import OrderedDict
          state_dict = model.state_dict()
          pretrained_state_dict = model_zoo.load_url(model_urls['resnet152'])
          for k, v in pretrained_state_dict.items():
              if k not in state_dict:
                  continue
              state_dict[k] = v
          model.load_state_dict(state_dict)
      return model