text: import math
  import torch.nn as nn
  from collections import OrderedDict
  import torch.utils.model_zoo as model_zoo
  from torchvision.models.resnet import BasicBlock, Bottleneck, model_urls
  
  
  class ResNet(nn.Module):
      """ ResNet network module. Allows extracting specific feature blocks."""
      def __init__(self, block, layers, output_layers, num_classes=1000, inplanes=64):
          self.inplanes = inplanes
          super(ResNet, self).__init__()
          self.output_layers = output_layers
          self.conv1 = nn.Conv2d(3, inplanes, kernel_size=7, stride=2, padding=3,
                                 bias=False)
          self.bn1 = nn.BatchNorm2d(inplanes)
          self.relu = nn.ReLU(inplace=True)
          self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
          self.layer1 = self._make_layer(block, inplanes, layers[0])
          self.layer2 = self._make_layer(block, inplanes*2, layers[1], stride=2)
          self.layer3 = self._make_layer(block, inplanes*4, layers[2], stride=2)
          self.layer4 = self._make_layer(block, inplanes*8, layers[3], stride=2)
          # self.avgpool = nn.AvgPool2d(7, stride=1)
          self.avgpool = nn.AdaptiveAvgPool2d((1,1))
          self.fc = nn.Linear(inplanes*8 * block.expansion, num_classes)
  
          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 _add_output_and_check(self, name, x, outputs, output_layers):
          if name in output_layers:
              outputs[name] = x
          return len(output_layers) == len(outputs)
  
      def forward(self, x, output_layers=None):
          """ Forward pass with input x. The output_layers specify the feature blocks which must be returned """
          outputs = OrderedDict()
  
          if output_layers is None:
              output_layers = self.output_layers
  
          x = self.conv1(x)
          x = self.bn1(x)
          x = self.relu(x)
  
          if self._add_output_and_check('conv1', x, outputs, output_layers):
              return outputs
  
          x = self.maxpool(x)
  
          x = self.layer1(x)
  
          if self._add_output_and_check('layer1', x, outputs, output_layers):
              return outputs
  
          x = self.layer2(x)
  
          if self._add_output_and_check('layer2', x, outputs, output_layers):
              return outputs
  
          x = self.layer3(x)
  
          if self._add_output_and_check('layer3', x, outputs, output_layers):
              return outputs
  
          x = self.layer4(x)
  
          if self._add_output_and_check('layer4', x, outputs, output_layers):
              return outputs
  
          x = self.avgpool(x)
          x = x.view(x.size(0), -1)
          x = self.fc(x)
  
          if self._add_output_and_check('fc', x, outputs, output_layers):
              return outputs
  
          if len(output_layers) == 1 and output_layers[0] == 'default':
              return x
  
          raise ValueError('output_layer is wrong.')
  
  
  def resnet18(output_layers=None, pretrained=False):
      """Constructs a ResNet-18 model.
      """
  
      if output_layers is None:
          output_layers = ['default']
      else:
          for l in output_layers:
              if l not in ['conv1', 'layer1', 'layer2', 'layer3', 'layer4', 'fc']:
                  raise ValueError('Unknown layer: {}'.format(l))
  
      model = ResNet(BasicBlock, [2, 2, 2, 2], output_layers)
  
      if pretrained:
          model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
      return model
  
  
  def resnet50(output_layers=None, pretrained=False):
      """Constructs a ResNet-50 model.
      """
  
      if output_layers is None:
          output_layers = ['default']
      else:
          for l in output_layers:
              if l not in ['conv1', 'layer1', 'layer2', 'layer3', 'layer4', 'fc']:
                  raise ValueError('Unknown layer: {}'.format(l))
  
      model = ResNet(Bottleneck, [3, 4, 6, 3], output_layers)
      if pretrained:
          model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
      return model
  