text: import torch.nn as nn
  from torch.hub import load_state_dict_from_url
  
  __all__ = [
      "ResNet",
      "resnet18",
      "resnet34",
      "resnet50",
      "resnet101",
      "resnet152",
      "resnext50_32x4d",
      "resnext101_32x8d",
  ]
  
  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",
      "resnext50_32x4d": "https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth",
      "resnext101_32x8d": "https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth",
  }
  
  
  def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
      """3x3 convolution with padding"""
      return nn.Conv2d(
          in_planes,
          out_planes,
          kernel_size=3,
          stride=stride,
          padding=dilation,
          groups=groups,
          bias=False,
          dilation=dilation,
      )
  
  
  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,
          groups=1,
          base_width=64,
          dilation=1,
          norm_layer=None,
      ):
          super(BasicBlock, self).__init__()
          if norm_layer is None:
              norm_layer = nn.BatchNorm2d
          if groups != 1 or base_width != 64:
              raise ValueError("BasicBlock only supports groups=1 and base_width=64")
          if dilation > 1:
              raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
          # Both self.conv1 and self.downsample layers downsample the input when stride != 1
          self.conv1 = conv3x3(inplanes, planes, stride)
          self.bn1 = norm_layer(planes)
          self.relu = nn.ReLU(inplace=True)
          self.conv2 = conv3x3(planes, planes)
          self.bn2 = norm_layer(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,
          groups=1,
          base_width=64,
          dilation=1,
          norm_layer=None,
      ):
          super(Bottleneck, self).__init__()
          if norm_layer is None:
              norm_layer = nn.BatchNorm2d
          width = int(planes * (base_width / 64.0)) * groups
          # Both self.conv2 and self.downsample layers downsample the input when stride != 1
          self.conv1 = conv1x1(inplanes, width)
          self.bn1 = norm_layer(width)
          self.conv2 = conv3x3(width, width, stride, groups, dilation)
          self.bn2 = norm_layer(width)
          self.conv3 = conv1x1(width, planes * self.expansion)
          self.bn3 = norm_layer(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,
          groups=1,
          width_per_group=64,
          replace_stride_with_dilation=None,
          norm_layer=None,
      ):
          super(ResNet, self).__init__()
          if norm_layer is None:
              norm_layer = nn.BatchNorm2d
          self._norm_layer = norm_layer
  
          self.inplanes = 64
          self.dilation = 1
          if replace_stride_with_dilation is None:
              # each element in the tuple indicates if we should replace
              # the 2x2 stride with a dilated convolution instead
              replace_stride_with_dilation = [False, False, False]
          if len(replace_stride_with_dilation) != 3:
              raise ValueError(
                  "replace_stride_with_dilation should be None "
                  "or a 3-element tuple, got {}".format(replace_stride_with_dilation)
              )
          self.groups = groups
          self.base_width = width_per_group
          self.conv1 = nn.Conv2d(
              3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False
          )
          self.bn1 = norm_layer(self.inplanes)
          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, dilate=replace_stride_with_dilation[0]
          )
          self.layer3 = self._make_layer(
              block, 256, layers[2], stride=2, dilate=replace_stride_with_dilation[1]
          )
          self.layer4 = self._make_layer(
              block, 512, layers[3], stride=2, dilate=replace_stride_with_dilation[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.GroupNorm)):
                  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, dilate=False):
          norm_layer = self._norm_layer
          downsample = None
          previous_dilation = self.dilation
          if dilate:
              self.dilation *= stride
              stride = 1
          if stride != 1 or self.inplanes != planes * block.expansion:
              downsample = nn.Sequential(
                  conv1x1(self.inplanes, planes * block.expansion, stride),
                  norm_layer(planes * block.expansion),
              )
  
          layers = []
          layers.append(
              block(
                  self.inplanes,
                  planes,
                  stride,
                  downsample,
                  self.groups,
                  self.base_width,
                  previous_dilation,
                  norm_layer,
              )
          )
          self.inplanes = planes * block.expansion
          for _ in range(1, blocks):
              layers.append(
                  block(
                      self.inplanes,
                      planes,
                      groups=self.groups,
                      base_width=self.base_width,
                      dilation=self.dilation,
                      norm_layer=norm_layer,
                  )
              )
  
          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.reshape(x.size(0), -1)
          x = self.fc(x)
  
          return x
  
  
  def _resnet(arch, block, layers, pretrained, progress, **kwargs):
      model = ResNet(block, layers, **kwargs)
      if pretrained:
          state_dict = load_state_dict_from_url(model_urls[arch], progress=progress)
          model.load_state_dict(state_dict)
      return model
  
  
  def resnet18(pretrained=False, progress=True, **kwargs):
      """Constructs a ResNet-18 model.
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
          progress (bool): If True, displays a progress bar of the download to stderr
      """
      return _resnet("resnet18", BasicBlock, [2, 2, 2, 2], pretrained, progress, **kwargs)
  
  
  def resnet34(pretrained=False, progress=True, **kwargs):
      """Constructs a ResNet-34 model.
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
          progress (bool): If True, displays a progress bar of the download to stderr
      """
      return _resnet("resnet34", BasicBlock, [3, 4, 6, 3], pretrained, progress, **kwargs)
  
  
  def resnet50(pretrained=False, progress=True, **kwargs):
      """Constructs a ResNet-50 model.
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
          progress (bool): If True, displays a progress bar of the download to stderr
      """
      return _resnet("resnet50", Bottleneck, [3, 4, 6, 3], pretrained, progress, **kwargs)
  
  
  def resnet101(pretrained=False, progress=True, **kwargs):
      """Constructs a ResNet-101 model.
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
          progress (bool): If True, displays a progress bar of the download to stderr
      """
      return _resnet(
          "resnet101", Bottleneck, [3, 4, 23, 3], pretrained, progress, **kwargs
      )
  
  
  def resnet152(pretrained=False, progress=True, **kwargs):
      """Constructs a ResNet-152 model.
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
          progress (bool): If True, displays a progress bar of the download to stderr
      """
      return _resnet(
          "resnet152", Bottleneck, [3, 8, 36, 3], pretrained, progress, **kwargs
      )
  
  
  def resnext50_32x4d(pretrained=False, progress=True, **kwargs):
      """Constructs a ResNeXt-50 32x4d model.
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
          progress (bool): If True, displays a progress bar of the download to stderr
      """
      kwargs["groups"] = 32
      kwargs["width_per_group"] = 4
      return _resnet(
          "resnext50_32x4d", Bottleneck, [3, 4, 6, 3], pretrained, progress, **kwargs
      )
  
  
  def resnext101_32x8d(pretrained=False, progress=True, **kwargs):
      """Constructs a ResNeXt-101 32x8d model.
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
          progress (bool): If True, displays a progress bar of the download to stderr
      """
      kwargs["groups"] = 32
      kwargs["width_per_group"] = 8
      return _resnet(
          "resnext101_32x8d", Bottleneck, [3, 4, 23, 3], pretrained, progress, **kwargs
      )
  
  
  model_dict = {
      "resnet18": resnet18,
      "resnet34": resnet34,
      "resnet50": resnet50,
      "resnet101": resnet101,
      "resnet152": resnet152,
      "resnext50_32x4d": resnext50_32x4d,
      "resnext101_32x8d": resnext101_32x8d,
  }
  