text: import torch
  import torch.nn as nn
  import pdb
  
  __all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
             'resnet152', 'resnext50_32x4d', 'resnext101_32x8d',
             'wide_resnet50_2', 'wide_resnet101_2']
  
  
  model_urls = {
      'resnet18': '/data_001/pixelai-2dface/Model-Zoo/ResNet/resnet18-5c106cde.pth',
      'resnet34': '/data_001/pixelai-2dface/Model-Zoo/ResNet/resnet34-333f7ec4.pth',
      'resnet50': '/data_001/pixelai-2dface/Model-Zoo/ResNet/resnet50-19c8e357.pth',
      'resnet101': '/data_003/pixelai-2dface/Model-Zoo/ResNet/resnet101-5d3b4d8f.pth',
      'resnet152': '/data_003/pixelai-2dface/Model-Zoo/ResNet/resnet152-b121ed2d.pth',
      'resnext50_32x4d': '/data_003/pixelai-2dface/Model-Zoo/ResNet/resnext50_32x4d-7cdf4587.pth',
      'resnext101_32x8d': '/data_003/pixelai-2dface/Model-Zoo/ResNet/resnext101_32x8d-8ba56ff5.pth',
      'wide_resnet50_2': '/data_003/pixelai-2dface/Model-Zoo/ResNet/wide_resnet50_2-95faca4d.pth',
      'wide_resnet101_2': '/data_003/pixelai-2dface/Model-Zoo/ResNet/wide_resnet101_2-32ee1156.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):
      # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2)
      # while original implementation places the stride at the first 1x1 convolution(self.conv1)
      # according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385.
      # This variant is also known as ResNet V1.5 and improves accuracy according to
      # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.
  
      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.)) * 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=318, 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))
          # building pts net
          self.pts_net = nn.Sequential(
              nn.Linear(512 * block.expansion, 256),
              nn.PReLU(),
              nn.Linear(256, 256),
              nn.PReLU(),
              nn.Linear(256, 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_impl(self, x):
          # See note [TorchScript super()]
          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 = torch.flatten(x, 1)
          pts = self.pts_net(x)
          
  
          return pts
  
      def forward(self, x):
          return self._forward_impl(x)
  
  
  def _resnet(arch, block, layers, pretrained, progress, **kwargs):
      model = ResNet(block, layers, **kwargs)
      if pretrained:
          #pdb.set_trace()
          state_dict = torch.load(model_urls[arch])
          del state_dict['fc.weight']
          del state_dict['fc.bias']
          model.load_state_dict(state_dict, strict=False)
      return model
  
  
  def resnet18(pretrained=False, progress=True, **kwargs):
      r"""ResNet-18 model from
      `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
      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):
      r"""ResNet-34 model from
      `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
      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):
      r"""ResNet-50 model from
      `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
      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):
      r"""ResNet-101 model from
      `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
      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):
      r"""ResNet-152 model from
      `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
      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):
      r"""ResNeXt-50 32x4d model from
      `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
      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):
      r"""ResNeXt-101 32x8d model from
      `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
      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)
  
  
  def wide_resnet50_2(pretrained=False, progress=True, **kwargs):
      r"""Wide ResNet-50-2 model from
      `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_
      The model is the same as ResNet except for the bottleneck number of channels
      which is twice larger in every block. The number of channels in outer 1x1
      convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
      channels, and in Wide ResNet-50-2 has 2048-1024-2048.
      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['width_per_group'] = 64 * 2
      return _resnet('wide_resnet50_2', Bottleneck, [3, 4, 6, 3],
                     pretrained, progress, **kwargs)
  
  
  def wide_resnet101_2(pretrained=False, progress=True, **kwargs):
      r"""Wide ResNet-101-2 model from
      `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_
      The model is the same as ResNet except for the bottleneck number of channels
      which is twice larger in every block. The number of channels in outer 1x1
      convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
      channels, and in Wide ResNet-50-2 has 2048-1024-2048.
      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['width_per_group'] = 64 * 2
      return _resnet('wide_resnet101_2', Bottleneck, [3, 4, 23, 3],
                     pretrained, progress, **kwargs)
  