text: import torch.nn as nn
  import torch.utils.model_zoo as model_zoo
  
  
  model_urls = {
      'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.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, **kwargs):
          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 IRBlock(nn.Module):
      expansion = 1
  
      def __init__(self, inplanes, planes, stride=1, downsample=None, use_se=True):
          super(IRBlock, self).__init__()
          self.bn0 = nn.BatchNorm2d(inplanes)
          self.conv1 = conv3x3(inplanes, inplanes)
          self.bn1 = nn.BatchNorm2d(inplanes)
          self.prelu = nn.PReLU()
          self.conv2 = conv3x3(inplanes, planes, stride)
          self.bn2 = nn.BatchNorm2d(planes)
          self.downsample = downsample
          self.stride = stride
          self.use_se = use_se
          if self.use_se:
              self.se = SEBlock(planes)
  
      def forward(self, x):
          residual = x
          out = self.bn0(x)
          out = self.conv1(out)
          out = self.bn1(out)
          out = self.prelu(out)
  
          out = self.conv2(out)
          out = self.bn2(out)
          if self.use_se:
              out = self.se(out)
  
          if self.downsample is not None:
              residual = self.downsample(x)
  
          out += residual
          out = self.prelu(out)
  
          return out
  
  
  class Bottleneck(nn.Module):
      expansion = 1
  
      def __init__(self, inplanes, planes, stride=1, downsample=None, **kwargs):
          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 * self.expansion, kernel_size=1, bias=False)
          self.bn3 = nn.BatchNorm2d(planes * self.expansion)
          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
  
  
  class SEBlock(nn.Module):
      def __init__(self, channel, reduction=16):
          super(SEBlock, self).__init__()
          self.avg_pool = nn.AdaptiveAvgPool2d(1)
          self.fc = nn.Sequential(
              nn.Linear(channel, channel // reduction),
              nn.PReLU(),
              nn.Linear(channel // reduction, channel),
              nn.Sigmoid()
          )
  
      def forward(self, x):
          b, c, _, _ = x.size()
          y = self.avg_pool(x).view(b, c)
          y = self.fc(y).view(b, c, 1, 1)
          return x * y
  
  
  class ResNetFace(nn.Module):
      def __init__(self, block, layers, use_se=True):
          self.inplanes = 64
          self.use_se = use_se
          super(ResNetFace, self).__init__()
          self.conv1 = nn.Conv2d(1, 64, kernel_size=3, padding=1, bias=False)
          self.bn1 = nn.BatchNorm2d(64)
          self.prelu = nn.PReLU()
          self.maxpool21 = nn.MaxPool2d(kernel_size=(2, 1), stride=(2, 1))
          self.maxpool22 = nn.MaxPool2d(kernel_size=2, stride=2)
          self.layer1 = self._make_layer(block, 128, layers[0])
          self.layer2 = self._make_layer(block, 256, layers[1])
          self.layer3 = self._make_layer(block, 512, layers[2])
          self.layer4 = self._make_layer(block, 128, layers[3])
          self.bn4 = nn.BatchNorm2d(128)
          self.dropout = nn.Dropout()
          self.fc5 = nn.Linear(12160, 512)
          self.bn5 = nn.BatchNorm1d(512)
  
          for m in self.modules():
              if isinstance(m, nn.Conv2d):
                  nn.init.xavier_normal_(m.weight)
              elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d):
                  nn.init.constant_(m.weight, 1)
                  nn.init.constant_(m.bias, 0)
              elif isinstance(m, nn.Linear):
                  nn.init.xavier_normal_(m.weight)
                  nn.init.constant_(m.bias, 0)
  
      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, use_se=self.use_se))
          self.inplanes = planes
          for i in range(1, blocks):
              layers.append(block(self.inplanes, planes, use_se=self.use_se))
  
          return nn.Sequential(*layers)
  
      def forward(self, x):
          x = self.conv1(x)
          x = self.bn1(x)
          x = self.prelu(x)
          x = self.maxpool22(x)
  
          x = self.layer1(x)
          x = self.maxpool22(x)
          x = self.layer2(x)
          x = self.maxpool22(x)
          x = self.layer3(x)
          x = self.maxpool22(x)
          x = self.layer4(x)
          x = self.maxpool21(x)
          x = self.bn4(x)
          x = self.dropout(x)
          x = x.view(x.size(0), -1)
          x = self.fc5(x)
          if x.shape[0] > 1:
              x = self.bn5(x)
  
          return x
  
  
  class ResNet(nn.Module):
  
      def __init__(self, block, layers):
          self.inplanes = 64
          super(ResNet, self).__init__()
          # self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
          #                        bias=False)
          self.conv1 = nn.Conv2d(1, 64, kernel_size=3, stride=1, padding=1,
                                 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], stride=2)
          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.AvgPool2d(8, stride=1)
          # self.fc = nn.Linear(512 * block.expansion, num_classes)
          self.fc5 = nn.Linear(512 * 8 * 8, 512)
  
          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.init.constant_(m.weight, 1)
                  nn.init.constant_(m.bias, 0)
  
      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)
  
          x = self.layer1(x)
          x = self.layer2(x)
          x = self.layer3(x)
          x = self.layer4(x)
          # x = nn.AvgPool2d(kernel_size=x.size()[2:])(x)
          # x = self.avgpool(x)
          x = x.view(x.size(0), -1)
          x = self.fc5(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']))
      return model
  
  
  class WrapModule(nn.Module):
      def __init__(self, block, layers, use_se=True, **kwargs):
          super(WrapModule, self).__init__()
          self.module = ResNetFace(block, layers, use_se, **kwargs)
  
      def _make_layer(self, block, planes, blocks, stride=1):
          return self.module._make_layer(block, planes, blocks, stride=1)
  
      def forward(self, x):
          return self.module(x)
  
  
  def resnet_face18(use_se=True, **kwargs):
      model = ResNetFace(IRBlock, [2, 3, 4, 3], use_se=use_se, **kwargs)
      return model
  
  
  def wrap_resnet_face18(use_se=True, **kwargs):
      model = WrapModule(IRBlock, [2, 3, 4, 3], use_se=use_se, **kwargs)
      return model
  