content: import torch.nn as nn
  import torch.nn.functional as F
  
  
  def conv1x1(in_planes, out_planes, stride=1):
      """1x1 convolution without padding"""
      return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, padding=0, bias=False)
  
  
  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):
      def __init__(self, in_planes, planes, stride=1):
          super().__init__()
          self.conv1 = conv3x3(in_planes, planes, stride)
          self.conv2 = conv3x3(planes, planes)
          self.bn1 = nn.BatchNorm2d(planes)
          self.bn2 = nn.BatchNorm2d(planes)
          self.relu = nn.ReLU(inplace=True)
  
          if stride == 1:
              self.downsample = None
          else:
              self.downsample = nn.Sequential(
                  conv1x1(in_planes, planes, stride=stride),
                  nn.BatchNorm2d(planes)
              )
  
      def forward(self, x):
          y = x
          y = self.relu(self.bn1(self.conv1(y)))
          y = self.bn2(self.conv2(y))
  
          if self.downsample is not None:
              x = self.downsample(x)
  
          return self.relu(x+y)
  
  
  class ResNetFPN_8_2(nn.Module):
      """
      ResNet+FPN, output resolution are 1/8 and 1/2.
      Each block has 2 layers.
      """
  
      def __init__(self, config):
          super().__init__()
          # Config
          block = BasicBlock
          initial_dim = config['initial_dim']
          block_dims = config['block_dims']
  
          # Class Variable
          self.in_planes = initial_dim
  
          # Networks
          self.conv1 = nn.Conv2d(1, initial_dim, kernel_size=7, stride=2, padding=3, bias=False)
          self.bn1 = nn.BatchNorm2d(initial_dim)
          self.relu = nn.ReLU(inplace=True)
  
          self.layer1 = self._make_layer(block, block_dims[0], stride=1)  # 1/2
          self.layer2 = self._make_layer(block, block_dims[1], stride=2)  # 1/4
          self.layer3 = self._make_layer(block, block_dims[2], stride=2)  # 1/8
  
          # 3. FPN upsample
          self.layer3_outconv = conv1x1(block_dims[2], block_dims[2])
          self.layer2_outconv = conv1x1(block_dims[1], block_dims[2])
          self.layer2_outconv2 = nn.Sequential(
              conv3x3(block_dims[2], block_dims[2]),
              nn.BatchNorm2d(block_dims[2]),
              nn.LeakyReLU(),
              conv3x3(block_dims[2], block_dims[1]),
          )
          self.layer1_outconv = conv1x1(block_dims[0], block_dims[1])
          self.layer1_outconv2 = nn.Sequential(
              conv3x3(block_dims[1], block_dims[1]),
              nn.BatchNorm2d(block_dims[1]),
              nn.LeakyReLU(),
              conv3x3(block_dims[1], block_dims[0]),
          )
  
          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)
  
      def _make_layer(self, block, dim, stride=1):
          layer1 = block(self.in_planes, dim, stride=stride)
          layer2 = block(dim, dim, stride=1)
          layers = (layer1, layer2)
  
          self.in_planes = dim
          return nn.Sequential(*layers)
  
      def forward(self, x):
          # ResNet Backbone
          x0 = self.relu(self.bn1(self.conv1(x)))
          x1 = self.layer1(x0)  # 1/2
          x2 = self.layer2(x1)  # 1/4
          x3 = self.layer3(x2)  # 1/8
  
          # FPN
          x3_out = self.layer3_outconv(x3)
  
          x3_out_2x = F.interpolate(x3_out, scale_factor=2., mode='bilinear', align_corners=True)
          x2_out = self.layer2_outconv(x2)
          x2_out = self.layer2_outconv2(x2_out+x3_out_2x)
  
          x2_out_2x = F.interpolate(x2_out, scale_factor=2., mode='bilinear', align_corners=True)
          x1_out = self.layer1_outconv(x1)
          x1_out = self.layer1_outconv2(x1_out+x2_out_2x)
  
          return [x3_out, x1_out]
  
  
  class ResNetFPN_16_4(nn.Module):
      """
      ResNet+FPN, output resolution are 1/16 and 1/4.
      Each block has 2 layers.
      """
  
      def __init__(self, config):
          super().__init__()
          # Config
          block = BasicBlock
          initial_dim = config['initial_dim']
          block_dims = config['block_dims']
  
          # Class Variable
          self.in_planes = initial_dim
  
          # Networks
          self.conv1 = nn.Conv2d(1, initial_dim, kernel_size=7, stride=2, padding=3, bias=False)
          self.bn1 = nn.BatchNorm2d(initial_dim)
          self.relu = nn.ReLU(inplace=True)
  
          self.layer1 = self._make_layer(block, block_dims[0], stride=1)  # 1/2
          self.layer2 = self._make_layer(block, block_dims[1], stride=2)  # 1/4
          self.layer3 = self._make_layer(block, block_dims[2], stride=2)  # 1/8
          self.layer4 = self._make_layer(block, block_dims[3], stride=2)  # 1/16
  
          # 3. FPN upsample
          self.layer4_outconv = conv1x1(block_dims[3], block_dims[3])
          self.layer3_outconv = conv1x1(block_dims[2], block_dims[3])
          self.layer3_outconv2 = nn.Sequential(
              conv3x3(block_dims[3], block_dims[3]),
              nn.BatchNorm2d(block_dims[3]),
              nn.LeakyReLU(),
              conv3x3(block_dims[3], block_dims[2]),
          )
  
          self.layer2_outconv = conv1x1(block_dims[1], block_dims[2])
          self.layer2_outconv2 = nn.Sequential(
              conv3x3(block_dims[2], block_dims[2]),
              nn.BatchNorm2d(block_dims[2]),
              nn.LeakyReLU(),
              conv3x3(block_dims[2], block_dims[1]),
          )
  
          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)
  
      def _make_layer(self, block, dim, stride=1):
          layer1 = block(self.in_planes, dim, stride=stride)
          layer2 = block(dim, dim, stride=1)
          layers = (layer1, layer2)
  
          self.in_planes = dim
          return nn.Sequential(*layers)
  
      def forward(self, x):
          # ResNet Backbone
          x0 = self.relu(self.bn1(self.conv1(x)))
          x1 = self.layer1(x0)  # 1/2
          x2 = self.layer2(x1)  # 1/4
          x3 = self.layer3(x2)  # 1/8
          x4 = self.layer4(x3)  # 1/16
  
          # FPN
          x4_out = self.layer4_outconv(x4)
  
          x4_out_2x = F.interpolate(x4_out, scale_factor=2., mode='bilinear', align_corners=True)
          x3_out = self.layer3_outconv(x3)
          x3_out = self.layer3_outconv2(x3_out+x4_out_2x)
  
          x3_out_2x = F.interpolate(x3_out, scale_factor=2., mode='bilinear', align_corners=True)
          x2_out = self.layer2_outconv(x2)
          x2_out = self.layer2_outconv2(x2_out+x3_out_2x)
  
          return [x4_out, x2_out]
  