content: import torch.nn as nn
  import math
  import torch.utils.model_zoo as model_zoo
  from modeling.sync_batchnorm.batchnorm import SynchronizedBatchNorm2d
  
  webroot = 'http://dl.yf.io/drn/'
  
  model_urls = {
      'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
      'drn-c-26': webroot + 'drn_c_26-ddedf421.pth',
      'drn-c-42': webroot + 'drn_c_42-9d336e8c.pth',
      'drn-c-58': webroot + 'drn_c_58-0a53a92c.pth',
      'drn-d-22': webroot + 'drn_d_22-4bd2f8ea.pth',
      'drn-d-38': webroot + 'drn_d_38-eebb45f0.pth',
      'drn-d-54': webroot + 'drn_d_54-0e0534ff.pth',
      'drn-d-105': webroot + 'drn_d_105-12b40979.pth'
  }
  
  
  def conv3x3(in_planes, out_planes, stride=1, padding=1, dilation=1):
      return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                       padding=padding, bias=False, dilation=dilation)
  
  
  class BasicBlock(nn.Module):
      expansion = 1
  
      def __init__(self, inplanes, planes, stride=1, downsample=None,
                   dilation=(1, 1), residual=True, BatchNorm=None):
          super(BasicBlock, self).__init__()
          self.conv1 = conv3x3(inplanes, planes, stride,
                               padding=dilation[0], dilation=dilation[0])
          self.bn1 = BatchNorm(planes)
          self.relu = nn.ReLU(inplace=True)
          self.conv2 = conv3x3(planes, planes,
                               padding=dilation[1], dilation=dilation[1])
          self.bn2 = BatchNorm(planes)
          self.downsample = downsample
          self.stride = stride
          self.residual = residual
  
      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)
          if self.residual:
              out += residual
          out = self.relu(out)
  
          return out
  
  
  class Bottleneck(nn.Module):
      expansion = 4
  
      def __init__(self, inplanes, planes, stride=1, downsample=None,
                   dilation=(1, 1), residual=True, BatchNorm=None):
          super(Bottleneck, self).__init__()
          self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
          self.bn1 = BatchNorm(planes)
          self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                                 padding=dilation[1], bias=False,
                                 dilation=dilation[1])
          self.bn2 = BatchNorm(planes)
          self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
          self.bn3 = BatchNorm(planes * 4)
          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 DRN(nn.Module):
  
      def __init__(self, block, layers, arch='D',
                   channels=(16, 32, 64, 128, 256, 512, 512, 512),
                   BatchNorm=None):
          super(DRN, self).__init__()
          self.inplanes = channels[0]
          self.out_dim = channels[-1]
          self.arch = arch
  
          if arch == 'C':
              self.conv1 = nn.Conv2d(3, channels[0], kernel_size=7, stride=1,
                                     padding=3, bias=False)
              self.bn1 = BatchNorm(channels[0])
              self.relu = nn.ReLU(inplace=True)
  
              self.layer1 = self._make_layer(
                  BasicBlock, channels[0], layers[0], stride=1, BatchNorm=BatchNorm)
              self.layer2 = self._make_layer(
                  BasicBlock, channels[1], layers[1], stride=2, BatchNorm=BatchNorm)
  
          elif arch == 'D':
              self.layer0 = nn.Sequential(
                  nn.Conv2d(3, channels[0], kernel_size=7, stride=1, padding=3,
                            bias=False),
                  BatchNorm(channels[0]),
                  nn.ReLU(inplace=True)
              )
  
              self.layer1 = self._make_conv_layers(
                  channels[0], layers[0], stride=1, BatchNorm=BatchNorm)
              self.layer2 = self._make_conv_layers(
                  channels[1], layers[1], stride=2, BatchNorm=BatchNorm)
  
          self.layer3 = self._make_layer(block, channels[2], layers[2], stride=2, BatchNorm=BatchNorm)
          self.layer4 = self._make_layer(block, channels[3], layers[3], stride=2, BatchNorm=BatchNorm)
          self.layer5 = self._make_layer(block, channels[4], layers[4],
                                         dilation=2, new_level=False, BatchNorm=BatchNorm)
          self.layer6 = None if layers[5] == 0 else \
              self._make_layer(block, channels[5], layers[5], dilation=4,
                               new_level=False, BatchNorm=BatchNorm)
  
          if arch == 'C':
              self.layer7 = None if layers[6] == 0 else \
                  self._make_layer(BasicBlock, channels[6], layers[6], dilation=2,
                                   new_level=False, residual=False, BatchNorm=BatchNorm)
              self.layer8 = None if layers[7] == 0 else \
                  self._make_layer(BasicBlock, channels[7], layers[7], dilation=1,
                                   new_level=False, residual=False, BatchNorm=BatchNorm)
          elif arch == 'D':
              self.layer7 = None if layers[6] == 0 else \
                  self._make_conv_layers(channels[6], layers[6], dilation=2, BatchNorm=BatchNorm)
              self.layer8 = None if layers[7] == 0 else \
                  self._make_conv_layers(channels[7], layers[7], dilation=1, BatchNorm=BatchNorm)
  
          self._init_weight()
  
      def _init_weight(self):
          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, SynchronizedBatchNorm2d):
                  m.weight.data.fill_(1)
                  m.bias.data.zero_()
              elif isinstance(m, nn.BatchNorm2d):
                  m.weight.data.fill_(1)
                  m.bias.data.zero_()
  
  
      def _make_layer(self, block, planes, blocks, stride=1, dilation=1,
                      new_level=True, residual=True, BatchNorm=None):
          assert dilation == 1 or dilation % 2 == 0
          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),
                  BatchNorm(planes * block.expansion),
              )
  
          layers = list()
          layers.append(block(
              self.inplanes, planes, stride, downsample,
              dilation=(1, 1) if dilation == 1 else (
                  dilation // 2 if new_level else dilation, dilation),
              residual=residual, BatchNorm=BatchNorm))
          self.inplanes = planes * block.expansion
          for i in range(1, blocks):
              layers.append(block(self.inplanes, planes, residual=residual,
                                  dilation=(dilation, dilation), BatchNorm=BatchNorm))
  
          return nn.Sequential(*layers)
  
      def _make_conv_layers(self, channels, convs, stride=1, dilation=1, BatchNorm=None):
          modules = []
          for i in range(convs):
              modules.extend([
                  nn.Conv2d(self.inplanes, channels, kernel_size=3,
                            stride=stride if i == 0 else 1,
                            padding=dilation, bias=False, dilation=dilation),
                  BatchNorm(channels),
                  nn.ReLU(inplace=True)])
              self.inplanes = channels
          return nn.Sequential(*modules)
  
      def forward(self, x):
          if self.arch == 'C':
              x = self.conv1(x)
              x = self.bn1(x)
              x = self.relu(x)
          elif self.arch == 'D':
              x = self.layer0(x)
  
          x = self.layer1(x)
          x = self.layer2(x)
  
          x = self.layer3(x)
          low_level_feat = x
  
          x = self.layer4(x)
          x = self.layer5(x)
  
          if self.layer6 is not None:
              x = self.layer6(x)
  
          if self.layer7 is not None:
              x = self.layer7(x)
  
          if self.layer8 is not None:
              x = self.layer8(x)
  
          return x, low_level_feat
  
  
  class DRN_A(nn.Module):
  
      def __init__(self, block, layers, BatchNorm=None):
          self.inplanes = 64
          super(DRN_A, self).__init__()
          self.out_dim = 512 * block.expansion
          self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                                 bias=False)
          self.bn1 = BatchNorm(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], BatchNorm=BatchNorm)
          self.layer2 = self._make_layer(block, 128, layers[1], stride=2, BatchNorm=BatchNorm)
          self.layer3 = self._make_layer(block, 256, layers[2], stride=1,
                                         dilation=2, BatchNorm=BatchNorm)
          self.layer4 = self._make_layer(block, 512, layers[3], stride=1,
                                         dilation=4, BatchNorm=BatchNorm)
  
          self._init_weight()
  
      def _init_weight(self):
          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, SynchronizedBatchNorm2d):
                  m.weight.data.fill_(1)
                  m.bias.data.zero_()
              elif isinstance(m, nn.BatchNorm2d):
                  m.weight.data.fill_(1)
                  m.bias.data.zero_()
  
      def _make_layer(self, block, planes, blocks, stride=1, dilation=1, BatchNorm=None):
          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),
                  BatchNorm(planes * block.expansion),
              )
  
          layers = []
          layers.append(block(self.inplanes, planes, stride, downsample, BatchNorm=BatchNorm))
          self.inplanes = planes * block.expansion
          for i in range(1, blocks):
              layers.append(block(self.inplanes, planes,
                                  dilation=(dilation, dilation, ), BatchNorm=BatchNorm))
  
          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)
  
          return x
  
  def drn_a_50(BatchNorm, pretrained=True):
      model = DRN_A(Bottleneck, [3, 4, 6, 3], BatchNorm=BatchNorm)
      if pretrained:
          model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
      return model
  
  
  def drn_c_26(BatchNorm, pretrained=True):
      model = DRN(BasicBlock, [1, 1, 2, 2, 2, 2, 1, 1], arch='C', BatchNorm=BatchNorm)
      if pretrained:
          pretrained = model_zoo.load_url(model_urls['drn-c-26'])
          del pretrained['fc.weight']
          del pretrained['fc.bias']
          model.load_state_dict(pretrained)
      return model
  
  
  def drn_c_42(BatchNorm, pretrained=True):
      model = DRN(BasicBlock, [1, 1, 3, 4, 6, 3, 1, 1], arch='C', BatchNorm=BatchNorm)
      if pretrained:
          pretrained = model_zoo.load_url(model_urls['drn-c-42'])
          del pretrained['fc.weight']
          del pretrained['fc.bias']
          model.load_state_dict(pretrained)
      return model
  
  
  def drn_c_58(BatchNorm, pretrained=True):
      model = DRN(Bottleneck, [1, 1, 3, 4, 6, 3, 1, 1], arch='C', BatchNorm=BatchNorm)
      if pretrained:
          pretrained = model_zoo.load_url(model_urls['drn-c-58'])
          del pretrained['fc.weight']
          del pretrained['fc.bias']
          model.load_state_dict(pretrained)
      return model
  
  
  def drn_d_22(BatchNorm, pretrained=True):
      model = DRN(BasicBlock, [1, 1, 2, 2, 2, 2, 1, 1], arch='D', BatchNorm=BatchNorm)
      if pretrained:
          pretrained = model_zoo.load_url(model_urls['drn-d-22'])
          del pretrained['fc.weight']
          del pretrained['fc.bias']
          model.load_state_dict(pretrained)
      return model
  
  
  def drn_d_24(BatchNorm, pretrained=True):
      model = DRN(BasicBlock, [1, 1, 2, 2, 2, 2, 2, 2], arch='D', BatchNorm=BatchNorm)
      if pretrained:
          pretrained = model_zoo.load_url(model_urls['drn-d-24'])
          del pretrained['fc.weight']
          del pretrained['fc.bias']
          model.load_state_dict(pretrained)
      return model
  
  
  def drn_d_38(BatchNorm, pretrained=True):
      model = DRN(BasicBlock, [1, 1, 3, 4, 6, 3, 1, 1], arch='D', BatchNorm=BatchNorm)
      if pretrained:
          pretrained = model_zoo.load_url(model_urls['drn-d-38'])
          del pretrained['fc.weight']
          del pretrained['fc.bias']
          model.load_state_dict(pretrained)
      return model
  
  
  def drn_d_40(BatchNorm, pretrained=True):
      model = DRN(BasicBlock, [1, 1, 3, 4, 6, 3, 2, 2], arch='D', BatchNorm=BatchNorm)
      if pretrained:
          pretrained = model_zoo.load_url(model_urls['drn-d-40'])
          del pretrained['fc.weight']
          del pretrained['fc.bias']
          model.load_state_dict(pretrained)
      return model
  
  
  def drn_d_54(BatchNorm, pretrained=True):
      model = DRN(Bottleneck, [1, 1, 3, 4, 6, 3, 1, 1], arch='D', BatchNorm=BatchNorm)
      if pretrained:
          pretrained = model_zoo.load_url(model_urls['drn-d-54'])
          del pretrained['fc.weight']
          del pretrained['fc.bias']
          model.load_state_dict(pretrained)
      return model
  
  
  def drn_d_105(BatchNorm, pretrained=True):
      model = DRN(Bottleneck, [1, 1, 3, 4, 23, 3, 1, 1], arch='D', BatchNorm=BatchNorm)
      if pretrained:
          pretrained = model_zoo.load_url(model_urls['drn-d-105'])
          del pretrained['fc.weight']
          del pretrained['fc.bias']
          model.load_state_dict(pretrained)
      return model
  
  if __name__ == "__main__":
      import torch
      model = drn_a_50(BatchNorm=nn.BatchNorm2d, pretrained=True)
      input = torch.rand(1, 3, 512, 512)
      output, low_level_feat = model(input)
      print(output.size())
      print(low_level_feat.size())
  