text: """Dilated ResNet"""
  import math
  import torch
  import torch.nn as nn
  import torch.utils.model_zoo as model_zoo
  
  __all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
             'resnet152', 'BasicBlock', 'Bottleneck']
  
  model_urls = {
      'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
      'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.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):
      """ResNet BasicBlock
      """
      expansion = 1
      def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None, previous_dilation=1,
                   norm_layer=None):
          super(BasicBlock, self).__init__()
          self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride,
                                 padding=dilation, dilation=dilation, bias=False)
          self.bn1 = norm_layer(planes)
          self.relu = nn.ReLU(inplace=True)
          self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1,
                                 padding=previous_dilation, dilation=previous_dilation, bias=False)
          self.bn2 = norm_layer(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 Bottleneck(nn.Module):
      """ResNet Bottleneck
      """
      # pylint: disable=unused-argument
      expansion = 4
      def __init__(self, inplanes, planes, stride=1, dilation=1,
                   downsample=None, previous_dilation=1, norm_layer=None):
          super(Bottleneck, self).__init__()
          self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
          self.bn1 = norm_layer(planes)
          self.conv2 = nn.Conv2d(
              planes, planes, kernel_size=3, stride=stride,
              padding=dilation, dilation=dilation, bias=False)
          self.bn2 = norm_layer(planes)
          self.conv3 = nn.Conv2d(
              planes, planes * 4, kernel_size=1, bias=False)
          self.bn3 = norm_layer(planes * 4)
          self.relu = nn.ReLU(inplace=True)
          self.downsample = downsample
          self.dilation = dilation
          self.stride = stride
  
      def _sum_each(self, x, y):
          assert(len(x) == len(y))
          z = []
          for i in range(len(x)):
              z.append(x[i]+y[i])
          return z
  
      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 ResNet(nn.Module):
      """Dilated Pre-trained ResNet Model, which preduces the stride of 8 featuremaps at conv5.
  
      Parameters
      ----------
      block : Block
          Class for the residual block. Options are BasicBlockV1, BottleneckV1.
      layers : list of int
          Numbers of layers in each block
      classes : int, default 1000
          Number of classification classes.
      dilated : bool, default False
          Applying dilation strategy to pretrained ResNet yielding a stride-8 model,
          typically used in Semantic Segmentation.
      norm_layer : object
          Normalization layer used in backbone network (default: :class:`mxnet.gluon.nn.BatchNorm`;
          for Synchronized Cross-GPU BachNormalization).
  
      Reference:
  
          - He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
  
          - Yu, Fisher, and Vladlen Koltun. "Multi-scale context aggregation by dilated convolutions."
      """
      # pylint: disable=unused-variable
      def __init__(self, block, layers, num_classes=1000, dilated=True,
                   deep_base=True, norm_layer=nn.BatchNorm2d, output_size=8):
          self.inplanes = 128 if deep_base else 64
          super(ResNet, self).__init__()
          if deep_base:
              self.conv1 = nn.Sequential(
                  nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, bias=False),
                  norm_layer(64),
                  nn.ReLU(inplace=True),
                  nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False),
                  norm_layer(64),
                  nn.ReLU(inplace=True),
                  nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1, bias=False),
              )
          else:
              self.conv1 = nn.Conv2d(3, 64, 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], norm_layer=norm_layer)
          self.layer2 = self._make_layer(block, 128, layers[1], stride=2, norm_layer=norm_layer)
  
          dilation_rate = 2
          if dilated and output_size <= 8:
              self.layer3 = self._make_layer(block, 256, layers[2], stride=1,
                                             dilation=dilation_rate, norm_layer=norm_layer)
              dilation_rate *= 2
          else:
              self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
                                             norm_layer=norm_layer)
  
          if dilated and output_size <= 16:
              self.layer4 = self._make_layer(block, 512, layers[3], stride=1,
                                             dilation=dilation_rate, norm_layer=norm_layer)
          else:
              self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
                                             norm_layer=norm_layer)
  
          self.avgpool = nn.AvgPool2d(7, stride=1)
          self.fc = nn.Linear(512 * block.expansion, num_classes)
  
          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, norm_layer):
                  m.weight.data.fill_(1)
                  m.bias.data.zero_()
  
      def _make_layer(self, block, planes, blocks, stride=1, dilation=1, norm_layer=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),
                  norm_layer(planes * block.expansion),
              )
  
          layers = []
          if dilation == 1 or dilation == 2:
              layers.append(block(self.inplanes, planes, stride, dilation=1,
                                  downsample=downsample, previous_dilation=dilation, norm_layer=norm_layer))
          elif dilation == 4:
              layers.append(block(self.inplanes, planes, stride, dilation=2,
                                  downsample=downsample, previous_dilation=dilation, norm_layer=norm_layer))
          else:
              raise RuntimeError("=> unknown dilation size: {}".format(dilation))
  
          self.inplanes = planes * block.expansion
          for i in range(1, blocks):
              layers.append(block(self.inplanes, planes, dilation=dilation, previous_dilation=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.view(x.size(0), -1)
          x = self.fc(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
  
  
  def resnet34(pretrained=False, **kwargs):
      """Constructs a ResNet-34 model.
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
      if pretrained:
          model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
      return model
  
  
  def resnet50(pretrained=False, root='~/.encoding/models', **kwargs):
      """Constructs a ResNet-50 model.
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
      if pretrained:
          from ..models.model_store import get_model_file
          model.load_state_dict(torch.load(
              get_model_file('resnet50', root=root)), strict=False)
      return model
  
  
  def resnet101(pretrained=False, root='~/.encoding/models', **kwargs):
      """Constructs a ResNet-101 model.
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
      if pretrained:
          from ..models.model_store import get_model_file
          model.load_state_dict(torch.load(
              get_model_file('resnet101', root=root)), strict=False)
      return model
  
  
  def resnet152(pretrained=False, root='~/.encoding/models', **kwargs):
      """Constructs a ResNet-152 model.
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
      if pretrained:
          from ..models.model_store import get_model_file
          model.load_state_dict(torch.load(
              get_model_file('resnet152', root=root)), strict=False)
      return model
  