text: # code from: https://github.com/jfzhang95/pytorch-deeplab-xception
  
  import math
  import torch.nn as nn
  import torch.utils.model_zoo as model_zoo
  # from modeling.sync_batchnorm.batchnorm import SynchronizedBatchNorm2d
  
  class Bottleneck(nn.Module):
      expansion = 4
  
      def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None, 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,
                                 dilation=dilation, padding=dilation, bias=False)
          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
          self.dilation = dilation
  
      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):
  
      def __init__(self, block, layers, output_stride, BatchNorm, pretrained=True):
          self.inplanes = 64
          super(ResNet, self).__init__()
          blocks = [1, 2, 4]
          if output_stride == 16:
              strides = [1, 2, 2, 1]
              dilations = [1, 1, 1, 2]
          elif output_stride == 8:
              strides = [1, 2, 1, 1]
              dilations = [1, 1, 2, 4]
          else:
              raise NotImplementedError
  
          # Modules
          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], stride=strides[0], dilation=dilations[0], BatchNorm=BatchNorm)
          self.layer2 = self._make_layer(block, 128, layers[1], stride=strides[1], dilation=dilations[1], BatchNorm=BatchNorm)
          self.layer3 = self._make_layer(block, 256, layers[2], stride=strides[2], dilation=dilations[2], BatchNorm=BatchNorm)
          self.layer4 = self._make_MG_unit(block, 512, blocks=blocks, stride=strides[3], dilation=dilations[3], BatchNorm=BatchNorm)
          # self.layer4 = self._make_layer(block, 512, layers[3], stride=strides[3], dilation=dilations[3], BatchNorm=BatchNorm)
          self._init_weight()
  
          if pretrained:
              self._load_pretrained_model()
  
      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, dilation, downsample, BatchNorm))
          self.inplanes = planes * block.expansion
          for i in range(1, blocks):
              layers.append(block(self.inplanes, planes, dilation=dilation, BatchNorm=BatchNorm))
  
          return nn.Sequential(*layers)
  
      def _make_MG_unit(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, dilation=blocks[0]*dilation,
                              downsample=downsample, BatchNorm=BatchNorm))
          self.inplanes = planes * block.expansion
          for i in range(1, len(blocks)):
              layers.append(block(self.inplanes, planes, stride=1,
                                  dilation=blocks[i]*dilation, BatchNorm=BatchNorm))
  
          return nn.Sequential(*layers)
  
      def forward(self, input):
          x = self.conv1(input)
          x = self.bn1(x)
          x = self.relu(x)
          x = self.maxpool(x)
  
          x = self.layer1(x)
          low_level_feat = x
          x = self.layer2(x)
          x = self.layer3(x)
          x = self.layer4(x)
          return x, low_level_feat
  
      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 _load_pretrained_model(self):
          pretrain_dict = model_zoo.load_url('https://download.pytorch.org/models/resnet101-5d3b4d8f.pth')
          model_dict = {}
          state_dict = self.state_dict()
          for k, v in pretrain_dict.items():
              if k in state_dict:
                  model_dict[k] = v
          state_dict.update(model_dict)
          self.load_state_dict(state_dict)
  
  def ResNet101(output_stride, BatchNorm, pretrained=True):
      """Constructs a ResNet-101 model.
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = ResNet(Bottleneck, [3, 4, 23, 3], output_stride, BatchNorm, pretrained=pretrained)
      return model
  
  if __name__ == "__main__":
      import torch
      model = ResNet101(BatchNorm=nn.BatchNorm2d, pretrained=True, output_stride=8)
      input = torch.rand(1, 3, 512, 512)
      output, low_level_feat = model(input)
      print(output.size())
      print(low_level_feat.size())