text: import jittor as jt
  from jittor import nn
  from jittor import Module
  from jittor import init
  from jittor.contrib import concat
  
  
  class Bottleneck(Module):
      expansion = 4
      def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None):
          super(Bottleneck, self).__init__()
          self.conv1 = nn.Conv(inplanes, planes, kernel_size=1, bias=False)
          self.bn1 = nn.BatchNorm(planes)
          self.conv2 = nn.Conv(planes, planes, kernel_size=3, stride=stride,
                                 dilation=dilation, padding=dilation, bias=False)
          self.bn2 = nn.BatchNorm(planes)
          self.conv3 = nn.Conv(planes, planes * 4, kernel_size=1, bias=False)
          self.bn3 = nn.BatchNorm(planes * 4)
          self.relu = nn.ReLU()
          self.downsample = downsample
          self.stride = stride
          self.dilation = dilation
  
      def execute(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(Module):
      def __init__(self, block, layers, output_stride):
          super(ResNet, self).__init__()
          self.inplanes = 128
          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.Sequential(
              nn.Conv(3, 64, kernel_size=3, stride=2, padding=1, bias=False),
              nn.BatchNorm(64),
              nn.ReLU(),
              nn.Conv(64, 64, kernel_size=3, stride=1, padding=1, bias=False),
              nn.BatchNorm(64),
              nn.ReLU(),
              nn.Conv(64, 128, kernel_size=3, stride=1, padding=1, bias=False))
  
  
          self.maxpool = nn.Pool(kernel_size=3, stride=2, padding=1)
  
          self.layer1 = self._make_layer(block, 64, layers[0], stride=strides[0], dilation=dilations[0])
          self.layer2 = self._make_layer(block, 128, layers[1], stride=strides[1], dilation=dilations[1])
          self.layer3 = self._make_layer(block, 256, layers[2], stride=strides[2], dilation=dilations[2])
          self.layer4 = self._make_MG_unit(block, 512, blocks=blocks, stride=strides[3], dilation=dilations[3])
  
  
      def _make_layer(self, block, planes, blocks, stride=1, dilation=1):
          downsample = None
          if stride != 1 or self.inplanes != planes * block.expansion:
              downsample = nn.Sequential(
                  nn.Conv(self.inplanes, planes * block.expansion,
                            kernel_size=1, stride=stride, bias=False),
                  nn.BatchNorm(planes * block.expansion),
              )
  
          layers = []
          layers.append(block(self.inplanes, planes, stride, dilation, downsample))
          self.inplanes = planes * block.expansion
          for i in range(1, blocks):
              layers.append(block(self.inplanes, planes, dilation=dilation))
  
          return nn.Sequential(*layers)
  
      def _make_MG_unit(self, block, planes, blocks, stride=1, dilation=1):
          downsample = None
          if stride != 1 or self.inplanes != planes * block.expansion:
              downsample = nn.Sequential(
                  nn.Conv(self.inplanes, planes * block.expansion,
                            kernel_size=1, stride=stride, bias=False),
                  nn.BatchNorm(planes * block.expansion),
              )
  
          layers = []
          layers.append(block(self.inplanes, planes, stride, dilation=blocks[0]*dilation,
                              downsample=downsample))
          self.inplanes = planes * block.expansion
          for i in range(1, len(blocks)):
              layers.append(block(self.inplanes, planes, stride=1,
                                  dilation=blocks[i]*dilation))
  
          return nn.Sequential(*layers)
  
      def execute(self, input):
          x = self.conv1(input)
  
          #x = self.bn1(x)
          #x = self.relu(x)
          x = self.maxpool(x)
  
          x = self.layer1(x)
  
  
          x_0 = x
          x_1 = self.layer2(x)
          x_2 = self.layer3(x_1)
          x_3 = self.layer4(x_2)
          return x_0, x_1, x_2, x_3
  
  def load_pretrained_model(model, params_path):
      pretrained_dict = torch.load(params_path)
      model_dict = {}
      param_name = model.parameters()
      name_list = [item.name() for item in param_name]
      for k, v in pretrained_dict.items():
          if k in name_list:
              # print (k)
              model_dict[k] = v
      model.load_parameters(model_dict)
  
  def resnet50(output_stride):
      model = ResNet(Bottleneck, [3,4,6,3], output_stride)
      # load imagenet pretrain model
      #model_path = './pretrained_models/resnet50.pth'
      model_path = 'model/pretrained_models/resnet50-ebb6acbb.pth'
      load_pretrained_model(model, model_path)
      return model
  
  def resnet101(output_stride):
      model = ResNet(Bottleneck, [3,4,23,3], output_stride)
      # load imagenet pretrain model
      #model_path = './pretrained_models/resnet101.pth'
      model_path = 'model/pretrained_models/resnet101-2a57e44d.pth'
      # load_pretrained_model(model, model_path)
      return model