code: import numpy as np
  import torch
  import torch.nn as nn
  from models.CCNet.CC import CC_module as CrissCrossAttention
  
  affine_par = True
  BatchNorm2d = nn.BatchNorm2d
  
  
  def outS(i):
      i = int(i)
      i = (i + 1) / 2
      i = int(np.ceil((i + 1) / 2.0))
      i = (i + 1) / 2
      return i
  
  
  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 Bottleneck(nn.Module):
      expansion = 4
  
      def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None, fist_dilation=1, multi_grid=1):
          super(Bottleneck, self).__init__()
          self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
          self.bn1 = BatchNorm2d(planes)
          self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                                 padding=dilation * multi_grid, dilation=dilation * multi_grid, bias=False)
          self.bn2 = BatchNorm2d(planes)
          self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
          self.bn3 = BatchNorm2d(planes * 4)
          self.relu = nn.ReLU(inplace=False)
          self.relu_inplace = nn.ReLU(inplace=True)
          self.downsample = downsample
          self.dilation = dilation
          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 = out + residual
          out = self.relu_inplace(out)
  
          return out
  
  
  class RCCAModule(nn.Module):
      def __init__(self, in_channels, out_channels, num_classes):
          super(RCCAModule, self).__init__()
          inter_channels = in_channels // 4
          self.conva = nn.Sequential(nn.Conv2d(in_channels, inter_channels, 3, padding=1, bias=False),
                                     BatchNorm2d(inter_channels), nn.ReLU(inplace=False))
          self.cca = CrissCrossAttention(inter_channels)
          self.convb = nn.Sequential(nn.Conv2d(inter_channels, inter_channels, 3, padding=1, bias=False),
                                     BatchNorm2d(inter_channels), nn.ReLU(inplace=False))
  
          self.bottleneck = nn.Sequential(
              nn.Conv2d(in_channels + inter_channels, out_channels, kernel_size=3, padding=1, dilation=1, bias=False),
              BatchNorm2d(out_channels), nn.ReLU(inplace=False),
              nn.Dropout2d(0.1),
              nn.Conv2d(512, num_classes, kernel_size=1, stride=1, padding=0, bias=True)
          )
  
      def forward(self, x, recurrence=2):
          output = self.conva(x)
          for i in range(recurrence):
              output = self.cca(output)
          output = self.convb(output)
  
          output = self.bottleneck(torch.cat([x, output], 1))
          return output
  
  
  class ResNet(nn.Module):
      def __init__(self, block, layers, num_classes, recurrence):
          self.inplanes = 128
          super(ResNet, self).__init__()
          self.conv1 = conv3x3(3, 64, stride=2)
          self.bn1 = BatchNorm2d(64)
          self.relu1 = nn.ReLU(inplace=False)
          self.conv2 = conv3x3(64, 64)
          self.bn2 = BatchNorm2d(64)
          self.relu2 = nn.ReLU(inplace=False)
          self.conv3 = conv3x3(64, 128)
          self.bn3 = BatchNorm2d(128)
          self.relu3 = nn.ReLU(inplace=False)
          self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
  
          self.relu = nn.ReLU(inplace=False)
          self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, ceil_mode=True)  # change
          self.layer1 = self._make_layer(block, 64, layers[0])
          self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
          self.layer3 = self._make_layer(block, 256, layers[2], stride=1, dilation=2)
          self.layer4 = self._make_layer(block, 512, layers[3], stride=1, dilation=4, multi_grid=(1, 1, 1))
          # self.layer5 = PSPModule(2048, 512)
          self.head = RCCAModule(2048, 512, num_classes)
  
          self.dsn = nn.Sequential(
              nn.Conv2d(1024, 512, kernel_size=3, stride=1, padding=1),
              BatchNorm2d(512), nn.ReLU(inplace=False),
              nn.Dropout2d(0.1),
              nn.Conv2d(512, num_classes, kernel_size=1, stride=1, padding=0, bias=True)
          )
          self.conv4 = nn.Conv2d(num_classes * 2, num_classes, kernel_size=1, stride=1, bias=False)
          # self.criterion = criterion
          self.recurrence = recurrence
  
      def _make_layer(self, block, planes, blocks, stride=1, dilation=1, multi_grid=1):
          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),
                  BatchNorm2d(planes * block.expansion, affine=affine_par))
  
          layers = []
          generate_multi_grid = lambda index, grids: grids[index % len(grids)] if isinstance(grids, tuple) else 1
          layers.append(block(self.inplanes, planes, stride, dilation=dilation, downsample=downsample,
                              multi_grid=generate_multi_grid(0, multi_grid)))
          self.inplanes = planes * block.expansion
          for i in range(1, blocks):
              layers.append(
                  block(self.inplanes, planes, dilation=dilation, multi_grid=generate_multi_grid(i, multi_grid)))
  
          return nn.Sequential(*layers)
  
      def forward(self, x, labels=None):
          size = (x.shape[2], x.shape[3])
          x = self.relu1(self.bn1(self.conv1(x)))
          x = self.relu2(self.bn2(self.conv2(x)))
          x = self.relu3(self.bn3(self.conv3(x)))
          x = self.maxpool(x)
          x = self.layer1(x)
          x = self.layer2(x)
          x = self.layer3(x)
          x_dsn = self.dsn(x)
          # print(x_dsn.shape)
          x = self.layer4(x)
          # print(x.shape)
          x = self.head(x, self.recurrence)
          # print(x.shape)
          outs = torch.cat([x, x_dsn], 1)
          # print(outs.shape)
          outs = self.conv4(outs)
          outs = nn.Upsample(size, mode='bilinear', align_corners=True)(outs)
          # print(outs)
          return outs
  
  
  def resnet152(num_classes=2, pretrained_model=None, recurrence=2, **kwargs):
      model = ResNet(Bottleneck, [3, 8, 36, 3], num_classes, recurrence)
      return model
  
  
  if __name__ == "__main__":
      device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
      model = resnet152()
      model = model.to(device)
      x = torch.rand((2, 3, 512, 512))
      x = x.to(device)
      print(x.shape)
      output = model(x)
      print(output.shape)
  
apis: torch.nn.MaxPool2d numpy.ceil torch.nn.Dropout2d torch.rand torch.nn.Upsample torch.cuda.is_available torch.nn.Conv2d torch.nn.Sequential torch.nn.ReLU torch.cat