text: import math
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  import torch.utils.model_zoo as model_zoo
  
  
  class Bottleneck(nn.Module):
      expansion = 4
  
      def __init__(self, inplanes, planes, stride=1, rate=1, downsample=None):
          super(Bottleneck, self).__init__()
          self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
          self.bn1 = nn.BatchNorm2d(planes)
          self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                                 dilation=rate, padding=rate, bias=False)
          self.bn2 = nn.BatchNorm2d(planes)
          self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
          self.bn3 = nn.BatchNorm2d(planes * 4)
          self.relu = nn.ReLU(inplace=True)
          self.downsample = downsample
          self.stride = stride
          self.rate = rate
  
      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, nInputChannels, block, layers, os=16, pretrained=False):
          self.inplanes = 64
          super(ResNet, self).__init__()
          if os == 16:
              strides = [1, 2, 2, 1]
              rates = [1, 1, 1, 2]
              blocks = [1, 2, 4]
          elif os == 8:
              strides = [1, 2, 1, 1]
              rates = [1, 1, 2, 2]
              blocks = [1, 2, 1]
          else:
              raise NotImplementedError
  
          # Modules
          self.conv1 = nn.Conv2d(nInputChannels, 64, kernel_size=7, stride=2, padding=3,
                                 bias=False)
          self.bn1 = nn.BatchNorm2d(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], rate=rates[0])
          self.layer2 = self._make_layer(block, 128, layers[1], stride=strides[1], rate=rates[1])
          self.layer3 = self._make_layer(block, 256, layers[2], stride=strides[2], rate=rates[2])
          self.layer4 = self._make_MG_unit(block, 512, blocks=blocks, stride=strides[3], rate=rates[3])
  
          self._init_weight()
  
          if pretrained:
              self._load_pretrained_model()
  
      def _make_layer(self, block, planes, blocks, stride=1, rate=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),
                  nn.BatchNorm2d(planes * block.expansion),
              )
  
          layers = []
          layers.append(block(self.inplanes, planes, stride, rate, downsample))
          self.inplanes = planes * block.expansion
          for i in range(1, blocks):
              layers.append(block(self.inplanes, planes))
  
          return nn.Sequential(*layers)
  
      def _make_MG_unit(self, block, planes, blocks=[1, 2, 4], stride=1, rate=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),
                  nn.BatchNorm2d(planes * block.expansion),
              )
  
          layers = []
          layers.append(block(self.inplanes, planes, stride, rate=blocks[0] * rate, downsample=downsample))
          self.inplanes = planes * block.expansion
          for i in range(1, len(blocks)):
              layers.append(block(self.inplanes, planes, stride=1, rate=blocks[i] * rate))
  
          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))
                  torch.nn.init.kaiming_normal_(m.weight)
              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(nInputChannels=3, os=16, pretrained=False):
      model = ResNet(nInputChannels, Bottleneck, [3, 4, 23, 3], os, pretrained=pretrained)
      return model
  
  
  class ASPP_module(nn.Module):
      def __init__(self, inplanes, planes, rate):
          super(ASPP_module, self).__init__()
          if rate == 1:
              kernel_size = 1
              padding = 0
          else:
              kernel_size = 3
              padding = rate
          self.atrous_convolution = nn.Conv2d(inplanes, planes, kernel_size=kernel_size,
                                              stride=1, padding=padding, dilation=rate, bias=False)
          self.bn = nn.BatchNorm2d(planes)
          self.relu = nn.ReLU()
  
          self._init_weight()
  
      def forward(self, x):
          x = self.atrous_convolution(x)
          x = self.bn(x)
  
          return self.relu(x)
  
      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))
                  torch.nn.init.kaiming_normal_(m.weight)
              elif isinstance(m, nn.BatchNorm2d):
                  m.weight.data.fill_(1)
                  m.bias.data.zero_()
  
  
  class DeepLabv3_plus(nn.Module):
      def __init__(self, nInputChannels=3, n_classes=21, os=16, pretrained=False, _print=True):
          if _print:
              print("Constructing DeepLabv3+ model...")
              print("Number of classes: {}".format(n_classes))
              print("Output stride: {}".format(os))
              print("Number of Input Channels: {}".format(nInputChannels))
          super(DeepLabv3_plus, self).__init__()
  
          # Atrous Conv
          self.resnet_features = ResNet101(nInputChannels, os, pretrained=pretrained)
  
          # ASPP
          if os == 16:
              rates = [1, 6, 12, 18]
          elif os == 8:
              rates = [1, 12, 24, 36]
          else:
              raise NotImplementedError
  
          self.aspp1 = ASPP_module(2048, 256, rate=rates[0])
          self.aspp2 = ASPP_module(2048, 256, rate=rates[1])
          self.aspp3 = ASPP_module(2048, 256, rate=rates[2])
          self.aspp4 = ASPP_module(2048, 256, rate=rates[3])
  
          self.relu = nn.ReLU()
  
          self.global_avg_pool = nn.Sequential(nn.AdaptiveAvgPool2d((1, 1)),
                                               nn.Conv2d(2048, 256, 1, stride=1, bias=False),
                                               # nn.BatchNorm2d(256),
                                               nn.ReLU())
  
          self.conv1 = nn.Conv2d(1280, 256, 1, bias=False)
          self.bn1 = nn.BatchNorm2d(256)
  
          # adopt [1x1, 48] for channel reduction.
          self.conv2 = nn.Conv2d(256, 48, 1, bias=False)
          self.bn2 = nn.BatchNorm2d(48)
  
          self.last_conv = nn.Sequential(nn.Conv2d(304, 256, kernel_size=3, stride=1, padding=1, bias=False),
                                         nn.BatchNorm2d(256),
                                         nn.ReLU(),
                                         nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False),
                                         nn.BatchNorm2d(256),
                                         nn.ReLU(),
                                         nn.Conv2d(256, n_classes, kernel_size=1, stride=1))
  
      def forward(self, input):
          x, low_level_features = self.resnet_features(input)
  
          x1 = self.aspp1(x)
          x2 = self.aspp2(x)
          x3 = self.aspp3(x)
          x4 = self.aspp4(x)
          x5 = self.global_avg_pool(x)
          x5 = F.upsample(x5, size=x4.size()[2:], mode='bilinear', align_corners=True)
  
          x = torch.cat((x1, x2, x3, x4, x5), dim=1)
  
          x = self.conv1(x)
          x = self.bn1(x)
          x = self.relu(x)
          x = F.upsample(x, size=(int(math.ceil(input.size()[-2] / 4)),
                                  int(math.ceil(input.size()[-1] / 4))), mode='bilinear', align_corners=True)
  
          low_level_features = self.conv2(low_level_features)
          low_level_features = self.bn2(low_level_features)
          low_level_features = self.relu(low_level_features)
  
          x = torch.cat((x, low_level_features), dim=1)
          x = self.last_conv(x)
          x = F.upsample(x, size=input.size()[2:], mode='bilinear', align_corners=True)
  
          return x
  
      def freeze_bn(self):
          for m in self.modules():
              if isinstance(m, nn.BatchNorm2d):
                  m.eval()
  
      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))
                  torch.nn.init.kaiming_normal_(m.weight)
              elif isinstance(m, nn.BatchNorm2d):
                  m.weight.data.fill_(1)
                  m.bias.data.zero_()
  
  
  def get_1x_lr_params(model):
      """
      This generator returns all the parameters of the net except for
      the last classification layer. Note that for each batchnorm layer,
      requires_grad is set to False in deeplab_resnet.py, therefore this function does not return
      any batchnorm parameter
      """
      b = [model.resnet_features]
      for i in range(len(b)):
          for k in b[i].parameters():
              if k.requires_grad:
                  yield k
  
  
  def get_10x_lr_params(model):
      """
      This generator returns all the parameters for the last layer of the net,
      which does the classification of pixel into classes
      """
      b = [model.aspp1, model.aspp2, model.aspp3, model.aspp4, model.conv1, model.conv2, model.last_conv]
      for j in range(len(b)):
          for k in b[j].parameters():
              if k.requires_grad:
                  yield k
  
  
  if __name__ == "__main__":
      model = DeepLabv3_plus(nInputChannels=1, n_classes=1, os=16, pretrained=False, _print=True)
      image = torch.randn(1, 1, 512, 512)
      with torch.no_grad():
          output = model(image)
      print(output.size())
  