content: import math
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from modeling.sync_batchnorm.batchnorm import SynchronizedBatchNorm2d
  
  class _ASPPModule(nn.Module):
      def __init__(self, inplanes, planes, kernel_size, padding, dilation, BatchNorm):
          super(_ASPPModule, self).__init__()
          self.atrous_conv = nn.Conv2d(inplanes, planes, kernel_size=kernel_size,
                                              stride=1, padding=padding, dilation=dilation, bias=False)
          self.bn = BatchNorm(planes)
          self.relu = nn.ReLU()
  
          self._init_weight()
  
      def forward(self, x):
          x = self.atrous_conv(x)
          x = self.bn(x)
  
          return self.relu(x)
  
      def _init_weight(self):
          for m in self.modules():
              if isinstance(m, nn.Conv2d):
                  torch.nn.init.kaiming_normal_(m.weight)
              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_()
  
  class ASPP(nn.Module):
      def __init__(self, backbone, output_stride, BatchNorm):
          super(ASPP, self).__init__()
          if backbone == 'drn':
              inplanes = 512
          elif backbone == 'mobilenet':
              inplanes = 320
          else:
              inplanes = 2048
          if output_stride == 16:
              dilations = [1, 6, 12, 18]
          elif output_stride == 8:
              dilations = [1, 12, 24, 36]
          else:
              raise NotImplementedError
  
          self.aspp1 = _ASPPModule(inplanes, 256, 1, padding=0, dilation=dilations[0], BatchNorm=BatchNorm)
          self.aspp2 = _ASPPModule(inplanes, 256, 3, padding=dilations[1], dilation=dilations[1], BatchNorm=BatchNorm)
          self.aspp3 = _ASPPModule(inplanes, 256, 3, padding=dilations[2], dilation=dilations[2], BatchNorm=BatchNorm)
          self.aspp4 = _ASPPModule(inplanes, 256, 3, padding=dilations[3], dilation=dilations[3], BatchNorm=BatchNorm)
  
          self.global_avg_pool = nn.Sequential(nn.AdaptiveAvgPool2d((1, 1)),
                                               nn.Conv2d(inplanes, 256, 1, stride=1, bias=False),
                                               BatchNorm(256),
                                               nn.ReLU())
          self.conv1 = nn.Conv2d(1280, 256, 1, bias=False)
          self.bn1 = BatchNorm(256)
          self.relu = nn.ReLU()
          self.dropout = nn.Dropout(0.5)
          self._init_weight()
  
      def forward(self, x):
          x1 = self.aspp1(x)
          x2 = self.aspp2(x)
          x3 = self.aspp3(x)
          x4 = self.aspp4(x)
          x5 = self.global_avg_pool(x)
          x5 = F.interpolate(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)
  
          return self.dropout(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, 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 build_aspp(backbone, output_stride, BatchNorm):
      return ASPP(backbone, output_stride, BatchNorm)