class_docstr: The Dual attention head.
  
  Args:
      num_classes (int): The unique number of target classes.
      in_channels (tuple): The number of input channels.
class_docstr_tok: ["The","Dual","attention","head",".","Args",":","num_classes","(","int",")",":","The","unique","number","of","target","classes",".","in_channels","(","tuple",")",":","The","number","of","input","channels","."]
code: def __init__(self, num_classes: int, in_channels: int):
      super().__init__()
      in_channels = in_channels[-1]
      inter_channels = in_channels // 4
  
      self.channel_conv = L.ConvBNReLU(in_channels, inter_channels, 3)
      self.position_conv = L.ConvBNReLU(in_channels, inter_channels, 3)
      self.pam = PAM(inter_channels)
      self.cam = CAM(inter_channels)
      self.conv1 = L.ConvBNReLU(inter_channels, inter_channels, 3)
      self.conv2 = L.ConvBNReLU(inter_channels, inter_channels, 3)
  
      self.aux_head = nn.Sequential(
          nn.Dropout2D(0.1), nn.Conv2D(in_channels, num_classes, 1)
      )
  
      self.aux_head_pam = nn.Sequential(
          nn.Dropout2D(0.1), nn.Conv2D(inter_channels, num_classes, 1)
      )
  
      self.aux_head_cam = nn.Sequential(
          nn.Dropout2D(0.1), nn.Conv2D(inter_channels, num_classes, 1)
      )
  
      self.cls_head = nn.Sequential(
          nn.Dropout2D(0.1), nn.Conv2D(inter_channels, num_classes, 1)
      )
  
imports: import os
  from typing import Union, List, Tuple
  import paddle
  from paddle import nn
  import paddle.nn.functional
  import numpy
  from paddlehub.module.module import moduleinfo
  import paddlehub.vision.segmentation_transforms
  from paddlehub.module.cv_module import ImageSegmentationModule
  from danet_resnet50_voc.resnet import ResNet50_vd
  import danet_resnet50_voc.layers
label_desc: Use image node_id 1 to create a new DAHead object from inherited base classes: nn with example: obj = DAHead(num_classes, in_channels)