class_docstr: Decoders Matter for Semantic Segmentation
  
  Reference:
      Zhi Tian, Tong He, Chunhua Shen, and Youliang Yan.
      "Decoders Matter for Semantic Segmentation:
      Data-Dependent Decoding Enables Flexible Feature Aggregation." CVPR, 2019
code: def __init__(
      self,
      num_classes,
      pretrained=True,
      backbone="resnet101",
      aux=False,
      **kwargs,
  ):
      super(DUNet, self).__init__(
          num_classes,
          pretrained=pretrained,
          aux=aux,
          backbone=backbone,
          **kwargs,
      )
      self.head = _DUHead(2144, **kwargs)
      self.dupsample = DUpsampling(
          256, num_classes, scale_factor=8, **kwargs
      )
      if aux:
          self.auxlayer = _FCNHead(1024, 256, **kwargs)
          self.aux_dupsample = DUpsampling(
              256, num_classes, scale_factor=8, **kwargs
          )
  
      self.__setattr__(
          "exclusive",
          ["dupsample", "head", "auxlayer", "aux_dupsample"]
          if aux
          else ["dupsample", "head"],
      )
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from pywick.models.segmentation.da_basenets.segbase import SegBaseModel
  from pywick.models.segmentation.da_basenets.fcn import _FCNHead
label_desc: Use image node_id 1 to create a new DUNet object from inherited base classes: SegBaseModel with example: obj = DUNet(num_classes, pretrained, backbone, aux)