class_docstr: The DecoupledSegNet implementation based on PaddlePaddle.
  
  The original article refers to
  Xiangtai Li, et, al. "Improving Semantic Segmentation via Decoupled Body and Edge Supervision"
  (https://arxiv.org/pdf/2007.10035.pdf)
  
  Args:
      num_classes (int): The unique number of target classes.
      backbone (paddle.nn.Layer): Backbone network, currently support Resnet50_vd/Resnet101_vd.
      backbone_indices (tuple, optional): Two values in the tuple indicate the indices of output of backbone.
         Default: (0, 3).
      aspp_ratios (tuple, optional): The dilation rate using in ASSP module.
          If output_stride=16, aspp_ratios should be set as (1, 6, 12, 18).
          If output_stride=8, aspp_ratios is (1, 12, 24, 36).
          Default: (1, 6, 12, 18).
      aspp_out_channels (int, optional): The output channels of ASPP module. Default: 256.
      align_corners (bool, optional): An argument of F.interpolate. It should be set to False when the feature size is even,
          e.g. 1024x512, otherwise it is True, e.g. 769x769. Default: False.
      pretrained (str, optional): The path or url of pretrained model. Default: None.
code: def __init__(
      self,
      num_classes,
      backbone,
      backbone_indices=(0, 3),
      aspp_ratios=(1, 6, 12, 18),
      aspp_out_channels=256,
      align_corners=False,
      pretrained=None,
  ):
      super().__init__()
      self.backbone = backbone
      backbone_channels = self.backbone.feat_channels
      self.head = DecoupledSegNetHead(
          num_classes,
          backbone_indices,
          backbone_channels,
          aspp_ratios,
          aspp_out_channels,
          align_corners,
      )
      self.align_corners = align_corners
      self.pretrained = pretrained
      self.init_weight()
  
imports: import cv2
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager
  from paddleseg.models import layers
  from paddleseg.models.backbones import resnet_vd
  from paddleseg.models import deeplab
  from paddleseg.utils import utils
label_desc: Use image node_id 1 to create a new DecoupledSegNet object from inherited base classes: nn with example: obj = DecoupledSegNet(num_classes, backbone, backbone_indices, aspp_ratios, aspp_out_channels, align_corners, pretrained)