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 forward(self, x):
      feat_list = self.backbone(x)
      logit_list = self.head(feat_list)
  
      seg_logit, body_logit, edge_logit = [
          F.interpolate(
              logit,
              paddle.shape(x)[2:],
              mode="bilinear",
              align_corners=self.align_corners,
          )
          for logit in logit_list
      ]
  
      if self.training:
          return [
              seg_logit,
              body_logit,
              edge_logit,
              (seg_logit, edge_logit),
          ]
      return [seg_logit]
  
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 2 for calling the DecoupledSegNet obj's underlying member method code with example usage: obj.forward(x) and returns: list, list