class_docstr: @article{cao2020learning,
          author = {Cao, Yi-Jun and Lin, Chuan and Li, Yong-Jie},
          year = {2020},
          title = {Learning Crisp Boundaries Using Deep Refinement Network and Adaptive Weighting Loss},
          journal = {IEEE Transactions on Multimedia},
          doi = {10.1109/TMM.2020.2987685}
  }
  
  dataset_name options: {'BSDS', 'NYUD'}
class_docstr_tok: ["@","article","{","cao2020learning",",","author","=","{","Cao",",","Yi-Jun","and","Lin",",","Chuan","and","Li",",","Yong-Jie","}",",","year","=","{","2020","}",",","title","=","{","Learning","Crisp","Boundaries","Using","Deep","Refinement","Network","and","Adaptive","Weighting","Loss","}",",","journal","=","{","IEEE","Transactions","on","Multimedia","}",",","doi","=","{","10.1109\/TMM.2020.2987685","}","}","dataset_name","options",":","{","'BSDS","'",",","'NYUD","'","}"]
code: def __init__(
      self, backbone_name="resnext101", dataset_name="BSDS", **_
  ):  # dataset_name options: {'BSDS', 'NYUD'}
      super(DRNet, self).__init__()
  
      self.encode = backbone(backbone_name, dataset=dataset_name)
      self.decode = decode(backbone=backbone_name, dataset=dataset_name)
  
imports: import torch.nn.functional
  from .utils import *
  from .backbone import *
label_desc: Use image node_id 1 to create a new DRNet object from inherited base classes: nn with example: obj = DRNet(backbone_name, dataset_name)