class_docstr: The DeepLabV2 implementation based on PaddlePaddle.
  
  The original article refers to:
      Chen, L. C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2017). Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence, 40(4), 834-848.
  
  Args:
      backbone (paddle.nn.Layer): Backbone network, currently support Resnet101.
      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.
      data_format(str, optional): Data format that specifies the layout of input. It can be "NCHW" or "NHWC". Default: "NCHW".
class_docstr_tok: ["The","DeepLabV2","implementation","based","on","PaddlePaddle",".","The","original","article","refers","to",":","Chen",",","L.","C.",",","Papandreou",",","G.",",","Kokkinos",",","I.",",","Murphy",",","K.",",","&","Yuille",",","A.","L.","(","2017",")",".","Deeplab",":","Semantic","image","segmentation","with","deep","convolutional","nets",",","atrous","convolution",",","and","fully","connected","crfs",".","IEEE","transactions","on","pattern","analysis","and","machine","intelligence",",","40","(","4",")",",","834-848",".","Args",":","backbone","(","paddle.nn.Layer",")",":","Backbone","network",",","currently","support","Resnet101",".","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",".","data_format","(","str",",","optional",")",":","Data","format","that","specifies","the","layout","of","input",".","It","can","be","``","NCHW","''","or","``","NHWC","''",".","Default",":","``","NCHW","''","."]
code: def __init__(
      self,
      backbone,
      align_corners=False,
      pretrained=None,
      shape_stream=False,
  ):
      super().__init__()
  
      self.backbone = backbone
      self.shape_stream = shape_stream
      self.head = edge_branch(
          inplanes=(64, 2048),
          out_channels=1024,
          dilation_series=[6, 12, 18, 24],
          padding_series=[6, 12, 18, 24],
          num_classes=2,
      )
  
      self.fusion = ClassifierModule(
          21, [6, 18, 30, 42], [6, 18, 30, 42], 19
      )
      self.align_corners = align_corners
      self.pretrained = pretrained
      self.init_weight()
  
file_path: PaddleSeg/contrib/DomainAdaptation/models/deeplabv2.py
imports: import os
  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.utils import utils, logger
  from .gscnn import GSCNNHead
  from .backbones.resnet import ClassifierModule
label_desc: Use image node_id 1 to create a new DeepLabV2 object from inherited base classes: nn with example: obj = DeepLabV2(backbone, align_corners, pretrained, shape_stream)