class_docstr: The DeepLabV3 implementation based on PaddlePaddle.
  
  The original article refers to
   Liang-Chieh Chen, et, al. "Rethinking Atrous Convolution for Semantic Image Segmentation"
   (https://arxiv.org/pdf/1706.05587.pdf).
  
  Args:
      Please Refer to DeepLabV3P above.
class_docstr_tok: ["The","DeepLabV3","implementation","based","on","PaddlePaddle",".","The","original","article","refers","to","Liang-Chieh","Chen",",","et",",","al",".","``","Rethinking","Atrous","Convolution","for","Semantic","Image","Segmentation","''","(","https",":","\/\/arxiv.org\/pdf\/1706.05587.pdf",")",".","Args",":","Please","Refer","to","DeepLabV3P","above","."]
code: def __init__(
      self,
      num_classes,
      backbone,
      backbone_indices=(3,),
      aspp_ratios=(1, 6, 12, 18),
      aspp_out_channels=256,
      align_corners=False,
      pretrained=None,
  ):
      super().__init__()
  
      self.backbone = backbone
      backbone_channels = [
          backbone.feat_channels[i] for i in backbone_indices
      ]
  
      self.head = DeepLabV3Head(
          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 paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager
  from paddleseg.models import layers
  from paddleseg.utils import utils
label_desc: Use image node_id 1 to create a new DeepLabV3 object from inherited base classes: nn with example: obj = DeepLabV3(num_classes, backbone, backbone_indices, aspp_ratios, aspp_out_channels, align_corners, pretrained)