class_docstr: The DeepLabV3PHead implementation based on PaddlePaddle.
  
  Args:
      num_classes (int): The unique number of target classes.
      backbone_indices (tuple): Two values in the tuple indicate the indices of output of backbone.
          the first index will be taken as a low-level feature in Decoder component;
          the second one will be taken as input of ASPP component.
          Usually backbone consists of four downsampling stage, and return an output of
          each stage. If we set it as (0, 3), it means taking feature map of the first
          stage in backbone as low-level feature used in Decoder, and feature map of the fourth
          stage as input of ASPP.
      backbone_channels (tuple): The same length with "backbone_indices". It indicates the channels of corresponding index.
      aspp_ratios (tuple): The dilation rates using in ASSP module.
      aspp_out_channels (int): The output channels of ASPP module.
      decoder_channels (int, optional): The channels of Decoder. 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.
      low_level_channels_projects (list, opitonal). The channels of low level features to output. Defualt: None.
class_docstr_tok: ["The","DeepLabV3PHead","implementation","based","on","PaddlePaddle",".","Args",":","num_classes","(","int",")",":","The","unique","number","of","target","classes",".","backbone_indices","(","tuple",")",":","Two","values","in","the","tuple","indicate","the","indices","of","output","of","backbone",".","the","first","index","will","be","taken","as","a","low-level","feature","in","Decoder","component",";","the","second","one","will","be","taken","as","input","of","ASPP","component",".","Usually","backbone","consists","of","four","downsampling","stage",",","and","return","an","output","of","each","stage",".","If","we","set","it","as","(","0",",","3",")",",","it","means","taking","feature","map","of","the","first","stage","in","backbone","as","low-level","feature","used","in","Decoder",",","and","feature","map","of","the","fourth","stage","as","input","of","ASPP",".","backbone_channels","(","tuple",")",":","The","same","length","with","``","backbone_indices","''",".","It","indicates","the","channels","of","corresponding","index",".","aspp_ratios","(","tuple",")",":","The","dilation","rates","using","in","ASSP","module",".","aspp_out_channels","(","int",")",":","The","output","channels","of","ASPP","module",".","decoder_channels","(","int",",","optional",")",":","The","channels","of","Decoder",".","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",".","low_level_channels_projects","(","list",",","opitonal",")",".","The","channels","of","low","level","features","to","output",".","Defualt",":","None","."]
code: def __init__(
      self,
      num_classes,
      backbone_indices,
      backbone_channels,
      aspp_ratios,
      aspp_out_channels,
      decoder_channels,
      align_corners,
      low_level_channels_projects,
      **kwargs,
  ):
      super().__init__()
      self.semantic_decoder = SinglePanopticDeepLabDecoder(
          backbone_indices=backbone_indices,
          backbone_channels=backbone_channels,
          aspp_ratios=aspp_ratios,
          aspp_out_channels=aspp_out_channels,
          decoder_channels=decoder_channels,
          align_corners=align_corners,
          low_level_channels_projects=low_level_channels_projects,
      )
      self.semantic_head = SinglePanopticDeepLabHead(
          num_classes=[num_classes],
          decoder_channels=decoder_channels,
          head_channels=decoder_channels,
          class_key=["semantic"],
      )
      self.instance_decoder = SinglePanopticDeepLabDecoder(
          backbone_indices=backbone_indices,
          backbone_channels=backbone_channels,
          aspp_ratios=aspp_ratios,
          aspp_out_channels=kwargs["instance_aspp_out_channels"],
          decoder_channels=kwargs["instance_decoder_channels"],
          align_corners=align_corners,
          low_level_channels_projects=kwargs[
              "instance_low_level_channels_projects"
          ],
      )
      self.instance_head = SinglePanopticDeepLabHead(
          num_classes=kwargs["instance_num_classes"],
          decoder_channels=kwargs["instance_decoder_channels"],
          head_channels=kwargs["instance_head_channels"],
          class_key=kwargs["instance_class_key"],
      )
  
imports: from collections import OrderedDict
  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 PanopticDeepLabHead object from inherited base classes: nn with example: obj = PanopticDeepLabHead(num_classes, backbone_indices, backbone_channels, aspp_ratios, aspp_out_channels, decoder_channels, align_corners, low_level_channels_projects)