class_docstr: The CCNet implementation based on PaddlePaddle.
  
  The original article refers to
  Zilong Huang, et al. "CCNet: Criss-Cross Attention for Semantic Segmentation"
  (https://arxiv.org/abs/1811.11721)
  
  Args:
      num_classes (int): The unique number of target classes.
      backbone (paddle.nn.Layer): Backbone network, currently support Resnet18_vd/Resnet34_vd/Resnet50_vd/Resnet101_vd.
      backbone_indices (tuple, list, optional): Two values in the tuple indicate the indices of output of backbone. Default: (2, 3).
      enable_auxiliary_loss (bool, optional): A bool value indicates whether adding auxiliary loss. Default: True.
      dropout_prob (float, optional): The probability of dropout. Default: 0.0.
      recurrence (int, optional): The number of recurrent operations. Defautl: 1.
      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.
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code: def __init__(
      self,
      num_classes,
      backbone,
      backbone_indices=(2, 3),
      enable_auxiliary_loss=True,
      dropout_prob=0.0,
      recurrence=1,
      align_corners=False,
      pretrained=None,
  ):
      super().__init__()
      self.enable_auxiliary_loss = enable_auxiliary_loss
      self.recurrence = recurrence
      self.align_corners = align_corners
  
      self.backbone = backbone
      self.backbone_indices = backbone_indices
      backbone_channels = [
          backbone.feat_channels[i] for i in backbone_indices
      ]
  
      if enable_auxiliary_loss:
          self.aux_head = layers.AuxLayer(
              backbone_channels[0],
              512,
              num_classes,
              dropout_prob=dropout_prob,
          )
      self.head = RCCAModule(
          backbone_channels[1],
          512,
          num_classes,
          dropout_prob=dropout_prob,
          recurrence=recurrence,
      )
      self.pretrained = pretrained
  
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 CCNet object from inherited base classes: nn with example: obj = CCNet(num_classes, backbone, backbone_indices, enable_auxiliary_loss, dropout_prob, recurrence, align_corners, pretrained)