class_docstr: The UPerNet  with CAE as backbone implementation with PaddlePaddle.
  
  The original paper refers to Unified Perceptual Parsing for Scene Understanding.
  (https://arxiv.org/abs/1807.10221)
  
  Args:
      num_classes (int): The unique number of target classes.
      backbone (Paddle.nn.Layer): Backbone network, currently support Resnet50/101.
      backbone_indices (tuple): Four values in the tuple indicate the indices of output of backbone.
      channels(int): Hidden layer channels of upernet head.
      fpn_channels(int): The fpn_channels of upernet head.
      head_channels(int): The inplane of upernet head.
      channels_fpn(int): The channels_fpn of upernet head.
      enable_auxiliary_loss (bool, optional): A bool value indicates whether adding auxiliary loss. Default: False.
      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.
      dropout_ratio(float): The dropout ratio of upernet head.
      pretrained (str, optional): The path or url of pretrained model. Default: None.
code: def __init__(
      self,
      num_classes,
      backbone,
      backbone_indices,
      channels,
      fpn_channels,
      head_channels,
      channels_fpn,
      enable_auxiliary_loss=True,
      align_corners=True,
      dropout_ratio=0.1,
      pretrained=None,
  ):
      super(UPerNetCAE, self).__init__()
      self._init_fpn(embed_dim=768, patch_size=16)
      self.backbone = backbone
      self.backbone_indices = backbone_indices
      self.align_corners = align_corners
      self.pretrained = pretrained
      self.enable_auxiliary_loss = enable_auxiliary_loss
  
      self.fpn_channels = fpn_channels
      self.head_channels = head_channels
      self.channels_fpn = channels_fpn
  
      self.decode_head = UPerNetHead(
          inplane=head_channels,
          num_class=num_classes,
          channels_fpn=channels_fpn,
          dropout_ratio=dropout_ratio,
          channels=channels,
          fpn_channels=fpn_channels,
          enable_auxiliary_loss=self.enable_auxiliary_loss,
      )
      self.init_weight()
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.utils import utils
  from paddleseg.cvlibs import manager
  from paddleseg.models import layers
  from paddleseg.models.backbones.transformer_utils import Identity
label_desc: Use image node_id 1 to create a new UPerNetCAE object from inherited base classes: nn with example: obj = UPerNetCAE(num_classes, backbone, backbone_indices, channels, fpn_channels, head_channels, channels_fpn, enable_auxiliary_loss, align_corners, dropout_ratio, pretrained)