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 forward(self, x):
      feats, feats_shape = self.backbone(x)  # [1, 1024, 768]
      B, _, Hp, Wp = feats_shape
  
      feats = [feats[i] for i in self.backbone_indices]
  
      for i, feat in enumerate(feats):
          feats[i] = paddle.reshape(
              paddle.transpose(self.norm(feat), perm=[0, 2, 1]),
              shape=[B, -1, Hp, Wp],
          )
  
      ops = [self.fpn1, self.fpn2, self.fpn3, self.fpn4]
      for i in range(len(feats)):
          feats[i] = ops[i](feats[i])
  
      logit_list = self.decode_head(feats)
      logit_list = [
          F.interpolate(
              logit,
              paddle.shape(x)[2:],
              mode="bilinear",
              align_corners=False,
          )
          for logit in logit_list
      ]
      return logit_list
  
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 3 for calling the UPerNetCAE obj's underlying member method code with example usage: obj.forward(x) and returns: logit_list