class_docstr: The PanopticDeeplab implementation based on PaddlePaddle.
  
  The original article refers to
   Bowen Cheng, et, al. "Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation"
   (https://arxiv.org/abs/1911.10194)
  
  Args:
      num_classes (int): The number of target semantic classes.
      backbone (paddle.nn.Layer): The backbone network. Currently supports ResNet50-vd/ResNet101-vd/Xception65.
      backbone_indices (tuple|None, optional): The indices of backbone output feature maps to use. 
          Default: [2, 1, 0, 3].
      aspp_ratios (tuple|None, optional): The dilation rate using in the ASSP module.
          If output_stride=16, `aspp_ratios` should be set as [1, 6, 12, 18].
          If output_stride=8, `aspp_ratios` should be [1, 12, 24, 36].
          Default: [1, 6, 12, 18].
      aspp_out_channels (int, optional): The output channels of the ASPP module. Default: 256.
      decoder_channels (int, optional): The channels of the decoder. Default: 256.
      low_level_channels_projects (list|None, opitonal): The channels of low-level features to extract. Defualt: None.
      align_corners (bool, optional): See `align_corners` of `paddle.nn.functional.interpolate`. Default: False.
      pretrained (str|None, optional): The path or url of pretrained model. If None, no pretrained model will be used.
          Default: None.
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code: def forward(self, x):
      feat_list = self.backbone(x)
      logit_dict = self.head(feat_list)
      results = self._upsample_predictions(logit_dict, x.shape[-2:])
  
      res = build_info_dict(
          _type_="net_out",
          sem_out=results["semantic"],
          center=results["center"],
          offset=results["offset"],
      )
      if not self.training:
          res["map_fields"].extend(["center", "offset"])
  
      return res
  
imports: from collections import OrderedDict
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.models import layers
  from paddleseg.utils import utils
  from paddlepanseg.cvlibs import manager, build_info_dict
label_desc: Use image node_id 3 for calling the PanopticDeepLab obj's underlying member method code with example usage: obj.forward(x) and returns: res