code: def predict(
      model,
      model_path,
      transforms,
      postprocessor,
      image_list,
      label_divisor,
      ignore_index,
      colormap=None,
      image_dir=None,
      save_dir="output",
  ):
      """
      Predict the panoptic segmentation results given the input of `image_list`.
  
      Args:
          model (nn.Layer): A panoptic segmentation model.
          model_path (str): The path of the pretrained model.
          transforms (paddleseg.transforms.Compose): The pipeline for data preprocessing of the input image.
          postprocessor (paddlepanseg.postprocessors.Postprocessor): Used to postprocess model output.
          image_list (list): A list of image path to be predicted.
          label_divisor (int): Used for conversion between semantic IDs and panoptic IDs.
          ignore_index (int): The class ID to be ignored. Default: 255.
          color_map (list, optional): The colormap used for visualization. Default: None.
          image_dir (str, optional): The root directory of the images to predict. Default: None.
          save_dir (str, optional): The directory for saving the predicted results. Default: 'output'.
      """
      paddleseg.utils.utils.load_entire_model(model, model_path)
      model.eval()
      nranks = paddle.distributed.get_world_size()
      local_rank = paddle.distributed.get_rank()
      if nranks > 1:
          img_lists = partition_list(image_list, nranks)
      else:
          img_lists = [image_list]
  
      logger.info("Start to predict...")
      progbar_pred = progbar.Progbar(
          target=len(img_lists[0]), verbose=1
      )
      with paddle.no_grad():
          for i, im_path in enumerate(img_lists[local_rank]):
              data = build_info_dict(
                  _type_="sample", img=im_path, img_path=im_path
              )
              data = transforms(data)
              # XXX: For efficiency only convert img to tensor.
              data["img"] = paddle.to_tensor(data["img"]).unsqueeze(0)
  
              pp_out = infer.inference(
                  model=model, data=data, postprocessor=postprocessor
              )
              # NOTE: We do not perform id conversion before visualization
              pan_pred = pp_out["pan_pred"][0, 0].numpy()
              sem_pred = pp_out["sem_pred"][0, 0].numpy()
              sem_vis = visualize_semantic(sem_pred, colormap)
              ins_vis = visualize_instance(pan_pred, ignore_ins_id=0)
              pan_vis = visualize_panoptic(
                  pan_pred,
                  label_divisor=label_divisor,
                  colormap=colormap,
                  ignore_index=ignore_index,
              )
  
              Image.fromarray(sem_vis).convert("RGB").save(
                  osp.join(
                      save_dir,
                      get_save_name(im_path, image_dir, "_sem.png"),
                  )
              )
              Image.fromarray(ins_vis).convert("RGB").save(
                  osp.join(
                      save_dir,
                      get_save_name(im_path, image_dir, "_ins.png"),
                  )
              )
              Image.fromarray(pan_vis).convert("RGB").save(
                  osp.join(
                      save_dir,
                      get_save_name(im_path, image_dir, "_pan.png"),
                  )
              )
  
              progbar_pred.update(i + 1)
  
file_path: PaddleSeg/contrib/PanopticSeg/paddlepanseg/core/predict.py
imports: import os
  import os.path
  import math
  import numpy
  import paddle
  import paddleseg
  from paddleseg.utils import logger, progbar
  from PIL import Image
  from paddlepanseg.utils.visualize import visualize_instance, visualize_semantic, visualize_panoptic
  from paddlepanseg.cvlibs import build_info_dict
  from .None import infer
label_desc: Use image node_id 4 for calling a global function with example usage: predict(model, model_path, transforms, postprocessor, image_list, label_divisor, ignore_index, colormap, image_dir, save_dir) without return types