code: def predict(
      model,
      model_path,
      val_dataset,
      image_list,
      image_dir=None,
      save_dir="output",
      custom_color=None,
  ):
      """
      predict and visualize the image_list.
  
      Args:
          model (nn.Layer): Used to predict for input image.
          model_path (str): The path of pretrained model.
          val_dataset (paddle.io.Dataset): Used to read validation information.
          image_list (list): A list of image path to be predicted.
          image_dir (str, optional): The root directory of the images predicted. Default: None.
          save_dir (str, optional): The directory to save the visualized results. Default: 'output'.
          custom_color (list, optional): Save images with a custom color map. Default: None, use paddleseg's default color map.
  
      """
      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]
  
      added_saved_dir = os.path.join(save_dir, "added_prediction")
      pred_saved_dir = os.path.join(save_dir, "pseudo_color_prediction")
      transforms = val_dataset.transforms
      cut_height = val_dataset.cut_height
      postprocessor = tusimple_processor.TusimpleProcessor(
          num_classes=val_dataset.num_classes,
          cut_height=cut_height,
          save_dir=save_dir,
      )
  
      logger.info("Start to predict...")
      progbar_pred = progbar.Progbar(
          target=len(img_lists[0]), verbose=1
      )
      color_map = visualize.get_color_map_list(
          256, custom_color=custom_color
      )
      with paddle.no_grad():
          for i, im_path in enumerate(img_lists[local_rank]):
              im = cv2.imread(im_path)
              ori_shape = im.shape[:2]
              im, _ = transforms(im)
              im = im[np.newaxis, ...]
              im = paddle.to_tensor(im)
  
              pred = infer.inference(
                  model,
                  im,
                  ori_shape=ori_shape,
                  transforms=transforms.transforms,
              )
  
              # get lane points
              postprocessor.predict(pred[1], im_path)
  
              pred = paddle.squeeze(pred[0])
              pred = pred.numpy().astype("uint8")
  
              # get the saved name
              if image_dir is not None:
                  im_file = im_path.replace(image_dir, "")
              else:
                  im_file = os.path.basename(im_path)
              if im_file[0] == "/" or im_file[0] == "\\":
                  im_file = im_file[1:]
  
              # save added image
              added_image = utils.visualize.visualize(
                  im_path, pred, color_map, weight=0.6
              )
              added_image_path = os.path.join(added_saved_dir, im_file)
              mkdir(added_image_path)
              cv2.imwrite(added_image_path, added_image)
  
              # save pseudo color prediction
              pred_mask = utils.visualize.get_pseudo_color_map(
                  pred, color_map
              )
              pred_saved_path = os.path.join(
                  pred_saved_dir, os.path.splitext(im_file)[0] + ".png"
              )
              mkdir(pred_saved_path)
              pred_mask.save(pred_saved_path)
  
              # pred_im = utils.visualize(im_path, pred, weight=0.0)
              # pred_saved_path = os.path.join(pred_saved_dir, im_file)
              # mkdir(pred_saved_path)
              # cv2.imwrite(pred_saved_path, pred_im)
  
              progbar_pred.update(i + 1)
  
file_path: PaddleSeg/contrib/LaneSeg/core/predict.py
imports: import os
  import math
  import cv2
  import numpy
  import paddle
  from paddleseg import utils
  from .None import infer
  from third_party import tusimple_processor
  from paddleseg.utils import logger, progbar, visualize
label_desc: Use image node_id 3 for calling a global function with example usage: predict(model, model_path, val_dataset, image_list, image_dir, save_dir, custom_color) without return types