code: def analyse(
      model,
      model_path,
      transforms,
      val_dataset,
      save_dir="output",
      aug_pred=False,
      scales=1.0,
      flip_horizontal=True,
      flip_vertical=False,
      is_slide=False,
      stride=None,
      crop_size=None,
      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.
          transforms (transform.Compose): Preprocess for input image.
          val_dataset (paddle.io.Dataset): Used to read and process validation datasets.
          save_dir (str, optional): The directory to save the visualized results. Default: 'output'.
          aug_pred (bool, optional): Whether to use mulit-scales and flip augment for predition. Default: False.
          scales (list|float, optional): Scales for augment. It is valid when `aug_pred` is True. Default: 1.0.
          flip_horizontal (bool, optional): Whether to use flip horizontally augment. It is valid when `aug_pred` is True. Default: True.
          flip_vertical (bool, optional): Whether to use flip vertically augment. It is valid when `aug_pred` is True. Default: False.
          is_slide (bool, optional): Whether to predict by sliding window. Default: False.
          stride (tuple|list, optional): The stride of sliding window, the first is width and the second is height.
              It should be provided when `is_slide` is True.
          crop_size (tuple|list, optional):  The crop size of sliding window, the first is width and the second is height.
              It should be provided when `is_slide` is True.
          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()
      file_list = val_dataset.file_list
      dataset_root = val_dataset.dataset_root
      nranks = paddle.distributed.get_world_size()
      local_rank = paddle.distributed.get_rank()
      if nranks > 1:
          paddle.distributed.init_parallel_env()
          file_list = partition_list(file_list, nranks)
      else:
          file_list = [file_list]
  
      added_saved_dir = os.path.join(save_dir, "added_prediction")
      pred_saved_dir = os.path.join(save_dir, "pseudo_color_prediction")
      results = {}
  
      logger.info("Start to predict...")
      progbar_pred = progbar.Progbar(
          target=len(file_list[0]), verbose=1
      )
      color_map = visualize.get_color_map_list(
          256, custom_color=custom_color
      )
      with paddle.no_grad():
          for i, (im_path, label_path) in enumerate(
              file_list[local_rank]
          ):
              data = preprocess(im_path, transforms)
  
              if aug_pred:
                  pred, _ = infer.aug_inference(
                      model,
                      data["img"],
                      trans_info=data["trans_info"],
                      scales=scales,
                      flip_horizontal=flip_horizontal,
                      flip_vertical=flip_vertical,
                      is_slide=is_slide,
                      stride=stride,
                      crop_size=crop_size,
                  )
              else:
                  pred, _ = infer.inference(
                      model,
                      data["img"],
                      trans_info=data["trans_info"],
                      is_slide=is_slide,
                      stride=stride,
                      crop_size=crop_size,
                  )
              pred = paddle.squeeze(pred)
  
              # calculate miou for the image
              label = paddle.to_tensor(
                  np.asarray(Image.open(label_path)), dtype=pred.dtype
              )
              (
                  intersect_area,
                  pred_area,
                  label_area,
              ) = metrics.calculate_area(
                  pred, label, val_dataset.num_classes
              )
              class_iou_per_img, miou_per_img = metrics.mean_iou(
                  intersect_area, pred_area, label_area
              )
              results[im_path] = {
                  "miou": miou_per_img,
                  "class_iou": list(class_iou_per_img),
                  "label_path": label_path,
              }
  
              pred = pred.numpy().astype("uint8")
              # get the saved name
              if dataset_root is not None:
                  im_file = im_path.replace(dataset_root, "")
              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)
              results[im_path]["added_path"] = added_image_path
  
              # 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)
              results[im_path]["prediction_path"] = pred_saved_path
  
              progbar_pred.update(i + 1)
      if nranks > 1:
          results_list = []
          paddle.distributed.all_gather_object(results_list, results)
          if local_rank == 0:
              results = {}
              for d in results_list:
                  results.update(d)
      if local_rank == 0:
          with open(
              os.path.join(save_dir, "analysis_results.json"), "w"
          ) as f:
              json.dump(results, f, indent=4)
  
      logger.info(
          "Samples analysis finished, the results are save in {}.".format(
              save_dir
          )
      )
  
file_path: PaddleSeg/paddleseg/core/analyse.py
imports: import os
  import json
  import math
  from unittest import result
  import cv2
  import numpy
  import paddle
  from PIL import Image
  from paddleseg import utils
  from paddleseg.core import infer
  from paddleseg.utils import logger, progbar, visualize, metrics
label_desc: Use image node_id 4 for calling a global function with example usage: analyse(model, model_path, transforms, val_dataset, save_dir, aug_pred, scales, flip_horizontal, flip_vertical, is_slide, stride, crop_size, custom_color) without return types