class_docstr: You need to to download and convert the [India Driving Dataset](https://idd.insaan.iiit.ac.in/evaluation/autonue21/#bm5)
  following the instructions of Segmentation Track. IDD_Dectection dataset also need for pseudo-labeling.
  
  And then, you need to organize data following the below structure.
  
  IDD_Segmentation
  |
  |--leftImg8bit
  |  |--train
  |  |--val
  |  |--test
  |
  |--gtFine
  |  |--train
  |  |--val
  |  |--test
  
  Args:
      transforms (list): Transforms for image.
      dataset_root (str): Cityscapes dataset directory.
      mode (str, optional): Which part of dataset to use. it is one of ('train', 'val', 'test'). Default: 'train'.
      coarse_multiple (float|int, optional): Multiple of the amount of coarse data relative to fine data. Default: 1
      add_val (bool, optional): Whether to add val set in training. Default: False
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code: def __init__(
      self,
      transforms,
      dataset_root,
      mode="train",
      coarse_multiple=1,
      add_val=False,
  ):
      self.dataset_root = dataset_root
      self.transforms = Compose(transforms)
      self.file_list = list()
      mode = mode.lower()
      self.mode = mode
      self.num_classes = 26
      self.ignore_index = 255
      self.coarse_multiple = coarse_multiple
  
      if mode not in ["train", "val", "test"]:
          raise ValueError(
              "mode should be 'train', 'val' or 'test', but got {}.".format(
                  mode
              )
          )
  
      if self.transforms is None:
          raise ValueError("`transforms` is necessary, but it is None.")
  
      img_dir = os.path.join(self.dataset_root, "leftImg8bit")
      label_dir = os.path.join(self.dataset_root, "gtFine")
      if (
          self.dataset_root is None
          or not os.path.isdir(self.dataset_root)
          or not os.path.isdir(img_dir)
          or not os.path.isdir(label_dir)
      ):
          raise ValueError(
              "The dataset is not Found or the folder structure is nonconfoumance."
          )
  
      label_files = sorted(
          glob.glob(
              os.path.join(
                  label_dir, mode, "*", "*_gtFine_labellevel3Ids.png"
              )
          )
      )
      img_files = sorted(
          glob.glob(os.path.join(img_dir, mode, "*", "*_leftImg8bit.*"))
      )
  
      self.file_list = [
          [img_path, label_path]
          for img_path, label_path in zip(img_files, label_files)
      ]
      # for ii in range(len(self.file_list)):
      #     print(self.file_list[ii])
      # print(len(self.file_list))
      self.num_files = len(self.file_list)
      self.total_num_files = self.num_files
  
      if mode == "train":
          # whether to add val set in training
          if add_val:
              label_files = sorted(
                  glob.glob(
                      os.path.join(
                          label_dir,
                          "val",
                          "*",
                          "*_gtFine_labellevel3Ids.png",
                      )
                  )
              )
              img_files = sorted(
                  glob.glob(
                      os.path.join(
                          img_dir, "val", "*", "*_leftImg8bit.*"
                      )
                  )
              )
              val_file_list = [
                  [img_path, label_path]
                  for img_path, label_path in zip(
                      img_files, label_files
                  )
              ]
              self.file_list.extend(val_file_list)
              for ii in range(len(self.file_list)):
                  print(self.file_list[ii])
              print(len(self.file_list))
              self.num_files = len(self.file_list)
              self.total_num_files = self.num_files
  
file_path: PaddleSeg/contrib/AutoNUE/datasets/auto_nue_crop.py
imports: import os
  import glob
  import random
  import paddle
  import numpy
  from PIL import Image
  from paddleseg.cvlibs import manager
  from paddleseg.transforms import Compose
label_desc: Use image node_id 1 to create a new AutoNueCrop object from inherited base classes: paddle.io with example: obj = AutoNueCrop(transforms, dataset_root, mode, coarse_multiple, add_val)