code: def __init__(
      self,
      root="./datasets/GTA5",
      list_path="./datasets/GTA5/list",
      split="train",
      base_size=769,
      crop_size=769,
      training=True,
      random_mirror=False,
      random_crop=False,
      resize=False,
      gaussian_blur=False,
      class_16=False,
      edge=True,
  ):
      # Args
      self.data_path = root
      self.list_path = list_path
      self.split = split
      self.base_size = to_tuple(base_size)
      self.crop_size = to_tuple(crop_size)
      self.training = training
      self.class_16 = False
      self.class_13 = False
      self.NUM_CLASSES = 19
      self.ignore_label = 255
      self.edge = edge
  
      assert class_16 == False
  
      # Augmentation
      self.random_mirror = random_mirror
      self.random_crop = random_crop
      self.resize = resize
      self.gaussian_blur = gaussian_blur
  
      # Files
      item_list_filepath = os.path.join(
          self.list_path, self.split + ".txt"
      )
      if not os.path.exists(item_list_filepath):
          print(item_list_filepath)
          raise Warning("split must be train/val/trainval/test/all")
      self.image_filepath = os.path.join(self.data_path, "images")
      self.gt_filepath = os.path.join(self.data_path, "labels")
      self.items = [id.strip() for id in open(item_list_filepath)]
  
      # Label map
      self.id_to_trainid = {
          7: 0,
          8: 1,
          11: 2,
          12: 3,
          13: 4,
          17: 5,
          19: 6,
          20: 7,
          21: 8,
          22: 9,
          23: 10,
          24: 11,
          25: 12,
          26: 13,
          27: 14,
          28: 15,
          31: 16,
          32: 17,
          33: 18,
      }
  
      print(
          "{} num images in GTA5 {} set have been loaded.".format(
              len(self.items), self.split
          )
      )
  
file_path: PaddleSeg/contrib/DomainAdaptation/datasets/gta5_noconfig.py
imports: import os
  import numpy
  from PIL import Image, ImageFile
  from datasets.cityscapes_noconfig import CityDataset, to_tuple
  import paddle
label_desc: Use image node_id 1 to create a new GTA5Dataset object from inherited base classes: CityDataset with example: obj = GTA5Dataset(root, list_path, split, base_size, crop_size, training, random_mirror, random_crop, resize, gaussian_blur, class_16, edge)