code: def __init__(
      self,
      root,
      list_path,
      split="train",
      base_size=769,
      crop_size=769,
      training=True,
      random_mirror=False,
      random_crop=False,
      resize=False,
      gaussian_blur=False,
      class_16=False,
      class_13=False,
      edge=True,
      logger=None,
  ):
      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.logger = logger
  
      # Augmentations
      self.random_mirror = random_mirror
      self.random_crop = random_crop
      self.resize = resize
      self.gaussian_blur = gaussian_blur
      self.NUM_CLASSES = 19
      self.ignore_label = 255
      self.edge = edge
  
      # Files
      item_list_filepath = os.path.join(
          self.list_path, self.split + ".txt"
      )
      if not os.path.exists(item_list_filepath):
          raise Warning("split must be train/val/trainval")
      self.image_filepath = os.path.join(self.data_path, "leftImg8bit")
      self.gt_filepath = os.path.join(self.data_path, "gtFine")
      self.items = [id.strip() for id in open(item_list_filepath)]
      self.id_to_trainid = cityscapes_id_to_trainid
  
      # In SYNTHIA-to-Cityscapes case, only consider 16 shared classes
      self.class_16 = class_16
      synthia_set_16 = [
          0,
          1,
          2,
          3,
          4,
          5,
          6,
          7,
          8,
          10,
          11,
          12,
          13,
          15,
          17,
          18,
      ]
      self.trainid_to_16id = {
          id: i for i, id in enumerate(synthia_set_16)
      }
  
      # In Cityscapes-to-NTHU case, only consider 13 shared classes
      self.class_13 = class_13
      synthia_set_13 = [0, 1, 2, 6, 7, 8, 10, 11, 12, 13, 15, 17, 18]
      self.trainid_to_13id = {
          id: i for i, id in enumerate(synthia_set_13)
      }
  
      print(
          "{} num images in Cityscapes {} set have been loaded.".format(
              len(self.items), self.split
          )
      )
  
file_path: PaddleSeg/contrib/DomainAdaptation/datasets/cityscapes_noconfig.py
imports: import os
  import random
  import numpy
  import collections.abc
  from PIL import Image, ImageOps, ImageFilter, ImageFile
  import paddle
  from paddle import io
  import paddleseg.transforms.functional
label_desc: Use image node_id 1 to create a new CityDataset object from inherited base classes: io with example: obj = CityDataset(root, list_path, split, base_size, crop_size, training, random_mirror, random_crop, resize, gaussian_blur, class_16, class_13, edge, logger)