code: def __init__(
      self,
      root="./datasets/SYNTHIA",
      list_path="./datasets/SYNTHIA/list",
      split="train",
      base_size=769,
      crop_size=769,
      training=True,
      class_16=False,
      random_mirror=False,
      random_crop=False,
      resize=False,
      gaussian_blur=False,
  ):
      # 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
  
      # Augmentations
      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):
          raise Warning("split must be train/val/trainavl/test")
      self.image_filepath = os.path.join(self.data_path, "RGB")
      self.gt_filepath = os.path.join(self.data_path, "GT/LABELS")
      self.items = [id.strip() for id in open(item_list_filepath)]
  
      # Label map
      self.id_to_trainid = {
          1: 10,
          2: 2,
          3: 0,
          4: 1,
          5: 4,
          6: 8,
          7: 5,
          8: 13,
          9: 7,
          10: 11,
          11: 18,
          12: 17,
          15: 6,
          16: 9,
          17: 12,
          18: 14,
          19: 15,
          20: 16,
          21: 3,
      }
  
      # Only consider 16 shared classes
      self.class_16 = class_16
      self.trainid_to_16id = {
          id: i for i, id in enumerate(synthia_set_16)
      }
      self.class_13 = False
  
      print(
          "{} num images in SYNTHIA {} set have been loaded.".format(
              len(self.items), self.split
          )
      )
  
file_path: PaddleSeg/contrib/DomainAdaptation/datasets/synthia.py
imports: import os
  import imageio
  import numpy
  from PIL import Image
  from datasets.cityscapes_noconfig import CityDataset, to_tuple
label_desc: Use image node_id 1 to create a new SYNTHIADataset object from inherited base classes: CityDataset with example: obj = SYNTHIADataset(root, list_path, split, base_size, crop_size, training, class_16, random_mirror, random_crop, resize, gaussian_blur)