text: import json
  import os
  from collections import namedtuple
  
  import torch
  import torch.utils.data as data
  from PIL import Image
  import numpy as np
  
  
  class CityscapesAdversarial(data.Dataset):
      """Cityscapes <http://www.cityscapes-dataset.com/> Dataset.
      
      **Parameters:**
          - **root** (string): Root directory of dataset where directory 'leftImg8bit' and 'gtFine' or 'gtCoarse' are located.
          - **split** (string, optional): The image split to use, 'train', 'test' or 'val' if mode="gtFine" otherwise 'train', 'train_extra' or 'val'
          - **mode** (string, optional): The quality mode to use, 'gtFine' or 'gtCoarse' or 'color'. Can also be a list to output a tuple with all specified target types.
          - **transform** (callable, optional): A function/transform that takes in a PIL image and returns a transformed version. E.g, ``transforms.RandomCrop``
          - **target_transform** (callable, optional): A function/transform that takes in the target and transforms it.
      """
  
      # Based on https://github.com/mcordts/cityscapesScripts
      CityscapesClass = namedtuple('CityscapesClass', ['name', 'id', 'train_id', 'category', 'category_id',
                                                       'has_instances', 'ignore_in_eval', 'color'])
      classes = [
          CityscapesClass('unlabeled',            0, 255, 'void', 0, False, True, (0, 0, 0)),
          CityscapesClass('ego vehicle',          1, 255, 'void', 0, False, True, (0, 0, 0)),
          CityscapesClass('rectification border', 2, 255, 'void', 0, False, True, (0, 0, 0)),
          CityscapesClass('out of roi',           3, 255, 'void', 0, False, True, (0, 0, 0)),
          CityscapesClass('static',               4, 255, 'void', 0, False, True, (0, 0, 0)),
          CityscapesClass('dynamic',              5, 255, 'void', 0, False, True, (111, 74, 0)),
          CityscapesClass('ground',               6, 255, 'void', 0, False, True, (81, 0, 81)),
          CityscapesClass('road',                 7, 0, 'flat', 1, False, False, (128, 64, 128)),
          CityscapesClass('sidewalk',             8, 1, 'flat', 1, False, False, (244, 35, 232)),
          CityscapesClass('parking',              9, 255, 'flat', 1, False, True, (250, 170, 160)),
          CityscapesClass('rail track',           10, 255, 'flat', 1, False, True, (230, 150, 140)),
          CityscapesClass('building',             11, 2, 'construction', 2, False, False, (70, 70, 70)),
          CityscapesClass('wall',                 12, 3, 'construction', 2, False, False, (102, 102, 156)),
          CityscapesClass('fence',                13, 4, 'construction', 2, False, False, (190, 153, 153)),
          CityscapesClass('guard rail',           14, 255, 'construction', 2, False, True, (180, 165, 180)),
          CityscapesClass('bridge',               15, 255, 'construction', 2, False, True, (150, 100, 100)),
          CityscapesClass('tunnel',               16, 255, 'construction', 2, False, True, (150, 120, 90)),
          CityscapesClass('pole',                 17, 5, 'object', 3, False, False, (153, 153, 153)),
          CityscapesClass('polegroup',            18, 255, 'object', 3, False, True, (153, 153, 153)),
          CityscapesClass('traffic light',        19, 6, 'object', 3, False, False, (250, 170, 30)),
          CityscapesClass('traffic sign',         20, 7, 'object', 3, False, False, (220, 220, 0)),
          CityscapesClass('vegetation',           21, 8, 'nature', 4, False, False, (107, 142, 35)),
          CityscapesClass('terrain',              22, 9, 'nature', 4, False, False, (152, 251, 152)),
          CityscapesClass('sky',                  23, 10, 'sky', 5, False, False, (70, 130, 180)),
          CityscapesClass('person',               24, 11, 'human', 6, True, False, (220, 20, 60)),
          CityscapesClass('rider',                25, 12, 'human', 6, True, False, (255, 0, 0)),
          CityscapesClass('car',                  26, 13, 'vehicle', 7, True, False, (0, 0, 142)),
          CityscapesClass('truck',                27, 14, 'vehicle', 7, True, False, (0, 0, 70)),
          CityscapesClass('bus',                  28, 15, 'vehicle', 7, True, False, (0, 60, 100)),
          CityscapesClass('caravan',              29, 255, 'vehicle', 7, True, True, (0, 0, 90)),
          CityscapesClass('trailer',              30, 255, 'vehicle', 7, True, True, (0, 0, 110)),
          CityscapesClass('train',                31, 16, 'vehicle', 7, True, False, (0, 80, 100)),
          CityscapesClass('motorcycle',           32, 17, 'vehicle', 7, True, False, (0, 0, 230)),
          CityscapesClass('bicycle',              33, 18, 'vehicle', 7, True, False, (119, 11, 32)),
          CityscapesClass('license plate',        -1, 255, 'vehicle', 7, False, True, (0, 0, 142)),
      ]
  
      train_id_to_color = [c.color for c in classes if (c.train_id != -1 and c.train_id != 255)]
      train_id_to_color.append([0, 0, 0])
      train_id_to_color = np.array(train_id_to_color)
      id_to_train_id = np.array([c.train_id for c in classes])
      
      # train_id_to_color = [(0, 0, 0), (128, 64, 128), (70, 70, 70), (153, 153, 153), (107, 142, 35),
      #                      (70, 130, 180), (220, 20, 60), (0, 0, 142)]
      # train_id_to_color = np.array(train_id_to_color)
      # id_to_train_id = np.array([c.category_id for c in classes], dtype='uint8') - 1
  
      def __init__(self, root, split='train', mode='fine', target_type='semantic', transform=None):
          self.root = os.path.expanduser(root)
          self.mode = 'gtFine'
          self.target_type = target_type
          # self.images_dir = os.path.join(self.root, 'leftImg8bit_trainvaltest', 'leftImg8bit', split)
          self.images_dir = os.path.join(self.root, 'leftImg8bit_trainvaltest', 'leftImg8bit', split + '_adversarial')
  
          self.targets_dir = os.path.join(self.root, 'gtFine_trainvaltest', self.mode, split)
          self.transform = transform
  
          self.split = split
          self.images = []
          self.targets = []
  
          if split not in ['train', 'test', 'val', 'val_adversarial']:
              raise ValueError('Invalid split for mode! Please use split="train", split="test"'
                               ' or split="val"')
          # self.images_dir = self.images_dir + '_adversarial'
          # print(self.images_dir)
          if not os.path.isdir(self.images_dir) or not os.path.isdir(self.targets_dir):
              raise RuntimeError('Dataset not found or incomplete. Please make sure all required folders for the'
                                 ' specified "split" and "mode" are inside the "root" directory')
  
          for file_name in os.listdir(self.images_dir):
              self.images.append(os.path.join(self.images_dir, file_name))
              # print(os.path.join(self.images_dir, file_name))
              # print(file_name)
              city = file_name.split('_')[0]
              file_name = file_name.split(city+'_')[1]
              # print('filename: ', file_name)
              # print(file_name.split('_leftImg8bit')[0])
              target_name = '{}_{}'.format(file_name.split('_leftImg8bit')[0],
                                           self._get_target_suffix(self.mode, self.target_type))
              target_name = city + '_' + target_name
              # print(target_name)
              self.targets.append(os.path.join(self.targets_dir, city, target_name))
  
      @classmethod
      def encode_target(cls, target):
          return cls.id_to_train_id[np.array(target)]
  
      @classmethod
      def decode_target(cls, target):
          target[target == 255] = 19
          # target = target.astype('uint8') + 1
          return cls.train_id_to_color[target]
  
      def __getitem__(self, index):
          """
          Args:
              index (int): Index
          Returns:
              tuple: (image, target) where target is a tuple of all target types if target_type is a list with more
              than one item. Otherwise target is a json object if target_type="polygon", else the image segmentation.
          """
          image = Image.open(self.images[index]).convert('RGB')
          target = Image.open(self.targets[index])
          if self.transform:
              image, target = self.transform(image, target)
          target = self.encode_target(target)
          return image, target
  
      def __len__(self):
          return len(self.images)
  
      def _load_json(self, path):
          with open(path, 'r') as file:
              data = json.load(file)
          return data
  
      def _get_target_suffix(self, mode, target_type):
          if target_type == 'instance':
              return '{}_instanceIds.png'.format(mode)
          elif target_type == 'semantic':
              return '{}_labelIds.png'.format(mode)
          elif target_type == 'color':
              return '{}_color.png'.format(mode)
          elif target_type == 'polygon':
              return '{}_polygons.json'.format(mode)
          elif target_type == 'depth':
              return '{}_disparity.png'.format(mode)