text: #!/usr/bin/env python3
  from slowfast.datasets.clevrer_resnet import Clevrerresnet_des
  import torch
  from torch.utils.data import DataLoader
  
  
  from slowfast.utils.parser import load_config, parse_args
  import slowfast.utils.logging as logging
  
  """
  Prints information about the dataset for testing and debugging
  
  Example:
  python3 clevrer_dev/baselines/test_resnet_ft.py \
    --cfg clevrer_dev/baselines/slowfast.yaml \
    DATA.PATH_TO_DATA_DIR /datasets/clevrer \
    DATA.PATH_PREFIX /datasets/clevrer
  """
  
  args = parse_args()
  cfg = load_config(args)
  
  logger = logging.get_logger(__name__)
  logging.setup_logging(cfg.OUTPUT_DIR)
  
  dataset = Clevrerresnet_des(cfg, 'train')
  print("Dataset len = {}".format(len(dataset)))
  
  #Test DataLoader
  dataloader = DataLoader(dataset, batch_size=2,
                          shuffle=True, num_workers=0)
  print("Vocab = {}".format(dataset.vocab))
  print("Ans_vocab = {}".format(dataset.ans_vocab))
  
  for i in range(10):
      print(dataset[i]['res_ft'].sum())
  
  for i_batch, sampled_batch in enumerate(dataloader):
      index = sampled_batch['index'][0].item()
      print("Video info = {}".format(dataset.get_video_info(index)))
      print(sampled_batch['res_ft'].size())
      print(sampled_batch['question_dict']['question'])
      print(sampled_batch['question_dict']['ans'])
      print(sampled_batch['question_dict']['len'])
      print(sampled_batch['index'])
  
      break
  
  dataset = Clevrerresnet_des(cfg, 'val')
  print("Dataset len = {}".format(len(dataset)))
  
  #Test DataLoader
  dataloader = DataLoader(dataset, batch_size=2,
                          shuffle=True, num_workers=0)
  print("Vocab = {}".format(dataset.vocab))
  print("Ans_vocab = {}".format(dataset.ans_vocab))
  
  for i_batch, sampled_batch in enumerate(dataloader):
      index = sampled_batch['index'][0].item()
      print("Video info = {}".format(dataset.get_video_info(index)))
      print(sampled_batch['res_ft'].size())
      print(sampled_batch['question_dict']['question'])
      print(sampled_batch['question_dict']['ans'])
      print(sampled_batch['question_dict']['len'])
      print(sampled_batch['index'])
      print(sampled_batch['res_ft'].sum())
  
      if i_batch > 50:
          break
  for i in range(10):
      print(dataset[i]['res_ft'].sum())
  