text: #!/usr/bin/env python
  
  import _init_paths
  import math, cv2, random
  import numpy as np
  import os.path as osp
  from copy import deepcopy
  import matplotlib.pyplot as plt
  from datasets.abstract_scene import abstract_scene
  from modules.abstract_trainer import SupervisedTrainer
  
  from abstract_utils import *
  from abstract_config import get_config
  
  
  def abstract_demo(config):
      transformer = image_normalize('background')
      train_db = abstract_scene(config, split='val', transform=transformer)   
      trainer = SupervisedTrainer(train_db)
      input_sentences = json_load('examples/abstract_samples.json')
      trainer.sample_demo(input_sentences)
  
  
  if __name__ == '__main__':
      cv2.setNumThreads(0)
      config, unparsed = get_config()
      config = abstract_arguments(config)
      np.random.seed(config.seed)
      random.seed(config.seed)
      torch.manual_seed(config.seed)
      if(config.cuda):
          torch.cuda.manual_seed_all(config.seed)
      prepare_directories(config)
  
      abstract_demo(config)
  