text: from utils import chair 
  import argparse
      
  parser = argparse.ArgumentParser()
  parser.add_argument("--annotation_path", type=str, default='coco/annotations')
  args = parser.parse_args()
  
  sentence_template = 'generated_sentences/%s.json'
  table1_tags = [('LRCN', 'lrcn_beam5_test', 'lrcn-sc_beam5_test'),
                 ('FC', 'fc_beam5_test', 'fc-sc_beam5_test'),
                 ('att2in', 'att2in_beam5_test', 'att2in-sc_beam5_test'),
                 ('TD', 'td_beam5_test', 'td-sc_beam5_test'),
                 ('TD-BB', 'td-bb_beam5_test', 'td-bb-sc_beam5_test'),
                 ('NBT', 'nbt_beam5_test'),
                 ('GAN', 'baseline-gan_beam5_test', 'gan_beam5_test')]
  
  _, imids, _ = chair.load_generated_captions(sentence_template %table1_tags[0][1])
  
  evaluator = chair.CHAIR(imids, args.annotation_path) 
  evaluator.get_annotations()
  
  print "\t\tCross Entropy\t\t\t\tSelf-Critical\t\t"
  print "Model\tSPICE\tMETEOR\tCIDEr\tCHAIRs\tCHAIRi\t|SPICE\tMETEOR\tCIDEr\tCHAIRs\tCHAIRi"
  
  for tag in table1_tags:
  
      cap_dict = evaluator.compute_chair(sentence_template %tag[1]) 
      metric_string_ce = chair.print_metrics(cap_dict, True)
      chair.save_hallucinated_words(sentence_template %tag[1], cap_dict)
      if len(tag) > 2:
          cap_dict = evaluator.compute_chair(sentence_template %tag[2]) 
          metric_string_sc = chair.print_metrics(cap_dict, True)
          chair.save_hallucinated_words(sentence_template %tag[2], cap_dict)
      else:
          metric_string_sc = "-\t-\t-\t-\t-"
      print "%s\t%s\t|%s" %(tag[0], metric_string_ce, metric_string_sc) 
  