def run_single_example()

in scripts/run_model.py [0:0]

• 55 lines of code
• 9 McCabe index (conditional complexity)

def run_single_example(args, model):
  dtype = torch.FloatTensor
  if args.use_gpu == 1:
    dtype = torch.cuda.FloatTensor

  # Build the CNN to use for feature extraction
  print('Loading CNN for feature extraction')
  cnn = build_cnn(args, dtype)

  # Load and preprocess the image
  img_size = (args.image_height, args.image_width)
  img = imread(args.image, mode='RGB')
  img = imresize(img, img_size, interp='bicubic')
  img = img.transpose(2, 0, 1)[None]
  mean = np.array([0.485, 0.456, 0.406]).reshape(1, 3, 1, 1)
  std = np.array([0.229, 0.224, 0.224]).reshape(1, 3, 1, 1)
  img = (img.astype(np.float32) / 255.0 - mean) / std

  # Use CNN to extract features for the image
  img_var = Variable(torch.FloatTensor(img).type(dtype), volatile=True)
  feats_var = cnn(img_var)

  # Tokenize the question
  vocab = load_vocab(args)
  question_tokens = tokenize(args.question,
                      punct_to_keep=[';', ','],
                      punct_to_remove=['?', '.'])
  question_encoded = encode(question_tokens,
                       vocab['question_token_to_idx'],
                       allow_unk=True)
  question_encoded = torch.LongTensor(question_encoded).view(1, -1)
  question_encoded = question_encoded.type(dtype).long()
  question_var = Variable(question_encoded, volatile=True)

  # Run the model
  print('Running the model\n')
  scores = None
  predicted_program = None
  if type(model) is tuple:
    program_generator, execution_engine = model
    program_generator.type(dtype)
    execution_engine.type(dtype)
    predicted_program = program_generator.reinforce_sample(
                          question_var,
                          temperature=args.temperature,
                          argmax=(args.sample_argmax == 1))
    scores = execution_engine(feats_var, predicted_program)
  else:
    model.type(dtype)
    scores = model(question_var, feats_var)

  # Print results
  _, predicted_answer_idx = scores.data.cpu()[0].max(dim=0)
  predicted_answer = vocab['answer_idx_to_token'][predicted_answer_idx[0]]

  print('Question: "%s"' % args.question)
  print('Predicted answer: ', predicted_answer)

  if predicted_program is not None:
    print()
    print('Predicted program:')
    program = predicted_program.data.cpu()[0]
    num_inputs = 1
    for fn_idx in program:
      fn_str = vocab['program_idx_to_token'][fn_idx]
      num_inputs += iep.programs.get_num_inputs(fn_str) - 1
      print(fn_str)
      if num_inputs == 0:
        break