def main()

in scripts/run_model.py [0:0]

• 38 lines of code
• 11 McCabe index (conditional complexity)

def main(args):
  print()
  model = None
  if args.baseline_model is not None:
    print('Loading baseline model from ', args.baseline_model)
    model, _ = utils.load_baseline(args.baseline_model)
    if args.vocab_json is not None:
      new_vocab = utils.load_vocab(args.vocab_json)
      model.rnn.expand_vocab(new_vocab['question_token_to_idx'])
  elif args.program_generator is not None and args.execution_engine is not None:
    print('Loading program generator from ', args.program_generator)
    program_generator, _ = utils.load_program_generator(args.program_generator)
    print('Loading execution engine from ', args.execution_engine)
    execution_engine, _ = utils.load_execution_engine(args.execution_engine, verbose=False)
    if args.vocab_json is not None:
      new_vocab = utils.load_vocab(args.vocab_json)
      program_generator.expand_encoder_vocab(new_vocab['question_token_to_idx'])
    model = (program_generator, execution_engine)
  else:
    print('Must give either --baseline_model or --program_generator and --execution_engine')
    return

  if args.question is not None and args.image is not None:
    run_single_example(args, model)
  else:
    vocab = load_vocab(args)
    loader_kwargs = {
      'question_h5': args.input_question_h5,
      'feature_h5': args.input_features_h5,
      'vocab': vocab,
      'batch_size': args.batch_size,
    }
    if args.num_samples is not None and args.num_samples > 0:
      loader_kwargs['max_samples'] = args.num_samples
    if args.family_split_file is not None:
      with open(args.family_split_file, 'r') as f:
        loader_kwargs['question_families'] = json.load(f)
    with ClevrDataLoader(**loader_kwargs) as loader:
      run_batch(args, model, loader)