def run_main()

in nmt/nmt.py [0:0]

• 54 lines of code
• 17 McCabe index (conditional complexity)

def run_main(flags, default_hparams, train_fn, inference_fn, target_session=""):
  """Run main."""
  # Job
  jobid = flags.jobid
  num_workers = flags.num_workers
  utils.print_out("# Job id %d" % jobid)

  # GPU device
  utils.print_out(
      "# Devices visible to TensorFlow: %s" % repr(tf.Session().list_devices()))

  # Random
  random_seed = flags.random_seed
  if random_seed is not None and random_seed > 0:
    utils.print_out("# Set random seed to %d" % random_seed)
    random.seed(random_seed + jobid)
    np.random.seed(random_seed + jobid)

  # Model output directory
  out_dir = flags.out_dir
  if out_dir and not tf.gfile.Exists(out_dir):
    utils.print_out("# Creating output directory %s ..." % out_dir)
    tf.gfile.MakeDirs(out_dir)

  # Load hparams.
  loaded_hparams = False
  if flags.ckpt:  # Try to load hparams from the same directory as ckpt
    ckpt_dir = os.path.dirname(flags.ckpt)
    ckpt_hparams_file = os.path.join(ckpt_dir, "hparams")
    if tf.gfile.Exists(ckpt_hparams_file) or flags.hparams_path:
      hparams = create_or_load_hparams(
          ckpt_dir, default_hparams, flags.hparams_path,
          save_hparams=False)
      loaded_hparams = True
  if not loaded_hparams:  # Try to load from out_dir
    assert out_dir
    hparams = create_or_load_hparams(
        out_dir, default_hparams, flags.hparams_path,
        save_hparams=(jobid == 0))

  ## Train / Decode
  if flags.inference_input_file:
    # Inference output directory
    trans_file = flags.inference_output_file
    assert trans_file
    trans_dir = os.path.dirname(trans_file)
    if not tf.gfile.Exists(trans_dir): tf.gfile.MakeDirs(trans_dir)

    # Inference indices
    hparams.inference_indices = None
    if flags.inference_list:
      (hparams.inference_indices) = (
          [int(token)  for token in flags.inference_list.split(",")])

    # Inference
    ckpt = flags.ckpt
    if not ckpt:
      ckpt = tf.train.latest_checkpoint(out_dir)
    inference_fn(ckpt, flags.inference_input_file,
                 trans_file, hparams, num_workers, jobid)

    # Evaluation
    ref_file = flags.inference_ref_file
    if ref_file and tf.gfile.Exists(trans_file):
      for metric in hparams.metrics:
        score = evaluation_utils.evaluate(
            ref_file,
            trans_file,
            metric,
            hparams.subword_option)
        utils.print_out("  %s: %.1f" % (metric, score))
  else:
    # Train
    train_fn(hparams, target_session=target_session)