982740ee6e56d674e3f220d7bf215ae4132c9c1c,magenta/models/onsets_frames_transcription/onsets_frames_transcription_infer.py,,main,#Any#,263

Before Change

        os.path.expanduser(FLAGS.acoustic_run_dir), "train",
        FLAGS.acoustic_checkpoint_filename)
  else:
    acoustic_checkpoint = tf.train.latest_checkpoint(
        os.path.join(os.path.expanduser(FLAGS.acoustic_run_dir), "train"))

  run_dir = os.path.expanduser(FLAGS.run_dir)

  hparams = tf_utils.merge_hparams(
      constants.DEFAULT_HPARAMS, model.get_default_hparams())
  hparams.parse(FLAGS.hparams)

  // Batch size should always be 1 for inference.
  hparams.batch_size = 1

  tf.logging.info(hparams)

  tf.gfile.MakeDirs(run_dir)

  with tf.gfile.Open(os.path.join(run_dir, "run_config.txt"), "w") as f:
    f.write(acoustic_checkpoint + "\n")
    f.write(FLAGS.examples_path + "\n")
    f.write(str(hparams) + "\n")

  model_inference(
      acoustic_checkpoint=acoustic_checkpoint,
      hparams=hparams,
      examples_path=FLAGS.examples_path,

After Change

    assert not FLAGS.checkpoint_path

    checkpoint_path = None
    while True:
      checkpoint_path = tf.contrib.training.wait_for_new_checkpoint(
          FLAGS.model_dir, last_checkpoint=checkpoint_path)
      model_inference(
          model_dir=FLAGS.model_dir,
          checkpoint_path=checkpoint_path,
          hparams=hparams,
          examples_path=FLAGS.examples_path,
          output_dir=output_dir,
          summary_writer=summary_writer,
          write_summary_every_step=False)
  else:
    model_inference(
        model_dir=FLAGS.model_dir,
        checkpoint_path=FLAGS.checkpoint_path,

Instances