Esempi in Python per run_eval

Esempio n. 1

File: pianoroll_rnn_nade_train.py Progetto: y0d4a/magenta

def main(unused_argv):
  tf.logging.set_verbosity(FLAGS.log)

  if not FLAGS.run_dir:
    tf.logging.fatal('--run_dir required')
    return
  if not FLAGS.sequence_example_file:
    tf.logging.fatal('--sequence_example_file required')
    return

  sequence_example_file = tf.gfile.Glob(
      os.path.expanduser(FLAGS.sequence_example_file))
  run_dir = os.path.expanduser(FLAGS.run_dir)

  config = pianoroll_rnn_nade_model.default_configs[FLAGS.config]
  config.hparams.parse(FLAGS.hparams)

  mode = 'eval' if FLAGS.eval else 'train'
  graph = pianoroll_rnn_nade_graph.build_graph(
      mode, config, sequence_example_file)

  train_dir = os.path.join(run_dir, 'train')
  tf.gfile.MakeDirs(train_dir)
  tf.logging.info('Train dir: %s', train_dir)

  if FLAGS.eval:
    eval_dir = os.path.join(run_dir, 'eval')
    tf.gfile.MakeDirs(eval_dir)
    tf.logging.info('Eval dir: %s', eval_dir)
    events_rnn_train.run_eval(graph, train_dir, eval_dir,
                              FLAGS.num_training_steps, FLAGS.summary_frequency)

  else:
    events_rnn_train.run_training(graph, train_dir, FLAGS.num_training_steps,
                                  FLAGS.summary_frequency)

Esempio n. 2

File: polyphony_rnn_train.py Progetto: Kaushikpatnaik/magenta

def main(unused_argv):
  tf.logging.set_verbosity(FLAGS.log)

  if not FLAGS.run_dir:
    tf.logging.fatal('--run_dir required')
    return
  if not FLAGS.sequence_example_file:
    tf.logging.fatal('--sequence_example_file required')
    return

  sequence_example_file = tf.gfile.Glob(
      os.path.expanduser(FLAGS.sequence_example_file))
  run_dir = os.path.expanduser(FLAGS.run_dir)

  config = polyphony_model.default_configs[FLAGS.config]

  mode = 'eval' if FLAGS.eval else 'train'
  graph = events_rnn_graph.build_graph(
      mode, config, sequence_example_file)

  train_dir = os.path.join(run_dir, 'train')
  tf.gfile.MakeDirs(train_dir)
  tf.logging.info('Train dir: %s', train_dir)

  if FLAGS.eval:
    eval_dir = os.path.join(run_dir, 'eval')
    tf.gfile.MakeDirs(eval_dir)
    tf.logging.info('Eval dir: %s', eval_dir)
    events_rnn_train.run_eval(graph, train_dir, eval_dir,
                              FLAGS.num_training_steps, FLAGS.summary_frequency)

  else:
    events_rnn_train.run_training(graph, train_dir, FLAGS.num_training_steps,
                                  FLAGS.summary_frequency)

Esempio n. 3

def main(unused_argv):
    tf.logging.set_verbosity(mt.FLAGS.log)
    data_dir = tgt.OUTPUT_DIR
    train_dir = os.path.join(tgt.MODEL_DIR, "logdir/train")
    if not os.path.exists(train_dir):
        tf.gfile.MakeDirs(train_dir)

    config = mt.melody_rnn_config_flags.config_from_flags()

    if not mt.FLAGS.eval:
        train_file = tf.gfile.Glob(
            os.path.join(data_dir, "training_melodies.tfrecord"))
        tf.logging.info("Train dir: %s", train_dir)
        with tf.gfile.Open(os.path.join(train_dir, "hparams"), mode="w") as f:
            f.write("\t".join([mt.FLAGS.config, mt.FLAGS.hparams]))

        graph = events_rnn_graph.build_graph("train", config, train_file)
        events_rnn_train.run_training(graph, train_dir,
                                      mt.FLAGS.num_training_steps,
                                      mt.FLAGS.summary_frequency)

    else:
        eval_file = tf.gfile.Glob(
            os.path.join(data_dir, "eval_melodies.tfrecord"))
        eval_dir = os.path.join(tgt.MODEL_DIR, "logdir/eval")
        if not os.path.exists(eval_dir):
            tf.gfile.MakeDirs(eval_dir)
        tf.logging.info("Eval dir: %s", eval_dir)

        graph = events_rnn_graph.build_graph("eval", config, eval_file)
        events_rnn_train.run_eval(graph, train_dir, eval_dir,
                                  mt.FLAGS.num_training_steps,
                                  mt.FLAGS.summary_frequency)

Esempio n. 4

File: drums_rnn_train.py Progetto: jmarwane/magenta

def main(unused_argv):
  tf.logging.set_verbosity(FLAGS.log)

  if not FLAGS.run_dir:
    tf.logging.fatal('--run_dir required')
    return
  if not FLAGS.sequence_example_file:
    tf.logging.fatal('--sequence_example_file required')
    return

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

  config = drums_rnn_config_flags.config_from_flags()

  mode = 'eval' if FLAGS.eval else 'train'
  graph = events_rnn_graph.build_graph(
      mode, config, sequence_example_file)

  train_dir = os.path.join(run_dir, 'train')
  if not os.path.exists(train_dir):
    tf.gfile.MakeDirs(train_dir)
  tf.logging.info('Train dir: %s', train_dir)

  if FLAGS.eval:
    eval_dir = os.path.join(run_dir, 'eval')
    if not os.path.exists(eval_dir):
      tf.gfile.MakeDirs(eval_dir)
    tf.logging.info('Eval dir: %s', eval_dir)
    events_rnn_train.run_eval(graph, train_dir, eval_dir,
                              FLAGS.num_training_steps, FLAGS.summary_frequency)

  else:
    events_rnn_train.run_training(graph, train_dir, FLAGS.num_training_steps,
                                  FLAGS.summary_frequency)

Esempio n. 5

File: melody_rnn_train.py Progetto: splch/vg-music-rnn

def main(unused_argv):
  tf.logging.set_verbosity(FLAGS.log)
  if not FLAGS.run_dir:
    tf.logging.fatal('--run_dir required')
    return
  if not FLAGS.sequence_example_file:
    tf.logging.fatal('--sequence_example_file required')
    return
  sequence_example_file_paths = tf.gfile.Glob(os.path.expanduser(FLAGS.sequence_example_file))
  run_dir = os.path.expanduser(FLAGS.run_dir)
  config = melody_rnn_config_flags.config_from_flags()
  mode = 'eval' if FLAGS.eval else 'train'
  graph = events_rnn_graph.build_graph(mode, config, sequence_example_file_paths)
  train_dir = os.path.join(run_dir, 'train')
  if not os.path.exists(train_dir):
    tf.gfile.MakeDirs(train_dir)
    tf.logging.info('Train dir: %s', train_dir)
  if FLAGS.eval:
    eval_dir = os.path.join(run_dir, 'eval')
    if not os.path.exists(eval_dir):
        tf.gfile.MakeDirs(eval_dir)
    tf.logging.info('Eval dir: %s', eval_dir)
    num_batches = ((FLAGS.num_eval_examples if FLAGS.num_eval_examples else magenta.common.count_records(sequence_example_file_paths)) config.hparams.batch_size)
    events_rnn_train.run_eval(graph, train_dir, eval_dir, num_batches)
  else:
    events_rnn_train.run_training(graph, train_dir, FLAGS.num_training_steps,FLAGS.summary_frequency,checkpoints_to_keep=FLAGS.num_checkpoints)

Esempio n. 6

File: melody_rnn_train.py Progetto: elektrowolf/melody-embedding

def main(unused_argv):
    tf.logging.set_verbosity(FLAGS.log)

    if not FLAGS.run_dir:
        tf.logging.fatal('--run_dir required')
        return
    if not FLAGS.sequence_example_file:
        tf.logging.fatal('--sequence_example_file required')
        return

    sequence_example_file_paths = tf.gfile.Glob(
        os.path.expanduser(FLAGS.sequence_example_file))
    run_dir = os.path.expanduser(FLAGS.run_dir)

    config = melody_rnn_config_flags.config_from_flags()

    if FLAGS.learn_initial_state:
        if not FLAGS.id_file:
            tf.logging.fatal('--id_file required')
            return

        # Count records for embedding
        id_file = os.path.expanduser(FLAGS.id_file)
        last_line = subprocess.check_output(['tail', '-1', id_file])
        config.num_records = int(last_line.split(',')[0]) + 1
        tf.logging.info('Counted %d records', config.num_records)

    mode = 'eval' if FLAGS.eval else 'train'
    graph = events_rnn_graph.build_graph(mode, config,
                                         sequence_example_file_paths)

    train_dir = os.path.join(run_dir, 'train')
    if not os.path.exists(train_dir):
        tf.gfile.MakeDirs(train_dir)
    tf.logging.info('Train dir: %s', train_dir)

    embedding_file = os.path.join(train_dir, 'embedding')

    if FLAGS.eval:
        eval_dir = os.path.join(run_dir, 'eval')
        if not os.path.exists(eval_dir):
            tf.gfile.MakeDirs(eval_dir)
        tf.logging.info('Eval dir: %s', eval_dir)
        events_rnn_train.run_eval(graph, train_dir, eval_dir,
                                  FLAGS.num_training_steps,
                                  FLAGS.summary_frequency)

    else:
        events_rnn_train.run_training(graph,
                                      train_dir,
                                      FLAGS.num_training_steps,
                                      FLAGS.summary_frequency,
                                      num_records=config.num_records,
                                      embedding_file=embedding_file)

Esempio n. 7

File: train_model.py Progetto: MizoTake/magenta_session

def main(unused_argv):
    tf.logging.set_verbosity(mt.FLAGS.log)
    data_dir = tgt.OUTPUT_DIR
    train_dir = os.path.join(tgt.MODEL_DIR, "logdir/train")
    if not os.path.exists(train_dir):
        tf.gfile.MakeDirs(train_dir)

    config = mt.melody_rnn_config_flags.config_from_flags()

    if not mt.FLAGS.eval:
        train_file = tf.gfile.Glob(
            os.path.join(data_dir, "training_melodies.tfrecord"))
        tf.logging.info("Train dir: %s", train_dir)
        with tf.gfile.Open(os.path.join(train_dir, "hparams"), mode="w") as f:
            f.write("\t".join([mt.FLAGS.config, mt.FLAGS.hparams]))

        graph = events_rnn_graph.get_build_graph_fn("train", config,
                                                    train_file)
        events_rnn_train.run_training(
            graph,
            train_dir,
            mt.FLAGS.num_training_steps,
            mt.FLAGS.summary_frequency,
            checkpoints_to_keep=mt.FLAGS.num_checkpoints)

    else:
        eval_file = tf.gfile.Glob(
            os.path.join(data_dir, "eval_melodies.tfrecord"))
        eval_dir = os.path.join(tgt.MODEL_DIR, "logdir/eval")
        if not os.path.exists(eval_dir):
            tf.gfile.MakeDirs(eval_dir)
        tf.logging.info("Eval dir: %s", eval_dir)

        examples = mt.FLAGS.num_eval_examples if mt.FLAGS.num_eval_examples else magenta.common.count_records(
            eval_file)

        if examples >= config.hparams.batch_size:
            num_batches = examples // config.hparams.batch_size
        else:
            config.hparams.batch_size = examples
            num_batches = 1

        graph = events_rnn_graph.get_build_graph_fn("eval", config, eval_file)
        events_rnn_train.run_eval(graph, train_dir, eval_dir, num_batches)

Esempio n. 8

File: performance_rnn_train.py Progetto: gradio-app/hub-magenta

def main(unused_argv):
    tf.logging.set_verbosity(FLAGS.log)

    if not FLAGS.run_dir:
        tf.logging.fatal('--run_dir required')
        return
    if not FLAGS.sequence_example_file:
        tf.logging.fatal('--sequence_example_file required')
        return

    sequence_example_file_paths = tf.gfile.Glob(
        os.path.expanduser(FLAGS.sequence_example_file))
    run_dir = os.path.expanduser(FLAGS.run_dir)

    config = performance_model.default_configs[FLAGS.config]
    config.hparams.parse(FLAGS.hparams)

    mode = 'eval' if FLAGS.eval else 'train'
    build_graph_fn = events_rnn_graph.get_build_graph_fn(
        mode, config, sequence_example_file_paths)

    train_dir = os.path.join(run_dir, 'train')
    tf.gfile.MakeDirs(train_dir)
    tf.logging.info('Train dir: %s', train_dir)

    if FLAGS.eval:
        eval_dir = os.path.join(run_dir, 'eval')
        tf.gfile.MakeDirs(eval_dir)
        tf.logging.info('Eval dir: %s', eval_dir)
        num_batches = (
            (FLAGS.num_eval_examples
             or magenta.common.count_records(sequence_example_file_paths)) //
            config.hparams.batch_size)
        events_rnn_train.run_eval(build_graph_fn, train_dir, eval_dir,
                                  num_batches)

    else:
        events_rnn_train.run_training(
            build_graph_fn,
            train_dir,
            FLAGS.num_training_steps,
            FLAGS.summary_frequency,
            checkpoints_to_keep=FLAGS.num_checkpoints,
            warm_start_bundle_file=FLAGS.warm_start_bundle_file)

Esempio n. 9

File: melody_rnn_train.py Progetto: lccodes/magenta

def main(unused_argv):
  tf.logging.set_verbosity(FLAGS.log)

  tf.logging.info('RUN FROM SOURCE')
  if not FLAGS.run_dir:
    tf.logging.fatal('--run_dir required')
    return
  if not FLAGS.sequence_example_file:
    tf.logging.fatal('--sequence_example_file required')
    return

  sequence_example_file_paths = tf.gfile.Glob(
      os.path.expanduser(FLAGS.sequence_example_file))
  run_dir = os.path.expanduser(FLAGS.run_dir)

  config = melody_rnn_config_flags.config_from_flags()

  mode = 'eval' if FLAGS.eval else 'train'
  graph = None
  if FLAGS.graph == 'old':
    tf.logging.info('Using old graph')
    graph = events_rnn_graph.build_graph(mode, config, sequence_example_file_paths)
  else:
    tf.logging.info('Using new graph')
    graph = banger_rnn_graph.build_graph(mode, config, sequence_example_file_paths)

  train_dir = os.path.join(run_dir, 'train')
  if not os.path.exists(train_dir):
    tf.gfile.MakeDirs(train_dir)
  tf.logging.info('Train dir: %s', train_dir)

  if FLAGS.eval:
    eval_dir = os.path.join(run_dir, 'eval')
    if not os.path.exists(eval_dir):
      tf.gfile.MakeDirs(eval_dir)
    tf.logging.info('Eval dir: %s', eval_dir)
    events_rnn_train.run_eval(graph, train_dir, eval_dir,
                              FLAGS.num_training_steps, FLAGS.summary_frequency)

  else:
    events_rnn_train.run_training(graph, train_dir, FLAGS.num_training_steps,
                                  FLAGS.summary_frequency)

Esempio n. 10

File: melody_rnn_train.py Progetto: ThierryGrb/magenta

def main(unused_argv):
  tf.logging.set_verbosity(FLAGS.log)

  if not FLAGS.run_dir:
    tf.logging.fatal('--run_dir required')
    return
  if not FLAGS.sequence_example_file:
    tf.logging.fatal('--sequence_example_file required')
    return

  sequence_example_file_paths = tf.gfile.Glob(
      os.path.expanduser(FLAGS.sequence_example_file))
  run_dir = os.path.expanduser(FLAGS.run_dir)

  config = melody_rnn_config_flags.config_from_flags()

  mode = 'eval' if FLAGS.eval else 'train'
  build_graph_fn = events_rnn_graph.get_build_graph_fn(
      mode, config, sequence_example_file_paths)

  train_dir = os.path.join(run_dir, 'train')
  if not os.path.exists(train_dir):
    tf.gfile.MakeDirs(train_dir)
  tf.logging.info('Train dir: %s', train_dir)

  if FLAGS.eval:
    eval_dir = os.path.join(run_dir, 'eval')
    if not os.path.exists(eval_dir):
      tf.gfile.MakeDirs(eval_dir)
    tf.logging.info('Eval dir: %s', eval_dir)
    num_batches = (
        (FLAGS.num_eval_examples if FLAGS.num_eval_examples else
         magenta.common.count_records(sequence_example_file_paths)) //
        config.hparams.batch_size)
    events_rnn_train.run_eval(build_graph_fn, train_dir, eval_dir, num_batches)

  else:
    events_rnn_train.run_training(build_graph_fn, train_dir,
                                  FLAGS.num_training_steps,
                                  FLAGS.summary_frequency,
                                  checkpoints_to_keep=FLAGS.num_checkpoints)

Esempio n. 11

File: pianoroll_rnn_nade_train.py Progetto: eraoul/magenta

def main(unused_argv):
  tf.logging.set_verbosity(FLAGS.log)

  if not FLAGS.run_dir:
    tf.logging.fatal('--run_dir required')
    return
  if not FLAGS.sequence_example_file:
    tf.logging.fatal('--sequence_example_file required')
    return

  sequence_example_file_paths = tf.gfile.Glob(
      os.path.expanduser(FLAGS.sequence_example_file))
  run_dir = os.path.expanduser(FLAGS.run_dir)

  config = pianoroll_rnn_nade_model.default_configs[FLAGS.config]
  config.hparams.parse(FLAGS.hparams)

  mode = 'eval' if FLAGS.eval else 'train'
  graph = pianoroll_rnn_nade_graph.build_graph(
      mode, config, sequence_example_file_paths)

  train_dir = os.path.join(run_dir, 'train')
  tf.gfile.MakeDirs(train_dir)
  tf.logging.info('Train dir: %s', train_dir)

  if FLAGS.eval:
    eval_dir = os.path.join(run_dir, 'eval')
    tf.gfile.MakeDirs(eval_dir)
    tf.logging.info('Eval dir: %s', eval_dir)
    num_batches = (
        (FLAGS.num_eval_examples if FLAGS.num_eval_examples else
         magenta.common.count_records(sequence_example_file_paths)) //
        config.hparams.batch_size)
    events_rnn_train.run_eval(graph, train_dir, eval_dir, num_batches)

  else:
    events_rnn_train.run_training(graph, train_dir, FLAGS.num_training_steps,
                                  FLAGS.summary_frequency,
                                  checkpoints_to_keep=FLAGS.num_checkpoints)