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 = melody_rnn_config.config_from_flags()
mode = 'eval' if FLAGS.eval else 'train'
graph = melody_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)
run_eval(graph, train_dir, eval_dir, FLAGS.num_training_steps,
FLAGS.summary_frequency)
else:
run_training(graph, train_dir, FLAGS.num_training_steps,
FLAGS.summary_frequency)
def run_from_flags():
tf.logging.set_verbosity(FLAGS.log)
config = melody_rnn_config.config_from_flags()
pipeline_instance = get_pipeline(config.encoder_decoder)
FLAGS.input = os.path.expanduser(FLAGS.input)
FLAGS.output_dir = os.path.expanduser(FLAGS.output_dir)
pipeline.run_pipeline_serial(
pipeline_instance,
pipeline.tf_record_iterator(FLAGS.input, pipeline_instance.input_type),
FLAGS.output_dir)
def main(unused_argv):
"""Saves bundle or runs generator based on flags."""
generator = melody_rnn_sequence_generator.MelodyRnnSequenceGenerator(
melody_rnn_config.config_from_flags(), FLAGS.steps_per_quarter,
get_checkpoint(), get_bundle())
if FLAGS.save_generator_bundle:
bundle_filename = os.path.expanduser(FLAGS.bundle_file)
tf.logging.info('Saving generator bundle to %s', bundle_filename)
generator.create_bundle_file(bundle_filename)
else:
run_with_flags(generator)