def main(unused_argv): """Saves bundle or runs generator based on flags.""" tf.logging.set_verbosity(FLAGS.log) bundle = get_bundle() if bundle: config_id = bundle.generator_details.id config = improv_rnn_model.default_configs[config_id] config.hparams.parse(FLAGS.hparams) else: config = improv_rnn_config_flags.config_from_flags() # Having too large of a batch size will slow generation down unnecessarily. config.hparams.batch_size = min(config.hparams.batch_size, FLAGS.beam_size * FLAGS.branch_factor) generator = improv_rnn_sequence_generator.ImprovRnnSequenceGenerator( model=improv_rnn_model.ImprovRnnModel(config), details=config.details, steps_per_quarter=config.steps_per_quarter, checkpoint=get_checkpoint(), bundle=bundle) if FLAGS.save_generator_bundle: bundle_filename = os.path.expanduser(FLAGS.bundle_file) if FLAGS.bundle_description is None: tf.logging.warning('No bundle description provided.') tf.logging.info('Saving generator bundle to %s', bundle_filename) generator.create_bundle_file(bundle_filename, FLAGS.bundle_description) else: run_with_flags(generator)
def main(unused_argv): """Saves bundle or runs generator based on flags.""" tf.logging.set_verbosity(FLAGS.log) config = improv_rnn_config_flags.config_from_flags() generator = improv_rnn_sequence_generator.ImprovRnnSequenceGenerator( model=improv_rnn_model.ImprovRnnModel(config), details=config.details, steps_per_quarter=config.steps_per_quarter, checkpoint=get_checkpoint(), bundle=get_bundle()) if FLAGS.save_generator_bundle: bundle_filename = os.path.expanduser(FLAGS.bundle_file) if FLAGS.bundle_description is None: tf.logging.warning('No bundle description provided.') tf.logging.info('Saving generator bundle to %s', bundle_filename) generator.create_bundle_file(bundle_filename, FLAGS.bundle_description) else: run_with_flags(generator)