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)