def main(unused_argv):
"""Saves bundle or runs generator based on flags."""
tf.logging.set_verbosity(FLAGS.log)
bundle = get_bundle()
config_id = bundle.generator_details.id if bundle else FLAGS.config
config = pianoroll_rnn_nade_model.default_configs[config_id]
config.hparams.parse(FLAGS.hparams)
# 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 = PianorollRnnNadeSequenceGenerator(
model=pianoroll_rnn_nade_model.PianorollRnnNadeModel(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 create_sequence_generator(config, **kwargs):
return PianorollRnnNadeSequenceGenerator(
pianoroll_rnn_nade_model.PianorollRnnNadeModel(config),
config.details,
steps_per_quarter=config.steps_per_quarter,
**kwargs)
pass
message = {
'status': 400,
'message': "File not found",
}
resp = jsonify(message)
resp.status_code = 400
return resp
if __name__ == '__main__':
bundle_file = 'pretrained/pianoroll_rnn_nade.mag'
with tf.Session():
tf.logging.set_verbosity(log)
bundle = get_bundle()
config_id = bundle.generator_details.id
config = pianoroll_rnn_nade_model.default_configs[config_id]
config.hparams.parse(hparams)
config.hparams.batch_size = min(config.hparams.batch_size,
beam_size * branch_factor)
generator = PianorollRnnNadeSequenceGenerator(
model=pianoroll_rnn_nade_model.PianorollRnnNadeModel(config),
details=config.details,
steps_per_quarter=config.steps_per_quarter,
checkpoint=None,
bundle=bundle)
app.run(debug=True, host='0.0.0.0', use_reloader=False)
