def __init__(self, checkpoint=None, bundle=None):
details = generator_pb2.GeneratorDetails(
id='test_generator',
description='Test Generator')
super(TestSequenceGenerator, self).__init__(
TestModel(), details, checkpoint, bundle)
def create_generator(checkpoint, steps_per_beat=4, hparams=None):
melody_encoder_decoder = basic_rnn_encoder_decoder.MelodyEncoderDecoder()
details = generator_pb2.GeneratorDetails(id='basic_rnn',
description='Basic RNN Generator')
return melody_rnn_sequence_generator.MelodyRnnSequenceGenerator(
details, checkpoint, melody_encoder_decoder,
basic_rnn_graph.build_graph, steps_per_beat,
{} if hparams is None else hparams)
def create_generator(train_dir, steps_per_beat=4, hparams=None):
melody_encoder_decoder = attention_rnn_encoder_decoder.MelodyEncoderDecoder(
)
details = generator_pb2.GeneratorDetails(
id='attention_rnn', description='Attention RNN Generator')
return melody_rnn_sequence_generator.MelodyRnnSequenceGenerator(
details, train_dir, melody_encoder_decoder,
attention_rnn_graph.build_graph, steps_per_beat,
{} if hparams is None else hparams)
def main(unused_argv):
melody_encoder_decoder = basic_rnn_encoder_decoder.MelodyEncoderDecoder()
details = generator_pb2.GeneratorDetails(id='basic_rnn',
description='Basic RNN Generator')
with melody_rnn_sequence_generator.MelodyRnnSequenceGenerator(
details, melody_rnn_generate.get_train_dir(),
melody_encoder_decoder, basic_rnn_graph.build_graph,
melody_rnn_generate.get_steps_per_beat(),
melody_rnn_generate.get_hparams()) as generator:
melody_rnn_generate.run_with_flags(generator)
def create_generator(checkpoint,
bundle,
steps_per_quarter=4,
hparams=None,
generator_id=DEFAULT_ID):
melody_encoder_decoder = basic_rnn_encoder_decoder.MelodyEncoderDecoder()
details = generator_pb2.GeneratorDetails(id=generator_id,
description='Basic RNN Generator')
return melody_rnn_sequence_generator.MelodyRnnSequenceGenerator(
details, checkpoint, bundle, melody_encoder_decoder,
basic_rnn_graph.build_graph, steps_per_quarter,
{} if hparams is None else hparams)
def testUseMatchingGeneratorId(self):
bundle = generator_pb2.GeneratorBundle(
generator_details=generator_pb2.GeneratorDetails(
id='test_generator'),
checkpoint_file=[b'foo.ckpt'],
metagraph_file=b'foo.ckpt.meta')
SeuenceGenerator(bundle=bundle)
bundle.generator_details.id = 'blarg'
with self.assertRaises(sequence_generator.SequenceGeneratorError):
SeuenceGenerator(bundle=bundle)
def create_generator(checkpoint, bundle, steps_per_quarter=4, hparams=None):
melody_encoder_decoder = attention_rnn_encoder_decoder.MelodyEncoderDecoder()
details = generator_pb2.GeneratorDetails(
id='attention_rnn',
description='Attention RNN Generator')
return melody_rnn_sequence_generator.MelodyRnnSequenceGenerator(
details,
checkpoint,
bundle,
melody_encoder_decoder,
attention_rnn_graph.build_graph,
steps_per_quarter,
{} if hparams is None else hparams)
def testSpecifyEitherCheckPointOrBundle(self):
bundle = generator_pb2.GeneratorBundle(
generator_details=generator_pb2.GeneratorDetails(
id='test_generator'),
checkpoint_file=[b'foo.ckpt'],
metagraph_file=b'foo.ckpt.meta')
with self.assertRaises(sequence_generator.SequenceGeneratorError):
SeuenceGenerator(checkpoint='foo.ckpt', bundle=bundle)
with self.assertRaises(sequence_generator.SequenceGeneratorError):
SeuenceGenerator(checkpoint=None, bundle=None)
SeuenceGenerator(checkpoint='foo.ckpt')
SeuenceGenerator(bundle=bundle)
def testGetBundleDetails(self):
# Test with non-bundle generator.
seq_gen = SeuenceGenerator(checkpoint='foo.ckpt')
self.assertEqual(None, seq_gen.bundle_details)
# Test with bundle-based generator.
bundle_details = generator_pb2.GeneratorBundle.BundleDetails(
description='bundle of joy')
bundle = generator_pb2.GeneratorBundle(
generator_details=generator_pb2.GeneratorDetails(
id='test_generator'),
bundle_details=bundle_details,
checkpoint_file=[b'foo.ckpt'],
metagraph_file=b'foo.ckpt.meta')
seq_gen = SeuenceGenerator(bundle=bundle)
self.assertEqual(bundle_details, seq_gen.bundle_details)
def main(unused_argv):
tf.logging.set_verbosity(tf.logging.INFO)
if not FLAGS.checkpoint_number:
tf.logging.fatal('--checkpoint_number required')
return
model_dict = {'LSTM': LSTMModel, 'LSTMAE': LSTMAE}
checkpoint_dir = FLAGS.checkpoint_dir
data_config = models.default_configs['performance_with_meta_128']
data_config.hparams.parse(FLAGS.hparams)
encoder_decoder = data_config.encoder_decoder
layers = FLAGS.layers
batch_size = 1
if FLAGS.classifier_weight:
config = LSTMConfig(encoder_decoder=encoder_decoder,
gpu=FLAGS.gpu,
layers=layers,
batch_size=batch_size,
label_classifier_weight=FLAGS.classifier_weight,
label_classifier_units=4)
else:
config = LSTMConfig(encoder_decoder=encoder_decoder,
gpu=FLAGS.gpu,
layers=layers,
batch_size=batch_size)
model = model_dict[FLAGS.model](config, 'generate')
number = FLAGS.checkpoint_number
bundle_file = checkpoint_dir + 'bundle.mag'
checkpoint_filename = os.path.join(checkpoint_dir,
'model.ckpt-' + str(number))
metagraph_filename = os.path.join(checkpoint_dir,
'model.ckpt-' + str(number) + '.meta')
tf.logging.info(checkpoint_dir)
with tf.Graph().as_default() as g:
model.build_graph_fn()
sess = tf.Session(graph=g)
saver = tf.train.Saver()
tf.logging.info('Checkpoint used: %s', checkpoint_filename)
saver.restore(sess, checkpoint_filename)
try:
tempdir = tempfile.mkdtemp()
checkpoint_filename = os.path.join(tempdir, 'model.ckpt')
saver = tf.train.Saver(sharded=False,
write_version=tf.train.SaverDef.V1)
saver.save(sess,
checkpoint_filename,
meta_graph_suffix='meta',
write_meta_graph=True)
metagraph_filename = checkpoint_filename + '.meta'
bundle = generator_pb2.GeneratorBundle()
details = generator_pb2.GeneratorDetails(
id='performance_with_dynamics',
description='Performance RNN with dynamics (compact input)')
bundle.generator_details.CopyFrom(details)
with tf.gfile.Open(checkpoint_filename, 'rb') as f:
bundle.checkpoint_file.append(f.read())
with tf.gfile.Open(metagraph_filename, 'rb') as f:
bundle.metagraph_file = f.read()
tf.logging.info('Writing to: ' + bundle_file)
with tf.gfile.Open(bundle_file, 'wb') as f:
f.write(bundle.SerializeToString())
finally:
if tempdir is not None:
tf.gfile.DeleteRecursively(tempdir)
