def run_from_flags(melody_encoder_decoder):
pipeline_instance = MelodyRNNPipeline(
melody_encoder_decoder, FLAGS.eval_ratio)
pipeline.run_pipeline_serial(
pipeline_instance,
pipeline.tf_record_iterator(FLAGS.input, pipeline_instance.input_type),
FLAGS.output_dir)
def run_from_flags(pipeline_instance):
tf.logging.set_verbosity(FLAGS.log)
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):
tf.logging.set_verbosity(FLAGS.log)
config = drums_rnn_config_flags.config_from_flags()
pipeline_instance = get_pipeline(
config, FLAGS.eval_ratio)
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):
tf.logging.set_verbosity(FLAGS.log)
pipeline_instance = get_pipeline(
min_events=32,
max_events=512,
eval_ratio=FLAGS.eval_ratio,
config=performance_model.default_configs[FLAGS.config])
input_dir = os.path.expanduser(FLAGS.input)
output_dir = os.path.expanduser(FLAGS.output_dir)
pipeline.run_pipeline_serial(
pipeline_instance,
pipeline.tf_record_iterator(input_dir, pipeline_instance.input_type),
output_dir)
def main(unused_argv):
tf.logging.set_verbosity(FLAGS.log)
pipeline_instance = polyphony_rnn_pipeline.get_pipeline(
min_steps=80, # 5 measures
max_steps=512,
eval_ratio=FLAGS.eval_ratio,
config=polyphony_model.default_configs['polyphony'])
input_dir = os.path.expanduser(FLAGS.input)
output_dir = os.path.expanduser(FLAGS.output_dir)
pipeline.run_pipeline_serial(
pipeline_instance,
pipeline.tf_record_iterator(input_dir, pipeline_instance.input_type),
output_dir)
def testRunPipelineSerial(self):
strings = ['abcdefg', 'helloworld!', 'qwerty']
root_dir = tempfile.mkdtemp(dir=self.get_temp_dir())
pipeline.run_pipeline_serial(MockPipeline(), iter(strings), root_dir)
dataset_1_dir = os.path.join(root_dir, 'dataset_1.tfrecord')
dataset_2_dir = os.path.join(root_dir, 'dataset_2.tfrecord')
self.assertTrue(tf.gfile.Exists(dataset_1_dir))
self.assertTrue(tf.gfile.Exists(dataset_2_dir))
dataset_1_reader = tf.python_io.tf_record_iterator(dataset_1_dir)
self.assertEqual(
set([('serialized:%s_A' % s).encode('utf-8')
for s in strings] + [('serialized:%s_B' % s).encode('utf-8')
for s in strings]),
set(dataset_1_reader))
dataset_2_reader = tf.python_io.tf_record_iterator(dataset_2_dir)
self.assertEqual(
set(('serialized:%s_C' % s).encode('utf-8') for s in strings),
set(dataset_2_reader))
def testRunPipelineSerial(self):
strings = ['abcdefg', 'helloworld!', 'qwerty']
root_dir = tempfile.mkdtemp(dir=self.get_temp_dir())
pipeline.run_pipeline_serial(
MockPipeline(), iter(strings), root_dir)
dataset_1_dir = os.path.join(root_dir, 'dataset_1.tfrecord')
dataset_2_dir = os.path.join(root_dir, 'dataset_2.tfrecord')
self.assertTrue(tf.gfile.Exists(dataset_1_dir))
self.assertTrue(tf.gfile.Exists(dataset_2_dir))
dataset_1_reader = tf.python_io.tf_record_iterator(dataset_1_dir)
self.assertEqual(
set([('serialized:%s_A' % s).encode('utf-8') for s in strings] +
[('serialized:%s_B' % s).encode('utf-8') for s in strings]),
set(dataset_1_reader))
dataset_2_reader = tf.python_io.tf_record_iterator(dataset_2_dir)
self.assertEqual(
set([('serialized:%s_C' % s).encode('utf-8') for s in strings]),
set(dataset_2_reader))
for file_in_dir in files_in_dir:
full_file_path = os.path.join(input_dir, file_in_dir)
print(full_file_path)
try:
sequence = midi_io.midi_to_sequence_proto(
tf.io.gfile.GFile(full_file_path, 'rb').read())
except midi_io.MIDIConversionError as e:
tf.logging.warning('Could not parse midi file %s. Error was: %s',
full_file_path, e)
sequence.collection_name = os.path.basename(work_dir)
sequence.filename = os.path.join(output_dir,
os.path.basename(full_file_path))
sequence.id = note_sequence_io.generate_note_sequence_id(
sequence.filename, sequence.collection_name, 'midi')
if sequence:
writer.write(sequence)
filenames = [anthems_file]
dataset = tf.data.TFRecordDataset(filenames)
config = melody_rnn_model.default_configs['attention_rnn']
pipeline_instance = melody_rnn_pipeline.get_pipeline(config, eval_ratio=0.0)
pipeline.run_pipeline_serial(
pipeline_instance,
pipeline.tf_record_iterator(anthems_file, pipeline_instance.input_type),
output_dir)
def run_from_flags(pipeline_instance):
tf.logging.set_verbosity(tf.logging.INFO)
pipeline.run_pipeline_serial(
pipeline_instance,
pipeline.tf_record_iterator(FLAGS.input, pipeline_instance.input_type),
FLAGS.output_dir)
def run_from_flags(pipeline_instance):
tf.logging.set_verbosity(tf.logging.INFO)
pipeline.run_pipeline_serial(
pipeline_instance,
pipeline.tf_record_iterator(FLAGS.input, pipeline_instance.input_type),
FLAGS.output_dir)
