def main(args):
config_file = args.config or os.path.join(args.logdir, 'model.yaml')
with open(config_file, 'rb') as f:
config = Configuration.from_yaml(f)
logger.debug(config)
model, trainer, encoding = config.configure(
_init,
logdir=args.logdir,
train_mode=(args.action == 'train'),
sampling_seed=getattr(args, 'seed', None))
if args.action == 'train':
trainer.train()
elif args.action == 'run':
trainer.load_variables(checkpoint_file=args.checkpoint)
data = pickle.load(args.input_file)
dataset = make_simple_dataset(_make_data_generator(encoding, data),
output_types=(tf.int32, tf.int32,
tf.int32),
output_shapes=([None], [None], [None]),
batch_size=args.batch_size)
output_ids = model.run(trainer.session, dataset, args.sample,
args.softmax_temperature)
output = [encoding.decode(seq) for seq in output_ids]
pickle.dump(output, args.output_file)
def run(self,
pipeline,
batch_size=None,
filters='program',
sample=False,
softmax_temperature=1.,
normalize_velocity=False,
options=None):
metadata_list = [] # gather metadata about each item of the dataset
apply_filters = '__program__' if filters == 'program' else True
dataset = make_simple_dataset(
self._load_data(tqdm.tqdm(pipeline),
apply_filters=apply_filters,
normalize_velocity=normalize_velocity,
metadata_list=metadata_list),
output_types=self.input_types,
output_shapes=self.input_shapes,
batch_size=batch_size
or self._cfg['data_prep'].get('val_batch_size'))
output_ids = self.model.run(self.trainer.session,
dataset,
sample,
softmax_temperature,
options=options) or []
sequences = [self.output_encoding.decode(ids) for ids in output_ids]
merged_sequences = []
instrument_id = 0
for seq, meta in zip(sequences, metadata_list):
instrument_id += 1
while meta['input_index'] > len(merged_sequences) - 1:
merged_sequences.append(music_pb2.NoteSequence())
instrument_id = 0
# Apply features (instrument, velocity)
if meta['note_features'] is not None:
if self._cfg['output_encoding'].get('use_velocity', False):
# If the output has velocity information, do not override it
del meta['note_features']['velocity']
set_note_fields(seq,
**meta['note_features'],
instrument=instrument_id)
else:
# If the style input had no notes, force the output to be empty
seq.Clear()
# Merge
merged_sequences[-1].notes.extend(seq.notes)
merged_sequences[-1].total_time = max(
merged_sequences[-1].total_time, seq.total_time)
instrument_info = merged_sequences[-1].instrument_infos.add()
instrument_info.instrument = instrument_id
instrument_info.name = meta['filter_name']
return merged_sequences
def run(self, args):
self.trainer.load_variables(checkpoint_file=args.checkpoint)
data = pickle.load(args.input_file)
def generator():
style_id = self.style_vocabulary.to_id(args.target_style)
for example in data:
segment_id, notes = example
yield self.input_encoding.encode(notes), style_id, [], []
dataset = make_simple_dataset(generator,
output_types=self.input_types,
output_shapes=self.input_shapes,
batch_size=args.batch_size)
output_ids = self.model.run(self.trainer.session, dataset, args.sample,
args.softmax_temperature)
outputs = [(segment_id, self.output_encoding.decode(seq))
for seq, (segment_id, _) in zip(output_ids, data)]
pickle.dump(outputs, args.output_file)
def _run(model, trainer, encoding, style_vocabulary, config, args):
trainer.load_variables(checkpoint_file=args.checkpoint)
data = pickle.load(args.input_file)
def generator():
style_id = style_vocabulary.to_id(args.target_style)
for example in data:
segment_id, notes = example
yield encoding.encode(notes, add_start=False,
add_end=False), style_id, [], []
dataset = make_simple_dataset(generator,
output_types=(tf.int32, tf.int32, tf.int32,
tf.int32),
output_shapes=([None], [], [None], [None]),
batch_size=args.batch_size)
output_ids = model.run(trainer.session, dataset, args.sample,
args.softmax_temperature)
output = [(segment_id, encoding.decode(seq))
for seq, (segment_id, _) in zip(output_ids, data)]
pickle.dump(output, args.output_file)