def get_pipeline(config, eval_ratio):
"""Returns the Pipeline instance which creates the RNN dataset.
Args:
config: A DrumsRnnConfig object.
eval_ratio: Fraction of input to set aside for evaluation set.
Returns:
A pipeline.Pipeline instance.
"""
quantizer = pipelines_common.Quantizer(steps_per_quarter=4)
drums_extractor_train = drum_pipelines.DrumsExtractor(
min_bars=7, max_steps=512, gap_bars=1.0, name='DrumsExtractorTrain')
drums_extractor_eval = drum_pipelines.DrumsExtractor(
min_bars=7, max_steps=512, gap_bars=1.0, name='DrumsExtractorEval')
encoder_pipeline_train = EncoderPipeline(config,
name='EncoderPipelineTrain')
encoder_pipeline_eval = EncoderPipeline(config, name='EncoderPipelineEval')
partitioner = pipelines_common.RandomPartition(
music_pb2.NoteSequence, ['eval_drum_tracks', 'training_drum_tracks'],
[eval_ratio])
dag = {
quantizer: dag_pipeline.Input(music_pb2.NoteSequence),
partitioner: quantizer,
drums_extractor_train: partitioner['training_drum_tracks'],
drums_extractor_eval: partitioner['eval_drum_tracks'],
encoder_pipeline_train: drums_extractor_train,
encoder_pipeline_eval: drums_extractor_eval,
dag_pipeline.Output('training_drum_tracks'): encoder_pipeline_train,
dag_pipeline.Output('eval_drum_tracks'): encoder_pipeline_eval
}
return dag_pipeline.DAGPipeline(dag)
def testDrumsExtractor(self):
note_sequence = common_testing_lib.parse_test_proto(
music_pb2.NoteSequence, """
time_signatures: {
numerator: 4
denominator: 4}
tempos: {
qpm: 60}""")
testing_lib.add_track_to_sequence(note_sequence,
0, [(12, 100, 2, 4), (11, 1, 6, 7),
(12, 1, 6, 8)],
is_drum=True)
testing_lib.add_track_to_sequence(note_sequence, 1, [(12, 127, 2, 4),
(14, 50, 6, 8)])
quantized_sequence = sequences_lib.quantize_note_sequence(
note_sequence, steps_per_quarter=1)
expected_events = [[
NO_DRUMS, NO_DRUMS,
DRUMS(12), NO_DRUMS, NO_DRUMS, NO_DRUMS,
DRUMS(11, 12)
]]
expected_drum_tracks = []
for events_list in expected_events:
drums = drums_lib.DrumTrack(events_list,
steps_per_quarter=1,
steps_per_bar=4)
expected_drum_tracks.append(drums)
unit = drum_pipelines.DrumsExtractor(min_bars=1, gap_bars=1)
self._unit_transform_test(unit, quantized_sequence,
expected_drum_tracks)
def get_pipeline(config, eval_ratio):
"""Returns the Pipeline instance which creates the RNN dataset.
Args:
config: A DrumsRnnConfig object.
eval_ratio: Fraction of input to set aside for evaluation set.
Returns:
A pipeline.Pipeline instance.
"""
partitioner = pipelines_common.RandomPartition(
music_pb2.NoteSequence,
['eval_drum_tracks', 'training_drum_tracks'],
[eval_ratio])
dag = {partitioner: dag_pipeline.DagInput(music_pb2.NoteSequence)}
for mode in ['eval', 'training']:
time_change_splitter = note_sequence_pipelines.TimeChangeSplitter(
name='TimeChangeSplitter_' + mode)
quantizer = note_sequence_pipelines.Quantizer(
steps_per_quarter=config.steps_per_quarter, name='Quantizer_' + mode)
drums_extractor = drum_pipelines.DrumsExtractor(
min_bars=7, max_steps=512, gap_bars=1.0, name='DrumsExtractor_' + mode)
encoder_pipeline = event_sequence_pipeline.EncoderPipeline(
magenta.music.DrumTrack, config.encoder_decoder,
name='EncoderPipeline_' + mode)
dag[time_change_splitter] = partitioner[mode + '_drum_tracks']
dag[quantizer] = time_change_splitter
dag[drums_extractor] = quantizer
dag[encoder_pipeline] = drums_extractor
dag[dag_pipeline.DagOutput(mode + '_drum_tracks')] = encoder_pipeline
return dag_pipeline.DAGPipeline(dag)
def testDrumsRNNPipeline(self):
note_sequence = magenta.common.testing_lib.parse_test_proto(
music_pb2.NoteSequence, """
time_signatures: {
numerator: 4
denominator: 4}
tempos: {
qpm: 120}""")
magenta.music.testing_lib.add_track_to_sequence(
note_sequence,
0, [(36, 100, 0.00, 2.0), (40, 55, 2.1, 5.0), (44, 80, 3.6, 5.0),
(41, 45, 5.1, 8.0), (64, 100, 6.6, 10.0), (55, 120, 8.1, 11.0),
(39, 110, 9.6, 9.7), (53, 99, 11.1, 14.1),
(51, 40, 12.6, 13.0), (55, 100, 14.1, 15.0),
(54, 90, 15.6, 17.0), (60, 100, 17.1, 18.0)],
is_drum=True)
quantizer = note_sequence_pipelines.Quantizer(steps_per_quarter=4)
drums_extractor = drum_pipelines.DrumsExtractor(min_bars=7,
gap_bars=1.0)
one_hot_encoding = magenta.music.OneHotEventSequenceEncoderDecoder(
magenta.music.MultiDrumOneHotEncoding())
quantized = quantizer.transform(note_sequence)[0]
drums = drums_extractor.transform(quantized)[0]
one_hot = one_hot_encoding.encode(drums)
expected_result = {
'training_drum_tracks': [one_hot],
'eval_drum_tracks': []
}
pipeline_inst = drums_rnn_pipeline.get_pipeline(self.config,
eval_ratio=0.0)
result = pipeline_inst.transform(note_sequence)
self.assertEqual(expected_result, result)
def testDrumsExtractor(self):
quantized_sequence = sequences_lib.QuantizedSequence()
quantized_sequence.steps_per_quarter = 1
testing_lib.add_quantized_track_to_sequence(
quantized_sequence, 0,
[(12, 100, 2, 4), (11, 1, 6, 7), (12, 1, 6, 8)],
is_drum=True)
testing_lib.add_quantized_track_to_sequence(
quantized_sequence, 1,
[(12, 127, 2, 4), (14, 50, 6, 8)])
expected_events = [
[NO_DRUMS, NO_DRUMS, DRUMS(12), NO_DRUMS, NO_DRUMS, NO_DRUMS,
DRUMS(11, 12)]]
expected_drum_tracks = []
for events_list in expected_events:
drums = drums_lib.DrumTrack(
events_list, steps_per_quarter=1, steps_per_bar=4)
expected_drum_tracks.append(drums)
unit = drum_pipelines.DrumsExtractor(min_bars=1, gap_bars=1)
self._unit_transform_test(unit, quantized_sequence, expected_drum_tracks)
