def get_pipeline(config, steps_per_quarter, min_steps, max_steps, eval_ratio):
"""Returns the Pipeline instance which creates the RNN dataset.
Args:
config: An EventSequenceRnnConfig.
steps_per_quarter: How many steps per quarter to use when quantizing.
min_steps: Minimum number of steps for an extracted sequence.
max_steps: Maximum number of steps for an extracted sequence.
eval_ratio: Fraction of input to set aside for evaluation set.
Returns:
A pipeline.Pipeline instance.
"""
quantizer = pipelines_common.Quantizer(steps_per_quarter=steps_per_quarter)
# Transpose up to a major third in either direction.
# Because our current dataset is Bach chorales, transposing more than a major
# third in either direction probably doesn't makes sense (e.g., because it is
# likely to exceed normal singing range).
transposition_range = range(-4, 5)
transposition_pipeline_train = sequences_lib.TranspositionPipeline(
transposition_range, name='TranspositionPipelineTrain')
transposition_pipeline_eval = sequences_lib.TranspositionPipeline(
transposition_range, name='TranspositionPipelineEval')
poly_extractor_train = PolyphonicSequenceExtractor(
min_steps=min_steps, max_steps=max_steps, name='PolyExtractorTrain')
poly_extractor_eval = PolyphonicSequenceExtractor(min_steps=min_steps,
max_steps=max_steps,
name='PolyExtractorEval')
encoder_pipeline_train = encoder_decoder.EncoderPipeline(
polyphony_lib.PolyphonicSequence,
config.encoder_decoder,
name='EncoderPipelineTrain')
encoder_pipeline_eval = encoder_decoder.EncoderPipeline(
polyphony_lib.PolyphonicSequence,
config.encoder_decoder,
name='EncoderPipelineEval')
partitioner = pipelines_common.RandomPartition(
music_pb2.NoteSequence, ['eval_poly_tracks', 'training_poly_tracks'],
[eval_ratio])
dag = {
quantizer: dag_pipeline.DagInput(music_pb2.NoteSequence),
partitioner: quantizer,
transposition_pipeline_train: partitioner['training_poly_tracks'],
transposition_pipeline_eval: partitioner['eval_poly_tracks'],
poly_extractor_train: transposition_pipeline_train,
poly_extractor_eval: transposition_pipeline_eval,
encoder_pipeline_train: poly_extractor_train,
encoder_pipeline_eval: poly_extractor_eval,
dag_pipeline.DagOutput('training_poly_tracks'): encoder_pipeline_train,
dag_pipeline.DagOutput('eval_poly_tracks'): encoder_pipeline_eval
}
return dag_pipeline.DAGPipeline(dag)
def testTranspositionPipeline(self):
tp = sequences_lib.TranspositionPipeline(range(0, 2))
testing_lib.add_track_to_sequence(self.note_sequence, 0,
[(12, 100, 1.0, 4.0)])
transposed = tp.transform(self.note_sequence)
self.assertEqual(2, len(transposed))
self.assertEqual(12, transposed[0].notes[0].pitch)
self.assertEqual(13, transposed[1].notes[0].pitch)
def get_pipeline(config, steps_per_quarter, min_steps, max_steps, eval_ratio):
"""Returns the Pipeline instance which creates the RNN dataset.
Args:
config: An EventSequenceRnnConfig.
steps_per_quarter: How many steps per quarter to use when quantizing.
min_steps: Minimum number of steps for an extracted sequence.
max_steps: Maximum number of steps for an extracted sequence.
eval_ratio: Fraction of input to set aside for evaluation set.
Returns:
A pipeline.Pipeline instance.
"""
# Transpose up to a major third in either direction.
# Because our current dataset is Bach chorales, transposing more than a major
# third in either direction probably doesn't makes sense (e.g., because it is
# likely to exceed normal singing range).
transposition_range = range(-4, 5)
partitioner = pipelines_common.RandomPartition(
music_pb2.NoteSequence, ['eval_poly_tracks', 'training_poly_tracks'],
[eval_ratio])
dag = {partitioner: dag_pipeline.DagInput(music_pb2.NoteSequence)}
for mode in ['eval', 'training']:
time_change_splitter = pipelines_common.TimeChangeSplitter(
name='TimeChangeSplitter_' + mode)
quantizer = pipelines_common.Quantizer(
steps_per_quarter=steps_per_quarter, name='Quantizer_' + mode)
transposition_pipeline = sequences_lib.TranspositionPipeline(
transposition_range, name='TranspositionPipeline_' + mode)
poly_extractor = PolyphonicSequenceExtractor(min_steps=min_steps,
max_steps=max_steps,
name='PolyExtractor_' +
mode)
encoder_pipeline = encoder_decoder.EncoderPipeline(
polyphony_lib.PolyphonicSequence,
config.encoder_decoder,
name='EncoderPipeline_' + mode)
dag[time_change_splitter] = partitioner[mode + '_poly_tracks']
dag[quantizer] = time_change_splitter
dag[transposition_pipeline] = quantizer
dag[poly_extractor] = transposition_pipeline
dag[encoder_pipeline] = poly_extractor
dag[dag_pipeline.DagOutput(mode + '_poly_tracks')] = encoder_pipeline
return dag_pipeline.DAGPipeline(dag)