def testIsTraining(self):
converter = data.OneHotDrumsConverter(
steps_per_quarter=1, slice_bars=1, max_tensors_per_notesequence=2)
self.is_training = True
self.assertEqual(2, len(converter.to_tensors(self.sequence)[0]))
converter.max_tensors_per_notesequence = None
self.assertEqual(5, len(converter.to_tensors(self.sequence)[0]))
def testMaxOutputsPerNoteSequence(self):
converter = data.OneHotDrumsConverter(
steps_per_quarter=1, slice_bars=1, max_tensors_per_notesequence=2)
self.assertEqual(2, len(converter.to_tensors(self.sequence)[0]))
converter.max_tensors_per_notesequence = 3
self.assertEqual(3, len(converter.to_tensors(self.sequence)[0]))
converter.max_tensors_per_notesequence = 100
self.assertEqual(5, len(converter.to_tensors(self.sequence)[0]))
def testToNoteSequence(self):
converter = data.OneHotDrumsConverter(
steps_per_quarter=1, slice_bars=2, max_tensors_per_notesequence=1)
_, output_tensors = converter.to_tensors(
filter_instrument(self.sequence, 1))
sequences = converter.to_notesequences(output_tensors)
self.assertEqual(1, len(sequences))
expected_sequence = music_pb2.NoteSequence(ticks_per_quarter=220)
expected_sequence.tempos.add(qpm=120)
testing_lib.add_track_to_sequence(
expected_sequence, 9,
[(38, 80, 0.5, 1.0),
(48, 80, 2.0, 2.5), (49, 80, 2.0, 2.5),
(51, 80, 3.5, 4.0)],
is_drum=True)
self.assertProtoEquals(expected_sequence, sequences[0])
config_map['cat-drums_2bar_small'] = Config(
model=MusicVAE(lstm_models.BidirectionalLstmEncoder(),
lstm_models.CategoricalLstmDecoder()),
hparams=merge_hparams(
lstm_models.get_default_hparams(),
HParams(
batch_size=512,
max_seq_len=32, # 2 bars w/ 16 steps per bar
z_size=256,
enc_rnn_size=[512],
dec_rnn_size=[256, 256],
)),
note_sequence_augmenter=None,
note_sequence_converter=data.OneHotDrumsConverter(
max_bars=100, # Truncate long drum sequences before slicing.
slice_bars=2,
steps_per_quarter=4,
binary_input=True),
train_examples_path=None,
eval_examples_path=None,
)
config_map['cat-drums_2bar_big'] = Config(
model=MusicVAE(lstm_models.BidirectionalLstmEncoder(),
lstm_models.CategoricalLstmDecoder()),
hparams=merge_hparams(
lstm_models.get_default_hparams(),
HParams(
batch_size=512,
max_seq_len=32, # 2 bars w/ 16 steps per bar
z_size=512,