aiexperiments-ai-duet/server/magenta/models/shared/melody_rnn_create_dataset_test.py

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2016-11-11 18:53:51 +00:00
# Copyright 2016 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for melody_rnn_create_dataset."""
# internal imports
import tensorflow as tf
from magenta.common import testing_lib as common_testing_lib
from magenta.models.shared import melody_rnn_create_dataset
from magenta.music import melodies_lib
from magenta.music import testing_lib
from magenta.pipelines import pipelines_common
from magenta.protobuf import music_pb2
FLAGS = tf.app.flags.FLAGS
class MelodyRNNPipelineTest(tf.test.TestCase):
def testMelodyRNNPipeline(self):
FLAGS.eval_ratio = 0.0
note_sequence = common_testing_lib.parse_test_proto(
music_pb2.NoteSequence,
"""
time_signatures: {
numerator: 4
denominator: 4}
tempos: {
qpm: 120}""")
testing_lib.add_track(
note_sequence, 0,
[(12, 100, 0.00, 2.0), (11, 55, 2.1, 5.0), (40, 45, 5.1, 8.0),
(55, 120, 8.1, 11.0), (53, 99, 11.1, 14.1)])
quantizer = pipelines_common.Quantizer(steps_per_quarter=4)
melody_extractor = pipelines_common.MonophonicMelodyExtractor(
min_bars=7, min_unique_pitches=5, gap_bars=1.0,
ignore_polyphonic_notes=False)
one_hot_encoder = melodies_lib.OneHotEncoderDecoder(0, 127, 0)
quantized = quantizer.transform(note_sequence)[0]
print quantized.tracks
melody = melody_extractor.transform(quantized)[0]
one_hot = one_hot_encoder.squash_and_encode(melody)
print one_hot
expected_result = {'training_melodies': [one_hot], 'eval_melodies': []}
pipeline_inst = melody_rnn_create_dataset.get_pipeline(one_hot_encoder)
result = pipeline_inst.transform(note_sequence)
self.assertEqual(expected_result, result)
if __name__ == '__main__':
tf.test.main()