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