132 lines
5.5 KiB
Python
132 lines
5.5 KiB
Python
# Copyright 2021 Huawei Technologies Co., Ltd
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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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# ==============================================================================
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"""
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Testing TimeStretch op in DE
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"""
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import numpy as np
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import pytest
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import mindspore.dataset as ds
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import mindspore.dataset.audio as c_audio
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from mindspore import log as logger
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CHANNEL_NUM = 2
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FREQ = 1025
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FRAME_NUM = 300
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COMPLEX = 2
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def gen(shape):
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np.random.seed(0)
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data = np.random.random(shape)
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yield (np.array(data, dtype=np.float32),)
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def count_unequal_element(data_expected, data_me, rtol, atol):
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assert data_expected.shape == data_me.shape
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total_count = len(data_expected.flatten())
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error = np.abs(data_expected - data_me)
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greater = np.greater(error, atol + np.abs(data_expected) * rtol)
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loss_count = np.count_nonzero(greater)
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assert (loss_count / total_count) < rtol, "\ndata_expected_std:{0}\ndata_me_error:{1}\nloss:{2}".format(
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data_expected[greater], data_me[greater], error[greater])
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def allclose_nparray(data_expected, data_me, rtol, atol, equal_nan=True):
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if np.any(np.isnan(data_expected)):
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assert np.allclose(data_me, data_expected, rtol, atol, equal_nan=equal_nan)
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elif not np.allclose(data_me, data_expected, rtol, atol, equal_nan=equal_nan):
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count_unequal_element(data_expected, data_me, rtol, atol)
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def test_time_stretch_pipeline():
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"""
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Test TimeStretch op. Pipeline.
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"""
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logger.info("test TimeStretch op")
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generator = gen([CHANNEL_NUM, FREQ, FRAME_NUM, COMPLEX])
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data1 = ds.GeneratorDataset(source=generator, column_names=["multi_dimensional_data"])
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transforms = [c_audio.TimeStretch(512, FREQ, 1.3)]
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data1 = data1.map(operations=transforms, input_columns=["multi_dimensional_data"])
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for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
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out_put = item["multi_dimensional_data"]
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assert out_put.shape == (CHANNEL_NUM, FREQ, np.ceil(FRAME_NUM / 1.3), COMPLEX)
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def test_time_stretch_pipeline_invalid_param():
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"""
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Test TimeStretch op. Set invalid param. Pipeline.
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"""
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logger.info("test TimeStretch op with invalid values")
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generator = gen([CHANNEL_NUM, FREQ, FRAME_NUM, COMPLEX])
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data1 = ds.GeneratorDataset(source=generator, column_names=["multi_dimensional_data"])
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with pytest.raises(ValueError, match=r"Input fixed_rate is not within the required interval of \(0, 16777216\]."):
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transforms = [c_audio.TimeStretch(512, FREQ, -1.3)]
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data1 = data1.map(operations=transforms, input_columns=["multi_dimensional_data"])
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for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
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out_put = item["multi_dimensional_data"]
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assert out_put.shape == (CHANNEL_NUM, FREQ, np.ceil(FRAME_NUM / 1.3), COMPLEX)
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def test_time_stretch_eager():
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"""
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Test TimeStretch op. Set param. Eager.
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"""
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logger.info("test TimeStretch op with customized parameter values")
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spectrogram = next(gen([CHANNEL_NUM, FREQ, FRAME_NUM, COMPLEX]))[0]
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out_put = c_audio.TimeStretch(512, FREQ, 1.3)(spectrogram)
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assert out_put.shape == (CHANNEL_NUM, FREQ, np.ceil(FRAME_NUM / 1.3), COMPLEX)
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def test_percision_time_stretch_eager():
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"""
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Test TimeStretch op. Compare precision. Eager.
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"""
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logger.info("test TimeStretch op with default values")
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spectrogram = np.array([[[[1.0402449369430542, 0.3807601034641266],
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[-1.120057225227356, -0.12819576263427734],
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[1.4303032159805298, -0.08839055150747299]],
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[[1.4198592901229858, 0.6900091767311096],
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[-1.8593409061431885, 0.16363371908664703],
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[-2.3349387645721436, -1.4366451501846313]]],
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[[[-0.7083967328071594, 0.9325454831123352],
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[-1.9133838415145874, 0.011225821450352669],
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[1.477278232574463, -1.0551637411117554]],
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[[-0.6668586134910583, -0.23143270611763],
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[-2.4390718936920166, 0.17638640105724335],
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[-0.4795735776424408, 0.1345423310995102]]]]).astype(np.float64)
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out_expect = np.array([[[[1.0402449369430542, 0.3807601034641266],
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[-1.302264928817749, -0.1490504890680313]],
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[[1.4198592901229858, 0.6900091767311096],
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[-2.382312774658203, 0.2096325159072876]]],
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[[[-0.7083966732025146, 0.9325454831123352],
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[-1.8545820713043213, 0.010880803689360619]],
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[[-0.6668586134910583, -0.23143276572227478],
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[-1.2737033367156982, 0.09211209416389465]]]]).astype(np.float64)
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out_ms = c_audio.TimeStretch(64, 2, 1.6)(spectrogram)
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allclose_nparray(out_ms, out_expect, 0.001, 0.001)
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if __name__ == '__main__':
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test_time_stretch_pipeline()
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test_time_stretch_pipeline_invalid_param()
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test_time_stretch_eager()
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test_percision_time_stretch_eager()
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