100 lines
3.7 KiB
Python
100 lines
3.7 KiB
Python
# Copyright 2021-2022 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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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 audio
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from mindspore import log as logger
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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, \
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"\ndata_expected_std:{0}\ndata_me_error:{1}\nloss:{2}". \
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format(data_expected[greater], data_me[greater], error[greater])
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def test_gain_eager():
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"""
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Feature: Gain
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Description: Test Gain in eager mode
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Expectation: The data is processed successfully
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"""
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logger.info("test Gain in eager mode")
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# Original waveform
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waveform = np.array([1, 2, 3, 4, 5, 6], dtype=np.float64)
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# Expect waveform
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expect_waveform = np.array([1.1220184, 2.2440369, 3.3660554, 4.4880738, 5.6100923, 6.7321107], dtype=np.float64)
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gain_op = audio.Gain()
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# Filtered waveform by Gain
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output = gain_op(waveform)
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count_unequal_element(expect_waveform, output, 0.00001, 0.00001)
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def test_gain_pipeline():
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"""
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Feature: Gain
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Description: Test Gain in pipeline mode
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Expectation: The data is processed successfully
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"""
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logger.info("test Gain in pipeline mode")
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# Original waveform
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waveform = np.array([[1, 2, 3], [0.1, 0.2, 0.3]], dtype=np.float64)
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# Expect waveform
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expect_waveform = np.array([[1.05925, 2.1185, 3.1778],
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[0.10592537, 0.21185075, 0.31777612]], dtype=np.float64)
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dataset = ds.NumpySlicesDataset(waveform, ["audio"], shuffle=False)
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gain_op = audio.Gain(0.5)
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# Filtered waveform by Gain
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dataset = dataset.map(input_columns=["audio"], operations=gain_op, num_parallel_workers=8)
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i = 0
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for item in dataset.create_dict_iterator(output_numpy=True):
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count_unequal_element(expect_waveform[i, :], item['audio'], 0.00001, 0.00001)
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i += 1
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def test_gain_invalid_input():
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"""
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Feature: Gain
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Description: Test param check of Gain
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Expectation: Throw correct error and message
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"""
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logger.info("test param check of Gain")
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def test_invalid_input(test_name, gain_db, error, error_msg):
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logger.info("Test Gain with bad input: {0}".format(test_name))
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with pytest.raises(error) as error_info:
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audio.Gain(gain_db)
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assert error_msg in str(error_info.value)
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test_invalid_input("invalid gain_db parameter type as a String", "1.0", TypeError,
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"Argument gain_db with value 1.0 is not of type [<class 'float'>, <class 'int'>],"
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" but got <class 'str'>.")
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test_invalid_input("invalid gain_db parameter value", 122323242445423534543, ValueError,
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"Input gain_db is not within the required interval of [-16777216, 16777216].")
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if __name__ == "__main__":
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test_gain_eager()
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test_gain_pipeline()
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test_gain_invalid_input()
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