104 lines
4.4 KiB
Python
104 lines
4.4 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, "\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 test_func_contrast_eager():
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"""
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Feature: Contrast op
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Description: Test Contrast op in eager mode with valid input
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Expectation: Output is equal to the expected output
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"""
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# Original waveform
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waveform = np.array([[1, 2], [3, 4]], dtype=np.float32)
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# Expect waveform
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expect_waveform = np.array([[1., -8.742277e-08],
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[-1., 1.748455e-07]],
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dtype=np.float32)
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contrast_op = audio.Contrast(75.0)
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# Filtered waveform by contrast
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output = contrast_op(waveform)
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count_unequal_element(expect_waveform, output, 0.0001, 0.0001)
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def test_func_contrast_pipeline():
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"""
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Feature: Contrast op
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Description: Test Contrast op in pipeline mode with valid input
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Expectation: Output is equal to the expected output
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"""
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# Original waveform
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waveform = np.array([[0.4941969, 0.53911686, 0.4846254], [0.10841596, 0.029320478, 0.52353495],
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[0.23657, 0.087965, 0.43579]], dtype=np.float64)
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# Expect waveform
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expect_waveform = np.array([[7.032282948493957520e-01, 7.328570485115051270e-01, 6.967759728431701660e-01],
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[2.311619222164154053e-01, 6.433061510324478149e-02,
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7.226532697677612305e-01],
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[4.539981484413146973e-01, 1.895205676555633545e-01, 6.622338891029357910e-01]],
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dtype=np.float64)
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dataset = ds.NumpySlicesDataset(waveform, ["audio"], shuffle=False)
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contrast_op = audio.Contrast()
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# Filtered waveform by contrast
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dataset = dataset.map(
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input_columns=["audio"], operations=contrast_op, num_parallel_workers=8)
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i = 0
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for item in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
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count_unequal_element(
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expect_waveform[i, :], item['audio'], 0.0001, 0.0001)
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i += 1
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def test_contrast_invalid_input():
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"""
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Feature: Contrast op
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Description: Test Contrast op with invalid input
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Expectation: Correct error and message are thrown as expected
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"""
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def test_invalid_input(test_name, enhancement_amount, error, error_msg):
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logger.info("Test Contrast with bad input: {0}".format(test_name))
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with pytest.raises(error) as error_info:
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audio.Contrast(enhancement_amount)
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assert error_msg in str(error_info.value)
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test_invalid_input("invalid enhancement_amount parameter type as a String", "75.0", TypeError,
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"Argument enhancement_amount with value 75.0 is not of type [<class 'float'>, <class 'int'>],"
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+ " but got <class 'str'>.")
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test_invalid_input("invalid enhancement_amount parameter value", -1, ValueError,
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"Input enhancement_amount is not within the required interval of [0, 100].")
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test_invalid_input("invalid enhancement_amount parameter value", 101, ValueError,
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"Input enhancement_amount is not within the required interval of [0, 100].")
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if __name__ == "__main__":
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test_func_contrast_eager()
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test_func_contrast_pipeline()
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test_contrast_invalid_input()
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