forked from mindspore-Ecosystem/mindspore
78 lines
3.5 KiB
Python
78 lines
3.5 KiB
Python
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# Copyright 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.audio.utils 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_linear_fbanks_normal():
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"""
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Feature: linear_fbanks.
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Description: Test normal operation.
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Expectation: The output data is the same as the result of torchaudio.functional.linear_fbanks.
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"""
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expect = np.array([[0.0000, 0.0000, 0.0000, 0.0000],
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[0.5357, 0.0000, 0.0000, 0.0000],
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[0.7202, 0.2798, 0.0000, 0.0000],
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[0.0000, 0.9762, 0.0238, 0.0000],
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[0.0000, 0.2321, 0.7679, 0.0000],
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[0.0000, 0.0000, 0.4881, 0.5119],
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[0.0000, 0.0000, 0.0000, 0.7440],
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[0.0000, 0.0000, 0.0000, 0.0000]], dtype=np.float64)
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output = audio.linear_fbanks(8, 2, 50, 4, 100)
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count_unequal_element(expect, output, 0.0001, 0.0001)
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def test_linear_fbanks_invalid_input():
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"""
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Feature: linear_fbanks.
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Description: Test operation with invalid input.
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Expectation: Throw exception as expected.
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"""
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def test_invalid_input(test_name, n_freqs, f_min, f_max, n_filter, sample_rate, error, error_msg):
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logger.info("Test linear_fbanks with bad input: {0}".format(test_name))
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with pytest.raises(error) as error_info:
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audio.linear_fbanks(n_freqs, f_min, f_max, n_filter, sample_rate)
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print(error_info)
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assert error_msg in str(error_info.value)
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test_invalid_input("invalid n_freqs parameter Value", 99999999999, 0, 50, 5, 100, ValueError, "n_freqs")
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test_invalid_input("invalid n_freqs parameter type", 10.5, 0, 50, 5, 100, TypeError, "n_freqs")
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test_invalid_input("invalid f_min parameter type", 10, None, 50, 5, 100, TypeError, "f_min")
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test_invalid_input("invalid f_max parameter type", 10, 0, None, 5, 100, TypeError, "f_max")
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test_invalid_input("invalid n_filter parameter type", 10, 0, 50, 10.1, 100, TypeError, "n_filter")
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test_invalid_input("invalid n_filter parameter Value", 20, 0, 50, 999999999999, 100, ValueError, "n_filter")
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test_invalid_input("invalid sample_rate parameter type", 10, 0, 50, 5, 100.1, TypeError, "sample_rate")
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test_invalid_input("invalid sample_rate parameter Value", 20, 0, 50, 5, 999999999999, ValueError, "sample_rate")
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if __name__ == "__main__":
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test_linear_fbanks_normal()
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test_linear_fbanks_invalid_input()
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