forked from huawei/mindspore2022
131 lines
6.0 KiB
Python
131 lines
6.0 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 AmplitudeToDB 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.transforms as c_audio
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from mindspore import log as logger
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from mindspore.dataset.audio.utils import ScaleType
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CHANNEL = 1
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FREQ = 20
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TIME = 15
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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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""" Precision calculation func """
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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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""" Precision calculation formula """
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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_func_amplitude_to_db_eager():
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""" mindspore eager mode normal testcase:amplitude_to_db op"""
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logger.info("check amplitude_to_db op output")
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ndarr_in = np.array([[[[-0.2197528, 0.3821656]]],
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[[[0.57418776, 0.46741104]]],
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[[[-0.20381108, -0.9303914]]],
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[[[0.3693608, -0.2017813]]],
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[[[-1.727381, -1.3708513]]],
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[[[1.259975, 0.4981323]]],
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[[[0.76986176, -0.5793846]]]]).astype(np.float32)
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# cal from benchmark
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out_expect = np.array([[[[-84.17748, -4.177484]]],
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[[[-2.4094608, -3.3030105]]],
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[[[-100., -100.]]],
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[[[-4.325492, -84.32549]]],
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[[[-100., -100.]]],
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[[[1.0036192, -3.0265532]]],
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[[[-1.1358725, -81.13587]]]]).astype(np.float32)
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amplitude_to_db_op = c_audio.AmplitudeToDB()
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out_mindspore = amplitude_to_db_op(ndarr_in)
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allclose_nparray(out_mindspore, out_expect, 0.0001, 0.0001)
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def test_func_amplitude_to_db_pipeline():
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""" mindspore pipeline mode normal testcase:amplitude_to_db op"""
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logger.info("test AmplitudeToDB op with default value")
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generator = gen([CHANNEL, FREQ, TIME])
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data1 = ds.GeneratorDataset(source=generator, column_names=["multi_dimensional_data"])
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transforms = [c_audio.AmplitudeToDB()]
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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, FREQ, TIME)
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def test_amplitude_to_db_invalid_input():
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def test_invalid_input(test_name, stype, ref_value, amin, top_db, error, error_msg):
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logger.info("Test AmplitudeToDB with bad input: {0}".format(test_name))
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with pytest.raises(error) as error_info:
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c_audio.AmplitudeToDB(stype=stype, ref_value=ref_value, amin=amin, top_db=top_db)
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assert error_msg in str(error_info.value)
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test_invalid_input("invalid stype parameter value", "test", 1.0, 1e-10, 80.0, TypeError,
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"Argument stype with value test is not of type [<enum 'ScaleType'>], but got <class 'str'>.")
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test_invalid_input("invalid ref_value parameter value", ScaleType.POWER, -1.0, 1e-10, 80.0, ValueError,
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"Input ref_value is not within the required interval of (0, 16777216]")
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test_invalid_input("invalid amin parameter value", ScaleType.POWER, 1.0, -1e-10, 80.0, ValueError,
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"Input amin is not within the required interval of (0, 16777216]")
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test_invalid_input("invalid top_db parameter value", ScaleType.POWER, 1.0, 1e-10, -80.0, ValueError,
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"Input top_db is not within the required interval of (0, 16777216]")
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test_invalid_input("invalid stype parameter value", True, 1.0, 1e-10, 80.0, TypeError,
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"Argument stype with value True is not of type [<enum 'ScaleType'>], but got <class 'bool'>.")
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test_invalid_input("invalid ref_value parameter value", ScaleType.POWER, "value", 1e-10, 80.0, TypeError,
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"Argument ref_value with value value is not of type [<class 'int'>, <class 'float'>], " +
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"but got <class 'str'>")
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test_invalid_input("invalid amin parameter value", ScaleType.POWER, 1.0, "value", -80.0, TypeError,
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"Argument amin with value value is not of type [<class 'int'>, <class 'float'>], " +
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"but got <class 'str'>")
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test_invalid_input("invalid top_db parameter value", ScaleType.POWER, 1.0, 1e-10, "value", TypeError,
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"Argument top_db with value value is not of type [<class 'int'>, <class 'float'>], " +
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"but got <class 'str'>")
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if __name__ == "__main__":
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test_func_amplitude_to_db_eager()
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test_func_amplitude_to_db_pipeline()
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test_amplitude_to_db_invalid_input()
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