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- # Copyright 2021 Huawei Technologies Co., Ltd
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- # ==============================================================================
- """
- Testing AmplitudeToDB op in DE
- """
- import numpy as np
- import pytest
-
- import mindspore.dataset as ds
- import mindspore.dataset.audio.transforms as c_audio
- from mindspore import log as logger
- from mindspore.dataset.audio.utils import ScaleType
-
- CHANNEL = 1
- FREQ = 20
- TIME = 15
-
-
- def gen(shape):
- np.random.seed(0)
- data = np.random.random(shape)
- yield (np.array(data, dtype=np.float32),)
-
-
- def count_unequal_element(data_expected, data_me, rtol, atol):
- """ Precision calculation func """
- assert data_expected.shape == data_me.shape
- total_count = len(data_expected.flatten())
- error = np.abs(data_expected - data_me)
- greater = np.greater(error, atol + np.abs(data_expected) * rtol)
- loss_count = np.count_nonzero(greater)
- assert (loss_count / total_count) < rtol, "\ndata_expected_std:{0}\ndata_me_error:{1}\nloss:{2}".format(
- data_expected[greater], data_me[greater], error[greater])
-
-
- def allclose_nparray(data_expected, data_me, rtol, atol, equal_nan=True):
- """ Precision calculation formula """
- if np.any(np.isnan(data_expected)):
- assert np.allclose(data_me, data_expected, rtol, atol, equal_nan=equal_nan)
- elif not np.allclose(data_me, data_expected, rtol, atol, equal_nan=equal_nan):
- count_unequal_element(data_expected, data_me, rtol, atol)
-
-
- def test_func_amplitude_to_db_eager():
- """ mindspore eager mode normal testcase:amplitude_to_db op"""
-
- logger.info("check amplitude_to_db op output")
- ndarr_in = np.array([[[[-0.2197528, 0.3821656]]],
- [[[0.57418776, 0.46741104]]],
- [[[-0.20381108, -0.9303914]]],
- [[[0.3693608, -0.2017813]]],
- [[[-1.727381, -1.3708513]]],
- [[[1.259975, 0.4981323]]],
- [[[0.76986176, -0.5793846]]]]).astype(np.float32)
- # cal from benchmark
- out_expect = np.array([[[[-84.17748, -4.177484]]],
- [[[-2.4094608, -3.3030105]]],
- [[[-100., -100.]]],
- [[[-4.325492, -84.32549]]],
- [[[-100., -100.]]],
- [[[1.0036192, -3.0265532]]],
- [[[-1.1358725, -81.13587]]]]).astype(np.float32)
-
- amplitude_to_db_op = c_audio.AmplitudeToDB()
- out_mindspore = amplitude_to_db_op(ndarr_in)
-
- allclose_nparray(out_mindspore, out_expect, 0.0001, 0.0001)
-
-
- def test_func_amplitude_to_db_pipeline():
- """ mindspore pipeline mode normal testcase:amplitude_to_db op"""
-
- logger.info("test AmplitudeToDB op with default value")
- generator = gen([CHANNEL, FREQ, TIME])
-
- data1 = ds.GeneratorDataset(source=generator, column_names=["multi_dimensional_data"])
-
- transforms = [c_audio.AmplitudeToDB()]
- data1 = data1.map(operations=transforms, input_columns=["multi_dimensional_data"])
-
- for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
- out_put = item["multi_dimensional_data"]
- assert out_put.shape == (CHANNEL, FREQ, TIME)
-
-
- def test_amplitude_to_db_invalid_input():
- def test_invalid_input(test_name, stype, ref_value, amin, top_db, error, error_msg):
- logger.info("Test AmplitudeToDB with bad input: {0}".format(test_name))
- with pytest.raises(error) as error_info:
- c_audio.AmplitudeToDB(stype=stype, ref_value=ref_value, amin=amin, top_db=top_db)
- assert error_msg in str(error_info.value)
-
- test_invalid_input("invalid stype parameter value", "test", 1.0, 1e-10, 80.0, TypeError,
- "Argument stype with value test is not of type [<enum 'ScaleType'>], but got <class 'str'>.")
- test_invalid_input("invalid ref_value parameter value", ScaleType.POWER, -1.0, 1e-10, 80.0, ValueError,
- "Input ref_value is not within the required interval of (0, 16777216]")
- test_invalid_input("invalid amin parameter value", ScaleType.POWER, 1.0, -1e-10, 80.0, ValueError,
- "Input amin is not within the required interval of (0, 16777216]")
- test_invalid_input("invalid top_db parameter value", ScaleType.POWER, 1.0, 1e-10, -80.0, ValueError,
- "Input top_db is not within the required interval of (0, 16777216]")
-
- test_invalid_input("invalid stype parameter value", True, 1.0, 1e-10, 80.0, TypeError,
- "Argument stype with value True is not of type [<enum 'ScaleType'>], but got <class 'bool'>.")
- test_invalid_input("invalid ref_value parameter value", ScaleType.POWER, "value", 1e-10, 80.0, TypeError,
- "Argument ref_value with value value is not of type [<class 'int'>, <class 'float'>], " +
- "but got <class 'str'>")
- test_invalid_input("invalid amin parameter value", ScaleType.POWER, 1.0, "value", -80.0, TypeError,
- "Argument amin with value value is not of type [<class 'int'>, <class 'float'>], " +
- "but got <class 'str'>")
- test_invalid_input("invalid top_db parameter value", ScaleType.POWER, 1.0, 1e-10, "value", TypeError,
- "Argument top_db with value value is not of type [<class 'int'>, <class 'float'>], " +
- "but got <class 'str'>")
-
-
- if __name__ == "__main__":
- test_func_amplitude_to_db_eager()
- test_func_amplitude_to_db_pipeline()
- test_amplitude_to_db_invalid_input()
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