mindspore/tests/ut/python/dataset/test_overdrive.py

101 lines
4.1 KiB
Python

# Copyright 2021-2022 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.
# ==============================================================================
import numpy as np
import pytest
import mindspore.dataset as ds
import mindspore.dataset.audio as audio
from mindspore import log as logger
def count_unequal_element(data_expected, data_me, rtol, atol):
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 test_overdrive_eager():
"""
Feature: Overdrive
Description: Test Overdrive in eager mode
Expectation: The results are as expected
"""
# Original waveform
waveform = np.array([[1.47, 4.722, 5.863], [0.492, 0.235, 0.56]], dtype=np.float32)
# Expect waveform
expect_waveform = np.array([[1., 1., 1.],
[0.74600005, 0.615, 0.77501255]], dtype=np.float32)
overdrive_op = audio.Overdrive()
# Filtered waveform by overdrive
output = overdrive_op(waveform)
count_unequal_element(expect_waveform, output, 0.0001, 0.0001)
def test_overdrive_pipeline():
"""
Feature: Overdrive
Description: Test Overdrive in pipeline mode
Expectation: The results are as expected
"""
# Original waveform
waveform = np.array([[0.1, 0.2], [0.4, 2.6]], dtype=np.float32)
# Expect waveform
expect_waveform = np.array([[0.29598799, 0.52081579],
[0.7, 1.]], dtype=np.float32)
dataset = ds.NumpySlicesDataset(waveform, ["waveform"], shuffle=False)
overdrive_op = audio.Overdrive(10.0, 5.0)
# Filtered waveform by overdrive
dataset = dataset.map(
input_columns=["waveform"], operations=overdrive_op)
i = 0
for item in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
count_unequal_element(expect_waveform[i, :],
item['waveform'], 0.0001, 0.0001)
i += 1
def test_overdrive_invalid_input():
"""
Feature: Overdrive
Description: Test invalid parameter of Overdrive
Expectation: Catch exceptions correctly
"""
def test_invalid_input(test_name, gain, color, error, error_msg):
logger.info("Test Overdrive with bad input: {0}".format(test_name))
with pytest.raises(error) as error_info:
audio.Overdrive(gain, color)
assert error_msg in str(error_info.value)
test_invalid_input("invalid gain parameter type as a str", "20", 20, TypeError,
"Argument gain with value 20 is not of type [<class 'float'>, <class 'int'>],"
+ " but got <class 'str'>.")
test_invalid_input("invalid color parameter type as a str", 10, "5", TypeError,
"Argument color with value 5 is not of type [<class 'float'>, <class 'int'>],"
+ " but got <class 'str'>.")
test_invalid_input("invalid gain out of range [0, 100]", 100.23, 5.0, ValueError,
"Input gain is not within the required interval of [0, 100].")
test_invalid_input("invalid color out of range [0, 100]", 30, -0.333, ValueError,
"Input color is not within the required interval of [0, 100].")
if __name__ == "__main__":
test_overdrive_eager()
test_overdrive_pipeline()
test_overdrive_invalid_input()