forked from huawei/mindspore2022
168 lines
7.2 KiB
Python
168 lines
7.2 KiB
Python
# Copyright 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 copy
|
|
import numpy as np
|
|
import pytest
|
|
|
|
import mindspore.dataset as ds
|
|
import mindspore.dataset.audio.transforms as atf
|
|
from mindspore import log as logger
|
|
|
|
CHANNEL = 1
|
|
FREQ = 5
|
|
TIME = 5
|
|
|
|
|
|
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 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 gen(shape):
|
|
np.random.seed(0)
|
|
data = np.random.random(shape)
|
|
yield(np.array(data, dtype=np.float32),)
|
|
|
|
|
|
def test_mask_along_axis_eager_random_input():
|
|
"""
|
|
Feature: MaskAlongAxis
|
|
Description: mindspore eager mode normal testcase with random input tensor
|
|
Expectation: the returned result is as expected
|
|
"""
|
|
logger.info("test Mask_Along_axis op")
|
|
spectrogram = next(gen((CHANNEL, FREQ, TIME)))[0]
|
|
expect_output = copy.deepcopy(spectrogram)
|
|
out_put = atf.MaskAlongAxis(mask_start=0, mask_width=1, mask_value=5.0, axis=2)(spectrogram)
|
|
for item in expect_output[0]:
|
|
item[0] = 5.0
|
|
assert out_put.shape == (CHANNEL, FREQ, TIME)
|
|
allclose_nparray(out_put, expect_output, 0.0001, 0.0001)
|
|
|
|
|
|
def test_mask_along_axis_eager_precision():
|
|
"""
|
|
Feature: MaskAlongAxis
|
|
Description: mindspore eager mode checking precision
|
|
Expectation: the returned result is as expected
|
|
"""
|
|
logger.info("test MaskAlongAxis op, checking precision")
|
|
spectrogram_0 = np.array([[[-0.0635, -0.6903],
|
|
[-1.7175, -0.0815],
|
|
[0.7981, -0.8297],
|
|
[-0.4589, -0.7506]],
|
|
[[0.6189, 1.1874],
|
|
[0.1856, -0.5536],
|
|
[1.0620, 0.2071],
|
|
[-0.3874, 0.0664]]]).astype(np.float32)
|
|
out_ms_0 = atf.MaskAlongAxis(mask_start=0, mask_width=1, mask_value=2.0, axis=2)(spectrogram_0)
|
|
spectrogram_1 = np.array([[[-0.0635, -0.6903],
|
|
[-1.7175, -0.0815],
|
|
[0.7981, -0.8297],
|
|
[-0.4589, -0.7506]],
|
|
[[0.6189, 1.1874],
|
|
[0.1856, -0.5536],
|
|
[1.0620, 0.2071],
|
|
[-0.3874, 0.0664]]]).astype(np.float64)
|
|
out_ms_1 = atf.MaskAlongAxis(mask_start=0, mask_width=1, mask_value=2.0, axis=2)(spectrogram_1)
|
|
out_benchmark = np.array([[[2.0000, -0.6903],
|
|
[2.0000, -0.0815],
|
|
[2.0000, -0.8297],
|
|
[2.0000, -0.7506]],
|
|
[[2.0000, 1.1874],
|
|
[2.0000, -0.5536],
|
|
[2.0000, 0.2071],
|
|
[2.0000, 0.0664]]]).astype(np.float32)
|
|
allclose_nparray(out_ms_0, out_benchmark, 0.0001, 0.0001)
|
|
allclose_nparray(out_ms_1, out_benchmark, 0.0001, 0.0001)
|
|
|
|
|
|
def test_mask_along_axis_pipeline():
|
|
"""
|
|
Feature: MaskAlongAxis
|
|
Description: mindspore pipeline mode normal testcase
|
|
Expectation: the returned result is as expected
|
|
"""
|
|
logger.info("test MaskAlongAxis op, pipeline")
|
|
|
|
generator = gen((CHANNEL, FREQ, TIME))
|
|
expect_output = copy.deepcopy(next(gen((CHANNEL, FREQ, TIME)))[0])
|
|
data1 = ds.GeneratorDataset(source=generator, column_names=["multi_dimensional_data"])
|
|
transforms = [atf.MaskAlongAxis(mask_start=2, mask_width=2, mask_value=2.0, axis=2)]
|
|
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"]
|
|
|
|
for item in expect_output[0]:
|
|
item[2] = 2.0
|
|
item[3] = 2.0
|
|
assert out_put.shape == (CHANNEL, FREQ, TIME)
|
|
allclose_nparray(out_put, expect_output, 0.0001, 0.0001)
|
|
|
|
|
|
def test_mask_along_axis_invalid_input():
|
|
"""
|
|
Feature: MaskAlongAxis
|
|
Description: mindspore eager mode with invalid input tensor
|
|
Expectation: throw correct error and message
|
|
"""
|
|
def test_invalid_param(test_name, mask_start, mask_width, mask_value, axis, error, error_msg):
|
|
"""
|
|
a function used for checking correct error and message with various input
|
|
"""
|
|
logger.info("Test MaskAlongAxis with wrong params: {0}".format(test_name))
|
|
with pytest.raises(error) as error_info:
|
|
atf.MaskAlongAxis(mask_start, mask_width, mask_value, axis)
|
|
assert error_msg in str(error_info.value)
|
|
|
|
test_invalid_param("invalid mask_start", -1, 10, 1.0, 1, ValueError,
|
|
"Input mask_start is not within the required interval of [0, 2147483647].")
|
|
test_invalid_param("invalid mask_width", 0, -1, 1.0, 1, ValueError,
|
|
"Input mask_width is not within the required interval of [1, 2147483647].")
|
|
test_invalid_param("invalid axis", 0, 10, 1.0, 1.0, TypeError,
|
|
"Argument axis with value 1.0 is not of type [<class 'int'>], but got <class 'float'>.")
|
|
test_invalid_param("invalid axis", 0, 10, 1.0, 0, ValueError,
|
|
"Input axis is not within the required interval of [1, 2].")
|
|
test_invalid_param("invalid axis", 0, 10, 1.0, 3, ValueError,
|
|
"Input axis is not within the required interval of [1, 2].")
|
|
test_invalid_param("invalid axis", 0, 10, 1.0, -1, ValueError,
|
|
"Input axis is not within the required interval of [1, 2].")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
test_mask_along_axis_eager_random_input()
|
|
test_mask_along_axis_eager_precision()
|
|
test_mask_along_axis_pipeline()
|
|
test_mask_along_axis_invalid_input()
|