130 lines
5.8 KiB
Python
130 lines
5.8 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 TimeMasking 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 as audio
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from mindspore import log as logger
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CHANNEL = 2
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FREQ = 20
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TIME = 30
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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_time_masking_eager_random_input():
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""" mindspore eager mode normal testcase:time_masking op"""
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logger.info("test time_masking op")
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spectrogram = next(gen((CHANNEL, FREQ, TIME)))[0]
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out_put = audio.TimeMasking(False, 3, 1, 10)(spectrogram)
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assert out_put.shape == (CHANNEL, FREQ, TIME)
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def test_func_time_masking_eager_precision():
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""" mindspore eager mode normal testcase:time_masking op"""
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logger.info("test time_masking op")
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spectrogram = np.array([[[0.17274511, 0.85174704, 0.07162686, -0.45436913],
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[-1.045921, -1.8204843, 0.62333095, -0.09532598],
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[1.8175547, -0.25779432, -0.58152324, -0.00221091]],
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[[-1.205032, 0.18922766, -0.5277673, -1.3090396],
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[1.8914849, -0.97001046, -0.23726775, 0.00525892],
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[-1.0271876, 0.33526883, 1.7413973, 0.12313101]]]).astype(np.float32)
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out_ms = audio.TimeMasking(False, 2, 0, 0)(spectrogram)
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out_benchmark = np.array([[[0., 0., 0.07162686, -0.45436913],
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[0., 0., 0.62333095, -0.09532598],
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[0., 0., -0.58152324, -0.00221091]],
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[[0., 0., -0.5277673, -1.3090396],
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[0., 0., -0.23726775, 0.00525892],
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[0., 0., 1.7413973, 0.12313101]]]).astype(np.float32)
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allclose_nparray(out_ms, out_benchmark, 0.0001, 0.0001)
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def test_func_time_masking_pipeline():
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""" mindspore pipeline mode normal testcase:time_masking op"""
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logger.info("test time_masking op, pipeline")
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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 = [audio.TimeMasking(True, 8)]
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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_time_masking_invalid_input():
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def test_invalid_param(test_name, iid_masks, time_mask_param, mask_start, error, error_msg):
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logger.info("Test TimeMasking with wrong params: {0}".format(test_name))
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with pytest.raises(error) as error_info:
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audio.TimeMasking(iid_masks, time_mask_param, mask_start)
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assert error_msg in str(error_info.value)
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def test_invalid_input(test_name, iid_masks, time_mask_param, mask_start, error, error_msg):
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logger.info("Test TimeMasking with wrong params: {0}".format(test_name))
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with pytest.raises(error) as error_info:
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spectrogram = next(gen((CHANNEL, FREQ, TIME)))[0]
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_ = audio.TimeMasking(iid_masks, time_mask_param, mask_start)(spectrogram)
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assert error_msg in str(error_info.value)
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test_invalid_param("invalid mask_start", True, 2, -10, ValueError,
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"Input mask_start is not within the required interval of [0, 16777216].")
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test_invalid_param("invalid mask_param", True, -2, 10, ValueError,
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"Input mask_param is not within the required interval of [0, 16777216].")
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test_invalid_param("invalid iid_masks", "True", 2, 10, TypeError,
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"Argument iid_masks with value True is not of type [<class 'bool'>], but got <class 'str'>.")
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test_invalid_input("invalid mask_start", False, 2, 100, RuntimeError,
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"'mask_start' should be less than the length of the masked dimension")
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test_invalid_input("invalid mask_width", False, 200, 2, RuntimeError,
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"'time_mask_param' should be less than or equal to the length of time dimension")
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if __name__ == "__main__":
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test_func_time_masking_eager_random_input()
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test_func_time_masking_eager_precision()
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test_func_time_masking_pipeline()
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test_time_masking_invalid_input()
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