forked from huawei/mindspore2022
85 lines
3.4 KiB
Python
Executable File
85 lines
3.4 KiB
Python
Executable File
# 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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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 a_c_trans
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def count_unequal_element(data_expected, data_me, rtol, atol):
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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 test_func_angle_001():
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"""
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Eager Test
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"""
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arr = np.array([[73.04, -13.00], [57.49, 13.20], [-57.64, 6.51], [-52.25, 30.67], [-30.11, -18.34],
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[-63.32, 99.33], [95.82, -24.76]], dtype=np.double)
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expected = np.array([-0.17614017, 0.22569334, 3.02912684, 2.6107975, -2.59450886, 2.13831337, -0.25286988],
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dtype=np.double)
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angle_op = a_c_trans.Angle()
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output = angle_op(arr)
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count_unequal_element(expected, output, 0.0001, 0.0001)
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def test_func_angle_002():
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"""
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Pipeline Test
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"""
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np.random.seed(6)
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arr = np.array([[[84.25, -85.92], [-92.23, 23.06], [-7.33, -44.17], [-62.95, -14.73]],
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[[93.09, 38.18], [-81.94, 71.34], [71.33, -39.00], [95.25, -32.94]]], dtype=np.double)
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expected = np.array([[-0.79521156, 2.89658848, -1.73524737, -2.91173309],
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[0.3892177, 2.42523905, -0.50034807, -0.33295219]], dtype=np.double)
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label = np.random.sample((2, 4, 1))
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data = (arr, label)
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dataset = ds.NumpySlicesDataset(data, column_names=["col1", "col2"], shuffle=False)
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angle_op = a_c_trans.Angle()
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dataset = dataset.map(operations=angle_op, input_columns=["col1"])
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for item1, item2 in zip(dataset.create_dict_iterator(num_epochs=1, output_numpy=True), expected):
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count_unequal_element(item2, item1['col1'], 0.0001, 0.0001)
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def test_func_angle_003():
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"""
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Pipeline Error Test
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"""
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np.random.seed(78)
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arr = np.array([["11", "22"], ["33", "44"], ["55", "66"], ["77", "88"]])
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label = np.random.sample((4, 1))
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data = (arr, label)
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dataset = ds.NumpySlicesDataset(data, column_names=["col1", 'col2'], shuffle=False)
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angle_op = a_c_trans.Angle()
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dataset = dataset.map(operations=angle_op, input_columns=["col1"])
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num_itr = 0
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with pytest.raises(RuntimeError, match="the data type of input tensor does not match the requirement of operator"):
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for _ in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
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num_itr += 1
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if __name__ == "__main__":
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test_func_angle_001()
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test_func_angle_002()
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test_func_angle_003()
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