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45 lines
1.7 KiB
45 lines
1.7 KiB
# Copyright 2020 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 unique op in DE
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"""
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import numpy as np
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import mindspore.dataset as ds
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import mindspore.dataset.transforms.c_transforms as ops
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def compare(array, res, idx, cnt):
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data = ds.NumpySlicesDataset([array], column_names="x")
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data = data.batch(2)
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data = data.map(operations=ops.Unique(), input_columns=["x"], output_columns=["x", "y", "z"],
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column_order=["x", "y", "z"])
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for d in data.create_dict_iterator(num_epochs=1, output_numpy=True):
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np.testing.assert_array_equal(res, d["x"])
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np.testing.assert_array_equal(idx, d["y"])
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np.testing.assert_array_equal(cnt, d["z"])
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def test_duplicate_basics():
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compare([0, 1, 2, 1, 2, 3], np.array([0, 1, 2, 3]),
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np.array([0, 1, 2, 1, 2, 3]), np.array([1, 2, 2, 1]))
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compare([0.0, 1.0, 2.0, 1.0, 2.0, 3.0], np.array([0.0, 1.0, 2.0, 3.0]),
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np.array([0, 1, 2, 1, 2, 3]), np.array([1, 2, 2, 1]))
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compare([1, 1, 1, 1, 1, 1], np.array([1]),
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np.array([0, 0, 0, 0, 0, 0]), np.array([6]))
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if __name__ == "__main__":
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test_duplicate_basics()
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