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- # Copyright 2020 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.
- # ==============================================================================
- """
- Testing unique op in DE
- """
- import numpy as np
-
- import mindspore.dataset as ds
- import mindspore.dataset.transforms.c_transforms as ops
-
-
- def compare(array, res, idx, cnt):
- data = ds.NumpySlicesDataset([array], column_names="x")
- data = data.batch(2)
- data = data.map(operations=ops.Unique(), input_columns=["x"], output_columns=["x", "y", "z"],
- column_order=["x", "y", "z"])
- for d in data.create_dict_iterator(num_epochs=1, output_numpy=True):
- np.testing.assert_array_equal(res, d["x"])
- np.testing.assert_array_equal(idx, d["y"])
- np.testing.assert_array_equal(cnt, d["z"])
-
- # the test function name code will be start with 'test' later
- def duplicate_basics():
- compare([0, 1, 2, 1, 2, 3], np.array([0, 1, 2, 3]),
- np.array([0, 1, 2, 1, 2, 3]), np.array([1, 2, 2, 1]))
- compare([0.0, 1.0, 2.0, 1.0, 2.0, 3.0], np.array([0.0, 1.0, 2.0, 3.0]),
- np.array([0, 1, 2, 1, 2, 3]), np.array([1, 2, 2, 1]))
- compare([1, 1, 1, 1, 1, 1], np.array([1]),
- np.array([0, 0, 0, 0, 0, 0]), np.array([6]))
-
-
- if __name__ == "__main__":
- test_duplicate_basics()
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