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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.
- # ============================================================================
- import numpy as np
-
- import mindspore.context as context
- import mindspore.nn as nn
- import mindspore.common.dtype as mstype
- from mindspore import Tensor
- from mindspore.ops import operations as P
-
- context.set_context(mode=context.GRAPH_MODE,
- device_target="Ascend")
-
-
- class Net(nn.Cell):
- def __init__(self, pad_num):
- super(Net, self).__init__()
- self.unique_with_pad = P.UniqueWithPad()
- self.pad_num = pad_num
-
- def construct(self, x):
- return self.unique_with_pad(x, self.pad_num)
-
-
- def test_unique_with_pad():
- x = Tensor(np.array([1, 1, 5, 5, 4, 4, 3, 3, 2, 2]), mstype.int32)
- pad_num = 8
- unique_with_pad = Net(pad_num)
- out = unique_with_pad(x)
- expect_val = ([1, 5, 4, 3, 2, 8, 8, 8, 8, 8], [0, 0, 1, 1, 2, 2, 3, 3, 4, 4])
- assert(out[0].asnumpy() == expect_val[0]).all()
- assert(out[1].asnumpy() == expect_val[1]).all()
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