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48 lines
1.5 KiB
48 lines
1.5 KiB
5 years ago
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# 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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import numpy as np
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.common import dtype as mstype
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from mindspore.ops import operations as P
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context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.uniq = P.Unique()
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def construct(self, x):
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return self.uniq(x)
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def test_net():
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x = Tensor(np.array([1, 2, 5, 2]), mstype.float32)
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uniq = Net()
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output = uniq(x)
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print("x:\n", x)
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print("y:\n", output[0])
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print("idx:\n", output[1])
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expect_y_result = [1., 2., 5.]
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expect_idx_result = [0, 1, 2, 1]
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assert (output[0].asnumpy() == expect_y_result).all()
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assert (output[1].asnumpy() == expect_idx_result).all()
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