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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 pytest
-
- import mindspore.context as context
- import mindspore.nn as nn
- from mindspore import Tensor
- from mindspore.ops import operations as P
-
-
- class Net(nn.Cell):
- def __init__(self):
- super(Net, self).__init__()
- self.select = P.Select()
-
- def construct(self, cond_op, input_x, input_y):
- return self.select(cond_op, input_x, input_y)
-
-
- cond = np.array([[True, False], [True, False]]).astype(np.bool)
- x = np.array([[1.2, 1], [1, 0]]).astype(np.float32)
- y = np.array([[1, 2], [3, 4.0]]).astype(np.float32)
-
-
- @pytest.mark.level0
- @pytest.mark.platform_x86_gpu_training
- @pytest.mark.env_onecard
- def test_select():
- context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
- select = Net()
- output = select(Tensor(cond), Tensor(x), Tensor(y))
- expect = [[1.2, 2], [1, 4.0]]
- error = np.ones(shape=[2, 2]) * 1.0e-6
- diff = output.asnumpy() - expect
- assert np.all(diff < error)
- assert np.all(-diff < error)
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