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- # Copyright 2021 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
-
- context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
-
-
- class Netnan(nn.Cell):
- def __init__(self):
- super(Netnan, self).__init__()
- self.isnan = P.IsNan()
-
- def construct(self, x):
- return self.isnan(x)
-
-
- x1 = np.array([[1.2, 2, np.nan, 88]]).astype(np.float32)
- x2 = np.array([[np.inf, 1, 88.0, 0]]).astype(np.float32)
- x3 = np.array([[1, 2], [3, 4], [5.0, 88.0]]).astype(np.float32)
-
-
- @pytest.mark.level0
- @pytest.mark.platform_x86_cpu
- @pytest.mark.env_onecard
- def test_nan():
- ms_isnan = Netnan()
- output1 = ms_isnan(Tensor(x1))
- expect1 = [[False, False, True, False]]
- assert (output1.asnumpy() == expect1).all()
-
- output2 = ms_isnan(Tensor(x2))
- expect2 = [[False, False, False, False]]
- assert (output2.asnumpy() == expect2).all()
-
- output3 = ms_isnan(Tensor(x3))
- expect3 = [[False, False], [False, False], [False, False]]
- assert (output3.asnumpy() == expect3).all()
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