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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.common import dtype as mstype
- from mindspore.ops import operations as P
-
-
- class NetArgmax(nn.Cell):
- def __init__(self):
- super(NetArgmax, self).__init__()
- axis1 = 0
- axis2 = -1
- self.argmax1 = P.Argmax(axis1, output_type=mstype.int32)
- self.argmax2 = P.Argmax(axis2, output_type=mstype.int32)
- self.argmax3 = P.Argmax(output_type=mstype.int32)
-
- def construct(self, x):
- return (self.argmax1(x), self.argmax2(x), self.argmax3(x))
-
-
- @pytest.mark.level0
- @pytest.mark.platform_x86_gpu_training
- @pytest.mark.env_onecard
- def test_argmax():
- x = Tensor(np.array([[1., 20., 5.],
- [67., 8., 9.],
- [130., 24., 15.],
- [0.3, -0.4, -15.]]).astype(np.float32))
- expect1 = np.array([2, 2, 2]).astype(np.int32)
- expect2 = np.array([1, 0, 0, 0]).astype(np.int32)
-
- context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
- argmax = NetArgmax()
- output = argmax(x)
- assert (output[0].asnumpy() == expect1).all()
- assert (output[1].asnumpy() == expect2).all()
- assert (output[2].asnumpy() == expect2).all()
-
- context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
- argmax1 = NetArgmax()
- output1 = argmax1(x)
- assert (output1[0].asnumpy() == expect1).all()
- assert (output1[1].asnumpy() == expect2).all()
- assert (output1[2].asnumpy() == expect2).all()
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