forked from mindspore-Ecosystem/mindspore
48 lines
1.7 KiB
Python
48 lines
1.7 KiB
Python
# Copyright 2019 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 pytest
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import numpy as np
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from mindspore import Tensor
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from mindspore.ops import operations as P
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import mindspore.nn as nn
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import mindspore.context as context
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class NetEqualCount(nn.Cell):
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def __init__(self):
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super(NetEqualCount, self).__init__()
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self.equalcount = P.EqualCount()
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def construct(self, x, y):
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return self.equalcount(x, y)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_equalcount():
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x = Tensor(np.array([1, 20, 5]).astype(np.int32))
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y = Tensor(np.array([2, 20, 5]).astype(np.int32))
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expect = np.array([2]).astype(np.int32)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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equal_count = NetEqualCount()
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output = equal_count(x, y)
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assert (output.asnumpy() == expect).all()
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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equal_count = NetEqualCount()
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output = equal_count(x, y)
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assert (output.asnumpy() == expect).all()
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