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1# Copyright 2019 Huawei Technologies Co., Ltd
2#
3# Licensed under the Apache License, Version 2.0 (the "License");
4# you may not use this file except in compliance with the License.
5# You may obtain a copy of the License at
6#
7# http://www.apache.org/licenses/LICENSE-2.0
8#
9# Unless required by applicable law or agreed to in writing, software
10# distributed under the License is distributed on an "AS IS" BASIS,
11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12# See the License for the specific language governing permissions and
13# limitations under the License.
14# ============================================================================
15
16import numpy as np
17import pytest
18
19import mindspore.context as context
20import mindspore.nn as nn
21from mindspore import Tensor
22from mindspore.ops import operations as P
23
24
25class NetEqualCount(nn.Cell):
26    def __init__(self):
27        super(NetEqualCount, self).__init__()
28        self.equalcount = P.EqualCount()
29
30    def construct(self, x, y):
31        return self.equalcount(x, y)
32
33
34@pytest.mark.level0
35@pytest.mark.platform_x86_gpu_training
36@pytest.mark.env_onecard
37def test_equalcount():
38    x = Tensor(np.array([1, 20, 5]).astype(np.int32))
39    y = Tensor(np.array([2, 20, 5]).astype(np.int32))
40    expect = np.array([2]).astype(np.int32)
41
42    context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
43    equal_count = NetEqualCount()
44    output = equal_count(x, y)
45    assert (output.asnumpy() == expect).all()
46
47    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
48    equal_count = NetEqualCount()
49    output = equal_count(x, y)
50    assert (output.asnumpy() == expect).all()
51