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1# Copyright 2020 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
24context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
25
26
27class Net2Inputs(nn.Cell):
28    def __init__(self):
29        super(Net2Inputs, self).__init__()
30        self.addn = P.AddN()
31
32    def construct(self, x, y):
33        return self.addn((x, y))
34
35
36@pytest.mark.level0
37@pytest.mark.platform_x86_cpu
38@pytest.mark.env_onecard
39def test_two_tensors_add():
40    x = np.arange(2 * 3 * 2).reshape((2, 3, 2))
41    y = np.arange(88, 2 * 3 * 2 + 88).reshape((2, 3, 2))
42    addn_net = Net2Inputs()
43    dtypes = (np.int32, np.float32)
44    for dtype in dtypes:
45        output = addn_net(Tensor(x.astype(dtype)), Tensor(y.astype(dtype)))
46        expect_result = (x + y).astype(dtype)
47        assert output.asnumpy().dtype == expect_result.dtype
48        assert np.array_equal(output.asnumpy(), expect_result)
49
50
51class Net4Inputs(nn.Cell):
52    def __init__(self):
53        super(Net4Inputs, self).__init__()
54        self.addn = P.AddN()
55
56    def construct(self, x, y, m, n):
57        return self.addn((x, y, m, n))
58
59
60@pytest.mark.level0
61@pytest.mark.platform_x86_cpu
62@pytest.mark.env_onecard
63def test_four_tensors_add():
64    x = np.arange(2 * 3).reshape((2, 3))
65    y = np.arange(1, 2 * 3 + 1).reshape((2, 3))
66    m = np.arange(2, 2 * 3 + 2).reshape((2, 3))
67    n = np.arange(3, 2 * 3 + 3).reshape((2, 3))
68    addn_net = Net4Inputs()
69    dtypes = (np.int32, np.float32)
70    for dtype in dtypes:
71        output = addn_net(Tensor(x.astype(dtype)), Tensor(y.astype(dtype)),
72                          Tensor(m.astype(dtype)), Tensor(n.astype(dtype)))
73        expect_result = (x + y + m + n).astype(dtype)
74        assert output.asnumpy().dtype == expect_result.dtype
75        assert np.array_equal(output.asnumpy(), expect_result)
76
77
78if __name__ == '__main__':
79    test_two_tensors_add()
80    test_four_tensors_add()
81