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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# ============================================================================
15import numpy as np
16
17import mindspore.context as context
18import mindspore.nn as nn
19import mindspore.ops.composite as C
20from mindspore import Tensor
21from mindspore.ops import operations as P
22
23context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
24
25
26class Net(nn.Cell):
27    def __init__(self):
28        super(Net, self).__init__()
29        self.add = P.AddN()
30
31    def construct(self, x, y):
32        return self.add((x, y))
33
34
35def test_net():
36    x = np.random.randn(1, 3, 3, 4).astype(np.float32)
37    y = np.random.randn(1, 3, 3, 4).astype(np.float32)
38    add = Net()
39    output = add(Tensor(x), Tensor(y))
40    print(x)
41    print(y)
42    print(output.asnumpy())
43    x = 1.0
44    y = 2.0
45    expect = 3.0
46    add = Net()
47    output = add(x, y)
48    assert output == expect
49
50
51def test_grad_addn_with_list():
52    grad_op = C.GradOperation(get_all=True)
53    class AddN(nn.Cell):
54        def __init__(self):
55            super().__init__()
56            self.add_n = P.AddN()
57
58        def construct(self, a, b):
59            return self.add_n([a, b])
60
61    inp = Tensor(np.ones([128, 96]).astype(np.float32))
62    grad_op(AddN())(inp, inp)
63