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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
19from mindspore import Tensor
20from mindspore.common.api import ms_function
21from mindspore.common.initializer import initializer
22from mindspore.common.parameter import Parameter
23from mindspore.ops import operations as P
24from mindspore.ops.composite import GradOperation
25
26context.set_context(device_target="Ascend")
27
28
29class Grad(nn.Cell):
30    def __init__(self, network):
31        super(Grad, self).__init__()
32        self.grad = GradOperation(get_all=True, sens_param=True)
33        self.network = network
34
35    @ms_function
36    def construct(self, input_, output_grad):
37        return self.grad(self.network)(input_, output_grad)
38
39
40class Net(nn.Cell):
41    def __init__(self):
42        super(Net, self).__init__()
43        out_channel = 512
44        kernel_size = 2048
45        self.conv = P.Conv2D(out_channel,
46                             (kernel_size, kernel_size),
47                             mode=1,
48                             pad_mode="same",
49                             pad=3,
50                             stride=2,
51                             dilation=1,
52                             group=1)
53        self.w = Parameter(initializer(
54            'normal', [512, 2048, 1, 1]), name='w')
55
56    @ms_function
57    def construct(self, x):
58        return self.conv(x, self.w)
59
60
61def test_net():
62    x = np.ones([32, 2048, 7, 7]).astype(np.float32)
63    sens = np.ones([32, 512, 7, 7]).astype(np.float32)
64    net = Grad(Net())
65    output = net(Tensor(x), Tensor(sens))
66    print(output.asnumpy())
67