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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# ============================================================================
15import numpy as np
16import pytest
17
18import mindspore.context as context
19import mindspore.nn as nn
20from mindspore import Tensor
21from mindspore.ops import operations as P
22from mindspore.ops.operations import _grad_ops as G
23
24context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
25
26class NetSqrtGrad(nn.Cell):
27    def __init__(self):
28        super(NetSqrtGrad, self).__init__()
29        self.sqrt_grad = G.SqrtGrad()
30
31    def construct(self, x, dx):
32        return self.sqrt_grad(x, dx)
33
34
35class Net(nn.Cell):
36    def __init__(self):
37        super(Net, self).__init__()
38        self.ops = P.Sqrt()
39
40    def construct(self, x):
41        return self.ops(x)
42
43@pytest.mark.level0
44@pytest.mark.platform_x86_cpu
45@pytest.mark.env_onecard
46def test_net():
47    x = np.abs(np.random.randn(2, 3, 3, 4)).astype(np.float32)
48    y_expect = np.sqrt(x)
49    net = Net()
50    out = net(Tensor(x))
51    diff = out.asnumpy() - y_expect
52    err = np.ones(shape=y_expect.shape) * 1.0e-5
53    assert np.all(diff < err)
54    assert out.shape == y_expect.shape
55
56
57@pytest.mark.level0
58@pytest.mark.platform_x86_cpu
59@pytest.mark.env_onecard
60def test_sqrt_grad():
61    x = Tensor(np.array([[[[-1, 1, 10],
62                           [5.9, 6.1, 6],
63                           [10, 1, -1]]]]).astype(np.float32))
64    dx = Tensor(np.array([[[[1, 1, 1],
65                            [2, 2, 2],
66                            [3, 3, 3]]]]).astype(np.float32))
67    expect = np.array([[[[-0.5, 0.5, 0.05,],
68                         [0.16949153, 0.16393442, 0.16666667,],
69                         [0.15, 1.5, -1.5,]]]]).astype(np.float32)
70    error = np.ones(shape=[3, 3]) * 1.0e-6
71
72    sqrt_grad = NetSqrtGrad()
73    output = sqrt_grad(x, dx)
74    diff = np.abs(output.asnumpy() - expect)
75    assert np.all(np.abs(diff) < error)
76