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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
26
27class NetSigmoidGrad(nn.Cell):
28    def __init__(self):
29        super(NetSigmoidGrad, self).__init__()
30        self.sigmoid_grad = G.SigmoidGrad()
31
32    def construct(self, y, dy):
33        return self.sigmoid_grad(y, dy)
34
35
36class Net(nn.Cell):
37    def __init__(self):
38        super(Net, self).__init__()
39        self.ops = P.Sigmoid()
40
41    def construct(self, x):
42        return self.ops(x)
43
44@pytest.mark.level0
45@pytest.mark.platform_x86_cpu
46@pytest.mark.env_onecard
47def test_net():
48    x = np.random.randn(2, 3, 3, 4).astype(np.float32)
49    y_expect = 1 / (1 + np.exp(-x))
50    net = Net()
51    out = net(Tensor(x))
52    diff = out.asnumpy() - y_expect
53    err = np.ones(shape=y_expect.shape) * 1.0e-5
54    assert np.all(diff < err)
55    assert out.shape == y_expect.shape
56
57
58@pytest.mark.level0
59@pytest.mark.platform_x86_cpu
60@pytest.mark.env_onecard
61def test_sigmoid_grad():
62    y = Tensor(np.array([[[[-1, 1, 2],
63                           [1, -1, 1],
64                           [2, 1, -1]]]]).astype(np.float32))
65    dy = Tensor(np.array([[[[-11, 2, 4],
66                            [-1, 1, -1],
67                            [-4, 4, -4]]]]).astype(np.float32))
68
69    expect = np.array([[[[22, 0, -8],
70                         [0, -2, 0],
71                         [8, 0, 8]]]]).astype(np.float32)
72
73    error = np.ones(shape=[1, 1, 3, 3]) * 1.0e-6
74
75    sigmoid_grad = NetSigmoidGrad()
76    output = sigmoid_grad(y, dy)
77    diff = np.abs(output.asnumpy() - expect)
78    assert np.all(abs(diff) < error)
79