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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
23from mindspore.ops.operations import _grad_ops as G
24
25context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
26
27class NetReLU6(nn.Cell):
28    def __init__(self):
29        super(NetReLU6, self).__init__()
30        self.relu6 = P.ReLU6()
31
32    def construct(self, x):
33        return self.relu6(x)
34
35class NetReLU6Grad(nn.Cell):
36    def __init__(self):
37        super(NetReLU6Grad, self).__init__()
38        self.relu6_grad = G.ReLU6Grad()
39
40    def construct(self, x, dy):
41        return self.relu6_grad(dy, x)
42
43@pytest.mark.level0
44@pytest.mark.platform_x86_cpu
45@pytest.mark.env_onecard
46def test_relu6():
47    x = Tensor(np.array([[[[-1, 1, 10],
48                           [5.9, 6.1, 6],
49                           [10, 1, -1]]]]).astype(np.float32))
50    expect = np.array([[[[0, 1, 6,],
51                         [5.9, 6, 6,],
52                         [6, 1, 0.]]]]).astype(np.float32)
53
54    relu6 = NetReLU6()
55    output = relu6(x)
56    assert (output.asnumpy() == expect).all()
57
58@pytest.mark.level0
59@pytest.mark.platform_x86_cpu
60@pytest.mark.env_onecard
61def test_relu6_grad():
62    x = Tensor(np.array([[[[-1, 1, 10],
63                           [5.9, 6.1, 6],
64                           [10, 1, -1]]]]).astype(np.float32))
65    dy = Tensor(np.array([[[[1, 1, 1],
66                            [1, 1, 1],
67                            [1, 1, 1]]]]).astype(np.float32))
68    expect = np.array([[[[0, 1, 0,],
69                         [1, 0, 1,],
70                         [0, 1, 0,]]]]).astype(np.float32)
71    error = np.ones(shape=[3, 3]) * 1.0e-6
72
73    relu6_grad = NetReLU6Grad()
74    output = relu6_grad(x, dy)
75    diff = np.abs(output.asnumpy() - expect)
76    assert np.all(np.abs(diff) < error)
77