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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
23
24context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
25
26
27class SoftplusNet(nn.Cell):
28    def __init__(self):
29        super(SoftplusNet, self).__init__()
30        self.softplus = P.Softplus()
31
32    def construct(self, x):
33        return self.softplus(x)
34
35
36def SoftplusCompute(x):
37    return np.log(1 + np.exp(x))
38
39
40@pytest.mark.level0
41@pytest.mark.platform_x86_gpu_training
42@pytest.mark.env_onecard
43def test_softplus_1d():
44    x_np = np.random.random((50,)).astype(np.float32)
45    y_np = SoftplusCompute(x_np)
46
47    x_ms = Tensor(x_np)
48    net = SoftplusNet()
49    y_ms = net(x_ms)
50
51    assert np.allclose(y_np, y_ms.asnumpy())
52
53
54@pytest.mark.level0
55@pytest.mark.platform_x86_gpu_training
56@pytest.mark.env_onecard
57def test_softplus_2d():
58    x_np = np.random.random((50, 40)).astype(np.float32)
59    y_np = SoftplusCompute(x_np)
60
61    x_ms = Tensor(x_np)
62    net = SoftplusNet()
63    y_ms = net(x_ms)
64
65    assert np.allclose(y_np, y_ms.asnumpy())
66
67
68@pytest.mark.level0
69@pytest.mark.platform_x86_gpu_training
70@pytest.mark.env_onecard
71def test_softplus_4d():
72    x_np = np.random.random((32, 3, 224, 224)).astype(np.float32)
73    y_np = SoftplusCompute(x_np)
74
75    x_ms = Tensor(x_np)
76    net = SoftplusNet()
77    y_ms = net(x_ms)
78
79    assert np.allclose(y_np, y_ms.asnumpy())
80
81
82@pytest.mark.level0
83@pytest.mark.platform_x86_gpu_training
84@pytest.mark.env_onecard
85def test_softplus_neg():
86    x_np = np.random.random((32, 3, 224, 224)).astype(np.float32) * -1
87    y_np = SoftplusCompute(x_np)
88
89    x_ms = Tensor(x_np)
90    net = SoftplusNet()
91    y_ms = net(x_ms)
92
93    assert np.allclose(y_np, y_ms.asnumpy())
94
95@pytest.mark.level0
96@pytest.mark.platform_x86_gpu_training
97@pytest.mark.env_onecard
98def test_softplus_4d_fp16():
99    x_np = np.random.random((32, 3, 224, 224)).astype(np.float16)
100    y_np = SoftplusCompute(x_np)
101
102    x_ms = Tensor(x_np)
103    net = SoftplusNet()
104    y_ms = net(x_ms)
105
106    assert np.allclose(y_np, y_ms.asnumpy(), rtol=5e-3)
107