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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
16import pytest
17
18import mindspore.context as context
19import mindspore.nn as nn
20from mindspore import Tensor
21from mindspore.ops import operations as P
22
23context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
24
25
26class Net(nn.Cell):
27    def __init__(self):
28        super(Net, self).__init__()
29        self.bias_add = P.BiasAdd()
30
31    def construct(self, x, b):
32        return self.bias_add(x, b)
33
34
35@pytest.mark.level0
36@pytest.mark.platform_x86_cpu
37@pytest.mark.env_onecard
38def test_bias_add4d():
39    x_shape = [2, 3, 4, 5]
40    x = np.ones(x_shape).astype(np.float32)
41    b = np.array([0.3, 0.5, 0.7]).astype(np.float32)
42    bias_add = Net()
43    output = bias_add(Tensor(x), Tensor(b))
44    expect_output = x
45    for i in range(x_shape[0]):
46        for j in range(x_shape[1]):
47            expect_output[i][j] = x[i][j] + b[j]
48    print(output)
49    assert np.all(output.asnumpy() == expect_output), "bias_add execute failed, please check current code commit"
50
51
52@pytest.mark.level0
53@pytest.mark.platform_x86_cpu
54@pytest.mark.env_onecard
55def test_bias_add2d():
56    x_shape = [2, 3]
57    x = np.ones(x_shape).astype(np.float32)
58    b = np.array([0.3, 0.5, 0.7]).astype(np.float32)
59    bias_add = Net()
60    output = bias_add(Tensor(x), Tensor(b))
61    expect_output = x
62    for i in range(x_shape[0]):
63        for j in range(x_shape[1]):
64            expect_output[i][j] = x[i][j] + b[j]
65    print(output)
66    assert np.all(output.asnumpy() == expect_output), "bias_add execute failed, please check current code commit"
67
68
69@pytest.mark.level0
70@pytest.mark.platform_x86_cpu
71@pytest.mark.env_onecard
72def test_bias_add3d():
73    x_shape = [2, 3, 4]
74    x = np.ones(x_shape).astype(np.float32)
75    b = np.array([0.3, 0.5, 0.7]).astype(np.float32)
76    bias_add = Net()
77    output = bias_add(Tensor(x), Tensor(b))
78    expect_output = x
79    for i in range(x_shape[0]):
80        for j in range(x_shape[1]):
81            expect_output[i][j] = x[i][j] + b[j]
82    print(output)
83    assert np.all(output.asnumpy() == expect_output), "bias_add execute failed, please check current code commit"
84
85@pytest.mark.level0
86@pytest.mark.platform_x86_cpu
87@pytest.mark.env_onecard
88def test_bias_add5d():
89    x_shape = [2, 5, 2, 3, 4]
90    x = np.ones(x_shape).astype(np.float32)
91    b = np.array([0.1, 0.3, 0.5, 0.7, 0.9]).astype(np.float32)
92    bias_add = Net()
93    output = bias_add(Tensor(x), Tensor(b))
94    expect_output = x
95    for i in range(x_shape[0]):
96        for j in range(x_shape[1]):
97            expect_output[i][j] = x[i][j] + b[j]
98    print(output)
99    assert np.all(output.asnumpy() == expect_output), "bias_add execute failed, please check current code commit"
100
101
102class Net2(nn.Cell):
103    def __init__(self):
104        super(Net2, self).__init__()
105        self.bias_add = P.BiasAdd()
106        self.mul = P.Mul()
107        self.div = P.Div()
108        self.add = P.Add()
109
110    def construct(self, x, y, z, w):
111        mul_ = self.mul(x, y)
112        div_ = self.div(z, w)
113        temp = self.bias_add(mul_, div_)
114        temp = self.bias_add(temp, div_)
115        return self.add(temp, x)
116
117
118@pytest.mark.level0
119@pytest.mark.platform_x86_cpu
120@pytest.mark.env_onecard
121def test_net2():
122    x_shape = [2, 3, 4]
123    x = np.ones(x_shape).astype(np.float32)
124    y = np.ones(x_shape).astype(np.float32)
125    z = np.array([1.1, 2.2, 3.4]).astype(np.float32)
126    w = np.array([10, 10, 10]).astype(np.float32)
127    net2 = Net2()
128    output = net2(Tensor(x), Tensor(y), Tensor(z), Tensor(w))
129    expect_out = (np.array([[[2.22, 2.22, 2.22, 2.22],
130                             [2.44, 2.44, 2.44, 2.44],
131                             [2.68, 2.68, 2.68, 2.68]],
132                            [[2.22, 2.22, 2.22, 2.22],
133                             [2.44, 2.44, 2.44, 2.44],
134                             [2.68, 2.68, 2.68, 2.68]]]))
135    assert np.allclose(output.asnumpy(), expect_out)
136