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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
24
25class NetRealDiv(nn.Cell):
26    def __init__(self):
27        super(NetRealDiv, self).__init__()
28        self.divide = P.RealDiv()
29
30    def construct(self, x, y):
31        return self.divide(x, y)
32
33
34@pytest.mark.level0
35@pytest.mark.platform_x86_cpu
36@pytest.mark.env_onecard
37def test_real_div():
38    x0_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float32)
39    y0_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float32)
40    x1_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float32)
41    y1_np = np.random.randint(1, 5, (2, 1, 4, 4)).astype(np.float32)
42    x2_np = np.random.randint(1, 5, (2, 1, 1, 4)).astype(np.float32)
43    y2_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float32)
44    x3_np = np.random.randint(1, 5, 1).astype(np.float32)
45    y3_np = np.random.randint(1, 5, 1).astype(np.float32)
46    x4_np = np.array(768).astype(np.float32)
47    y4_np = np.array(3072.5).astype(np.float32)
48
49    x0 = Tensor(x0_np)
50    y0 = Tensor(y0_np)
51    x1 = Tensor(x1_np)
52    y1 = Tensor(y1_np)
53    x2 = Tensor(x2_np)
54    y2 = Tensor(y2_np)
55    x3 = Tensor(x3_np)
56    y3 = Tensor(y3_np)
57    x4 = Tensor(x4_np)
58    y4 = Tensor(y4_np)
59
60    context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
61    real_div = NetRealDiv()
62    output0 = real_div(x0, y0)
63    expect0 = np.divide(x0_np, y0_np)
64    diff0 = output0.asnumpy() - expect0
65    error0 = np.ones(shape=expect0.shape) * 1.0e-5
66    assert np.all(diff0 < error0)
67    assert output0.shape == expect0.shape
68
69    output1 = real_div(x1, y1)
70    expect1 = np.divide(x1_np, y1_np)
71    diff1 = output1.asnumpy() - expect1
72    error1 = np.ones(shape=expect1.shape) * 1.0e-5
73    assert np.all(diff1 < error1)
74    assert output1.shape == expect1.shape
75
76    output2 = real_div(x2, y2)
77    expect2 = np.divide(x2_np, y2_np)
78    diff2 = output2.asnumpy() - expect2
79    error2 = np.ones(shape=expect2.shape) * 1.0e-5
80    assert np.all(diff2 < error2)
81    assert output2.shape == expect2.shape
82
83    output3 = real_div(x3, y3)
84    expect3 = np.divide(x3_np, y3_np)
85    diff3 = output3.asnumpy() - expect3
86    error3 = np.ones(shape=expect3.shape) * 1.0e-5
87    assert np.all(diff3 < error3)
88    assert output3.shape == expect3.shape
89
90    output4 = real_div(x4, y4)
91    expect4 = np.divide(x4_np, y4_np)
92    diff4 = output4.asnumpy() - expect4
93    error4 = np.ones(shape=expect4.shape) * 1.0e-5
94    assert np.all(diff4 < error4)
95    assert output4.shape == expect4.shape
96