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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='CPU')
25
26
27class Net(nn.Cell):
28    def __init__(self):
29        super(Net, self).__init__()
30        self.ops = P.IsFinite()
31
32    def construct(self, x):
33        return self.ops(x)
34
35
36@pytest.mark.level0
37@pytest.mark.platform_x86_cpu
38@pytest.mark.env_onecard
39def test_net():
40    x0 = Tensor(np.array([np.log(-1), 0.4, np.log(0)]).astype(np.float16))
41    x1 = Tensor(np.array([np.log(-1), 0.4, np.log(0)]).astype(np.float32))
42    x2 = Tensor(np.array([np.log(-1), 0.4, np.log(0)]).astype(np.float64))
43    x3 = Tensor(np.array([4, 1, -5]).astype(np.int8))
44    x4 = Tensor(np.array([4, 1, -5]).astype(np.int16))
45    x5 = Tensor(np.array([4, 1, -5]).astype(np.int32))
46    x6 = Tensor(np.array([4, 1, -5]).astype(np.int64))
47    x7 = Tensor(np.array([4, 1, -5]).astype(np.uint8))
48    x8 = Tensor(np.array([4, 1, -5]).astype(np.uint16))
49    x9 = Tensor(np.array([4, 1, -5]).astype(np.uint32))
50    x10 = Tensor(np.array([4, 1, -5]).astype(np.uint64))
51    x11 = Tensor(np.array([False, True, False]).astype(np.bool_))
52
53    net = Net()
54    out = net(x0).asnumpy()
55    expect = [False, True, False]
56    assert np.all(out == expect)
57
58    out = net(x1).asnumpy()
59    expect = [False, True, False]
60    assert np.all(out == expect)
61
62    out = net(x2).asnumpy()
63    expect = [False, True, False]
64    assert np.all(out == expect)
65
66    out = net(x3).asnumpy()
67    expect = [True, True, True]
68    assert np.all(out == expect)
69
70    out = net(x4).asnumpy()
71    expect = [True, True, True]
72    assert np.all(out == expect)
73
74    out = net(x5).asnumpy()
75    expect = [True, True, True]
76    assert np.all(out == expect)
77
78    out = net(x6).asnumpy()
79    expect = [True, True, True]
80    assert np.all(out == expect)
81
82    out = net(x7).asnumpy()
83    expect = [True, True, True]
84    assert np.all(out == expect)
85
86    out = net(x8).asnumpy()
87    expect = [True, True, True]
88    assert np.all(out == expect)
89
90    out = net(x9).asnumpy()
91    expect = [True, True, True]
92    assert np.all(out == expect)
93
94    out = net(x10).asnumpy()
95    expect = [True, True, True]
96    assert np.all(out == expect)
97
98    out = net(x11).asnumpy()
99    expect = [True, True, True]
100    assert np.all(out == expect)
101