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1# Copyright 2021 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
18import mindspore.context as context
19from mindspore import Tensor
20from mindspore.nn import Cell
21import mindspore.ops as P
22
23context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
24
25
26class SqueezeNet(Cell):
27    def __init__(self):
28        super(SqueezeNet, self).__init__()
29        self.squeeze = P.Squeeze()
30
31    def construct(self, x):
32        return self.squeeze(x)
33
34
35@pytest.mark.level0
36@pytest.mark.platform_x86_cpu
37@pytest.mark.env_onecard
38def test_squeeze_shape_float32():
39    x = np.ones(shape=[1, 2, 1, 1, 8, 3, 1]).astype(np.float32)
40    expect = np.ones(shape=[2, 8, 3]).astype(np.float32)
41    net = SqueezeNet()
42    result = net(Tensor(x))
43    assert np.allclose(result.asnumpy(), expect, rtol=1.e-4,
44                       atol=1.e-8, equal_nan=True)
45
46
47@pytest.mark.level0
48@pytest.mark.platform_x86_cpu
49@pytest.mark.env_onecard
50def test_squeeze_shape_int32():
51    x = np.array([[7], [11]]).astype(np.int32)
52    expect = np.array([7, 11]).astype(np.int32)
53    net = SqueezeNet()
54    result = net(Tensor(x))
55    assert np.allclose(result.asnumpy(), expect, rtol=1.e-4,
56                       atol=1.e-8, equal_nan=True)
57
58
59@pytest.mark.level0
60@pytest.mark.platform_x86_cpu
61@pytest.mark.env_onecard
62def test_squeeze_shape_bool():
63    x = np.array([[True], [False]]).astype(np.bool_)
64    expect = np.array([True, False]).astype(np.bool_)
65    net = SqueezeNet()
66    result = net(Tensor(x))
67    assert np.allclose(result.asnumpy(), expect, rtol=1.e-4,
68                       atol=1.e-8, equal_nan=True)
69
70
71@pytest.mark.level0
72@pytest.mark.platform_x86_cpu
73@pytest.mark.env_onecard
74def test_squeeze_shape_float64():
75    x = np.random.random([1, 2, 1, 1, 8, 3, 1]).astype(np.float64)
76    expect = np.squeeze(x)
77    net = SqueezeNet()
78    result = net(Tensor(x))
79    print(result.asnumpy()[0][0], expect[0][0])
80    assert np.allclose(result.asnumpy(), expect, rtol=1.e-4,
81                       atol=1.e-8, equal_nan=True)
82
83
84@pytest.mark.level0
85@pytest.mark.platform_x86_cpu
86@pytest.mark.env_onecard
87def test_squeeze_shape_uint16():
88    x = np.random.random([1, 2, 1, 1, 8, 3, 1]).astype(np.uint16)
89    expect = np.squeeze(x)
90    net = SqueezeNet()
91    result = net(Tensor(x))
92    print(result.asnumpy()[0][0], expect[0][0])
93    assert np.allclose(result.asnumpy(), expect, rtol=1.e-4,
94                       atol=1.e-8, equal_nan=True)
95