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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.PYNATIVE_MODE, device_target="GPU")
25
26
27class NetOnesLike(nn.Cell):
28    def __init__(self):
29        super(NetOnesLike, self).__init__()
30        self.ones_like = P.OnesLike()
31
32    def construct(self, x):
33        return self.ones_like(x)
34
35
36@pytest.mark.level0
37@pytest.mark.platform_x86_gpu_training
38@pytest.mark.env_onecard
39def test_OnesLike():
40    x0_np = np.random.uniform(-2, 2, (2, 3, 4, 4)).astype(np.float32)
41    x1_np = np.random.uniform(-2, 2, 1).astype(np.float16)
42    x2_np = np.zeros([3, 3, 3], dtype=np.int32)
43
44    x0 = Tensor(x0_np)
45    x1 = Tensor(x1_np)
46    x2 = Tensor(x2_np)
47
48    context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
49    ones_like = NetOnesLike()
50    output0 = ones_like(x0)
51    expect0 = np.ones_like(x0_np)
52    diff0 = output0.asnumpy() - expect0
53    error0 = np.ones(shape=expect0.shape) * 1.0e-5
54    assert np.all(diff0 < error0)
55    assert output0.shape == expect0.shape
56
57    output1 = ones_like(x1)
58    expect1 = np.ones_like(x1_np)
59    diff1 = output1.asnumpy() - expect1
60    error1 = np.ones(shape=expect1.shape) * 1.0e-5
61    assert np.all(diff1 < error1)
62    assert output1.shape == expect1.shape
63
64    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
65    ones_like = NetOnesLike()
66    output0 = ones_like(x0)
67    expect0 = np.ones_like(x0_np)
68    diff0 = output0.asnumpy() - expect0
69    error0 = np.ones(shape=expect0.shape) * 1.0e-5
70    assert np.all(diff0 < error0)
71    assert output0.shape == expect0.shape
72
73    output1 = ones_like(x1)
74    expect1 = np.ones_like(x1_np)
75    diff1 = output1.asnumpy() - expect1
76    error1 = np.ones(shape=expect1.shape) * 1.0e-5
77    assert np.all(diff1 < error1)
78    assert output1.shape == expect1.shape
79
80    output2 = ones_like(x2)
81    expect2 = np.ones_like(x2_np)
82    diff2 = output2.asnumpy() - expect2
83    error2 = np.ones(shape=expect2.shape) * 1.0e-5
84    assert np.all(diff2 < error2)
85    assert output2.shape == expect2.shape
86