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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
20from mindspore.common.tensor import Tensor
21from mindspore.nn import Cell
22from mindspore.ops import operations as P
23
24
25class Net(Cell):
26    def __init__(self):
27        super(Net, self).__init__()
28        self.lessequal = P.LessEqual()
29
30    def construct(self, x, y):
31        return self.lessequal(x, y)
32
33
34@pytest.mark.level0
35@pytest.mark.platform_x86_gpu_training
36@pytest.mark.env_onecard
37def test_lessequal():
38    x = Tensor(np.array([[1, 2, 3]]).astype(np.float32))
39    y = Tensor(np.array([[2, 2, 2]]).astype(np.float32))
40    expect = np.array([[True, True, False]])
41    x1 = Tensor(np.array([[1, 2, 3]]).astype(np.int16))
42    y1 = Tensor(np.array([[2]]).astype(np.int16))
43    expect1 = np.array([[True, True, False]])
44    x2 = Tensor(np.array([[1, 2, 3]]).astype(np.uint8))
45    y2 = Tensor(np.array([[2]]).astype(np.uint8))
46    expect2 = np.array([[True, True, False]])
47    x3 = Tensor(np.array([[1, 2, 3]]).astype(np.float64))
48    y3 = Tensor(np.array([[2]]).astype(np.float64))
49    expect3 = np.array([[True, True, False]])
50    x4 = Tensor(np.array([[1, 2, 3]]).astype(np.float16))
51    y4 = Tensor(np.array([[2]]).astype(np.float16))
52    expect4 = np.array([[True, True, False]])
53    x5 = Tensor(np.array([[1, 2, 3]]).astype(np.int64))
54    y5 = Tensor(np.array([[2]]).astype(np.int64))
55    expect5 = np.array([[True, True, False]])
56    x6 = Tensor(np.array([[1, 2, 3]]).astype(np.int32))
57    y6 = Tensor(np.array([[2, 2, 2]]).astype(np.int32))
58    expect6 = np.array([[True, True, False]])
59    x7 = Tensor(np.array([[1, 2, 3]]).astype(np.int8))
60    y7 = Tensor(np.array([[2]]).astype(np.int8))
61    expect7 = np.array([[True, True, False]])
62
63    x = [x, x1, x2, x3, x4, x5, x6, x7]
64    y = [y, y1, y2, y3, y4, y5, y6, y7]
65    expect = [expect, expect1, expect2, expect3, expect4, expect5, expect6, expect7]
66
67    context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
68    lessequal = Net()
69    for i, xi in enumerate(x):
70        output = lessequal(xi, y[i])
71        assert np.all(output.asnumpy() == expect[i])
72        assert output.shape == expect[i].shape
73        print('test [%d/%d] passed!' % (i, len(x)))
74
75    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
76    lessequal = Net()
77    for i, xi in enumerate(x):
78        output = lessequal(xi, y[i])
79        assert np.all(output.asnumpy() == expect[i])
80        assert output.shape == expect[i].shape
81        print('test [%d/%d] passed!' % (i, len(x)))
82