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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
17
18import mindspore.context as context
19import mindspore.nn as nn
20from mindspore import Tensor
21from mindspore.common import dtype as mstype
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.uniq = P.Unique()
31
32    def construct(self, x):
33        return self.uniq(x)
34
35
36def test_net_fp32():
37    x = Tensor(np.array([1, 2, 5, 2]), mstype.float32)
38    uniq = Net()
39    output = uniq(x)
40    print("x:\n", x)
41    print("y:\n", output[0])
42    print("idx:\n", output[1])
43    expect_y_result = [1., 2., 5.]
44    expect_idx_result = [0, 1, 2, 1]
45
46    assert (output[0].asnumpy() == expect_y_result).all()
47    assert (output[1].asnumpy() == expect_idx_result).all()
48
49def test_net_fp16():
50    x = Tensor(np.array([1, 5, 2, 2]), mstype.float16)
51    uniq = Net()
52    output = uniq(x)
53    print("x:\n", x)
54    print("y:\n", output[0])
55    print("idx:\n", output[1])
56    expect_y_result = [1., 5., 2.]
57    expect_idx_result = [0, 1, 2, 2]
58
59    assert (output[0].asnumpy() == expect_y_result).all()
60    assert (output[1].asnumpy() == expect_idx_result).all()
61
62def test_net_int32():
63    x = Tensor(np.array([1, 2, 5, 2]), mstype.int32)
64    uniq = Net()
65    output = uniq(x)
66    print("x:\n", x)
67    print("y:\n", output[0])
68    print("idx:\n", output[1])
69    expect_y_result = [1, 2, 5]
70    expect_idx_result = [0, 1, 2, 1]
71
72    assert (output[0].asnumpy() == expect_y_result).all()
73    assert (output[1].asnumpy() == expect_idx_result).all()
74
75
76def test_net_int64():
77    x = Tensor(np.array([1, 2, 5, 2]), mstype.int64)
78    uniq = Net()
79    output = uniq(x)
80    print("x:\n", x)
81    print("y:\n", output[0])
82    print("idx:\n", output[1])
83    expect_y_result = [1, 2, 5]
84    expect_idx_result = [0, 1, 2, 1]
85
86    assert (output[0].asnumpy() == expect_y_result).all()
87    assert (output[1].asnumpy() == expect_idx_result).all()
88