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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"""test_metric_factory"""
16import math
17import numpy as np
18
19from mindspore import Tensor
20from mindspore.nn.metrics import get_metric_fn
21from mindspore.nn.metrics.metric import rearrange_inputs
22
23
24def test_classification_accuracy():
25    x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
26    y = Tensor(np.array([1, 0, 1]))
27    metric = get_metric_fn('accuracy', eval_type='classification')
28    metric.clear()
29    metric.update(x, y)
30    accuracy = metric.eval()
31    assert math.isclose(accuracy, 2 / 3)
32
33
34def test_classification_accuracy_by_alias():
35    x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
36    y = Tensor(np.array([1, 0, 1]))
37    metric = get_metric_fn('acc', eval_type='classification')
38    metric.clear()
39    metric.update(x, y)
40    accuracy = metric.eval()
41    assert math.isclose(accuracy, 2 / 3)
42
43
44def test_classification_precision():
45    x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
46    y = Tensor(np.array([1, 0, 1]))
47    metric = get_metric_fn('precision', eval_type='classification')
48    metric.clear()
49    metric.update(x, y)
50    precision = metric.eval()
51
52    assert np.equal(precision, np.array([0.5, 1])).all()
53
54
55class RearrangeInputsDemo:
56    def __init__(self):
57        self._indexes = None
58
59    @property
60    def indexes(self):
61        return getattr(self, '_indexes', None)
62
63    def set_indexes(self, indexes):
64        self._indexes = indexes
65        return self
66
67    @rearrange_inputs
68    def update(self, *inputs):
69        return inputs
70
71
72def test_rearrange_inputs_without_arrange():
73    mini_decorator = RearrangeInputsDemo()
74    outs = mini_decorator.update(5, 9)
75    assert outs == (5, 9)
76
77
78def test_rearrange_inputs_with_arrange():
79    mini_decorator = RearrangeInputsDemo().set_indexes([1, 0])
80    outs = mini_decorator.update(5, 9)
81    assert outs == (9, 5)
82
83
84def test_rearrange_inputs_with_multi_inputs():
85    mini_decorator = RearrangeInputsDemo().set_indexes([1, 3])
86    outs = mini_decorator.update(0, 9, 0, 5)
87    assert outs == (9, 5)
88