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- # Copyright 2020 Huawei Technologies Co., Ltd
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- # ============================================================================
- """test_metric_factory"""
- import math
- import numpy as np
-
- from mindspore import Tensor
- from mindspore.nn.metrics import get_metric_fn
- from mindspore.nn.metrics.metric import rearrange_inputs
-
-
- def test_classification_accuracy():
- x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
- y = Tensor(np.array([1, 0, 1]))
- metric = get_metric_fn('accuracy', eval_type='classification')
- metric.clear()
- metric.update(x, y)
- accuracy = metric.eval()
- assert math.isclose(accuracy, 2 / 3)
-
-
- def test_classification_accuracy_by_alias():
- x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
- y = Tensor(np.array([1, 0, 1]))
- metric = get_metric_fn('acc', eval_type='classification')
- metric.clear()
- metric.update(x, y)
- accuracy = metric.eval()
- assert math.isclose(accuracy, 2 / 3)
-
-
- def test_classification_precision():
- x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
- y = Tensor(np.array([1, 0, 1]))
- metric = get_metric_fn('precision', eval_type='classification')
- metric.clear()
- metric.update(x, y)
- precision = metric.eval()
-
- assert np.equal(precision, np.array([0.5, 1])).all()
-
-
- class RearrangeInputsDemo:
- def __init__(self):
- self._indexes = None
-
- @property
- def indexes(self):
- return getattr(self, '_indexes', None)
-
- def set_indexes(self, indexes):
- self._indexes = indexes
- return self
-
- @rearrange_inputs
- def update(self, *inputs):
- return inputs
-
-
- def test_rearrange_inputs_without_arrange():
- mini_decorator = RearrangeInputsDemo()
- outs = mini_decorator.update(5, 9)
- assert outs == (5, 9)
-
-
- def test_rearrange_inputs_with_arrange():
- mini_decorator = RearrangeInputsDemo().set_indexes([1, 0])
- outs = mini_decorator.update(5, 9)
- assert outs == (9, 5)
-
-
- def test_rearrange_inputs_with_multi_inputs():
- mini_decorator = RearrangeInputsDemo().set_indexes([1, 3])
- outs = mini_decorator.update(0, 9, 0, 5)
- assert outs == (9, 5)
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