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