51 lines
1.7 KiB
Python
51 lines
1.7 KiB
Python
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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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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import numpy as np
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class TableMetric(object):
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def __init__(self, main_indicator='acc', **kwargs):
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self.main_indicator = main_indicator
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self.reset()
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def __call__(self, pred, batch, *args, **kwargs):
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structure_probs = pred['structure_probs'].numpy()
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structure_labels = batch[1]
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correct_num = 0
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all_num = 0
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structure_probs = np.argmax(structure_probs, axis=2)
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structure_labels = structure_labels[:, 1:]
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batch_size = structure_probs.shape[0]
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for bno in range(batch_size):
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all_num += 1
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if (structure_probs[bno] == structure_labels[bno]).all():
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correct_num += 1
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self.correct_num += correct_num
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self.all_num += all_num
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return {
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'acc': correct_num * 1.0 / all_num,
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}
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def get_metric(self):
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"""
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return metrics {
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'acc': 0,
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}
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"""
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acc = 1.0 * self.correct_num / self.all_num
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self.reset()
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return {'acc': acc}
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def reset(self):
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self.correct_num = 0
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self.all_num = 0
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