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| # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. | |
| # | |
| # 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. | |
| # The code is refer from: https://github.com/open-mmlab/mmocr/blob/main/mmocr/core/evaluation/kie_metric.py | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| import numpy as np | |
| import paddle | |
| __all__ = ['KIEMetric'] | |
| class KIEMetric(object): | |
| def __init__(self, main_indicator='hmean', **kwargs): | |
| self.main_indicator = main_indicator | |
| self.reset() | |
| self.node = [] | |
| self.gt = [] | |
| def __call__(self, preds, batch, **kwargs): | |
| nodes, _ = preds | |
| gts, tag = batch[4].squeeze(0), batch[5].tolist()[0] | |
| gts = gts[:tag[0], :1].reshape([-1]) | |
| self.node.append(nodes.numpy()) | |
| self.gt.append(gts) | |
| # result = self.compute_f1_score(nodes, gts) | |
| # self.results.append(result) | |
| def compute_f1_score(self, preds, gts): | |
| ignores = [0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 25] | |
| C = preds.shape[1] | |
| classes = np.array(sorted(set(range(C)) - set(ignores))) | |
| hist = np.bincount( | |
| (gts * C).astype('int64') + preds.argmax(1), minlength=C | |
| **2).reshape([C, C]).astype('float32') | |
| diag = np.diag(hist) | |
| recalls = diag / hist.sum(1).clip(min=1) | |
| precisions = diag / hist.sum(0).clip(min=1) | |
| f1 = 2 * recalls * precisions / (recalls + precisions).clip(min=1e-8) | |
| return f1[classes] | |
| def combine_results(self, results): | |
| node = np.concatenate(self.node, 0) | |
| gts = np.concatenate(self.gt, 0) | |
| results = self.compute_f1_score(node, gts) | |
| data = {'hmean': results.mean()} | |
| return data | |
| def get_metric(self): | |
| metrics = self.combine_results(self.results) | |
| self.reset() | |
| return metrics | |
| def reset(self): | |
| self.results = [] # clear results | |
| self.node = [] | |
| self.gt = [] | |