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