mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
synced 2025-10-31 09:49:30 +00:00
72 lines
2.4 KiB
Python
72 lines
2.4 KiB
Python
# 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):
|
|
|
|
metircs = self.combine_results(self.results)
|
|
self.reset()
|
|
return metircs
|
|
|
|
def reset(self):
|
|
self.results = [] # clear results
|
|
self.node = []
|
|
self.gt = []
|