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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 = []
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