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# Copyright (c) 2020 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
__all__ = ['DetMetric', 'DetFCEMetric']
from .eval_det_iou import DetectionIoUEvaluator
class DetMetric(object):
def __init__(self, main_indicator='hmean', **kwargs):
self.evaluator = DetectionIoUEvaluator()
self.main_indicator = main_indicator
self.reset()
def __call__(self, preds, batch, **kwargs):
'''
batch: a list produced by dataloaders.
image: np.ndarray of shape (N, C, H, W).
ratio_list: np.ndarray of shape(N,2)
polygons: np.ndarray of shape (N, K, 4, 2), the polygons of objective regions.
ignore_tags: np.ndarray of shape (N, K), indicates whether a region is ignorable or not.
preds: a list of dict produced by post process
points: np.ndarray of shape (N, K, 4, 2), the polygons of objective regions.
'''
gt_polyons_batch = batch[2]
ignore_tags_batch = batch[3]
for pred, gt_polyons, ignore_tags in zip(preds, gt_polyons_batch,
ignore_tags_batch):
# prepare gt
gt_info_list = [{
'points': gt_polyon,
'text': '',
'ignore': ignore_tag
} for gt_polyon, ignore_tag in zip(gt_polyons, ignore_tags)]
# prepare det
det_info_list = [{
'points': det_polyon,
'text': ''
} for det_polyon in pred['points']]
result = self.evaluator.evaluate_image(gt_info_list, det_info_list)
self.results.append(result)
def get_metric(self):
"""
return metrics {
'precision': 0,
'recall': 0,
'hmean': 0
}
"""
metrics = self.evaluator.combine_results(self.results)
self.reset()
return metrics
def reset(self):
self.results = [] # clear results
class DetFCEMetric(object):
def __init__(self, main_indicator='hmean', **kwargs):
self.evaluator = DetectionIoUEvaluator()
self.main_indicator = main_indicator
self.reset()
def __call__(self, preds, batch, **kwargs):
'''
batch: a list produced by dataloaders.
image: np.ndarray of shape (N, C, H, W).
ratio_list: np.ndarray of shape(N,2)
polygons: np.ndarray of shape (N, K, 4, 2), the polygons of objective regions.
ignore_tags: np.ndarray of shape (N, K), indicates whether a region is ignorable or not.
preds: a list of dict produced by post process
points: np.ndarray of shape (N, K, 4, 2), the polygons of objective regions.
'''
gt_polyons_batch = batch[2]
ignore_tags_batch = batch[3]
for pred, gt_polyons, ignore_tags in zip(preds, gt_polyons_batch,
ignore_tags_batch):
# prepare gt
gt_info_list = [{
'points': gt_polyon,
'text': '',
'ignore': ignore_tag
} for gt_polyon, ignore_tag in zip(gt_polyons, ignore_tags)]
# prepare det
det_info_list = [{
'points': det_polyon,
'text': '',
'score': score
} for det_polyon, score in zip(pred['points'], pred['scores'])]
for score_thr in self.results.keys():
det_info_list_thr = [
det_info for det_info in det_info_list
if det_info['score'] >= score_thr
]
result = self.evaluator.evaluate_image(gt_info_list,
det_info_list_thr)
self.results[score_thr].append(result)
def get_metric(self):
"""
return metrics {'heman':0,
'thr 0.3':'precision: 0 recall: 0 hmean: 0',
'thr 0.4':'precision: 0 recall: 0 hmean: 0',
'thr 0.5':'precision: 0 recall: 0 hmean: 0',
'thr 0.6':'precision: 0 recall: 0 hmean: 0',
'thr 0.7':'precision: 0 recall: 0 hmean: 0',
'thr 0.8':'precision: 0 recall: 0 hmean: 0',
'thr 0.9':'precision: 0 recall: 0 hmean: 0',
}
"""
metrics = {}
hmean = 0
for score_thr in self.results.keys():
metric = self.evaluator.combine_results(self.results[score_thr])
# for key, value in metric.items():
# metrics['{}_{}'.format(key, score_thr)] = value
metric_str = 'precision:{:.5f} recall:{:.5f} hmean:{:.5f}'.format(
metric['precision'], metric['recall'], metric['hmean'])
metrics['thr {}'.format(score_thr)] = metric_str
hmean = max(hmean, metric['hmean'])
metrics['hmean'] = hmean
self.reset()
return metrics
def reset(self):
self.results = {
0.3: [],
0.4: [],
0.5: [],
0.6: [],
0.7: [],
0.8: [],
0.9: []
} # clear results
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