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90 lines
2.9 KiB
Python
90 lines
2.9 KiB
Python
# Copyright (c) 2022 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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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 copy
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import sys
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from collections import OrderedDict
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from .coco_utils import get_infer_results, cocoapi_eval
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class COCOMetric(object):
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def __init__(self, coco_gt, **kwargs):
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self.clsid2catid = {
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i: cat['id']
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for i, cat in enumerate(coco_gt.loadCats(coco_gt.getCatIds()))
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}
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self.coco_gt = coco_gt
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self.classwise = kwargs.get('classwise', False)
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self.bias = 0
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self.reset()
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def reset(self):
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# only bbox and mask evaluation support currently
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self.details = {
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'gt': copy.deepcopy(self.coco_gt.dataset),
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'bbox': [],
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'mask': []
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}
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self.eval_stats = {}
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def update(self, im_id, outputs):
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outs = {}
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# outputs Tensor -> numpy.ndarray
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for k, v in outputs.items():
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outs[k] = v
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outs['im_id'] = im_id
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infer_results = get_infer_results(
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outs, self.clsid2catid, bias=self.bias)
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self.details['bbox'] += infer_results[
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'bbox'] if 'bbox' in infer_results else []
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self.details['mask'] += infer_results[
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'mask'] if 'mask' in infer_results else []
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def accumulate(self):
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if len(self.details['bbox']) > 0:
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bbox_stats = cocoapi_eval(
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copy.deepcopy(self.details['bbox']),
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'bbox',
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coco_gt=self.coco_gt,
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classwise=self.classwise)
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self.eval_stats['bbox'] = bbox_stats
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sys.stdout.flush()
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if len(self.details['mask']) > 0:
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seg_stats = cocoapi_eval(
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copy.deepcopy(self.details['mask']),
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'segm',
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coco_gt=self.coco_gt,
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classwise=self.classwise)
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self.eval_stats['mask'] = seg_stats
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sys.stdout.flush()
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def log(self):
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pass
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def get(self):
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if 'bbox' not in self.eval_stats:
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return {'bbox_mmap': 0.}
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if 'mask' in self.eval_stats:
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return OrderedDict(
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zip(['bbox_mmap', 'segm_mmap'],
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[self.eval_stats['bbox'][0], self.eval_stats['mask'][0]]))
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else:
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return {'bbox_mmap': self.eval_stats['bbox'][0]}
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