Spaces:
Build error
Build error
File size: 1,899 Bytes
28c256d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from argparse import ArgumentParser
import mmengine
from mmengine.logging import print_log
from mmdet.datasets import CocoDataset
from mmdet.evaluation import CocoOccludedSeparatedMetric
def main():
parser = ArgumentParser(
description='Compute recall of COCO occluded and separated masks '
'presented in paper https://arxiv.org/abs/2210.10046.')
parser.add_argument('result', help='result file (pkl format) path')
parser.add_argument('--out', help='file path to save evaluation results')
parser.add_argument(
'--score-thr',
type=float,
default=0.3,
help='Score threshold for the recall calculation. Defaults to 0.3')
parser.add_argument(
'--iou-thr',
type=float,
default=0.75,
help='IoU threshold for the recall calculation. Defaults to 0.75.')
parser.add_argument(
'--ann',
default='data/coco/annotations/instances_val2017.json',
help='coco annotation file path')
args = parser.parse_args()
results = mmengine.load(args.result)
assert 'masks' in results[0]['pred_instances'], \
'The results must be predicted by instance segmentation model.'
metric = CocoOccludedSeparatedMetric(
ann_file=args.ann, iou_thr=args.iou_thr, score_thr=args.score_thr)
metric.dataset_meta = CocoDataset.METAINFO
for datasample in results:
metric.process(data_batch=None, data_samples=[datasample])
metric_res = metric.compute_metrics(metric.results)
if args.out is not None:
mmengine.dump(metric_res, args.out)
print_log(f'Evaluation results have been saved to {args.out}.')
if __name__ == '__main__':
main()
|