| |
| 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() |
|
|
