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from argparse import ArgumentParser |
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import mmengine |
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from mmengine.logging import print_log |
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from mmdet.datasets import CocoDataset |
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from mmdet.evaluation import CocoOccludedSeparatedMetric |
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def main(): |
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parser = ArgumentParser( |
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description='Compute recall of COCO occluded and separated masks ' |
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'presented in paper https://arxiv.org/abs/2210.10046.') |
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parser.add_argument('result', help='result file (pkl format) path') |
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parser.add_argument('--out', help='file path to save evaluation results') |
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parser.add_argument( |
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'--score-thr', |
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type=float, |
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default=0.3, |
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help='Score threshold for the recall calculation. Defaults to 0.3') |
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parser.add_argument( |
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'--iou-thr', |
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type=float, |
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default=0.75, |
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help='IoU threshold for the recall calculation. Defaults to 0.75.') |
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parser.add_argument( |
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'--ann', |
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default='data/coco/annotations/instances_val2017.json', |
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help='coco annotation file path') |
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args = parser.parse_args() |
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results = mmengine.load(args.result) |
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assert 'masks' in results[0]['pred_instances'], \ |
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'The results must be predicted by instance segmentation model.' |
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metric = CocoOccludedSeparatedMetric( |
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ann_file=args.ann, iou_thr=args.iou_thr, score_thr=args.score_thr) |
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metric.dataset_meta = CocoDataset.METAINFO |
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for datasample in results: |
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metric.process(data_batch=None, data_samples=[datasample]) |
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metric_res = metric.compute_metrics(metric.results) |
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if args.out is not None: |
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mmengine.dump(metric_res, args.out) |
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print_log(f'Evaluation results have been saved to {args.out}.') |
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if __name__ == '__main__': |
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main() |
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