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import os.path as osp |
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from tempfile import TemporaryDirectory |
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import mmengine |
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import numpy as np |
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from mmdet.datasets import CocoDataset |
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from mmdet.evaluation import CocoOccludedSeparatedMetric |
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def test_coco_occluded_separated_metric(): |
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ann = [[ |
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'fake1.jpg', 'person', 8, [219.9, 176.12, 11.14, 34.23], { |
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'size': [480, 640], |
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'counts': b'nYW31n>2N2FNbA48Kf=?XBDe=m0OM3M4YOPB8_>L4JXao5' |
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} |
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]] * 3 |
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dummy_mask = np.zeros((10, 10), dtype=np.uint8) |
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dummy_mask[:5, :5] = 1 |
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rle = { |
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'size': [480, 640], |
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'counts': b'nYW31n>2N2FNbA48Kf=?XBDe=m0OM3M4YOPB8_>L4JXao5' |
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} |
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res = [(None, |
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dict( |
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img_id=0, |
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bboxes=np.array([[50, 60, 70, 80]] * 2), |
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masks=[rle] * 2, |
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labels=np.array([0, 1], dtype=np.int64), |
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scores=np.array([0.77, 0.77])))] * 3 |
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tempdir = TemporaryDirectory() |
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ann_path = osp.join(tempdir.name, 'coco_occluded.pkl') |
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mmengine.dump(ann, ann_path) |
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metric = CocoOccludedSeparatedMetric( |
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ann_file='tests/data/coco_sample.json', |
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occluded_ann=ann_path, |
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separated_ann=ann_path, |
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metric=[]) |
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metric.dataset_meta = CocoDataset.METAINFO |
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eval_res = metric.compute_metrics(res) |
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assert isinstance(eval_res, dict) |
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assert eval_res['occluded_recall'] == 100 |
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assert eval_res['separated_recall'] == 100 |
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