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import os.path as osp |
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import tempfile |
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from unittest import TestCase |
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import numpy as np |
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import torch |
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from mmdet.evaluation import CrowdHumanMetric |
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class TestCrowdHumanMetric(TestCase): |
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def _create_dummy_results(self): |
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bboxes = np.array([[1330, 317, 418, 1338], [792, 24, 723, 2017], |
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[693, 291, 307, 894], [522, 290, 285, 826], |
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[728, 336, 175, 602], [92, 337, 267, 681]]) |
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bboxes[:, 2:4] += bboxes[:, 0:2] |
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scores = np.array([1.0, 1.0, 1.0, 1.0, 1.0, 1.0]) |
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return dict( |
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bboxes=torch.from_numpy(bboxes), scores=torch.from_numpy(scores)) |
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def setUp(self): |
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self.tmp_dir = tempfile.TemporaryDirectory() |
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self.ann_file_path = \ |
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'tests/data/crowdhuman_dataset/test_annotation_train.odgt', |
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def tearDown(self): |
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self.tmp_dir.cleanup() |
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def test_init(self): |
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with self.assertRaisesRegex(KeyError, 'metric should be one of'): |
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CrowdHumanMetric(ann_file=self.ann_file_path[0], metric='unknown') |
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def test_evaluate(self): |
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dummy_pred = self._create_dummy_results() |
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crowdhuman_metric = CrowdHumanMetric( |
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ann_file=self.ann_file_path[0], |
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outfile_prefix=f'{self.tmp_dir.name}/test') |
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crowdhuman_metric.process({}, [ |
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dict( |
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pred_instances=dummy_pred, |
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img_id='283554,35288000868e92d4', |
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ori_shape=(1640, 1640)) |
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]) |
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eval_results = crowdhuman_metric.evaluate(size=1) |
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target = { |
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'crowd_human/mAP': 0.8333, |
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'crowd_human/mMR': 0.0, |
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'crowd_human/JI': 1.0 |
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} |
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self.assertDictEqual(eval_results, target) |
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self.assertTrue(osp.isfile(osp.join(self.tmp_dir.name, 'test.json'))) |
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