| import numpy as np | |
| import pytest | |
| import torch | |
| from mmengine.structures import InstanceData | |
| from mmdet3d.evaluation.metrics import KittiMetric | |
| from mmdet3d.structures import Det3DDataSample, LiDARInstance3DBoxes | |
| data_root = 'tests/data/kitti' | |
| def _init_evaluate_input(): | |
| metainfo = dict(sample_idx=0) | |
| predictions = Det3DDataSample() | |
| pred_instances_3d = InstanceData() | |
| pred_instances_3d.bboxes_3d = LiDARInstance3DBoxes( | |
| torch.tensor( | |
| [[8.7314, -1.8559, -1.5997, 0.4800, 1.2000, 1.8900, 0.0100]])) | |
| pred_instances_3d.scores_3d = torch.Tensor([0.9]) | |
| pred_instances_3d.labels_3d = torch.Tensor([0]) | |
| predictions.pred_instances_3d = pred_instances_3d | |
| predictions.pred_instances = InstanceData() | |
| predictions.set_metainfo(metainfo) | |
| predictions = predictions.to_dict() | |
| return {}, [predictions] | |
| def _init_multi_modal_evaluate_input(): | |
| metainfo = dict(sample_idx=0) | |
| predictions = Det3DDataSample() | |
| pred_instances_3d = InstanceData() | |
| pred_instances = InstanceData() | |
| pred_instances.bboxes = torch.tensor([[712.4, 143, 810.7, 307.92]]) | |
| pred_instances.scores = torch.Tensor([0.9]) | |
| pred_instances.labels = torch.Tensor([0]) | |
| pred_instances_3d.bboxes_3d = LiDARInstance3DBoxes( | |
| torch.tensor( | |
| [[8.7314, -1.8559, -1.5997, 0.4800, 1.2000, 1.8900, 0.0100]])) | |
| pred_instances_3d.scores_3d = torch.Tensor([0.9]) | |
| pred_instances_3d.labels_3d = torch.Tensor([0]) | |
| predictions.pred_instances_3d = pred_instances_3d | |
| predictions.pred_instances = pred_instances | |
| predictions.set_metainfo(metainfo) | |
| predictions = predictions.to_dict() | |
| return {}, [predictions] | |
| def test_multi_modal_kitti_metric(): | |
| if not torch.cuda.is_available(): | |
| pytest.skip('test requires GPU and torch+cuda') | |
| kittimetric = KittiMetric( | |
| data_root + '/kitti_infos_train.pkl', metric=['mAP']) | |
| kittimetric.dataset_meta = dict(classes=['Pedestrian', 'Cyclist', 'Car']) | |
| data_batch, predictions = _init_multi_modal_evaluate_input() | |
| kittimetric.process(data_batch, predictions) | |
| ap_dict = kittimetric.compute_metrics(kittimetric.results) | |
| assert np.isclose(ap_dict['pred_instances_3d/KITTI/Overall_3D_AP11_easy'], | |
| 3.0303030303030307) | |
| assert np.isclose(ap_dict['pred_instances_3d/KITTI/Overall_BEV_AP11_easy'], | |
| 3.0303030303030307) | |
| assert np.isclose(ap_dict['pred_instances_3d/KITTI/Overall_2D_AP11_easy'], | |
| 3.0303030303030307) | |
| assert np.isclose(ap_dict['pred_instances/KITTI/Overall_2D_AP11_easy'], | |
| 3.0303030303030307) | |
| assert np.isclose(ap_dict['pred_instances/KITTI/Overall_2D_AP11_moderate'], | |
| 3.0303030303030307) | |
| assert np.isclose(ap_dict['pred_instances/KITTI/Overall_2D_AP11_hard'], | |
| 3.0303030303030307) | |
| def test_kitti_metric_mAP(): | |
| if not torch.cuda.is_available(): | |
| pytest.skip('test requires GPU and torch+cuda') | |
| kittimetric = KittiMetric( | |
| data_root + '/kitti_infos_train.pkl', metric=['mAP']) | |
| kittimetric.dataset_meta = dict(classes=['Pedestrian', 'Cyclist', 'Car']) | |
| data_batch, predictions = _init_evaluate_input() | |
| kittimetric.process(data_batch, predictions) | |
| ap_dict = kittimetric.compute_metrics(kittimetric.results) | |
| assert np.isclose(ap_dict['pred_instances_3d/KITTI/Overall_3D_AP11_easy'], | |
| 3.0303030303030307) | |
| assert np.isclose( | |
| ap_dict['pred_instances_3d/KITTI/Overall_3D_AP11_moderate'], | |
| 3.0303030303030307) | |
| assert np.isclose(ap_dict['pred_instances_3d/KITTI/Overall_3D_AP11_hard'], | |
| 3.0303030303030307) | |