import numpy as np import os import pytest import tempfile import torch from mmdet3d.core.bbox import LiDARInstance3DBoxes from mmdet3d.datasets import KittiDataset def _generate_kitti_dataset_config(): data_root = 'tests/data/kitti' ann_file = 'tests/data/kitti/kitti_infos_train.pkl' classes = ['Pedestrian', 'Cyclist', 'Car'] pts_prefix = 'velodyne_reduced' pipeline = [ dict( type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4, file_client_args=dict(backend='disk')), dict( type='MultiScaleFlipAug3D', img_scale=(1333, 800), pts_scale_ratio=1, flip=False, transforms=[ dict( type='GlobalRotScaleTrans', rot_range=[0, 0], scale_ratio_range=[1.0, 1.0], translation_std=[0, 0, 0]), dict(type='RandomFlip3D'), dict( type='PointsRangeFilter', point_cloud_range=[0, -40, -3, 70.4, 40, 1]), dict( type='DefaultFormatBundle3D', class_names=classes, with_label=False), dict(type='Collect3D', keys=['points']) ]) ] modality = dict(use_lidar=True, use_camera=False) split = 'training' return data_root, ann_file, classes, pts_prefix, pipeline, modality, split def _generate_kitti_multi_modality_dataset_config(): data_root = 'tests/data/kitti' ann_file = 'tests/data/kitti/kitti_infos_train.pkl' classes = ['Pedestrian', 'Cyclist', 'Car'] pts_prefix = 'velodyne_reduced' img_norm_cfg = dict( mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) pipeline = [ dict( type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4, file_client_args=dict(backend='disk')), dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug3D', img_scale=(1333, 800), pts_scale_ratio=1, flip=False, transforms=[ dict(type='Resize', multiscale_mode='value', keep_ratio=True), dict( type='GlobalRotScaleTrans', rot_range=[0, 0], scale_ratio_range=[1., 1.], translation_std=[0, 0, 0]), dict(type='RandomFlip3D'), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict( type='PointsRangeFilter', point_cloud_range=[0, -40, -3, 70.4, 40, 1]), dict( type='DefaultFormatBundle3D', class_names=classes, with_label=False), dict(type='Collect3D', keys=['points', 'img']) ]) ] modality = dict(use_lidar=True, use_camera=True) split = 'training' return data_root, ann_file, classes, pts_prefix, pipeline, modality, split def test_getitem(): np.random.seed(0) data_root, ann_file, classes, pts_prefix, \ _, modality, split = _generate_kitti_dataset_config() pipeline = [ dict( type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4, file_client_args=dict(backend='disk')), dict( type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True, file_client_args=dict(backend='disk')), dict( type='ObjectSample', db_sampler=dict( data_root='tests/data/kitti/', info_path='tests/data/kitti/kitti_dbinfos_train.pkl', rate=1.0, prepare=dict( filter_by_difficulty=[-1], filter_by_min_points=dict(Pedestrian=10)), classes=['Pedestrian', 'Cyclist', 'Car'], sample_groups=dict(Pedestrian=6))), dict( type='ObjectNoise', num_try=100, translation_std=[1.0, 1.0, 0.5], global_rot_range=[0.0, 0.0], rot_range=[-0.78539816, 0.78539816]), dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5), dict( type='GlobalRotScaleTrans', rot_range=[-0.78539816, 0.78539816], scale_ratio_range=[0.95, 1.05]), dict( type='PointsRangeFilter', point_cloud_range=[0, -40, -3, 70.4, 40, 1]), dict( type='ObjectRangeFilter', point_cloud_range=[0, -40, -3, 70.4, 40, 1]), dict(type='PointShuffle'), dict( type='DefaultFormatBundle3D', class_names=['Pedestrian', 'Cyclist', 'Car']), dict( type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d']) ] kitti_dataset = KittiDataset(data_root, ann_file, split, pts_prefix, pipeline, classes, modality) data = kitti_dataset[0] points = data['points']._data gt_bboxes_3d = data['gt_bboxes_3d']._data gt_labels_3d = data['gt_labels_3d']._data expected_gt_bboxes_3d = torch.tensor( [[9.5081, -5.2269, -1.1370, 0.4915, 1.2288, 1.9353, -2.7136]]) expected_gt_labels_3d = torch.tensor([0]) assert points.shape == (780, 4) assert torch.allclose( gt_bboxes_3d.tensor, expected_gt_bboxes_3d, atol=1e-4) assert torch.all(gt_labels_3d == expected_gt_labels_3d) # test multi-modality KITTI dataset np.random.seed(0) point_cloud_range = [0, -40, -3, 70.4, 40, 1] img_norm_cfg = dict( mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) multi_modality_pipeline = [ dict( type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4), dict(type='LoadImageFromFile'), dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True), dict( type='Resize', img_scale=[(640, 192), (2560, 768)], multiscale_mode='range', keep_ratio=True), dict( type='GlobalRotScaleTrans', rot_range=[-0.78539816, 0.78539816], scale_ratio_range=[0.95, 1.05], translation_std=[0.2, 0.2, 0.2]), dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5), dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range), dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range), dict(type='PointShuffle'), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='DefaultFormatBundle3D', class_names=classes), dict( type='Collect3D', keys=['points', 'img', 'gt_bboxes_3d', 'gt_labels_3d']), ] modality = dict(use_lidar=True, use_camera=True) kitti_dataset = KittiDataset(data_root, ann_file, split, pts_prefix, multi_modality_pipeline, classes, modality) data = kitti_dataset[0] img = data['img']._data lidar2img = data['img_metas']._data['lidar2img'] expected_lidar2img = np.array( [[6.02943726e+02, -7.07913330e+02, -1.22748432e+01, -1.70942719e+02], [1.76777252e+02, 8.80879879e+00, -7.07936157e+02, -1.02568634e+02], [9.99984801e-01, -1.52826728e-03, -5.29071223e-03, -3.27567995e-01], [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.00000000e+00]]) assert img.shape[:] == (3, 416, 1344) assert np.allclose(lidar2img, expected_lidar2img) def test_evaluate(): if not torch.cuda.is_available(): pytest.skip('test requires GPU and torch+cuda') data_root, ann_file, classes, pts_prefix, \ pipeline, modality, split = _generate_kitti_dataset_config() kitti_dataset = KittiDataset(data_root, ann_file, split, pts_prefix, pipeline, classes, modality) boxes_3d = LiDARInstance3DBoxes( torch.tensor( [[8.7314, -1.8559, -1.5997, 0.4800, 1.2000, 1.8900, 0.0100]])) labels_3d = torch.tensor([ 0, ]) scores_3d = torch.tensor([0.5]) metric = ['mAP'] result = dict(boxes_3d=boxes_3d, labels_3d=labels_3d, scores_3d=scores_3d) ap_dict = kitti_dataset.evaluate([result], metric) assert np.isclose(ap_dict['KITTI/Overall_3D_easy'], 3.0303030303030307) assert np.isclose(ap_dict['KITTI/Overall_3D_moderate'], 3.0303030303030307) assert np.isclose(ap_dict['KITTI/Overall_3D_hard'], 3.0303030303030307) def test_show(): import mmcv from os import path as osp from mmdet3d.core.bbox import LiDARInstance3DBoxes tmp_dir = tempfile.TemporaryDirectory() temp_dir = tmp_dir.name data_root, ann_file, classes, pts_prefix, \ pipeline, modality, split = _generate_kitti_dataset_config() kitti_dataset = KittiDataset( data_root, ann_file, split=split, modality=modality, pipeline=pipeline) boxes_3d = LiDARInstance3DBoxes( torch.tensor( [[46.1218, -4.6496, -0.9275, 0.5316, 1.4442, 1.7450, 1.1749], [33.3189, 0.1981, 0.3136, 0.5656, 1.2301, 1.7985, 1.5723], [46.1366, -4.6404, -0.9510, 0.5162, 1.6501, 1.7540, 1.3778], [33.2646, 0.2297, 0.3446, 0.5746, 1.3365, 1.7947, 1.5430], [58.9079, 16.6272, -1.5829, 1.5656, 3.9313, 1.4899, 1.5505]])) scores_3d = torch.tensor([0.1815, 0.1663, 0.5792, 0.2194, 0.2780]) labels_3d = torch.tensor([0, 0, 1, 1, 2]) result = dict(boxes_3d=boxes_3d, scores_3d=scores_3d, labels_3d=labels_3d) results = [result] kitti_dataset.show(results, temp_dir, show=False) pts_file_path = osp.join(temp_dir, '000000', '000000_points.obj') gt_file_path = osp.join(temp_dir, '000000', '000000_gt.obj') pred_file_path = osp.join(temp_dir, '000000', '000000_pred.obj') mmcv.check_file_exist(pts_file_path) mmcv.check_file_exist(gt_file_path) mmcv.check_file_exist(pred_file_path) tmp_dir.cleanup() # test show with pipeline eval_pipeline = [ dict( type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4), dict( type='DefaultFormatBundle3D', class_names=classes, with_label=False), dict(type='Collect3D', keys=['points']) ] tmp_dir = tempfile.TemporaryDirectory() temp_dir = tmp_dir.name kitti_dataset.show(results, temp_dir, show=False, pipeline=eval_pipeline) pts_file_path = osp.join(temp_dir, '000000', '000000_points.obj') gt_file_path = osp.join(temp_dir, '000000', '000000_gt.obj') pred_file_path = osp.join(temp_dir, '000000', '000000_pred.obj') mmcv.check_file_exist(pts_file_path) mmcv.check_file_exist(gt_file_path) mmcv.check_file_exist(pred_file_path) tmp_dir.cleanup() # test multi-modality show tmp_dir = tempfile.TemporaryDirectory() temp_dir = tmp_dir.name _, _, _, _, multi_modality_pipeline, modality, _ = \ _generate_kitti_multi_modality_dataset_config() kitti_dataset = KittiDataset(data_root, ann_file, split, pts_prefix, multi_modality_pipeline, classes, modality) kitti_dataset.show(results, temp_dir, show=False) pts_file_path = osp.join(temp_dir, '000000', '000000_points.obj') gt_file_path = osp.join(temp_dir, '000000', '000000_gt.obj') pred_file_path = osp.join(temp_dir, '000000', '000000_pred.obj') img_file_path = osp.join(temp_dir, '000000', '000000_img.png') img_pred_path = osp.join(temp_dir, '000000', '000000_pred.png') img_gt_file = osp.join(temp_dir, '000000', '000000_gt.png') mmcv.check_file_exist(pts_file_path) mmcv.check_file_exist(gt_file_path) mmcv.check_file_exist(pred_file_path) mmcv.check_file_exist(img_file_path) mmcv.check_file_exist(img_pred_path) mmcv.check_file_exist(img_gt_file) tmp_dir.cleanup() # test multi-modality show with pipeline eval_pipeline = [ dict( type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4), dict(type='LoadImageFromFile'), dict( type='DefaultFormatBundle3D', class_names=classes, with_label=False), dict(type='Collect3D', keys=['points', 'img']) ] tmp_dir = tempfile.TemporaryDirectory() temp_dir = tmp_dir.name kitti_dataset.show(results, temp_dir, show=False, pipeline=eval_pipeline) pts_file_path = osp.join(temp_dir, '000000', '000000_points.obj') gt_file_path = osp.join(temp_dir, '000000', '000000_gt.obj') pred_file_path = osp.join(temp_dir, '000000', '000000_pred.obj') img_file_path = osp.join(temp_dir, '000000', '000000_img.png') img_pred_path = osp.join(temp_dir, '000000', '000000_pred.png') img_gt_file = osp.join(temp_dir, '000000', '000000_gt.png') mmcv.check_file_exist(pts_file_path) mmcv.check_file_exist(gt_file_path) mmcv.check_file_exist(pred_file_path) mmcv.check_file_exist(img_file_path) mmcv.check_file_exist(img_pred_path) mmcv.check_file_exist(img_gt_file) tmp_dir.cleanup() def test_format_results(): from mmdet3d.core.bbox import LiDARInstance3DBoxes data_root, ann_file, classes, pts_prefix, \ pipeline, modality, split = _generate_kitti_dataset_config() kitti_dataset = KittiDataset(data_root, ann_file, split, pts_prefix, pipeline, classes, modality) boxes_3d = LiDARInstance3DBoxes( torch.tensor( [[8.7314, -1.8559, -1.5997, 0.4800, 1.2000, 1.8900, 0.0100]])) labels_3d = torch.tensor([ 0, ]) scores_3d = torch.tensor([0.5]) result = dict(boxes_3d=boxes_3d, labels_3d=labels_3d, scores_3d=scores_3d) results = [result] result_files, tmp_dir = kitti_dataset.format_results(results) expected_name = np.array(['Pedestrian']) expected_truncated = np.array([0.]) expected_occluded = np.array([0]) expected_alpha = np.array([-3.3410306]) expected_bbox = np.array([[710.443, 144.00221, 820.29114, 307.58667]]) expected_dimensions = np.array([[1.2, 1.89, 0.48]]) expected_location = np.array([[1.8399826, 1.4700007, 8.410018]]) expected_rotation_y = np.array([-3.1315928]) expected_score = np.array([0.5]) expected_sample_idx = np.array([0]) assert np.all(result_files[0]['name'] == expected_name) assert np.allclose(result_files[0]['truncated'], expected_truncated) assert np.all(result_files[0]['occluded'] == expected_occluded) assert np.allclose(result_files[0]['alpha'], expected_alpha) assert np.allclose(result_files[0]['bbox'], expected_bbox) assert np.allclose(result_files[0]['dimensions'], expected_dimensions) assert np.allclose(result_files[0]['location'], expected_location) assert np.allclose(result_files[0]['rotation_y'], expected_rotation_y) assert np.allclose(result_files[0]['score'], expected_score) assert np.allclose(result_files[0]['sample_idx'], expected_sample_idx) tmp_dir.cleanup() def test_bbox2result_kitti(): data_root, ann_file, classes, pts_prefix, \ pipeline, modality, split = _generate_kitti_dataset_config() kitti_dataset = KittiDataset(data_root, ann_file, split, pts_prefix, pipeline, classes, modality) boxes_3d = LiDARInstance3DBoxes( torch.tensor( [[8.7314, -1.8559, -1.5997, 0.4800, 1.2000, 1.8900, 0.0100]])) labels_3d = torch.tensor([ 0, ]) scores_3d = torch.tensor([0.5]) result = dict(boxes_3d=boxes_3d, labels_3d=labels_3d, scores_3d=scores_3d) results = [result] tmp_dir = tempfile.TemporaryDirectory() temp_kitti_result_dir = tmp_dir.name det_annos = kitti_dataset.bbox2result_kitti( results, classes, submission_prefix=temp_kitti_result_dir) expected_file_path = os.path.join(temp_kitti_result_dir, '000000.txt') expected_name = np.array(['Pedestrian']) expected_dimensions = np.array([1.2000, 1.8900, 0.4800]) expected_rotation_y = np.array([0.0100]) - np.pi expected_score = np.array([0.5]) assert np.all(det_annos[0]['name'] == expected_name) assert np.allclose(det_annos[0]['rotation_y'], expected_rotation_y) assert np.allclose(det_annos[0]['score'], expected_score) assert np.allclose(det_annos[0]['dimensions'], expected_dimensions) assert os.path.exists(expected_file_path) tmp_dir.cleanup() tmp_dir = tempfile.TemporaryDirectory() temp_kitti_result_dir = tmp_dir.name boxes_3d = LiDARInstance3DBoxes(torch.tensor([])) labels_3d = torch.tensor([]) scores_3d = torch.tensor([]) empty_result = dict( boxes_3d=boxes_3d, labels_3d=labels_3d, scores_3d=scores_3d) results = [empty_result] det_annos = kitti_dataset.bbox2result_kitti( results, classes, submission_prefix=temp_kitti_result_dir) expected_file_path = os.path.join(temp_kitti_result_dir, '000000.txt') assert os.path.exists(expected_file_path) tmp_dir.cleanup() def test_bbox2result_kitti2d(): data_root, ann_file, classes, pts_prefix, \ pipeline, modality, split = _generate_kitti_dataset_config() kitti_dataset = KittiDataset(data_root, ann_file, split, pts_prefix, pipeline, classes, modality) bboxes = np.array([[[46.1218, -4.6496, -0.9275, 0.5316, 0.5], [33.3189, 0.1981, 0.3136, 0.5656, 0.5]], [[46.1366, -4.6404, -0.9510, 0.5162, 0.5], [33.2646, 0.2297, 0.3446, 0.5746, 0.5]]]) det_annos = kitti_dataset.bbox2result_kitti2d([bboxes], classes) expected_name = np.array( ['Pedestrian', 'Pedestrian', 'Cyclist', 'Cyclist']) expected_bbox = np.array([[46.1218, -4.6496, -0.9275, 0.5316], [33.3189, 0.1981, 0.3136, 0.5656], [46.1366, -4.6404, -0.951, 0.5162], [33.2646, 0.2297, 0.3446, 0.5746]]) expected_score = np.array([0.5, 0.5, 0.5, 0.5]) assert np.all(det_annos[0]['name'] == expected_name) assert np.allclose(det_annos[0]['bbox'], expected_bbox) assert np.allclose(det_annos[0]['score'], expected_score)