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| # dataset settings | |
| dataset_type = 'KittiDataset' | |
| data_root = 'data/kitti/' | |
| class_names = ['Pedestrian', 'Cyclist', 'Car'] | |
| point_cloud_range = [0, -40, -3, 70.4, 40, 1] | |
| input_modality = dict(use_lidar=True, use_camera=False) | |
| db_sampler = dict( | |
| data_root=data_root, | |
| info_path=data_root + 'kitti_dbinfos_train.pkl', | |
| rate=1.0, | |
| prepare=dict( | |
| filter_by_difficulty=[-1], | |
| filter_by_min_points=dict(Car=5, Pedestrian=10, Cyclist=10)), | |
| classes=class_names, | |
| sample_groups=dict(Car=12, Pedestrian=6, Cyclist=6)) | |
| file_client_args = dict(backend='disk') | |
| # Uncomment the following if use ceph or other file clients. | |
| # See https://mmcv.readthedocs.io/en/latest/api.html#mmcv.fileio.FileClient | |
| # for more details. | |
| # file_client_args = dict( | |
| # backend='petrel', path_mapping=dict(data='s3://kitti_data/')) | |
| train_pipeline = [ | |
| dict( | |
| type='LoadPointsFromFile', | |
| coord_type='LIDAR', | |
| load_dim=4, | |
| use_dim=4, | |
| file_client_args=file_client_args), | |
| dict( | |
| type='LoadAnnotations3D', | |
| with_bbox_3d=True, | |
| with_label_3d=True, | |
| file_client_args=file_client_args), | |
| dict(type='ObjectSample', db_sampler=db_sampler), | |
| 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=point_cloud_range), | |
| dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range), | |
| dict(type='PointShuffle'), | |
| dict(type='DefaultFormatBundle3D', class_names=class_names), | |
| dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d']) | |
| ] | |
| test_pipeline = [ | |
| dict( | |
| type='LoadPointsFromFile', | |
| coord_type='LIDAR', | |
| load_dim=4, | |
| use_dim=4, | |
| file_client_args=file_client_args), | |
| 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., 1.], | |
| translation_std=[0, 0, 0]), | |
| dict(type='RandomFlip3D'), | |
| dict( | |
| type='PointsRangeFilter', point_cloud_range=point_cloud_range), | |
| dict( | |
| type='DefaultFormatBundle3D', | |
| class_names=class_names, | |
| with_label=False), | |
| dict(type='Collect3D', keys=['points']) | |
| ]) | |
| ] | |
| # construct a pipeline for data and gt loading in show function | |
| # please keep its loading function consistent with test_pipeline (e.g. client) | |
| eval_pipeline = [ | |
| dict( | |
| type='LoadPointsFromFile', | |
| coord_type='LIDAR', | |
| load_dim=4, | |
| use_dim=4, | |
| file_client_args=file_client_args), | |
| dict( | |
| type='DefaultFormatBundle3D', | |
| class_names=class_names, | |
| with_label=False), | |
| dict(type='Collect3D', keys=['points']) | |
| ] | |
| data = dict( | |
| samples_per_gpu=6, | |
| workers_per_gpu=4, | |
| train=dict( | |
| type='RepeatDataset', | |
| times=2, | |
| dataset=dict( | |
| type=dataset_type, | |
| data_root=data_root, | |
| ann_file=data_root + 'kitti_infos_train.pkl', | |
| split='training', | |
| pts_prefix='velodyne_reduced', | |
| pipeline=train_pipeline, | |
| modality=input_modality, | |
| classes=class_names, | |
| test_mode=False, | |
| # we use box_type_3d='LiDAR' in kitti and nuscenes dataset | |
| # and box_type_3d='Depth' in sunrgbd and scannet dataset. | |
| box_type_3d='LiDAR')), | |
| val=dict( | |
| type=dataset_type, | |
| data_root=data_root, | |
| ann_file=data_root + 'kitti_infos_val.pkl', | |
| split='training', | |
| pts_prefix='velodyne_reduced', | |
| pipeline=test_pipeline, | |
| modality=input_modality, | |
| classes=class_names, | |
| test_mode=True, | |
| box_type_3d='LiDAR'), | |
| test=dict( | |
| type=dataset_type, | |
| data_root=data_root, | |
| ann_file=data_root + 'kitti_infos_val.pkl', | |
| split='training', | |
| pts_prefix='velodyne_reduced', | |
| pipeline=test_pipeline, | |
| modality=input_modality, | |
| classes=class_names, | |
| test_mode=True, | |
| box_type_3d='LiDAR')) | |
| evaluation = dict(interval=1, pipeline=eval_pipeline) | |