Spaces:
No application file
No application file
| # dataset settings | |
| # D5 in the config name means the whole dataset is divided into 5 folds | |
| # We only use one fold for efficient experiments | |
| dataset_type = 'WaymoDataset' | |
| data_root = 'data/waymo/kitti_format/' | |
| 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://waymo_data/')) | |
| class_names = ['Car'] | |
| point_cloud_range = [-74.88, -74.88, -2, 74.88, 74.88, 4] | |
| input_modality = dict(use_lidar=True, use_camera=False) | |
| db_sampler = dict( | |
| data_root=data_root, | |
| info_path=data_root + 'waymo_dbinfos_train.pkl', | |
| rate=1.0, | |
| prepare=dict(filter_by_difficulty=[-1], filter_by_min_points=dict(Car=5)), | |
| classes=class_names, | |
| sample_groups=dict(Car=15), | |
| points_loader=dict( | |
| type='LoadPointsFromFile', | |
| coord_type='LIDAR', | |
| load_dim=5, | |
| use_dim=[0, 1, 2, 3, 4], | |
| file_client_args=file_client_args)) | |
| train_pipeline = [ | |
| dict( | |
| type='LoadPointsFromFile', | |
| coord_type='LIDAR', | |
| load_dim=6, | |
| use_dim=5, | |
| 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='RandomFlip3D', | |
| sync_2d=False, | |
| flip_ratio_bev_horizontal=0.5, | |
| flip_ratio_bev_vertical=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=6, | |
| use_dim=5, | |
| 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=6, | |
| use_dim=5, | |
| 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=2, | |
| workers_per_gpu=4, | |
| train=dict( | |
| type='RepeatDataset', | |
| times=2, | |
| dataset=dict( | |
| type=dataset_type, | |
| data_root=data_root, | |
| ann_file=data_root + 'waymo_infos_train.pkl', | |
| split='training', | |
| 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', | |
| # load one frame every five frames | |
| load_interval=5)), | |
| val=dict( | |
| type=dataset_type, | |
| data_root=data_root, | |
| ann_file=data_root + 'waymo_infos_val.pkl', | |
| split='training', | |
| 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 + 'waymo_infos_val.pkl', | |
| split='training', | |
| pipeline=test_pipeline, | |
| modality=input_modality, | |
| classes=class_names, | |
| test_mode=True, | |
| box_type_3d='LiDAR')) | |
| evaluation = dict(interval=24, pipeline=eval_pipeline) | |