point_cloud_range = [-50, -50, -5, 50, 50, 3] class_names = [ 'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle', 'motorcycle', 'pedestrian', 'traffic_cone', 'barrier' ] dataset_type = 'NuScenesDataset' data_root = 'data/nuscenes/' file_client_args = dict(backend='disk') train_pipeline = [ dict( type='LoadPointsFromFile', load_dim=5, use_dim=5, file_client_args=file_client_args), dict( type='LoadPointsFromMultiSweeps', sweeps_num=10, file_client_args=file_client_args), dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True), dict( type='GlobalRotScale', rot_uniform_noise=[-0.3925, 0.3925], scaling_uniform_noise=[0.95, 1.05], trans_normal_noise=[0, 0, 0]), dict(type='RandomFlip3D', flip_ratio=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='DefaultFormatBundle3D', class_names=class_names), dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d']) ] test_pipeline = [ dict( type='LoadPointsFromFile', load_dim=5, use_dim=5, file_client_args=file_client_args), dict( type='LoadPointsFromMultiSweeps', sweeps_num=10, file_client_args=file_client_args), dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range), dict(type='RandomFlip3D', flip_ratio=0), dict( type='DefaultFormatBundle3D', class_names=class_names, with_label=False), dict(type='Collect3D', keys=['points']) ] data = dict( samples_per_gpu=4, workers_per_gpu=4, train=dict( type=dataset_type, data_root=data_root, ann_file=data_root + 'nuscenes_infos_train.pkl', pipeline=train_pipeline, classes=class_names, test_mode=False), val=dict( type=dataset_type, data_root=data_root, ann_file=data_root + 'nuscenes_infos_val.pkl', pipeline=test_pipeline, classes=class_names, test_mode=True), test=dict( type=dataset_type, data_root=data_root, ann_file=data_root + 'nuscenes_infos_val.pkl', pipeline=test_pipeline, classes=class_names, test_mode=True))