point_cloud_range = [-15.0, -30.0, -2.0, 15.0, 30.0, 2.0] class_names = [ 'car', 'van', 'truck', 'bicycle', 'traffic_sign', 'traffic_cone', 'traffic_light', 'pedestrian', 'others' ] dataset_type = 'B2D_DriveTransformer_GoalPoint_Dataset' data_root = 'data/bench2drive' input_modality = dict( use_lidar=False, use_camera=True, use_radar=False, use_map=False, use_external=True) file_client_args = dict(backend='disk') train_pipeline = [ dict(type='LoadMultiViewImageFromFiles', to_float32=True), dict(type='ResizeCropFlipImage'), dict( type='NormalizeMultiviewImage', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict( type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True, with_attr_label=True), dict( type='CustomObjectRangeFilter', point_cloud_range=[-15.0, -30.0, -2.0, 15.0, 30.0, 2.0]), dict( type='CustomObjectNameFilter', classes=[ 'car', 'van', 'truck', 'bicycle', 'traffic_sign', 'traffic_cone', 'traffic_light', 'pedestrian', 'others' ]), dict( type='TrajPreprocess', pc_range=[-15.0, -30.0, -2.0, 15.0, 30.0, 2.0], with_ego_fix_dist=True, ego_fut_offset_input=False, assign_class_for_ego=False), dict( type='CustomFormatBundle3D', class_names=[ 'car', 'van', 'truck', 'bicycle', 'traffic_sign', 'traffic_cone', 'traffic_light', 'pedestrian', 'others' ], with_ego=True, collect_keys=[ 'lidar2img', 'cam_intrinsic', 'cam_extrinsic', 'timestamp', 'ego_pose', 'ego_pose_inv', 'pad_shape', 'gt_traj_fut_classes', 'ego_fut_classes' ]), dict( type='CustomCollect3D', keys=[ 'gt_bboxes_3d', 'gt_labels_3d', 'img', 'ego_his_trajs', 'fut_valid_flag_fix_time', 'ego_fut_trajs_fix_time', 'ego_fut_masks_fix_time', 'fut_valid_flag_fix_dist', 'ego_fut_trajs_fix_dist', 'ego_fut_masks_fix_dist', 'ego_fut_cmd', 'ego_lcf_feat', 'gt_attr_labels', 'prev_exists', 'index', 'lidar2img', 'cam_intrinsic', 'cam_extrinsic', 'timestamp', 'ego_pose', 'ego_pose_inv', 'pad_shape', 'gt_traj_fut_classes', 'ego_fut_classes' ]) ] test_pipeline = [ dict(type='LoadMultiViewImageFromFiles', to_float32=True), dict(type='ResizeCropFlipImage'), dict( type='NormalizeMultiviewImage', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict( type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True, with_attr_label=True), dict( type='CustomObjectRangeFilter', point_cloud_range=[-15.0, -30.0, -2.0, 15.0, 30.0, 2.0]), dict( type='CustomObjectNameFilter', classes=[ 'car', 'van', 'truck', 'bicycle', 'traffic_sign', 'traffic_cone', 'traffic_light', 'pedestrian', 'others' ]), dict( type='TrajPreprocess', pc_range=[-15.0, -30.0, -2.0, 15.0, 30.0, 2.0], with_ego_fix_dist=True, ego_fut_offset_input=False, assign_class_for_ego=False), dict( type='CustomFormatBundle3D', class_names=[ 'car', 'van', 'truck', 'bicycle', 'traffic_sign', 'traffic_cone', 'traffic_light', 'pedestrian', 'others' ], with_ego=True, collect_keys=[ 'lidar2img', 'cam_intrinsic', 'cam_extrinsic', 'timestamp', 'ego_pose', 'ego_pose_inv', 'pad_shape', 'gt_traj_fut_classes', 'ego_fut_classes' ]), dict( type='CustomCollect3D', keys=[ 'gt_bboxes_3d', 'gt_labels_3d', 'img', 'ego_his_trajs', 'fut_valid_flag_fix_time', 'ego_fut_trajs_fix_time', 'ego_fut_masks_fix_time', 'fut_valid_flag_fix_dist', 'ego_fut_trajs_fix_dist', 'ego_fut_masks_fix_dist', 'ego_fut_cmd', 'ego_lcf_feat', 'gt_attr_labels', 'prev_exists', 'index', 'lidar2img', 'cam_intrinsic', 'cam_extrinsic', 'timestamp', 'ego_pose', 'ego_pose_inv', 'pad_shape', 'gt_traj_fut_classes', 'ego_fut_classes' ]) ] eval_pipeline = [ dict( type='LoadPointsFromFile', coord_type='LIDAR', load_dim=5, use_dim=5, file_client_args=dict(backend='disk')), dict( type='LoadPointsFromMultiSweeps', sweeps_num=10, file_client_args=dict(backend='disk')), dict( type='DefaultFormatBundle3D', class_names=[ 'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle', 'motorcycle', 'pedestrian', 'traffic_cone', 'barrier' ], with_label=False), dict(type='Collect3D', keys=['points']) ] data = dict( samples_per_gpu=6, workers_per_gpu=8, train=dict( type='B2D_DriveTransformer_GoalPoint_Dataset', data_root='data/bench2drive', ann_file='data/infos/b2d_infos_v1_train_drivetransformer_meta.pkl', pipeline=[ dict(type='LoadMultiViewImageFromFiles', to_float32=True), dict(type='ResizeCropFlipImage'), dict( type='NormalizeMultiviewImage', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict( type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True, with_attr_label=True), dict( type='CustomObjectRangeFilter', point_cloud_range=[-15.0, -30.0, -2.0, 15.0, 30.0, 2.0]), dict( type='CustomObjectNameFilter', classes=[ 'car', 'van', 'truck', 'bicycle', 'traffic_sign', 'traffic_cone', 'traffic_light', 'pedestrian', 'others' ]), dict( type='TrajPreprocess', pc_range=[-15.0, -30.0, -2.0, 15.0, 30.0, 2.0], with_ego_fix_dist=True, ego_fut_offset_input=False, assign_class_for_ego=False), dict( type='CustomFormatBundle3D', class_names=[ 'car', 'van', 'truck', 'bicycle', 'traffic_sign', 'traffic_cone', 'traffic_light', 'pedestrian', 'others' ], with_ego=True, collect_keys=[ 'lidar2img', 'cam_intrinsic', 'cam_extrinsic', 'timestamp', 'ego_pose', 'ego_pose_inv', 'pad_shape', 'gt_traj_fut_classes', 'ego_fut_classes' ]), dict( type='CustomCollect3D', keys=[ 'gt_bboxes_3d', 'gt_labels_3d', 'img', 'ego_his_trajs', 'fut_valid_flag_fix_time', 'ego_fut_trajs_fix_time', 'ego_fut_masks_fix_time', 'fut_valid_flag_fix_dist', 'ego_fut_trajs_fix_dist', 'ego_fut_masks_fix_dist', 'ego_fut_cmd', 'ego_lcf_feat', 'gt_attr_labels', 'prev_exists', 'index', 'lidar2img', 'cam_intrinsic', 'cam_extrinsic', 'timestamp', 'ego_pose', 'ego_pose_inv', 'pad_shape', 'gt_traj_fut_classes', 'ego_fut_classes' ]) ], classes=[ 'car', 'van', 'truck', 'bicycle', 'traffic_sign', 'traffic_cone', 'traffic_light', 'pedestrian', 'others' ], modality=dict( use_lidar=False, use_camera=True, use_radar=False, use_map=False, use_external=True), test_mode=False, box_type_3d='LiDAR', data_aug_conf=dict( resize_lim=(0.64, 0.69), final_dim=(384, 1056), bot_pct_lim=(0.0, 0.0), rot_lim=(-5.4, 5.4), H=900, W=1600, rand_flip=True, rot3d_range=[0, 0]), name_mapping=dict({ 'vehicle.bh.crossbike': 'bicycle', 'vehicle.diamondback.century': 'bicycle', 'vehicle.gazelle.omafiets': 'bicycle', 'vehicle.audi.etron': 'car', 'vehicle.chevrolet.impala': 'car', 'vehicle.dodge.charger_2020': 'car', 'vehicle.dodge.charger_police': 'car', 'vehicle.dodge.charger_police_2020': 'car', 'vehicle.lincoln.mkz_2017': 'car', 'vehicle.lincoln.mkz_2020': 'car', 'vehicle.mini.cooper_s_2021': 'car', 'vehicle.mercedes.coupe_2020': 'car', 'vehicle.ford.mustang': 'car', 'vehicle.nissan.patrol_2021': 'car', 'vehicle.audi.tt': 'car', 'vehicle.ford.crown': 'car', 'vehicle.tesla.model3': 'car', '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/FordCrown/SM_FordCrown_parked.SM_FordCrown_parked': 'car', '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/Charger/SM_ChargerParked.SM_ChargerParked': 'car', '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/Lincoln/SM_LincolnParked.SM_LincolnParked': 'car', '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/MercedesCCC/SM_MercedesCCC_Parked.SM_MercedesCCC_Parked': 'car', '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/Mini2021/SM_Mini2021_parked.SM_Mini2021_parked': 'car', '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/NissanPatrol2021/SM_NissanPatrol2021_parked.SM_NissanPatrol2021_parked': 'car', '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/TeslaM3/SM_TeslaM3_parked.SM_TeslaM3_parked': 'car', '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/VolkswagenT2/SM_VolkswagenT2_2021_Parked.SM_VolkswagenT2_2021_Parked': 'van', 'vehicle.ford.ambulance': 'van', 'vehicle.carlamotors.firetruck': 'truck', 'traffic.speed_limit.30': 'traffic_sign', 'traffic.speed_limit.40': 'traffic_sign', 'traffic.speed_limit.50': 'traffic_sign', 'traffic.speed_limit.60': 'traffic_sign', 'traffic.speed_limit.90': 'traffic_sign', 'traffic.speed_limit.120': 'traffic_sign', 'traffic.stop': 'traffic_sign', 'traffic.yield': 'traffic_sign', 'traffic.traffic_light': 'traffic_light', 'static.prop.warningconstruction': 'traffic_cone', 'static.prop.warningaccident': 'traffic_cone', 'static.prop.trafficwarning': 'traffic_cone', 'static.prop.constructioncone': 'traffic_cone', 'walker.pedestrian.0001': 'pedestrian', 'walker.pedestrian.0003': 'pedestrian', 'walker.pedestrian.0004': 'pedestrian', 'walker.pedestrian.0005': 'pedestrian', 'walker.pedestrian.0007': 'pedestrian', 'walker.pedestrian.0010': 'pedestrian', 'walker.pedestrian.0013': 'pedestrian', 'walker.pedestrian.0014': 'pedestrian', 'walker.pedestrian.0015': 'pedestrian', 'walker.pedestrian.0016': 'pedestrian', 'walker.pedestrian.0017': 'pedestrian', 'walker.pedestrian.0018': 'pedestrian', 'walker.pedestrian.0019': 'pedestrian', 'walker.pedestrian.0020': 'pedestrian', 'walker.pedestrian.0021': 'pedestrian', 'walker.pedestrian.0022': 'pedestrian', 'walker.pedestrian.0025': 'pedestrian', 'walker.pedestrian.0027': 'pedestrian', 'walker.pedestrian.0030': 'pedestrian', 'walker.pedestrian.0031': 'pedestrian', 'walker.pedestrian.0032': 'pedestrian', 'walker.pedestrian.0034': 'pedestrian', 'walker.pedestrian.0035': 'pedestrian', 'walker.pedestrian.0041': 'pedestrian', 'walker.pedestrian.0042': 'pedestrian', 'walker.pedestrian.0046': 'pedestrian', 'walker.pedestrian.0047': 'pedestrian', 'static.prop.dirtdebris01': 'others', 'static.prop.dirtdebris02': 'others' }), map_file='data/infos/b2d_map_infos.pkl', point_cloud_range=[-15.0, -30.0, -2.0, 15.0, 30.0, 2.0], collect_keys=[ 'lidar2img', 'cam_intrinsic', 'cam_extrinsic', 'timestamp', 'ego_pose', 'ego_pose_inv', 'pad_shape', 'gt_traj_fut_classes', 'ego_fut_classes' ], polyline_points_num=20, filter_empty_gt=False, sub_seq_lenth=-1, use_splited_data=True, cache_lenth=7, future_frames=6, future_frames_ego_fix_time=30, future_frames_ego_fix_dist=20, sample_interval_ego_fut=1, sample_interval=5, fix_future_dis=1, use_angle_as_dis_traj=True, use_raw_goalpoint=False), val=dict( type='B2D_DriveTransformer_GoalPoint_Dataset', ann_file='data/infos/b2d_infos_v1_val_drivetransformer_meta.pkl', pipeline=[ dict(type='LoadMultiViewImageFromFiles', to_float32=True), dict(type='ResizeCropFlipImage'), dict( type='NormalizeMultiviewImage', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict( type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True, with_attr_label=True), dict( type='CustomObjectRangeFilter', point_cloud_range=[-15.0, -30.0, -2.0, 15.0, 30.0, 2.0]), dict( type='CustomObjectNameFilter', classes=[ 'car', 'van', 'truck', 'bicycle', 'traffic_sign', 'traffic_cone', 'traffic_light', 'pedestrian', 'others' ]), dict( type='TrajPreprocess', pc_range=[-15.0, -30.0, -2.0, 15.0, 30.0, 2.0], with_ego_fix_dist=True, ego_fut_offset_input=False, assign_class_for_ego=False), dict( type='CustomFormatBundle3D', class_names=[ 'car', 'van', 'truck', 'bicycle', 'traffic_sign', 'traffic_cone', 'traffic_light', 'pedestrian', 'others' ], with_ego=True, collect_keys=[ 'lidar2img', 'cam_intrinsic', 'cam_extrinsic', 'timestamp', 'ego_pose', 'ego_pose_inv', 'pad_shape', 'gt_traj_fut_classes', 'ego_fut_classes' ]), dict( type='CustomCollect3D', keys=[ 'gt_bboxes_3d', 'gt_labels_3d', 'img', 'ego_his_trajs', 'fut_valid_flag_fix_time', 'ego_fut_trajs_fix_time', 'ego_fut_masks_fix_time', 'fut_valid_flag_fix_dist', 'ego_fut_trajs_fix_dist', 'ego_fut_masks_fix_dist', 'ego_fut_cmd', 'ego_lcf_feat', 'gt_attr_labels', 'prev_exists', 'index', 'lidar2img', 'cam_intrinsic', 'cam_extrinsic', 'timestamp', 'ego_pose', 'ego_pose_inv', 'pad_shape', 'gt_traj_fut_classes', 'ego_fut_classes' ]) ], classes=[ 'car', 'van', 'truck', 'bicycle', 'traffic_sign', 'traffic_cone', 'traffic_light', 'pedestrian', 'others' ], modality=dict( use_lidar=False, use_camera=True, use_radar=False, use_map=False, use_external=True), test_mode=True, box_type_3d='LiDAR', data_root='data/bench2drive', data_aug_conf=dict( resize_lim=(0.64, 0.69), final_dim=(384, 1056), bot_pct_lim=(0.0, 0.0), rot_lim=(-5.4, 5.4), H=900, W=1600, rand_flip=True, rot3d_range=[0, 0]), name_mapping=dict({ 'vehicle.bh.crossbike': 'bicycle', 'vehicle.diamondback.century': 'bicycle', 'vehicle.gazelle.omafiets': 'bicycle', 'vehicle.audi.etron': 'car', 'vehicle.chevrolet.impala': 'car', 'vehicle.dodge.charger_2020': 'car', 'vehicle.dodge.charger_police': 'car', 'vehicle.dodge.charger_police_2020': 'car', 'vehicle.lincoln.mkz_2017': 'car', 'vehicle.lincoln.mkz_2020': 'car', 'vehicle.mini.cooper_s_2021': 'car', 'vehicle.mercedes.coupe_2020': 'car', 'vehicle.ford.mustang': 'car', 'vehicle.nissan.patrol_2021': 'car', 'vehicle.audi.tt': 'car', 'vehicle.ford.crown': 'car', 'vehicle.tesla.model3': 'car', '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/FordCrown/SM_FordCrown_parked.SM_FordCrown_parked': 'car', '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/Charger/SM_ChargerParked.SM_ChargerParked': 'car', '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/Lincoln/SM_LincolnParked.SM_LincolnParked': 'car', '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/MercedesCCC/SM_MercedesCCC_Parked.SM_MercedesCCC_Parked': 'car', '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/Mini2021/SM_Mini2021_parked.SM_Mini2021_parked': 'car', '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/NissanPatrol2021/SM_NissanPatrol2021_parked.SM_NissanPatrol2021_parked': 'car', '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/TeslaM3/SM_TeslaM3_parked.SM_TeslaM3_parked': 'car', '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/VolkswagenT2/SM_VolkswagenT2_2021_Parked.SM_VolkswagenT2_2021_Parked': 'van', 'vehicle.ford.ambulance': 'van', 'vehicle.carlamotors.firetruck': 'truck', 'traffic.speed_limit.30': 'traffic_sign', 'traffic.speed_limit.40': 'traffic_sign', 'traffic.speed_limit.50': 'traffic_sign', 'traffic.speed_limit.60': 'traffic_sign', 'traffic.speed_limit.90': 'traffic_sign', 'traffic.speed_limit.120': 'traffic_sign', 'traffic.stop': 'traffic_sign', 'traffic.yield': 'traffic_sign', 'traffic.traffic_light': 'traffic_light', 'static.prop.warningconstruction': 'traffic_cone', 'static.prop.warningaccident': 'traffic_cone', 'static.prop.trafficwarning': 'traffic_cone', 'static.prop.constructioncone': 'traffic_cone', 'walker.pedestrian.0001': 'pedestrian', 'walker.pedestrian.0003': 'pedestrian', 'walker.pedestrian.0004': 'pedestrian', 'walker.pedestrian.0005': 'pedestrian', 'walker.pedestrian.0007': 'pedestrian', 'walker.pedestrian.0010': 'pedestrian', 'walker.pedestrian.0013': 'pedestrian', 'walker.pedestrian.0014': 'pedestrian', 'walker.pedestrian.0015': 'pedestrian', 'walker.pedestrian.0016': 'pedestrian', 'walker.pedestrian.0017': 'pedestrian', 'walker.pedestrian.0018': 'pedestrian', 'walker.pedestrian.0019': 'pedestrian', 'walker.pedestrian.0020': 'pedestrian', 'walker.pedestrian.0021': 'pedestrian', 'walker.pedestrian.0022': 'pedestrian', 'walker.pedestrian.0025': 'pedestrian', 'walker.pedestrian.0027': 'pedestrian', 'walker.pedestrian.0030': 'pedestrian', 'walker.pedestrian.0031': 'pedestrian', 'walker.pedestrian.0032': 'pedestrian', 'walker.pedestrian.0034': 'pedestrian', 'walker.pedestrian.0035': 'pedestrian', 'walker.pedestrian.0041': 'pedestrian', 'walker.pedestrian.0042': 'pedestrian', 'walker.pedestrian.0046': 'pedestrian', 'walker.pedestrian.0047': 'pedestrian', 'static.prop.dirtdebris01': 'others', 'static.prop.dirtdebris02': 'others' }), map_file='data/infos/b2d_map_infos.pkl', point_cloud_range=[-15.0, -30.0, -2.0, 15.0, 30.0, 2.0], collect_keys=[ 'lidar2img', 'cam_intrinsic', 'cam_extrinsic', 'timestamp', 'ego_pose', 'ego_pose_inv', 'pad_shape', 'gt_traj_fut_classes', 'ego_fut_classes' ], polyline_points_num=20, filter_empty_gt=False, use_splited_data=True, cache_lenth=7, future_frames=6, future_frames_ego_fix_time=30, future_frames_ego_fix_dist=20, sample_interval_ego_fut=1, sample_interval=5, fix_future_dis=1, use_angle_as_dis_traj=True, use_raw_goalpoint=False), test=dict( type='B2D_DriveTransformer_GoalPoint_Dataset', data_root='data/bench2drive', ann_file='data/infos/b2d_infos_v1_val_drivetransformer_meta.pkl', pipeline=[ dict(type='LoadMultiViewImageFromFiles', to_float32=True), dict(type='ResizeCropFlipImage'), dict( type='NormalizeMultiviewImage', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict( type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True, with_attr_label=True), dict( type='CustomObjectRangeFilter', point_cloud_range=[-15.0, -30.0, -2.0, 15.0, 30.0, 2.0]), dict( type='CustomObjectNameFilter', classes=[ 'car', 'van', 'truck', 'bicycle', 'traffic_sign', 'traffic_cone', 'traffic_light', 'pedestrian', 'others' ]), dict( type='TrajPreprocess', pc_range=[-15.0, -30.0, -2.0, 15.0, 30.0, 2.0], with_ego_fix_dist=True, ego_fut_offset_input=False, assign_class_for_ego=False), dict( type='CustomFormatBundle3D', class_names=[ 'car', 'van', 'truck', 'bicycle', 'traffic_sign', 'traffic_cone', 'traffic_light', 'pedestrian', 'others' ], with_ego=True, collect_keys=[ 'lidar2img', 'cam_intrinsic', 'cam_extrinsic', 'timestamp', 'ego_pose', 'ego_pose_inv', 'pad_shape', 'gt_traj_fut_classes', 'ego_fut_classes' ]), dict( type='CustomCollect3D', keys=[ 'gt_bboxes_3d', 'gt_labels_3d', 'img', 'ego_his_trajs', 'fut_valid_flag_fix_time', 'ego_fut_trajs_fix_time', 'ego_fut_masks_fix_time', 'fut_valid_flag_fix_dist', 'ego_fut_trajs_fix_dist', 'ego_fut_masks_fix_dist', 'ego_fut_cmd', 'ego_lcf_feat', 'gt_attr_labels', 'prev_exists', 'index', 'lidar2img', 'cam_intrinsic', 'cam_extrinsic', 'timestamp', 'ego_pose', 'ego_pose_inv', 'pad_shape', 'gt_traj_fut_classes', 'ego_fut_classes' ]) ], classes=[ 'car', 'van', 'truck', 'bicycle', 'traffic_sign', 'traffic_cone', 'traffic_light', 'pedestrian', 'others' ], modality=dict( use_lidar=False, use_camera=True, use_radar=False, use_map=False, use_external=True), test_mode=True, box_type_3d='LiDAR', data_aug_conf=dict( resize_lim=(0.64, 0.69), final_dim=(384, 1056), bot_pct_lim=(0.0, 0.0), rot_lim=(-5.4, 5.4), H=900, W=1600, rand_flip=True, rot3d_range=[0, 0]), name_mapping=dict({ 'vehicle.bh.crossbike': 'bicycle', 'vehicle.diamondback.century': 'bicycle', 'vehicle.gazelle.omafiets': 'bicycle', 'vehicle.audi.etron': 'car', 'vehicle.chevrolet.impala': 'car', 'vehicle.dodge.charger_2020': 'car', 'vehicle.dodge.charger_police': 'car', 'vehicle.dodge.charger_police_2020': 'car', 'vehicle.lincoln.mkz_2017': 'car', 'vehicle.lincoln.mkz_2020': 'car', 'vehicle.mini.cooper_s_2021': 'car', 'vehicle.mercedes.coupe_2020': 'car', 'vehicle.ford.mustang': 'car', 'vehicle.nissan.patrol_2021': 'car', 'vehicle.audi.tt': 'car', 'vehicle.ford.crown': 'car', 'vehicle.tesla.model3': 'car', '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/FordCrown/SM_FordCrown_parked.SM_FordCrown_parked': 'car', '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/Charger/SM_ChargerParked.SM_ChargerParked': 'car', '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/Lincoln/SM_LincolnParked.SM_LincolnParked': 'car', '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/MercedesCCC/SM_MercedesCCC_Parked.SM_MercedesCCC_Parked': 'car', '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/Mini2021/SM_Mini2021_parked.SM_Mini2021_parked': 'car', '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/NissanPatrol2021/SM_NissanPatrol2021_parked.SM_NissanPatrol2021_parked': 'car', '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/TeslaM3/SM_TeslaM3_parked.SM_TeslaM3_parked': 'car', '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/VolkswagenT2/SM_VolkswagenT2_2021_Parked.SM_VolkswagenT2_2021_Parked': 'van', 'vehicle.ford.ambulance': 'van', 'vehicle.carlamotors.firetruck': 'truck', 'traffic.speed_limit.30': 'traffic_sign', 'traffic.speed_limit.40': 'traffic_sign', 'traffic.speed_limit.50': 'traffic_sign', 'traffic.speed_limit.60': 'traffic_sign', 'traffic.speed_limit.90': 'traffic_sign', 'traffic.speed_limit.120': 'traffic_sign', 'traffic.stop': 'traffic_sign', 'traffic.yield': 'traffic_sign', 'traffic.traffic_light': 'traffic_light', 'static.prop.warningconstruction': 'traffic_cone', 'static.prop.warningaccident': 'traffic_cone', 'static.prop.trafficwarning': 'traffic_cone', 'static.prop.constructioncone': 'traffic_cone', 'walker.pedestrian.0001': 'pedestrian', 'walker.pedestrian.0003': 'pedestrian', 'walker.pedestrian.0004': 'pedestrian', 'walker.pedestrian.0005': 'pedestrian', 'walker.pedestrian.0007': 'pedestrian', 'walker.pedestrian.0010': 'pedestrian', 'walker.pedestrian.0013': 'pedestrian', 'walker.pedestrian.0014': 'pedestrian', 'walker.pedestrian.0015': 'pedestrian', 'walker.pedestrian.0016': 'pedestrian', 'walker.pedestrian.0017': 'pedestrian', 'walker.pedestrian.0018': 'pedestrian', 'walker.pedestrian.0019': 'pedestrian', 'walker.pedestrian.0020': 'pedestrian', 'walker.pedestrian.0021': 'pedestrian', 'walker.pedestrian.0022': 'pedestrian', 'walker.pedestrian.0025': 'pedestrian', 'walker.pedestrian.0027': 'pedestrian', 'walker.pedestrian.0030': 'pedestrian', 'walker.pedestrian.0031': 'pedestrian', 'walker.pedestrian.0032': 'pedestrian', 'walker.pedestrian.0034': 'pedestrian', 'walker.pedestrian.0035': 'pedestrian', 'walker.pedestrian.0041': 'pedestrian', 'walker.pedestrian.0042': 'pedestrian', 'walker.pedestrian.0046': 'pedestrian', 'walker.pedestrian.0047': 'pedestrian', 'static.prop.dirtdebris01': 'others', 'static.prop.dirtdebris02': 'others' }), map_file='data/infos/b2d_map_infos.pkl', point_cloud_range=[-15.0, -30.0, -2.0, 15.0, 30.0, 2.0], collect_keys=[ 'lidar2img', 'cam_intrinsic', 'cam_extrinsic', 'timestamp', 'ego_pose', 'ego_pose_inv', 'pad_shape', 'gt_traj_fut_classes', 'ego_fut_classes' ], polyline_points_num=20, filter_empty_gt=False, use_splited_data=True, cache_lenth=7, future_frames=6, future_frames_ego_fix_time=30, future_frames_ego_fix_dist=20, sample_interval_ego_fut=1, sample_interval=5, fix_future_dis=1, use_angle_as_dis_traj=True, use_raw_goalpoint=False), shuffler_sampler=dict(type='InfiniteGroupEachSampleInBatchSampler'), nonshuffler_sampler=dict(type='DistributedSampler')) evaluation = dict( interval=24, pipeline=[ dict( type='LoadPointsFromFile', coord_type='LIDAR', load_dim=5, use_dim=5, file_client_args=dict(backend='disk')), dict( type='LoadPointsFromMultiSweeps', sweeps_num=10, file_client_args=dict(backend='disk')), dict( type='DefaultFormatBundle3D', class_names=[ 'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle', 'motorcycle', 'pedestrian', 'traffic_cone', 'barrier' ], with_label=False), dict(type='Collect3D', keys=['points']) ]) checkpoint_config = dict(interval=1) log_config = dict( interval=50, hooks=[dict(type='TextLoggerHook'), dict(type='TensorboardLoggerHook')]) dist_params = dict(backend='nccl') log_level = 'INFO' work_dir = './work_dirs/drivetransformer_goalpoint_1gpu' load_from = None resume_from = './work_dirs/drivetransformer_goalpoint_1gpu/epoch_12.pth' workflow = [('train', 1)] plugin = True plugin_dir = 'adzoo/drivetransformer/mmdet3d_plugin/' voxel_size = [0.15, 0.15, 4] img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) NameMapping = dict({ 'vehicle.bh.crossbike': 'bicycle', 'vehicle.diamondback.century': 'bicycle', 'vehicle.gazelle.omafiets': 'bicycle', 'vehicle.audi.etron': 'car', 'vehicle.chevrolet.impala': 'car', 'vehicle.dodge.charger_2020': 'car', 'vehicle.dodge.charger_police': 'car', 'vehicle.dodge.charger_police_2020': 'car', 'vehicle.lincoln.mkz_2017': 'car', 'vehicle.lincoln.mkz_2020': 'car', 'vehicle.mini.cooper_s_2021': 'car', 'vehicle.mercedes.coupe_2020': 'car', 'vehicle.ford.mustang': 'car', 'vehicle.nissan.patrol_2021': 'car', 'vehicle.audi.tt': 'car', 'vehicle.ford.crown': 'car', 'vehicle.tesla.model3': 'car', '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/FordCrown/SM_FordCrown_parked.SM_FordCrown_parked': 'car', '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/Charger/SM_ChargerParked.SM_ChargerParked': 'car', '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/Lincoln/SM_LincolnParked.SM_LincolnParked': 'car', '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/MercedesCCC/SM_MercedesCCC_Parked.SM_MercedesCCC_Parked': 'car', '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/Mini2021/SM_Mini2021_parked.SM_Mini2021_parked': 'car', '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/NissanPatrol2021/SM_NissanPatrol2021_parked.SM_NissanPatrol2021_parked': 'car', '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/TeslaM3/SM_TeslaM3_parked.SM_TeslaM3_parked': 'car', '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/VolkswagenT2/SM_VolkswagenT2_2021_Parked.SM_VolkswagenT2_2021_Parked': 'van', 'vehicle.ford.ambulance': 'van', 'vehicle.carlamotors.firetruck': 'truck', 'traffic.speed_limit.30': 'traffic_sign', 'traffic.speed_limit.40': 'traffic_sign', 'traffic.speed_limit.50': 'traffic_sign', 'traffic.speed_limit.60': 'traffic_sign', 'traffic.speed_limit.90': 'traffic_sign', 'traffic.speed_limit.120': 'traffic_sign', 'traffic.stop': 'traffic_sign', 'traffic.yield': 'traffic_sign', 'traffic.traffic_light': 'traffic_light', 'static.prop.warningconstruction': 'traffic_cone', 'static.prop.warningaccident': 'traffic_cone', 'static.prop.trafficwarning': 'traffic_cone', 'static.prop.constructioncone': 'traffic_cone', 'walker.pedestrian.0001': 'pedestrian', 'walker.pedestrian.0003': 'pedestrian', 'walker.pedestrian.0004': 'pedestrian', 'walker.pedestrian.0005': 'pedestrian', 'walker.pedestrian.0007': 'pedestrian', 'walker.pedestrian.0010': 'pedestrian', 'walker.pedestrian.0013': 'pedestrian', 'walker.pedestrian.0014': 'pedestrian', 'walker.pedestrian.0015': 'pedestrian', 'walker.pedestrian.0016': 'pedestrian', 'walker.pedestrian.0017': 'pedestrian', 'walker.pedestrian.0018': 'pedestrian', 'walker.pedestrian.0019': 'pedestrian', 'walker.pedestrian.0020': 'pedestrian', 'walker.pedestrian.0021': 'pedestrian', 'walker.pedestrian.0022': 'pedestrian', 'walker.pedestrian.0025': 'pedestrian', 'walker.pedestrian.0027': 'pedestrian', 'walker.pedestrian.0030': 'pedestrian', 'walker.pedestrian.0031': 'pedestrian', 'walker.pedestrian.0032': 'pedestrian', 'walker.pedestrian.0034': 'pedestrian', 'walker.pedestrian.0035': 'pedestrian', 'walker.pedestrian.0041': 'pedestrian', 'walker.pedestrian.0042': 'pedestrian', 'walker.pedestrian.0046': 'pedestrian', 'walker.pedestrian.0047': 'pedestrian', 'static.prop.dirtdebris01': 'others', 'static.prop.dirtdebris02': 'others' }) collect_keys = [ 'lidar2img', 'cam_intrinsic', 'cam_extrinsic', 'timestamp', 'ego_pose', 'ego_pose_inv', 'pad_shape', 'gt_traj_fut_classes', 'ego_fut_classes' ] num_classes = 9 map_classes = [ 'Broken', 'Solid', 'SolidSolid', 'Center', 'TrafficLight', 'StopSign' ] map_fixed_ptsnum_per_gt_line = 20 map_fixed_ptsnum_per_pred_line = 20 map_eval_use_same_gt_sample_num_flag = True map_num_classes = 6 agent_query_num_vec = 900 agent_num_topk_sift = 900 agent_num_propagated = 50 map_query_num_vec = 100 map_num_topk_sift = 100 map_num_propagated = 50 memory_len_frame = 10 num_mode = 6 num_gpus = 1 batch_size = 6 num_iters_per_epoch = 1041 data_aug_conf = dict( resize_lim=(0.64, 0.69), final_dim=(384, 1056), bot_pct_lim=(0.0, 0.0), rot_lim=(-5.4, 5.4), H=900, W=1600, rand_flip=True, rot3d_range=[0, 0]) _dim_ = 512 queue_length = 1 total_epochs = 24 dropout = 0.1 model = dict( type='DriveTransformer', use_grid_mask=False, pretrained=dict(img='./ckpts/resnet50-19c8e357.pth'), img_backbone=dict( type='ResNet', depth=50, num_stages=4, out_indices=(3, ), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=False), norm_eval=True, style='pytorch'), img_neck=dict( type='FPN', in_channels=[2048], out_channels=512, start_level=0, add_extra_convs='on_output', num_outs=1, relu_before_extra_convs=True), pts_bbox_head=dict( type='DriveTransformerlHead', ego_lcf_feat_idx=[0, 1, 2, 3, 4, 5, 6, 7, 8], ego_command_dim=128, img_stride=32, embed_dims=512, num_reg_fcs=2, num_cls_fcs=2, agent_num_propagated=50, map_num_propagated=50, memory_len_frame=10, agent_num_query=900, agent_num_query_sifted=900, fut_mode=6, fut_ego_mode=1, fut_ts=6, fut_ego_fix_dist=True, fut_ts_ego_fix_dist=20, fut_ts_ego_fix_time=30, num_classes=9, code_size=10, map_num_query=100, map_num_query_sifted=100, map_num_classes=6, map_num_pts_per_vec=20, map_num_pts_per_gt_vec=20, map_query_embed_type='instance_pts', map_transform_method='minmax', map_gt_shift_pts_pattern='v2', map_dir_interval=1, map_code_size=2, map_code_weights=[1.0, 1.0, 1.0, 1.0], sync_cls_avg_factor=True, with_box_refine=True, LID=True, with_ego_pos=True, position_range=[-15.0, -30.0, -2.0, 15.0, 30.0, 2.0], depth_start=1, depth_step=0.8, depth_num=64, agent_prep_decoder=dict( type='DriveTransformerPreDecoder', num_layers=1, return_intermediate=False, transformerlayers=dict( type='DriveTransformerPreDecoderLayer', attn_cfgs=[ dict( type='AttentionLayer', embed_dims=512, head_dim=64, attn_drop=0.1), dict( type='AttentionLayer', embed_dims=512, head_dim=64, attn_drop=0.1) ], ffn_cfgs=dict( type='SwiGLULayer', embed_dims=512, feedforward_channels=2048, ffn_drop=0.1), with_cp=False, operation_order=('cross_attn', 'norm', 'self_attn', 'norm', 'ffn', 'norm'))), map_prep_decoder=dict( type='DriveTransformerPreDecoder', num_layers=1, return_intermediate=False, transformerlayers=dict( type='DriveTransformerPreDecoderLayer', attn_cfgs=[ dict( type='AttentionLayer', embed_dims=512, head_dim=64, attn_drop=0.1), dict( type='AttentionLayer', embed_dims=512, head_dim=64, attn_drop=0.1) ], ffn_cfgs=dict( type='SwiGLULayer', embed_dims=512, feedforward_channels=2048, ffn_drop=0.1), with_cp=False, operation_order=('cross_attn', 'norm', 'self_attn', 'norm', 'ffn', 'norm'))), transformer=dict( type='DriveTransformerWrapper', embed_dims=512, decoder=dict( type='DriveTransformerDecoder', num_layers=6, fut_mode=6, agent_num_query=900, map_num_query=100, map_num_pts_per_vec=20, return_intermediate=True, embed_dims=512, refine=True, transformerlayers=dict( type='DriveTransformerDecoderLayer', agent_query_num=900, map_query_num=100, memory_len_frame=10, agent_num_propagated=50, map_num_propagated=50, map_pts_per_vec=20, feedforward_channels=2048, ffn_dropout=0.1, with_cp=False, attn_cfgs=[ dict( type='AttentionLayer', embed_dims=512, head_dim=64, attn_drop=0.1, layer_scale=0.01), dict( type='AttentionLayer', embed_dims=512, head_dim=64, attn_drop=0.1, layer_scale=0.01), dict( type='AttentionLayer', embed_dims=512, head_dim=64, attn_drop=0.1, no_wq=True) ], ffn_cfgs=dict( type='SwiGLULayer', embed_dims=512, feedforward_channels=2048, ffn_drop=0.1), operation_order=('task_self_attn', 'norm', 'temporal_cross_attn', 'norm', 'sensor_cross_attn', 'norm', 'ffn', 'norm')))), bbox_coder=dict( type='CustomNMSFreeCoder', post_center_range=[-20, -35, -10.0, 20, 35, 10.0], pc_range=[-15.0, -30.0, -2.0, 15.0, 30.0, 2.0], max_num=100, voxel_size=[0.15, 0.15, 4], num_classes=9), loss_cls=dict( type='FocalLoss', use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=2.0), loss_bbox=dict(type='L1Loss', loss_weight=0.25), loss_traj=dict(type='L1Loss', loss_weight=0.2), loss_traj_cls=dict( type='FocalLoss', use_sigmoid=True, gamma=2.0, alpha=0.5, loss_weight=0.2), map_bbox_coder=dict( type='MapNMSFreeCoder', post_center_range=[-20, -35, -20, -35, 20, 35, 20, 35], pc_range=[-15.0, -30.0, -2.0, 15.0, 30.0, 2.0], max_num=50, voxel_size=[0.15, 0.15, 4], num_classes=6), loss_map_cls=dict( type='FocalLoss', use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=2.0), loss_map_pts=dict(type='PtsL1Loss', loss_weight=1.0), loss_map_dir=dict(type='PtsDirCosLoss', loss_weight=0.005), loss_plan_reg_fix_time=dict(type='L1Loss', loss_weight=3.5), loss_plan_reg_fix_dist=dict(type='L1Loss', loss_weight=10.0), loss_plan_cls=dict( type='FocalLoss', use_sigmoid=True, gamma=4.0, alpha=0.5, loss_weight=20.0)), train_cfg=dict( pts=dict( grid_size=[512, 512, 1], voxel_size=[0.15, 0.15, 4], point_cloud_range=[-15.0, -30.0, -2.0, 15.0, 30.0, 2.0], out_size_factor=4, assigner=dict( type='HungarianAssigner3D', cls_cost=dict( type='FocalLossCost', weight=2.0, gamma=2.0, alpha=0.25), reg_cost=dict(type='BBox3DL1Cost', weight=0.25), iou_cost=dict(type='IoUCost', weight=0.0), pc_range=[-15.0, -30.0, -2.0, 15.0, 30.0, 2.0]), map_assigner=dict( type='MapHungarianAssigner3D', cls_cost=dict( type='FocalLossCost', weight=2.0, gamma=2.0, alpha=0.25), reg_cost=dict( type='BBoxL1Cost', weight=0.0, box_format='xywh'), iou_cost=dict(type='IoUCost', iou_mode='giou', weight=0.0), pts_cost=dict(type='OrderedPtsL1Cost', weight=1.0), pc_range=[-15.0, -30.0, -2.0, 15.0, 30.0, 2.0])))) info_root = 'data/infos' map_root = 'data/bench2drive/maps' map_file = 'data/infos/b2d_map_infos.pkl' ann_file_train = 'data/infos/b2d_infos_v1_train_drivetransformer_meta.pkl' ann_file_val = 'data/infos/b2d_infos_v1_val_drivetransformer_meta.pkl' ann_file_test = 'data/infos/b2d_infos_v1_val_drivetransformer_meta.pkl' optimizer = dict(type='AdamW', lr=0.0001, weight_decay=0.01) optimizer_config = dict( grad_clip=dict(max_norm=35, norm_type=2), type='GradientCumulativeFp16OptimizerHook', cumulative_iters=32) lr_config = dict( policy='CosineAnnealing', warmup='linear', warmup_iters=1000, warmup_ratio=0.1, min_lr_ratio=0.01) runner = dict(type='EpochBasedRunner', max_epochs=24) fp16 = dict(loss_scale=512.0) find_unused_parameters = True custom_hooks = [dict(type='CustomSetEpochInfoHook')] cumulative_iters = 32 gpu_ids = range(0, 1)