Invalid JSON:Unexpected non-whitespace character after JSONat line 2, column 1
| {"env_info": "MMCV: 0.0.1", "config": "point_cloud_range = [-15.0, -30.0, -2.0, 15.0, 30.0, 2.0]\nclass_names = [\n 'car', 'van', 'truck', 'bicycle', 'traffic_sign', 'traffic_cone',\n 'traffic_light', 'pedestrian', 'others'\n]\ndataset_type = 'B2D_VAD_Dataset'\ndata_root = 'data/bench2drive'\ninput_modality = dict(\n use_lidar=False,\n use_camera=True,\n use_radar=False,\n use_map=False,\n use_external=True)\nfile_client_args = dict(backend='disk')\ntrain_pipeline = [\n dict(type='LoadMultiViewImageFromFiles', to_float32=True),\n dict(type='PhotoMetricDistortionMultiViewImage'),\n dict(\n type='LoadAnnotations3D',\n with_bbox_3d=True,\n with_label_3d=True,\n with_attr_label=True),\n dict(\n type='VADObjectRangeFilter',\n point_cloud_range=[-15.0, -30.0, -2.0, 15.0, 30.0, 2.0]),\n dict(\n type='VADObjectNameFilter',\n classes=[\n 'car', 'van', 'truck', 'bicycle', 'traffic_sign', 'traffic_cone',\n 'traffic_light', 'pedestrian', 'others'\n ]),\n dict(\n type='NormalizeMultiviewImage',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='RandomScaleImageMultiViewImage', scales=[0.8]),\n dict(type='PadMultiViewImage', size_divisor=32),\n dict(\n type='VADFormatBundle3D',\n class_names=[\n 'car', 'van', 'truck', 'bicycle', 'traffic_sign', 'traffic_cone',\n 'traffic_light', 'pedestrian', 'others'\n ],\n with_ego=True),\n dict(\n type='CustomCollect3D',\n keys=[\n 'gt_bboxes_3d', 'gt_labels_3d', 'img', 'ego_his_trajs',\n 'gt_attr_labels', 'ego_fut_trajs', 'ego_fut_masks', 'ego_fut_cmd',\n 'ego_lcf_feat'\n ])\n]\ntest_pipeline = [\n dict(type='LoadMultiViewImageFromFiles', to_float32=True),\n dict(\n type='LoadAnnotations3D',\n with_bbox_3d=True,\n with_label_3d=True,\n with_attr_label=True),\n dict(\n type='VADObjectRangeFilter',\n point_cloud_range=[-15.0, -30.0, -2.0, 15.0, 30.0, 2.0]),\n dict(\n type='VADObjectNameFilter',\n classes=[\n 'car', 'van', 'truck', 'bicycle', 'traffic_sign', 'traffic_cone',\n 'traffic_light', 'pedestrian', 'others'\n ]),\n dict(\n type='NormalizeMultiviewImage',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(\n type='MultiScaleFlipAug3D',\n img_scale=(1600, 900),\n pts_scale_ratio=1,\n flip=False,\n transforms=[\n dict(type='RandomScaleImageMultiViewImage', scales=[0.8]),\n dict(type='PadMultiViewImage', size_divisor=32),\n dict(\n type='VADFormatBundle3D',\n class_names=[\n 'car', 'van', 'truck', 'bicycle', 'traffic_sign',\n 'traffic_cone', 'traffic_light', 'pedestrian', 'others'\n ],\n with_label=False,\n with_ego=True),\n dict(\n type='CustomCollect3D',\n keys=[\n 'gt_bboxes_3d', 'gt_labels_3d', 'img', 'fut_valid_flag',\n 'ego_his_trajs', 'ego_fut_trajs', 'ego_fut_masks',\n 'ego_fut_cmd', 'ego_lcf_feat', 'gt_attr_labels'\n ])\n ])\n]\neval_pipeline = [\n dict(\n type='LoadPointsFromFile',\n coord_type='LIDAR',\n load_dim=5,\n use_dim=5,\n file_client_args=dict(backend='disk')),\n dict(\n type='LoadPointsFromMultiSweeps',\n sweeps_num=10,\n file_client_args=dict(backend='disk')),\n dict(\n type='DefaultFormatBundle3D',\n class_names=[\n 'car', 'truck', 'trailer', 'bus', 'construction_vehicle',\n 'bicycle', 'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'\n ],\n with_label=False),\n dict(type='Collect3D', keys=['points'])\n]\ndata = dict(\n samples_per_gpu=1,\n workers_per_gpu=4,\n train=dict(\n type='B2D_VAD_Dataset',\n data_root='data/bench2drive',\n ann_file='data/infos/b2d_infos_train.pkl',\n pipeline=[\n dict(type='LoadMultiViewImageFromFiles', to_float32=True),\n dict(type='PhotoMetricDistortionMultiViewImage'),\n dict(\n type='LoadAnnotations3D',\n with_bbox_3d=True,\n with_label_3d=True,\n with_attr_label=True),\n dict(\n type='VADObjectRangeFilter',\n point_cloud_range=[-15.0, -30.0, -2.0, 15.0, 30.0, 2.0]),\n dict(\n type='VADObjectNameFilter',\n classes=[\n 'car', 'van', 'truck', 'bicycle', 'traffic_sign',\n 'traffic_cone', 'traffic_light', 'pedestrian', 'others'\n ]),\n dict(\n type='NormalizeMultiviewImage',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='RandomScaleImageMultiViewImage', scales=[0.8]),\n dict(type='PadMultiViewImage', size_divisor=32),\n dict(\n type='VADFormatBundle3D',\n class_names=[\n 'car', 'van', 'truck', 'bicycle', 'traffic_sign',\n 'traffic_cone', 'traffic_light', 'pedestrian', 'others'\n ],\n with_ego=True),\n dict(\n type='CustomCollect3D',\n keys=[\n 'gt_bboxes_3d', 'gt_labels_3d', 'img', 'ego_his_trajs',\n 'gt_attr_labels', 'ego_fut_trajs', 'ego_fut_masks',\n 'ego_fut_cmd', 'ego_lcf_feat'\n ])\n ],\n classes=[\n 'car', 'van', 'truck', 'bicycle', 'traffic_sign', 'traffic_cone',\n 'traffic_light', 'pedestrian', 'others'\n ],\n modality=dict(\n use_lidar=False,\n use_camera=True,\n use_radar=False,\n use_map=False,\n use_external=True),\n test_mode=False,\n box_type_3d='LiDAR',\n name_mapping=dict({\n 'vehicle.bh.crossbike':\n 'bicycle',\n 'vehicle.diamondback.century':\n 'bicycle',\n 'vehicle.gazelle.omafiets':\n 'bicycle',\n 'vehicle.chevrolet.impala':\n 'car',\n 'vehicle.dodge.charger_2020':\n 'car',\n 'vehicle.dodge.charger_police':\n 'car',\n 'vehicle.dodge.charger_police_2020':\n 'car',\n 'vehicle.lincoln.mkz_2017':\n 'car',\n 'vehicle.lincoln.mkz_2020':\n 'car',\n 'vehicle.mini.cooper_s_2021':\n 'car',\n 'vehicle.mercedes.coupe_2020':\n 'car',\n 'vehicle.ford.mustang':\n 'car',\n 'vehicle.nissan.patrol_2021':\n 'car',\n 'vehicle.audi.tt':\n 'car',\n 'vehicle.audi.etron':\n 'car',\n 'vehicle.ford.crown':\n 'car',\n 'vehicle.tesla.model3':\n 'car',\n '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/FordCrown/SM_FordCrown_parked.SM_FordCrown_parked':\n 'car',\n '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/Charger/SM_ChargerParked.SM_ChargerParked':\n 'car',\n '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/Lincoln/SM_LincolnParked.SM_LincolnParked':\n 'car',\n '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/MercedesCCC/SM_MercedesCCC_Parked.SM_MercedesCCC_Parked':\n 'car',\n '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/Mini2021/SM_Mini2021_parked.SM_Mini2021_parked':\n 'car',\n '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/NissanPatrol2021/SM_NissanPatrol2021_parked.SM_NissanPatrol2021_parked':\n 'car',\n '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/TeslaM3/SM_TeslaM3_parked.SM_TeslaM3_parked':\n 'car',\n '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/VolkswagenT2/SM_VolkswagenT2_2021_Parked.SM_VolkswagenT2_2021_Parked':\n 'van',\n 'vehicle.ford.ambulance':\n 'van',\n 'vehicle.carlamotors.firetruck':\n 'truck',\n 'traffic.speed_limit.30':\n 'traffic_sign',\n 'traffic.speed_limit.40':\n 'traffic_sign',\n 'traffic.speed_limit.50':\n 'traffic_sign',\n 'traffic.speed_limit.60':\n 'traffic_sign',\n 'traffic.speed_limit.90':\n 'traffic_sign',\n 'traffic.speed_limit.120':\n 'traffic_sign',\n 'traffic.stop':\n 'traffic_sign',\n 'traffic.yield':\n 'traffic_sign',\n 'traffic.traffic_light':\n 'traffic_light',\n 'static.prop.warningconstruction':\n 'traffic_cone',\n 'static.prop.warningaccident':\n 'traffic_cone',\n 'static.prop.trafficwarning':\n 'traffic_cone',\n 'static.prop.constructioncone':\n 'traffic_cone',\n 'walker.pedestrian.0001':\n 'pedestrian',\n 'walker.pedestrian.0004':\n 'pedestrian',\n 'walker.pedestrian.0005':\n 'pedestrian',\n 'walker.pedestrian.0007':\n 'pedestrian',\n 'walker.pedestrian.0013':\n 'pedestrian',\n 'walker.pedestrian.0014':\n 'pedestrian',\n 'walker.pedestrian.0017':\n 'pedestrian',\n 'walker.pedestrian.0018':\n 'pedestrian',\n 'walker.pedestrian.0019':\n 'pedestrian',\n 'walker.pedestrian.0020':\n 'pedestrian',\n 'walker.pedestrian.0022':\n 'pedestrian',\n 'walker.pedestrian.0025':\n 'pedestrian',\n 'walker.pedestrian.0035':\n 'pedestrian',\n 'walker.pedestrian.0041':\n 'pedestrian',\n 'walker.pedestrian.0046':\n 'pedestrian',\n 'walker.pedestrian.0047':\n 'pedestrian',\n 'static.prop.dirtdebris01':\n 'others',\n 'static.prop.dirtdebris02':\n 'others'\n }),\n map_root='data/bench2drive/maps',\n map_file='data/infos/b2d_map_infos.pkl',\n bev_size=(100, 100),\n queue_length=3,\n past_frames=2,\n future_frames=6,\n point_cloud_range=[-15.0, -30.0, -2.0, 15.0, 30.0, 2.0],\n polyline_points_num=20),\n val=dict(\n type='B2D_VAD_Dataset',\n ann_file='data/infos/b2d_infos_val.pkl',\n pipeline=[\n dict(type='LoadMultiViewImageFromFiles', to_float32=True),\n dict(\n type='LoadAnnotations3D',\n with_bbox_3d=True,\n with_label_3d=True,\n with_attr_label=True),\n dict(\n type='VADObjectRangeFilter',\n point_cloud_range=[-15.0, -30.0, -2.0, 15.0, 30.0, 2.0]),\n dict(\n type='VADObjectNameFilter',\n classes=[\n 'car', 'van', 'truck', 'bicycle', 'traffic_sign',\n 'traffic_cone', 'traffic_light', 'pedestrian', 'others'\n ]),\n dict(\n type='NormalizeMultiviewImage',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(\n type='MultiScaleFlipAug3D',\n img_scale=(1600, 900),\n pts_scale_ratio=1,\n flip=False,\n transforms=[\n dict(type='RandomScaleImageMultiViewImage', scales=[0.8]),\n dict(type='PadMultiViewImage', size_divisor=32),\n dict(\n type='VADFormatBundle3D',\n class_names=[\n 'car', 'van', 'truck', 'bicycle', 'traffic_sign',\n 'traffic_cone', 'traffic_light', 'pedestrian',\n 'others'\n ],\n with_label=False,\n with_ego=True),\n dict(\n type='CustomCollect3D',\n keys=[\n 'gt_bboxes_3d', 'gt_labels_3d', 'img',\n 'fut_valid_flag', 'ego_his_trajs', 'ego_fut_trajs',\n 'ego_fut_masks', 'ego_fut_cmd', 'ego_lcf_feat',\n 'gt_attr_labels'\n ])\n ])\n ],\n classes=[\n 'car', 'van', 'truck', 'bicycle', 'traffic_sign', 'traffic_cone',\n 'traffic_light', 'pedestrian', 'others'\n ],\n modality=dict(\n use_lidar=False,\n use_camera=True,\n use_radar=False,\n use_map=False,\n use_external=True),\n test_mode=True,\n box_type_3d='LiDAR',\n data_root='data/bench2drive',\n name_mapping=dict({\n 'vehicle.bh.crossbike':\n 'bicycle',\n 'vehicle.diamondback.century':\n 'bicycle',\n 'vehicle.gazelle.omafiets':\n 'bicycle',\n 'vehicle.chevrolet.impala':\n 'car',\n 'vehicle.dodge.charger_2020':\n 'car',\n 'vehicle.dodge.charger_police':\n 'car',\n 'vehicle.dodge.charger_police_2020':\n 'car',\n 'vehicle.lincoln.mkz_2017':\n 'car',\n 'vehicle.lincoln.mkz_2020':\n 'car',\n 'vehicle.mini.cooper_s_2021':\n 'car',\n 'vehicle.mercedes.coupe_2020':\n 'car',\n 'vehicle.ford.mustang':\n 'car',\n 'vehicle.nissan.patrol_2021':\n 'car',\n 'vehicle.audi.tt':\n 'car',\n 'vehicle.audi.etron':\n 'car',\n 'vehicle.ford.crown':\n 'car',\n 'vehicle.tesla.model3':\n 'car',\n '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/FordCrown/SM_FordCrown_parked.SM_FordCrown_parked':\n 'car',\n '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/Charger/SM_ChargerParked.SM_ChargerParked':\n 'car',\n '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/Lincoln/SM_LincolnParked.SM_LincolnParked':\n 'car',\n '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/MercedesCCC/SM_MercedesCCC_Parked.SM_MercedesCCC_Parked':\n 'car',\n '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/Mini2021/SM_Mini2021_parked.SM_Mini2021_parked':\n 'car',\n '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/NissanPatrol2021/SM_NissanPatrol2021_parked.SM_NissanPatrol2021_parked':\n 'car',\n '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/TeslaM3/SM_TeslaM3_parked.SM_TeslaM3_parked':\n 'car',\n '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/VolkswagenT2/SM_VolkswagenT2_2021_Parked.SM_VolkswagenT2_2021_Parked':\n 'van',\n 'vehicle.ford.ambulance':\n 'van',\n 'vehicle.carlamotors.firetruck':\n 'truck',\n 'traffic.speed_limit.30':\n 'traffic_sign',\n 'traffic.speed_limit.40':\n 'traffic_sign',\n 'traffic.speed_limit.50':\n 'traffic_sign',\n 'traffic.speed_limit.60':\n 'traffic_sign',\n 'traffic.speed_limit.90':\n 'traffic_sign',\n 'traffic.speed_limit.120':\n 'traffic_sign',\n 'traffic.stop':\n 'traffic_sign',\n 'traffic.yield':\n 'traffic_sign',\n 'traffic.traffic_light':\n 'traffic_light',\n 'static.prop.warningconstruction':\n 'traffic_cone',\n 'static.prop.warningaccident':\n 'traffic_cone',\n 'static.prop.trafficwarning':\n 'traffic_cone',\n 'static.prop.constructioncone':\n 'traffic_cone',\n 'walker.pedestrian.0001':\n 'pedestrian',\n 'walker.pedestrian.0004':\n 'pedestrian',\n 'walker.pedestrian.0005':\n 'pedestrian',\n 'walker.pedestrian.0007':\n 'pedestrian',\n 'walker.pedestrian.0013':\n 'pedestrian',\n 'walker.pedestrian.0014':\n 'pedestrian',\n 'walker.pedestrian.0017':\n 'pedestrian',\n 'walker.pedestrian.0018':\n 'pedestrian',\n 'walker.pedestrian.0019':\n 'pedestrian',\n 'walker.pedestrian.0020':\n 'pedestrian',\n 'walker.pedestrian.0022':\n 'pedestrian',\n 'walker.pedestrian.0025':\n 'pedestrian',\n 'walker.pedestrian.0035':\n 'pedestrian',\n 'walker.pedestrian.0041':\n 'pedestrian',\n 'walker.pedestrian.0046':\n 'pedestrian',\n 'walker.pedestrian.0047':\n 'pedestrian',\n 'static.prop.dirtdebris01':\n 'others',\n 'static.prop.dirtdebris02':\n 'others'\n }),\n map_root='data/bench2drive/maps',\n map_file='data/infos/b2d_map_infos.pkl',\n bev_size=(100, 100),\n queue_length=3,\n past_frames=2,\n future_frames=6,\n point_cloud_range=[-15.0, -30.0, -2.0, 15.0, 30.0, 2.0],\n polyline_points_num=20,\n eval_cfg=dict(\n dist_ths=[0.5, 1.0, 2.0, 4.0],\n dist_th_tp=2.0,\n min_recall=0.1,\n min_precision=0.1,\n mean_ap_weight=5,\n class_names=[\n 'car', 'van', 'truck', 'bicycle', 'traffic_sign',\n 'traffic_cone', 'traffic_light', 'pedestrian'\n ],\n tp_metrics=['trans_err', 'scale_err', 'orient_err', 'vel_err'],\n err_name_maping=dict(\n trans_err='mATE',\n scale_err='mASE',\n orient_err='mAOE',\n vel_err='mAVE',\n attr_err='mAAE'),\n class_range=dict(\n car=(50, 50),\n van=(50, 50),\n truck=(50, 50),\n bicycle=(40, 40),\n traffic_sign=(30, 30),\n traffic_cone=(30, 30),\n traffic_light=(30, 30),\n pedestrian=(40, 40)))),\n test=dict(\n type='B2D_VAD_Dataset',\n data_root='data/bench2drive',\n ann_file='data/infos/b2d_infos_val.pkl',\n pipeline=[\n dict(type='LoadMultiViewImageFromFiles', to_float32=True),\n dict(\n type='LoadAnnotations3D',\n with_bbox_3d=True,\n with_label_3d=True,\n with_attr_label=True),\n dict(\n type='VADObjectRangeFilter',\n point_cloud_range=[-15.0, -30.0, -2.0, 15.0, 30.0, 2.0]),\n dict(\n type='VADObjectNameFilter',\n classes=[\n 'car', 'van', 'truck', 'bicycle', 'traffic_sign',\n 'traffic_cone', 'traffic_light', 'pedestrian', 'others'\n ]),\n dict(\n type='NormalizeMultiviewImage',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(\n type='MultiScaleFlipAug3D',\n img_scale=(1600, 900),\n pts_scale_ratio=1,\n flip=False,\n transforms=[\n dict(type='RandomScaleImageMultiViewImage', scales=[0.8]),\n dict(type='PadMultiViewImage', size_divisor=32),\n dict(\n type='VADFormatBundle3D',\n class_names=[\n 'car', 'van', 'truck', 'bicycle', 'traffic_sign',\n 'traffic_cone', 'traffic_light', 'pedestrian',\n 'others'\n ],\n with_label=False,\n with_ego=True),\n dict(\n type='CustomCollect3D',\n keys=[\n 'gt_bboxes_3d', 'gt_labels_3d', 'img',\n 'fut_valid_flag', 'ego_his_trajs', 'ego_fut_trajs',\n 'ego_fut_masks', 'ego_fut_cmd', 'ego_lcf_feat',\n 'gt_attr_labels'\n ])\n ])\n ],\n classes=[\n 'car', 'van', 'truck', 'bicycle', 'traffic_sign', 'traffic_cone',\n 'traffic_light', 'pedestrian', 'others'\n ],\n modality=dict(\n use_lidar=False,\n use_camera=True,\n use_radar=False,\n use_map=False,\n use_external=True),\n test_mode=True,\n box_type_3d='LiDAR',\n name_mapping=dict({\n 'vehicle.bh.crossbike':\n 'bicycle',\n 'vehicle.diamondback.century':\n 'bicycle',\n 'vehicle.gazelle.omafiets':\n 'bicycle',\n 'vehicle.chevrolet.impala':\n 'car',\n 'vehicle.dodge.charger_2020':\n 'car',\n 'vehicle.dodge.charger_police':\n 'car',\n 'vehicle.dodge.charger_police_2020':\n 'car',\n 'vehicle.lincoln.mkz_2017':\n 'car',\n 'vehicle.lincoln.mkz_2020':\n 'car',\n 'vehicle.mini.cooper_s_2021':\n 'car',\n 'vehicle.mercedes.coupe_2020':\n 'car',\n 'vehicle.ford.mustang':\n 'car',\n 'vehicle.nissan.patrol_2021':\n 'car',\n 'vehicle.audi.tt':\n 'car',\n 'vehicle.audi.etron':\n 'car',\n 'vehicle.ford.crown':\n 'car',\n 'vehicle.tesla.model3':\n 'car',\n '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/FordCrown/SM_FordCrown_parked.SM_FordCrown_parked':\n 'car',\n '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/Charger/SM_ChargerParked.SM_ChargerParked':\n 'car',\n '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/Lincoln/SM_LincolnParked.SM_LincolnParked':\n 'car',\n '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/MercedesCCC/SM_MercedesCCC_Parked.SM_MercedesCCC_Parked':\n 'car',\n '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/Mini2021/SM_Mini2021_parked.SM_Mini2021_parked':\n 'car',\n '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/NissanPatrol2021/SM_NissanPatrol2021_parked.SM_NissanPatrol2021_parked':\n 'car',\n '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/TeslaM3/SM_TeslaM3_parked.SM_TeslaM3_parked':\n 'car',\n '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/VolkswagenT2/SM_VolkswagenT2_2021_Parked.SM_VolkswagenT2_2021_Parked':\n 'van',\n 'vehicle.ford.ambulance':\n 'van',\n 'vehicle.carlamotors.firetruck':\n 'truck',\n 'traffic.speed_limit.30':\n 'traffic_sign',\n 'traffic.speed_limit.40':\n 'traffic_sign',\n 'traffic.speed_limit.50':\n 'traffic_sign',\n 'traffic.speed_limit.60':\n 'traffic_sign',\n 'traffic.speed_limit.90':\n 'traffic_sign',\n 'traffic.speed_limit.120':\n 'traffic_sign',\n 'traffic.stop':\n 'traffic_sign',\n 'traffic.yield':\n 'traffic_sign',\n 'traffic.traffic_light':\n 'traffic_light',\n 'static.prop.warningconstruction':\n 'traffic_cone',\n 'static.prop.warningaccident':\n 'traffic_cone',\n 'static.prop.trafficwarning':\n 'traffic_cone',\n 'static.prop.constructioncone':\n 'traffic_cone',\n 'walker.pedestrian.0001':\n 'pedestrian',\n 'walker.pedestrian.0004':\n 'pedestrian',\n 'walker.pedestrian.0005':\n 'pedestrian',\n 'walker.pedestrian.0007':\n 'pedestrian',\n 'walker.pedestrian.0013':\n 'pedestrian',\n 'walker.pedestrian.0014':\n 'pedestrian',\n 'walker.pedestrian.0017':\n 'pedestrian',\n 'walker.pedestrian.0018':\n 'pedestrian',\n 'walker.pedestrian.0019':\n 'pedestrian',\n 'walker.pedestrian.0020':\n 'pedestrian',\n 'walker.pedestrian.0022':\n 'pedestrian',\n 'walker.pedestrian.0025':\n 'pedestrian',\n 'walker.pedestrian.0035':\n 'pedestrian',\n 'walker.pedestrian.0041':\n 'pedestrian',\n 'walker.pedestrian.0046':\n 'pedestrian',\n 'walker.pedestrian.0047':\n 'pedestrian',\n 'static.prop.dirtdebris01':\n 'others',\n 'static.prop.dirtdebris02':\n 'others'\n }),\n map_root='data/bench2drive/maps',\n map_file='data/infos/b2d_map_infos.pkl',\n bev_size=(100, 100),\n queue_length=3,\n past_frames=2,\n future_frames=6,\n point_cloud_range=[-15.0, -30.0, -2.0, 15.0, 30.0, 2.0],\n polyline_points_num=20,\n eval_cfg=dict(\n dist_ths=[0.5, 1.0, 2.0, 4.0],\n dist_th_tp=2.0,\n min_recall=0.1,\n min_precision=0.1,\n mean_ap_weight=5,\n class_names=[\n 'car', 'van', 'truck', 'bicycle', 'traffic_sign',\n 'traffic_cone', 'traffic_light', 'pedestrian'\n ],\n tp_metrics=['trans_err', 'scale_err', 'orient_err', 'vel_err'],\n err_name_maping=dict(\n trans_err='mATE',\n scale_err='mASE',\n orient_err='mAOE',\n vel_err='mAVE',\n attr_err='mAAE'),\n class_range=dict(\n car=(50, 50),\n van=(50, 50),\n truck=(50, 50),\n bicycle=(40, 40),\n traffic_sign=(30, 30),\n traffic_cone=(30, 30),\n traffic_light=(30, 30),\n pedestrian=(40, 40)))),\n shuffler_sampler=dict(type='DistributedGroupSampler'),\n nonshuffler_sampler=dict(type='DistributedSampler'))\nevaluation = dict(\n interval=6,\n pipeline=[\n dict(type='LoadMultiViewImageFromFiles', to_float32=True),\n dict(\n type='LoadAnnotations3D',\n with_bbox_3d=True,\n with_label_3d=True,\n with_attr_label=True),\n dict(\n type='VADObjectRangeFilter',\n point_cloud_range=[-15.0, -30.0, -2.0, 15.0, 30.0, 2.0]),\n dict(\n type='VADObjectNameFilter',\n classes=[\n 'car', 'van', 'truck', 'bicycle', 'traffic_sign',\n 'traffic_cone', 'traffic_light', 'pedestrian', 'others'\n ]),\n dict(\n type='NormalizeMultiviewImage',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(\n type='MultiScaleFlipAug3D',\n img_scale=(1600, 900),\n pts_scale_ratio=1,\n flip=False,\n transforms=[\n dict(type='RandomScaleImageMultiViewImage', scales=[0.8]),\n dict(type='PadMultiViewImage', size_divisor=32),\n dict(\n type='VADFormatBundle3D',\n class_names=[\n 'car', 'van', 'truck', 'bicycle', 'traffic_sign',\n 'traffic_cone', 'traffic_light', 'pedestrian', 'others'\n ],\n with_label=False,\n with_ego=True),\n dict(\n type='CustomCollect3D',\n keys=[\n 'gt_bboxes_3d', 'gt_labels_3d', 'img',\n 'fut_valid_flag', 'ego_his_trajs', 'ego_fut_trajs',\n 'ego_fut_masks', 'ego_fut_cmd', 'ego_lcf_feat',\n 'gt_attr_labels'\n ])\n ])\n ],\n metric='bbox',\n map_metric='chamfer')\ncheckpoint_config = dict(interval=1, max_keep_ckpts=6)\nlog_config = dict(\n interval=50,\n hooks=[dict(type='TextLoggerHook'),\n dict(type='TensorboardLoggerHook')])\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nwork_dir = './work_dirs/GenAD_config_b2d'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\nvoxel_size = [0.15, 0.15, 4]\nimg_norm_cfg = dict(\n mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\nNameMapping = dict({\n 'vehicle.bh.crossbike':\n 'bicycle',\n 'vehicle.diamondback.century':\n 'bicycle',\n 'vehicle.gazelle.omafiets':\n 'bicycle',\n 'vehicle.chevrolet.impala':\n 'car',\n 'vehicle.dodge.charger_2020':\n 'car',\n 'vehicle.dodge.charger_police':\n 'car',\n 'vehicle.dodge.charger_police_2020':\n 'car',\n 'vehicle.lincoln.mkz_2017':\n 'car',\n 'vehicle.lincoln.mkz_2020':\n 'car',\n 'vehicle.mini.cooper_s_2021':\n 'car',\n 'vehicle.mercedes.coupe_2020':\n 'car',\n 'vehicle.ford.mustang':\n 'car',\n 'vehicle.nissan.patrol_2021':\n 'car',\n 'vehicle.audi.tt':\n 'car',\n 'vehicle.audi.etron':\n 'car',\n 'vehicle.ford.crown':\n 'car',\n 'vehicle.tesla.model3':\n 'car',\n '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/FordCrown/SM_FordCrown_parked.SM_FordCrown_parked':\n 'car',\n '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/Charger/SM_ChargerParked.SM_ChargerParked':\n 'car',\n '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/Lincoln/SM_LincolnParked.SM_LincolnParked':\n 'car',\n '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/MercedesCCC/SM_MercedesCCC_Parked.SM_MercedesCCC_Parked':\n 'car',\n '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/Mini2021/SM_Mini2021_parked.SM_Mini2021_parked':\n 'car',\n '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/NissanPatrol2021/SM_NissanPatrol2021_parked.SM_NissanPatrol2021_parked':\n 'car',\n '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/TeslaM3/SM_TeslaM3_parked.SM_TeslaM3_parked':\n 'car',\n '/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/VolkswagenT2/SM_VolkswagenT2_2021_Parked.SM_VolkswagenT2_2021_Parked':\n 'van',\n 'vehicle.ford.ambulance':\n 'van',\n 'vehicle.carlamotors.firetruck':\n 'truck',\n 'traffic.speed_limit.30':\n 'traffic_sign',\n 'traffic.speed_limit.40':\n 'traffic_sign',\n 'traffic.speed_limit.50':\n 'traffic_sign',\n 'traffic.speed_limit.60':\n 'traffic_sign',\n 'traffic.speed_limit.90':\n 'traffic_sign',\n 'traffic.speed_limit.120':\n 'traffic_sign',\n 'traffic.stop':\n 'traffic_sign',\n 'traffic.yield':\n 'traffic_sign',\n 'traffic.traffic_light':\n 'traffic_light',\n 'static.prop.warningconstruction':\n 'traffic_cone',\n 'static.prop.warningaccident':\n 'traffic_cone',\n 'static.prop.trafficwarning':\n 'traffic_cone',\n 'static.prop.constructioncone':\n 'traffic_cone',\n 'walker.pedestrian.0001':\n 'pedestrian',\n 'walker.pedestrian.0004':\n 'pedestrian',\n 'walker.pedestrian.0005':\n 'pedestrian',\n 'walker.pedestrian.0007':\n 'pedestrian',\n 'walker.pedestrian.0013':\n 'pedestrian',\n 'walker.pedestrian.0014':\n 'pedestrian',\n 'walker.pedestrian.0017':\n 'pedestrian',\n 'walker.pedestrian.0018':\n 'pedestrian',\n 'walker.pedestrian.0019':\n 'pedestrian',\n 'walker.pedestrian.0020':\n 'pedestrian',\n 'walker.pedestrian.0022':\n 'pedestrian',\n 'walker.pedestrian.0025':\n 'pedestrian',\n 'walker.pedestrian.0035':\n 'pedestrian',\n 'walker.pedestrian.0041':\n 'pedestrian',\n 'walker.pedestrian.0046':\n 'pedestrian',\n 'walker.pedestrian.0047':\n 'pedestrian',\n 'static.prop.dirtdebris01':\n 'others',\n 'static.prop.dirtdebris02':\n 'others'\n})\neval_cfg = dict(\n dist_ths=[0.5, 1.0, 2.0, 4.0],\n dist_th_tp=2.0,\n min_recall=0.1,\n min_precision=0.1,\n mean_ap_weight=5,\n class_names=[\n 'car', 'van', 'truck', 'bicycle', 'traffic_sign', 'traffic_cone',\n 'traffic_light', 'pedestrian'\n ],\n tp_metrics=['trans_err', 'scale_err', 'orient_err', 'vel_err'],\n err_name_maping=dict(\n trans_err='mATE',\n scale_err='mASE',\n orient_err='mAOE',\n vel_err='mAVE',\n attr_err='mAAE'),\n class_range=dict(\n car=(50, 50),\n van=(50, 50),\n truck=(50, 50),\n bicycle=(40, 40),\n traffic_sign=(30, 30),\n traffic_cone=(30, 30),\n traffic_light=(30, 30),\n pedestrian=(40, 40)))\nnum_classes = 9\nmap_classes = [\n 'Broken', 'Solid', 'SolidSolid', 'Center', 'TrafficLight', 'StopSign'\n]\nmap_num_vec = 100\nmap_fixed_ptsnum_per_gt_line = 20\nmap_fixed_ptsnum_per_pred_line = 20\nmap_eval_use_same_gt_sample_num_flag = True\nmap_num_classes = 6\npast_frames = 2\nfuture_frames = 6\n_dim_ = 256\n_pos_dim_ = 128\n_ffn_dim_ = 512\n_num_levels_ = 1\nbev_h_ = 100\nbev_w_ = 100\nqueue_length = 3\ntotal_epochs = 6\nmodel = dict(\n type='GenAD',\n use_grid_mask=True,\n video_test_mode=True,\n pretrained=dict(img='ckpts/resnet50-19c8e357.pth'),\n img_backbone=dict(\n type='ResNet',\n depth=50,\n num_stages=4,\n out_indices=(3, ),\n frozen_stages=1,\n norm_cfg=dict(type='BN', requires_grad=False),\n norm_eval=True,\n style='pytorch'),\n img_neck=dict(\n type='FPN',\n in_channels=[2048],\n out_channels=256,\n start_level=0,\n add_extra_convs='on_output',\n num_outs=1,\n relu_before_extra_convs=True),\n pts_bbox_head=dict(\n type='GenADHead',\n map_thresh=0.5,\n dis_thresh=0.2,\n pe_normalization=True,\n tot_epoch=6,\n use_traj_lr_warmup=False,\n query_thresh=0.0,\n query_use_fix_pad=False,\n ego_his_encoder=None,\n ego_lcf_feat_idx=None,\n valid_fut_ts=6,\n ego_fut_mode=6,\n agent_dim=300,\n ego_agent_decoder=dict(\n type='CustomTransformerDecoder',\n num_layers=1,\n return_intermediate=False,\n transformerlayers=dict(\n type='BaseTransformerLayer',\n attn_cfgs=[\n dict(\n type='MultiheadAttention',\n embed_dims=256,\n num_heads=8,\n dropout=0.0)\n ],\n feedforward_channels=512,\n ffn_dropout=0.0,\n operation_order=('cross_attn', 'norm', 'ffn', 'norm'))),\n ego_map_decoder=dict(\n type='CustomTransformerDecoder',\n num_layers=1,\n return_intermediate=False,\n transformerlayers=dict(\n type='BaseTransformerLayer',\n attn_cfgs=[\n dict(\n type='MultiheadAttention',\n embed_dims=256,\n num_heads=8,\n dropout=0.0)\n ],\n feedforward_channels=512,\n ffn_dropout=0.0,\n operation_order=('cross_attn', 'norm', 'ffn', 'norm'))),\n motion_decoder=dict(\n type='CustomTransformerDecoder',\n num_layers=1,\n return_intermediate=False,\n transformerlayers=dict(\n type='BaseTransformerLayer',\n attn_cfgs=[\n dict(\n type='MultiheadAttention',\n embed_dims=256,\n num_heads=8,\n dropout=0.0)\n ],\n feedforward_channels=512,\n ffn_dropout=0.0,\n operation_order=('cross_attn', 'norm', 'ffn', 'norm'))),\n motion_map_decoder=dict(\n type='CustomTransformerDecoder',\n num_layers=1,\n return_intermediate=False,\n transformerlayers=dict(\n type='BaseTransformerLayer',\n attn_cfgs=[\n dict(\n type='MultiheadAttention',\n embed_dims=256,\n num_heads=8,\n dropout=0.0)\n ],\n feedforward_channels=512,\n ffn_dropout=0.0,\n operation_order=('cross_attn', 'norm', 'ffn', 'norm'))),\n use_pe=True,\n bev_h=100,\n bev_w=100,\n num_query=300,\n num_classes=9,\n in_channels=256,\n sync_cls_avg_factor=True,\n with_box_refine=True,\n as_two_stage=False,\n map_num_vec=100,\n map_num_classes=6,\n map_num_pts_per_vec=20,\n map_num_pts_per_gt_vec=20,\n map_query_embed_type='instance_pts',\n map_transform_method='minmax',\n map_gt_shift_pts_pattern='v2',\n map_dir_interval=1,\n map_code_size=2,\n map_code_weights=[1.0, 1.0, 1.0, 1.0],\n transformer=dict(\n type='VADPerceptionTransformer',\n map_num_vec=100,\n map_num_pts_per_vec=20,\n rotate_prev_bev=True,\n use_shift=True,\n use_can_bus=True,\n embed_dims=256,\n encoder=dict(\n type='BEVFormerEncoder',\n num_layers=3,\n pc_range=[-15.0, -30.0, -2.0, 15.0, 30.0, 2.0],\n num_points_in_pillar=4,\n return_intermediate=False,\n transformerlayers=dict(\n type='BEVFormerLayer',\n attn_cfgs=[\n dict(\n type='TemporalSelfAttention',\n embed_dims=256,\n num_levels=1),\n dict(\n type='SpatialCrossAttention',\n pc_range=[-15.0, -30.0, -2.0, 15.0, 30.0, 2.0],\n deformable_attention=dict(\n type='MSDeformableAttention3D',\n embed_dims=256,\n num_points=8,\n num_levels=1),\n embed_dims=256)\n ],\n feedforward_channels=512,\n ffn_dropout=0.0,\n operation_order=('self_attn', 'norm', 'cross_attn', 'norm',\n 'ffn', 'norm'))),\n decoder=dict(\n type='DetectionTransformerDecoder',\n num_layers=3,\n return_intermediate=True,\n transformerlayers=dict(\n type='DetrTransformerDecoderLayer',\n attn_cfgs=[\n dict(\n type='MultiheadAttention',\n embed_dims=256,\n num_heads=8,\n dropout=0.0),\n dict(\n type='CustomMSDeformableAttention',\n embed_dims=256,\n num_levels=1)\n ],\n feedforward_channels=512,\n ffn_dropout=0.0,\n operation_order=('self_attn', 'norm', 'cross_attn', 'norm',\n 'ffn', 'norm'))),\n map_decoder=dict(\n type='MapDetectionTransformerDecoder',\n num_layers=3,\n return_intermediate=True,\n transformerlayers=dict(\n type='DetrTransformerDecoderLayer',\n attn_cfgs=[\n dict(\n type='MultiheadAttention',\n embed_dims=256,\n num_heads=8,\n dropout=0.0),\n dict(\n type='CustomMSDeformableAttention',\n embed_dims=256,\n num_levels=1)\n ],\n feedforward_channels=512,\n ffn_dropout=0.0,\n operation_order=('self_attn', 'norm', 'cross_attn', 'norm',\n 'ffn', 'norm')))),\n bbox_coder=dict(\n type='CustomNMSFreeCoder',\n post_center_range=[-20, -35, -10.0, 20, 35, 10.0],\n pc_range=[-15.0, -30.0, -2.0, 15.0, 30.0, 2.0],\n max_num=100,\n voxel_size=[0.15, 0.15, 4],\n num_classes=9),\n map_bbox_coder=dict(\n type='MapNMSFreeCoder',\n post_center_range=[-20, -35, -20, -35, 20, 35, 20, 35],\n pc_range=[-15.0, -30.0, -2.0, 15.0, 30.0, 2.0],\n max_num=50,\n voxel_size=[0.15, 0.15, 4],\n num_classes=6),\n positional_encoding=dict(\n type='LearnedPositionalEncoding',\n num_feats=128,\n row_num_embed=100,\n col_num_embed=100),\n loss_cls=dict(\n type='FocalLoss',\n use_sigmoid=True,\n gamma=2.0,\n alpha=0.25,\n loss_weight=2.0),\n loss_bbox=dict(type='L1Loss', loss_weight=0.25),\n loss_traj=dict(type='L1Loss', loss_weight=0.2),\n loss_traj_cls=dict(\n type='FocalLoss',\n use_sigmoid=True,\n gamma=2.0,\n alpha=0.25,\n loss_weight=0.2),\n loss_iou=dict(type='GIoULoss', loss_weight=0.0),\n loss_map_cls=dict(\n type='FocalLoss',\n use_sigmoid=True,\n gamma=2.0,\n alpha=0.25,\n loss_weight=2.0),\n loss_map_bbox=dict(type='L1Loss', loss_weight=0.0),\n loss_map_iou=dict(type='GIoULoss', loss_weight=0.0),\n loss_map_pts=dict(type='PtsL1Loss', loss_weight=1.0),\n loss_map_dir=dict(type='PtsDirCosLoss', loss_weight=0.005),\n loss_plan_reg=dict(type='L1Loss', loss_weight=1.0),\n loss_plan_bound=dict(\n type='PlanMapBoundLoss', loss_weight=1.0, dis_thresh=1.0),\n loss_plan_col=dict(type='PlanCollisionLoss', loss_weight=1.0),\n loss_plan_dir=dict(type='PlanMapDirectionLoss', loss_weight=0.5),\n loss_vae_gen=dict(type='ProbabilisticLoss', loss_weight=1.0)),\n train_cfg=dict(\n pts=dict(\n grid_size=[512, 512, 1],\n voxel_size=[0.15, 0.15, 4],\n point_cloud_range=[-15.0, -30.0, -2.0, 15.0, 30.0, 2.0],\n out_size_factor=4,\n assigner=dict(\n type='HungarianAssigner3D',\n cls_cost=dict(type='FocalLossCost', weight=2.0),\n reg_cost=dict(type='BBox3DL1Cost', weight=0.25),\n iou_cost=dict(type='IoUCost', weight=0.0),\n pc_range=[-15.0, -30.0, -2.0, 15.0, 30.0, 2.0]),\n map_assigner=dict(\n type='MapHungarianAssigner3D',\n cls_cost=dict(type='FocalLossCost', weight=2.0),\n reg_cost=dict(\n type='BBoxL1Cost', weight=0.0, box_format='xywh'),\n iou_cost=dict(type='IoUCost', iou_mode='giou', weight=0.0),\n pts_cost=dict(type='OrderedPtsL1Cost', weight=1.0),\n pc_range=[-15.0, -30.0, -2.0, 15.0, 30.0, 2.0]))))\ninfo_root = 'data/infos'\nmap_root = 'data/bench2drive/maps'\nmap_file = 'data/infos/b2d_map_infos.pkl'\nann_file_train = 'data/infos/b2d_infos_train.pkl'\nann_file_val = 'data/infos/b2d_infos_val.pkl'\nann_file_test = 'data/infos/b2d_infos_val.pkl'\ninference_only_pipeline = [\n dict(type='LoadMultiViewImageFromFiles', to_float32=True),\n dict(\n type='NormalizeMultiviewImage',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='PadMultiViewImage', size_divisor=32),\n dict(\n type='MultiScaleFlipAug3D',\n img_scale=(1600, 900),\n pts_scale_ratio=1,\n flip=False,\n transforms=[\n dict(type='RandomScaleImageMultiViewImage', scales=[0.8]),\n dict(type='PadMultiViewImage', size_divisor=32),\n dict(\n type='VADFormatBundle3D',\n class_names=[\n 'car', 'van', 'truck', 'bicycle', 'traffic_sign',\n 'traffic_cone', 'traffic_light', 'pedestrian', 'others'\n ],\n with_label=False,\n with_ego=True),\n dict(type='CustomCollect3D', keys=['img', 'ego_fut_cmd'])\n ])\n]\noptimizer = dict(\n type='AdamW',\n lr=0.0002,\n paramwise_cfg=dict(custom_keys=dict(img_backbone=dict(lr_mult=0.1))),\n weight_decay=0.01)\noptimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))\nlr_config = dict(\n by_epoch=False,\n policy='CosineAnnealing',\n warmup='linear',\n warmup_iters=500,\n warmup_ratio=0.3333333333333333,\n min_lr_ratio=0.001)\nrunner = dict(type='EpochBasedRunner', max_epochs=6)\nfind_unused_parameters = True\ncustom_hooks = [\n dict(type='CustomSetEpochInfoHook'),\n dict(\n type='MLflowHook',\n tracking_uri='https://dagshub.com/YSH-research/YSH-Bench2Drive.mlflow',\n exp_name='GenAD-Bench2Drive',\n log_interval=100,\n log_model=True,\n tags=dict(model='GenAD', dataset='Bench2Drive'))\n]\ngpu_ids = range(0, 1)\n", "seed": 0, "exp_name": "GenAD_config_b2d.py"} | |
| {"mode": "train", "epoch": 1, "iter": 50, "lr": 8e-05, "memory": 8494, "data_time": 0.23037, "loss_cls": 1.42193, "loss_bbox": 1.69824, "loss_traj": 1.73606, "loss_traj_cls": 0.14672, "loss_map_cls": 1.33139, "loss_map_bbox": 0.0, "loss_map_iou": 0.0, "loss_map_pts": 6.34393, "loss_map_dir": 0.09262, "loss_plan_reg": 0.17051, "loss_plan_bound": 0.0, "loss_plan_col": 0.0, "loss_plan_dir": 0.0, "d0.loss_cls": 1.45761, "d0.loss_bbox": 1.70494, "d1.loss_cls": 1.41068, "d1.loss_bbox": 1.71901, "d0.loss_map_cls": 1.3727, "d0.loss_map_bbox": 0.0, "d0.loss_map_iou": 0.0, "d0.loss_map_pts": 6.71792, "d0.loss_map_dir": 0.09491, "d1.loss_map_cls": 1.3365, "d1.loss_map_bbox": 0.0, "d1.loss_map_iou": 0.0, "d1.loss_map_pts": 6.43207, "d1.loss_map_dir": 0.09359, "loss_vae_gen": 0.02997, "loss": 35.3113, "grad_norm": 40.92718, "time": 0.693} | |
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