_dim_ = 256 _ffn_dim_ = 512 _num_levels_ = 1 _pos_dim_ = 128 auto_scale_lr = dict(base_batch_size=16, enable=False) bev_h_ = 50 bev_w_ = 50 by_epoch = False class_names = [ 'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone', ] custom_hooks = [ dict( by_epoch=False, clean_local=False, interval=1, repo_id='5421Project', type='CheckpointUploader'), dict(repo_id='5421Project', resume_type='last', type='CheckpointResumer'), ] data = dict( nonshuffler_sampler=dict(type='DistributedSampler'), samples_per_gpu=1, shuffler_sampler=dict(type='DistributedGroupSampler'), test=dict( ann_file='data/nuscenes/v1.0-mini/nuscenes_infos_temporal_val.pkl', bev_size=( 50, 50, ), classes=[ 'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone', ], data_root='data/nuscenes/v1.0-mini/', frame=[ -3, -2, -1, ], modality=dict( use_camera=True, use_external=False, use_lidar=False, use_map=False, use_radar=False), pipeline=[ dict(to_float32=True, type='LoadMultiViewImageFromFiles'), dict( mean=[ 123.675, 116.28, 103.53, ], std=[ 58.395, 57.12, 57.375, ], to_rgb=True, type='NormalizeMultiviewImage'), dict( flip=False, img_scale=( 800, 450, ), pts_scale_ratio=[ 1.0, ], transforms=[ dict( scales=[ 0.5, ], type='RandomScaleImageMultiViewImage'), dict(size_divisor=32, type='PadMultiViewImage'), dict( class_names=[ 'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone', ], type='CustomDefaultFormatBundle3D'), dict(keys=[ 'img', ], type='CustomCollect3D'), ], type='MultiScaleFlipAug3D'), ], test_mode=True, type='CustomNuScenesDataset'), train=dict( ann_file='data/nuscenes/v1.0-mini/nuscenes_infos_temporal_train.pkl', bev_size=( 50, 50, ), box_type_3d='LiDAR', classes=[ 'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone', ], data_root='data/nuscenes/v1.0-mini/', modality=dict( use_camera=True, use_external=False, use_lidar=False, use_map=False, use_radar=False), pipeline=[ dict(to_float32=True, type='LoadMultiViewImageFromFiles'), dict( type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True), dict( point_cloud_range=[ -51.2, -51.2, -5.0, 51.2, 51.2, 3.0, ], type='ObjectRangeFilter'), dict( classes=[ 'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone', ], type='ObjectNameFilter'), dict(type='PhotoMetricDistortionMultiViewImage'), dict( mean=[ 123.675, 116.28, 103.53, ], std=[ 58.395, 57.12, 57.375, ], to_rgb=True, type='NormalizeMultiviewImage'), dict(scales=[ 0.5, ], type='RandomScaleImageMultiViewImage'), dict(size_divisor=32, type='PadMultiViewImage'), dict( class_names=[ 'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone', ], type='CustomDefaultFormatBundle3D'), dict( keys=[ 'gt_bboxes_3d', 'gt_labels_3d', 'img', ], type='CustomCollect3D'), dict(type='TypeConverter'), ], queue_length=4, test_mode=False, type='CustomNuScenesDataset', use_valid_flag=True), val=dict( ann_file='data/nuscenes/v1.0-mini/nuscenes_infos_temporal_val.pkl', bev_size=( 50, 50, ), classes=[ 'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone', ], data_root='data/nuscenes/v1.0-mini/', frame=(), frames=[ -3, -2, -1, ], modality=dict( use_camera=True, use_external=False, use_lidar=False, use_map=False, use_radar=False), pipeline=[ dict(to_float32=True, type='LoadMultiViewImageFromFiles'), dict( mean=[ 123.675, 116.28, 103.53, ], std=[ 58.395, 57.12, 57.375, ], to_rgb=True, type='NormalizeMultiviewImage'), dict( flip=False, img_scale=( 800, 450, ), pts_scale_ratio=[ 1.0, ], transforms=[ dict( scales=[ 0.5, ], type='RandomScaleImageMultiViewImage'), dict(size_divisor=32, type='PadMultiViewImage'), dict( class_names=[ 'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone', ], type='CustomDefaultFormatBundle3D'), dict(keys=[ 'img', ], type='CustomCollect3D'), ], type='MultiScaleFlipAug3D'), ], samples_per_gpu=1, test_mode=True, type='CustomNuScenesDataset'), workers_per_gpu=4) data_root = 'data/nuscenes/v1.0-mini/' dataset_type = 'CustomNuScenesDataset' decoder = dict( num_layers=6, return_intermediate=True, transformerlayers=dict( attn_cfgs=[ dict( dropout=0.1, embed_dims=256, num_heads=8, type='MultiheadAttention'), dict( embed_dims=256, num_levels=1, type='CustomMSDeformableAttention'), ], ffn_cfgs=dict( feedforward_channels=512, ffn_drop=0.1, num_fcs=2, type='FFN'), operation_order=( 'self_attn', 'norm', 'cross_attn', 'norm', 'ffn', 'norm', ), type='DetrTransformerDecoderLayer'), type='DetectionTransformerDecoder') default_hooks = dict( checkpoint=dict( by_epoch=False, interval=1, max_keep_ckpts=1, save_best=[ 'loss', 'mAP', 'NDS', ], type='CheckpointHookV2'), logger=dict( interval=1, interval_exp_name=1000, log_metric_by_epoch=False, type='LoggerHook'), param_scheduler=dict(type='ParamSchedulerHook'), runtime_info=dict(type='RuntimeInfoHook'), sampler_seed=dict(type='DistSamplerSeedHook'), timer=dict(type='IterTimerHook')) encoder = dict( num_layers=3, num_points_in_pillar=8, pc_range=[ -51.2, -51.2, -5.0, 51.2, 51.2, 3.0, ], return_intermediate=False, transformerlayers=dict( attn_cfgs=[ dict(embed_dims=256, num_levels=1, type='TemporalSelfAttention'), dict( deformable_attention=dict( embed_dims=256, num_levels=1, num_points=8, type='MSDeformableAttention3D'), embed_dims=256, pc_range=[ -51.2, -51.2, -5.0, 51.2, 51.2, 3.0, ], type='SpatialCrossAttention'), ], ffn_cfgs=dict( feedforward_channels=512, ffn_drop=0.1, num_fcs=2, type='FFN'), operation_order=( 'self_attn', 'norm', 'cross_attn', 'norm', 'ffn', 'norm', ), type='BEVFormerLayer'), type='BEVFormerEncoder') env_cfg = dict(dist_cfg=dict(backend='nccl')) experiment_name = 'debug' file_client_args = dict(backend='disk') frames = [ -3, -2, -1, ] gpu_ids = range(0, 1) img_norm_cfg = dict( mean=[ 123.675, 116.28, 103.53, ], std=[ 58.395, 57.12, 57.375, ], to_rgb=True) input_modality = dict( use_camera=True, use_external=False, use_lidar=False, use_map=False, use_radar=False) interval = 1 launcher = 'none' load_from = None log_interval = 1 log_processor = dict(window_size=20) lr_config = dict( min_lr_ratio=0.001, policy='CosineAnnealing', warmup='linear', warmup_iters=500, warmup_ratio=0.3333333333333333) max_epochs = 5 max_iters = 2 model = dict( img_backbone=dict( depth=50, frozen_stages=1, norm_cfg=dict(requires_grad=False, type='BN'), norm_eval=True, num_stages=4, out_indices=(3, ), style='pytorch', type='ResNet'), img_neck=dict( add_extra_convs='on_output', in_channels=[ 2048, ], num_outs=1, out_channels=256, relu_before_extra_convs=True, start_level=0, type='FPN'), pretrained=dict(img='torchvision://resnet50'), pts_bbox_head=dict( as_two_stage=False, bbox_coder=dict( max_num=300, num_classes=10, pc_range=[ -51.2, -51.2, -5.0, 51.2, 51.2, 3.0, ], post_center_range=[ -61.2, -61.2, -10.0, 61.2, 61.2, 10.0, ], type='NMSFreeCoder', voxel_size=[ 0.2, 0.2, 8, ]), bev_h=50, bev_w=50, in_channels=256, loss_bbox=dict(loss_weight=0.5, type='L1Loss'), loss_cls=dict( alpha=0.25, gamma=2.0, loss_weight=2.0, type='FocalLoss', use_sigmoid=True), loss_iou=dict(loss_weight=0.25, type='GIoULoss'), num_classes=10, num_query=900, positional_encoding=dict( col_num_embed=50, num_feats=128, row_num_embed=50, type='LearnedPositionalEncoding'), sync_cls_avg_factor=True, transformer=dict( decoder=dict( num_layers=6, return_intermediate=True, transformerlayers=dict( attn_cfgs=[ dict( dropout=0.1, embed_dims=256, num_heads=8, type='MultiheadAttention'), dict( embed_dims=256, num_levels=1, type='CustomMSDeformableAttention'), ], ffn_cfgs=dict( feedforward_channels=512, ffn_drop=0.1, num_fcs=2, type='FFN'), operation_order=( 'self_attn', 'norm', 'cross_attn', 'norm', 'ffn', 'norm', ), type='DetrTransformerDecoderLayer'), type='DetectionTransformerDecoder'), embed_dims=256, encoder=dict( num_layers=3, num_points_in_pillar=8, pc_range=[ -51.2, -51.2, -5.0, 51.2, 51.2, 3.0, ], return_intermediate=False, transformerlayers=dict( attn_cfgs=[ dict( embed_dims=256, num_levels=1, type='TemporalSelfAttention'), dict( deformable_attention=dict( embed_dims=256, num_levels=1, num_points=8, type='MSDeformableAttention3D'), embed_dims=256, pc_range=[ -51.2, -51.2, -5.0, 51.2, 51.2, 3.0, ], type='SpatialCrossAttention'), ], ffn_cfgs=dict( feedforward_channels=512, ffn_drop=0.1, num_fcs=2, type='FFN'), operation_order=( 'self_attn', 'norm', 'cross_attn', 'norm', 'ffn', 'norm', ), type='BEVFormerLayer'), type='BEVFormerEncoder'), num_cams=6, num_feature_levels=1, rotate_prev_bev=True, type='PerceptionTransformer', use_can_bus=True, use_shift=True), type='BEVFormerHead', with_box_refine=True), train_cfg=dict( pts=dict( assigner=dict( cls_cost=dict(type='FocalCost', weight=2.0), iou_cost=dict(type='SmoothL1Cost', weight=0.25), pc_range=[ -51.2, -51.2, -5.0, 51.2, 51.2, 3.0, ], reg_cost=dict(type='BBox3DL1Cost', weight=0.25), type='HungarianAssigner3D'), grid_size=[ 512, 512, 1, ], out_size_factor=4, point_cloud_range=[ -51.2, -51.2, -5.0, 51.2, 51.2, 3.0, ], voxel_size=[ 0.2, 0.2, 8, ])), type='BEVFormerDetector', use_grid_mask=True, video_test_mode=True) optim_wrapper = dict( optimizer=dict(lr=0.0001, type='AdamW', weight_decay=0.01), type='OptimWrapper') optimizer = dict(lr=0.0001, type='AdamW', weight_decay=0.01) param_scheduler = dict( milestones=[ 1, 2, ], type='MultiStepLR') point_cloud_range = [ -51.2, -51.2, -5.0, 51.2, 51.2, 3.0, ] pts_bbox_head = dict( as_two_stage=False, bbox_coder=dict( max_num=300, num_classes=10, pc_range=[ -51.2, -51.2, -5.0, 51.2, 51.2, 3.0, ], post_center_range=[ -61.2, -61.2, -10.0, 61.2, 61.2, 10.0, ], type='NMSFreeCoder', voxel_size=[ 0.2, 0.2, 8, ]), bev_h=50, bev_w=50, in_channels=256, loss_bbox=dict(loss_weight=0.5, type='L1Loss'), loss_cls=dict( alpha=0.25, gamma=2.0, loss_weight=2.0, type='FocalLoss', use_sigmoid=True), loss_iou=dict(loss_weight=0.25, type='GIoULoss'), num_classes=10, num_query=900, positional_encoding=dict( col_num_embed=50, num_feats=128, row_num_embed=50, type='LearnedPositionalEncoding'), sync_cls_avg_factor=True, transformer=dict( decoder=dict( num_layers=6, return_intermediate=True, transformerlayers=dict( attn_cfgs=[ dict( dropout=0.1, embed_dims=256, num_heads=8, type='MultiheadAttention'), dict( embed_dims=256, num_levels=1, type='CustomMSDeformableAttention'), ], ffn_cfgs=dict( feedforward_channels=512, ffn_drop=0.1, num_fcs=2, type='FFN'), operation_order=( 'self_attn', 'norm', 'cross_attn', 'norm', 'ffn', 'norm', ), type='DetrTransformerDecoderLayer'), type='DetectionTransformerDecoder'), embed_dims=256, encoder=dict( num_layers=3, num_points_in_pillar=8, pc_range=[ -51.2, -51.2, -5.0, 51.2, 51.2, 3.0, ], return_intermediate=False, transformerlayers=dict( attn_cfgs=[ dict( embed_dims=256, num_levels=1, type='TemporalSelfAttention'), dict( deformable_attention=dict( embed_dims=256, num_levels=1, num_points=8, type='MSDeformableAttention3D'), embed_dims=256, pc_range=[ -51.2, -51.2, -5.0, 51.2, 51.2, 3.0, ], type='SpatialCrossAttention'), ], ffn_cfgs=dict( feedforward_channels=512, ffn_drop=0.1, num_fcs=2, type='FFN'), operation_order=( 'self_attn', 'norm', 'cross_attn', 'norm', 'ffn', 'norm', ), type='BEVFormerLayer'), type='BEVFormerEncoder'), num_cams=6, num_feature_levels=1, rotate_prev_bev=True, type='PerceptionTransformer', use_can_bus=True, use_shift=True), type='BEVFormerHead', with_box_refine=True) queue_length = 4 resume = False scales = [ 0.5, ] test_cfg = dict(max_iters=1) test_dataloader = dict( batch_size=1, collate_fn=dict(type='test_collate'), dataset=dict( ann_file='data/nuscenes/v1.0-mini/nuscenes_infos_temporal_val.pkl', bev_size=( 50, 50, ), classes=[ 'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone', ], data_root='data/nuscenes/v1.0-mini/', frame=[ -3, -2, -1, ], modality=dict( use_camera=True, use_external=False, use_lidar=False, use_map=False, use_radar=False), pipeline=[ dict(to_float32=True, type='LoadMultiViewImageFromFiles'), dict( mean=[ 123.675, 116.28, 103.53, ], std=[ 58.395, 57.12, 57.375, ], to_rgb=True, type='NormalizeMultiviewImage'), dict( flip=False, img_scale=( 800, 450, ), pts_scale_ratio=[ 1.0, ], transforms=[ dict( scales=[ 0.5, ], type='RandomScaleImageMultiViewImage'), dict(size_divisor=32, type='PadMultiViewImage'), dict( class_names=[ 'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone', ], type='CustomDefaultFormatBundle3D'), dict(keys=[ 'img', ], type='CustomCollect3D'), ], type='MultiScaleFlipAug3D'), ], test_mode=True, type='CustomNuScenesDataset'), num_workers=0, sampler=dict(shuffle=True, type='DefaultSampler')) test_evaluator = dict(metrics=[ dict( ann_file='data/nuscenes/v1.0-mini/nuscenes_infos_temporal_val.pkl', data_root='data/nuscenes/v1.0-mini/', type='src.NuScenesMetric', version='v1.0-mini'), ]) test_max_iters = 1 test_pipeline = [ dict(to_float32=True, type='LoadMultiViewImageFromFiles'), dict( mean=[ 123.675, 116.28, 103.53, ], std=[ 58.395, 57.12, 57.375, ], to_rgb=True, type='NormalizeMultiviewImage'), dict( flip=False, img_scale=( 800, 450, ), pts_scale_ratio=[ 1.0, ], transforms=[ dict(scales=[ 0.5, ], type='RandomScaleImageMultiViewImage'), dict(size_divisor=32, type='PadMultiViewImage'), dict( class_names=[ 'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone', ], type='CustomDefaultFormatBundle3D'), dict(keys=[ 'img', ], type='CustomCollect3D'), ], type='MultiScaleFlipAug3D'), ] train_cfg = dict(by_epoch=False, max_epochs=5, max_iters=2, val_interval=1) train_dataloader = dict( batch_size=1, collate_fn=dict(type='train_collate'), dataset=dict( ann_file='data/nuscenes/v1.0-mini/nuscenes_infos_temporal_train.pkl', bev_size=( 50, 50, ), box_type_3d='LiDAR', classes=[ 'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone', ], data_root='data/nuscenes/v1.0-mini/', modality=dict( use_camera=True, use_external=False, use_lidar=False, use_map=False, use_radar=False), pipeline=[ dict(to_float32=True, type='LoadMultiViewImageFromFiles'), dict( type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True), dict( point_cloud_range=[ -51.2, -51.2, -5.0, 51.2, 51.2, 3.0, ], type='ObjectRangeFilter'), dict( classes=[ 'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone', ], type='ObjectNameFilter'), dict(type='PhotoMetricDistortionMultiViewImage'), dict( mean=[ 123.675, 116.28, 103.53, ], std=[ 58.395, 57.12, 57.375, ], to_rgb=True, type='NormalizeMultiviewImage'), dict(scales=[ 0.5, ], type='RandomScaleImageMultiViewImage'), dict(size_divisor=32, type='PadMultiViewImage'), dict( class_names=[ 'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone', ], type='CustomDefaultFormatBundle3D'), dict( keys=[ 'gt_bboxes_3d', 'gt_labels_3d', 'img', ], type='CustomCollect3D'), dict(type='TypeConverter'), ], queue_length=4, test_mode=False, type='CustomNuScenesDataset', use_valid_flag=True), num_workers=0, sampler=dict(shuffle=True, type='DefaultSampler')) train_pipeline = [ dict(to_float32=True, type='LoadMultiViewImageFromFiles'), dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True), dict( point_cloud_range=[ -51.2, -51.2, -5.0, 51.2, 51.2, 3.0, ], type='ObjectRangeFilter'), dict( classes=[ 'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone', ], type='ObjectNameFilter'), dict(type='PhotoMetricDistortionMultiViewImage'), dict( mean=[ 123.675, 116.28, 103.53, ], std=[ 58.395, 57.12, 57.375, ], to_rgb=True, type='NormalizeMultiviewImage'), dict(scales=[ 0.5, ], type='RandomScaleImageMultiViewImage'), dict(size_divisor=32, type='PadMultiViewImage'), dict( class_names=[ 'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone', ], type='CustomDefaultFormatBundle3D'), dict( keys=[ 'gt_bboxes_3d', 'gt_labels_3d', 'img', ], type='CustomCollect3D'), dict(type='TypeConverter'), ] transformer = dict( decoder=dict( num_layers=6, return_intermediate=True, transformerlayers=dict( attn_cfgs=[ dict( dropout=0.1, embed_dims=256, num_heads=8, type='MultiheadAttention'), dict( embed_dims=256, num_levels=1, type='CustomMSDeformableAttention'), ], ffn_cfgs=dict( feedforward_channels=512, ffn_drop=0.1, num_fcs=2, type='FFN'), operation_order=( 'self_attn', 'norm', 'cross_attn', 'norm', 'ffn', 'norm', ), type='DetrTransformerDecoderLayer'), type='DetectionTransformerDecoder'), embed_dims=256, encoder=dict( num_layers=3, num_points_in_pillar=8, pc_range=[ -51.2, -51.2, -5.0, 51.2, 51.2, 3.0, ], return_intermediate=False, transformerlayers=dict( attn_cfgs=[ dict( embed_dims=256, num_levels=1, type='TemporalSelfAttention'), dict( deformable_attention=dict( embed_dims=256, num_levels=1, num_points=8, type='MSDeformableAttention3D'), embed_dims=256, pc_range=[ -51.2, -51.2, -5.0, 51.2, 51.2, 3.0, ], type='SpatialCrossAttention'), ], ffn_cfgs=dict( feedforward_channels=512, ffn_drop=0.1, num_fcs=2, type='FFN'), operation_order=( 'self_attn', 'norm', 'cross_attn', 'norm', 'ffn', 'norm', ), type='BEVFormerLayer'), type='BEVFormerEncoder'), num_cams=6, num_feature_levels=1, rotate_prev_bev=True, type='PerceptionTransformer', use_can_bus=True, use_shift=True) val_cfg = dict(max_iters=1) val_dataloader = dict( batch_size=1, collate_fn=dict(type='test_collate'), dataset=dict( ann_file='data/nuscenes/v1.0-mini/nuscenes_infos_temporal_val.pkl', bev_size=( 50, 50, ), classes=[ 'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone', ], data_root='data/nuscenes/v1.0-mini/', frame=(), frames=[ -3, -2, -1, ], modality=dict( use_camera=True, use_external=False, use_lidar=False, use_map=False, use_radar=False), pipeline=[ dict(to_float32=True, type='LoadMultiViewImageFromFiles'), dict( mean=[ 123.675, 116.28, 103.53, ], std=[ 58.395, 57.12, 57.375, ], to_rgb=True, type='NormalizeMultiviewImage'), dict( flip=False, img_scale=( 800, 450, ), pts_scale_ratio=[ 1.0, ], transforms=[ dict( scales=[ 0.5, ], type='RandomScaleImageMultiViewImage'), dict(size_divisor=32, type='PadMultiViewImage'), dict( class_names=[ 'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone', ], type='CustomDefaultFormatBundle3D'), dict(keys=[ 'img', ], type='CustomCollect3D'), ], type='MultiScaleFlipAug3D'), ], samples_per_gpu=1, test_mode=True, type='CustomNuScenesDataset'), num_workers=0, sampler=dict(shuffle=True, type='DefaultSampler')) val_evaluator = dict(metrics=[ dict( ann_file='data/nuscenes/v1.0-mini/nuscenes_infos_temporal_val.pkl', classes=[ 'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone', ], data_root='data/nuscenes/v1.0-mini/', jsonfile_prefix='results', modality=dict( use_camera=True, use_external=False, use_lidar=False, use_map=False, use_radar=False), plot_every_run=True, plot_examples=1, type='src.NuScenesMetric', version='v1.0-mini'), ]) val_interval = 1 val_max_iters = 1 version = 'v1.0-mini' visualizer = dict( type='Visualizer', vis_backends=[ dict(type='LocalVisBackend'), dict(type='TensorboardVisBackend'), ]) voxel_size = [ 0.2, 0.2, 8, ] work_dir = 'experiment'