| point_cloud_range = [-51.2, -51.2, -5.0, 51.2, 51.2, 3.0] | |
| class_names = [ | |
| 'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier', | |
| 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone' | |
| ] | |
| dataset_type = 'CustomNuScenesDataset' | |
| data_root = 'data/nuscenes/' | |
| 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='LoadMultiViewImageFromFilesInCeph', | |
| to_float32=True, | |
| file_client_args=dict(backend='disk'), | |
| img_root=''), | |
| dict(type='PhotoMetricDistortionMultiViewImage'), | |
| dict( | |
| type='LoadAnnotations3D', | |
| with_bbox_3d=True, | |
| with_label_3d=True, | |
| with_attr_label=False), | |
| dict( | |
| type='ObjectRangeFilter', | |
| point_cloud_range=[-51.2, -51.2, -5.0, 51.2, 51.2, 3.0]), | |
| dict( | |
| type='ObjectNameFilter', | |
| classes=[ | |
| 'car', 'truck', 'construction_vehicle', 'bus', 'trailer', | |
| 'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone' | |
| ]), | |
| dict( | |
| type='NormalizeMultiviewImage', | |
| mean=[103.53, 116.28, 123.675], | |
| std=[1.0, 1.0, 1.0], | |
| to_rgb=False), | |
| dict(type='PadMultiViewImage', size_divisor=32), | |
| dict( | |
| type='DefaultFormatBundle3D', | |
| class_names=[ | |
| 'car', 'truck', 'construction_vehicle', 'bus', 'trailer', | |
| 'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone' | |
| ]), | |
| dict(type='CustomCollect3D', keys=['gt_bboxes_3d', 'gt_labels_3d', 'img']) | |
| ] | |
| test_pipeline = [ | |
| dict( | |
| type='LoadMultiViewImageFromFilesInCeph', | |
| to_float32=True, | |
| file_client_args=dict(backend='disk'), | |
| img_root=''), | |
| dict( | |
| type='NormalizeMultiviewImage', | |
| mean=[103.53, 116.28, 123.675], | |
| std=[1.0, 1.0, 1.0], | |
| to_rgb=False), | |
| dict(type='PadMultiViewImage', size_divisor=32), | |
| dict( | |
| type='MultiScaleFlipAug3D', | |
| img_scale=(1600, 900), | |
| pts_scale_ratio=1, | |
| flip=False, | |
| transforms=[ | |
| dict( | |
| type='DefaultFormatBundle3D', | |
| class_names=[ | |
| 'car', 'truck', 'construction_vehicle', 'bus', 'trailer', | |
| 'barrier', 'motorcycle', 'bicycle', 'pedestrian', | |
| 'traffic_cone' | |
| ], | |
| with_label=False), | |
| dict(type='CustomCollect3D', keys=['img']) | |
| ]) | |
| ] | |
| 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=1, | |
| workers_per_gpu=4, | |
| train=dict( | |
| type='CustomNuScenesDataset', | |
| data_root='data/nuscenes/', | |
| ann_file='data/infos/nuscenes_infos_temporal_train.pkl', | |
| pipeline=[ | |
| dict( | |
| type='LoadMultiViewImageFromFilesInCeph', | |
| to_float32=True, | |
| file_client_args=dict(backend='disk'), | |
| img_root=''), | |
| dict(type='PhotoMetricDistortionMultiViewImage'), | |
| dict( | |
| type='LoadAnnotations3D', | |
| with_bbox_3d=True, | |
| with_label_3d=True, | |
| with_attr_label=False), | |
| dict( | |
| type='ObjectRangeFilter', | |
| point_cloud_range=[-51.2, -51.2, -5.0, 51.2, 51.2, 3.0]), | |
| dict( | |
| type='ObjectNameFilter', | |
| classes=[ | |
| 'car', 'truck', 'construction_vehicle', 'bus', 'trailer', | |
| 'barrier', 'motorcycle', 'bicycle', 'pedestrian', | |
| 'traffic_cone' | |
| ]), | |
| dict( | |
| type='NormalizeMultiviewImage', | |
| mean=[103.53, 116.28, 123.675], | |
| std=[1.0, 1.0, 1.0], | |
| to_rgb=False), | |
| dict(type='PadMultiViewImage', size_divisor=32), | |
| dict( | |
| type='DefaultFormatBundle3D', | |
| class_names=[ | |
| 'car', 'truck', 'construction_vehicle', 'bus', 'trailer', | |
| 'barrier', 'motorcycle', 'bicycle', 'pedestrian', | |
| 'traffic_cone' | |
| ]), | |
| dict( | |
| type='CustomCollect3D', | |
| keys=['gt_bboxes_3d', 'gt_labels_3d', 'img']) | |
| ], | |
| classes=[ | |
| 'car', 'truck', 'construction_vehicle', 'bus', 'trailer', | |
| 'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone' | |
| ], | |
| modality=dict( | |
| use_lidar=False, | |
| use_camera=True, | |
| use_radar=False, | |
| use_map=False, | |
| use_external=True), | |
| test_mode=False, | |
| box_type_3d='LiDAR', | |
| use_valid_flag=True, | |
| bev_size=(200, 200), | |
| queue_length=4), | |
| val=dict( | |
| type='CustomNuScenesDataset', | |
| data_root='data/nuscenes/', | |
| ann_file='data/infos/nuscenes_infos_temporal_val.pkl', | |
| pipeline=[ | |
| dict( | |
| type='LoadMultiViewImageFromFilesInCeph', | |
| to_float32=True, | |
| file_client_args=dict(backend='disk'), | |
| img_root=''), | |
| dict( | |
| type='NormalizeMultiviewImage', | |
| mean=[103.53, 116.28, 123.675], | |
| std=[1.0, 1.0, 1.0], | |
| to_rgb=False), | |
| dict(type='PadMultiViewImage', size_divisor=32), | |
| dict( | |
| type='MultiScaleFlipAug3D', | |
| img_scale=(1600, 900), | |
| pts_scale_ratio=1, | |
| flip=False, | |
| transforms=[ | |
| dict( | |
| type='DefaultFormatBundle3D', | |
| class_names=[ | |
| 'car', 'truck', 'construction_vehicle', 'bus', | |
| 'trailer', 'barrier', 'motorcycle', 'bicycle', | |
| 'pedestrian', 'traffic_cone' | |
| ], | |
| with_label=False), | |
| dict(type='CustomCollect3D', keys=['img']) | |
| ]) | |
| ], | |
| classes=[ | |
| 'car', 'truck', 'construction_vehicle', 'bus', 'trailer', | |
| 'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone' | |
| ], | |
| modality=dict( | |
| use_lidar=False, | |
| use_camera=True, | |
| use_radar=False, | |
| use_map=False, | |
| use_external=True), | |
| test_mode=True, | |
| box_type_3d='LiDAR', | |
| bev_size=(200, 200), | |
| samples_per_gpu=1), | |
| test=dict( | |
| type='CustomNuScenesDataset', | |
| data_root='data/nuscenes/', | |
| ann_file='data/infos/nuscenes_infos_temporal_val.pkl', | |
| pipeline=[ | |
| dict( | |
| type='LoadMultiViewImageFromFilesInCeph', | |
| to_float32=True, | |
| file_client_args=dict(backend='disk'), | |
| img_root=''), | |
| dict( | |
| type='NormalizeMultiviewImage', | |
| mean=[103.53, 116.28, 123.675], | |
| std=[1.0, 1.0, 1.0], | |
| to_rgb=False), | |
| dict(type='PadMultiViewImage', size_divisor=32), | |
| dict( | |
| type='MultiScaleFlipAug3D', | |
| img_scale=(1600, 900), | |
| pts_scale_ratio=1, | |
| flip=False, | |
| transforms=[ | |
| dict( | |
| type='DefaultFormatBundle3D', | |
| class_names=[ | |
| 'car', 'truck', 'construction_vehicle', 'bus', | |
| 'trailer', 'barrier', 'motorcycle', 'bicycle', | |
| 'pedestrian', 'traffic_cone' | |
| ], | |
| with_label=False), | |
| dict(type='CustomCollect3D', keys=['img']) | |
| ]) | |
| ], | |
| classes=[ | |
| 'car', 'truck', 'construction_vehicle', 'bus', 'trailer', | |
| 'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone' | |
| ], | |
| modality=dict( | |
| use_lidar=False, | |
| use_camera=True, | |
| use_radar=False, | |
| use_map=False, | |
| use_external=True), | |
| test_mode=True, | |
| box_type_3d='LiDAR', | |
| bev_size=(200, 200)), | |
| shuffler_sampler=dict(type='DistributedGroupSampler'), | |
| nonshuffler_sampler=dict(type='DistributedSampler')) | |
| evaluation = dict( | |
| interval=6, | |
| pipeline=[ | |
| dict( | |
| type='LoadMultiViewImageFromFilesInCeph', | |
| to_float32=True, | |
| file_client_args=dict(backend='disk'), | |
| img_root=''), | |
| dict( | |
| type='NormalizeMultiviewImage', | |
| mean=[103.53, 116.28, 123.675], | |
| std=[1.0, 1.0, 1.0], | |
| to_rgb=False), | |
| dict(type='PadMultiViewImage', size_divisor=32), | |
| dict( | |
| type='MultiScaleFlipAug3D', | |
| img_scale=(1600, 900), | |
| pts_scale_ratio=1, | |
| flip=False, | |
| transforms=[ | |
| dict( | |
| type='DefaultFormatBundle3D', | |
| class_names=[ | |
| 'car', 'truck', 'construction_vehicle', 'bus', | |
| 'trailer', 'barrier', 'motorcycle', 'bicycle', | |
| 'pedestrian', 'traffic_cone' | |
| ], | |
| with_label=False), | |
| dict(type='CustomCollect3D', keys=['img']) | |
| ]) | |
| ]) | |
| 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 = 'projects/work_dirs/bevformer/base_bevformer/' | |
| load_from = 'ckpts/r101_dcn_fcos3d_pretrain.pth' | |
| resume_from = None | |
| workflow = [('train', 1)] | |
| plugin = True | |
| plugin_dir = 'projects/mmdet3d_plugin/' | |
| voxel_size = [0.2, 0.2, 8] | |
| img_norm_cfg = dict( | |
| mean=[103.53, 116.28, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) | |
| _dim_ = 256 | |
| _pos_dim_ = 128 | |
| _ffn_dim_ = 512 | |
| _num_levels_ = 4 | |
| bev_h_ = 200 | |
| bev_w_ = 200 | |
| queue_length = 4 | |
| model = dict( | |
| type='BEVFormer', | |
| use_grid_mask=True, | |
| video_test_mode=True, | |
| img_backbone=dict( | |
| type='ResNet', | |
| depth=101, | |
| num_stages=4, | |
| out_indices=(1, 2, 3), | |
| frozen_stages=1, | |
| norm_cfg=dict(type='BN2d', requires_grad=False), | |
| norm_eval=True, | |
| style='caffe', | |
| dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), | |
| stage_with_dcn=(False, False, True, True)), | |
| img_neck=dict( | |
| type='FPN', | |
| in_channels=[512, 1024, 2048], | |
| out_channels=256, | |
| start_level=0, | |
| add_extra_convs='on_output', | |
| num_outs=4, | |
| relu_before_extra_convs=True), | |
| pts_bbox_head=dict( | |
| type='BEVFormerHead', | |
| bev_h=200, | |
| bev_w=200, | |
| num_query=900, | |
| num_classes=10, | |
| in_channels=256, | |
| sync_cls_avg_factor=True, | |
| with_box_refine=True, | |
| as_two_stage=False, | |
| transformer=dict( | |
| type='PerceptionTransformer', | |
| rotate_prev_bev=True, | |
| use_shift=True, | |
| use_can_bus=True, | |
| embed_dims=256, | |
| encoder=dict( | |
| type='BEVFormerEncoder', | |
| num_layers=6, | |
| pc_range=[-51.2, -51.2, -5.0, 51.2, 51.2, 3.0], | |
| num_points_in_pillar=4, | |
| return_intermediate=False, | |
| transformerlayers=dict( | |
| type='BEVFormerLayer', | |
| attn_cfgs=[ | |
| dict( | |
| type='TemporalSelfAttention', | |
| embed_dims=256, | |
| num_levels=1), | |
| dict( | |
| type='SpatialCrossAttention', | |
| pc_range=[-51.2, -51.2, -5.0, 51.2, 51.2, 3.0], | |
| deformable_attention=dict( | |
| type='MSDeformableAttention3D', | |
| embed_dims=256, | |
| num_points=8, | |
| num_levels=4), | |
| embed_dims=256) | |
| ], | |
| feedforward_channels=512, | |
| ffn_dropout=0.1, | |
| operation_order=('self_attn', 'norm', 'cross_attn', 'norm', | |
| 'ffn', 'norm'))), | |
| decoder=dict( | |
| type='DetectionTransformerDecoder', | |
| num_layers=6, | |
| return_intermediate=True, | |
| transformerlayers=dict( | |
| type='DetrTransformerDecoderLayer', | |
| attn_cfgs=[ | |
| dict( | |
| type='MultiheadAttention', | |
| embed_dims=256, | |
| num_heads=8, | |
| dropout=0.1), | |
| dict( | |
| type='CustomMSDeformableAttention', | |
| embed_dims=256, | |
| num_levels=1) | |
| ], | |
| feedforward_channels=512, | |
| ffn_dropout=0.1, | |
| operation_order=('self_attn', 'norm', 'cross_attn', 'norm', | |
| 'ffn', 'norm')))), | |
| bbox_coder=dict( | |
| type='NMSFreeCoder', | |
| post_center_range=[-61.2, -61.2, -10.0, 61.2, 61.2, 10.0], | |
| pc_range=[-51.2, -51.2, -5.0, 51.2, 51.2, 3.0], | |
| max_num=300, | |
| voxel_size=[0.2, 0.2, 8], | |
| num_classes=10), | |
| positional_encoding=dict( | |
| type='LearnedPositionalEncoding', | |
| num_feats=128, | |
| row_num_embed=200, | |
| col_num_embed=200), | |
| 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_iou=dict(type='GIoULoss', loss_weight=0.0)), | |
| train_cfg=dict( | |
| pts=dict( | |
| grid_size=[512, 512, 1], | |
| voxel_size=[0.2, 0.2, 8], | |
| point_cloud_range=[-51.2, -51.2, -5.0, 51.2, 51.2, 3.0], | |
| out_size_factor=4, | |
| assigner=dict( | |
| type='HungarianAssigner3D', | |
| cls_cost=dict(type='FocalLossCost', weight=2.0), | |
| reg_cost=dict(type='BBox3DL1Cost', weight=0.25), | |
| iou_cost=dict(type='IoUCost', weight=0.0), | |
| pc_range=[-51.2, -51.2, -5.0, 51.2, 51.2, 3.0])))) | |
| info_root = 'data/infos/' | |
| ann_file_train = 'data/infos/nuscenes_infos_temporal_train.pkl' | |
| ann_file_val = 'data/infos/nuscenes_infos_temporal_val.pkl' | |
| ann_file_test = 'data/infos/nuscenes_infos_temporal_val.pkl' | |
| optimizer = dict( | |
| type='AdamW', | |
| lr=0.0002, | |
| paramwise_cfg=dict(custom_keys=dict(img_backbone=dict(lr_mult=0.1))), | |
| weight_decay=0.01) | |
| optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) | |
| lr_config = dict( | |
| policy='CosineAnnealing', | |
| warmup='linear', | |
| warmup_iters=500, | |
| warmup_ratio=0.3333333333333333, | |
| min_lr_ratio=0.001) | |
| total_epochs = 24 | |
| runner = dict(type='EpochBasedRunner', max_epochs=24) | |
| logger_name = 'mmdet' | |
| gpu_ids = range(0, 1) | |