311 lines
7.2 KiB
YAML
311 lines
7.2 KiB
YAML
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# BEVFusion 多任务配置：同时进行3D检测和BEV分割
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# 继承基础配置
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_base_:
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- ../default.yaml
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model:
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type: BEVFusion
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# 多任务头配置
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heads:
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# 任务1：3D目标检测
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object:
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type: TransFusionHead
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num_proposals: 200
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auxiliary: true
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in_channels: 512
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num_classes: ${num_classes}
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num_heads: 8
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nms_kernel_size: 3
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ffn_channel: 256
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dropout: 0.1
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bn_momentum: 0.1
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activation: relu
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common_heads:
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center: [2, 2]
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height: [1, 2]
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dim: [3, 2]
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rot: [2, 2]
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vel: [2, 2]
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bbox_coder:
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type: TransFusionBBoxCoder
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pc_range: ${point_cloud_range[:2]}
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post_center_range: [-61.2, -61.2, -10.0, 61.2, 61.2, 10.0]
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score_threshold: 0.0
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out_size_factor: 8
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voxel_size: ${voxel_size[:2]}
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code_size: 10
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loss_cls:
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type: FocalLoss
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use_sigmoid: true
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gamma: 2.0
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alpha: 0.25
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reduction: mean
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loss_weight: 1.0
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loss_bbox:
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type: L1Loss
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reduction: mean
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loss_weight: 0.25
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loss_iou:
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type: GIoULoss
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reduction: mean
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loss_weight: 0.0
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train_cfg:
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point_cloud_range: ${point_cloud_range}
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grid_size: [1440, 1440, 41]
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voxel_size: ${voxel_size}
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out_size_factor: 8
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gaussian_overlap: 0.1
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min_radius: 2
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pos_weight: -1
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code_weights: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.2, 0.2]
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assigner:
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type: HungarianAssigner3D
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iou_calculator:
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type: BboxOverlaps3D
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coordinate: lidar
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cls_cost:
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type: FocalLossCost
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gamma: 2.0
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alpha: 0.25
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weight: 0.15
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reg_cost:
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type: BBoxBEVL1Cost
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weight: 0.25
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iou_cost:
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type: IoU3DCost
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weight: 0.25
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test_cfg:
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point_cloud_range: ${point_cloud_range}
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grid_size: [1440, 1440, 41]
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voxel_size: ${voxel_size}
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out_size_factor: 8
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nms_type: null
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# 任务2：BEV地图分割
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map:
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type: BEVSegmentationHead
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in_channels: 512
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grid_transform:
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input_scope: [[-54.0, 54.0, 0.75], [-54.0, 54.0, 0.75]]
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output_scope: [[-50, 50, 0.5], [-50, 50, 0.5]]
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classes: ${map_classes}
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loss: focal
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# 损失权重配置（可根据实际情况调整）
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loss_scale:
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object: 1.0 # 检测任务权重
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map: 1.0 # 分割任务权重
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# 训练数据pipeline（需要同时包含检测和分割标注）
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train_pipeline:
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- type: LoadMultiViewImageFromFiles
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to_float32: true
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- type: LoadPointsFromFile
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coord_type: LIDAR
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load_dim: ${load_dim}
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use_dim: ${use_dim}
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- type: LoadPointsFromMultiSweeps
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sweeps_num: 9
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load_dim: ${load_dim}
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use_dim: ${use_dim}
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pad_empty_sweeps: true
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remove_close: true
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- type: LoadAnnotations3D
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with_bbox_3d: true
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with_label_3d: true
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with_attr_label: false
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- type: LoadBEVSegmentation # 加载BEV分割标注
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classes: ${map_classes}
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- type: ObjectPaste
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stop_epoch: -1
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db_sampler:
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dataset_root: ${dataset_root}
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info_path: ${dataset_root + "nuscenes_dbinfos_train.pkl"}
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rate: 1.0
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prepare:
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filter_by_difficulty: [-1]
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filter_by_min_points:
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car: 5
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truck: 5
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bus: 5
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trailer: 5
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construction_vehicle: 5
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traffic_cone: 5
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barrier: 5
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motorcycle: 5
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bicycle: 5
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pedestrian: 5
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classes: ${object_classes}
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sample_groups:
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car: 2
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truck: 3
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construction_vehicle: 7
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bus: 4
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trailer: 6
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barrier: 2
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motorcycle: 6
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bicycle: 6
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pedestrian: 2
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traffic_cone: 2
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points_loader:
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type: LoadPointsFromFile
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coord_type: LIDAR
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load_dim: ${load_dim}
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use_dim: ${use_dim}
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- type: ImageAug3D
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final_dim: ${image_size}
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resize_lim: ${augment2d.resize[0]}
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bot_pct_lim: [0.0, 0.0]
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rot_lim: ${augment2d.rotate}
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rand_flip: true
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is_train: true
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- type: GlobalRotScaleTrans
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resize_lim: ${augment3d.scale}
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rot_lim: ${augment3d.rotate}
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trans_lim: ${augment3d.translate}
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is_train: true
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- type: RandomFlip3D
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- type: PointsRangeFilter
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point_cloud_range: ${point_cloud_range}
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- type: ObjectRangeFilter
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point_cloud_range: ${point_cloud_range}
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- type: ObjectNameFilter
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classes: ${object_classes}
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- type: ImageNormalize
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mean: [0.485, 0.456, 0.406]
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std: [0.229, 0.224, 0.225]
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- type: GridMask
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use_h: true
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use_w: true
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max_epoch: ${max_epochs}
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rotate: 1
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offset: false
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ratio: 0.5
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mode: 1
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prob: ${augment2d.gridmask.prob}
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fixed_prob: ${augment2d.gridmask.fixed_prob}
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- type: PointShuffle
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- type: DefaultFormatBundle3D
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classes: ${object_classes}
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- type: Collect3D
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keys:
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- img
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- points
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- gt_bboxes_3d
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- gt_labels_3d
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- gt_masks_bev # 添加分割标注
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meta_keys:
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- camera_intrinsics
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- camera2ego
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- lidar2ego
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- lidar2camera
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- camera2lidar
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- lidar2image
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- img_aug_matrix
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- lidar_aug_matrix
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# 验证和测试pipeline也需要支持分割
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test_pipeline:
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- type: LoadMultiViewImageFromFiles
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to_float32: true
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- type: LoadPointsFromFile
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coord_type: LIDAR
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load_dim: ${load_dim}
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use_dim: ${use_dim}
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- type: LoadPointsFromMultiSweeps
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sweeps_num: 9
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load_dim: ${load_dim}
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use_dim: ${use_dim}
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pad_empty_sweeps: true
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remove_close: true
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- type: LoadAnnotations3D
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with_bbox_3d: true
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with_label_3d: true
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with_attr_label: false
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- type: LoadBEVSegmentation
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classes: ${map_classes}
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- type: ImageAug3D
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final_dim: ${image_size}
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resize_lim: ${augment2d.resize[1]}
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bot_pct_lim: [0.0, 0.0]
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rot_lim: [0.0, 0.0]
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rand_flip: false
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is_train: false
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- type: ImageNormalize
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mean: [0.485, 0.456, 0.406]
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std: [0.229, 0.224, 0.225]
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- type: DefaultFormatBundle3D
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classes: ${object_classes}
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- type: Collect3D
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keys:
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- img
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- points
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- gt_bboxes_3d
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- gt_labels_3d
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- gt_masks_bev
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meta_keys:
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- camera_intrinsics
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- camera2ego
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- lidar2ego
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- lidar2camera
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- camera2lidar
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- lidar2image
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- img_aug_matrix
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- lidar_aug_matrix
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# 评估配置：同时评估检测和分割
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evaluation:
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interval: 1
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pipeline: ${test_pipeline}
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# 检测评估指标
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metric:
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- bbox
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- map
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# 优化器配置（多任务可能需要更大学习率）
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optimizer:
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type: AdamW
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lr: 2.0e-4
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weight_decay: 0.01
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paramwise_cfg:
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custom_keys:
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absolute_pos_embed:
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decay_mult: 0
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relative_position_bias_table:
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decay_mult: 0
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optimizer_config:
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grad_clip:
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max_norm: 35
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norm_type: 2
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# 学习率调度
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lr_config:
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policy: CosineAnnealing
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warmup: linear
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warmup_iters: 500
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warmup_ratio: 0.33333333
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min_lr_ratio: 1.0e-3
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# 训练配置
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runner:
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type: EpochBasedRunner
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max_epochs: 20
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# 检查点和日志
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checkpoint_config:
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interval: 1
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max_keep_ckpts: 5
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log_config:
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interval: 50
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hooks:
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- type: TextLoggerHook
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# 其他配置
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find_unused_parameters: false
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sync_bn: true
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cudnn_benchmark: true
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