247 lines
5.4 KiB
YAML
247 lines
5.4 KiB
YAML
# 三任务配置: 3D检测 + BEV分割 + 矢量地图
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# 基于SwinTransformer + ConvFuser架构
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_base_: ./default.yaml
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# 模型配置
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model:
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type: BEVFusion
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# Encoder配置（复用训练好的）
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encoders:
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camera:
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backbone:
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type: SwinTransformer
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embed_dims: 96
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depths: [2, 2, 6, 2]
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num_heads: [3, 6, 12, 24]
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window_size: 7
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mlp_ratio: 4
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qkv_bias: true
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qk_scale: null
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drop_rate: 0.
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attn_drop_rate: 0.
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drop_path_rate: 0.2
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patch_norm: true
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out_indices: [1, 2, 3]
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with_cp: false
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convert_weights: true
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init_cfg:
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type: Pretrained
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checkpoint: https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth
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neck:
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type: GeneralizedLSSFPN
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in_channels: [192, 384, 768]
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out_channels: 256
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start_level: 0
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num_outs: 3
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norm_cfg:
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type: BN2d
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requires_grad: true
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act_cfg:
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type: ReLU
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inplace: true
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upsample_cfg:
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mode: bilinear
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align_corners: false
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vtransform:
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type: DepthLSSTransform
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in_channels: 256
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out_channels: 80
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image_size: ${image_size}
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feature_size: [${[image_size[0] // 8, image_size[1] // 8]}]
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xbound: [-54.0, 54.0, 0.3]
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ybound: [-54.0, 54.0, 0.3]
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zbound: [-10.0, 10.0, 20.0]
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dbound: [1.0, 60.0, 0.5]
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downsample: 2
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lidar:
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voxelize:
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max_num_points: 10
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point_cloud_range: ${point_cloud_range}
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voxel_size: ${voxel_size}
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max_voxels: [120000, 160000]
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backbone:
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type: SparseEncoder
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in_channels: 5
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sparse_shape: [1440, 1440, 41]
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output_channels: 128
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order:
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- conv
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- norm
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- act
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encoder_channels:
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- [16, 16, 32]
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- [32, 32, 64]
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- [64, 64, 128]
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- [128, 128]
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encoder_paddings:
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- [0, 0, 1]
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- [0, 0, 1]
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- [0, 0, [1, 1, 0]]
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- [0, 0]
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block_type: basicblock
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# Fuser配置
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fuser:
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type: ConvFuser
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in_channels: [80, 256]
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out_channels: 256
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# Decoder配置
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decoder:
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backbone:
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type: SECOND
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in_channels: 256
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out_channels: [128, 256]
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layer_nums: [5, 5]
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layer_strides: [1, 2]
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norm_cfg:
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type: BN
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eps: 1.0e-3
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momentum: 0.01
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conv_cfg:
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type: Conv2d
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bias: false
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neck:
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type: SECONDFPN
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in_channels: [128, 256]
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out_channels: [256, 256]
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upsample_strides: [1, 2]
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norm_cfg:
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type: BN
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eps: 1.0e-3
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momentum: 0.01
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upsample_cfg:
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type: deconv
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bias: false
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use_conv_for_no_stride: true
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# 三个任务头
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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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hidden_channel: 128
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num_classes: 10
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num_decoder_layers: 1
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num_heads: 8
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nms_kernel_size: 3
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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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voxel_size: ${voxel_size[:2]}
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out_size_factor: 8
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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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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_heatmap:
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type: GaussianFocalLoss
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reduction: mean
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loss_weight: 1.0
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# 任务2: BEV地图分割
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map:
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type: VanillaSegmentationHead
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in_channels: 512
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num_classes: 6
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align_corners: false
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loss_decode:
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type: CrossEntropyLoss
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use_sigmoid: false
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class_weight: [1.0, 2.0, 2.0, 2.0, 2.0, 2.0]
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loss_weight: 1.0
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# 任务3: 矢量地图预测 🆕
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vector_map:
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type: MapTRHead
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in_channels: 512
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num_classes: 3
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num_queries: 50
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num_points: 20
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embed_dims: 256
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num_decoder_layers: 6
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num_heads: 8
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dropout: 0.1
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loss_cls_weight: 2.0
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loss_reg_weight: 5.0
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loss_chamfer_weight: 2.0
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score_threshold: 0.3
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nms_threshold: 0.5
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# 损失权重
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loss_scale:
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object: 1.0
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map: 1.0
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vector_map: 1.0 # 矢量地图任务权重
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# 训练配置
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max_epochs: 20
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batch_size_per_gpu: 1
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num_workers_per_gpu: 0 # 避免shared memory问题
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# 学习率（三任务可能需要稍微调整）
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optimizer:
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type: AdamW
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lr: 1.5e-4 # 稍微降低学习率
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weight_decay: 0.01
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# 评估配置
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evaluation:
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interval: 1
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pipeline: ${val_pipeline}
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metric:
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- bbox # 3D检测mAP
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- map # BEV分割mIoU
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- vector # 矢量地图AP 🆕
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# Checkpoint
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checkpoint_config:
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interval: 1
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max_keep_ckpts: 5
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# 日志
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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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- type: TensorboardLoggerHook
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# 运行时
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dist_params:
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backend: nccl
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log_level: INFO
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work_dir: runs/three_tasks
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load_from: null # 从当前训练的多任务模型加载
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resume_from: null
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workflow: [['train', 1]]
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