154 lines
3.5 KiB
YAML
154 lines
3.5 KiB
YAML
# BEVFusion 多任务配置：检测 + 分割（SwinTransformer版本）
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_base_:
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- ../det/transfusion/secfpn/camera+lidar/swint_v0p075/convfuser.yaml
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voxel_size: [0.075, 0.075, 0.2]
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point_cloud_range: [-54.0, -54.0, -5.0, 54.0, 54.0, 3.0]
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model:
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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: pretrained/swint-nuimages-pretrained.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:
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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:
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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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heads:
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# 3D检测头
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object:
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in_channels: 512
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train_cfg:
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grid_size: [1440, 1440, 41]
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test_cfg:
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grid_size: [1440, 1440, 41]
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# BEV分割头
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map:
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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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# 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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# 训练超参数
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max_epochs: 20
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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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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 # 可选：启用tensorboard
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