84 lines
1.7 KiB
YAML
84 lines
1.7 KiB
YAML
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image_size: [256, 704]
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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: ResNet
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depth: 50
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num_stages: 4
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out_indices: [0, 1, 2, 3]
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norm_cfg:
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type: BN2d
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requires_grad: true
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norm_eval: false
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init_cfg:
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type: Pretrained
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checkpoint: torchvision://resnet50
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neck:
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type: SECONDFPN
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in_channels: [256, 512, 1024, 2048]
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out_channels: [128, 128, 128, 128]
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upsample_strides: [0.25, 0.5, 1, 2]
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vtransform:
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type: LSSTransform
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in_channels: 512
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out_channels: 64
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image_size: ${image_size}
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feature_size: ${[image_size[0] // 16, image_size[1] // 16]}
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xbound: [-51.2, 51.2, 0.8]
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ybound: [-51.2, 51.2, 0.8]
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zbound: [-10.0, 10.0, 20.0]
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dbound: [1.0, 60.0, 1.0]
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downsample: 1
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decoder:
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backbone:
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type: GeneralizedResNet
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in_channels: 64
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blocks:
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- [2, 128, 2]
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- [2, 256, 2]
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- [2, 512, 1]
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neck:
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type: LSSFPN
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in_indices: [-1, 0]
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in_channels: [512, 128]
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out_channels: 256
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scale_factor: 2
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heads:
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object:
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train_cfg:
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code_weights: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
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fuser:
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type: ConvFuser
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in_channels: [64, 64]
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out_channels: 64
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optimizer:
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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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# encoders.camera.backbone:
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# lr_mult: 0.1
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# lr_config:
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# policy: cyclic
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# target_ratio: 5.0
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# cyclic_times: 1
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# step_ratio_up: 0.4
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# momentum_config:
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# policy: cyclic
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# cyclic_times: 1
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# step_ratio_up: 0.4
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data:
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samples_per_gpu: 4
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