# data:
#   train:
#     dataset:
#       ann_file: nuscenes_radar/nuscenes_radar_infos_train_radar.pkl
#   val:
#     ann_file: nuscenes_radar/nuscenes_radar_infos_val_radar.pkl
#   test:
#     ann_file: nuscenes_radar/nuscenes_radar_infos_val_radar.pkl

model:
  decoder:
    backbone:
      type: GeneralizedResNet
      in_channels: 336
      blocks:
        - [2, 160, 2]
        - [2, 320, 2]
        - [2, 640, 1]
    neck:
      type: LSSFPN
      in_indices: [-1, 0]
      in_channels: [640, 160]
      out_channels: 256
      scale_factor: 2
  heads:
    object:
      in_channels: 256

optimizer:
  type: AdamW
  lr: 2.0e-4
  weight_decay: 0.01
  paramwise_cfg:
    custom_keys:
      absolute_pos_embed: 
        decay_mult: 0
      relative_position_bias_table:
        decay_mult: 0

optimizer_config:
  grad_clip:
    max_norm: 35
    norm_type: 2

lr_config:
  policy: CosineAnnealing
  warmup: linear
  warmup_iters: 500
  warmup_ratio: 0.33333333
  min_lr_ratio: 1.0e-3