petr_vovnet_gridmask_p4_800x320.yml

batch_size: 1
epochs: 24

train_dataset:
  type: NuscenesMVDataset
  dataset_root: data/nuscenes/
  ann_file: data/nuscenes/petr_nuscenes_annotation_train.pkl
  mode: train
  class_names: [
            'car', 'truck', 'construction_vehicle', 'bus', 'trailer',
            'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
        ]
  transforms:
    - type: LoadMultiViewImageFromFiles
      to_float32: True
    - type: LoadAnnotations3D
      with_bbox_3d: True
      with_label_3d: True
    - type: SampleRangeFilter
      point_cloud_range: [-51.2, -51.2, -5.0, 51.2, 51.2, 3.0]
    - type: SampleNameFilter
      classes: [
            'car', 'truck', 'construction_vehicle', 'bus', 'trailer',
            'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
        ]
    - type: ResizeCropFlipImage
      sample_aug_cfg:
        resize_lim: [0.47, 0.625]
        final_dim: [320, 800]
        bot_pct_lim: [0.0, 0.0]
        rot_lim: [0.0, 0.0]
        H: 900
        W: 1600
        rand_flip: True
      training: True
    - type: GlobalRotScaleTransImage
      rot_range: [-0.3925, 0.3925]
      translation_std: [0, 0, 0]
      scale_ratio_range: [0.95, 1.05]
      reverse_angle: True
      training: True
    - type: NormalizeMultiviewImage
      mean: [103.530, 116.280, 123.675]
      std: [57.375, 57.120, 58.395]
    - type: PadMultiViewImage
      size_divisor: 32
    - type: SampleFilerByKey
      keys: ['gt_bboxes_3d', 'gt_labels_3d', 'img']

val_dataset:
  type: NuscenesMVDataset
  dataset_root: data/nuscenes/
  ann_file: data/nuscenes/petr_nuscenes_annotation_val.pkl
  mode: val
  class_names: ['car', 'truck', 'construction_vehicle', 'bus', 'trailer',
                    'barrier', 'motorcycle', 'bicycle', 'pedestrian',
                    'traffic_cone']
  transforms:
    - type: LoadMultiViewImageFromFiles
      to_float32: True
    - type: ResizeCropFlipImage
      sample_aug_cfg:
          resize_lim: [0.47, 0.625]
          final_dim: [320, 800]
          bot_pct_lim: [0.0, 0.0]
          rot_lim: [0.0, 0.0]
          H: 900
          W: 1600
          rand_flip: True
      training: False
    - type: NormalizeMultiviewImage
      mean: [103.530, 116.280, 123.675]
      std: [57.375, 57.120, 58.395]
    - type: PadMultiViewImage
      size_divisor: 32
    - type: SampleFilerByKey
      keys: ['img']

model:
  type: Petr3D
  use_recompute: True
  use_grid_mask: True
  backbone:
    # use recompute to save memory
    type: VoVNetCP
    spec_name: V-99-eSE
    norm_eval: True
    frozen_stages: -1
    input_ch: 3
    out_features: ('stage4','stage5',)
  neck:
    # remove unused parameters
    type: CPFPN
    in_channels: [768, 1024]
    out_channels: 256
    num_outs: 2
  pts_bbox_head:
    type: PETRHead
    num_classes: 10
    in_channels: 256
    num_query: 900
    LID: true
    with_multiview: true
    position_range: [-61.2, -61.2, -10.0, 61.2, 61.2, 10.0]
    normedlinear: False
    transformer:
      type: PETRTransformer
      decoder_embed_dims: 256
      decoder:
        type: PETRTransformerDecoder
        return_intermediate: True
        num_layers: 6
        transformerlayers:
          type: PETRTransformerDecoderLayer
          attns:
            - type: MultiHeadAttention
              embed_dims: 256
              num_heads: 8
              attn_drop: 0.1
              drop_prob: 0.1
            - type: PETRMultiheadAttention
              embed_dims: 256
              num_heads: 8
              attn_drop: 0.1
              drop_prob: 0.1
              batch_first: True
          feedforward_channels: 2048
          ffn_dropout: 0.1
          operation_order: ['self_attn', 'norm', 'cross_attn', 'norm', 'ffn', 'norm']
    positional_encoding:
      type: SinePositionalEncoding3D
      num_feats: 128
      normalize: True
    bbox_coder:
      type: NMSFreeCoder
      post_center_range: [-61.2, -61.2, -10.0, 61.2, 61.2, 10.0]
      point_cloud_range: [-51.2, -51.2, -5.0, 51.2, 51.2, 3.0]
      max_num: 300
      voxel_size: [0.2, 0.2, 8]
      num_classes: 10
    loss_cls:
      type: WeightedFocalLoss
      gamma: 2.0
      alpha: 0.25
      loss_weight: 2.0
      reduction: sum
    loss_bbox:
      type: WeightedL1Loss
      loss_weight: 0.25
      reduction: sum


optimizer:
  type: AdamW
  weight_decay: 0.01
  grad_clip:
    type: ClipGradByGlobalNorm
    clip_norm: 35

lr_scheduler:
  type: LinearWarmup
  learning_rate:
    type: CosineAnnealingDecay
    learning_rate: 0.0002
    T_max: 84408 # 3517 * 24e
    eta_min: 0.0000002
  warmup_steps: 500
  start_lr: 0.0
  end_lr: 0.0002