pretrain_shapenet.yaml

optimizer : {
  type: AdamW,
  kwargs: {
  lr : 0.0005, 
  weight_decay : 0.05
}}

scheduler: {
  type: CosLR,
  kwargs: {
    epochs: 300,
    initial_epochs : 3
}}

dataset : {
  train : { _base_: cfgs/dataset_configs/ShapeNet-55.yaml, 
            others: {subset: 'train', npoints: 1024, whole: True}},
  val : { _base_: cfgs/dataset_configs/ModelNet40.yaml, 
            others: {subset: 'test'}},
  extra_train : { _base_: cfgs/dataset_configs/ModelNet40.yaml, 
            others: {subset: 'train'}}}
model : {
  NAME: MaskPoint,
  m: 0.999,
  T: 0.07,
  K: 16384,
  pos_embed_hidden_dim: 128,

  transformer_config: {
    mask_ratio: 0.90,
    trans_dim: 384,
    depth: 12,
    dec_depth: 1,
    use_sigmoid: true,
    use_moco_loss: false,
    query_loss_weight: 1.0,
    moco_loss_weight: 0.01,
    use_focal_loss: true,
    focal_loss_alpha: 0.25,
    focal_loss_gamma: 2,
    ambiguous_threshold: -1,
    ambiguous_dynamic_threshold: 256,
    dec_query_mode: points,     # [center, points]
    dec_query_real_num: 256,
    dec_query_fake_num: 256,
    drop_path_rate: 0.1,
    cls_dim: 512,
    num_heads: 6,
    group_size: 32, 
    num_group: 64, 
    encoder_dims: 256,
  }
}

total_bs : 128
step_per_update : 1
grad_norm_clip : 10
max_epoch : 300