Point-LGMask.yaml

optimizer : {
  type: AdamW,
  kwargs: {
  lr : 0.001,
  weight_decay : 0.05
}}

scheduler: {
  type: CosLR,
  kwargs: {
    epochs: 300,
    initial_epochs : 10
}}

criterion: {
    ent_weight: 0.0,
    me_max: true,
    memax_weight: 1.0,
    num_proto: 40,
    start_sharpen: 0.25,
    final_sharpen: 0.25,
    temperature: 0.1,
    use_ent: true,
    use_sinkhorn: false,
    output_dim: 256,
}

data: {
  pin_mem: true,
  label_smoothing: 0.0,
  rand_views: 1,
  focal_views: 2,
}

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: Point_BERT,
  m: 0.999, 

  transformer_config: {
    enc_arch: point_lgmask,
    mask_ratio: 0.6,
    mask_type: 'rand',
    trans_dim: 384,
    encoder_dims: 384,
    hidden_dim: 256,
    cls_dim: 40,
    use_bn_fc: true,
    output_dim_fc: 128,
    depth: 12,
    drop_path_rate: 0.1,
    decoder_depth: 4,
    decoder_num_heads: 6,
    replace_pob: 0.,
    num_heads: 6,
    group_size: 32,
    num_group: 64,
    total_bs : 128,
  }}

total_bs : 128
step_per_update : 1
max_epoch : 300