pretrain.yaml

optimizer: { type: AdamW, kwargs: { lr: 0.00006, weight_decay: 0.05 } }

scheduler: { type: CosLR, kwargs: { epochs: 600, initial_epochs: 80 } }

dataset:
  {
    train:
      {
        _base_: cfgs/dataset_configs/UnlabeledHybrid.yaml,
        others: { subset: "train", npoints: 1024 },
      },
    val:
      {
        _base_: cfgs/dataset_configs/UnlabeledHybrid.yaml,
        others: { subset: "test", npoints: 1024 },
      },
    test:
      {
        _base_: cfgs/dataset_configs/UnlabeledHybrid.yaml,
        others: { subset: "test", npoints: 1024 },
      },
  }

model:
  {
    NAME: PointGPT,
    cls_dim: 40,
    group_size: 32,
    num_group: 64,
    loss: cdl12,
    weight_center: 1,
    transformer_config:
      {
        mask_ratio: 0.7,
        mask_type: "rand",
        trans_dim: 1024,
        encoder_dims: 1024,
        depth: 24,
        drop_path_rate: 0.1,
        num_heads: 16,
        decoder_depth: 4,
        decoder_num_heads: 12,
      },
  }

npoints: 1024
total_bs: 128
step_per_update: 1
max_epoch: 350