config/l2p-vit-cifar100-b10-10-10.yaml

dataset: &dataset cifar100
init_cls_num: &init_cls_num 10
inc_cls_num: &inc_cls_num 10
total_cls_num: &total_cls_num 100
task_num: &task_num 10
image_size: &image_size 224

init_cls_num: *init_cls_num
inc_cls_num: *inc_cls_num
task_num: *task_num
epoch: 1 # 5
val_per_epoch: 5

batch_size: 16 # Source code is 16 per device * 8 devices, since we don't use distribution device, set batch_size to 16
testing_times: 1

seed: 2

train_trfms:
  - RandomResizedCrop: 
      size: *image_size
      scale: [0.05, 1.0]
      ratio: [0.75, 1.3333] # [0.75, 1.3333333333]
      interpolation: BILINEAR
  - RandomHorizontalFlip: 
      p: 0.5
  - ToTensor: {}

test_trfms: 
  - Resize:
      size: 256 # Stated in source code of L2P
      interpolation: BICUBIC # 3 # LANCZOS
  - CenterCrop: 
      size: *image_size
  - ToTensor: {}

optimizer:
  name: Adam
  kwargs:
    lr: 0.001875 # 0.03
    betas: [0.9, 0.999]
    weight_decay: 0

lr_scheduler:
  name: Constant

backbone:
  name: vit_pt_imnet
  kwargs:
    num_classes: 100
    pretrained: true
    model_name : vit_base_patch16_224

classifier:
  name: L2P
  kwargs:
    init_cls_num: *init_cls_num
    inc_cls_num: *inc_cls_num
    num_class: *total_cls_num
    task_num: *task_num
    feat_dim: 768
    prompt_length: 5 # L_p in paper
    pool_size: 10 # M in paper
    top_k: 5 # N in paper
    pull_constraint_coeff: 1.0 # -0.5 in paper, 1.0 in source code