config/codaprompt.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

image_size: *image_size
init_cls_num: *init_cls_num
inc_cls_num: *inc_cls_num
task_num: *task_num

epoch: 20
val_per_epoch: 20

batch_size: 128

train_trfms:
  - RandomResizedCrop: 
      size: *image_size
  - RandomHorizontalFlip: {}
  - ToTensor: {}
  - Normalize:
      mean: [0., 0., 0.]
      std: [1., 1., 1.]

test_trfms: 
  - Resize: 
      size: *image_size
  - ToTensor: {}
  - Normalize:
      mean: [0., 0., 0.]
      std: [1., 1., 1.]

optimizer:
  name: Adam
  kwargs:
    lr: 0.001
    betas: [0.9, 0.999]
    weight_decay: 0

#lr_scheduler:
#  name: MultiStepLR
#  kwargs:
#    gamma: 0.1
#    milestones: [80, 120]

lr_scheduler:
  name: CosineSchedule
  kwargs:
    K: 20

backbone:
  name: vit_pt_imnet
  kwargs:
    num_classes: *total_cls_num
    pretrained: true
    model_name : vit_base_patch16_224

classifier:
  name: CodaPrompt
  kwargs:
    num_class: *total_cls_num
    task_num: *task_num
    init_cls_num: *init_cls_num
    inc_cls_num: *inc_cls_num
    feat_dim: 768
    prompt_length: 8
    pool_size: 100
    mu: 0.0