dataset: &dataset "imagenet-r"
data_root: "/home/lvqiexuan/temp_data/imagenet-r/"

init_cls_num: &init_cls_num 10
inc_cls_num: &inc_cls_num 10
total_cls_num: &total_cls_num 200
task_num: &task_num 20
image_size: &image_size 224

dataset: *dataset
task_num: *task_num
init_cls_num: *init_cls_num
inc_cls_num: *inc_cls_num
total_cls_num: *total_cls_num
epoch: 4
val_per_epoch: 4

train_batch_size: 128
test_batch_size: 64

testing_times: 10

#setting: task-agnostic 
setting: task-aware

optimizer:
  name: AdamW
  kwargs:
    lr: 1e-3
    weight_decay: 0.

lr_scheduler:
  name: CosineAnnealingWarmUp
  kwargs:
    T_max: 0 # Will be replaced in trainter.py with epoch * len(dataloader)
    warmup_length: 30

backbone:
  name: clip
  kwargs:
    model_name : ViT-B/16
    pretrained : True
    block_layer: ResidualAttentionBlock_MLP
    act_layer: QuickGELU
    norm_layer: LayerNorm

classifier:
  name: DMNSP
  kwargs:
    init_cls_num: *init_cls_num
    inc_cls_num: *inc_cls_num
    prompt_template : "a bad photo of a {}."
    label_smoothing: 0.
    lamda_scale: 30