LibContinual / config /rapf50-10.yaml
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includesL:
- headers/data.yaml
- headers/device.yaml
- headers/model.yaml
data_root: /home/xtx/datasets/cifar100
image_size: &image_size 224
num_workers: &num_workers 16
save_path: ./
class_order: &class_order [87, 0, 52, 58, 44, 91, 68, 97, 51, 15, 94, 92, 10, 72, 49, 78, 61, 14, 8, 86, 84, 96, 18, 24, 32, 45, 88, 11, 4, 67, 69, 66, 77, 47, 79, 93, 29, 50, 57, 83, 17, 81, 41, 12, 37, 59, 25, 20, 80, 73, 1, 28, 6, 46, 62, 82, 53, 9, 31, 75, 38, 63, 33, 74, 27, 22, 36, 3, 16, 21, 60, 19, 70, 90, 89, 43, 5, 42, 65, 76, 40, 30, 23, 85, 2, 95, 56, 48, 71, 64, 98, 13, 99, 7, 34, 55, 54, 26, 35, 39]
seed: &seed 1919810
is_rapf: True
# Control B and
init_cls_num: &init_cls_num 50
inc_cls_num: &inc_cls_num 10
task_num: &task_num 6
epoch: &epoch 15
batch_size: &batch_size 128
train_batch_size: &train_batch_size 100
n_gpu: 1
beta: &beta 2
shrinkage: &shrinkage False
threshold: &threshold 0.55
val_per_epoch: &val_per_epoch 10
optimizer:
name: Adam
kwargs:
lr: 0.001
weight_decay: 0.0000
lr_scheduler:
name: MultiStepLR
kwargs:
gamma: 0.1
milestones: [4, 10]
last_epoch: -1
backbone:
name: clip
kwargs:
model_name: ViT-B/16
device: cuda
experts_num: 1
block_layer: ResidualAttentionBlock_MoE_MLP
top_k : 1
step: 1
act_layer: QuickGELU
norm_layer: LayerNorm
classifier:
name: RAPF
kwargs:
init_cls_num: *init_cls_num
inc_cls_num: *inc_cls_num
threshold: *threshold
beta: *beta
shrinkage: *shrinkage
train_batch_size: *train_batch_size
batch_size: *batch_size
num_workers: *num_workers
prompt_template: "a good photo of a {}"
seed: *seed
fp16: False
class_order: *class_order
mix_bias: 0.6