Res-Tuning组件

from swift import ResTuningConfig
config = ResTuningConfig(
    dims=768,
    root_modules=r'.*blocks.0$',
    stem_modules=r'.*blocks\.\d+$',
    target_modules=r'norm',
    tuner_cfg='res_adapter'
)

from swift import Swift
import timm, torch
model = timm.create_model("vit_base_patch16_224", pretrained=False, num_classes=100)
model_tune = Swift.prepare_model(model, config)
print(model_tune.get_trainable_parameters())
print(model(torch.ones(1, 3, 224, 224)).shape)

@inproceedings{jiang2023restuning,
  title={Res-Tuning: A Flexible and Efficient Tuning Paradigm via Unbinding Tuner from Backbone},
  author={Jiang, Zeyinzi and Mao, Chaojie and Huang, Ziyuan and Ma, Ao and Lv, Yiliang and Shen, Yujun and Zhao, Deli and Zhou, Jingren},
  booktitle={Advances in Neural Information Processing Systems},
  year={2023}
}