tangent-h/m_spt_annotations

[ICCV 2023 oral] This is the official repository for our paper: ''Sensitivity-Aware Visual Parameter-Efficient Fine-Tuning''.

Sensitivity-Aware Visual Parameter-Efficient Fine-Tuning

[ICCV 2023] (oral) This is the official repository for our paper: Sensitivity-Aware Visual Parameter-Efficient Fine-Tuning by Haoyu He, Jianfei Cai, Jing Zhang, Dacheng Tao and Bohan Zhuang.

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🚀 News

[2023-09-12]: Release code.

[2023-08-12]: Got accepted by ICCV 2023 for oral presentation!

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Introduction:

Instead of presenting another architecture with learnable parameters in parameter-effcient fine-tuning (PEFT), our work emphasizes the importance to put PEFT architectures to optimal positions tailored for diverse tasks!

Our SPT consists of two stages.

Stage 1 is a very quick one-shot parameter sensitivity estimation (several seconds) to find where to introduce trainable parameters. The following are some interesting sensitivity patterns in various pre-trained ViTs with top-0.4% sensitive parameters. We find that the proportions exhibit task-specific varying patterns in terms of network depth and task-agnostic similar patterns in terms of operations.

Stage 2 is standard PEFT that keep the majority parameters frozen and only fine-tune the trainable ones. Our SPT introduces trainable parameters to the sensitive positions in two granularities: the unstructured neurons and structured PEFT modules (e.g., LoRA or Adapter) to achieve good performance!

If you find this repository or our paper useful, please consider cite and star us!

@inproceedings{he2023sensitivity,
  title={Sensitivity-Aware Visual Parameter-Efficient Fine-Tuning},
  author={He, Haoyu and Cai, Jianfei and Zhang, Jing and Tao, Dacheng and Zhuang, Bohan},
  booktitle={ICCV},
  year={2023}
}

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Getting started on SPT:

Install dependency:

We have tested our code on both Torch 1.8.0, and 1.10.0. Please install the other dependencies with the following code in the home directory:

pip install -r requirements.txt

Data preparation:

We provide training and inference code for our main benchmark VTAB-1k.

cd data/vtab-source
python get_vtab1k.py

PS: You may have to manually install Sun397. Please refer to VTAB-1k.

Download pre-trained models:

Please download the backbones with the following code:

cd checkpoints

# Supervised pre-trained ViT-B/16
wget https://console.cloud.google.com/storage/browser/_details/vit_models/imagenet21k/ViT-B_16.npz

# MAE pre-trained ViT-B/16
wget https://dl.fbaipublicfiles.com/mae/pretrain/mae_pretrain_vit_base.pth

# MoCo V3 pre-trained ViT-B/16
wget https://dl.fbaipublicfiles.com/moco-v3/vit-b-300ep/linear-vit-b-300ep.pth.tar

Get parameter sensitivity:

We have provided the following code (we have already provided the sensitivity for supervised pre-trained ViT-B/16 in sensitivity_spt_supervised_lora_a10 and sensitivity_spt_supervised_adapter_a10).

# SPT-ADAPTER and SPT-LORA with supervised pre-trained ViT-B/16
bash configs/vtab_mae_spt_lora_sensitivity.sh
bash configs/vtab_mae_spt_adapter_sensitivity.sh

# SPT-ADAPTER and SPT-LORA with MAE pre-trained ViT-B/16
bash configs/vtab_mae_spt_lora_sensitivity.sh
bash configs/vtab_mae_spt_adapter_sensitivity.sh

# SPT-ADAPTER and SPT-LORA with MoCo V3 pre-trained ViT-B/16 
bash configs/vtab_mae_spt_lora_sensitivity.sh
bash configs/vtab_mae_spt_adapter_sensitivity.sh

PEFT with SPT:

We have provided the following training code:

# SPT-ADAPTER and SPT-LORA with supervised pre-trained ViT-B/16
bash configs/vtab_supervised_spt_lora.sh
bash configs/vtab_supervised_spt_adapter.sh

# SPT-ADAPTER and SPT-LORA with MAE pre-trained ViT-B/16
bash configs/vtab_mae_spt_lora.sh
bash configs/vtab_mae_spt_adapter.sh

# SPT-ADAPTER and SPT-LORA with MoCo V3 pre-trained ViT-B/16 
bash configs/vtab_moco_spt_lora.sh
bash configs/vtab_moco_spt_adapter.sh

PS: we sweep different trainable parameter budgets to seek potential better results (from 0.2M to 1.0M).

TODO:

- [x] Release code for SPT on ViTs.
- [ ] Release code for FGVC benchmark training (ETA October).
- [ ] Release code for SPT on Swin (ETA October).
- [ ] Release code for SPT on ConvNext (ETA October).
- [ ] Integrate to [PEFT](https://github.com/huggingface/peft) package.

Acknowledgements:

Our code is modified from NOAH, CoOp, AutoFormer, timm, and mmcv. We thank the authors for their open-sourced code.