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NOAH

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[TPAMI] Searching prompt modules for parameter-efficient transfer learning.

파일 탐색기

최종 버전 다운로드 (.zip)

- - AUTHORS
  - CONTRIBUTING.md
  - get_vtab1k.py
  - LICENSE
  - README.md
  - setup.py
- - ViT-B_prompt_adapter_8.yaml
- - ViT-B_prompt_lora_8.yaml
- - ViT-B_prompt_vpt_1.yaml
  - ViT-B_prompt_vpt_100.yaml
  - ViT-B_prompt_vpt_1000.yaml
  - ViT-B_prompt_vpt_200.yaml
  - ViT-B_prompt_vpt_25.yaml
  - ViT-B_prompt_vpt_250.yaml
  - ViT-B_prompt_vpt_5.yaml
  - ViT-B_prompt_vpt_50.yaml
  - ViT-B_prompt_vpt_500.yaml
- motivation.pdf
- motivation.png
- table1
- table1.jpg
- config.py
- datasets.py
- imagenet_withhold.py
- samplers.py
- subImageNet.py
- utils.py
- - __init__.py
  - adapter_super.py
  - embedding_super.py
  - layernorm_super.py
  - Linear_super.py
  - multihead_super.py
  - multihead_super_prompt.py
  - prompt_tuning_super.py
  - qkv_super.py
  - qkv_super_prompt.py
- __init__.py
- supernet_transformer_prompt.py
- supernet_vision_transformer_timm.py
- utils.py
.gitignore
evolution.py
LICENSE
README.md
requirements.txt
supernet_engine_prompt.py
supernet_train_prompt.py

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/ZhangYuanhan-AI/NOAH

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd NOAH

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Python

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

pip install -r requirements.txt

requirements.txt 등에 명시된 파이썬 라이브러리를 설치합니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

// repository documentation

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<div align="center">

<h1>Neural Prompt Search</h1>

<div>
    <a href='https://zhangyuanhan-ai.github.io/' target='_blank'>Yuanhan Zhang</a>&emsp;
    <a href='https://kaiyangzhou.github.io/' target='_blank'>Kaiyang Zhou</a>&emsp;
    <a href='https://liuziwei7.github.io/' target='_blank'>Ziwei Liu</a>
</div>
<div>
    S-Lab, Nanyang Technological University
</div>

<img src="figures/motivation.png">

<h3>TL;DR</h3>
    
The idea is simple: we view existing parameter-efficient tuning modules, including [Adapter](https://arxiv.org/abs/1902.00751), [LoRA](https://arxiv.org/abs/2106.09685) and [VPT](https://arxiv.org/abs/2203.12119), as prompt modules and propose to search the optimal configuration via neural architecture search. Our approach is named **NOAH** (Neural prOmpt seArcH).

---

<p align="center">
  <a href="https://arxiv.org/abs/2206.04673" target='_blank'>[arXiv]</a> •
  <a href="https://zhangyuanhan-ai.github.io/NOAH" target='_blank'>[project page]</a>
</p>

</div>

## Updatas
[05/2022] [arXiv](https://arxiv.org/abs/2206.04673) paper has been **released**.

## Environment Setup
```
conda create -n NOAH python=3.8
conda activate NOAH
pip install -r requirements.txt
```

## Data Preparation

### 1. Visual Task Adaptation Benchmark (VTAB)
```
cd data/vtab-source
python get_vtab1k.py
```

### 2. Few-Shot and Domain Generation

- Images

Please refer to [DATASETS.md](https://github.com/KaiyangZhou/CoOp/blob/main/DATASETS.md) to download the datasets.

- Train/Val/Test splits

Please refer to files under `data/XXX/XXX/annotations` for the detail information.

## Quick Start For NOAH
We use the VTAB experiments as examples.

### 1. Downloading the Pre-trained Model
| Model | Link |
|-------|------|
|ViT B/16 | [link](https://storage.googleapis.com/vit_models/imagenet21k/ViT-B_16.npz)|

### 2. Supernet Training
```
sh configs/NOAH/VTAB/supernet/slurm_train_vtab.sh PATH-TO-YOUR-PRETRAINED-MODEL
```

### 3. Subnet Search
```
sh configs/NOAH/VTAB/search/slurm_search_vtab.sh PARAMETERS-LIMITES
```
### 4. Subnet Retraining
```
sh configs/NOAH/VTAB/subnet/slurm_retrain_vtab.sh PATH-TO-YOUR-PRETRAINED-MODEL
```
We add the optimal subnet architecture of each dataset in the ``experiments/NOAH/subnet/VTAB``.

### 5. Performance
![fig1](https://raw.githubusercontent.com/ZhangYuanhan-AI/NOAH/main/figures/table1.jpg)

## Citation
If you use this code in your research, please kindly cite this work.
```
@misc{zhang2022neural,
      title={Neural Prompt Search}, 
      author={Yuanhan Zhang and Kaiyang Zhou and Ziwei Liu},
      year={2022},
      eprint={2206.04673},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
```

## Acknoledgments
Part of the code is borrowed from [CoOp](https://github.com/KaiyangZhou/CoOp), [AutoFormer](https://github.com/microsoft/Cream/tree/main/AutoFormer), [timm](https://github.com/rwightman/pytorch-image-models) and [mmcv](https://github.com/open-mmlab/mmcv).

Thanks to Chong Zhou (https://chongzhou96.github.io/) for the code of downloading the VTAB-1k.

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