README.md

PINAT: A Permutation INvariance Augmented Transformer for NAS Predictor (AAAI 2023 Oral)

This is the code for our paper "PINAT: A Permutation INvariance Augmented Transformer for NAS Predictor". As AAAI does not publish the supplementary material, we provide this file in the folder of assets.

Prerequisites and Dependencies

This repository contains the code of PINAT. To run the codes, please see the prerequisites below:

1. Download the datasets of NAS-Bench-101 and NAS-Bench-201, and pre-trained checkpoints from the Google Drive.
2. Install the necessary packages below via pip or conda. We used Anaconda Python 3.8 for our experiments.

dgl==0.6.1
h5py==3.7.0
matplotlib==3.6.2
networkx==2.8.7
opencv-python==4.6.0.66
pandas==1.5.1
Pillow==9.2.0
prettytable==3.4.1
pynvml==11.4.1
PyYAML==6.0
schema==0.7.5
scikit-learn==1.1.2
scipy==1.9.3
seaborn==0.12.1
tensorboard==2.10.1
tensorflow-gpu==2.10.0
torch==1.9.0
torch-cluster==1.6.0
torch-geometric==2.1.0.post1
torch-scatter==2.0.9
torch-sparse==0.6.12
torchvision==0.10.0
tqdm==4.64.1

• We also summarize our environments into the requirements.txt. To install a same environment, simply run:

pip install -r requirements.txt

NAS-Bench Experiments

Go to the folder of nasbench and please refer to nasbench/README.md

DARTS Experiments

Go to the folder of darts and please refer to darts/README.md

Citation

If you find this work helpful in your research, please consider citing our paper:

@inproceedings{lu2023pinat,
  title     = {PINAT: A Permutation INvariance Augmented Transformer for NAS Predictor},
  author    = {Lu, Shun and Hu, Yu and Wang, Peihao and Han, Yan and Tan, Jianchao and Li, Jixiang and Yang, Sen and Liu, Ji},
  booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)},
  year      = {2023}
}