Official Pytorch Implementation of When NAS Meets Trees: An Efficient Algorithm for Neural Architecture Search
WARNING: this is not the final version. But a demo version. We are still working on this project in part time to push it for a conference or journal. After the paper gets accepted, the final version will be released.
git clone git@github.com:guochengqian/TNAS.git
cd TNAS
source env_install.sh
CUDA_VISIBLE_DEVICES=0 python exps/NATS-algos/search-cell-tnas.py --cfg cfgs/search_cell/tnas.yaml
sbatch --array=0-4 --time=5:00:00 a100_tnas_alpha.sh cfgs/search_cell/tnas_warmup.yaml d_a=4
This code is highly relied on NATS-Bench.
If you find that this project helps your research, please consider citing the related paper:
@inproceedings{qian2022meets,
title={When NAS Meets Trees: An Efficient Algorithm for Neural Architecture Search},
author={Qian, Guocheng and Zhang, Xuanyang and Li, Guohao and Zhao, Chen and Chen, Yukang and Zhang, Xiangyu and Ghanem, Bernard and Sun, Jian},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={2782--2787},
year={2022}
}