Code accompanying the paper
[ICLR'2021] [Outstanding Paper Award]: Rethinking Architecture Selection in Differentiable NAS
Ruochen Wang, Minhao Cheng, Xiangning Chen, Xiaocheng Tang, Cho-Jui Hsieh
Python >= 3.7
PyTorch >= 1.5
tensorboard == 2.0.1
gpustat
The scripts for running experiments can be found in the exp_scripts/
directory.
-
Download the NAS-Bench-201-v1_0-e61699.pth and save it under
./data
folder. -
Install NasBench201 via pip. (Note: We use the
[2020-02-25]
version of the NAS-Bench-201 API. If you have the newer version installed, you might addhp="200"
toapi.query_by_arch()
innasbench201/train_search.py
)
pip install nas-bench-201
The ckpts and logs will be saved to ./experiments/nasbench201/search-{script_name}-{seed}/
. For example, the ckpt dir would be ./experiments/nasbench201/search-darts-201-1/
for the command below.
bash darts-201.sh
The projection script loads ckpts from experiments/nasbench201/{resume_expid}
bash darts-proj-201.sh --resume_epoch 100 --resume_expid search-darts-201-1
bash blank-201.sh
bash blank-proj-201.sh --resume_expid search-blank-201-1
The ckpts and logs will be saved to ./experiments/sota/{dataset}/search-{script_name}-{space_id}-{seed}/
. For example, ./experiments/sota/cifar10/search-darts-sota-s3-1/
(script: darts-sota, space: s3, seed: 1).
bash darts-sota.sh --space [s1/s2/s3/s4] --dataset [cifar10/cifar100/svhn]
bash darts-proj-sota.sh --space [s1/s2/s3/s4] --dataset [cifar10/cifar100/svhn] --resume_expid search-darts-sota-[s1/s2/s3/s4]-2
bash blank-sota.sh --space [s1/s2/s3/s4] --dataset [cifar10/cifar100/svhn]
bash blank-proj-201.sh --space [s1/s2/s3/s4] --dataset [cifar10/cifar100/svhn] --resume_expid search-blank-sota-[s1/s2/s3/s4]-2
bash eval.sh --arch [genotype_name]
bash eval-c100.sh --arch [genotype_name]
bash eval-svhn.sh --arch [genotype_name]
bash darts-sota.sh
bash darts-proj-sota.sh --resume_expid search-blank-sota-s5-2
bash blank-sota.sh
bash blank-proj-201.sh --resume_expid search-blank-sota-s5-2
bash eval.sh --arch [genotype_name]
@inproceedings{
ruochenwang2021dartspt,
title={Rethinking Architecture Selection in Differentiable NAS},
author={Ruochen Wang, Minhao Cheng, Xiangning Chen, Xiaocheng Tang, Cho-Jui Hsieh},
booktitle={International Conference on Learning Representations (ICLR)},
year={2021}
}