DARTS-PT

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

Requirements

Python >= 3.7
PyTorch >= 1.5
tensorboard == 2.0.1
gpustat

Experiments on NAS-Bench-201

The scripts for running experiments can be found in the exp_scripts/ directory.

Dataset preparation

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 add hp="200" to api.query_by_arch() in nasbench201/train_search.py)

pip install nas-bench-201

Running DARTS-PT on NAS-Bench-201

Supernet training

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

Architecture selection (projection)

The projection script loads ckpts from experiments/nasbench201/{resume_expid}

bash darts-proj-201.sh --resume_epoch 100 --resume_expid search-darts-201-1

Fix-alpha version (blank-pt):

bash blank-201.sh
bash blank-proj-201.sh --resume_expid search-blank-201-1

Experiments on S1-S4

Supernet training

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]

Architecture selection (projection)

bash darts-proj-sota.sh --space [s1/s2/s3/s4] --dataset [cifar10/cifar100/svhn] --resume_expid search-darts-sota-[s1/s2/s3/s4]-2

Fix-alpha version (blank-pt):

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

Evaluation

bash eval.sh --arch [genotype_name]
bash eval-c100.sh --arch [genotype_name]
bash eval-svhn.sh --arch [genotype_name]

Expeirments on DARTS Space

Supernet training

bash darts-sota.sh

Archtiecture selection (projection)

bash darts-proj-sota.sh --resume_expid search-blank-sota-s5-2

Fix-alpha version (blank-pt)

bash blank-sota.sh
bash blank-proj-201.sh --resume_expid search-blank-sota-s5-2

Evaluation

bash eval.sh --arch [genotype_name]

Citation

@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}
}

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darts-pt's Introduction

DARTS-PT

Requirements

Experiments on NAS-Bench-201

Dataset preparation

Running DARTS-PT on NAS-Bench-201

Supernet training

Architecture selection (projection)

Fix-alpha version (blank-pt):

Experiments on S1-S4

Supernet training

Architecture selection (projection)

Fix-alpha version (blank-pt):

Evaluation

Expeirments on DARTS Space

Supernet training

Archtiecture selection (projection)

Fix-alpha version (blank-pt)

Evaluation

Citation

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