1.9 KiB
1.9 KiB
One-Shot Neural Architecture Search via Self-Evaluated Template Network
Highlight: we equip one-shot NAS with an architecture sampler and train network weights using uniformly sampling.
One-Shot Neural Architecture Search via Self-Evaluated Template Network is accepted by ICCV 2019.
Requirements and Preparation
Please install Python>=3.6 and PyTorch>=1.2.0.
Usefull tools
- Compute the number of parameters and FLOPs of a model:
from utils import get_model_infos
flop, param = get_model_infos(net, (1,3,32,32))
- Different NAS-searched architectures are defined here.
Usage
Please use the following scripts to train the searched SETN-searched CNN on CIFAR-10, CIFAR-100, and ImageNet.
CUDA_VISIBLE_DEVICES=0 bash ./scripts/nas-infer-train.sh cifar10 SETN 96 -1
CUDA_VISIBLE_DEVICES=0 bash ./scripts/nas-infer-train.sh cifar100 SETN 96 -1
CUDA_VISIBLE_DEVICES=0,1,2,3 bash ./scripts/nas-infer-train.sh imagenet-1k SETN 256 -1
Searching on the NAS-Bench-201 search space
The searching codes of SETN on a small search space (NAS-Bench-201).
CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/SETN.sh cifar10 1 -1
Searching on the NASNet search space is not ready yet.
Citation
If you find that this project helps your research, please consider citing the following paper:
@inproceedings{dong2019one,
title = {One-Shot Neural Architecture Search via Self-Evaluated Template Network},
author = {Dong, Xuanyi and Yang, Yi},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
pages = {3681--3690},
year = {2019}
}