autodl-projects/docs/ICCV-2019-SETN.md
2021-05-19 07:23:50 +00:00

54 lines
1.9 KiB
Markdown

# [One-Shot Neural Architecture Search via Self-Evaluated Template Network](https://arxiv.org/abs/1910.05733)
<img align="right" src="http://xuanyidong.com/resources/paper-icon/ICCV-2019-SETN.png" width="450">
<strong>Highlight</strong>: 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
1. 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))
```
2. Different NAS-searched architectures are defined [here](https://github.com/D-X-Y/AutoDL-Projects/blob/main/lib/nas_infer_model/DXYs/genotypes.py).
## 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}
}
```