54 lines
1.9 KiB
Markdown
54 lines
1.9 KiB
Markdown
# [One-Shot Neural Architecture Search via Self-Evaluated Template Network](https://arxiv.org/abs/1910.05733)
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<img align="right" src="http://xuanyidong.com/resources/paper-icon/ICCV-2019-SETN.png" width="450">
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<strong>Highlight</strong>: we equip one-shot NAS with an architecture sampler and train network weights using uniformly sampling.
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One-Shot Neural Architecture Search via Self-Evaluated Template Network is accepted by ICCV 2019.
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## Requirements and Preparation
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Please install `Python>=3.6` and `PyTorch>=1.2.0`.
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### Usefull tools
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1. Compute the number of parameters and FLOPs of a model:
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```
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from utils import get_model_infos
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flop, param = get_model_infos(net, (1,3,32,32))
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```
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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).
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## Usage
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Please use the following scripts to train the searched SETN-searched CNN on CIFAR-10, CIFAR-100, and ImageNet.
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```
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CUDA_VISIBLE_DEVICES=0 bash ./scripts/nas-infer-train.sh cifar10 SETN 96 -1
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CUDA_VISIBLE_DEVICES=0 bash ./scripts/nas-infer-train.sh cifar100 SETN 96 -1
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CUDA_VISIBLE_DEVICES=0,1,2,3 bash ./scripts/nas-infer-train.sh imagenet-1k SETN 256 -1
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```
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### Searching on the NAS-Bench-201 search space
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The searching codes of SETN on a small search space (NAS-Bench-201).
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```
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CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/SETN.sh cifar10 1 -1
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```
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**Searching on the NASNet search space** is not ready yet.
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# Citation
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If you find that this project helps your research, please consider citing the following paper:
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```
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@inproceedings{dong2019one,
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title = {One-Shot Neural Architecture Search via Self-Evaluated Template Network},
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author = {Dong, Xuanyi and Yang, Yi},
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booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
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pages = {3681--3690},
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year = {2019}
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}
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```
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