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< p align = "center" >
< img src = "https://xuanyidong.com/resources/images/AutoDL-log.png" width = "400" / >
< / p >
---------
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[](../LICENSE.md)
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自动深度学习库 (AutoDL-Projects) 是一个开源的,轻量级的,功能强大的项目。
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该项目实现了多种网络结构搜索(NAS)和超参数优化(HPO)算法。
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**谁应该考虑使用AutoDL-Projects**
- 想尝试不同AutoDL算法的初学者
- 想调研AutoDL在特定问题上的有效性的工程师
- 想轻松实现和实验新AutoDL算法的研究员
**为什么我们要用AutoDL-Projects**
- 最简化的python依赖库
- 所有算法都在一个代码库下
- 积极地维护
## AutoDL-Projects 能力简述
目前,该项目提供了下列算法和以及对应的运行脚本。请点击每个算法对应的链接看他们的细节描述。
< table >
< tbody >
< tr align = "center" valign = "bottom" >
< th > Type< / th >
< th > ABBRV< / th >
< th > Algorithms< / th >
< th > Description< / th >
< / tr >
< tr > <!-- (1 - st row) -->
< td rowspan = "6" align = "center" valign = "middle" halign = "middle" > NAS < / td >
< td align = "center" valign = "middle" > TAS < / td >
< td align = "center" valign = "middle" > < a href = "https://arxiv.org/abs/1905.09717" > Network Pruning via Transformable Architecture Search< / a > < / td >
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< td align = "center" valign = "middle" > < a href = "https://github.com/D-X-Y/AutoDL-Projects/tree/main/docs/NeurIPS-2019-TAS.md" > NeurIPS-2019-TAS.md< / a > < / td >
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< / tr >
< tr > <!-- (2 - nd row) -->
< td align = "center" valign = "middle" > DARTS < / td >
< td align = "center" valign = "middle" > < a href = "https://arxiv.org/abs/1806.09055" > DARTS: Differentiable Architecture Search< / a > < / td >
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< td align = "center" valign = "middle" > < a href = "https://github.com/D-X-Y/AutoDL-Projects/tree/main/docs/ICLR-2019-DARTS.md" > ICLR-2019-DARTS.md< / a > < / td >
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< / tr >
< tr > <!-- (3 - nd row) -->
< td align = "center" valign = "middle" > GDAS < / td >
< td align = "center" valign = "middle" > < a href = "https://arxiv.org/abs/1910.04465" > Searching for A Robust Neural Architecture in Four GPU Hours< / a > < / td >
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< td align = "center" valign = "middle" > < a href = "https://github.com/D-X-Y/AutoDL-Projects/tree/main/docs/CVPR-2019-GDAS.md" > CVPR-2019-GDAS.md< / a > < / td >
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< / tr >
< tr > <!-- (4 - rd row) -->
< td align = "center" valign = "middle" > SETN < / td >
< td align = "center" valign = "middle" > < a href = "https://arxiv.org/abs/1910.05733" > One-Shot Neural Architecture Search via Self-Evaluated Template Network< / a > < / td >
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< td align = "center" valign = "middle" > < a href = "https://github.com/D-X-Y/AutoDL-Projects/tree/main/docs/ICCV-2019-SETN.md" > ICCV-2019-SETN.md< / a > < / td >
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< / tr >
< tr > <!-- (5 - th row) -->
< td align = "center" valign = "middle" > NAS-Bench-201 < / td >
< td align = "center" valign = "middle" > < a href = "https://openreview.net/forum?id=HJxyZkBKDr" > NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search< / a > < / td >
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< td align = "center" valign = "middle" > < a href = "https://github.com/D-X-Y/AutoDL-Projects/tree/main/docs/NAS-Bench-201.md" > NAS-Bench-201.md< / a > < / td >
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< / tr >
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< tr > <!-- (6 - th row) -->
< td align = "center" valign = "middle" > NATS-Bench < / td >
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< td align = "center" valign = "middle" > < a href = "https://xuanyidong.com/assets/projects/NATS-Bench" > NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size< / a > < / td >
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< td align = "center" valign = "middle" > < a href = "https://github.com/D-X-Y/NATS-Bench" > NATS-Bench.md< / a > < / td >
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< / tr >
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< tr > <!-- (7 - th row) -->
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< td align = "center" valign = "middle" > ... < / td >
< td align = "center" valign = "middle" > ENAS / REA / REINFORCE / BOHB < / td >
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< td align = "center" valign = "middle" > Please check the original papers. < / td >
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< td align = "center" valign = "middle" > < a href = "https://github.com/D-X-Y/AutoDL-Projects/tree/main/docs/NAS-Bench-201.md" > NAS-Bench-201.md< / a > < a href = "https://github.com/D-X-Y/NATS-Bench" > NATS-Bench.md< / a > < / td >
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< / tr >
< tr > <!-- (start second block) -->
< td rowspan = "1" align = "center" valign = "middle" halign = "middle" > HPO < / td >
< td align = "center" valign = "middle" > HPO-CG < / td >
< td align = "center" valign = "middle" > Hyperparameter optimization with approximate gradient < / td >
< td align = "center" valign = "middle" > coming soon < / a > < / td >
< / tr >
< tr > <!-- (start third block) -->
< td rowspan = "1" align = "center" valign = "middle" halign = "middle" > Basic < / td >
< td align = "center" valign = "middle" > ResNet < / td >
< td align = "center" valign = "middle" > Deep Learning-based Image Classification < / td >
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< td align = "center" valign = "middle" > < a href = "https://github.com/D-X-Y/AutoDL-Projects/tree/main/docs/BASELINE.md" > BASELINE.md< / a > < / a > < / td >
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< / tr >
< / tbody >
< / table >
## 准备工作
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请使用`3.6`以上的`Python`, 更多的Python包参见[requirements.txt](requirements.txt).
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请下载并且解压缩`CIFAR`和`ImageNet`到`$TORCH_HOME`.
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## 引用
如果您发现该项目对您的科研或工程有帮助,请考虑引用下列的某些文献:
```
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@inproceedings {dong2021autohas,
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title = {{AutoHAS}: Efficient Hyperparameter and Architecture Search},
author = {Dong, Xuanyi and Tan, Mingxing and Yu, Adams Wei and Peng, Daiyi and Gabrys, Bogdan and Le, Quoc V},
booktitle = {2nd Workshop on Neural Architecture Search at International Conference on Learning Representations (ICLR)},
year = {2021}
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}
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@article {dong2021nats,
title = {{NATS-Bench}: Benchmarking NAS Algorithms for Architecture Topology and Size},
author = {Dong, Xuanyi and Liu, Lu and Musial, Katarzyna and Gabrys, Bogdan},
doi = {10.1109/TPAMI.2021.3054824},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
year = {2021},
note = {\mbox{doi}:\url{10.1109/TPAMI.2021.3054824}}
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}
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@inproceedings {dong2020nasbench201,
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title = {{NAS-Bench-201}: Extending the Scope of Reproducible Neural Architecture Search},
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author = {Dong, Xuanyi and Yang, Yi},
booktitle = {International Conference on Learning Representations (ICLR)},
url = {https://openreview.net/forum?id=HJxyZkBKDr},
year = {2020}
}
@inproceedings {dong2019tas,
title = {Network Pruning via Transformable Architecture Search},
author = {Dong, Xuanyi and Yang, Yi},
booktitle = {Neural Information Processing Systems (NeurIPS)},
year = {2019}
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pages = {760--771},
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}
@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}
}
@inproceedings {dong2019search,
title = {Searching for A Robust Neural Architecture in Four GPU Hours},
author = {Dong, Xuanyi and Yang, Yi},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
pages = {1761--1770},
year = {2019}
}
```
# 其他
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如果你想要给这份代码库做贡献,请看[CONTRIBUTING.md](../.github/CONTRIBUTING.md)。
此外,使用规范请参考[CODE-OF-CONDUCT.md](../.github/CODE-OF-CONDUCT.md)。
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# 许可证
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The entire codebase is under [MIT license ](../LICENSE.md )