150 lines
7.1 KiB
Markdown
150 lines
7.1 KiB
Markdown
<p align="center">
|
||
<img src="https://xuanyidong.com/resources/images/AutoDL-log.png" width="400"/>
|
||
</p>
|
||
|
||
---------
|
||
[](../LICENSE.md)
|
||
|
||
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。
|
||
该项目实现了多种网络结构搜索(NAS)和超参数优化(HPO)算法。
|
||
|
||
**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>
|
||
<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>
|
||
</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>
|
||
<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>
|
||
</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>
|
||
<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>
|
||
</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>
|
||
<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>
|
||
</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>
|
||
<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>
|
||
</tr>
|
||
<tr> <!-- (6-th row) -->
|
||
<td align="center" valign="middle"> NATS-Bench </td>
|
||
<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>
|
||
<td align="center" valign="middle"> <a href="https://github.com/D-X-Y/NATS-Bench">NATS-Bench.md</a> </td>
|
||
</tr>
|
||
<tr> <!-- (7-th row) -->
|
||
<td align="center" valign="middle"> ... </td>
|
||
<td align="center" valign="middle"> ENAS / REA / REINFORCE / BOHB </td>
|
||
<td align="center" valign="middle"> Please check the original papers. </td>
|
||
<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>
|
||
</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>
|
||
<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>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
|
||
|
||
## 准备工作
|
||
|
||
请使用`3.6`以上的`Python`,更多的Python包参见[requirements.txt](requirements.txt).
|
||
|
||
请下载并且解压缩`CIFAR`和`ImageNet`到`$TORCH_HOME`.
|
||
|
||
## 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:
|
||
```
|
||
@inproceedings{dong2021autohas,
|
||
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}
|
||
}
|
||
@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}}
|
||
}
|
||
@inproceedings{dong2020nasbench201,
|
||
title = {{NAS-Bench-201}: Extending the Scope of Reproducible Neural Architecture Search},
|
||
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}
|
||
pages = {760--771},
|
||
}
|
||
@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
|
||
|
||
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)。
|
||
|
||
# 许可证
|
||
The entire codebase is under [MIT license](../LICENSE.md)
|