update GDAS reduction cell
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Xuanyi Dong
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---------
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[](LICENSE.md)
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Automated Deep Learning (AutoDL-Projects) is an open source, lightweight, but useful project for researchers.
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Automated Deep Learning (AutoDL-Projects) is an open source, lightweight, but useful project for everyone.
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This project implemented several neural architecture search (NAS) and hyper-parameter optimization (HPO) algorithms.
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中文介绍见[README_CN.md](README_CN.md)
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**Who should consider using AutoDL-Projects**
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130
README_CN.md
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130
README_CN.md
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@ -0,0 +1,130 @@
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<p align="center">
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<img src="https://xuanyidong.com/resources/images/AutoDL-log.png" width="400"/>
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</p>
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---------
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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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台项目目前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**
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- 想尝试不同AutoDL算法的初学者
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- 想调研AutoDL在特定问题上的有效性的工程师
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- 想轻松实现和实验新AutoDL算法的研究员
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**为什么我们要用AutoDL-Projects**
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- 最简化的python依赖库
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- 所有算法都在一个代码库下
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- 积极地维护
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## AutoDL-Projects 能力简述
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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。
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<table>
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<tbody>
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<tr align="center" valign="bottom">
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<th>Type</th>
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<th>ABBRV</th>
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<th>Algorithms</th>
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<th>Description</th>
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</tr>
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<tr> <!-- (1-st row) -->
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<td rowspan="6" align="center" valign="middle" halign="middle"> NAS </td>
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<td align="center" valign="middle"> TAS </td>
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<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/master/docs/NIPS-2019-TAS.md">NIPS-2019-TAS.md</a> </td>
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</tr>
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<tr> <!-- (2-nd row) -->
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<td align="center" valign="middle"> DARTS </td>
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<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/master/docs/ICLR-2019-DARTS.md">ICLR-2019-DARTS.md</a> </td>
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</tr>
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<tr> <!-- (3-nd row) -->
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<td align="center" valign="middle"> GDAS </td>
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<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/master/docs/CVPR-2019-GDAS.md">CVPR-2019-GDAS.md</a> </td>
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</tr>
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<tr> <!-- (4-rd row) -->
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<td align="center" valign="middle"> SETN </td>
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<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/master/docs/ICCV-2019-SETN.md">ICCV-2019-SETN.md</a> </td>
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</tr>
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<tr> <!-- (5-th row) -->
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<td align="center" valign="middle"> NAS-Bench-201 </td>
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<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/master/docs/NAS-Bench-201.md">NAS-Bench-201.md</a> </td>
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</tr>
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<tr> <!-- (6-th row) -->
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<td align="center" valign="middle"> ... </td>
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<td align="center" valign="middle"> ENAS / REA / REINFORCE / BOHB </td>
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<td align="center" valign="middle"> <a href="https://github.com/D-X-Y/AutoDL-Projects/tree/master/docs/NAS-Bench-201.md">NAS-Bench-201.md</a> </td>
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</tr>
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<tr> <!-- (start second block) -->
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<td rowspan="1" align="center" valign="middle" halign="middle"> HPO </td>
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<td align="center" valign="middle"> HPO-CG </td>
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<td align="center" valign="middle"> Hyperparameter optimization with approximate gradient </td>
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<td align="center" valign="middle"> coming soon </a> </td>
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</tr>
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<tr> <!-- (start third block) -->
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<td rowspan="1" align="center" valign="middle" halign="middle"> Basic </td>
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<td align="center" valign="middle"> ResNet </td>
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<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/master/docs/BASELINE.md">BASELINE.md</a> </a> </td>
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</tr>
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</tbody>
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</table>
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## 准备工作
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Please install `Python>=3.6` and `PyTorch>=1.3.0`. (You could also run this project in lower versions of Python and PyTorch, but may have bugs).
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Some visualization codes may require `opencv`.
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CIFAR and ImageNet should be downloaded and extracted into `$TORCH_HOME`.
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Some methods use knowledge distillation (KD), which require pre-trained models. Please download these models from [Google Drive](https://drive.google.com/open?id=1ANmiYEGX-IQZTfH8w0aSpj-Wypg-0DR-) (or train by yourself) and save into `.latent-data`.
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## 引用
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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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```
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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},
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booktitle = {International Conference on Learning Representations (ICLR)},
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url = {https://openreview.net/forum?id=HJxyZkBKDr},
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year = {2020}
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}
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@inproceedings{dong2019tas,
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title = {Network Pruning via Transformable Architecture Search},
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author = {Dong, Xuanyi and Yang, Yi},
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booktitle = {Neural Information Processing Systems (NeurIPS)},
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year = {2019}
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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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@inproceedings{dong2019search,
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title = {Searching for A Robust Neural Architecture in Four GPU Hours},
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author = {Dong, Xuanyi and Yang, Yi},
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booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
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pages = {1761--1770},
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year = {2019}
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}
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```
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# 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)。
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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)
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@ -234,3 +234,58 @@ class PartAwareOp(nn.Module):
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final_fea = torch.cat((x,features), dim=1)
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outputs = self.last( final_fea )
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return outputs
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# Searching for A Robust Neural Architecture in Four GPU Hours
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class GDAS_Reduction_Cell(nn.Module):
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def __init__(self, C_prev_prev, C_prev, C, reduction_prev, multiplier, affine, track_running_stats):
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super(GDAS_Reduction_Cell, self).__init__()
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if reduction_prev:
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self.preprocess0 = FactorizedReduce(C_prev_prev, C, 2, affine, track_running_stats)
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else:
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self.preprocess0 = ReLUConvBN(C_prev_prev, C, 1, 1, 0, 1, affine, track_running_stats)
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self.preprocess1 = ReLUConvBN(C_prev, C, 1, 1, 0, 1, affine, track_running_stats)
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self.multiplier = multiplier
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self.reduction = True
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self.ops1 = nn.ModuleList(
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[nn.Sequential(
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nn.ReLU(inplace=False),
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nn.Conv2d(C, C, (1, 3), stride=(1, 2), padding=(0, 1), groups=8, bias=False),
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nn.Conv2d(C, C, (3, 1), stride=(2, 1), padding=(1, 0), groups=8, bias=False),
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nn.BatchNorm2d(C, affine=True),
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nn.ReLU(inplace=False),
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nn.Conv2d(C, C, 1, stride=1, padding=0, bias=False),
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nn.BatchNorm2d(C, affine=True)),
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nn.Sequential(
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nn.ReLU(inplace=False),
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nn.Conv2d(C, C, (1, 3), stride=(1, 2), padding=(0, 1), groups=8, bias=False),
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nn.Conv2d(C, C, (3, 1), stride=(2, 1), padding=(1, 0), groups=8, bias=False),
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nn.BatchNorm2d(C, affine=True),
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nn.ReLU(inplace=False),
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nn.Conv2d(C, C, 1, stride=1, padding=0, bias=False),
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nn.BatchNorm2d(C, affine=True))])
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self.ops2 = nn.ModuleList(
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[nn.Sequential(
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nn.MaxPool2d(3, stride=1, padding=1),
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nn.BatchNorm2d(C, affine=True)),
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nn.Sequential(
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nn.MaxPool2d(3, stride=2, padding=1),
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nn.BatchNorm2d(C, affine=True))])
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def forward(self, s0, s1, drop_prob = -1):
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s0 = self.preprocess0(s0)
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s1 = self.preprocess1(s1)
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X0 = self.ops1[0] (s0)
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X1 = self.ops1[1] (s1)
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if self.training and drop_prob > 0.:
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X0, X1 = drop_path(X0, drop_prob), drop_path(X1, drop_prob)
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X2 = self.ops2[0] (X0+X1)
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X3 = self.ops2[1] (s1)
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if self.training and drop_prob > 0.:
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X2, X3 = drop_path(X2, drop_prob), drop_path(X3, drop_prob)
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return torch.cat([X0, X1, X2, X3], dim=1)
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