diff --git a/README.md b/README.md index 4fdd656..e72cab9 100644 --- a/README.md +++ b/README.md @@ -5,8 +5,9 @@ --------- [![MIT licensed](https://img.shields.io/badge/license-MIT-brightgreen.svg)](LICENSE.md) -Automated Deep Learning (AutoDL-Projects) is an open source, lightweight, but useful project for researchers. +Automated Deep Learning (AutoDL-Projects) is an open source, lightweight, but useful project for everyone. This project implemented several neural architecture search (NAS) and hyper-parameter optimization (HPO) algorithms. +中文介绍见[README_CN.md](README_CN.md) **Who should consider using AutoDL-Projects** diff --git a/README_CN.md b/README_CN.md new file mode 100644 index 0000000..c01e1ba --- /dev/null +++ b/README_CN.md @@ -0,0 +1,130 @@ +

+ +

+ +--------- +[![MIT licensed](https://img.shields.io/badge/license-MIT-brightgreen.svg)](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。 +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。 + +**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。 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeABBRVAlgorithmsDescription
NAS TAS Network Pruning via Transformable Architecture Search NIPS-2019-TAS.md
DARTS DARTS: Differentiable Architecture Search ICLR-2019-DARTS.md
GDAS Searching for A Robust Neural Architecture in Four GPU Hours CVPR-2019-GDAS.md
SETN One-Shot Neural Architecture Search via Self-Evaluated Template Network ICCV-2019-SETN.md
NAS-Bench-201 NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search NAS-Bench-201.md
... ENAS / REA / REINFORCE / BOHB NAS-Bench-201.md
HPO HPO-CG Hyperparameter optimization with approximate gradient coming soon
Basic ResNet Deep Learning-based Image Classification BASELINE.md
+ + +## 准备工作 + +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). +Some visualization codes may require `opencv`. + +CIFAR and ImageNet should be downloaded and extracted into `$TORCH_HOME`. +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`. + +## 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{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} +} +@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) diff --git a/lib/models/cell_operations.py b/lib/models/cell_operations.py index 021bdef..f9e6a20 100644 --- a/lib/models/cell_operations.py +++ b/lib/models/cell_operations.py @@ -234,3 +234,58 @@ class PartAwareOp(nn.Module): final_fea = torch.cat((x,features), dim=1) outputs = self.last( final_fea ) return outputs + + +# Searching for A Robust Neural Architecture in Four GPU Hours +class GDAS_Reduction_Cell(nn.Module): + + def __init__(self, C_prev_prev, C_prev, C, reduction_prev, multiplier, affine, track_running_stats): + super(GDAS_Reduction_Cell, self).__init__() + if reduction_prev: + self.preprocess0 = FactorizedReduce(C_prev_prev, C, 2, affine, track_running_stats) + else: + self.preprocess0 = ReLUConvBN(C_prev_prev, C, 1, 1, 0, 1, affine, track_running_stats) + self.preprocess1 = ReLUConvBN(C_prev, C, 1, 1, 0, 1, affine, track_running_stats) + self.multiplier = multiplier + + self.reduction = True + self.ops1 = nn.ModuleList( + [nn.Sequential( + nn.ReLU(inplace=False), + nn.Conv2d(C, C, (1, 3), stride=(1, 2), padding=(0, 1), groups=8, bias=False), + nn.Conv2d(C, C, (3, 1), stride=(2, 1), padding=(1, 0), groups=8, bias=False), + nn.BatchNorm2d(C, affine=True), + nn.ReLU(inplace=False), + nn.Conv2d(C, C, 1, stride=1, padding=0, bias=False), + nn.BatchNorm2d(C, affine=True)), + nn.Sequential( + nn.ReLU(inplace=False), + nn.Conv2d(C, C, (1, 3), stride=(1, 2), padding=(0, 1), groups=8, bias=False), + nn.Conv2d(C, C, (3, 1), stride=(2, 1), padding=(1, 0), groups=8, bias=False), + nn.BatchNorm2d(C, affine=True), + nn.ReLU(inplace=False), + nn.Conv2d(C, C, 1, stride=1, padding=0, bias=False), + nn.BatchNorm2d(C, affine=True))]) + + self.ops2 = nn.ModuleList( + [nn.Sequential( + nn.MaxPool2d(3, stride=1, padding=1), + nn.BatchNorm2d(C, affine=True)), + nn.Sequential( + nn.MaxPool2d(3, stride=2, padding=1), + nn.BatchNorm2d(C, affine=True))]) + + def forward(self, s0, s1, drop_prob = -1): + s0 = self.preprocess0(s0) + s1 = self.preprocess1(s1) + + X0 = self.ops1[0] (s0) + X1 = self.ops1[1] (s1) + if self.training and drop_prob > 0.: + X0, X1 = drop_path(X0, drop_prob), drop_path(X1, drop_prob) + + X2 = self.ops2[0] (X0+X1) + X3 = self.ops2[1] (s1) + if self.training and drop_prob > 0.: + X2, X3 = drop_path(X2, drop_prob), drop_path(X3, drop_prob) + return torch.cat([X0, X1, X2, X3], dim=1)