master -> main

2021-03-01 21:02:29 +08:00
parent af3ce50a65
commit 493478800c
13 changed files with 32 additions and 35 deletions
--- a/docs/CVPR-2019-GDAS.md
+++ b/docs/CVPR-2019-GDAS.md
@@ -22,7 +22,7 @@ from utils import get_model_infos
 flop, param  = get_model_infos(net, (1,3,32,32))
 ```

-2. Different NAS-searched architectures are defined [here](https://github.com/D-X-Y/AutoDL-Projects/blob/master/lib/nas_infer_model/DXYs/genotypes.py).
+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).


 ## Usage
@@ -34,7 +34,7 @@ CUDA_VISIBLE_DEVICES=0 bash ./scripts/nas-infer-train.sh cifar10  GDAS_V1 96 -1
 CUDA_VISIBLE_DEVICES=0 bash ./scripts/nas-infer-train.sh cifar100 GDAS_V1 96 -1
 CUDA_VISIBLE_DEVICES=0,1,2,3 bash ./scripts/nas-infer-train.sh imagenet-1k GDAS_V1 256 -1
 ```
-If you are interested in the configs of each NAS-searched architecture, they are defined at [genotypes.py](https://github.com/D-X-Y/AutoDL-Projects/blob/master/lib/nas_infer_model/DXYs/genotypes.py).
+If you are interested in the configs of each NAS-searched architecture, they are defined at [genotypes.py](https://github.com/D-X-Y/AutoDL-Projects/blob/main/lib/nas_infer_model/DXYs/genotypes.py).

 ### Searching on the NASNet search space

--- a/docs/ICCV-2019-SETN.md
+++ b/docs/ICCV-2019-SETN.md
@@ -18,7 +18,7 @@ from utils import get_model_infos
 flop, param  = get_model_infos(net, (1,3,32,32))
 ```

-2. Different NAS-searched architectures are defined [here](https://github.com/D-X-Y/AutoDL-Projects/blob/master/lib/nas_infer_model/DXYs/genotypes.py).
+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).


 ## Usage
--- a/docs/ICLR-2019-DARTS.md
+++ b/docs/ICLR-2019-DARTS.md
@@ -16,7 +16,7 @@ This command will start to use the first-order DARTS to search architectures on
 CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/DARTS1V-search-NASNet-space.sh cifar10 -1
 ```

-After searching, if you want to train the searched architecture found by the above scripts, you need to add the config of that architecture (will be printed in log) in [genotypes.py](https://github.com/D-X-Y/AutoDL-Projects/blob/master/lib/nas_infer_model/DXYs/genotypes.py).
+After searching, if you want to train the searched architecture found by the above scripts, you need to add the config of that architecture (will be printed in log) in [genotypes.py](https://github.com/D-X-Y/AutoDL-Projects/blob/main/lib/nas_infer_model/DXYs/genotypes.py).
 In future, I will add a more eligent way to train the searched architecture from the DARTS search space.


--- a/docs/NAS-Bench-201-PURE.md
+++ b/docs/NAS-Bench-201-PURE.md
@@ -1,6 +1,6 @@
 # [NAS-BENCH-201: Extending the Scope of Reproducible Neural Architecture Search](https://openreview.net/forum?id=HJxyZkBKDr)

-**Since our NAS-BENCH-201 has been extended to NATS-Bench, this `README` is deprecated and not maintained. Please use [NATS-Bench](https://github.com/D-X-Y/AutoDL-Projects/blob/master/docs/NATS-Bench.md), which has 5x more architecture information and faster API than NAS-BENCH-201.**
+**Since our NAS-BENCH-201 has been extended to NATS-Bench, this `README` is deprecated and not maintained. Please use [NATS-Bench](https://github.com/D-X-Y/AutoDL-Projects/blob/main/docs/NATS-Bench.md), which has 5x more architecture information and faster API than NAS-BENCH-201.**

 We propose an algorithm-agnostic NAS benchmark (NAS-Bench-201) with a fixed search space, which provides a unified benchmark for almost any up-to-date NAS algorithms.
 The design of our search space is inspired by that used in the most popular cell-based searching algorithms, where a cell is represented as a directed acyclic graph.
@@ -44,7 +44,7 @@ It is recommended to put these data into `$TORCH_HOME` (`~/.torch/` by default).

 ## How to Use NAS-Bench-201

-**More usage can be found in [our test codes](https://github.com/D-X-Y/AutoDL-Projects/blob/master/exps/NAS-Bench-201/test-nas-api.py)**.
+**More usage can be found in [our test codes](https://github.com/D-X-Y/AutoDL-Projects/blob/main/exps/NAS-Bench-201/test-nas-api.py)**.

 1. Creating an API instance from a file:
 ```
@@ -161,7 +161,7 @@ api.reload('{:}/{:}'.format(os.environ['TORCH_HOME'], 'NAS-BENCH-201-4-v1.0-arch
 weights = api.get_net_param(3, 'cifar10', None) # Obtaining the weights of all trials for the 3-th architecture on cifar10. It will returns a dict, where the key is the seed and the value is the trained weights.
 ```

-To obtain the training and evaluation information (please see the comments [here](https://github.com/D-X-Y/AutoDL-Projects/blob/master/lib/nas_201_api/api_201.py#L142)):
+To obtain the training and evaluation information (please see the comments [here](https://github.com/D-X-Y/AutoDL-Projects/blob/main/lib/nas_201_api/api_201.py#L142)):
 ```
 api.get_more_info(112, 'cifar10', None, hp='200', is_random=True)
 # Query info of last training epoch for 112-th architecture
--- a/docs/NAS-Bench-201.md
+++ b/docs/NAS-Bench-201.md
@@ -1,6 +1,6 @@
 # [NAS-BENCH-201: Extending the Scope of Reproducible Neural Architecture Search](https://openreview.net/forum?id=HJxyZkBKDr)

-**Since our NAS-BENCH-201 has been extended to NATS-Bench, this README is deprecated and not maintained. Please use [NATS-Bench](https://github.com/D-X-Y/AutoDL-Projects/blob/master/docs/NATS-Bench.md), which has 5x more architecture information and faster API than NAS-BENCH-201.**
+**Since our NAS-BENCH-201 has been extended to NATS-Bench, this README is deprecated and not maintained. Please use [NATS-Bench](https://github.com/D-X-Y/AutoDL-Projects/blob/main/docs/NATS-Bench.md), which has 5x more architecture information and faster API than NAS-BENCH-201.**

 We propose an algorithm-agnostic NAS benchmark (NAS-Bench-201) with a fixed search space, which provides a unified benchmark for almost any up-to-date NAS algorithms.
 The design of our search space is inspired by that used in the most popular cell-based searching algorithms, where a cell is represented as a directed acyclic graph.
@@ -42,7 +42,7 @@ It is recommended to put these data into `$TORCH_HOME` (`~/.torch/` by default).

 ## How to Use NAS-Bench-201

-**More usage can be found in [our test codes](https://github.com/D-X-Y/AutoDL-Projects/blob/master/exps/NAS-Bench-201/test-nas-api.py)**.
+**More usage can be found in [our test codes](https://github.com/D-X-Y/AutoDL-Projects/blob/main/exps/NAS-Bench-201/test-nas-api.py)**.

 1. Creating an API instance from a file:
 ```
@@ -159,7 +159,7 @@ api.reload('{:}/{:}'.format(os.environ['TORCH_HOME'], 'NAS-BENCH-201-4-v1.0-arch
 weights = api.get_net_param(3, 'cifar10', None) # Obtaining the weights of all trials for the 3-th architecture on cifar10. It will returns a dict, where the key is the seed and the value is the trained weights.
 ```

-To obtain the training and evaluation information (please see the comments [here](https://github.com/D-X-Y/AutoDL-Projects/blob/master/lib/nas_201_api/api_201.py#L142)):
+To obtain the training and evaluation information (please see the comments [here](https://github.com/D-X-Y/AutoDL-Projects/blob/main/lib/nas_201_api/api_201.py#L142)):
 ```
 api.get_more_info(112, 'cifar10', None, hp='200', is_random=True)
 # Query info of last training epoch for 112-th architecture
--- a/docs/NeurIPS-2019-TAS.md
+++ b/docs/NeurIPS-2019-TAS.md
@@ -31,7 +31,7 @@ args: `cifar10` indicates the dataset name, `ResNet56` indicates the basemodel n

 **Model Configuration**

-The searched shapes for ResNet-20/32/56/110/164 and ResNet-18/50 in Table 3/4 in the original paper are listed in [`configs/NeurIPS-2019`](https://github.com/D-X-Y/AutoDL-Projects/tree/master/configs/NeurIPS-2019).
+The searched shapes for ResNet-20/32/56/110/164 and ResNet-18/50 in Table 3/4 in the original paper are listed in [`configs/NeurIPS-2019`](https://github.com/D-X-Y/AutoDL-Projects/tree/main/configs/NeurIPS-2019).

 **Search for the depth configuration of ResNet**
 ```
--- a/docs/README_CN.md
+++ b/docs/README_CN.md
@@ -0,0 +1,145 @@
+<p align="center">
+<img src="https://xuanyidong.com/resources/images/AutoDL-log.png" width="400"/>
+</p>
+
+---------
+[![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。
+
+
+<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/AutoDL-Projects/tree/main/docs/NATS-Bench.md">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/AutoDL-Projects/tree/main/docs/NATS-Bench.md">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
+
+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：
+```
+@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)