Merge pull request #14 from D-X-Y/master

Update Chinese documents and fix typos

NAS-Modeels/README.md → NAS-Models/README.md

@@ -5,6 +5,8 @@ Nine of them are automatically searched models using different Neural Architectu
 We provide codes and scripts to train these models on both CIFAR-10 and CIFAR-100.
 We use the standard data augmentation, i.e., random crop, random flip, and normalization.
 
+Please find the Chinese documentation (中文文档) in `README_CN.md`.
+
 ---
 ## Table of Contents
 - [Installation](#installation)
@@ -67,7 +69,7 @@ Please use `./scripts/base-train.sh` for ResNet and use `./scripts/train-nas.sh`
 
 
 ### Citation
-If you find that this project helps your research, please consider citing these papers:
+If you find that this project helps your research, please consider citing some of these papers:
 ```
 @inproceedings{dong2019one,
   title     = {One-Shot Neural Architecture Search via Self-Evaluated Template Network},

NAS-Models/README_CN.md

+# 基于神经网络搜索的图片分类
+
+该项目包含了10种图片分类模型。其中的九种模型是由不同的神经网络搜索(NAS)算法自动搜索出来的，另外一种是ResNet模型。
+我们提供了代码和训练脚本来在CIFAR-10和CIFAR-100数据集上训练和测试这些模型。
+在训练过程中，我们使用了标准的数据增强技术，即随机裁剪，随机翻转，和归一化。
+
+---
+## 内容概括
+- [安装说明](#安装说明)
+- [数据准备](#数据准备)
+- [训练模型](#训练模型)
+- [项目文件结构介绍](#项目文件结构)
+- [引用](#引用)
+
+
+### 安装说明
+这个项目依赖于以下一些软件包：
+- Python = 3.6
+- PadddlePaddle Fluid >= v0.15.0
+- numpy, tarfile, cPickle, PIL
+
+
+### 数据准备
+请在运行代码前下载 [CIFAR-10](https://dataset.bj.bcebos.com/cifar/cifar-10-python.tar.gz) 和 [CIFAR-100](https://dataset.bj.bcebos.com/cifar/cifar-100-python.tar.gz)。
+请注意CIFAR-10-Python压缩文件的MD5值是`c58f30108f718f92721af3b95e74349a`，CIFAR-100-Python压缩文件的MD5值是`eb9058c3a382ffc7106e4002c42a8d85`。
+请将这两个下载文件保存在`${TORCH_HOME}/cifar.python`路径下。在数据准备之后，应该有两个文件：`${TORCH_HOME}/cifar.python/cifar-10-python.tar.gz`和`${TORCH_HOME}/cifar.python/cifar-100-python.tar.gz`。
+
+
+### Training Models
+
+在设置好环境和准备好数据之后，您可以开始训练模型了。训练的主要入口文件是在`train_cifar.py`中，我们提供了方便的脚本可以直接训练，如下：
+```
+bash ./scripts/base-train.sh 0 cifar-10 ResNet110
+bash ./scripts/train-nas.sh  0 cifar-10 GDAS_V1
+bash ./scripts/train-nas.sh  0 cifar-10 GDAS_V2
+bash ./scripts/train-nas.sh  0 cifar-10  SETN
+bash ./scripts/train-nas.sh  0 cifar-10 NASNet
+bash ./scripts/train-nas.sh  0 cifar-10 ENASNet
+bash ./scripts/train-nas.sh  0 cifar-10 AmoebaNet
+bash ./scripts/train-nas.sh  0 cifar-10 PNASNet
+bash ./scripts/train-nas.sh  0 cifar-100 SETN
+```
+第一个参数指定在哪块GPU上运行该的程序(GPU-ID)，第二个参数指定数据集名称(`cifar-10`或`cifar-100`)，第三个参数是指定了模型名称。
+如果您要训练ResNet模型，请使用`./scripts/base-train.sh`；如果您要训练NAS搜索出的模型，请使用`./scripts/train-nas.sh`。
+
+
+### 项目文件结构
+```
+.
+├──train_cifar.py [训练卷积神经网络模型的文件]
+├──lib [数据集，模型，及其他相关库]
+│  └──models  
+│     ├──__init__.py [引用一些模型相关的函数和类]
+│     ├──resnet.py [定义ResNet模型]
+│     ├──operations.py [定义了NAS搜索空间中的一些原子级操作]
+│     ├──genotypes.py [定义了不同的NAS搜索出的模型的拓扑结构]
+│     └──nas_net.py [定义了NAS模型的宏观结构]
+│  └──utils
+│     ├──__init__.py [引用一些辅助模块]
+│     ├──meter.py [定义了AverageMeter类来统计模型的准确率和损失函数值]
+│     ├──time_utils.py [定义了打印时间和转换时间度量的函数]
+│     └──data_utils.py [定义了数据集相关的读取和数据增强相关的函数]
+└──scripts [运行脚本]
+```
+
+
+### 引用
+如果您发现这个项目对您的研究有帮助，请考虑引用下面的某些论文：
+```
+@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)},
+  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}
+}
+@inproceedings{liu2018darts,
+  title     = {Darts: Differentiable architecture search},
+  author    = {Liu, Hanxiao and Simonyan, Karen and Yang, Yiming},
+  booktitle = {International Conference on Learning Representations (ICLR)},
+  year      = {2018}
+}
+@inproceedings{pham2018efficient,
+  title     = {Efficient Neural Architecture Search via Parameter Sharing},
+  author    = {Pham, Hieu and Guan, Melody and Zoph, Barret and Le, Quoc and Dean, Jeff},
+  booktitle = {International Conference on Machine Learning (ICML)},
+  pages     = {4092--4101},
+  year      = {2018}
+}
+@inproceedings{liu2018progressive,
+  title     = {Progressive neural architecture search},
+  author    = {Liu, Chenxi and Zoph, Barret and Neumann, Maxim and Shlens, Jonathon and Hua, Wei and Li, Li-Jia and Fei-Fei, Li and Yuille, Alan and Huang, Jonathan and Murphy, Kevin},
+  booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
+  pages     = {19--34},
+  year      = {2018}
+}
+@inproceedings{zoph2018learning,
+  title     = {Learning transferable architectures for scalable image recognition},
+  author    = {Zoph, Barret and Vasudevan, Vijay and Shlens, Jonathon and Le, Quoc V},
+  booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
+  pages     = {8697--8710},
+  year      = {2018}
+}
+@inproceedings{real2019regularized,
+  title     = {Regularized evolution for image classifier architecture search},
+  author    = {Real, Esteban and Aggarwal, Alok and Huang, Yanping and Le, Quoc V},
+  booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)},
+  pages     = {4780--4789},
+  year      = {2019}
+}
+```

NAS-Modeels/lib/models/genotypes.py → NAS-Models/lib/models/genotypes.py

-##################################################
-# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
-##################################################
 from collections import namedtuple
 
 Genotype = namedtuple('Genotype', 'normal normal_concat reduce reduce_concat')

NAS-Modeels/lib/utils/__init__.py → NAS-Models/lib/utils/__init__.py

-##################################################
-# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
-##################################################
 from .meter import AverageMeter
 from .time_utils import time_for_file, time_string, time_string_short, time_print, convert_size2str, convert_secs2time
 from .data_utils import reader_creator