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!9553 update nasnet readme cn
From: @pandoublefeng Reviewed-by: @c_34 Signed-off-by: @c_34tags/v1.1.0
mindspore-ci-bot
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5 years ago
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model_zoo/official/cv/nasnet/README.md
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| @@ -4,7 +4,7 @@ | |||
| - [Model Architecture](#model-architecture) | |||
| - [Dataset](#dataset) | |||
| - [Environment Requirements](#environment-requirements) | |||
| - [Quick Start](#quick-start) | |||
| - [Quick Start](#quick-start) | |||
| - [Script Description](#script-description) | |||
| - [Script and Sample Code](#script-and-sample-code) | |||
| - [Script Parameters](#script-parameters) | |||
| @@ -18,7 +18,6 @@ | |||
| # [NASNet Description](#contents) | |||
| [Paper](https://arxiv.org/abs/1707.07012): Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc V. Le. Learning Transferable Architectures for Scalable Image Recognition. 2017. | |||
| # [Model architecture](#contents) | |||
| @@ -27,27 +26,25 @@ The overall network architecture of NASNet is show below: | |||
| [Link](https://arxiv.org/abs/1707.07012) | |||
| # [Dataset](#contents) | |||
| Dataset used: [imagenet](http://www.image-net.org/) | |||
| - Dataset size: ~125G, 1.2W colorful images in 1000 classes | |||
| - Train: 120G, 1.2W images | |||
| - Test: 5G, 50000 images | |||
| - Train: 120G, 1.2W images | |||
| - Test: 5G, 50000 images | |||
| - Data format: RGB images. | |||
| - Note: Data will be processed in src/dataset.py | |||
| - Note: Data will be processed in src/dataset.py | |||
| # [Environment Requirements](#contents) | |||
| - Hardware GPU | |||
| - Prepare hardware environment with GPU processor. | |||
| - Prepare hardware environment with GPU processor. | |||
| - Framework | |||
| - [MindSpore](https://www.mindspore.cn/install/en) | |||
| - [MindSpore](https://www.mindspore.cn/install/en) | |||
| - For more information, please check the resources below： | |||
| - [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html) | |||
| - [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html) | |||
| - [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html) | |||
| - [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html) | |||
| # [Script description](#contents) | |||
| @@ -77,7 +74,7 @@ Dataset used: [imagenet](http://www.image-net.org/) | |||
| Parameters for both training and evaluating can be set in config.py. | |||
| ``` | |||
| ```python | |||
| 'random_seed': 1, # fix random seed | |||
| 'rank': 0, # local rank of distributed | |||
| 'group_size': 1, # world size of distributed | |||
| @@ -102,9 +99,9 @@ Parameters for both training and evaluating can be set in config.py. | |||
| ## [Training Process](#contents) | |||
| #### Usage | |||
| ### Usage | |||
| ``` | |||
| ```bash | |||
| GPU: | |||
| # distribute training example(8p) | |||
| sh run_distribute_train_for_gpu.sh DATA_DIR | |||
| @@ -112,7 +109,7 @@ GPU: | |||
| sh run_standalone_train_for_gpu.sh DEVICE_ID DATA_DIR | |||
| ``` | |||
| #### Launch | |||
| ### Launch | |||
| ```bash | |||
| # distributed training example(8p) for GPU | |||
| @@ -127,25 +124,23 @@ You can find checkpoint file together with result in log. | |||
| ### Usage | |||
| ``` | |||
| ```bash | |||
| # Evaluation | |||
| sh run_eval_for_gpu.sh DEVICE_ID DATA_DIR PATH_CHECKPOINT | |||
| ``` | |||
| #### Launch | |||
| ### Launch | |||
| ```bash | |||
| # Evaluation with checkpoint | |||
| sh scripts/run_eval_for_gpu.sh 0 /dataset/val ./checkpoint/nasnet-a-mobile-rank0-248_10009.ckpt | |||
| ``` | |||
| #### Result | |||
| ### Result | |||
| Evaluation result will be stored in the scripts path. Under this, you can find result like the followings in log. | |||
| ``` | |||
| acc=73.5%(TOP1) | |||
| ``` | |||
| # [Model description](#contents) | |||
| @@ -163,7 +158,6 @@ acc=73.5%(TOP1) | |||
| | Optimizer | Momentum | | |||
| | Loss Function | SoftmaxCrossEntropyWithLogits | | |||
| | Loss | 1.8965 | | |||
| | Accuracy | 73.5%(TOP1) | | |||
| | Total time | 144 h 8ps | | |||
| | Checkpoint for Fine tuning | 89 M(.ckpt file) | | |||
| @@ -173,13 +167,12 @@ acc=73.5%(TOP1) | |||
| | -------------------------- | ------------------------- | | |||
| | Resource | NV SMX2 V100-32G | | |||
| | uploaded Date | 09/24/2020 | | |||
| | MindSpore Version | 1.0.0 | | |||
| | Dataset | ImageNet, 1.2W | | |||
| | MindSpore Version | 1.0.0 | | |||
| | Dataset | ImageNet, 1.2W | | |||
| | batch_size | 32 | | |||
| | outputs | probability | | |||
| | Accuracy | acc=73.5%(TOP1) | | |||
| # [ModelZoo Homepage](#contents) | |||
| Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo). | |||
| Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo). | |||
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model_zoo/official/cv/nasnet/README_CN.md
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| @@ -1,60 +1,86 @@ | |||
| # NASNet示例 | |||
| # 目录 | |||
| <!-- TOC --> | |||
| - [NASNet示例](#nasnet示例) | |||
| - [概述](#概述) | |||
| - [要求](#要求) | |||
| - [结构](#结构) | |||
| - [参数配置](#参数配置) | |||
| - [运行示例](#运行示例) | |||
| - [训练](#训练) | |||
| - [用法](#用法) | |||
| - [运行](#运行) | |||
| - [结果](#结果) | |||
| - [评估](#评估) | |||
| - [用法](#用法-1) | |||
| - [启动](#启动) | |||
| - [结果](#结果-1) | |||
| - [目录](#目录) | |||
| - [NASNet概述](#NASNet概述) | |||
| - [模型架构](#模型架构) | |||
| - [数据集](#数据集) | |||
| - [环境要求](#环境要求) | |||
| - [脚本说明](#脚本说明) | |||
| - [脚本和样例代码](#脚本和样例代码) | |||
| - [脚本参数](#脚本参数) | |||
| - [训练过程](#训练过程) | |||
| - [评估过程](#评估过程) | |||
| - [模型描述](#模型描述) | |||
| - [性能](#性能) | |||
| - [训练性能](#训练性能) | |||
| - [评估性能](#评估性能) | |||
| - [ModelZoo主页](#modelzoo主页) | |||
| <!-- /TOC --> | |||
| ## 概述 | |||
| # NASNet概述 | |||
| 此为MindSpore中训练NASNet-A-Mobile的示例。 | |||
| [论文](https://arxiv.org/abs/1707.07012): Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc V. Le. Learning Transferable Architectures for Scalable Image Recognition. 2017. | |||
| ## 要求 | |||
| # 模型架构 | |||
| - 安装[Mindspore](http://www.mindspore.cn/install/en)。 | |||
| - 下载数据集。 | |||
| NASNet总体网络架构如下： | |||
| ## 结构 | |||
| [链接](https://arxiv.org/abs/1707.07012) | |||
| ```shell | |||
| # 数据集 | |||
| 使用的数据集：[imagenet](http://www.image-net.org/) | |||
| - 数据集大小：125G，共1000个类、1.2万张彩色图像 | |||
| - 训练集：120G，共1.2万张图像 | |||
| - 测试集：5G，共5万张图像 | |||
| - 数据格式：RGB | |||
| * 注：数据在src/dataset.py中处理。 | |||
| # 环境要求 | |||
| - 硬件：GPU | |||
| - 使用GPU处理器来搭建硬件环境。 | |||
| - 框架 | |||
| - [MindSpore](https://www.mindspore.cn/install) | |||
| - 如需查看详情，请参见如下资源： | |||
| - [MindSpore教程](https://www.mindspore.cn/tutorial/training/en/master/index.html) | |||
| - [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html) | |||
| # 脚本说明 | |||
| ## 脚本及样例代码 | |||
| ```python | |||
| . | |||
| └─nasnet | |||
| └─nasnet | |||
| ├─README.md | |||
| ├─scripts | |||
| ├─run_standalone_train_for_gpu.sh # 使用GPU平台启动单机训练（单卡） | |||
| ├─Run_distribute_train_for_gpu.sh # 使用GPU平台启动分布式训练（8卡） | |||
| └─Run_eval_for_gpu.sh # 使用GPU平台进行启动评估 | |||
| ├─README_CN.md | |||
| ├─scripts | |||
| ├─run_standalone_train_for_gpu.sh # 使用GPU平台启动单机训练（单卡） | |||
| ├─run_distribute_train_for_gpu.sh # 使用GPU平台启动分布式训练（8卡） | |||
| └─run_eval_for_gpu.sh # 使用GPU平台进行启动评估 | |||
| ├─src | |||
| ├─config.py # 参数配置 | |||
| ├─dataset.py # 数据预处理 | |||
| ├─loss.py # 自定义交叉熵损失函数 | |||
| ├─lr_generator.py # 学习率生成器 | |||
| ├─nasnet_a_mobile.py # 网络定义 | |||
| ├─eval.py # 评估网络 | |||
| ├─export.py # 转换检查点 | |||
| └─train.py # 训练网络 | |||
| ├─nasnet_a_mobile.py # 网络定义 | |||
| ├─eval.py # 评估网络 | |||
| ├─export.py # 转换检查点 | |||
| └─train.py # 训练网络 | |||
| ``` | |||
| ## 参数配置 | |||
| ## 脚本参数 | |||
| 在config.py中可以同时配置训练参数和评估参数。 | |||
| ``` | |||
| ```python | |||
| 'random_seed':1, # 固定随机种子 | |||
| 'rank':0, # 分布式训练进程序号 | |||
| 'group_size':1, # 分布式训练分组大小 | |||
| @@ -75,25 +101,20 @@ | |||
| 'opt_eps':1.0, # epsilon参数 | |||
| 'rmsprop_decay':0.9, # rmsprop衰减 | |||
| 'loss_scale':1, # 损失规模 | |||
| ``` | |||
| ## 训练过程 | |||
| ### 用法 | |||
| ## 运行示例 | |||
| ### 训练 | |||
| #### 用法 | |||
| ``` | |||
| ```bash | |||
| # 分布式训练示例（8卡） | |||
| sh run_distribute_train_for_gpu.sh DATA_DIR | |||
| sh run_distribute_train_for_gpu.sh DATA_DIR | |||
| # 单机训练 | |||
| sh run_standalone_train_for_gpu.sh DEVICE_ID DATA_DIR | |||
| ``` | |||
| #### 运行 | |||
| ### 运行 | |||
| ```bash | |||
| # GPU分布式训练示例（8卡） | |||
| @@ -102,20 +123,20 @@ sh scripts/run_distribute_train_for_gpu.sh /dataset/train | |||
| sh scripts/run_standalone_train_for_gpu.sh 0 /dataset/train | |||
| ``` | |||
| #### 结果 | |||
| ### 结果 | |||
| 可以在日志中找到检查点文件及结果。 | |||
| ### 评估 | |||
| ## 评估过程 | |||
| #### 用法 | |||
| ### 用法 | |||
| ``` | |||
| ```bash | |||
| # 评估 | |||
| sh run_eval_for_gpu.sh DEVICE_ID DATA_DIR PATH_CHECKPOINT | |||
| ``` | |||
| #### 启动 | |||
| ### 启动 | |||
| ```bash | |||
| # 检查点评估 | |||
| @@ -124,7 +145,43 @@ sh scripts/run_eval_for_gpu.sh 0 /dataset/val ./checkpoint/nasnet-a-mobile-rank0 | |||
| > 训练过程中可以生成检查点。 | |||
| #### 结果 | |||
| ### 结果 | |||
| 评估结果保存在脚本路径下。路径下的日志中，可以找到如下结果： | |||
| acc=73.5%(TOP1) | |||
| # 模型描述 | |||
| ## 性能 | |||
| ### 训练性能 | |||
| | 参数 | NASNet | | |||
| | -------------------------- | ------------------------- | | |||
| | 资源 | NV SMX2 V100-32G | | |||
| | 上传日期 | 2020-09-24 | | |||
| | MindSpore版本 | 1.0.0 | | |||
| | 数据集 | ImageNet | | |||
| | 训练参数 | src/config.py | | |||
| | 优化器 | Momentum | | |||
| | 损失函数 | SoftmaxCrossEntropyWithLogits | | |||
| | 损失值 | 1.8965 | | |||
| | 总时间 | 8卡运行约144个小时 | | |||
| | 检查点文件大小 | 89 M(.ckpt文件) | | |||
| ### 评估性能 | |||
| | 参数 | | | |||
| | -------------------------- | ------------------------- | | |||
| | 资源 | NV SMX2 V100-32G | | |||
| | 上传日期 | 2020-09-24 | | |||
| | MindSpore版本 | 1.0.0 | | |||
| | 数据及 | ImageNet, 1.2W | | |||
| | batch_size | 32 | | |||
| | 输出 | 概率 | | |||
| | 精确度 | acc=73.5%(TOP1) | | |||
| # ModelZoo主页 | |||
| 请浏览官网[主页](https://gitee.com/mindspore/mindspore/tree/master/model_zoo)。 | |||