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# 目录
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<!-- TOC -->
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- [概述](#概述)
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- [数据集](#环境要求)
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- [环境要求](#环境要求)
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- [快速入门](#快速入门)
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- [工程目录](#工程目录)
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<!-- /TOC -->
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# 概述
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本文主要讲解如何在端侧基于[efficientNet](https://arxiv.org/abs/1905.11946)模型迁移学习训练。首先在服务器或个人笔记本上进行模型转换；然后在安卓设备上训练模型。示例代码中使用efficientNet预训练模型，除最后全连接层外均冻结。这种训练模式能有效降低计算能耗，适用于端侧训练。
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# 数据集
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[Places dataset](http://places2.csail.mit.edu/)数据集包含不同分辨率的图片，总大小约100GB。本例使用大小仅有500MB的验证集 [validation data of small images](http://places2.csail.mit.edu/download.html)。
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- 数据集大小：501M，36, 500, 224*224 images 共365类
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- 数据格式：jpeg
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> 注意
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> - 当前发布版本中，数据通过dataset.cc中自定义的`DataSet`类加载。我们使用[ImageMagick convert tool](https://imagemagick.org/)进行数据预处理，包括图像裁剪、转换为BMP格式。
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> - 本例将使用10分类而不是365类。
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> - 训练、验证和测试数据集的比例分别是3:1:1。
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- 验证集数据目录结构如下:
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```text
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places
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├── val_256
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│ ├── Places365_val_00000001.jpg
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│ ├── Places365_val_00000002.jpg
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│ ├── ...
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│ ├── Places365_val_00036499.jpg
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│ └── Places365_val_00036500.jpg
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```
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# 环境要求
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- 服务端
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- [MindSpore Framework](https://www.mindspore.cn/install/en) - 建议使用安装docker环境
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- [MindSpore ToD Framework](https://www.mindspore.cn/tutorial/tod/en/use/prparation.html)
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- [Android NDK r20b](https://dl.google.com/android/repository/android-ndk-r20b-linux-x86_64.zip)
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- [Android SDK](https://developer.android.com/studio?hl=zh-cn#cmdline-tools)
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- [ImageMagick convert tool](https://imagemagick.org/)
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- Android设备端
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# 快速入门
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安装完毕，在`./mindspore/mindspore/lite/examples/transfer_learning`目录下执行脚本，命令如下：
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```bash
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sh ./prepare_and_run.sh -D DATASET_PATH [-d MINDSPORE_DOCKER] [-r RELEASE.tar.gz] [-t arm64|x86]
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```
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其中，`DATASET_PATH`是数据集路径；`MINDSPORE_DOCKER`是运行MindSpore的docker镜像，如果没有使用docker环境，则使用本地运行；`REALEASE.tar.gz`为端侧运行时训练工具压缩包绝对路径；`-t`选项为设备处理器架构，默认为`arm64`，如果输入`x86`则本地运行。注意：若在不同平台执行训练，需在先执行脚本前运行`make clean`指令。
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# 脚本详述
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`prepare_and_run.sh`脚本的功能如下：
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- 将Python模型文件转换为`.ms`文件。
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- 编译训练源码并将相关文件传输到设备端
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- 设备端执行训练
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运行命令参见[快速入门](#快速入门)
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## 模型准备
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脚本`prepare_model.sh`会基于MIndSpore架构将Python模型转换为`lenet_tod.mindir`模型；然后，使用MindSpore ToD 模型转换工具将`lenet_tod.mindir`文件转换为`lenet_tod.ms`文件。如果没有docker环境，则本地执行转换。
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## 模型训练
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首先编译`/src`文件夹中训练代码源码，生成的二进制文件在`./bin`目录下；然后将`transfer_learning_tod.ms`模型文件、训练脚本、MindSpore ToD库文件、编译生成的`/bin`目录和预处理后的`Places`数据集拷贝到`package`文件夹；最后使用`adb`工具将`package`文件夹传输至设备端并执行训练。
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# 工程目录
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```text
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transfer_learning/
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├── Makefile # Makefile of src code
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├── model
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│ ├── effnet.py # Python implementation of efficientNet
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│ ├── transfer_learning_export.py # Python script that exports the LeNet model to .mindir
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│ ├── prepare_model.sh # script that export model (using docker) then converts it
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│ └── train_utils.py # utility function used during the export
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├── prepare_and_run.sh # main script that creates model, compiles it and send to device for running
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├── prepare_dataset.sh # prepares the Places dataset (crop/convert/organizing folders)
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├── README.md # this manual
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├── scripts
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│ ├── eval.sh # script that load the train model and evaluates its accuracy
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│ ├── eval_untrained.sh # script that load the untrained model and evaluates its accuracy
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│ ├── places365_val.txt # association of images to classes withiin the Places 365 dataset
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│ └── train.sh # script that load the initial model and trains it
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├── src
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│ ├── dataset.cc # dataset handler
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│ ├── dataset.h # dataset class header
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│ ├── net_runner.cc # program that runs training/evaluation of models
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│ └── net_runner.h # net_runner header
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```
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在脚本`prepare_and_run.sh`运行前，必须确保以下目录结构正确，这些文件将被传入设备用于训练。
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```text
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package-arm64/
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├── bin
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│ └── net_runner # the executable that performs the training/evaluation
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├── dataset
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│ ├── 0 # folder containing images 0-99 belonging to 0'th class
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│ │ ├── 0.bmp
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│ │ ├── 1.bmp
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│ │ ├── ....
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│ │ ├── 98.bmp
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│ │ └── 99.bmp
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│ ├── ... # folders containing images 0-99 belonging to 1'st-8'th classes
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│ ├── 9 # folder containing images 0-99 belonging to 9'th class
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│ │ ├── 0.bmp
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│ │ ├── 1.bmp
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│ │ ├── ....
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│ │ ├── 98.bmp
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│ │ └── 99.bmp
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├── lib
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│ └── libmindspore-lite.so # MindSpore Lite library
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├── model
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│ └── transfer_learning_tod.ms # model to train
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├── eval.sh # script that load the train model and evaluates its accuracy
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├── eval_untrained.sh # script that load the untrain model and evaluates its accuracy
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└── train.sh # script that load the initial model and train it
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```
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mindspore-ci-bot
4 years ago
committed by
Gitee