在MCU或小型系统上执行推理

查看源文件

概述

本教程介绍面向IoT边缘侧设备的超轻量AI部署方案。

相较于移动设备，IoT设备上通常使用MicroControllerUnits(MCUs)，不仅设备系统ROM资源非常有限，而且硬件资源内存和算力都非常弱小。 因此IOT设备上的AI应用对AI模型推理的运行时内存和功耗都有严格限制。 MindSpore Lite针对MCUs部署硬件后端，提供了一种超轻量Micro AI部署解决方案：离线阶段直接将模型生成轻量化代码，不再需要在线解析模型和图编译，生成的Micro推理代码非常直观易懂，运行时内存小，代码体积也更小。 用户使用MindSpore Lite转换工具converter_lite非常容易生成可在x86/ARM64/ARM32/Cortex-M平台部署的推理代码。

通过Micro部署一个模型进行推理，通常包含以下四步：模型推理代码生成、Micro库获取、代码集成、编译部署。

模型推理代码生成

概述

通过MindSpore Lite转换工具converter_lite，并在转换工具的参数配置文件中，配置Micro配置项，就能为输入模型生成推理代码。 此章只介绍转换工具中生成代码的相关功能，关于转换工具的基本使用方法，请参考推理模型转换。

环境准备

以Linux环境下使用转换工具为例，需要进行如下环境准备。

  1. 转换工具运行所需的系统环境

    本例采用Linux下的系统环境，推荐使用Ubuntu 18.04.02LTS。

  2. 获取转换工具

    可以通过两种方式获取转换工具：

    • MindSpore官网下载Release版本。

      用户需下载操作系统为Linux-x86_64，硬件平台为CPU的发布包。

    • 从源码开始编译构建。

  3. 解压下载的包

    tar -zxf mindspore-lite-${version}-linux-x64.tar.gz

    ${version}是发布包的版本号。

  4. 将转换工具运行时需要的动态链接库加入环境变量LD_LIBRARY_PATH

    export LD_LIBRARY_PATH=${PACKAGE_ROOT_PATH}/tools/converter/lib:${LD_LIBRARY_PATH}

    ${PACKAGE_ROOT_PATH}是解压得到的文件夹路径。

执行converter_lite生成推理代码

  1. 进入转换目录

    cd ${PACKAGE_ROOT_PATH}/tools/converter/converter

  2. 设置Micro配置项

    在当前目录下新建micro.cfg文件，文件内容如下：

    [micro_param]

# enable code-generation for MCU HW

enable_micro=true

# specify HW target, support x86,Cortex-M, AMR32A, ARM64 only.

target=x86

# enable parallel inference or not.

support_parallel=false

    配置文件中，第一行的[micro_param]表明后续的变量参数属于Micro配置项micro_param，这些参数用于控制代码生成，各参数含义如下表1所示。 本例中，我们将生成适用底层架构为x86_64的Linux系统上的推理代码，故设置target=x86以声明生成的推理代码将用于底层架构为x86_64的Linux系统。

  3. 准备要生成推理代码的模型

    用户可点击此处下载本例中用到的MNIST手写数字识别模型。 下载后，解压包，得到mnist.tflite，该模型为已经训练完的MNIST分类模型，为TFLITE模型。将mnist.tflite模型拷贝到当前所在的转换工具目录。

  4. 执行converter_lite，生成代码

    ./converter_lite --fmk=TFLITE --modelFile=mnist.tflite --outputFile=mnist --configFile=micro.cfg

    运行成功后的结果显示为：

    CONVERTER RESULT SUCCESS:0

    用户若想了解converter_lite转换工具的相关参数，可参考converter参数说明。

    在转换工具执行成功后，生成的代码被保存在用户指定的outputFile路径下，在本例中，为当前转换目录下的mnist文件夹，内容如下：

    mnist                          # 指定的生成代码根目录名称
├── benchmark                  # 对模型推理代码进行集成调用的benchmark例程
│   ├── benchmark.c
│   ├── calib_output.c
│   ├── calib_output.h
│   ├── load_input.c
│   └── load_input.h
├── CMakeLists.txt             # benchmark例程的cmake工程文件
└── src                        # 模型推理代码目录
    ├── CMakeLists.txt
    ├── net.bin                # 二进制形式的模型权重
    ├── net.c
    ├── net.cmake
    ├── net.h
    ├── model.c
    ├── context.c
    ├── context.h
    ├── tensor.c
    ├── tensor.h
    ├── weight.c
    └── weight.h

    用户可通过参考benchmark例程中对推理代码的接口调用，来对代码进行集成以实现自身的应用，关于接口的详细说明，请参考API文档。

表1：micro_param参数定义

参数

是否必选

参数说明

取值范围

默认值

enable_micro

是

模型会生成代码，否则生成.ms

true, false

false

target

是

生成代码针对的平台

x86, Cortex-M, ARM32, ARM64

x86

support_parallel

否

是否生成多线程推理代码，仅在x86, ARM32, ARM64平台可设置为true

true, false

false

Micro库获取

在生成模型推理代码之后，用户在对代码进行集成开发之前，需要获得生成的推理代码所依赖的Micro库。

不同平台的推理代码依赖对应平台的Micro库，用户需根据使用的平台，在生成代码时，通过Micro配置项target指定该平台，并在获取Micro库时，获得该平台的Micro库。 用户可通过MindSpore官网下载对应平台的Release版本。

在模型推理代码生成章节，我们得到了x86_64架构Linux平台的模型推理代码，而该代码所依赖的Micro库，就在转换工具所使用的发布包内。 发布包内，推理代码所依赖的库和头文件如下：

mindspore-lite-{version}-linux-x64
├── runtime
│   └── include
│       └── c_api            # MindSpore Lite集成开发的C API头文件
└── tools
    └── codegen # 代码生成的source code 依赖include和lib
        ├── include          # 推理框架头文件
        │   ├── nnacl        # nnacl 算子头文件
        │   └── wrapper      # wrapper 算子头文件
        ├── lib
        │   ├── libwrapper.a # MindSpore Lite codegen生成代码依赖的部分算子静态库
        │   └── libnnacl.a   # MindSpore Lite codegen生成代码依赖的nnacl算子静态库
        └── third_party
            ├── include
            │   └── CMSIS    # ARM CMSIS NN 算子头文件
            └── lib
                └── libcmsis_nn.a # ARM CMSIS NN 算子静态库

代码集成及编译部署

不同的平台在代码集成和编译部署上会有不同的差异

在MCU上执行推理

概述

本教程以MNIST模型在STM32F767芯片的部署为例，演示如何在Cortex-M架构的MCU上部署推理模型，主要包括以下几步：

  • 通过converter_lite转换工具，生成适配Cortex-M架构的模型推理代码

  • 下载得到该Cortex-M架构对应的Micro库

  • 对得到的推理代码和Micro库进行集成，编译并部署验证

    在Windows平台，我们演示了如何通过IAR进行推理代码的集成开发，在Linux平台上，我们演示了如何通过MakeFile交叉编译的方式进行代码集成开发。

生成MCU推理代码

为MCU生成推理代码，请参考模型推理代码生成章节，只需将Micro配置项中的target=x86改为target=Cortex-M，就可以为MCU生成推理代码。 生成成功之后，文件夹内容如下所示：

mnist                          # 指定的生成代码根目录名称
├── benchmark                  # 对模型推理代码进行集成调用的benchmark例程
│   ├── benchmark.c
│   ├── calib_output.c
│   ├── calib_output.h
│   ├── data.c
│   ├── data.h
│   ├── load_input.c
│   └── load_input.h
├── build.sh                   # 一键编译脚本
├── CMakeLists.txt             # benchmark例程的cmake工程文件
├── cortex-m7.toolchain.cmake  # cortex-m7的交叉编译cmake文件
└── src                        # 模型推理代码目录
    ├── CMakeLists.txt
    ├── context.c
    ├── context.h
    ├── model.c
    ├── net.c
    ├── net.cmake
    ├── net.h
    ├── tensor.c
    ├── tensor.h
    ├── weight.c
    └── weight.h

下载Cortex-M架构Micro库

STM32F767芯片为Cortex-M7架构，可以通过以下两种方式获取该架构的Micro库：

  • MindSpore官网下载Release版本。

    用户需下载操作系统为None，硬件平台为Cortex-M7的发布包。

  • 从源码开始编译构建。

    用户可通过MSLITE_MICRO_PLATFORM=cortex-m7 bash build.sh -I x86_64命令，来编译得到Cortex-M7的发布包。

对于暂未提供发布包进行下载的其他Cortex-M架构平台，用户可参考从源码编译构建的方式，修改MindSpore源码，进行手动编译，得到发布包。

在Windows上的代码集成及编译部署：通过IAR进行集成开发

本例通过IAR进行代码集成及烧录，演示如何在Windows上对生成的推理代码进行集成开发。主要分为以下几步：

  • 下载所需要的相关软件，做好集成的环境准备

  • 通过STM32CubeMX软件生成所需要的MCU启动代码及演示工程

  • 在IAR内集成模型推理代码及Micro库

  • 编译并仿真运行

环境准备

  • STM32CubeMX Windows版本 >= 6.0.1

    • STM32CubeMX是意法半导体提供的STM32芯片图形化配置工具，该工具用于生成STM芯片的启动代码及工程。

  • IAR EWARM >= 9.1

    • IAR EWARM是一款IARSystems公司为ARM微处理器开发的一个集成开发环境。

获取MCU启动代码及工程

如果用户已经有自己的MCU工程，请忽略该章节。 本章，以生成STM32F767芯片的启动工程为例，演示如何通过STM32CubeMX生成STM32芯片的MCU工程。

  • 启动STM32CubeMX，在File选项中选择New Project来新建工程。

  • 在MCU/MPU Selector窗口，搜索并选择STM32F767IGT6，点击Start Project创建该芯片的工程。

  • 在Project Manager界面，配置工程名及生成的工程路径，在Toolchain / IDE选项选择EWARM，以指定生成IAR工程。

  • 点击上方的GENERATE CODE生成代码。

  • 在已安装IAR的PC机上，双击生成工程内EWARM目录下的Project.eww即可打开该IAR工程。

集成模型推理代码及Micro库

  • 将生成的推理代码拷贝到工程内，并将下载Cortex-M架构Micro库章节获得的压缩包解压后放到生成的推理代码目录内，目录如下图所示：

    test_stm32f767                                   # MCU工程目录
├── Core
│   ├── Inc
│   └── Src
│       ├── main.c
│       └── ...
├── Drivers
├── EWARM                                        # IAR工程文件目录
└── mnist                                        # 生成代码根目录
    ├── benchmark                                # 对模型推理代码进行集成调用的benchmark例程
    │   ├── benchmark.c
    │   ├── data.c
    │   ├── data.h
    │   └── ...
    │── mindspore-lite-1.8.0-none-cortex-m7      # 下载的Cortex-M7架构`Micro`库
    ├── src                                      # 模型推理代码目录
    └── ...

  • 向IAR工程导入源文件

    打开IAR工程，在Workspace界面，右键该项目，选择Add -> Add Group，添加一个mnist分组，右键点击该分组，重复新建分组操作，新建src和benchmark分组。 在各自分组下，选择Add -> Add Files，将mnist文件夹内src和benchmark目录下的源文件引入各自分组。

  • 加入依赖的头文件路径和静态库

    在Workspace界面，右键该项目，选择Options，打开项目选项窗口。在项目选项窗口左侧选择C/C++ Compiler选项，在右侧的子窗口中，选择Preprocessor子界面，将推理代码依赖的头文件路径加入到列表中。本例中添加的头文件路径如下：

    $PROJ_DIR$/../mnist/mindspore-lite-1.8.0-none-cortex-m7/runtime
$PROJ_DIR$/../mnist/mindspore-lite-1.8.0-none-cortex-m7/runtime/include
$PROJ_DIR$/../mnist/mindspore-lite-1.8.0-none-cortex-m7/tools/codegen/include
$PROJ_DIR$/../mnist/mindspore-lite-1.8.0-none-cortex-m7/tools/codegen/third_party/include/CMSIS/Core
$PROJ_DIR$/../mnist/mindspore-lite-1.8.0-none-cortex-m7/tools/codegen/third_party/include/CMSIS/DSP
$PROJ_DIR$/../mnist/mindspore-lite-1.8.0-none-cortex-m7/tools/codegen/third_party/include/CMSIS/NN
$PROJ_DIR$/../mnist

    在项目选项窗口左侧选择Linker选项，在右侧的子窗口中，选择Library子界面，将推理代码依赖的算子静态库文件加入到列表中。本例中添加的静态库文件如下：

    $PROJ_DIR$/../mnist/mindspore-lite-1.8.0-none-cortex-m7/tools/codegen/lib/libwrapper.a
$PROJ_DIR$/../mnist/mindspore-lite-1.8.0-none-cortex-m7/tools/codegen/lib/libnnacl.a
$PROJ_DIR$/../mnist/mindspore-lite-1.8.0-none-cortex-m7/tools/codegen/third_party/lib/libcmsis_nn.a

  • 修改main.c文件，调用benchmark函数

    在main.c开头增加头文件引用，并在main函数中调用benchmark.c中的benchmark函数，benchmark文件夹内的程序为对生成的src内的推理代码进行推理调用并比较输出的示范样例程序，用户可以自由对它进行修改。

    #include "benchmark/benchmark.h"
...
int main(void)
{
  ...
  if (benchmark() == 0) {
      printf("\nrun success.\n");
  } else {
      printf("\nrun failed.\n");
  }
  ...
}

  • 修改mnist/benchmark/data.c文件，将标杆输入输出数据存放在程序内以进行对比

    在benchmark例程内，会设置模型的输入数据，并将推理结果和设定的期望结果进行对比，得到误差偏移值。 在本例中，通过修改data.c的calib_input0_data数组，设置模型的输入数据，通过修改calib_output0_data，设定期望结果。

    float calib_input0_data[NET_INPUT0_SIZE] = {0.54881352186203,0.7151893377304077,0.6027633547782898,0.5448831915855408,0.42365479469299316,0.6458941102027893,0.4375872015953064,0.891772985458374,0.9636627435684204,0.3834415078163147,0.7917250394821167,0.5288949012756348,0.5680445432662964,0.9255966544151306,0.07103605568408966,0.08712930232286453,0.020218396559357643,0.832619845867157,0.7781567573547363,0.8700121641159058,0.978618323802948,0.7991585731506348,0.4614793658256531,0.7805292010307312,0.11827442795038223,0.6399210095405579,0.14335328340530396,0.9446688890457153,0.5218483209609985,0.4146619439125061,0.26455560326576233,0.7742336988449097,0.4561503231525421,0.568433940410614,0.018789799883961678,0.6176354885101318,0.6120957136154175,0.6169340014457703,0.9437480568885803,0.681820273399353,0.35950788855552673,0.43703195452690125,0.6976311802864075,0.0602254718542099,0.6667667031288147,0.670637845993042,0.21038256585597992,0.12892629206180573,0.31542834639549255,0.36371076107025146,0.5701967477798462,0.4386015236377716,0.9883738160133362,0.10204481333494186,0.20887675881385803,0.16130951046943665,0.6531082987785339,0.25329160690307617,0.4663107693195343,0.24442559480667114,0.15896958112716675,0.11037514358758926,0.6563295722007751,0.13818295300006866,0.1965823620557785,0.3687251806259155,0.8209932446479797,0.09710127860307693,0.8379449248313904,0.0960984081029892,0.9764594435691833,0.4686512053012848,0.9767611026763916,0.6048455238342285,0.7392635941505432,0.03918779268860817,0.28280696272850037,0.12019655853509903,0.296140193939209,0.11872772127389908,0.3179831802845001,0.414262980222702,0.06414749473333359,0.6924721002578735,0.5666014552116394,0.26538950204849243,0.5232480764389038,0.09394051134586334,0.5759465098381042,0.9292961955070496,0.3185689449310303,0.6674103736877441,0.13179786503314972,0.7163271903991699,0.28940609097480774,0.18319135904312134,0.5865129232406616,0.02010754682123661,0.8289400339126587,0.004695476032793522,0.6778165102005005,0.2700079679489136,0.7351940274238586,0.9621885418891907,0.2487531453371048,0.5761573314666748,0.5920419096946716,0.5722519159317017,0.22308163344860077,0.9527490139007568,0.4471253752708435,0.8464086651802063,0.6994792819023132,0.2974369525909424,0.8137978315353394,0.396505743265152,0.8811032176017761,0.5812729001045227,0.8817353844642639,0.6925315856933594,0.7252542972564697,0.5013243556022644,0.9560836553573608,0.6439902186393738,0.4238550364971161,0.6063932180404663,0.019193198531866074,0.30157482624053955,0.6601735353469849,0.2900775969028473,0.6180154085159302,0.42876869440078735,0.1354740709066391,0.29828232526779175,0.5699648857116699,0.5908727645874023,0.5743252635002136,0.6532008051872253,0.6521032452583313,0.43141844868659973,0.8965466022491455,0.36756187677383423,0.4358649253845215,0.8919233679771423,0.806194007396698,0.7038885951042175,0.10022688657045364,0.9194825887680054,0.7142413258552551,0.9988470077514648,0.14944830536842346,0.8681260347366333,0.16249293088912964,0.6155595779418945,0.1238199844956398,0.8480082154273987,0.8073189854621887,0.5691007375717163,0.40718328952789307,0.06916699558496475,0.6974287629127502,0.45354267954826355,0.7220556139945984,0.8663823008537292,0.9755215048789978,0.855803370475769,0.011714084073901176,0.359978049993515,0.729990541934967,0.17162968218326569,0.5210366249084473,0.054337989538908005,0.19999653100967407,0.01852179504930973,0.793697714805603,0.2239246815443039,0.3453516662120819,0.9280812740325928,0.704414427280426,0.031838931143283844,0.1646941602230072,0.6214783787727356,0.5772286057472229,0.23789282143115997,0.9342139959335327,0.6139659285545349,0.5356327891349792,0.5899099707603455,0.7301220297813416,0.31194499135017395,0.39822107553482056,0.20984375476837158,0.18619300425052643,0.9443724155426025,0.739550769329071,0.49045881628990173,0.22741462290287018,0.2543564736843109,0.058029159903526306,0.43441662192344666,0.3117958903312683,0.6963434815406799,0.37775182723999023,0.1796036809682846,0.024678727611899376,0.06724963337182999,0.6793927550315857,0.4536968469619751,0.5365791916847229,0.8966712951660156,0.990338921546936,0.21689698100090027,0.6630781888961792,0.2633223831653595,0.02065099962055683,0.7583786249160767,0.32001715898513794,0.38346388936042786,0.5883170962333679,0.8310484290122986,0.6289818286895752,0.872650682926178,0.27354204654693604,0.7980468273162842,0.18563593924045563,0.9527916312217712,0.6874882578849792,0.21550767123699188,0.9473705887794495,0.7308558225631714,0.2539416551589966,0.21331197023391724,0.518200695514679,0.02566271834075451,0.20747007429599762,0.4246854782104492,0.3741699755191803,0.46357542276382446,0.27762871980667114,0.5867843627929688,0.8638556003570557,0.11753185838460922,0.517379105091095,0.13206811249256134,0.7168596982955933,0.39605969190597534,0.5654212832450867,0.1832798421382904,0.14484776556491852,0.4880562722682953,0.35561272501945496,0.9404319524765015,0.7653252482414246,0.748663604259491,0.9037197232246399,0.08342243731021881,0.5521924495697021,0.5844760537147522,0.961936354637146,0.2921475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42349433898926,0.1390727013349533,0.4269043505191803,0.842854917049408,0.8180332779884338};
float calib_output0_data[NET_OUTPUT0_SIZE] = {3.5647096e-05,6.824297e-08,0.009327697,3.2340475e-05,1.1117579e-05,1.5117058e-06,4.6314454e-07,5.161628e-11,0.9905911,3.8835238e-10};

编译并仿真运行

在本例中，采用软件仿真的方式对推理结果进行查看分析。 在Workspace界面，右键该项目，选择Options，打开项目选项窗口。在项目选项窗口左侧选择Debugger选项，在右侧的Setup子窗口中，Driver选择为Simulator，使能软件仿真。

关闭项目选项窗口，点击菜单栏Project -> Download and Debug，进行项目编译并仿真。通过在benchmark调用处加入断点，可以观察到仿真运行的推理结果，以及benchmark()函数的返回值。

在Linux上的代码集成及编译部署：通过MakeFile进行代码集成

本章教程以在Linux平台上，通过MakeFile对生成的模型代码进行集成开发为例，演示了在Linux上进行MCU推理代码集成开发的一般步骤。 主要分为以下几步：

  • 下载所需要的相关软件，准备好交叉编译及烧录环境

  • 通过STM32CubeMX软件生成所需要的MCU启动代码及演示工程

  • 修改MakeFile集成模型推理代码及Micro库

  • 编译工程及烧录

  • 读取板端运行结果并验证

本例构建完成的完整demo代码，可点击此处下载。

环境准备

  • CMake >= 3.18.3

  • GNU Arm Embedded Toolchain >= 10-2020-q4-major-x86_64-linux

    • 该工具为适用Cortex-M的Linux下交叉编译工具。

    • 下载x86_64-linux版本的gcc-arm-none-eabi，解压缩后，将目录下的bin路径加入到PATH环境变量中：export PATH=gcc-arm-none-eabi路径/bin:$PATH。

  • STM32CubeMX-Lin >= 6.5.0

    • STM32CubeMX是意法半导体提供的STM32芯片图形化配置工具，该工具用于生成STM芯片的启动代码及工程。

  • STM32CubePrg-Lin >= 6.5.0

    • 该工具是意法半导体提供的烧录工具，可用于程序烧录和数据读取。

获取MCU启动代码及工程

如果用户已经有自己的MCU工程，请忽略该章节。 本章，以生成STM32F767芯片的启动工程为例，演示如何通过STM32CubeMX生成STM32芯片的MCU工程。

  • 启动STM32CubeMX，在File选项中选择New Project来新建工程。

  • 在MCU/MPU Selector窗口，搜索并选择STM32F767IGT6，点击Start Project创建该芯片的工程。

  • 在Project Manager界面，配置工程名及生成的工程路径，在Toolchain / IDE选项选择Makefile，以👈指定生成MakeFile工程。

  • 点击上方的GENERATE CODE生成代码

  • 在生成的工程目录下执行make，测试代码是否成功编译。

集成模型推理代码

  • 将生成的推理代码拷贝到MCU工程内，并将下载Cortex-M架构Micro库章节获得的压缩包解压后放到生成的推理代码目录内，目录如下图所示：

    stm32f767                                       # MCU工程目录
├── Core
│   ├── Inc
│   └── Src
│       ├── main.c
│       └── ...
├── Drivers
├── mnist                                        # 生成代码根目录
│   ├── benchmark                                # 对模型推理代码进行集成调用的benchmark例程
│   │   ├── benchmark.c
│   │   ├── data.c
│   │   ├── data.h
│   │   └── ...
│   │── mindspore-lite-1.8.0-none-cortex-m7      # 下载的Cortex-M7架构`Micro`库
│   ├── src                                      # 模型推理代码目录
│   └── ...
├── Makefile
├── startup_stm32f767xx.s
└── STM32F767IGTx_FLASH.ld

  • 修改MakeFile，将模型推理代码及依赖库加入工程

    本例中要加入工程的源代码包括src下的模型推理代码和benchmark目录下的模型推理调用示例代码， 修改MakeFile中的C_SOURCES变量定义，加入源文件路径：

    C_SOURCES =  \
mnist/src/context.c \
mnist/src/model.c \
mnist/src/net.c \
mnist/src/tensor.c \
mnist/src/weight.c \
mnist/benchmark/benchmark.c \
mnist/benchmark/calib_output.c \
mnist/benchmark/load_input.c \
mnist/benchmark/data.c \
...

    加入依赖的头文件路径：修改MakeFile中的C_INCLUDES变量定义，加入以下路径：

    LITE_PACK = mindspore-lite-1.8.0-none-cortex-m7

C_INCLUDES =  \
-Imnist/$(LITE_PACK)/runtime \
-Imnist/$(LITE_PACK)/runtime/include \
-Imnist/$(LITE_PACK)/tools/codegen/include \
-Imnist/$(LITE_PACK)/tools/codegen/third_party/include/CMSIS/Core \
-Imnist/$(LITE_PACK)/tools/codegen/third_party/include/CMSIS/DSP \
-Imnist/$(LITE_PACK)/tools/codegen/third_party/include/CMSIS/NN \
-Imnist \
...

    加入依赖的算子库（-lnnacl -lwrapper -lcmsis_nn），声明算子库文件所在路径，增加链接时编译选项（-specs=nosys.specs）。 本例中修改后的相关变量定义如下：

    LIBS = -lc -lm -lnosys -lnnacl -lwrapper -lcmsis_nn
LIBDIR = -Lmnist/$(LITE_PACK)/tools/codegen/lib -Lmnist/$(LITE_PACK)/tools/codegen/third_party/lib
LDFLAGS = $(MCU) -specs=nosys.specs -specs=nano.specs -T$(LDSCRIPT) $(LIBDIR) $(LIBS) -Wl,-Map=$(BUILD_DIR)/$(TARGET).map,--cref -Wl,--gc-sections

  • 修改main.c文件，调用benchmark函数

    在main函数中调用benchmark.c中的benchmark函数，benchmark文件夹内的程序为对生成的src内的推理代码进行推理调用并比较输出的示范样例程序，用户可以自由对它进行修改，在本例中，我们直接调用benchmark函数，并根据返回结果，赋值run_dnn_flag变量。

    run_dnn_flag = '0';
if (benchmark() == 0) {
    printf("\nrun success.\n");
    run_dnn_flag = '1';
} else {
    printf("\nrun failed.\n");
    run_dnn_flag = '2';
}

    在main.c开头增加头文件引用和run_dnn_flag变量的定义。

    #include "benchmark/benchmark.h"

char run_dnn_flag __attribute__((section(".myram"))) ;//测试用数组

    在本例中，为方便直接使用烧录器对推理结果进行读取，把变量定义在了自定义的section段（myram）中，用户可以使用下面方式设置自定义的section段，或者忽略该声明，采用串口或其他交互方式得到推理结果。

    自定义section段的设置方法如下： 修改STM32F767IGTx_FLASH.ld中的MEMORY段，增加一个自定义内存段MYRAM(在本例中，将RAM内存起始地址加4，以腾出内存给MYRAM)；接着在SECTIONS段内增加自定义的myram段声明。

    MEMORY
{
MYRAM (xrw)     : ORIGIN = 0x20000000, LENGTH = 1
RAM (xrw)      : ORIGIN = 0x20000004, LENGTH = 524284
...
}
...
SECTIONS
{
  ...
  .myram (NOLOAD):
  {
    . = ALIGN(4);
    _smyram = .;        /* create a global symbol at data start */
    *(.sram)           /* .data sections */
    *(.sram*)          /* .data* sections */

    . = ALIGN(4);
    _emyram = .;        /* define a global symbol at data end */
  } >MYRAM AT> FLASH
}

  • 修改mnist/benchmark/data.c文件，将标杆输入输出数据存放在程序内以进行对比

    在benchmark例程内，会设置模型的输入数据，并将推理结果和设定的期望结果进行对比，得到误差偏移值。 在本例中，通过修改data.c的calib_input0_data数组，设置模型的输入数据，通过修改calib_output0_data，设定期望结果。

    float calib_input0_data[NET_INPUT0_SIZE] = {0.54881352186203,0.7151893377304077,0.6027633547782898,0.5448831915855408,0.42365479469299316,0.6458941102027893,0.4375872015953064,0.891772985458374,0.9636627435684204,0.3834415078163147,0.7917250394821167,0.5288949012756348,0.5680445432662964,0.9255966544151306,0.07103605568408966,0.08712930232286453,0.020218396559357643,0.832619845867157,0.7781567573547363,0.8700121641159058,0.978618323802948,0.7991585731506348,0.4614793658256531,0.7805292010307312,0.11827442795038223,0.6399210095405579,0.14335328340530396,0.9446688890457153,0.5218483209609985,0.4146619439125061,0.26455560326576233,0.7742336988449097,0.4561503231525421,0.568433940410614,0.018789799883961678,0.6176354885101318,0.6120957136154175,0.6169340014457703,0.9437480568885803,0.681820273399353,0.35950788855552673,0.43703195452690125,0.6976311802864075,0.0602254718542099,0.6667667031288147,0.670637845993042,0.21038256585597992,0.12892629206180573,0.31542834639549255,0.36371076107025146,0.5701967477798462,0.4386015236377716,0.9883738160133362,0.10204481333494186,0.20887675881385803,0.16130951046943665,0.6531082987785339,0.25329160690307617,0.4663107693195343,0.24442559480667114,0.15896958112716675,0.11037514358758926,0.6563295722007751,0.13818295300006866,0.1965823620557785,0.3687251806259155,0.8209932446479797,0.09710127860307693,0.8379449248313904,0.0960984081029892,0.9764594435691833,0.4686512053012848,0.9767611026763916,0.6048455238342285,0.7392635941505432,0.03918779268860817,0.28280696272850037,0.12019655853509903,0.296140193939209,0.11872772127389908,0.3179831802845001,0.414262980222702,0.06414749473333359,0.6924721002578735,0.5666014552116394,0.26538950204849243,0.5232480764389038,0.09394051134586334,0.5759465098381042,0.9292961955070496,0.3185689449310303,0.6674103736877441,0.13179786503314972,0.7163271903991699,0.28940609097480774,0.18319135904312134,0.5865129232406616,0.02010754682123661,0.8289400339126587,0.004695476032793522,0.6778165102005005,0.2700079679489136,0.7351940274238586,0.9621885418891907,0.2487531453371048,0.5761573314666748,0.5920419096946716,0.5722519159317017,0.22308163344860077,0.9527490139007568,0.4471253752708435,0.8464086651802063,0.6994792819023132,0.2974369525909424,0.8137978315353394,0.396505743265152,0.8811032176017761,0.5812729001045227,0.8817353844642639,0.6925315856933594,0.7252542972564697,0.5013243556022644,0.9560836553573608,0.6439902186393738,0.4238550364971161,0.6063932180404663,0.019193198531866074,0.30157482624053955,0.6601735353469849,0.2900775969028473,0.6180154085159302,0.42876869440078735,0.1354740709066391,0.29828232526779175,0.5699648857116699,0.5908727645874023,0.5743252635002136,0.6532008051872253,0.6521032452583313,0.43141844868659973,0.8965466022491455,0.36756187677383423,0.4358649253845215,0.8919233679771423,0.806194007396698,0.7038885951042175,0.10022688657045364,0.9194825887680054,0.7142413258552551,0.9988470077514648,0.14944830536842346,0.8681260347366333,0.16249293088912964,0.6155595779418945,0.1238199844956398,0.8480082154273987,0.8073189854621887,0.5691007375717163,0.40718328952789307,0.06916699558496475,0.6974287629127502,0.45354267954826355,0.7220556139945984,0.8663823008537292,0.9755215048789978,0.855803370475769,0.011714084073901176,0.359978049993515,0.729990541934967,0.17162968218326569,0.5210366249084473,0.054337989538908005,0.19999653100967407,0.01852179504930973,0.793697714805603,0.2239246815443039,0.3453516662120819,0.9280812740325928,0.704414427280426,0.031838931143283844,0.1646941602230072,0.6214783787727356,0.5772286057472229,0.23789282143115997,0.9342139959335327,0.6139659285545349,0.5356327891349792,0.5899099707603455,0.7301220297813416,0.31194499135017395,0.39822107553482056,0.20984375476837158,0.18619300425052643,0.9443724155426025,0.739550769329071,0.49045881628990173,0.22741462290287018,0.2543564736843109,0.058029159903526306,0.43441662192344666,0.3117958903312683,0.6963434815406799,0.37775182723999023,0.1796036809682846,0.024678727611899376,0.06724963337182999,0.6793927550315857,0.4536968469619751,0.5365791916847229,0.8966712951660156,0.990338921546936,0.21689698100090027,0.6630781888961792,0.2633223831653595,0.02065099962055683,0.7583786249160767,0.32001715898513794,0.38346388936042786,0.5883170962333679,0.8310484290122986,0.6289818286895752,0.872650682926178,0.27354204654693604,0.7980468273162842,0.18563593924045563,0.9527916312217712,0.6874882578849792,0.21550767123699188,0.9473705887794495,0.7308558225631714,0.2539416551589966,0.21331197023391724,0.518200695514679,0.02566271834075451,0.20747007429599762,0.4246854782104492,0.3741699755191803,0.46357542276382446,0.27762871980667114,0.5867843627929688,0.8638556003570557,0.11753185838460922,0.517379105091095,0.13206811249256134,0.7168596982955933,0.39605969190597534,0.5654212832450867,0.1832798421382904,0.14484776556491852,0.4880562722682953,0.35561272501945496,0.9404319524765015,0.7653252482414246,0.748663604259491,0.9037197232246399,0.08342243731021881,0.5521924495697021,0.5844760537147522,0.961936354637146,0.2921475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2,0.879234790802002,0.5436780452728271,0.2826996445655823,0.030235258862376213,0.7103368043899536,0.007884103804826736,0.37267908453941345,0.5305371880531311,0.922111451625824,0.08949454873800278,0.40594232082366943,0.024313200265169144,0.3426109850406647,0.6222310662269592,0.2790679335594177,0.2097499519586563,0.11570323258638382,0.5771402716636658,0.6952700018882751,0.6719571352005005,0.9488610029220581,0.002703213831409812,0.6471966505050659,0.60039222240448,0.5887396335601807,0.9627703428268433,0.016871673986315727,0.6964824199676514,0.8136786222457886,0.5098071694374084,0.33396488428115845,0.7908401489257812,0.09724292904138565,0.44203564524650574,0.5199523568153381,0.6939564347267151,0.09088572859764099,0.2277594953775406,0.4103015661239624,0.6232946515083313,0.8869608044624329,0.618826150894165,0.13346147537231445,0.9805801510810852,0.8717857599258423,0.5027207732200623,0.9223479628562927,0.5413808226585388,0.9233060479164124,0.8298973441123962,0.968286395072937,0.919782817363739,0.03603381663560867,0.1747720092535019,0.3891346752643585,0.9521427154541016,0.300028920173645,0.16046763956546783,0.8863046765327454,0.4463944137096405,0.9078755974769592,0.16023047268390656,0.6611174941062927,0.4402637481689453,0.07648676633834839,0.6964631676673889,0.2473987489938736,0.03961552307009697,0.05994429811835289,0.06107853725552559,0.9077329635620117,0.7398838996887207,0.8980623483657837,0.6725823283195496,0.5289399027824402,0.30444636940956116,0.997962236404419,0.36218905448913574,0.47064894437789917,0.37824517488479614,0.979526937007904,0.1746583878993988,0.32798799872398376,0.6803486943244934,0.06320761889219284,0.60724937915802,0.47764649987220764,0.2839999794960022,0.2384132742881775,0.5145127177238464,0.36792758107185364,0.4565199017524719,0.3374773859977722,0.9704936742782593,0.13343943655490875,0.09680395573377609,0.3433917164802551,0.5910269021987915,0.6591764688491821,0.3972567617893219,0.9992780089378357,0.35189300775527954,0.7214066386222839,0.6375827193260193,0.8130538463592529,0.9762256741523743,0.8897936344146729,0.7645619511604309,0.6982485055923462,0.335498183965683,0.14768557250499725,0.06263600289821625,0.2419017106294632,0.432281494140625,0.521996259689331,0.7730835676193237,0.9587409496307373,0.1173204779624939,0.10700414329767227,0.5896947383880615,0.7453980445861816,0.848150372505188,0.9358320832252502,0.9834262132644653,0.39980170130729675,0.3803351819515228,0.14780867099761963,0.6849344372749329,0.6567619442939758,0.8620625734329224,0.09725799411535263,0.49777689576148987,0.5810819268226624,0.2415570467710495,0.16902540624141693,0.8595808148384094,0.05853492394089699,0.47062090039253235,0.11583399772644043,0.45705875754356384,0.9799623489379883,0.4237063527107239,0.857124924659729,0.11731556057929993,0.2712520658969879,0.40379273891448975,0.39981213212013245,0.6713835000991821,0.3447181284427643,0.713766872882843,0.6391869187355042,0.399161159992218,0.43176013231277466,0.614527702331543,0.0700421929359436,0.8224067091941833,0.65342116355896,0.7263424396514893,0.5369229912757874,0.11047711223363876,0.4050356149673462,0.40537357330322266,0.3210429847240448,0.029950324445962906,0.73725426197052,0.10978446155786514,0.6063081622123718,0.7032175064086914,0.6347863078117371,0.95914226770401,0.10329815745353699,0.8671671748161316,0.02919023483991623,0.534916877746582,0.4042436182498932,0.5241838693618774,0.36509987711906433,0.19056691229343414,0.01912289671599865,0.5181497931480408,0.8427768349647522,0.3732159435749054,0.2228638231754303,0.080532006919384,0.0853109210729599,0.22139644622802734,0.10001406073570251,0.26503971219062805,0.06614946573972702,0.06560486555099487,0.8562761545181274,0.1621202677488327,0.5596824288368225,0.7734555602073669,0.4564095735549927,0.15336887538433075,0.19959613680839539,0.43298420310020447,0.52823406457901,0.3494403064250946,0.7814795970916748,0.7510216236114502,0.9272118210792542,0.028952548280358315,0.8956912755966187,0.39256879687309265,0.8783724904060364,0.690784752368927,0.987348735332489,0.7592824697494507,0.3645446300506592,0.5010631680488586,0.37638914585113525,0.364911824464798,0.2609044909477234,0.49597030878067017,0.6817399263381958,0.27734026312828064,0.5243797898292542,0.117380291223526,0.1598452925682068,0.04680635407567024,0.9707314372062683,0.0038603513967245817,0.17857997119426727,0.6128667593002319,0.08136960119009018,0.8818964958190918,0.7196201682090759,0.9663899540901184,0.5076355338096619,0.3004036843776703,0.549500584602356,0.9308187365531921,0.5207614302635193,0.2672070264816284,0.8773987889289856,0.3719187378883362,0.0013833499979227781,0.2476850152015686,0.31823351979255676,0.8587774634361267,0.4585031569004059,0.4445872902870178,0.33610227704048157,0.880678117275238,0.9450267553329468,0.9918903112411499,0.3767412602901459,0.9661474227905273,0.7918795943260193,0.675689160823822,0.24488948285579681,0.21645726263523102,0.1660478264093399,0.9227566123008728,0.2940766513347626,0.4530942440032959,0.49395784735679626,0.7781715989112854,0.8442349433898926,0.1390727013349533,0.4269043505191803,0.842854917049408,0.8180332779884338};
float calib_output0_data[NET_OUTPUT0_SIZE] = {3.5647096e-05,6.824297e-08,0.009327697,3.2340475e-05,1.1117579e-05,1.5117058e-06,4.6314454e-07,5.161628e-11,0.9905911,3.8835238e-10};

编译工程及烧录

  • 编译

    在MCU工程目录下，执行make命令进行编译，编译成功后显示如下，test_stm767为本例的MCU工程名：

    arm-none-eabi-size build/test_stm767.elf
text      data    bss    dec       hex      filename
120316    3620    87885  211821    33b6d    build/test_stm767.elf
arm-none-eabi-objcopy -O ihex build/test_stm767.elf build/test_stm767.hex
arm-none-eabi-objcopy -O binary -S build/test_stm767.elf build/test_stm767.bin

  • 烧录运行

    我们可以通过STMSTM32CubePrg烧录工具进行代码烧录并运行。在PC机上，通过STLink连接一块可烧录的开发板，然后在当前MCU工程目录下运行以下命令，执行烧录并运行程序：

    bash ${STMSTM32CubePrg_PATH}/bin/STM32_Programmer.sh -c port=SWD -w build/test_stm767.bin 0x08000000 -s 0x08000000

    ${STMSTM32CubePrg_PATH为}为STMSTM32CubePrg安装路径。关于命令中的各参数含义，请参考STMSTM32CubePrg的使用手册。

推理结果验证

本例中，我们把benchmark运行结果标志保存在了起始地址为0x20000000且大小为1字节的内存段内，故可以直接通过烧录器获取该处地址的数据，以得到程序返回结果。 在PC机上，通过STLink连接一块已烧录好程序的开发板，通过执行以下命令读取内存数据：

bash ${STMSTM32CubePrg_PATH为}/bin/STM32_Programmer.sh -c port=SWD model=HOTPLUG --upload 0x20000000 0x1 ret.bin

${STMSTM32CubePrg_PATH为}为STMSTM32CubePrg安装路径。关于命令中的各参数含义，请参考STMSTM32CubePrg的使用手册。

读取的数据被保存在ret.bin文件内，运行cat ret.bin，如果板端推理成功，ret.bin内保存着字符1，会显示如下：

1

在轻鸿蒙设备上执行推理

轻鸿蒙编译环境准备

用户可以通过OpenHarmony官网来学习轻鸿蒙环境下的编译及烧录。 本教程以Hi3516开发板为例，演示如何在轻鸿蒙环境上使用Micro部署推理模型。

编译模型

使用converter_lite编译lenet模型，生成对应轻鸿蒙平台的推理代码，命令如下：

./converter_lite --fmk=TFLITE --modelFile=mnist.tflite --outputFile=${SOURCE_CODE_DIR} --configFile=${COFIG_FILE}

其中config配置文件设置target = ARM32。

编写构建脚本

轻鸿蒙应用程序开发请先参考运行Hello OHOS。将上一步生成的mnist目录拷贝到任意鸿蒙源码路径下，假设为applications/sample/，然后新建BUILD.gn文件：

<harmony-source-path>/applications/sample/mnist
├── benchmark
├── CMakeLists.txt
├── BUILD.gn
└── src

下载适用于OpenHarmony的预编译推理runtime包，然后将其解压至任意鸿蒙源码路径下。编写BUILD.gn文件：

import("//build/lite/config/component/lite_component.gni")
import("//build/lite/ndk/ndk.gni")

lite_component("mnist_benchmark") {
    target_type = "executable"
    sources = [
        "benchmark/benchmark.cc",
        "benchmark/calib_output.cc",
        "benchmark/load_input.c",
        "src/net.c",
        "src/weight.c",
        "src/session.cc",
        "src/tensor.cc",
    ]
    features = []
    include_dirs = [
        "<YOUR MINDSPORE LITE RUNTIME PATH>/runtime",
        "<YOUR MINDSPORE LITE RUNTIME PATH>/tools/codegen/include",
        "//applications/sample/mnist/benchmark",
        "//applications/sample/mnist/src",
    ]
    ldflags = [
        "-fno-strict-aliasing",
        "-Wall",
        "-pedantic",
        "-std=gnu99",
    ]
    libs = [
        "<YOUR MINDSPORE LITE RUNTIME PATH>/runtime/lib/libmindspore-lite.a",
        "<YOUR MINDSPORE LITE RUNTIME PATH>/tools/codegen/lib/libwrapper.a",
    ]
    defines = [
        "NOT_USE_STL",
        "ENABLE_NEON",
        "ENABLE_ARM",
        "ENABLE_ARM32"
    ]
    cflags = [
        "-fno-strict-aliasing",
        "-Wall",
        "-pedantic",
        "-std=gnu99",
    ]
    cflags_cc = [
        "-fno-strict-aliasing",
        "-Wall",
        "-pedantic",
        "-std=c++17",
    ]
}

<YOUR MINDSPORE LITE RUNTIME PATH>是解压出来的推理runtime包路径，比如//applications/sample/mnist/mindspore-lite-1.3.0-ohos-aarch32。 修改文件build/lite/components/applications.json，添加组件mnist_benchmark的配置：

{
    "component": "mnist_benchmark",
    "description": "Communication related samples.",
    "optional": "true",
    "dirs": [
    "applications/sample/mnist"
    ],
    "targets": [
    "//applications/sample/mnist:mnist_benchmark"
    ],
    "rom": "",
    "ram": "",
    "output": [],
    "adapted_kernel": [ "liteos_a" ],
    "features": [],
    "deps": {
    "components": [],
    "third_party": []
    }
},

修改文件vendor/hisilicon/hispark_taurus/config.json，新增mnist_benchmark组件的条目:

{ "component": "mnist_benchmark", "features":[] }

编译benchmark

cd <openharmony-source-path>
hb set(设置编译路径)
.（选择当前路径）
选择ipcamera_hispark_taurus@hisilicon并回车
hb build mnist_benchmark（执行编译）

生成结果文件out/hispark_taurus/ipcamera_hispark_taurus/bin/mnist_benchmark。

执行benchmark

将mnist_benchmark、权重文件（mnist/src/net.bin）以及输入文件解压后拷贝到开发板上，然后执行：

OHOS # ./mnist_benchmark mnist_input.bin net.bin 1
OHOS # =======run benchmark======
input 0: mnist_input.bin

loop count: 1
total time: 10.11800ms, per time: 10.11800ms

outputs:
name: int8toft32_Softmax-7_post0/output-0, DataType: 43, Elements: 10, Shape: [1 10 ], Data:
0.000000, 0.000000, 0.003906, 0.000000, 0.000000, 0.992188, 0.000000, 0.000000, 0.000000, 0.000000,
========run success=======