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README
Apache-2.0

Paddle-Lite-Demo

Paddle-Lite 提供了多个应用场景的 demo,并支持 Android、iOS 和 ArmLinux 三个平台:

关于 Paddle-Lite 更多示例,请参考如下文档链接:

要求

  • iOS

    • macOS+Xcode,已验证的环境:Xcode Version 11.5 (11E608c) on macOS Catalina(10.15.5)
    • Xcode 11.3会报"Invalid bitcode version ..."的编译错误,请将Xcode升级到11.4及以上的版本后重新编译
    • 对于ios 12.x版本,如果提示“xxx. which may not be supported by this version of Xcode”,请下载对应的工具包, 下载完成后解压放到/Applications/Xcode.app/Contents/Developer/Platforms/iPhoneOS.platform/DeviceSupport目录,重启xcode
  • Android

    • Android Studio 4.2;
    • adb调试工具;
    • Android手机或开发版;
    • 华为手机支持NPU的 Demo(NPU的功能暂时只在nova5、mate30和mate30 5G上进行了测试,用户可自行尝试其它搭载了麒麟810和990芯片的华为手机(如nova5i pro、mate30 pro、荣耀v30,mate40或p40,且需要将系统更新到最新版)
  • ARMLinux

    $ sudo apt-get update
    $ sudo apt-get install gcc g++ make wget unzip libopencv-dev pkg-config
    $ wget https://www.cmake.org/files/v3.10/cmake-3.10.3.tar.gz
    $ tar -zxvf cmake-3.10.3.tar.gz
    $ cd cmake-3.10.3
    $ ./configure
    $ make
    $ sudo make install

安装

$ git clone https://github.com/PaddlePaddle/Paddle-Lite-Demo

  • iOS

    • 在PaddleLite-ios-demo目录下执行download_dependencies.sh脚本,该脚本会离线下载并解压ios demo所需要的依赖, 包括paddle-lite 预测库,demo所需要的模型,opencv framework
    $ chmod +x download_dependencies.sh
    $ ./download_dependencies.sh
    • 打开xcode,点击“Open another project…”打开Paddle-Lite-Demo/PaddleLite-ios-demo/ios-xxx_demo/目录下的xcode工程;
    • 在选中左上角“project navigator”,选择“classification_demo”,修改“General”信息;
    • 插入ios真机(已验证:iphone8, iphonexr),选择Device为插入的真机;
    • 点击左上角“build and run”按钮;
  • Android

    • 打开Android Studio,在"Welcome to Android Studio"窗口点击"Open an existing Android Studio project",在弹出的路径选择窗口中进入"image_classification_demo"目录,然后点击右下角的"Open"按钮即可导入工程
    • 通过USB连接Android手机或开发板;
    • 载入工程后,点击菜单栏的Run->Run 'App'按钮,在弹出的"Select Deployment Target"窗口选择已经连接的Android设备(连接失败请检查本机adb工具是否正常),然后点击"OK"按钮;
    • 由于Demo所用到的库和模型均通过app/build.gradle脚本在线下载,因此,第一次编译耗时较长(取决于网络下载速度),请耐心等待;
    • 对于图像分类Demo,如果库和模型下载失败,建议手动下载并拷贝到相应目录下:
      1. paddle_lite_libs.tar.gz:解压后将 inference_*/cxx 拷贝至 Paddle-Lite-Demo/image_classification/android/app/cxx/image_classification/app/PaddleLite/cxx,将 inference_*/java 拷贝至 Paddle-Lite-Demo/image_classification/android/app/cxx/image_classification/app/PaddleLite/java
      2. mobilenet_v1_for_cpu.tar.gz:解压至 Paddle-Lite-Demo/image_classification/android/app/cxx/image_classification/app/app/src/main/assets/models/mobilenet_v1_for_cpu 目录
    • 在图像分类Demo中,你还可以通过上方的"Gallery"和"Take Photo"按钮从相册或相机中加载测试图像;
  • ARMLinux or Shell

    • 预测库下载
    $ cd Paddle-Lite-Demo/libs
    $ ./download.sh # 下载预测库
    • 图像分类Demo的编译与运行(以下所有命令均在设备上操作)
    $ cd Paddle-Lite-Demo/image_classification/assets
    $ ./download.sh # 下载模型、测试图片和标签文件
    $ cd Paddle-Lite-Demo/image_classification/android/shell/cxx/image_classification
    $ ./build.sh armv8 # 编译可执行文件,并运行程序

    在终端打印预测结果和性能数据。

    • 目标检测Demo的编译与运行(以下所有命令均在设备上操作)
    $ cd Paddle-Lite-Demo/object_detection/assets
    $ ./download.sh # 下载模型、测试图片和标签文件
    $ cd Paddle-Lite-Demo/object_detection/android/shell/cxx/yolov3_mobilenet_v3
    $ ./build.sh armv8 # 编译可执行文件,并运行程序

    在终端打印预测结果和性能数据,同时在build目录中生成dog_yolo_v3_mobilenetv3_detection_result.jpg.jpg。

效果展示

  • 图像分类

    Android iOS Armlinux
    android_image_classification_cat_cpu width="200" height="500" ios_static width="200" height="500" armlinux_image_classification_raspberry_pi width="200" height="500"
  • 目标检测

    Android iOS
    android_object_detection_picodet_cpu width="300" height="500" ios_picodet_static width="300" height="500"
  • OCR

    Android iOS
  • 人脸检测

    Android iOS
    android_face_detection_cpu 补充中
  • 人脸关键点检测

    Android iOS
    android_face_keypoints_detection_cpu 补充中
  • 口罩识别

    Android iOS
    android_mask_detection_cpu 补充中
  • 人像分割

    Android iOS
  • PP 识图

    Android iOS

性能优化

  • 多线程设置:
    • demo 中线程数默认是1,用户可以根据手机大核个数,设置最大线程数。如小米9,它有4个A76 大核,线程数最大设置为4;
    • 设置方法:可以通过界面的setting 按钮进行更新,也可以通过修改源码(config.set_threads())的线程数进行更新。
  • FP16 推理:
    • demo 中模型默认是FP32 模型,如果你是在armv8.2 架构以上的手机运行如小米9,则可选用FP16 模型进行推理;否则,不能使用FP16 模型进行推理。
    • FP16 推理方法:如果 APP 中 assets/model 目录下提供了FP16 模型(nb 模型以_fp16结尾),用户只需更新源码模型路径就行;否则,用户需要下载 OPT 可执行文件,并参考 OPT 使用文档重新转换模型(将 enable_fp16 设置为 true,如 --enable_fp16=1),然后更新源码中模型路径即可。
    • 如果提供的预测库没有包含FP16 kernel 算子话,用户需要从 release 仓库中下载含有FP16 kernel新的预测库。
  • Int8/稀疏推理:
    • 用户可以使用 PaddleSlim 工具 完成模型量化/稀疏化处理,进一步提升模型性能。
    • 使用方法:用 PaddleSlim 生成新模型,然后参考 FP16 推理方法,更新 demo 中预测模型即可。
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