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Apache-2.0

Paddle-Lite-Demo

Paddle-Lite提供IOS、Android和ARMLinux的示例,具体如下:

  • iOS示例:
    • 基于MobileNetV1的图像分类(支持视频流);
    • 基于MobileNetV1-SSD的目标检测(支持视频流);
  • Android示例:
    • 基于MobileNetV1的图像分类;
    • 基于MobileNetV1-SSD的目标检测;
    • 基于Ultra-Light-Fast-Generic-Face-Detector-1MB的人脸检测;
    • 基于DeeplabV3+MobilNetV2的人像分割;
    • 基于视频流的人脸检测+口罩识别;
    • 基于YOLOV3-MobileNetV3的目标检测;
  • ARMLinux示例:
    • 基于MobileNetV1的图像分类;
    • 基于MobileNetV1-SSD的目标检测;

关于Paddle-Lite和示例,请参考本文剩余章节和如下文档链接:

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 3.4
    • Android手机或开发版,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,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java/PaddlePredictor.jar拷贝至Paddle-Lite-Demo/PaddleLite-android-demo/image_classification_demo/app/libs,将java/libs/armeabi-v7a/libpaddle_lite_jni.so拷贝至Paddle-Lite-Demo/PaddleLite-android-demo/image_classification_demo/app/src/main/jniLibs/armeabi-v7a/libpaddle_lite_jni.so,将java/libs/armeabi-v8a/libpaddle_lite_jni.so拷贝至Paddle-Lite-Demo/PaddleLite-android-demo/image_classification_demo/app/src/main/jniLibs/arm64-v8a/libpaddle_lite_jni.so 2)mobilenet_v1_for_cpu.tar.gz:解压至Paddle-Lite-Demo/PaddleLite-android-demo/image_classification_demo/app/src/main/assets/models/mobilenet_v1_for_cpu 3)mobilenet_v1_for_npu.tar.gz:解压至Paddle-Lite-Demo/PaddleLite-android-demo/image_classification_demo/app/src/main/assets/models/mobilenet_v1_for_npu
    • 在图像分类Demo中,默认会载入一张猫的图像,并会在图像下方给出CPU的预测结果,如果你使用的是麒麟810或990芯片的华为手机(如Nova5系列),可以在右上角的上下文菜单选择"Settings..."打开设置窗口切换NPU模型进行预测;
    • 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

    • 模型和预测库下载
    $ cd Paddle-Lite-Demo/PaddleLite-armlinux-demo
    $ ./download_models_and_libs.sh # 下载模型和预测库
    • 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/PaddleLite-armlinux-demo/image_classification_demo
    $ ./run.sh armv8 # RK3399
    $ ./run.sh armv7hf # 树莓派3B

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

    • 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/PaddleLite-armlinux-demo/object_detection_demo
    $ ./run.sh armv8 # RK3399
    $ ./run.sh armv7hf # 树莓派3B

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

更新到最新的预测库

IOS更新预测库

  • 替换库文件:产出的lib目录替换Paddle-Lite-Demo/PaddleLite-ios-demo/ios-classification_demo/classification_demo/lib目录
  • 替换头文件:产出的include目录下的文件替换Paddle-Lite-Demo/PaddleLite-ios-demo/ios-classification_demo/classification_demo/paddle_lite目录下的文件

Android更新预测库

  • 替换jar文件:将生成的build.lite.android.xxx.gcc/inference_lite_lib.android.xxx/java/jar/PaddlePredictor.jar替换demo中的Paddle-Lite-Demo/PaddleLite-android-demo/image_classification_demo/app/libs/PaddlePredictor.jar
  • 替换arm64-v8a jni库文件:将生成build.lite.android.armv8.gcc/inference_lite_lib.android.armv8/java/so/libpaddle_lite_jni.so库替换demo中的Paddle-Lite-Demo/PaddleLite-android-demo/image_classification_demo/app/src/main/jniLibs/arm64-v8a/libpaddle_lite_jni.so
  • 替换armeabi-v7a jni库文件:将生成的build.lite.android.armv7.gcc/inference_lite_lib.android.armv7/java/so/libpaddle_lite_jni.so库替换demo中的Paddle-Lite-Demo/PaddleLite-android-demo/image_classification_demo/app/src/main/jniLibs/armeabi-v7a/libpaddle_lite_jni.so.

ARMLinux更新预测库

  • 替换头文件目录,将生成的cxx中的include目录替换Paddle-Lite-Demo/PaddleLite-armlinux-demo/Paddle-Lite/include目录;
  • 替换armv8动态库,将生成的cxx/libs中的libpaddle_light_api_shared.so替换Paddle-Lite-Demo/PaddleLite-armlinux-demo/Paddle-Lite/libs/armv8/libpaddle_light_api_shared.so
  • 替换armv7hf动态库,将生成的cxx/libs中的libpaddle_light_api_shared.so替换Paddle-Lite-Demo/PaddleLite-armlinux-demo/Paddle-Lite/libs/armv7hf/libpaddle_light_api_shared.so

效果展示

  • iOS

    • 基于MobileNetV1的图像分类

    ios_static ios_video

    • 基于MobileNetV1-SSD的目标检测

    ios_static ios_video

  • Android

    • 基于MobileNetV1的图像分类

      • CPU预测结果(测试环境:华为nova5)

      android_image_classification_cat_cpu android_image_classification_keyboard_cpu

      • NPU预测结果(测试环境:华为nova5)

      android_image_classification_cat_npu android_image_classification_keyboard_npu

    • 基于MobileNetV1-SSD的目标检测

      • CPU预测结果(测试环境:华为nova5)

      android_object_detection_npu

      • NPU预测结果(测试环境:华为nova5)

      待支持

    • 基于Ultra-Light-Fast-Generic-Face-Detector-1MB的人脸检测

      • CPU预测结果(测试环境:华为nova5)

      android_face_detection_cpu

      • NPU预测结果

      待支持

    • 基于DeeplabV3+MobilNetV2的人像分割

      • CPU预测结果(测试环境:华为nova5)

      android_human_segmentation_cpu

      • NPU预测结果

      待支持

    • 基于视频流的人脸检测+口罩识别

      • CPU预测结果(测试环境:华为mate30)

      android_mask_detection_cpu

      • NPU预测结果

      待支持

    • 基于视频流的人脸关键点检测

      • CPU预测结果(测试环境:OnePlus 7)

      android_face_keypoints_detection_cpu

      • NPU预测结果

      待支持

    • 基于YOLOV3-MobileNetV3的目标检测

      • CPU预测结果(测试环境:华为p40,预测总耗时:55.9ms)

      android_yolo_detection_cpu

      • CPU+NPU异构计算预测结果(预测总耗时:27.1ms);

      android_yolo_detection_hybrid_cpu_npu

      注意:CPU+NPU的异构计算需要基于原始Paddle模型配置文件进行手动分割子图,子图分割结果如图所示:MobileNetV3被包裹在subgraph op内并Offload到NPU上执行(未做任何优化,后续将加入zero copy并对相关op进行针对性优化,届时性能将获更大的提升),yolo_box和multiclass_nms等算子在CPU上执行。

  • ARMLinux

    • 基于MobileNetV1的图像分类

    armlinux_image_classification_raspberry_pi

    • 基于MobileNetV1-SSD的目标检测

    armlinux_object_detection_raspberry_pi

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简介

Paddle Lite Demo提供了应用Paddle Lite实现的IOS、Android和ARMLinux的代码示例,包括图像分类、目标检测、人脸检测等多个示例。 展开 收起
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