arm平台上的MNN编译与运行 |
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1.编译准备 在GitHub - alibaba/MNN: MNN is a blazing fast, lightweight deep learning framework, battle-tested by business-critical use cases in Alibaba下载code->downloadZIP unzip MNN-master.zip cd MNN-master vim CMakeLists.txt将其中的MNN_BUILD_DEMO、MNN_BUILD_TOOLS、MNN_BUILD_QUANTOOLS、MNN_BUILD_CONVERTER的option改为ON 2.生成Makefile 查看arm的架构，如armv7、v8等，赋值给CMAKE_SYSTEM_PROCESSOR 确定编译结果保存文件的路径赋值给CMAKE_INSTALL_PREFIX 确定编译工具链的位置，赋值给CMAKE_C_COMPILER、CMAKE_CXX_COMPILER whereis arm-linux-gnueabihf-gcc arm-linux-gnueabihf-gcc: /root/arm-linux-compiler/gcc-linaro-12.2.1-2023.01-x86_64_arm-linux-gnueabihf/bin/arm-linux-gnueabihf-gcc whereis arm-linux-gnueabihf-g++ arm-linux-gnueabihf-g++: /root/arm-linux-compiler/gcc-linaro-12.2.1-2023.01-x86_64_arm-linux-gnueabihf/bin/arm-linux-gnueabihf-g++ mkdir install && mkdir build && cd build cmake .. \ -DCMAKE_BUILD_TYPE=Release \ -DMNN_BUILD_DEMO=ON \ -DMNN_BUILD_BENCHMARK=true \ -DMNN_BUILD_CONVERTER=true \ -DMNN_BUILD_QUANTOOLS=true \ -DMNN_USE_INT8_FAST=true \ -DMNN_BUILD_TEST=true\ -DMNN_OPENCL=ON \ -DCMAKE_SYSTEM_NAME=Linux \ -DCMAKE_SYSTEM_VERSION=1 \ -DCMAKE_SYSTEM_PROCESSOR=armv7 \ -DCMAKE_INSTALL_PREFIX=/root/mnn/MNN-master/install \ -DCMAKE_C_COMPILER=/root/arm-linux-compiler/gcc-linaro-12.2.1-2023.01-x86_64_arm-linux-gnueabihf/bin/arm-linux-gnueabihf-gcc \ -DCMAKE_CXX_COMPILER=/root/arm-linux-compiler/gcc-linaro-12.2.1-2023.01-x86_64_arm-linux-gnueabihf/bin/arm-linux-gnueabihf-g++3.编译 执行make 如果遇到报错： /root/mnn/MNN-master/source/math/Vec.hpp:342:71: error: cannot convert ‘int16x8_t’ to ‘int8x16_t’ 342 | auto m0m1 = vtrnq_s8(reinterpret_cast(vec0.value), reinterpret_cast(vec1.value)); | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | | | int16x8_t In file included from /root/mnn/MNN-master/source/math/Vec.hpp:15: /root/arm-linux-compiler/gcc-linaro-12.2.1-2023.01-x86_64_arm-linux-gnueabihf/lib/gcc/arm-linux-gnueabihf/12.2.1/include/arm_neon.h:9532:36: note: initializing argument 2 of ‘int8x16x2_t vtrnq_s8(int8x16_t, int8x16_t)’ 9532 | vtrnq_s8 (int8x16_t __a, int8x16_t __b) | ~~~~~~~~~~^~~ /root/mnn/MNN-master/source/math/Vec.hpp:343:71: error: cannot convert ‘int16x8_t’ to ‘int8x16_t’ 343 | auto m2m3 = vtrnq_s8(reinterpret_cast(vec2.value), reinterpret_cast(vec3.value)); | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | | | int16x8_t则将/root/mnn/MNN-master/source/math/Vec.hpp的342、343行 从 auto m0m1 = vtrnq_s8(reinterpret_cast(vec0.value), reinterpret_cast(vec1.value)); auto m2m3 = vtrnq_s8(reinterpret_cast(vec2.value), reinterpret_cast(vec3.value));改为 auto m0m1 = vtrnq_s8(reinterpret_cast(vec0.value), reinterpret_cast(vec1.value)); auto m2m3 = vtrnq_s8(reinterpret_cast(vec2.value), reinterpret_cast(vec3.value));重新make -j4 如果发生make进度卡住的情况，则不要加-j4 成果物是libMNN.so 4.模型准备 在Model Zoo · 语雀中找合适的模型，比如：GitHub - Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB: 💎1MB lightweight face detection model (1MB轻量级人脸检测模型) |
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