一、Ubuntu下使用：1、克隆下载NNPACKgit clone https://github.com/Maratyszcza/NNPACK.git2、安装nijia，并编译Install ninja build systemsudo apt-get install ninja-build || brew install ninjaInstall PeachPy assembler and confu configuration system[sudo] pip install --upgrade git+https://github.com/Maratyszcza/PeachPy [sudo] pip install --upgrade git+https://github.com/Maratyszcza/confuThen clone NNPACK, install dependencies, configure, and buildcd NNPACK confu setup python ./configure.py ninja3、链接的时候还需要用到pthread库，否在会遇到找不到实现函数。target_link_libraries(NNPACK libnnpack.a libpthreadpool.a pthread)
需要加入pthread库，否这会报错。4、测试代码#include #include "nnpack.h" #include #include using namespace std; float test_nnpack(){ //init nnpack enum nnp_status init_status = nnp_initialize(); if (init_status != nnp_status_success) { return 0; } enum nnp_convolution_algorithm algorithm = nnp_convolution_algorithm_auto; enum nnp_convolution_transform_strategy strategy=nnp_convolution_transform_strategy_tuple_based; const size_t batch_size = 1; const size_t input_channels = 128; const size_t output_channels = 128; const struct nnp_padding input_padding = { 1, 1, 1, 1 }; const struct nnp_size input_size ={ 256, 256}; const struct nnp_size kernel_size = { 5, 5 }; const struct nnp_size stride={.width=2,.height=2}; const struct nnp_size output_size = { .width = (input_padding.left + input_size.width + input_padding.right - kernel_size.width)/stride.width + 1, .height =(input_padding.top + input_size.height + input_padding.bottom - kernel_size.height)/stride.height + 1 }; //malloc memory for input, kernel, output, bias float* input = (float*)malloc(batch_size * input_channels *input_size.height *input_size.width * sizeof(float)); float* kernel = (float*)malloc(input_channels * output_channels * kernel_size.height * kernel_size.width * sizeof(float)); float* output = (float*)malloc(batch_size* output_channels * output_size.height * output_size.width * sizeof(float)); float* bias = (float*)malloc(output_channels * sizeof(float)); pthreadpool_t threadpool= nullptr; struct nnp_profile computation_profile;//use for compute time; //init input data int i,j; for(int c=0; cout; for(int i=0;i
在mxnet、tiny-dnn中的引用方法：https://www.insight.io/github.com/dmlc/mxnet/blob/master/src/operator/nnpack/nnpack_convolution-inl.hhttps://github.com/tiny-dnn/tiny-dnn/blob/master/tiny_dnn/core/kernels/conv2d_op_nnpack.h在tiny-dnn中，底层调用nnpack为啥只采用了一个线程，于是我测试了多线程的速度，经过测试发现nnpack的弱点，原来在于多线程：
二、android使用1、添加ndk-build环境变量路径：export NDK_ROOT=/home/hjimce/Android/Sdk/ndk-bundle/cd到nnpack所在的目录，更改jni APP_PLATFORM := android-212、直接运行：${NDK_ROOT}/ndk-build 3、代码中引用cmake要指定相关的库文件：arguments "-DANDROID_ARM_NEON=TRUE", "-DANDROID_TOOLCHAIN=clang" ndk { // Specifies the ABI configurations of your native // libraries Gradle should build and package with your APK. abiFilters 'x86','armeabi-v7a' }手机速度测试：

修改：多线程时间测试不能使用time.clock(),可使用auto begin =std::chrono::high_resolution_clock::now();