pytorch 移动端框架 thnets 附c示例代码

最新推荐文章于 2023-02-26 22:03:12 发布

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最新推荐文章于 2023-02-26 22:03:12 发布

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文章标签：人工智能内存管理

原文链接：http://www.cnblogs.com/cpuimage/p/8366792.html

本文分享了一个移动端图像识别项目的实践经验，对比了mxnet、thnets、caffe及tensorflow等框架的使用心得，重点介绍了thnets在iOS下的应用及性能优化。

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前年年前做一个手机移动端图像识别项目的时候，

先后尝试了mxnet,thnets,caffe,tensorflow.

当时的情况是,mxnet内存管理奇差，内存经常由于模型运算分配不足，app挂掉。

后来调研了下caffe发现也很不友好。

最后发现thnets相对比较轻巧，

经过算法调优之后，性能还不错，

特别是在ios下启用了Accelerate加速库。

后来tensorflow快速发展，就切到tensorflow上了。

最近看了下thnets，作者 mvitez 看来不怎么上心了。

至今为止，改进的不多。

thnets的移动端样例代码，可以参考:

https://github.com/e-lab/apps-iOs

https://github.com/e-lab/apps-android

有一段时间nnpack加速库起来了，就想着把thnets给patch一下nnpack.

但是由于项目太赶，没那个时间去做。

后来也因为切换到tensorflow上了。

thnets就被雪藏了。

向作者提交了两个建议，1，改用stb_image加载图片 2, 支持windows平台

这个两个工作，我都做了。

作者合了1。

2 我关闭了。几天前去看历史记录，作者当时问我关闭的原因,我没回。

真正的原因是。。。thnets被我遗忘了，而windows 版本的存在意义并不大。

今天稍微花了点时间，在windows写个thnets的demo样例，给有需要的网友~

项目地址:

https://github.com/cpuimage/thnets

代码示例见:demo.c

#include <string.h> 
#include <stdlib.h> 
#include <stdio.h> 
#include "thnets.h"
// http://cpuimage.cnblogs.com/
THNETWORK * net;

char * labels[] = { "lamp"," bottle"," watch"," pen"," grass"," shoe"," wall"," chair"," mug"," fork"," table"," book"," tablet"," bookcase"," pencil"," door"," face"," ceiling"," sofa"," bicycle"," aluminum - can"," window"," road"," stairs"," floor"," painting"," toy"," remote"," computer"," plant"," television"," dog"," laptop"," microwave"," cat"," tree"," knife"," car"," motorcycle"," person"," cup"," sidewalk"," telephone"," spoon"," hand"," sofabed" };

int main(int argc, char ** argv) {
    img_t image = { 0 };
    //test.jpg 
    char * pic_file = argv[1];
    //model
    char * model_path = argv[2];

    int dropclassifier = 0;
    loadimage(pic_file, &image);
    THInit();
    printf("init_thnets.");
    net = THLoadNetwork(model_path);
    if (net) {
        THUseSpatialConvolutionMM(net, 2);
        if (dropclassifier == 1) {
            if (net->net->modules[net->net->nelem - 1].type == MT_SoftMax)
                net->net->nelem--;
            if (net->net->modules[net->net->nelem - 1].type == MT_Linear)
                net->net->nelem--;
            if (net->net->modules[net->net->nelem - 1].type == MT_View)
                net->net->nelem--;
        }
    }
    else {
        printf("Shiiiiit went down.");
        return -1;
    }
    float * percentages = 0;
    int outwidth, outheight;

    if (!net) {
        return 0;
    }
    int i = 0;
    int nbatch = 1;
    unsigned char *bitmaps[256];
    for (i = 0; i < nbatch; i++)
        bitmaps[i] = image.bitmap;
    int size = THProcessImages(net, bitmaps, nbatch, image.width, image.height, image.cp * image.width, &percentages, &outwidth, &outheight, 0);
    for (i = 0; i < nbatch; i++)
        if (bitmaps[i])
            free(bitmaps[i]);
    if (percentages)
    {
        float max[3] = { 0 };
        int maxPos[3] = { 0 };
        for (int j = 0; j < size; j++) {
            if (percentages[j] > max[0]) {
                maxPos[0] = j;
                max[0] = percentages[j];
            }
            else if (percentages[j] > max[1]) {
                maxPos[1] = j;
                max[1] = percentages[j];
            }
            else if (percentages[j] > max[2]) {
                maxPos[2] = j;
                max[2] = percentages[j];
            }
        }
        for (int index = 0; index < 3; index += 1) {
            const float predictionValue = max[index];
            if (predictionValue > 0.05f) {
                printf(" %f %s  \n", predictionValue, labels[maxPos[index] % size]);
            }
        }
        free(percentages);
    }
    getchar();
}