【R329开发板评测】基于R2329-AIPU的动态手势识别及实机部署运行

budbool LV 8

一、模型与算法
采用MIT开源的TSM算法，论文作者通过对特征进行shift操作，在不增加额外参数和算力的情况下进行时间建模，然后移植到了各种手机上进行了各种算法的对比。作者代码也移植到了jetson nano上能达到实时运行(没有使用CUDA)，甚至在FPGA上也进行了移植，满满的工业风。

二、效果展示

模型backbone是mobileV2,然而运行只有18FPS，不应该啊,看了直播才知道，初代AIPU对分组卷积不太友好，ok好吧。我也尝试了在R329 CPU上运行，只有3FPS。模型在笔记本i7 cpu下TF pb模型能有65FPS，ONNX 120FPS。 4009491768-6129cbf2ef7a3.jfif

三、移植到AIPU

3.1模型量化

为了防止出现未知错误，我直接把作者的pytorch代码重构为TensoFlow代码，后面会开源。
由于涉及到时间建模，模型输入有11个输入矩阵。1个图像矩阵，10个转移矩阵，输出也是11个矩阵，1个分类矩阵，10个转移矩阵。
cfg配置如下：

[[Common]
mode = build
use_aqt = True

[Parser]
model_name = mobilenet_v2
model_type = tensorflow 
detection_postprocess = 
model_domain = image_classification
output = fully_connected/BiasAdd,Slice,Slice_2,Slice_4,Slice_6,Slice_8,Slice_10,Slice_12,Slice_14,Slice_16,Slice_18
input_model = ./input/model3.pb
input = x_input,x_input0,x_input1,x_input2,x_input3,x_input4,x_input5,x_input6,x_input7,x_input8,x_input9
input_shape = [1, 224, 224, 3],[1, 56, 56, 3],[1, 28, 28, 4],[1, 28, 28, 4],[1, 14, 14, 8],[1, 14, 14, 8],[1, 14, 14, 8],[1, 14, 14, 12],[1, 14, 14, 12],[1, 7, 7, 20],[1, 7, 7, 20]
output_dir = ./

[AutoQuantizationTool]
quantize_method = SYMMETRIC
quant_precision = int8
ops_per_channel = DepthwiseConv
reverse_rgb = False
label_id_offset = 
dataset_name = 
detection_postprocess = 
anchor_generator = 
ts_max_file = ./input/max_dict2.npy
ts_min_file = ./input/min_dict2.npy
[GBuilder]
outputs = aipu_mobilenet_v2Z2_1104.bin
target = Z1_0701]

由于模型涉及到多输入多输出，故不能直接使用训练集模型自动量化，所以需要手动求出每个节点的最值，按照技术手册里规定的格式，分别保存max，min的值为dict文件。

3.2模型推理
需要对输出结果微调，以下为推理和微调代码。

/**
 * @file main.cpp
 * @brief 
 * 
 * Copyright (c) 2021 Sipeed team
 * ********************************
 * Modify by @zhai
 * *******************************
 */
extern "C" {
#include <stdio.h>
#include <unistd.h>
#include <stdint.h>
#include <string.h>
#include "fbviewer.h"
#include "label.h"
#include <sys/wait.h>
#include <sys/types.h>
}

#include "standard_api.h"
#include <iostream>
#include <sys/time.h>
#include<list>
#include<vector>
#include "opencv2/opencv.hpp"
using namespace cv;
#define DBG_LINE() printf("###L%d\r\n", __LINE__)

int label_oft = 0;

typedef struct {
    int index;
    int8_t val;
} int8_data_t;

typedef struct {
    int index;
    uint8_t val;
} uint8_data_t;

std::list<int8_t*> listbuffs;


int8_t flag_frame_num=0 ;


std::vector<int> history;



int FindMax(int8_t a[], int n, int*pMaxPos)
{
  int i;
  int8_t max;
  max = a[0];       
  *pMaxPos = 0;      

  for (i=1; i<n; i++)
  {
    if (a[i] > max)
    {
      max = a[i];
      *pMaxPos = i;   
    }
  }
  return max ;
}
cv::VideoCapture capture(0);

int init_cam(void)
{
    int x,y;
    getCurrentRes(&x, &y);
    printf("LCD width is %d, height is %d\n", x, y);
    cv::Mat img;
    VideoCapture cap;
    cap.isOpened();
    
    if(!capture.isOpened())
    {
        std::cout<<"video not open."<<std::endl;
        return 1;
    }
    //get default video fps, set fps to 30fps
    double rate = capture.get(CAP_PROP_FPS);
    printf("rate is %lf\n", rate);
    capture.set(CAP_PROP_FPS, 30);
    rate = capture.get(CAP_PROP_FPS);
    printf("rate is %lf\n", rate);
    //get default video frame info
    double frame_width = capture.get(CAP_PROP_FRAME_WIDTH);
    double frame_height = capture.get(CAP_PROP_FRAME_HEIGHT);
    printf("frame_width is %lf, frame_height is %lf\n", frame_width, frame_height);
    //set video frame size to QVGA (then we crop to 224x224)
    frame_width = 320;
    frame_height = 240;
    if(!capture.set(CAP_PROP_FRAME_WIDTH,frame_width))
    {
        printf("set width failed\n");
        return 2;
    }
    if(!capture.set(CAP_PROP_FRAME_HEIGHT, frame_height))
    {
        printf("set width failed\n");
        return 3;
    } 
    return 0;
}

int init_graph(char* file_model, aipu_ctx_handle_t ** ctx, aipu_graph_desc_t* gdesc, aipu_buffer_alloc_info_t* info)
{
    const char* status_msg =NULL;
    aipu_status_t status = AIPU_STATUS_SUCCESS;
    int ret = 0;
    //Step1: init ctx handle
    status = AIPU_init_ctx(ctx);       
    if (status != AIPU_STATUS_SUCCESS) {
        AIPU_get_status_msg(status, &status_msg);
        printf("[DEMO ERROR] AIPU_init_ctx: %s\n", status_msg);
        ret = -1;
        //goto out;
    }

    //Step2: load graph
    status = AIPU_load_graph_helper(*ctx, file_model, gdesc);
    if (status != AIPU_STATUS_SUCCESS) {
        AIPU_get_status_msg(status, &status_msg);
        printf("[DEMO ERROR] AIPU_load_graph_helper: %s\n", status_msg);
        ret = -2;
        //goto deinit_ctx;
    }
    printf("[DEMO INFO] AIPU load graph successfully.\n");

    //Step3: alloc tensor buffers
    status = AIPU_alloc_tensor_buffers(*ctx, gdesc, info);
    if (status != AIPU_STATUS_SUCCESS) {
        AIPU_get_status_msg(status, &status_msg);
        printf("[DEMO ERROR] AIPU_alloc_tensor_buffers: %s\n", status_msg);
        ret = -3;
        //goto unload_graph;
    }
    
    return ret;
}

int cap_img(Mat* lcd_frame, Mat* ai_frame)
{    
    Rect roi(40, 0, 240/4*3, 240);  //16/9 -> 4/3  
    Rect input_roi(8, 8, 224, 224);
    Size dsize = Size(240, 240);
    if(!capture.read(*lcd_frame))
    {
        printf("no video frame\r\n");
        return -1;
    }
    *lcd_frame = (*lcd_frame)(roi).clone();
    rotate(*lcd_frame, *lcd_frame, ROTATE_180);
    resize(*lcd_frame, *lcd_frame, dsize);
    cvtColor(*lcd_frame, *lcd_frame, COLOR_BGR2RGB);
    *ai_frame = (*lcd_frame)(input_roi).clone();
    //*ai_frame = (*lcd_frame)(input_roi).clone() + Scalar(-123, -117,-104);
     ai_frame->convertTo(*ai_frame,CV_16SC3);
     add(Scalar(-127, -127,-127),*ai_frame,*ai_frame);
     ai_frame->convertTo(*ai_frame,CV_8SC3);

    return 0;
}

int infer_img(Mat* ai_frame,Mat* x0,Mat* x1,Mat* x2,Mat* x3,Mat* x4,Mat* x5,Mat* x6,Mat* x7,Mat* x8,Mat* x9, aipu_ctx_handle_t ** ctx, aipu_graph_desc_t* gdesc, aipu_buffer_alloc_info_t* info, int signed_flag, int* label_idx, int* label_prob)
{
    uint32_t job_id=0;
    const char* status_msg =NULL;
    int32_t time_out=-1;
    bool finish_job_successfully = true;
    aipu_status_t status = AIPU_STATUS_SUCCESS;
    int ret = 0;

    //std::cout<<* ai_frame<<std::endl;

    // printf("input_x:%d\n",info->inputs.tensors[0].size);
    // printf("input_x0:%d\n",info->inputs.tensors[1].size);
    // printf("input_x1:%d\n",info->inputs.tensors[2].size);
    // printf("input_x2:%d\n",info->inputs.tensors[3].size);
    // printf("input_x3:%d\n",info->inputs.tensors[4].size);
    // printf("input_x4:%d\n",info->inputs.tensors[5].size);
    // printf("input_x5:%d\n",info->inputs.tensors[6].size);
    // printf("input_x6:%d\n",info->inputs.tensors[7].size);
    // printf("input_x7:%d\n",info->inputs.tensors[8].size);
    // printf("input_x8:%d\n",info->inputs.tensors[9].size);
    // printf("input_x9:%d\n",info->inputs.tensors[10].size);
    
   
    memcpy(info->inputs.tensors[0].va, ai_frame->data,info->inputs.tensors[0].size);


    memcpy(info->inputs.tensors[1].va, x0->data, info->inputs.tensors[1].size);
    memcpy(info->inputs.tensors[2].va, x1->data, info->inputs.tensors[2].size);
    memcpy(info->inputs.tensors[3].va, x2->data,info->inputs.tensors[3].size);
    memcpy(info->inputs.tensors[4].va, x3->data, info->inputs.tensors[4].size);
    memcpy(info->inputs.tensors[5].va, x4->data,info->inputs.tensors[5].size);
    memcpy(info->inputs.tensors[6].va, x5->data,info->inputs.tensors[6].size);
    memcpy(info->inputs.tensors[7].va, x6->data, info->inputs.tensors[7].size);
    memcpy(info->inputs.tensors[8].va, x7->data,info->inputs.tensors[8].size);
    memcpy(info->inputs.tensors[9].va, x8->data, info->inputs.tensors[9].size);
    memcpy(info->inputs.tensors[10].va, x9->data,info->inputs.tensors[10].size);

    flag_frame_num=1;
    

    status = AIPU_create_job(*ctx, gdesc, info->handle, &job_id);
    //std::cout<<status<<std::endl;
    if (status != AIPU_STATUS_SUCCESS) {
        AIPU_get_status_msg(status, &status_msg);
        printf("[DEMO ERROR] AIPU_create_job: %s\n", status_msg);
        ret = -1;
        //goto free_tensor_buffers;
    }
    status = AIPU_finish_job(*ctx, job_id, time_out);
    if (status != AIPU_STATUS_SUCCESS) {
        AIPU_get_status_msg(status, &status_msg);
        printf("[DEMO ERROR] AIPU_finish_job: %s\n", status_msg);
        finish_job_successfully = false;
    } else {
        finish_job_successfully = true;
    }

    if (finish_job_successfully) {
        int8_t *result = (int8_t *)info->outputs.tensors[0].va;
        
        uint32_t size = info->outputs.tensors[0].size;
     

        int8_t *buff=(int8_t *)malloc(27*sizeof(int8_t));
        memcpy(buff, result, sizeof(int8_t)*27);
        if(listbuffs.size()>5){listbuffs.pop_front();}
        listbuffs.push_back(buff);



        int8_t *buffsum;
        buffsum=(int8_t *)calloc(27,sizeof(int8_t));

        int Len=listbuffs.size();
        int k=0;
        std::list<int8_t *>::iterator iter;
        for(iter=listbuffs.begin();iter!=listbuffs.end();iter++){

           for(k=0;k<27;k++){buffsum[k]=buffsum[k]+(*iter)[k];}

        }

        int i=0;
   
        memcpy((x0->data),info->outputs.tensors[1].va, info->outputs.tensors[1].size);  
        memcpy((x1->data),info->outputs.tensors[2].va,  info->outputs.tensors[2].size);
        memcpy((x2->data),info->outputs.tensors[3].va,  info->outputs.tensors[3].size);
        memcpy((x3->data),info->outputs.tensors[4].va,  info->outputs.tensors[4].size);
        memcpy((x4->data),info->outputs.tensors[5].va,  info->outputs.tensors[5].size);
        memcpy((x5->data),info->outputs.tensors[6].va,  info->outputs.tensors[6].size);
        memcpy((x6->data),info->outputs.tensors[7].va,  info->outputs.tensors[7].size);
        memcpy((x7->data),info->outputs.tensors[8].va,  info->outputs.tensors[8].size);
        memcpy((x8->data),info->outputs.tensors[9].va,  info->outputs.tensors[9].size);
        memcpy((x9->data),info->outputs.tensors[10].va,  info->outputs.tensors[10].size);




        int idx;
        
        FindMax(buffsum, 27, &idx);
        printf("idx:%d\n",idx);
        int label_covert=2;
        int errors_fre[]={7, 8, 21, 22, 3,10,11,12,13};
        i=0;
        for(i=0;i<9;i++){
            if(idx==errors_fre[i]){idx=history.back();}
        }

        if(idx==0){idx=history.back();}

        if(idx!=history.back()){
            if(history[history.size()-3]!=history.back()||history[history.size()-2]!=history.back()){idx=history.back();}
        }

        history.push_back(idx);

        *label_idx=history.back();
        if(history.size()>20){history.erase(history.begin());}
    
    }

    status = AIPU_clean_job(*ctx, job_id);
    if (status != AIPU_STATUS_SUCCESS) {
        AIPU_get_status_msg(status, &status_msg);
        printf("[TEST ERROR] AIPU_clean_job: %s\n", status_msg);
        ret = -2;
        //goto free_tensor_buffers;
    }
    return ret;
}


float cal_fps(struct timeval start, struct timeval end)
{
    struct timeval interval;
    if (end.tv_usec >= start.tv_usec) {
        interval.tv_usec = end.tv_usec - start.tv_usec;
        interval.tv_sec = end.tv_sec - start.tv_sec;
    } else  {
        interval.tv_usec = 1000000 + end.tv_usec - start.tv_usec;
        interval.tv_sec = end.tv_sec - 1 - start.tv_sec;
    }
    float fps = 1000000.0 / interval.tv_usec;
    return fps;
}

volatile int exit_flag = 0;
void my_handler(int s){
    printf("Caught signal %d\n",s);
    exit_flag = 1;
    return;
}



int main(int argc, char *argv[])
{
    int ret = 0;
    uint32_t job_id=0;
    int32_t time_out=-1;
    bool finish_job_successfully = true;
    int model_inw, model_inh, model_inch, model_outw, model_outh, model_outch, img_size;    
    int8_t* bmpbuf;
    cv::Mat lcd_frame;
    cv::Mat ai_frame;
    int label_idx, label_prob;
    struct timeval start, end;

    int flag_frame=0;
    float fps=0;

    // cv::Mat x0 = cv::Mat::zeros(56,56 , CV_32FC(3)); 
    // cv::Mat x1 = cv::Mat::zeros(28, 28, CV_32FC(4)); 
    // cv::Mat x2 = cv::Mat::zeros(28, 28, CV_32FC(4)); 
    // cv::Mat x3 = cv::Mat::zeros(14, 14, CV_32FC(8)); 
    // cv::Mat x4 = cv::Mat::zeros(14,14 , CV_32FC(8)); 
    // cv::Mat x5 = cv::Mat::zeros(14,14 , CV_32FC(8)); 
    // cv::Mat x6 = cv::Mat::zeros(14, 14, CV_32FC(12)); 
    // cv::Mat x7 = cv::Mat::zeros(14,14 , CV_32FC(12)); 
    // cv::Mat x8 = cv::Mat::zeros(7,7 , CV_32FC(20)); 
    // cv::Mat x9 = cv::Mat::zeros(7, 7, CV_32FC(20 ));

    cv::Mat x0 = cv::Mat::zeros(56,56 , CV_8SC(3)); 
    cv::Mat x1 = cv::Mat::zeros(28, 28, CV_8SC(4)); 
    cv::Mat x2 = cv::Mat::zeros(28, 28, CV_8SC(4)); 
    cv::Mat x3 = cv::Mat::zeros(14, 14, CV_8SC(8)); 
    cv::Mat x4 = cv::Mat::zeros(14,14 , CV_8SC(8)); 
    cv::Mat x5 = cv::Mat::zeros(14,14 , CV_8SC(8)); 
    cv::Mat x6 = cv::Mat::zeros(14, 14, CV_8SC(12)); 
    cv::Mat x7 = cv::Mat::zeros(14,14 , CV_8SC(12)); 
    cv::Mat x8 = cv::Mat::zeros(7,7 , CV_8SC(20)); 
    cv::Mat x9 = cv::Mat::zeros(7, 7, CV_8SC(20 ));




   
    

    signal(SIGINT, my_handler); 

    


    history.push_back(2);
    history.push_back(2);
    
    
    printf("Zhouyi Cam test program: \r\n");
    printf("Usage: \r\n");
    printf("    ./zhouyi aipu.bin signed [label_oft]\r\n");
    printf("    signed=0, uint8 output; =1, int8 output\r\n");
    printf("    real_label_idx = predict_idx-label_oft, \r\n");
    printf("    NOTE: default cal with 224x224\r\n");

    aipu_ctx_handle_t * ctx = NULL;
    aipu_status_t status = AIPU_STATUS_SUCCESS;
    const char* status_msg =NULL;
    aipu_graph_desc_t gdesc;
    aipu_buffer_alloc_info_t info;

    //Step 0: parse input argv
    if(argc < 3) {
        printf("argc=%d error\r\n", argc);
        return -1;
    }
    if(argc >3) label_oft = atoi(argv[3]);
    char* file_model= argv[1];
    int signed_flag = 1;
    
    //Step 1: set USB camera
    ret = init_cam();DBG_LINE();
    if(ret != 0) {
        printf("[DEMO ERROR] init_cam err: %s\n", ret);
        goto out;
    }
    
    //Step 2: init model graph
    ret = init_graph(file_model, &ctx, &gdesc, &info);DBG_LINE();
    if(ret == -1) goto out;
    else if(ret == -2) goto deinit_ctx;
    else if(ret == -3) goto unload_graph;
    
    //MAIN LOOP
    
    while(!exit_flag)
    {
        //1. cap cam img
        if(cap_img(&lcd_frame, &ai_frame) != 0) {
            break;
        }
        //2. infer cam img, get label
        gettimeofday(&start, NULL);
        
       if(flag_frame%2==0){
            ret = infer_img(&ai_frame,&x0,&x1,&x2,&x3,&x4,&x5,&x6,&x7,&x8,&x9, &ctx, &gdesc, &info, signed_flag, &label_idx, &label_prob);
           
            
       
       
        if(ret != 0) goto free_tensor_buffers;
        gettimeofday(&end, NULL);

         fps = cal_fps(start, end);

         }
         flag_frame+=1;
        //3. draw lcd
        flip(lcd_frame, lcd_frame, 1);
        putText(lcd_frame, labels[label_idx], Point(0, 224), cv::FONT_HERSHEY_PLAIN, 1, Scalar(255,0,0), 2);
        
        char fps_str[16];
        sprintf(fps_str, "%.1ffps", fps);
        putText(lcd_frame, fps_str, Point(0, 16), cv::FONT_HERSHEY_PLAIN, 1, Scalar(255,0,0), 2);
       
        
        fb_display(lcd_frame.data, 0, 240, 240, 0, 0, 0, 0);
        
    }

free_tensor_buffers:
    status = AIPU_free_tensor_buffers(ctx, info.handle);
    if (status != AIPU_STATUS_SUCCESS) {
        AIPU_get_status_msg(status, &status_msg);
        printf("[DEMO ERROR] AIPU_free_tensor_buffers: %s\n", status_msg);
        ret = -1;
    }
unload_graph:
    status = AIPU_unload_graph(ctx, &gdesc);
    if (status != AIPU_STATUS_SUCCESS) {
        AIPU_get_status_msg(status, &status_msg);
        printf("[DEMO ERROR] AIPU_unload_graph: %s\n", status_msg);
        ret = -1;
    }
deinit_ctx:
    status = AIPU_deinit_ctx(ctx);
    if (status != AIPU_STATUS_SUCCESS) {
        AIPU_get_status_msg(status, &status_msg);
        printf("[DEMO ERROR] AIPU_deinit_ctx: %s\n", status_msg);
        ret = -1;
    }

out:

    return ret;
}

四、代码地址
https://github.com/ZhaiFengYun/R329-AIPU-gesture-recognition

（文章转载自：极术社区 zhai）
（原文链接：https://aijishu.com/a/1060000000230937 ）