YOLOv3：Darknet代码解析（三）卷积操作

淡淡的烟草味﹌ 2022-05-18 06:18 377阅读 0赞

目的：读懂YOLO\_v3中的卷积操作以及相关语句

相关：YOLOv3：Darknet代码解析（六）简化的程序与卷积拆分 [https://blog.csdn.net/weixin\_36474809/article/details/81739771][https_blog.csdn.net_weixin_36474809_article_details_81739771]

[YOLOv3：Darknet代码解析（一）安装Darknet][YOLOv3_Darknet_Darknet]

[YOLOv3：Darknet代码解析（二）代码初步][YOLOv3_Darknet]

[YOLOv3：Darknet代码解析（三）卷积操作][YOLOv3_Darknet 1]

[YOLOv3：Darknet代码解析（四）结构更改与训练][YOLOv3_Darknet 2]

[YOLOv3：Darknet代码解析（五）权重与特征存储][YOLOv3_Darknet 3]

[YOLOv3：Darknet代码解析（六）简化的程序与卷积拆分][https_blog.csdn.net_weixin_36474809_article_details_81739771]

**目录**

1.卷积内容

2. gemm（重要）

2.1 线性代数中的gemm

2.2 卷积的语句： gemm\_nn

2.3 另一个角度的卷积

3.其他内容

3.1 自上而下迭代到卷积

主函数

test\_detector子函数

实现网络语句parse\_network\_cfg

加载权重load\_weight

实现卷积层的代码

--------------------

# 1.卷积内容 #

卷积层中核心代码，前馈的卷积层，反向传播的卷积层。

void forward_convolutional_layer(convolutional_layer l, network_state state)
    {
        int out_h = convolutional_out_height(l);
        int out_w = convolutional_out_width(l);
        int i;
    
        fill_cpu(l.outputs*l.batch, 0, l.output, 1);
    
        if(l.xnor){
            binarize_weights(l.weights, l.n, l.c*l.size*l.size, l.binary_weights);
            swap_binary(&l);
            binarize_cpu(state.input, l.c*l.h*l.w*l.batch, l.binary_input);
            state.input = l.binary_input;
        }
    
        int m = l.n;
        int k = l.size*l.size*l.c;
        int n = out_h*out_w;
        printf("Layer number - %d\n", l.layer_num);
        int hw = 0;
    
        if (l.layer_num == 0) hw = 1;
        else hw = 0;
    
        printf("\nValue of m (l.n) = %d, k (l.size*l.size*l.c) = %d, n (out_h*out_w) = %d, l.c = %d\n", m,k,n,l.c);
        if (l.xnor && l.c%32 == 0 && AI2) {
            forward_xnor_layer(l, state);
            printf("xnor\n");
        } else {
    
            float *a = l.weights;
            float *b = state.workspace;
            float *c = l.output;
    
            for(i = 0; i < l.batch; ++i){
                im2col_cpu(state.input, l.c, l.h, l.w, 
                        l.size, l.stride, l.pad, b);
                if(hw)
                  gemm_cpu_hw(0,0,m,n,k,1,a,k,b,n,1,c,n);
                else
                  gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
     
                c += n*m;
                state.input += l.c*l.h*l.w;
            }
        }
    
        if(l.batch_normalize){
            forward_batchnorm_layer(l, state);
        }
        add_bias(l.output, l.biases, l.batch, l.n, out_h*out_w);
    
        activate_array(l.output, m*n*l.batch, l.activation);
        if(l.binary || l.xnor) swap_binary(&l);
    }

其中，核心语句为  gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);这个语句表示卷积中的矩阵乘。相应的图像转为取框矩阵的格式与权重相乘。把一个三维卷积等价转换为一个二维的矩阵乘。

# 2. gemm（重要） #

## 2.1 线性代数中的gemm ##

为卷积操作中的矩阵乘法。在gemm.c中。gemm即线性代数中的矩阵相乘。几种,gemm\_nn 、gemm\_nt 、gemm\_tn 、gemm\_  
tt这几个分别表示两个矩阵是否进行转置。

void gemm(int TA, int TB, int M, int N, int K, float ALPHA, 
            float *A, int lda, 
            float *B, int ldb,
            float BETA,
            float *C, int ldc)
    {
        gemm_cpu( TA,  TB,  M, N, K, ALPHA,A,lda, B, ldb,BETA,C,ldc);
    }
    
    void gemm_cpu(int TA, int TB, int M, int N, int K, float ALPHA, 
            float *A, int lda, 
            float *B, int ldb,
            float BETA,
            float *C, int ldc)
    {
        //printf("cpu: %d %d %d %d %d %f %d %d %f %d\n",TA, TB, M, N, K, ALPHA, lda, ldb, BETA, ldc);
        int i, j;
        for(i = 0; i < M; ++i){
            for(j = 0; j < N; ++j){
                C[i*ldc + j] *= BETA;
            }
        }
        if(!TA && !TB)
            gemm_nn(M, N, K, ALPHA,A,lda, B, ldb,C,ldc);
        else if(TA && !TB)
            gemm_tn(M, N, K, ALPHA,A,lda, B, ldb,C,ldc);
        else if(!TA && TB)
            gemm_nt(M, N, K, ALPHA,A,lda, B, ldb,C,ldc);
        else
            gemm_tt(M, N, K, ALPHA,A,lda, B, ldb,C,ldc);
    }

前两个TA，TB决定了是否转置。后面运算函数为gemm\_nn还是gemm\_tn，gemm\_nt，gemm\_tt。输入层中输入TA，TB都为0，即不进行转置。因此用到的函数为gemm\_nn。

## 2.2 卷积的语句： gemm\_nn ##

void gemm_nn(int M, int N, int K, float ALPHA, 
            float *A, int lda, 
            float *B, int ldb,
            float *C, int ldc)
    {
        printf ("GEMM NN SW\n");
        int i,j,k;
        for(i = 0; i < M; ++i){
            for(k = 0; k < K; ++k){
                register float A_PART = ALPHA*A[i*lda+k];
                for(j = 0; j < N; ++j){
                    C[i*ldc+j] += A_PART*B[k*ldb+j];
                }
            }
        }
    }

A,B,C都为指针，在forward\_convolutional\_layer.c中，A为权重，B为图像值，C为输出

float *a = l.weights;
    float *b = state.workspace;
    float *c = l.output;

M，N，K为输出的featureMap的坐标，对应于下面的m，n，k。lda，ldb，ldc对应于下面的K，N，N

int M = l.n;
        int K = l.size*l.size*l.c;
        int N = out_h*out_w;

*  M卷积核的个数，
 *  K卷积核的大小，
 *  N输出的feature-map的大小
 *  权重M\*K，输入 K\*N， 输出 M\*N

整个卷积过程有两个重要步骤，一个是M，K，N的值的大小，另一个是指针a，b，c指向的位置。

核心语句为三个for循环里面，

C\[i\*ldc+j\] += A\[i\*lda+k\]  \*  B\[k\*ldb+j\];

即输入C\[ i\*N + j \]+=A\[ i\*K +k \]  \*  B\[k\*N + j \]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl8zNjQ3NDgwOQ_size_16_color_FFFFFF_t_70][]

## 2.3 另一个角度的卷积 ##

YOLOv3：Darknet代码解析（五）简化的程序与卷积拆分  [https://blog.csdn.net/weixin\_36474809/article/details/81739771][https_blog.csdn.net_weixin_36474809_article_details_81739771]

MTCNN（七）卷积更改为嵌套for循环格式 [https://blog.csdn.net/weixin\_36474809/article/details/83145601][https_blog.csdn.net_weixin_36474809_article_details_83145601]

// l.output\[filters\]\[width\]\[height\] +=  
    //        state.input\[channels\]\[width\]\[height\] \*  
    //        l.weights\[filters\]\[channels\]\[filter\_width\]\[filter\_height\];

// -------------convulution in 2D matrix format-------------------
    // input kernel matrix  *  input feature matrix(Trans) = output feature matrix
    // height (outChannels)    height (3D_KernelSize)        height (outChannels)
    // width  (3D_KernelSize)  width  (outFeatureSize)       width  (outFeatureSize)
    
    //C=αAB + βC :   outpBox=weightIn*matrixIn(T)
    	//       A_transpose       B_transpose
    	gemm_cpu(0,                1,            \
    	//A row C row             B col C col        A col B row        alpha
    	weightIn->out_ChannelNum, matrixIn->height,  matrixIn->width,   1,   \
    	//A*            A'col           B*              B'col             beta
    	weightIn->pdata,matrixIn->width,matrixIn->pdata,matrixIn->width,  0, \
    	//C*             C'col
    	outpBox->pdata,  matrixIn->height);

# 3.其他内容 #

运行YOLO时的命令行为：./darknet detect cfg/yolov3-tiny.cfg yolov3-tiny.weights data/dog.jpg

我们需要找到m,k,n的大小，和a，b，c指针的位置。

## 3.1 自上而下迭代到卷积 ##

找出一次实现网络之中迭代到卷积的函数

### 主函数 ###

darknet.c
    int main(int argc, char **argv)
    {
        if(argc < 2){
            fprintf(stderr, "usage: %s <function>\n", argv[0]);
            return 0;
        }
        gpu_index = find_int_arg(argc, argv, "-i", 0);
        if(find_arg(argc, argv, "-nogpu")) {
            gpu_index = -1;
        }
    
    #ifndef GPU
        gpu_index = -1;
    #else
        if(gpu_index >= 0){
            cuda_set_device(gpu_index);
        }
    #endif
    
        if (0 == strcmp(argv[1], "average")){
            average(argc, argv);
        } else if (0 == strcmp(argv[1], "detect")){
            float thresh = find_float_arg(argc, argv, "-thresh", .25);
            char *filename = (argc > 4) ? argv[4]: 0;
            test_detector("cfg/coco.data", argv[2], argv[3], filename, thresh);
        } else {
            fprintf(stderr, "Not an option: %s\n", argv[1]);
        }
        return 0;
    }

### test\_detector子函数 ###

主函数中test\_detector子函数中，argv\[2\]为yolov3-tiny.cfg,  argv\[3\]为yolov3-tiny.weights

//detector.c
    void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh)
    {
        list *options = read_data_cfg(datacfg);
        char *name_list = option_find_str(options, "names", "data/names.list");
        char **names = get_labels(name_list);
    
        image **alphabet = load_alphabet();
        network net = parse_network_cfg(cfgfile);
        if(weightfile){
            load_weights(&net, weightfile);
        }
        layer l = net.layers[net.n-1];
        set_batch_network(&net, 1);
        srand(2222222);
        clock_t time;
        char buff[256];
        char *input = buff;
        int j;
        float nms=.4;
        box *boxes = calloc(l.w*l.h*l.n, sizeof(box));
        float **probs = calloc(l.w*l.h*l.n, sizeof(float *));
        for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = calloc(l.classes, sizeof(float *));
        while(1){
            if(filename){
                strncpy(input, filename, 256);
            } else {
                printf("Enter Image Path: ");
                fflush(stdout);
                input = fgets(input, 256, stdin);
                if(!input) return;
                strtok(input, "\n");
            }
            image im = load_image_color(input,0,0);
            image sized = resize_image(im, net.w, net.h);
            float *X = sized.data;
            time=clock();
            network_predict(net, X);
            printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
            get_region_boxes(l, 1, 1, thresh, probs, boxes, 0);
            if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, l.classes, nms);
            draw_detections(im, l.w*l.h*l.n, thresh, boxes, probs, names, alphabet, l.classes);
            save_image(im, "predictions");
            show_image(im, "predictions");
    
            free_image(im);
            free_image(sized);
    
            if (filename) break;
        }
    }

### 实现网络语句parse\_network\_cfg ###

函数中，确定网络的语句：

network net = parse_network_cfg(cfgfile);
        if(weightfile){
            load_weights(&net, weightfile);
        }
        layer l = net.layers[net.n-1];
        set_batch_network(&net, 1);

### 加载权重load\_weight ###

函数中重要的为parse\_network\_cfg,此函数为根据相应的cfg文件创建神经网络，根据相应的load\_weights加载相应的参数。

//parser.c
    network parse_network_cfg(char *filename)
    {
        list *sections = read_cfg(filename);
        node *n = sections->front;
        if(!n) error("Config file has no sections");
        network net = make_network(sections->size - 1);
        net.gpu_index = gpu_index;
        size_params params;
    
        section *s = (section *)n->val;
        list *options = s->options;
        if(!is_network(s)) error("First section must be [net] or [network]");
        parse_net_options(options, &net);
    
        params.h = net.h;
        params.w = net.w;
        params.c = net.c;
        params.inputs = net.inputs;
        params.batch = net.batch;
        params.time_steps = net.time_steps;
        params.net = net;
    
        size_t workspace_size = 0;
        n = n->next;
        int count = 0;
        free_section(s);
        fprintf(stderr, "layer     filters    size              input                output\n");
        while(n){
            params.index = count;
            fprintf(stderr, "%5d ", count);
            s = (section *)n->val;
            options = s->options;
            layer l = {0};
            LAYER_TYPE lt = string_to_layer_type(s->type);
            if(lt == CONVOLUTIONAL){
                l = parse_convolutional(options, params);
            }else if(lt == LOCAL){
                l = parse_local(options, params);
            }else if(lt == DROPOUT){
                l = parse_dropout(options, params);
                l.output = net.layers[count-1].output;
                l.delta = net.layers[count-1].delta;
            }else{
                fprintf(stderr, "Type not recognized: %s\n", s->type);
            }
            l.dontload = option_find_int_quiet(options, "dontload", 0);
            l.dontloadscales = option_find_int_quiet(options, "dontloadscales", 0);
            option_unused(options);
            net.layers[count] = l;
            if (l.workspace_size > workspace_size) workspace_size = l.workspace_size;
            free_section(s);
            n = n->next;
            ++count;
            if(n){
                params.h = l.out_h;
                params.w = l.out_w;
                params.c = l.out_c;
                params.inputs = l.outputs;
            }
        }   
        free_list(sections);
        net.outputs = get_network_output_size(net);
        net.output = get_network_output(net);
        if(workspace_size){
            net.workspace = calloc(1, workspace_size);
        }
        return net;
    }

###  实现卷积层的代码 ###

//parser.c
    convolutional_layer parse_convolutional(list *options, size_params params)
    {
        int n = option_find_int(options, "filters",1);
        int size = option_find_int(options, "size",1);
        int stride = option_find_int(options, "stride",1);
        int pad = option_find_int_quiet(options, "pad",0);
        int padding = option_find_int_quiet(options, "padding",0);
        if(pad) padding = size/2;
    
        char *activation_s = option_find_str(options, "activation", "logistic");
        ACTIVATION activation = get_activation(activation_s);
    
        int batch,h,w,c;
        h = params.h;
        w = params.w;
        c = params.c;
        batch=params.batch;
        if(!(h && w && c)) error("Layer before convolutional layer must output image.");
        int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
        int binary = option_find_int_quiet(options, "binary", 0);
        int xnor = option_find_int_quiet(options, "xnor", 0);
    
        convolutional_layer layer = make_convolutional_layer(batch,h,w,c,n,size,stride,padding,activation, batch_normalize, binary, xnor, params.net.adam);
        layer.flipped = option_find_int_quiet(options, "flipped", 0);
        layer.dot = option_find_float_quiet(options, "dot", 0);
        if(params.net.adam){
            layer.B1 = params.net.B1;
            layer.B2 = params.net.B2;
            layer.eps = params.net.eps;
        }
    
        return layer;
    }

[https_blog.csdn.net_weixin_36474809_article_details_81739771]: https://blog.csdn.net/weixin_36474809/article/details/81739771
[YOLOv3_Darknet_Darknet]: https://blog.csdn.net/weixin_36474809/article/details/80881043
[YOLOv3_Darknet]: https://blog.csdn.net/weixin_36474809/article/details/80975845
[YOLOv3_Darknet 1]: https://blog.csdn.net/weixin_36474809/article/details/81296612
[YOLOv3_Darknet 2]: https://blog.csdn.net/weixin_36474809/article/details/81326286
[YOLOv3_Darknet 3]: https://blog.csdn.net/weixin_36474809/article/details/81331175
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl8zNjQ3NDgwOQ_size_16_color_FFFFFF_t_70]: /images/20220518/50bfe37476ff493797cc6aa5128acac2.png
[https_blog.csdn.net_weixin_36474809_article_details_83145601]: https://blog.csdn.net/weixin_36474809/article/details/83145601