YOLO源码详解（三）- 前向传播（forward）

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目录(?)[+]

本系列作者：木凌 
时间：2016年11月。 
文章连接：http://blog.csdn.net/u014540717

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一、主函数void forward_network(network net, network_state state)

//network.cvoid forward_network(network net, network_state state){    state.workspace = net.workspace;    int i;    for(i = 0; i < net.n; ++i){        state.index = i;        layer l = net.layers[i];        //如果delta不为零，那么就把所有的输入值输入乘一个系数，用float *delta指针指向它        if(l.delta){            scal_cpu(l.outputs * l.batch, 0, l.delta, 1);        }        //从这里开始我们可以一层一层分析了，重复的层就不再分析了，顺序如下：        //[convolutional]        //[maxpool]        //[local]        //[dropout]        //[connected]        //[detection]        l.forward(l, state);        state.input = l.output;    }}
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1、前向传播-convolutional层

//convolutional_layer.cvoid forward_convolutional_layer(convolutional_layer l, network_state state){    //获取卷积层输出的长、宽    int out_h = convolutional_out_height(l);    int out_w = convolutional_out_width(l);    int i;    //初始化，将输出的数据全部赋值０    fill_cpu(l.outputs*l.batch, 0, l.output, 1);    /*       if(l.binary){       binarize_weights(l.weights, l.n, l.c*l.size*l.size, l.binary_weights);       binarize_weights2(l.weights, l.n, l.c*l.size*l.size, l.cweights, l.scales);       swap_binary(&l);       }     */    /*       if(l.binary){       int m = l.n;       int k = l.size*l.size*l.c;       int n = out_h*out_w;       char  *a = l.cweights;       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);       gemm_bin(m,n,k,1,a,k,b,n,c,n);       c += n*m;       state.input += l.c*l.h*l.w;       }       scale_bias(l.output, l.scales, l.batch, l.n, out_h*out_w);       add_bias(l.output, l.biases, l.batch, l.n, out_h*out_w);       activate_array(l.output, m*n*l.batch, l.activation);       return;       }     */    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;    }    //m是卷积核的个数，k是每个卷积核的参数数量（l.size是卷积核的大小），n是每个输出feature map的像素个数    int m = l.n;    int k = l.size*l.size*l.c;    int n = out_h*out_w;    if (l.xnor && l.c%32 == 0 && AI2) {        forward_xnor_layer(l, state);        printf("xnor\n");    } else {        //weights顾名思义，就是卷积核的参数，`$grep -rn "l.weights"`可以看到：        //l.weights = calloc(c*n*size*size, sizeof(float))        //说白了a是指向权重的指针，b是指向工作空间指针，c是指向输出的指针        float *a = l.weights;        float *b = state.workspace;        float *c = l.output;        for(i = 0; i < l.batch; ++i){            //im2col就是image to column,就是将图像依照卷积核的大小拉伸为列向量，方便矩阵运算            im2col_cpu(state.input, l.c, l.h, l.w,                    l.size, l.stride, l.pad, b);            //这个函数实现矩阵运算，也就是卷积运算            gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);            c += n*m;            //更新输入            state.input += l.c*l.h*l.w;        }    }    //BN层，加速收敛    if(l.batch_normalize){        forward_batchnorm_layer(l, state);    }    //添加偏置项    add_bias(l.output, l.biases, l.batch, l.n, out_h*out_w);    //非线性变化，leaky RELU层，非常简单，不多做介绍    activate_array(l.output, m*n*l.batch, l.activation);    //不太清楚binary和xnor是什么意思，希望有了解的留言，谢谢～    if(l.binary || l.xnor) swap_binary(&l);}
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函数剖析

a. im2col_cpu()

这个函数还是很重要的，我们来分析下。这个函数是从caffe中移植过来的

//im2col.c//From Berkeley Vision's Caffe!//https://github.com/BVLC/caffe/blob/master/LICENSEvoid im2col_cpu(float* data_im,     int channels,  int height,  int width,     int ksize,  int stride, int pad, float* data_col) {    int c,h,w;    int height_col = (height + 2*pad - ksize) / stride + 1;    int width_col = (width + 2*pad - ksize) / stride + 1;    int channels_col = channels * ksize * ksize;    //最外层循环是每个卷积核的参数个数    for (c = 0; c < channels_col; ++c) {        int w_offset = c % ksize;        int h_offset = (c / ksize) % ksize;        int c_im = c / ksize / ksize;        //这两层循环是用卷积核把图像遍历一遍，这说起来比较晦涩，一会儿画个图来理解，很简单～        for (h = 0; h < height_col; ++h) {            for (w = 0; w < width_col; ++w) {                int im_row = h_offset + h * stride;                int im_col = w_offset + w * stride;                int col_index = (c * height_col + h) * width_col + w;                data_col[col_index] = im2col_get_pixel(data_im, height, width, channels,                        im_row, im_col, c_im, pad);            }        }    }}float im2col_get_pixel(float *im, int height, int width, int channels,                        int row, int col, int channel, int pad){    row -= pad;    col -= pad;    if (row < 0 || col < 0 ||        row >= height || col >= width) return 0;    return im[col + width*(row + height*channel)];}
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我画了下面的图来帮助理解下im2col_cpu()这个函数，为了方便理解，这里假设图像尺寸是5*5， stride=2，kernel_size=3 

float *b指向state.workspace这个工作空间，也就是把原始数据变成行向量放到工作空间里，然后进行卷积计算

b. gemm()

这个函数实现了卷积的运算

//gemm.cvoid 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)        //调用这个函数        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);}void gemm_nn(int M, int N, int K, float ALPHA,        float *A, int lda,        float *B, int ldb,        float *C, int ldc){    int i,j,k;    //这个函数同样一会儿画图表示吧，说起来太费劲。。。    for(i = 0; i < M; ++i){        for(k = 0; k < K; ++k){            //关键字请求编译器尽可能的将变量存在CPU内部寄存器中，而不是通过内存寻址访问，以提高效率。            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];            }        }    }}
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这个函数的将卷积后的值放入c所指向的内存中（最终生成number of kernel个2*2的feature map） 

函数结束后，开始循环每一个batch，卷积计算结果依次向后放

c. forward_batchnorm_layer()

这个函数实现batch normalization，加速了训练的收敛过程，详细见这篇论文 Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

//batchnorm_layer.cvoid forward_batchnorm_layer(layer l, network_state state){    if(l.type == BATCHNORM) copy_cpu(l.outputs*l.batch, state.input, 1, l.output, 1);    if(l.type == CONNECTED){        l.out_c = l.outputs;        l.out_h = l.out_w = 1;    }    if(state.train){        mean_cpu(l.output, l.batch, l.out_c, l.out_h*l.out_w, l.mean);        variance_cpu(l.output, l.mean, l.batch, l.out_c, l.out_h*l.out_w, l.variance);        normalize_cpu(l.output, l.mean, l.variance, l.batch, l.out_c, l.out_h*l.out_w);    } else {        normalize_cpu(l.output, l.rolling_mean, l.rolling_variance, l.batch, l.out_c, l.out_h*l.out_w);    }    scale_bias(l.output, l.scales, l.batch, l.out_c, l.out_h*l.out_w);}
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//blas.c//计算均值void mean_cpu(float *x, int batch, int filters, int spatial, float *mean){    float scale = 1./(batch * spatial);    int i,j,k;    //注意，这里的均值是不同batch的同一维度的feature的均值    for(i = 0; i < filters; ++i){        mean[i] = 0;        for(j = 0; j < batch; ++j){            for(k = 0; k < spatial; ++k){                int index = j*filters*spatial + i*spatial + k;                mean[i] += x[index];            }        }        mean[i] *= scale;    }}//计算方差void variance_cpu(float *x, float *mean, int batch, int filters, int spatial, float *variance){    float scale = 1./(batch * spatial - 1);    int i,j,k;    for(i = 0; i < filters; ++i){        variance[i] = 0;        for(j = 0; j < batch; ++j){            for(k = 0; k < spatial; ++k){                int index = j*filters*spatial + i*spatial + k;                variance[i] += pow((x[index] - mean[i]), 2);            }        }        variance[i] *= scale;    }}//归一化void normalize_cpu(float *x, float *mean, float *variance, int batch, int filters, int spatial){    int b, f, i;    for(b = 0; b < batch; ++b){        for(f = 0; f < filters; ++f){            for(i = 0; i < spatial; ++i){                int index = b*filters*spatial + f*spatial + i;                //公式中的ε=.000001f                x[index] = (x[index] - mean[f])/(sqrt(variance[f]) + .000001f);            }        }    }}
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//convolutional_layer.cvoid scale_bias(float *output, float *scales, int batch, int n, int size){    int i,j,b;    for(b = 0; b < batch; ++b){        for(i = 0; i < n; ++i){            for(j = 0; j < size; ++j){                //scales就是创建convolutional_layer时分配的l.scales，值全是1                output[(b*n + i)*size + j] *= scales[i];            }        }    }}
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z这三个函数分别对应论文中的如下公式： 

d. add_bias()

这个函数和scale_bias()一模一样，是什么意思？明白的给解释一下呗～

//convolutional_layer.cvoid scale_bias(float *output, float *scales, int batch, int n, int size){    int i,j,b;    for(b = 0; b < batch; ++b){        for(i = 0; i < n; ++i){            for(j = 0; j < size; ++j){                output[(b*n + i)*size + j] *= scales[i];            }        }    }}
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2、前向传播-maxpool layer

//池化层相对要简单很多，如果理解了卷基层，这一层就很好理解

void forward_maxpool_layer(const maxpool_layer l, network_state state){    int b,i,j,k,m,n;    int w_offset = -l.pad;    int h_offset = -l.pad;    int h = l.out_h;    int w = l.out_w;    int c = l.c;    for(b = 0; b < l.batch; ++b){        for(k = 0; k < c; ++k){            //注意这里的h和w是maxpooling层输出的高度和宽度            for(i = 0; i < h; ++i){                for(j = 0; j < w; ++j){                    int out_index = j + w*(i + h*(k + c*b));                    float max = -FLT_MAX;                    int max_i = -1;                    //寻找最大值                    for(n = 0; n < l.size; ++n){                        for(m = 0; m < l.size; ++m){                            int cur_h = h_offset + i*l.stride + n;                            int cur_w = w_offset + j*l.stride + m;                            int index = cur_w + l.w*(cur_h + l.h*(k + b*l.c));                            int valid = (cur_h >= 0 && cur_h < l.h &&                                         cur_w >= 0 && cur_w < l.w);                            float val = (valid != 0) ? state.input[index] : -FLT_MAX;                            max_i = (val > max) ? index : max_i;                            max   = (val > max) ? val   : max;                        }                    }                    l.output[out_index] = max;                    l.indexes[out_index] = max_i;                }            }        }    }}
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3、前向传播-local layer

顾名思义，local层就是只看前一层的一部分，作者这里选择了左上角的区域

void forward_local_layer(const local_layer l, network_state state){    int out_h = local_out_height(l);    int out_w = local_out_width(l);    int i, j;    int locations = out_h * out_w;    for(i = 0; i < l.batch; ++i){        copy_cpu(l.outputs, l.biases, 1, l.output + i*l.outputs, 1);    }    for(i = 0; i < l.batch; ++i){        float *input = state.input + i*l.w*l.h*l.c;        im2col_cpu(input, l.c, l.h, l.w,                 l.size, l.stride, l.pad, l.col_image);        float *output = l.output + i*l.outputs;        for(j = 0; j < locations; ++j){            float *a = l.weights + j*l.size*l.size*l.c*l.n;            float *b = l.col_image + j;            float *c = output + j;            int m = l.n;            //n=1说明了作者只取了左上角的local区域，很容易想明白～            int n = 1;            int k = l.size*l.size*l.c;            gemm(0,0,m,n,k,1,a,k,b,locations,1,c,locations);        }    }    activate_array(l.output, l.outputs*l.batch, l.activation);}
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4、前向传播-dropout layer

dropout层主要是为了防止过拟合，详细可以自己搜索，这里不做过多介绍

void forward_dropout_layer(dropout_layer l, network_state state){    int i;    //dropout层只在训练阶段有效    if (!state.train) return;    for(i = 0; i < l.batch * l.inputs; ++i){        //产生0~1之间的随机数        float r = rand_uniform(0, 1);        l.rand[i] = r;        //如果小于给定概率值，就把相应的输入项赋0        if(r < l.probability) state.input[i] = 0;        else state.input[i] *= l.scale;    }}
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5、前向传播-connected layer

void forward_connected_layer(connected_layer l, network_state state){    int i;    fill_cpu(l.outputs*l.batch, 0, l.output, 1);    int m = l.batch;    int k = l.inputs;    int n = l.outputs;    float *a = state.input;    float *b = l.weights;    float *c = l.output;    //注意这里的TB=1了，所以调用了gemm_tn()这个函数，下面会有介绍    gemm(0,1,m,n,k,1,a,k,b,k,1,c,n);    if(l.batch_normalize){        if(state.train){            mean_cpu(l.output, l.batch, l.outputs, 1, l.mean);            variance_cpu(l.output, l.mean, l.batch, l.outputs, 1, l.variance);            //将l.rolling_mean所有值赋0.95（移动平均什么意思呢？自己百度吧，数据分析用的很多～）            scal_cpu(l.outputs, .95, l.rolling_mean, 1);            //将l.rolling_mean的值加上0.5*l.mean            axpy_cpu(l.outputs, .05, l.mean, 1, l.rolling_mean, 1);            //将l.rolling_variance所有值赋0.95            scal_cpu(l.outputs, .95, l.rolling_variance, 1);            //将l.rolling_variance的值加上0.5*l.variance            axpy_cpu(l.outputs, .05, l.variance, 1, l.rolling_variance, 1);            //将l.output的值赋值到l.x，此时l.x是没有经过BN的            copy_cpu(l.outputs*l.batch, l.output, 1, l.x, 1);            //BN            normalize_cpu(l.output, l.mean, l.variance, l.batch, l.outputs, 1);            //将l.output的值赋值到l.x_norm，此时l.x_norm是经过BN之后的数据            copy_cpu(l.outputs*l.batch, l.output, 1, l.x_norm, 1);        } else {            normalize_cpu(l.output, l.rolling_mean, l.rolling_variance, l.batch, l.outputs, 1);        }        scale_bias(l.output, l.scales, l.batch, l.outputs, 1);    }    for(i = 0; i < l.batch; ++i){        axpy_cpu(l.outputs, 1, l.biases, 1, l.output + i*l.outputs, 1);    }    //线性变换返回值不变    activate_array(l.output, l.outputs*l.batch, l.activation);}
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//gemm.cvoid gemm_nt(int M, int N, int K, float ALPHA,         float *A, int lda,         float *B, int ldb,        float *C, int ldc){    int i,j,k;    //M=batch，每个样本有N(yolo.train.cfg中是1715=S×S×(B∗5+C))个输出    for(i = 0; i < M; ++i){        for(j = 0; j < N; ++j){            register float sum = 0;            //K是inputs，即输入个数            for(k = 0; k < K; ++k){                //输入项和权重项对应相乘相加                sum += ALPHA*A[i*lda+k]*B[j*ldb + k];            }            C[i*ldc+j] += sum;        }    }}
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6、前向传播-detectiondetection layer

这是最后一层了，这也是作者论文的精髓所在，希望大家能对比论文认真看一下。 
前向传播终于要结束了，这篇博文有点小长～～

void forward_detection_layer(const detection_layer l, network_state state){    int locations = l.side*l.side;    int i,j;    memcpy(l.output, state.input, l.outputs*l.batch*sizeof(float));    //if(l.reorg) reorg(l.output, l.w*l.h, size*l.n, l.batch, 1);    int b;    //这里的softmax=0，所以最后竟然都没有softmax层……    if (l.softmax){        for(b = 0; b < l.batch; ++b){            int index = b*l.inputs;            for (i = 0; i < locations; ++i) {                int offset = i*l.classes;                softmax(l.output + index + offset, l.classes, 1,                        l.output + index + offset);            }        }    }    //训练的时候才需要cost function    if(state.train){        float avg_iou = 0;        float avg_cat = 0;        float avg_allcat = 0;        float avg_obj = 0;        float avg_anyobj = 0;        int count = 0;        *(l.cost) = 0;        int size = l.inputs * l.batch;        /*void *memset(void *s, int ch, size_t n);        memset是计算机中C/C++语言函数。将s所指向的某一块内存中的前n个 字节的内容全部设置        为ch指定的ASCII值*/        memset(l.delta, 0, size * sizeof(float));//l.delta存放的loss function的没一项        for (b = 0; b < l.batch; ++b){            int index = b*l.inputs;            //locations = 7*7            for (i = 0; i < locations; ++i) {                //coords包括x, y, w, h，1代表的是置信度                //truth_index是真实值的坐标索引                int truth_index = (b*locations + i)*(1+l.coords+l.classes);                int is_obj = state.truth[truth_index];                //计算置信度的损失                for (j = 0; j < l.n; ++j) {                    //p_index是预测值的坐标索引，每个网格预测l.n个框，这里l.n=3（cfg文件中的num值）,论文中是2                    int p_index = index + locations*l.classes + i*l.n + j;                    l.delta[p_index] = l.noobject_scale*(0 - l.output[p_index]);                    //对应论文公式，这里先假设B个框中都没有物体                    *(l.cost) += l.noobject_scale*pow(l.output[p_index], 2);                    avg_anyobj += l.output[p_index];                }                int best_index = -1;                float best_iou = 0;                float best_rmse = 20;                if (!is_obj){                    continue;                }                int class_index = index + i*l.classes;                                for(j = 0; j < l.classes; ++j) {                    //计算类别的损失                    l.delta[class_index+j] = l.class_scale * (state.truth[truth_index+1+j] - l.output[class_index+j]);                    *(l.cost) += l.class_scale * pow(state.truth[truth_index+1+j] - l.output[class_index+j], 2);                    if(state.truth[truth_index + 1 + j]) avg_cat += l.output[class_index+j];                    avg_allcat += l.output[class_index+j];                }                //计算位置信息的损失                box truth = float_to_box(state.truth + truth_index + 1 + l.classes);                truth.x /= l.side;                truth.y /= l.side;                /*寻找最后预测框（We only predict one set of class probabilities per grid cell,                 regardless of the number of boxes B）*/                for(j = 0; j < l.n; ++j){                    int box_index = index + locations*(l.classes + l.n) + (i*l.n + j) * l.coords;                    box out = float_to_box(l.output + box_index);                    out.x /= l.side;                    out.y /= l.side;                    if (l.sqrt){                        out.w = out.w*out.w;                        out.h = out.h*out.h;                    }                    //计算iou的值                    float iou  = box_iou(out, truth);                    //iou = 0;                    //计算均方根误差（root-mean-square error）                    float rmse = box_rmse(out, truth);                    //选出iou最大或者均方根误差最小的那个框作为最后预测框～                    if(best_iou > 0 || iou > 0){                        if(iou > best_iou){                            best_iou = iou;                            best_index = j;                        }                    }else{                        if(rmse < best_rmse){                            best_rmse = rmse;                            best_index = j;                        }                    }                }                //强制确定一个最后预测框                if(l.forced){                    if(truth.w*truth.h < .1){                        best_index = 1;                    }else{                        best_index = 0;                    }                }                //随机确定一个最后预测框～                if(l.random && *(state.net.seen) < 64000){                    best_index = rand()%l.n;                }                //预测的框的索引                int box_index = index + locations*(l.classes + l.n) + (i*l.n + best_index) * l.coords;                //真实框的索引                int tbox_index = truth_index + 1 + l.classes;                box out = float_to_box(l.output + box_index);                out.x /= l.side;                out.y /= l.side;                if (l.sqrt) {                    out.w = out.w*out.w;                    out.h = out.h*out.h;                }                float iou  = box_iou(out, truth);                //printf("%d,", best_index);                int p_index = index + locations*l.classes + i*l.n + best_index;                //前面假设了B个框中都没有物体来计算损失，这里再把有物体的那个减掉                *(l.cost) -= l.noobject_scale * pow(l.output[p_index], 2);                *(l.cost) += l.object_scale * pow(1-l.output[p_index], 2);                avg_obj += l.output[p_index];                l.delta[p_index] = l.object_scale * (1.-l.output[p_index]);                if(l.rescore){                    l.delta[p_index] = l.object_scale * (iou - l.output[p_index]);                }                l.delta[box_index+0] = l.coord_scale*(state.truth[tbox_index + 0] - l.output[box_index + 0]);                l.delta[box_index+1] = l.coord_scale*(state.truth[tbox_index + 1] - l.output[box_index + 1]);                l.delta[box_index+2] = l.coord_scale*(state.truth[tbox_index + 2] - l.output[box_index + 2]);                l.delta[box_index+3] = l.coord_scale*(state.truth[tbox_index + 3] - l.output[box_index + 3]);                if(l.sqrt){                    l.delta[box_index+2] = l.coord_scale*(sqrt(state.truth[tbox_index + 2]) - l.output[box_index + 2]);                    l.delta[box_index+3] = l.coord_scale*(sqrt(state.truth[tbox_index + 3]) - l.output[box_index + 3]);                }                //把iou作为损失，这包含了x,y,w,h四个参数，其实后来没用iou来计算损失，而是论文中给的公式                *(l.cost) += pow(1-iou, 2);                avg_iou += iou;                ++count;            }        }        //论文中没用到        if(0){            float *costs = calloc(l.batch*locations*l.n, sizeof(float));            for (b = 0; b < l.batch; ++b) {                int index = b*l.inputs;                for (i = 0; i < locations; ++i) {                    for (j = 0; j < l.n; ++j) {                        int p_index = index + locations*l.classes + i*l.n + j;                        costs[b*locations*l.n + i*l.n + j] = l.delta[p_index]*l.delta[p_index];                    }                }            }            int indexes[100];            top_k(costs, l.batch*locations*l.n, 100, indexes);            float cutoff = costs[indexes[99]];            for (b = 0; b < l.batch; ++b) {                int index = b*l.inputs;                for (i = 0; i < locations; ++i) {                    for (j = 0; j < l.n; ++j) {                        int p_index = index + locations*l.classes + i*l.n + j;                        if (l.delta[p_index]*l.delta[p_index] < cutoff) l.delta[p_index] = 0;                    }                }            }            free(costs);        }        //前面的*(l.cost)其实可以注释掉了，因为前面都没用，到这里才计算loss        *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2);        //打印log        printf("Detection Avg IOU: %f, Pos Cat: %f, All Cat: %f, Pos Obj: %f, Any Obj: %f, count: %d\n", avg_iou/count, avg_cat/count, avg_allcat/(count*l.classes), avg_obj/count, avg_anyobj/(l.batch*locations*l.n), count);        //if(l.reorg) reorg(l.delta, l.w*l.h, size*l.n, l.batch, 0);    }}
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二、总结

读到这里，我们已经完全掌握了YOLO代码的框架，我们大概总结下darknet的优缺点。 
优点：

1. 代码依赖项少，只有cuda，甚至连opencv都可以不需要，如果你在cpu平台，cuda都可以扔了（当然darknet的cup代码并没有做什么优化，跑起来就很慢）。这对于做工程的人来说是非常好的消息，因为我们可以很easy的将代码移植到其他平台

缺点：

1. 在darknet中，所有层的lr都一样，这对微调造成了很大的困难，因为微调需要把前面几层的lr都设置的很小很小，然后主要训练最后一层的权重

2. 总的来说就是darknet的接口确实很差，如果想把网络改成inception或者resnet的构架，需要改大量的代码，这对于验证模型可行性来说，非常浪费时间。你也应该能理解到为什么我们想要将代码移植到mxnet或者caffe上，然后在mxnet上做模型压缩了

下一篇是反向传播部分的代码，这部分是很重要的，但对与移植或者进一步改进网络来说，其实没必要理解反向传播部分的代码，但如果你想对CNN更深入的了解，可以继续看一下篇关于反向传播部分的内容。

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