darknet之detection_layer源码分析

• 最关键的函数，以yolov1为例
• yolov1没有anchor，直接使用网络输出计算IOU

void forward_detection_layer(const detection_layer l, network_state state)
{
   
   
    // locations是cell的个数，yolov1是49
    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;
    // 如果需要，计算softmax
    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, 1);
            }
        }
    }
    // 后面的代码都在这个if里
    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;
        // yolov1 : l.inputs = 7*7*(5*2+20)
        int size = l.inputs * l.batch;
        memset(l.delta, 0, size * sizeof(float));
        for (b = 0; b < l.batch; ++b){
   
   
            int index = b*l.inputs;
            // 对每7*7*(5*2+20)个数据，后面的索引都是在一组7*7*(5*2+20)里说
            for (i = 0; i < locations; ++i) {
   
   
                int truth_index = (b*locations + i)*(1+l.coords+l.classes);
                // 索引0的值
                int is_obj = state.truth[truth_index];
                // l.n是框的个数
                for (j = 0; j < l.n; ++j) {
   
   
                    int p_index = index + locations*l.classes + i*l.n + j;