YOLO detection 学习

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detection_layer make_detection_layer(int batch, int inputs, int n, int side, int classes, int coords, int rescore){    detection_layer l = {0};    l.type = DETECTION;    l.n = n;//对应论文里面的B,表示每个cell生成的bounding box数。    l.batch = batch;    l.inputs = inputs;//输入detection的维度，计算方式根据论文是S*S*(B*5 + C)，论文里面对应7*7*（2*5 + 20）    l.classes = classes;//类别数，论文里面对应是20    l.coords = coords;//坐标，（x,y,w,h）, (x,y)是相对于网格的数值[0,1],(w,h)，是相对于整个图片的大小。    l.rescore = rescore;    l.side = side;//网格side*side    assert(side*side*((1 + l.coords)*l.n + l.classes) == inputs);    l.cost = calloc(1, sizeof(float));    l.outputs = l.inputs;    l.truths = l.side*l.side*(1+l.coords+l.classes);    l.output = calloc(batch*l.outputs, sizeof(float));    l.delta = calloc(batch*l.outputs, sizeof(float));#ifdef GPU    l.output_gpu = cuda_make_array(l.output, batch*l.outputs);    l.delta_gpu = cuda_make_array(l.delta, batch*l.outputs);#endif    fprintf(stderr, "Detection Layer\n");    srand(0);    return l;}

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));    int b;    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_array(l.output + index + offset, l.classes, 1,                    l.output + index + offset);            }            int offset = locations*l.classes;            activate_array(l.output + index + offset, locations*l.n*(1 + l.coords), LOGISTIC);        }    }    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;        memset(l.delta, 0, size * sizeof(float));        for (b = 0; b < l.batch; ++b){            int index = b*l.inputs;            for (i = 0; i < locations; ++i) {                //truth 的存储方式是 [l.side*l.side*(1+l.classes+l.coords)]                int truth_index = (b*locations + i)*(1 + l.coords + l.classes);//这个地方表示的是输入图片的gound truth的index                int is_obj = state.truth[truth_index];//表示这个cell是否是object，数据存放在state里面                for (j = 0; j < l.n; ++j) {//计算noobj这一项的损失                    int p_index = index + locations*l.classes + i*l.n + j;                    //b*l.inputs + locations*l.classes + i*l.n + j;                    //inputs 的存储方式是 [l.side*l.side*l.classes][l.side*l.side*l.n][l.side*l.side*l.n*l.coords]                    l.delta[p_index] = l.noobject_scale*(0 - l.output[p_index]);//对应论文损失的第四项。这个计算很是奇怪，论文里面还是要判断一下是否是noobj？？？？他怎么没有判断就直接计算了                    *(l.cost) += l.noobject_scale*pow(l.output[p_index], 2);//同上                    avg_anyobj += l.output[p_index];                }                int best_index = -1;                float best_iou = 0;                float best_rmse = 20;                if (!is_obj){                    continue;                }                //后面都是对应于object的情况。                int class_index = index + i*l.classes;                for (j = 0; j < l.classes; ++j) {                    l.delta[class_index + j] = l.class_scale * (state.truth[truth_index + 1 + j] - l.output[class_index + j]);//对应论文五项损失的第五项                    *(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];//float类型做判断这样写不太好。                    avg_allcat += l.output[class_index + j];                }//l.classes                box truth = float_to_box(state.truth + truth_index + 1 + l.classes);//进行了loaction 对应 truth的获取。                truth.x /= l.side;//这个地方的计算不清楚。                truth.y /= l.side;                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);//cell里面预测的boundingbox                    out.x /= l.side;//同上面的不清楚                    out.y /= l.side;                    if (l.sqrt){                        out.w = out.w*out.w;                        out.h = out.h*out.h;                    }//if                    float iou = box_iou(out, truth);//计算iou值，即out与truth的交除以他们的并。                    //iou = 0;                    float rmse = box_rmse(out, truth);//这个是计算boundingbox各项之间的差的平方和的开根号。                    if (best_iou > 0 || iou > 0){                        if (iou > best_iou){                            best_iou = iou;                            best_index = j;//存放cell里面最好的预测的boundingbox的idx                        }                    }                    else{                        if (rmse < best_rmse){                            best_rmse = rmse;                            best_index = j;                        }                    }//if                }//for                if (l.forced){//？？                    if (truth.w*truth.h < .1){                        best_index = 1;                    }                    else{                        best_index = 0;                    }                }                int box_index = index + locations*(l.classes + l.n) + (i*l.n + best_index) * l.coords;                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;                //对应于论文里面的第三项。                *(l.cost) -= l.noobject_scale * pow(l.output[p_index], 2);//这里揭开前面的问题，这里相当于已经判定是objcet，就把之前算在里面noobjcet的减掉。                *(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]);//这里就覆盖到之前被noobject赋值的数。                if (l.rescore){                    l.delta[p_index] = l.object_scale * (iou - l.output[p_index]);                }                //下面的四个对应于论文里面损失的第一项和第二项。                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]);                }                *(l.cost) += pow(1 - iou, 2);                avg_iou += iou;                ++count;            }            if (l.softmax){                gradient_array(l.output + index + locations*l.classes, locations*l.n*(1 + l.coords),                    LOGISTIC, l.delta + index + locations*l.classes);            }        }//loaction        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);    }//batchsize}

λcoord∑i=0S2∑j=0B1objij((xi−xi^)2+(yj+yj^)2)
+λcoord∑i=0S2∑j=0B1objij((wi−−√−wi^−−√)2+(hi−−√−hi^−−√)2
+∑i=0S2∑j=0B1objij(ci−ci^)2
+λnoobj∑i=0S2∑j=0B1noobjij(ci−ci^)2
+∑i=0S21obji(∑c∈classes(pi(c)−pi^(c))2)

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