yolov1代码阅读

yolov1使用的backbone是由GoogLeNet启发而来，有24个卷积层，最后接2个全连接层，详细结构如下图：

检测网络的输入分辨率是448X448，最后的特征图大小为7X7。在特征图的每一个位置都预测如下数据项：

1、一个C维的向量，表示在该位置含有物体的条件下，含有的物体属于C个类别中每一类别的条件概率；

2、一个B维的向量，网络为每个位置预测了B个bounding boxes，每个bounding boxes都有一个“分数”，表示该box与真正的物体框的IOU，也可以理解成该bounding box含有物体的概率。这里的“分数”和1中的每个类别的分数相乘就是每个框含有每一类物体的概率；

3、一个B*4维的向量。前两个数是bbox的中心点坐标，坐标值是在7X7的尺度下，相对于feature map当前位置的偏移量，范围为[0, 1]，当前位置坐标和偏移量相加即为实际的中心点坐标。这个中心点坐标的范围实际上就在以当前位置为左上角顶点的grid cell内。

综上输出的Tensor元素的排列顺序为：7*7*C-->7*7*B-->7*7*4*B，而标签Tensor元素的排列顺序为(1-->C-->4)*7*7。

yolov1的Loss由4部分组成，如下图：

yolov1最后一层的代码如下：

/*
l.coord_scale=5
l.object_scale=1
l.noobject_scale=0.5
l.class_scale=1
l.delta[box_index+0] = l.coord_scale*(net.truth[tbox_index + 0] - l.output[box_index + 0]);
l.delta[box_index+1] = l.coord_scale*(net.truth[tbox_index + 1] - l.output[box_index + 1]);
l.delta[box_index+2] = l.coord_scale*(sqrt(net.truth[tbox_index + 2]) - l.output[box_index + 2]);
l.delta[box_index+3] = l.coord_scale*(sqrt(net.truth[tbox_index + 3]) - l.output[box_index + 3]);
l.delta[p_index] = l.object_scale * (iou - l.output[p_index]);
l.delta[p_index] = l.noobject_scale*(0 - l.output[p_index]);
l.delta[class_index+j] = l.class_scale * (net.truth[truth_index+1+j] - l.output[class_index+j]);
*/
void forward_detection_layer(const detection_layer l, network net)
{
    int locations = l.side*l.side;
    int i,j;
    memcpy(l.output, net.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;
    // l.softmax=0
    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, 1,
                        l.output + index + offset);
            }
        }
    }
    if(net.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) {
                int truth_index = (b*locations + i)*(1+l.coords+l.classes);
                int is_obj = net.truth[truth_index];
                for (j = 0; j < l.n; ++j) {
                    int p_index = index + locations*l.classes + i*l.n + j;
                    // l.noobject_scale=0.5
                    l.delta[p_index] = l.noobject_scale*(0 - l.output[p_index]);
                    *(l.cost) += l.noobject_scale*pow(l.output[p_index], 2);
                    avg_anyobj += l.output[p_index];
                }
                if (!is_obj){
                    continue;
                }

                // l.class_scale=1
                int class_index = index + i*l.classes;
                for(j = 0; j < l.classes; ++j) {
                    l.delta[class_index+j] = l.class_scale * (net.truth[truth_index+1+j] - l.output[class_index+j]);
                    *(l.cost) += l.class_scale * pow(net.truth[truth_index+1+j] - l.output[class_index+j], 2);
                    if(net.truth[truth_index + 1 + j]) avg_cat += l.output[class_index+j];
                    avg_allcat += l.output[class_index+j];
                }
                int best_index = -1;
                float best_iou = 0;
                float best_rmse = 20;
                int row = i / l.side;
                int col = i % l.side;
                box truth = float_to_box(net.truth + truth_index + 1 + l.classes, 1);
                truth.x = (truth.x + col) / l.side;
                truth.y = (truth.y + row) / 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, 1);
                    out.x = (out.x + col) / l.side;
                    out.y = (out.y + row) / l.side;
                    // l.sqrt=1
                    if (l.sqrt){
                        out.w = out.w*out.w;
                        out.h = out.h*out.h;
                    }
                    float iou  = box_iou(out, truth);
                    float rmse = box_rmse(out, truth);
                    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;
                        }
                    }
                }
                // l.forced=0
                if(l.forced){
                    if(truth.w*truth.h < .1){
                        best_index = 1;
                    }else{
                        best_index = 0;
                    }
                }
                // l.random=0
                if(l.random && *(net.seen) < 64000){
                    best_index = rand()%l.n;
                }

                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, 1);
                out.x = (out.x + col) / l.side;
                out.y = (out.y + row) / l.side;
                if (l.sqrt) {
                    out.w = out.w*out.w;
                    out.h = out.h*out.h;
                }
                float iou  = box_iou(out, truth);
                // l.noobject_scale=0.5, l.object_scale=1
                int p_index = index + locations*l.classes + i*l.n + best_index;
                *(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]);
                // l.rescore=1
                if(l.rescore){
                    l.delta[p_index] = l.object_scale * (iou - l.output[p_index]);
                }
                // l.coord_scale=5
                l.delta[box_index+0] = l.coord_scale*(net.truth[tbox_index + 0] - l.output[box_index + 0]);
                l.delta[box_index+1] = l.coord_scale*(net.truth[tbox_index + 1] - l.output[box_index + 1]);
                l.delta[box_index+2] = l.coord_scale*(net.truth[tbox_index + 2] - l.output[box_index + 2]);
                l.delta[box_index+3] = l.coord_scale*(net.truth[tbox_index + 3] - l.output[box_index + 3]);
                if(l.sqrt){
                    l.delta[box_index+2] = l.coord_scale*(sqrt(net.truth[tbox_index + 2]) - l.output[box_index + 2]);
                    l.delta[box_index+3] = l.coord_scale*(sqrt(net.truth[tbox_index + 3]) - l.output[box_index + 3]);
                }

                *(l.cost) += pow(1-iou, 2);
                avg_iou += iou;
                ++count;
            }
        }
        *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2);
        printf("Detection Avg IOU: %f, Pos Cat: %f, All Cat: %f, Pos Obj: %f, Any Obj: %f, count: %d
", 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);
    }
}

void backward_detection_layer(const detection_layer l, network net)
{
    axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, net.delta, 1);
}

预测时根据最终的输出得到bbox的代码如下：（值得注意的是，在yolov1的训练阶段，会使用原图的宽、高将标注的bbox归一化，在预测阶段输出的bbox坐标也是归一化的。）

void get_detection_detections(layer l, int w, int h, float thresh, detection *dets)
{
    int i,j,n;
    float *predictions = l.output;
    //int per_cell = 5*num+classes;
    for (i = 0; i < l.side*l.side; ++i){
        int row = i / l.side;
        int col = i % l.side;
        for(n = 0; n < l.n; ++n){
            int index = i*l.n + n;
            int p_index = l.side*l.side*l.classes + i*l.n + n;
            float scale = predictions[p_index];
            int box_index = l.side*l.side*(l.classes + l.n) + (i*l.n + n)*4;
            box b;
//            b.x = (predictions[box_index + 0] + col) / l.side * w;
//            b.y = (predictions[box_index + 1] + row) / l.side * h;
//            b.w = pow(predictions[box_index + 2], (l.sqrt?2:1)) * w;
//            b.h = pow(predictions[box_index + 3], (l.sqrt?2:1)) * h;
            b.x = (predictions[box_index + 0] + col) / l.side;
            b.y = (predictions[box_index + 1] + row) / l.side;
            b.w = pow(predictions[box_index + 2], (l.sqrt?2:1));
            b.h = pow(predictions[box_index + 3], (l.sqrt?2:1));
            dets[index].bbox = b;
            dets[index].objectness = scale;
            for(j = 0; j < l.classes; ++j){
                int class_index = i*l.classes;
                float prob = scale*predictions[class_index+j];
              dets[index].prob[j] = (prob > thresh) ? prob : 0;
            }
        }
    }
}

生成net.truth的关键代码如下：

void fill_truth_region(char *path, float *truth, int classes, int num_boxes, int flip, float dx, float dy, float sx, float sy)
{
    char labelpath[4096];
    find_replace(path, "images", "labels", labelpath);
    find_replace(labelpath, "JPEGImages", "labels", labelpath);

    find_replace(labelpath, ".jpg", ".txt", labelpath);
    find_replace(labelpath, ".png", ".txt", labelpath);
    find_replace(labelpath, ".JPG", ".txt", labelpath);
    find_replace(labelpath, ".JPEG", ".txt", labelpath);
    int count = 0;
    box_label *boxes = read_boxes(labelpath, &count);
    randomize_boxes(boxes, count);
    correct_boxes(boxes, count, dx, dy, sx, sy, flip);
    float x,y,w,h;
    int id;
    int i;

    for (i = 0; i < count; ++i) {
        x =  boxes[i].x;
        y =  boxes[i].y;
        w =  boxes[i].w;
        h =  boxes[i].h;
        id = boxes[i].id;

        if (w < .005 || h < .005) continue;

        // num_boxes is S in article, yolov1 divides the input image into SxS grid
        int col = (int)(x*num_boxes);
        int row = (int)(y*num_boxes);

        x = x*num_boxes - col;
        y = y*num_boxes - row;

        int index = (col+row*num_boxes)*(5+classes);
        if (truth[index]) continue;
        truth[index++] = 1;

        if (id < classes) truth[index+id] = 1;
        index += classes;

        truth[index++] = x;
        truth[index++] = y;
        truth[index++] = w;
        truth[index++] = h;
    }
    free(boxes);
}