Yolo of Darknet_ Forward of layer_ yolo_ Layer comments

• Did two things ： Determine the positive and negative samples , Calculation loss

void forward_yolo_layer(const layer l, network_state state)
{
    
    int i, j, b, t, n;
    // batch*3*85*w*h
    memcpy(l.output, state.input, l.outputs*l.batch * sizeof(float));

#ifndef GPU
    for (b = 0; b < l.batch; ++b) {
    
        for (n = 0; n < l.n; ++n) {
    
            int index = entry_index(l, b, n*l.w*l.h, 0);
            // 0,1 Location execution sigmoid
            activate_array(l.output + index, 2 * l.w*l.h, LOGISTIC);        // x,y,
            scal_add_cpu(2 * l.w*l.h, l.scale_x_y, -0.5*(l.scale_x_y - 1), l.output + index, 1);    // scale x,y
            index = entry_index(l, b, n*l.w*l.h, 4);
            // 4~84 Location execution sigmoid, Confidence and category probability 
            activate_array(l.output + index, (1 + l.classes)*l.w*l.h, LOGISTIC);
        }
    }
#endif

    // delta is zeroed
    memset(l.delta, 0, l.outputs * l.batch * sizeof(float));
    if (!state.train) return;
    //float avg_iou = 0;
    float tot_iou = 0;
    float tot_giou = 0;
    float tot_diou = 0;
    float tot_ciou = 0;
    float tot_iou_loss = 0;
    float tot_giou_loss = 0;
    float tot_diou_loss = 0;
    float tot_ciou_loss = 0;
    float recall = 0;
    float recall75 = 0;
    float avg_cat = 0;
    float avg_obj = 0;
    float avg_anyobj = 0;
    int count = 0;
    int class_count = 0;
    *(l.cost) = 0;
    for (b = 0; b < l.batch; ++b) {
    
        //  Calculation error ： Traverse anchor, And gt Maximum IOU Greater than the threshold is a positive sample 
        for (j = 0; j < l.h; ++j) {
    
            for (i = 0; i < l.w; ++i) {
    
                for (n = 0; n < l.n; ++n) {
    
                    int box_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 0);
                    //  Get... According to the index box Information about 
                    box pred = get_yolo_box(l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.w*l.h);
                    float best_match_iou = 0;
                    int best_match_t = 0;
                    float best_iou = 0;
                    int best_t = 0;
                    //  Calculate the number of... With the current grid n individual anchor The best match gt
                    for (t = 0; t < l.max_boxes; ++t) {
    
                        // batch*90*5
                        //  Take the current picture t individual gt
                        box truth = float_to_box_stride(state.truth + t*(4 + 1) + b*l.truths, 1);
                        int class_id = state.truth[t*(4 + 1) + b*l.truths + 4];
                        if (class_id >= l.classes || class_id < 0) {
    
                            printf("\n Warning: in txt-labels class_id=%d >= classes=%d in cfg-file. In txt-labels class_id should be [from 0 to %d] \n", class_id, l.classes, l.classes - 1);
                            printf("\n truth.x = %f, truth.y = %f, truth.w = %f, truth.h = %f, class_id = %d \n", truth.x, truth.y, truth.w, truth.h, class_id);
                            if (check_mistakes) getchar();
                            continue; // if label contains class_id more than number of classes in the cfg-file and class_id check garbage value
                        }
                        if (!truth.x) break;  // continue;

                        int class_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4 + 1);
                        int obj_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4);
                        float objectness = l.output[obj_index];
                        if (isnan(objectness) || isinf(objectness)) l.output[obj_index] = 0;
                        //  Judge output Is there a category probability greater than the threshold in 
                        int class_id_match = compare_yolo_class(l.output, l.classes, class_index, l.w*l.h, objectness, class_id, 0.25f);

                        float iou = box_iou(pred, truth);
                        if (iou > best_match_iou && class_id_match == 1) {
    
                            best_match_iou = iou;
                            best_match_t = t;
                        }
                        if (iou > best_iou) {
    
                            best_iou = iou;
                            best_t = t;
                        }
                    }
                    // x,y,w,h,c Medium c
                    int obj_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4);
                    avg_anyobj += l.output[obj_index];
                    //  Error term , Initialize to negative sample error ,iou If the threshold is met, zero is assigned , Wait for the following assignment positive sample error or ignore 
                    l.delta[obj_index] = l.cls_normalizer * (0 - l.output[obj_index]);
                    if (best_match_iou > l.ignore_thresh) {
    
                        l.delta[obj_index] = 0;
                    }
                    //  Multiply the negative sample error by one scale
                    else if (state.net.adversarial) {
    
                        // 80 Index of the first of the categories 
                        int class_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4 + 1);
                        int stride = l.w*l.h;
                        float scale = pred.w * pred.h;
                        if (scale > 0) scale = sqrt(scale);
                        l.delta[obj_index] = scale * l.cls_normalizer * (0 - l.output[obj_index]);
                        int cl_id;
                        for (cl_id = 0; cl_id < l.classes; ++cl_id) {
    
                            if(l.output[class_index + stride*cl_id] * l.output[obj_index] > 0.25)
                                l.delta[class_index + stride*cl_id] = scale * (0 - l.output[class_index + stride*cl_id]);
                        }
                    }
                    if (best_iou > l.truth_thresh) {
    
                        //  Positive sample error 
                        l.delta[obj_index] = l.cls_normalizer * (1 - l.output[obj_index]);

                        //  Best match gt Categories 
                        int class_id = state.truth[best_t*(4 + 1) + b*l.truths + 4];
                        if (l.map) class_id = l.map[class_id];
                        // 80 Index of the first of the categories 
                        int class_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4 + 1);
                        //  Calculate the category error 
                        delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0, l.focal_loss, l.label_smooth_eps, l.classes_multipliers);
                        box truth = float_to_box_stride(state.truth + best_t*(4 + 1) + b*l.truths, 1);
                        const float class_multiplier = (l.classes_multipliers) ? l.classes_multipliers[class_id] : 1.0f;
                        //  Calculation xywh error 
                        delta_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer * class_multiplier, l.iou_loss, 1, l.max_delta);
                    }
                }
            }
        }
        //  Calculation gt With the grid anchor Maximum IOU, Greater than the threshold is a positive sample 
        for (t = 0; t < l.max_boxes; ++t) {
    
            //  take gt Information about 
            box truth = float_to_box_stride(state.truth + t*(4 + 1) + b*l.truths, 1);
            if (truth.x < 0 || truth.y < 0 || truth.x > 1 || truth.y > 1 || truth.w < 0 || truth.h < 0) {
    
                char buff[256];
                printf(" Wrong label: truth.x = %f, truth.y = %f, truth.w = %f, truth.h = %f \n", truth.x, truth.y, truth.w, truth.h);
                sprintf(buff, "echo \"Wrong label: truth.x = %f, truth.y = %f, truth.w = %f, truth.h = %f\" >> bad_label.list",
                    truth.x, truth.y, truth.w, truth.h);
                system(buff);
            }
            int class_id = state.truth[t*(4 + 1) + b*l.truths + 4];
            if (class_id >= l.classes || class_id < 0) continue; // if label contains class_id more than number of classes in the cfg-file and class_id check garbage value

            if (!truth.x) break;  // continue;
            //  Calculation gt With which anchor Best match 
            float best_iou = 0;
            int best_n = 0;
            i = (truth.x * l.w);
            j = (truth.y * l.h);
            box truth_shift = truth;
            truth_shift.x = truth_shift.y = 0;
            for (n = 0; n < l.total; ++n) {
    
                box pred = {
     0 };
                pred.w = l.biases[2 * n] / state.net.w;
                pred.h = l.biases[2 * n + 1] / state.net.h;
                float iou = box_iou(pred, truth_shift);
                if (iou > best_iou) {
    
                    best_iou = iou;
                    best_n = n;
                }
            }

            //  Judge anchor Whether it belongs to this layer 
            int mask_n = int_index(l.mask, best_n, l.n);
            if (mask_n >= 0) {
    
                int class_id = state.truth[t*(4 + 1) + b*l.truths + 4];
                if (l.map) class_id = l.map[class_id];

                int box_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0);
                const float class_multiplier = (l.classes_multipliers) ? l.classes_multipliers[class_id] : 1.0f;
                ious all_ious = delta_yolo_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer * class_multiplier, l.iou_loss, 1, l.max_delta);

                // range is 0 <= 1
                tot_iou += all_ious.iou;
                tot_iou_loss += 1 - all_ious.iou;
                // range is -1 <= giou <= 1
                tot_giou += all_ious.giou;
                tot_giou_loss += 1 - all_ious.giou;

                tot_diou += all_ious.diou;
                tot_diou_loss += 1 - all_ious.diou;

                tot_ciou += all_ious.ciou;
                tot_ciou_loss += 1 - all_ious.ciou;

                int obj_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4);
                avg_obj += l.output[obj_index];
                l.delta[obj_index] = class_multiplier * l.cls_normalizer * (1 - l.output[obj_index]);

                int class_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4 + 1);
                delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.focal_loss, l.label_smooth_eps, l.classes_multipliers);

                //printf(" label: class_id = %d, truth.x = %f, truth.y = %f, truth.w = %f, truth.h = %f \n", class_id, truth.x, truth.y, truth.w, truth.h);
                //printf(" mask_n = %d, l.output[obj_index] = %f, l.output[class_index + class_id] = %f \n\n", mask_n, l.output[obj_index], l.output[class_index + class_id]);

                ++count;
                ++class_count;
                if (all_ious.iou > .5) recall += 1;
                if (all_ious.iou > .75) recall75 += 1;
            }

            // iou_thresh
            // gt Grid IOU Greater than the threshold anchor It is also a positive sample 
            for (n = 0; n < l.total; ++n) {
    
                int mask_n = int_index(l.mask, n, l.n);
                if (mask_n >= 0 && n != best_n && l.iou_thresh < 1.0f) {
    
                    box pred = {
     0 };
                    pred.w = l.biases[2 * n] / state.net.w;
                    pred.h = l.biases[2 * n + 1] / state.net.h;
                    float iou = box_iou_kind(pred, truth_shift, l.iou_thresh_kind); // IOU, GIOU, MSE, DIOU, CIOU
                    // iou, n

                    if (iou > l.iou_thresh) {
    
                        int class_id = state.truth[t*(4 + 1) + b*l.truths + 4];
                        if (l.map) class_id = l.map[class_id];

                        int box_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0);
                        const float class_multiplier = (l.classes_multipliers) ? l.classes_multipliers[class_id] : 1.0f;
                        ious all_ious = delta_yolo_box(truth, l.output, l.biases, n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer * class_multiplier, l.iou_loss, 1, l.max_delta);

                        // range is 0 <= 1
                        tot_iou += all_ious.iou;
                        tot_iou_loss += 1 - all_ious.iou;
                        // range is -1 <= giou <= 1
                        tot_giou += all_ious.giou;
                        tot_giou_loss += 1 - all_ious.giou;

                        tot_diou += all_ious.diou;
                        tot_diou_loss += 1 - all_ious.diou;

                        tot_ciou += all_ious.ciou;
                        tot_ciou_loss += 1 - all_ious.ciou;

                        int obj_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4);
                        avg_obj += l.output[obj_index];
                        l.delta[obj_index] = class_multiplier * l.cls_normalizer * (1 - l.output[obj_index]);

                        int class_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4 + 1);
                        delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.focal_loss, l.label_smooth_eps, l.classes_multipliers);

                        ++count;
                        ++class_count;
                        if (all_ious.iou > .5) recall += 1;
                        if (all_ious.iou > .75) recall75 += 1;
                    }
                }
            }
        }

        // averages the deltas obtained by the function: delta_yolo_box()_accumulate
        //  Average the number of categories 
        for (j = 0; j < l.h; ++j) {
    
            for (i = 0; i < l.w; ++i) {
    
                for (n = 0; n < l.n; ++n) {
    
                    int box_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 0);
                    int class_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4 + 1);
                    const int stride = l.w*l.h;

                    averages_yolo_deltas(class_index, box_index, stride, l.classes, l.delta);
                }
            }
        }
    }

    if (count == 0) count = 1;
    if (class_count == 0) class_count = 1;

    //*(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2);
    //printf("Region %d Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d\n", state.index, avg_iou / count, avg_cat / class_count, avg_obj / count, avg_anyobj / (l.w*l.h*l.n*l.batch), recall / count, recall75 / count, count);

    int stride = l.w*l.h;
    float* no_iou_loss_delta = (float *)calloc(l.batch * l.outputs, sizeof(float));
    memcpy(no_iou_loss_delta, l.delta, l.batch * l.outputs * sizeof(float));
    for (b = 0; b < l.batch; ++b) {
    
        for (j = 0; j < l.h; ++j) {
    
            for (i = 0; i < l.w; ++i) {
    
                for (n = 0; n < l.n; ++n) {
    
                    int index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 0);
                    no_iou_loss_delta[index + 0 * stride] = 0;
                    no_iou_loss_delta[index + 1 * stride] = 0;
                    no_iou_loss_delta[index + 2 * stride] = 0;
                    no_iou_loss_delta[index + 3 * stride] = 0;
                }
            }
        }
    }
    float classification_loss = l.cls_normalizer * pow(mag_array(no_iou_loss_delta, l.outputs * l.batch), 2);
    free(no_iou_loss_delta);
    float loss = pow(mag_array(l.delta, l.outputs * l.batch), 2);
    float iou_loss = loss - classification_loss;

    float avg_iou_loss = 0;
    // gIOU loss + MSE (objectness) loss
    if (l.iou_loss == MSE) {
    
        *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2);
    }
    else {
    
        // Always compute classification loss both for iou + cls loss and for logging with mse loss
        // TODO: remove IOU loss fields before computing MSE on class
        // probably split into two arrays
        if (l.iou_loss == GIOU) {
    
            avg_iou_loss = count > 0 ? l.iou_normalizer * (tot_giou_loss / count) : 0;
        }
        else {
    
            avg_iou_loss = count > 0 ? l.iou_normalizer * (tot_iou_loss / count) : 0;
        }
        *(l.cost) = avg_iou_loss + classification_loss;
    }

    loss /= l.batch;
    classification_loss /= l.batch;
    iou_loss /= l.batch;

    fprintf(stderr, "v3 (%s loss, Normalizer: (iou: %.2f, cls: %.2f) Region %d Avg (IOU: %f, GIOU: %f), Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d, class_loss = %f, iou_loss = %f, total_loss = %f \n",
        (l.iou_loss == MSE ? "mse" : (l.iou_loss == GIOU ? "giou" : "iou")), l.iou_normalizer, l.cls_normalizer, state.index, tot_iou / count, tot_giou / count, avg_cat / class_count, avg_obj / count, avg_anyobj / (l.w*l.h*l.n*l.batch), recall / count, recall75 / count, count,
        classification_loss, iou_loss, loss);
}