派生自 Algorithm/baseDetector
lib/detecter_tools/darknet/gaussian_yolo_layer.c
@@ -1,899 +1,899 @@ // Gaussian YOLOv3 implementation // Author: Jiwoong Choi // ICCV 2019 Paper: http://openaccess.thecvf.com/content_ICCV_2019/html/Choi_Gaussian_YOLOv3_An_Accurate_and_Fast_Object_Detector_Using_Localization_ICCV_2019_paper.html // arxiv.org: https://arxiv.org/abs/1904.04620v2 // source code: https://github.com/jwchoi384/Gaussian_YOLOv3 #include "gaussian_yolo_layer.h" #include "activations.h" #include "blas.h" #include "box.h" #include "dark_cuda.h" #include "utils.h" #include <stdio.h> #include <assert.h> #include <string.h> #include <stdlib.h> #ifndef M_PI #define M_PI 3.141592 #endif extern int check_mistakes; layer make_gaussian_yolo_layer(int batch, int w, int h, int n, int total, int *mask, int classes, int max_boxes) { int i; layer l = { (LAYER_TYPE)0 }; l.type = GAUSSIAN_YOLO; l.n = n; l.total = total; l.batch = batch; l.h = h; l.w = w; l.c = n*(classes + 8 + 1); l.out_w = l.w; l.out_h = l.h; l.out_c = l.c; l.classes = classes; l.cost = (float*)calloc(1, sizeof(float)); l.biases = (float*)calloc(total*2, sizeof(float)); if(mask) l.mask = mask; else{ l.mask = (int*)calloc(n, sizeof(int)); for(i = 0; i < n; ++i){ l.mask[i] = i; } } l.bias_updates = (float*)calloc(n*2, sizeof(float)); l.outputs = h*w*n*(classes + 8 + 1); l.inputs = l.outputs; l.max_boxes = max_boxes; l.truths = l.max_boxes*(4 + 1); l.delta = (float*)calloc(batch*l.outputs, sizeof(float)); l.output = (float*)calloc(batch*l.outputs, sizeof(float)); for(i = 0; i < total*2; ++i){ l.biases[i] = .5; } l.forward = forward_gaussian_yolo_layer; l.backward = backward_gaussian_yolo_layer; #ifdef GPU l.forward_gpu = forward_gaussian_yolo_layer_gpu; l.backward_gpu = backward_gaussian_yolo_layer_gpu; l.output_gpu = cuda_make_array(l.output, batch*l.outputs); l.delta_gpu = cuda_make_array(l.delta, batch*l.outputs); free(l.output); if (cudaSuccess == cudaHostAlloc(&l.output, batch*l.outputs * sizeof(float), cudaHostRegisterMapped)) l.output_pinned = 1; else { cudaGetLastError(); // reset CUDA-error l.output = (float*)calloc(batch * l.outputs, sizeof(float)); } free(l.delta); if (cudaSuccess == cudaHostAlloc(&l.delta, batch*l.outputs * sizeof(float), cudaHostRegisterMapped)) l.delta_pinned = 1; else { cudaGetLastError(); // reset CUDA-error l.delta = (float*)calloc(batch * l.outputs, sizeof(float)); } #endif //fprintf(stderr, "Gaussian_yolo\n"); srand(time(0)); return l; } void resize_gaussian_yolo_layer(layer *l, int w, int h) { l->w = w; l->h = h; l->outputs = h*w*l->n*(l->classes + 8 + 1); l->inputs = l->outputs; //l->output = (float *)realloc(l->output, l->batch*l->outputs * sizeof(float)); //l->delta = (float *)realloc(l->delta, l->batch*l->outputs * sizeof(float)); if (!l->output_pinned) l->output = (float*)realloc(l->output, l->batch*l->outputs * sizeof(float)); if (!l->delta_pinned) l->delta = (float*)realloc(l->delta, l->batch*l->outputs * sizeof(float)); #ifdef GPU if (l->output_pinned) { CHECK_CUDA(cudaFreeHost(l->output)); if (cudaSuccess != cudaHostAlloc(&l->output, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) { cudaGetLastError(); // reset CUDA-error l->output = (float*)calloc(l->batch * l->outputs, sizeof(float)); l->output_pinned = 0; } } if (l->delta_pinned) { CHECK_CUDA(cudaFreeHost(l->delta)); if (cudaSuccess != cudaHostAlloc(&l->delta, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) { cudaGetLastError(); // reset CUDA-error l->delta = (float*)calloc(l->batch * l->outputs, sizeof(float)); l->delta_pinned = 0; } } cuda_free(l->delta_gpu); cuda_free(l->output_gpu); l->delta_gpu = cuda_make_array(l->delta, l->batch*l->outputs); l->output_gpu = cuda_make_array(l->output, l->batch*l->outputs); #endif } box get_gaussian_yolo_box(float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, int stride, YOLO_POINT yolo_point) { box b; b.w = exp(x[index + 4 * stride]) * biases[2 * n] / w; b.h = exp(x[index + 6 * stride]) * biases[2 * n + 1] / h; b.x = (i + x[index + 0 * stride]) / lw; b.y = (j + x[index + 2 * stride]) / lh; if (yolo_point == YOLO_CENTER) { } else if (yolo_point == YOLO_LEFT_TOP) { b.x = (i + x[index + 0 * stride]) / lw + b.w / 2; b.y = (j + x[index + 2 * stride]) / lh + b.h / 2; } else if (yolo_point == YOLO_RIGHT_BOTTOM) { b.x = (i + x[index + 0 * stride]) / lw - b.w / 2; b.y = (j + x[index + 2 * stride]) / lh - b.h / 2; } return b; } static inline float fix_nan_inf(float val) { if (isnan(val) || isinf(val)) val = 0; return val; } static inline float clip_value(float val, const float max_val) { if (val > max_val) val = max_val; else if (val < -max_val) val = -max_val; return val; } float delta_gaussian_yolo_box(box truth, float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, float *delta, float scale, int stride, float iou_normalizer, IOU_LOSS iou_loss, float uc_normalizer, int accumulate, YOLO_POINT yolo_point, float max_delta) { box pred = get_gaussian_yolo_box(x, biases, n, index, i, j, lw, lh, w, h, stride, yolo_point); float iou; ious all_ious = { 0 }; all_ious.iou = box_iou(pred, truth); all_ious.giou = box_giou(pred, truth); all_ious.diou = box_diou(pred, truth); all_ious.ciou = box_ciou(pred, truth); if (pred.w == 0) { pred.w = 1.0; } if (pred.h == 0) { pred.h = 1.0; } float sigma_const = 0.3; float epsi = pow(10,-9); float dx, dy, dw, dh; iou = all_ious.iou; float tx, ty, tw, th; tx = (truth.x*lw - i); ty = (truth.y*lh - j); tw = log(truth.w*w / biases[2 * n]); th = log(truth.h*h / biases[2 * n + 1]); if (yolo_point == YOLO_CENTER) { } else if (yolo_point == YOLO_LEFT_TOP) { tx = ((truth.x - truth.w / 2)*lw - i); ty = ((truth.y - truth.h / 2)*lh - j); } else if (yolo_point == YOLO_RIGHT_BOTTOM) { tx = ((truth.x + truth.w / 2)*lw - i); ty = ((truth.y + truth.h / 2)*lh - j); } dx = (tx - x[index + 0 * stride]); dy = (ty - x[index + 2 * stride]); dw = (tw - x[index + 4 * stride]); dh = (th - x[index + 6 * stride]); // Gaussian float in_exp_x = dx / x[index+1*stride]; float in_exp_x_2 = pow(in_exp_x, 2); float normal_dist_x = exp(in_exp_x_2*(-1./2.))/(sqrt(M_PI * 2.0)*(x[index+1*stride]+sigma_const)); float in_exp_y = dy / x[index+3*stride]; float in_exp_y_2 = pow(in_exp_y, 2); float normal_dist_y = exp(in_exp_y_2*(-1./2.))/(sqrt(M_PI * 2.0)*(x[index+3*stride]+sigma_const)); float in_exp_w = dw / x[index+5*stride]; float in_exp_w_2 = pow(in_exp_w, 2); float normal_dist_w = exp(in_exp_w_2*(-1./2.))/(sqrt(M_PI * 2.0)*(x[index+5*stride]+sigma_const)); float in_exp_h = dh / x[index+7*stride]; float in_exp_h_2 = pow(in_exp_h, 2); float normal_dist_h = exp(in_exp_h_2*(-1./2.))/(sqrt(M_PI * 2.0)*(x[index+7*stride]+sigma_const)); float temp_x = (1./2.) * 1./(normal_dist_x+epsi) * normal_dist_x * scale; float temp_y = (1./2.) * 1./(normal_dist_y+epsi) * normal_dist_y * scale; float temp_w = (1./2.) * 1./(normal_dist_w+epsi) * normal_dist_w * scale; float temp_h = (1./2.) * 1./(normal_dist_h+epsi) * normal_dist_h * scale; if (!accumulate) { delta[index + 0 * stride] = 0; delta[index + 1 * stride] = 0; delta[index + 2 * stride] = 0; delta[index + 3 * stride] = 0; delta[index + 4 * stride] = 0; delta[index + 5 * stride] = 0; delta[index + 6 * stride] = 0; delta[index + 7 * stride] = 0; } float delta_x = temp_x * in_exp_x * (1. / x[index + 1 * stride]); float delta_y = temp_y * in_exp_y * (1. / x[index + 3 * stride]); float delta_w = temp_w * in_exp_w * (1. / x[index + 5 * stride]); float delta_h = temp_h * in_exp_h * (1. / x[index + 7 * stride]); float delta_ux = temp_x * (in_exp_x_2 / x[index + 1 * stride] - 1. / (x[index + 1 * stride] + sigma_const)); float delta_uy = temp_y * (in_exp_y_2 / x[index + 3 * stride] - 1. / (x[index + 3 * stride] + sigma_const)); float delta_uw = temp_w * (in_exp_w_2 / x[index + 5 * stride] - 1. / (x[index + 5 * stride] + sigma_const)); float delta_uh = temp_h * (in_exp_h_2 / x[index + 7 * stride] - 1. / (x[index + 7 * stride] + sigma_const)); if (iou_loss != MSE) { // GIoU iou = all_ious.giou; // https://github.com/generalized-iou/g-darknet // https://arxiv.org/abs/1902.09630v2 // https://giou.stanford.edu/ // https://arxiv.org/abs/1911.08287v1 // https://github.com/Zzh-tju/DIoU-darknet all_ious.dx_iou = dx_box_iou(pred, truth, iou_loss); float dx, dy, dw, dh; dx = all_ious.dx_iou.dt; dy = all_ious.dx_iou.db; dw = all_ious.dx_iou.dl; dh = all_ious.dx_iou.dr; if (yolo_point == YOLO_CENTER) { } else if (yolo_point == YOLO_LEFT_TOP) { dx = dx - dw/2; dy = dy - dh/2; } else if (yolo_point == YOLO_RIGHT_BOTTOM) { dx = dx + dw / 2; dy = dy + dh / 2; } // jacobian^t (transpose) //float dx = (all_ious.dx_iou.dl + all_ious.dx_iou.dr); //float dy = (all_ious.dx_iou.dt + all_ious.dx_iou.db); //float dw = ((-0.5 * all_ious.dx_iou.dl) + (0.5 * all_ious.dx_iou.dr)); //float dh = ((-0.5 * all_ious.dx_iou.dt) + (0.5 * all_ious.dx_iou.db)); // predict exponential, apply gradient of e^delta_t ONLY for w,h dw *= exp(x[index + 4 * stride]); dh *= exp(x[index + 6 * stride]); delta_x = dx; delta_y = dy; delta_w = dw; delta_h = dh; } // normalize iou weight, for GIoU delta_x *= iou_normalizer; delta_y *= iou_normalizer; delta_w *= iou_normalizer; delta_h *= iou_normalizer; // normalize Uncertainty weight delta_ux *= uc_normalizer; delta_uy *= uc_normalizer; delta_uw *= uc_normalizer; delta_uh *= uc_normalizer; delta_x = fix_nan_inf(delta_x); delta_y = fix_nan_inf(delta_y); delta_w = fix_nan_inf(delta_w); delta_h = fix_nan_inf(delta_h); delta_ux = fix_nan_inf(delta_ux); delta_uy = fix_nan_inf(delta_uy); delta_uw = fix_nan_inf(delta_uw); delta_uh = fix_nan_inf(delta_uh); if (max_delta != FLT_MAX) { delta_x = clip_value(delta_x, max_delta); delta_y = clip_value(delta_y, max_delta); delta_w = clip_value(delta_w, max_delta); delta_h = clip_value(delta_h, max_delta); delta_ux = clip_value(delta_ux, max_delta); delta_uy = clip_value(delta_uy, max_delta); delta_uw = clip_value(delta_uw, max_delta); delta_uh = clip_value(delta_uh, max_delta); } delta[index + 0 * stride] += delta_x; delta[index + 2 * stride] += delta_y; delta[index + 4 * stride] += delta_w; delta[index + 6 * stride] += delta_h; delta[index + 1 * stride] += delta_ux; delta[index + 3 * stride] += delta_uy; delta[index + 5 * stride] += delta_uw; delta[index + 7 * stride] += delta_uh; return iou; } void averages_gaussian_yolo_deltas(int class_index, int box_index, int stride, int classes, float *delta) { int classes_in_one_box = 0; int c; for (c = 0; c < classes; ++c) { if (delta[class_index + stride*c] > 0) classes_in_one_box++; } if (classes_in_one_box > 0) { delta[box_index + 0 * stride] /= classes_in_one_box; delta[box_index + 1 * stride] /= classes_in_one_box; delta[box_index + 2 * stride] /= classes_in_one_box; delta[box_index + 3 * stride] /= classes_in_one_box; delta[box_index + 4 * stride] /= classes_in_one_box; delta[box_index + 5 * stride] /= classes_in_one_box; delta[box_index + 6 * stride] /= classes_in_one_box; delta[box_index + 7 * stride] /= classes_in_one_box; } } void delta_gaussian_yolo_class(float *output, float *delta, int index, int class_id, int classes, int stride, float *avg_cat, float label_smooth_eps, float *classes_multipliers) { int n; if (delta[index]){ float y_true = 1; if (label_smooth_eps) y_true = y_true * (1 - label_smooth_eps) + 0.5*label_smooth_eps; delta[index + stride*class_id] = y_true - output[index + stride*class_id]; //delta[index + stride*class_id] = 1 - output[index + stride*class_id]; if (classes_multipliers) delta[index + stride*class_id] *= classes_multipliers[class_id]; if(avg_cat) *avg_cat += output[index + stride*class_id]; return; } for(n = 0; n < classes; ++n){ float y_true = ((n == class_id) ? 1 : 0); if (label_smooth_eps) y_true = y_true * (1 - label_smooth_eps) + 0.5*label_smooth_eps; delta[index + stride*n] = y_true - output[index + stride*n]; if (classes_multipliers && n == class_id) delta[index + stride*class_id] *= classes_multipliers[class_id]; if(n == class_id && avg_cat) *avg_cat += output[index + stride*n]; } } int compare_gaussian_yolo_class(float *output, int classes, int class_index, int stride, float objectness, int class_id, float conf_thresh) { int j; for (j = 0; j < classes; ++j) { //float prob = objectness * output[class_index + stride*j]; float prob = output[class_index + stride*j]; if (prob > conf_thresh) { return 1; } } return 0; } static int entry_gaussian_index(layer l, int batch, int location, int entry) { int n = location / (l.w*l.h); int loc = location % (l.w*l.h); return batch*l.outputs + n*l.w*l.h*(8+l.classes+1) + entry*l.w*l.h + loc; } void forward_gaussian_yolo_layer(const layer l, network_state state) { int i,j,b,t,n; 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){ // x : mu, sigma int index = entry_gaussian_index(l, b, n*l.w*l.h, 0); activate_array(l.output + index, 2*l.w*l.h, LOGISTIC); scal_add_cpu(l.w*l.h, l.scale_x_y, -0.5*(l.scale_x_y - 1), l.output + index, 1); // scale x // y : mu, sigma index = entry_gaussian_index(l, b, n*l.w*l.h, 2); activate_array(l.output + index, 2*l.w*l.h, LOGISTIC); scal_add_cpu(l.w*l.h, l.scale_x_y, -0.5*(l.scale_x_y - 1), l.output + index, 1); // scale y // w : sigma index = entry_gaussian_index(l, b, n*l.w*l.h, 5); activate_array(l.output + index, l.w*l.h, LOGISTIC); // h : sigma index = entry_gaussian_index(l, b, n*l.w*l.h, 7); activate_array(l.output + index, l.w*l.h, LOGISTIC); // objectness & class index = entry_gaussian_index(l, b, n*l.w*l.h, 8); activate_array(l.output + index, (1+l.classes)*l.w*l.h, LOGISTIC); } } #endif memset(l.delta, 0, l.outputs * l.batch * sizeof(float)); if (!state.train) return; float avg_iou = 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) { for (j = 0; j < l.h; ++j) { for (i = 0; i < l.w; ++i) { for (n = 0; n < l.n; ++n) { const int class_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 9); const int obj_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 8); const int box_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 0); const int stride = l.w*l.h; box pred = get_gaussian_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, l.yolo_point); float best_match_iou = 0; int best_match_t = 0; float best_iou = 0; int best_t = 0; for(t = 0; t < l.max_boxes; ++t){ 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) { 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(" 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 } if(!truth.x) break; float objectness = l.output[obj_index]; int class_id_match = compare_gaussian_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; } } avg_anyobj += l.output[obj_index]; l.delta[obj_index] = l.cls_normalizer * (0 - l.output[obj_index]); if (best_match_iou > l.ignore_thresh) { const float iou_multiplier = best_match_iou*best_match_iou;// (best_match_iou - l.ignore_thresh) / (1.0 - l.ignore_thresh); if (l.objectness_smooth) { l.delta[obj_index] = l.cls_normalizer * (iou_multiplier - l.output[obj_index]); int class_id = state.truth[best_match_t*(4 + 1) + b*l.truths + 4]; if (l.map) class_id = l.map[class_id]; const float class_multiplier = (l.classes_multipliers) ? l.classes_multipliers[class_id] : 1.0f; l.delta[class_index + stride*class_id] = class_multiplier * (iou_multiplier - l.output[class_index + stride*class_id]); } else l.delta[obj_index] = 0; } else if (state.net.adversarial) { 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) { const float iou_multiplier = best_iou*best_iou;// (best_iou - l.truth_thresh) / (1.0 - l.truth_thresh); if (l.objectness_smooth) l.delta[obj_index] = l.cls_normalizer * (iou_multiplier - l.output[obj_index]); else l.delta[obj_index] = l.cls_normalizer * (1 - l.output[obj_index]); //l.delta[obj_index] = l.cls_normalizer * (1 - l.output[obj_index]); int class_id = state.truth[best_t*(4 + 1) + b*l.truths + 4]; if (l.map) class_id = l.map[class_id]; delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0, l.label_smooth_eps, l.classes_multipliers); const float class_multiplier = (l.classes_multipliers) ? l.classes_multipliers[class_id] : 1.0f; if (l.objectness_smooth) l.delta[class_index + stride*class_id] = class_multiplier * (iou_multiplier - l.output[class_index + stride*class_id]); box truth = float_to_box_stride(state.truth + best_t*(4 + 1) + b*l.truths, 1); delta_gaussian_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, l.uc_normalizer, 1, l.yolo_point, l.max_delta); } } } } for(t = 0; t < l.max_boxes; ++t){ box truth = float_to_box_stride(state.truth + t*(4 + 1) + b*l.truths, 1); if(!truth.x) break; float best_iou = 0; int best_n = 0; i = (truth.x * l.w); j = (truth.y * l.h); if (l.yolo_point == YOLO_CENTER) { } else if (l.yolo_point == YOLO_LEFT_TOP) { i = min_val_cmp(l.w-1, max_val_cmp(0, ((truth.x - truth.w / 2) * l.w))); j = min_val_cmp(l.h-1, max_val_cmp(0, ((truth.y - truth.h / 2) * l.h))); } else if (l.yolo_point == YOLO_RIGHT_BOTTOM) { i = min_val_cmp(l.w-1, max_val_cmp(0, ((truth.x + truth.w / 2) * l.w))); j = min_val_cmp(l.h-1, max_val_cmp(0, ((truth.y + truth.h / 2) * 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; } } 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_gaussian_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; float iou = delta_gaussian_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, l.uc_normalizer, 1, l.yolo_point, l.max_delta); int obj_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 8); avg_obj += l.output[obj_index]; l.delta[obj_index] = class_multiplier * l.cls_normalizer * (1 - l.output[obj_index]); int class_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 9); delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.label_smooth_eps, l.classes_multipliers); ++count; ++class_count; if(iou > .5) recall += 1; if(iou > .75) recall75 += 1; avg_iou += iou; } // iou_thresh 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_gaussian_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; float iou = delta_gaussian_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, l.uc_normalizer, 1, l.yolo_point, l.max_delta); int obj_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 8); avg_obj += l.output[obj_index]; l.delta[obj_index] = class_multiplier * l.cls_normalizer * (1 - l.output[obj_index]); int class_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 9); delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.label_smooth_eps, l.classes_multipliers); ++count; ++class_count; if (iou > .5) recall += 1; if (iou > .75) recall75 += 1; avg_iou += iou; } } } } // averages the deltas obtained by the function: delta_yolo_box()_accumulate 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_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 0); int class_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 9); const int stride = l.w*l.h; averages_gaussian_yolo_deltas(class_index, box_index, stride, l.classes, l.delta); } } } } // calculate: Classification-loss, IoU-loss and Uncertainty-loss const int stride = l.w*l.h; float* classification_lost = (float *)calloc(l.batch * l.outputs, sizeof(float)); memcpy(classification_lost, 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 box_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 0); classification_lost[box_index + 0 * stride] = 0; classification_lost[box_index + 1 * stride] = 0; classification_lost[box_index + 2 * stride] = 0; classification_lost[box_index + 3 * stride] = 0; classification_lost[box_index + 4 * stride] = 0; classification_lost[box_index + 5 * stride] = 0; classification_lost[box_index + 6 * stride] = 0; classification_lost[box_index + 7 * stride] = 0; } } } } float class_loss = pow(mag_array(classification_lost, l.outputs * l.batch), 2); free(classification_lost); float* except_uncertainty_lost = (float *)calloc(l.batch * l.outputs, sizeof(float)); memcpy(except_uncertainty_lost, 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 box_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 0); except_uncertainty_lost[box_index + 4 * stride] = 0; except_uncertainty_lost[box_index + 5 * stride] = 0; except_uncertainty_lost[box_index + 6 * stride] = 0; except_uncertainty_lost[box_index + 7 * stride] = 0; } } } } float except_uc_loss = pow(mag_array(except_uncertainty_lost, l.outputs * l.batch), 2); free(except_uncertainty_lost); *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2); float loss = pow(mag_array(l.delta, l.outputs * l.batch), 2); float uc_loss = loss - except_uc_loss; float iou_loss = except_uc_loss - class_loss; loss /= l.batch; class_loss /= l.batch; uc_loss /= l.batch; iou_loss /= l.batch; fprintf(stderr, "Region %d Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d, class_loss = %.2f, iou_loss = %.2f, uc_loss = %.2f, total_loss = %.2f \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, class_loss, iou_loss, uc_loss, loss); } void backward_gaussian_yolo_layer(const layer l, network_state state) { axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, state.delta, 1); } void correct_gaussian_yolo_boxes(detection *dets, int n, int w, int h, int netw, int neth, int relative, int letter) { int i; int new_w=0; int new_h=0; if (letter) { if (((float)netw / w) < ((float)neth / h)) { new_w = netw; new_h = (h * netw) / w; } else { new_h = neth; new_w = (w * neth) / h; } } else { new_w = netw; new_h = neth; } /* if (((float)netw/w) < ((float)neth/h)) { new_w = netw; new_h = (h * netw)/w; } else { new_h = neth; new_w = (w * neth)/h; } */ for (i = 0; i < n; ++i){ box b = dets[i].bbox; b.x = (b.x - (netw - new_w)/2./netw) / ((float)new_w/netw); b.y = (b.y - (neth - new_h)/2./neth) / ((float)new_h/neth); b.w *= (float)netw/new_w; b.h *= (float)neth/new_h; if(!relative){ b.x *= w; b.w *= w; b.y *= h; b.h *= h; } dets[i].bbox = b; } } int gaussian_yolo_num_detections(layer l, float thresh) { int i, n; int count = 0; for (i = 0; i < l.w*l.h; ++i){ for(n = 0; n < l.n; ++n){ int obj_index = entry_gaussian_index(l, 0, n*l.w*l.h + i, 8); if(l.output[obj_index] > thresh){ ++count; } } } return count; } /* void avg_flipped_gaussian_yolo(layer l) { int i,j,n,z; float *flip = l.output + l.outputs; for (j = 0; j < l.h; ++j) { for (i = 0; i < l.w/2; ++i) { for (n = 0; n < l.n; ++n) { for(z = 0; z < l.classes + 8 + 1; ++z){ int i1 = z*l.w*l.h*l.n + n*l.w*l.h + j*l.w + i; int i2 = z*l.w*l.h*l.n + n*l.w*l.h + j*l.w + (l.w - i - 1); float swap = flip[i1]; flip[i1] = flip[i2]; flip[i2] = swap; if(z == 0){ flip[i1] = -flip[i1]; flip[i2] = -flip[i2]; } } } } } for(i = 0; i < l.outputs; ++i){ l.output[i] = (l.output[i] + flip[i])/2.; } } */ int get_gaussian_yolo_detections(layer l, int w, int h, int netw, int neth, float thresh, int *map, int relative, detection *dets, int letter) { int i,j,n; float *predictions = l.output; //if (l.batch == 2) avg_flipped_gaussian_yolo(l); int count = 0; for (i = 0; i < l.w*l.h; ++i){ int row = i / l.w; int col = i % l.w; for(n = 0; n < l.n; ++n){ int obj_index = entry_gaussian_index(l, 0, n*l.w*l.h + i, 8); float objectness = predictions[obj_index]; if (objectness <= thresh) continue; // incorrect behavior for Nan values if (objectness > thresh) { int box_index = entry_gaussian_index(l, 0, n*l.w*l.h + i, 0); dets[count].bbox = get_gaussian_yolo_box(predictions, l.biases, l.mask[n], box_index, col, row, l.w, l.h, netw, neth, l.w*l.h, l.yolo_point); dets[count].objectness = objectness; dets[count].classes = l.classes; dets[count].uc[0] = predictions[entry_gaussian_index(l, 0, n*l.w*l.h + i, 1)]; // tx uncertainty dets[count].uc[1] = predictions[entry_gaussian_index(l, 0, n*l.w*l.h + i, 3)]; // ty uncertainty dets[count].uc[2] = predictions[entry_gaussian_index(l, 0, n*l.w*l.h + i, 5)]; // tw uncertainty dets[count].uc[3] = predictions[entry_gaussian_index(l, 0, n*l.w*l.h + i, 7)]; // th uncertainty dets[count].points = l.yolo_point; //if (l.yolo_point != YOLO_CENTER) dets[count].objectness = objectness = 0; for (j = 0; j < l.classes; ++j) { int class_index = entry_gaussian_index(l, 0, n*l.w*l.h + i, 9 + j); float uc_aver = (dets[count].uc[0] + dets[count].uc[1] + dets[count].uc[2] + dets[count].uc[3]) / 4.0; float prob = objectness*predictions[class_index] * (1.0 - uc_aver); dets[count].prob[j] = (prob > thresh) ? prob : 0; } ++count; } } } correct_gaussian_yolo_boxes(dets, count, w, h, netw, neth, relative, letter); return count; } #ifdef GPU void forward_gaussian_yolo_layer_gpu(const layer l, network_state state) { copy_ongpu(l.batch*l.inputs, state.input, 1, l.output_gpu, 1); int b, n; for (b = 0; b < l.batch; ++b) { for(n = 0; n < l.n; ++n) { // x : mu, sigma int index = entry_gaussian_index(l, b, n*l.w*l.h, 0); activate_array_ongpu(l.output_gpu + index, 2*l.w*l.h, LOGISTIC); scal_add_ongpu(l.w*l.h, l.scale_x_y, -0.5*(l.scale_x_y - 1), l.output_gpu + index, 1); // scale x // y : mu, sigma index = entry_gaussian_index(l, b, n*l.w*l.h, 2); activate_array_ongpu(l.output_gpu + index, 2*l.w*l.h, LOGISTIC); scal_add_ongpu(l.w*l.h, l.scale_x_y, -0.5*(l.scale_x_y - 1), l.output_gpu + index, 1); // scale y // w : sigma index = entry_gaussian_index(l, b, n*l.w*l.h, 5); activate_array_ongpu(l.output_gpu + index, l.w*l.h, LOGISTIC); // h : sigma index = entry_gaussian_index(l, b, n*l.w*l.h, 7); activate_array_ongpu(l.output_gpu + index, l.w*l.h, LOGISTIC); // objectness & class index = entry_gaussian_index(l, b, n*l.w*l.h, 8); activate_array_ongpu(l.output_gpu + index, (1+l.classes)*l.w*l.h, LOGISTIC); } } if (!state.train || l.onlyforward) { //cuda_pull_array(l.output_gpu, l.output, l.batch*l.outputs); cuda_pull_array_async(l.output_gpu, l.output, l.batch*l.outputs); CHECK_CUDA(cudaPeekAtLastError()); return; } float *in_cpu = (float *)calloc(l.batch*l.inputs, sizeof(float)); cuda_pull_array(l.output_gpu, l.output, l.batch*l.outputs); memcpy(in_cpu, l.output, l.batch*l.outputs * sizeof(float)); float *truth_cpu = 0; if (state.truth) { int num_truth = l.batch*l.truths; truth_cpu = (float *)calloc(num_truth, sizeof(float)); cuda_pull_array(state.truth, truth_cpu, num_truth); } network_state cpu_state = state; cpu_state.net = state.net; cpu_state.index = state.index; cpu_state.train = state.train; cpu_state.truth = truth_cpu; cpu_state.input = in_cpu; forward_gaussian_yolo_layer(l, cpu_state); //forward_yolo_layer(l, state); cuda_push_array(l.delta_gpu, l.delta, l.batch*l.outputs); free(in_cpu); if (cpu_state.truth) free(cpu_state.truth); } void backward_gaussian_yolo_layer_gpu(const layer l, network_state state) { axpy_ongpu(l.batch*l.inputs, 1, l.delta_gpu, 1, state.delta, 1); } #endif // Gaussian YOLOv3 implementation // Author: Jiwoong Choi // ICCV 2019 Paper: http://openaccess.thecvf.com/content_ICCV_2019/html/Choi_Gaussian_YOLOv3_An_Accurate_and_Fast_Object_Detector_Using_Localization_ICCV_2019_paper.html // arxiv.org: https://arxiv.org/abs/1904.04620v2 // source code: https://github.com/jwchoi384/Gaussian_YOLOv3 #include "gaussian_yolo_layer.h" #include "activations.h" #include "blas.h" #include "box.h" #include "dark_cuda.h" #include "utils.h" #include <stdio.h> #include <assert.h> #include <string.h> #include <stdlib.h> #ifndef M_PI #define M_PI 3.141592 #endif extern int check_mistakes; layer make_gaussian_yolo_layer(int batch, int w, int h, int n, int total, int *mask, int classes, int max_boxes) { int i; layer l = { (LAYER_TYPE)0 }; l.type = GAUSSIAN_YOLO; l.n = n; l.total = total; l.batch = batch; l.h = h; l.w = w; l.c = n*(classes + 8 + 1); l.out_w = l.w; l.out_h = l.h; l.out_c = l.c; l.classes = classes; l.cost = (float*)calloc(1, sizeof(float)); l.biases = (float*)calloc(total*2, sizeof(float)); if(mask) l.mask = mask; else{ l.mask = (int*)calloc(n, sizeof(int)); for(i = 0; i < n; ++i){ l.mask[i] = i; } } l.bias_updates = (float*)calloc(n*2, sizeof(float)); l.outputs = h*w*n*(classes + 8 + 1); l.inputs = l.outputs; l.max_boxes = max_boxes; l.truth_size = 4 + 2; l.truths = l.max_boxes*l.truth_size; l.delta = (float*)calloc(batch*l.outputs, sizeof(float)); l.output = (float*)calloc(batch*l.outputs, sizeof(float)); for(i = 0; i < total*2; ++i){ l.biases[i] = .5; } l.forward = forward_gaussian_yolo_layer; l.backward = backward_gaussian_yolo_layer; #ifdef GPU l.forward_gpu = forward_gaussian_yolo_layer_gpu; l.backward_gpu = backward_gaussian_yolo_layer_gpu; l.output_gpu = cuda_make_array(l.output, batch*l.outputs); l.delta_gpu = cuda_make_array(l.delta, batch*l.outputs); free(l.output); if (cudaSuccess == cudaHostAlloc(&l.output, batch*l.outputs * sizeof(float), cudaHostRegisterMapped)) l.output_pinned = 1; else { cudaGetLastError(); // reset CUDA-error l.output = (float*)calloc(batch * l.outputs, sizeof(float)); } free(l.delta); if (cudaSuccess == cudaHostAlloc(&l.delta, batch*l.outputs * sizeof(float), cudaHostRegisterMapped)) l.delta_pinned = 1; else { cudaGetLastError(); // reset CUDA-error l.delta = (float*)calloc(batch * l.outputs, sizeof(float)); } #endif //fprintf(stderr, "Gaussian_yolo\n"); srand(time(0)); return l; } void resize_gaussian_yolo_layer(layer *l, int w, int h) { l->w = w; l->h = h; l->outputs = h*w*l->n*(l->classes + 8 + 1); l->inputs = l->outputs; //l->output = (float *)realloc(l->output, l->batch*l->outputs * sizeof(float)); //l->delta = (float *)realloc(l->delta, l->batch*l->outputs * sizeof(float)); if (!l->output_pinned) l->output = (float*)realloc(l->output, l->batch*l->outputs * sizeof(float)); if (!l->delta_pinned) l->delta = (float*)realloc(l->delta, l->batch*l->outputs * sizeof(float)); #ifdef GPU if (l->output_pinned) { CHECK_CUDA(cudaFreeHost(l->output)); if (cudaSuccess != cudaHostAlloc(&l->output, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) { cudaGetLastError(); // reset CUDA-error l->output = (float*)calloc(l->batch * l->outputs, sizeof(float)); l->output_pinned = 0; } } if (l->delta_pinned) { CHECK_CUDA(cudaFreeHost(l->delta)); if (cudaSuccess != cudaHostAlloc(&l->delta, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) { cudaGetLastError(); // reset CUDA-error l->delta = (float*)calloc(l->batch * l->outputs, sizeof(float)); l->delta_pinned = 0; } } cuda_free(l->delta_gpu); cuda_free(l->output_gpu); l->delta_gpu = cuda_make_array(l->delta, l->batch*l->outputs); l->output_gpu = cuda_make_array(l->output, l->batch*l->outputs); #endif } box get_gaussian_yolo_box(float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, int stride, YOLO_POINT yolo_point) { box b; b.w = exp(x[index + 4 * stride]) * biases[2 * n] / w; b.h = exp(x[index + 6 * stride]) * biases[2 * n + 1] / h; b.x = (i + x[index + 0 * stride]) / lw; b.y = (j + x[index + 2 * stride]) / lh; if (yolo_point == YOLO_CENTER) { } else if (yolo_point == YOLO_LEFT_TOP) { b.x = (i + x[index + 0 * stride]) / lw + b.w / 2; b.y = (j + x[index + 2 * stride]) / lh + b.h / 2; } else if (yolo_point == YOLO_RIGHT_BOTTOM) { b.x = (i + x[index + 0 * stride]) / lw - b.w / 2; b.y = (j + x[index + 2 * stride]) / lh - b.h / 2; } return b; } static inline float fix_nan_inf(float val) { if (isnan(val) || isinf(val)) val = 0; return val; } static inline float clip_value(float val, const float max_val) { if (val > max_val) val = max_val; else if (val < -max_val) val = -max_val; return val; } float delta_gaussian_yolo_box(box truth, float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, float *delta, float scale, int stride, float iou_normalizer, IOU_LOSS iou_loss, float uc_normalizer, int accumulate, YOLO_POINT yolo_point, float max_delta) { box pred = get_gaussian_yolo_box(x, biases, n, index, i, j, lw, lh, w, h, stride, yolo_point); float iou; ious all_ious = { 0 }; all_ious.iou = box_iou(pred, truth); all_ious.giou = box_giou(pred, truth); all_ious.diou = box_diou(pred, truth); all_ious.ciou = box_ciou(pred, truth); if (pred.w == 0) { pred.w = 1.0; } if (pred.h == 0) { pred.h = 1.0; } float sigma_const = 0.3; float epsi = pow(10,-9); float dx, dy, dw, dh; iou = all_ious.iou; float tx, ty, tw, th; tx = (truth.x*lw - i); ty = (truth.y*lh - j); tw = log(truth.w*w / biases[2 * n]); th = log(truth.h*h / biases[2 * n + 1]); if (yolo_point == YOLO_CENTER) { } else if (yolo_point == YOLO_LEFT_TOP) { tx = ((truth.x - truth.w / 2)*lw - i); ty = ((truth.y - truth.h / 2)*lh - j); } else if (yolo_point == YOLO_RIGHT_BOTTOM) { tx = ((truth.x + truth.w / 2)*lw - i); ty = ((truth.y + truth.h / 2)*lh - j); } dx = (tx - x[index + 0 * stride]); dy = (ty - x[index + 2 * stride]); dw = (tw - x[index + 4 * stride]); dh = (th - x[index + 6 * stride]); // Gaussian float in_exp_x = dx / x[index+1*stride]; float in_exp_x_2 = pow(in_exp_x, 2); float normal_dist_x = exp(in_exp_x_2*(-1./2.))/(sqrt(M_PI * 2.0)*(x[index+1*stride]+sigma_const)); float in_exp_y = dy / x[index+3*stride]; float in_exp_y_2 = pow(in_exp_y, 2); float normal_dist_y = exp(in_exp_y_2*(-1./2.))/(sqrt(M_PI * 2.0)*(x[index+3*stride]+sigma_const)); float in_exp_w = dw / x[index+5*stride]; float in_exp_w_2 = pow(in_exp_w, 2); float normal_dist_w = exp(in_exp_w_2*(-1./2.))/(sqrt(M_PI * 2.0)*(x[index+5*stride]+sigma_const)); float in_exp_h = dh / x[index+7*stride]; float in_exp_h_2 = pow(in_exp_h, 2); float normal_dist_h = exp(in_exp_h_2*(-1./2.))/(sqrt(M_PI * 2.0)*(x[index+7*stride]+sigma_const)); float temp_x = (1./2.) * 1./(normal_dist_x+epsi) * normal_dist_x * scale; float temp_y = (1./2.) * 1./(normal_dist_y+epsi) * normal_dist_y * scale; float temp_w = (1./2.) * 1./(normal_dist_w+epsi) * normal_dist_w * scale; float temp_h = (1./2.) * 1./(normal_dist_h+epsi) * normal_dist_h * scale; if (!accumulate) { delta[index + 0 * stride] = 0; delta[index + 1 * stride] = 0; delta[index + 2 * stride] = 0; delta[index + 3 * stride] = 0; delta[index + 4 * stride] = 0; delta[index + 5 * stride] = 0; delta[index + 6 * stride] = 0; delta[index + 7 * stride] = 0; } float delta_x = temp_x * in_exp_x * (1. / x[index + 1 * stride]); float delta_y = temp_y * in_exp_y * (1. / x[index + 3 * stride]); float delta_w = temp_w * in_exp_w * (1. / x[index + 5 * stride]); float delta_h = temp_h * in_exp_h * (1. / x[index + 7 * stride]); float delta_ux = temp_x * (in_exp_x_2 / x[index + 1 * stride] - 1. / (x[index + 1 * stride] + sigma_const)); float delta_uy = temp_y * (in_exp_y_2 / x[index + 3 * stride] - 1. / (x[index + 3 * stride] + sigma_const)); float delta_uw = temp_w * (in_exp_w_2 / x[index + 5 * stride] - 1. / (x[index + 5 * stride] + sigma_const)); float delta_uh = temp_h * (in_exp_h_2 / x[index + 7 * stride] - 1. / (x[index + 7 * stride] + sigma_const)); if (iou_loss != MSE) { // GIoU iou = all_ious.giou; // https://github.com/generalized-iou/g-darknet // https://arxiv.org/abs/1902.09630v2 // https://giou.stanford.edu/ // https://arxiv.org/abs/1911.08287v1 // https://github.com/Zzh-tju/DIoU-darknet all_ious.dx_iou = dx_box_iou(pred, truth, iou_loss); float dx, dy, dw, dh; dx = all_ious.dx_iou.dt; dy = all_ious.dx_iou.db; dw = all_ious.dx_iou.dl; dh = all_ious.dx_iou.dr; if (yolo_point == YOLO_CENTER) { } else if (yolo_point == YOLO_LEFT_TOP) { dx = dx - dw/2; dy = dy - dh/2; } else if (yolo_point == YOLO_RIGHT_BOTTOM) { dx = dx + dw / 2; dy = dy + dh / 2; } // jacobian^t (transpose) //float dx = (all_ious.dx_iou.dl + all_ious.dx_iou.dr); //float dy = (all_ious.dx_iou.dt + all_ious.dx_iou.db); //float dw = ((-0.5 * all_ious.dx_iou.dl) + (0.5 * all_ious.dx_iou.dr)); //float dh = ((-0.5 * all_ious.dx_iou.dt) + (0.5 * all_ious.dx_iou.db)); // predict exponential, apply gradient of e^delta_t ONLY for w,h dw *= exp(x[index + 4 * stride]); dh *= exp(x[index + 6 * stride]); delta_x = dx; delta_y = dy; delta_w = dw; delta_h = dh; } // normalize iou weight, for GIoU delta_x *= iou_normalizer; delta_y *= iou_normalizer; delta_w *= iou_normalizer; delta_h *= iou_normalizer; // normalize Uncertainty weight delta_ux *= uc_normalizer; delta_uy *= uc_normalizer; delta_uw *= uc_normalizer; delta_uh *= uc_normalizer; delta_x = fix_nan_inf(delta_x); delta_y = fix_nan_inf(delta_y); delta_w = fix_nan_inf(delta_w); delta_h = fix_nan_inf(delta_h); delta_ux = fix_nan_inf(delta_ux); delta_uy = fix_nan_inf(delta_uy); delta_uw = fix_nan_inf(delta_uw); delta_uh = fix_nan_inf(delta_uh); if (max_delta != FLT_MAX) { delta_x = clip_value(delta_x, max_delta); delta_y = clip_value(delta_y, max_delta); delta_w = clip_value(delta_w, max_delta); delta_h = clip_value(delta_h, max_delta); delta_ux = clip_value(delta_ux, max_delta); delta_uy = clip_value(delta_uy, max_delta); delta_uw = clip_value(delta_uw, max_delta); delta_uh = clip_value(delta_uh, max_delta); } delta[index + 0 * stride] += delta_x; delta[index + 2 * stride] += delta_y; delta[index + 4 * stride] += delta_w; delta[index + 6 * stride] += delta_h; delta[index + 1 * stride] += delta_ux; delta[index + 3 * stride] += delta_uy; delta[index + 5 * stride] += delta_uw; delta[index + 7 * stride] += delta_uh; return iou; } void averages_gaussian_yolo_deltas(int class_index, int box_index, int stride, int classes, float *delta) { int classes_in_one_box = 0; int c; for (c = 0; c < classes; ++c) { if (delta[class_index + stride*c] > 0) classes_in_one_box++; } if (classes_in_one_box > 0) { delta[box_index + 0 * stride] /= classes_in_one_box; delta[box_index + 1 * stride] /= classes_in_one_box; delta[box_index + 2 * stride] /= classes_in_one_box; delta[box_index + 3 * stride] /= classes_in_one_box; delta[box_index + 4 * stride] /= classes_in_one_box; delta[box_index + 5 * stride] /= classes_in_one_box; delta[box_index + 6 * stride] /= classes_in_one_box; delta[box_index + 7 * stride] /= classes_in_one_box; } } void delta_gaussian_yolo_class(float *output, float *delta, int index, int class_id, int classes, int stride, float *avg_cat, float label_smooth_eps, float *classes_multipliers, float cls_normalizer) { int n; if (delta[index]){ float y_true = 1; if (label_smooth_eps) y_true = y_true * (1 - label_smooth_eps) + 0.5*label_smooth_eps; delta[index + stride*class_id] = y_true - output[index + stride*class_id]; //delta[index + stride*class_id] = 1 - output[index + stride*class_id]; if (classes_multipliers) delta[index + stride*class_id] *= classes_multipliers[class_id]; if(avg_cat) *avg_cat += output[index + stride*class_id]; return; } for(n = 0; n < classes; ++n){ float y_true = ((n == class_id) ? 1 : 0); if (label_smooth_eps) y_true = y_true * (1 - label_smooth_eps) + 0.5*label_smooth_eps; delta[index + stride*n] = y_true - output[index + stride*n]; if (classes_multipliers && n == class_id) delta[index + stride*class_id] *= classes_multipliers[class_id] * cls_normalizer; if(n == class_id && avg_cat) *avg_cat += output[index + stride*n]; } } int compare_gaussian_yolo_class(float *output, int classes, int class_index, int stride, float objectness, int class_id, float conf_thresh) { int j; for (j = 0; j < classes; ++j) { //float prob = objectness * output[class_index + stride*j]; float prob = output[class_index + stride*j]; if (prob > conf_thresh) { return 1; } } return 0; } static int entry_gaussian_index(layer l, int batch, int location, int entry) { int n = location / (l.w*l.h); int loc = location % (l.w*l.h); return batch*l.outputs + n*l.w*l.h*(8+l.classes+1) + entry*l.w*l.h + loc; } void forward_gaussian_yolo_layer(const layer l, network_state state) { int i,j,b,t,n; 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){ // x : mu, sigma int index = entry_gaussian_index(l, b, n*l.w*l.h, 0); activate_array(l.output + index, 2*l.w*l.h, LOGISTIC); scal_add_cpu(l.w*l.h, l.scale_x_y, -0.5*(l.scale_x_y - 1), l.output + index, 1); // scale x // y : mu, sigma index = entry_gaussian_index(l, b, n*l.w*l.h, 2); activate_array(l.output + index, 2*l.w*l.h, LOGISTIC); scal_add_cpu(l.w*l.h, l.scale_x_y, -0.5*(l.scale_x_y - 1), l.output + index, 1); // scale y // w : sigma index = entry_gaussian_index(l, b, n*l.w*l.h, 5); activate_array(l.output + index, l.w*l.h, LOGISTIC); // h : sigma index = entry_gaussian_index(l, b, n*l.w*l.h, 7); activate_array(l.output + index, l.w*l.h, LOGISTIC); // objectness & class index = entry_gaussian_index(l, b, n*l.w*l.h, 8); activate_array(l.output + index, (1+l.classes)*l.w*l.h, LOGISTIC); } } #endif memset(l.delta, 0, l.outputs * l.batch * sizeof(float)); if (!state.train) return; float avg_iou = 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) { for (j = 0; j < l.h; ++j) { for (i = 0; i < l.w; ++i) { for (n = 0; n < l.n; ++n) { const int class_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 9); const int obj_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 8); const int box_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 0); const int stride = l.w*l.h; box pred = get_gaussian_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, l.yolo_point); float best_match_iou = 0; int best_match_t = 0; float best_iou = 0; int best_t = 0; for(t = 0; t < l.max_boxes; ++t){ box truth = float_to_box_stride(state.truth + t*l.truth_size + b*l.truths, 1); int class_id = state.truth[t*l.truth_size + b*l.truths + 4]; if (class_id >= l.classes) { 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(" 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 } if(!truth.x) break; float objectness = l.output[obj_index]; int class_id_match = compare_gaussian_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; } } avg_anyobj += l.output[obj_index]; l.delta[obj_index] = l.obj_normalizer * (0 - l.output[obj_index]); if (best_match_iou > l.ignore_thresh) { const float iou_multiplier = best_match_iou*best_match_iou;// (best_match_iou - l.ignore_thresh) / (1.0 - l.ignore_thresh); if (l.objectness_smooth) { l.delta[obj_index] = l.obj_normalizer * (iou_multiplier - l.output[obj_index]); int class_id = state.truth[best_match_t*l.truth_size + b*l.truths + 4]; if (l.map) class_id = l.map[class_id]; delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0, l.label_smooth_eps, l.classes_multipliers, l.cls_normalizer); } else l.delta[obj_index] = 0; } else if (state.net.adversarial) { float scale = pred.w * pred.h; if (scale > 0) scale = sqrt(scale); l.delta[obj_index] = scale * l.obj_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) { const float iou_multiplier = best_iou*best_iou;// (best_iou - l.truth_thresh) / (1.0 - l.truth_thresh); if (l.objectness_smooth) l.delta[obj_index] = l.obj_normalizer * (iou_multiplier - l.output[obj_index]); else l.delta[obj_index] = l.obj_normalizer * (1 - l.output[obj_index]); //l.delta[obj_index] = l.obj_normalizer * (1 - l.output[obj_index]); int class_id = state.truth[best_t*l.truth_size + b*l.truths + 4]; if (l.map) class_id = l.map[class_id]; delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0, l.label_smooth_eps, l.classes_multipliers, l.cls_normalizer); const float class_multiplier = (l.classes_multipliers) ? l.classes_multipliers[class_id] : 1.0f; if (l.objectness_smooth) l.delta[class_index + stride*class_id] = class_multiplier * (iou_multiplier - l.output[class_index + stride*class_id]); box truth = float_to_box_stride(state.truth + best_t*l.truth_size + b*l.truths, 1); delta_gaussian_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, l.uc_normalizer, 1, l.yolo_point, l.max_delta); } } } } for(t = 0; t < l.max_boxes; ++t){ box truth = float_to_box_stride(state.truth + t*l.truth_size + b*l.truths, 1); if(!truth.x) break; float best_iou = 0; int best_n = 0; i = (truth.x * l.w); j = (truth.y * l.h); if (l.yolo_point == YOLO_CENTER) { } else if (l.yolo_point == YOLO_LEFT_TOP) { i = min_val_cmp(l.w-1, max_val_cmp(0, ((truth.x - truth.w / 2) * l.w))); j = min_val_cmp(l.h-1, max_val_cmp(0, ((truth.y - truth.h / 2) * l.h))); } else if (l.yolo_point == YOLO_RIGHT_BOTTOM) { i = min_val_cmp(l.w-1, max_val_cmp(0, ((truth.x + truth.w / 2) * l.w))); j = min_val_cmp(l.h-1, max_val_cmp(0, ((truth.y + truth.h / 2) * 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; } } int mask_n = int_index(l.mask, best_n, l.n); if(mask_n >= 0){ int class_id = state.truth[t*l.truth_size + b*l.truths + 4]; if (l.map) class_id = l.map[class_id]; int box_index = entry_gaussian_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; float iou = delta_gaussian_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, l.uc_normalizer, 1, l.yolo_point, l.max_delta); int obj_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 8); avg_obj += l.output[obj_index]; l.delta[obj_index] = class_multiplier * l.obj_normalizer * (1 - l.output[obj_index]); int class_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 9); delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.label_smooth_eps, l.classes_multipliers, l.cls_normalizer); ++count; ++class_count; if(iou > .5) recall += 1; if(iou > .75) recall75 += 1; avg_iou += iou; } // iou_thresh 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*l.truth_size + b*l.truths + 4]; if (l.map) class_id = l.map[class_id]; int box_index = entry_gaussian_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; float iou = delta_gaussian_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, l.uc_normalizer, 1, l.yolo_point, l.max_delta); int obj_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 8); avg_obj += l.output[obj_index]; l.delta[obj_index] = class_multiplier * l.obj_normalizer * (1 - l.output[obj_index]); int class_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 9); delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.label_smooth_eps, l.classes_multipliers, l.cls_normalizer); ++count; ++class_count; if (iou > .5) recall += 1; if (iou > .75) recall75 += 1; avg_iou += iou; } } } } // averages the deltas obtained by the function: delta_yolo_box()_accumulate 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_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 0); int class_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 9); const int stride = l.w*l.h; averages_gaussian_yolo_deltas(class_index, box_index, stride, l.classes, l.delta); } } } } // calculate: Classification-loss, IoU-loss and Uncertainty-loss const int stride = l.w*l.h; float* classification_lost = (float *)calloc(l.batch * l.outputs, sizeof(float)); memcpy(classification_lost, 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 box_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 0); classification_lost[box_index + 0 * stride] = 0; classification_lost[box_index + 1 * stride] = 0; classification_lost[box_index + 2 * stride] = 0; classification_lost[box_index + 3 * stride] = 0; classification_lost[box_index + 4 * stride] = 0; classification_lost[box_index + 5 * stride] = 0; classification_lost[box_index + 6 * stride] = 0; classification_lost[box_index + 7 * stride] = 0; } } } } float class_loss = pow(mag_array(classification_lost, l.outputs * l.batch), 2); free(classification_lost); float* except_uncertainty_lost = (float *)calloc(l.batch * l.outputs, sizeof(float)); memcpy(except_uncertainty_lost, 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 box_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 0); except_uncertainty_lost[box_index + 4 * stride] = 0; except_uncertainty_lost[box_index + 5 * stride] = 0; except_uncertainty_lost[box_index + 6 * stride] = 0; except_uncertainty_lost[box_index + 7 * stride] = 0; } } } } float except_uc_loss = pow(mag_array(except_uncertainty_lost, l.outputs * l.batch), 2); free(except_uncertainty_lost); *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2); float loss = pow(mag_array(l.delta, l.outputs * l.batch), 2); float uc_loss = loss - except_uc_loss; float iou_loss = except_uc_loss - class_loss; loss /= l.batch; class_loss /= l.batch; uc_loss /= l.batch; iou_loss /= l.batch; fprintf(stderr, "Region %d Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d, class_loss = %.2f, iou_loss = %.2f, uc_loss = %.2f, total_loss = %.2f \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, class_loss, iou_loss, uc_loss, loss); } void backward_gaussian_yolo_layer(const layer l, network_state state) { axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, state.delta, 1); } void correct_gaussian_yolo_boxes(detection *dets, int n, int w, int h, int netw, int neth, int relative, int letter) { int i; int new_w=0; int new_h=0; if (letter) { if (((float)netw / w) < ((float)neth / h)) { new_w = netw; new_h = (h * netw) / w; } else { new_h = neth; new_w = (w * neth) / h; } } else { new_w = netw; new_h = neth; } /* if (((float)netw/w) < ((float)neth/h)) { new_w = netw; new_h = (h * netw)/w; } else { new_h = neth; new_w = (w * neth)/h; } */ for (i = 0; i < n; ++i){ box b = dets[i].bbox; b.x = (b.x - (netw - new_w)/2./netw) / ((float)new_w/netw); b.y = (b.y - (neth - new_h)/2./neth) / ((float)new_h/neth); b.w *= (float)netw/new_w; b.h *= (float)neth/new_h; if(!relative){ b.x *= w; b.w *= w; b.y *= h; b.h *= h; } dets[i].bbox = b; } } int gaussian_yolo_num_detections(layer l, float thresh) { int i, n; int count = 0; for (i = 0; i < l.w*l.h; ++i){ for(n = 0; n < l.n; ++n){ int obj_index = entry_gaussian_index(l, 0, n*l.w*l.h + i, 8); if(l.output[obj_index] > thresh){ ++count; } } } return count; } /* void avg_flipped_gaussian_yolo(layer l) { int i,j,n,z; float *flip = l.output + l.outputs; for (j = 0; j < l.h; ++j) { for (i = 0; i < l.w/2; ++i) { for (n = 0; n < l.n; ++n) { for(z = 0; z < l.classes + 8 + 1; ++z){ int i1 = z*l.w*l.h*l.n + n*l.w*l.h + j*l.w + i; int i2 = z*l.w*l.h*l.n + n*l.w*l.h + j*l.w + (l.w - i - 1); float swap = flip[i1]; flip[i1] = flip[i2]; flip[i2] = swap; if(z == 0){ flip[i1] = -flip[i1]; flip[i2] = -flip[i2]; } } } } } for(i = 0; i < l.outputs; ++i){ l.output[i] = (l.output[i] + flip[i])/2.; } } */ int get_gaussian_yolo_detections(layer l, int w, int h, int netw, int neth, float thresh, int *map, int relative, detection *dets, int letter) { int i,j,n; float *predictions = l.output; //if (l.batch == 2) avg_flipped_gaussian_yolo(l); int count = 0; for (i = 0; i < l.w*l.h; ++i){ int row = i / l.w; int col = i % l.w; for(n = 0; n < l.n; ++n){ int obj_index = entry_gaussian_index(l, 0, n*l.w*l.h + i, 8); float objectness = predictions[obj_index]; if (objectness <= thresh) continue; // incorrect behavior for Nan values if (objectness > thresh) { int box_index = entry_gaussian_index(l, 0, n*l.w*l.h + i, 0); dets[count].bbox = get_gaussian_yolo_box(predictions, l.biases, l.mask[n], box_index, col, row, l.w, l.h, netw, neth, l.w*l.h, l.yolo_point); dets[count].objectness = objectness; dets[count].classes = l.classes; dets[count].uc[0] = predictions[entry_gaussian_index(l, 0, n*l.w*l.h + i, 1)]; // tx uncertainty dets[count].uc[1] = predictions[entry_gaussian_index(l, 0, n*l.w*l.h + i, 3)]; // ty uncertainty dets[count].uc[2] = predictions[entry_gaussian_index(l, 0, n*l.w*l.h + i, 5)]; // tw uncertainty dets[count].uc[3] = predictions[entry_gaussian_index(l, 0, n*l.w*l.h + i, 7)]; // th uncertainty dets[count].points = l.yolo_point; //if (l.yolo_point != YOLO_CENTER) dets[count].objectness = objectness = 0; for (j = 0; j < l.classes; ++j) { int class_index = entry_gaussian_index(l, 0, n*l.w*l.h + i, 9 + j); float uc_aver = (dets[count].uc[0] + dets[count].uc[1] + dets[count].uc[2] + dets[count].uc[3]) / 4.0; float prob = objectness*predictions[class_index] * (1.0 - uc_aver); dets[count].prob[j] = (prob > thresh) ? prob : 0; } ++count; } } } correct_gaussian_yolo_boxes(dets, count, w, h, netw, neth, relative, letter); return count; } #ifdef GPU void forward_gaussian_yolo_layer_gpu(const layer l, network_state state) { copy_ongpu(l.batch*l.inputs, state.input, 1, l.output_gpu, 1); int b, n; for (b = 0; b < l.batch; ++b) { for(n = 0; n < l.n; ++n) { // x : mu, sigma int index = entry_gaussian_index(l, b, n*l.w*l.h, 0); activate_array_ongpu(l.output_gpu + index, 2*l.w*l.h, LOGISTIC); scal_add_ongpu(l.w*l.h, l.scale_x_y, -0.5*(l.scale_x_y - 1), l.output_gpu + index, 1); // scale x // y : mu, sigma index = entry_gaussian_index(l, b, n*l.w*l.h, 2); activate_array_ongpu(l.output_gpu + index, 2*l.w*l.h, LOGISTIC); scal_add_ongpu(l.w*l.h, l.scale_x_y, -0.5*(l.scale_x_y - 1), l.output_gpu + index, 1); // scale y // w : sigma index = entry_gaussian_index(l, b, n*l.w*l.h, 5); activate_array_ongpu(l.output_gpu + index, l.w*l.h, LOGISTIC); // h : sigma index = entry_gaussian_index(l, b, n*l.w*l.h, 7); activate_array_ongpu(l.output_gpu + index, l.w*l.h, LOGISTIC); // objectness & class index = entry_gaussian_index(l, b, n*l.w*l.h, 8); activate_array_ongpu(l.output_gpu + index, (1+l.classes)*l.w*l.h, LOGISTIC); } } if (!state.train || l.onlyforward) { //cuda_pull_array(l.output_gpu, l.output, l.batch*l.outputs); cuda_pull_array_async(l.output_gpu, l.output, l.batch*l.outputs); CHECK_CUDA(cudaPeekAtLastError()); return; } float *in_cpu = (float *)calloc(l.batch*l.inputs, sizeof(float)); cuda_pull_array(l.output_gpu, l.output, l.batch*l.outputs); memcpy(in_cpu, l.output, l.batch*l.outputs * sizeof(float)); float *truth_cpu = 0; if (state.truth) { int num_truth = l.batch*l.truths; truth_cpu = (float *)calloc(num_truth, sizeof(float)); cuda_pull_array(state.truth, truth_cpu, num_truth); } network_state cpu_state = state; cpu_state.net = state.net; cpu_state.index = state.index; cpu_state.train = state.train; cpu_state.truth = truth_cpu; cpu_state.input = in_cpu; forward_gaussian_yolo_layer(l, cpu_state); //forward_yolo_layer(l, state); cuda_push_array(l.delta_gpu, l.delta, l.batch*l.outputs); free(in_cpu); if (cpu_state.truth) free(cpu_state.truth); } void backward_gaussian_yolo_layer_gpu(const layer l, network_state state) { axpy_ongpu(l.batch*l.inputs, l.delta_normalizer, l.delta_gpu, 1, state.delta, 1); } #endif
