text: // 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
  
  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) {
          cudaFreeHost(l->output);
          if (cudaSuccess != cudaHostAlloc(&l->output, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {
              cudaGetLastError(); // reset CUDA-error
              l->output = (float*)realloc(l->output, l->batch * l->outputs * sizeof(float));
              l->output_pinned = 0;
          }
      }
  
      if (l->delta_pinned) {
          cudaFreeHost(l->delta);
          if (cudaSuccess != cudaHostAlloc(&l->delta, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {
              cudaGetLastError(); // reset CUDA-error
              l->delta = (float*)realloc(l->delta, 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;
  }
  
  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)
  {
      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]);
  
          // normalize iou weight, for GIoU
          dx *= iou_normalizer;
          dy *= iou_normalizer;
          dw *= iou_normalizer;
          dh *= iou_normalizer;
  
          delta_x = dx;
          delta_y = dy;
          delta_w = dw;
          delta_h = dh;
      }
  
      // normalize Uncertainty weight
      delta_ux *= uc_normalizer;
      delta_uy *= uc_normalizer;
      delta_uw *= uc_normalizer;
      delta_uh *= uc_normalizer;
  
      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)
  {
      int n;
      if (delta[index]){
          delta[index + stride*class_id] = 1 - output[index + stride*class_id];
          if(avg_cat) *avg_cat += output[index + stride*class_id];
          return;
      }
      for(n = 0; n < classes; ++n){
          delta[index + stride*n] = ((n == class_id)?1 : 0) - output[index + stride*n];
          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) {
                      int box_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 0);
                      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(" 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);
                              getchar();
                              continue; // if label contains class_id more than number of classes in the cfg-file
                          }
                          if(!truth.x) break;
  
                          int class_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 9);
                          int obj_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 8);
                          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;
                          }
                      }
                      int obj_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 8);
                      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) {
                          l.delta[obj_index] = 0;
                      }
                      if (best_iou > l.truth_thresh) {
                          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];
                          int class_index = entry_gaussian_index(l, b, 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, 0);
                          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, l.iou_loss, l.uc_normalizer, 1, l.yolo_point);
                      }
                  }
              }
          }
          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) {
                  i = (truth.x * l.w);
                  j = (truth.y * l.h);
              }
              else if (l.yolo_point == YOLO_LEFT_TOP) {
                  i = ((truth.x - truth.w / 2) * l.w);
                  j = ((truth.y - truth.h / 2) * l.h);
              }
              else if (l.yolo_point == YOLO_RIGHT_BOTTOM) {
                  i = ((truth.x + truth.w / 2) * l.w);
                  j = ((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 box_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0);
                  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, l.iou_loss, l.uc_normalizer, 1, l.yolo_point);
  
                  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] = l.cls_normalizer * (1 - l.output[obj_index]);
  
                  int class_id = state.truth[t*(4 + 1) + b*l.truths + 4];
                  if (l.map) class_id = l.map[class_id];
                  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);
  
                  ++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(pred, truth_shift);
                      // iou, n
  
                      if (iou > l.iou_thresh) {
                          int box_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0);
                          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, l.iou_loss, l.uc_normalizer, 1, l.yolo_point);
  
                          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] = l.cls_normalizer * (1 - l.output[obj_index]);
  
                          int class_id = state.truth[t*(4 + 1) + b*l.truths + 4];
                          if (l.map) class_id = l.map[class_id];
                          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);
  
                          ++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;
  
      printf("Region %d Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f,  count: %d, loss = %.2f, class_loss = %.2f, iou_loss = %.2f, uc_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,
          loss, class_loss, iou_loss, uc_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
  