text: #include "region_layer.h"
  #include "activations.h"
  #include "blas.h"
  #include "box.h"
  #include "cuda.h"
  #include "utils.h"
  
  #include <stdio.h>
  #include <assert.h>
  #include <string.h>
  #include <stdlib.h>
  
  layer make_region_layer(int batch, int w, int h, int n, int classes, int coords)
  {
      layer l = {0};
      l.type = REGION;
  
      l.n = n;
      l.batch = batch;
      l.h = h;
      l.w = w;
      l.c = n*(classes + coords + 1);
      l.out_w = l.w;
      l.out_h = l.h;
      l.out_c = l.c;
      l.classes = classes;
      l.coords = coords;
      l.cost = calloc(1, sizeof(float));
      l.biases = calloc(n*2, sizeof(float));
      l.bias_updates = calloc(n*2, sizeof(float));
      l.outputs = h*w*n*(classes + coords + 1);
      l.inputs = l.outputs;
      l.truths = 30*(l.coords + 1);
      l.delta = calloc(batch*l.outputs, sizeof(float));
      l.output = calloc(batch*l.outputs, sizeof(float));
      int i;
      for(i = 0; i < n*2; ++i){
          l.biases[i] = .5;
      }
  
      l.forward = forward_region_layer;
      l.backward = backward_region_layer;
  #ifdef GPU
      l.forward_gpu = forward_region_layer_gpu;
      l.backward_gpu = backward_region_layer_gpu;
      l.output_gpu = cuda_make_array(l.output, batch*l.outputs);
      l.delta_gpu = cuda_make_array(l.delta, batch*l.outputs);
  #endif
  
      fprintf(stderr, "detection\n");
      srand(0);
  
      return l;
  }
  
  void resize_region_layer(layer *l, int w, int h)
  {
      l->w = w;
      l->h = h;
  
      l->outputs = h*w*l->n*(l->classes + l->coords + 1);
      l->inputs = l->outputs;
  
      l->output = realloc(l->output, l->batch*l->outputs*sizeof(float));
      l->delta = realloc(l->delta, l->batch*l->outputs*sizeof(float));
  
  #ifdef GPU
      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_region_box(float *x, float *biases, int n, int index, int i, int j, int w, int h, int stride)
  {
      box b;
      b.x = (i + x[index + 0*stride]) / w;
      b.y = (j + x[index + 1*stride]) / h;
      b.w = exp(x[index + 2*stride]) * biases[2*n]   / w;
      b.h = exp(x[index + 3*stride]) * biases[2*n+1] / h;
      return b;
  }
  
  float delta_region_box(box truth, float *x, float *biases, int n, int index, int i, int j, int w, int h, float *delta, float scale, int stride)
  {
      box pred = get_region_box(x, biases, n, index, i, j, w, h, stride);
      float iou = box_iou(pred, truth);
  
      float tx = (truth.x*w - i);
      float ty = (truth.y*h - j);
      float tw = log(truth.w*w / biases[2*n]);
      float th = log(truth.h*h / biases[2*n + 1]);
  
      delta[index + 0*stride] = scale * (tx - x[index + 0*stride]);
      delta[index + 1*stride] = scale * (ty - x[index + 1*stride]);
      delta[index + 2*stride] = scale * (tw - x[index + 2*stride]);
      delta[index + 3*stride] = scale * (th - x[index + 3*stride]);
      return iou;
  }
  
  void delta_region_mask(float *truth, float *x, int n, int index, float *delta, int stride, int scale)
  {
      int i;
      for(i = 0; i < n; ++i){
          delta[index + i*stride] = scale*(truth[i] - x[index + i*stride]);
      }
  }
  
  
  void delta_region_class(float *output, float *delta, int index, int class, int classes, tree *hier, float scale, int stride, float *avg_cat, int tag)
  {
      int i, n;
      if(hier){
          float pred = 1;
          while(class >= 0){
              pred *= output[index + stride*class];
              int g = hier->group[class];
              int offset = hier->group_offset[g];
              for(i = 0; i < hier->group_size[g]; ++i){
                  delta[index + stride*(offset + i)] = scale * (0 - output[index + stride*(offset + i)]);
              }
              delta[index + stride*class] = scale * (1 - output[index + stride*class]);
  
              class = hier->parent[class];
          }
          *avg_cat += pred;
      } else {
          if (delta[index] && tag){
              delta[index + stride*class] = scale * (1 - output[index + stride*class]);
              return;
          }
          for(n = 0; n < classes; ++n){
              delta[index + stride*n] = scale * (((n == class)?1 : 0) - output[index + stride*n]);
              if(n == class) *avg_cat += output[index + stride*n];
          }
      }
  }
  
  float logit(float x)
  {
      return log(x/(1.-x));
  }
  
  float tisnan(float x)
  {
      return (x != x);
  }
  
  int entry_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*(l.coords+l.classes+1) + entry*l.w*l.h + loc;
  }
  
  void forward_region_layer(const layer l, network net)
  {
      int i,j,b,t,n;
      memcpy(l.output, net.input, l.outputs*l.batch*sizeof(float));
  
  #ifndef GPU
      for (b = 0; b < l.batch; ++b){
          for(n = 0; n < l.n; ++n){
              int index = entry_index(l, b, n*l.w*l.h, 0);
              activate_array(l.output + index, 2*l.w*l.h, LOGISTIC);
              index = entry_index(l, b, n*l.w*l.h, l.coords);
              if(!l.background) activate_array(l.output + index,   l.w*l.h, LOGISTIC);
              index = entry_index(l, b, n*l.w*l.h, l.coords + 1);
              if(!l.softmax && !l.softmax_tree) activate_array(l.output + index, l.classes*l.w*l.h, LOGISTIC);
          }
      }
      if (l.softmax_tree){
          int i;
          int count = l.coords + 1;
          for (i = 0; i < l.softmax_tree->groups; ++i) {
              int group_size = l.softmax_tree->group_size[i];
              softmax_cpu(net.input + count, group_size, l.batch, l.inputs, l.n*l.w*l.h, 1, l.n*l.w*l.h, l.temperature, l.output + count);
              count += group_size;
          }
      } else if (l.softmax){
          int index = entry_index(l, 0, 0, l.coords + !l.background);
          softmax_cpu(net.input + index, l.classes + l.background, l.batch*l.n, l.inputs/l.n, l.w*l.h, 1, l.w*l.h, 1, l.output + index);
      }
  #endif
  
      memset(l.delta, 0, l.outputs * l.batch * sizeof(float));
      if(!net.train) return;
      float avg_iou = 0;
      float recall = 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) {
          if(l.softmax_tree){
              int onlyclass = 0;
              for(t = 0; t < 30; ++t){
                  box truth = float_to_box(net.truth + t*(l.coords + 1) + b*l.truths, 1);
                  if(!truth.x) break;
                  int class = net.truth[t*(l.coords + 1) + b*l.truths + l.coords];
                  float maxp = 0;
                  int maxi = 0;
                  if(truth.x > 100000 && truth.y > 100000){
                      for(n = 0; n < l.n*l.w*l.h; ++n){
                          int class_index = entry_index(l, b, n, l.coords + 1);
                          int obj_index = entry_index(l, b, n, l.coords);
                          float scale =  l.output[obj_index];
                          l.delta[obj_index] = l.noobject_scale * (0 - l.output[obj_index]);
                          float p = scale*get_hierarchy_probability(l.output + class_index, l.softmax_tree, class, l.w*l.h);
                          if(p > maxp){
                              maxp = p;
                              maxi = n;
                          }
                      }
                      int class_index = entry_index(l, b, maxi, l.coords + 1);
                      int obj_index = entry_index(l, b, maxi, l.coords);
                      delta_region_class(l.output, l.delta, class_index, class, l.classes, l.softmax_tree, l.class_scale, l.w*l.h, &avg_cat, !l.softmax);
                      if(l.output[obj_index] < .3) l.delta[obj_index] = l.object_scale * (.3 - l.output[obj_index]);
                      else  l.delta[obj_index] = 0;
                      l.delta[obj_index] = 0;
                      ++class_count;
                      onlyclass = 1;
                      break;
                  }
              }
              if(onlyclass) continue;
          }
          for (j = 0; j < l.h; ++j) {
              for (i = 0; i < l.w; ++i) {
                  for (n = 0; n < l.n; ++n) {
                      int box_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 0);
                      box pred = get_region_box(l.output, l.biases, n, box_index, i, j, l.w, l.h, l.w*l.h);
                      float best_iou = 0;
                      for(t = 0; t < 30; ++t){
                          box truth = float_to_box(net.truth + t*(l.coords + 1) + b*l.truths, 1);
                          if(!truth.x) break;
                          float iou = box_iou(pred, truth);
                          if (iou > best_iou) {
                              best_iou = iou;
                          }
                      }
                      int obj_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, l.coords);
                      avg_anyobj += l.output[obj_index];
                      l.delta[obj_index] = l.noobject_scale * (0 - l.output[obj_index]);
                      if(l.background) l.delta[obj_index] = l.noobject_scale * (1 - l.output[obj_index]);
                      if (best_iou > l.thresh) {
                          l.delta[obj_index] = 0;
                      }
  
                      if(*(net.seen) < 12800){
                          box truth = {0};
                          truth.x = (i + .5)/l.w;
                          truth.y = (j + .5)/l.h;
                          truth.w = l.biases[2*n]/l.w;
                          truth.h = l.biases[2*n+1]/l.h;
                          delta_region_box(truth, l.output, l.biases, n, box_index, i, j, l.w, l.h, l.delta, .01, l.w*l.h);
                      }
                  }
              }
          }
          for(t = 0; t < 30; ++t){
              box truth = float_to_box(net.truth + t*(l.coords + 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);
              //printf("%d %f %d %f\n", i, truth.x*l.w, j, truth.y*l.h);
              box truth_shift = truth;
              truth_shift.x = 0;
              truth_shift.y = 0;
              //printf("index %d %d\n",i, j);
              for(n = 0; n < l.n; ++n){
                  int box_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 0);
                  box pred = get_region_box(l.output, l.biases, n, box_index, i, j, l.w, l.h, l.w*l.h);
                  if(l.bias_match){
                      pred.w = l.biases[2*n]/l.w;
                      pred.h = l.biases[2*n+1]/l.h;
                  }
                  //printf("pred: (%f, %f) %f x %f\n", pred.x, pred.y, pred.w, pred.h);
                  pred.x = 0;
                  pred.y = 0;
                  float iou = box_iou(pred, truth_shift);
                  if (iou > best_iou){
                      best_iou = iou;
                      best_n = n;
                  }
              }
              //printf("%d %f (%f, %f) %f x %f\n", best_n, best_iou, truth.x, truth.y, truth.w, truth.h);
  
              int box_index = entry_index(l, b, best_n*l.w*l.h + j*l.w + i, 0);
              float iou = delta_region_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, l.delta, l.coord_scale *  (2 - truth.w*truth.h), l.w*l.h);
              if(l.coords > 4){
                  int mask_index = entry_index(l, b, best_n*l.w*l.h + j*l.w + i, 4);
                  delta_region_mask(net.truth + t*(l.coords + 1) + b*l.truths + 5, l.output, l.coords - 4, mask_index, l.delta, l.w*l.h, l.mask_scale);
              }
              if(iou > .5) recall += 1;
              avg_iou += iou;
  
              //l.delta[best_index + 4] = iou - l.output[best_index + 4];
              int obj_index = entry_index(l, b, best_n*l.w*l.h + j*l.w + i, l.coords);
              avg_obj += l.output[obj_index];
              l.delta[obj_index] = l.object_scale * (1 - l.output[obj_index]);
              if (l.rescore) {
                  l.delta[obj_index] = l.object_scale * (iou - l.output[obj_index]);
              }
              if(l.background){
                  l.delta[obj_index] = l.object_scale * (0 - l.output[obj_index]);
              }
  
              int class = net.truth[t*(l.coords + 1) + b*l.truths + l.coords];
              if (l.map) class = l.map[class];
              int class_index = entry_index(l, b, best_n*l.w*l.h + j*l.w + i, l.coords + 1);
              delta_region_class(l.output, l.delta, class_index, class, l.classes, l.softmax_tree, l.class_scale, l.w*l.h, &avg_cat, !l.softmax);
              ++count;
              ++class_count;
          }
      }
      //printf("\n");
      *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2);
      printf("Region Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, Avg Recall: %f,  count: %d\n", avg_iou/count, avg_cat/class_count, avg_obj/count, avg_anyobj/(l.w*l.h*l.n*l.batch), recall/count, count);
  }
  
  void backward_region_layer(const layer l, network net)
  {
      /*
         int b;
         int size = l.coords + l.classes + 1;
         for (b = 0; b < l.batch*l.n; ++b){
         int index = (b*size + 4)*l.w*l.h;
         gradient_array(l.output + index, l.w*l.h, LOGISTIC, l.delta + index);
         }
         axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, net.delta, 1);
       */
  }
  
  void correct_region_boxes(box *boxes, int n, int w, int h, int netw, int neth, int relative)
  {
      int i;
      int new_w=0;
      int new_h=0;
      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 = boxes[i];
          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;
          }
          boxes[i] = b;
      }
  }
  
  void get_region_boxes(layer l, int w, int h, int netw, int neth, float thresh, float **probs, box *boxes, float **masks, int only_objectness, int *map, float tree_thresh, int relative)
  {
      int i,j,n,z;
      float *predictions = l.output;
      if (l.batch == 2) {
          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 + l.coords + 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.;
          }
      }
      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 index = n*l.w*l.h + i;
              for(j = 0; j < l.classes; ++j){
                  probs[index][j] = 0;
              }
              int obj_index  = entry_index(l, 0, n*l.w*l.h + i, l.coords);
              int box_index  = entry_index(l, 0, n*l.w*l.h + i, 0);
              int mask_index = entry_index(l, 0, n*l.w*l.h + i, 4);
              float scale = l.background ? 1 : predictions[obj_index];
              boxes[index] = get_region_box(predictions, l.biases, n, box_index, col, row, l.w, l.h, l.w*l.h);
              if(masks){
                  for(j = 0; j < l.coords - 4; ++j){
                      masks[index][j] = l.output[mask_index + j*l.w*l.h];
                  }
              }
  
              int class_index = entry_index(l, 0, n*l.w*l.h + i, l.coords + !l.background);
              if(l.softmax_tree){
  
                  hierarchy_predictions(predictions + class_index, l.classes, l.softmax_tree, 0, l.w*l.h);
                  if(map){
                      for(j = 0; j < 200; ++j){
                          int class_index = entry_index(l, 0, n*l.w*l.h + i, l.coords + 1 + map[j]);
                          float prob = scale*predictions[class_index];
                          probs[index][j] = (prob > thresh) ? prob : 0;
                      }
                  } else {
                      int j =  hierarchy_top_prediction(predictions + class_index, l.softmax_tree, tree_thresh, l.w*l.h);
                      probs[index][j] = (scale > thresh) ? scale : 0;
                      probs[index][l.classes] = scale;
                  }
              } else {
                  float max = 0;
                  for(j = 0; j < l.classes; ++j){
                      int class_index = entry_index(l, 0, n*l.w*l.h + i, l.coords + 1 + j);
                      float prob = scale*predictions[class_index];
                      probs[index][j] = (prob > thresh) ? prob : 0;
                      if(prob > max) max = prob;
                      // TODO REMOVE
                      // if (j == 56 ) probs[index][j] = 0; 
                      /*
                         if (j != 0) probs[index][j] = 0; 
                         int blacklist[] = {121, 497, 482, 504, 122, 518,481, 418, 542, 491, 914, 478, 120, 510,500};
                         int bb;
                         for (bb = 0; bb < sizeof(blacklist)/sizeof(int); ++bb){
                         if(index == blacklist[bb]) probs[index][j] = 0;
                         }
                       */
                  }
                  probs[index][l.classes] = max;
              }
              if(only_objectness){
                  probs[index][0] = scale;
              }
          }
      }
      correct_region_boxes(boxes, l.w*l.h*l.n, w, h, netw, neth, relative);
  }
  
  #ifdef GPU
  
  void forward_region_layer_gpu(const layer l, network net)
  {
      copy_gpu(l.batch*l.inputs, net.input_gpu, 1, l.output_gpu, 1);
      int b, n;
      for (b = 0; b < l.batch; ++b){
          for(n = 0; n < l.n; ++n){
              int index = entry_index(l, b, n*l.w*l.h, 0);
              activate_array_gpu(l.output_gpu + index, 2*l.w*l.h, LOGISTIC);
              if(l.coords > 4){
                  index = entry_index(l, b, n*l.w*l.h, 4);
                  activate_array_gpu(l.output_gpu + index, (l.coords - 4)*l.w*l.h, LOGISTIC);
              }
              index = entry_index(l, b, n*l.w*l.h, l.coords);
              if(!l.background) activate_array_gpu(l.output_gpu + index,   l.w*l.h, LOGISTIC);
              index = entry_index(l, b, n*l.w*l.h, l.coords + 1);
              if(!l.softmax && !l.softmax_tree) activate_array_gpu(l.output_gpu + index, l.classes*l.w*l.h, LOGISTIC);
          }
      }
      if (l.softmax_tree){
          int index = entry_index(l, 0, 0, l.coords + 1);
          softmax_tree(net.input_gpu + index, l.w*l.h, l.batch*l.n, l.inputs/l.n, 1, l.output_gpu + index, *l.softmax_tree);
      /*
          int mmin = 9000;
          int mmax = 0;
          int i;
          for(i = 0; i < l.softmax_tree->groups; ++i){
              int group_size = l.softmax_tree->group_size[i];
              if (group_size < mmin) mmin = group_size;
              if (group_size > mmax) mmax = group_size;
          }
          //printf("%d %d %d \n", l.softmax_tree->groups, mmin, mmax);
          */
          /*
          // TIMING CODE
          int zz;
          int number = 1000;
          int count = 0;
          int i;
          for (i = 0; i < l.softmax_tree->groups; ++i) {
          int group_size = l.softmax_tree->group_size[i];
          count += group_size;
          }
          printf("%d %d\n", l.softmax_tree->groups, count);
          {
          double then = what_time_is_it_now();
          for(zz = 0; zz < number; ++zz){
          int index = entry_index(l, 0, 0, 5);
          softmax_tree(net.input_gpu + index, l.w*l.h, l.batch*l.n, l.inputs/l.n, 1, l.output_gpu + index, *l.softmax_tree);
          }
          cudaDeviceSynchronize();
          printf("Good GPU Timing: %f\n", what_time_is_it_now() - then);
          } 
          {
          double then = what_time_is_it_now();
          for(zz = 0; zz < number; ++zz){
          int i;
          int count = 5;
          for (i = 0; i < l.softmax_tree->groups; ++i) {
          int group_size = l.softmax_tree->group_size[i];
          int index = entry_index(l, 0, 0, count);
          softmax_gpu(net.input_gpu + index, group_size, l.batch*l.n, l.inputs/l.n, l.w*l.h, 1, l.w*l.h, 1, l.output_gpu + index);
          count += group_size;
          }
          }
          cudaDeviceSynchronize();
          printf("Bad GPU Timing: %f\n", what_time_is_it_now() - then);
          }
          {
          double then = what_time_is_it_now();
          for(zz = 0; zz < number; ++zz){
          int i;
          int count = 5;
          for (i = 0; i < l.softmax_tree->groups; ++i) {
          int group_size = l.softmax_tree->group_size[i];
          softmax_cpu(net.input + count, group_size, l.batch, l.inputs, l.n*l.w*l.h, 1, l.n*l.w*l.h, l.temperature, l.output + count);
          count += group_size;
          }
          }
          cudaDeviceSynchronize();
          printf("CPU Timing: %f\n", what_time_is_it_now() - then);
          }
           */
          /*
             int i;
             int count = 5;
             for (i = 0; i < l.softmax_tree->groups; ++i) {
             int group_size = l.softmax_tree->group_size[i];
             int index = entry_index(l, 0, 0, count);
             softmax_gpu(net.input_gpu + index, group_size, l.batch*l.n, l.inputs/l.n, l.w*l.h, 1, l.w*l.h, 1, l.output_gpu + index);
             count += group_size;
             }
           */
      } else if (l.softmax) {
          int index = entry_index(l, 0, 0, l.coords + !l.background);
          //printf("%d\n", index);
          softmax_gpu(net.input_gpu + index, l.classes + l.background, l.batch*l.n, l.inputs/l.n, l.w*l.h, 1, l.w*l.h, 1, l.output_gpu + index);
      }
      if(!net.train || l.onlyforward){
          cuda_pull_array(l.output_gpu, l.output, l.batch*l.outputs);
          return;
      }
  
      cuda_pull_array(l.output_gpu, net.input, l.batch*l.inputs);
      forward_region_layer(l, net);
      //cuda_push_array(l.output_gpu, l.output, l.batch*l.outputs);
      if(!net.train) return;
      cuda_push_array(l.delta_gpu, l.delta, l.batch*l.outputs);
  }
  
  void backward_region_layer_gpu(const layer l, network net)
  {
      int b, n;
      for (b = 0; b < l.batch; ++b){
          for(n = 0; n < l.n; ++n){
              int index = entry_index(l, b, n*l.w*l.h, 0);
              gradient_array_gpu(l.output_gpu + index, 2*l.w*l.h, LOGISTIC, l.delta_gpu + index);
              if(l.coords > 4){
                  index = entry_index(l, b, n*l.w*l.h, 4);
                  gradient_array_gpu(l.output_gpu + index, (l.coords - 4)*l.w*l.h, LOGISTIC, l.delta_gpu + index);
              }
              index = entry_index(l, b, n*l.w*l.h, l.coords);
              if(!l.background) gradient_array_gpu(l.output_gpu + index,   l.w*l.h, LOGISTIC, l.delta_gpu + index);
          }
      }
      axpy_gpu(l.batch*l.inputs, 1, l.delta_gpu, 1, net.delta_gpu, 1);
  }
  #endif
  
  void zero_objectness(layer l)
  {
      int i, n;
      for (i = 0; i < l.w*l.h; ++i){
          for(n = 0; n < l.n; ++n){
              int obj_index = entry_index(l, 0, n*l.w*l.h + i, l.coords);
              l.output[obj_index] = 0;
          }
      }
  }
  
  