text: #include "detection_layer.h"
  #include "activations.h"
  #include "softmax_layer.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>
  
  detection_layer make_detection_layer(int batch, int inputs, int n, int side, int classes, int coords, int rescore)
  {
      detection_layer l = {0};
      l.type = DETECTION;
  
      l.n = n;
      l.batch = batch;
      l.inputs = inputs;
      l.classes = classes;
      l.coords = coords;
      l.rescore = rescore;
      l.side = side;
      l.w = side;
      l.h = side;
      assert(side*side*((1 + l.coords)*l.n + l.classes) == inputs);
      l.cost = calloc(1, sizeof(float));
      l.outputs = l.inputs;
      l.truths = l.side*l.side*(1+l.coords+l.classes);
      l.output = calloc(batch*l.outputs, sizeof(float));
      l.delta = calloc(batch*l.outputs, sizeof(float));
  
      l.forward = forward_detection_layer;
      l.backward = backward_detection_layer;
  #ifdef GPU
      l.forward_gpu = forward_detection_layer_gpu;
      l.backward_gpu = backward_detection_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 Layer\n");
      srand(0);
  
      return l;
  }
  
  void forward_detection_layer(const detection_layer l, network_state state)
  {
      int locations = l.side*l.side;
      int i,j;
      memcpy(l.output, state.input, l.outputs*l.batch*sizeof(float));
      //if(l.reorg) reorg(l.output, l.w*l.h, size*l.n, l.batch, 1);
      int b;
      if (l.softmax){
          for(b = 0; b < l.batch; ++b){
              int index = b*l.inputs;
              for (i = 0; i < locations; ++i) {
                  int offset = i*l.classes;
                  softmax(l.output + index + offset, l.classes, 1,
                          l.output + index + offset);
              }
          }
      }
      if(state.train){
          float avg_iou = 0;
          float avg_cat = 0;
          float avg_allcat = 0;
          float avg_obj = 0;
          float avg_anyobj = 0;
          int count = 0;
          *(l.cost) = 0;
          int size = l.inputs * l.batch;
          memset(l.delta, 0, size * sizeof(float));
          for (b = 0; b < l.batch; ++b){
              int index = b*l.inputs;
              for (i = 0; i < locations; ++i) {
                  int truth_index = (b*locations + i)*(1+l.coords+l.classes);
                  int is_obj = state.truth[truth_index];
                  for (j = 0; j < l.n; ++j) {
                      int p_index = index + locations*l.classes + i*l.n + j;
                      l.delta[p_index] = l.noobject_scale*(0 - l.output[p_index]);
                      *(l.cost) += l.noobject_scale*pow(l.output[p_index], 2);
                      avg_anyobj += l.output[p_index];
                  }
  
                  int best_index = -1;
                  float best_iou = 0;
                  float best_rmse = 20;
  
                  if (!is_obj){
                      continue;
                  }
  
                  int class_index = index + i*l.classes;
                  for(j = 0; j < l.classes; ++j) {
                      l.delta[class_index+j] = l.class_scale * (state.truth[truth_index+1+j] - l.output[class_index+j]);
                      *(l.cost) += l.class_scale * pow(state.truth[truth_index+1+j] - l.output[class_index+j], 2);
                      if(state.truth[truth_index + 1 + j]) avg_cat += l.output[class_index+j];
                      avg_allcat += l.output[class_index+j];
                  }
  
                  box truth = float_to_box(state.truth + truth_index + 1 + l.classes);
                  truth.x /= l.side;
                  truth.y /= l.side;
  
                  for(j = 0; j < l.n; ++j){
                      int box_index = index + locations*(l.classes + l.n) + (i*l.n + j) * l.coords;
                      box out = float_to_box(l.output + box_index);
                      out.x /= l.side;
                      out.y /= l.side;
  
                      if (l.sqrt){
                          out.w = out.w*out.w;
                          out.h = out.h*out.h;
                      }
  
                      float iou  = box_iou(out, truth);
                      //iou = 0;
                      float rmse = box_rmse(out, truth);
                      if(best_iou > 0 || iou > 0){
                          if(iou > best_iou){
                              best_iou = iou;
                              best_index = j;
                          }
                      }else{
                          if(rmse < best_rmse){
                              best_rmse = rmse;
                              best_index = j;
                          }
                      }
                  }
  
                  if(l.forced){
                      if(truth.w*truth.h < .1){
                          best_index = 1;
                      }else{
                          best_index = 0;
                      }
                  }
                  if(l.random && *(state.net.seen) < 64000){
                      best_index = rand()%l.n;
                  }
  
                  int box_index = index + locations*(l.classes + l.n) + (i*l.n + best_index) * l.coords;
                  int tbox_index = truth_index + 1 + l.classes;
  
                  box out = float_to_box(l.output + box_index);
                  out.x /= l.side;
                  out.y /= l.side;
                  if (l.sqrt) {
                      out.w = out.w*out.w;
                      out.h = out.h*out.h;
                  }
                  float iou  = box_iou(out, truth);
  
                  //printf("%d,", best_index);
                  int p_index = index + locations*l.classes + i*l.n + best_index;
                  *(l.cost) -= l.noobject_scale * pow(l.output[p_index], 2);
                  *(l.cost) += l.object_scale * pow(1-l.output[p_index], 2);
                  avg_obj += l.output[p_index];
                  l.delta[p_index] = l.object_scale * (1.-l.output[p_index]);
  
                  if(l.rescore){
                      l.delta[p_index] = l.object_scale * (iou - l.output[p_index]);
                  }
  
                  l.delta[box_index+0] = l.coord_scale*(state.truth[tbox_index + 0] - l.output[box_index + 0]);
                  l.delta[box_index+1] = l.coord_scale*(state.truth[tbox_index + 1] - l.output[box_index + 1]);
                  l.delta[box_index+2] = l.coord_scale*(state.truth[tbox_index + 2] - l.output[box_index + 2]);
                  l.delta[box_index+3] = l.coord_scale*(state.truth[tbox_index + 3] - l.output[box_index + 3]);
                  if(l.sqrt){
                      l.delta[box_index+2] = l.coord_scale*(sqrt(state.truth[tbox_index + 2]) - l.output[box_index + 2]);
                      l.delta[box_index+3] = l.coord_scale*(sqrt(state.truth[tbox_index + 3]) - l.output[box_index + 3]);
                  }
  
                  *(l.cost) += pow(1-iou, 2);
                  avg_iou += iou;
                  ++count;
              }
          }
  
          if(0){
              float *costs = calloc(l.batch*locations*l.n, sizeof(float));
              for (b = 0; b < l.batch; ++b) {
                  int index = b*l.inputs;
                  for (i = 0; i < locations; ++i) {
                      for (j = 0; j < l.n; ++j) {
                          int p_index = index + locations*l.classes + i*l.n + j;
                          costs[b*locations*l.n + i*l.n + j] = l.delta[p_index]*l.delta[p_index];
                      }
                  }
              }
              int indexes[100];
              top_k(costs, l.batch*locations*l.n, 100, indexes);
              float cutoff = costs[indexes[99]];
              for (b = 0; b < l.batch; ++b) {
                  int index = b*l.inputs;
                  for (i = 0; i < locations; ++i) {
                      for (j = 0; j < l.n; ++j) {
                          int p_index = index + locations*l.classes + i*l.n + j;
                          if (l.delta[p_index]*l.delta[p_index] < cutoff) l.delta[p_index] = 0;
                      }
                  }
              }
              free(costs);
          }
  
  
          *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2);
  
  
          printf("Detection Avg IOU: %f, Pos Cat: %f, All Cat: %f, Pos Obj: %f, Any Obj: %f, count: %d\n", avg_iou/count, avg_cat/count, avg_allcat/(count*l.classes), avg_obj/count, avg_anyobj/(l.batch*locations*l.n), count);
          //if(l.reorg) reorg(l.delta, l.w*l.h, size*l.n, l.batch, 0);
      }
  }
  
  void backward_detection_layer(const detection_layer l, network_state state)
  {
      axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, state.delta, 1);
  }
  
  void get_detection_boxes(layer l, int w, int h, float thresh, float **probs, box *boxes, int only_objectness)
  {
      int i,j,n;
      float *predictions = l.output;
      //int per_cell = 5*num+classes;
      for (i = 0; i < l.side*l.side; ++i){
          int row = i / l.side;
          int col = i % l.side;
          for(n = 0; n < l.n; ++n){
              int index = i*l.n + n;
              int p_index = l.side*l.side*l.classes + i*l.n + n;
              float scale = predictions[p_index];
              int box_index = l.side*l.side*(l.classes + l.n) + (i*l.n + n)*4;
              boxes[index].x = (predictions[box_index + 0] + col) / l.side * w;
              boxes[index].y = (predictions[box_index + 1] + row) / l.side * h;
              boxes[index].w = pow(predictions[box_index + 2], (l.sqrt?2:1)) * w;
              boxes[index].h = pow(predictions[box_index + 3], (l.sqrt?2:1)) * h;
              for(j = 0; j < l.classes; ++j){
                  int class_index = i*l.classes;
                  float prob = scale*predictions[class_index+j];
                  probs[index][j] = (prob > thresh) ? prob : 0;
              }
              if(only_objectness){
                  probs[index][0] = scale;
              }
          }
      }
  }
  
  #ifdef GPU
  
  void forward_detection_layer_gpu(const detection_layer l, network_state state)
  {
      if(!state.train){
          copy_ongpu(l.batch*l.inputs, state.input, 1, l.output_gpu, 1);
          return;
      }
  
      float *in_cpu = calloc(l.batch*l.inputs, sizeof(float));
      float *truth_cpu = 0;
      if(state.truth){
          int num_truth = l.batch*l.side*l.side*(1+l.coords+l.classes);
          truth_cpu = calloc(num_truth, sizeof(float));
          cuda_pull_array(state.truth, truth_cpu, num_truth);
      }
      cuda_pull_array(state.input, in_cpu, l.batch*l.inputs);
      network_state cpu_state = state;
      cpu_state.train = state.train;
      cpu_state.truth = truth_cpu;
      cpu_state.input = in_cpu;
      forward_detection_layer(l, cpu_state);
      cuda_push_array(l.output_gpu, l.output, l.batch*l.outputs);
      cuda_push_array(l.delta_gpu, l.delta, l.batch*l.inputs);
      free(cpu_state.input);
      if(cpu_state.truth) free(cpu_state.truth);
  }
  
  void backward_detection_layer_gpu(detection_layer l, network_state state)
  {
      axpy_ongpu(l.batch*l.inputs, 1, l.delta_gpu, 1, state.delta, 1);
      //copy_ongpu(l.batch*l.inputs, l.delta_gpu, 1, state.delta, 1);
  }
  #endif
  
  