text: #include "region_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>
  
  #define DOABS 1
  
  region_layer make_region_layer(int batch, int w, int h, int n, int classes, int coords, int max_boxes)
  {
      region_layer l = { (LAYER_TYPE)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 = (float*)xcalloc(1, sizeof(float));
      l.biases = (float*)xcalloc(n * 2, sizeof(float));
      l.bias_updates = (float*)xcalloc(n * 2, sizeof(float));
      l.outputs = h*w*n*(classes + coords + 1);
      l.inputs = l.outputs;
      l.max_boxes = max_boxes;
      l.truth_size = 4 + 2;
      l.truths = max_boxes*l.truth_size;
      l.delta = (float*)xcalloc(batch * l.outputs, sizeof(float));
      l.output = (float*)xcalloc(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(time(0));
  
      return l;
  }
  
  void resize_region_layer(layer *l, int w, int h)
  {
  #ifdef GPU
      int old_w = l->w;
      int old_h = l->h;
  #endif
      l->w = w;
      l->h = h;
  
      l->outputs = h*w*l->n*(l->classes + l->coords + 1);
      l->inputs = l->outputs;
  
      l->output = (float*)xrealloc(l->output, l->batch * l->outputs * sizeof(float));
      l->delta = (float*)xrealloc(l->delta, l->batch * l->outputs * sizeof(float));
  
  #ifdef GPU
      //if (old_w < w || old_h < h)
      {
          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)
  {
      box b;
      b.x = (i + logistic_activate(x[index + 0])) / w;
      b.y = (j + logistic_activate(x[index + 1])) / h;
      b.w = exp(x[index + 2]) * biases[2*n];
      b.h = exp(x[index + 3]) * biases[2*n+1];
      if(DOABS){
          b.w = exp(x[index + 2]) * biases[2*n]   / w;
          b.h = exp(x[index + 3]) * 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)
  {
      box pred = get_region_box(x, biases, n, index, i, j, w, h);
      float iou = box_iou(pred, truth);
  
      float tx = (truth.x*w - i);
      float ty = (truth.y*h - j);
      float tw = log(truth.w / biases[2*n]);
      float th = log(truth.h / biases[2*n + 1]);
      if(DOABS){
          tw = log(truth.w*w / biases[2*n]);
          th = log(truth.h*h / biases[2*n + 1]);
      }
  
      delta[index + 0] = scale * (tx - logistic_activate(x[index + 0])) * logistic_gradient(logistic_activate(x[index + 0]));
      delta[index + 1] = scale * (ty - logistic_activate(x[index + 1])) * logistic_gradient(logistic_activate(x[index + 1]));
      delta[index + 2] = scale * (tw - x[index + 2]);
      delta[index + 3] = scale * (th - x[index + 3]);
      return iou;
  }
  
  void delta_region_class(float *output, float *delta, int index, int class_id, int classes, tree *hier, float scale, float *avg_cat, int focal_loss)
  {
      int i, n;
      if(hier){
          float pred = 1;
          while(class_id >= 0){
              pred *= output[index + class_id];
              int g = hier->group[class_id];
              int offset = hier->group_offset[g];
              for(i = 0; i < hier->group_size[g]; ++i){
                  delta[index + offset + i] = scale * (0 - output[index + offset + i]);
              }
              delta[index + class_id] = scale * (1 - output[index + class_id]);
  
              class_id = hier->parent[class_id];
          }
          *avg_cat += pred;
      } else {
          // Focal loss
          if (focal_loss) {
              // Focal Loss
              float alpha = 0.5;    // 0.25 or 0.5
              //float gamma = 2;    // hardcoded in many places of the grad-formula
  
              int ti = index + class_id;
              float pt = output[ti] + 0.000000000000001F;
              // http://fooplot.com/#W3sidHlwZSI6MCwiZXEiOiItKDEteCkqKDIqeCpsb2coeCkreC0xKSIsImNvbG9yIjoiIzAwMDAwMCJ9LHsidHlwZSI6MTAwMH1d
              float grad = -(1 - pt) * (2 * pt*logf(pt) + pt - 1);    // http://blog.csdn.net/linmingan/article/details/77885832
              //float grad = (1 - pt) * (2 * pt*logf(pt) + pt - 1);    // https://github.com/unsky/focal-loss
  
              for (n = 0; n < classes; ++n) {
                  delta[index + n] = scale * (((n == class_id) ? 1 : 0) - output[index + n]);
  
                  delta[index + n] *= alpha*grad;
  
                  if (n == class_id) *avg_cat += output[index + n];
              }
          }
          else {
              // default
              for (n = 0; n < classes; ++n) {
                  delta[index + n] = scale * (((n == class_id) ? 1 : 0) - output[index + n]);
                  if (n == class_id) *avg_cat += output[index + n];
              }
          }
      }
  }
  
  float logit(float x)
  {
      return log(x/(1.-x));
  }
  
  float tisnan(float x)
  {
      return (x != x);
  }
  
  static 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 softmax_tree(float *input, int batch, int inputs, float temp, tree *hierarchy, float *output);
  void forward_region_layer(const region_layer l, network_state state)
  {
      int i,j,b,t,n;
      int size = l.coords + l.classes + 1;
      memcpy(l.output, state.input, l.outputs*l.batch*sizeof(float));
      #ifndef GPU
      flatten(l.output, l.w*l.h, size*l.n, l.batch, 1);
      #endif
      for (b = 0; b < l.batch; ++b){
          for(i = 0; i < l.h*l.w*l.n; ++i){
              int index = size*i + b*l.outputs;
              l.output[index + 4] = logistic_activate(l.output[index + 4]);
          }
      }
  
  
  #ifndef GPU
      if (l.softmax_tree){
          for (b = 0; b < l.batch; ++b){
              for(i = 0; i < l.h*l.w*l.n; ++i){
                  int index = size*i + b*l.outputs;
                  softmax_tree(l.output + index + 5, 1, 0, 1, l.softmax_tree, l.output + index + 5);
              }
          }
      } else if (l.softmax){
          for (b = 0; b < l.batch; ++b){
              for(i = 0; i < l.h*l.w*l.n; ++i){
                  int index = size*i + b*l.outputs;
                  softmax(l.output + index + 5, l.classes, 1, l.output + index + 5, 1);
              }
          }
      }
  #endif
      if(!state.train) return;
      memset(l.delta, 0, l.outputs * l.batch * sizeof(float));
      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_id = 0;
              for(t = 0; t < l.max_boxes; ++t){
                  box truth = float_to_box(state.truth + t*l.truth_size + b*l.truths);
                  if(!truth.x) break; // continue;
                  int class_id = state.truth[t*l.truth_size + b*l.truths + 4];
                  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 index = size*n + b*l.outputs + 5;
                          float scale =  l.output[index-1];
                          float p = scale*get_hierarchy_probability(l.output + index, l.softmax_tree, class_id);
                          if(p > maxp){
                              maxp = p;
                              maxi = n;
                          }
                      }
                      int index = size*maxi + b*l.outputs + 5;
                      delta_region_class(l.output, l.delta, index, class_id, l.classes, l.softmax_tree, l.class_scale, &avg_cat, l.focal_loss);
                      ++class_count;
                      onlyclass_id = 1;
                      break;
                  }
              }
              if(onlyclass_id) continue;
          }
          for (j = 0; j < l.h; ++j) {
              for (i = 0; i < l.w; ++i) {
                  for (n = 0; n < l.n; ++n) {
                      int index = size*(j*l.w*l.n + i*l.n + n) + b*l.outputs;
                      box pred = get_region_box(l.output, l.biases, n, index, i, j, l.w, l.h);
                      float best_iou = 0;
                      int best_class_id = -1;
                      for(t = 0; t < l.max_boxes; ++t){
                          box truth = float_to_box(state.truth + t*l.truth_size + b*l.truths);
                          int class_id = state.truth[t * l.truth_size + b*l.truths + 4];
                          if (class_id >= l.classes) continue; // if label contains class_id more than number of classes in the cfg-file
                          if(!truth.x) break; // continue;
                          float iou = box_iou(pred, truth);
                          if (iou > best_iou) {
                              best_class_id = state.truth[t*l.truth_size + b*l.truths + 4];
                              best_iou = iou;
                          }
                      }
                      avg_anyobj += l.output[index + 4];
                      l.delta[index + 4] = l.noobject_scale * ((0 - l.output[index + 4]) * logistic_gradient(l.output[index + 4]));
                      if(l.classfix == -1) l.delta[index + 4] = l.noobject_scale * ((best_iou - l.output[index + 4]) * logistic_gradient(l.output[index + 4]));
                      else{
                          if (best_iou > l.thresh) {
                              l.delta[index + 4] = 0;
                              if(l.classfix > 0){
                                  delta_region_class(l.output, l.delta, index + 5, best_class_id, l.classes, l.softmax_tree, l.class_scale*(l.classfix == 2 ? l.output[index + 4] : 1), &avg_cat, l.focal_loss);
                                  ++class_count;
                              }
                          }
                      }
  
                      if(*(state.net.seen) < 12800){
                          box truth = {0};
                          truth.x = (i + .5)/l.w;
                          truth.y = (j + .5)/l.h;
                          truth.w = l.biases[2*n];
                          truth.h = l.biases[2*n+1];
                          if(DOABS){
                              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, index, i, j, l.w, l.h, l.delta, .01);
                      }
                  }
              }
          }
          for(t = 0; t < l.max_boxes; ++t){
              box truth = float_to_box(state.truth + t*l.truth_size + b*l.truths);
              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);
                  getchar();
                  continue; // if label contains class_id more than number of classes in the cfg-file
              }
  
              if(!truth.x) break; // continue;
              float best_iou = 0;
              int best_index = 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 index = size*(j*l.w*l.n + i*l.n + n) + b*l.outputs;
                  box pred = get_region_box(l.output, l.biases, n, index, i, j, l.w, l.h);
                  if(l.bias_match){
                      pred.w = l.biases[2*n];
                      pred.h = l.biases[2*n+1];
                      if(DOABS){
                          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_index = index;
                      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);
  
              float iou = delta_region_box(truth, l.output, l.biases, best_n, best_index, i, j, l.w, l.h, l.delta, l.coord_scale);
              if(iou > .5) recall += 1;
              avg_iou += iou;
  
              //l.delta[best_index + 4] = iou - l.output[best_index + 4];
              avg_obj += l.output[best_index + 4];
              l.delta[best_index + 4] = l.object_scale * (1 - l.output[best_index + 4]) * logistic_gradient(l.output[best_index + 4]);
              if (l.rescore) {
                  l.delta[best_index + 4] = l.object_scale * (iou - l.output[best_index + 4]) * logistic_gradient(l.output[best_index + 4]);
              }
  
              if (l.map) class_id = l.map[class_id];
              delta_region_class(l.output, l.delta, best_index + 5, class_id, l.classes, l.softmax_tree, l.class_scale, &avg_cat, l.focal_loss);
              ++count;
              ++class_count;
          }
      }
      //printf("\n");
      #ifndef GPU
      flatten(l.delta, l.w*l.h, size*l.n, l.batch, 0);
      #endif
      *(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 region_layer l, network_state state)
  {
      axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, state.delta, 1);
  }
  
  void get_region_boxes(layer l, int w, int h, float thresh, float **probs, box *boxes, int only_objectness, int *map)
  {
      int i;
      float *const predictions = l.output;
      #pragma omp parallel for
      for (i = 0; i < l.w*l.h; ++i){
          int j, n;
          int row = i / l.w;
          int col = i % l.w;
          for(n = 0; n < l.n; ++n){
              int index = i*l.n + n;
              int p_index = index * (l.classes + 5) + 4;
              float scale = predictions[p_index];
              if(l.classfix == -1 && scale < .5) scale = 0;
              int box_index = index * (l.classes + 5);
              boxes[index] = get_region_box(predictions, l.biases, n, box_index, col, row, l.w, l.h);
              boxes[index].x *= w;
              boxes[index].y *= h;
              boxes[index].w *= w;
              boxes[index].h *= h;
  
              int class_index = index * (l.classes + 5) + 5;
              if(l.softmax_tree){
  
                  hierarchy_predictions(predictions + class_index, l.classes, l.softmax_tree, 0);
                  int found = 0;
                  if(map){
                      for(j = 0; j < 200; ++j){
                          float prob = scale*predictions[class_index+map[j]];
                          probs[index][j] = (prob > thresh) ? prob : 0;
                      }
                  } else {
                      for(j = l.classes - 1; j >= 0; --j){
                          if(!found && predictions[class_index + j] > .5){
                              found = 1;
                          } else {
                              predictions[class_index + j] = 0;
                          }
                          float prob = predictions[class_index+j];
                          probs[index][j] = (scale > thresh) ? prob : 0;
                      }
                  }
              } else {
                  for(j = 0; j < l.classes; ++j){
                      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_region_layer_gpu(const region_layer l, network_state state)
  {
      /*
         if(!state.train){
         copy_ongpu(l.batch*l.inputs, state.input, 1, l.output_gpu, 1);
         return;
         }
       */
      flatten_ongpu(state.input, l.h*l.w, l.n*(l.coords + l.classes + 1), l.batch, 1, l.output_gpu);
      if(l.softmax_tree){
          int i;
          int count = 5;
          for (i = 0; i < l.softmax_tree->groups; ++i) {
              int group_size = l.softmax_tree->group_size[i];
              softmax_gpu(l.output_gpu+count, group_size, l.classes + 5, l.w*l.h*l.n*l.batch, 1, l.output_gpu + count);
              count += group_size;
          }
      }else if (l.softmax){
          softmax_gpu(l.output_gpu+5, l.classes, l.classes + 5, l.w*l.h*l.n*l.batch, 1, l.output_gpu + 5);
      }
  
      float* in_cpu = (float*)xcalloc(l.batch * l.inputs, sizeof(float));
      float *truth_cpu = 0;
      if(state.truth){
          int num_truth = l.batch*l.truths;
          truth_cpu = (float*)xcalloc(num_truth, sizeof(float));
          cuda_pull_array(state.truth, truth_cpu, num_truth);
      }
      cuda_pull_array(l.output_gpu, in_cpu, l.batch*l.inputs);
      //cudaStreamSynchronize(get_cuda_stream());
      network_state cpu_state = state;
      cpu_state.train = state.train;
      cpu_state.truth = truth_cpu;
      cpu_state.input = in_cpu;
      forward_region_layer(l, cpu_state);
      //cuda_push_array(l.output_gpu, l.output, l.batch*l.outputs);
      free(cpu_state.input);
      if(!state.train) return;
      cuda_push_array(l.delta_gpu, l.delta, l.batch*l.outputs);
      //cudaStreamSynchronize(get_cuda_stream());
      if(cpu_state.truth) free(cpu_state.truth);
  }
  
  void backward_region_layer_gpu(region_layer l, network_state state)
  {
      flatten_ongpu(l.delta_gpu, l.h*l.w, l.n*(l.coords + l.classes + 1), l.batch, 0, state.delta);
  }
  #endif
  
  
  void correct_region_boxes(detection *dets, 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 = 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;
      }
  }
  
  
  void get_region_detections(layer l, int w, int h, int netw, int neth, float thresh, int *map, float tree_thresh, int relative, detection *dets)
  {
      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) {
                  dets[index].prob[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];
              dets[index].bbox = get_region_box(predictions, l.biases, n, box_index, col, row, l.w, l.h);// , l.w*l.h);
              dets[index].objectness = scale > thresh ? scale : 0;
              if (dets[index].mask) {
                  for (j = 0; j < l.coords - 4; ++j) {
                      dets[index].mask[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];
                          dets[index].prob[j] = (prob > thresh) ? prob : 0;
                      }
                  }
                  else {
                      int j = hierarchy_top_prediction(predictions + class_index, l.softmax_tree, tree_thresh, l.w*l.h);
                      dets[index].prob[j] = (scale > thresh) ? scale : 0;
                  }
              }
              else {
                  if (dets[index].objectness) {
                      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];
                          dets[index].prob[j] = (prob > thresh) ? prob : 0;
                      }
                  }
              }
          }
      }
      correct_region_boxes(dets, l.w*l.h*l.n, w, h, netw, neth, relative);
  }
  
  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;
          }
      }
  }
  