text: #include "yolo_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_yolo_layer(int batch, int w, int h, int n, int total, int *mask, int classes, int max_boxes)
  {
      int i;
      layer l = {0};
      l.type = YOLO;
  
      l.n = n;
      l.total = total;
      l.batch = batch;
      l.h = h;
      l.w = w;
      l.c = n*(classes + 4 + 1);
      l.out_w = l.w;
      l.out_h = l.h;
      l.out_c = l.c;
      l.classes = classes;
      l.cost = calloc(1, sizeof(float));
      l.biases = calloc(total*2, sizeof(float));
      if(mask) l.mask = mask;
      else{
          l.mask = calloc(n, sizeof(int));
          for(i = 0; i < n; ++i){
              l.mask[i] = i;
          }
      }
      l.bias_updates = calloc(n*2, sizeof(float));
      l.outputs = h*w*n*(classes + 4 + 1);
      l.inputs = l.outputs;
      l.max_boxes = max_boxes;
      l.truths = l.max_boxes*(4 + 1);    // 90*(4 + 1);
      l.delta = calloc(batch*l.outputs, sizeof(float));
      l.output = calloc(batch*l.outputs, sizeof(float));
      for(i = 0; i < total*2; ++i){
          l.biases[i] = .5;
      }
  
      l.forward = forward_yolo_layer;
      l.backward = backward_yolo_layer;
  #ifdef GPU
      l.forward_gpu = forward_yolo_layer_gpu;
      l.backward_gpu = backward_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 = 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 = calloc(batch*l.outputs, sizeof(float));
      }
  #endif
  
      fprintf(stderr, "yolo\n");
      srand(0);
  
      return l;
  }
  
  void resize_yolo_layer(layer *l, int w, int h)
  {
      l->w = w;
      l->h = h;
  
      l->outputs = h*w*l->n*(l->classes + 4 + 1);
      l->inputs = l->outputs;
  
      if (!l->output_pinned) l->output = realloc(l->output, l->batch*l->outputs * sizeof(float));
      if (!l->delta_pinned) l->delta = 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 = 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 = 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_yolo_box(float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, int stride)
  {
      box b;
      // ln - natural logarithm (base = e)
      // x` = t.x * lw - i;   // x = ln(x`/(1-x`))   // x - output of previous conv-layer
      // y` = t.y * lh - i;   // y = ln(y`/(1-y`))   // y - output of previous conv-layer
                              // w = ln(t.w * net.w / anchors_w); // w - output of previous conv-layer
                              // h = ln(t.h * net.h / anchors_h); // h - output of previous conv-layer
      b.x = (i + x[index + 0*stride]) / lw;
      b.y = (j + x[index + 1*stride]) / lh;
      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_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)
  {
      box pred = get_yolo_box(x, biases, n, index, i, j, lw, lh, w, h, stride);
      float iou = box_iou(pred, truth);
  
      float tx = (truth.x*lw - i);
      float ty = (truth.y*lh - 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_yolo_class(float *output, float *delta, int index, int class_id, int classes, int stride, float *avg_cat, int focal_loss)
  {
      int n;
      if (delta[index + stride*class_id]){
          delta[index + stride*class_id] = 1 - output[index + stride*class_id];
          if(avg_cat) *avg_cat += output[index + stride*class_id];
          return;
      }
      // 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 + stride*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 + stride*n] = (((n == class_id) ? 1 : 0) - output[index + stride*n]);
  
              delta[index + stride*n] *= alpha*grad;
  
              if (n == class_id) *avg_cat += output[index + stride*n];
          }
      }
      else {
          // default
          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];
          }
      }
  }
  
  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*(4+l.classes+1) + entry*l.w*l.h + loc;
  }
  
  static box float_to_box_stride(float *f, int stride)
  {
      box b = { 0 };
      b.x = f[0];
      b.y = f[1 * stride];
      b.w = f[2 * stride];
      b.h = f[3 * stride];
      return b;
  }
  
  void forward_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){
              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, 4);
              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_index(l, b, n*l.w*l.h + j*l.w + i, 0);
                      box pred = get_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);
                      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);
                              getchar();
                              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_iou = iou;
                              best_t = t;
                          }
                      }
                      int obj_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4);
                      avg_anyobj += l.output[obj_index];
                      l.delta[obj_index] = 0 - l.output[obj_index];
                      if (best_iou > l.ignore_thresh) {
                          l.delta[obj_index] = 0;
                      }
                      if (best_iou > l.truth_thresh) {
                          l.delta[obj_index] = 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_index(l, b, n*l.w*l.h + j*l.w + i, 4 + 1);
                          delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0, l.focal_loss);
                          box truth = float_to_box_stride(state.truth + best_t*(4 + 1) + b*l.truths, 1);
                          delta_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);
                      }
                  }
              }
          }
          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) 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_n = 0;
              i = (truth.x * l.w);
              j = (truth.y * 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_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0);
                  float iou = delta_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);
  
                  int obj_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4);
                  avg_obj += l.output[obj_index];
                  l.delta[obj_index] = 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_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4 + 1);
                  delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.focal_loss);
  
                  ++count;
                  ++class_count;
                  if(iou > .5) recall += 1;
                  if(iou > .75) recall75 += 1;
                  avg_iou += iou;
              }
          }
      }
      *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2);
      printf("Region %d Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f,  count: %d\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);
  }
  
  void backward_yolo_layer(const layer l, network_state state)
  {
     axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, state.delta, 1);
  }
  
  void correct_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;
      }
      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 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_index(l, 0, n*l.w*l.h + i, 4);
              if(l.output[obj_index] > thresh){
                  ++count;
              }
          }
      }
      return count;
  }
  
  void avg_flipped_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 + 4 + 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_yolo_detections(layer l, int w, int h, int netw, int neth, float thresh, int *map, int relative, detection *dets, int letter)
  {
      //printf("\n l.batch = %d, l.w = %d, l.h = %d, l.n = %d \n", l.batch, l.w, l.h, l.n);
      int i,j,n;
      float *predictions = l.output;
      if (l.batch == 2) avg_flipped_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_index(l, 0, n*l.w*l.h + i, 4);
              float objectness = predictions[obj_index];
              //if(objectness <= thresh) continue;    // incorrect behavior for Nan values
              if (objectness > thresh) {
                  //printf("\n objectness = %f, thresh = %f, i = %d, n = %d \n", objectness, thresh, i, n);
                  int box_index = entry_index(l, 0, n*l.w*l.h + i, 0);
                  dets[count].bbox = get_yolo_box(predictions, l.biases, l.mask[n], box_index, col, row, l.w, l.h, netw, neth, l.w*l.h);
                  dets[count].objectness = objectness;
                  dets[count].classes = l.classes;
                  for (j = 0; j < l.classes; ++j) {
                      int class_index = entry_index(l, 0, n*l.w*l.h + i, 4 + 1 + j);
                      float prob = objectness*predictions[class_index];
                      dets[count].prob[j] = (prob > thresh) ? prob : 0;
                  }
                  ++count;
              }
          }
      }
      correct_yolo_boxes(dets, count, w, h, netw, neth, relative, letter);
      return count;
  }
  
  #ifdef GPU
  
  void forward_yolo_layer_gpu(const layer l, network_state state)
  {
      //copy_ongpu(l.batch*l.inputs, state.input, 1, l.output_gpu, 1);
      simple_copy_ongpu(l.batch*l.inputs, state.input, l.output_gpu);
      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);
              // y = 1./(1. + exp(-x))
              // x = ln(y/(1-y))  // ln - natural logarithm (base = e)
              // if(y->1) x -> inf
              // if(y->0) x -> -inf
              activate_array_ongpu(l.output_gpu + index, 2*l.w*l.h, LOGISTIC);    // x,y
              index = entry_index(l, b, n*l.w*l.h, 4);
              activate_array_ongpu(l.output_gpu + index, (1+l.classes)*l.w*l.h, LOGISTIC); // classes and objectness
          }
      }
      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);
          return;
      }
  
      float *in_cpu = 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 = 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_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_yolo_layer_gpu(const layer l, network_state state)
  {
      axpy_ongpu(l.batch*l.inputs, 1, l.delta_gpu, 1, state.delta, 1);
  }
  #endif
  
  