text: #include <stdio.h>
  #include <string.h>
  #include <stdlib.h>
  #include <stdint.h>
  
  #include "activation_layer.h"
  #include "activations.h"
  #include "assert.h"
  #include "avgpool_layer.h"
  #include "batchnorm_layer.h"
  #include "blas.h"
  #include "connected_layer.h"
  #include "convolutional_layer.h"
  #include "cost_layer.h"
  #include "crnn_layer.h"
  #include "crop_layer.h"
  #include "detection_layer.h"
  #include "dropout_layer.h"
  #include "gru_layer.h"
  #include "list.h"
  #include "local_layer.h"
  #include "lstm_layer.h"
  #include "conv_lstm_layer.h"
  #include "maxpool_layer.h"
  #include "normalization_layer.h"
  #include "option_list.h"
  #include "parser.h"
  #include "region_layer.h"
  #include "reorg_layer.h"
  #include "reorg_old_layer.h"
  #include "rnn_layer.h"
  #include "route_layer.h"
  #include "shortcut_layer.h"
  #include "scale_channels_layer.h"
  #include "sam_layer.h"
  #include "softmax_layer.h"
  #include "utils.h"
  #include "upsample_layer.h"
  #include "version.h"
  #include "yolo_layer.h"
  #include "gaussian_yolo_layer.h"
  
  typedef struct{
      char *type;
      list *options;
  }section;
  
  list *read_cfg(char *filename);
  
  LAYER_TYPE string_to_layer_type(char * type)
  {
  
      if (strcmp(type, "[shortcut]")==0) return SHORTCUT;
      if (strcmp(type, "[scale_channels]") == 0) return SCALE_CHANNELS;
      if (strcmp(type, "[sam]") == 0) return SAM;
      if (strcmp(type, "[crop]")==0) return CROP;
      if (strcmp(type, "[cost]")==0) return COST;
      if (strcmp(type, "[detection]")==0) return DETECTION;
      if (strcmp(type, "[region]")==0) return REGION;
      if (strcmp(type, "[yolo]") == 0) return YOLO;
      if (strcmp(type, "[Gaussian_yolo]") == 0) return GAUSSIAN_YOLO;
      if (strcmp(type, "[local]")==0) return LOCAL;
      if (strcmp(type, "[conv]")==0
              || strcmp(type, "[convolutional]")==0) return CONVOLUTIONAL;
      if (strcmp(type, "[activation]")==0) return ACTIVE;
      if (strcmp(type, "[net]")==0
              || strcmp(type, "[network]")==0) return NETWORK;
      if (strcmp(type, "[crnn]")==0) return CRNN;
      if (strcmp(type, "[gru]")==0) return GRU;
      if (strcmp(type, "[lstm]")==0) return LSTM;
      if (strcmp(type, "[conv_lstm]") == 0) return CONV_LSTM;
      if (strcmp(type, "[rnn]")==0) return RNN;
      if (strcmp(type, "[conn]")==0
              || strcmp(type, "[connected]")==0) return CONNECTED;
      if (strcmp(type, "[max]")==0
              || strcmp(type, "[maxpool]")==0) return MAXPOOL;
      if (strcmp(type, "[local_avg]") == 0
          || strcmp(type, "[local_avgpool]") == 0) return LOCAL_AVGPOOL;
      if (strcmp(type, "[reorg3d]")==0) return REORG;
      if (strcmp(type, "[reorg]") == 0) return REORG_OLD;
      if (strcmp(type, "[avg]")==0
              || strcmp(type, "[avgpool]")==0) return AVGPOOL;
      if (strcmp(type, "[dropout]")==0) return DROPOUT;
      if (strcmp(type, "[lrn]")==0
              || strcmp(type, "[normalization]")==0) return NORMALIZATION;
      if (strcmp(type, "[batchnorm]")==0) return BATCHNORM;
      if (strcmp(type, "[soft]")==0
              || strcmp(type, "[softmax]")==0) return SOFTMAX;
      if (strcmp(type, "[route]")==0) return ROUTE;
      if (strcmp(type, "[upsample]") == 0) return UPSAMPLE;
      if (strcmp(type, "[empty]") == 0) return EMPTY;
      return BLANK;
  }
  
  void free_section(section *s)
  {
      free(s->type);
      node *n = s->options->front;
      while(n){
          kvp *pair = (kvp *)n->val;
          free(pair->key);
          free(pair);
          node *next = n->next;
          free(n);
          n = next;
      }
      free(s->options);
      free(s);
  }
  
  void parse_data(char *data, float *a, int n)
  {
      int i;
      if(!data) return;
      char *curr = data;
      char *next = data;
      int done = 0;
      for(i = 0; i < n && !done; ++i){
          while(*++next !='\0' && *next != ',');
          if(*next == '\0') done = 1;
          *next = '\0';
          sscanf(curr, "%g", &a[i]);
          curr = next+1;
      }
  }
  
  typedef struct size_params{
      int batch;
      int inputs;
      int h;
      int w;
      int c;
      int index;
      int time_steps;
      int train;
      network net;
  } size_params;
  
  local_layer parse_local(list *options, size_params params)
  {
      int n = option_find_int(options, "filters",1);
      int size = option_find_int(options, "size",1);
      int stride = option_find_int(options, "stride",1);
      int pad = option_find_int(options, "pad",0);
      char *activation_s = option_find_str(options, "activation", "logistic");
      ACTIVATION activation = get_activation(activation_s);
  
      int batch,h,w,c;
      h = params.h;
      w = params.w;
      c = params.c;
      batch=params.batch;
      if(!(h && w && c)) error("Layer before local layer must output image.");
  
      local_layer layer = make_local_layer(batch,h,w,c,n,size,stride,pad,activation);
  
      return layer;
  }
  
  convolutional_layer parse_convolutional(list *options, size_params params)
  {
      int n = option_find_int(options, "filters",1);
      int groups = option_find_int_quiet(options, "groups", 1);
      int size = option_find_int(options, "size",1);
      int stride = -1;
      //int stride = option_find_int(options, "stride",1);
      int stride_x = option_find_int_quiet(options, "stride_x", -1);
      int stride_y = option_find_int_quiet(options, "stride_y", -1);
      if (stride_x < 1 || stride_y < 1) {
          stride = option_find_int(options, "stride", 1);
          if (stride_x < 1) stride_x = stride;
          if (stride_y < 1) stride_y = stride;
      }
      else {
          stride = option_find_int_quiet(options, "stride", 1);
      }
      int dilation = option_find_int_quiet(options, "dilation", 1);
      int antialiasing = option_find_int_quiet(options, "antialiasing", 0);
      if (size == 1) dilation = 1;
      int pad = option_find_int_quiet(options, "pad",0);
      int padding = option_find_int_quiet(options, "padding",0);
      if(pad) padding = size/2;
  
      char *activation_s = option_find_str(options, "activation", "logistic");
      ACTIVATION activation = get_activation(activation_s);
  
      int assisted_excitation = option_find_float_quiet(options, "assisted_excitation", 0);
  
      int share_index = option_find_int_quiet(options, "share_index", -1000000000);
      convolutional_layer *share_layer = NULL;
      if(share_index >= 0) share_layer = &params.net.layers[share_index];
      else if(share_index != -1000000000) share_layer = &params.net.layers[params.index + share_index];
  
      int batch,h,w,c;
      h = params.h;
      w = params.w;
      c = params.c;
      batch=params.batch;
      if(!(h && w && c)) error("Layer before convolutional layer must output image.");
      int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
      int cbn = option_find_int_quiet(options, "cbn", 0);
      if (cbn) batch_normalize = 2;
      int binary = option_find_int_quiet(options, "binary", 0);
      int xnor = option_find_int_quiet(options, "xnor", 0);
      int use_bin_output = option_find_int_quiet(options, "bin_output", 0);
      int sway = option_find_int_quiet(options, "sway", 0);
      int rotate = option_find_int_quiet(options, "rotate", 0);
      int stretch = option_find_int_quiet(options, "stretch", 0);
      int stretch_sway = option_find_int_quiet(options, "stretch_sway", 0);
      if ((sway + rotate + stretch + stretch_sway) > 1) {
          printf(" Error: should be used only 1 param: sway=1, rotate=1 or stretch=1 in the [convolutional] layer \n");
          exit(0);
      }
      int deform = sway || rotate || stretch || stretch_sway;
      if (deform && size == 1) {
          printf(" Error: params (sway=1, rotate=1 or stretch=1) should be used only with size >=3 in the [convolutional] layer \n");
          exit(0);
      }
  
      convolutional_layer layer = make_convolutional_layer(batch,1,h,w,c,n,groups,size,stride_x,stride_y,dilation,padding,activation, batch_normalize, binary, xnor, params.net.adam, use_bin_output, params.index, antialiasing, share_layer, assisted_excitation, deform, params.train);
      layer.flipped = option_find_int_quiet(options, "flipped", 0);
      layer.dot = option_find_float_quiet(options, "dot", 0);
      layer.sway = sway;
      layer.rotate = rotate;
      layer.stretch = stretch;
      layer.stretch_sway = stretch_sway;
      layer.angle = option_find_float_quiet(options, "angle", 15);
  
      if(params.net.adam){
          layer.B1 = params.net.B1;
          layer.B2 = params.net.B2;
          layer.eps = params.net.eps;
      }
  
      return layer;
  }
  
  layer parse_crnn(list *options, size_params params)
  {
      int size = option_find_int_quiet(options, "size", 3);
      int stride = option_find_int_quiet(options, "stride", 1);
      int dilation = option_find_int_quiet(options, "dilation", 1);
      int pad = option_find_int_quiet(options, "pad", 0);
      int padding = option_find_int_quiet(options, "padding", 0);
      if (pad) padding = size / 2;
  
      int output_filters = option_find_int(options, "output",1);
      int hidden_filters = option_find_int(options, "hidden",1);
      int groups = option_find_int_quiet(options, "groups", 1);
      char *activation_s = option_find_str(options, "activation", "logistic");
      ACTIVATION activation = get_activation(activation_s);
      int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
      int xnor = option_find_int_quiet(options, "xnor", 0);
  
      layer l = make_crnn_layer(params.batch, params.h, params.w, params.c, hidden_filters, output_filters, groups, params.time_steps, size, stride, dilation, padding, activation, batch_normalize, xnor, params.train);
  
      l.shortcut = option_find_int_quiet(options, "shortcut", 0);
  
      return l;
  }
  
  layer parse_rnn(list *options, size_params params)
  {
      int output = option_find_int(options, "output",1);
      int hidden = option_find_int(options, "hidden",1);
      char *activation_s = option_find_str(options, "activation", "logistic");
      ACTIVATION activation = get_activation(activation_s);
      int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
      int logistic = option_find_int_quiet(options, "logistic", 0);
  
      layer l = make_rnn_layer(params.batch, params.inputs, hidden, output, params.time_steps, activation, batch_normalize, logistic);
  
      l.shortcut = option_find_int_quiet(options, "shortcut", 0);
  
      return l;
  }
  
  layer parse_gru(list *options, size_params params)
  {
      int output = option_find_int(options, "output",1);
      int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
  
      layer l = make_gru_layer(params.batch, params.inputs, output, params.time_steps, batch_normalize);
  
      return l;
  }
  
  layer parse_lstm(list *options, size_params params)
  {
      int output = option_find_int(options, "output",1);
      int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
  
      layer l = make_lstm_layer(params.batch, params.inputs, output, params.time_steps, batch_normalize);
  
      return l;
  }
  
  layer parse_conv_lstm(list *options, size_params params)
  {
      // a ConvLSTM with a larger transitional kernel should be able to capture faster motions
      int size = option_find_int_quiet(options, "size", 3);
      int stride = option_find_int_quiet(options, "stride", 1);
      int dilation = option_find_int_quiet(options, "dilation", 1);
      int pad = option_find_int_quiet(options, "pad", 0);
      int padding = option_find_int_quiet(options, "padding", 0);
      if (pad) padding = size / 2;
  
      int output_filters = option_find_int(options, "output", 1);
      int groups = option_find_int_quiet(options, "groups", 1);
      char *activation_s = option_find_str(options, "activation", "LINEAR");
      ACTIVATION activation = get_activation(activation_s);
      int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
      int xnor = option_find_int_quiet(options, "xnor", 0);
      int peephole = option_find_int_quiet(options, "peephole", 0);
  
      layer l = make_conv_lstm_layer(params.batch, params.h, params.w, params.c, output_filters, groups, params.time_steps, size, stride, dilation, padding, activation, batch_normalize, peephole, xnor, params.train);
  
      l.state_constrain = option_find_int_quiet(options, "state_constrain", params.time_steps * 32);
      l.shortcut = option_find_int_quiet(options, "shortcut", 0);
  
      return l;
  }
  
  connected_layer parse_connected(list *options, size_params params)
  {
      int output = option_find_int(options, "output",1);
      char *activation_s = option_find_str(options, "activation", "logistic");
      ACTIVATION activation = get_activation(activation_s);
      int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
  
      connected_layer layer = make_connected_layer(params.batch, 1, params.inputs, output, activation, batch_normalize);
  
      return layer;
  }
  
  softmax_layer parse_softmax(list *options, size_params params)
  {
  	int groups = option_find_int_quiet(options, "groups", 1);
  	softmax_layer layer = make_softmax_layer(params.batch, params.inputs, groups);
  	layer.temperature = option_find_float_quiet(options, "temperature", 1);
  	char *tree_file = option_find_str(options, "tree", 0);
  	if (tree_file) layer.softmax_tree = read_tree(tree_file);
  	layer.w = params.w;
  	layer.h = params.h;
  	layer.c = params.c;
  	layer.spatial = option_find_float_quiet(options, "spatial", 0);
  	layer.noloss = option_find_int_quiet(options, "noloss", 0);
  	return layer;
  }
  
  int *parse_yolo_mask(char *a, int *num)
  {
      int *mask = 0;
      if (a) {
          int len = strlen(a);
          int n = 1;
          int i;
          for (i = 0; i < len; ++i) {
              if (a[i] == ',') ++n;
          }
          mask = (int*)xcalloc(n, sizeof(int));
          for (i = 0; i < n; ++i) {
              int val = atoi(a);
              mask[i] = val;
              a = strchr(a, ',') + 1;
          }
          *num = n;
      }
      return mask;
  }
  
  float *get_classes_multipliers(char *cpc, const int classes)
  {
      float *classes_multipliers = NULL;
      if (cpc) {
          int classes_counters = classes;
          int *counters_per_class = parse_yolo_mask(cpc, &classes_counters);
          if (classes_counters != classes) {
              printf(" number of values in counters_per_class = %d doesn't match with classes = %d \n", classes_counters, classes);
              exit(0);
          }
          float max_counter = 0;
          int i;
          for (i = 0; i < classes_counters; ++i) if (max_counter < counters_per_class[i]) max_counter = counters_per_class[i];
          classes_multipliers = (float *)calloc(classes_counters, sizeof(float));
          for (i = 0; i < classes_counters; ++i) classes_multipliers[i] = max_counter / counters_per_class[i];
          free(counters_per_class);
          printf(" classes_multipliers: ");
          for (i = 0; i < classes_counters; ++i) printf("%.1f, ", classes_multipliers[i]);
          printf("\n");
      }
      return classes_multipliers;
  }
  
  layer parse_yolo(list *options, size_params params)
  {
      int classes = option_find_int(options, "classes", 20);
      int total = option_find_int(options, "num", 1);
      int num = total;
      char *a = option_find_str(options, "mask", 0);
      int *mask = parse_yolo_mask(a, &num);
      int max_boxes = option_find_int_quiet(options, "max", 90);
      layer l = make_yolo_layer(params.batch, params.w, params.h, num, total, mask, classes, max_boxes);
      if (l.outputs != params.inputs) {
          printf("Error: l.outputs == params.inputs \n");
          printf("filters= in the [convolutional]-layer doesn't correspond to classes= or mask= in [yolo]-layer \n");
          exit(EXIT_FAILURE);
      }
      //assert(l.outputs == params.inputs);
  
      char *cpc = option_find_str(options, "counters_per_class", 0);
      l.classes_multipliers = get_classes_multipliers(cpc, classes);
  
      l.label_smooth_eps = option_find_float_quiet(options, "label_smooth_eps", 0.0f);
      l.scale_x_y = option_find_float_quiet(options, "scale_x_y", 1);
      l.objectness_smooth = option_find_int_quiet(options, "objectness_smooth", 0);
      l.max_delta = option_find_float_quiet(options, "max_delta", FLT_MAX);   // set 10
      l.iou_normalizer = option_find_float_quiet(options, "iou_normalizer", 0.75);
      l.cls_normalizer = option_find_float_quiet(options, "cls_normalizer", 1);
      char *iou_loss = option_find_str_quiet(options, "iou_loss", "mse");   //  "iou");
  
      if (strcmp(iou_loss, "mse") == 0) l.iou_loss = MSE;
      else if (strcmp(iou_loss, "giou") == 0) l.iou_loss = GIOU;
      else if (strcmp(iou_loss, "diou") == 0) l.iou_loss = DIOU;
      else if (strcmp(iou_loss, "ciou") == 0) l.iou_loss = CIOU;
      else l.iou_loss = IOU;
      fprintf(stderr, "[yolo] params: iou loss: %s (%d), iou_norm: %2.2f, cls_norm: %2.2f, scale_x_y: %2.2f\n",
          iou_loss, l.iou_loss, l.iou_normalizer, l.cls_normalizer, l.scale_x_y);
  
      char *iou_thresh_kind_str = option_find_str_quiet(options, "iou_thresh_kind", "iou");
      if (strcmp(iou_thresh_kind_str, "iou") == 0) l.iou_thresh_kind = IOU;
      else if (strcmp(iou_thresh_kind_str, "giou") == 0) l.iou_thresh_kind = GIOU;
      else if (strcmp(iou_thresh_kind_str, "diou") == 0) l.iou_thresh_kind = DIOU;
      else if (strcmp(iou_thresh_kind_str, "ciou") == 0) l.iou_thresh_kind = CIOU;
      else {
          fprintf(stderr, " Wrong iou_thresh_kind = %s \n", iou_thresh_kind_str);
          l.iou_thresh_kind = IOU;
      }
  
      l.beta_nms = option_find_float_quiet(options, "beta_nms", 0.6);
      char *nms_kind = option_find_str_quiet(options, "nms_kind", "default");
      if (strcmp(nms_kind, "default") == 0) l.nms_kind = DEFAULT_NMS;
      else {
          if (strcmp(nms_kind, "greedynms") == 0) l.nms_kind = GREEDY_NMS;
          else if (strcmp(nms_kind, "diounms") == 0) l.nms_kind = DIOU_NMS;
          else l.nms_kind = DEFAULT_NMS;
          printf("nms_kind: %s (%d), beta = %f \n", nms_kind, l.nms_kind, l.beta_nms);
      }
  
      l.jitter = option_find_float(options, "jitter", .2);
      l.focal_loss = option_find_int_quiet(options, "focal_loss", 0);
  
      l.ignore_thresh = option_find_float(options, "ignore_thresh", .5);
      l.truth_thresh = option_find_float(options, "truth_thresh", 1);
      l.iou_thresh = option_find_float_quiet(options, "iou_thresh", 1); // recommended to use iou_thresh=0.213 in [yolo]
      l.random = option_find_float_quiet(options, "random", 0);
  
      char *map_file = option_find_str(options, "map", 0);
      if (map_file) l.map = read_map(map_file);
  
      a = option_find_str(options, "anchors", 0);
      if (a) {
          int len = strlen(a);
          int n = 1;
          int i;
          for (i = 0; i < len; ++i) {
              if (a[i] == ',') ++n;
          }
          for (i = 0; i < n && i < total*2; ++i) {
              float bias = atof(a);
              l.biases[i] = bias;
              a = strchr(a, ',') + 1;
          }
      }
      return l;
  }
  
  
  int *parse_gaussian_yolo_mask(char *a, int *num) // Gaussian_YOLOv3
  {
      int *mask = 0;
      if (a) {
          int len = strlen(a);
          int n = 1;
          int i;
          for (i = 0; i < len; ++i) {
              if (a[i] == ',') ++n;
          }
          mask = (int *)calloc(n, sizeof(int));
          for (i = 0; i < n; ++i) {
              int val = atoi(a);
              mask[i] = val;
              a = strchr(a, ',') + 1;
          }
          *num = n;
      }
      return mask;
  }
  
  
  layer parse_gaussian_yolo(list *options, size_params params) // Gaussian_YOLOv3
  {
      int classes = option_find_int(options, "classes", 20);
      int max_boxes = option_find_int_quiet(options, "max", 90);
      int total = option_find_int(options, "num", 1);
      int num = total;
  
      char *a = option_find_str(options, "mask", 0);
      int *mask = parse_gaussian_yolo_mask(a, &num);
      layer l = make_gaussian_yolo_layer(params.batch, params.w, params.h, num, total, mask, classes, max_boxes);
      if (l.outputs != params.inputs) {
          printf("Error: l.outputs == params.inputs \n");
          printf("filters= in the [convolutional]-layer doesn't correspond to classes= or mask= in [Gaussian_yolo]-layer \n");
          exit(EXIT_FAILURE);
      }
      //assert(l.outputs == params.inputs);
  
      char *cpc = option_find_str(options, "counters_per_class", 0);
      l.classes_multipliers = get_classes_multipliers(cpc, classes);
  
      l.label_smooth_eps = option_find_float_quiet(options, "label_smooth_eps", 0.0f);
      l.scale_x_y = option_find_float_quiet(options, "scale_x_y", 1);
      l.objectness_smooth = option_find_int_quiet(options, "objectness_smooth", 0);
      l.max_delta = option_find_float_quiet(options, "max_delta", FLT_MAX);   // set 10
      l.uc_normalizer = option_find_float_quiet(options, "uc_normalizer", 1.0);
      l.iou_normalizer = option_find_float_quiet(options, "iou_normalizer", 0.75);
      l.cls_normalizer = option_find_float_quiet(options, "cls_normalizer", 1.0);
      char *iou_loss = option_find_str_quiet(options, "iou_loss", "mse");   //  "iou");
  
      if (strcmp(iou_loss, "mse") == 0) l.iou_loss = MSE;
      else if (strcmp(iou_loss, "giou") == 0) l.iou_loss = GIOU;
      else if (strcmp(iou_loss, "diou") == 0) l.iou_loss = DIOU;
      else if (strcmp(iou_loss, "ciou") == 0) l.iou_loss = CIOU;
      else l.iou_loss = IOU;
  
      char *iou_thresh_kind_str = option_find_str_quiet(options, "iou_thresh_kind", "iou");
      if (strcmp(iou_thresh_kind_str, "iou") == 0) l.iou_thresh_kind = IOU;
      else if (strcmp(iou_thresh_kind_str, "giou") == 0) l.iou_thresh_kind = GIOU;
      else if (strcmp(iou_thresh_kind_str, "diou") == 0) l.iou_thresh_kind = DIOU;
      else if (strcmp(iou_thresh_kind_str, "ciou") == 0) l.iou_thresh_kind = CIOU;
      else {
          fprintf(stderr, " Wrong iou_thresh_kind = %s \n", iou_thresh_kind_str);
          l.iou_thresh_kind = IOU;
      }
  
      l.beta_nms = option_find_float_quiet(options, "beta_nms", 0.6);
      char *nms_kind = option_find_str_quiet(options, "nms_kind", "default");
      if (strcmp(nms_kind, "default") == 0) l.nms_kind = DEFAULT_NMS;
      else {
          if (strcmp(nms_kind, "greedynms") == 0) l.nms_kind = GREEDY_NMS;
          else if (strcmp(nms_kind, "diounms") == 0) l.nms_kind = DIOU_NMS;
          else if (strcmp(nms_kind, "cornersnms") == 0) l.nms_kind = CORNERS_NMS;
          else l.nms_kind = DEFAULT_NMS;
          printf("nms_kind: %s (%d), beta = %f \n", nms_kind, l.nms_kind, l.beta_nms);
      }
  
      char *yolo_point = option_find_str_quiet(options, "yolo_point", "center");
      if (strcmp(yolo_point, "left_top") == 0) l.yolo_point = YOLO_LEFT_TOP;
      else if (strcmp(yolo_point, "right_bottom") == 0) l.yolo_point = YOLO_RIGHT_BOTTOM;
      else l.yolo_point = YOLO_CENTER;
  
      fprintf(stderr, "[Gaussian_yolo] iou loss: %s (%d), iou_norm: %2.2f, cls_norm: %2.2f, scale: %2.2f, point: %d\n",
          iou_loss, l.iou_loss, l.iou_normalizer, l.cls_normalizer, l.scale_x_y, l.yolo_point);
  
      l.jitter = option_find_float(options, "jitter", .2);
  
      l.ignore_thresh = option_find_float(options, "ignore_thresh", .5);
      l.truth_thresh = option_find_float(options, "truth_thresh", 1);
      l.iou_thresh = option_find_float_quiet(options, "iou_thresh", 1); // recommended to use iou_thresh=0.213 in [yolo]
      l.random = option_find_float_quiet(options, "random", 0);
  
      char *map_file = option_find_str(options, "map", 0);
      if (map_file) l.map = read_map(map_file);
  
      a = option_find_str(options, "anchors", 0);
      if (a) {
          int len = strlen(a);
          int n = 1;
          int i;
          for (i = 0; i < len; ++i) {
              if (a[i] == ',') ++n;
          }
          for (i = 0; i < n; ++i) {
              float bias = atof(a);
              l.biases[i] = bias;
              a = strchr(a, ',') + 1;
          }
      }
      return l;
  }
  
  layer parse_region(list *options, size_params params)
  {
      int coords = option_find_int(options, "coords", 4);
      int classes = option_find_int(options, "classes", 20);
      int num = option_find_int(options, "num", 1);
      int max_boxes = option_find_int_quiet(options, "max", 90);
  
      layer l = make_region_layer(params.batch, params.w, params.h, num, classes, coords, max_boxes);
      if (l.outputs != params.inputs) {
          printf("Error: l.outputs == params.inputs \n");
          printf("filters= in the [convolutional]-layer doesn't correspond to classes= or num= in [region]-layer \n");
          exit(EXIT_FAILURE);
      }
      //assert(l.outputs == params.inputs);
  
      l.log = option_find_int_quiet(options, "log", 0);
      l.sqrt = option_find_int_quiet(options, "sqrt", 0);
  
      l.softmax = option_find_int(options, "softmax", 0);
      l.focal_loss = option_find_int_quiet(options, "focal_loss", 0);
      //l.max_boxes = option_find_int_quiet(options, "max",30);
      l.jitter = option_find_float(options, "jitter", .2);
      l.rescore = option_find_int_quiet(options, "rescore",0);
  
      l.thresh = option_find_float(options, "thresh", .5);
      l.classfix = option_find_int_quiet(options, "classfix", 0);
      l.absolute = option_find_int_quiet(options, "absolute", 0);
      l.random = option_find_float_quiet(options, "random", 0);
  
      l.coord_scale = option_find_float(options, "coord_scale", 1);
      l.object_scale = option_find_float(options, "object_scale", 1);
      l.noobject_scale = option_find_float(options, "noobject_scale", 1);
      l.mask_scale = option_find_float(options, "mask_scale", 1);
      l.class_scale = option_find_float(options, "class_scale", 1);
      l.bias_match = option_find_int_quiet(options, "bias_match",0);
  
      char *tree_file = option_find_str(options, "tree", 0);
      if (tree_file) l.softmax_tree = read_tree(tree_file);
      char *map_file = option_find_str(options, "map", 0);
      if (map_file) l.map = read_map(map_file);
  
      char *a = option_find_str(options, "anchors", 0);
      if(a){
          int len = strlen(a);
          int n = 1;
          int i;
          for(i = 0; i < len; ++i){
              if (a[i] == ',') ++n;
          }
          for(i = 0; i < n && i < num*2; ++i){
              float bias = atof(a);
              l.biases[i] = bias;
              a = strchr(a, ',')+1;
          }
      }
      return l;
  }
  detection_layer parse_detection(list *options, size_params params)
  {
      int coords = option_find_int(options, "coords", 1);
      int classes = option_find_int(options, "classes", 1);
      int rescore = option_find_int(options, "rescore", 0);
      int num = option_find_int(options, "num", 1);
      int side = option_find_int(options, "side", 7);
      detection_layer layer = make_detection_layer(params.batch, params.inputs, num, side, classes, coords, rescore);
  
      layer.softmax = option_find_int(options, "softmax", 0);
      layer.sqrt = option_find_int(options, "sqrt", 0);
  
      layer.max_boxes = option_find_int_quiet(options, "max",30);
      layer.coord_scale = option_find_float(options, "coord_scale", 1);
      layer.forced = option_find_int(options, "forced", 0);
      layer.object_scale = option_find_float(options, "object_scale", 1);
      layer.noobject_scale = option_find_float(options, "noobject_scale", 1);
      layer.class_scale = option_find_float(options, "class_scale", 1);
      layer.jitter = option_find_float(options, "jitter", .2);
      layer.random = option_find_float_quiet(options, "random", 0);
      layer.reorg = option_find_int_quiet(options, "reorg", 0);
      return layer;
  }
  
  cost_layer parse_cost(list *options, size_params params)
  {
      char *type_s = option_find_str(options, "type", "sse");
      COST_TYPE type = get_cost_type(type_s);
      float scale = option_find_float_quiet(options, "scale",1);
      cost_layer layer = make_cost_layer(params.batch, params.inputs, type, scale);
      layer.ratio =  option_find_float_quiet(options, "ratio",0);
      return layer;
  }
  
  crop_layer parse_crop(list *options, size_params params)
  {
      int crop_height = option_find_int(options, "crop_height",1);
      int crop_width = option_find_int(options, "crop_width",1);
      int flip = option_find_int(options, "flip",0);
      float angle = option_find_float(options, "angle",0);
      float saturation = option_find_float(options, "saturation",1);
      float exposure = option_find_float(options, "exposure",1);
  
      int batch,h,w,c;
      h = params.h;
      w = params.w;
      c = params.c;
      batch=params.batch;
      if(!(h && w && c)) error("Layer before crop layer must output image.");
  
      int noadjust = option_find_int_quiet(options, "noadjust",0);
  
      crop_layer l = make_crop_layer(batch,h,w,c,crop_height,crop_width,flip, angle, saturation, exposure);
      l.shift = option_find_float(options, "shift", 0);
      l.noadjust = noadjust;
      return l;
  }
  
  layer parse_reorg(list *options, size_params params)
  {
      int stride = option_find_int(options, "stride",1);
      int reverse = option_find_int_quiet(options, "reverse",0);
  
      int batch,h,w,c;
      h = params.h;
      w = params.w;
      c = params.c;
      batch=params.batch;
      if(!(h && w && c)) error("Layer before reorg layer must output image.");
  
      layer layer = make_reorg_layer(batch,w,h,c,stride,reverse);
      return layer;
  }
  
  layer parse_reorg_old(list *options, size_params params)
  {
      printf("\n reorg_old \n");
      int stride = option_find_int(options, "stride", 1);
      int reverse = option_find_int_quiet(options, "reverse", 0);
  
      int batch, h, w, c;
      h = params.h;
      w = params.w;
      c = params.c;
      batch = params.batch;
      if (!(h && w && c)) error("Layer before reorg layer must output image.");
  
      layer layer = make_reorg_old_layer(batch, w, h, c, stride, reverse);
      return layer;
  }
  
  maxpool_layer parse_local_avgpool(list *options, size_params params)
  {
      int stride = option_find_int(options, "stride", 1);
      int stride_x = option_find_int_quiet(options, "stride_x", stride);
      int stride_y = option_find_int_quiet(options, "stride_y", stride);
      int size = option_find_int(options, "size", stride);
      int padding = option_find_int_quiet(options, "padding", size - 1);
      int maxpool_depth = 0;
      int out_channels = 1;
      int antialiasing = 0;
      const int avgpool = 1;
  
      int batch, h, w, c;
      h = params.h;
      w = params.w;
      c = params.c;
      batch = params.batch;
      if (!(h && w && c)) error("Layer before [local_avgpool] layer must output image.");
  
      maxpool_layer layer = make_maxpool_layer(batch, h, w, c, size, stride_x, stride_y, padding, maxpool_depth, out_channels, antialiasing, avgpool, params.train);
      return layer;
  }
  
  maxpool_layer parse_maxpool(list *options, size_params params)
  {
      int stride = option_find_int(options, "stride",1);
      int stride_x = option_find_int_quiet(options, "stride_x", stride);
      int stride_y = option_find_int_quiet(options, "stride_y", stride);
      int size = option_find_int(options, "size",stride);
      int padding = option_find_int_quiet(options, "padding", size-1);
      int maxpool_depth = option_find_int_quiet(options, "maxpool_depth", 0);
      int out_channels = option_find_int_quiet(options, "out_channels", 1);
      int antialiasing = option_find_int_quiet(options, "antialiasing", 0);
      const int avgpool = 0;
  
      int batch,h,w,c;
      h = params.h;
      w = params.w;
      c = params.c;
      batch=params.batch;
      if(!(h && w && c)) error("Layer before [maxpool] layer must output image.");
  
      maxpool_layer layer = make_maxpool_layer(batch, h, w, c, size, stride_x, stride_y, padding, maxpool_depth, out_channels, antialiasing, avgpool, params.train);
      return layer;
  }
  
  avgpool_layer parse_avgpool(list *options, size_params params)
  {
      int batch,w,h,c;
      w = params.w;
      h = params.h;
      c = params.c;
      batch=params.batch;
      if(!(h && w && c)) error("Layer before avgpool layer must output image.");
  
      avgpool_layer layer = make_avgpool_layer(batch,w,h,c);
      return layer;
  }
  
  dropout_layer parse_dropout(list *options, size_params params)
  {
      float probability = option_find_float(options, "probability", .2);
      int dropblock = option_find_int_quiet(options, "dropblock", 0);
      float dropblock_size_rel = option_find_float_quiet(options, "dropblock_size_rel", 0);
      int dropblock_size_abs = option_find_float_quiet(options, "dropblock_size_abs", 0);
      if (dropblock_size_abs > params.w || dropblock_size_abs > params.h) {
          printf(" [dropout] - dropblock_size_abs = %d that is bigger than layer size %d x %d \n", dropblock_size_abs, params.w, params.h);
          dropblock_size_abs = min_val_cmp(params.w, params.h);
      }
      if (dropblock && !dropblock_size_rel && !dropblock_size_abs) {
          printf(" [dropout] - None of the parameters (dropblock_size_rel or dropblock_size_abs) are set, will be used: dropblock_size_abs = 7 \n");
          dropblock_size_abs = 7;
      }
      if (dropblock_size_rel && dropblock_size_abs) {
          printf(" [dropout] - Both parameters are set, only the parameter will be used: dropblock_size_abs = %d \n", dropblock_size_abs);
          dropblock_size_rel = 0;
      }
      dropout_layer layer = make_dropout_layer(params.batch, params.inputs, probability, dropblock, dropblock_size_rel, dropblock_size_abs, params.w, params.h, params.c);
      layer.out_w = params.w;
      layer.out_h = params.h;
      layer.out_c = params.c;
      return layer;
  }
  
  layer parse_normalization(list *options, size_params params)
  {
      float alpha = option_find_float(options, "alpha", .0001);
      float beta =  option_find_float(options, "beta" , .75);
      float kappa = option_find_float(options, "kappa", 1);
      int size = option_find_int(options, "size", 5);
      layer l = make_normalization_layer(params.batch, params.w, params.h, params.c, size, alpha, beta, kappa);
      return l;
  }
  
  layer parse_batchnorm(list *options, size_params params)
  {
      layer l = make_batchnorm_layer(params.batch, params.w, params.h, params.c, params.train);
      return l;
  }
  
  layer parse_shortcut(list *options, size_params params, network net)
  {
      char *activation_s = option_find_str(options, "activation", "linear");
      ACTIVATION activation = get_activation(activation_s);
  
      char *weights_type_str = option_find_str_quiet(options, "weights_type", "none");
      WEIGHTS_TYPE_T weights_type = NO_WEIGHTS;
      if(strcmp(weights_type_str, "per_feature") == 0 || strcmp(weights_type_str, "per_layer") == 0) weights_type = PER_FEATURE;
      else if (strcmp(weights_type_str, "per_channel") == 0) weights_type = PER_CHANNEL;
      else if (strcmp(weights_type_str, "none") != 0) {
          printf("Error: Incorrect weights_type = %s \n Use one of: none, per_feature, per_channel \n", weights_type_str);
          getchar();
          exit(0);
      }
  
      char *weights_normalization_str = option_find_str_quiet(options, "weights_normalization", "none");
      WEIGHTS_NORMALIZATION_T weights_normalization = NO_NORMALIZATION;
      if (strcmp(weights_normalization_str, "relu") == 0 || strcmp(weights_normalization_str, "avg_relu") == 0) weights_normalization = RELU_NORMALIZATION;
      else if (strcmp(weights_normalization_str, "softmax") == 0) weights_normalization = SOFTMAX_NORMALIZATION;
      else if (strcmp(weights_type_str, "none") != 0) {
          printf("Error: Incorrect weights_normalization = %s \n Use one of: none, relu, softmax \n", weights_normalization_str);
          getchar();
          exit(0);
      }
  
      char *l = option_find(options, "from");
      int len = strlen(l);
      if (!l) error("Route Layer must specify input layers: from = ...");
      int n = 1;
      int i;
      for (i = 0; i < len; ++i) {
          if (l[i] == ',') ++n;
      }
  
      int* layers = (int*)calloc(n, sizeof(int));
      int* sizes = (int*)calloc(n, sizeof(int));
      float **layers_output = (float **)calloc(n, sizeof(float *));
      float **layers_delta = (float **)calloc(n, sizeof(float *));
      float **layers_output_gpu = (float **)calloc(n, sizeof(float *));
      float **layers_delta_gpu = (float **)calloc(n, sizeof(float *));
  
      for (i = 0; i < n; ++i) {
          int index = atoi(l);
          l = strchr(l, ',') + 1;
          if (index < 0) index = params.index + index;
          layers[i] = index;
          sizes[i] = params.net.layers[index].outputs;
          layers_output[i] = params.net.layers[index].output;
          layers_delta[i] = params.net.layers[index].delta;
      }
  
  #ifdef GPU
      for (i = 0; i < n; ++i) {
          layers_output_gpu[i] = params.net.layers[layers[i]].output_gpu;
          layers_delta_gpu[i] = params.net.layers[layers[i]].delta_gpu;
      }
  #endif// GPU
  
      layer s = make_shortcut_layer(params.batch, n, layers, sizes, params.w, params.h, params.c, layers_output, layers_delta,
          layers_output_gpu, layers_delta_gpu, weights_type, weights_normalization, activation, params.train);
  
      free(layers_output_gpu);
      free(layers_delta_gpu);
  
      for (i = 0; i < n; ++i) {
          int index = layers[i];
          assert(params.w == net.layers[index].out_w && params.h == net.layers[index].out_h);
  
          if (params.w != net.layers[index].out_w || params.h != net.layers[index].out_h || params.c != net.layers[index].out_c)
              fprintf(stderr, " (%4d x%4d x%4d) + (%4d x%4d x%4d) \n",
                  params.w, params.h, params.c, net.layers[index].out_w, net.layers[index].out_h, params.net.layers[index].out_c);
      }
  
      return s;
  }
  
  
  layer parse_scale_channels(list *options, size_params params, network net)
  {
      char *l = option_find(options, "from");
      int index = atoi(l);
      if (index < 0) index = params.index + index;
      int scale_wh = option_find_int_quiet(options, "scale_wh", 0);
  
      int batch = params.batch;
      layer from = net.layers[index];
  
      layer s = make_scale_channels_layer(batch, index, params.w, params.h, params.c, from.out_w, from.out_h, from.out_c, scale_wh);
  
      char *activation_s = option_find_str_quiet(options, "activation", "linear");
      ACTIVATION activation = get_activation(activation_s);
      s.activation = activation;
      if (activation == SWISH || activation == MISH) {
          printf(" [scale_channels] layer doesn't support SWISH or MISH activations \n");
      }
      return s;
  }
  
  layer parse_sam(list *options, size_params params, network net)
  {
      char *l = option_find(options, "from");
      int index = atoi(l);
      if (index < 0) index = params.index + index;
  
      int batch = params.batch;
      layer from = net.layers[index];
  
      layer s = make_sam_layer(batch, index, params.w, params.h, params.c, from.out_w, from.out_h, from.out_c);
  
      char *activation_s = option_find_str_quiet(options, "activation", "linear");
      ACTIVATION activation = get_activation(activation_s);
      s.activation = activation;
      if (activation == SWISH || activation == MISH) {
          printf(" [sam] layer doesn't support SWISH or MISH activations \n");
      }
      return s;
  }
  
  
  layer parse_activation(list *options, size_params params)
  {
      char *activation_s = option_find_str(options, "activation", "linear");
      ACTIVATION activation = get_activation(activation_s);
  
      layer l = make_activation_layer(params.batch, params.inputs, activation);
  
      l.out_h = params.h;
      l.out_w = params.w;
      l.out_c = params.c;
      l.h = params.h;
      l.w = params.w;
      l.c = params.c;
  
      return l;
  }
  
  layer parse_upsample(list *options, size_params params, network net)
  {
  
      int stride = option_find_int(options, "stride", 2);
      layer l = make_upsample_layer(params.batch, params.w, params.h, params.c, stride);
      l.scale = option_find_float_quiet(options, "scale", 1);
      return l;
  }
  
  route_layer parse_route(list *options, size_params params)
  {
      char *l = option_find(options, "layers");
      if(!l) error("Route Layer must specify input layers");
      int len = strlen(l);
      int n = 1;
      int i;
      for(i = 0; i < len; ++i){
          if (l[i] == ',') ++n;
      }
  
      int* layers = (int*)xcalloc(n, sizeof(int));
      int* sizes = (int*)xcalloc(n, sizeof(int));
      for(i = 0; i < n; ++i){
          int index = atoi(l);
          l = strchr(l, ',')+1;
          if(index < 0) index = params.index + index;
          layers[i] = index;
          sizes[i] = params.net.layers[index].outputs;
      }
      int batch = params.batch;
  
      int groups = option_find_int_quiet(options, "groups", 1);
      int group_id = option_find_int_quiet(options, "group_id", 0);
  
      route_layer layer = make_route_layer(batch, n, layers, sizes, groups, group_id);
  
      convolutional_layer first = params.net.layers[layers[0]];
      layer.out_w = first.out_w;
      layer.out_h = first.out_h;
      layer.out_c = first.out_c;
      for(i = 1; i < n; ++i){
          int index = layers[i];
          convolutional_layer next = params.net.layers[index];
          if(next.out_w == first.out_w && next.out_h == first.out_h){
              layer.out_c += next.out_c;
          }else{
              fprintf(stderr, " The width and height of the input layers are different. \n");
              layer.out_h = layer.out_w = layer.out_c = 0;
          }
      }
      layer.out_c = layer.out_c / layer.groups;
  
      layer.w = first.w;
      layer.h = first.h;
      layer.c = layer.out_c;
  
      if (n > 3) fprintf(stderr, " \t    ");
      else if (n > 1) fprintf(stderr, " \t            ");
      else fprintf(stderr, " \t\t            ");
  
      fprintf(stderr, "           ");
      if (layer.groups > 1) fprintf(stderr, "%d/%d", layer.group_id, layer.groups);
      else fprintf(stderr, "   ");
      fprintf(stderr, " -> %4d x%4d x%4d \n", layer.out_w, layer.out_h, layer.out_c);
  
      return layer;
  }
  
  learning_rate_policy get_policy(char *s)
  {
      if (strcmp(s, "random")==0) return RANDOM;
      if (strcmp(s, "poly")==0) return POLY;
      if (strcmp(s, "constant")==0) return CONSTANT;
      if (strcmp(s, "step")==0) return STEP;
      if (strcmp(s, "exp")==0) return EXP;
      if (strcmp(s, "sigmoid")==0) return SIG;
      if (strcmp(s, "steps")==0) return STEPS;
      if (strcmp(s, "sgdr")==0) return SGDR;
      fprintf(stderr, "Couldn't find policy %s, going with constant\n", s);
      return CONSTANT;
  }
  
  void parse_net_options(list *options, network *net)
  {
      net->max_batches = option_find_int(options, "max_batches", 0);
      net->batch = option_find_int(options, "batch",1);
      net->learning_rate = option_find_float(options, "learning_rate", .001);
      net->learning_rate_min = option_find_float_quiet(options, "learning_rate_min", .00001);
      net->batches_per_cycle = option_find_int_quiet(options, "sgdr_cycle", net->max_batches);
      net->batches_cycle_mult = option_find_int_quiet(options, "sgdr_mult", 2);
      net->momentum = option_find_float(options, "momentum", .9);
      net->decay = option_find_float(options, "decay", .0001);
      int subdivs = option_find_int(options, "subdivisions",1);
      net->time_steps = option_find_int_quiet(options, "time_steps",1);
      net->track = option_find_int_quiet(options, "track", 0);
      net->augment_speed = option_find_int_quiet(options, "augment_speed", 2);
      net->init_sequential_subdivisions = net->sequential_subdivisions = option_find_int_quiet(options, "sequential_subdivisions", subdivs);
      if (net->sequential_subdivisions > subdivs) net->init_sequential_subdivisions = net->sequential_subdivisions = subdivs;
      net->try_fix_nan = option_find_int_quiet(options, "try_fix_nan", 0);
      net->batch /= subdivs;
      net->batch *= net->time_steps;
      net->subdivisions = subdivs;
  
      *net->seen = 0;
      *net->cur_iteration = 0;
      net->loss_scale = option_find_float_quiet(options, "loss_scale", 1);
      net->dynamic_minibatch = option_find_int_quiet(options, "dynamic_minibatch", 0);
      net->optimized_memory = option_find_int_quiet(options, "optimized_memory", 0);
      net->workspace_size_limit = (size_t)1024*1024 * option_find_float_quiet(options, "workspace_size_limit_MB", 1024);  // 1024 MB by default
  
      net->adam = option_find_int_quiet(options, "adam", 0);
      if(net->adam){
          net->B1 = option_find_float(options, "B1", .9);
          net->B2 = option_find_float(options, "B2", .999);
          net->eps = option_find_float(options, "eps", .000001);
      }
  
      net->h = option_find_int_quiet(options, "height",0);
      net->w = option_find_int_quiet(options, "width",0);
      net->c = option_find_int_quiet(options, "channels",0);
      net->inputs = option_find_int_quiet(options, "inputs", net->h * net->w * net->c);
      net->max_crop = option_find_int_quiet(options, "max_crop",net->w*2);
      net->min_crop = option_find_int_quiet(options, "min_crop",net->w);
      net->flip = option_find_int_quiet(options, "flip", 1);
      net->blur = option_find_int_quiet(options, "blur", 0);
      net->gaussian_noise = option_find_int_quiet(options, "gaussian_noise", 0);
      net->mixup = option_find_int_quiet(options, "mixup", 0);
      int cutmix = option_find_int_quiet(options, "cutmix", 0);
      int mosaic = option_find_int_quiet(options, "mosaic", 0);
      if (mosaic && cutmix) net->mixup = 4;
      else if (cutmix) net->mixup = 2;
      else if (mosaic) net->mixup = 3;
      net->letter_box = option_find_int_quiet(options, "letter_box", 0);
      net->label_smooth_eps = option_find_float_quiet(options, "label_smooth_eps", 0.0f);
      net->resize_step = option_find_float_quiet(options, "resize_step", 32);
      net->attention = option_find_int_quiet(options, "attention", 0);
      net->adversarial_lr = option_find_float_quiet(options, "adversarial_lr", 0);
  
      net->angle = option_find_float_quiet(options, "angle", 0);
      net->aspect = option_find_float_quiet(options, "aspect", 1);
      net->saturation = option_find_float_quiet(options, "saturation", 1);
      net->exposure = option_find_float_quiet(options, "exposure", 1);
      net->hue = option_find_float_quiet(options, "hue", 0);
      net->power = option_find_float_quiet(options, "power", 4);
  
      if(!net->inputs && !(net->h && net->w && net->c)) error("No input parameters supplied");
  
      char *policy_s = option_find_str(options, "policy", "constant");
      net->policy = get_policy(policy_s);
      net->burn_in = option_find_int_quiet(options, "burn_in", 0);
  #ifdef GPU
      if (net->gpu_index >= 0) {
          char device_name[1024];
          int compute_capability = get_gpu_compute_capability(net->gpu_index, device_name);
  #ifdef CUDNN_HALF
          if (compute_capability >= 700) net->cudnn_half = 1;
          else net->cudnn_half = 0;
  #endif// CUDNN_HALF
          fprintf(stderr, " %d : compute_capability = %d, cudnn_half = %d, GPU: %s \n", net->gpu_index, compute_capability, net->cudnn_half, device_name);
      }
      else fprintf(stderr, " GPU isn't used \n");
  #endif// GPU
      if(net->policy == STEP){
          net->step = option_find_int(options, "step", 1);
          net->scale = option_find_float(options, "scale", 1);
      } else if (net->policy == STEPS || net->policy == SGDR){
          char *l = option_find(options, "steps");
          char *p = option_find(options, "scales");
          char *s = option_find(options, "seq_scales");
          if(net->policy == STEPS && (!l || !p)) error("STEPS policy must have steps and scales in cfg file");
  
          if (l) {
              int len = strlen(l);
              int n = 1;
              int i;
              for (i = 0; i < len; ++i) {
                  if (l[i] == ',') ++n;
              }
              int* steps = (int*)xcalloc(n, sizeof(int));
              float* scales = (float*)xcalloc(n, sizeof(float));
              float* seq_scales = (float*)xcalloc(n, sizeof(float));
              for (i = 0; i < n; ++i) {
                  float scale = 1.0;
                  if (p) {
                      scale = atof(p);
                      p = strchr(p, ',') + 1;
                  }
                  float sequence_scale = 1.0;
                  if (s) {
                      sequence_scale = atof(s);
                      s = strchr(s, ',') + 1;
                  }
                  int step = atoi(l);
                  l = strchr(l, ',') + 1;
                  steps[i] = step;
                  scales[i] = scale;
                  seq_scales[i] = sequence_scale;
              }
              net->scales = scales;
              net->steps = steps;
              net->seq_scales = seq_scales;
              net->num_steps = n;
          }
      } else if (net->policy == EXP){
          net->gamma = option_find_float(options, "gamma", 1);
      } else if (net->policy == SIG){
          net->gamma = option_find_float(options, "gamma", 1);
          net->step = option_find_int(options, "step", 1);
      } else if (net->policy == POLY || net->policy == RANDOM){
          //net->power = option_find_float(options, "power", 1);
      }
  
  }
  
  int is_network(section *s)
  {
      return (strcmp(s->type, "[net]")==0
              || strcmp(s->type, "[network]")==0);
  }
  
  void set_train_only_bn(network net)
  {
      int train_only_bn = 0;
      int i;
      for (i = net.n - 1; i >= 0; --i) {
          if (net.layers[i].train_only_bn) train_only_bn = net.layers[i].train_only_bn;  // set l.train_only_bn for all previous layers
          if (train_only_bn) {
              net.layers[i].train_only_bn = train_only_bn;
  
              if (net.layers[i].type == CONV_LSTM) {
                  net.layers[i].wf->train_only_bn = train_only_bn;
                  net.layers[i].wi->train_only_bn = train_only_bn;
                  net.layers[i].wg->train_only_bn = train_only_bn;
                  net.layers[i].wo->train_only_bn = train_only_bn;
                  net.layers[i].uf->train_only_bn = train_only_bn;
                  net.layers[i].ui->train_only_bn = train_only_bn;
                  net.layers[i].ug->train_only_bn = train_only_bn;
                  net.layers[i].uo->train_only_bn = train_only_bn;
                  if (net.layers[i].peephole) {
                      net.layers[i].vf->train_only_bn = train_only_bn;
                      net.layers[i].vi->train_only_bn = train_only_bn;
                      net.layers[i].vo->train_only_bn = train_only_bn;
                  }
              }
              else if (net.layers[i].type == CRNN) {
                  net.layers[i].input_layer->train_only_bn = train_only_bn;
                  net.layers[i].self_layer->train_only_bn = train_only_bn;
                  net.layers[i].output_layer->train_only_bn = train_only_bn;
              }
          }
      }
  }
  
  network parse_network_cfg(char *filename)
  {
      return parse_network_cfg_custom(filename, 0, 0);
  }
  
  network parse_network_cfg_custom(char *filename, int batch, int time_steps)
  {
      list *sections = read_cfg(filename);
      node *n = sections->front;
      if(!n) error("Config file has no sections");
      network net = make_network(sections->size - 1);
      net.gpu_index = gpu_index;
      size_params params;
  
      if (batch > 0) params.train = 0;    // allocates memory for Detection only
      else params.train = 1;              // allocates memory for Detection & Training
  
      section *s = (section *)n->val;
      list *options = s->options;
      if(!is_network(s)) error("First section must be [net] or [network]");
      parse_net_options(options, &net);
  
  #ifdef GPU
      printf("net.optimized_memory = %d \n", net.optimized_memory);
      if (net.optimized_memory >= 2 && params.train) {
          pre_allocate_pinned_memory((size_t)1024 * 1024 * 1024 * 8);   // pre-allocate 8 GB CPU-RAM for pinned memory
      }
  #endif  // GPU
  
      params.h = net.h;
      params.w = net.w;
      params.c = net.c;
      params.inputs = net.inputs;
      if (batch > 0) net.batch = batch;
      if (time_steps > 0) net.time_steps = time_steps;
      if (net.batch < 1) net.batch = 1;
      if (net.time_steps < 1) net.time_steps = 1;
      if (net.batch < net.time_steps) net.batch = net.time_steps;
      params.batch = net.batch;
      params.time_steps = net.time_steps;
      params.net = net;
      printf("mini_batch = %d, batch = %d, time_steps = %d, train = %d \n", net.batch, net.batch * net.subdivisions, net.time_steps, params.train);
  
      int avg_outputs = 0;
      int avg_counter = 0;
      float bflops = 0;
      size_t workspace_size = 0;
      size_t max_inputs = 0;
      size_t max_outputs = 0;
      int receptive_w = 1, receptive_h = 1;
      int receptive_w_scale = 1, receptive_h_scale = 1;
      const int show_receptive_field = option_find_float_quiet(options, "show_receptive_field", 0);
  
      n = n->next;
      int count = 0;
      free_section(s);
      fprintf(stderr, "   layer   filters  size/strd(dil)      input                output\n");
      while(n){
          params.index = count;
          fprintf(stderr, "%4d ", count);
          s = (section *)n->val;
          options = s->options;
          layer l = { (LAYER_TYPE)0 };
          LAYER_TYPE lt = string_to_layer_type(s->type);
          if(lt == CONVOLUTIONAL){
              l = parse_convolutional(options, params);
          }else if(lt == LOCAL){
              l = parse_local(options, params);
          }else if(lt == ACTIVE){
              l = parse_activation(options, params);
          }else if(lt == RNN){
              l = parse_rnn(options, params);
          }else if(lt == GRU){
              l = parse_gru(options, params);
          }else if(lt == LSTM){
              l = parse_lstm(options, params);
          }else if (lt == CONV_LSTM) {
              l = parse_conv_lstm(options, params);
          }else if(lt == CRNN){
              l = parse_crnn(options, params);
          }else if(lt == CONNECTED){
              l = parse_connected(options, params);
          }else if(lt == CROP){
              l = parse_crop(options, params);
          }else if(lt == COST){
              l = parse_cost(options, params);
              l.keep_delta_gpu = 1;
          }else if(lt == REGION){
              l = parse_region(options, params);
              l.keep_delta_gpu = 1;
          }else if (lt == YOLO) {
              l = parse_yolo(options, params);
              l.keep_delta_gpu = 1;
          }else if (lt == GAUSSIAN_YOLO) {
              l = parse_gaussian_yolo(options, params);
              l.keep_delta_gpu = 1;
          }else if(lt == DETECTION){
              l = parse_detection(options, params);
          }else if(lt == SOFTMAX){
              l = parse_softmax(options, params);
              net.hierarchy = l.softmax_tree;
              l.keep_delta_gpu = 1;
          }else if(lt == NORMALIZATION){
              l = parse_normalization(options, params);
          }else if(lt == BATCHNORM){
              l = parse_batchnorm(options, params);
          }else if(lt == MAXPOOL){
              l = parse_maxpool(options, params);
          }else if (lt == LOCAL_AVGPOOL) {
              l = parse_local_avgpool(options, params);
          }else if(lt == REORG){
              l = parse_reorg(options, params);        }
          else if (lt == REORG_OLD) {
              l = parse_reorg_old(options, params);
          }else if(lt == AVGPOOL){
              l = parse_avgpool(options, params);
          }else if(lt == ROUTE){
              l = parse_route(options, params);
              int k;
              for (k = 0; k < l.n; ++k) {
                  net.layers[l.input_layers[k]].use_bin_output = 0;
                  net.layers[l.input_layers[k]].keep_delta_gpu = 1;
              }
          }else if (lt == UPSAMPLE) {
              l = parse_upsample(options, params, net);
          }else if(lt == SHORTCUT){
              l = parse_shortcut(options, params, net);
              net.layers[count - 1].use_bin_output = 0;
              net.layers[l.index].use_bin_output = 0;
              net.layers[l.index].keep_delta_gpu = 1;
          }else if (lt == SCALE_CHANNELS) {
              l = parse_scale_channels(options, params, net);
              net.layers[count - 1].use_bin_output = 0;
              net.layers[l.index].use_bin_output = 0;
              net.layers[l.index].keep_delta_gpu = 1;
          }
          else if (lt == SAM) {
              l = parse_sam(options, params, net);
              net.layers[count - 1].use_bin_output = 0;
              net.layers[l.index].use_bin_output = 0;
              net.layers[l.index].keep_delta_gpu = 1;
          }else if(lt == DROPOUT){
              l = parse_dropout(options, params);
              l.output = net.layers[count-1].output;
              l.delta = net.layers[count-1].delta;
  #ifdef GPU
              l.output_gpu = net.layers[count-1].output_gpu;
              l.delta_gpu = net.layers[count-1].delta_gpu;
              l.keep_delta_gpu = 1;
  #endif
          }
          else if (lt == EMPTY) {
              layer empty_layer = {(LAYER_TYPE)0};
              empty_layer.out_w = params.w;
              empty_layer.out_h = params.h;
              empty_layer.out_c = params.c;
              l = empty_layer;
              l.output = net.layers[count - 1].output;
              l.delta = net.layers[count - 1].delta;
  #ifdef GPU
              l.output_gpu = net.layers[count - 1].output_gpu;
              l.delta_gpu = net.layers[count - 1].delta_gpu;
  #endif
          }else{
              fprintf(stderr, "Type not recognized: %s\n", s->type);
          }
  
          // calculate receptive field
          if(show_receptive_field)
          {
              int dilation = max_val_cmp(1, l.dilation);
              int stride = max_val_cmp(1, l.stride);
              int size = max_val_cmp(1, l.size);
  
              if (l.type == UPSAMPLE || (l.type == REORG))
              {
  
                  l.receptive_w = receptive_w;
                  l.receptive_h = receptive_h;
                  l.receptive_w_scale = receptive_w_scale = receptive_w_scale / stride;
                  l.receptive_h_scale = receptive_h_scale = receptive_h_scale / stride;
  
              }
              else {
                  if (l.type == ROUTE) {
                      receptive_w = receptive_h = receptive_w_scale = receptive_h_scale = 0;
                      int k;
                      for (k = 0; k < l.n; ++k) {
                          layer route_l = net.layers[l.input_layers[k]];
                          receptive_w = max_val_cmp(receptive_w, route_l.receptive_w);
                          receptive_h = max_val_cmp(receptive_h, route_l.receptive_h);
                          receptive_w_scale = max_val_cmp(receptive_w_scale, route_l.receptive_w_scale);
                          receptive_h_scale = max_val_cmp(receptive_h_scale, route_l.receptive_h_scale);
                      }
                  }
                  else
                  {
                      int increase_receptive = size + (dilation - 1) * 2 - 1;// stride;
                      increase_receptive = max_val_cmp(0, increase_receptive);
  
                      receptive_w += increase_receptive * receptive_w_scale;
                      receptive_h += increase_receptive * receptive_h_scale;
                      receptive_w_scale *= stride;
                      receptive_h_scale *= stride;
                  }
  
                  l.receptive_w = receptive_w;
                  l.receptive_h = receptive_h;
                  l.receptive_w_scale = receptive_w_scale;
                  l.receptive_h_scale = receptive_h_scale;
              }
              //printf(" size = %d, dilation = %d, stride = %d, receptive_w = %d, receptive_w_scale = %d - ", size, dilation, stride, receptive_w, receptive_w_scale);
  
              int cur_receptive_w = receptive_w;
              int cur_receptive_h = receptive_h;
  
              fprintf(stderr, "%4d - receptive field: %d x %d \n", count, cur_receptive_w, cur_receptive_h);
          }
  
  #ifdef GPU
          // futher GPU-memory optimization: net.optimized_memory == 2
          if (net.optimized_memory >= 2 && params.train && l.type != DROPOUT)
          {
              l.optimized_memory = net.optimized_memory;
              if (l.output_gpu) {
                  cuda_free(l.output_gpu);
                  //l.output_gpu = cuda_make_array_pinned(l.output, l.batch*l.outputs); // l.steps
                  l.output_gpu = cuda_make_array_pinned_preallocated(NULL, l.batch*l.outputs); // l.steps
              }
              if (l.activation_input_gpu) {
                  cuda_free(l.activation_input_gpu);
                  l.activation_input_gpu = cuda_make_array_pinned_preallocated(NULL, l.batch*l.outputs); // l.steps
              }
  
              if (l.x_gpu) {
                  cuda_free(l.x_gpu);
                  l.x_gpu = cuda_make_array_pinned_preallocated(NULL, l.batch*l.outputs); // l.steps
              }
  
              // maximum optimization
              if (net.optimized_memory >= 3 && l.type != DROPOUT) {
                  if (l.delta_gpu) {
                      cuda_free(l.delta_gpu);
                      //l.delta_gpu = cuda_make_array_pinned_preallocated(NULL, l.batch*l.outputs); // l.steps
                      //printf("\n\n PINNED DELTA GPU = %d \n", l.batch*l.outputs);
                  }
              }
  
              if (l.type == CONVOLUTIONAL) {
                  set_specified_workspace_limit(&l, net.workspace_size_limit);   // workspace size limit 1 GB
              }
          }
  #endif // GPU
  
          l.clip = option_find_float_quiet(options, "clip", 0);
          l.dynamic_minibatch = net.dynamic_minibatch;
          l.onlyforward = option_find_int_quiet(options, "onlyforward", 0);
          l.dont_update = option_find_int_quiet(options, "dont_update", 0);
          l.burnin_update = option_find_int_quiet(options, "burnin_update", 0);
          l.stopbackward = option_find_int_quiet(options, "stopbackward", 0);
          l.train_only_bn = option_find_int_quiet(options, "train_only_bn", 0);
          l.dontload = option_find_int_quiet(options, "dontload", 0);
          l.dontloadscales = option_find_int_quiet(options, "dontloadscales", 0);
          l.learning_rate_scale = option_find_float_quiet(options, "learning_rate", 1);
          option_unused(options);
          net.layers[count] = l;
          if (l.workspace_size > workspace_size) workspace_size = l.workspace_size;
          if (l.inputs > max_inputs) max_inputs = l.inputs;
          if (l.outputs > max_outputs) max_outputs = l.outputs;
          free_section(s);
          n = n->next;
          ++count;
          if(n){
              if (l.antialiasing) {
                  params.h = l.input_layer->out_h;
                  params.w = l.input_layer->out_w;
                  params.c = l.input_layer->out_c;
                  params.inputs = l.input_layer->outputs;
              }
              else {
                  params.h = l.out_h;
                  params.w = l.out_w;
                  params.c = l.out_c;
                  params.inputs = l.outputs;
              }
          }
          if (l.bflops > 0) bflops += l.bflops;
  
          if (l.w > 1 && l.h > 1) {
              avg_outputs += l.outputs;
              avg_counter++;
          }
      }
      free_list(sections);
  
  #ifdef GPU
      if (net.optimized_memory && params.train)
      {
          int k;
          for (k = 0; k < net.n; ++k) {
              layer l = net.layers[k];
              // delta GPU-memory optimization: net.optimized_memory == 1
              if (!l.keep_delta_gpu) {
                  const size_t delta_size = l.outputs*l.batch; // l.steps
                  if (net.max_delta_gpu_size < delta_size) {
                      net.max_delta_gpu_size = delta_size;
                      if (net.global_delta_gpu) cuda_free(net.global_delta_gpu);
                      if (net.state_delta_gpu) cuda_free(net.state_delta_gpu);
                      assert(net.max_delta_gpu_size > 0);
                      net.global_delta_gpu = (float *)cuda_make_array(NULL, net.max_delta_gpu_size);
                      net.state_delta_gpu = (float *)cuda_make_array(NULL, net.max_delta_gpu_size);
                  }
                  if (l.delta_gpu) {
                      if (net.optimized_memory >= 3) {}
                      else cuda_free(l.delta_gpu);
                  }
                  l.delta_gpu = net.global_delta_gpu;
              }
  
              // maximum optimization
              if (net.optimized_memory >= 3 && l.type != DROPOUT) {
                  if (l.delta_gpu && l.keep_delta_gpu) {
                      //cuda_free(l.delta_gpu);   // already called above
                      l.delta_gpu = cuda_make_array_pinned_preallocated(NULL, l.batch*l.outputs); // l.steps
                      //printf("\n\n PINNED DELTA GPU = %d \n", l.batch*l.outputs);
                  }
              }
  
              net.layers[k] = l;
          }
      }
  #endif
  
      set_train_only_bn(net); // set l.train_only_bn for all required layers
  
      net.outputs = get_network_output_size(net);
      net.output = get_network_output(net);
      avg_outputs = avg_outputs / avg_counter;
      fprintf(stderr, "Total BFLOPS %5.3f \n", bflops);
      fprintf(stderr, "avg_outputs = %d \n", avg_outputs);
  #ifdef GPU
      get_cuda_stream();
      get_cuda_memcpy_stream();
      if (gpu_index >= 0)
      {
          int size = get_network_input_size(net) * net.batch;
          net.input_state_gpu = cuda_make_array(0, size);
          if (cudaSuccess == cudaHostAlloc(&net.input_pinned_cpu, size * sizeof(float), cudaHostRegisterMapped)) net.input_pinned_cpu_flag = 1;
          else {
              cudaGetLastError(); // reset CUDA-error
              net.input_pinned_cpu = (float*)xcalloc(size, sizeof(float));
          }
  
          // pre-allocate memory for inference on Tensor Cores (fp16)
          if (net.cudnn_half) {
              *net.max_input16_size = max_inputs;
              CHECK_CUDA(cudaMalloc((void **)net.input16_gpu, *net.max_input16_size * sizeof(short))); //sizeof(half)
              *net.max_output16_size = max_outputs;
              CHECK_CUDA(cudaMalloc((void **)net.output16_gpu, *net.max_output16_size * sizeof(short))); //sizeof(half)
          }
          if (workspace_size) {
              fprintf(stderr, " Allocate additional workspace_size = %1.2f MB \n", (float)workspace_size/1000000);
              net.workspace = cuda_make_array(0, workspace_size / sizeof(float) + 1);
          }
          else {
              net.workspace = (float*)xcalloc(1, workspace_size);
          }
      }
  #else
          if (workspace_size) {
              net.workspace = (float*)xcalloc(1, workspace_size);
          }
  #endif
  
      LAYER_TYPE lt = net.layers[net.n - 1].type;
      if ((net.w % 32 != 0 || net.h % 32 != 0) && (lt == YOLO || lt == REGION || lt == DETECTION)) {
          printf("\n Warning: width=%d and height=%d in cfg-file must be divisible by 32 for default networks Yolo v1/v2/v3!!! \n\n",
              net.w, net.h);
      }
      return net;
  }
  
  
  
  list *read_cfg(char *filename)
  {
      FILE *file = fopen(filename, "r");
      if(file == 0) file_error(filename);
      char *line;
      int nu = 0;
      list *sections = make_list();
      section *current = 0;
      while((line=fgetl(file)) != 0){
          ++ nu;
          strip(line);
          switch(line[0]){
              case '[':
                  current = (section*)xmalloc(sizeof(section));
                  list_insert(sections, current);
                  current->options = make_list();
                  current->type = line;
                  break;
              case '\0':
              case '#':
              case ';':
                  free(line);
                  break;
              default:
                  if(!read_option(line, current->options)){
                      fprintf(stderr, "Config file error line %d, could parse: %s\n", nu, line);
                      free(line);
                  }
                  break;
          }
      }
      fclose(file);
      return sections;
  }
  
  void save_convolutional_weights_binary(layer l, FILE *fp)
  {
  #ifdef GPU
      if(gpu_index >= 0){
          pull_convolutional_layer(l);
      }
  #endif
      int size = (l.c/l.groups)*l.size*l.size;
      binarize_weights(l.weights, l.n, size, l.binary_weights);
      int i, j, k;
      fwrite(l.biases, sizeof(float), l.n, fp);
      if (l.batch_normalize){
          fwrite(l.scales, sizeof(float), l.n, fp);
          fwrite(l.rolling_mean, sizeof(float), l.n, fp);
          fwrite(l.rolling_variance, sizeof(float), l.n, fp);
      }
      for(i = 0; i < l.n; ++i){
          float mean = l.binary_weights[i*size];
          if(mean < 0) mean = -mean;
          fwrite(&mean, sizeof(float), 1, fp);
          for(j = 0; j < size/8; ++j){
              int index = i*size + j*8;
              unsigned char c = 0;
              for(k = 0; k < 8; ++k){
                  if (j*8 + k >= size) break;
                  if (l.binary_weights[index + k] > 0) c = (c | 1<<k);
              }
              fwrite(&c, sizeof(char), 1, fp);
          }
      }
  }
  
  void save_shortcut_weights(layer l, FILE *fp)
  {
  #ifdef GPU
      if (gpu_index >= 0) {
          pull_shortcut_layer(l);
          printf("\n pull_shortcut_layer \n");
      }
  #endif
      int i;
      for (i = 0; i < l.nweights; ++i) printf(" %f, ", l.weight_updates[i]);
      printf(" l.nweights = %d - update \n", l.nweights);
      for (i = 0; i < l.nweights; ++i) printf(" %f, ", l.weights[i]);
      printf(" l.nweights = %d \n\n", l.nweights);
  
      int num = l.nweights;
      fwrite(l.weights, sizeof(float), num, fp);
  }
  
  void save_convolutional_weights(layer l, FILE *fp)
  {
      if(l.binary){
          //save_convolutional_weights_binary(l, fp);
          //return;
      }
  #ifdef GPU
      if(gpu_index >= 0){
          pull_convolutional_layer(l);
      }
  #endif
      int num = l.nweights;
      fwrite(l.biases, sizeof(float), l.n, fp);
      if (l.batch_normalize){
          fwrite(l.scales, sizeof(float), l.n, fp);
          fwrite(l.rolling_mean, sizeof(float), l.n, fp);
          fwrite(l.rolling_variance, sizeof(float), l.n, fp);
      }
      fwrite(l.weights, sizeof(float), num, fp);
      //if(l.adam){
      //    fwrite(l.m, sizeof(float), num, fp);
      //    fwrite(l.v, sizeof(float), num, fp);
      //}
  }
  
  void save_batchnorm_weights(layer l, FILE *fp)
  {
  #ifdef GPU
      if(gpu_index >= 0){
          pull_batchnorm_layer(l);
      }
  #endif
      fwrite(l.biases, sizeof(float), l.c, fp);
      fwrite(l.scales, sizeof(float), l.c, fp);
      fwrite(l.rolling_mean, sizeof(float), l.c, fp);
      fwrite(l.rolling_variance, sizeof(float), l.c, fp);
  }
  
  void save_connected_weights(layer l, FILE *fp)
  {
  #ifdef GPU
      if(gpu_index >= 0){
          pull_connected_layer(l);
      }
  #endif
      fwrite(l.biases, sizeof(float), l.outputs, fp);
      fwrite(l.weights, sizeof(float), l.outputs*l.inputs, fp);
      if (l.batch_normalize){
          fwrite(l.scales, sizeof(float), l.outputs, fp);
          fwrite(l.rolling_mean, sizeof(float), l.outputs, fp);
          fwrite(l.rolling_variance, sizeof(float), l.outputs, fp);
      }
  }
  
  void save_weights_upto(network net, char *filename, int cutoff)
  {
  #ifdef GPU
      if(net.gpu_index >= 0){
          cuda_set_device(net.gpu_index);
      }
  #endif
      fprintf(stderr, "Saving weights to %s\n", filename);
      FILE *fp = fopen(filename, "wb");
      if(!fp) file_error(filename);
  
      int major = MAJOR_VERSION;
      int minor = MINOR_VERSION;
      int revision = PATCH_VERSION;
      fwrite(&major, sizeof(int), 1, fp);
      fwrite(&minor, sizeof(int), 1, fp);
      fwrite(&revision, sizeof(int), 1, fp);
      (*net.seen) = get_current_iteration(net) * net.batch * net.subdivisions; // remove this line, when you will save to weights-file both: seen & cur_iteration
      fwrite(net.seen, sizeof(uint64_t), 1, fp);
  
      int i;
      for(i = 0; i < net.n && i < cutoff; ++i){
          layer l = net.layers[i];
          if (l.type == CONVOLUTIONAL && l.share_layer == NULL) {
              save_convolutional_weights(l, fp);
          } if (l.type == SHORTCUT && l.nweights > 0) {
              save_shortcut_weights(l, fp);
          } if(l.type == CONNECTED){
              save_connected_weights(l, fp);
          } if(l.type == BATCHNORM){
              save_batchnorm_weights(l, fp);
          } if(l.type == RNN){
              save_connected_weights(*(l.input_layer), fp);
              save_connected_weights(*(l.self_layer), fp);
              save_connected_weights(*(l.output_layer), fp);
          } if(l.type == GRU){
              save_connected_weights(*(l.input_z_layer), fp);
              save_connected_weights(*(l.input_r_layer), fp);
              save_connected_weights(*(l.input_h_layer), fp);
              save_connected_weights(*(l.state_z_layer), fp);
              save_connected_weights(*(l.state_r_layer), fp);
              save_connected_weights(*(l.state_h_layer), fp);
          } if(l.type == LSTM){
              save_connected_weights(*(l.wf), fp);
              save_connected_weights(*(l.wi), fp);
              save_connected_weights(*(l.wg), fp);
              save_connected_weights(*(l.wo), fp);
              save_connected_weights(*(l.uf), fp);
              save_connected_weights(*(l.ui), fp);
              save_connected_weights(*(l.ug), fp);
              save_connected_weights(*(l.uo), fp);
          } if (l.type == CONV_LSTM) {
              if (l.peephole) {
                  save_convolutional_weights(*(l.vf), fp);
                  save_convolutional_weights(*(l.vi), fp);
                  save_convolutional_weights(*(l.vo), fp);
              }
              save_convolutional_weights(*(l.wf), fp);
              save_convolutional_weights(*(l.wi), fp);
              save_convolutional_weights(*(l.wg), fp);
              save_convolutional_weights(*(l.wo), fp);
              save_convolutional_weights(*(l.uf), fp);
              save_convolutional_weights(*(l.ui), fp);
              save_convolutional_weights(*(l.ug), fp);
              save_convolutional_weights(*(l.uo), fp);
          } if(l.type == CRNN){
              save_convolutional_weights(*(l.input_layer), fp);
              save_convolutional_weights(*(l.self_layer), fp);
              save_convolutional_weights(*(l.output_layer), fp);
          } if(l.type == LOCAL){
  #ifdef GPU
              if(gpu_index >= 0){
                  pull_local_layer(l);
              }
  #endif
              int locations = l.out_w*l.out_h;
              int size = l.size*l.size*l.c*l.n*locations;
              fwrite(l.biases, sizeof(float), l.outputs, fp);
              fwrite(l.weights, sizeof(float), size, fp);
          }
      }
      fclose(fp);
  }
  void save_weights(network net, char *filename)
  {
      save_weights_upto(net, filename, net.n);
  }
  
  void transpose_matrix(float *a, int rows, int cols)
  {
      float* transpose = (float*)xcalloc(rows * cols, sizeof(float));
      int x, y;
      for(x = 0; x < rows; ++x){
          for(y = 0; y < cols; ++y){
              transpose[y*rows + x] = a[x*cols + y];
          }
      }
      memcpy(a, transpose, rows*cols*sizeof(float));
      free(transpose);
  }
  
  void load_connected_weights(layer l, FILE *fp, int transpose)
  {
      fread(l.biases, sizeof(float), l.outputs, fp);
      fread(l.weights, sizeof(float), l.outputs*l.inputs, fp);
      if(transpose){
          transpose_matrix(l.weights, l.inputs, l.outputs);
      }
      //printf("Biases: %f mean %f variance\n", mean_array(l.biases, l.outputs), variance_array(l.biases, l.outputs));
      //printf("Weights: %f mean %f variance\n", mean_array(l.weights, l.outputs*l.inputs), variance_array(l.weights, l.outputs*l.inputs));
      if (l.batch_normalize && (!l.dontloadscales)){
          fread(l.scales, sizeof(float), l.outputs, fp);
          fread(l.rolling_mean, sizeof(float), l.outputs, fp);
          fread(l.rolling_variance, sizeof(float), l.outputs, fp);
          //printf("Scales: %f mean %f variance\n", mean_array(l.scales, l.outputs), variance_array(l.scales, l.outputs));
          //printf("rolling_mean: %f mean %f variance\n", mean_array(l.rolling_mean, l.outputs), variance_array(l.rolling_mean, l.outputs));
          //printf("rolling_variance: %f mean %f variance\n", mean_array(l.rolling_variance, l.outputs), variance_array(l.rolling_variance, l.outputs));
      }
  #ifdef GPU
      if(gpu_index >= 0){
          push_connected_layer(l);
      }
  #endif
  }
  
  void load_batchnorm_weights(layer l, FILE *fp)
  {
      fread(l.biases, sizeof(float), l.c, fp);
      fread(l.scales, sizeof(float), l.c, fp);
      fread(l.rolling_mean, sizeof(float), l.c, fp);
      fread(l.rolling_variance, sizeof(float), l.c, fp);
  #ifdef GPU
      if(gpu_index >= 0){
          push_batchnorm_layer(l);
      }
  #endif
  }
  
  void load_convolutional_weights_binary(layer l, FILE *fp)
  {
      fread(l.biases, sizeof(float), l.n, fp);
      if (l.batch_normalize && (!l.dontloadscales)){
          fread(l.scales, sizeof(float), l.n, fp);
          fread(l.rolling_mean, sizeof(float), l.n, fp);
          fread(l.rolling_variance, sizeof(float), l.n, fp);
      }
      int size = (l.c / l.groups)*l.size*l.size;
      int i, j, k;
      for(i = 0; i < l.n; ++i){
          float mean = 0;
          fread(&mean, sizeof(float), 1, fp);
          for(j = 0; j < size/8; ++j){
              int index = i*size + j*8;
              unsigned char c = 0;
              fread(&c, sizeof(char), 1, fp);
              for(k = 0; k < 8; ++k){
                  if (j*8 + k >= size) break;
                  l.weights[index + k] = (c & 1<<k) ? mean : -mean;
              }
          }
      }
  #ifdef GPU
      if(gpu_index >= 0){
          push_convolutional_layer(l);
      }
  #endif
  }
  
  void load_convolutional_weights(layer l, FILE *fp)
  {
      if(l.binary){
          //load_convolutional_weights_binary(l, fp);
          //return;
      }
      int num = l.nweights;
      int read_bytes;
      read_bytes = fread(l.biases, sizeof(float), l.n, fp);
      if (read_bytes > 0 && read_bytes < l.n) printf("\n Warning: Unexpected end of wights-file! l.biases - l.index = %d \n", l.index);
      //fread(l.weights, sizeof(float), num, fp); // as in connected layer
      if (l.batch_normalize && (!l.dontloadscales)){
          read_bytes = fread(l.scales, sizeof(float), l.n, fp);
          if (read_bytes > 0 && read_bytes < l.n) printf("\n Warning: Unexpected end of wights-file! l.scales - l.index = %d \n", l.index);
          read_bytes = fread(l.rolling_mean, sizeof(float), l.n, fp);
          if (read_bytes > 0 && read_bytes < l.n) printf("\n Warning: Unexpected end of wights-file! l.rolling_mean - l.index = %d \n", l.index);
          read_bytes = fread(l.rolling_variance, sizeof(float), l.n, fp);
          if (read_bytes > 0 && read_bytes < l.n) printf("\n Warning: Unexpected end of wights-file! l.rolling_variance - l.index = %d \n", l.index);
          if(0){
              int i;
              for(i = 0; i < l.n; ++i){
                  printf("%g, ", l.rolling_mean[i]);
              }
              printf("\n");
              for(i = 0; i < l.n; ++i){
                  printf("%g, ", l.rolling_variance[i]);
              }
              printf("\n");
          }
          if(0){
              fill_cpu(l.n, 0, l.rolling_mean, 1);
              fill_cpu(l.n, 0, l.rolling_variance, 1);
          }
      }
      read_bytes = fread(l.weights, sizeof(float), num, fp);
      if (read_bytes > 0 && read_bytes < l.n) printf("\n Warning: Unexpected end of wights-file! l.weights - l.index = %d \n", l.index);
      //if(l.adam){
      //    fread(l.m, sizeof(float), num, fp);
      //    fread(l.v, sizeof(float), num, fp);
      //}
      //if(l.c == 3) scal_cpu(num, 1./256, l.weights, 1);
      if (l.flipped) {
          transpose_matrix(l.weights, (l.c/l.groups)*l.size*l.size, l.n);
      }
      //if (l.binary) binarize_weights(l.weights, l.n, (l.c/l.groups)*l.size*l.size, l.weights);
  #ifdef GPU
      if(gpu_index >= 0){
          push_convolutional_layer(l);
      }
  #endif
  }
  
  void load_shortcut_weights(layer l, FILE *fp)
  {
      int num = l.nweights;
      int read_bytes;
      read_bytes = fread(l.weights, sizeof(float), num, fp);
      if (read_bytes > 0 && read_bytes < num) printf("\n Warning: Unexpected end of wights-file! l.weights - l.index = %d \n", l.index);
      //for (int i = 0; i < l.nweights; ++i) printf(" %f, ", l.weights[i]);
      //printf(" read_bytes = %d \n\n", read_bytes);
  #ifdef GPU
      if (gpu_index >= 0) {
          push_shortcut_layer(l);
      }
  #endif
  }
  
  void load_weights_upto(network *net, char *filename, int cutoff)
  {
  #ifdef GPU
      if(net->gpu_index >= 0){
          cuda_set_device(net->gpu_index);
      }
  #endif
      fprintf(stderr, "Loading weights from %s...", filename);
      fflush(stdout);
      FILE *fp = fopen(filename, "rb");
      if(!fp) file_error(filename);
  
      int major;
      int minor;
      int revision;
      fread(&major, sizeof(int), 1, fp);
      fread(&minor, sizeof(int), 1, fp);
      fread(&revision, sizeof(int), 1, fp);
      if ((major * 10 + minor) >= 2) {
          printf("\n seen 64");
          uint64_t iseen = 0;
          fread(&iseen, sizeof(uint64_t), 1, fp);
          *net->seen = iseen;
      }
      else {
          printf("\n seen 32");
          uint32_t iseen = 0;
          fread(&iseen, sizeof(uint32_t), 1, fp);
          *net->seen = iseen;
      }
      *net->cur_iteration = get_current_batch(*net);
      printf(", trained: %.0f K-images (%.0f Kilo-batches_64) \n", (float)(*net->seen / 1000), (float)(*net->seen / 64000));
      int transpose = (major > 1000) || (minor > 1000);
  
      int i;
      for(i = 0; i < net->n && i < cutoff; ++i){
          layer l = net->layers[i];
          if (l.dontload) continue;
          if(l.type == CONVOLUTIONAL && l.share_layer == NULL){
              load_convolutional_weights(l, fp);
          }
          if (l.type == SHORTCUT && l.nweights > 0) {
              load_shortcut_weights(l, fp);
          }
          if(l.type == CONNECTED){
              load_connected_weights(l, fp, transpose);
          }
          if(l.type == BATCHNORM){
              load_batchnorm_weights(l, fp);
          }
          if(l.type == CRNN){
              load_convolutional_weights(*(l.input_layer), fp);
              load_convolutional_weights(*(l.self_layer), fp);
              load_convolutional_weights(*(l.output_layer), fp);
          }
          if(l.type == RNN){
              load_connected_weights(*(l.input_layer), fp, transpose);
              load_connected_weights(*(l.self_layer), fp, transpose);
              load_connected_weights(*(l.output_layer), fp, transpose);
          }
          if(l.type == GRU){
              load_connected_weights(*(l.input_z_layer), fp, transpose);
              load_connected_weights(*(l.input_r_layer), fp, transpose);
              load_connected_weights(*(l.input_h_layer), fp, transpose);
              load_connected_weights(*(l.state_z_layer), fp, transpose);
              load_connected_weights(*(l.state_r_layer), fp, transpose);
              load_connected_weights(*(l.state_h_layer), fp, transpose);
          }
          if(l.type == LSTM){
              load_connected_weights(*(l.wf), fp, transpose);
              load_connected_weights(*(l.wi), fp, transpose);
              load_connected_weights(*(l.wg), fp, transpose);
              load_connected_weights(*(l.wo), fp, transpose);
              load_connected_weights(*(l.uf), fp, transpose);
              load_connected_weights(*(l.ui), fp, transpose);
              load_connected_weights(*(l.ug), fp, transpose);
              load_connected_weights(*(l.uo), fp, transpose);
          }
          if (l.type == CONV_LSTM) {
              if (l.peephole) {
                  load_convolutional_weights(*(l.vf), fp);
                  load_convolutional_weights(*(l.vi), fp);
                  load_convolutional_weights(*(l.vo), fp);
              }
              load_convolutional_weights(*(l.wf), fp);
              load_convolutional_weights(*(l.wi), fp);
              load_convolutional_weights(*(l.wg), fp);
              load_convolutional_weights(*(l.wo), fp);
              load_convolutional_weights(*(l.uf), fp);
              load_convolutional_weights(*(l.ui), fp);
              load_convolutional_weights(*(l.ug), fp);
              load_convolutional_weights(*(l.uo), fp);
          }
          if(l.type == LOCAL){
              int locations = l.out_w*l.out_h;
              int size = l.size*l.size*l.c*l.n*locations;
              fread(l.biases, sizeof(float), l.outputs, fp);
              fread(l.weights, sizeof(float), size, fp);
  #ifdef GPU
              if(gpu_index >= 0){
                  push_local_layer(l);
              }
  #endif
          }
          if (feof(fp)) break;
      }
      fprintf(stderr, "Done! Loaded %d layers from weights-file \n", i);
      fclose(fp);
  }
  
  void load_weights(network *net, char *filename)
  {
      load_weights_upto(net, filename, net->n);
  }
  
  // load network & force - set batch size
  network *load_network_custom(char *cfg, char *weights, int clear, int batch)
  {
      printf(" Try to load cfg: %s, weights: %s, clear = %d \n", cfg, weights, clear);
      network* net = (network*)xcalloc(1, sizeof(network));
      *net = parse_network_cfg_custom(cfg, batch, 1);
      if (weights && weights[0] != 0) {
          printf(" Try to load weights: %s \n", weights);
          load_weights(net, weights);
      }
      fuse_conv_batchnorm(*net);
      if (clear) {
          (*net->seen) = 0;
          (*net->cur_iteration) = 0;
      }
      return net;
  }
  
  // load network & get batch size from cfg-file
  network *load_network(char *cfg, char *weights, int clear)
  {
      printf(" Try to load cfg: %s, clear = %d \n", cfg, clear);
      network* net = (network*)xcalloc(1, sizeof(network));
      *net = parse_network_cfg(cfg);
      if (weights && weights[0] != 0) {
          printf(" Try to load weights: %s \n", weights);
          load_weights(net, weights);
      }
      if (clear) {
          (*net->seen) = 0;
          (*net->cur_iteration) = 0;
      }
      return net;
  }
  