text: #include <stdio.h>
  #include <string.h>
  #include <stdlib.h>
  
  #include "darknet/activation_layer.h"
  #include "darknet/activations.h"
  #include "assert.h"
  #include "darknet/avgpool_layer.h"
  #include "darknet/batchnorm_layer.h"
  #include "darknet/blas.h"
  #include "darknet/connected_layer.h"
  #include "darknet/convolutional_layer.h"
  #include "darknet/cost_layer.h"
  #include "darknet/crnn_layer.h"
  #include "darknet/crop_layer.h"
  #include "darknet/detection_layer.h"
  #include "darknet/dropout_layer.h"
  #include "darknet/gru_layer.h"
  #include "darknet/list.h"
  #include "darknet/local_layer.h"
  #include "darknet/maxpool_layer.h"
  #include "darknet/normalization_layer.h"
  #include "darknet/option_list.h"
  #include "darknet/parser.h"
  #include "darknet/region_layer.h"
  #include "darknet/reorg_layer.h"
  #include "darknet/rnn_layer.h"
  #include "darknet/route_layer.h"
  #include "darknet/shortcut_layer.h"
  #include "darknet/softmax_layer.h"
  #include "darknet/utils.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, "[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, "[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, "[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, "[reorg]")==0) return REORG;
      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;
      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;
      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 size = option_find_int(options, "size",1);
      int stride = option_find_int(options, "stride",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 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 binary = option_find_int_quiet(options, "binary", 0);
      int xnor = option_find_int_quiet(options, "xnor", 0);
  
      convolutional_layer layer = make_convolutional_layer(batch,h,w,c,n,size,stride,padding,activation, batch_normalize, binary, xnor, params.net.adam);
      layer.flipped = option_find_int_quiet(options, "flipped", 0);
      layer.dot = option_find_float_quiet(options, "dot", 0);
      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 output_filters = option_find_int(options, "output_filters",1);
      int hidden_filters = option_find_int(options, "hidden_filters",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);
  
      layer l = make_crnn_layer(params.batch, params.w, params.h, params.c, hidden_filters, output_filters, params.time_steps, activation, batch_normalize);
  
      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;
  }
  
  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, 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);
      return layer;
  }
  
  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);
  
      layer l = make_region_layer(params.batch, params.w, params.h, num, classes, coords);
      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.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_int_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.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){
              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_int_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;
  }
  
  maxpool_layer parse_maxpool(list *options, size_params params)
  {
      int stride = option_find_int(options, "stride",1);
      int size = option_find_int(options, "size",stride);
      int padding = option_find_int_quiet(options, "padding", (size-1)/2);
  
      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,padding);
      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", .5);
      dropout_layer layer = make_dropout_layer(params.batch, params.inputs, probability);
      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);
      return l;
  }
  
  layer parse_shortcut(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_shortcut_layer(batch, index, params.w, params.h, params.c, from.out_w, from.out_h, from.out_c);
  
      char *activation_s = option_find_str(options, "activation", "linear");
      ACTIVATION activation = get_activation(activation_s);
      s.activation = activation;
      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;
  }
  
  route_layer parse_route(list *options, size_params params, network net)
  {
      char *l = option_find(options, "layers");   
      int len = strlen(l);
      if(!l) error("Route Layer must specify input layers");
      int n = 1;
      int i;
      for(i = 0; i < len; ++i){
          if (l[i] == ',') ++n;
      }
  
      int *layers = calloc(n, sizeof(int));
      int *sizes = calloc(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] = net.layers[index].outputs;
      }
      int batch = params.batch;
  
      route_layer layer = make_route_layer(batch, n, layers, sizes);
  
      convolutional_layer first = 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 = net.layers[index];
          if(next.out_w == first.out_w && next.out_h == first.out_h){
              layer.out_c += next.out_c;
          }else{
              layer.out_h = layer.out_w = layer.out_c = 0;
          }
      }
  
      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;
      fprintf(stderr, "Couldn't find policy %s, going with constant\n", s);
      return CONSTANT;
  }
  
  void parse_net_options(list *options, network *net)
  {
      net->batch = option_find_int(options, "batch",1);
      net->learning_rate = option_find_float(options, "learning_rate", .001);
      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->batch /= subdivs;
      net->batch *= net->time_steps;
      net->subdivisions = subdivs;
  
      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->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);
  
      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);
      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){
          char *l = option_find(options, "steps");   
          char *p = option_find(options, "scales");   
          if(!l || !p) error("STEPS policy must have steps and scales in cfg file");
  
          int len = strlen(l);
          int n = 1;
          int i;
          for(i = 0; i < len; ++i){
              if (l[i] == ',') ++n;
          }
          int *steps = calloc(n, sizeof(int));
          float *scales = calloc(n, sizeof(float));
          for(i = 0; i < n; ++i){
              int step    = atoi(l);
              float scale = atof(p);
              l = strchr(l, ',')+1;
              p = strchr(p, ',')+1;
              steps[i] = step;
              scales[i] = scale;
          }
          net->scales = scales;
          net->steps = steps;
          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);
      }
      net->max_batches = option_find_int(options, "max_batches", 0);
  }
  
  int is_network(section *s)
  {
      return (strcmp(s->type, "[net]")==0
              || strcmp(s->type, "[network]")==0);
  }
  
  network parse_network_cfg(char *filename)
  {
      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;
  
      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);
  
      params.h = net.h;
      params.w = net.w;
      params.c = net.c;
      params.inputs = net.inputs;
      params.batch = net.batch;
      params.time_steps = net.time_steps;
      params.net = net;
  
      size_t workspace_size = 0;
      n = n->next;
      int count = 0;
      free_section(s);
      fprintf(stderr, "layer     filters    size              input                output\n");
      while(n){
          params.index = count;
          fprintf(stderr, "%5d ", count);
          s = (section *)n->val;
          options = s->options;
          layer l = {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 == 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);
          }else if(lt == REGION){
              l = parse_region(options, params);
          }else if(lt == DETECTION){
              l = parse_detection(options, params);
          }else if(lt == SOFTMAX){
              l = parse_softmax(options, params);
              net.hierarchy = l.softmax_tree;
          }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 == REORG){
              l = parse_reorg(options, params);
          }else if(lt == AVGPOOL){
              l = parse_avgpool(options, params);
          }else if(lt == ROUTE){
              l = parse_route(options, params, net);
          }else if(lt == SHORTCUT){
              l = parse_shortcut(options, params, net);
          }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;
  #endif
          }else{
              fprintf(stderr, "Type not recognized: %s\n", s->type);
          }
          l.dontload = option_find_int_quiet(options, "dontload", 0);
          l.dontloadscales = option_find_int_quiet(options, "dontloadscales", 0);
          option_unused(options);
          net.layers[count] = l;
          if (l.workspace_size > workspace_size) workspace_size = l.workspace_size;
          free_section(s);
          n = n->next;
          ++count;
          if(n){
              params.h = l.out_h;
              params.w = l.out_w;
              params.c = l.out_c;
              params.inputs = l.outputs;
          }
      }   
      free_list(sections);
      net.outputs = get_network_output_size(net);
      net.output = get_network_output(net);
      if(workspace_size){
          //printf("%ld\n", workspace_size);
  #ifdef GPU
          if(gpu_index >= 0){
              net.workspace = cuda_make_array(0, (workspace_size-1)/sizeof(float)+1);
          }else {
              net.workspace = calloc(1, workspace_size);
          }
  #else
          net.workspace = calloc(1, workspace_size);
  #endif
      }
      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 = malloc(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
      binarize_weights(l.weights, l.n, l.c*l.size*l.size, l.binary_weights);
      int size = l.c*l.size*l.size;
      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_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.n*l.c*l.size*l.size;
      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.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, "w");
      if(!fp) file_error(filename);
  
      int major = 0;
      int minor = 1;
      int revision = 0;
      fwrite(&major, sizeof(int), 1, fp);
      fwrite(&minor, sizeof(int), 1, fp);
      fwrite(&revision, sizeof(int), 1, fp);
      fwrite(net.seen, sizeof(int), 1, fp);
  
      int i;
      for(i = 0; i < net.n && i < cutoff; ++i){
          layer l = net.layers[i];
          if(l.type == CONVOLUTIONAL){
              save_convolutional_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 == 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 = calloc(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.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.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.n*l.c*l.size*l.size;
      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);
          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);
          }
      }
      fread(l.weights, sizeof(float), num, fp);
      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.size*l.size, l.n);
      }
      //if (l.binary) binarize_weights(l.weights, l.n, l.c*l.size*l.size, l.weights);
  #ifdef GPU
      if(gpu_index >= 0){
          push_convolutional_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);
      fread(net->seen, sizeof(int), 1, fp);
      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){
              load_convolutional_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 == 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
          }
      }
      fprintf(stderr, "Done!\n");
      fclose(fp);
  }
  
  void load_weights(network *net, char *filename)
  {
      load_weights_upto(net, filename, net->n);
  }
  
  