text: #include "darknet.h"
  #include <time.h>
  #include <stdlib.h>
  #include <stdio.h>
  #if defined(_MSC_VER) && defined(_DEBUG)
  #include <crtdbg.h>
  #endif
  
  #include "parser.h"
  #include "utils.h"
  #include "dark_cuda.h"
  #include "blas.h"
  #include "connected_layer.h"
  
  
  extern void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top);
  extern void run_voxel(int argc, char **argv);
  extern void run_yolo(int argc, char **argv);
  extern void run_detector(int argc, char **argv);
  extern void run_coco(int argc, char **argv);
  extern void run_writing(int argc, char **argv);
  extern void run_captcha(int argc, char **argv);
  extern void run_nightmare(int argc, char **argv);
  extern void run_dice(int argc, char **argv);
  extern void run_compare(int argc, char **argv);
  extern void run_classifier(int argc, char **argv);
  extern void run_char_rnn(int argc, char **argv);
  extern void run_vid_rnn(int argc, char **argv);
  extern void run_tag(int argc, char **argv);
  extern void run_cifar(int argc, char **argv);
  extern void run_go(int argc, char **argv);
  extern void run_art(int argc, char **argv);
  extern void run_super(int argc, char **argv);
  
  void average(int argc, char *argv[])
  {
      char *cfgfile = argv[2];
      char *outfile = argv[3];
      gpu_index = -1;
      network net = parse_network_cfg(cfgfile);
      network sum = parse_network_cfg(cfgfile);
  
      char *weightfile = argv[4];
      load_weights(&sum, weightfile);
  
      int i, j;
      int n = argc - 5;
      for(i = 0; i < n; ++i){
          weightfile = argv[i+5];
          load_weights(&net, weightfile);
          for(j = 0; j < net.n; ++j){
              layer l = net.layers[j];
              layer out = sum.layers[j];
              if(l.type == CONVOLUTIONAL){
                  int num = l.n*l.c*l.size*l.size;
                  axpy_cpu(l.n, 1, l.biases, 1, out.biases, 1);
                  axpy_cpu(num, 1, l.weights, 1, out.weights, 1);
                  if(l.batch_normalize){
                      axpy_cpu(l.n, 1, l.scales, 1, out.scales, 1);
                      axpy_cpu(l.n, 1, l.rolling_mean, 1, out.rolling_mean, 1);
                      axpy_cpu(l.n, 1, l.rolling_variance, 1, out.rolling_variance, 1);
                  }
              }
              if(l.type == CONNECTED){
                  axpy_cpu(l.outputs, 1, l.biases, 1, out.biases, 1);
                  axpy_cpu(l.outputs*l.inputs, 1, l.weights, 1, out.weights, 1);
              }
          }
      }
      n = n+1;
      for(j = 0; j < net.n; ++j){
          layer l = sum.layers[j];
          if(l.type == CONVOLUTIONAL){
              int num = l.n*l.c*l.size*l.size;
              scal_cpu(l.n, 1./n, l.biases, 1);
              scal_cpu(num, 1./n, l.weights, 1);
                  if(l.batch_normalize){
                      scal_cpu(l.n, 1./n, l.scales, 1);
                      scal_cpu(l.n, 1./n, l.rolling_mean, 1);
                      scal_cpu(l.n, 1./n, l.rolling_variance, 1);
                  }
          }
          if(l.type == CONNECTED){
              scal_cpu(l.outputs, 1./n, l.biases, 1);
              scal_cpu(l.outputs*l.inputs, 1./n, l.weights, 1);
          }
      }
      save_weights(sum, outfile);
  }
  
  void speed(char *cfgfile, int tics)
  {
      if (tics == 0) tics = 1000;
      network net = parse_network_cfg(cfgfile);
      set_batch_network(&net, 1);
      int i;
      time_t start = time(0);
      image im = make_image(net.w, net.h, net.c);
      for(i = 0; i < tics; ++i){
          network_predict(net, im.data);
      }
      double t = difftime(time(0), start);
      printf("\n%d evals, %f Seconds\n", tics, t);
      printf("Speed: %f sec/eval\n", t/tics);
      printf("Speed: %f Hz\n", tics/t);
  }
  
  void operations(char *cfgfile)
  {
      gpu_index = -1;
      network net = parse_network_cfg(cfgfile);
      int i;
      long ops = 0;
      for(i = 0; i < net.n; ++i){
          layer l = net.layers[i];
          if(l.type == CONVOLUTIONAL){
              ops += 2l * l.n * l.size*l.size*l.c * l.out_h*l.out_w;
          } else if(l.type == CONNECTED){
              ops += 2l * l.inputs * l.outputs;
  		} else if (l.type == RNN){
              ops += 2l * l.input_layer->inputs * l.input_layer->outputs;
              ops += 2l * l.self_layer->inputs * l.self_layer->outputs;
              ops += 2l * l.output_layer->inputs * l.output_layer->outputs;
          } else if (l.type == GRU){
              ops += 2l * l.uz->inputs * l.uz->outputs;
              ops += 2l * l.uh->inputs * l.uh->outputs;
              ops += 2l * l.ur->inputs * l.ur->outputs;
              ops += 2l * l.wz->inputs * l.wz->outputs;
              ops += 2l * l.wh->inputs * l.wh->outputs;
              ops += 2l * l.wr->inputs * l.wr->outputs;
          } else if (l.type == LSTM){
              ops += 2l * l.uf->inputs * l.uf->outputs;
              ops += 2l * l.ui->inputs * l.ui->outputs;
              ops += 2l * l.ug->inputs * l.ug->outputs;
              ops += 2l * l.uo->inputs * l.uo->outputs;
              ops += 2l * l.wf->inputs * l.wf->outputs;
              ops += 2l * l.wi->inputs * l.wi->outputs;
              ops += 2l * l.wg->inputs * l.wg->outputs;
              ops += 2l * l.wo->inputs * l.wo->outputs;
          }
      }
      printf("Floating Point Operations: %ld\n", ops);
      printf("Floating Point Operations: %.2f Bn\n", (float)ops/1000000000.);
  }
  
  void oneoff(char *cfgfile, char *weightfile, char *outfile)
  {
      gpu_index = -1;
      network net = parse_network_cfg(cfgfile);
      int oldn = net.layers[net.n - 2].n;
      int c = net.layers[net.n - 2].c;
      net.layers[net.n - 2].n = 9372;
      net.layers[net.n - 2].biases += 5;
      net.layers[net.n - 2].weights += 5*c;
      if(weightfile){
          load_weights(&net, weightfile);
      }
      net.layers[net.n - 2].biases -= 5;
      net.layers[net.n - 2].weights -= 5*c;
      net.layers[net.n - 2].n = oldn;
      printf("%d\n", oldn);
      layer l = net.layers[net.n - 2];
      copy_cpu(l.n/3, l.biases, 1, l.biases +   l.n/3, 1);
      copy_cpu(l.n/3, l.biases, 1, l.biases + 2*l.n/3, 1);
      copy_cpu(l.n/3*l.c, l.weights, 1, l.weights +   l.n/3*l.c, 1);
      copy_cpu(l.n/3*l.c, l.weights, 1, l.weights + 2*l.n/3*l.c, 1);
      *net.seen = 0;
      save_weights(net, outfile);
  }
  
  void partial(char *cfgfile, char *weightfile, char *outfile, int max)
  {
      gpu_index = -1;
      network net = parse_network_cfg(cfgfile);
      if(weightfile){
          load_weights_upto(&net, weightfile, max);
      }
      *net.seen = 0;
      save_weights_upto(net, outfile, max);
  }
  
  #include "convolutional_layer.h"
  void rescale_net(char *cfgfile, char *weightfile, char *outfile)
  {
      gpu_index = -1;
      network net = parse_network_cfg(cfgfile);
      if(weightfile){
          load_weights(&net, weightfile);
      }
      int i;
      for(i = 0; i < net.n; ++i){
          layer l = net.layers[i];
          if(l.type == CONVOLUTIONAL){
              rescale_weights(l, 2, -.5);
              break;
          }
      }
      save_weights(net, outfile);
  }
  
  void rgbgr_net(char *cfgfile, char *weightfile, char *outfile)
  {
      gpu_index = -1;
      network net = parse_network_cfg(cfgfile);
      if(weightfile){
          load_weights(&net, weightfile);
      }
      int i;
      for(i = 0; i < net.n; ++i){
          layer l = net.layers[i];
          if(l.type == CONVOLUTIONAL){
              rgbgr_weights(l);
              break;
          }
      }
      save_weights(net, outfile);
  }
  
  void reset_normalize_net(char *cfgfile, char *weightfile, char *outfile)
  {
      gpu_index = -1;
      network net = parse_network_cfg(cfgfile);
      if (weightfile) {
          load_weights(&net, weightfile);
      }
      int i;
      for (i = 0; i < net.n; ++i) {
          layer l = net.layers[i];
          if (l.type == CONVOLUTIONAL && l.batch_normalize) {
              denormalize_convolutional_layer(l);
          }
          if (l.type == CONNECTED && l.batch_normalize) {
              denormalize_connected_layer(l);
          }
          if (l.type == GRU && l.batch_normalize) {
              denormalize_connected_layer(*l.input_z_layer);
              denormalize_connected_layer(*l.input_r_layer);
              denormalize_connected_layer(*l.input_h_layer);
              denormalize_connected_layer(*l.state_z_layer);
              denormalize_connected_layer(*l.state_r_layer);
              denormalize_connected_layer(*l.state_h_layer);
          }
          if (l.type == LSTM && l.batch_normalize) {
              denormalize_connected_layer(*l.wf);
              denormalize_connected_layer(*l.wi);
              denormalize_connected_layer(*l.wg);
              denormalize_connected_layer(*l.wo);
              denormalize_connected_layer(*l.uf);
              denormalize_connected_layer(*l.ui);
              denormalize_connected_layer(*l.ug);
              denormalize_connected_layer(*l.uo);
  		}
      }
      save_weights(net, outfile);
  }
  
  layer normalize_layer(layer l, int n)
  {
      int j;
      l.batch_normalize=1;
      l.scales = (float*)calloc(n, sizeof(float));
      for(j = 0; j < n; ++j){
          l.scales[j] = 1;
      }
      l.rolling_mean = (float*)calloc(n, sizeof(float));
      l.rolling_variance = (float*)calloc(n, sizeof(float));
      return l;
  }
  
  void normalize_net(char *cfgfile, char *weightfile, char *outfile)
  {
      gpu_index = -1;
      network net = parse_network_cfg(cfgfile);
      if(weightfile){
          load_weights(&net, weightfile);
      }
      int i;
      for(i = 0; i < net.n; ++i){
          layer l = net.layers[i];
          if(l.type == CONVOLUTIONAL && !l.batch_normalize){
              net.layers[i] = normalize_layer(l, l.n);
          }
          if (l.type == CONNECTED && !l.batch_normalize) {
              net.layers[i] = normalize_layer(l, l.outputs);
          }
          if (l.type == GRU && l.batch_normalize) {
              *l.input_z_layer = normalize_layer(*l.input_z_layer, l.input_z_layer->outputs);
              *l.input_r_layer = normalize_layer(*l.input_r_layer, l.input_r_layer->outputs);
              *l.input_h_layer = normalize_layer(*l.input_h_layer, l.input_h_layer->outputs);
              *l.state_z_layer = normalize_layer(*l.state_z_layer, l.state_z_layer->outputs);
              *l.state_r_layer = normalize_layer(*l.state_r_layer, l.state_r_layer->outputs);
              *l.state_h_layer = normalize_layer(*l.state_h_layer, l.state_h_layer->outputs);
              net.layers[i].batch_normalize=1;
          }
          if (l.type == LSTM && l.batch_normalize) {
              *l.wf = normalize_layer(*l.wf, l.wf->outputs);
              *l.wi = normalize_layer(*l.wi, l.wi->outputs);
              *l.wg = normalize_layer(*l.wg, l.wg->outputs);
              *l.wo = normalize_layer(*l.wo, l.wo->outputs);
              *l.uf = normalize_layer(*l.uf, l.uf->outputs);
              *l.ui = normalize_layer(*l.ui, l.ui->outputs);
              *l.ug = normalize_layer(*l.ug, l.ug->outputs);
              *l.uo = normalize_layer(*l.uo, l.uo->outputs);
              net.layers[i].batch_normalize=1;
          }
      }
      save_weights(net, outfile);
  }
  
  void statistics_net(char *cfgfile, char *weightfile)
  {
      gpu_index = -1;
      network net = parse_network_cfg(cfgfile);
      if (weightfile) {
          load_weights(&net, weightfile);
      }
      int i;
      for (i = 0; i < net.n; ++i) {
          layer l = net.layers[i];
          if (l.type == CONNECTED && l.batch_normalize) {
              printf("Connected Layer %d\n", i);
              statistics_connected_layer(l);
          }
          if (l.type == GRU && l.batch_normalize) {
              printf("GRU Layer %d\n", i);
              printf("Input Z\n");
              statistics_connected_layer(*l.input_z_layer);
              printf("Input R\n");
              statistics_connected_layer(*l.input_r_layer);
              printf("Input H\n");
              statistics_connected_layer(*l.input_h_layer);
              printf("State Z\n");
              statistics_connected_layer(*l.state_z_layer);
              printf("State R\n");
              statistics_connected_layer(*l.state_r_layer);
              printf("State H\n");
              statistics_connected_layer(*l.state_h_layer);
          }
          if (l.type == LSTM && l.batch_normalize) {
              printf("LSTM Layer %d\n", i);
              printf("wf\n");
              statistics_connected_layer(*l.wf);
              printf("wi\n");
              statistics_connected_layer(*l.wi);
              printf("wg\n");
              statistics_connected_layer(*l.wg);
              printf("wo\n");
              statistics_connected_layer(*l.wo);
              printf("uf\n");
              statistics_connected_layer(*l.uf);
              printf("ui\n");
              statistics_connected_layer(*l.ui);
              printf("ug\n");
              statistics_connected_layer(*l.ug);
              printf("uo\n");
              statistics_connected_layer(*l.uo);
          }
          printf("\n");
      }
  }
  
  void denormalize_net(char *cfgfile, char *weightfile, char *outfile)
  {
      gpu_index = -1;
      network net = parse_network_cfg(cfgfile);
      if (weightfile) {
          load_weights(&net, weightfile);
      }
      int i;
      for (i = 0; i < net.n; ++i) {
          layer l = net.layers[i];
          if (l.type == CONVOLUTIONAL && l.batch_normalize) {
              denormalize_convolutional_layer(l);
              net.layers[i].batch_normalize=0;
          }
          if (l.type == CONNECTED && l.batch_normalize) {
              denormalize_connected_layer(l);
              net.layers[i].batch_normalize=0;
          }
          if (l.type == GRU && l.batch_normalize) {
              denormalize_connected_layer(*l.input_z_layer);
              denormalize_connected_layer(*l.input_r_layer);
              denormalize_connected_layer(*l.input_h_layer);
              denormalize_connected_layer(*l.state_z_layer);
              denormalize_connected_layer(*l.state_r_layer);
              denormalize_connected_layer(*l.state_h_layer);
              l.input_z_layer->batch_normalize = 0;
              l.input_r_layer->batch_normalize = 0;
              l.input_h_layer->batch_normalize = 0;
              l.state_z_layer->batch_normalize = 0;
              l.state_r_layer->batch_normalize = 0;
              l.state_h_layer->batch_normalize = 0;
              net.layers[i].batch_normalize=0;
          }
          if (l.type == GRU && l.batch_normalize) {
              denormalize_connected_layer(*l.wf);
              denormalize_connected_layer(*l.wi);
              denormalize_connected_layer(*l.wg);
              denormalize_connected_layer(*l.wo);
              denormalize_connected_layer(*l.uf);
              denormalize_connected_layer(*l.ui);
              denormalize_connected_layer(*l.ug);
              denormalize_connected_layer(*l.uo);
              l.wf->batch_normalize = 0;
              l.wi->batch_normalize = 0;
              l.wg->batch_normalize = 0;
              l.wo->batch_normalize = 0;
              l.uf->batch_normalize = 0;
              l.ui->batch_normalize = 0;
              l.ug->batch_normalize = 0;
              l.uo->batch_normalize = 0;
              net.layers[i].batch_normalize=0;
  		}
      }
      save_weights(net, outfile);
  }
  
  void visualize(char *cfgfile, char *weightfile)
  {
      network net = parse_network_cfg(cfgfile);
      if(weightfile){
          load_weights(&net, weightfile);
      }
      visualize_network(net);
  #ifdef OPENCV
      wait_until_press_key_cv();
  #endif
  }
  
  int main(int argc, char **argv)
  {
  #ifdef _DEBUG
  	_CrtSetDbgFlag(_CRTDBG_ALLOC_MEM_DF | _CRTDBG_LEAK_CHECK_DF);
  #endif
  
  	int i;
  	for (i = 0; i < argc; ++i) {
  		if (!argv[i]) continue;
  		strip_args(argv[i]);
  	}
  
      //test_resize("data/bad.jpg");
      //test_box();
      //test_convolutional_layer();
      if(argc < 2){
          fprintf(stderr, "usage: %s <function>\n", argv[0]);
          return 0;
      }
      gpu_index = find_int_arg(argc, argv, "-i", 0);
      if(find_arg(argc, argv, "-nogpu")) {
          gpu_index = -1;
          printf("\n Currently Darknet doesn't support -nogpu flag. If you want to use CPU - please compile Darknet with GPU=0 in the Makefile, or compile darknet_no_gpu.sln on Windows.\n");
          exit(-1);
      }
  
  #ifndef GPU
      gpu_index = -1;
  #else
      if(gpu_index >= 0){
          cuda_set_device(gpu_index);
          CHECK_CUDA(cudaSetDeviceFlags(cudaDeviceScheduleBlockingSync));
      }
  #endif
  
      if (0 == strcmp(argv[1], "average")){
          average(argc, argv);
      } else if (0 == strcmp(argv[1], "yolo")){
          run_yolo(argc, argv);
      } else if (0 == strcmp(argv[1], "voxel")){
          run_voxel(argc, argv);
      } else if (0 == strcmp(argv[1], "super")){
          run_super(argc, argv);
      } else if (0 == strcmp(argv[1], "detector")){
          run_detector(argc, argv);
      } else if (0 == strcmp(argv[1], "detect")){
          float thresh = find_float_arg(argc, argv, "-thresh", .24);
  		int ext_output = find_arg(argc, argv, "-ext_output");
          char *filename = (argc > 4) ? argv[4]: 0;
          test_detector("cfg/coco.data", argv[2], argv[3], filename, thresh, 0.5, 0, ext_output, 0, NULL);
      } else if (0 == strcmp(argv[1], "cifar")){
          run_cifar(argc, argv);
      } else if (0 == strcmp(argv[1], "go")){
          run_go(argc, argv);
      } else if (0 == strcmp(argv[1], "rnn")){
          run_char_rnn(argc, argv);
      } else if (0 == strcmp(argv[1], "vid")){
          run_vid_rnn(argc, argv);
      } else if (0 == strcmp(argv[1], "coco")){
          run_coco(argc, argv);
      } else if (0 == strcmp(argv[1], "classify")){
          predict_classifier("cfg/imagenet1k.data", argv[2], argv[3], argv[4], 5);
      } else if (0 == strcmp(argv[1], "classifier")){
          run_classifier(argc, argv);
      } else if (0 == strcmp(argv[1], "art")){
          run_art(argc, argv);
      } else if (0 == strcmp(argv[1], "tag")){
          run_tag(argc, argv);
      } else if (0 == strcmp(argv[1], "compare")){
          run_compare(argc, argv);
      } else if (0 == strcmp(argv[1], "dice")){
          run_dice(argc, argv);
      } else if (0 == strcmp(argv[1], "writing")){
          run_writing(argc, argv);
      } else if (0 == strcmp(argv[1], "3d")){
          composite_3d(argv[2], argv[3], argv[4], (argc > 5) ? atof(argv[5]) : 0);
      } else if (0 == strcmp(argv[1], "test")){
          test_resize(argv[2]);
      } else if (0 == strcmp(argv[1], "captcha")){
          run_captcha(argc, argv);
      } else if (0 == strcmp(argv[1], "nightmare")){
          run_nightmare(argc, argv);
      } else if (0 == strcmp(argv[1], "rgbgr")){
          rgbgr_net(argv[2], argv[3], argv[4]);
      } else if (0 == strcmp(argv[1], "reset")){
          reset_normalize_net(argv[2], argv[3], argv[4]);
      } else if (0 == strcmp(argv[1], "denormalize")){
          denormalize_net(argv[2], argv[3], argv[4]);
      } else if (0 == strcmp(argv[1], "statistics")){
          statistics_net(argv[2], argv[3]);
      } else if (0 == strcmp(argv[1], "normalize")){
          normalize_net(argv[2], argv[3], argv[4]);
      } else if (0 == strcmp(argv[1], "rescale")){
          rescale_net(argv[2], argv[3], argv[4]);
      } else if (0 == strcmp(argv[1], "ops")){
          operations(argv[2]);
      } else if (0 == strcmp(argv[1], "speed")){
          speed(argv[2], (argc > 3 && argv[3]) ? atoi(argv[3]) : 0);
      } else if (0 == strcmp(argv[1], "oneoff")){
          oneoff(argv[2], argv[3], argv[4]);
      } else if (0 == strcmp(argv[1], "partial")){
          partial(argv[2], argv[3], argv[4], atoi(argv[5]));
      } else if (0 == strcmp(argv[1], "average")){
          average(argc, argv);
      } else if (0 == strcmp(argv[1], "visualize")){
          visualize(argv[2], (argc > 3) ? argv[3] : 0);
      } else if (0 == strcmp(argv[1], "imtest")){
          test_resize(argv[2]);
      } else {
          fprintf(stderr, "Not an option: %s\n", argv[1]);
      }
      return 0;
  }
  