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https://github.com/pjreddie/darknet.git
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@ -33,7 +33,6 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus,
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cuda_set_device(gpus[i]);
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#endif
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nets[i] = load_network(cfgfile, weightfile, clear);
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nets[i].learning_rate *= ngpus;
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}
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srand(time(0));
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network net = nets[0];
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@ -313,7 +313,7 @@ void test_coco(char *cfgfile, char *weightfile, char *filename, float thresh)
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if(weightfile){
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load_weights(&net, weightfile);
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}
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detection_layer l = net.layers[net.n-1];
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layer l = net.layers[net.n-1];
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set_batch_network(&net, 1);
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srand(2222222);
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float nms = .4;
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@ -22,12 +22,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
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#ifdef GPU
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cuda_set_device(gpus[i]);
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#endif
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nets[i] = parse_network_cfg(cfgfile);
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if(weightfile){
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load_weights(&nets[i], weightfile);
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}
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if(clear) *nets[i].seen = 0;
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nets[i].learning_rate *= ngpus;
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nets[i] = load_network(cfgfile, weightfile, clear);
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}
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srand(time(0));
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network net = nets[0];
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@ -451,7 +451,7 @@ void train_dcgan(char *cfg, char *weight, char *acfg, char *aweight, int clear,
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printf("%s\n", base);
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network gnet = load_network(cfg, weight, clear);
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network anet = load_network(acfg, aweight, clear);
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float orig_rate = anet.learning_rate;
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//float orig_rate = anet.learning_rate;
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int start = 0;
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int i, j, k;
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@ -494,7 +494,7 @@ void train_dcgan(char *cfg, char *weight, char *acfg, char *aweight, int clear,
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int y_size = gnet.truths*gnet.batch;
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float *imerror = cuda_make_array(0, y_size);
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int ay_size = anet.truths*anet.batch;
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//int ay_size = anet.truths*anet.batch;
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float aloss_avg = -1;
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@ -664,14 +664,14 @@ void train_colorizer(char *cfg, char *weight, char *acfg, char *aweight, int cle
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clock_t time;
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int x_size = net.inputs*net.batch;
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int y_size = x_size;
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//int y_size = x_size;
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net.delta = 0;
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net.train = 1;
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float *pixs = calloc(x_size, sizeof(float));
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float *graypixs = calloc(x_size, sizeof(float));
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float *y = calloc(y_size, sizeof(float));
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//float *y = calloc(y_size, sizeof(float));
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int ay_size = anet.outputs*anet.batch;
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//int ay_size = anet.outputs*anet.batch;
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anet.delta = 0;
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anet.train = 1;
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@ -1,5 +1,7 @@
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#include "darknet.h"
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#include <math.h>
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// ./darknet nightmare cfg/extractor.recon.cfg ~/trained/yolo-coco.conv frame6.png -reconstruct -iters 500 -i 3 -lambda .1 -rate .01 -smooth 2
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float abs_mean(float *x, int n)
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@ -128,11 +130,11 @@ void smooth(image recon, image update, float lambda, int num)
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void reconstruct_picture(network net, float *features, image recon, image update, float rate, float momentum, float lambda, int smooth_size, int iters)
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{
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int iter = 0;
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layer l = get_network_output_layer(net);
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for (iter = 0; iter < iters; ++iter) {
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image delta = make_image(recon.w, recon.h, recon.c);
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#ifdef GPU
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layer l = get_network_output_layer(net);
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cuda_push_array(net.input_gpu, recon.data, recon.w*recon.h*recon.c);
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//cuda_push_array(net.truth_gpu, features, net.truths);
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net.delta_gpu = cuda_make_array(delta.data, delta.w*delta.h*delta.c);
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@ -1,5 +1,7 @@
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#include "darknet.h"
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#include <math.h>
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typedef struct {
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float *x;
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float *y;
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@ -24,7 +24,6 @@ void train_segmenter(char *datacfg, char *cfgfile, char *weightfile, int *gpus,
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load_weights(&nets[i], weightfile);
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}
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if(clear) *nets[i].seen = 0;
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nets[i].learning_rate *= ngpus;
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}
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srand(time(0));
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network net = nets[0];
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@ -76,6 +75,15 @@ void train_segmenter(char *datacfg, char *cfgfile, char *weightfile, int *gpus,
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pthread_join(load_thread, 0);
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train = buffer;
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load_thread = load_data(args);
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image tr = float_to_image(net.w, net.h, 81, train.y.vals[0]);
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image im = float_to_image(net.w, net.h, net.c, train.X.vals[0]);
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image mask = mask_to_rgb(tr);
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show_image(im, "input");
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show_image(mask, "truth");
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#ifdef OPENCV
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cvWaitKey(100);
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#endif
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free_image(mask);
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printf("Loaded: %lf seconds\n", sec(clock()-time));
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time=clock();
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@ -279,7 +279,7 @@ void test_yolo(char *cfgfile, char *weightfile, char *filename, float thresh)
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if(weightfile){
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load_weights(&net, weightfile);
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}
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detection_layer l = net.layers[net.n-1];
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layer l = net.layers[net.n-1];
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set_batch_network(&net, 1);
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srand(2222222);
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clock_t time;
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