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examples/classifier.c

@@ -33,7 +33,6 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus,
         cuda_set_device(gpus[i]);
 #endif
         nets[i] = load_network(cfgfile, weightfile, clear);
-        nets[i].learning_rate *= ngpus;
     }
     srand(time(0));
     network net = nets[0];

examples/coco.c

@@ -313,7 +313,7 @@ void test_coco(char *cfgfile, char *weightfile, char *filename, float thresh)
     if(weightfile){
         load_weights(&net, weightfile);
     }
-    detection_layer l = net.layers[net.n-1];
+    layer l = net.layers[net.n-1];
     set_batch_network(&net, 1);
     srand(2222222);
     float nms = .4;

examples/detector.c

@@ -22,12 +22,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
 #ifdef GPU
         cuda_set_device(gpus[i]);
 #endif
-        nets[i] = parse_network_cfg(cfgfile);
-        if(weightfile){
-            load_weights(&nets[i], weightfile);
-        }
-        if(clear) *nets[i].seen = 0;
-        nets[i].learning_rate *= ngpus;
+        nets[i] = load_network(cfgfile, weightfile, clear);
     }
     srand(time(0));
     network net = nets[0];

examples/lsd.c

@@ -451,7 +451,7 @@ void train_dcgan(char *cfg, char *weight, char *acfg, char *aweight, int clear,
     printf("%s\n", base);
     network gnet = load_network(cfg, weight, clear);
     network anet = load_network(acfg, aweight, clear);
-    float orig_rate = anet.learning_rate;
+    //float orig_rate = anet.learning_rate;
 
     int start = 0;
     int i, j, k;
@@ -494,7 +494,7 @@ void train_dcgan(char *cfg, char *weight, char *acfg, char *aweight, int clear,
     int y_size = gnet.truths*gnet.batch;
     float *imerror = cuda_make_array(0, y_size);
 
-    int ay_size = anet.truths*anet.batch;
+    //int ay_size = anet.truths*anet.batch;
 
     float aloss_avg = -1;
 
@@ -664,14 +664,14 @@ void train_colorizer(char *cfg, char *weight, char *acfg, char *aweight, int cle
     clock_t time;
 
     int x_size = net.inputs*net.batch;
-    int y_size = x_size;
+    //int y_size = x_size;
     net.delta = 0;
     net.train = 1;
     float *pixs = calloc(x_size, sizeof(float));
     float *graypixs = calloc(x_size, sizeof(float));
-    float *y = calloc(y_size, sizeof(float));
+    //float *y = calloc(y_size, sizeof(float));
 
-    int ay_size = anet.outputs*anet.batch;
+    //int ay_size = anet.outputs*anet.batch;
     anet.delta = 0;
     anet.train = 1;
 

examples/nightmare.c

@@ -1,5 +1,7 @@
 #include "darknet.h"
 
+#include <math.h>
+
 // ./darknet nightmare cfg/extractor.recon.cfg ~/trained/yolo-coco.conv frame6.png -reconstruct -iters 500 -i 3 -lambda .1 -rate .01 -smooth 2
 
 float abs_mean(float *x, int n)
@@ -128,11 +130,11 @@ void smooth(image recon, image update, float lambda, int num)
 void reconstruct_picture(network net, float *features, image recon, image update, float rate, float momentum, float lambda, int smooth_size, int iters)
 {
     int iter = 0;
-    layer l = get_network_output_layer(net);
     for (iter = 0; iter < iters; ++iter) {
         image delta = make_image(recon.w, recon.h, recon.c);
 
 #ifdef GPU
+        layer l = get_network_output_layer(net);
         cuda_push_array(net.input_gpu, recon.data, recon.w*recon.h*recon.c);
         //cuda_push_array(net.truth_gpu, features, net.truths);
         net.delta_gpu = cuda_make_array(delta.data, delta.w*delta.h*delta.c);

examples/rnn.c

@@ -1,5 +1,7 @@
 #include "darknet.h"
 
+#include <math.h>
+
 typedef struct {
     float *x;
     float *y;

examples/segmenter.c

@@ -24,7 +24,6 @@ void train_segmenter(char *datacfg, char *cfgfile, char *weightfile, int *gpus,
             load_weights(&nets[i], weightfile);
         }
         if(clear) *nets[i].seen = 0;
-        nets[i].learning_rate *= ngpus;
     }
     srand(time(0));
     network net = nets[0];
@@ -76,6 +75,15 @@ void train_segmenter(char *datacfg, char *cfgfile, char *weightfile, int *gpus,
         pthread_join(load_thread, 0);
         train = buffer;
         load_thread = load_data(args);
+        image tr = float_to_image(net.w, net.h, 81, train.y.vals[0]);
+        image im = float_to_image(net.w, net.h, net.c, train.X.vals[0]);
+        image mask = mask_to_rgb(tr);
+        show_image(im, "input");
+        show_image(mask, "truth");
+#ifdef OPENCV
+        cvWaitKey(100);
+#endif
+        free_image(mask);
 
         printf("Loaded: %lf seconds\n", sec(clock()-time));
         time=clock();

examples/yolo.c

@@ -279,7 +279,7 @@ void test_yolo(char *cfgfile, char *weightfile, char *filename, float thresh)
     if(weightfile){
         load_weights(&net, weightfile);
     }
-    detection_layer l = net.layers[net.n-1];
+    layer l = net.layers[net.n-1];
     set_batch_network(&net, 1);
     srand(2222222);
     clock_t time;