Set cur_iteration = 0; initially

src/classifier.c

@@ -48,7 +48,10 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus,
         if(weightfile){
             load_weights(&nets[i], weightfile);
         }
-        if(clear) *nets[i].seen = 0;
+        if (clear) {
+            *nets[i].seen = 0;
+            *nets[i].cur_iteration = 0;
+        }
         nets[i].learning_rate *= ngpus;
     }
     srand(time(0));

src/darknet.c

@@ -165,6 +165,7 @@ void oneoff(char *cfgfile, char *weightfile, char *outfile)
     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;
+    *net.cur_iteration = 0;
     save_weights(net, outfile);
 }
 
@@ -176,6 +177,7 @@ void partial(char *cfgfile, char *weightfile, char *outfile, int max)
         load_weights_upto(&net, weightfile, max);
     }
     *net.seen = 0;
+    *net.cur_iteration = 0;
     save_weights_upto(net, outfile, max);
 }
 

src/detector.c

@@ -77,7 +77,10 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
         if (weightfile) {
             load_weights(&nets[k], weightfile);
         }
-        if (clear) *nets[k].seen = 0;
+        if (clear) {
+            *nets[k].seen = 0;
+            *nets[k].cur_iteration = 0;
+        }
         nets[k].learning_rate *= ngpus;
     }
     srand(time(0));

src/parser.c

@@ -1973,7 +1973,10 @@ network *load_network_custom(char *cfg, char *weights, int clear, int batch)
         load_weights(net, weights);
     }
     fuse_conv_batchnorm(*net);
-    if (clear) (*net->seen) = 0;
+    if (clear) {
+        (*net->seen) = 0;
+        (*net->cur_iteration) = 0;
+    }
     return net;
 }
 
@@ -1987,6 +1990,9 @@ network *load_network(char *cfg, char *weights, int clear)
         printf(" Try to load weights: %s \n", weights);
         load_weights(net, weights);
     }
-    if (clear) (*net->seen) = 0;
+    if (clear) {
+        (*net->seen) = 0;
+        (*net->cur_iteration) = 0;
+    }
     return net;
 }

src/rnn.c

@@ -155,7 +155,10 @@ void train_char_rnn(char *cfgfile, char *weightfile, char *filename, int clear,
     fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
     int batch = net.batch;
     int steps = net.time_steps;
-    if(clear) *net.seen = 0;
+    if (clear) {
+        *net.seen = 0;
+        *net.cur_iteration = 0;
+    }
     int i = (*net.seen)/net.batch;
 
     int streams = batch/steps;

src/tag.c

@@ -13,7 +13,10 @@ void train_tag(char *cfgfile, char *weightfile, int clear)
     if(weightfile){
         load_weights(&net, weightfile);
     }
-    if(clear) *net.seen = 0;
+    if (clear) {
+        *net.seen = 0;
+        *net.cur_iteration = 0;
+    }
     printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
     int imgs = 1024;
     list* plist = get_paths("tag/train.list");