Fix mAP chart

src/detector.c

@@ -222,7 +222,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
 
         i = get_current_batch(net);
         if (net.cudnn_half) {
-            if (i < net.burn_in) printf("\n Tensor Cores are disabled until the first %d iterations are reached.", 2*net.burn_in);
+            if (i < net.burn_in*3) printf("\n Tensor Cores are disabled until the first %d iterations are reached.", 3*net.burn_in);
             else printf("\n Tensor Cores are used.");
         }
         printf("\n %d: %f, %f avg loss, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), (what_time_is_it_now()-time), i*imgs);
@@ -232,6 +232,19 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
             int draw_precision = 0;
             int calc_map_for_each = 4 * train_images_num / (net.batch * net.subdivisions);
             if (calc_map && (i >= (iter_map + calc_map_for_each) || i == net.max_batches) && i >= net.burn_in && i >= 1000) {
+                if (l.random) {
+                    printf("Resizing to initial size: %d x %d \n", init_w, init_h);
+                    args.w = init_w;
+                    args.h = init_h;
+                    pthread_join(load_thread, 0);
+                    train = buffer;
+                    load_thread = load_data(args);
+                    int k;
+                    for (k = 0; k < ngpus; ++k) {
+                        resize_network(nets + k, init_w, init_h);
+                    }
+                    net = nets[0];
+                }
                 iter_map = i;
                 mean_average_precision = validate_detector_map(datacfg, cfgfile, weightfile, 0.25, 0.5, &net);
                 printf("\n mean_average_precision = %f \n", mean_average_precision);

src/image.c

@@ -737,6 +737,7 @@ void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_
     if (draw_precision) {
         static float old_precision = 0;
         static int iteration_old = 0;
+        static int text_iteration_old = 0;
         if(iteration_old == 0) cvPutText(img, "mAP%", cvPoint(0, 12), &font, CV_RGB(255, 0, 0));
 
         cvLine(img,
@@ -744,8 +745,8 @@ void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_
             cvPoint(img_offset + draw_size * (float)current_batch / max_batches, draw_size * (1 - precision)),
             CV_RGB(255, 0, 0), 1, 8, 0);
 
-        old_precision = precision;
-        iteration_old = current_batch;
+        if (((int)(old_precision*10) != (int)(precision*10)) || (current_batch - text_iteration_old) >= max_batches/10) {
+            text_iteration_old = current_batch;
             sprintf(char_buff, "%2.0f%% ", precision * 100);
             CvFont font3;
             cvInitFont(&font3, CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, 0.7, 0, 5, CV_AA);
@@ -755,6 +756,9 @@ void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_
             cvInitFont(&font2, CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, 0.7, 0, 1, CV_AA);
             cvPutText(img, char_buff, cvPoint(pt1.x - 30, draw_size * (1 - precision) + 15), &font2, CV_RGB(200, 0, 0));
         }
+        old_precision = precision;
+        iteration_old = current_batch;
+    }
 
     sprintf(char_buff, "current avg loss = %2.4f    iteration = %d", avg_loss, current_batch);
     pt1.x = 55, pt1.y = 10;
@@ -763,8 +767,6 @@ void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_
     pt1.y += 15;
     cvPutText(img, char_buff, pt1, &font, CV_RGB(0, 0, 0));
 
-
-
     cvShowImage("average loss", img);
     int k = cvWaitKey(20);
     if (k == 's' || current_batch == (max_batches - 1) || current_batch % 100 == 0) {