From 8f1b4e0962857d402f9d017fcbf387ef0eceb7c4 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Thu, 01 Sep 2016 23:48:41 +0000
Subject: [PATCH] updates and things

---
 src/coco.c |   69 +++++++++++-----------------------
 1 files changed, 23 insertions(+), 46 deletions(-)

diff --git a/src/coco.c b/src/coco.c
index 17d0654..1371870 100644
--- a/src/coco.c
+++ b/src/coco.c
@@ -6,11 +6,14 @@
 #include "utils.h"
 #include "parser.h"
 #include "box.h"
+#include "demo.h"
 
 #ifdef OPENCV
 #include "opencv2/highgui/highgui_c.h"
 #endif
 
+void convert_detections(float *predictions, int classes, int num, int square, int side, int w, int h, float thresh, float **probs, box *boxes, int only_objectness);
+
 char *coco_classes[] = {"person","bicycle","car","motorcycle","airplane","bus","train","truck","boat","traffic light","fire hydrant","stop sign","parking meter","bench","bird","cat","dog","horse","sheep","cow","elephant","bear","zebra","giraffe","backpack","umbrella","handbag","tie","suitcase","frisbee","skis","snowboard","sports ball","kite","baseball bat","baseball glove","skateboard","surfboard","tennis racket","bottle","wine glass","cup","fork","knife","spoon","bowl","banana","apple","sandwich","orange","broccoli","carrot","hot dog","pizza","donut","cake","chair","couch","potted plant","bed","dining table","toilet","tv","laptop","mouse","remote","keyboard","cell phone","microwave","oven","toaster","sink","refrigerator","book","clock","vase","scissors","teddy bear","hair drier","toothbrush"};
 
 int coco_ids[] = {1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90};
@@ -20,7 +23,9 @@
 void train_coco(char *cfgfile, char *weightfile)
 {
     //char *train_images = "/home/pjreddie/data/voc/test/train.txt";
-    char *train_images = "/home/pjreddie/data/coco/train.txt";
+    //char *train_images = "/home/pjreddie/data/coco/train.txt";
+    char *train_images = "data/coco.trainval.txt";
+    //char *train_images = "data/bags.train.list";
     char *backup_directory = "/home/pjreddie/backup/";
     srand(time(0));
     data_seed = time(0);
@@ -59,6 +64,11 @@
     args.d = &buffer;
     args.type = REGION_DATA;
 
+    args.angle = net.angle;
+    args.exposure = net.exposure;
+    args.saturation = net.saturation;
+    args.hue = net.hue;
+
     pthread_t load_thread = load_data_in_thread(args);
     clock_t time;
     //while(i*imgs < N*120){
@@ -85,11 +95,16 @@
         avg_loss = avg_loss*.9 + loss*.1;
 
         printf("%d: %f, %f avg, %f rate, %lf seconds, %d images\n", i, loss, avg_loss, get_current_rate(net), sec(clock()-time), i*imgs);
-        if(i%1000==0){
+        if(i%1000==0 || (i < 1000 && i%100 == 0)){
             char buff[256];
             sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
             save_weights(net, buff);
         }
+        if(i%100==0){
+            char buff[256];
+            sprintf(buff, "%s/%s.backup", backup_directory, base);
+            save_weights(net, buff);
+        }
         free_data(train);
     }
     char buff[256];
@@ -97,34 +112,6 @@
     save_weights(net, buff);
 }
 
-void convert_coco_detections(float *predictions, int classes, int num, int square, int side, int w, int h, float thresh, float **probs, box *boxes, int only_objectness)
-{
-    int i,j,n;
-    //int per_cell = 5*num+classes;
-    for (i = 0; i < side*side; ++i){
-        int row = i / side;
-        int col = i % side;
-        for(n = 0; n < num; ++n){
-            int index = i*num + n;
-            int p_index = side*side*classes + i*num + n;
-            float scale = predictions[p_index];
-            int box_index = side*side*(classes + num) + (i*num + n)*4;
-            boxes[index].x = (predictions[box_index + 0] + col) / side * w;
-            boxes[index].y = (predictions[box_index + 1] + row) / side * h;
-            boxes[index].w = pow(predictions[box_index + 2], (square?2:1)) * w;
-            boxes[index].h = pow(predictions[box_index + 3], (square?2:1)) * h;
-            for(j = 0; j < classes; ++j){
-                int class_index = i*classes;
-                float prob = scale*predictions[class_index+j];
-                probs[index][j] = (prob > thresh) ? prob : 0;
-            }
-            if(only_objectness){
-                probs[index][0] = scale;
-            }
-        }
-    }
-}
-
 void print_cocos(FILE *fp, int image_id, box *boxes, float **probs, int num_boxes, int classes, int w, int h)
 {
     int i, j;
@@ -234,7 +221,7 @@
             float *predictions = network_predict(net, X);
             int w = val[t].w;
             int h = val[t].h;
-            convert_coco_detections(predictions, classes, l.n, square, side, w, h, thresh, probs, boxes, 0);
+            convert_detections(predictions, classes, l.n, square, side, w, h, thresh, probs, boxes, 0);
             if (nms) do_nms_sort(boxes, probs, side*side*l.n, classes, iou_thresh);
             print_cocos(fp, image_id, boxes, probs, side*side*l.n, classes, w, h);
             free_image(val[t]);
@@ -297,7 +284,7 @@
         image sized = resize_image(orig, net.w, net.h);
         char *id = basecfg(path);
         float *predictions = network_predict(net, sized.data);
-        convert_coco_detections(predictions, classes, l.n, square, side, 1, 1, thresh, probs, boxes, 1);
+        convert_detections(predictions, classes, l.n, square, side, 1, 1, thresh, probs, boxes, 1);
         if (nms) do_nms(boxes, probs, side*side*l.n, 1, nms_thresh);
 
         char *labelpath = find_replace(path, "images", "labels");
@@ -369,12 +356,11 @@
         time=clock();
         float *predictions = network_predict(net, X);
         printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
-        convert_coco_detections(predictions, l.classes, l.n, l.sqrt, l.side, 1, 1, thresh, probs, boxes, 0);
+        convert_detections(predictions, l.classes, l.n, l.sqrt, l.side, 1, 1, thresh, probs, boxes, 0);
         if (nms) do_nms_sort(boxes, probs, l.side*l.side*l.n, l.classes, nms);
         draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, coco_classes, coco_labels, 80);
+        save_image(im, "prediction");
         show_image(im, "predictions");
-
-        show_image(sized, "resized");
         free_image(im);
         free_image(sized);
 #ifdef OPENCV
@@ -385,12 +371,6 @@
     }
 }
 
-#ifdef OPENCV
-#ifdef GPU
-void demo_coco(char *cfgfile, char *weightfile, float thresh, int cam_index);
-#endif
-#endif
-
 void run_coco(int argc, char **argv)
 {
     int i;
@@ -401,6 +381,7 @@
     }
     float thresh = find_float_arg(argc, argv, "-thresh", .2);
     int cam_index = find_int_arg(argc, argv, "-c", 0);
+    int frame_skip = find_int_arg(argc, argv, "-s", 0);
 
     if(argc < 4){
         fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
@@ -414,9 +395,5 @@
     else if(0==strcmp(argv[2], "train")) train_coco(cfg, weights);
     else if(0==strcmp(argv[2], "valid")) validate_coco(cfg, weights);
     else if(0==strcmp(argv[2], "recall")) validate_coco_recall(cfg, weights);
-#ifdef OPENCV
-#ifdef GPU
-    else if(0==strcmp(argv[2], "demo")) demo_coco(cfg, weights, thresh, cam_index);
-#endif
-#endif
+    else if(0==strcmp(argv[2], "demo")) demo(cfg, weights, thresh, cam_index, filename, coco_classes, coco_labels, 80, frame_skip);
 }

--
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