From 8bcdee86585f496afe1a8a38d608ea0504a11243 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Tue, 01 Sep 2015 18:22:03 +0000
Subject: [PATCH] Some bug fixes, random stuff
---
src/coco.c | 279 ++++++++++++++++++++++++++++++++++++++++++++++++-------
1 files changed, 244 insertions(+), 35 deletions(-)
diff --git a/src/coco.c b/src/coco.c
index d2a108a..62ae429 100644
--- a/src/coco.c
+++ b/src/coco.c
@@ -17,7 +17,7 @@
void draw_coco(image im, float *pred, int side, char *label)
{
- int classes = 81;
+ int classes = 1;
int elems = 4+classes;
int j;
int r, c;
@@ -26,10 +26,9 @@
for(c = 0; c < side; ++c){
j = (r*side + c) * elems;
int class = max_index(pred+j, classes);
- if (class == 0) continue;
if (pred[j+class] > 0.2){
int width = pred[j+class]*5 + 1;
- printf("%f %s\n", pred[j+class], coco_classes[class-1]);
+ printf("%f %s\n", pred[j+class], "object"); //coco_classes[class-1]);
float red = get_color(0,class,classes);
float green = get_color(1,class,classes);
float blue = get_color(2,class,classes);
@@ -37,10 +36,10 @@
j += classes;
box predict = {pred[j+0], pred[j+1], pred[j+2], pred[j+3]};
- box anchor = {(c+.5)/side, (r+.5)/side, .5, .5};
- box decode = decode_box(predict, anchor);
+ predict.x = (predict.x+c)/side;
+ predict.y = (predict.y+r)/side;
- draw_bbox(im, decode, width, red, green, blue);
+ draw_bbox(im, predict, width, red, green, blue);
}
}
}
@@ -49,7 +48,8 @@
void train_coco(char *cfgfile, char *weightfile)
{
- char *train_images = "/home/pjreddie/data/coco/train.txt";
+ //char *train_images = "/home/pjreddie/data/coco/train.txt";
+ char *train_images = "/home/pjreddie/data/voc/test/train.txt";
char *backup_directory = "/home/pjreddie/backup/";
srand(time(0));
data_seed = time(0);
@@ -65,8 +65,11 @@
int i = net.seen/imgs;
data train, buffer;
- int classes = 81;
- int side = 7;
+
+ layer l = net.layers[net.n - 1];
+
+ int side = l.side;
+ int classes = l.classes;
list *plist = get_paths(train_images);
int N = plist->size;
@@ -95,9 +98,9 @@
printf("Loaded: %lf seconds\n", sec(clock()-time));
/*
- image im = float_to_image(net.w, net.h, 3, train.X.vals[114]);
+ image im = float_to_image(net.w, net.h, 3, train.X.vals[113]);
image copy = copy_image(im);
- draw_coco(copy, train.y.vals[114], 7, "truth");
+ draw_coco(copy, train.y.vals[113], 7, "truth");
cvWaitKey(0);
free_image(copy);
*/
@@ -109,12 +112,19 @@
avg_loss = avg_loss*.9 + loss*.1;
printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), i*imgs);
- if((i-1)*imgs <= 80*N && i*imgs > N*80){
- fprintf(stderr, "First stage done.\n");
+ if((i-1)*imgs <= N && i*imgs > N){
+ fprintf(stderr, "First stage done\n");
+ net.learning_rate *= 10;
char buff[256];
sprintf(buff, "%s/%s_first_stage.weights", backup_directory, base);
save_weights(net, buff);
- return;
+ }
+
+ if((i-1)*imgs <= 80*N && i*imgs > N*80){
+ fprintf(stderr, "Second stage done.\n");
+ char buff[256];
+ sprintf(buff, "%s/%s_second_stage.weights", backup_directory, base);
+ save_weights(net, buff);
}
if(i%1000==0){
char buff[256];
@@ -128,25 +138,52 @@
save_weights(net, buff);
}
-void convert_cocos(float *predictions, int classes, int objectness, int background, int num_boxes, int w, int h, float thresh, float **probs, box *boxes)
+void get_probs(float *predictions, int total, int classes, int inc, float **probs)
{
int i,j;
- int per_box = 4+classes+(background || objectness);
- for (i = 0; i < num_boxes*num_boxes; ++i){
- float scale = 1;
- if(objectness) scale = 1-predictions[i*per_box];
- int offset = i*per_box+(background||objectness);
+ for (i = 0; i < total; ++i){
+ int index = i*inc;
+ float scale = predictions[index];
+ probs[i][0] = scale;
for(j = 0; j < classes; ++j){
- float prob = scale*predictions[offset+j];
+ probs[i][j] = scale*predictions[index+j+1];
+ }
+ }
+}
+void get_boxes(float *predictions, int n, int num_boxes, int per_box, box *boxes)
+{
+ int i,j;
+ for (i = 0; i < num_boxes*num_boxes; ++i){
+ for(j = 0; j < n; ++j){
+ int index = i*n+j;
+ int offset = index*per_box;
+ int row = i / num_boxes;
+ int col = i % num_boxes;
+ boxes[index].x = (predictions[offset + 0] + col) / num_boxes;
+ boxes[index].y = (predictions[offset + 1] + row) / num_boxes;
+ boxes[index].w = predictions[offset + 2];
+ boxes[index].h = predictions[offset + 3];
+ }
+ }
+}
+
+void convert_cocos(float *predictions, int classes, int num_boxes, int num, int w, int h, float thresh, float **probs, box *boxes)
+{
+ int i,j;
+ int per_box = 4+classes;
+ for (i = 0; i < num_boxes*num_boxes*num; ++i){
+ int offset = i*per_box;
+ for(j = 0; j < classes; ++j){
+ float prob = predictions[offset+j];
probs[i][j] = (prob > thresh) ? prob : 0;
}
int row = i / num_boxes;
int col = i % num_boxes;
offset += classes;
- boxes[i].x = (predictions[offset + 0] + col) / num_boxes * w;
- boxes[i].y = (predictions[offset + 1] + row) / num_boxes * h;
- boxes[i].w = pow(predictions[offset + 2], 2) * w;
- boxes[i].h = pow(predictions[offset + 3], 2) * h;
+ boxes[i].x = (predictions[offset + 0] + col) / num_boxes;
+ boxes[i].y = (predictions[offset + 1] + row) / num_boxes;
+ boxes[i].w = predictions[offset + 2];
+ boxes[i].h = predictions[offset + 3];
}
}
@@ -181,6 +218,179 @@
return atoi(p+1);
}
+void validate_recall(char *cfgfile, char *weightfile)
+{
+ network net = parse_network_cfg(cfgfile);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ set_batch_network(&net, 1);
+ fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
+ srand(time(0));
+
+ char *val_images = "/home/pjreddie/data/voc/test/2007_test.txt";
+ list *plist = get_paths(val_images);
+ char **paths = (char **)list_to_array(plist);
+
+ layer l = net.layers[net.n - 1];
+
+ int num_boxes = l.side;
+ int num = l.n;
+ int classes = l.classes;
+
+ int j;
+
+ box *boxes = calloc(num_boxes*num_boxes*num, sizeof(box));
+ float **probs = calloc(num_boxes*num_boxes*num, sizeof(float *));
+ for(j = 0; j < num_boxes*num_boxes*num; ++j) probs[j] = calloc(classes+1, sizeof(float *));
+
+ int N = plist->size;
+ int i=0;
+ int k;
+
+ float iou_thresh = .5;
+ float thresh = .1;
+ int total = 0;
+ int correct = 0;
+ float avg_iou = 0;
+ int nms = 0;
+ int proposals = 0;
+
+ for (i = 0; i < N; ++i) {
+ char *path = paths[i];
+ image orig = load_image_color(path, 0, 0);
+ image resized = resize_image(orig, net.w, net.h);
+
+ float *X = resized.data;
+ float *predictions = network_predict(net, X);
+ get_boxes(predictions+1+classes, num, num_boxes, 5+classes, boxes);
+ get_probs(predictions, num*num_boxes*num_boxes, classes, 5+classes, probs);
+ if (nms) do_nms(boxes, probs, num*num_boxes*num_boxes, (classes>0) ? classes : 1, iou_thresh);
+
+ char *labelpath = find_replace(path, "images", "labels");
+ labelpath = find_replace(labelpath, "JPEGImages", "labels");
+ labelpath = find_replace(labelpath, ".jpg", ".txt");
+ labelpath = find_replace(labelpath, ".JPEG", ".txt");
+
+ int num_labels = 0;
+ box_label *truth = read_boxes(labelpath, &num_labels);
+ for(k = 0; k < num_boxes*num_boxes*num; ++k){
+ if(probs[k][0] > thresh){
+ ++proposals;
+ }
+ }
+ for (j = 0; j < num_labels; ++j) {
+ ++total;
+ box t = {truth[j].x, truth[j].y, truth[j].w, truth[j].h};
+ float best_iou = 0;
+ for(k = 0; k < num_boxes*num_boxes*num; ++k){
+ float iou = box_iou(boxes[k], t);
+ if(probs[k][0] > thresh && iou > best_iou){
+ best_iou = iou;
+ }
+ }
+ avg_iou += best_iou;
+ if(best_iou > iou_thresh){
+ ++correct;
+ }
+ }
+ free(truth);
+ free_image(orig);
+ free_image(resized);
+ fprintf(stderr, "%5d %5d %5d\tRPs/Img: %.2f\tIOU: %.2f%%\tRecall:%.2f%%\n", i, correct, total, (float)proposals/(i+1), avg_iou*100/total, 100.*correct/total);
+ }
+}
+
+void extract_boxes(char *cfgfile, char *weightfile)
+{
+ network net = parse_network_cfg(cfgfile);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ set_batch_network(&net, 1);
+ fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
+ srand(time(0));
+
+ char *val_images = "/home/pjreddie/data/voc/test/train.txt";
+ list *plist = get_paths(val_images);
+ char **paths = (char **)list_to_array(plist);
+
+ layer l = net.layers[net.n - 1];
+
+ int num_boxes = l.side;
+ int num = l.n;
+ int classes = l.classes;
+
+ int j;
+
+ box *boxes = calloc(num_boxes*num_boxes*num, sizeof(box));
+ float **probs = calloc(num_boxes*num_boxes*num, sizeof(float *));
+ for(j = 0; j < num_boxes*num_boxes*num; ++j) probs[j] = calloc(classes+1, sizeof(float *));
+
+ int N = plist->size;
+ int i=0;
+ int k;
+
+ int count = 0;
+ float iou_thresh = .1;
+
+ for (i = 0; i < N; ++i) {
+ fprintf(stderr, "%5d %5d\n", i, count);
+ char *path = paths[i];
+ image orig = load_image_color(path, 0, 0);
+ image resized = resize_image(orig, net.w, net.h);
+
+ float *X = resized.data;
+ float *predictions = network_predict(net, X);
+ get_boxes(predictions+1+classes, num, num_boxes, 5+classes, boxes);
+ get_probs(predictions, num*num_boxes*num_boxes, classes, 5+classes, probs);
+
+ char *labelpath = find_replace(path, "images", "labels");
+ labelpath = find_replace(labelpath, "JPEGImages", "labels");
+ labelpath = find_replace(labelpath, ".jpg", ".txt");
+ labelpath = find_replace(labelpath, ".JPEG", ".txt");
+
+ int num_labels = 0;
+ box_label *truth = read_boxes(labelpath, &num_labels);
+ FILE *label = stdin;
+ for(k = 0; k < num_boxes*num_boxes*num; ++k){
+ int overlaps = 0;
+ for (j = 0; j < num_labels; ++j) {
+ box t = {truth[j].x, truth[j].y, truth[j].w, truth[j].h};
+ float iou = box_iou(boxes[k], t);
+ if (iou > iou_thresh){
+ if (!overlaps) {
+ char buff[256];
+ sprintf(buff, "/home/pjreddie/extracted/labels/%d.txt", count);
+ label = fopen(buff, "w");
+ overlaps = 1;
+ }
+ fprintf(label, "%d %f\n", truth[j].id, iou);
+ }
+ }
+ if (overlaps) {
+ char buff[256];
+ sprintf(buff, "/home/pjreddie/extracted/imgs/%d", count++);
+ int dx = (boxes[k].x - boxes[k].w/2) * orig.w;
+ int dy = (boxes[k].y - boxes[k].h/2) * orig.h;
+ int w = boxes[k].w * orig.w;
+ int h = boxes[k].h * orig.h;
+ image cropped = crop_image(orig, dx, dy, w, h);
+ image sized = resize_image(cropped, 224, 224);
+ #ifdef OPENCV
+ save_image_jpg(sized, buff);
+ #endif
+ free_image(sized);
+ free_image(cropped);
+ fclose(label);
+ }
+ }
+ free(truth);
+ free_image(orig);
+ free_image(resized);
+ }
+}
+
void validate_coco(char *cfgfile, char *weightfile)
{
network net = parse_network_cfg(cfgfile);
@@ -188,7 +398,6 @@
load_weights(&net, weightfile);
}
set_batch_network(&net, 1);
- detection_layer layer = get_network_detection_layer(net);
fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
srand(time(0));
@@ -196,10 +405,9 @@
list *plist = get_paths("data/coco_val_5k.list");
char **paths = (char **)list_to_array(plist);
- int classes = layer.classes;
- int objectness = layer.objectness;
- int background = layer.background;
- int num_boxes = sqrt(get_detection_layer_locations(layer));
+ int num_boxes = 9;
+ int num = 4;
+ int classes = 1;
int j;
char buff[1024];
@@ -207,9 +415,9 @@
FILE *fp = fopen(buff, "w");
fprintf(fp, "[\n");
- box *boxes = calloc(num_boxes*num_boxes, sizeof(box));
- float **probs = calloc(num_boxes*num_boxes, sizeof(float *));
- for(j = 0; j < num_boxes*num_boxes; ++j) probs[j] = calloc(classes, sizeof(float *));
+ box *boxes = calloc(num_boxes*num_boxes*num, sizeof(box));
+ float **probs = calloc(num_boxes*num_boxes*num, sizeof(float *));
+ for(j = 0; j < num_boxes*num_boxes*num; ++j) probs[j] = calloc(classes, sizeof(float *));
int m = plist->size;
int i=0;
@@ -257,7 +465,7 @@
float *predictions = network_predict(net, X);
int w = val[t].w;
int h = val[t].h;
- convert_cocos(predictions, classes, objectness, background, num_boxes, w, h, thresh, probs, boxes);
+ convert_cocos(predictions, classes, num_boxes, num, w, h, thresh, probs, boxes);
if (nms) do_nms(boxes, probs, num_boxes, classes, iou_thresh);
print_cocos(fp, image_id, boxes, probs, num_boxes, classes, w, h);
free_image(val[t]);
@@ -319,5 +527,6 @@
char *filename = (argc > 5) ? argv[5]: 0;
if(0==strcmp(argv[2], "test")) test_coco(cfg, weights, filename);
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], "extract")) extract_boxes(cfg, weights);
+ else if(0==strcmp(argv[2], "valid")) validate_recall(cfg, weights);
}
--
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