From 0305fb4d99cf1efc7d4aa4d2ee2d65d54500d437 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Thu, 26 Nov 2015 19:48:01 +0000
Subject: [PATCH] Some changes
---
src/coco.c | 507 ++++++++++++++++++++-----------------------------------
1 files changed, 188 insertions(+), 319 deletions(-)
diff --git a/src/coco.c b/src/coco.c
index 87f3dca..17d0654 100644
--- a/src/coco.c
+++ b/src/coco.c
@@ -15,41 +15,12 @@
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};
-void draw_coco(image im, float *pred, int side, char *label)
-{
- int classes = 1;
- int elems = 4+classes;
- int j;
- int r, c;
-
- for(r = 0; r < side; ++r){
- for(c = 0; c < side; ++c){
- j = (r*side + c) * elems;
- int class = max_index(pred+j, classes);
- if (pred[j+class] > 0.2){
- int width = pred[j+class]*5 + 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);
-
- j += classes;
-
- box predict = {pred[j+0], pred[j+1], pred[j+2], pred[j+3]};
- predict.x = (predict.x+c)/side;
- predict.y = (predict.y+r)/side;
-
- draw_bbox(im, predict, width, red, green, blue);
- }
- }
- }
- show_image(im, label);
-}
+image coco_labels[80];
void train_coco(char *cfgfile, char *weightfile)
{
- //char *train_images = "/home/pjreddie/data/coco/train.txt";
- char *train_images = "/home/pjreddie/data/voc/test/train.txt";
+ //char *train_images = "/home/pjreddie/data/voc/test/train.txt";
+ char *train_images = "/home/pjreddie/data/coco/train.txt";
char *backup_directory = "/home/pjreddie/backup/";
srand(time(0));
data_seed = time(0);
@@ -61,7 +32,7 @@
load_weights(&net, weightfile);
}
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
- int imgs = 128;
+ int imgs = net.batch*net.subdivisions;
int i = *net.seen/imgs;
data train, buffer;
@@ -70,9 +41,10 @@
int side = l.side;
int classes = l.classes;
+ float jitter = l.jitter;
list *plist = get_paths(train_images);
- int N = plist->size;
+ //int N = plist->size;
char **paths = (char **)list_to_array(plist);
load_args args = {0};
@@ -82,13 +54,15 @@
args.n = imgs;
args.m = plist->size;
args.classes = classes;
+ args.jitter = jitter;
args.num_boxes = side;
args.d = &buffer;
args.type = REGION_DATA;
pthread_t load_thread = load_data_in_thread(args);
clock_t time;
- while(i*imgs < N*120){
+ //while(i*imgs < N*120){
+ while(get_current_batch(net) < net.max_batches){
i += 1;
time=clock();
pthread_join(load_thread, 0);
@@ -97,34 +71,20 @@
printf("Loaded: %lf seconds\n", sec(clock()-time));
-/*
- 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[113], 7, "truth");
- cvWaitKey(0);
- free_image(copy);
- */
+ /*
+ 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[113], 7, "truth");
+ cvWaitKey(0);
+ free_image(copy);
+ */
time=clock();
float loss = train_network(net, train);
if (avg_loss < 0) avg_loss = loss;
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 <= 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);
- }
-
- 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);
- }
+ 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){
char buff[256];
sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
@@ -137,59 +97,38 @@
save_weights(net, buff);
}
-void get_probs(float *predictions, int total, int classes, int inc, float **probs)
+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;
- for (i = 0; i < total; ++i){
- int index = i*inc;
- float scale = predictions[index];
- probs[i][0] = scale;
- for(j = 0; j < classes; ++j){
- probs[i][j] = scale*predictions[index+j+1];
+ 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 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;
- boxes[i].y = (predictions[offset + 1] + row) / num_boxes;
- boxes[i].w = predictions[offset + 2];
- boxes[i].h = predictions[offset + 3];
- }
-}
void print_cocos(FILE *fp, int image_id, box *boxes, float **probs, int num_boxes, int classes, int w, int h)
{
int i, j;
- for(i = 0; i < num_boxes*num_boxes; ++i){
+ for(i = 0; i < num_boxes; ++i){
float xmin = boxes[i].x - boxes[i].w/2.;
float xmax = boxes[i].x + boxes[i].w/2.;
float ymin = boxes[i].y - boxes[i].h/2.;
@@ -217,201 +156,6 @@
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 = 1;
- int proposals = 0;
- int save = 1;
-
- 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;
- if(save){
- char buff[256];
- sprintf(buff, "/data/extracted/nms_preds/%d", proposals);
- 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);
- sprintf(buff, "/data/extracted/nms_pred_boxes/%d.txt", proposals);
- char *im_id = basecfg(path);
- FILE *fp = fopen(buff, "w");
- fprintf(fp, "%s %d %d %d %d\n", im_id, dx, dy, dx+w, dy+h);
- fclose(fp);
- free(im_id);
- }
- }
- }
- 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 = .3;
-
- 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, "/data/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, "/data/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);
@@ -422,13 +166,16 @@
fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
srand(time(0));
- char *base = "/home/pjreddie/backup/";
+ char *base = "results/";
list *plist = get_paths("data/coco_val_5k.list");
+ //list *plist = get_paths("/home/pjreddie/data/people-art/test.txt");
+ //list *plist = get_paths("/home/pjreddie/data/voc/test/2007_test.txt");
char **paths = (char **)list_to_array(plist);
- int num_boxes = 9;
- int num = 4;
- int classes = 1;
+ layer l = net.layers[net.n-1];
+ int classes = l.classes;
+ int square = l.sqrt;
+ int side = l.side;
int j;
char buff[1024];
@@ -436,9 +183,9 @@
FILE *fp = fopen(buff, "w");
fprintf(fp, "[\n");
- 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 *));
+ box *boxes = calloc(side*side*l.n, sizeof(box));
+ float **probs = calloc(side*side*l.n, sizeof(float *));
+ for(j = 0; j < side*side*l.n; ++j) probs[j] = calloc(classes, sizeof(float *));
int m = plist->size;
int i=0;
@@ -448,17 +195,18 @@
int nms = 1;
float iou_thresh = .5;
- load_args args = {0};
- args.w = net.w;
- args.h = net.h;
- args.type = IMAGE_DATA;
-
int nthreads = 8;
image *val = calloc(nthreads, sizeof(image));
image *val_resized = calloc(nthreads, sizeof(image));
image *buf = calloc(nthreads, sizeof(image));
image *buf_resized = calloc(nthreads, sizeof(image));
pthread_t *thr = calloc(nthreads, sizeof(pthread_t));
+
+ load_args args = {0};
+ args.w = net.w;
+ args.h = net.h;
+ args.type = IMAGE_DATA;
+
for(t = 0; t < nthreads; ++t){
args.path = paths[i+t];
args.im = &buf[t];
@@ -486,9 +234,9 @@
float *predictions = network_predict(net, X);
int w = val[t].w;
int h = val[t].h;
- 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);
+ convert_coco_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]);
free_image(val_resized[t]);
}
@@ -496,27 +244,123 @@
fseek(fp, -2, SEEK_CUR);
fprintf(fp, "\n]\n");
fclose(fp);
+
fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start));
}
-void test_coco(char *cfgfile, char *weightfile, char *filename)
+void validate_coco_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 *base = "results/comp4_det_test_";
+ list *plist = get_paths("/home/pjreddie/data/voc/test/2007_test.txt");
+ char **paths = (char **)list_to_array(plist);
+
+ layer l = net.layers[net.n-1];
+ int classes = l.classes;
+ int square = l.sqrt;
+ int side = l.side;
+
+ int j, k;
+ FILE **fps = calloc(classes, sizeof(FILE *));
+ for(j = 0; j < classes; ++j){
+ char buff[1024];
+ snprintf(buff, 1024, "%s%s.txt", base, coco_classes[j]);
+ fps[j] = fopen(buff, "w");
+ }
+ box *boxes = calloc(side*side*l.n, sizeof(box));
+ float **probs = calloc(side*side*l.n, sizeof(float *));
+ for(j = 0; j < side*side*l.n; ++j) probs[j] = calloc(classes, sizeof(float *));
+
+ int m = plist->size;
+ int i=0;
+
+ float thresh = .001;
+ int nms = 0;
+ float iou_thresh = .5;
+ float nms_thresh = .5;
+
+ int total = 0;
+ int correct = 0;
+ int proposals = 0;
+ float avg_iou = 0;
+
+ for(i = 0; i < m; ++i){
+ char *path = paths[i];
+ image orig = load_image_color(path, 0, 0);
+ 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);
+ if (nms) do_nms(boxes, probs, side*side*l.n, 1, nms_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 < side*side*l.n; ++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 < side*side*l.n; ++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;
+ }
+ }
+
+ 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);
+ free(id);
+ free_image(orig);
+ free_image(sized);
+ }
+}
+
+void test_coco(char *cfgfile, char *weightfile, char *filename, float thresh)
{
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
+ detection_layer l = net.layers[net.n-1];
set_batch_network(&net, 1);
srand(2222222);
+ float nms = .4;
clock_t time;
- char input[256];
+ char buff[256];
+ char *input = buff;
+ int j;
+ box *boxes = calloc(l.side*l.side*l.n, sizeof(box));
+ float **probs = calloc(l.side*l.side*l.n, sizeof(float *));
+ for(j = 0; j < l.side*l.side*l.n; ++j) probs[j] = calloc(l.classes, sizeof(float *));
while(1){
if(filename){
strncpy(input, filename, 256);
} else {
printf("Enter Image Path: ");
fflush(stdout);
- fgets(input, 256, stdin);
+ input = fgets(input, 256, stdin);
+ if(!input) return;
strtok(input, "\n");
}
image im = load_image_color(input,0,0);
@@ -525,7 +369,12 @@
time=clock();
float *predictions = network_predict(net, X);
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
- draw_coco(im, predictions, 7, "predictions");
+ convert_coco_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);
+ show_image(im, "predictions");
+
+ show_image(sized, "resized");
free_image(im);
free_image(sized);
#ifdef OPENCV
@@ -536,8 +385,23 @@
}
}
+#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;
+ for(i = 0; i < 80; ++i){
+ char buff[256];
+ sprintf(buff, "data/labels/%s.png", coco_classes[i]);
+ coco_labels[i] = load_image_color(buff, 0, 0);
+ }
+ float thresh = find_float_arg(argc, argv, "-thresh", .2);
+ int cam_index = find_int_arg(argc, argv, "-c", 0);
+
if(argc < 4){
fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
return;
@@ -546,8 +410,13 @@
char *cfg = argv[3];
char *weights = (argc > 4) ? argv[4] : 0;
char *filename = (argc > 5) ? argv[5]: 0;
- if(0==strcmp(argv[2], "test")) test_coco(cfg, weights, filename);
+ if(0==strcmp(argv[2], "test")) test_coco(cfg, weights, filename, thresh);
else if(0==strcmp(argv[2], "train")) train_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);
+ 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
}
--
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