From 16d06ec0db241261d0d030722e440206ed8aad77 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Mon, 29 Feb 2016 21:54:12 +0000
Subject: [PATCH] stuff
---
src/classifier.c | 169 +++++++++++++++++++++++++++++++++++++++++++++++++++-----
1 files changed, 153 insertions(+), 16 deletions(-)
diff --git a/src/classifier.c b/src/classifier.c
index 9924c37..fdbe534 100644
--- a/src/classifier.c
+++ b/src/classifier.c
@@ -70,6 +70,11 @@
load_args args = {0};
args.w = net.w;
args.h = net.h;
+
+ args.min = net.w;
+ args.max = net.max_crop;
+ args.size = net.w;
+
args.paths = paths;
args.classes = classes;
args.n = imgs;
@@ -88,6 +93,16 @@
load_thread = load_data_in_thread(args);
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
+
+/*
+ int u;
+ for(u = 0; u < net.batch; ++u){
+ image im = float_to_image(net.w, net.h, 3, train.X.vals[u]);
+ show_image(im, "loaded");
+ cvWaitKey(0);
+ }
+ */
+
float loss = train_network(net, train);
if(avg_loss == -1) avg_loss = loss;
avg_loss = avg_loss*.9 + loss*.1;
@@ -99,7 +114,7 @@
sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
save_weights(net, buff);
}
- if(*net.seen%1000 == 0){
+ if(*net.seen%100 == 0){
char buff[256];
sprintf(buff, "%s/%s.backup",backup_directory,base);
save_weights(net, buff);
@@ -152,13 +167,14 @@
load_args args = {0};
args.w = net.w;
args.h = net.h;
+
args.paths = paths;
args.classes = classes;
args.n = num;
args.m = 0;
args.labels = labels;
args.d = &buffer;
- args.type = CLASSIFICATION_DATA;
+ args.type = OLD_CLASSIFICATION_DATA;
pthread_t load_thread = load_data_in_thread(args);
for(i = 1; i <= splits; ++i){
@@ -221,19 +237,22 @@
break;
}
}
- image im = load_image_color(paths[i], 256, 256);
+ int w = net.w;
+ int h = net.h;
+ image im = load_image_color(paths[i], w, h);
+ int shift = 32;
image images[10];
- images[0] = crop_image(im, -16, -16, 256, 256);
- images[1] = crop_image(im, 16, -16, 256, 256);
- images[2] = crop_image(im, 0, 0, 256, 256);
- images[3] = crop_image(im, -16, 16, 256, 256);
- images[4] = crop_image(im, 16, 16, 256, 256);
+ images[0] = crop_image(im, -shift, -shift, w, h);
+ images[1] = crop_image(im, shift, -shift, w, h);
+ images[2] = crop_image(im, 0, 0, w, h);
+ images[3] = crop_image(im, -shift, shift, w, h);
+ images[4] = crop_image(im, shift, shift, w, h);
flip_image(im);
- images[5] = crop_image(im, -16, -16, 256, 256);
- images[6] = crop_image(im, 16, -16, 256, 256);
- images[7] = crop_image(im, 0, 0, 256, 256);
- images[8] = crop_image(im, -16, 16, 256, 256);
- images[9] = crop_image(im, 16, 16, 256, 256);
+ images[5] = crop_image(im, -shift, -shift, w, h);
+ images[6] = crop_image(im, shift, -shift, w, h);
+ images[7] = crop_image(im, 0, 0, w, h);
+ images[8] = crop_image(im, -shift, shift, w, h);
+ images[9] = crop_image(im, shift, shift, w, h);
float *pred = calloc(classes, sizeof(float));
for(j = 0; j < 10; ++j){
float *p = network_predict(net, images[j].data);
@@ -252,6 +271,122 @@
}
}
+void validate_classifier_full(char *datacfg, char *filename, char *weightfile)
+{
+ int i, j;
+ network net = parse_network_cfg(filename);
+ set_batch_network(&net, 1);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ srand(time(0));
+
+ list *options = read_data_cfg(datacfg);
+
+ char *label_list = option_find_str(options, "labels", "data/labels.list");
+ char *valid_list = option_find_str(options, "valid", "data/train.list");
+ int classes = option_find_int(options, "classes", 2);
+ int topk = option_find_int(options, "top", 1);
+
+ char **labels = get_labels(label_list);
+ list *plist = get_paths(valid_list);
+
+ char **paths = (char **)list_to_array(plist);
+ int m = plist->size;
+ free_list(plist);
+
+ float avg_acc = 0;
+ float avg_topk = 0;
+ int *indexes = calloc(topk, sizeof(int));
+
+ for(i = 0; i < m; ++i){
+ int class = -1;
+ char *path = paths[i];
+ for(j = 0; j < classes; ++j){
+ if(strstr(path, labels[j])){
+ class = j;
+ break;
+ }
+ }
+ image im = load_image_color(paths[i], 0, 0);
+ resize_network(&net, im.w, im.h);
+ //show_image(im, "orig");
+ //show_image(crop, "cropped");
+ //cvWaitKey(0);
+ float *pred = network_predict(net, im.data);
+
+ free_image(im);
+ top_k(pred, classes, topk, indexes);
+
+ if(indexes[0] == class) avg_acc += 1;
+ for(j = 0; j < topk; ++j){
+ if(indexes[j] == class) avg_topk += 1;
+ }
+
+ printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1));
+ }
+}
+
+
+void validate_classifier_single(char *datacfg, char *filename, char *weightfile)
+{
+ int i, j;
+ network net = parse_network_cfg(filename);
+ set_batch_network(&net, 1);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ srand(time(0));
+
+ list *options = read_data_cfg(datacfg);
+
+ char *label_list = option_find_str(options, "labels", "data/labels.list");
+ char *valid_list = option_find_str(options, "valid", "data/train.list");
+ int classes = option_find_int(options, "classes", 2);
+ int topk = option_find_int(options, "top", 1);
+
+ char **labels = get_labels(label_list);
+ list *plist = get_paths(valid_list);
+
+ char **paths = (char **)list_to_array(plist);
+ int m = plist->size;
+ free_list(plist);
+
+ float avg_acc = 0;
+ float avg_topk = 0;
+ int *indexes = calloc(topk, sizeof(int));
+
+ for(i = 0; i < m; ++i){
+ int class = -1;
+ char *path = paths[i];
+ for(j = 0; j < classes; ++j){
+ if(strstr(path, labels[j])){
+ class = j;
+ break;
+ }
+ }
+ image im = load_image_color(paths[i], 0, 0);
+ image resized = resize_min(im, net.w);
+ image crop = crop_image(resized, (resized.w - net.w)/2, (resized.h - net.h)/2, net.w, net.h);
+ //show_image(im, "orig");
+ //show_image(crop, "cropped");
+ //cvWaitKey(0);
+ float *pred = network_predict(net, crop.data);
+
+ free_image(im);
+ free_image(resized);
+ free_image(crop);
+ top_k(pred, classes, topk, indexes);
+
+ if(indexes[0] == class) avg_acc += 1;
+ for(j = 0; j < topk; ++j){
+ if(indexes[j] == class) avg_topk += 1;
+ }
+
+ printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1));
+ }
+}
+
void validate_classifier_multi(char *datacfg, char *filename, char *weightfile)
{
int i, j;
@@ -271,7 +406,7 @@
char **labels = get_labels(label_list);
list *plist = get_paths(valid_list);
- int scales[] = {224, 256, 384, 480, 640};
+ int scales[] = {224, 256, 384, 480, 512};
int nscales = sizeof(scales)/sizeof(scales[0]);
char **paths = (char **)list_to_array(plist);
@@ -402,7 +537,7 @@
args.m = 0;
args.labels = 0;
args.d = &buffer;
- args.type = CLASSIFICATION_DATA;
+ args.type = OLD_CLASSIFICATION_DATA;
pthread_t load_thread = load_data_in_thread(args);
for(curr = net.batch; curr < m; curr += net.batch){
@@ -420,7 +555,7 @@
time=clock();
matrix pred = network_predict_data(net, val);
-
+
int i, j;
if (target_layer >= 0){
//layer l = net.layers[target_layer];
@@ -461,6 +596,8 @@
else if(0==strcmp(argv[2], "valid")) validate_classifier(data, cfg, weights);
else if(0==strcmp(argv[2], "valid10")) validate_classifier_10(data, cfg, weights);
else if(0==strcmp(argv[2], "validmulti")) validate_classifier_multi(data, cfg, weights);
+ else if(0==strcmp(argv[2], "validsingle")) validate_classifier_single(data, cfg, weights);
+ else if(0==strcmp(argv[2], "validfull")) validate_classifier_full(data, cfg, weights);
}
--
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