From 352ae7e65b6a74bcd768aa88b866a44c713284c8 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Wed, 26 Oct 2016 15:35:44 +0000
Subject: [PATCH] ADAM
---
src/classifier.c | 49 +++++++++++++++++++++++++++++++++++++------------
1 files changed, 37 insertions(+), 12 deletions(-)
diff --git a/src/classifier.c b/src/classifier.c
index e588af5..a77f9df 100644
--- a/src/classifier.c
+++ b/src/classifier.c
@@ -41,7 +41,7 @@
return options;
}
-void hierarchy_predictions(float *predictions, int n, tree *hier)
+void hierarchy_predictions(float *predictions, int n, tree *hier, int only_leaves)
{
int j;
for(j = 0; j < n; ++j){
@@ -50,8 +50,10 @@
predictions[j] *= predictions[parent];
}
}
- for(j = 0; j < n; ++j){
- if(!hier->leaf[j]) predictions[j] = 0;
+ if(only_leaves){
+ for(j = 0; j < n; ++j){
+ if(!hier->leaf[j]) predictions[j] = 0;
+ }
}
}
@@ -410,7 +412,7 @@
float *pred = calloc(classes, sizeof(float));
for(j = 0; j < 10; ++j){
float *p = network_predict(net, images[j].data);
- if(net.hierarchy) hierarchy_predictions(p, net.outputs, net.hierarchy);
+ if(net.hierarchy) hierarchy_predictions(p, net.outputs, net.hierarchy, 1);
axpy_cpu(classes, 1, p, 1, pred, 1);
free_image(images[j]);
}
@@ -471,7 +473,7 @@
//show_image(crop, "cropped");
//cvWaitKey(0);
float *pred = network_predict(net, resized.data);
- if(net.hierarchy) hierarchy_predictions(pred, net.outputs, net.hierarchy);
+ if(net.hierarchy) hierarchy_predictions(pred, net.outputs, net.hierarchy, 1);
free_image(im);
free_image(resized);
@@ -486,6 +488,26 @@
}
}
+void change_leaves(tree *t, char *leaf_list)
+{
+ list *llist = get_paths(leaf_list);
+ char **leaves = (char **)list_to_array(llist);
+ int n = llist->size;
+ int i,j;
+ int found = 0;
+ for(i = 0; i < t->n; ++i){
+ t->leaf[i] = 0;
+ for(j = 0; j < n; ++j){
+ if (0==strcmp(t->name[i], leaves[j])){
+ t->leaf[i] = 1;
+ ++found;
+ break;
+ }
+ }
+ }
+ fprintf(stderr, "Found %d leaves.\n", found);
+}
+
void validate_classifier_single(char *datacfg, char *filename, char *weightfile)
{
@@ -500,6 +522,8 @@
list *options = read_data_cfg(datacfg);
char *label_list = option_find_str(options, "labels", "data/labels.list");
+ char *leaf_list = option_find_str(options, "leaves", 0);
+ if(leaf_list) change_leaves(net.hierarchy, leaf_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);
@@ -531,7 +555,7 @@
//show_image(crop, "cropped");
//cvWaitKey(0);
float *pred = network_predict(net, crop.data);
- if(net.hierarchy) hierarchy_predictions(pred, net.outputs, net.hierarchy);
+ if(net.hierarchy) hierarchy_predictions(pred, net.outputs, net.hierarchy, 1);
if(resized.data != im.data) free_image(resized);
free_image(im);
@@ -592,7 +616,7 @@
image r = resize_min(im, scales[j]);
resize_network(&net, r.w, r.h);
float *p = network_predict(net, r.data);
- if(net.hierarchy) hierarchy_predictions(p, net.outputs, net.hierarchy);
+ if(net.hierarchy) hierarchy_predictions(p, net.outputs, net.hierarchy, 1);
axpy_cpu(classes, 1, p, 1, pred, 1);
flip_image(r);
p = network_predict(net, r.data);
@@ -692,7 +716,7 @@
}
}
-void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename)
+void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top)
{
network net = parse_network_cfg(cfgfile);
if(weightfile){
@@ -705,7 +729,7 @@
char *name_list = option_find_str(options, "names", 0);
if(!name_list) name_list = option_find_str(options, "labels", "data/labels.list");
- int top = option_find_int(options, "top", 1);
+ if(top == 0) top = option_find_int(options, "top", 1);
int i = 0;
char **names = get_labels(name_list);
@@ -732,7 +756,7 @@
float *X = r.data;
time=clock();
float *predictions = network_predict(net, X);
- if(net.hierarchy) hierarchy_predictions(predictions, net.outputs, net.hierarchy);
+ if(net.hierarchy) hierarchy_predictions(predictions, net.outputs, net.hierarchy, 0);
top_k(predictions, net.outputs, top, indexes);
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
for(i = 0; i < top; ++i){
@@ -1113,7 +1137,7 @@
show_image(in, "Classifier");
float *predictions = network_predict(net, in_s.data);
- if(net.hierarchy) hierarchy_predictions(predictions, net.outputs, net.hierarchy);
+ if(net.hierarchy) hierarchy_predictions(predictions, net.outputs, net.hierarchy, 1);
top_predictions(net, top, indexes);
printf("\033[2J");
@@ -1165,6 +1189,7 @@
}
int cam_index = find_int_arg(argc, argv, "-c", 0);
+ int top = find_int_arg(argc, argv, "-t", 0);
int clear = find_arg(argc, argv, "-clear");
char *data = argv[3];
char *cfg = argv[4];
@@ -1172,7 +1197,7 @@
char *filename = (argc > 6) ? argv[6]: 0;
char *layer_s = (argc > 7) ? argv[7]: 0;
int layer = layer_s ? atoi(layer_s) : -1;
- if(0==strcmp(argv[2], "predict")) predict_classifier(data, cfg, weights, filename);
+ if(0==strcmp(argv[2], "predict")) predict_classifier(data, cfg, weights, filename, top);
else if(0==strcmp(argv[2], "try")) try_classifier(data, cfg, weights, filename, atoi(layer_s));
else if(0==strcmp(argv[2], "train")) train_classifier(data, cfg, weights, clear);
else if(0==strcmp(argv[2], "trainm")) train_classifier_multi(data, cfg, weights, gpus, ngpus, clear);
--
Gitblit v1.10.0