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 | 75 ++++++++++++++++++++++++++++++-------
1 files changed, 61 insertions(+), 14 deletions(-)
diff --git a/src/classifier.c b/src/classifier.c
index 208b7ed..a77f9df 100644
--- a/src/classifier.c
+++ b/src/classifier.c
@@ -41,6 +41,22 @@
return options;
}
+void hierarchy_predictions(float *predictions, int n, tree *hier, int only_leaves)
+{
+ int j;
+ for(j = 0; j < n; ++j){
+ int parent = hier->parent[j];
+ if(parent >= 0){
+ predictions[j] *= predictions[parent];
+ }
+ }
+ if(only_leaves){
+ for(j = 0; j < n; ++j){
+ if(!hier->leaf[j]) predictions[j] = 0;
+ }
+ }
+}
+
float *get_regression_values(char **labels, int n)
{
float *v = calloc(n, sizeof(float));
@@ -99,7 +115,8 @@
load_args args = {0};
args.w = net.w;
args.h = net.h;
- args.threads = 16;
+ args.threads = 32;
+ args.hierarchy = net.hierarchy;
args.min = net.min_crop;
args.max = net.max_crop;
@@ -206,6 +223,7 @@
args.saturation = net.saturation;
args.hue = net.hue;
args.size = net.w;
+ args.hierarchy = net.hierarchy;
args.paths = paths;
args.classes = classes;
@@ -394,6 +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, 1);
axpy_cpu(classes, 1, p, 1, pred, 1);
free_image(images[j]);
}
@@ -454,6 +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, 1);
free_image(im);
free_image(resized);
@@ -468,6 +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)
{
@@ -482,6 +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);
@@ -513,6 +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, 1);
if(resized.data != im.data) free_image(resized);
free_image(im);
@@ -573,6 +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, 1);
axpy_cpu(classes, 1, p, 1, pred, 1);
flip_image(r);
p = network_predict(net, r.data);
@@ -672,8 +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){
@@ -686,7 +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);
@@ -713,11 +756,13 @@
float *X = r.data;
time=clock();
float *predictions = network_predict(net, X);
- top_predictions(net, top, indexes);
+ 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){
int index = indexes[i];
- printf("%s: %f\n", names[index], predictions[index]);
+ if(net.hierarchy) printf("%d, %s: %f, parent: %s \n",index, names[index], predictions[index], (net.hierarchy->parent[index] >= 0) ? names[net.hierarchy->parent[index]] : "Root");
+ else printf("%s: %f\n",names[index], predictions[index]);
}
if(r.data != im.data) free_image(r);
free_image(im);
@@ -899,15 +944,15 @@
float curr_threat = 0;
if(1){
curr_threat = predictions[0] * 0 +
- predictions[1] * .6 +
- predictions[2];
+ predictions[1] * .6 +
+ predictions[2];
} else {
curr_threat = predictions[218] +
- predictions[539] +
- predictions[540] +
- predictions[368] +
- predictions[369] +
- predictions[370];
+ predictions[539] +
+ predictions[540] +
+ predictions[368] +
+ predictions[369] +
+ predictions[370];
}
threat = roll * curr_threat + (1-roll) * threat;
@@ -1092,6 +1137,7 @@
show_image(in, "Classifier");
float *predictions = network_predict(net, in_s.data);
+ if(net.hierarchy) hierarchy_predictions(predictions, net.outputs, net.hierarchy, 1);
top_predictions(net, top, indexes);
printf("\033[2J");
@@ -1143,6 +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];
@@ -1150,7 +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);
--
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