From c521f87c9eb3ee70258f315ad0d72d7cf174e8b7 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Mon, 25 May 2015 18:53:10 +0000
Subject: [PATCH] ?
---
src/detection.c | 51 +++++++++++++++++++++++++++++----------------------
1 files changed, 29 insertions(+), 22 deletions(-)
diff --git a/src/detection.c b/src/detection.c
index 1f1114f..c012848 100644
--- a/src/detection.c
+++ b/src/detection.c
@@ -47,6 +47,8 @@
int top = (y-h/2)*im.h;
int bot = (y+h/2)*im.h;
draw_box(im, left, top, right, bot, red, green, blue);
+ draw_box(im, left+1, top+1, right+1, bot+1, red, green, blue);
+ draw_box(im, left-1, top-1, right-1, bot-1, red, green, blue);
}
}
}
@@ -115,7 +117,12 @@
time=clock();
float loss = train_network(net, train);
+ //TODO
+ #ifdef GPU
float *out = get_network_output_gpu(net);
+ #else
+ float *out = get_network_output(net);
+ #endif
image im = float_to_image(net.w, net.h, 3, train.X.vals[127]);
image copy = copy_image(im);
draw_localization(copy, &(out[63*80]));
@@ -149,7 +156,7 @@
if(weightfile){
load_weights(&net, weightfile);
}
- detection_layer *layer = get_network_detection_layer(net);
+ detection_layer layer = get_network_detection_layer(net);
net.learning_rate = 0;
net.decay = 0;
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
@@ -157,9 +164,9 @@
int i = net.seen/imgs;
data train, buffer;
- int classes = layer->classes;
- int background = layer->background;
- int side = sqrt(get_detection_layer_locations(*layer));
+ int classes = layer.classes;
+ int background = layer.background;
+ int side = sqrt(get_detection_layer_locations(layer));
char **paths;
list *plist;
@@ -174,7 +181,7 @@
paths = (char **)list_to_array(plist);
pthread_t load_thread = load_data_detection_thread(imgs, paths, plist->size, classes, net.w, net.h, side, side, background, &buffer);
clock_t time;
- cost_layer clayer = *((cost_layer *)net.layers[net.n-1]);
+ cost_layer clayer = net.layers[net.n-1];
while(1){
i += 1;
time=clock();
@@ -212,7 +219,7 @@
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 == 100){
- net.learning_rate *= 10;
+ //net.learning_rate *= 10;
}
if(i%100==0){
char buff[256];
@@ -235,15 +242,15 @@
if(weightfile){
load_weights(&net, weightfile);
}
- detection_layer *layer = get_network_detection_layer(net);
+ detection_layer layer = get_network_detection_layer(net);
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
int imgs = 128;
int i = net.seen/imgs;
data train, buffer;
- int classes = layer->classes;
- int background = layer->background;
- int side = sqrt(get_detection_layer_locations(*layer));
+ int classes = layer.classes;
+ int background = layer.background;
+ int side = sqrt(get_detection_layer_locations(layer));
char **paths;
list *plist;
@@ -308,8 +315,8 @@
float y = (pred.vals[j][ci + 1] + row)/num_boxes;
float w = pred.vals[j][ci + 2]; //* distance_from_edge(row, num_boxes);
float h = pred.vals[j][ci + 3]; //* distance_from_edge(col, num_boxes);
- w = w*w;
- h = h*h;
+ w = pow(w, 2);
+ h = pow(h, 2);
float prob = scale*pred.vals[j][k+class+background+nuisance];
if(prob < threshold) continue;
printf("%d %d %f %f %f %f %f\n", offset + j, class, prob, x, y, w, h);
@@ -325,7 +332,7 @@
if(weightfile){
load_weights(&net, weightfile);
}
- detection_layer *layer = get_network_detection_layer(net);
+ 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));
@@ -336,10 +343,10 @@
//list *plist = get_paths("/home/pjreddie/data/voc/train.txt");
char **paths = (char **)list_to_array(plist);
- int classes = layer->classes;
- int nuisance = layer->nuisance;
- int background = (layer->background && !nuisance);
- int num_boxes = sqrt(get_detection_layer_locations(*layer));
+ int classes = layer.classes;
+ int nuisance = layer.nuisance;
+ int background = (layer.background && !nuisance);
+ int num_boxes = sqrt(get_detection_layer_locations(layer));
int per_box = 4+classes+background+nuisance;
int num_output = num_boxes*num_boxes*per_box;
@@ -393,7 +400,7 @@
load_weights(&post, "/home/pjreddie/imagenet_backup/localize_1000.weights");
set_batch_network(&post, 1);
- detection_layer *layer = get_network_detection_layer(net);
+ 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));
@@ -404,10 +411,10 @@
//list *plist = get_paths("/home/pjreddie/data/voc/train.txt");
char **paths = (char **)list_to_array(plist);
- int classes = layer->classes;
- int nuisance = layer->nuisance;
- int background = (layer->background && !nuisance);
- int num_boxes = sqrt(get_detection_layer_locations(*layer));
+ int classes = layer.classes;
+ int nuisance = layer.nuisance;
+ int background = (layer.background && !nuisance);
+ int num_boxes = sqrt(get_detection_layer_locations(layer));
int per_box = 4+classes+background+nuisance;
--
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