From e92f7d301c971b4d27aa3dcd1e4047e94f04b3fc Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Wed, 25 Mar 2015 01:27:12 +0000
Subject: [PATCH] smaller gridsize in bias
---
src/detection.c | 28 ++++++++++++++++------------
1 files changed, 16 insertions(+), 12 deletions(-)
diff --git a/src/detection.c b/src/detection.c
index f861347..1800ca6 100644
--- a/src/detection.c
+++ b/src/detection.c
@@ -3,11 +3,11 @@
#include "parser.h"
-char *class_names[] = {"aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"};
+char *class_names[] = {"bg", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"};
#define AMNT 3
void draw_detection(image im, float *box, int side)
{
- int classes = 20;
+ int classes = 21;
int elems = 4+classes;
int j;
int r, c;
@@ -50,6 +50,7 @@
if(weightfile){
load_weights(&net, weightfile);
}
+ //net.seen = 0;
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
int imgs = 128;
srand(time(0));
@@ -72,12 +73,12 @@
train = buffer;
load_thread = load_data_detection_thread(imgs, paths, plist->size, classes, im_dim, im_dim, 7, 7, jitter, background, &buffer);
- /*
- image im = float_to_image(im_dim - jitter, im_dim-jitter, 3, train.X.vals[0]);
- draw_detection(im, train.y.vals[0], 7);
+/*
+ image im = float_to_image(im_dim - jitter, im_dim-jitter, 3, train.X.vals[114]);
+ draw_detection(im, train.y.vals[114], 7);
show_image(im, "truth");
cvWaitKey(0);
- */
+*/
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
@@ -108,8 +109,9 @@
char **paths = (char **)list_to_array(plist);
int im_size = 448;
int classes = 20;
- int background = 0;
- int num_output = 7*7*(4+classes+background);
+ int background = 1;
+ int nuisance = 0;
+ int num_output = 7*7*(4+classes+background+nuisance);
int m = plist->size;
int i = 0;
@@ -133,17 +135,19 @@
matrix pred = network_predict_data(net, val);
int j, k, class;
for(j = 0; j < pred.rows; ++j){
- for(k = 0; k < pred.cols; k += classes+4+background){
+ for(k = 0; k < pred.cols; k += classes+4+background+nuisance){
+ float scale = 1.;
+ if(nuisance) scale = pred.vals[j][k];
for(class = 0; class < classes; ++class){
- int index = (k)/(classes+4+background);
+ int index = (k)/(classes+4+background+nuisance);
int r = index/7;
int c = index%7;
- int ci = k+classes+background;
+ int ci = k+classes+background+nuisance;
float y = (r + pred.vals[j][ci + 0])/7.;
float x = (c + pred.vals[j][ci + 1])/7.;
float h = pred.vals[j][ci + 2];
float w = pred.vals[j][ci + 3];
- printf("%d %d %f %f %f %f %f\n", (i-1)*m/splits + j, class, pred.vals[j][k+class], y, x, h, w);
+ printf("%d %d %f %f %f %f %f\n", (i-1)*m/splits + j, class, scale*pred.vals[j][k+class+background+nuisance], y, x, h, w);
}
}
}
--
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