From bc902b277e9131cc169751056786de5393da737d Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Mon, 24 Feb 2014 20:21:31 +0000
Subject: [PATCH] Imagenet Features\!
---
src/tests.c | 105 +++++++++++++++++++++++++++++++++++++++++++++-------
1 files changed, 90 insertions(+), 15 deletions(-)
diff --git a/src/tests.c b/src/tests.c
index c72e900..91ee4bf 100644
--- a/src/tests.c
+++ b/src/tests.c
@@ -188,37 +188,64 @@
free_data(train);
}
-void test_full()
+void train_full()
{
- network net = parse_network_cfg("full.cfg");
+ network net = parse_network_cfg("cfg/imagenet.cfg");
srand(2222222);
- int i = 800;
+ int i = 0;
char *labels[] = {"cat","dog"};
float lr = .00001;
float momentum = .9;
float decay = 0.01;
- while(i++ < 1000 || 1){
- visualize_network(net);
- cvWaitKey(100);
- data train = load_data_image_pathfile_random("train_paths.txt", 1000, labels, 2, 256, 256);
+ while(1){
+ i += 1000;
+ data train = load_data_image_pathfile_random("images/assira/train.list", 1000, labels, 2, 256, 256);
image im = float_to_image(256, 256, 3,train.X.vals[0]);
- show_image(im, "input");
- cvWaitKey(100);
+ //visualize_network(net);
+ //cvWaitKey(100);
+ //show_image(im, "input");
+ //cvWaitKey(100);
//scale_data_rows(train, 1./255.);
normalize_data_rows(train);
clock_t start = clock(), end;
- float loss = train_network_sgd(net, train, 100, lr, momentum, decay);
+ float loss = train_network_sgd(net, train, 1000, lr, momentum, decay);
end = clock();
printf("%d: %f, Time: %lf seconds, LR: %f, Momentum: %f, Decay: %f\n", i, loss, (float)(end-start)/CLOCKS_PER_SEC, lr, momentum, decay);
free_data(train);
- if(i%100==0){
+ if(i%10000==0){
char buff[256];
- sprintf(buff, "backup_%d.cfg", i);
- //save_network(net, buff);
+ sprintf(buff, "cfg/assira_backup_%d.cfg", i);
+ save_network(net, buff);
}
//lr *= .99;
}
}
+void test_full()
+{
+ network net = parse_network_cfg("cfg/backup_1300.cfg");
+ srand(2222222);
+ int i,j;
+ int total = 100;
+ char *labels[] = {"cat","dog"};
+ FILE *fp = fopen("preds.txt","w");
+ for(i = 0; i < total; ++i){
+ visualize_network(net);
+ cvWaitKey(100);
+ data test = load_data_image_pathfile_part("images/assira/test.list", i, total, labels, 2, 256, 256);
+ image im = float_to_image(256, 256, 3,test.X.vals[0]);
+ show_image(im, "input");
+ cvWaitKey(100);
+ normalize_data_rows(test);
+ for(j = 0; j < test.X.rows; ++j){
+ float *x = test.X.vals[j];
+ forward_network(net, x);
+ int class = get_predicted_class_network(net);
+ fprintf(fp, "%d\n", class);
+ }
+ free_data(test);
+ }
+ fclose(fp);
+}
void test_nist()
{
@@ -400,6 +427,7 @@
{
x = x-1+3;
x = x-1+3;
+ x = x-1+3;
x = (x-1)*2+1;
x = x-1+5;
x = (x-1)*2+1;
@@ -411,13 +439,14 @@
{
int h = voc_size(outh);
int w = voc_size(outw);
+ printf("%d %d\n", h, w);
IplImage *sized = cvCreateImage(cvSize(w,h), src->depth, src->nChannels);
cvResize(src, sized, CV_INTER_LINEAR);
image im = ipl_to_image(sized);
reset_network_size(net, im.h, im.w, im.c);
forward_network(net, im.data);
- image out = get_network_image_layer(net, 5);
+ image out = get_network_image_layer(net, 6);
//printf("%d %d\n%d %d\n", outh, out.h, outw, out.w);
free_image(im);
cvReleaseImage(&sized);
@@ -500,7 +529,7 @@
void features_VOC_image(char *image_file, char *image_dir, char *out_dir)
{
int i,j;
- network net = parse_network_cfg("cfg/voc_features.cfg");
+ network net = parse_network_cfg("cfg/imagenet.cfg");
char image_path[1024];
sprintf(image_path, "%s%s",image_dir, image_file);
char out_path[1024];
@@ -557,8 +586,54 @@
cvReleaseImage(&src);
}
+void test_distribution()
+{
+ IplImage* img = 0;
+ if( (img = cvLoadImage("im_small.jpg",-1)) == 0 ) file_error("im_small.jpg");
+ network net = parse_network_cfg("cfg/voc_features.cfg");
+ int h = img->height/8-2;
+ int w = img->width/8-2;
+ image out = features_output_size(net, img, h, w);
+ int c = out.c;
+ out.c = 1;
+ show_image(out, "output");
+ out.c = c;
+ image input = ipl_to_image(img);
+ show_image(input, "input");
+ CvScalar s;
+ int i,j;
+ image affects = make_image(input.h, input.w, 1);
+ int count = 0;
+ for(i = 0; i<img->height; i += 1){
+ for(j = 0; j < img->width; j += 1){
+ IplImage *copy = cvCloneImage(img);
+ s=cvGet2D(copy,i,j); // get the (i,j) pixel value
+ printf("%d/%d\n", count++, img->height*img->width);
+ s.val[0]=0;
+ s.val[1]=0;
+ s.val[2]=0;
+ cvSet2D(copy,i,j,s); // set the (i,j) pixel value
+ image mod = features_output_size(net, copy, h, w);
+ image dist = image_distance(out, mod);
+ show_image(affects, "affects");
+ cvWaitKey(1);
+ cvReleaseImage(©);
+ //affects.data[i*affects.w + j] += dist.data[3*dist.w+5];
+ affects.data[i*affects.w + j] += dist.data[1*dist.w+1];
+ free_image(mod);
+ free_image(dist);
+ }
+ }
+ show_image(affects, "Origins");
+ cvWaitKey(0);
+ cvWaitKey(0);
+}
+
+
int main(int argc, char *argv[])
{
+ //train_full();
+ //test_distribution();
//feenableexcept(FE_DIVBYZERO | FE_INVALID | FE_OVERFLOW);
//test_blas();
--
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