From cc06817efa24f20811ef6b32143c6700a91c5f2a Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Fri, 11 Apr 2014 08:00:27 +0000
Subject: [PATCH] Attempt at visualizing ImageNet Features
---
src/tests.c | 233 +++++++++++++++++++++++++++++++++++-----------------------
1 files changed, 141 insertions(+), 92 deletions(-)
diff --git a/src/tests.c b/src/tests.c
index 557f0fb..5d9136d 100644
--- a/src/tests.c
+++ b/src/tests.c
@@ -77,7 +77,7 @@
int size = 3;
float eps = .00000001;
image test = make_random_image(5,5, 1);
- convolutional_layer layer = *make_convolutional_layer(test.h,test.w,test.c, n, size, stride, RELU);
+ convolutional_layer layer = *make_convolutional_layer(1,test.h,test.w,test.c, n, size, stride, RELU);
image out = get_convolutional_image(layer);
float **jacobian = calloc(test.h*test.w*test.c, sizeof(float));
@@ -200,7 +200,7 @@
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]);
+ //image im = float_to_image(256, 256, 3,train.X.vals[0]);
//visualize_network(net);
//cvWaitKey(100);
//show_image(im, "input");
@@ -220,6 +220,14 @@
//lr *= .99;
}
}
+
+void test_visualize()
+{
+ network net = parse_network_cfg("cfg/imagenet.cfg");
+ srand(2222222);
+ visualize_network(net);
+ cvWaitKey(0);
+}
void test_full()
{
network net = parse_network_cfg("cfg/backup_1300.cfg");
@@ -247,38 +255,84 @@
fclose(fp);
}
+void test_cifar10()
+{
+ data test = load_cifar10_data("images/cifar10/test_batch.bin");
+ scale_data_rows(test, 1./255);
+ network net = parse_network_cfg("cfg/cifar10.cfg");
+ int count = 0;
+ float lr = .000005;
+ float momentum = .99;
+ float decay = 0.001;
+ decay = 0;
+ int batch = 10000;
+ while(++count <= 10000){
+ char buff[256];
+ sprintf(buff, "images/cifar10/data_batch_%d.bin", rand()%5+1);
+ data train = load_cifar10_data(buff);
+ scale_data_rows(train, 1./255);
+ train_network_sgd(net, train, batch, lr, momentum, decay);
+ //printf("%5f %5f\n",(double)count*batch/train.X.rows, loss);
+
+ float test_acc = network_accuracy(net, test);
+ printf("%5f %5f\n",(double)count*batch/train.X.rows/5, 1-test_acc);
+ free_data(train);
+ }
+
+}
+
+void test_vince()
+{
+ network net = parse_network_cfg("cfg/vince.cfg");
+ data train = load_categorical_data_csv("images/vince.txt", 144, 2);
+ normalize_data_rows(train);
+
+ int count = 0;
+ float lr = .00005;
+ float momentum = .9;
+ float decay = 0.0001;
+ decay = 0;
+ int batch = 10000;
+ while(++count <= 10000){
+ float loss = train_network_sgd(net, train, batch, lr, momentum, decay);
+ printf("%5f %5f\n",(double)count*batch/train.X.rows, loss);
+ }
+}
+
void test_nist()
{
srand(444444);
srand(888888);
- network net = parse_network_cfg("nist.cfg");
+ network net = parse_network_cfg("cfg/nist_basic.cfg");
data train = load_categorical_data_csv("mnist/mnist_train.csv", 0, 10);
data test = load_categorical_data_csv("mnist/mnist_test.csv",0,10);
normalize_data_rows(train);
normalize_data_rows(test);
//randomize_data(train);
int count = 0;
- float lr = .0005;
+ float lr = .00005;
float momentum = .9;
- float decay = 0.001;
- clock_t start = clock(), end;
- while(++count <= 100){
- //visualize_network(net);
- float loss = train_network_sgd(net, train, 1000, lr, momentum, decay);
- printf("%5d Training Loss: %lf, Params: %f %f %f, ",count*100, loss, lr, momentum, decay);
- end = clock();
- printf("Time: %lf seconds\n", (float)(end-start)/CLOCKS_PER_SEC);
- start=end;
- //cvWaitKey(100);
- //lr /= 2;
- if(count%5 == 0){
- float train_acc = network_accuracy(net, train);
- fprintf(stderr, "\nTRAIN: %f\n", train_acc);
- float test_acc = network_accuracy(net, test);
- fprintf(stderr, "TEST: %f\n\n", test_acc);
- printf("%d, %f, %f\n", count, train_acc, test_acc);
- //lr *= .5;
+ float decay = 0.0001;
+ decay = 0;
+ //clock_t start = clock(), end;
+ int batch = 10000;
+ while(++count <= 10000){
+ float loss = train_network_sgd(net, train, batch, lr, momentum, decay);
+ printf("%5f %5f\n",(double)count*batch/train.X.rows, loss);
+ //printf("%5d Training Loss: %lf, Params: %f %f %f, ",count*1000, loss, lr, momentum, decay);
+ //end = clock();
+ //printf("Time: %lf seconds\n", (float)(end-start)/CLOCKS_PER_SEC);
+ //start=end;
+ /*
+ if(count%5 == 0){
+ float train_acc = network_accuracy(net, train);
+ fprintf(stderr, "\nTRAIN: %f\n", train_acc);
+ float test_acc = network_accuracy(net, test);
+ fprintf(stderr, "TEST: %f\n\n", test_acc);
+ printf("%d, %f, %f\n", count, train_acc, test_acc);
+ //lr *= .5;
}
+ */
}
}
@@ -439,91 +493,77 @@
{
int h = voc_size(outh);
int w = voc_size(outw);
- printf("%d %d\n", h, w);
+ fprintf(stderr, "%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);
+ resize_network(net, im.h, im.w, im.c);
forward_network(net, im.data);
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);
return copy_image(out);
}
-void features_VOC(int part, int total)
+void features_VOC_image_size(char *image_path, int h, int w)
{
- int i,j, count = 0;
+ int j;
network net = parse_network_cfg("cfg/voc_imagenet.cfg");
- char *path_file = "images/VOC2012/all_paths.txt";
- char *out_dir = "voc_features/";
- list *paths = get_paths(path_file);
- node *n = paths->front;
- int size = paths->size;
- for(count = 0; count < part*size/total; ++count) n = n->next;
- while(n && count++ < (part+1)*size/total){
- char *path = (char *)n->val;
- char buff[1024];
- sprintf(buff, "%s%s.txt",out_dir, path);
- printf("%s\n", path);
- FILE *fp = fopen(buff, "w");
- if(fp == 0) file_error(buff);
+ fprintf(stderr, "%s\n", image_path);
- IplImage* src = 0;
- if( (src = cvLoadImage(path,-1)) == 0 )
- {
- printf("Cannot load file image %s\n", path);
- exit(0);
- }
- int w = src->width;
- int h = src->height;
- int sbin = 8;
- int interval = 10;
- double scale = pow(2., 1./interval);
- int m = (w<h)?w:h;
- int max_scale = 1+floor((double)log((double)m/(5.*sbin))/log(scale));
- image *ims = calloc(max_scale+interval, sizeof(image));
+ IplImage* src = 0;
+ if( (src = cvLoadImage(image_path,-1)) == 0 ) file_error(image_path);
+ image out = features_output_size(net, src, h, w);
+ for(j = 0; j < out.c*out.h*out.w; ++j){
+ if(j != 0) printf(",");
+ printf("%g", out.data[j]);
+ }
+ printf("\n");
+ free_image(out);
+ cvReleaseImage(&src);
+}
- for(i = 0; i < interval; ++i){
- double factor = 1./pow(scale, i);
- double ih = round(h*factor);
- double iw = round(w*factor);
- int ex_h = round(ih/4.) - 2;
- int ex_w = round(iw/4.) - 2;
- ims[i] = features_output_size(net, src, ex_h, ex_w);
+void visualize_imagenet_features(char *filename)
+{
+ int i,j,k;
+ network net = parse_network_cfg("cfg/voc_imagenet.cfg");
+ list *plist = get_paths(filename);
+ node *n = plist->front;
+ int h = voc_size(1), w = voc_size(1);
+ int num = get_network_image(net).c;
+ image *vizs = calloc(num, sizeof(image));
+ for(i = 0; i < num; ++i) vizs[i] = make_image(h, w, 3);
+ while(n){
+ char *image_path = (char *)n->val;
+ image im = load_image(image_path, 0, 0);
+ printf("Processing %dx%d image\n", im.h, im.w);
+ resize_network(net, im.h, im.w, im.c);
+ forward_network(net, im.data);
+ image out = get_network_image(net);
- ih = round(h*factor);
- iw = round(w*factor);
- ex_h = round(ih/8.) - 2;
- ex_w = round(iw/8.) - 2;
- ims[i+interval] = features_output_size(net, src, ex_h, ex_w);
- for(j = i+interval; j < max_scale; j += interval){
- factor /= 2.;
- ih = round(h*factor);
- iw = round(w*factor);
- ex_h = round(ih/8.) - 2;
- ex_w = round(iw/8.) - 2;
- ims[j+interval] = features_output_size(net, src, ex_h, ex_w);
+ int dh = (im.h - h)/h;
+ int dw = (im.w - w)/w;
+ for(i = 0; i < out.h; ++i){
+ for(j = 0; j < out.w; ++j){
+ image sub = get_sub_image(im, dh*i, dw*j, h, w);
+ for(k = 0; k < out.c; ++k){
+ float val = get_pixel(out, i, j, k);
+ //printf("%f, ", val);
+ image sub_c = copy_image(sub);
+ scale_image(sub_c, val);
+ add_into_image(sub_c, vizs[k], 0, 0);
+ free_image(sub_c);
+ }
+ free_image(sub);
}
}
- for(i = 0; i < max_scale+interval; ++i){
- image out = ims[i];
- //printf("%d, %d\n", out.h, out.w);
- fprintf(fp, "%d, %d, %d\n",out.c, out.h, out.w);
- for(j = 0; j < out.c*out.h*out.w; ++j){
- if(j != 0)fprintf(fp, ",");
- fprintf(fp, "%g", out.data[j]);
- }
- fprintf(fp, "\n");
- free_image(out);
- }
- free(ims);
- fclose(fp);
- cvReleaseImage(&src);
+ //printf("\n");
+ show_images(vizs, 10, "IMAGENET Visualization");
+ cvWaitKey(1000);
n = n->next;
}
+ cvWaitKey(0);
}
void features_VOC_image(char *image_file, char *image_dir, char *out_dir)
@@ -531,9 +571,9 @@
int i,j;
network net = parse_network_cfg("cfg/voc_imagenet.cfg");
char image_path[1024];
- sprintf(image_path, "%s%s",image_dir, image_file);
+ sprintf(image_path, "%s/%s",image_dir, image_file);
char out_path[1024];
- sprintf(out_path, "%s%s.txt",out_dir, image_file);
+ sprintf(out_path, "%s/%s.txt",out_dir, image_file);
printf("%s\n", image_file);
FILE *fp = fopen(out_path, "w");
if(fp == 0) file_error(out_path);
@@ -543,10 +583,11 @@
int w = src->width;
int h = src->height;
int sbin = 8;
- int interval = 10;
+ int interval = 4;
double scale = pow(2., 1./interval);
int m = (w<h)?w:h;
int max_scale = 1+floor((double)log((double)m/(5.*sbin))/log(scale));
+ if(max_scale < interval) error("max_scale must be >= interval");
image *ims = calloc(max_scale+interval, sizeof(image));
for(i = 0; i < interval; ++i){
@@ -637,15 +678,23 @@
//feenableexcept(FE_DIVBYZERO | FE_INVALID | FE_OVERFLOW);
//test_blas();
+ //test_visualize();
+ //test_gpu_blas();
+ //test_blas();
//test_convolve_matrix();
// test_im2row();
//test_split();
//test_ensemble();
//test_nist();
+ //test_cifar10();
+ //test_vince();
//test_full();
//train_VOC();
- features_VOC_image(argv[1], argv[2], argv[3]);
- printf("Success!\n");
+ //features_VOC_image(argv[1], argv[2], argv[3]);
+ //features_VOC_image_size(argv[1], atoi(argv[2]), atoi(argv[3]));
+ //visualize_imagenet_features("data/assira/train.list");
+ visualize_imagenet_features("data/VOC2011.list");
+ fprintf(stderr, "Success!\n");
//test_random_preprocess();
//test_random_classify();
//test_parser();
--
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