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Imagenet Features\!

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This commit is contained in:
Joseph Redmon 2014-02-24 12:21:31 -08:00
parent 2c6d4ba1d5
commit bc902b277e
6 changed files with 141 additions and 48 deletions
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1
src/data.c
View File

@ -10,6 +10,7 @@ list *get_paths(char *filename)
{
char *path;
FILE *file = fopen(filename, "r");
if(!file) file_error(filename);
list *lines = make_list();
while((path=fgetl(file))){
list_insert(lines, path);

23
src/image.c
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@ -4,6 +4,21 @@
int windows = 0;
image image_distance(image a, image b)
{
int i,j;
image dist = make_image(a.h, a.w, 1);
for(i = 0; i < a.c; ++i){
for(j = 0; j < a.h*a.w; ++j){
dist.data[j] += pow(a.data[i*a.h*a.w+j]-b.data[i*a.h*a.w+j],2);
}
}
for(j = 0; j < a.h*a.w; ++j){
dist.data[j] = sqrt(dist.data[j]);
}
return dist;
}
void subtract_image(image a, image b)
{
int i;
@ -370,9 +385,11 @@ image load_image(char *filename, int h, int w)
printf("Cannot load file image %s\n", filename);
exit(0);
}
IplImage *resized = resizeImage(src, h, w, 1);
cvReleaseImage(&src);
src = resized;
if(h && w ){
IplImage *resized = resizeImage(src, h, w, 1);
cvReleaseImage(&src);
src = resized;
}
image out = ipl_to_image(src);
cvReleaseImage(&src);
return out;

1
src/image.h
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@ -10,6 +10,7 @@ typedef struct {
float *data;
} image;
image image_distance(image a, image b);
void scale_image(image m, float s);
void add_scalar_image(image m, float s);
void normalize_image(image p);

55
src/network.c
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@ -21,18 +21,18 @@ network make_network(int n)
return net;
}
void print_convolutional_cfg(FILE *fp, convolutional_layer *l)
void print_convolutional_cfg(FILE *fp, convolutional_layer *l, int first)
{
int i;
fprintf(fp, "[convolutional]\n"
"height=%d\n"
"width=%d\n"
"channels=%d\n"
"filters=%d\n"
fprintf(fp, "[convolutional]\n");
if(first) fprintf(fp, "height=%d\n"
"width=%d\n"
"channels=%d\n",
l->h, l->w, l->c);
fprintf(fp, "filters=%d\n"
"size=%d\n"
"stride=%d\n"
"activation=%s\n",
l->h, l->w, l->c,
l->n, l->size, l->stride,
get_activation_string(l->activation));
fprintf(fp, "data=");
@ -40,14 +40,14 @@ void print_convolutional_cfg(FILE *fp, convolutional_layer *l)
for(i = 0; i < l->n*l->c*l->size*l->size; ++i) fprintf(fp, "%g,", l->filters[i]);
fprintf(fp, "\n\n");
}
void print_connected_cfg(FILE *fp, connected_layer *l)
void print_connected_cfg(FILE *fp, connected_layer *l, int first)
{
int i;
fprintf(fp, "[connected]\n"
"input=%d\n"
"output=%d\n"
fprintf(fp, "[connected]\n");
if(first) fprintf(fp, "input=%d\n", l->inputs);
fprintf(fp, "output=%d\n"
"activation=%s\n",
l->inputs, l->outputs,
l->outputs,
get_activation_string(l->activation));
fprintf(fp, "data=");
for(i = 0; i < l->outputs; ++i) fprintf(fp, "%g,", l->biases[i]);
@ -55,22 +55,21 @@ void print_connected_cfg(FILE *fp, connected_layer *l)
fprintf(fp, "\n\n");
}
void print_maxpool_cfg(FILE *fp, maxpool_layer *l)
void print_maxpool_cfg(FILE *fp, maxpool_layer *l, int first)
{
fprintf(fp, "[maxpool]\n"
"height=%d\n"
"width=%d\n"
"channels=%d\n"
"stride=%d\n\n",
l->h, l->w, l->c,
l->stride);
fprintf(fp, "[maxpool]\n");
if(first) fprintf(fp, "height=%d\n"
"width=%d\n"
"channels=%d\n",
l->h, l->w, l->c);
fprintf(fp, "stride=%d\n\n", l->stride);
}
void print_softmax_cfg(FILE *fp, softmax_layer *l)
void print_softmax_cfg(FILE *fp, softmax_layer *l, int first)
{
fprintf(fp, "[softmax]\n"
"input=%d\n\n",
l->inputs);
fprintf(fp, "[softmax]\n");
if(first) fprintf(fp, "input=%d\n", l->inputs);
fprintf(fp, "\n");
}
void save_network(network net, char *filename)
@ -81,13 +80,13 @@ void save_network(network net, char *filename)
for(i = 0; i < net.n; ++i)
{
if(net.types[i] == CONVOLUTIONAL)
print_convolutional_cfg(fp, (convolutional_layer *)net.layers[i]);
print_convolutional_cfg(fp, (convolutional_layer *)net.layers[i], i==0);
else if(net.types[i] == CONNECTED)
print_connected_cfg(fp, (connected_layer *)net.layers[i]);
print_connected_cfg(fp, (connected_layer *)net.layers[i], i==0);
else if(net.types[i] == MAXPOOL)
print_maxpool_cfg(fp, (maxpool_layer *)net.layers[i]);
print_maxpool_cfg(fp, (maxpool_layer *)net.layers[i], i==0);
else if(net.types[i] == SOFTMAX)
print_softmax_cfg(fp, (softmax_layer *)net.layers[i]);
print_softmax_cfg(fp, (softmax_layer *)net.layers[i], i==0);
}
fclose(fp);
}

4
src/softmax_layer.c
View File

@ -36,9 +36,9 @@ void forward_softmax_layer(const softmax_layer layer, float *input)
}
for(i = 0; i < layer.inputs; ++i){
sum += exp(input[i]-largest);
printf("%f, ", input[i]);
//printf("%f, ", input[i]);
}
printf("\n");
//printf("\n");
if(sum) sum = largest+log(sum);
else sum = largest-100;
for(i = 0; i < layer.inputs; ++i){

105
src/tests.c
View File

@ -188,37 +188,64 @@ void test_data()
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()
{
@ -398,6 +425,7 @@ void train_VOC()
int voc_size(int x)
{
x = x-1+3;
x = x-1+3;
x = x-1+3;
x = (x-1)*2+1;
@ -411,13 +439,14 @@ image features_output_size(network net, IplImage *src, int outh, int outw)
{
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(int part, int total)
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 @@ void features_VOC_image(char *image_file, char *image_dir, char *out_dir)
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(&copy);
//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();