mirror/darknet
1
0
Fork 0
mirror of https://github.com/pjreddie/darknet.git synced 2023-08-10 21:13:14 +03:00
Code Issues Projects Releases Wiki Activity

captcha stuff

Browse Source
This commit is contained in:
Joseph Redmon 2015-02-23 18:52:05 -08:00
parent 0f645836f1
commit 5f4a5f59b0
13 changed files with 414 additions and 180 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
src/cost_layer.c
View File

@ -49,7 +49,7 @@ void forward_cost_layer(cost_layer layer, float *input, float *truth)
if(layer.type == DETECTION){ if(layer.type == DETECTION){
int i; int i;
for(i = 0; i < layer.batch*layer.inputs; ++i){ for(i = 0; i < layer.batch*layer.inputs; ++i){
if((i%5) && !truth[(i/5)*5]) layer.delta[i] = 0; if((i%25) && !truth[(i/25)*25]) layer.delta[i] = 0;
} }
} }
*(layer.output) = dot_cpu(layer.batch*layer.inputs, layer.delta, 1, layer.delta, 1); *(layer.output) = dot_cpu(layer.batch*layer.inputs, layer.delta, 1, layer.delta, 1);
@ -71,7 +71,7 @@ void forward_cost_layer_gpu(cost_layer layer, float * input, float * truth)
axpy_ongpu(layer.batch*layer.inputs, -1, input, 1, layer.delta_gpu, 1); axpy_ongpu(layer.batch*layer.inputs, -1, input, 1, layer.delta_gpu, 1);
if(layer.type==DETECTION){ if(layer.type==DETECTION){
mask_ongpu(layer.inputs*layer.batch, layer.delta_gpu, truth, 5); mask_ongpu(layer.inputs*layer.batch, layer.delta_gpu, truth, 25);
} }
cuda_pull_array(layer.delta_gpu, layer.delta, layer.batch*layer.inputs); cuda_pull_array(layer.delta_gpu, layer.delta, layer.batch*layer.inputs);

261
src/darknet.c
View File

@ -31,14 +31,17 @@ void test_parser()
save_network(net, "cfg/trained_imagenet_smaller.cfg"); save_network(net, "cfg/trained_imagenet_smaller.cfg");
} }
char *class_names[] = {"aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"};
#define AMNT 3 #define AMNT 3
void draw_detection(image im, float *box, int side) void draw_detection(image im, float *box, int side)
{ {
int classes = 20;
int elems = 4+classes+1;
int j; int j;
int r, c; int r, c;
float amount[AMNT] = {0}; float amount[AMNT] = {0};
for(r = 0; r < side*side; ++r){ for(r = 0; r < side*side; ++r){
float val = box[r*5]; float val = box[r*elems];
for(j = 0; j < AMNT; ++j){ for(j = 0; j < AMNT; ++j){
if(val > amount[j]) { if(val > amount[j]) {
float swap = val; float swap = val;
@ -51,21 +54,29 @@ void draw_detection(image im, float *box, int side)
for(r = 0; r < side; ++r){ for(r = 0; r < side; ++r){
for(c = 0; c < side; ++c){ for(c = 0; c < side; ++c){
j = (r*side + c) * 5; j = (r*side + c) * elems;
printf("Prob: %f\n", box[j]); //printf("%d\n", j);
//printf("Prob: %f\n", box[j]);
if(box[j] >= smallest){ if(box[j] >= smallest){
int class = max_index(box+j+1, classes);
int z;
for(z = 0; z < classes; ++z) printf("%f %s\n", box[j+1+z], class_names[z]);
printf("%f %s\n", box[j+1+class], class_names[class]);
float red = get_color(0,class,classes);
float green = get_color(1,class,classes);
float blue = get_color(2,class,classes);
j += classes;
int d = im.w/side; int d = im.w/side;
int y = r*d+box[j+1]*d; int y = r*d+box[j+1]*d;
int x = c*d+box[j+2]*d; int x = c*d+box[j+2]*d;
int h = box[j+3]*im.h; int h = box[j+3]*im.h;
int w = box[j+4]*im.w; int w = box[j+4]*im.w;
//printf("%f %f %f %f\n", box[j+1], box[j+2], box[j+3], box[j+4]); draw_box(im, x-w/2, y-h/2, x+w/2, y+h/2,red,green,blue);
//printf("%d %d %d %d\n", x, y, w, h);
//printf("%d %d %d %d\n", x-w/2, y-h/2, x+w/2, y+h/2);
draw_box(im, x-w/2, y-h/2, x+w/2, y+h/2);
} }
} }
} }
//printf("Done\n");
show_image(im, "box"); show_image(im, "box");
cvWaitKey(0); cvWaitKey(0);
} }
@ -100,24 +111,24 @@ void train_detection_net(char *cfgfile, char *weightfile)
srand(time(0)); srand(time(0));
//srand(23410); //srand(23410);
int i = net.seen/imgs; int i = net.seen/imgs;
list *plist = get_paths("/home/pjreddie/data/imagenet/horse_pos.txt"); list *plist = get_paths("/home/pjreddie/data/voc/train.txt");
char **paths = (char **)list_to_array(plist); char **paths = (char **)list_to_array(plist);
printf("%d\n", plist->size); printf("%d\n", plist->size);
data train, buffer; data train, buffer;
int im_dim = 512; int im_dim = 512;
int jitter = 64; int jitter = 64;
pthread_t load_thread = load_data_detection_thread(imgs, paths, plist->size, im_dim, im_dim, 7, 7, jitter, &buffer); pthread_t load_thread = load_data_detection_thread(imgs, paths, plist->size, 20, im_dim, im_dim, 7, 7, jitter, &buffer);
clock_t time; clock_t time;
while(1){ while(1){
i += 1; i += 1;
time=clock(); time=clock();
pthread_join(load_thread, 0); pthread_join(load_thread, 0);
train = buffer; train = buffer;
load_thread = load_data_detection_thread(imgs, paths, plist->size, im_dim, im_dim, 7, 7, jitter, &buffer); load_thread = load_data_detection_thread(imgs, paths, plist->size, 20, im_dim, im_dim, 7, 7, jitter, &buffer);
/* /*
image im = float_to_image(im_dim - jitter, im_dim-jitter, 3, train.X.vals[923]); image im = float_to_image(im_dim - jitter, im_dim-jitter, 3, train.X.vals[0]);
draw_detection(im, train.y.vals[923], 7); draw_detection(im, train.y.vals[0], 7);
show_image(im, "truth"); show_image(im, "truth");
cvWaitKey(0); cvWaitKey(0);
*/ */
@ -128,7 +139,7 @@ void train_detection_net(char *cfgfile, char *weightfile)
net.seen += imgs; net.seen += imgs;
avg_loss = avg_loss*.9 + loss*.1; 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); printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), i*imgs);
if(i%100==0){ if(i%800==0){
char buff[256]; char buff[256];
sprintf(buff, "/home/pjreddie/imagenet_backup/%s_%d.weights",base, i); sprintf(buff, "/home/pjreddie/imagenet_backup/%s_%d.weights",base, i);
save_weights(net, buff); save_weights(net, buff);
@ -146,17 +157,20 @@ void validate_detection_net(char *cfgfile, char *weightfile)
fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
srand(time(0)); srand(time(0));
list *plist = get_paths("/home/pjreddie/data/imagenet/detection.val"); list *plist = get_paths("/home/pjreddie/data/voc/val.txt");
char **paths = (char **)list_to_array(plist); char **paths = (char **)list_to_array(plist);
int num_output = 1225;
int im_size = 448;
int classes = 20;
int m = plist->size; int m = plist->size;
int i = 0; int i = 0;
int splits = 50; int splits = 100;
int num = (i+1)*m/splits - i*m/splits; int num = (i+1)*m/splits - i*m/splits;
fprintf(stderr, "%d\n", m); fprintf(stderr, "%d\n", m);
data val, buffer; data val, buffer;
pthread_t load_thread = load_data_thread(paths, num, 0, 0, 245, 224, 224, &buffer); pthread_t load_thread = load_data_thread(paths, num, 0, 0, num_output, im_size, im_size, &buffer);
clock_t time; clock_t time;
for(i = 1; i <= splits; ++i){ for(i = 1; i <= splits; ++i){
time=clock(); time=clock();
@ -165,23 +179,33 @@ void validate_detection_net(char *cfgfile, char *weightfile)
num = (i+1)*m/splits - i*m/splits; num = (i+1)*m/splits - i*m/splits;
char **part = paths+(i*m/splits); char **part = paths+(i*m/splits);
if(i != splits) load_thread = load_data_thread(part, num, 0, 0, 245, 224, 224, &buffer); if(i != splits) load_thread = load_data_thread(part, num, 0, 0, num_output, im_size, im_size, &buffer);
fprintf(stderr, "Loaded: %lf seconds\n", sec(clock()-time)); fprintf(stderr, "%d: Loaded: %lf seconds\n", i, sec(clock()-time));
matrix pred = network_predict_data(net, val); matrix pred = network_predict_data(net, val);
int j, k; int j, k, class;
for(j = 0; j < pred.rows; ++j){ for(j = 0; j < pred.rows; ++j){
for(k = 0; k < pred.cols; k += 5){ for(k = 0; k < pred.cols; k += classes+4+1){
if (pred.vals[j][k] > .005){
int index = k/5; /*
int z;
for(z = 0; z < 25; ++z) printf("%f, ", pred.vals[j][k+z]);
printf("\n");
*/
float p = pred.vals[j][k];
//if (pred.vals[j][k] > .001){
for(class = 0; class < classes; ++class){
int index = (k)/(classes+4+1);
int r = index/7; int r = index/7;
int c = index%7; int c = index%7;
float y = (32.*(r + pred.vals[j][k+1]))/224.; float y = (r + pred.vals[j][k+1+classes])/7.;
float x = (32.*(c + pred.vals[j][k+2]))/224.; float x = (c + pred.vals[j][k+2+classes])/7.;
float h = (256.*(pred.vals[j][k+3]))/224.; float h = pred.vals[j][k+3+classes];
float w = (256.*(pred.vals[j][k+4]))/224.; float w = pred.vals[j][k+4+classes];
printf("%d %f %f %f %f %f\n", (i-1)*m/splits + j + 1, pred.vals[j][k], y, x, h, w); printf("%d %d %f %f %f %f %f\n", (i-1)*m/splits + j, class, p*pred.vals[j][k+class+1], y, x, h, w);
} }
//}
} }
} }
@ -191,44 +215,44 @@ void validate_detection_net(char *cfgfile, char *weightfile)
} }
/* /*
void train_imagenet_distributed(char *address) void train_imagenet_distributed(char *address)
{ {
float avg_loss = 1; float avg_loss = 1;
srand(time(0)); srand(time(0));
network net = parse_network_cfg("cfg/net.cfg"); network net = parse_network_cfg("cfg/net.cfg");
set_learning_network(&net, 0, 1, 0); set_learning_network(&net, 0, 1, 0);
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
int imgs = net.batch; int imgs = net.batch;
int i = 0; int i = 0;
char **labels = get_labels("/home/pjreddie/data/imagenet/cls.labels.list"); char **labels = get_labels("/home/pjreddie/data/imagenet/cls.labels.list");
list *plist = get_paths("/data/imagenet/cls.train.list"); list *plist = get_paths("/data/imagenet/cls.train.list");
char **paths = (char **)list_to_array(plist); char **paths = (char **)list_to_array(plist);
printf("%d\n", plist->size); printf("%d\n", plist->size);
clock_t time; clock_t time;
data train, buffer; data train, buffer;
pthread_t load_thread = load_data_thread(paths, imgs, plist->size, labels, 1000, 224, 224, &buffer); pthread_t load_thread = load_data_thread(paths, imgs, plist->size, labels, 1000, 224, 224, &buffer);
while(1){ while(1){
i += 1; i += 1;
time=clock(); time=clock();
client_update(net, address); client_update(net, address);
printf("Updated: %lf seconds\n", sec(clock()-time)); printf("Updated: %lf seconds\n", sec(clock()-time));
time=clock(); time=clock();
pthread_join(load_thread, 0); pthread_join(load_thread, 0);
train = buffer; train = buffer;
normalize_data_rows(train); normalize_data_rows(train);
load_thread = load_data_thread(paths, imgs, plist->size, labels, 1000, 224, 224, &buffer); load_thread = load_data_thread(paths, imgs, plist->size, labels, 1000, 224, 224, &buffer);
printf("Loaded: %lf seconds\n", sec(clock()-time)); printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock(); time=clock();
float loss = train_network(net, train); float loss = train_network(net, train);
avg_loss = avg_loss*.9 + loss*.1; 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); printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), i*imgs);
free_data(train); free_data(train);
} }
} }
*/ */
void convert(char *cfgfile, char *outfile, char *weightfile) void convert(char *cfgfile, char *outfile, char *weightfile)
{ {
@ -239,6 +263,111 @@ void convert(char *cfgfile, char *outfile, char *weightfile)
save_network(net, outfile); save_network(net, outfile);
} }
void train_captcha(char *cfgfile, char *weightfile)
{
float avg_loss = -1;
srand(time(0));
char *base = basename(cfgfile);
printf("%s\n", base);
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
int imgs = 1024;
int i = net.seen/imgs;
list *plist = get_paths("/data/captcha/train.list");
char **paths = (char **)list_to_array(plist);
printf("%d\n", plist->size);
clock_t time;
while(1){
++i;
time=clock();
data train = load_data_captcha(paths, imgs, plist->size, 10, 60, 200);
translate_data_rows(train, -128);
scale_data_rows(train, 1./128);
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
float loss = train_network(net, train);
net.seen += imgs;
if(avg_loss == -1) avg_loss = loss;
avg_loss = avg_loss*.9 + loss*.1;
printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), net.seen);
free_data(train);
if(i%100==0){
char buff[256];
sprintf(buff, "/home/pjreddie/imagenet_backup/%s_%d.weights",base, i);
save_weights(net, buff);
}
}
}
void validate_captcha(char *cfgfile, char *weightfile)
{
srand(time(0));
char *base = basename(cfgfile);
printf("%s\n", base);
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
int imgs = 1000;
int numchars = 37;
list *plist = get_paths("/data/captcha/valid.list");
char **paths = (char **)list_to_array(plist);
data valid = load_data_captcha(paths, imgs, 0, 10, 60, 200);
translate_data_rows(valid, -128);
scale_data_rows(valid, 1./128);
matrix pred = network_predict_data(net, valid);
int i, k;
int correct = 0;
int total = 0;
int accuracy = 0;
for(i = 0; i < imgs; ++i){
int allcorrect = 1;
for(k = 0; k < 10; ++k){
char truth = int_to_alphanum(max_index(valid.y.vals[i]+k*numchars, numchars));
char prediction = int_to_alphanum(max_index(pred.vals[i]+k*numchars, numchars));
if (truth != prediction) allcorrect=0;
if (truth != '.' && truth == prediction) ++correct;
if (truth != '.' || truth != prediction) ++total;
}
accuracy += allcorrect;
}
printf("Word Accuracy: %f, Char Accuracy %f\n", (float)accuracy/imgs, (float)correct/total);
free_data(valid);
}
void test_captcha(char *cfgfile, char *weightfile)
{
srand(time(0));
char *base = basename(cfgfile);
printf("%s\n", base);
network net = parse_network_cfg(cfgfile);
set_batch_network(&net, 1);
if(weightfile){
load_weights(&net, weightfile);
}
clock_t time;
char filename[256];
while(1){
printf("Enter filename: ");
fgets(filename, 256, stdin);
strtok(filename, "\n");
time = clock();
image im = load_image_color(filename, 60, 200);
translate_image(im, -128);
scale_image(im, 1/128.);
float *X = im.data;
time=clock();
float *predictions = network_predict(net, X);
printf("Predicted in %f\n", sec(clock() - time));
print_letters(predictions, 10);
free_image(im);
}
}
void train_imagenet(char *cfgfile, char *weightfile) void train_imagenet(char *cfgfile, char *weightfile)
{ {
float avg_loss = -1; float avg_loss = -1;
@ -333,6 +462,7 @@ void test_detection(char *cfgfile, char *weightfile)
if(weightfile){ if(weightfile){
load_weights(&net, weightfile); load_weights(&net, weightfile);
} }
int im_size = 224;
set_batch_network(&net, 1); set_batch_network(&net, 1);
srand(2222222); srand(2222222);
clock_t time; clock_t time;
@ -340,7 +470,7 @@ void test_detection(char *cfgfile, char *weightfile)
while(1){ while(1){
fgets(filename, 256, stdin); fgets(filename, 256, stdin);
strtok(filename, "\n"); strtok(filename, "\n");
image im = load_image_color(filename, 224, 224); image im = load_image_color(filename, im_size, im_size);
translate_image(im, -128); translate_image(im, -128);
scale_image(im, 1/128.); scale_image(im, 1/128.);
printf("%d %d %d\n", im.h, im.w, im.c); printf("%d %d %d\n", im.h, im.w, im.c);
@ -814,6 +944,9 @@ int main(int argc, char **argv)
else if(0==strcmp(argv[1], "nist")) train_nist(argv[2]); else if(0==strcmp(argv[1], "nist")) train_nist(argv[2]);
else if(0==strcmp(argv[1], "ctest")) test_cifar10(argv[2]); else if(0==strcmp(argv[1], "ctest")) test_cifar10(argv[2]);
else if(0==strcmp(argv[1], "train")) train_imagenet(argv[2], (argc > 3)? argv[3] : 0); else if(0==strcmp(argv[1], "train")) train_imagenet(argv[2], (argc > 3)? argv[3] : 0);
else if(0==strcmp(argv[1], "captcha")) train_captcha(argv[2], (argc > 3)? argv[3] : 0);
else if(0==strcmp(argv[1], "tcaptcha")) test_captcha(argv[2], (argc > 3)? argv[3] : 0);
else if(0==strcmp(argv[1], "vcaptcha")) validate_captcha(argv[2], (argc > 3)? argv[3] : 0);
else if(0==strcmp(argv[1], "testseg")) test_voc_segment(argv[2], (argc > 3)? argv[3] : 0); else if(0==strcmp(argv[1], "testseg")) test_voc_segment(argv[2], (argc > 3)? argv[3] : 0);
//else if(0==strcmp(argv[1], "client")) train_imagenet_distributed(argv[2]); //else if(0==strcmp(argv[1], "client")) train_imagenet_distributed(argv[2]);
else if(0==strcmp(argv[1], "detect")) test_detection(argv[2], (argc > 3)? argv[3] : 0); else if(0==strcmp(argv[1], "detect")) test_detection(argv[2], (argc > 3)? argv[3] : 0);

183
src/data.c
View File

@ -17,6 +17,7 @@ struct load_args{
int nh; int nh;
int nw; int nw;
int jitter; int jitter;
int classes;
data *d; data *d;
}; };
@ -33,53 +34,16 @@ list *get_paths(char *filename)
return lines; return lines;
} }
void fill_truth_detection(char *path, float *truth, int height, int width, int num_height, int num_width, int dy, int dx, int jitter) char **get_random_paths(char **paths, int n, int m)
{
int box_height = height/num_height;
int box_width = width/num_width;
char *labelpath = find_replace(path, "imgs", "det/train");
labelpath = find_replace(labelpath, ".JPEG", ".txt");
FILE *file = fopen(labelpath, "r");
if(!file) file_error(labelpath);
float x, y, h, w;
while(fscanf(file, "%f %f %f %f", &x, &y, &w, &h) == 4){
x *= width + jitter;
y *= height + jitter;
x -= dx;
y -= dy;
int i = x/box_width;
int j = y/box_height;
if(i < 0) i = 0;
if(i >= num_width) i = num_width-1;
if(j < 0) j = 0;
if(j >= num_height) j = num_height-1;
float dw = (x - i*box_width)/box_width;
float dh = (y - j*box_height)/box_height;
//printf("%d %d %f %f\n", i, j, dh, dw);
int index = (i+j*num_width)*5;
truth[index++] = 1;
truth[index++] = dh;
truth[index++] = dw;
truth[index++] = h*(height+jitter)/height;
truth[index++] = w*(width+jitter)/width;
}
fclose(file);
}
void fill_truth(char *path, char **labels, int k, float *truth)
{ {
char **random_paths = calloc(n, sizeof(char*));
int i; int i;
memset(truth, 0, k*sizeof(float)); for(i = 0; i < n; ++i){
int count = 0; int index = rand()%m;
for(i = 0; i < k; ++i){ random_paths[i] = paths[index];
if(strstr(path, labels[i])){ if(i == 0) printf("%s\n", paths[index]);
truth[i] = 1;
++count;
}
} }
if(count != 1) printf("%d, %s\n", count, path); return random_paths;
} }
matrix load_image_paths(char **paths, int n, int h, int w) matrix load_image_paths(char **paths, int n, int h, int w)
@ -98,16 +62,100 @@ matrix load_image_paths(char **paths, int n, int h, int w)
return X; return X;
} }
char **get_random_paths(char **paths, int n, int m) void fill_truth_detection(char *path, float *truth, int classes, int height, int width, int num_height, int num_width, int dy, int dx, int jitter, int flip)
{
int box_height = height/num_height;
int box_width = width/num_width;
char *labelpath = find_replace(path, "VOC2012/JPEGImages", "labels");
labelpath = find_replace(labelpath, ".jpg", ".txt");
FILE *file = fopen(labelpath, "r");
if(!file) file_error(labelpath);
float x, y, h, w;
int id;
while(fscanf(file, "%d %f %f %f %f", &id, &x, &y, &w, &h) == 5){
if(flip) x = 1-x;
x *= width + jitter;
y *= height + jitter;
x -= dx;
y -= dy;
int i = x/box_width;
int j = y/box_height;
if(i < 0) i = 0;
if(i >= num_width) i = num_width-1;
if(j < 0) j = 0;
if(j >= num_height) j = num_height-1;
float dw = (x - i*box_width)/box_width;
float dh = (y - j*box_height)/box_height;
//printf("%d %d %d %f %f\n", id, i, j, dh, dw);
int index = (i+j*num_width)*(4+classes+1);
truth[index++] = 1;
truth[index+id] = 1;
index += classes;
truth[index++] = dh;
truth[index++] = dw;
truth[index++] = h*(height+jitter)/height;
truth[index++] = w*(width+jitter)/width;
}
fclose(file);
}
#define NUMCHARS 37
void print_letters(float *pred, int n)
{ {
char **random_paths = calloc(n, sizeof(char*));
int i; int i;
for(i = 0; i < n; ++i){ for(i = 0; i < n; ++i){
int index = rand()%m; int index = max_index(pred+i*NUMCHARS, NUMCHARS);
random_paths[i] = paths[index]; printf("%c", int_to_alphanum(index));
if(i == 0) printf("%s\n", paths[index]);
} }
return random_paths; printf("\n");
}
void fill_truth_captcha(char *path, int n, float *truth)
{
char *begin = strrchr(path, '/');
++begin;
int i;
for(i = 0; i < strlen(begin) && i < n && begin[i] != '.'; ++i){
int index = alphanum_to_int(begin[i]);
if(index > 35) printf("Bad %c\n", begin[i]);
truth[i*NUMCHARS+index] = 1;
}
for(;i < n; ++i){
truth[i*NUMCHARS + NUMCHARS-1] = 1;
}
}
data load_data_captcha(char **paths, int n, int m, int k, int h, int w)
{
if(m) paths = get_random_paths(paths, n, m);
data d;
d.shallow = 0;
d.X = load_image_paths(paths, n, h, w);
d.y = make_matrix(n, k*NUMCHARS);
int i;
for(i = 0; i < n; ++i){
fill_truth_captcha(paths[i], k, d.y.vals[i]);
}
if(m) free(paths);
return d;
}
void fill_truth(char *path, char **labels, int k, float *truth)
{
int i;
memset(truth, 0, k*sizeof(float));
int count = 0;
for(i = 0; i < k; ++i){
if(strstr(path, labels[i])){
truth[i] = 1;
++count;
}
}
if(count != 1) printf("%d, %s\n", count, path);
} }
matrix load_labels_paths(char **paths, int n, char **labels, int k) matrix load_labels_paths(char **paths, int n, char **labels, int k)
@ -120,17 +168,6 @@ matrix load_labels_paths(char **paths, int n, char **labels, int k)
return y; return y;
} }
matrix load_labels_detection(char **paths, int n, int height, int width, int num_height, int num_width)
{
int k = num_height*num_width*5;
matrix y = make_matrix(n, k);
int i;
for(i = 0; i < n; ++i){
fill_truth_detection(paths[i], y.vals[i], height, width, num_height, num_width, 0, 0, 0);
}
return y;
}
data load_data_image_pathfile(char *filename, char **labels, int k, int h, int w) data load_data_image_pathfile(char *filename, char **labels, int k, int h, int w)
{ {
list *plist = get_paths(filename); list *plist = get_paths(filename);
@ -165,20 +202,22 @@ void free_data(data d)
} }
} }
data load_data_detection_jitter_random(int n, char **paths, int m, int h, int w, int nh, int nw, int jitter) data load_data_detection_jitter_random(int n, char **paths, int m, int classes, int h, int w, int nh, int nw, int jitter)
{ {
char **random_paths = get_random_paths(paths, n, m); char **random_paths = get_random_paths(paths, n, m);
int i; int i;
data d; data d;
d.shallow = 0; d.shallow = 0;
d.X = load_image_paths(random_paths, n, h, w); d.X = load_image_paths(random_paths, n, h, w);
int k = nh*nw*5; int k = nh*nw*(4+classes+1);
d.y = make_matrix(n, k); d.y = make_matrix(n, k);
for(i = 0; i < n; ++i){ for(i = 0; i < n; ++i){
int dx = rand()%jitter; int dx = rand()%jitter;
int dy = rand()%jitter; int dy = rand()%jitter;
fill_truth_detection(random_paths[i], d.y.vals[i], h-jitter, w-jitter, nh, nw, dy, dx, jitter); int flip = rand()%2;
fill_truth_detection(random_paths[i], d.y.vals[i], classes, h-jitter, w-jitter, nh, nw, dy, dx, jitter, flip);
image a = float_to_image(h, w, 3, d.X.vals[i]); image a = float_to_image(h, w, 3, d.X.vals[i]);
if(flip) flip_image(a);
jitter_image(a,h-jitter,w-jitter,dy,dx); jitter_image(a,h-jitter,w-jitter,dy,dx);
} }
d.X.cols = (h-jitter)*(w-jitter)*3; d.X.cols = (h-jitter)*(w-jitter)*3;
@ -189,14 +228,14 @@ data load_data_detection_jitter_random(int n, char **paths, int m, int h, int w,
void *load_detection_thread(void *ptr) void *load_detection_thread(void *ptr)
{ {
struct load_args a = *(struct load_args*)ptr; struct load_args a = *(struct load_args*)ptr;
*a.d = load_data_detection_jitter_random(a.n, a.paths, a.m, a.h, a.w, a.nh, a.nw, a.jitter); *a.d = load_data_detection_jitter_random(a.n, a.paths, a.m, a.classes, a.h, a.w, a.nh, a.nw, a.jitter);
translate_data_rows(*a.d, -128); translate_data_rows(*a.d, -128);
scale_data_rows(*a.d, 1./128); scale_data_rows(*a.d, 1./128);
free(ptr); free(ptr);
return 0; return 0;
} }
pthread_t load_data_detection_thread(int n, char **paths, int m, int h, int w, int nh, int nw, int jitter, data *d) pthread_t load_data_detection_thread(int n, char **paths, int m, int classes, int h, int w, int nh, int nw, int jitter, data *d)
{ {
pthread_t thread; pthread_t thread;
struct load_args *args = calloc(1, sizeof(struct load_args)); struct load_args *args = calloc(1, sizeof(struct load_args));
@ -207,6 +246,7 @@ pthread_t load_data_detection_thread(int n, char **paths, int m, int h, int w, i
args->w = w; args->w = w;
args->nh = nh; args->nh = nh;
args->nw = nw; args->nw = nw;
args->classes = classes;
args->jitter = jitter; args->jitter = jitter;
args->d = d; args->d = d;
if(pthread_create(&thread, 0, load_detection_thread, args)) { if(pthread_create(&thread, 0, load_detection_thread, args)) {
@ -215,17 +255,6 @@ pthread_t load_data_detection_thread(int n, char **paths, int m, int h, int w, i
return thread; return thread;
} }
data load_data_detection_random(int n, char **paths, int m, int h, int w, int nh, int nw)
{
char **random_paths = get_random_paths(paths, n, m);
data d;
d.shallow = 0;
d.X = load_image_paths(random_paths, n, h, w);
d.y = load_labels_detection(random_paths, n, h, w, nh, nw);
free(random_paths);
return d;
}
data load_data(char **paths, int n, int m, char **labels, int k, int h, int w) data load_data(char **paths, int n, int m, char **labels, int k, int h, int w)
{ {
if(m) paths = get_random_paths(paths, n, m); if(m) paths = get_random_paths(paths, n, m);

7
src/data.h
View File

@ -14,12 +14,13 @@ typedef struct{
void free_data(data d); void free_data(data d);
void print_letters(float *pred, int n);
data load_data_captcha(char **paths, int n, int m, int k, int h, int w);
data load_data(char **paths, int n, int m, char **labels, int k, int h, int w); data load_data(char **paths, int n, int m, char **labels, int k, int h, int w);
pthread_t load_data_thread(char **paths, int n, int m, char **labels, int k, int h, int w, data *d); pthread_t load_data_thread(char **paths, int n, int m, char **labels, int k, int h, int w, data *d);
pthread_t load_data_detection_thread(int n, char **paths, int m, int h, int w, int nh, int nw, int jitter, data *d); pthread_t load_data_detection_thread(int n, char **paths, int m, int classes, int h, int w, int nh, int nw, int jitter, data *d);
data load_data_detection_jitter_random(int n, char **paths, int m, int h, int w, int nh, int nw, int jitter); data load_data_detection_jitter_random(int n, char **paths, int m, int classes, int h, int w, int nh, int nw, int jitter);
data load_data_detection_random(int n, char **paths, int m, int h, int w, int nh, int nw);
data load_data_image_pathfile(char *filename, char **labels, int k, int h, int w); data load_data_image_pathfile(char *filename, char **labels, int k, int h, int w);
data load_cifar10_data(char *filename); data load_cifar10_data(char *filename);

62
src/image.c
View File

@ -4,9 +4,23 @@
int windows = 0; int windows = 0;
void draw_box(image a, int x1, int y1, int x2, int y2) float colors[6][3] = { {1,0,1}, {0,0,1},{0,1,1},{0,1,0},{1,1,0},{1,0,0} };
float get_color(int c, int x, int max)
{ {
int i, c; float ratio = ((float)x/max)*5;
int i = floor(ratio);
int j = ceil(ratio);
ratio -= i;
float r = (1-ratio) * colors[i][c] + ratio*colors[j][c];
printf("%f\n", r);
return r;
}
void draw_box(image a, int x1, int y1, int x2, int y2, float r, float g, float b)
{
normalize_image(a);
int i;
if(x1 < 0) x1 = 0; if(x1 < 0) x1 = 0;
if(x1 >= a.w) x1 = a.w-1; if(x1 >= a.w) x1 = a.w-1;
if(x2 < 0) x2 = 0; if(x2 < 0) x2 = 0;
@ -17,17 +31,25 @@ void draw_box(image a, int x1, int y1, int x2, int y2)
if(y2 < 0) y2 = 0; if(y2 < 0) y2 = 0;
if(y2 >= a.h) y2 = a.h-1; if(y2 >= a.h) y2 = a.h-1;
for(c = 0; c < a.c; ++c){ for(i = x1; i < x2; ++i){
for(i = x1; i < x2; ++i){ a.data[i + y1*a.w + 0*a.w*a.h] = b;
a.data[i + y1*a.w + c*a.w*a.h] = (c==0)?1:-1; a.data[i + y2*a.w + 0*a.w*a.h] = b;
a.data[i + y2*a.w + c*a.w*a.h] = (c==0)?1:-1;
} a.data[i + y1*a.w + 1*a.w*a.h] = g;
a.data[i + y2*a.w + 1*a.w*a.h] = g;
a.data[i + y1*a.w + 2*a.w*a.h] = r;
a.data[i + y2*a.w + 2*a.w*a.h] = r;
} }
for(c = 0; c < a.c; ++c){ for(i = y1; i < y2; ++i){
for(i = y1; i < y2; ++i){ a.data[x1 + i*a.w + 0*a.w*a.h] = b;
a.data[x1 + i*a.w + c*a.w*a.h] = (c==0)?1:-1; a.data[x2 + i*a.w + 0*a.w*a.h] = b;
a.data[x2 + i*a.w + c*a.w*a.h] = (c==0)?1:-1;
} a.data[x1 + i*a.w + 1*a.w*a.h] = g;
a.data[x2 + i*a.w + 1*a.w*a.h] = g;
a.data[x1 + i*a.w + 2*a.w*a.h] = r;
a.data[x2 + i*a.w + 2*a.w*a.h] = r;
} }
} }
@ -46,6 +68,22 @@ void jitter_image(image a, int h, int w, int dh, int dw)
} }
} }
void flip_image(image a)
{
int i,j,k;
for(k = 0; k < a.c; ++k){
for(i = 0; i < a.h; ++i){
for(j = 0; j < a.w/2; ++j){
int index = j + a.w*(i + a.h*(k));
int flip = (a.w - j - 1) + a.w*(i + a.h*(k));
float swap = a.data[flip];
a.data[flip] = a.data[index];
a.data[index] = swap;
}
}
}
}
image image_distance(image a, image b) image image_distance(image a, image b)
{ {
int i,j; int i,j;

4
src/image.h
View File

@ -11,8 +11,10 @@ typedef struct {
float *data; float *data;
} image; } image;
float get_color(int c, int x, int max);
void jitter_image(image a, int h, int w, int dh, int dw); void jitter_image(image a, int h, int w, int dh, int dw);
void draw_box(image a, int x1, int y1, int x2, int y2); void flip_image(image a);
void draw_box(image a, int x1, int y1, int x2, int y2, float r, float g, float b);
image image_distance(image a, image b); image image_distance(image a, image b);
void scale_image(image m, float s); void scale_image(image m, float s);
void translate_image(image m, float s); void translate_image(image m, float s);

5
src/network_kernels.cu
View File

@ -21,6 +21,7 @@ extern "C" {
extern "C" float * get_network_output_gpu_layer(network net, int i); extern "C" float * get_network_output_gpu_layer(network net, int i);
extern "C" float * get_network_delta_gpu_layer(network net, int i); extern "C" float * get_network_delta_gpu_layer(network net, int i);
float *get_network_output_gpu(network net);
void forward_network_gpu(network net, float * input, float * truth, int train) void forward_network_gpu(network net, float * input, float * truth, int train)
{ {
@ -219,6 +220,10 @@ float train_network_datum_gpu(network net, float *x, float *y)
//time = clock(); //time = clock();
update_network_gpu(net); update_network_gpu(net);
float error = get_network_cost(net); float error = get_network_cost(net);
//print_letters(y, 50);
//float *out = get_network_output_gpu(net);
//print_letters(out, 50);
//printf("updt %f\n", sec(clock() - time)); //printf("updt %f\n", sec(clock() - time));
//time = clock(); //time = clock();
return error; return error;

3
src/parser.c
View File

@ -191,6 +191,7 @@ connected_layer *parse_connected(list *options, network *net, int count)
softmax_layer *parse_softmax(list *options, network *net, int count) softmax_layer *parse_softmax(list *options, network *net, int count)
{ {
int input; int input;
int groups = option_find_int(options, "groups",1);
if(count == 0){ if(count == 0){
input = option_find_int(options, "input",1); input = option_find_int(options, "input",1);
net->batch = option_find_int(options, "batch",1); net->batch = option_find_int(options, "batch",1);
@ -198,7 +199,7 @@ softmax_layer *parse_softmax(list *options, network *net, int count)
}else{ }else{
input = get_network_output_size_layer(*net, count-1); input = get_network_output_size_layer(*net, count-1);
} }
softmax_layer *layer = make_softmax_layer(net->batch, input); softmax_layer *layer = make_softmax_layer(net->batch, groups, input);
option_unused(options); option_unused(options);
return layer; return layer;
} }

44
src/softmax_layer.c
View File

@ -5,16 +5,18 @@
#include <math.h> #include <math.h>
#include <stdlib.h> #include <stdlib.h>
#include <stdio.h> #include <stdio.h>
#include <assert.h>
softmax_layer *make_softmax_layer(int batch, int inputs) softmax_layer *make_softmax_layer(int batch, int groups, int inputs)
{ {
assert(inputs%groups == 0);
fprintf(stderr, "Softmax Layer: %d inputs\n", inputs); fprintf(stderr, "Softmax Layer: %d inputs\n", inputs);
softmax_layer *layer = calloc(1, sizeof(softmax_layer)); softmax_layer *layer = calloc(1, sizeof(softmax_layer));
layer->batch = batch; layer->batch = batch;
layer->groups = groups;
layer->inputs = inputs; layer->inputs = inputs;
layer->output = calloc(inputs*batch, sizeof(float)); layer->output = calloc(inputs*batch, sizeof(float));
layer->delta = calloc(inputs*batch, sizeof(float)); layer->delta = calloc(inputs*batch, sizeof(float));
layer->jacobian = calloc(inputs*inputs*batch, sizeof(float));
#ifdef GPU #ifdef GPU
layer->output_gpu = cuda_make_array(layer->output, inputs*batch); layer->output_gpu = cuda_make_array(layer->output, inputs*batch);
layer->delta_gpu = cuda_make_array(layer->delta, inputs*batch); layer->delta_gpu = cuda_make_array(layer->delta, inputs*batch);
@ -22,23 +24,31 @@ softmax_layer *make_softmax_layer(int batch, int inputs)
return layer; return layer;
} }
void softmax_array(float *input, int n, float *output)
{
int i;
float sum = 0;
float largest = -FLT_MAX;
for(i = 0; i < n; ++i){
if(input[i] > largest) largest = input[i];
}
for(i = 0; i < n; ++i){
sum += exp(input[i]-largest);
}
if(sum) sum = largest+log(sum);
else sum = largest-100;
for(i = 0; i < n; ++i){
output[i] = exp(input[i]-sum);
}
}
void forward_softmax_layer(const softmax_layer layer, float *input) void forward_softmax_layer(const softmax_layer layer, float *input)
{ {
int i,b; int b;
for(b = 0; b < layer.batch; ++b){ int inputs = layer.inputs / layer.groups;
float sum = 0; int batch = layer.batch * layer.groups;
float largest = -FLT_MAX; for(b = 0; b < batch; ++b){
for(i = 0; i < layer.inputs; ++i){ softmax_array(input+b*inputs, inputs, layer.output+b*inputs);
if(input[i+b*layer.inputs] > largest) largest = input[i+b*layer.inputs];
}
for(i = 0; i < layer.inputs; ++i){
sum += exp(input[i+b*layer.inputs]-largest);
}
if(sum) sum = largest+log(sum);
else sum = largest-100;
for(i = 0; i < layer.inputs; ++i){
layer.output[i+b*layer.inputs] = exp(input[i+b*layer.inputs]-sum);
}
} }
} }

4
src/softmax_layer.h
View File

@ -4,16 +4,16 @@
typedef struct { typedef struct {
int inputs; int inputs;
int batch; int batch;
int groups;
float *delta; float *delta;
float *output; float *output;
float *jacobian;
#ifdef GPU #ifdef GPU
float * delta_gpu; float * delta_gpu;
float * output_gpu; float * output_gpu;
#endif #endif
} softmax_layer; } softmax_layer;
softmax_layer *make_softmax_layer(int batch, int inputs); softmax_layer *make_softmax_layer(int batch, int groups, int inputs);
void forward_softmax_layer(const softmax_layer layer, float *input); void forward_softmax_layer(const softmax_layer layer, float *input);
void backward_softmax_layer(const softmax_layer layer, float *delta); void backward_softmax_layer(const softmax_layer layer, float *delta);

4
src/softmax_layer_kernels.cu
View File

@ -34,7 +34,9 @@ extern "C" void pull_softmax_layer_output(const softmax_layer layer)
extern "C" void forward_softmax_layer_gpu(const softmax_layer layer, float *input) extern "C" void forward_softmax_layer_gpu(const softmax_layer layer, float *input)
{ {
forward_softmax_layer_kernel<<<cuda_gridsize(layer.batch), BLOCK>>>(layer.inputs, layer.batch, input, layer.output_gpu); int inputs = layer.inputs / layer.groups;
int batch = layer.batch * layer.groups;
forward_softmax_layer_kernel<<<cuda_gridsize(batch), BLOCK>>>(inputs, batch, input, layer.output_gpu);
check_error(cudaPeekAtLastError()); check_error(cudaPeekAtLastError());
/* /*

11
src/utils.c
View File

@ -8,6 +8,17 @@
#include "utils.h" #include "utils.h"
int alphanum_to_int(char c)
{
return (c < 58) ? c - 48 : c-87;
}
char int_to_alphanum(int i)
{
if (i == 36) return '.';
return (i < 10) ? i + 48 : i + 87;
}
void pm(int M, int N, float *A) void pm(int M, int N, float *A)
{ {
int i,j; int i,j;

2
src/utils.h
View File

@ -4,6 +4,8 @@
#include <time.h> #include <time.h>
#include "list.h" #include "list.h"
int alphanum_to_int(char c);
char int_to_alphanum(int i);
void read_all(int fd, char *buffer, size_t bytes); void read_all(int fd, char *buffer, size_t bytes);
void write_all(int fd, char *buffer, size_t bytes); void write_all(int fd, char *buffer, size_t bytes);
char *find_replace(char *str, char *orig, char *rep); char *find_replace(char *str, char *orig, char *rep);