From 5f4a5f59b072d4029107422d30b04941424c48b1 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Tue, 24 Feb 2015 02:52:05 +0000
Subject: [PATCH] captcha stuff
---
src/softmax_layer_kernels.cu | 4
src/cost_layer.c | 4
src/softmax_layer.h | 4
src/utils.h | 2
src/network_kernels.cu | 5
src/data.c | 183 ++++++++++-------
src/softmax_layer.c | 44 ++-
src/data.h | 7
src/image.c | 62 +++++-
src/parser.c | 3
src/darknet.c | 261 +++++++++++++++++++------
src/image.h | 4
src/utils.c | 11 +
13 files changed, 414 insertions(+), 180 deletions(-)
diff --git a/src/cost_layer.c b/src/cost_layer.c
index a08562b..34c8fb5 100644
--- a/src/cost_layer.c
+++ b/src/cost_layer.c
@@ -49,7 +49,7 @@
if(layer.type == DETECTION){
int 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);
@@ -71,7 +71,7 @@
axpy_ongpu(layer.batch*layer.inputs, -1, input, 1, layer.delta_gpu, 1);
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);
diff --git a/src/darknet.c b/src/darknet.c
index 92a9196..fc58f3d 100644
--- a/src/darknet.c
+++ b/src/darknet.c
@@ -31,14 +31,17 @@
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
void draw_detection(image im, float *box, int side)
{
+ int classes = 20;
+ int elems = 4+classes+1;
int j;
int r, c;
float amount[AMNT] = {0};
for(r = 0; r < side*side; ++r){
- float val = box[r*5];
+ float val = box[r*elems];
for(j = 0; j < AMNT; ++j){
if(val > amount[j]) {
float swap = val;
@@ -51,21 +54,29 @@
for(r = 0; r < side; ++r){
for(c = 0; c < side; ++c){
- j = (r*side + c) * 5;
- printf("Prob: %f\n", box[j]);
+ j = (r*side + c) * elems;
+ //printf("%d\n", j);
+ //printf("Prob: %f\n", box[j]);
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 y = r*d+box[j+1]*d;
int x = c*d+box[j+2]*d;
int h = box[j+3]*im.h;
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]);
- //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);
+ draw_box(im, x-w/2, y-h/2, x+w/2, y+h/2,red,green,blue);
}
}
}
+ //printf("Done\n");
show_image(im, "box");
cvWaitKey(0);
}
@@ -100,24 +111,24 @@
srand(time(0));
//srand(23410);
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);
printf("%d\n", plist->size);
data train, buffer;
int im_dim = 512;
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;
while(1){
i += 1;
time=clock();
pthread_join(load_thread, 0);
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]);
- draw_detection(im, train.y.vals[923], 7);
+/*
+ image im = float_to_image(im_dim - jitter, im_dim-jitter, 3, train.X.vals[0]);
+ draw_detection(im, train.y.vals[0], 7);
show_image(im, "truth");
cvWaitKey(0);
*/
@@ -128,7 +139,7 @@
net.seen += imgs;
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);
- if(i%100==0){
+ if(i%800==0){
char buff[256];
sprintf(buff, "/home/pjreddie/imagenet_backup/%s_%d.weights",base, i);
save_weights(net, buff);
@@ -146,17 +157,20 @@
fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
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);
+ int num_output = 1225;
+ int im_size = 448;
+ int classes = 20;
int m = plist->size;
int i = 0;
- int splits = 50;
+ int splits = 100;
int num = (i+1)*m/splits - i*m/splits;
fprintf(stderr, "%d\n", m);
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;
for(i = 1; i <= splits; ++i){
time=clock();
@@ -165,23 +179,33 @@
num = (i+1)*m/splits - 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);
- int j, k;
+ int j, k, class;
for(j = 0; j < pred.rows; ++j){
- for(k = 0; k < pred.cols; k += 5){
- if (pred.vals[j][k] > .005){
- int index = k/5;
+ for(k = 0; k < pred.cols; k += classes+4+1){
+
+ /*
+ 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 c = index%7;
- float y = (32.*(r + pred.vals[j][k+1]))/224.;
- float x = (32.*(c + pred.vals[j][k+2]))/224.;
- float h = (256.*(pred.vals[j][k+3]))/224.;
- float w = (256.*(pred.vals[j][k+4]))/224.;
- printf("%d %f %f %f %f %f\n", (i-1)*m/splits + j + 1, pred.vals[j][k], y, x, h, w);
+ float y = (r + pred.vals[j][k+1+classes])/7.;
+ float x = (c + pred.vals[j][k+2+classes])/7.;
+ float h = pred.vals[j][k+3+classes];
+ float w = pred.vals[j][k+4+classes];
+ 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 train_imagenet_distributed(char *address)
-{
- float avg_loss = 1;
- srand(time(0));
- network net = parse_network_cfg("cfg/net.cfg");
- set_learning_network(&net, 0, 1, 0);
- printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
- int imgs = net.batch;
- int i = 0;
- char **labels = get_labels("/home/pjreddie/data/imagenet/cls.labels.list");
- list *plist = get_paths("/data/imagenet/cls.train.list");
- char **paths = (char **)list_to_array(plist);
- printf("%d\n", plist->size);
- clock_t time;
- data train, buffer;
- pthread_t load_thread = load_data_thread(paths, imgs, plist->size, labels, 1000, 224, 224, &buffer);
- while(1){
- i += 1;
+ void train_imagenet_distributed(char *address)
+ {
+ float avg_loss = 1;
+ srand(time(0));
+ network net = parse_network_cfg("cfg/net.cfg");
+ set_learning_network(&net, 0, 1, 0);
+ printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
+ int imgs = net.batch;
+ int i = 0;
+ char **labels = get_labels("/home/pjreddie/data/imagenet/cls.labels.list");
+ list *plist = get_paths("/data/imagenet/cls.train.list");
+ char **paths = (char **)list_to_array(plist);
+ printf("%d\n", plist->size);
+ clock_t time;
+ data train, buffer;
+ pthread_t load_thread = load_data_thread(paths, imgs, plist->size, labels, 1000, 224, 224, &buffer);
+ while(1){
+ i += 1;
- time=clock();
- client_update(net, address);
- printf("Updated: %lf seconds\n", sec(clock()-time));
+ time=clock();
+ client_update(net, address);
+ printf("Updated: %lf seconds\n", sec(clock()-time));
- time=clock();
- pthread_join(load_thread, 0);
- train = buffer;
- normalize_data_rows(train);
- load_thread = load_data_thread(paths, imgs, plist->size, labels, 1000, 224, 224, &buffer);
- printf("Loaded: %lf seconds\n", sec(clock()-time));
- time=clock();
+ time=clock();
+ pthread_join(load_thread, 0);
+ train = buffer;
+ normalize_data_rows(train);
+ load_thread = load_data_thread(paths, imgs, plist->size, labels, 1000, 224, 224, &buffer);
+ printf("Loaded: %lf seconds\n", sec(clock()-time));
+ time=clock();
- float loss = train_network(net, train);
- 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);
- free_data(train);
- }
-}
-*/
+ float loss = train_network(net, train);
+ 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);
+ free_data(train);
+ }
+ }
+ */
void convert(char *cfgfile, char *outfile, char *weightfile)
{
@@ -239,6 +263,111 @@
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)
{
float avg_loss = -1;
@@ -333,6 +462,7 @@
if(weightfile){
load_weights(&net, weightfile);
}
+ int im_size = 224;
set_batch_network(&net, 1);
srand(2222222);
clock_t time;
@@ -340,7 +470,7 @@
while(1){
fgets(filename, 256, stdin);
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);
scale_image(im, 1/128.);
printf("%d %d %d\n", im.h, im.w, im.c);
@@ -814,6 +944,9 @@
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], "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], "client")) train_imagenet_distributed(argv[2]);
else if(0==strcmp(argv[1], "detect")) test_detection(argv[2], (argc > 3)? argv[3] : 0);
diff --git a/src/data.c b/src/data.c
index fd6b722..a6b6db3 100644
--- a/src/data.c
+++ b/src/data.c
@@ -17,6 +17,7 @@
int nh;
int nw;
int jitter;
+ int classes;
data *d;
};
@@ -33,53 +34,16 @@
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;
- memset(truth, 0, k*sizeof(float));
- int count = 0;
- for(i = 0; i < k; ++i){
- if(strstr(path, labels[i])){
- truth[i] = 1;
- ++count;
- }
+ for(i = 0; i < n; ++i){
+ int index = rand()%m;
+ random_paths[i] = paths[index];
+ if(i == 0) printf("%s\n", paths[index]);
}
- if(count != 1) printf("%d, %s\n", count, path);
+ return random_paths;
}
matrix load_image_paths(char **paths, int n, int h, int w)
@@ -98,16 +62,100 @@
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)
{
- char **random_paths = calloc(n, sizeof(char*));
+ 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)
+{
int i;
for(i = 0; i < n; ++i){
- int index = rand()%m;
- random_paths[i] = paths[index];
- if(i == 0) printf("%s\n", paths[index]);
+ int index = max_index(pred+i*NUMCHARS, NUMCHARS);
+ printf("%c", int_to_alphanum(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)
@@ -120,17 +168,6 @@
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)
{
list *plist = get_paths(filename);
@@ -165,20 +202,22 @@
}
}
-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);
int i;
data d;
d.shallow = 0;
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);
for(i = 0; i < n; ++i){
int dx = 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]);
+ if(flip) flip_image(a);
jitter_image(a,h-jitter,w-jitter,dy,dx);
}
d.X.cols = (h-jitter)*(w-jitter)*3;
@@ -189,14 +228,14 @@
void *load_detection_thread(void *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);
scale_data_rows(*a.d, 1./128);
free(ptr);
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;
struct load_args *args = calloc(1, sizeof(struct load_args));
@@ -207,6 +246,7 @@
args->w = w;
args->nh = nh;
args->nw = nw;
+ args->classes = classes;
args->jitter = jitter;
args->d = d;
if(pthread_create(&thread, 0, load_detection_thread, args)) {
@@ -215,17 +255,6 @@
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)
{
if(m) paths = get_random_paths(paths, n, m);
diff --git a/src/data.h b/src/data.h
index 13b62d8..6a08c88 100644
--- a/src/data.h
+++ b/src/data.h
@@ -14,12 +14,13 @@
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);
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);
-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_random(int n, char **paths, int m, int h, int w, int nh, int nw);
+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 classes, int h, int w, int nh, int nw, int jitter);
data load_data_image_pathfile(char *filename, char **labels, int k, int h, int w);
data load_cifar10_data(char *filename);
diff --git a/src/image.c b/src/image.c
index a686a3e..53cf281 100644
--- a/src/image.c
+++ b/src/image.c
@@ -4,9 +4,23 @@
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 >= a.w) x1 = a.w-1;
if(x2 < 0) x2 = 0;
@@ -17,17 +31,25 @@
if(y2 < 0) y2 = 0;
if(y2 >= a.h) y2 = a.h-1;
- for(c = 0; c < a.c; ++c){
- for(i = x1; i < x2; ++i){
- a.data[i + y1*a.w + c*a.w*a.h] = (c==0)?1:-1;
- a.data[i + y2*a.w + c*a.w*a.h] = (c==0)?1:-1;
- }
+ for(i = x1; i < x2; ++i){
+ a.data[i + y1*a.w + 0*a.w*a.h] = b;
+ a.data[i + y2*a.w + 0*a.w*a.h] = b;
+
+ 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){
- a.data[x1 + i*a.w + c*a.w*a.h] = (c==0)?1:-1;
- a.data[x2 + i*a.w + c*a.w*a.h] = (c==0)?1:-1;
- }
+ for(i = y1; i < y2; ++i){
+ a.data[x1 + i*a.w + 0*a.w*a.h] = b;
+ a.data[x2 + i*a.w + 0*a.w*a.h] = b;
+
+ 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 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)
{
int i,j;
diff --git a/src/image.h b/src/image.h
index 219798d..93b9e7e 100644
--- a/src/image.h
+++ b/src/image.h
@@ -11,8 +11,10 @@
float *data;
} image;
+float get_color(int c, int x, int max);
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);
void scale_image(image m, float s);
void translate_image(image m, float s);
diff --git a/src/network_kernels.cu b/src/network_kernels.cu
index 1f3f2e0..b83d056 100644
--- a/src/network_kernels.cu
+++ b/src/network_kernels.cu
@@ -21,6 +21,7 @@
extern "C" float * get_network_output_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)
{
@@ -219,6 +220,10 @@
//time = clock();
update_network_gpu(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));
//time = clock();
return error;
diff --git a/src/parser.c b/src/parser.c
index 3f94c80..850cc38 100644
--- a/src/parser.c
+++ b/src/parser.c
@@ -191,6 +191,7 @@
softmax_layer *parse_softmax(list *options, network *net, int count)
{
int input;
+ int groups = option_find_int(options, "groups",1);
if(count == 0){
input = option_find_int(options, "input",1);
net->batch = option_find_int(options, "batch",1);
@@ -198,7 +199,7 @@
}else{
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);
return layer;
}
diff --git a/src/softmax_layer.c b/src/softmax_layer.c
index aa5ab06..a200ae5 100644
--- a/src/softmax_layer.c
+++ b/src/softmax_layer.c
@@ -5,16 +5,18 @@
#include <math.h>
#include <stdlib.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);
softmax_layer *layer = calloc(1, sizeof(softmax_layer));
layer->batch = batch;
+ layer->groups = groups;
layer->inputs = inputs;
layer->output = calloc(inputs*batch, sizeof(float));
layer->delta = calloc(inputs*batch, sizeof(float));
- layer->jacobian = calloc(inputs*inputs*batch, sizeof(float));
#ifdef GPU
layer->output_gpu = cuda_make_array(layer->output, inputs*batch);
layer->delta_gpu = cuda_make_array(layer->delta, inputs*batch);
@@ -22,23 +24,31 @@
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)
{
- int i,b;
- for(b = 0; b < layer.batch; ++b){
- float sum = 0;
- float largest = -FLT_MAX;
- for(i = 0; i < layer.inputs; ++i){
- 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);
- }
+ int b;
+ int inputs = layer.inputs / layer.groups;
+ int batch = layer.batch * layer.groups;
+ for(b = 0; b < batch; ++b){
+ softmax_array(input+b*inputs, inputs, layer.output+b*inputs);
}
}
diff --git a/src/softmax_layer.h b/src/softmax_layer.h
index 0cc9d53..1c1cdae 100644
--- a/src/softmax_layer.h
+++ b/src/softmax_layer.h
@@ -4,16 +4,16 @@
typedef struct {
int inputs;
int batch;
+ int groups;
float *delta;
float *output;
- float *jacobian;
#ifdef GPU
float * delta_gpu;
float * output_gpu;
#endif
} 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 backward_softmax_layer(const softmax_layer layer, float *delta);
diff --git a/src/softmax_layer_kernels.cu b/src/softmax_layer_kernels.cu
index 61dc607..c0e8bc3 100644
--- a/src/softmax_layer_kernels.cu
+++ b/src/softmax_layer_kernels.cu
@@ -34,7 +34,9 @@
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());
/*
diff --git a/src/utils.c b/src/utils.c
index bf02ff3..1db8101 100644
--- a/src/utils.c
+++ b/src/utils.c
@@ -8,6 +8,17 @@
#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)
{
int i,j;
diff --git a/src/utils.h b/src/utils.h
index e233da8..7ae8a8d 100644
--- a/src/utils.h
+++ b/src/utils.h
@@ -4,6 +4,8 @@
#include <time.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 write_all(int fd, char *buffer, size_t bytes);
char *find_replace(char *str, char *orig, char *rep);
--
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