From fb9e0fe33681280112e4e33939c5844dba994dca Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Wed, 04 Mar 2015 22:56:38 +0000
Subject: [PATCH] Big changes to detection
---
src/darknet.c | 443 +++++++++++++++++++++++++++++++++++++++---------------
1 files changed, 317 insertions(+), 126 deletions(-)
diff --git a/src/darknet.c b/src/darknet.c
index 64012e0..413d7f2 100644
--- a/src/darknet.c
+++ b/src/darknet.c
@@ -31,133 +31,169 @@
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;
int j;
int r, c;
- float amount[AMNT] = {0};
- for(r = 0; r < side*side; ++r){
- float val = box[r*5];
- for(j = 0; j < AMNT; ++j){
- if(val > amount[j]) {
- float swap = val;
- val = amount[j];
- amount[j] = swap;
- }
- }
- }
- float smallest = amount[AMNT-1];
for(r = 0; r < side; ++r){
for(c = 0; c < side; ++c){
- j = (r*side + c) * 5;
- printf("Prob: %f\n", box[j]);
- if(box[j] >= smallest){
+ j = (r*side + c) * elems;
+ //printf("%d\n", j);
+ //printf("Prob: %f\n", box[j]);
+ int class = max_index(box+j, classes);
+ if(box[j+class] > .02 || 1){
+ //int z;
+ //for(z = 0; z < classes; ++z) printf("%f %s\n", box[j+z], class_names[z]);
+ printf("%f %s\n", box[j+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]*256;
- int w = box[j+4]*256;
- //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);
+ int y = r*d+box[j]*d;
+ int x = c*d+box[j+1]*d;
+ int h = box[j+2]*im.h;
+ int w = box[j+3]*im.w;
+ 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);
}
-
-void train_detection_net(char *cfgfile)
+char *basename(char *cfgfile)
{
+ char *c = cfgfile;
+ char *next;
+ while((next = strchr(c, '/')))
+ {
+ c = next+1;
+ }
+ c = copy_string(c);
+ next = strchr(c, '_');
+ if (next) *next = 0;
+ next = strchr(c, '.');
+ if (next) *next = 0;
+ return c;
+}
+
+void train_detection_net(char *cfgfile, char *weightfile)
+{
+ char *base = basename(cfgfile);
+ printf("%s\n", base);
float avg_loss = 1;
- //network net = parse_network_cfg("/home/pjreddie/imagenet_backup/alexnet_1270.cfg");
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 imgs = 128;
srand(time(0));
//srand(23410);
- int i = 0;
- list *plist = get_paths("/home/pjreddie/data/imagenet/horse.txt");
+ int i = net.seen/imgs;
+ 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;
- pthread_t load_thread = load_data_detection_thread(imgs, paths, plist->size, 256, 256, 7, 7, 256, &buffer);
+ int im_dim = 512;
+ int jitter = 64;
+ int classes = 21;
+ pthread_t load_thread = load_data_detection_thread(imgs, paths, plist->size, classes, 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, 256, 256, 7, 7, 256, &buffer);
- //data train = load_data_detection_random(imgs, paths, plist->size, 224, 224, 7, 7, 256);
+ load_thread = load_data_detection_thread(imgs, paths, plist->size, classes, im_dim, im_dim, 7, 7, jitter, &buffer);
-/*
- image im = float_to_image(224, 224, 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);
+ */
- normalize_data_rows(train);
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
float loss = train_network(net, train);
+ 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){
char buff[256];
- sprintf(buff, "/home/pjreddie/imagenet_backup/detnet_%d.cfg", i);
- save_network(net, buff);
+ sprintf(buff, "/home/pjreddie/imagenet_backup/%s_%d.weights",base, i);
+ save_weights(net, buff);
}
free_data(train);
}
}
-void validate_detection_net(char *cfgfile)
+void validate_detection_net(char *cfgfile, char *weightfile)
{
network net = parse_network_cfg(cfgfile);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
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 = 21;
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();
pthread_join(load_thread, 0);
val = buffer;
- normalize_data_rows(val);
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);
-
- fprintf(stderr, "Loaded: %lf seconds\n", sec(clock()-time));
+ if(i != splits) load_thread = load_data_thread(part, num, 0, 0, num_output, im_size, im_size, &buffer);
+
+ 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){
+
+ /*
+ int z;
+ for(z = 0; z < 25; ++z) printf("%f, ", pred.vals[j][k+z]);
+ printf("\n");
+ */
+
+ //if (pred.vals[j][k] > .001){
+ for(class = 0; class < classes-1; ++class){
+ int index = (k)/(classes+4);
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+0+classes])/7.;
+ float x = (c + pred.vals[j][k+1+classes])/7.;
+ float h = pred.vals[j][k+2+classes];
+ float w = pred.vals[j][k+3+classes];
+ printf("%d %d %f %f %f %f %f\n", (i-1)*m/splits + j, class, pred.vals[j][k+class], y, x, h, w);
}
+ //}
}
}
@@ -167,52 +203,169 @@
}
/*
-void train_imagenet_distributed(char *address)
+ 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();
+ 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);
+ }
+ }
+ */
+
+void convert(char *cfgfile, char *outfile, char *weightfile)
{
- float avg_loss = 1;
+ network net = parse_network_cfg(cfgfile);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ save_network(net, outfile);
+}
+
+void train_captcha(char *cfgfile, char *weightfile)
+{
+ float avg_loss = -1;
srand(time(0));
- network net = parse_network_cfg("cfg/net.cfg");
- set_learning_network(&net, 0, 1, 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 = 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");
+ 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;
- data train, buffer;
- pthread_t load_thread = load_data_thread(paths, imgs, plist->size, labels, 1000, 224, 224, &buffer);
while(1){
- i += 1;
-
+ ++i;
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);
+ 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), i*imgs);
+ 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 train_imagenet(char *cfgfile)
+
+void validate_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);
- //test_learn_bias(*(convolutional_layer *)net.layers[1]);
- //set_learning_network(&net, net.learning_rate, 0, net.decay);
+ 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;
+ 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;
@@ -235,26 +388,30 @@
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/vgg_%d.cfg", i);
- save_network(net, buff);
+ sprintf(buff, "/home/pjreddie/imagenet_backup/%s_%d.weights",base, i);
+ save_weights(net, buff);
}
}
}
-void validate_imagenet(char *filename)
+void validate_imagenet(char *filename, char *weightfile)
{
int i = 0;
network net = parse_network_cfg(filename);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
srand(time(0));
char **labels = get_labels("/home/pjreddie/data/imagenet/cls.val.labels.list");
- list *plist = get_paths("/home/pjreddie/data/imagenet/cls.val.list");
+ list *plist = get_paths("/data/imagenet/cls.val.list");
char **paths = (char **)list_to_array(plist);
int m = plist->size;
free_list(plist);
@@ -272,7 +429,6 @@
pthread_join(load_thread, 0);
val = buffer;
- //normalize_data_rows(val);
num = (i+1)*m/splits - i*m/splits;
char **part = paths+(i*m/splits);
@@ -288,9 +444,13 @@
}
}
-void test_detection(char *cfgfile)
+void test_detection(char *cfgfile, char *weightfile)
{
network net = parse_network_cfg(cfgfile);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ int im_size = 448;
set_batch_network(&net, 1);
srand(2222222);
clock_t time;
@@ -298,8 +458,9 @@
while(1){
fgets(filename, 256, stdin);
strtok(filename, "\n");
- image im = load_image_color(filename, 224, 224);
- z_normalize_image(im);
+ 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);
float *X = im.data;
time=clock();
@@ -312,6 +473,7 @@
void test_init(char *cfgfile)
{
+ gpu_index = -1;
network net = parse_network_cfg(cfgfile);
set_batch_network(&net, 1);
srand(2222222);
@@ -345,20 +507,44 @@
}
void test_dog(char *cfgfile)
{
- image im = load_image_color("data/dog.jpg", 224, 224);
+ image im = load_image_color("data/dog.jpg", 256, 256);
translate_image(im, -128);
print_image(im);
float *X = im.data;
network net = parse_network_cfg(cfgfile);
set_batch_network(&net, 1);
- float *predictions = network_predict(net, X);
+ network_predict(net, X);
image crop = get_network_image_layer(net, 0);
- //show_image(crop, "cropped");
- // print_image(crop);
- //show_image(im, "orig");
+ show_image(crop, "cropped");
+ print_image(crop);
+ show_image(im, "orig");
float * inter = get_network_output(net);
pm(1000, 1, inter);
- //cvWaitKey(0);
+ cvWaitKey(0);
+}
+
+void test_voc_segment(char *cfgfile, char *weightfile)
+{
+ network net = parse_network_cfg(cfgfile);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ set_batch_network(&net, 1);
+ while(1){
+ char filename[256];
+ fgets(filename, 256, stdin);
+ strtok(filename, "\n");
+ image im = load_image_color(filename, 500, 500);
+ //resize_network(net, im.h, im.w, im.c);
+ translate_image(im, -128);
+ scale_image(im, 1/128.);
+ //float *predictions = network_predict(net, im.data);
+ network_predict(net, im.data);
+ free_image(im);
+ image output = get_network_image_layer(net, net.n-2);
+ show_image(output, "Segment Output");
+ cvWaitKey(0);
+ }
}
void test_imagenet(char *cfgfile)
@@ -377,7 +563,7 @@
strtok(filename, "\n");
image im = load_image_color(filename, 256, 256);
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);
float *X = im.data;
time=clock();
@@ -559,26 +745,6 @@
cvWaitKey(0);
}
-#ifdef GPU
-void test_convolutional_layer()
-{
- network net = parse_network_cfg("cfg/nist_conv.cfg");
- int size = get_network_input_size(net);
- float *in = calloc(size, sizeof(float));
- int i;
- for(i = 0; i < size; ++i) in[i] = rand_normal();
- float *in_gpu = cuda_make_array(in, size);
- convolutional_layer layer = *(convolutional_layer *)net.layers[0];
- int out_size = convolutional_out_height(layer)*convolutional_out_width(layer)*layer.batch;
- cuda_compare(layer.output_gpu, layer.output, out_size, "nothing");
- cuda_compare(layer.biases_gpu, layer.biases, layer.n, "biases");
- cuda_compare(layer.filters_gpu, layer.filters, layer.n*layer.size*layer.size*layer.c, "filters");
- bias_output(layer);
- bias_output_gpu(layer);
- cuda_compare(layer.output_gpu, layer.output, out_size, "biased output");
-}
-#endif
-
void test_correct_nist()
{
network net = parse_network_cfg("cfg/nist_conv.cfg");
@@ -684,14 +850,18 @@
{
int i;
for(i = index; i < argc-1; ++i) argv[i] = argv[i+1];
+ argv[i] = 0;
}
int find_arg(int argc, char* argv[], char *arg)
{
int i;
- for(i = 0; i < argc; ++i) if(0==strcmp(argv[i], arg)) {
- del_arg(argc, argv, i);
- return 1;
+ for(i = 0; i < argc; ++i) {
+ if(!argv[i]) continue;
+ if(0==strcmp(argv[i], arg)) {
+ del_arg(argc, argv, i);
+ return 1;
+ }
}
return 0;
}
@@ -700,6 +870,7 @@
{
int i;
for(i = 0; i < argc-1; ++i){
+ if(!argv[i]) continue;
if(0==strcmp(argv[i], arg)){
def = atoi(argv[i+1]);
del_arg(argc, argv, i);
@@ -710,6 +881,20 @@
return def;
}
+void scale_rate(char *filename, float scale)
+{
+ // Ready for some weird shit??
+ FILE *fp = fopen(filename, "r+b");
+ if(!fp) file_error(filename);
+ float rate = 0;
+ fread(&rate, sizeof(float), 1, fp);
+ printf("Scaling learning rate from %f to %f\n", rate, rate*scale);
+ rate = rate*scale;
+ fseek(fp, 0, SEEK_SET);
+ fwrite(&rate, sizeof(float), 1, fp);
+ fclose(fp);
+}
+
int main(int argc, char **argv)
{
//test_convolutional_layer();
@@ -740,25 +925,31 @@
fprintf(stderr, "usage: %s <function> <filename>\n", argv[0]);
return 0;
}
- else if(0==strcmp(argv[1], "detection")) train_detection_net(argv[2]);
+ else if(0==strcmp(argv[1], "detection")) train_detection_net(argv[2], (argc > 3)? argv[3] : 0);
else if(0==strcmp(argv[1], "test")) test_imagenet(argv[2]);
else if(0==strcmp(argv[1], "dog")) test_dog(argv[2]);
else if(0==strcmp(argv[1], "ctrain")) train_cifar10(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], "train")) train_imagenet(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]);
+ else if(0==strcmp(argv[1], "detect")) test_detection(argv[2], (argc > 3)? argv[3] : 0);
else if(0==strcmp(argv[1], "init")) test_init(argv[2]);
else if(0==strcmp(argv[1], "visualize")) test_visualize(argv[2]);
- else if(0==strcmp(argv[1], "valid")) validate_imagenet(argv[2]);
+ else if(0==strcmp(argv[1], "valid")) validate_imagenet(argv[2], (argc > 3)? argv[3] : 0);
else if(0==strcmp(argv[1], "testnist")) test_nist(argv[2]);
- else if(0==strcmp(argv[1], "validetect")) validate_detection_net(argv[2]);
+ else if(0==strcmp(argv[1], "validetect")) validate_detection_net(argv[2], (argc > 3)? argv[3] : 0);
else if(argc < 4){
fprintf(stderr, "usage: %s <function> <filename> <filename>\n", argv[0]);
return 0;
}
else if(0==strcmp(argv[1], "compare")) compare_nist(argv[2], argv[3]);
+ else if(0==strcmp(argv[1], "convert")) convert(argv[2], argv[3], (argc > 4)? argv[4] : 0);
+ else if(0==strcmp(argv[1], "scale")) scale_rate(argv[2], atof(argv[3]));
fprintf(stderr, "Success!\n");
return 0;
}
--
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