From 76e258520edb50e8bb897ba15aa9467579e70a6a Mon Sep 17 00:00:00 2001
From: AlexeyAB <alexeyab84@gmail.com>
Date: Wed, 20 Jun 2018 10:28:25 +0000
Subject: [PATCH] Minor fix
---
src/classifier.c | 495 ++++++++++++++++++++++++++++--------------------------
1 files changed, 258 insertions(+), 237 deletions(-)
diff --git a/src/classifier.c b/src/classifier.c
index a77f9df..b38f2fe 100644
--- a/src/classifier.c
+++ b/src/classifier.c
@@ -6,56 +6,28 @@
#include "assert.h"
#include "classifier.h"
#include "cuda.h"
+#ifdef WIN32
+#include <time.h>
+#include <winsock.h>
+#include "gettimeofday.h"
+#else
#include <sys/time.h>
+#endif
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
-image get_image_from_stream(CvCapture *cap);
+#include "opencv2/core/version.hpp"
+#ifndef CV_VERSION_EPOCH
+#include "opencv2/videoio/videoio_c.h"
#endif
+image get_image_from_stream(CvCapture *cap);
+image get_image_from_stream_cpp(CvCapture *cap);
+#include "http_stream.h"
-list *read_data_cfg(char *filename)
-{
- FILE *file = fopen(filename, "r");
- if(file == 0) file_error(filename);
- char *line;
- int nu = 0;
- list *options = make_list();
- while((line=fgetl(file)) != 0){
- ++ nu;
- strip(line);
- switch(line[0]){
- case '\0':
- case '#':
- case ';':
- free(line);
- break;
- default:
- if(!read_option(line, options)){
- fprintf(stderr, "Config file error line %d, could parse: %s\n", nu, line);
- free(line);
- }
- break;
- }
- }
- fclose(file);
- return options;
-}
+IplImage* draw_train_chart(float max_img_loss, int max_batches, int number_of_lines, int img_size);
+void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches);
-void hierarchy_predictions(float *predictions, int n, tree *hier, int only_leaves)
-{
- int j;
- for(j = 0; j < n; ++j){
- int parent = hier->parent[j];
- if(parent >= 0){
- predictions[j] *= predictions[parent];
- }
- }
- if(only_leaves){
- for(j = 0; j < n; ++j){
- if(!hier->leaf[j]) predictions[j] = 0;
- }
- }
-}
+#endif
float *get_regression_values(char **labels, int n)
{
@@ -69,9 +41,8 @@
return v;
}
-void train_classifier_multi(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear)
+void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show)
{
-#ifdef GPU
int i;
float avg_loss = -1;
@@ -84,7 +55,9 @@
int seed = rand();
for(i = 0; i < ngpus; ++i){
srand(seed);
+#ifdef GPU
cuda_set_device(gpus[i]);
+#endif
nets[i] = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&nets[i], weightfile);
@@ -120,6 +93,7 @@
args.min = net.min_crop;
args.max = net.max_crop;
+ args.flip = net.flip;
args.angle = net.angle;
args.aspect = net.aspect;
args.exposure = net.exposure;
@@ -134,154 +108,77 @@
args.labels = labels;
args.type = CLASSIFICATION_DATA;
- data train;
- data buffer;
- pthread_t load_thread;
- args.d = &buffer;
- load_thread = load_data(args);
-
- int epoch = (*net.seen)/N;
- while(get_current_batch(net) < net.max_batches || net.max_batches == 0){
- time=clock();
-
- pthread_join(load_thread, 0);
- train = buffer;
- load_thread = load_data(args);
-
- printf("Loaded: %lf seconds\n", sec(clock()-time));
- time=clock();
-
- float loss = train_networks(nets, ngpus, train, 4);
- if(avg_loss == -1) avg_loss = loss;
- avg_loss = avg_loss*.9 + loss*.1;
- printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen);
- free_data(train);
- if(*net.seen/N > epoch){
- epoch = *net.seen/N;
- char buff[256];
- sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
- save_weights(net, buff);
- }
- if(get_current_batch(net)%100 == 0){
- char buff[256];
- sprintf(buff, "%s/%s.backup",backup_directory,base);
- save_weights(net, buff);
- }
- }
- char buff[256];
- sprintf(buff, "%s/%s.weights", backup_directory, base);
- save_weights(net, buff);
-
- free_network(net);
- free_ptrs((void**)labels, classes);
- free_ptrs((void**)paths, plist->size);
- free_list(plist);
- free(base);
-#endif
-}
-
-
-void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int clear)
-{
- srand(time(0));
- float avg_loss = -1;
- char *base = basecfg(cfgfile);
- printf("%s\n", base);
- network net = parse_network_cfg(cfgfile);
- if(weightfile){
- load_weights(&net, weightfile);
- }
- if(clear) *net.seen = 0;
-
- int imgs = net.batch * net.subdivisions;
-
- printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
- list *options = read_data_cfg(datacfg);
-
- char *backup_directory = option_find_str(options, "backup", "/backup/");
- char *label_list = option_find_str(options, "labels", "data/labels.list");
- char *train_list = option_find_str(options, "train", "data/train.list");
- int classes = option_find_int(options, "classes", 2);
-
- char **labels = get_labels(label_list);
- list *plist = get_paths(train_list);
- char **paths = (char **)list_to_array(plist);
- printf("%d\n", plist->size);
- int N = plist->size;
- clock_t time;
-
- load_args args = {0};
- args.w = net.w;
- args.h = net.h;
- args.threads = 8;
-
- args.min = net.min_crop;
- args.max = net.max_crop;
- args.angle = net.angle;
- args.aspect = net.aspect;
- args.exposure = net.exposure;
- args.saturation = net.saturation;
- args.hue = net.hue;
- args.size = net.w;
- args.hierarchy = net.hierarchy;
-
- args.paths = paths;
- args.classes = classes;
- args.n = imgs;
- args.m = N;
- args.labels = labels;
- args.type = CLASSIFICATION_DATA;
-
- data train;
- data buffer;
- pthread_t load_thread;
- args.d = &buffer;
- load_thread = load_data(args);
-
- int epoch = (*net.seen)/N;
- while(get_current_batch(net) < net.max_batches || net.max_batches == 0){
- time=clock();
-
- pthread_join(load_thread, 0);
- train = buffer;
- load_thread = load_data(args);
-
- printf("Loaded: %lf seconds\n", sec(clock()-time));
- time=clock();
-
#ifdef OPENCV
- if(0){
- int u;
- for(u = 0; u < imgs; ++u){
- image im = float_to_image(net.w, net.h, 3, train.X.vals[u]);
- show_image(im, "loaded");
- cvWaitKey(0);
- }
+ args.threads = 3;
+ IplImage* img = NULL;
+ float max_img_loss = 5;
+ int number_of_lines = 100;
+ int img_size = 1000;
+ if (!dont_show)
+ img = draw_train_chart(max_img_loss, net.max_batches, number_of_lines, img_size);
+#endif //OPENCV
+
+ data train;
+ data buffer;
+ pthread_t load_thread;
+ args.d = &buffer;
+ load_thread = load_data(args);
+
+ int iter_save = get_current_batch(net);
+ while(get_current_batch(net) < net.max_batches || net.max_batches == 0){
+ time=clock();
+
+ pthread_join(load_thread, 0);
+ train = buffer;
+ load_thread = load_data(args);
+
+ printf("Loaded: %lf seconds\n", sec(clock()-time));
+ time=clock();
+
+ float loss = 0;
+#ifdef GPU
+ if(ngpus == 1){
+ loss = train_network(net, train);
+ } else {
+ loss = train_networks(nets, ngpus, train, 4);
}
+#else
+ loss = train_network(net, train);
#endif
-
- float loss = train_network(net, train);
- free_data(train);
-
if(avg_loss == -1) avg_loss = loss;
avg_loss = avg_loss*.9 + loss*.1;
+
+ i = get_current_batch(net);
+
printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen);
- if(*net.seen/N > epoch){
- epoch = *net.seen/N;
+#ifdef OPENCV
+ if(!dont_show)
+ draw_train_loss(img, img_size, avg_loss, max_img_loss, i, net.max_batches);
+#endif // OPENCV
+
+ if (i >= (iter_save + 100)) {
+ iter_save = i;
+#ifdef GPU
+ if (ngpus != 1) sync_nets(nets, ngpus, 0);
+#endif
char buff[256];
- sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
+ sprintf(buff, "%s/%s_%d.weights",backup_directory,base, i);
save_weights(net, buff);
}
- if(get_current_batch(net)%100 == 0){
- char buff[256];
- sprintf(buff, "%s/%s.backup",backup_directory,base);
- save_weights(net, buff);
- }
+ free_data(train);
}
+#ifdef GPU
+ if (ngpus != 1) sync_nets(nets, ngpus, 0);
+#endif
char buff[256];
- sprintf(buff, "%s/%s.weights", backup_directory, base);
+ sprintf(buff, "%s/%s_final.weights", backup_directory, base);
save_weights(net, buff);
+#ifdef OPENCV
+ cvReleaseImage(&img);
+ cvDestroyAllWindows();
+#endif
+
free_network(net);
free_ptrs((void**)labels, classes);
free_ptrs((void**)paths, plist->size);
@@ -289,6 +186,118 @@
free(base);
}
+
+/*
+ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int clear)
+ {
+ srand(time(0));
+ float avg_loss = -1;
+ char *base = basecfg(cfgfile);
+ printf("%s\n", base);
+ network net = parse_network_cfg(cfgfile);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ if(clear) *net.seen = 0;
+
+ int imgs = net.batch * net.subdivisions;
+
+ printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
+ list *options = read_data_cfg(datacfg);
+
+ char *backup_directory = option_find_str(options, "backup", "/backup/");
+ char *label_list = option_find_str(options, "labels", "data/labels.list");
+ char *train_list = option_find_str(options, "train", "data/train.list");
+ int classes = option_find_int(options, "classes", 2);
+
+ char **labels = get_labels(label_list);
+ list *plist = get_paths(train_list);
+ char **paths = (char **)list_to_array(plist);
+ printf("%d\n", plist->size);
+ int N = plist->size;
+ clock_t time;
+
+ load_args args = {0};
+ args.w = net.w;
+ args.h = net.h;
+ args.threads = 8;
+
+ args.min = net.min_crop;
+ args.max = net.max_crop;
+ args.flip = net.flip;
+ args.angle = net.angle;
+ args.aspect = net.aspect;
+ args.exposure = net.exposure;
+ args.saturation = net.saturation;
+ args.hue = net.hue;
+ args.size = net.w;
+ args.hierarchy = net.hierarchy;
+
+ args.paths = paths;
+ args.classes = classes;
+ args.n = imgs;
+ args.m = N;
+ args.labels = labels;
+ args.type = CLASSIFICATION_DATA;
+
+ data train;
+ data buffer;
+ pthread_t load_thread;
+ args.d = &buffer;
+ load_thread = load_data(args);
+
+ int epoch = (*net.seen)/N;
+ while(get_current_batch(net) < net.max_batches || net.max_batches == 0){
+ time=clock();
+
+ pthread_join(load_thread, 0);
+ train = buffer;
+ load_thread = load_data(args);
+
+ printf("Loaded: %lf seconds\n", sec(clock()-time));
+ time=clock();
+
+#ifdef OPENCV
+if(0){
+int u;
+for(u = 0; u < imgs; ++u){
+ image im = float_to_image(net.w, net.h, 3, train.X.vals[u]);
+ show_image(im, "loaded");
+ cvWaitKey(0);
+}
+}
+#endif
+
+float loss = train_network(net, train);
+free_data(train);
+
+if(avg_loss == -1) avg_loss = loss;
+avg_loss = avg_loss*.9 + loss*.1;
+printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen);
+if(*net.seen/N > epoch){
+ epoch = *net.seen/N;
+ char buff[256];
+ sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
+ save_weights(net, buff);
+}
+if(get_current_batch(net)%100 == 0){
+ char buff[256];
+ sprintf(buff, "%s/%s.backup",backup_directory,base);
+ save_weights(net, buff);
+}
+}
+char buff[256];
+sprintf(buff, "%s/%s.weights", backup_directory, base);
+save_weights(net, buff);
+
+free_network(net);
+free_ptrs((void**)labels, classes);
+free_ptrs((void**)paths, plist->size);
+free_list(plist);
+free(base);
+}
+*/
+
void validate_classifier_crop(char *datacfg, char *filename, char *weightfile)
{
int i = 0;
@@ -304,6 +313,7 @@
char *valid_list = option_find_str(options, "valid", "data/train.list");
int classes = option_find_int(options, "classes", 2);
int topk = option_find_int(options, "top", 1);
+ if (topk > classes) topk = classes;
char **labels = get_labels(label_list);
list *plist = get_paths(valid_list);
@@ -372,6 +382,7 @@
char *valid_list = option_find_str(options, "valid", "data/train.list");
int classes = option_find_int(options, "classes", 2);
int topk = option_find_int(options, "top", 1);
+ if (topk > classes) topk = classes;
char **labels = get_labels(label_list);
list *plist = get_paths(valid_list);
@@ -385,11 +396,11 @@
int *indexes = calloc(topk, sizeof(int));
for(i = 0; i < m; ++i){
- int class = -1;
+ int class_id = -1;
char *path = paths[i];
for(j = 0; j < classes; ++j){
if(strstr(path, labels[j])){
- class = j;
+ class_id = j;
break;
}
}
@@ -419,9 +430,9 @@
free_image(im);
top_k(pred, classes, topk, indexes);
free(pred);
- if(indexes[0] == class) avg_acc += 1;
+ if(indexes[0] == class_id) avg_acc += 1;
for(j = 0; j < topk; ++j){
- if(indexes[j] == class) avg_topk += 1;
+ if(indexes[j] == class_id) avg_topk += 1;
}
printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1));
@@ -444,6 +455,7 @@
char *valid_list = option_find_str(options, "valid", "data/train.list");
int classes = option_find_int(options, "classes", 2);
int topk = option_find_int(options, "top", 1);
+ if (topk > classes) topk = classes;
char **labels = get_labels(label_list);
list *plist = get_paths(valid_list);
@@ -458,11 +470,11 @@
int size = net.w;
for(i = 0; i < m; ++i){
- int class = -1;
+ int class_id = -1;
char *path = paths[i];
for(j = 0; j < classes; ++j){
if(strstr(path, labels[j])){
- class = j;
+ class_id = j;
break;
}
}
@@ -479,35 +491,15 @@
free_image(resized);
top_k(pred, classes, topk, indexes);
- if(indexes[0] == class) avg_acc += 1;
+ if(indexes[0] == class_id) avg_acc += 1;
for(j = 0; j < topk; ++j){
- if(indexes[j] == class) avg_topk += 1;
+ if(indexes[j] == class_id) avg_topk += 1;
}
printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1));
}
}
-void change_leaves(tree *t, char *leaf_list)
-{
- list *llist = get_paths(leaf_list);
- char **leaves = (char **)list_to_array(llist);
- int n = llist->size;
- int i,j;
- int found = 0;
- for(i = 0; i < t->n; ++i){
- t->leaf[i] = 0;
- for(j = 0; j < n; ++j){
- if (0==strcmp(t->name[i], leaves[j])){
- t->leaf[i] = 1;
- ++found;
- break;
- }
- }
- }
- fprintf(stderr, "Found %d leaves.\n", found);
-}
-
void validate_classifier_single(char *datacfg, char *filename, char *weightfile)
{
@@ -527,6 +519,7 @@
char *valid_list = option_find_str(options, "valid", "data/train.list");
int classes = option_find_int(options, "classes", 2);
int topk = option_find_int(options, "top", 1);
+ if (topk > classes) topk = classes;
char **labels = get_labels(label_list);
list *plist = get_paths(valid_list);
@@ -540,11 +533,11 @@
int *indexes = calloc(topk, sizeof(int));
for(i = 0; i < m; ++i){
- int class = -1;
+ int class_id = -1;
char *path = paths[i];
for(j = 0; j < classes; ++j){
if(strstr(path, labels[j])){
- class = j;
+ class_id = j;
break;
}
}
@@ -562,9 +555,9 @@
free_image(crop);
top_k(pred, classes, topk, indexes);
- if(indexes[0] == class) avg_acc += 1;
+ if(indexes[0] == class_id) avg_acc += 1;
for(j = 0; j < topk; ++j){
- if(indexes[j] == class) avg_topk += 1;
+ if(indexes[j] == class_id) avg_topk += 1;
}
printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1));
@@ -587,6 +580,7 @@
char *valid_list = option_find_str(options, "valid", "data/train.list");
int classes = option_find_int(options, "classes", 2);
int topk = option_find_int(options, "top", 1);
+ if (topk > classes) topk = classes;
char **labels = get_labels(label_list);
list *plist = get_paths(valid_list);
@@ -602,11 +596,11 @@
int *indexes = calloc(topk, sizeof(int));
for(i = 0; i < m; ++i){
- int class = -1;
+ int class_id = -1;
char *path = paths[i];
for(j = 0; j < classes; ++j){
if(strstr(path, labels[j])){
- class = j;
+ class_id = j;
break;
}
}
@@ -626,9 +620,9 @@
free_image(im);
top_k(pred, classes, topk, indexes);
free(pred);
- if(indexes[0] == class) avg_acc += 1;
+ if(indexes[0] == class_id) avg_acc += 1;
for(j = 0; j < topk; ++j){
- if(indexes[j] == class) avg_topk += 1;
+ if(indexes[j] == class_id) avg_topk += 1;
}
printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1));
@@ -637,7 +631,7 @@
void try_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int layer_num)
{
- network net = parse_network_cfg(cfgfile);
+ network net = parse_network_cfg_custom(cfgfile, 1);
if(weightfile){
load_weights(&net, weightfile);
}
@@ -648,7 +642,9 @@
char *name_list = option_find_str(options, "names", 0);
if(!name_list) name_list = option_find_str(options, "labels", "data/labels.list");
+ int classes = option_find_int(options, "classes", 2);
int top = option_find_int(options, "top", 1);
+ if (top > classes) top = classes;
int i = 0;
char **names = get_labels(name_list);
@@ -718,7 +714,7 @@
void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top)
{
- network net = parse_network_cfg(cfgfile);
+ network net = parse_network_cfg_custom(cfgfile, 1);
if(weightfile){
load_weights(&net, weightfile);
}
@@ -729,7 +725,9 @@
char *name_list = option_find_str(options, "names", 0);
if(!name_list) name_list = option_find_str(options, "labels", "data/labels.list");
- if(top == 0) top = option_find_int(options, "top", 1);
+ int classes = option_find_int(options, "classes", 2);
+ if (top == 0) top = option_find_int(options, "top", 1);
+ if (top > classes) top = classes;
int i = 0;
char **names = get_labels(name_list);
@@ -749,8 +747,9 @@
strtok(input, "\n");
}
image im = load_image_color(input, 0, 0);
- image r = resize_min(im, size);
- resize_network(&net, r.w, r.h);
+ image r = letterbox_image(im, net.w, net.h);
+ //image r = resize_min(im, size);
+ //resize_network(&net, r.w, r.h);
printf("%d %d\n", r.w, r.h);
float *X = r.data;
@@ -900,13 +899,18 @@
srand(2222222);
CvCapture * cap;
- if(filename){
- cap = cvCaptureFromFile(filename);
- }else{
- cap = cvCaptureFromCAM(cam_index);
+ if (filename) {
+ //cap = cvCaptureFromFile(filename);
+ cap = get_capture_video_stream(filename);
+ }
+ else {
+ //cap = cvCaptureFromCAM(cam_index);
+ cap = get_capture_webcam(cam_index);
}
+ int classes = option_find_int(options, "classes", 2);
int top = option_find_int(options, "top", 1);
+ if (top > classes) top = classes;
char *name_list = option_find_str(options, "names", 0);
char **names = get_labels(name_list);
@@ -926,7 +930,8 @@
struct timeval tval_before, tval_after, tval_result;
gettimeofday(&tval_before, NULL);
- image in = get_image_from_stream(cap);
+ //image in = get_image_from_stream(cap);
+ image in = get_image_from_stream_cpp(cap);
if(!in.data) break;
image in_s = resize_image(in, net.w, net.h);
@@ -1032,13 +1037,18 @@
srand(2222222);
CvCapture * cap;
- if(filename){
- cap = cvCaptureFromFile(filename);
- }else{
- cap = cvCaptureFromCAM(cam_index);
+ if (filename) {
+ //cap = cvCaptureFromFile(filename);
+ cap = get_capture_video_stream(filename);
+ }
+ else {
+ //cap = cvCaptureFromCAM(cam_index);
+ cap = get_capture_webcam(cam_index);
}
+ int classes = option_find_int(options, "classes", 2);
int top = option_find_int(options, "top", 1);
+ if (top > classes) top = classes;
char *name_list = option_find_str(options, "names", 0);
char **names = get_labels(name_list);
@@ -1055,7 +1065,8 @@
struct timeval tval_before, tval_after, tval_result;
gettimeofday(&tval_before, NULL);
- image in = get_image_from_stream(cap);
+ //image in = get_image_from_stream(cap);
+ image in = get_image_from_stream_cpp(cap);
image in_s = resize_image(in, net.w, net.h);
show_image(in, "Threat Detection");
@@ -1099,7 +1110,7 @@
{
#ifdef OPENCV
printf("Classifier Demo\n");
- network net = parse_network_cfg(cfgfile);
+ network net = parse_network_cfg_custom(cfgfile, 1);
if(weightfile){
load_weights(&net, weightfile);
}
@@ -1110,12 +1121,16 @@
CvCapture * cap;
if(filename){
- cap = cvCaptureFromFile(filename);
+ //cap = cvCaptureFromFile(filename);
+ cap = get_capture_video_stream(filename);
}else{
- cap = cvCaptureFromCAM(cam_index);
+ //cap = cvCaptureFromCAM(cam_index);
+ cap = get_capture_webcam(cam_index);
}
+ int classes = option_find_int(options, "classes", 2);
int top = option_find_int(options, "top", 1);
+ if (top > classes) top = classes;
char *name_list = option_find_str(options, "names", 0);
char **names = get_labels(name_list);
@@ -1132,7 +1147,8 @@
struct timeval tval_before, tval_after, tval_result;
gettimeofday(&tval_before, NULL);
- image in = get_image_from_stream(cap);
+ //image in = get_image_from_stream(cap);
+ image in = get_image_from_stream_cpp(cap);
image in_s = resize_image(in, net.w, net.h);
show_image(in, "Classifier");
@@ -1172,6 +1188,7 @@
char *gpu_list = find_char_arg(argc, argv, "-gpus", 0);
int *gpus = 0;
+ int gpu = 0;
int ngpus = 0;
if(gpu_list){
printf("%s\n", gpu_list);
@@ -1186,8 +1203,13 @@
gpus[i] = atoi(gpu_list);
gpu_list = strchr(gpu_list, ',')+1;
}
+ } else {
+ gpu = gpu_index;
+ gpus = &gpu;
+ ngpus = 1;
}
+ int dont_show = find_arg(argc, argv, "-dont_show");
int cam_index = find_int_arg(argc, argv, "-c", 0);
int top = find_int_arg(argc, argv, "-t", 0);
int clear = find_arg(argc, argv, "-clear");
@@ -1199,8 +1221,7 @@
int layer = layer_s ? atoi(layer_s) : -1;
if(0==strcmp(argv[2], "predict")) predict_classifier(data, cfg, weights, filename, top);
else if(0==strcmp(argv[2], "try")) try_classifier(data, cfg, weights, filename, atoi(layer_s));
- else if(0==strcmp(argv[2], "train")) train_classifier(data, cfg, weights, clear);
- else if(0==strcmp(argv[2], "trainm")) train_classifier_multi(data, cfg, weights, gpus, ngpus, clear);
+ else if(0==strcmp(argv[2], "train")) train_classifier(data, cfg, weights, gpus, ngpus, clear, dont_show);
else if(0==strcmp(argv[2], "demo")) demo_classifier(data, cfg, weights, cam_index, filename);
else if(0==strcmp(argv[2], "gun")) gun_classifier(data, cfg, weights, cam_index, filename);
else if(0==strcmp(argv[2], "threat")) threat_classifier(data, cfg, weights, cam_index, filename);
--
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