From 1b5afb45838e603fa6780762eb8cc59246dc2d81 Mon Sep 17 00:00:00 2001
From: IlyaOvodov <b@ovdv.ru>
Date: Tue, 08 May 2018 11:09:35 +0000
Subject: [PATCH] Output improvements for detector results: When printing detector results, output was done in random order, obfuscating results for interpreting. Now: 1. Text output includes coordinates of rects in (left,right,top,bottom in pixels) along with label and score 2. Text output is sorted by rect lefts to simplify finding appropriate rects on image 3. If several class probs are > thresh for some detection, the most probable is written first and coordinates for others are not repeated 4. Rects are imprinted in image in order by their best class prob, so most probable rects are always on top and not overlayed by less probable ones 5. Most probable label for rect is always written first Also: 6. Message about low GPU memory include required amount
---
src/classifier.c | 986 ++++++++++++++++++++++++++++++++++++++++++++++++++++++---
1 files changed, 922 insertions(+), 64 deletions(-)
diff --git a/src/classifier.c b/src/classifier.c
index 8a3ae5a..e45f5a1 100644
--- a/src/classifier.c
+++ b/src/classifier.c
@@ -2,53 +2,69 @@
#include "utils.h"
#include "parser.h"
#include "option_list.h"
+#include "blas.h"
+#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"
+#include "opencv2/core/version.hpp"
+#ifndef CV_VERSION_EPOCH
+#include "opencv2/videoio/videoio_c.h"
+#endif
+image get_image_from_stream(CvCapture *cap);
#endif
-list *read_data_cfg(char *filename)
+float *get_regression_values(char **labels, int n)
{
- 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;
- }
+ float *v = calloc(n, sizeof(float));
+ int i;
+ for(i = 0; i < n; ++i){
+ char *p = strchr(labels[i], ' ');
+ *p = 0;
+ v[i] = atof(p+1);
}
- fclose(file);
- return options;
+ return v;
}
-void train_classifier(char *datacfg, char *cfgfile, char *weightfile)
+void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear)
{
- data_seed = time(0);
- srand(time(0));
+ int i;
+
float avg_loss = -1;
char *base = basecfg(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;
+ printf("%d\n", ngpus);
+ network *nets = calloc(ngpus, sizeof(network));
+ srand(time(0));
+ 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);
+ }
+ if(clear) *nets[i].seen = 0;
+ nets[i].learning_rate *= ngpus;
+ }
+ srand(time(0));
+ network net = nets[0];
+
+ int imgs = net.batch * net.subdivisions * ngpus;
+
+ 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/");
@@ -62,32 +78,57 @@
printf("%d\n", plist->size);
int N = plist->size;
clock_t time;
- pthread_t load_thread;
- data train;
- data buffer;
load_args args = {0};
args.w = net.w;
args.h = net.h;
+ args.threads = 32;
+ args.hierarchy = net.hierarchy;
+
+ 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.paths = paths;
args.classes = classes;
args.n = imgs;
args.m = N;
args.labels = labels;
- args.d = &buffer;
args.type = CLASSIFICATION_DATA;
- load_thread = load_data_in_thread(args);
+ 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);
- load_thread = load_data_in_thread(args);
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
- float loss = train_network(net, train);
+
+ 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
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);
@@ -98,7 +139,7 @@
sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
save_weights(net, buff);
}
- if(*net.seen%1000 == 0){
+ if(get_current_batch(net)%100 == 0){
char buff[256];
sprintf(buff, "%s/%s.backup",backup_directory,base);
save_weights(net, buff);
@@ -108,8 +149,6 @@
sprintf(buff, "%s/%s.weights", backup_directory, base);
save_weights(net, buff);
- pthread_join(load_thread, 0);
- free_data(buffer);
free_network(net);
free_ptrs((void**)labels, classes);
free_ptrs((void**)paths, plist->size);
@@ -117,7 +156,119 @@
free(base);
}
-void validate_classifier(char *datacfg, char *filename, char *weightfile)
+
+/*
+ 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;
network net = parse_network_cfg(filename);
@@ -143,7 +294,7 @@
clock_t time;
float avg_acc = 0;
float avg_topk = 0;
- int splits = 50;
+ int splits = m/1000;
int num = (i+1)*m/splits - i*m/splits;
data val, buffer;
@@ -151,13 +302,14 @@
load_args args = {0};
args.w = net.w;
args.h = net.h;
+
args.paths = paths;
args.classes = classes;
args.n = num;
args.m = 0;
args.labels = labels;
args.d = &buffer;
- args.type = CLASSIFICATION_DATA;
+ args.type = OLD_CLASSIFICATION_DATA;
pthread_t load_thread = load_data_in_thread(args);
for(i = 1; i <= splits; ++i){
@@ -183,7 +335,266 @@
}
}
-void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename)
+void validate_classifier_10(char *datacfg, char *filename, char *weightfile)
+{
+ int i, j;
+ network net = parse_network_cfg(filename);
+ set_batch_network(&net, 1);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ srand(time(0));
+
+ list *options = read_data_cfg(datacfg);
+
+ char *label_list = option_find_str(options, "labels", "data/labels.list");
+ 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);
+
+ char **labels = get_labels(label_list);
+ list *plist = get_paths(valid_list);
+
+ char **paths = (char **)list_to_array(plist);
+ int m = plist->size;
+ free_list(plist);
+
+ float avg_acc = 0;
+ float avg_topk = 0;
+ int *indexes = calloc(topk, sizeof(int));
+
+ for(i = 0; i < m; ++i){
+ int class_id = -1;
+ char *path = paths[i];
+ for(j = 0; j < classes; ++j){
+ if(strstr(path, labels[j])){
+ class_id = j;
+ break;
+ }
+ }
+ int w = net.w;
+ int h = net.h;
+ int shift = 32;
+ image im = load_image_color(paths[i], w+shift, h+shift);
+ image images[10];
+ images[0] = crop_image(im, -shift, -shift, w, h);
+ images[1] = crop_image(im, shift, -shift, w, h);
+ images[2] = crop_image(im, 0, 0, w, h);
+ images[3] = crop_image(im, -shift, shift, w, h);
+ images[4] = crop_image(im, shift, shift, w, h);
+ flip_image(im);
+ images[5] = crop_image(im, -shift, -shift, w, h);
+ images[6] = crop_image(im, shift, -shift, w, h);
+ images[7] = crop_image(im, 0, 0, w, h);
+ images[8] = crop_image(im, -shift, shift, w, h);
+ images[9] = crop_image(im, shift, shift, w, h);
+ float *pred = calloc(classes, sizeof(float));
+ for(j = 0; j < 10; ++j){
+ float *p = network_predict(net, images[j].data);
+ if(net.hierarchy) hierarchy_predictions(p, net.outputs, net.hierarchy, 1);
+ axpy_cpu(classes, 1, p, 1, pred, 1);
+ free_image(images[j]);
+ }
+ free_image(im);
+ top_k(pred, classes, topk, indexes);
+ free(pred);
+ if(indexes[0] == class_id) avg_acc += 1;
+ for(j = 0; j < topk; ++j){
+ 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 validate_classifier_full(char *datacfg, char *filename, char *weightfile)
+{
+ int i, j;
+ network net = parse_network_cfg(filename);
+ set_batch_network(&net, 1);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ srand(time(0));
+
+ list *options = read_data_cfg(datacfg);
+
+ char *label_list = option_find_str(options, "labels", "data/labels.list");
+ 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);
+
+ char **labels = get_labels(label_list);
+ list *plist = get_paths(valid_list);
+
+ char **paths = (char **)list_to_array(plist);
+ int m = plist->size;
+ free_list(plist);
+
+ float avg_acc = 0;
+ float avg_topk = 0;
+ int *indexes = calloc(topk, sizeof(int));
+
+ int size = net.w;
+ for(i = 0; i < m; ++i){
+ int class_id = -1;
+ char *path = paths[i];
+ for(j = 0; j < classes; ++j){
+ if(strstr(path, labels[j])){
+ class_id = j;
+ break;
+ }
+ }
+ image im = load_image_color(paths[i], 0, 0);
+ image resized = resize_min(im, size);
+ resize_network(&net, resized.w, resized.h);
+ //show_image(im, "orig");
+ //show_image(crop, "cropped");
+ //cvWaitKey(0);
+ float *pred = network_predict(net, resized.data);
+ if(net.hierarchy) hierarchy_predictions(pred, net.outputs, net.hierarchy, 1);
+
+ free_image(im);
+ free_image(resized);
+ top_k(pred, classes, topk, indexes);
+
+ if(indexes[0] == class_id) avg_acc += 1;
+ for(j = 0; j < topk; ++j){
+ 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 validate_classifier_single(char *datacfg, char *filename, char *weightfile)
+{
+ int i, j;
+ network net = parse_network_cfg(filename);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ set_batch_network(&net, 1);
+ srand(time(0));
+
+ list *options = read_data_cfg(datacfg);
+
+ char *label_list = option_find_str(options, "labels", "data/labels.list");
+ char *leaf_list = option_find_str(options, "leaves", 0);
+ if(leaf_list) change_leaves(net.hierarchy, leaf_list);
+ 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);
+
+ char **labels = get_labels(label_list);
+ list *plist = get_paths(valid_list);
+
+ char **paths = (char **)list_to_array(plist);
+ int m = plist->size;
+ free_list(plist);
+
+ float avg_acc = 0;
+ float avg_topk = 0;
+ int *indexes = calloc(topk, sizeof(int));
+
+ for(i = 0; i < m; ++i){
+ int class_id = -1;
+ char *path = paths[i];
+ for(j = 0; j < classes; ++j){
+ if(strstr(path, labels[j])){
+ class_id = j;
+ break;
+ }
+ }
+ image im = load_image_color(paths[i], 0, 0);
+ image resized = resize_min(im, net.w);
+ image crop = crop_image(resized, (resized.w - net.w)/2, (resized.h - net.h)/2, net.w, net.h);
+ //show_image(im, "orig");
+ //show_image(crop, "cropped");
+ //cvWaitKey(0);
+ float *pred = network_predict(net, crop.data);
+ if(net.hierarchy) hierarchy_predictions(pred, net.outputs, net.hierarchy, 1);
+
+ if(resized.data != im.data) free_image(resized);
+ free_image(im);
+ free_image(crop);
+ top_k(pred, classes, topk, indexes);
+
+ if(indexes[0] == class_id) avg_acc += 1;
+ for(j = 0; j < topk; ++j){
+ 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 validate_classifier_multi(char *datacfg, char *filename, char *weightfile)
+{
+ int i, j;
+ network net = parse_network_cfg(filename);
+ set_batch_network(&net, 1);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ srand(time(0));
+
+ list *options = read_data_cfg(datacfg);
+
+ char *label_list = option_find_str(options, "labels", "data/labels.list");
+ 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);
+
+ char **labels = get_labels(label_list);
+ list *plist = get_paths(valid_list);
+ int scales[] = {224, 288, 320, 352, 384};
+ int nscales = sizeof(scales)/sizeof(scales[0]);
+
+ char **paths = (char **)list_to_array(plist);
+ int m = plist->size;
+ free_list(plist);
+
+ float avg_acc = 0;
+ float avg_topk = 0;
+ int *indexes = calloc(topk, sizeof(int));
+
+ for(i = 0; i < m; ++i){
+ int class_id = -1;
+ char *path = paths[i];
+ for(j = 0; j < classes; ++j){
+ if(strstr(path, labels[j])){
+ class_id = j;
+ break;
+ }
+ }
+ float *pred = calloc(classes, sizeof(float));
+ image im = load_image_color(paths[i], 0, 0);
+ for(j = 0; j < nscales; ++j){
+ image r = resize_min(im, scales[j]);
+ resize_network(&net, r.w, r.h);
+ float *p = network_predict(net, r.data);
+ if(net.hierarchy) hierarchy_predictions(p, net.outputs, net.hierarchy, 1);
+ axpy_cpu(classes, 1, p, 1, pred, 1);
+ flip_image(r);
+ p = network_predict(net, r.data);
+ axpy_cpu(classes, 1, p, 1, pred, 1);
+ if(r.data != im.data) free_image(r);
+ }
+ free_image(im);
+ top_k(pred, classes, topk, indexes);
+ free(pred);
+ if(indexes[0] == class_id) avg_acc += 1;
+ for(j = 0; j < topk; ++j){
+ 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 try_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int layer_num)
{
network net = parse_network_cfg(cfgfile);
if(weightfile){
@@ -201,7 +612,7 @@
int i = 0;
char **names = get_labels(name_list);
clock_t time;
- int indexes[10];
+ int *indexes = calloc(top, sizeof(int));
char buff[256];
char *input = buff;
while(1){
@@ -214,10 +625,45 @@
if(!input) return;
strtok(input, "\n");
}
- image im = load_image_color(input, 256, 256);
+ image orig = load_image_color(input, 0, 0);
+ image r = resize_min(orig, 256);
+ image im = crop_image(r, (r.w - 224 - 1)/2 + 1, (r.h - 224 - 1)/2 + 1, 224, 224);
+ float mean[] = {0.48263312050943, 0.45230225481413, 0.40099074308742};
+ float std[] = {0.22590347483426, 0.22120921437787, 0.22103996251583};
+ float var[3];
+ var[0] = std[0]*std[0];
+ var[1] = std[1]*std[1];
+ var[2] = std[2]*std[2];
+
+ normalize_cpu(im.data, mean, var, 1, 3, im.w*im.h);
+
float *X = im.data;
time=clock();
float *predictions = network_predict(net, X);
+
+ layer l = net.layers[layer_num];
+ for(i = 0; i < l.c; ++i){
+ if(l.rolling_mean) printf("%f %f %f\n", l.rolling_mean[i], l.rolling_variance[i], l.scales[i]);
+ }
+#ifdef GPU
+ cuda_pull_array(l.output_gpu, l.output, l.outputs);
+#endif
+ for(i = 0; i < l.outputs; ++i){
+ printf("%f\n", l.output[i]);
+ }
+ /*
+
+ printf("\n\nWeights\n");
+ for(i = 0; i < l.n*l.size*l.size*l.c; ++i){
+ printf("%f\n", l.filters[i]);
+ }
+
+ printf("\n\nBiases\n");
+ for(i = 0; i < l.n; ++i){
+ printf("%f\n", l.biases[i]);
+ }
+ */
+
top_predictions(net, top, indexes);
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
for(i = 0; i < top; ++i){
@@ -229,10 +675,105 @@
}
}
-void test_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int target_layer)
+void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top)
+{
+ network net = parse_network_cfg(cfgfile);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ set_batch_network(&net, 1);
+ srand(2222222);
+
+ list *options = read_data_cfg(datacfg);
+
+ 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 i = 0;
+ char **names = get_labels(name_list);
+ clock_t time;
+ int *indexes = calloc(top, sizeof(int));
+ char buff[256];
+ char *input = buff;
+ int size = net.w;
+ while(1){
+ if(filename){
+ strncpy(input, filename, 256);
+ }else{
+ printf("Enter Image Path: ");
+ fflush(stdout);
+ input = fgets(input, 256, stdin);
+ if(!input) return;
+ strtok(input, "\n");
+ }
+ image im = load_image_color(input, 0, 0);
+ 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;
+ time=clock();
+ float *predictions = network_predict(net, X);
+ if(net.hierarchy) hierarchy_predictions(predictions, net.outputs, net.hierarchy, 0);
+ top_k(predictions, net.outputs, top, indexes);
+ printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
+ for(i = 0; i < top; ++i){
+ int index = indexes[i];
+ if(net.hierarchy) printf("%d, %s: %f, parent: %s \n",index, names[index], predictions[index], (net.hierarchy->parent[index] >= 0) ? names[net.hierarchy->parent[index]] : "Root");
+ else printf("%s: %f\n",names[index], predictions[index]);
+ }
+ if(r.data != im.data) free_image(r);
+ free_image(im);
+ if (filename) break;
+ }
+}
+
+
+void label_classifier(char *datacfg, char *filename, char *weightfile)
+{
+ int i;
+ network net = parse_network_cfg(filename);
+ set_batch_network(&net, 1);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ srand(time(0));
+
+ list *options = read_data_cfg(datacfg);
+
+ char *label_list = option_find_str(options, "names", "data/labels.list");
+ char *test_list = option_find_str(options, "test", "data/train.list");
+ int classes = option_find_int(options, "classes", 2);
+
+ char **labels = get_labels(label_list);
+ list *plist = get_paths(test_list);
+
+ char **paths = (char **)list_to_array(plist);
+ int m = plist->size;
+ free_list(plist);
+
+ for(i = 0; i < m; ++i){
+ image im = load_image_color(paths[i], 0, 0);
+ image resized = resize_min(im, net.w);
+ image crop = crop_image(resized, (resized.w - net.w)/2, (resized.h - net.h)/2, net.w, net.h);
+ float *pred = network_predict(net, crop.data);
+
+ if(resized.data != im.data) free_image(resized);
+ free_image(im);
+ free_image(crop);
+ int ind = max_index(pred, classes);
+
+ printf("%s\n", labels[ind]);
+ }
+}
+
+
+void test_classifier(char *datacfg, char *cfgfile, char *weightfile, int target_layer)
{
int curr = 0;
- network net = parse_network_cfg(filename);
+ network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
@@ -241,10 +782,8 @@
list *options = read_data_cfg(datacfg);
char *test_list = option_find_str(options, "test", "data/test.list");
- char *label_list = option_find_str(options, "labels", "data/labels.list");
int classes = option_find_int(options, "classes", 2);
- char **labels = get_labels(label_list);
list *plist = get_paths(test_list);
char **paths = (char **)list_to_array(plist);
@@ -262,9 +801,9 @@
args.classes = classes;
args.n = net.batch;
args.m = 0;
- args.labels = labels;
+ args.labels = 0;
args.d = &buffer;
- args.type = CLASSIFICATION_DATA;
+ args.type = OLD_CLASSIFICATION_DATA;
pthread_t load_thread = load_data_in_thread(args);
for(curr = net.batch; curr < m; curr += net.batch){
@@ -282,24 +821,308 @@
time=clock();
matrix pred = network_predict_data(net, val);
-
- int i;
+
+ int i, j;
if (target_layer >= 0){
//layer l = net.layers[target_layer];
}
- for(i = 0; i < val.X.rows; ++i){
-
+ for(i = 0; i < pred.rows; ++i){
+ printf("%s", paths[curr-net.batch+i]);
+ for(j = 0; j < pred.cols; ++j){
+ printf("\t%g", pred.vals[i][j]);
+ }
+ printf("\n");
}
free_matrix(pred);
- fprintf(stderr, "%lf seconds, %d images\n", sec(clock()-time), val.X.rows);
+ fprintf(stderr, "%lf seconds, %d images, %d total\n", sec(clock()-time), val.X.rows, curr);
free_data(val);
}
}
+void threat_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename)
+{
+#ifdef OPENCV
+ float threat = 0;
+ float roll = .2;
+
+ printf("Classifier Demo\n");
+ network net = parse_network_cfg(cfgfile);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ set_batch_network(&net, 1);
+ list *options = read_data_cfg(datacfg);
+
+ srand(2222222);
+ CvCapture * cap;
+
+ if(filename){
+ cap = cvCaptureFromFile(filename);
+ }else{
+ cap = cvCaptureFromCAM(cam_index);
+ }
+
+ int top = option_find_int(options, "top", 1);
+
+ char *name_list = option_find_str(options, "names", 0);
+ char **names = get_labels(name_list);
+
+ int *indexes = calloc(top, sizeof(int));
+
+ if(!cap) error("Couldn't connect to webcam.\n");
+ //cvNamedWindow("Threat", CV_WINDOW_NORMAL);
+ //cvResizeWindow("Threat", 512, 512);
+ float fps = 0;
+ int i;
+
+ int count = 0;
+
+ while(1){
+ ++count;
+ struct timeval tval_before, tval_after, tval_result;
+ gettimeofday(&tval_before, NULL);
+
+ image in = get_image_from_stream(cap);
+ if(!in.data) break;
+ image in_s = resize_image(in, net.w, net.h);
+
+ image out = in;
+ int x1 = out.w / 20;
+ int y1 = out.h / 20;
+ int x2 = 2*x1;
+ int y2 = out.h - out.h/20;
+
+ int border = .01*out.h;
+ int h = y2 - y1 - 2*border;
+ int w = x2 - x1 - 2*border;
+
+ float *predictions = network_predict(net, in_s.data);
+ float curr_threat = 0;
+ if(1){
+ curr_threat = predictions[0] * 0 +
+ predictions[1] * .6 +
+ predictions[2];
+ } else {
+ curr_threat = predictions[218] +
+ predictions[539] +
+ predictions[540] +
+ predictions[368] +
+ predictions[369] +
+ predictions[370];
+ }
+ threat = roll * curr_threat + (1-roll) * threat;
+
+ draw_box_width(out, x2 + border, y1 + .02*h, x2 + .5 * w, y1 + .02*h + border, border, 0,0,0);
+ if(threat > .97) {
+ draw_box_width(out, x2 + .5 * w + border,
+ y1 + .02*h - 2*border,
+ x2 + .5 * w + 6*border,
+ y1 + .02*h + 3*border, 3*border, 1,0,0);
+ }
+ draw_box_width(out, x2 + .5 * w + border,
+ y1 + .02*h - 2*border,
+ x2 + .5 * w + 6*border,
+ y1 + .02*h + 3*border, .5*border, 0,0,0);
+ draw_box_width(out, x2 + border, y1 + .42*h, x2 + .5 * w, y1 + .42*h + border, border, 0,0,0);
+ if(threat > .57) {
+ draw_box_width(out, x2 + .5 * w + border,
+ y1 + .42*h - 2*border,
+ x2 + .5 * w + 6*border,
+ y1 + .42*h + 3*border, 3*border, 1,1,0);
+ }
+ draw_box_width(out, x2 + .5 * w + border,
+ y1 + .42*h - 2*border,
+ x2 + .5 * w + 6*border,
+ y1 + .42*h + 3*border, .5*border, 0,0,0);
+
+ draw_box_width(out, x1, y1, x2, y2, border, 0,0,0);
+ for(i = 0; i < threat * h ; ++i){
+ float ratio = (float) i / h;
+ float r = (ratio < .5) ? (2*(ratio)) : 1;
+ float g = (ratio < .5) ? 1 : 1 - 2*(ratio - .5);
+ draw_box_width(out, x1 + border, y2 - border - i, x2 - border, y2 - border - i, 1, r, g, 0);
+ }
+ top_predictions(net, top, indexes);
+ char buff[256];
+ sprintf(buff, "/home/pjreddie/tmp/threat_%06d", count);
+ //save_image(out, buff);
+
+ printf("\033[2J");
+ printf("\033[1;1H");
+ printf("\nFPS:%.0f\n",fps);
+
+ for(i = 0; i < top; ++i){
+ int index = indexes[i];
+ printf("%.1f%%: %s\n", predictions[index]*100, names[index]);
+ }
+
+ if(1){
+ show_image(out, "Threat");
+ cvWaitKey(10);
+ }
+ free_image(in_s);
+ free_image(in);
+
+ gettimeofday(&tval_after, NULL);
+ timersub(&tval_after, &tval_before, &tval_result);
+ float curr = 1000000.f/((long int)tval_result.tv_usec);
+ fps = .9*fps + .1*curr;
+ }
+#endif
+}
+
+
+void gun_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename)
+{
+#ifdef OPENCV
+ int bad_cats[] = {218, 539, 540, 1213, 1501, 1742, 1911, 2415, 4348, 19223, 368, 369, 370, 1133, 1200, 1306, 2122, 2301, 2537, 2823, 3179, 3596, 3639, 4489, 5107, 5140, 5289, 6240, 6631, 6762, 7048, 7171, 7969, 7984, 7989, 8824, 8927, 9915, 10270, 10448, 13401, 15205, 18358, 18894, 18895, 19249, 19697};
+
+ printf("Classifier Demo\n");
+ network net = parse_network_cfg(cfgfile);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ set_batch_network(&net, 1);
+ list *options = read_data_cfg(datacfg);
+
+ srand(2222222);
+ CvCapture * cap;
+
+ if(filename){
+ cap = cvCaptureFromFile(filename);
+ }else{
+ cap = cvCaptureFromCAM(cam_index);
+ }
+
+ int top = option_find_int(options, "top", 1);
+
+ char *name_list = option_find_str(options, "names", 0);
+ char **names = get_labels(name_list);
+
+ int *indexes = calloc(top, sizeof(int));
+
+ if(!cap) error("Couldn't connect to webcam.\n");
+ cvNamedWindow("Threat Detection", CV_WINDOW_NORMAL);
+ cvResizeWindow("Threat Detection", 512, 512);
+ float fps = 0;
+ int i;
+
+ while(1){
+ struct timeval tval_before, tval_after, tval_result;
+ gettimeofday(&tval_before, NULL);
+
+ image in = get_image_from_stream(cap);
+ image in_s = resize_image(in, net.w, net.h);
+ show_image(in, "Threat Detection");
+
+ float *predictions = network_predict(net, in_s.data);
+ top_predictions(net, top, indexes);
+
+ printf("\033[2J");
+ printf("\033[1;1H");
+
+ int threat = 0;
+ for(i = 0; i < sizeof(bad_cats)/sizeof(bad_cats[0]); ++i){
+ int index = bad_cats[i];
+ if(predictions[index] > .01){
+ printf("Threat Detected!\n");
+ threat = 1;
+ break;
+ }
+ }
+ if(!threat) printf("Scanning...\n");
+ for(i = 0; i < sizeof(bad_cats)/sizeof(bad_cats[0]); ++i){
+ int index = bad_cats[i];
+ if(predictions[index] > .01){
+ printf("%s\n", names[index]);
+ }
+ }
+
+ free_image(in_s);
+ free_image(in);
+
+ cvWaitKey(10);
+
+ gettimeofday(&tval_after, NULL);
+ timersub(&tval_after, &tval_before, &tval_result);
+ float curr = 1000000.f/((long int)tval_result.tv_usec);
+ fps = .9*fps + .1*curr;
+ }
+#endif
+}
+
+void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename)
+{
+#ifdef OPENCV
+ printf("Classifier Demo\n");
+ network net = parse_network_cfg(cfgfile);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ set_batch_network(&net, 1);
+ list *options = read_data_cfg(datacfg);
+
+ srand(2222222);
+ CvCapture * cap;
+
+ if(filename){
+ cap = cvCaptureFromFile(filename);
+ }else{
+ cap = cvCaptureFromCAM(cam_index);
+ }
+
+ int top = option_find_int(options, "top", 1);
+
+ char *name_list = option_find_str(options, "names", 0);
+ char **names = get_labels(name_list);
+
+ int *indexes = calloc(top, sizeof(int));
+
+ if(!cap) error("Couldn't connect to webcam.\n");
+ cvNamedWindow("Classifier", CV_WINDOW_NORMAL);
+ cvResizeWindow("Classifier", 512, 512);
+ float fps = 0;
+ int i;
+
+ while(1){
+ struct timeval tval_before, tval_after, tval_result;
+ gettimeofday(&tval_before, NULL);
+
+ image in = get_image_from_stream(cap);
+ image in_s = resize_image(in, net.w, net.h);
+ show_image(in, "Classifier");
+
+ float *predictions = network_predict(net, in_s.data);
+ if(net.hierarchy) hierarchy_predictions(predictions, net.outputs, net.hierarchy, 1);
+ top_predictions(net, top, indexes);
+
+ printf("\033[2J");
+ printf("\033[1;1H");
+ printf("\nFPS:%.0f\n",fps);
+
+ for(i = 0; i < top; ++i){
+ int index = indexes[i];
+ printf("%.1f%%: %s\n", predictions[index]*100, names[index]);
+ }
+
+ free_image(in_s);
+ free_image(in);
+
+ cvWaitKey(10);
+
+ gettimeofday(&tval_after, NULL);
+ timersub(&tval_after, &tval_before, &tval_result);
+ float curr = 1000000.f/((long int)tval_result.tv_usec);
+ fps = .9*fps + .1*curr;
+ }
+#endif
+}
+
+
void run_classifier(int argc, char **argv)
{
if(argc < 4){
@@ -307,16 +1130,51 @@
return;
}
+ 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);
+ int len = strlen(gpu_list);
+ ngpus = 1;
+ int i;
+ for(i = 0; i < len; ++i){
+ if (gpu_list[i] == ',') ++ngpus;
+ }
+ gpus = calloc(ngpus, sizeof(int));
+ for(i = 0; i < ngpus; ++i){
+ gpus[i] = atoi(gpu_list);
+ gpu_list = strchr(gpu_list, ',')+1;
+ }
+ } else {
+ gpu = gpu_index;
+ gpus = &gpu;
+ ngpus = 1;
+ }
+
+ 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");
char *data = argv[3];
char *cfg = argv[4];
char *weights = (argc > 5) ? argv[5] : 0;
char *filename = (argc > 6) ? argv[6]: 0;
char *layer_s = (argc > 7) ? argv[7]: 0;
int layer = layer_s ? atoi(layer_s) : -1;
- if(0==strcmp(argv[2], "predict")) predict_classifier(data, cfg, weights, filename);
- else if(0==strcmp(argv[2], "train")) train_classifier(data, cfg, weights);
- else if(0==strcmp(argv[2], "test")) test_classifier(data, cfg, weights,filename, layer);
- else if(0==strcmp(argv[2], "valid")) validate_classifier(data, cfg, weights);
+ 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, gpus, ngpus, clear);
+ 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);
+ else if(0==strcmp(argv[2], "test")) test_classifier(data, cfg, weights, layer);
+ else if(0==strcmp(argv[2], "label")) label_classifier(data, cfg, weights);
+ else if(0==strcmp(argv[2], "valid")) validate_classifier_single(data, cfg, weights);
+ else if(0==strcmp(argv[2], "validmulti")) validate_classifier_multi(data, cfg, weights);
+ else if(0==strcmp(argv[2], "valid10")) validate_classifier_10(data, cfg, weights);
+ else if(0==strcmp(argv[2], "validcrop")) validate_classifier_crop(data, cfg, weights);
+ else if(0==strcmp(argv[2], "validfull")) validate_classifier_full(data, cfg, weights);
}
--
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