From 729ce43e6ec45cfdb58e06e227428a0f81c5de0f Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Fri, 10 Jun 2016 00:20:31 +0000
Subject: [PATCH] stuff
---
src/classifier.c | 414 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++-
1 files changed, 404 insertions(+), 10 deletions(-)
diff --git a/src/classifier.c b/src/classifier.c
index ddd88b1..24b28b5 100644
--- a/src/classifier.c
+++ b/src/classifier.c
@@ -2,6 +2,9 @@
#include "utils.h"
#include "parser.h"
#include "option_list.h"
+#include "blas.h"
+#include "classifier.h"
+#include <sys/time.h>
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
@@ -35,7 +38,7 @@
return options;
}
-void train_classifier(char *datacfg, char *cfgfile, char *weightfile)
+void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int clear)
{
data_seed = time(0);
srand(time(0));
@@ -46,8 +49,9 @@
if(weightfile){
load_weights(&net, weightfile);
}
+ if(clear) *net.seen = 0;
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
- int imgs = 1024;
+ int imgs = net.batch;
list *options = read_data_cfg(datacfg);
@@ -69,6 +73,11 @@
load_args args = {0};
args.w = net.w;
args.h = net.h;
+
+ args.min = net.min_crop;
+ args.max = net.max_crop;
+ args.size = net.w;
+
args.paths = paths;
args.classes = classes;
args.n = imgs;
@@ -87,6 +96,16 @@
load_thread = load_data_in_thread(args);
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
+
+ /*
+ int u;
+ for(u = 0; u < net.batch; ++u){
+ image im = float_to_image(net.w, net.h, 3, train.X.vals[u]);
+ show_image(im, "loaded");
+ cvWaitKey(0);
+ }
+ */
+
float loss = train_network(net, train);
if(avg_loss == -1) avg_loss = loss;
avg_loss = avg_loss*.9 + loss*.1;
@@ -98,7 +117,7 @@
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);
@@ -151,13 +170,14 @@
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){
@@ -183,6 +203,259 @@
}
}
+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 = -1;
+ char *path = paths[i];
+ for(j = 0; j < classes; ++j){
+ if(strstr(path, labels[j])){
+ class = 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);
+ 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) avg_acc += 1;
+ for(j = 0; j < topk; ++j){
+ if(indexes[j] == class) 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 = -1;
+ char *path = paths[i];
+ for(j = 0; j < classes; ++j){
+ if(strstr(path, labels[j])){
+ class = 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);
+
+ free_image(im);
+ free_image(resized);
+ top_k(pred, classes, topk, indexes);
+
+ if(indexes[0] == class) avg_acc += 1;
+ for(j = 0; j < topk; ++j){
+ if(indexes[j] == class) 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);
+ 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 = -1;
+ char *path = paths[i];
+ for(j = 0; j < classes; ++j){
+ if(strstr(path, labels[j])){
+ class = 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(resized.data != im.data) free_image(resized);
+ free_image(im);
+ free_image(crop);
+ top_k(pred, classes, topk, indexes);
+
+ if(indexes[0] == class) avg_acc += 1;
+ for(j = 0; j < topk; ++j){
+ if(indexes[j] == class) 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[] = {192, 224, 288, 320, 352};
+ 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 = -1;
+ char *path = paths[i];
+ for(j = 0; j < classes; ++j){
+ if(strstr(path, labels[j])){
+ class = 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);
+ 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) avg_acc += 1;
+ for(j = 0; j < topk; ++j){
+ if(indexes[j] == class) avg_topk += 1;
+ }
+
+ printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1));
+ }
+}
+
void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename)
{
network net = parse_network_cfg(cfgfile);
@@ -204,6 +477,7 @@
int *indexes = calloc(top, sizeof(int));
char buff[256];
char *input = buff;
+ int size = net.w;
while(1){
if(filename){
strncpy(input, filename, 256);
@@ -214,8 +488,12 @@
if(!input) return;
strtok(input, "\n");
}
- image im = load_image_color(input, net.w, net.h);
- float *X = im.data;
+ image im = load_image_color(input, 0, 0);
+ 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);
top_predictions(net, top, indexes);
@@ -224,11 +502,52 @@
int index = indexes[i];
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;
@@ -262,7 +581,7 @@
args.m = 0;
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){
@@ -280,7 +599,7 @@
time=clock();
matrix pred = network_predict_data(net, val);
-
+
int i, j;
if (target_layer >= 0){
//layer l = net.layers[target_layer];
@@ -296,12 +615,79 @@
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 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);
+ 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){
@@ -309,6 +695,8 @@
return;
}
+ int cam_index = find_int_arg(argc, argv, "-c", 0);
+ int clear = find_arg(argc, argv, "-clear");
char *data = argv[3];
char *cfg = argv[4];
char *weights = (argc > 5) ? argv[5] : 0;
@@ -316,9 +704,15 @@
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], "train")) train_classifier(data, cfg, weights, clear);
+ else if(0==strcmp(argv[2], "demo")) demo_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(data, cfg, weights);
+ else if(0==strcmp(argv[2], "valid10")) validate_classifier_10(data, cfg, weights);
+ else if(0==strcmp(argv[2], "validmulti")) validate_classifier_multi(data, cfg, weights);
+ else if(0==strcmp(argv[2], "validsingle")) validate_classifier_single(data, cfg, weights);
+ else if(0==strcmp(argv[2], "validfull")) validate_classifier_full(data, cfg, weights);
}
--
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