From cd8a3dcb4ca42f22ad8f46a95e00977c92be6bbd Mon Sep 17 00:00:00 2001
From: AlexeyAB <alexeyab84@gmail.com>
Date: Thu, 08 Feb 2018 23:22:42 +0000
Subject: [PATCH] Compile fixes
---
src/classifier.c | 283 +++++++++++++++++++++++++++++---------------------------
1 files changed, 146 insertions(+), 137 deletions(-)
diff --git a/src/classifier.c b/src/classifier.c
index 208b7ed..37f02d5 100644
--- a/src/classifier.c
+++ b/src/classifier.c
@@ -6,41 +6,23 @@
#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)
-{
- 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;
-}
-
float *get_regression_values(char **labels, int n)
{
float *v = calloc(n, sizeof(float));
@@ -53,9 +35,8 @@
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)
{
-#ifdef GPU
int i;
float avg_loss = -1;
@@ -68,7 +49,9 @@
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);
@@ -99,7 +82,8 @@
load_args args = {0};
args.w = net.w;
args.h = net.h;
- args.threads = 16;
+ args.threads = 32;
+ args.hierarchy = net.hierarchy;
args.min = net.min_crop;
args.max = net.max_crop;
@@ -134,7 +118,16 @@
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
- float loss = train_networks(nets, ngpus, train, 4);
+ 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);
@@ -160,116 +153,118 @@
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;
+/*
+ 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;
+ 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);
+ 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 *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;
+ 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;
+ 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.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;
+ 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);
+ 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();
+ 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);
+ pthread_join(load_thread, 0);
+ train = buffer;
+ load_thread = load_data(args);
- printf("Loaded: %lf seconds\n", sec(clock()-time));
- time=clock();
+ 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);
- }
- }
+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);
+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);
- }
- }
+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.weights", backup_directory, base);
+ sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
save_weights(net, buff);
-
- free_network(net);
- free_ptrs((void**)labels, classes);
- free_ptrs((void**)paths, plist->size);
- free_list(plist);
- free(base);
}
+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)
{
@@ -394,6 +389,7 @@
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]);
}
@@ -454,6 +450,7 @@
//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);
@@ -482,6 +479,8 @@
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);
@@ -513,6 +512,7 @@
//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);
@@ -573,6 +573,7 @@
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);
@@ -672,8 +673,7 @@
}
}
-
-void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename)
+void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top)
{
network net = parse_network_cfg(cfgfile);
if(weightfile){
@@ -686,7 +686,7 @@
char *name_list = option_find_str(options, "names", 0);
if(!name_list) name_list = option_find_str(options, "labels", "data/labels.list");
- int top = option_find_int(options, "top", 1);
+ if(top == 0) top = option_find_int(options, "top", 1);
int i = 0;
char **names = get_labels(name_list);
@@ -706,18 +706,21 @@
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;
time=clock();
float *predictions = network_predict(net, X);
- top_predictions(net, top, indexes);
+ 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];
- printf("%s: %f\n", names[index], predictions[index]);
+ 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);
@@ -899,15 +902,15 @@
float curr_threat = 0;
if(1){
curr_threat = predictions[0] * 0 +
- predictions[1] * .6 +
- predictions[2];
+ predictions[1] * .6 +
+ predictions[2];
} else {
curr_threat = predictions[218] +
- predictions[539] +
- predictions[540] +
- predictions[368] +
- predictions[369] +
- predictions[370];
+ predictions[539] +
+ predictions[540] +
+ predictions[368] +
+ predictions[369] +
+ predictions[370];
}
threat = roll * curr_threat + (1-roll) * threat;
@@ -1092,6 +1095,7 @@
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");
@@ -1126,6 +1130,7 @@
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);
@@ -1140,9 +1145,14 @@
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];
@@ -1150,10 +1160,9 @@
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);
+ 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);
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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