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classifier.c
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#include "network.h"
#include "utils.h"
#include "parser.h"
#include "option_list.h"
#include "blas.h"
#include "assert.h"
#include "classifier.h"
#include "dark_cuda.h"
#ifdef WIN32
#include <time.h>
#include "gettimeofday.h"
#else
#include <sys/time.h>
#endif
float validate_classifier_single(char *datacfg, char *filename, char *weightfile, network *existing_net, int topk_custom);
float *get_regression_values(char **labels, int n)
{
float* v = (float*)xcalloc(n, sizeof(float));
int i;
for(i = 0; i < n; ++i){
char *p = strchr(labels[i], ' ');
*p = 0;
v[i] = atof(p+1);
}
return v;
}
void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dontuse_opencv, int dont_show, int mjpeg_port, int calc_topk, int show_imgs, char* chart_path)
{
int i;
float avg_loss = -1;
float avg_contrastive_acc = 0;
char *base = basecfg(cfgfile);
printf("%s\n", base);
printf("%d\n", ngpus);
network* nets = (network*)xcalloc(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].cur_iteration = 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/");
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);
int topk_data = option_find_int(options, "top", 5);
char topk_buff[10];
sprintf(topk_buff, "top%d", topk_data);
layer l = net.layers[net.n - 1];
if (classes != l.outputs && (l.type == SOFTMAX || l.type == COST)) {
printf("\n Error: num of filters = %d in the last conv-layer in cfg-file doesn't match to classes = %d in data-file \n",
l.outputs, classes);
getchar();
}
char **labels = get_labels(label_list);
if (net.unsupervised) {
free(labels);
labels = NULL;
}
list *plist = get_paths(train_list);
char **paths = (char **)list_to_array(plist);
printf("%d\n", plist->size);
int train_images_num = plist->size;
clock_t time;
load_args args = {0};
args.w = net.w;
args.h = net.h;
args.c = net.c;
args.threads = 32;
if (net.contrastive && args.threads > net.batch/2) args.threads = net.batch / 2;
args.hierarchy = net.hierarchy;
args.contrastive = net.contrastive;
args.dontuse_opencv = dontuse_opencv;
args.min = net.min_crop;
args.max = net.max_crop;
args.flip = net.flip;
args.blur = net.blur;
args.angle = net.angle;
args.aspect = net.aspect;
args.exposure = net.exposure;
args.saturation = net.saturation;
args.hue = net.hue;
args.size = net.w > net.h ? net.w : net.h;
args.label_smooth_eps = net.label_smooth_eps;
args.mixup = net.mixup;
if (dont_show && show_imgs) show_imgs = 2;
args.show_imgs = show_imgs;
args.paths = paths;
args.classes = classes;
args.n = imgs;
args.m = train_images_num;
args.labels = labels;
args.type = CLASSIFICATION_DATA;
#ifdef OPENCV
//args.threads = 3;
mat_cv* img = NULL;
float max_img_loss = net.max_chart_loss;
int number_of_lines = 100;
int img_size = 1000;
char windows_name[100];
sprintf(windows_name, "chart_%s.png", base);
if (!dontuse_opencv) img = draw_train_chart(windows_name, max_img_loss, net.max_batches, number_of_lines, img_size, dont_show, chart_path);
#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);
int iter_save_last = get_current_batch(net);
int iter_topk = get_current_batch(net);
float topk = 0;
int count = 0;
double start, time_remaining, avg_time = -1, alpha_time = 0.01;
start = what_time_is_it_now();
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
if(avg_loss == -1 || isnan(avg_loss) || isinf(avg_loss)) avg_loss = loss;
avg_loss = avg_loss*.9 + loss*.1;
i = get_current_batch(net);
int calc_topk_for_each = iter_topk + 2 * train_images_num / (net.batch * net.subdivisions); // calculate TOPk for each 2 Epochs
calc_topk_for_each = fmax(calc_topk_for_each, net.burn_in);
calc_topk_for_each = fmax(calc_topk_for_each, 100);
if (i % 10 == 0) {
if (calc_topk) {
fprintf(stderr, "\n (next TOP%d calculation at %d iterations) ", topk_data, calc_topk_for_each);
if (topk > 0) fprintf(stderr, " Last accuracy TOP%d = %2.2f %% \n", topk_data, topk * 100);
}
if (net.cudnn_half) {
if (i < net.burn_in * 3) fprintf(stderr, " Tensor Cores are disabled until the first %d iterations are reached.\n", 3 * net.burn_in);
else fprintf(stderr, " Tensor Cores are used.\n");
}
}
int draw_precision = 0;
if (calc_topk && (i >= calc_topk_for_each || i == net.max_batches)) {
iter_topk = i;
if (net.contrastive && l.type != SOFTMAX && l.type != COST) {
int k;
for (k = 0; k < net.n; ++k) if (net.layers[k].type == CONTRASTIVE) break;
topk = *(net.layers[k].loss) / 100;
sprintf(topk_buff, "Contr");
}
else {
topk = validate_classifier_single(datacfg, cfgfile, weightfile, &net, topk_data); // calc TOP-n
printf("\n accuracy %s = %f \n", topk_buff, topk);
}
draw_precision = 1;
}
time_remaining = ((net.max_batches - i) / ngpus) * (what_time_is_it_now() - start) / 60 / 60;
// set initial value, even if resume training from 10000 iteration
if (avg_time < 0) avg_time = time_remaining;
else avg_time = alpha_time * time_remaining + (1 - alpha_time) * avg_time;
start = what_time_is_it_now();
printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images, %f hours left\n", get_current_batch(net), (float)(*net.seen)/ train_images_num, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen, avg_time);
#ifdef OPENCV
if (net.contrastive) {
float cur_con_acc = -1;
int k;
for (k = 0; k < net.n; ++k)
if (net.layers[k].type == CONTRASTIVE) cur_con_acc = *net.layers[k].loss;
if (cur_con_acc >= 0) avg_contrastive_acc = avg_contrastive_acc*0.99 + cur_con_acc * 0.01;
printf(" avg_contrastive_acc = %f \n", avg_contrastive_acc);
}
if (!dontuse_opencv) draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, i, net.max_batches, topk, draw_precision, topk_buff, avg_contrastive_acc / 100, dont_show, mjpeg_port, avg_time);
#endif // OPENCV
if (i >= (iter_save + 1000)) {
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, i);
save_weights(net, buff);
}
if (i >= (iter_save_last + 100)) {
iter_save_last = i;
#ifdef GPU
if (ngpus != 1) sync_nets(nets, ngpus, 0);
#endif
char buff[256];
sprintf(buff, "%s/%s_last.weights", 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_final.weights", backup_directory, base);
save_weights(net, buff);
#ifdef OPENCV
release_mat(&img);
destroy_all_windows_cv();
#endif
pthread_join(load_thread, 0);
free_data(buffer);
//free_network(net);
for (i = 0; i < ngpus; ++i) free_network(nets[i]);
free(nets);
//free_ptrs((void**)labels, classes);
if(labels) free(labels);
free_ptrs((void**)paths, plist->size);
free_list(plist);
free(base);
free_list_contents_kvp(options);
free_list(options);
}
/*
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);
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);
if (topk > classes) topk = classes;
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);
clock_t time;
float avg_acc = 0;
float avg_topk = 0;
int splits = m/1000;
int num = (i+1)*m/splits - i*m/splits;
data val, buffer;
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 = OLD_CLASSIFICATION_DATA;
pthread_t load_thread = load_data_in_thread(args);
for(i = 1; i <= splits; ++i){
time=clock();
pthread_join(load_thread, 0);
val = buffer;
num = (i+1)*m/splits - i*m/splits;
char **part = paths+(i*m/splits);
if(i != splits){
args.paths = part;
load_thread = load_data_in_thread(args);
}
printf("Loaded: %d images in %lf seconds\n", val.X.rows, sec(clock()-time));
time=clock();
float *acc = network_accuracies(net, val, topk);
avg_acc += acc[0];
avg_topk += acc[1];
printf("%d: top 1: %f, top %d: %f, %lf seconds, %d images\n", i, avg_acc/i, topk, avg_topk/i, sec(clock()-time), val.X.rows);
free_data(val);
}
}
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);
if (topk > classes) topk = classes;
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 = (int*)xcalloc(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 = (float*)xcalloc(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));
}
free(indexes);
}
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);
if (topk > classes) topk = classes;
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 = (int*)xcalloc(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));
}
free(indexes);
}
float validate_classifier_single(char *datacfg, char *filename, char *weightfile, network *existing_net, int topk_custom)
{
int i, j;
network net;
int old_batch = -1;
if (existing_net) {
net = *existing_net; // for validation during training
old_batch = net.batch;
set_batch_network(&net, 1);
}
else {
net = parse_network_cfg_custom(filename, 1, 0);
if (weightfile) {
load_weights(&net, weightfile);
}
//set_batch_network(&net, 1);
fuse_conv_batchnorm(net);
calculate_binary_weights(net);
}
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);
if (topk_custom > 0) topk = topk_custom; // for validation during training
if (topk > classes) topk = classes;
printf(" TOP calculation...\n");
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 = (int*)xcalloc(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;
}
if (existing_net) printf("\r");
else printf("\n");
printf("%d: top 1: %f, top %d: %f", i, avg_acc/(i+1), topk, avg_topk/(i+1));
}
free(indexes);
if (existing_net) {
set_batch_network(&net, old_batch);
}
float topk_result = avg_topk / i;
return topk_result;
}
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);
if (topk > classes) topk = classes;
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 = (int*)xcalloc(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 = (float*)xcalloc(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));
}
free(indexes);
}
void try_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int layer_num)
{
network net = parse_network_cfg_custom(cfgfile, 1, 0);
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");
int classes = option_find_int(options, "classes", 2);
int top = option_find_int(options, "top", 1);
if (top > classes) top = classes;
char **names = get_labels(name_list);
clock_t time;
int* indexes = (int*)xcalloc(top, sizeof(int));
char buff[256];
char *input = buff;
while(1){
if(filename){
strncpy(input, filename, 256);
}else{
printf("Enter Image Path: ");
fflush(stdout);
input = fgets(input, 256, stdin);
if(!input) break;
strtok(input, "\n");
}
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];
int i;
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){
int index = indexes[i];
printf("%s: %f\n", names[index], predictions[index]);
}
free_image(im);
if (filename) break;
}
free(indexes);
}
void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top)
{
network net = parse_network_cfg_custom(cfgfile, 1, 0);
if(weightfile){
load_weights(&net, weightfile);
}
set_batch_network(&net, 1);
srand(2222222);
fuse_conv_batchnorm(net);
calculate_binary_weights(net);
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");
int classes = option_find_int(options, "classes", 2);
printf(" classes = %d, output in cfg = %d \n", classes, net.layers[net.n - 1].c);
layer l = net.layers[net.n - 1];
if (classes != l.outputs && (l.type == SOFTMAX || l.type == COST)) {
printf("\n Error: num of filters = %d in the last conv-layer in cfg-file doesn't match to classes = %d in data-file \n",
l.outputs, classes);
getchar();
}
if (top == 0) top = option_find_int(options, "top", 1);
if (top > classes) top = classes;
int i = 0;
char **names = get_labels(name_list);
clock_t time;
int* indexes = (int*)xcalloc(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) break;
strtok(input, "\n");
}
image im = load_image_color(input, 0, 0);
image resized = resize_min(im, net.w);
image cropped = crop_image(resized, (resized.w - net.w)/2, (resized.h - net.h)/2, net.w, net.h);
printf("%d %d\n", cropped.w, cropped.h);
float *X = cropped.data;
double time = get_time_point();
float *predictions = network_predict(net, X);
printf("%s: Predicted in %lf milli-seconds.\n", input, ((double)get_time_point() - time) / 1000);
if(net.hierarchy) hierarchy_predictions(predictions, net.outputs, net.hierarchy, 0);
top_k(predictions, net.outputs, top, indexes);
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]);
}
free_image(cropped);
if (resized.data != im.data) {
free_image(resized);
}
free_image(im);
if (filename) break;
}
free(indexes);
free_network(net);
free_list_contents_kvp(options);
free_list(options);
}
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(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
srand(time(0));
fuse_conv_batchnorm(net);
calculate_binary_weights(net);
list *options = read_data_cfg(datacfg);
char *test_list = option_find_str(options, "test", "data/test.list");
int classes = option_find_int(options, "classes", 2);
list *plist = get_paths(test_list);
char **paths = (char **)list_to_array(plist);
int m = plist->size;
free_list(plist);
clock_t time;
data val, buffer;
load_args args = {0};
args.w = net.w;
args.h = net.h;
args.paths = paths;
args.classes = classes;
args.n = net.batch;
args.m = 0;
args.labels = 0;
args.d = &buffer;
args.type = OLD_CLASSIFICATION_DATA;
pthread_t load_thread = load_data_in_thread(args);
for(curr = net.batch; curr < m; curr += net.batch){
time=clock();
pthread_join(load_thread, 0);
val = buffer;
if(curr < m){
args.paths = paths + curr;
if (curr + net.batch > m) args.n = m - curr;
load_thread = load_data_in_thread(args);
}
fprintf(stderr, "Loaded: %d images in %lf seconds\n", val.X.rows, sec(clock()-time));
time=clock();
matrix pred = network_predict_data(net, val);
int i, j;
if (target_layer >= 0){
//layer l = net.layers[target_layer];