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Fix various multithreading related issues

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augustin64 2023-01-14 15:02:57 +01:00
parent dd6fb046c7
commit df48b92cf2
1 changed files with 58 additions and 56 deletions
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114
src/cnn/train.c
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@ -205,74 +205,76 @@ void train(int dataset_type, char* images_file, char* labels_file, char* data_di
batches_epoques = div_up(nb_images_total, BATCHES);
nb_images_total_remaining = nb_images_total;
#ifndef USE_MULTITHREADING
train_params->nb_images = BATCHES;
train_params->nb_images = BATCHES;
#endif
for (int j=0; j < batches_epoques; j++) {
#ifdef USE_MULTITHREADING
if (j == batches_epoques-1) {
nb_remaining_images = nb_images_total_remaining;
nb_images_total_remaining = 0;
} else {
nb_images_total_remaining -= BATCHES;
nb_remaining_images = BATCHES;
}
for (int k=0; k < nb_threads; k++) {
if (k == nb_threads-1) {
train_parameters[k]->nb_images = nb_remaining_images;
nb_remaining_images = 0;
if (j == batches_epoques-1) {
nb_remaining_images = nb_images_total_remaining;
nb_images_total_remaining = 0;
} else {
nb_remaining_images -= BATCHES / nb_threads;
nb_images_total_remaining -= BATCHES;
nb_remaining_images = BATCHES;
}
train_parameters[k]->start = BATCHES*j + (BATCHES/nb_threads)*k;
train_parameters[k]->network = copy_network(network);
if (train_parameters[k]->start+train_parameters[k]->nb_images >= nb_images_total) {
train_parameters[k]->nb_images = nb_images_total - train_parameters[k]->start -1;
for (int k=0; k < nb_threads; k++) {
if (k == nb_threads-1) {
train_parameters[k]->nb_images = nb_remaining_images;
nb_remaining_images = 0;
} else {
nb_remaining_images -= BATCHES / nb_threads;
}
train_parameters[k]->start = BATCHES*j + (BATCHES/nb_threads)*k;
if (train_parameters[k]->start+train_parameters[k]->nb_images >= nb_images_total) {
train_parameters[k]->nb_images = nb_images_total - train_parameters[k]->start -1;
}
if (train_parameters[k]->nb_images > 0) {
train_parameters[k]->network = copy_network(network);
pthread_create( &tid[k], NULL, train_thread, (void*) train_parameters[k]);
} else {
train_parameters[k]->network = NULL;
}
}
if (train_parameters[k]->nb_images > 0) {
pthread_create( &tid[k], NULL, train_thread, (void*) train_parameters[k]);
} else {
tid[k] = 0;
for (int k=0; k < nb_threads; k++) {
// On attend la terminaison de chaque thread un à un
if (train_parameters[k]->network) {
pthread_join( tid[k], NULL );
accuracy += train_parameters[k]->accuracy / (float) nb_images_total;
}
}
}
for (int k=0; k < nb_threads; k++) {
// On attend la terminaison de chaque thread un à un
if (tid[k] != 0) {
pthread_join( tid[k], NULL );
accuracy += train_parameters[k]->accuracy / (float) nb_images_total;
// On attend que tous les fils aient fini avant d'appliquer des modifications au réseau principal
for (int k=0; k < nb_threads; k++) {
if (train_parameters[k]->network) { // Si le fil a été utilisé
update_weights(network, train_parameters[k]->network, train_parameters[k]->nb_images);
update_bias(network, train_parameters[k]->network, train_parameters[k]->nb_images);
free_network(train_parameters[k]->network);
}
}
}
// On attend que tous les fils aient fini avant d'appliquer des modifications au réseau principal
for (int k=0; k < nb_threads; k++) {
update_weights(network, train_parameters[k]->network, train_parameters[k]->nb_images);
update_bias(network, train_parameters[k]->network, train_parameters[k]->nb_images);
free_network(train_parameters[k]->network);
}
current_accuracy = accuracy * nb_images_total/((j+1)*BATCHES);
printf("\rThreads [%d]\tÉpoque [%d/%d]\tImage [%d/%d]\tAccuracy: "YELLOW"%0.2f%%"RESET" ", nb_threads, i, epochs, BATCHES*(j+1), nb_images_total, current_accuracy*100);
fflush(stdout);
current_accuracy = accuracy * nb_images_total/((j+1)*BATCHES);
printf("\rThreads [%d]\tÉpoque [%d/%d]\tImage [%d/%d]\tAccuracy: "YELLOW"%0.2f%%"RESET" ", nb_threads, i, epochs, BATCHES*(j+1), nb_images_total, current_accuracy*100);
fflush(stdout);
#else
(void)nb_images_total_remaining; // Juste pour enlever un warning
(void)nb_images_total_remaining; // Juste pour enlever un warning
train_params->start = j*BATCHES;
train_params->start = j*BATCHES;
// Ne pas dépasser le nombre d'images à cause de la partie entière
if (j == batches_epoques-1) {
train_params->nb_images = nb_images_total - j*BATCHES;
}
train_thread((void*)train_params);
accuracy += train_params->accuracy / (float) nb_images_total;
current_accuracy = accuracy * nb_images_total/((j+1)*BATCHES);
update_weights(network, network, train_params->nb_images);
update_bias(network, network, train_params->nb_images);
printf("\rÉpoque [%d/%d]\tImage [%d/%d]\tAccuracy: "YELLOW"%0.4f%%"RESET" ", i, epochs, BATCHES*(j+1), nb_images_total, current_accuracy*100);
fflush(stdout);
// Ne pas dépasser le nombre d'images à cause de la partie entière
if (j == batches_epoques-1) {
train_params->nb_images = nb_images_total - j*BATCHES;
}
train_thread((void*)train_params);
accuracy += train_params->accuracy / (float) nb_images_total;
current_accuracy = accuracy * nb_images_total/((j+1)*BATCHES);
update_weights(network, network, train_params->nb_images);
update_bias(network, network, train_params->nb_images);
printf("\rÉpoque [%d/%d]\tImage [%d/%d]\tAccuracy: "YELLOW"%0.4f%%"RESET" ", i, epochs, BATCHES*(j+1), nb_images_total, current_accuracy*100);
fflush(stdout);
#endif
}
end_time = omp_get_wtime();