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@crossbot I will murder you in your sleep ⚔️
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Joseph Redmon
273
examples/cifar.c
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273
examples/cifar.c
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#include "darknet/network.h"
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#include "darknet/utils.h"
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#include "darknet/parser.h"
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#include "darknet/option_list.h"
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#include "darknet/blas.h"
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void train_cifar(char *cfgfile, char *weightfile)
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{
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srand(time(0));
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float avg_loss = -1;
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char *base = basecfg(cfgfile);
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printf("%s\n", base);
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network net = parse_network_cfg(cfgfile);
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if(weightfile){
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load_weights(&net, weightfile);
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}
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printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
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char *backup_directory = "/home/pjreddie/backup/";
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int classes = 10;
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int N = 50000;
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char **labels = get_labels("data/cifar/labels.txt");
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int epoch = (*net.seen)/N;
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data train = load_all_cifar10();
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while(get_current_batch(net) < net.max_batches || net.max_batches == 0){
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clock_t time=clock();
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float loss = train_network_sgd(net, train, 1);
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if(avg_loss == -1) avg_loss = loss;
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avg_loss = avg_loss*.95 + loss*.05;
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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);
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if(*net.seen/N > epoch){
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epoch = *net.seen/N;
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char buff[256];
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sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
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save_weights(net, buff);
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}
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if(get_current_batch(net)%100 == 0){
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char buff[256];
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sprintf(buff, "%s/%s.backup",backup_directory,base);
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save_weights(net, buff);
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}
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}
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char buff[256];
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sprintf(buff, "%s/%s.weights", backup_directory, base);
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save_weights(net, buff);
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free_network(net);
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free_ptrs((void**)labels, classes);
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free(base);
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free_data(train);
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}
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void train_cifar_distill(char *cfgfile, char *weightfile)
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{
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srand(time(0));
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float avg_loss = -1;
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char *base = basecfg(cfgfile);
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printf("%s\n", base);
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network net = parse_network_cfg(cfgfile);
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if(weightfile){
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load_weights(&net, weightfile);
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}
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printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
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char *backup_directory = "/home/pjreddie/backup/";
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int classes = 10;
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int N = 50000;
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char **labels = get_labels("data/cifar/labels.txt");
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int epoch = (*net.seen)/N;
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data train = load_all_cifar10();
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matrix soft = csv_to_matrix("results/ensemble.csv");
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float weight = .9;
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scale_matrix(soft, weight);
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scale_matrix(train.y, 1. - weight);
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matrix_add_matrix(soft, train.y);
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while(get_current_batch(net) < net.max_batches || net.max_batches == 0){
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clock_t time=clock();
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float loss = train_network_sgd(net, train, 1);
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if(avg_loss == -1) avg_loss = loss;
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avg_loss = avg_loss*.95 + loss*.05;
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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);
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if(*net.seen/N > epoch){
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epoch = *net.seen/N;
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char buff[256];
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sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
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save_weights(net, buff);
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}
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if(get_current_batch(net)%100 == 0){
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char buff[256];
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sprintf(buff, "%s/%s.backup",backup_directory,base);
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save_weights(net, buff);
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}
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}
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char buff[256];
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sprintf(buff, "%s/%s.weights", backup_directory, base);
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save_weights(net, buff);
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free_network(net);
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free_ptrs((void**)labels, classes);
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free(base);
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free_data(train);
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}
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void test_cifar_multi(char *filename, char *weightfile)
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{
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network net = parse_network_cfg(filename);
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if(weightfile){
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load_weights(&net, weightfile);
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}
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set_batch_network(&net, 1);
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srand(time(0));
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float avg_acc = 0;
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data test = load_cifar10_data("data/cifar/cifar-10-batches-bin/test_batch.bin");
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int i;
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for(i = 0; i < test.X.rows; ++i){
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image im = float_to_image(32, 32, 3, test.X.vals[i]);
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float pred[10] = {0};
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float *p = network_predict(net, im.data);
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axpy_cpu(10, 1, p, 1, pred, 1);
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flip_image(im);
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p = network_predict(net, im.data);
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axpy_cpu(10, 1, p, 1, pred, 1);
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int index = max_index(pred, 10);
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int class = max_index(test.y.vals[i], 10);
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if(index == class) avg_acc += 1;
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free_image(im);
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printf("%4d: %.2f%%\n", i, 100.*avg_acc/(i+1));
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}
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}
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void test_cifar(char *filename, char *weightfile)
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{
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network net = parse_network_cfg(filename);
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if(weightfile){
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load_weights(&net, weightfile);
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}
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srand(time(0));
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clock_t time;
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float avg_acc = 0;
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float avg_top5 = 0;
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data test = load_cifar10_data("data/cifar/cifar-10-batches-bin/test_batch.bin");
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time=clock();
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float *acc = network_accuracies(net, test, 2);
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avg_acc += acc[0];
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avg_top5 += acc[1];
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printf("top1: %f, %lf seconds, %d images\n", avg_acc, sec(clock()-time), test.X.rows);
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free_data(test);
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}
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void extract_cifar()
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{
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char *labels[] = {"airplane","automobile","bird","cat","deer","dog","frog","horse","ship","truck"};
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int i;
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data train = load_all_cifar10();
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data test = load_cifar10_data("data/cifar/cifar-10-batches-bin/test_batch.bin");
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for(i = 0; i < train.X.rows; ++i){
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image im = float_to_image(32, 32, 3, train.X.vals[i]);
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int class = max_index(train.y.vals[i], 10);
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char buff[256];
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sprintf(buff, "data/cifar/train/%d_%s",i,labels[class]);
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save_image_png(im, buff);
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}
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for(i = 0; i < test.X.rows; ++i){
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image im = float_to_image(32, 32, 3, test.X.vals[i]);
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int class = max_index(test.y.vals[i], 10);
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char buff[256];
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sprintf(buff, "data/cifar/test/%d_%s",i,labels[class]);
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save_image_png(im, buff);
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}
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}
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void test_cifar_csv(char *filename, char *weightfile)
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{
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network net = parse_network_cfg(filename);
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if(weightfile){
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load_weights(&net, weightfile);
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}
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srand(time(0));
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data test = load_cifar10_data("data/cifar/cifar-10-batches-bin/test_batch.bin");
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matrix pred = network_predict_data(net, test);
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int i;
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for(i = 0; i < test.X.rows; ++i){
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image im = float_to_image(32, 32, 3, test.X.vals[i]);
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flip_image(im);
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}
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matrix pred2 = network_predict_data(net, test);
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scale_matrix(pred, .5);
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scale_matrix(pred2, .5);
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matrix_add_matrix(pred2, pred);
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matrix_to_csv(pred);
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fprintf(stderr, "Accuracy: %f\n", matrix_topk_accuracy(test.y, pred, 1));
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free_data(test);
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}
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void test_cifar_csvtrain(char *filename, char *weightfile)
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{
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network net = parse_network_cfg(filename);
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if(weightfile){
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load_weights(&net, weightfile);
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}
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srand(time(0));
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data test = load_all_cifar10();
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matrix pred = network_predict_data(net, test);
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int i;
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for(i = 0; i < test.X.rows; ++i){
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image im = float_to_image(32, 32, 3, test.X.vals[i]);
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flip_image(im);
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}
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matrix pred2 = network_predict_data(net, test);
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scale_matrix(pred, .5);
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scale_matrix(pred2, .5);
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matrix_add_matrix(pred2, pred);
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matrix_to_csv(pred);
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fprintf(stderr, "Accuracy: %f\n", matrix_topk_accuracy(test.y, pred, 1));
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free_data(test);
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}
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void eval_cifar_csv()
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{
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data test = load_cifar10_data("data/cifar/cifar-10-batches-bin/test_batch.bin");
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matrix pred = csv_to_matrix("results/combined.csv");
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fprintf(stderr, "%d %d\n", pred.rows, pred.cols);
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fprintf(stderr, "Accuracy: %f\n", matrix_topk_accuracy(test.y, pred, 1));
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free_data(test);
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free_matrix(pred);
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}
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void run_cifar(int argc, char **argv)
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{
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if(argc < 4){
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fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
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return;
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}
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char *cfg = argv[3];
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char *weights = (argc > 4) ? argv[4] : 0;
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if(0==strcmp(argv[2], "train")) train_cifar(cfg, weights);
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else if(0==strcmp(argv[2], "extract")) extract_cifar();
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else if(0==strcmp(argv[2], "distill")) train_cifar_distill(cfg, weights);
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else if(0==strcmp(argv[2], "test")) test_cifar(cfg, weights);
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else if(0==strcmp(argv[2], "multi")) test_cifar_multi(cfg, weights);
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else if(0==strcmp(argv[2], "csv")) test_cifar_csv(cfg, weights);
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else if(0==strcmp(argv[2], "csvtrain")) test_cifar_csvtrain(cfg, weights);
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else if(0==strcmp(argv[2], "eval")) eval_cifar_csv();
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}
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