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