| | |
| | | srand(2222222); |
| | | int i = 0; |
| | | char *labels[] = {"cat","dog"}; |
| | | clock_t time; |
| | | while(1){ |
| | | i += 1000; |
| | | time=clock(); |
| | | data train = load_data_image_pathfile_random("data/assira/train.list", imgs*net.batch, labels, 2, 256, 256); |
| | | normalize_data_rows(train); |
| | | clock_t start = clock(), end; |
| | | float loss = train_network_sgd_gpu(net, train, imgs); |
| | | end = clock(); |
| | | printf("%d: %f, Time: %lf seconds\n", i, loss, (float)(end-start)/CLOCKS_PER_SEC ); |
| | | printf("Loaded: %lf seconds\n", sec(clock()-time)); |
| | | time=clock(); |
| | | float loss = train_network_sgd(net, train, imgs); |
| | | printf("%d: %f, Time: %lf seconds\n", i, loss, sec(clock()-time)); |
| | | free_data(train); |
| | | if(i%10000==0){ |
| | | char buff[256]; |
| | |
| | | } |
| | | } |
| | | |
| | | void train_imagenet() |
| | | { |
| | | network net = parse_network_cfg("cfg/imagenet_backup_710.cfg"); |
| | | printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); |
| | | int imgs = 1000/net.batch+1; |
| | | //imgs=1; |
| | | srand(888888); |
| | | int i = 0; |
| | | char **labels = get_labels("/home/pjreddie/data/imagenet/cls.labels.list"); |
| | | list *plist = get_paths("/home/pjreddie/data/imagenet/cls.cropped.list"); |
| | | char **paths = (char **)list_to_array(plist); |
| | | clock_t time; |
| | | while(1){ |
| | | i += 1; |
| | | time=clock(); |
| | | data train = load_data_random(imgs*net.batch, paths, plist->size, labels, 1000, 256, 256); |
| | | normalize_data_rows(train); |
| | | printf("Loaded: %lf seconds\n", sec(clock()-time)); |
| | | time=clock(); |
| | | #ifdef GPU |
| | | float loss = train_network_sgd_gpu(net, train, imgs); |
| | | printf("%d: %f, %lf seconds, %d images\n", i, loss, sec(clock()-time), i*imgs*net.batch); |
| | | #endif |
| | | free_data(train); |
| | | if(i%10==0){ |
| | | char buff[256]; |
| | | sprintf(buff, "/home/pjreddie/imagenet_backup/imagenet_backup_%d.cfg", i); |
| | | save_network(net, buff); |
| | | } |
| | | } |
| | | } |
| | | |
| | | void test_imagenet() |
| | | { |
| | | network net = parse_network_cfg("cfg/imagenet_test.cfg"); |
| | | //imgs=1; |
| | | srand(2222222); |
| | | int i = 0; |
| | | char **names = get_labels("cfg/shortnames.txt"); |
| | | clock_t time; |
| | | char filename[256]; |
| | | int indexes[10]; |
| | | while(1){ |
| | | gets(filename); |
| | | image im = load_image_color(filename, 256, 256); |
| | | normalize_image(im); |
| | | printf("%d %d %d\n", im.h, im.w, im.c); |
| | | float *X = im.data; |
| | | time=clock(); |
| | | float *predictions = network_predict(net, X); |
| | | top_predictions(net, 10, indexes); |
| | | printf("%s: Predicted in %f seconds.\n", filename, sec(clock()-time)); |
| | | for(i = 0; i < 10; ++i){ |
| | | int index = indexes[i]; |
| | | printf("%s: %f\n", names[index], predictions[index]); |
| | | } |
| | | free_image(im); |
| | | } |
| | | } |
| | | |
| | | void test_visualize() |
| | | { |
| | | network net = parse_network_cfg("cfg/voc_imagenet.cfg"); |
| | | network net = parse_network_cfg("cfg/assira_backup_740000.cfg"); |
| | | srand(2222222); |
| | | visualize_network(net); |
| | | cvWaitKey(0); |
| | |
| | | for(i = 0; i < total; ++i){ |
| | | visualize_network(net); |
| | | cvWaitKey(100); |
| | | data test = load_data_image_pathfile_part("images/assira/test.list", i, total, labels, 2, 256, 256); |
| | | data test = load_data_image_pathfile_part("data/assira/test.list", i, total, labels, 2, 256, 256); |
| | | image im = float_to_image(256, 256, 3,test.X.vals[0]); |
| | | show_image(im, "input"); |
| | | cvWaitKey(100); |
| | |
| | | |
| | | int main(int argc, char *argv[]) |
| | | { |
| | | //test_blas(); |
| | | train_assira(); |
| | | //test_distribution(); |
| | | //feenableexcept(FE_DIVBYZERO | FE_INVALID | FE_OVERFLOW); |
| | | |
| | | //test_blas(); |
| | | //test_visualize(); |
| | | //test_gpu_blas(); |
| | | //test_blas(); |
| | | //test_convolve_matrix(); |
| | | // test_im2row(); |
| | | //test_split(); |
| | | //test_ensemble(); |
| | | //test_nist_single(); |
| | | //test_nist(); |
| | | test_gpu_blas(); |
| | | //train_imagenet(); |
| | | //train_nist(); |
| | | //test_convolutional_layer(); |
| | | //test_col2im(); |
| | | //test_cifar10(); |
| | | //train_cifar10(); |
| | | //test_vince(); |
| | | //test_full(); |
| | | //tune_VOC(); |
| | | //features_VOC_image(argv[1], argv[2], argv[3], 0); |
| | | //features_VOC_image(argv[1], argv[2], argv[3], 1); |
| | | //train_VOC(); |
| | | //features_VOC_image(argv[1], argv[2], argv[3], 0, 4); |
| | | //features_VOC_image(argv[1], argv[2], argv[3], 1, 4); |
| | | //features_VOC_image_size(argv[1], atoi(argv[2]), atoi(argv[3])); |
| | | //visualize_imagenet_features("data/assira/train.list"); |
| | | //visualize_imagenet_topk("data/VOC2012.list"); |
| | | //visualize_cat(); |
| | | //flip_network(); |
| | | //test_visualize(); |
| | | //test_parser(); |
| | | fprintf(stderr, "Success!\n"); |
| | | //test_random_preprocess(); |
| | | //test_random_classify(); |
| | | //test_parser(); |
| | | //test_backpropagate(); |
| | | //test_ann(); |
| | | //test_convolve(); |
| | | //test_upsample(); |
| | | //test_rotate(); |
| | | //test_load(); |
| | | //test_network(); |
| | | //test_convolutional_layer(); |
| | | //verify_convolutional_layer(); |
| | | //test_color(); |
| | | //cvWaitKey(0); |
| | | return 0; |
| | | } |