def load_trans_data(args, trans): dl = Data_Loader() x_train, x_test, y_test = dl.get_dataset(args.dataset, true_label=args.class_ind) x_train_trans, labels = transform_data(x_train, trans) x_test_trans, _ = transform_data(x_test, trans) x_test_trans, x_train_trans = x_test_trans.transpose( 0, 3, 1, 2), x_train_trans.transpose(0, 3, 1, 2) y_test = np.array(y_test) == args.class_ind return x_train_trans, x_test_trans, y_test
def load_trans_data(args, trans): dl = Data_Loader() x_train, x_test, y_test = dl.get_dataset(args.dataset, true_label=args.class_ind, flip_ones_and_zeros=args.flip) print("Computing transformed data for train data") x_train_trans, labels = transform_data(x_train, trans) print("Computing transformed data for test data") x_test_trans, _ = transform_data(x_test, trans) x_test_trans, x_train_trans = x_test_trans.transpose(0, 3, 1, 2), x_train_trans.transpose(0, 3, 1, 2) y_test = np.array(y_test) == args.class_ind return x_train_trans, x_test_trans, y_test
def load_trans_data(args): dl = Data_Loader() train_real, val_real, val_fake = dl.get_dataset(args.dataset, args.c_pr) y_test_fscore = np.concatenate([np.zeros(len(val_real)), np.ones(len(val_fake))]) ratio = 100.0 * len(val_real) / (len(val_real) + len(val_fake)) n_train, n_dims = train_real.shape rots = np.random.randn(args.n_rots, n_dims, args.d_out) print('Calculating transforms') x_train = np.stack([train_real.dot(rot) for rot in rots], 2) val_real_xs = np.stack([val_real.dot(rot) for rot in rots], 2) val_fake_xs = np.stack([val_fake.dot(rot) for rot in rots], 2) x_test = np.concatenate([val_real_xs, val_fake_xs]) return x_train, x_test, y_test_fscore, ratio
def load_trans_data(args, trans): dl = Data_Loader() x_train, x_test, y_test = dl.get_dataset(args.dataset, true_label=args.class_ind) print("Non Augmented Data Shape: ", x_train.shape) #DATA AUGMENTATION normal_data_transformer = Transformer_non90(0, 0, 30) transformations_inds_aug = np.tile( np.arange(normal_data_transformer.n_transforms), len(x_train)) print("Num Data Augments: ", normal_data_transformer.n_transforms) x_train_aug = normal_data_transformer.transform_batch( np.repeat(x_train, normal_data_transformer.n_transforms, axis=0), transformations_inds_aug) print("Augmented Data Shape ", x_train_aug.shape) x_train_trans, labels = transform_data(x_train_aug, trans) x_test_trans, _ = transform_data(x_test, trans) x_test_trans, x_train_trans = x_test_trans.transpose( 0, 3, 1, 2), x_train_trans.transpose(0, 3, 1, 2) y_test = np.array(y_test) == args.class_ind return x_train_trans, x_test_trans, y_test