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
