def load_dataset(data_file=('%s/%s' % (DATA_DIR, DATA_FILE))):
dataset = utils_backdoor.load_dataset(data_file, keys=['X_test', 'Y_test'])
X_test = np.array(dataset['X_test'], dtype='float32')
Y_test = np.array(dataset['Y_test'], dtype='float32')
print('X_test shape %s' % str(X_test.shape))
print('Y_test shape %s' % str(Y_test.shape))
return X_test, Y_test
def load_dataset(data_file=('%s/%s' % (DATA_DIR, DATA_FILE))):
dataset = utils_backdoor.load_dataset(data_file, keys=['X_test', 'Y_test'])
X_test = np.array(dataset['X_test'], dtype='float32')
Y_test = np.array(dataset['Y_test'], dtype='float32')
if CHANNELS_FIRST:
# X_test = np.moveaxis(X_test, -1, 1)
X_test = np.rollaxis(X_test, 3, 1)
print('X_test shape %s' % str(X_test.shape))
print('Y_test shape %s' % str(Y_test.shape))
return X_test, Y_test
def load_dataset(data_file=('%s/%s' % (DATA_DIR, DATA_FILE))):
if not os.path.exists(data_file):
print(
"The data file does not exist. Please download the file and put in data/ directory from https://drive.google.com/file/d/1kcveaJC3Ra-XDuaNqHzYeomMvU8d1npj/view?usp=sharing"
)
exit(1)
dataset = utils_backdoor.load_dataset(
data_file, keys=['X_train', 'Y_train', 'X_test', 'Y_test'])
X_train = dataset['X_train']
Y_train = dataset['Y_train']
X_test = dataset['X_test']
Y_test = dataset['Y_test']
return X_train, Y_train, X_test, Y_test
def load_dataset(data_file=('%s/%s' % (DATA_DIR, DATA_FILE))):
if not os.path.exists(data_file):
print(
"The data file does not exist. Please download the file and put in data/ directory from https://drive.google.com/file/d/1kcveaJC3Ra-XDuaNqHzYeomMvU8d1npj/view?usp=sharing"
)
exit(1)
dataset = utils_backdoor.load_dataset(
data_file, keys=['X_train', 'Y_train', 'X_test', 'Y_test'])
X_train = np.transpose(np.array(dataset['X_train'], dtype='float32'),
(0, 3, 1, 2))
Y_train = np.array(dataset['Y_train'], dtype='int64')
Y_train = np.asarray([np.where(r == 1)[0][0] for r in Y_train])
X_test = np.transpose(np.array(dataset['X_test'], dtype='float32'),
(0, 3, 1, 2))
Y_test = np.array(dataset['Y_test'], dtype='int64')
Y_test = np.asarray([np.where(r == 1)[0][0] for r in Y_test])
#tensor_x, tensor_y = torch.Tensor(X), torch.Tensor(Y)
#dataset = torch.utils.data.TensorDataset(tensor_x, tensor_y)
#generator = torch.utils.data.DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=False)
return X_train, Y_train, X_test, Y_test