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npapernot 2019-03-18 16:47:36 +00:00
parent 4784b0f31e
commit b6c932ec66
1 changed files with 9 additions and 9 deletions
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18
research/pate_2017/input.py
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@ -130,7 +130,7 @@ def extract_svhn(local_url):
data_dict = loadmat(file_obj) data_dict = loadmat(file_obj)
# Extract each dictionary (one for data, one for labels) # Extract each dictionary (one for data, one for labels)
data, labels = data_dict["X"], data_dict["y"] data, labels = data_dict['X'], data_dict['y']
# Set np type # Set np type
data = np.asarray(data, dtype=np.float32) data = np.asarray(data, dtype=np.float32)
@ -197,8 +197,8 @@ def extract_cifar10(local_url, data_dir):
else: else:
# Do everything from scratch # Do everything from scratch
# Define lists of all files we should extract # Define lists of all files we should extract
train_files = ["data_batch_" + str(i) for i in xrange(1, 6)] train_files = ['data_batch_' + str(i) for i in xrange(1, 6)]
test_file = ["test_batch"] test_file = ['test_batch']
cifar10_files = train_files + test_file cifar10_files = train_files + test_file
# Check if all files have already been extracted # Check if all files have already been extracted
@ -217,7 +217,7 @@ def extract_cifar10(local_url, data_dir):
labels = [] labels = []
for file in train_files: for file in train_files:
# Construct filename # Construct filename
filename = data_dir + "/cifar-10-batches-py/" + file filename = data_dir + '/cifar-10-batches-py/' + file
# Unpickle dictionary and extract images and labels # Unpickle dictionary and extract images and labels
images_tmp, labels_tmp = unpickle_cifar_dic(filename) images_tmp, labels_tmp = unpickle_cifar_dic(filename)
@ -236,7 +236,7 @@ def extract_cifar10(local_url, data_dir):
np.save(data_dir + preprocessed_files[1], train_labels) np.save(data_dir + preprocessed_files[1], train_labels)
# Construct filename for test file # Construct filename for test file
filename = data_dir + "/cifar-10-batches-py/" + test_file[0] filename = data_dir + '/cifar-10-batches-py/' + test_file[0]
# Load test images and labels # Load test images and labels
test_data, test_images = unpickle_cifar_dic(filename) test_data, test_images = unpickle_cifar_dic(filename)
@ -260,7 +260,7 @@ def extract_mnist_data(filename, num_images, image_size, pixel_depth):
Values are rescaled from [0, 255] down to [-0.5, 0.5]. Values are rescaled from [0, 255] down to [-0.5, 0.5].
""" """
# if not os.path.exists(file): # if not os.path.exists(file):
if not tf.gfile.Exists(filename+".npy"): if not tf.gfile.Exists(filename+'.npy'):
with gzip.open(filename) as bytestream: with gzip.open(filename) as bytestream:
bytestream.read(16) bytestream.read(16)
buf = bytestream.read(image_size * image_size * num_images) buf = bytestream.read(image_size * image_size * num_images)
@ -270,7 +270,7 @@ def extract_mnist_data(filename, num_images, image_size, pixel_depth):
np.save(filename, data) np.save(filename, data)
return data return data
else: else:
with tf.gfile.Open(filename+".npy", mode='r') as file_obj: with tf.gfile.Open(filename+'.npy', mode='r') as file_obj:
return np.load(file_obj) return np.load(file_obj)
@ -279,7 +279,7 @@ def extract_mnist_labels(filename, num_images):
Extract the labels into a vector of int64 label IDs. Extract the labels into a vector of int64 label IDs.
""" """
# if not os.path.exists(file): # if not os.path.exists(file):
if not tf.gfile.Exists(filename+".npy"): if not tf.gfile.Exists(filename+'.npy'):
with gzip.open(filename) as bytestream: with gzip.open(filename) as bytestream:
bytestream.read(8) bytestream.read(8)
buf = bytestream.read(1 * num_images) buf = bytestream.read(1 * num_images)
@ -287,7 +287,7 @@ def extract_mnist_labels(filename, num_images):
np.save(filename, labels) np.save(filename, labels)
return labels return labels
else: else:
with tf.gfile.Open(filename+".npy", mode='r') as file_obj: with tf.gfile.Open(filename+'.npy', mode='r') as file_obj:
return np.load(file_obj) return np.load(file_obj)