docstrings

research/pate_2017/input.py

@@ -34,11 +34,7 @@ FLAGS = tf.flags.FLAGS
 
 
 def create_dir_if_needed(dest_directory):
-  """
-  Create directory if doesn't exist
-  :param dest_directory:
-  :return: True if everything went well
-  """
+  """Create directory if doesn't exist."""
   if not tf.gfile.IsDirectory(dest_directory):
     tf.gfile.MakeDirs(dest_directory)
 
@@ -46,11 +42,8 @@ def create_dir_if_needed(dest_directory):
 
 
 def maybe_download(file_urls, directory):
-  """
-  Download a set of files in temporary local folder
-  :param directory: the directory where to download
-  :return: a tuple of filepaths corresponding to the files given as input
-  """
+  """Download a set of files in temporary local folder."""
+
   # Create directory if doesn't exist
   assert create_dir_if_needed(directory)
 
@@ -91,8 +84,6 @@ def image_whitening(data):
   """
   Subtracts mean of image and divides by adjusted standard variance (for
   stability). Operations are per image but performed for the entire array.
-  :param image: 4D array (ID, Height, Weight, Channel)
-  :return: 4D array (ID, Height, Weight, Channel)
   """
   assert len(np.shape(data)) == 4
 
@@ -119,11 +110,7 @@ def image_whitening(data):
 
 
 def extract_svhn(local_url):
-  """
-  Extract a MATLAB matrix into two numpy arrays with data and labels
-  :param local_url:
-  :return:
-  """
+  """Extract a MATLAB matrix into two numpy arrays with data and labels"""
 
   with tf.gfile.Open(local_url, mode='r') as file_obj:
     # Load MATLAB matrix using scipy IO
@@ -149,11 +136,7 @@ def extract_svhn(local_url):
 
 
 def unpickle_cifar_dic(file_path):
-  """
-  Helper function: unpickles a dictionary (used for loading CIFAR)
-  :param file_path: filename of the pickle
-  :return: tuple of (images, labels)
-  """
+  """Helper function: unpickles a dictionary (used for loading CIFAR)"""
   file_obj = open(file_path, 'rb')
   data_dict = pickle.load(file_obj)
   file_obj.close()
@@ -161,12 +144,8 @@ def unpickle_cifar_dic(file_path):
 
 
 def extract_cifar10(local_url, data_dir):
-  """
-  Extracts the CIFAR-10 dataset and return numpy arrays with the different sets
-  :param local_url: where the tar.gz archive is located locally
-  :param data_dir: where to extract the archive's file
-  :return: a tuple (train data, train labels, test data, test labels)
-  """
+  """Extracts CIFAR-10 and return numpy arrays with the different sets"""
+
   # These numpy dumps can be reloaded to avoid performing the pre-processing
   # if they exist in the working directory.
   # Changing the order of this list will ruin the indices below.
@@ -203,8 +182,8 @@ def extract_cifar10(local_url, data_dir):
 
     # Check if all files have already been extracted
     need_to_unpack = False
-    for file in cifar10_files:
-      if not tf.gfile.Exists(file):
+    for file_name in cifar10_files:
+      if not tf.gfile.Exists(file_name):
         need_to_unpack = True
         break
 
@@ -292,9 +271,10 @@ def extract_mnist_labels(filename, num_images):
 def ld_svhn(extended=False, test_only=False):
   """
   Load the original SVHN data
-  :param extended: include extended training data in the returned array
-  :param test_only: disables loading of both train and extra -> large speed up
-  :return: tuple of arrays which depend on the parameters
+
+  Args:
+    extended: include extended training data in the returned array
+    test_only: disables loading of both train and extra -> large speed up
   """
   # Define files to be downloaded
   # WARNING: changing the order of this list will break indices (cf. below)
@@ -334,12 +314,8 @@ def ld_svhn(extended=False, test_only=False):
 
 
 def ld_cifar10(test_only=False):
-  """
-  Load the original CIFAR10 data
-  :param extended: include extended training data in the returned array
-  :param test_only: disables loading of both train and extra -> large speed up
-  :return: tuple of arrays which depend on the parameters
-  """
+  """Load the original CIFAR10 data"""
+
   # Define files to be downloaded
   file_urls = ['https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz']
 
@@ -363,12 +339,7 @@ def ld_cifar10(test_only=False):
 
 
 def ld_mnist(test_only=False):
-  """
-  Load the MNIST dataset
-  :param extended: include extended training data in the returned array
-  :param test_only: disables loading of both train and extra -> large speed up
-  :return: tuple of arrays which depend on the parameters
-  """
+  """Load the MNIST dataset."""
   # Define files to be downloaded
   # WARNING: changing the order of this list will break indices (cf. below)
   file_urls = ['http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz',
@@ -396,12 +367,13 @@ def partition_dataset(data, labels, nb_teachers, teacher_id):
   """
   Simple partitioning algorithm that returns the right portion of the data
   needed by a given teacher out of a certain nb of teachers
-  :param data: input data to be partitioned
-  :param labels: output data to be partitioned
-  :param nb_teachers: number of teachers in the ensemble (affects size of each
+
+  Args:
+    data: input data to be partitioned
+    labels: output data to be partitioned
+    nb_teachers: number of teachers in the ensemble (affects size of each
                       partition)
-  :param teacher_id: id of partition to retrieve
-  :return:
+    teacher_id: id of partition to retrieve
   """
 
   # Sanity check