pylint edits

research/pate_2017/input.py

@@ -100,14 +100,14 @@ def image_whitening(data):
   nb_pixels = np.shape(data)[1] * np.shape(data)[2] * np.shape(data)[3]
 
   # Subtract mean
-  mean = np.mean(data, axis=(1,2,3))
+  mean = np.mean(data, axis=(1, 2, 3))
 
   ones = np.ones(np.shape(data)[1:4], dtype=np.float32)
   for i in xrange(len(data)):
     data[i, :, :, :] -= mean[i] * ones
 
   # Compute adjusted standard variance
-  adj_std_var = np.maximum(np.ones(len(data), dtype=np.float32) / math.sqrt(nb_pixels), np.std(data, axis=(1,2,3))) #NOLINT(long-line)
+  adj_std_var = np.maximum(np.ones(len(data), dtype=np.float32) / math.sqrt(nb_pixels), np.std(data, axis=(1, 2, 3)))  # pylint: disable=line-too-long
 
   # Divide image
   for i in xrange(len(data)):
@@ -148,15 +148,15 @@ def extract_svhn(local_url):
     return data, labels
 
 
-def unpickle_cifar_dic(file):
+def unpickle_cifar_dic(file):  # pylint: disable=redefined-builtin
   """
   Helper function: unpickles a dictionary (used for loading CIFAR)
   :param file: filename of the pickle
   :return: tuple of (images, labels)
   """
-  fo = open(file, 'rb')
+  file_obj = open(file, 'rb')
-  data_dict = pickle.load(fo)
+  data_dict = pickle.load(file_obj)
-  fo.close()
+  file_obj.close()
   return data_dict['data'], data_dict['labels']
 
 
@@ -176,8 +176,8 @@ def extract_cifar10(local_url, data_dir):
                         '/cifar10_test_labels.npy']
 
   all_preprocessed = True
-  for file in preprocessed_files:
+  for file_name in preprocessed_files:
-    if not tf.gfile.Exists(data_dir + file):
+    if not tf.gfile.Exists(data_dir + file_name):
       all_preprocessed = False
       break
 
@@ -197,7 +197,7 @@ def extract_cifar10(local_url, data_dir):
   else:
     # Do everything from scratch
     # 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"]
     cifar10_files = train_files + test_file
 
@@ -227,7 +227,7 @@ def extract_cifar10(local_url, data_dir):
       labels.append(labels_tmp)
 
     # Convert to numpy arrays and reshape in the expected format
-    train_data = np.asarray(images, dtype=np.float32).reshape((50000,3,32,32))
+    train_data = np.asarray(images, dtype=np.float32).reshape((50000, 3, 32, 32))
     train_data = np.swapaxes(train_data, 1, 3)
     train_labels = np.asarray(labels, dtype=np.int32).reshape(50000)
 
@@ -242,7 +242,7 @@ def extract_cifar10(local_url, data_dir):
     test_data, test_images = unpickle_cifar_dic(filename)
 
     # Convert to numpy arrays and reshape in the expected format
-    test_data = np.asarray(test_data,dtype=np.float32).reshape((10000,3,32,32))
+    test_data = np.asarray(test_data, dtype=np.float32).reshape((10000, 3, 32, 32))
     test_data = np.swapaxes(test_data, 1, 3)
     test_labels = np.asarray(test_images, dtype=np.int32).reshape(10000)
 
@@ -332,7 +332,7 @@ def ld_svhn(extended=False, test_only=False):
       return train_data, train_labels, test_data, test_labels
     else:
       # Return training and extended training data separately
-      return train_data,train_labels, test_data,test_labels, ext_data,ext_labels
+      return train_data, train_labels, test_data, test_labels, ext_data, ext_labels
 
 
 def ld_cifar10(test_only=False):
@@ -377,7 +377,7 @@ def ld_mnist(test_only=False):
                'http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz',
                'http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz',
                'http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz',
-               ]
+              ]
 
   # Maybe download data and retrieve local storage urls
   local_urls = maybe_download(file_urls, FLAGS.data_dir)