Merge branch 'master' into switch_mnist_mirror

README.md

@@ -243,6 +243,7 @@ The following authors contributed 100 lines or more (ordered according to the Gi
 * Erfan Noury (UMBC)
 * Robert Wagner (Case Western Reserve University)
 * Erh-Chung Chen (National Tsing Hua University)
+* Joel Frank (Ruhr-University Bochum)
 
 ## Copyright
 

cleverhans/__init__.py

@@ -3,4 +3,4 @@
 
 # If possible attach a hex digest to the version string to keep track of
 # changes in the development branch
-__version__ = append_dev_version('4.0.0')
+__version__ = append_dev_version("4.0.0")

cleverhans/devtools/autopep8_all.py

@@ -12,5 +12,5 @@
 
 for f in list_files(".py"):
 
-  command = ["autopep8", "-i", "--indent-size", "2", f]
-  shell_call(command)
+    command = ["autopep8", "-i", "--indent-size", "2", f]
+    shell_call(command)

cleverhans/devtools/checks.py

@@ -13,19 +13,20 @@
 
 import numpy as np
 
+
 class CleverHansTest(unittest.TestCase):
-  """TestCase with some extra features"""
+    """TestCase with some extra features"""
 
-  def setUp(self):
-    self.test_start = time.time()
-    # seed the randomness
-    np.random.seed(1234)
+    def setUp(self):
+        self.test_start = time.time()
+        # seed the randomness
+        np.random.seed(1234)
 
-  def tearDown(self):
-    print(self.id(), "took", time.time() - self.test_start, "seconds")
+    def tearDown(self):
+        print(self.id(), "took", time.time() - self.test_start, "seconds")
 
-  def assertClose(self, x, y, *args, **kwargs):
-    """Assert that `x` and `y` have close to the same value"""
-    # self.assertTrue(np.allclose(x, y)) doesn't give a useful message
-    # on failure
-    assert np.allclose(x, y, *args, **kwargs), (x, y)
+    def assertClose(self, x, y, *args, **kwargs):
+        """Assert that `x` and `y` have close to the same value"""
+        # self.assertTrue(np.allclose(x, y)) doesn't give a useful message
+        # on failure
+        assert np.allclose(x, y, *args, **kwargs), (x, y)

cleverhans/devtools/list_files.py

@@ -4,68 +4,74 @@
 
 
 def list_files(suffix=""):
-  """
-  Returns a list of all files in CleverHans with the given suffix.
+    """
+    Returns a list of all files in CleverHans with the given suffix.
 
-  Parameters
-  ----------
-  suffix : str
+    Parameters
+    ----------
+    suffix : str
 
-  Returns
-  -------
+    Returns
+    -------
 
-  file_list : list
-      A list of all files in CleverHans whose filepath ends with `suffix`.
-  """
+    file_list : list
+        A list of all files in CleverHans whose filepath ends with `suffix`.
+    """
 
-  cleverhans_path = os.path.abspath(cleverhans.__path__[0])
-  # In some environments cleverhans_path does not point to a real directory.
-  # In such case return empty list.
-  if not os.path.isdir(cleverhans_path):
-    return []
-  repo_path = os.path.abspath(os.path.join(cleverhans_path, os.pardir))
-  file_list = _list_files(cleverhans_path, suffix)
+    cleverhans_path = os.path.abspath(cleverhans.__path__[0])
+    # In some environments cleverhans_path does not point to a real directory.
+    # In such case return empty list.
+    if not os.path.isdir(cleverhans_path):
+        return []
+    repo_path = os.path.abspath(os.path.join(cleverhans_path, os.pardir))
+    file_list = _list_files(cleverhans_path, suffix)
 
-  extra_dirs = ['cleverhans_tutorials', 'examples', 'scripts', 'tests_tf', 'tests_pytorch']
+    extra_dirs = [
+        "cleverhans_tutorials",
+        "examples",
+        "scripts",
+        "tests_tf",
+        "tests_pytorch",
+    ]
 
-  for extra_dir in extra_dirs:
-    extra_path = os.path.join(repo_path, extra_dir)
-    if os.path.isdir(extra_path):
-      extra_files = _list_files(extra_path, suffix)
-      extra_files = [os.path.join(os.pardir, path) for path in extra_files]
-      file_list = file_list + extra_files
+    for extra_dir in extra_dirs:
+        extra_path = os.path.join(repo_path, extra_dir)
+        if os.path.isdir(extra_path):
+            extra_files = _list_files(extra_path, suffix)
+            extra_files = [os.path.join(os.pardir, path) for path in extra_files]
+            file_list = file_list + extra_files
 
-  return file_list
+    return file_list
 
 
 def _list_files(path, suffix=""):
-  """
-  Returns a list of all files ending in `suffix` contained within `path`.
+    """
+    Returns a list of all files ending in `suffix` contained within `path`.
 
-  Parameters
-  ----------
-  path : str
-      a filepath
-  suffix : str
+    Parameters
+    ----------
+    path : str
+        a filepath
+    suffix : str
 
-  Returns
-  -------
-  l : list
-      A list of all files ending in `suffix` contained within `path`.
-      (If `path` is a file rather than a directory, it is considered
-      to "contain" itself)
-  """
-  if os.path.isdir(path):
-    incomplete = os.listdir(path)
-    complete = [os.path.join(path, entry) for entry in incomplete]
-    lists = [_list_files(subpath, suffix) for subpath in complete]
-    flattened = []
-    for one_list in lists:
-      for elem in one_list:
-        flattened.append(elem)
-    return flattened
-  else:
-    assert os.path.exists(path), "couldn't find file '%s'" % path
-    if path.endswith(suffix):
-      return [path]
-    return []
+    Returns
+    -------
+    l : list
+        A list of all files ending in `suffix` contained within `path`.
+        (If `path` is a file rather than a directory, it is considered
+        to "contain" itself)
+    """
+    if os.path.isdir(path):
+        incomplete = os.listdir(path)
+        complete = [os.path.join(path, entry) for entry in incomplete]
+        lists = [_list_files(subpath, suffix) for subpath in complete]
+        flattened = []
+        for one_list in lists:
+            for elem in one_list:
+                flattened.append(elem)
+        return flattened
+    else:
+        assert os.path.exists(path), "couldn't find file '%s'" % path
+        if path.endswith(suffix):
+            return [path]
+        return []

cleverhans/devtools/mocks.py

@@ -14,52 +14,70 @@
 
 
 def random_feed_dict(rng, placeholders):
-  """
-  Returns random data to be used with `feed_dict`.
-  :param rng: A numpy.random.RandomState instance
-  :param placeholders: List of tensorflow placeholders
-  :return: A dict mapping placeholders to random numpy values
-  """
+    """
+    Returns random data to be used with `feed_dict`.
+    :param rng: A numpy.random.RandomState instance
+    :param placeholders: List of tensorflow placeholders
+    :return: A dict mapping placeholders to random numpy values
+    """
 
-  output = {}
+    output = {}
 
-  for placeholder in placeholders:
-    if placeholder.dtype != 'float32':
-      raise NotImplementedError()
-    value = rng.randn(*placeholder.shape).astype('float32')
-    output[placeholder] = value
+    for placeholder in placeholders:
+        if placeholder.dtype != "float32":
+            raise NotImplementedError()
+        value = rng.randn(*placeholder.shape).astype("float32")
+        output[placeholder] = value
+
+    return output
 
-  return output
 
 class SimpleDataset(Dataset):
-  """
-  A dataset containing random values.
-  Values are uniformly distributed, either in [0, max_val] or [-1, max_val].
-  """
+    """
+    A dataset containing random values.
+    Values are uniformly distributed, either in [0, max_val] or [-1, max_val].
+    """
 
-  def __init__(self, dim=2, train_start=0, train_end=3, test_start=0, test_end=5, center=False, max_val=1.,
-               nb_classes=5):
-    kwargs = copy.copy(locals())
-    del kwargs['self']
-    if "__class__" in kwargs:
-      del kwargs["__class__"]
-    super(SimpleDataset, self).__init__(kwargs)
-    self.__dict__.update(kwargs)
-    train_x_rng = np.random.RandomState([2018, 11, 9, 1])
-    # Even if train_start is not 0, we should still generate the first training examples from the rng.
-    # This way the dataset looks like it is an array of deterministic data that we index using train_start.
-    self.x_train = train_x_rng.uniform(- center * max_val, max_val, (train_end, dim))[train_start:]
-    # Use a second rng for the test set so that it also looks like an array of deterministic data that we
-    # index into, unaffected by the number of training examples.
-    test_x_rng = np.random.RandomState([2018, 11, 9, 2])
-    self.x_test = test_x_rng.uniform(- center * max_val, max_val, (test_end, dim))[test_start:]
-    # Likewise, to keep the number of examples read from the rng affecting the values of the labels, we
-    # must generate the labels from a different rng
-    train_y_rng = np.random.RandomState([2018, 11, 9, 3])
-    self.y_train = train_y_rng.randint(low=0, high=nb_classes, size=(train_end, 1))[train_start:]
-    test_y_rng = np.random.RandomState([2018, 11, 9, 4])
-    self.y_test = test_y_rng.randint(low=0, high=nb_classes, size=(test_end, 1))[test_start:]
-    assert self.x_train.shape[0] == self.y_train.shape[0]
-    assert self.x_test.shape[0] == self.y_test.shape[0]
-    self.y_train = np_utils.to_categorical(self.y_train, nb_classes)
-    self.y_test = np_utils.to_categorical(self.y_test, nb_classes)
+    def __init__(
+        self,
+        dim=2,
+        train_start=0,
+        train_end=3,
+        test_start=0,
+        test_end=5,
+        center=False,
+        max_val=1.0,
+        nb_classes=5,
+    ):
+        kwargs = copy.copy(locals())
+        del kwargs["self"]
+        if "__class__" in kwargs:
+            del kwargs["__class__"]
+        super(SimpleDataset, self).__init__(kwargs)
+        self.__dict__.update(kwargs)
+        train_x_rng = np.random.RandomState([2018, 11, 9, 1])
+        # Even if train_start is not 0, we should still generate the first training examples from the rng.
+        # This way the dataset looks like it is an array of deterministic data that we index using train_start.
+        self.x_train = train_x_rng.uniform(
+            -center * max_val, max_val, (train_end, dim)
+        )[train_start:]
+        # Use a second rng for the test set so that it also looks like an array of deterministic data that we
+        # index into, unaffected by the number of training examples.
+        test_x_rng = np.random.RandomState([2018, 11, 9, 2])
+        self.x_test = test_x_rng.uniform(-center * max_val, max_val, (test_end, dim))[
+            test_start:
+        ]
+        # Likewise, to keep the number of examples read from the rng affecting the values of the labels, we
+        # must generate the labels from a different rng
+        train_y_rng = np.random.RandomState([2018, 11, 9, 3])
+        self.y_train = train_y_rng.randint(low=0, high=nb_classes, size=(train_end, 1))[
+            train_start:
+        ]
+        test_y_rng = np.random.RandomState([2018, 11, 9, 4])
+        self.y_test = test_y_rng.randint(low=0, high=nb_classes, size=(test_end, 1))[
+            test_start:
+        ]
+        assert self.x_train.shape[0] == self.y_train.shape[0]
+        assert self.x_test.shape[0] == self.y_test.shape[0]
+        self.y_train = np_utils.to_categorical(self.y_train, nb_classes)
+        self.y_test = np_utils.to_categorical(self.y_test, nb_classes)