BoghdadyJR/codesearch_python · Data Studio

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def database_admin_api(self): """Helper for session-related API calls.""" if self._database_admin_api is None: self._database_admin_api = DatabaseAdminClient( credentials=self.credentials, client_info=_CLIENT_INFO ) return self._database_admin_api
def copy(self): """Make a copy of this client. Copies the local data stored as simple types but does not copy the current state of any open connections with the Cloud Bigtable API. :rtype: :class:`.Client` :returns: A copy of the current client. """ return self....
def list_instance_configs(self, page_size=None, page_token=None): """List available instance configurations for the client's project. .. _RPC docs: https://cloud.google.com/spanner/docs/reference/rpc/\ google.spanner.admin.instance.v1#google.spanner.admin.\ i...
def instance( self, instance_id, configuration_name=None, display_name=None, node_count=DEFAULT_NODE_COUNT, ): """Factory to create a instance associated with this client. :type instance_id: str :param instance_id: The ID of the instance. :ty...
def list_instances(self, filter_="", page_size=None, page_token=None): """List instances for the client's project. See https://cloud.google.com/spanner/reference/rpc/google.spanner.admin.database.v1#google.spanner.admin.database.v1.InstanceAdmin.ListInstances :type filter_: string ...
def _should_retry(exc): """Predicate for determining when to retry. We retry if and only if the 'reason' is 'backendError' or 'rateLimitExceeded'. """ if not hasattr(exc, "errors"): return False if len(exc.errors) == 0: # Check for unstructured error returns, e.g. from GFE ...
def default(session, django_dep=('django',)): """Default unit test session. """ # Install all test dependencies, then install this package in-place. deps = UNIT_TEST_DEPS deps += django_dep session.install(*deps) for local_dep in LOCAL_DEPS: session.install('-e', local_dep) ses...
def unit(session): """Run the unit test suite.""" # Testing multiple version of django # See https://www.djangoproject.com/download/ for supported version django_deps_27 = [ ('django==1.8.19',), ('django >= 1.11.0, < 2.0.0dev',), ] if session.virtualenv.interpreter == '2.7': ...
def system(session): """Run the system test suite.""" # Sanity check: Only run system tests if the environment variable is set. if not os.environ.get('GOOGLE_APPLICATION_CREDENTIALS', ''): session.skip('Credentials must be set via environment variable.') # Use pre-release gRPC for system tests...
def entry_from_resource(resource, client, loggers): """Detect correct entry type from resource and instantiate. :type resource: dict :param resource: One entry resource from API response. :type client: :class:`~google.cloud.logging.client.Client` :param client: Client that owns the log entry. ...
def retrieve_metadata_server(metadata_key): """Retrieve the metadata key in the metadata server. See: https://cloud.google.com/compute/docs/storing-retrieving-metadata :type metadata_key: str :param metadata_key: Key of the metadata which will form the url. You can also supply...
def model_loss(y, model, mean=True): """ Define loss of TF graph :param y: correct labels :param model: output of the model :param mean: boolean indicating whether should return mean of loss or vector of losses for each input of the batch :return: return mean of loss if True, otherwise return...
def initialize_uninitialized_global_variables(sess): """ Only initializes the variables of a TensorFlow session that were not already initialized. :param sess: the TensorFlow session :return: """ # List all global variables global_vars = tf.global_variables() # Find initialized status for all variabl...
def train(sess, loss, x, y, X_train, Y_train, save=False, init_all=False, evaluate=None, feed=None, args=None, rng=None, var_list=None, fprop_args=None, optimizer=None): """ Train a TF graph. This function is deprecated. Prefer cleverhans.train.train when possible. cleverhans.train.train sup...
def model_eval(sess, x, y, predictions, X_test=None, Y_test=None, feed=None, args=None): """ Compute the accuracy of a TF model on some data :param sess: TF session to use :param x: input placeholder :param y: output placeholder (for labels) :param predictions: model output predictions :par...
def batch_eval(args, *kwargs): """ Wrapper around deprecated function. """ # Inside function to avoid circular import from cleverhans.evaluation import batch_eval as new_batch_eval warnings.warn("batch_eval has moved to cleverhans.evaluation. " "batch_eval will be removed from utils_tf on ...
def model_argmax(sess, x, predictions, samples, feed=None): """ Helper function that computes the current class prediction :param sess: TF session :param x: the input placeholder :param predictions: the model's symbolic output :param samples: numpy array with input samples (dims must match x) :param feed:...
def l2_batch_normalize(x, epsilon=1e-12, scope=None): """ Helper function to normalize a batch of vectors. :param x: the input placeholder :param epsilon: stabilizes division :return: the batch of l2 normalized vector """ with tf.name_scope(scope, "l2_batch_normalize") as name_scope: x_shape = tf.shap...
def kl_with_logits(p_logits, q_logits, scope=None, loss_collection=tf.GraphKeys.REGULARIZATION_LOSSES): """Helper function to compute kl-divergence KL(p \|\| q) """ with tf.name_scope(scope, "kl_divergence") as name: p = tf.nn.softmax(p_logits) p_log = tf.nn.log_softmax(p_logits) q_lo...
def clip_eta(eta, ord, eps): """ Helper function to clip the perturbation to epsilon norm ball. :param eta: A tensor with the current perturbation. :param ord: Order of the norm (mimics Numpy). Possible values: np.inf, 1 or 2. :param eps: Epsilon, bound of the perturbation. """ # Clipping p...
def infer_devices(devices=None): """ Returns the list of devices that multi-replica code should use. :param devices: list of string device names, e.g. ["/GPU:0"] If the user specifies this, `infer_devices` checks that it is valid, and then uses this user-specified list. If the user does not spec...
def get_available_gpus(): """ Returns a list of string names of all available GPUs """ local_device_protos = device_lib.list_local_devices() return [x.name for x in local_device_protos if x.device_type == 'GPU']
def clip_by_value(t, clip_value_min, clip_value_max, name=None): """ A wrapper for clip_by_value that casts the clipping range if needed. """ def cast_clip(clip): """ Cast clipping range argument if needed. """ if t.dtype in (tf.float32, tf.float64): if hasattr(clip, 'dtype'): # Co...
def mul(a, b): """ A wrapper around tf multiplication that does more automatic casting of the input. """ def multiply(a, b): """Multiplication""" return a * b return op_with_scalar_cast(a, b, multiply)
def div(a, b): """ A wrapper around tf division that does more automatic casting of the input. """ def divide(a, b): """Division""" return a / b return op_with_scalar_cast(a, b, divide)
def op_with_scalar_cast(a, b, f): """ Builds the graph to compute f(a, b). If only one of the two arguments is a scalar and the operation would cause a type error without casting, casts the scalar to match the tensor. :param a: a tf-compatible array or scalar :param b: a tf-compatible array or scalar ""...
def jacobian_graph(predictions, x, nb_classes): """ Create the Jacobian graph to be ran later in a TF session :param predictions: the model's symbolic output (linear output, pre-softmax) :param x: the input placeholder :param nb_classes: the number of classes the model has :return: """ # This fun...
def jacobian_augmentation(sess, x, X_sub_prev, Y_sub, grads, lmbda, aug_batch_size=512, feed=None): """ Augment an adversary's substit...
def evaluate_model(filepath, train_start=0, train_end=60000, test_start=0, test_end=10000, batch_size=128, testing=False, num_threads=None): """ Run evaluation on a saved model :param filepath: path to model to evaluate :param train_start: index of first ...
def set_input(self, X_batch=None): """ Preprocessing the inputs before calling session.run() :param X_batch: A dictionary of inputs to the first sub-graph :return: A tuple, `(fetches, fd)`, with `fetches` being a list of Tensors to be fetches and `fd` the feed dictionary. """ input...
def proc_fvals(self, fvals): """ Postprocess the outputs of the Session.run(). Move the outputs of sub-graphs to next ones and return the output of the last sub-graph. :param fvals: A list of fetched values returned by Session.run() :return: A dictionary of fetched values returned by the last sub-g...
def _write_single_batch_images_internal(self, batch_id, client_batch): """Helper method to write images from single batch into datastore.""" client = self._datastore_client batch_key = client.key(self._entity_kind_batches, batch_id) for img_id, img in iteritems(self._data[batch_id]['images']): img...
def write_to_datastore(self): """Writes all image batches to the datastore.""" client = self._datastore_client with client.no_transact_batch() as client_batch: for batch_id, batch_data in iteritems(self._data): batch_key = client.key(self._entity_kind_batches, batch_id) batch_entity = ...
def write_single_batch_images_to_datastore(self, batch_id): """Writes only images from one batch to the datastore.""" client = self._datastore_client with client.no_transact_batch() as client_batch: self._write_single_batch_images_internal(batch_id, client_batch)
def init_from_datastore(self): """Initializes batches by reading from the datastore.""" self._data = {} for entity in self._datastore_client.query_fetch( kind=self._entity_kind_batches): batch_id = entity.key.flat_path[-1] self._data[batch_id] = dict(entity) self._data[batch_id]['i...
def add_batch(self, batch_id, batch_properties=None): """Adds batch with give ID and list of properties.""" if batch_properties is None: batch_properties = {} if not isinstance(batch_properties, dict): raise ValueError('batch_properties has to be dict, however it was: ' + ...
def add_image(self, batch_id, image_id, image_properties=None): """Adds image to given batch.""" if batch_id not in self._data: raise KeyError('Batch with ID "{0}" does not exist'.format(batch_id)) if image_properties is None: image_properties = {} if not isinstance(image_properties, dict): ...
def _read_image_list(self, skip_image_ids=None): """Reads list of dataset images from the datastore.""" if skip_image_ids is None: skip_image_ids = [] images = self._storage_client.list_blobs( prefix=os.path.join('dataset', self._dataset_name) + '/') zip_files = [i for i in images if i.end...
def init_from_storage_write_to_datastore(self, batch_size=100, allowed_epsilon=None, skip_image_ids=None, max_num_images=None): """Initializes d...
def init_from_dataset_and_submissions_write_to_datastore( self, dataset_batches, attack_submission_ids): """Init list of adversarial batches from dataset batches and submissions. Args: dataset_batches: instances of DatasetBatches attack_submission_ids: iterable with IDs of all (targeted and n...
def count_generated_adv_examples(self): """Returns total number of all generated adversarial examples.""" result = {} for v in itervalues(self.data): s_id = v['submission_id'] result[s_id] = result.get(s_id, 0) + len(v['images']) return result
def make_confidence_report_bundled(filepath, train_start=TRAIN_START, train_end=TRAIN_END, test_start=TEST_START, test_end=TEST_END, which_set=WHICH_SET, recipe=RECIPE, report_path=REPORT_PATH, ...
def print_stats(correctness, confidence, name): """ Prints out accuracy, coverage, etc. statistics :param correctness: ndarray One bool per example specifying whether it was correctly classified :param confidence: ndarray The probability associated with each prediction :param name: str The name of...
def make_confidence_report(filepath, train_start=TRAIN_START, train_end=TRAIN_END, test_start=TEST_START, test_end=TEST_END, batch_size=BATCH_SIZE, which_set=WHICH_SET, mc_batch_size=MC_BATCH_SIZE, ...
def mnist_tutorial(train_start=0, train_end=60000, test_start=0, test_end=10000, nb_epochs=NB_EPOCHS, batch_size=BATCH_SIZE, learning_rate=LEARNING_RATE, train_dir=TRAIN_DIR, filename=FILENAME, load_model=LOAD_MODEL, testing=False, label_smooth...
def generate(self, x, kwargs): """ Generate symbolic graph for adversarial examples and return. :param x: The model's symbolic inputs. :param kwargs: Keyword arguments for the base attacker """ assert self.parse_params(kwargs) labels, _nb_classes = self.get_or_guess_labels(x, kwargs) ...
def attack(self, x, true_y): """ Runs the untargeted attack. :param x: The input :param true_y: The correct label for `x`. This attack aims to produce misclassification. """ adv_x_cls = [] prob_cls = [] m = tf.shape(x)[0] true_y_idx = tf.argmax(true_y, axis=1) expanded_x = tf.co...
def attack_class(self, x, target_y): """ Run the attack on a specific target class. :param x: tf Tensor. The input example. :param target_y: tf Tensor. The attacker's desired target class. Returns: A targeted adversarial example, intended to be classified as the target class. """ adv =...
def batch_indices(batch_nb, data_length, batch_size): """ This helper function computes a batch start and end index :param batch_nb: the batch number :param data_length: the total length of the data being parsed by batches :param batch_size: the number of inputs in each batch :return: pair of (start, end) i...
def other_classes(nb_classes, class_ind): """ Returns a list of class indices excluding the class indexed by class_ind :param nb_classes: number of classes in the task :param class_ind: the class index to be omitted :return: list of class indices excluding the class indexed by class_ind """ if class_ind <...
def to_categorical(y, nb_classes, num_classes=None): """ Converts a class vector (integers) to binary class matrix. This is adapted from the Keras function with the same name. :param y: class vector to be converted into a matrix (integers from 0 to nb_classes). :param nb_classes: nb_classes: total...
def random_targets(gt, nb_classes): """ Take in an array of correct labels and randomly select a different label for each label in the array. This is typically used to randomly select a target class in targeted adversarial examples attacks (i.e., when the search algorithm takes in both a source class and targ...
def pair_visual(args, *kwargs): """Deprecation wrapper""" warnings.warn("`pair_visual` has moved to `cleverhans.plot.pyplot_image`. " "cleverhans.utils.pair_visual may be removed on or after " "2019-04-24.") from cleverhans.plot.pyplot_image import pair_visual as new_pair_visual ...
def grid_visual(args, *kwargs): """Deprecation wrapper""" warnings.warn("`grid_visual` has moved to `cleverhans.plot.pyplot_image`. " "cleverhans.utils.grid_visual may be removed on or after " "2019-04-24.") from cleverhans.plot.pyplot_image import grid_visual as new_grid_visual ...
def get_logits_over_interval(args, *kwargs): """Deprecation wrapper""" warnings.warn("`get_logits_over_interval` has moved to " "`cleverhans.plot.pyplot_image`. " "cleverhans.utils.get_logits_over_interval may be removed on " "or after 2019-04-24.") # pylint:disab...
def linear_extrapolation_plot(args, *kwargs): """Deprecation wrapper""" warnings.warn("`linear_extrapolation_plot` has moved to " "`cleverhans.plot.pyplot_image`. " "cleverhans.utils.linear_extrapolation_plot may be removed on " "or after 2019-04-24.") # pylint:di...
def create_logger(name): """ Create a logger object with the given name. If this is the first time that we call this method, then initialize the formatter. """ base = logging.getLogger("cleverhans") if len(base.handlers) == 0: ch = logging.StreamHandler() formatter = logging.Formatter('[%(levelna...
def deterministic_dict(normal_dict): """ Returns a version of `normal_dict` whose iteration order is always the same """ out = OrderedDict() for key in sorted(normal_dict.keys()): out[key] = normal_dict[key] return out
def ordered_union(l1, l2): """ Return the union of l1 and l2, with a deterministic ordering. (Union of python sets does not necessarily have a consisten iteration order) :param l1: list of items :param l2: list of items :returns: list containing one copy of each item that is in l1 or in l2 """ out = [...
def safe_zip(*args): """like zip but with these properties: - returns a list, rather than an iterator. This is the old Python2 zip behavior. - a guarantee that all arguments are the same length. (normal zip silently drops entries to make them the same length) """ length = len(args[0]) if not all(len(arg) ...
def shell_call(command, **kwargs): """Calls shell command with argument substitution. Args: command: command represented as a list. Each element of the list is one token of the command. For example "cp a b" becomes ['cp', 'a', 'b'] If any element of the list looks like '${NAME}' then it will be rep...
def deep_copy(numpy_dict): """ Returns a copy of a dictionary whose values are numpy arrays. Copies their values rather than copying references to them. """ out = {} for key in numpy_dict: out[key] = numpy_dict[key].copy() return out
def data_mnist(datadir=tempfile.gettempdir(), train_start=0, train_end=60000, test_start=0, test_end=10000): """ Load and preprocess MNIST dataset :param datadir: path to folder where data should be stored :param train_start: index of first training set example :param train_end: index of last t...
def data_cifar10(train_start=0, train_end=50000, test_start=0, test_end=10000): """ Preprocess CIFAR10 dataset :return: """ # These values are specific to CIFAR10 img_rows = 32 img_cols = 32 nb_classes = 10 # the data, shuffled and split between train and test sets (x_train, y_train), (x_test, y_...
def print_accuracies(filepath, train_start=TRAIN_START, train_end=TRAIN_END, test_start=TEST_START, test_end=TEST_END, batch_size=BATCH_SIZE, which_set=WHICH_SET, base_eps_iter=BASE_EPS_ITER, nb_iter=NB_ITER): """ Load a saved model...
def impl(sess, model, dataset, factory, x_data, y_data, base_eps_iter=BASE_EPS_ITER, nb_iter=NB_ITER, batch_size=BATCH_SIZE): """ The actual implementation of the evaluation. :param sess: tf.Session :param model: cleverhans.model.Model :param dataset: cleverhans.dataset.Dataset :param fact...
def main(argv=None): """ Print accuracies """ try: _name_of_script, filepath = argv except ValueError: raise ValueError(argv) print_accuracies(filepath=filepath, test_start=FLAGS.test_start, test_end=FLAGS.test_end, which_set=FLAGS.which_set, nb_iter=FLAGS.nb_it...
def fast_gradient_method(model_fn, x, eps, ord, clip_min=None, clip_max=None, y=None, targeted=False, sanity_checks=False): """ PyTorch implementation of the Fast Gradient Method. :param model_fn: a callable that takes an input tensor and returns the model logits. :param x: input tensor...
def load_images(input_dir, batch_shape): """Read png images from input directory in batches. Args: input_dir: input directory batch_shape: shape of minibatch array, i.e. [batch_size, height, width, 3] Yields: filenames: list file names without path of each image Lenght of this list could be le...
def save_images(images, filenames, output_dir): """Saves images to the output directory. Args: images: array with minibatch of images filenames: list of filenames without path If number of file names in this list less than number of images in the minibatch then only first len(filenames) images ...
def main(_): """Run the sample attack""" # Images for inception classifier are normalized to be in [-1, 1] interval, # eps is a difference between pixels so it should be in [0, 2] interval. # Renormalizing epsilon from [0, 255] to [0, 2]. eps = 2.0 * FLAGS.max_epsilon / 255.0 batch_shape = [FLAGS.batch_size...
def ld_cifar10(): """Load training and test data.""" train_transforms = torchvision.transforms.Compose([torchvision.transforms.ToTensor()]) test_transforms = torchvision.transforms.Compose([torchvision.transforms.ToTensor()]) train_dataset = torchvision.datasets.CIFAR10(root='/tmp/data', train=True, transform=t...
def plot_report_from_path(path, success_name=DEFAULT_SUCCESS_NAME, fail_names=DEFAULT_FAIL_NAMES, label=None, is_max_confidence=True, linewidth=LINEWIDTH, plot_upper_bound=True): """ Plots a success-fail curve fr...
def plot_report(report, success_name, fail_names, label=None, is_max_confidence=True, linewidth=LINEWIDTH, plot_upper_bound=True): """ Plot a success fail curve from a confidence report :param report: A confidence report (the type of object saved by make_confide...
def make_curve(report, success_name, fail_names): """ Make a success-failure curve. :param report: A confidence report (the type of object saved by make_confidence_report.py) :param success_name: see plot_report_from_path :param fail_names: see plot_report_from_path :returns: fail_optimal: list of f...
def model_train(self): """ Train a TF graph :param sess: TF session to use when training the graph :param x: input placeholder :param y: output placeholder (for labels) :param predictions: model output predictions :param X_train: numpy array with training inputs :param Y_train: numpy arr...
def clone_g0_inputs_on_ngpus(self, inputs, outputs, g0_inputs): """ Clone variables unused by the attack on all GPUs. Specifically, the ground-truth label, y, has to be preserved until the training step. :param inputs: A list of dictionaries as the inputs to each step. :param outputs: A list of dic...
def generate(self, x, **kwargs): """ Return a tensor that constructs adversarial examples for the given input. Generate uses tf.py_func in order to operate over tensors. :param x: (required) A tensor with the inputs. :param kwargs: See `parse_params` """ assert self.sess is not None, \ ...
def parse_params(self, y_target=None, batch_size=1, binary_search_steps=5, max_iterations=1000, initial_const=1e-2, clip_min=0, clip_max=1): """ :param y_target: (optional) A tens...
def attack(self, x_val, targets): """ Perform the attack on the given instance for the given targets. """ def lbfgs_objective(adv_x, self, targets, oimgs, CONST): """ returns the function value and the gradient for fmin_l_bfgs_b """ loss = self.sess.run( self.loss, feed_...
def set_device(self, device_name): """ Set the device before the next fprop to create a new graph on the specified device. """ device_name = unify_device_name(device_name) self.device_name = device_name for layer in self.layers: layer.device_name = device_name
def create_sync_ops(self, host_device): """ Return a list of assignment operations that syncs the parameters of all model copies with the one on host_device. :param host_device: (required str) the name of the device with latest parameters """ host_device = unify_device_na...
def get_variable(self, name, initializer): """ Create and initialize a variable using a numpy array and set trainable. :param name: (required str) name of the variable :param initializer: a numpy array or a tensor """ v = tf.get_variable(name, shape=initializer.shape, ini...
def set_input_shape_ngpu(self, new_input_shape): """ Create and initialize layer parameters on the device previously set in self.device_name. :param new_input_shape: a list or tuple for the shape of the input. """ assert self.device_name, "Device name has not been set." device_name = self....
def create_sync_ops(self, host_device): """Create an assignment operation for each weight on all devices. The weight is assigned the value of the copy on the `host_device'. """ sync_ops = [] host_params = self.params_device[host_device] for device, params in (self.params_device).iteritems(): ...
def vatm(model, x, logits, eps, num_iterations=1, xi=1e-6, clip_min=None, clip_max=None, scope=None): """ Tensorflow implementation of the perturbation method used for virtual adversarial training: https://arxiv.org/abs/1507.00677 :param mo...
def generate(self, x, kwargs): """ Generate symbolic graph for adversarial examples and return. :param x: The model's symbolic inputs. :param kwargs: See `parse_params` """ # Parse and save attack-specific parameters assert self.parse_params(kwargs) return vatm( self.model,...
def parse_params(self, eps=2.0, nb_iter=None, xi=1e-6, clip_min=None, clip_max=None, num_iterations=None, **kwargs): """ Take in a dictionary of parameters and applies attack-spec...
def iterate_with_exp_backoff(base_iter, max_num_tries=6, max_backoff=300.0, start_backoff=4.0, backoff_multiplier=2.0, frac_random_backoff=0.25): """Iterate with exponential...
def list_blobs(self, prefix=''): """Lists names of all blobs by their prefix.""" return [b.name for b in self.bucket.list_blobs(prefix=prefix)]
def begin(self): """Begins a batch.""" if self._cur_batch: raise ValueError('Previous batch is not committed.') self._cur_batch = self._client.batch() self._cur_batch.begin() self._num_mutations = 0
def rollback(self): """Rolls back pending mutations. Keep in mind that NoTransactionBatch splits all mutations into smaller batches and commit them as soon as mutation buffer reaches maximum length. That's why rollback method will only roll back pending mutations from the buffer, but won't be able ...
def put(self, entity): """Adds mutation of the entity to the mutation buffer. If mutation buffer reaches its capacity then this method commit all pending mutations from the buffer and emties it. Args: entity: entity which should be put into the datastore """ self._cur_batch.put(entity) ...
def delete(self, key): """Adds deletion of the entity with given key to the mutation buffer. If mutation buffer reaches its capacity then this method commit all pending mutations from the buffer and emties it. Args: key: key of the entity which should be deleted """ self._cur_batch.delet...
def get(self, key, transaction=None): """Retrieves an entity given its key.""" return self._client.get(key, transaction=transaction)
def mnist_tutorial_cw(train_start=0, train_end=60000, test_start=0, test_end=10000, viz_enabled=VIZ_ENABLED, nb_epochs=NB_EPOCHS, batch_size=BATCH_SIZE, source_samples=SOURCE_SAMPLES, learning_rate=LEARNING_RATE, ...
def attack_selection(attack_string): """ Selects the Attack Class using string input. :param attack_string: adversarial attack name in string format :return: attack class defined in cleverhans.attacks_eager """ # List of Implemented attacks attacks_list = AVAILABLE_ATTACKS.keys() # Checking for reque...
def mnist_tutorial(train_start=0, train_end=60000, test_start=0, test_end=10000, nb_epochs=NB_EPOCHS, batch_size=BATCH_SIZE, learning_rate=LEARNING_RATE, clean_train=True, testing=False, backprop_through_attack=False, ...
def sudo_remove_dirtree(dir_name): """Removes directory tree as a superuser. Args: dir_name: name of the directory to remove. This function is necessary to cleanup directories created from inside a Docker, since they usually written as a root, thus have to be removed as a root. """ try: subproce...
def main(args): """Main function which runs worker.""" title = '## Starting evaluation of round {0} ##'.format(args.round_name) logging.info('\n' + '#' * len(title) + '\n' + '#' * len(title) + '\n' + '##' + ' ' * (len(title)-2) + '##' + '\n' + title + '\...

def database_admin_api(self): """Helper for session-related API calls.""" if self._database_admin_api is None: self._database_admin_api = DatabaseAdminClient( credentials=self.credentials, client_info=_CLIENT_INFO ) return self._database_admin_api

def copy(self): """Make a copy of this client. Copies the local data stored as simple types but does not copy the current state of any open connections with the Cloud Bigtable API. :rtype: :class:`.Client` :returns: A copy of the current client. """ return self....

def list_instance_configs(self, page_size=None, page_token=None): """List available instance configurations for the client's project. .. _RPC docs: https://cloud.google.com/spanner/docs/reference/rpc/\ google.spanner.admin.instance.v1#google.spanner.admin.\ i...

def instance( self, instance_id, configuration_name=None, display_name=None, node_count=DEFAULT_NODE_COUNT, ): """Factory to create a instance associated with this client. :type instance_id: str :param instance_id: The ID of the instance. :ty...

def list_instances(self, filter_="", page_size=None, page_token=None): """List instances for the client's project. See https://cloud.google.com/spanner/reference/rpc/google.spanner.admin.database.v1#google.spanner.admin.database.v1.InstanceAdmin.ListInstances :type filter_: string ...

def _should_retry(exc): """Predicate for determining when to retry. We retry if and only if the 'reason' is 'backendError' or 'rateLimitExceeded'. """ if not hasattr(exc, "errors"): return False if len(exc.errors) == 0: # Check for unstructured error returns, e.g. from GFE ...

def default(session, django_dep=('django',)): """Default unit test session. """ # Install all test dependencies, then install this package in-place. deps = UNIT_TEST_DEPS deps += django_dep session.install(*deps) for local_dep in LOCAL_DEPS: session.install('-e', local_dep) ses...

def unit(session): """Run the unit test suite.""" # Testing multiple version of django # See https://www.djangoproject.com/download/ for supported version django_deps_27 = [ ('django==1.8.19',), ('django >= 1.11.0, < 2.0.0dev',), ] if session.virtualenv.interpreter == '2.7': ...

def system(session): """Run the system test suite.""" # Sanity check: Only run system tests if the environment variable is set. if not os.environ.get('GOOGLE_APPLICATION_CREDENTIALS', ''): session.skip('Credentials must be set via environment variable.') # Use pre-release gRPC for system tests...

def entry_from_resource(resource, client, loggers): """Detect correct entry type from resource and instantiate. :type resource: dict :param resource: One entry resource from API response. :type client: :class:`~google.cloud.logging.client.Client` :param client: Client that owns the log entry. ...

def retrieve_metadata_server(metadata_key): """Retrieve the metadata key in the metadata server. See: https://cloud.google.com/compute/docs/storing-retrieving-metadata :type metadata_key: str :param metadata_key: Key of the metadata which will form the url. You can also supply...

def model_loss(y, model, mean=True): """ Define loss of TF graph :param y: correct labels :param model: output of the model :param mean: boolean indicating whether should return mean of loss or vector of losses for each input of the batch :return: return mean of loss if True, otherwise return...

def initialize_uninitialized_global_variables(sess): """ Only initializes the variables of a TensorFlow session that were not already initialized. :param sess: the TensorFlow session :return: """ # List all global variables global_vars = tf.global_variables() # Find initialized status for all variabl...

def train(sess, loss, x, y, X_train, Y_train, save=False, init_all=False, evaluate=None, feed=None, args=None, rng=None, var_list=None, fprop_args=None, optimizer=None): """ Train a TF graph. This function is deprecated. Prefer cleverhans.train.train when possible. cleverhans.train.train sup...

def model_eval(sess, x, y, predictions, X_test=None, Y_test=None, feed=None, args=None): """ Compute the accuracy of a TF model on some data :param sess: TF session to use :param x: input placeholder :param y: output placeholder (for labels) :param predictions: model output predictions :par...

def batch_eval(*args, **kwargs): """ Wrapper around deprecated function. """ # Inside function to avoid circular import from cleverhans.evaluation import batch_eval as new_batch_eval warnings.warn("batch_eval has moved to cleverhans.evaluation. " "batch_eval will be removed from utils_tf on ...

def model_argmax(sess, x, predictions, samples, feed=None): """ Helper function that computes the current class prediction :param sess: TF session :param x: the input placeholder :param predictions: the model's symbolic output :param samples: numpy array with input samples (dims must match x) :param feed:...

def l2_batch_normalize(x, epsilon=1e-12, scope=None): """ Helper function to normalize a batch of vectors. :param x: the input placeholder :param epsilon: stabilizes division :return: the batch of l2 normalized vector """ with tf.name_scope(scope, "l2_batch_normalize") as name_scope: x_shape = tf.shap...

def kl_with_logits(p_logits, q_logits, scope=None, loss_collection=tf.GraphKeys.REGULARIZATION_LOSSES): """Helper function to compute kl-divergence KL(p || q) """ with tf.name_scope(scope, "kl_divergence") as name: p = tf.nn.softmax(p_logits) p_log = tf.nn.log_softmax(p_logits) q_lo...

def clip_eta(eta, ord, eps): """ Helper function to clip the perturbation to epsilon norm ball. :param eta: A tensor with the current perturbation. :param ord: Order of the norm (mimics Numpy). Possible values: np.inf, 1 or 2. :param eps: Epsilon, bound of the perturbation. """ # Clipping p...

def infer_devices(devices=None): """ Returns the list of devices that multi-replica code should use. :param devices: list of string device names, e.g. ["/GPU:0"] If the user specifies this, `infer_devices` checks that it is valid, and then uses this user-specified list. If the user does not spec...

def get_available_gpus(): """ Returns a list of string names of all available GPUs """ local_device_protos = device_lib.list_local_devices() return [x.name for x in local_device_protos if x.device_type == 'GPU']

def clip_by_value(t, clip_value_min, clip_value_max, name=None): """ A wrapper for clip_by_value that casts the clipping range if needed. """ def cast_clip(clip): """ Cast clipping range argument if needed. """ if t.dtype in (tf.float32, tf.float64): if hasattr(clip, 'dtype'): # Co...

def mul(a, b): """ A wrapper around tf multiplication that does more automatic casting of the input. """ def multiply(a, b): """Multiplication""" return a * b return op_with_scalar_cast(a, b, multiply)

def div(a, b): """ A wrapper around tf division that does more automatic casting of the input. """ def divide(a, b): """Division""" return a / b return op_with_scalar_cast(a, b, divide)

def op_with_scalar_cast(a, b, f): """ Builds the graph to compute f(a, b). If only one of the two arguments is a scalar and the operation would cause a type error without casting, casts the scalar to match the tensor. :param a: a tf-compatible array or scalar :param b: a tf-compatible array or scalar ""...

def jacobian_graph(predictions, x, nb_classes): """ Create the Jacobian graph to be ran later in a TF session :param predictions: the model's symbolic output (linear output, pre-softmax) :param x: the input placeholder :param nb_classes: the number of classes the model has :return: """ # This fun...

def jacobian_augmentation(sess, x, X_sub_prev, Y_sub, grads, lmbda, aug_batch_size=512, feed=None): """ Augment an adversary's substit...

def evaluate_model(filepath, train_start=0, train_end=60000, test_start=0, test_end=10000, batch_size=128, testing=False, num_threads=None): """ Run evaluation on a saved model :param filepath: path to model to evaluate :param train_start: index of first ...

def set_input(self, X_batch=None): """ Preprocessing the inputs before calling session.run() :param X_batch: A dictionary of inputs to the first sub-graph :return: A tuple, `(fetches, fd)`, with `fetches` being a list of Tensors to be fetches and `fd` the feed dictionary. """ input...

def proc_fvals(self, fvals): """ Postprocess the outputs of the Session.run(). Move the outputs of sub-graphs to next ones and return the output of the last sub-graph. :param fvals: A list of fetched values returned by Session.run() :return: A dictionary of fetched values returned by the last sub-g...

def _write_single_batch_images_internal(self, batch_id, client_batch): """Helper method to write images from single batch into datastore.""" client = self._datastore_client batch_key = client.key(self._entity_kind_batches, batch_id) for img_id, img in iteritems(self._data[batch_id]['images']): img...

def write_to_datastore(self): """Writes all image batches to the datastore.""" client = self._datastore_client with client.no_transact_batch() as client_batch: for batch_id, batch_data in iteritems(self._data): batch_key = client.key(self._entity_kind_batches, batch_id) batch_entity = ...

def write_single_batch_images_to_datastore(self, batch_id): """Writes only images from one batch to the datastore.""" client = self._datastore_client with client.no_transact_batch() as client_batch: self._write_single_batch_images_internal(batch_id, client_batch)

def init_from_datastore(self): """Initializes batches by reading from the datastore.""" self._data = {} for entity in self._datastore_client.query_fetch( kind=self._entity_kind_batches): batch_id = entity.key.flat_path[-1] self._data[batch_id] = dict(entity) self._data[batch_id]['i...

def add_batch(self, batch_id, batch_properties=None): """Adds batch with give ID and list of properties.""" if batch_properties is None: batch_properties = {} if not isinstance(batch_properties, dict): raise ValueError('batch_properties has to be dict, however it was: ' + ...

def add_image(self, batch_id, image_id, image_properties=None): """Adds image to given batch.""" if batch_id not in self._data: raise KeyError('Batch with ID "{0}" does not exist'.format(batch_id)) if image_properties is None: image_properties = {} if not isinstance(image_properties, dict): ...

def _read_image_list(self, skip_image_ids=None): """Reads list of dataset images from the datastore.""" if skip_image_ids is None: skip_image_ids = [] images = self._storage_client.list_blobs( prefix=os.path.join('dataset', self._dataset_name) + '/') zip_files = [i for i in images if i.end...

def init_from_storage_write_to_datastore(self, batch_size=100, allowed_epsilon=None, skip_image_ids=None, max_num_images=None): """Initializes d...

def init_from_dataset_and_submissions_write_to_datastore( self, dataset_batches, attack_submission_ids): """Init list of adversarial batches from dataset batches and submissions. Args: dataset_batches: instances of DatasetBatches attack_submission_ids: iterable with IDs of all (targeted and n...

def count_generated_adv_examples(self): """Returns total number of all generated adversarial examples.""" result = {} for v in itervalues(self.data): s_id = v['submission_id'] result[s_id] = result.get(s_id, 0) + len(v['images']) return result

def make_confidence_report_bundled(filepath, train_start=TRAIN_START, train_end=TRAIN_END, test_start=TEST_START, test_end=TEST_END, which_set=WHICH_SET, recipe=RECIPE, report_path=REPORT_PATH, ...

def print_stats(correctness, confidence, name): """ Prints out accuracy, coverage, etc. statistics :param correctness: ndarray One bool per example specifying whether it was correctly classified :param confidence: ndarray The probability associated with each prediction :param name: str The name of...

def make_confidence_report(filepath, train_start=TRAIN_START, train_end=TRAIN_END, test_start=TEST_START, test_end=TEST_END, batch_size=BATCH_SIZE, which_set=WHICH_SET, mc_batch_size=MC_BATCH_SIZE, ...

def mnist_tutorial(train_start=0, train_end=60000, test_start=0, test_end=10000, nb_epochs=NB_EPOCHS, batch_size=BATCH_SIZE, learning_rate=LEARNING_RATE, train_dir=TRAIN_DIR, filename=FILENAME, load_model=LOAD_MODEL, testing=False, label_smooth...

def generate(self, x, **kwargs): """ Generate symbolic graph for adversarial examples and return. :param x: The model's symbolic inputs. :param kwargs: Keyword arguments for the base attacker """ assert self.parse_params(**kwargs) labels, _nb_classes = self.get_or_guess_labels(x, kwargs) ...

def attack(self, x, true_y): """ Runs the untargeted attack. :param x: The input :param true_y: The correct label for `x`. This attack aims to produce misclassification. """ adv_x_cls = [] prob_cls = [] m = tf.shape(x)[0] true_y_idx = tf.argmax(true_y, axis=1) expanded_x = tf.co...

def attack_class(self, x, target_y): """ Run the attack on a specific target class. :param x: tf Tensor. The input example. :param target_y: tf Tensor. The attacker's desired target class. Returns: A targeted adversarial example, intended to be classified as the target class. """ adv =...

def batch_indices(batch_nb, data_length, batch_size): """ This helper function computes a batch start and end index :param batch_nb: the batch number :param data_length: the total length of the data being parsed by batches :param batch_size: the number of inputs in each batch :return: pair of (start, end) i...

def other_classes(nb_classes, class_ind): """ Returns a list of class indices excluding the class indexed by class_ind :param nb_classes: number of classes in the task :param class_ind: the class index to be omitted :return: list of class indices excluding the class indexed by class_ind """ if class_ind <...

def to_categorical(y, nb_classes, num_classes=None): """ Converts a class vector (integers) to binary class matrix. This is adapted from the Keras function with the same name. :param y: class vector to be converted into a matrix (integers from 0 to nb_classes). :param nb_classes: nb_classes: total...

def random_targets(gt, nb_classes): """ Take in an array of correct labels and randomly select a different label for each label in the array. This is typically used to randomly select a target class in targeted adversarial examples attacks (i.e., when the search algorithm takes in both a source class and targ...

def pair_visual(*args, **kwargs): """Deprecation wrapper""" warnings.warn("`pair_visual` has moved to `cleverhans.plot.pyplot_image`. " "cleverhans.utils.pair_visual may be removed on or after " "2019-04-24.") from cleverhans.plot.pyplot_image import pair_visual as new_pair_visual ...

def grid_visual(*args, **kwargs): """Deprecation wrapper""" warnings.warn("`grid_visual` has moved to `cleverhans.plot.pyplot_image`. " "cleverhans.utils.grid_visual may be removed on or after " "2019-04-24.") from cleverhans.plot.pyplot_image import grid_visual as new_grid_visual ...

def get_logits_over_interval(*args, **kwargs): """Deprecation wrapper""" warnings.warn("`get_logits_over_interval` has moved to " "`cleverhans.plot.pyplot_image`. " "cleverhans.utils.get_logits_over_interval may be removed on " "or after 2019-04-24.") # pylint:disab...

def linear_extrapolation_plot(*args, **kwargs): """Deprecation wrapper""" warnings.warn("`linear_extrapolation_plot` has moved to " "`cleverhans.plot.pyplot_image`. " "cleverhans.utils.linear_extrapolation_plot may be removed on " "or after 2019-04-24.") # pylint:di...

def create_logger(name): """ Create a logger object with the given name. If this is the first time that we call this method, then initialize the formatter. """ base = logging.getLogger("cleverhans") if len(base.handlers) == 0: ch = logging.StreamHandler() formatter = logging.Formatter('[%(levelna...

def deterministic_dict(normal_dict): """ Returns a version of `normal_dict` whose iteration order is always the same """ out = OrderedDict() for key in sorted(normal_dict.keys()): out[key] = normal_dict[key] return out

def ordered_union(l1, l2): """ Return the union of l1 and l2, with a deterministic ordering. (Union of python sets does not necessarily have a consisten iteration order) :param l1: list of items :param l2: list of items :returns: list containing one copy of each item that is in l1 or in l2 """ out = [...

def safe_zip(*args): """like zip but with these properties: - returns a list, rather than an iterator. This is the old Python2 zip behavior. - a guarantee that all arguments are the same length. (normal zip silently drops entries to make them the same length) """ length = len(args[0]) if not all(len(arg) ...

def shell_call(command, **kwargs): """Calls shell command with argument substitution. Args: command: command represented as a list. Each element of the list is one token of the command. For example "cp a b" becomes ['cp', 'a', 'b'] If any element of the list looks like '${NAME}' then it will be rep...

def deep_copy(numpy_dict): """ Returns a copy of a dictionary whose values are numpy arrays. Copies their values rather than copying references to them. """ out = {} for key in numpy_dict: out[key] = numpy_dict[key].copy() return out

def data_mnist(datadir=tempfile.gettempdir(), train_start=0, train_end=60000, test_start=0, test_end=10000): """ Load and preprocess MNIST dataset :param datadir: path to folder where data should be stored :param train_start: index of first training set example :param train_end: index of last t...

def data_cifar10(train_start=0, train_end=50000, test_start=0, test_end=10000): """ Preprocess CIFAR10 dataset :return: """ # These values are specific to CIFAR10 img_rows = 32 img_cols = 32 nb_classes = 10 # the data, shuffled and split between train and test sets (x_train, y_train), (x_test, y_...

def print_accuracies(filepath, train_start=TRAIN_START, train_end=TRAIN_END, test_start=TEST_START, test_end=TEST_END, batch_size=BATCH_SIZE, which_set=WHICH_SET, base_eps_iter=BASE_EPS_ITER, nb_iter=NB_ITER): """ Load a saved model...

def impl(sess, model, dataset, factory, x_data, y_data, base_eps_iter=BASE_EPS_ITER, nb_iter=NB_ITER, batch_size=BATCH_SIZE): """ The actual implementation of the evaluation. :param sess: tf.Session :param model: cleverhans.model.Model :param dataset: cleverhans.dataset.Dataset :param fact...

def main(argv=None): """ Print accuracies """ try: _name_of_script, filepath = argv except ValueError: raise ValueError(argv) print_accuracies(filepath=filepath, test_start=FLAGS.test_start, test_end=FLAGS.test_end, which_set=FLAGS.which_set, nb_iter=FLAGS.nb_it...

def fast_gradient_method(model_fn, x, eps, ord, clip_min=None, clip_max=None, y=None, targeted=False, sanity_checks=False): """ PyTorch implementation of the Fast Gradient Method. :param model_fn: a callable that takes an input tensor and returns the model logits. :param x: input tensor...

def load_images(input_dir, batch_shape): """Read png images from input directory in batches. Args: input_dir: input directory batch_shape: shape of minibatch array, i.e. [batch_size, height, width, 3] Yields: filenames: list file names without path of each image Lenght of this list could be le...

def save_images(images, filenames, output_dir): """Saves images to the output directory. Args: images: array with minibatch of images filenames: list of filenames without path If number of file names in this list less than number of images in the minibatch then only first len(filenames) images ...

def main(_): """Run the sample attack""" # Images for inception classifier are normalized to be in [-1, 1] interval, # eps is a difference between pixels so it should be in [0, 2] interval. # Renormalizing epsilon from [0, 255] to [0, 2]. eps = 2.0 * FLAGS.max_epsilon / 255.0 batch_shape = [FLAGS.batch_size...

def ld_cifar10(): """Load training and test data.""" train_transforms = torchvision.transforms.Compose([torchvision.transforms.ToTensor()]) test_transforms = torchvision.transforms.Compose([torchvision.transforms.ToTensor()]) train_dataset = torchvision.datasets.CIFAR10(root='/tmp/data', train=True, transform=t...

def plot_report_from_path(path, success_name=DEFAULT_SUCCESS_NAME, fail_names=DEFAULT_FAIL_NAMES, label=None, is_max_confidence=True, linewidth=LINEWIDTH, plot_upper_bound=True): """ Plots a success-fail curve fr...

def plot_report(report, success_name, fail_names, label=None, is_max_confidence=True, linewidth=LINEWIDTH, plot_upper_bound=True): """ Plot a success fail curve from a confidence report :param report: A confidence report (the type of object saved by make_confide...

def make_curve(report, success_name, fail_names): """ Make a success-failure curve. :param report: A confidence report (the type of object saved by make_confidence_report.py) :param success_name: see plot_report_from_path :param fail_names: see plot_report_from_path :returns: fail_optimal: list of f...

def model_train(self): """ Train a TF graph :param sess: TF session to use when training the graph :param x: input placeholder :param y: output placeholder (for labels) :param predictions: model output predictions :param X_train: numpy array with training inputs :param Y_train: numpy arr...

def clone_g0_inputs_on_ngpus(self, inputs, outputs, g0_inputs): """ Clone variables unused by the attack on all GPUs. Specifically, the ground-truth label, y, has to be preserved until the training step. :param inputs: A list of dictionaries as the inputs to each step. :param outputs: A list of dic...

def generate(self, x, **kwargs): """ Return a tensor that constructs adversarial examples for the given input. Generate uses tf.py_func in order to operate over tensors. :param x: (required) A tensor with the inputs. :param kwargs: See `parse_params` """ assert self.sess is not None, \ ...

def parse_params(self, y_target=None, batch_size=1, binary_search_steps=5, max_iterations=1000, initial_const=1e-2, clip_min=0, clip_max=1): """ :param y_target: (optional) A tens...

def attack(self, x_val, targets): """ Perform the attack on the given instance for the given targets. """ def lbfgs_objective(adv_x, self, targets, oimgs, CONST): """ returns the function value and the gradient for fmin_l_bfgs_b """ loss = self.sess.run( self.loss, feed_...

def set_device(self, device_name): """ Set the device before the next fprop to create a new graph on the specified device. """ device_name = unify_device_name(device_name) self.device_name = device_name for layer in self.layers: layer.device_name = device_name

def create_sync_ops(self, host_device): """ Return a list of assignment operations that syncs the parameters of all model copies with the one on host_device. :param host_device: (required str) the name of the device with latest parameters """ host_device = unify_device_na...

def get_variable(self, name, initializer): """ Create and initialize a variable using a numpy array and set trainable. :param name: (required str) name of the variable :param initializer: a numpy array or a tensor """ v = tf.get_variable(name, shape=initializer.shape, ini...

def set_input_shape_ngpu(self, new_input_shape): """ Create and initialize layer parameters on the device previously set in self.device_name. :param new_input_shape: a list or tuple for the shape of the input. """ assert self.device_name, "Device name has not been set." device_name = self....

def create_sync_ops(self, host_device): """Create an assignment operation for each weight on all devices. The weight is assigned the value of the copy on the `host_device'. """ sync_ops = [] host_params = self.params_device[host_device] for device, params in (self.params_device).iteritems(): ...

def vatm(model, x, logits, eps, num_iterations=1, xi=1e-6, clip_min=None, clip_max=None, scope=None): """ Tensorflow implementation of the perturbation method used for virtual adversarial training: https://arxiv.org/abs/1507.00677 :param mo...

def generate(self, x, **kwargs): """ Generate symbolic graph for adversarial examples and return. :param x: The model's symbolic inputs. :param kwargs: See `parse_params` """ # Parse and save attack-specific parameters assert self.parse_params(**kwargs) return vatm( self.model,...

def parse_params(self, eps=2.0, nb_iter=None, xi=1e-6, clip_min=None, clip_max=None, num_iterations=None, **kwargs): """ Take in a dictionary of parameters and applies attack-spec...

def iterate_with_exp_backoff(base_iter, max_num_tries=6, max_backoff=300.0, start_backoff=4.0, backoff_multiplier=2.0, frac_random_backoff=0.25): """Iterate with exponential...

def list_blobs(self, prefix=''): """Lists names of all blobs by their prefix.""" return [b.name for b in self.bucket.list_blobs(prefix=prefix)]

def begin(self): """Begins a batch.""" if self._cur_batch: raise ValueError('Previous batch is not committed.') self._cur_batch = self._client.batch() self._cur_batch.begin() self._num_mutations = 0

def rollback(self): """Rolls back pending mutations. Keep in mind that NoTransactionBatch splits all mutations into smaller batches and commit them as soon as mutation buffer reaches maximum length. That's why rollback method will only roll back pending mutations from the buffer, but won't be able ...

def put(self, entity): """Adds mutation of the entity to the mutation buffer. If mutation buffer reaches its capacity then this method commit all pending mutations from the buffer and emties it. Args: entity: entity which should be put into the datastore """ self._cur_batch.put(entity) ...

def delete(self, key): """Adds deletion of the entity with given key to the mutation buffer. If mutation buffer reaches its capacity then this method commit all pending mutations from the buffer and emties it. Args: key: key of the entity which should be deleted """ self._cur_batch.delet...

def get(self, key, transaction=None): """Retrieves an entity given its key.""" return self._client.get(key, transaction=transaction)

def mnist_tutorial_cw(train_start=0, train_end=60000, test_start=0, test_end=10000, viz_enabled=VIZ_ENABLED, nb_epochs=NB_EPOCHS, batch_size=BATCH_SIZE, source_samples=SOURCE_SAMPLES, learning_rate=LEARNING_RATE, ...

def attack_selection(attack_string): """ Selects the Attack Class using string input. :param attack_string: adversarial attack name in string format :return: attack class defined in cleverhans.attacks_eager """ # List of Implemented attacks attacks_list = AVAILABLE_ATTACKS.keys() # Checking for reque...

def mnist_tutorial(train_start=0, train_end=60000, test_start=0, test_end=10000, nb_epochs=NB_EPOCHS, batch_size=BATCH_SIZE, learning_rate=LEARNING_RATE, clean_train=True, testing=False, backprop_through_attack=False, ...

def sudo_remove_dirtree(dir_name): """Removes directory tree as a superuser. Args: dir_name: name of the directory to remove. This function is necessary to cleanup directories created from inside a Docker, since they usually written as a root, thus have to be removed as a root. """ try: subproce...

def main(args): """Main function which runs worker.""" title = '## Starting evaluation of round {0} ##'.format(args.round_name) logging.info('\n' + '#' * len(title) + '\n' + '#' * len(title) + '\n' + '##' + ' ' * (len(title)-2) + '##' + '\n' + title + '\...