Python errors.OpError方法代碼示例

# 需要導入模塊: from tensorflow.python.framework import errors [as 別名]
# 或者: from tensorflow.python.framework.errors import OpError [as 別名]
def GetTempDir():
  """Return a temporary directory for tests to use."""
  global _googletest_temp_dir
  if not _googletest_temp_dir:
    first_frame = tf_inspect.stack()[-1][0]
    temp_dir = os.path.join(tempfile.gettempdir(),
                            os.path.basename(tf_inspect.getfile(first_frame)))
    temp_dir = tempfile.mkdtemp(prefix=temp_dir.rstrip('.py'))

    def delete_temp_dir(dirname=temp_dir):
      try:
        file_io.delete_recursively(dirname)
      except errors.OpError as e:
        logging.error('Error removing %s: %s', dirname, e)

    atexit.register(delete_temp_dir)
    _googletest_temp_dir = temp_dir

  return _googletest_temp_dir 

# 需要導入模塊: from tensorflow.python.framework import errors [as 別名]
# 或者: from tensorflow.python.framework.errors import OpError [as 別名]
def _do_call(self, fn, *args):
    try:
      return fn(*args)
    except errors.OpError as e:
      message = compat.as_text(e.message)
      m = BaseSession._NODEDEF_NAME_RE.search(message)
      node_def = None
      op = None
      if m is not None:
        node_name = m.group(1)
        try:
          op = self._graph.get_operation_by_name(node_name)
          node_def = op.node_def
        except KeyError:
          pass
      raise type(e)(node_def, op, message) 

# 需要導入模塊: from tensorflow.python.framework import errors [as 別名]
# 或者: from tensorflow.python.framework.errors import OpError [as 別名]
def file_exists(filename):
  """Determines whether a path exists or not.

  Args:
    filename: string, a path

  Returns:
    True if the path exists, whether its a file or a directory.
    False if the path does not exist and there are no filesystem errors.

  Raises:
    errors.OpError: Propagates any errors reported by the FileSystem API.
  """
  try:
    with errors.raise_exception_on_not_ok_status() as status:
      pywrap_tensorflow.FileExists(compat.as_bytes(filename), status)
  except errors.NotFoundError:
    return False
  return True 

# 需要導入模塊: from tensorflow.python.framework import errors [as 別名]
# 或者: from tensorflow.python.framework.errors import OpError [as 別名]
def read_file_to_string(filename, binary_mode=False):
  """Reads the entire contents of a file to a string.

  Args:
    filename: string, path to a file
    binary_mode: whether to open the file in binary mode or not. This changes
        the type of the object returned.

  Returns:
    contents of the file as a string or bytes.

  Raises:
    errors.OpError: Raises variety of errors that are subtypes e.g.
    NotFoundError etc.
  """
  if binary_mode:
    f = FileIO(filename, mode="rb")
  else:
    f = FileIO(filename, mode="r")
  return f.read() 

# 需要導入模塊: from tensorflow.python.framework import errors [as 別名]
# 或者: from tensorflow.python.framework.errors import OpError [as 別名]
def stat(filename):
  """Returns file statistics for a given path.

  Args:
    filename: string, path to a file

  Returns:
    FileStatistics struct that contains information about the path

  Raises:
    errors.OpError: If the operation fails.
  """
  file_statistics = pywrap_tensorflow.FileStatistics()
  with errors.raise_exception_on_not_ok_status() as status:
    pywrap_tensorflow.Stat(compat.as_bytes(filename), file_statistics, status)
    return file_statistics 

# 需要導入模塊: from tensorflow.python.framework import errors [as 別名]
# 或者: from tensorflow.python.framework.errors import OpError [as 別名]
def Load(self):
    """Loads new values.

    The watcher will load from one path at a time; as soon as that path stops
    yielding events, it will move on to the next path. We assume that old paths
    are never modified after a newer path has been written. As a result, Load()
    can be called multiple times in a row without losing events that have not
    been yielded yet. In other words, we guarantee that every event will be
    yielded exactly once.

    Yields:
      All values that have not been yielded yet.

    Raises:
      DirectoryDeletedError: If the directory has been permanently deleted
        (as opposed to being temporarily unavailable).
    """
    try:
      for event in self._LoadInternal():
        yield event
    except errors.OpError:
      if not gfile.Exists(self._directory):
        raise DirectoryDeletedError(
            'Directory %s has been permanently deleted' % self._directory) 

# 需要導入模塊: from tensorflow.python.framework import errors [as 別名]
# 或者: from tensorflow.python.framework.errors import OpError [as 別名]
def _SetPath(self, path):
    """Sets the current path to watch for new events.

    This also records the size of the old path, if any. If the size can't be
    found, an error is logged.

    Args:
      path: The full path of the file to watch.
    """
    old_path = self._path
    if old_path and not io_wrapper.IsGCSPath(old_path):
      try:
        # We're done with the path, so store its size.
        size = gfile.Stat(old_path).length
        logging.debug('Setting latest size of %s to %d', old_path, size)
        self._finalized_sizes[old_path] = size
      except errors.OpError as e:
        logging.error('Unable to get size of %s: %s', old_path, e)

    self._path = path
    self._loader = self._loader_factory(path) 

# 需要導入模塊: from tensorflow.python.framework import errors [as 別名]
# 或者: from tensorflow.python.framework.errors import OpError [as 別名]
def get_matching_files(filename):
  """Returns a list of files that match the given pattern.

  Args:
    filename: string, the pattern

  Returns:
    Returns a list of strings containing filenames that match the given pattern.

  Raises:
    errors.OpError: If there are filesystem / directory listing errors.
  """
  with errors.raise_exception_on_not_ok_status() as status:
    # Convert each element to string, since the return values of the
    # vector of string should be interpreted as strings, not bytes.
    return [compat.as_str_any(matching_filename)
            for matching_filename in pywrap_tensorflow.GetMatchingFiles(
                compat.as_bytes(filename), status)] 

# 需要導入模塊: from tensorflow.python.framework import errors [as 別名]
# 或者: from tensorflow.python.framework.errors import OpError [as 別名]
def test_linear_model_mismatched_dense_values(self):
    column = fc.weighted_categorical_column(
        categorical_column=fc.categorical_column_with_identity(
            key='ids', num_buckets=3),
        weight_feature_key='values')
    with ops.Graph().as_default():
      model = linear.LinearModel((column,), sparse_combiner='mean')
      predictions = model({
          'ids':
              sparse_tensor.SparseTensorValue(
                  indices=((0, 0), (1, 0), (1, 1)),
                  values=(0, 2, 1),
                  dense_shape=(2, 2)),
          'values': ((.5,), (1.,))
      })
      # Disabling the constant folding optimizer here since it changes the
      # error message differently on CPU and GPU.
      config = config_pb2.ConfigProto()
      config.graph_options.rewrite_options.constant_folding = (
          rewriter_config_pb2.RewriterConfig.OFF)
      with _initialized_session(config):
        with self.assertRaisesRegexp(errors.OpError, 'Incompatible shapes'):
          self.evaluate(predictions) 

# 需要導入模塊: from tensorflow.python.framework import errors [as 別名]
# 或者: from tensorflow.python.framework.errors import OpError [as 別名]
def get_summary_writer(tensorboard_dir):
  """Creates a directory for writing summaries and returns a writer."""
  tf.logging.info('TensorBoard directory: %s', tensorboard_dir)
  tf.logging.info('Deleting prior data if exists...')
  try:
    gfile.DeleteRecursively(tensorboard_dir)
  except errors.OpError as err:
    tf.logging.error('Directory did not exist? Error: %s', err)
  tf.logging.info('Deleted! Creating the directory again...')
  gfile.MakeDirs(tensorboard_dir)
  tf.logging.info('Created! Instatiating SummaryWriter...')
  summary_writer = tf.summary.FileWriter(tensorboard_dir)
  return summary_writer 

# 需要導入模塊: from tensorflow.python.framework import errors [as 別名]
# 或者: from tensorflow.python.framework.errors import OpError [as 別名]
def __init__(self,
               performed_action,
               run_metadata=None,
               client_graph_def=None,
               tf_error=None):
    """Constructor for `OnRunEndRequest`.

    Args:
      performed_action: (`OnRunStartAction`) Actually-performed action by the
        debug-wrapper session.
      run_metadata: run_metadata output from the run() call (if any).
      client_graph_def: (GraphDef) GraphDef from the client side, i.e., from
        the python front end of TensorFlow. Can be obtained with
        session.graph.as_graph_def().
      tf_error: (errors.OpError subtypes) TensorFlow OpError that occurred
        during the run (if any).
    """

    _check_type(performed_action, str)
    self.performed_action = performed_action

    if run_metadata is not None:
      _check_type(run_metadata, config_pb2.RunMetadata)
    self.run_metadata = run_metadata
    self.client_graph_def = client_graph_def
    self.tf_error = tf_error 

# 需要導入模塊: from tensorflow.python.framework import errors [as 別名]
# 或者: from tensorflow.python.framework.errors import OpError [as 別名]
def close(self):
    """Closes this session.

    Calling this method frees all resources associated with the session.

    Raises:
      tf.errors.OpError: Or one of its subclasses if an error occurs while
        closing the TensorFlow session.
    """
    with self._extend_lock:
      if self._opened and not self._closed:
        self._closed = True
        with errors.raise_exception_on_not_ok_status() as status:
          tf_session.TF_CloseDeprecatedSession(self._session, status) 

# 需要導入模塊: from tensorflow.python.framework import errors [as 別名]
# 或者: from tensorflow.python.framework.errors import OpError [as 別名]
def __exit__(self, exec_type, exec_value, exec_tb):
    if exec_type is errors.OpError:
      logging.error('Session closing due to OpError: %s', (exec_value,))
    self._default_session_context_manager.__exit__(
        exec_type, exec_value, exec_tb)
    self._default_graph_context_manager.__exit__(exec_type, exec_value, exec_tb)

    self._default_session_context_manager = None
    self._default_graph_context_manager = None

    self.close() 

# 需要導入模塊: from tensorflow.python.framework import errors [as 別名]
# 或者: from tensorflow.python.framework.errors import OpError [as 別名]
def reset(target, containers=None, config=None):
    """Resets resource containers on `target`, and close all connected sessions.

    A resource container is distributed across all workers in the
    same cluster as `target`.  When a resource container on `target`
    is reset, resources associated with that container will be cleared.
    In particular, all Variables in the container will become undefined:
    they lose their values and shapes.

    NOTE:
    (i) reset() is currently only implemented for distributed sessions.
    (ii) Any sessions on the master named by `target` will be closed.

    If no resource containers are provided, all containers are reset.

    Args:
      target: The execution engine to connect to.
      containers: A list of resource container name strings, or `None` if all of
        all the containers are to be reset.
      config: (Optional.) Protocol buffer with configuration options.

    Raises:
      tf.errors.OpError: Or one of its subclasses if an error occurs while
        resetting containers.
    """
    if target is not None:
      target = compat.as_bytes(target)
    if containers is not None:
      containers = [compat.as_bytes(c) for c in containers]
    else:
      containers = []
    tf_session.TF_Reset(target, containers, config) 

# 需要導入模塊: from tensorflow.python.framework import errors [as 別名]
# 或者: from tensorflow.python.framework.errors import OpError [as 別名]
def delete_file(filename):
  """Deletes the file located at 'filename'.

  Args:
    filename: string, a filename

  Raises:
    errors.OpError: Propagates any errors reported by the FileSystem API.  E.g.,
    NotFoundError if the file does not exist.
  """
  with errors.raise_exception_on_not_ok_status() as status:
    pywrap_tensorflow.DeleteFile(compat.as_bytes(filename), status)