Python tensorflow.python.framework.errors.OpError() Examples

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 

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) 

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) 

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 

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) 

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) 

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) 

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) 

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() 

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 

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) 

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 

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) 

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) 

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)] 

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 

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 

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 

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 

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 

def test_runtime_batch_size_mismatch(self):
    price1 = fc.numeric_column('price1')
    price2 = fc.numeric_column('price2')
    with ops.Graph().as_default():
      features = {
          'price1': array_ops.placeholder(dtype=dtypes.int64),  # batchsize = 3
          'price2': [[3.], [4.]]  # batchsize = 2
      }
      model = linear.LinearModel([price1, price2])
      predictions = model(features)
      with _initialized_session() as sess:
        with self.assertRaisesRegexp(errors.OpError,
                                     'must have the same size and shape'):
          sess.run(
              predictions, feed_dict={features['price1']: [[1.], [5.], [7.]]}) 

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 

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 

def testRunWithError(self):
    """Test the debug tensor dumping when error occurs in graph runtime."""

    with session.Session() as sess:
      ph = tf.placeholder(tf.float32, name="mismatch/ph")
      x = tf.transpose(ph, name="mismatch/x")
      m = constant_op.constant(
          np.array(
              [[1.0, 2.0]], dtype=np.float32), name="mismatch/m")
      y = math_ops.matmul(m, x, name="mismatch/y")

      run_options = config_pb2.RunOptions(output_partition_graphs=True)
      debug_utils.watch_graph(
          run_options,
          sess.graph,
          debug_ops=["DebugIdentity"],
          debug_urls="file://%s" % self._dump_root)

      with self.assertRaises(errors.OpError):
        sess.run(y,
                 options=run_options,
                 feed_dict={ph: np.array([[-3.0], [0.0]])})

      dump = debug_data.DebugDumpDir(self._dump_root)
      self.assertFalse(dump.loaded_partition_graphs())

      m_dumps = dump.watch_key_to_data("mismatch/m:0:DebugIdentity")
      self.assertEqual(1, len(m_dumps))
      self.assertAllClose(np.array([[1.0, 2.0]]), m_dumps[0].get_tensor())

      x_dumps = dump.watch_key_to_data("mismatch/x:0:DebugIdentity")
      self.assertEqual(1, len(x_dumps))
      self.assertAllClose(np.array([[-3.0, 0.0]]), x_dumps[0].get_tensor()) 

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 

def rename(oldname, newname, overwrite=False):
  """Rename or move a file / directory.

  Args:
    oldname: string, pathname for a file
    newname: string, pathname to which the file needs to be moved
    overwrite: boolean, if false its an error for newpath to be occupied by an
        existing file.

  Raises:
    errors.OpError: If the operation fails.
  """
  with errors.raise_exception_on_not_ok_status() as status:
    pywrap_tensorflow.RenameFile(
        compat.as_bytes(oldname), compat.as_bytes(newname), overwrite, status) 

def assertRaisesOpError(self, expected_err_re_or_predicate):
    return self.assertRaisesWithPredicateMatch(errors.OpError,
                                               expected_err_re_or_predicate) 

def assertRaisesWithPredicateMatch(self, exception_type,
                                     expected_err_re_or_predicate):
    """Returns a context manager to enclose code expected to raise an exception.

    If the exception is an OpError, the op stack is also included in the message
    predicate search.

    Args:
      exception_type: The expected type of exception that should be raised.
      expected_err_re_or_predicate: If this is callable, it should be a function
        of one argument that inspects the passed-in exception and
        returns True (success) or False (please fail the test). Otherwise, the
        error message is expected to match this regular expression partially.

    Returns:
      A context manager to surround code that is expected to raise an
      exception.
    """
    if callable(expected_err_re_or_predicate):
      predicate = expected_err_re_or_predicate
    else:
      def predicate(e):
        err_str = e.message if isinstance(e, errors.OpError) else str(e)
        op = e.op if isinstance(e, errors.OpError) else None
        while op is not None:
          err_str += "\nCaused by: " + op.name
          op = op._original_op
        logging.info("Searching within error strings: '%s' within '%s'",
                     expected_err_re_or_predicate, err_str)
        return re.search(expected_err_re_or_predicate, err_str)
    try:
      yield
      self.fail(exception_type.__name__ + " not raised")
    except Exception as e:  # pylint: disable=broad-except
      if not isinstance(e, exception_type) or not predicate(e):
        raise AssertionError("Exception of type %s: %s" %
                             (str(type(e)), str(e)))
  # pylint: enable=g-doc-return-or-yield 

def delete_recursively(dirname):
  """Deletes everything under dirname recursively.

  Args:
    dirname: string, a path to a directory

  Raises:
    errors.OpError: If the operation fails.
  """
  with errors.raise_exception_on_not_ok_status() as status:
    pywrap_tensorflow.DeleteRecursively(compat.as_bytes(dirname), status) 

def copy(oldpath, newpath, overwrite=False):
  """Copies data from oldpath to newpath.

  Args:
    oldpath: string, name of the file who's contents need to be copied
    newpath: string, name of the file to which to copy to
    overwrite: boolean, if false its an error for newpath to be occupied by an
        existing file.

  Raises:
    errors.OpError: If the operation fails.
  """
  with errors.raise_exception_on_not_ok_status() as status:
    pywrap_tensorflow.CopyFile(
        compat.as_bytes(oldpath), compat.as_bytes(newpath), overwrite, status)