Python gfile.IsDirectory方法代碼示例

# 需要導入模塊: from tensorflow.python.platform import gfile [as 別名]
# 或者: from tensorflow.python.platform.gfile import IsDirectory [as 別名]
def __init__(self, dump_root, partition_graphs=None, validate=True):
    """`DebugDumpDir` constructor.

    Args:
      dump_root: (`str`) path to the dump root directory.
      partition_graphs: A repeated field of GraphDefs representing the
          partition graphs executed by the TensorFlow runtime.
      validate: (`bool`) whether the dump files are to be validated against the
          partition graphs.

    Raises:
      IOError: If dump_root does not exist as a directory.
    """

    if not gfile.IsDirectory(dump_root):
      raise IOError("Dump root directory %s does not exist" % dump_root)

    self._core_metadata = None
    self._load_dumps(dump_root)
    self._create_tensor_watch_maps()
    self._load_partition_graphs(partition_graphs, validate)

    self._python_graph = None 

# 需要導入模塊: from tensorflow.python.platform import gfile [as 別名]
# 或者: from tensorflow.python.platform.gfile import IsDirectory [as 別名]
def ListPlugins(logdir):
  """List all the plugins that have registered assets in logdir.

  If the plugins_dir does not exist, it returns an empty list. This maintains
  compatibility with old directories that have no plugins written.

  Args:
    logdir: A directory that was created by a TensorFlow events writer.

  Returns:
    a list of plugin names, as strings
  """
  plugins_dir = os.path.join(logdir, _PLUGINS_DIR)
  if not gfile.IsDirectory(plugins_dir):
    return []
  entries = gfile.ListDirectory(plugins_dir)
  return [x for x in entries if _IsDirectory(plugins_dir, x)] 

# 需要導入模塊: from tensorflow.python.platform import gfile [as 別名]
# 或者: from tensorflow.python.platform.gfile import IsDirectory [as 別名]
def ListAssets(logdir, plugin_name):
  """List all the assets that are available for given plugin in a logdir.

  Args:
    logdir: A directory that was created by a TensorFlow summary.FileWriter.
    plugin_name: A string name of a plugin to list assets for.

  Returns:
    A string list of available plugin assets. If the plugin subdirectory does
    not exist (either because the logdir doesn't exist, or because the plugin
    didn't register) an empty list is returned.
  """
  plugin_dir = PluginDirectory(logdir, plugin_name)
  if not gfile.IsDirectory(plugin_dir):
    return []
  entries = gfile.ListDirectory(plugin_dir)
  return [x for x in entries if not _IsDirectory(plugin_dir, x)] 

# 需要導入模塊: from tensorflow.python.platform import gfile [as 別名]
# 或者: from tensorflow.python.platform.gfile import IsDirectory [as 別名]
def __init__(self, dump_root, partition_graphs=None, validate=True):
    """`DebugDumpDir` constructor.

    Args:
      dump_root: (`str`) path to the dump root directory.
      partition_graphs: A repeated field of GraphDefs representing the
          partition graphs executed by the TensorFlow runtime.
      validate: (`bool`) whether the dump files are to be validated against the
          partition graphs.

    Raises:
      IOError: If dump_root does not exist as a directory.
    """

    if not gfile.IsDirectory(dump_root):
      raise IOError("Dump root directory %s does not exist" % dump_root)

    self._load_dumps(dump_root)
    self._create_tensor_watch_maps()
    self._load_partition_graphs(partition_graphs, validate)

    self._python_graph = None 

# 需要導入模塊: from tensorflow.python.platform import gfile [as 別名]
# 或者: from tensorflow.python.platform.gfile import IsDirectory [as 別名]
def _get_checkpoint_filename(ckpt_dir_or_file):
  """Returns checkpoint filename given directory or specific checkpoint file."""
  if gfile.IsDirectory(ckpt_dir_or_file):
    return saver.latest_checkpoint(ckpt_dir_or_file)
  return ckpt_dir_or_file 

# 需要導入模塊: from tensorflow.python.platform import gfile [as 別名]
# 或者: from tensorflow.python.platform.gfile import IsDirectory [as 別名]
def __init__(self, logdir, max_queue=10, flush_secs=120,
               filename_suffix=None):
    """Creates a `EventFileWriter` and an event file to write to.

    On construction the summary writer creates a new event file in `logdir`.
    This event file will contain `Event` protocol buffers, which are written to
    disk via the add_event method.

    The other arguments to the constructor control the asynchronous writes to
    the event file:

    *  `flush_secs`: How often, in seconds, to flush the added summaries
       and events to disk.
    *  `max_queue`: Maximum number of summaries or events pending to be
       written to disk before one of the 'add' calls block.

    Args:
      logdir: A string. Directory where event file will be written.
      max_queue: Integer. Size of the queue for pending events and summaries.
      flush_secs: Number. How often, in seconds, to flush the
        pending events and summaries to disk.
      filename_suffix: A string. Every event file's name is suffixed with
        `filename_suffix`.
    """
    self._logdir = logdir
    if not gfile.IsDirectory(self._logdir):
      gfile.MakeDirs(self._logdir)
    self._event_queue = six.moves.queue.Queue(max_queue)
    self._ev_writer = pywrap_tensorflow.EventsWriter(
        compat.as_bytes(os.path.join(self._logdir, "events")))
    self._flush_secs = flush_secs
    self._sentinel_event = self._get_sentinel_event()
    if filename_suffix:
      self._ev_writer.InitWithSuffix(compat.as_bytes(filename_suffix))
    self._closed = False
    self._worker = _EventLoggerThread(self._event_queue, self._ev_writer,
                                      self._flush_secs, self._sentinel_event)

    self._worker.start() 

# 需要導入模塊: from tensorflow.python.platform import gfile [as 別名]
# 或者: from tensorflow.python.platform.gfile import IsDirectory [as 別名]
def _IsDirectory(parent, item):
  """Helper that returns if parent/item is a directory."""
  return gfile.IsDirectory(os.path.join(parent, item)) 

# 需要導入模塊: from tensorflow.python.platform import gfile [as 別名]
# 或者: from tensorflow.python.platform.gfile import IsDirectory [as 別名]
def _get_checkpoint_filename(filepattern):
  """Returns checkpoint filename given directory or specific filepattern."""
  if gfile.IsDirectory(filepattern):
    return saver.latest_checkpoint(filepattern)
  return filepattern 

# 需要導入模塊: from tensorflow.python.platform import gfile [as 別名]
# 或者: from tensorflow.python.platform.gfile import IsDirectory [as 別名]
def testConstructWrapperWithExistingFileDumpRootRaisesException(self):
    file_path = os.path.join(self.session_root, "foo")
    open(file_path, "a").close()  # Create the file
    self.assertTrue(gfile.Exists(file_path))
    self.assertFalse(gfile.IsDirectory(file_path))
    with self.assertRaisesRegexp(ValueError,
                                 "session_root path points to a file"):
      dumping_wrapper.DumpingDebugWrapperSession(
          session.Session(), session_root=file_path, log_usage=False) 

# 需要導入模塊: from tensorflow.python.platform import gfile [as 別名]
# 或者: from tensorflow.python.platform.gfile import IsDirectory [as 別名]
def __init__(self, sess, session_root, watch_fn=None, log_usage=True):
    """Constructor of DumpingDebugWrapperSession.

    Args:
      sess: The TensorFlow `Session` object being wrapped.
      session_root: (`str`) Path to the session root directory. Must be a
        directory that does not exist or an empty directory. If the directory
        does not exist, it will be created by the debugger core during debug
        [`Session.run()`](../../../g3doc/api_docs/python/client.md#session.run)
        calls.
        As the `run()` calls occur, subdirectories will be added to
        `session_root`. The subdirectories' names has the following pattern:
          run_<epoch_time_stamp>_<uuid>
        E.g., run_1480734393835964_ad4c953a85444900ae79fc1b652fb324
      watch_fn: (`Callable`) A Callable that can be used to define per-run
        debug ops and watched tensors. See the doc of
        `NonInteractiveDebugWrapperSession.__init__()` for details.
      log_usage: (`bool`) whether the usage of this class is to be logged.

    Raises:
       ValueError: If `session_root` is an existing and non-empty directory or
       if `session_root` is a file.
    """

    if log_usage:
      pass  # No logging for open-source.

    framework.NonInteractiveDebugWrapperSession.__init__(
        self, sess, watch_fn=watch_fn)

    if gfile.Exists(session_root):
      if not gfile.IsDirectory(session_root):
        raise ValueError(
            "session_root path points to a file: %s" % session_root)
      elif gfile.ListDirectory(session_root):
        raise ValueError(
            "session_root path points to a non-empty directory: %s" %
            session_root)
    self._session_root = session_root 

# 需要導入模塊: from tensorflow.python.platform import gfile [as 別名]
# 或者: from tensorflow.python.platform.gfile import IsDirectory [as 別名]
def __init__(self, logdir, max_queue=10, flush_secs=120):
    """Creates a `EventFileWriter` and an event file to write to.

    On construction the summary writer creates a new event file in `logdir`.
    This event file will contain `Event` protocol buffers, which are written to
    disk via the add_event method.

    The other arguments to the constructor control the asynchronous writes to
    the event file:

    *  `flush_secs`: How often, in seconds, to flush the added summaries
       and events to disk.
    *  `max_queue`: Maximum number of summaries or events pending to be
       written to disk before one of the 'add' calls block.

    Args:
      logdir: A string. Directory where event file will be written.
      max_queue: Integer. Size of the queue for pending events and summaries.
      flush_secs: Number. How often, in seconds, to flush the
        pending events and summaries to disk.
    """
    self._logdir = logdir
    if not gfile.IsDirectory(self._logdir):
      gfile.MakeDirs(self._logdir)
    self._event_queue = six.moves.queue.Queue(max_queue)
    self._ev_writer = pywrap_tensorflow.EventsWriter(
        compat.as_bytes(os.path.join(self._logdir, "events")))
    self._closed = False
    self._worker = _EventLoggerThread(self._event_queue, self._ev_writer,
                                      flush_secs)

    self._worker.start() 

# 需要導入模塊: from tensorflow.python.platform import gfile [as 別名]
# 或者: from tensorflow.python.platform.gfile import IsDirectory [as 別名]
def _get_checkpoint_filename(ckpt_dir_or_file):
  """Returns checkpoint filename given directory or specific checkpoint file."""
  if gfile.IsDirectory(ckpt_dir_or_file):
    return checkpoint_management.latest_checkpoint(ckpt_dir_or_file)
  return ckpt_dir_or_file 

# 需要導入模塊: from tensorflow.python.platform import gfile [as 別名]
# 或者: from tensorflow.python.platform.gfile import IsDirectory [as 別名]
def testGraphFromMetaGraphBecomesAvailable(self):
    """Test accumulator by writing values and then reading them."""

    directory = os.path.join(self.get_temp_dir(), 'metagraph_test_values_dir')
    if gfile.IsDirectory(directory):
      gfile.DeleteRecursively(directory)
    gfile.MkDir(directory)

    writer = tf.train.SummaryWriter(directory, max_queue=100)

    with tf.Graph().as_default() as graph:
      _ = tf.constant([2.0, 1.0])
    # Add a graph to the summary writer.
    meta_graph_def = saver.export_meta_graph(
        graph_def=graph.as_graph_def(add_shapes=True))
    writer.add_meta_graph(meta_graph_def)

    writer.flush()

    # Verify that we can load those events properly
    acc = ea.EventAccumulator(directory)
    acc.Reload()
    self.assertTagsEqual(
        acc.Tags(),
        {
            ea.IMAGES: [],
            ea.AUDIO: [],
            ea.SCALARS: [],
            ea.HISTOGRAMS: [],
            ea.COMPRESSED_HISTOGRAMS: [],
            ea.GRAPH: True,
            ea.META_GRAPH: True,
            ea.RUN_METADATA: []
        })
    self.assertProtoEquals(graph.as_graph_def(add_shapes=True), acc.Graph())
    self.assertProtoEquals(meta_graph_def, acc.MetaGraph()) 

# 需要導入模塊: from tensorflow.python.platform import gfile [as 別名]
# 或者: from tensorflow.python.platform.gfile import IsDirectory [as 別名]
def GetLogdirSubdirectories(path):
  """Returns subdirectories with event files on path."""
  if gfile.Exists(path) and not gfile.IsDirectory(path):
    raise ValueError('GetLogdirSubdirectories: path exists and is not a '
                     'directory, %s' % path)

  # ListRecursively just yields nothing if the path doesn't exist.
  return (
      subdir
      for (subdir, files) in io_wrapper.ListRecursively(path)
      if list(filter(event_accumulator.IsTensorFlowEventsFile, files))
  ) 

# 需要導入模塊: from tensorflow.python.platform import gfile [as 別名]
# 或者: from tensorflow.python.platform.gfile import IsDirectory [as 別名]
def _AddEvents(path):
  if not gfile.IsDirectory(path):
    gfile.MakeDirs(path)
  fpath = os.path.join(path, 'hypothetical.tfevents.out')
  with gfile.GFile(fpath, 'w') as f:
    f.write('')
    return fpath