Python file_io.atomic_write_string_to_file方法代碼示例

# 需要導入模塊: from tensorflow.python.lib.io import file_io [as 別名]
# 或者: from tensorflow.python.lib.io.file_io import atomic_write_string_to_file [as 別名]
def upload_schema(self):  # type: () -> None
        if not self.schema:
            raise ValueError(
                "Cannot upload a schema since no schema_path was provided. Either provide one, or "
                "use write_stats_and_schema so that a schema can be inferred first."
            )
        file_io.atomic_write_string_to_file(self.schema_snapshot_path,
                                            self.schema.SerializeToString()) 

# 需要導入模塊: from tensorflow.python.lib.io import file_io [as 別名]
# 或者: from tensorflow.python.lib.io.file_io import atomic_write_string_to_file [as 別名]
def upload_anomalies(self):  # type: () -> None
        if self.anomalies.anomaly_info:
            file_io.atomic_write_string_to_file(self.anomalies_path,
                                                self.anomalies.SerializeToString()) 

# 需要導入模塊: from tensorflow.python.lib.io import file_io [as 別名]
# 或者: from tensorflow.python.lib.io.file_io import atomic_write_string_to_file [as 別名]
def _SetState(self, state):
    file_io.atomic_write_string_to_file(self._state_file,
                                        text_format.MessageToString(state)) 

# 需要導入模塊: from tensorflow.python.lib.io import file_io [as 別名]
# 或者: from tensorflow.python.lib.io.file_io import atomic_write_string_to_file [as 別名]
def update_checkpoint_state(save_dir,
                            model_checkpoint_path,
                            all_model_checkpoint_paths=None,
                            latest_filename=None):
  """Updates the content of the 'checkpoint' file.

  This updates the checkpoint file containing a CheckpointState
  proto.

  Args:
    save_dir: Directory where the model was saved.
    model_checkpoint_path: The checkpoint file.
    all_model_checkpoint_paths: List of strings.  Paths to all not-yet-deleted
      checkpoints, sorted from oldest to newest.  If this is a non-empty list,
      the last element must be equal to model_checkpoint_path.  These paths
      are also saved in the CheckpointState proto.
    latest_filename: Optional name of the checkpoint file.  Default to
      'checkpoint'.

  Raises:
    RuntimeError: If the save paths conflict.
  """
  # Writes the "checkpoint" file for the coordinator for later restoration.
  coord_checkpoint_filename = _GetCheckpointFilename(save_dir, latest_filename)
  ckpt = generate_checkpoint_state_proto(
      save_dir,
      model_checkpoint_path,
      all_model_checkpoint_paths=all_model_checkpoint_paths)

  if coord_checkpoint_filename == ckpt.model_checkpoint_path:
    raise RuntimeError("Save path '%s' conflicts with path used for "
                       "checkpoint state.  Please use a different save path." %
                       model_checkpoint_path)

  # Preventing potential read/write race condition by *atomically* writing to a
  # file.
  file_io.atomic_write_string_to_file(coord_checkpoint_filename,
                                      text_format.MessageToString(ckpt)) 

# 需要導入模塊: from tensorflow.python.lib.io import file_io [as 別名]
# 或者: from tensorflow.python.lib.io.file_io import atomic_write_string_to_file [as 別名]
def write_graph(graph_or_graph_def, logdir, name, as_text=True):
  """Writes a graph proto to a file.

  The graph is written as a binary proto unless `as_text` is `True`.

  ```python
  v = tf.Variable(0, name='my_variable')
  sess = tf.Session()
  tf.train.write_graph(sess.graph_def, '/tmp/my-model', 'train.pbtxt')
  ```

  or

  ```python
  v = tf.Variable(0, name='my_variable')
  sess = tf.Session()
  tf.train.write_graph(sess.graph, '/tmp/my-model', 'train.pbtxt')
  ```

  Args:
    graph_or_graph_def: A `Graph` or a `GraphDef` protocol buffer.
    logdir: Directory where to write the graph. This can refer to remote
      filesystems, such as Google Cloud Storage (GCS).
    name: Filename for the graph.
    as_text: If `True`, writes the graph as an ASCII proto.
  """
  if isinstance(graph_or_graph_def, ops.Graph):
    graph_def = graph_or_graph_def.as_graph_def()
  else:
    graph_def = graph_or_graph_def

  # gcs does not have the concept of directory at the moment.
  if not file_io.file_exists(logdir) and not logdir.startswith('gs:'):
    file_io.recursive_create_dir(logdir)
  path = os.path.join(logdir, name)
  if as_text:
    file_io.atomic_write_string_to_file(path, str(graph_def))
  else:
    file_io.atomic_write_string_to_file(path, graph_def.SerializeToString()) 

# 需要導入模塊: from tensorflow.python.lib.io import file_io [as 別名]
# 或者: from tensorflow.python.lib.io.file_io import atomic_write_string_to_file [as 別名]
def write_metadata(metadata, path):
  """Write metadata to given path, in JSON format.

  Args:
    metadata: A `DatasetMetadata` to write.
    path: a path to a directory where metadata should be written.
  """
  if not file_io.file_exists(path):
    file_io.recursive_create_dir(path)
  schema_file = os.path.join(path, 'schema.pbtxt')
  ascii_proto = text_format.MessageToString(metadata.schema)
  file_io.atomic_write_string_to_file(schema_file, ascii_proto, overwrite=True) 

# 需要導入模塊: from tensorflow.python.lib.io import file_io [as 別名]
# 或者: from tensorflow.python.lib.io.file_io import atomic_write_string_to_file [as 別名]
def _update_checkpoint_state(save_dir,
                             model_checkpoint_path,
                             all_model_checkpoint_paths=None,
                             latest_filename=None,
                             save_relative_paths=False):
  """Updates the content of the 'checkpoint' file.

  This updates the checkpoint file containing a CheckpointState
  proto.

  Args:
    save_dir: Directory where the model was saved.
    model_checkpoint_path: The checkpoint file.
    all_model_checkpoint_paths: List of strings.  Paths to all not-yet-deleted
      checkpoints, sorted from oldest to newest.  If this is a non-empty list,
      the last element must be equal to model_checkpoint_path.  These paths
      are also saved in the CheckpointState proto.
    latest_filename: Optional name of the checkpoint file.  Default to
      'checkpoint'.
    save_relative_paths: If `True`, will write relative paths to the checkpoint
      state file.

  Raises:
    RuntimeError: If any of the model checkpoint paths conflict with the file
      containing CheckpointSate.
  """
  # Writes the "checkpoint" file for the coordinator for later restoration.
  coord_checkpoint_filename = _GetCheckpointFilename(save_dir, latest_filename)
  if save_relative_paths:
    if os.path.isabs(model_checkpoint_path):
      rel_model_checkpoint_path = os.path.relpath(
          model_checkpoint_path, save_dir)
    else:
      rel_model_checkpoint_path = model_checkpoint_path
    rel_all_model_checkpoint_paths = []
    for p in all_model_checkpoint_paths:
      if os.path.isabs(p):
        rel_all_model_checkpoint_paths.append(os.path.relpath(p, save_dir))
      else:
        rel_all_model_checkpoint_paths.append(p)
    ckpt = generate_checkpoint_state_proto(
        save_dir,
        rel_model_checkpoint_path,
        all_model_checkpoint_paths=rel_all_model_checkpoint_paths)
  else:
    ckpt = generate_checkpoint_state_proto(
        save_dir,
        model_checkpoint_path,
        all_model_checkpoint_paths=all_model_checkpoint_paths)

  if coord_checkpoint_filename == ckpt.model_checkpoint_path:
    raise RuntimeError("Save path '%s' conflicts with path used for "
                       "checkpoint state.  Please use a different save path." %
                       model_checkpoint_path)

  # Preventing potential read/write race condition by *atomically* writing to a
  # file.
  file_io.atomic_write_string_to_file(coord_checkpoint_filename,
                                      text_format.MessageToString(ckpt)) 

# 需要導入模塊: from tensorflow.python.lib.io import file_io [as 別名]
# 或者: from tensorflow.python.lib.io.file_io import atomic_write_string_to_file [as 別名]
def write_graph(graph_or_graph_def, logdir, name, as_text=True):
  """Writes a graph proto to a file.

  The graph is written as a binary proto unless `as_text` is `True`.

  ```python
  v = tf.Variable(0, name='my_variable')
  sess = tf.Session()
  tf.train.write_graph(sess.graph_def, '/tmp/my-model', 'train.pbtxt')
  ```

  or

  ```python
  v = tf.Variable(0, name='my_variable')
  sess = tf.Session()
  tf.train.write_graph(sess.graph, '/tmp/my-model', 'train.pbtxt')
  ```

  Args:
    graph_or_graph_def: A `Graph` or a `GraphDef` protocol buffer.
    logdir: Directory where to write the graph. This can refer to remote
      filesystems, such as Google Cloud Storage (GCS).
    name: Filename for the graph.
    as_text: If `True`, writes the graph as an ASCII proto.

  Returns:
    The path of the output proto file.
  """
  if isinstance(graph_or_graph_def, ops.Graph):
    graph_def = graph_or_graph_def.as_graph_def()
  else:
    graph_def = graph_or_graph_def

  # gcs does not have the concept of directory at the moment.
  if not file_io.file_exists(logdir) and not logdir.startswith('gs:'):
    file_io.recursive_create_dir(logdir)
  path = os.path.join(logdir, name)
  if as_text:
    file_io.atomic_write_string_to_file(path, str(graph_def))
  else:
    file_io.atomic_write_string_to_file(path, graph_def.SerializeToString())
  return path 

# 需要導入模塊: from tensorflow.python.lib.io import file_io [as 別名]
# 或者: from tensorflow.python.lib.io.file_io import atomic_write_string_to_file [as 別名]
def write_graph(graph_or_graph_def, logdir, name, as_text=True):
  """Writes a graph proto to a file.

  The graph is written as a text proto unless `as_text` is `False`.

  ```python
  v = tf.Variable(0, name='my_variable')
  sess = tf.Session()
  tf.train.write_graph(sess.graph_def, '/tmp/my-model', 'train.pbtxt')
  ```

  or

  ```python
  v = tf.Variable(0, name='my_variable')
  sess = tf.Session()
  tf.train.write_graph(sess.graph, '/tmp/my-model', 'train.pbtxt')
  ```

  Args:
    graph_or_graph_def: A `Graph` or a `GraphDef` protocol buffer.
    logdir: Directory where to write the graph. This can refer to remote
      filesystems, such as Google Cloud Storage (GCS).
    name: Filename for the graph.
    as_text: If `True`, writes the graph as an ASCII proto.

  Returns:
    The path of the output proto file.
  """
  if isinstance(graph_or_graph_def, ops.Graph):
    graph_def = graph_or_graph_def.as_graph_def()
  else:
    graph_def = graph_or_graph_def

  # gcs does not have the concept of directory at the moment.
  if not file_io.file_exists(logdir) and not logdir.startswith('gs:'):
    file_io.recursive_create_dir(logdir)
  path = os.path.join(logdir, name)
  if as_text:
    file_io.atomic_write_string_to_file(path,
                                        text_format.MessageToString(graph_def))
  else:
    file_io.atomic_write_string_to_file(path, graph_def.SerializeToString())
  return path