Python pywrap_tensorflow.TF_Message方法代码示例

# 需要导入模块: from tensorflow.python import pywrap_tensorflow [as 别名]
# 或者: from tensorflow.python.pywrap_tensorflow import TF_Message [as 别名]
def raise_exception_on_not_ok_status():
  status = pywrap_tensorflow.TF_NewStatus()
  try:
    yield status
    if pywrap_tensorflow.TF_GetCode(status) != 0:
      raise _make_specific_exception(
          None, None,
          compat.as_text(pywrap_tensorflow.TF_Message(status)),
          pywrap_tensorflow.TF_GetCode(status))
  finally:
    pywrap_tensorflow.TF_DeleteStatus(status) 

# 需要导入模块: from tensorflow.python import pywrap_tensorflow [as 别名]
# 或者: from tensorflow.python.pywrap_tensorflow import TF_Message [as 别名]
def load_file_system_library(library_filename):
  """Loads a TensorFlow plugin, containing file system implementation.

  Pass `library_filename` to a platform-specific mechanism for dynamically
  loading a library. The rules for determining the exact location of the
  library are platform-specific and are not documented here.

  Args:
    library_filename: Path to the plugin.
      Relative or absolute filesystem path to a dynamic library file.

  Returns:
    None.

  Raises:
    RuntimeError: when unable to load the library.
  """
  status = py_tf.TF_NewStatus()
  lib_handle = py_tf.TF_LoadLibrary(library_filename, status)
  try:
    error_code = py_tf.TF_GetCode(status)
    if error_code != 0:
      error_msg = compat.as_text(py_tf.TF_Message(status))
      # pylint: disable=protected-access
      raise errors_impl._make_specific_exception(
          None, None, error_msg, error_code)
      # pylint: enable=protected-access
  finally:
    py_tf.TF_DeleteStatus(status) 

# 需要导入模块: from tensorflow.python import pywrap_tensorflow [as 别名]
# 或者: from tensorflow.python.pywrap_tensorflow import TF_Message [as 别名]
def raise_exception_on_not_ok_status():
  try:
    status = pywrap_tensorflow.TF_NewStatus()
    yield status
    if pywrap_tensorflow.TF_GetCode(status) != 0:
      raise _make_specific_exception(
          None, None,
          compat.as_text(pywrap_tensorflow.TF_Message(status)),
          pywrap_tensorflow.TF_GetCode(status))
  finally:
    pywrap_tensorflow.TF_DeleteStatus(status) 

# 需要导入模块: from tensorflow.python import pywrap_tensorflow [as 别名]
# 或者: from tensorflow.python.pywrap_tensorflow import TF_Message [as 别名]
def __exit__(self, type_arg, value_arg, traceback_arg):
    try:
      if c_api.TF_GetCode(self.status.status) != 0:
        raise _make_specific_exception(
            None, None,
            compat.as_text(c_api.TF_Message(self.status.status)),
            c_api.TF_GetCode(self.status.status))
    # Delete the underlying status object from memory otherwise it stays alive
    # as there is a reference to status from this from the traceback due to
    # raise.
    finally:
      del self.status
    return False  # False values do not suppress exceptions 

# 需要导入模块: from tensorflow.python import pywrap_tensorflow [as 别名]
# 或者: from tensorflow.python.pywrap_tensorflow import TF_Message [as 别名]
def load_op_library(library_filename):
  """Loads a TensorFlow plugin, containing custom ops and kernels.

  Pass "library_filename" to a platform-specific mechanism for dynamically
  loading a library. The rules for determining the exact location of the
  library are platform-specific and are not documented here. When the
  library is loaded, ops and kernels registered in the library via the
  `REGISTER_*` macros are made available in the TensorFlow process. Note
  that ops with the same name as an existing op are rejected and not
  registered with the process.

  Args:
    library_filename: Path to the plugin.
      Relative or absolute filesystem path to a dynamic library file.

  Returns:
    A python module containing the Python wrappers for Ops defined in
    the plugin.

  Raises:
    RuntimeError: when unable to load the library or get the python wrappers.
  """
  status = py_tf.TF_NewStatus()

  lib_handle = py_tf.TF_LoadLibrary(library_filename, status)
  try:
    error_code = py_tf.TF_GetCode(status)
    if error_code != 0:
      error_msg = compat.as_text(py_tf.TF_Message(status))
      # pylint: disable=protected-access
      raise errors_impl._make_specific_exception(
          None, None, error_msg, error_code)
      # pylint: enable=protected-access
  finally:
    py_tf.TF_DeleteStatus(status)

  op_list_str = py_tf.TF_GetOpList(lib_handle)
  op_list = op_def_pb2.OpList()
  op_list.ParseFromString(compat.as_bytes(op_list_str))
  wrappers = py_tf.GetPythonWrappers(op_list_str)

  # Delete the library handle to release any memory held in C
  # that are no longer needed.
  py_tf.TF_DeleteLibraryHandle(lib_handle)

  # Get a unique name for the module.
  module_name = hashlib.md5(wrappers).hexdigest()
  if module_name in sys.modules:
    return sys.modules[module_name]
  module = imp.new_module(module_name)
  # pylint: disable=exec-used
  exec(wrappers, module.__dict__)
  # Stash away the library handle for making calls into the dynamic library.
  module.LIB_HANDLE = lib_handle
  # OpDefs of the list of ops defined in the library.
  module.OP_LIST = op_list
  sys.modules[module_name] = module
  return module 

# 需要导入模块: from tensorflow.python import pywrap_tensorflow [as 别名]
# 或者: from tensorflow.python.pywrap_tensorflow import TF_Message [as 别名]
def load_op_library(library_filename):
  """Loads a TensorFlow plugin, containing custom ops and kernels.

  Pass "library_filename" to a platform-specific mechanism for dynamically
  loading a library. The rules for determining the exact location of the
  library are platform-specific and are not documented here. When the
  library is loaded, ops and kernels registered in the library via the
  `REGISTER_*` macros are made available in the TensorFlow process. Note
  that ops with the same name as an existing op are rejected and not
  registered with the process.

  Args:
    library_filename: Path to the plugin.
      Relative or absolute filesystem path to a dynamic library file.

  Returns:
    A python module containing the Python wrappers for Ops defined in
    the plugin.

  Raises:
    RuntimeError: when unable to load the library or get the python wrappers.
  """
  status = py_tf.TF_NewStatus()

  lib_handle = py_tf.TF_LoadLibrary(library_filename, status)
  try:
    error_code = py_tf.TF_GetCode(status)
    if error_code != 0:
      error_msg = compat.as_text(py_tf.TF_Message(status))
      # pylint: disable=protected-access
      raise errors_impl._make_specific_exception(
          None, None, error_msg, error_code)
      # pylint: enable=protected-access
  finally:
    py_tf.TF_DeleteStatus(status)

  op_list_str = py_tf.TF_GetOpList(lib_handle)
  op_list = op_def_pb2.OpList()
  op_list.ParseFromString(compat.as_bytes(op_list_str))
  wrappers = py_tf.GetPythonWrappers(op_list_str)

  # Get a unique name for the module.
  module_name = hashlib.md5(wrappers).hexdigest()
  if module_name in sys.modules:
    return sys.modules[module_name]
  module = imp.new_module(module_name)
  # pylint: disable=exec-used
  exec(wrappers, module.__dict__)
  # Stash away the library handle for making calls into the dynamic library.
  module.LIB_HANDLE = lib_handle
  # OpDefs of the list of ops defined in the library.
  module.OP_LIST = op_list
  sys.modules[module_name] = module
  return module