Python pywrap_tensorflow.GetPythonWrappers方法代碼示例

# 需要導入模塊: from tensorflow.python import pywrap_tensorflow [as 別名]
# 或者: from tensorflow.python.pywrap_tensorflow import GetPythonWrappers [as 別名]
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 

# 需要導入模塊: from tensorflow.python import pywrap_tensorflow [as 別名]
# 或者: from tensorflow.python.pywrap_tensorflow import GetPythonWrappers [as 別名]
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 

# 需要導入模塊: from tensorflow.python import pywrap_tensorflow [as 別名]
# 或者: from tensorflow.python.pywrap_tensorflow import GetPythonWrappers [as 別名]
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.
  """
  with errors_impl.raise_exception_on_not_ok_status() as status:
    lib_handle = py_tf.TF_LoadLibrary(library_filename, 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