定义用于加载TensorFlow插件的功能

2018-10-27 17:26 更新

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# ============================================================================

""用于加载 TensorFlow 插件的功能""

"""Function for loading TensorFlow plugins.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import hashlib import imp import sys import threading from tensorflow.core.framework import op_def_pb2 from tensorflow.core.lib.core import error_codes_pb2 from tensorflow.python import pywrap_tensorflow as py_tf from tensorflow.python.framework import errors_impl from tensorflow.python.util import compat 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 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)
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