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._make_specific_exception(None, None, error_msg,
error_code)
# pylint: enable=protected-access
finally:
py_tf.TF_DeleteStatus(status)
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))
with _OP_LIBRARY_MAP_LOCK:
if (error_code == error_codes_pb2.ALREADY_EXISTS
and 'has already been loaded' in error_msg
and library_filename in _OP_LIBRARY_MAP):
return _OP_LIBRARY_MAP[library_filename]
# pylint: disable=protected-access
raise errors._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()
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
# Memoize the filename to module mapping.
with _OP_LIBRARY_MAP_LOCK:
_OP_LIBRARY_MAP[library_filename] = module
return module
def raise_exception_on_not_ok_status():
status = c_api_util.ScopedTFStatus()
yield status.status
if c_api.TF_GetCode(status) != 0:
raise _make_specific_exception(
None, None,
compat.as_text(c_api.TF_Message(status)),
c_api.TF_GetCode(status))
def _do_run(self, target_list, fetch_list, feed_dict):
"""Runs a step based on the given fetches and feeds.
Args:
target_list: A list of byte arrays corresponding to names of tensors
or operations to be run to, but not fetched.
fetch_list: A list of byte arrays corresponding to names of tensors to
be fetched and operations to be run.
feed_dict: A dictionary that maps tensor names (as byte arrays) to
numpy ndarrays.
Returns:
A list of numpy ndarrays, corresponding to the elements of
`fetch_list`. If the ith element of `fetch_list` contains the
name of an operation, the first Tensor output of that operation
will be returned for that element.
"""
try:
# Ensure any changes to the graph are reflected in the runtime.
with self._extend_lock:
if self._graph.version > self._current_version:
graph_def = self._graph.as_graph_def(
from_version=self._current_version)
try:
status = tf_session.TF_NewStatus()
tf_session.TF_ExtendGraph(
self._session, graph_def.SerializeToString(),
status)
if tf_session.TF_GetCode(status) != 0:
raise RuntimeError(
compat.as_text(tf_session.TF_Message(status)))
self._opened = True
finally:
tf_session.TF_DeleteStatus(status)
self._current_version = self._graph.version
return tf_session.TF_Run(self._session, feed_dict, fetch_list,
target_list)
except tf_session.StatusNotOK as e:
e_type, e_value, e_traceback = sys.exc_info()
error_message = compat.as_text(e.error_message)
m = BaseSession._NODEDEF_NAME_RE.search(error_message)
if m is not None:
node_name = m.group(1)
node_def = None
try:
op = self._graph.get_operation_by_name(node_name)
node_def = op.node_def
except KeyError:
op = None
# pylint: disable=protected-access
raise errors._make_specific_exception(node_def, op,
error_message, e.code)
# pylint: enable=protected-access
six.reraise(e_type, e_value, e_traceback)
def __del__(self):
self.close()
try:
status = tf_session.TF_NewStatus()
if self._session is not None:
tf_session.TF_DeleteSession(self._session, status)
if tf_session.TF_GetCode(status) != 0:
raise RuntimeError(tf_session.TF_Message(status))
self._session = None
finally:
tf_session.TF_DeleteStatus(status)
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)
def raise_exception_on_not_ok_status():
status = c_api_util.ScopedTFStatus()
yield status.status
try:
if c_api.TF_GetCode(status.status) != 0:
raise _make_specific_exception(
None, None, compat.as_text(c_api.TF_Message(status.status)),
c_api.TF_GetCode(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 status
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
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.
Expects the symbols "RegisterOps", "RegisterKernels", and "GetOpList", to be
defined in the library.
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:
if py_tf.TF_GetCode(status) != 0:
raise RuntimeError(compat.as_text(py_tf.TF_Message(status)))
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, len(op_list_str))
# Get a unique name for the module.
module_name = hashlib.md5(wrappers).hexdigest()
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 close(self):
"""Closes this session.
Calling this method frees all resources associated with the session.
Raises:
RuntimeError: If an error occurs while closing the session.
"""
with self._extend_lock:
if self._opened and not self._closed:
self._closed = True
try:
status = tf_session.TF_NewStatus()
tf_session.TF_CloseSession(self._session, status)
if tf_session.TF_GetCode(status) != 0:
raise RuntimeError(tf_session.TF_Message(status))
finally:
tf_session.TF_DeleteStatus(status)
def _extend_graph(self):
# Ensure any changes to the graph are reflected in the runtime.
with self._extend_lock:
if self._graph.version > self._current_version:
graph_def = self._graph.as_graph_def(
from_version=self._current_version)
try:
status = tf_session.TF_NewStatus()
tf_session.TF_ExtendGraph(
self._session, graph_def.SerializeToString(), status)
if tf_session.TF_GetCode(status) != 0:
raise RuntimeError(compat.as_text(tf_session.TF_Message(status)))
self._opened = True
finally:
tf_session.TF_DeleteStatus(status)
self._current_version = self._graph.version
def __init__(self, target='', graph=None, config=None):
"""Constructs a new TensorFlow session.
Args:
target: (Optional) The TensorFlow execution engine to connect to.
graph: (Optional) The graph to be used. If this argument is None,
the default graph will be used.
config: (Optional) ConfigProto proto used to configure the session.
Raises:
RuntimeError: If an error occurs while creating the TensorFlow
session.
"""
if graph is None:
self._graph = ops.get_default_graph()
else:
self._graph = graph
self._opened = False
self._closed = False
self._current_version = 0
self._extend_lock = threading.Lock()
self._target = target
self._delete_lock = threading.Lock()
self._dead_handles = []
self._session = None
opts = tf_session.TF_NewSessionOptions(target=target, config=config)
try:
status = tf_session.TF_NewStatus()
try:
self._session = tf_session.TF_NewSession(opts, status)
if tf_session.TF_GetCode(status) != 0:
raise RuntimeError(
compat.as_text(tf_session.TF_Message(status)))
finally:
tf_session.TF_DeleteStatus(status)
finally:
tf_session.TF_DeleteSessionOptions(opts)