def _run_fn(session, feed_dict, fetch_list, target_list, options,
run_outputs):
# Ensure any changes to the graph are reflected in the runtime.
self._extend_graph()
if options:
return tf_session.TF_Run(session, options, feed_dict,
fetch_list, target_list, run_outputs)
else:
return tf_session.TF_Run(session, None, feed_dict, fetch_list,
target_list, None)
def _run_fn(session, feed_dict, fetch_list, target_list, options,
run_metadata):
# Ensure any changes to the graph are reflected in the runtime.
self._extend_graph()
with errors.raise_exception_on_not_ok_status() as status:
if options:
return tf_session.TF_Run(session, options, feed_dict,
fetch_list, target_list, status,
run_metadata)
else:
return tf_session.TF_Run(session, None, feed_dict,
fetch_list, target_list, status,
None)
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 fast_tf():
return tf_session.TF_Run(session, options,
feed_dict, fetch_list, target_list,
status, run_metadata)
def _run_fn(session, feed_dict, fetch_list, target_list):
# Ensure any changes to the graph are reflected in the runtime.
self._extend_graph()
return tf_session.TF_Run(session, feed_dict, fetch_list,
target_list)
