def _make_callable(self, feed_arrays, feed_symbols, symbol_vals,
session):
"""
Generates a callable that runs the graph.
Arguments:
feed_arrays: List of input tensors to be fed Numpy arrays at runtime.
feed_symbols: List of input tensors to be fed symbolic tensors at runtime.
symbol_vals: List of symbolic tensors to be fed to `feed_symbols`.
session: Session to use to generate the callable.
Returns:
Function that runs the graph according to the above options.
"""
# Prepare callable options.
callable_opts = config_pb2.CallableOptions()
# Handle external-data feed.
for x in feed_arrays:
callable_opts.feed.append(x.name)
if self.feed_dict:
for key in sorted(self.feed_dict.keys()):
callable_opts.feed.append(key.name)
# Handle symbolic feed.
for x, y in zip(feed_symbols, symbol_vals):
connection = callable_opts.tensor_connection.add()
if x.dtype != y.dtype:
y = math_ops.cast(y, x.dtype)
from_tensor = ops._as_graph_element(y)
if from_tensor is None:
from_tensor = y
connection.from_tensor = from_tensor.name # Data tensor
connection.to_tensor = x.name # Placeholder
# Handle fetches.
for x in self.outputs + self.fetches:
callable_opts.fetch.append(x.name)
# Handle updates.
if self.updates_op:
callable_opts.target.append(self.updates_op.name)
# Handle run_options.
if self.run_options:
callable_opts.run_options.CopyFrom(self.run_options)
# Create callable.
callable_fn = session._make_callable_from_options(callable_opts)
# Cache parameters corresponding to the generated callable, so that
# we can detect future mismatches and refresh the callable.
self._callable_fn = callable_fn
self._feed_arrays = feed_arrays
self._feed_symbols = feed_symbols
self._symbol_vals = symbol_vals
self._fetches = list(self.fetches)
self._session = session
def control_dependencies(self, control_inputs):
"""Handles control dependencies.
FuncGraph wraps Graph's control_dependencies logic by first filtering out
any external tensors / operations and storing them in the graph's
control_captures member. Any consumers of this function graph must then
decide how to handle the control captures.
Args:
control_inputs: A list of `Operation` or `Tensor` objects which
must be executed or computed before running the operations
defined in the context. Can also be `None` to clear the control
dependencies.
Returns:
A context manager that specifies control dependencies for all
operations constructed within the context.
Raises:
TypeError: If `control_inputs` is not a list of `Operation` or
`Tensor` objects.
"""
if control_inputs is None:
return super(FuncGraph, self).control_dependencies(control_inputs)
filtered_control_inputs = []
for c in control_inputs:
# Check for _UnreadVariable
if (isinstance(c, ops.IndexedSlices)
or (hasattr(c, "_handle") and hasattr(c, "op"))):
c = c.op
graph_element = ops._as_graph_element(c) # pylint: disable=protected-access
if graph_element is None:
graph_element = c
if graph_element is not None and getattr(graph_element, "graph",
None) is not self:
self.control_captures.add(graph_element)
else:
filtered_control_inputs.append(graph_element)
return super(FuncGraph,
self).control_dependencies(filtered_control_inputs)
def control_dependencies(self, control_inputs):
"""Handles control dependencies.
FuncGraph wraps Graph's control_dependencies logic by first filtering out
any external tensors / operations and storing them in the graph's
control_captures member. Any consumers of this function graph must then
decide how to handle the control captures.
Args:
control_inputs: A list of `Operation` or `Tensor` objects which
must be executed or computed before running the operations
defined in the context. Can also be `None` to clear the control
dependencies.
Returns:
A context manager that specifies control dependencies for all
operations constructed within the context.
Raises:
TypeError: If `control_inputs` is not a list of `Operation` or
`Tensor` objects.
"""
if control_inputs is None:
return super(FuncGraph, self).control_dependencies(control_inputs)
filtered_control_inputs = []
for c in control_inputs:
# Check for _UnreadVariable
if (isinstance(c, ops.IndexedSlices) or
(hasattr(c, "_handle") and hasattr(c, "op"))):
c = c.op
graph_element = ops._as_graph_element(c) # pylint: disable=protected-access
if graph_element is None:
graph_element = c
if graph_element is not None and getattr(
graph_element, "graph", None) is not self:
self.control_captures.add(graph_element)
else:
filtered_control_inputs.append(graph_element)
return super(FuncGraph, self).control_dependencies(filtered_control_inputs)
def _make_callable(self, feed_arrays, feed_symbols, symbol_values, all_fetches):
callable_opts = config_pb2.CallableOptions()
for x in feed_arrays:
callable_opts.feed.append(x.name)
if self.feed_dict:
for key in sorted(self.feed_dict.keys()):
callable_opts.feed.appned(key.name)
for x, y in zip(feed_symbols, symbol_values):
connection = callable_opts.tensor_connection.add()
if x.dtype != y.dtype:
y = tf.cast(y, x.dtype)
from_tensor = tf_ops._as_graph_element(y)
if from_tensor is None:
from_tensor = y
connection.from_tensor = from_tensor.name
connection.to_tensor = x.name
self._all_fetches = all_fetches
self._fetch_handler = _FetchHandler(
graph=self.graph or tf.get_default_graph(),
fetches=self._all_fetches, feeds={})
for x in self._fetch_handler.fetches():
callable_opts.fetch.append(x.name)
callable_opts.target.append(self.updates_ops.name)
if self.run_options:
callable_opts.run_options.CopyFrom(self.run_options)
callable_fn = self.tf_sess._make_callable_from_options(callable_opts)
self._callable_fn = callable_fn
self._feed_arrays = feed_arrays
self._feed_symbols = feed_symbols
self._symbol_values = symbol_values
