def function_def_to_graph(fdef, input_shapes=None):
"""Converts a FunctionDef to a FuncGraph (sub-class Graph).
The returned FuncGraph's `name`, `inputs` and `outputs` fields will be set.
The input tensors are represented as placeholders.
Note: `FuncGraph.inputs` and `FuncGraph.captures` are not set and may be set
by the caller.
Args:
fdef: FunctionDef.
input_shapes: Optional. A list of TensorShape objects of the shapes of
function inputs. If specified, its length must match length of
`fdef.signature.input_arg`. If a shape is None, the corresponding input
placeholder will have unknown shape.
Returns:
A FuncGraph.
"""
func_graph = FuncGraph(fdef.signature.name)
graph_def, nested_to_flat_tensor_name = function_def_to_graph_def(
fdef, input_shapes)
with func_graph.as_default():
# Add all function nodes to the graph.
importer.import_graph_def(graph_def, name="")
# Initialize fields specific to FuncGraph.
# inputs
input_tensor_names = [
nested_to_flat_tensor_name[arg.name]
for arg in fdef.signature.input_arg
]
func_graph.inputs = [
func_graph.get_tensor_by_name(name) for name in input_tensor_names
]
# outputs
output_tensor_names = [
nested_to_flat_tensor_name[fdef.ret[arg.name]]
for arg in fdef.signature.output_arg
]
func_graph.outputs = [
func_graph.get_tensor_by_name(name) for name in output_tensor_names
]
func_graph.control_outputs = [
func_graph.get_operation_by_name(fdef.control_ret[ret_name])
for ret_name in fdef.signature.control_output
]
return func_graph
def function_def_to_graph(fdef,
structured_input_signature=None,
structured_outputs=None,
input_shapes=None):
"""Converts a FunctionDef to a FuncGraph (sub-class Graph).
The returned FuncGraph's `name`, `inputs` and `outputs` fields will be set.
The input tensors are represented as placeholders.
Note: `FuncGraph.inputs` and `FuncGraph.captures` are not set and may be set
by the caller.
Args:
fdef: FunctionDef.
structured_input_signature: Optional. The structured input signature to
use for initializing the FuncGraph. See the docstring for FuncGraph for
more information.
structured_outputs: Optional. The structured outputs to use for
initializing the FuncGraph. See the docstring for FuncGraph for more
information.
input_shapes: Optional. A list of TensorShape objects of the shapes of
function inputs. Defaults to the function's "_input_shapes" attribute. If
specified, its length must match length of `fdef.signature.input_arg`. If
a shape is None, the corresponding input placeholder will have unknown
shape.
Returns:
A FuncGraph.
"""
func_graph = FuncGraph(fdef.signature.name,
structured_input_signature=structured_input_signature,
structured_outputs=structured_outputs)
if input_shapes is None:
input_shapes_attr = fdef.attr.get("_input_shapes", None)
if input_shapes_attr is not None:
raw_input_shapes = input_shapes_attr.list.shape
# Replace resource handle shapes in the inputs to disable shape inference.
# Setting the shape to either the variable handle shape (which is always
# `[]`) or the variable shape can cause shape inference issues.
input_shapes = []
for input_shape, arg_def in zip(raw_input_shapes,
fdef.signature.input_arg):
if arg_def.type == types_pb2.DT_RESOURCE and arg_def.handle_data:
input_shapes.append(None)
else:
input_shapes.append(input_shape)
graph_def, nested_to_flat_tensor_name = function_def_to_graph_def(
fdef, input_shapes)
with func_graph.as_default():
# Add all function nodes to the graph.
importer.import_graph_def_for_function(graph_def, name="")
# Initialize fields specific to FuncGraph.
# inputs
input_tensor_names = [
nested_to_flat_tensor_name[arg.name] for arg in fdef.signature.input_arg
]
func_graph.inputs = [
func_graph.get_tensor_by_name(name) for name in input_tensor_names
]
# outputs
output_tensor_names = [
nested_to_flat_tensor_name[fdef.ret[arg.name]]
for arg in fdef.signature.output_arg
]
func_graph.outputs = [
func_graph.get_tensor_by_name(name) for name in output_tensor_names
]
func_graph.control_outputs = [
func_graph.get_operation_by_name(fdef.control_ret[ret_name])
for ret_name in fdef.signature.control_output
]
_set_handle_data(func_graph, fdef)
for node in graph_def.node:
output_shapes = node.attr.get("_output_shapes", None)
if output_shapes is not None:
op = func_graph.get_operation_by_name(node.name)
# _output_shapes for functions can sometimes be too long because the
# output-intermediates-for-gradients version of the function was
# substituted before saving. We'll accept that here. (See b/133666530).
for output_index, shape in enumerate(
output_shapes.list.shape[:len(op.outputs)]):
op.outputs[output_index].set_shape(shape)
output_names = {}
for ret_arg_def, tensor_name in zip(
fdef.signature.output_arg, output_tensor_names):
output_names[ops.tensor_id(
func_graph.get_tensor_by_name(tensor_name))] = (
ret_arg_def.name)
func_graph._output_names = output_names # pylint: disable=protected-access
return func_graph
def function_def_to_graph(fdef, input_shapes=None, copy_functions=True):
"""Converts a FunctionDef to a FuncGraph (sub-class Graph).
The returned FuncGraph's `name`, `inputs` and `outputs` fields will be set.
The input tensors are represented as placeholders.
Note: `FuncGraph.inputs` and `FuncGraph.captures` are not set and may be set
by the caller.
Args:
fdef: FunctionDef.
input_shapes: Optional. A list of TensorShape objects of the shapes of
function inputs. Defaults to the function's "_input_shapes" attribute. If
specified, its length must match length of `fdef.signature.input_arg`. If
a shape is None, the corresponding input placeholder will have unknown
shape.
copy_functions: Whether to copy all functions that exists in default graph
(independently of being used or not) to the created FuncGraph.
Returns:
A FuncGraph.
"""
func_graph = FuncGraph(fdef.signature.name)
if input_shapes is None:
input_shapes_attr = fdef.attr.get("_input_shapes", None)
if input_shapes_attr is not None:
input_shapes = input_shapes_attr.list.shape
graph_def, nested_to_flat_tensor_name = function_def_to_graph_def(
fdef, input_shapes, copy_functions)
with func_graph.as_default():
# Add all function nodes to the graph.
importer.import_graph_def(graph_def, name="")
# Initialize fields specific to FuncGraph.
# inputs
input_tensor_names = [
nested_to_flat_tensor_name[arg.name] for arg in fdef.signature.input_arg
]
func_graph.inputs = [
func_graph.get_tensor_by_name(name) for name in input_tensor_names
]
# outputs
output_tensor_names = [
nested_to_flat_tensor_name[fdef.ret[arg.name]]
for arg in fdef.signature.output_arg
]
func_graph.outputs = [
func_graph.get_tensor_by_name(name) for name in output_tensor_names
]
func_graph.control_outputs = [
func_graph.get_operation_by_name(fdef.control_ret[ret_name])
for ret_name in fdef.signature.control_output
]
for node in graph_def.node:
output_shapes = node.attr.get("_output_shapes", None)
if output_shapes is not None:
op = func_graph.get_operation_by_name(node.name)
# _output_shapes for functions can sometimes be too long because the
# output-intermediates-for-gradients version of the function was
# substituted before saving. We'll accept that here. (See b/133666530).
for output_index, shape in enumerate(
output_shapes.list.shape[:len(op.outputs)]):
op.outputs[output_index].set_shape(shape)
return func_graph
def function_def_to_graph(fdef, input_shapes=None):
"""Converts a FunctionDef to a FuncGraph (sub-class Graph).
The returned FuncGraph's `name`, `inputs` and `outputs` fields will be set.
The input tensors are represented as placeholders.
Note: `FuncGraph.inputs` and `FuncGraph.captures` are not set and may be set
by the caller.
Args:
fdef: FunctionDef.
input_shapes: Optional. A list of TensorShape objects of the shapes of
function inputs. Defaults to the function's "_input_shapes" attribute. If
specified, its length must match length of `fdef.signature.input_arg`. If
a shape is None, the corresponding input placeholder will have unknown
shape.
Returns:
A FuncGraph.
"""
func_graph = FuncGraph(fdef.signature.name)
if input_shapes is None:
input_shapes_attr = fdef.attr.get("_input_shapes", None)
if input_shapes_attr is not None:
input_shapes = input_shapes_attr.list.shape
graph_def, nested_to_flat_tensor_name = function_def_to_graph_def(
fdef, input_shapes)
with func_graph.as_default():
# Add all function nodes to the graph.
importer.import_graph_def(graph_def, name="")
# Initialize fields specific to FuncGraph.
# inputs
input_tensor_names = [
nested_to_flat_tensor_name[arg.name] for arg in fdef.signature.input_arg
]
func_graph.inputs = [
func_graph.get_tensor_by_name(name) for name in input_tensor_names
]
# outputs
output_tensor_names = [
nested_to_flat_tensor_name[fdef.ret[arg.name]]
for arg in fdef.signature.output_arg
]
func_graph.outputs = [
func_graph.get_tensor_by_name(name) for name in output_tensor_names
]
func_graph.control_outputs = [
func_graph.get_operation_by_name(fdef.control_ret[ret_name])
for ret_name in fdef.signature.control_output
]
for node in graph_def.node:
output_shapes = node.attr.get("_output_shapes", None)
if output_shapes is not None:
op = func_graph.get_operation_by_name(node.name)
for output_index, shape in enumerate(output_shapes.list.shape):
op.outputs[output_index].set_shape(shape)
return func_graph