def __init__(self, object_graph_proto, saved_model_proto, export_dir):
meta_graph = saved_model_proto.meta_graphs[0]
self._asset_file_def = meta_graph.asset_file_def
self._proto = object_graph_proto
self._export_dir = export_dir
self._defined_functions = {}
for defined_function in function_lib.from_library(
meta_graph.graph_def.library):
# TODO(allenl): Do we need to do name mapping here? Not quite sure what
# happens when loaded names collide with existing names.
defined_function.add_to_graph(None)
self._defined_functions[defined_function.name] = defined_function
self._load_all()
def import_graph_def(graph_def,
input_map=None,
return_elements=None,
name=None,
op_dict=None,
producer_op_list=None):
"""Imports the graph from `graph_def` into the current default `Graph`.
This function provides a way to import a serialized TensorFlow
[`GraphDef`](https://www.tensorflow.org/code/tensorflow/core/framework/graph.proto)
protocol buffer, and extract individual objects in the `GraphDef` as
`tf.Tensor` and `tf.Operation` objects. Once extracted,
these objects are placed into the current default `Graph`. See
`tf.Graph.as_graph_def` for a way to create a `GraphDef`
proto.
Args:
graph_def: A `GraphDef` proto containing operations to be imported into
the default graph.
input_map: A dictionary mapping input names (as strings) in `graph_def`
to `Tensor` objects. The values of the named input tensors in the
imported graph will be re-mapped to the respective `Tensor` values.
return_elements: A list of strings containing operation names in
`graph_def` that will be returned as `Operation` objects; and/or
tensor names in `graph_def` that will be returned as `Tensor` objects.
name: (Optional.) A prefix that will be prepended to the names in
`graph_def`. Note that this does not apply to imported function names.
Defaults to `"import"`.
op_dict: (Optional.) Deprecated, do not use.
producer_op_list: (Optional.) An `OpList` proto with the (possibly stripped)
list of `OpDef`s used by the producer of the graph. If provided,
unrecognized attrs for ops in `graph_def` that have their default value
according to `producer_op_list` will be removed. This will allow some more
`GraphDef`s produced by later binaries to be accepted by earlier binaries.
Returns:
A list of `Operation` and/or `Tensor` objects from the imported graph,
corresponding to the names in `return_elements`,
and None if `returns_elements` is None.
Raises:
TypeError: If `graph_def` is not a `GraphDef` proto,
`input_map` is not a dictionary mapping strings to `Tensor` objects,
or `return_elements` is not a list of strings.
ValueError: If `input_map`, or `return_elements` contains names that
do not appear in `graph_def`, or `graph_def` is not well-formed (e.g.
it refers to an unknown tensor).
"""
op_dict = op_def_registry.get_registered_ops()
graph_def = _ProcessGraphDefParam(graph_def, op_dict)
input_map = _ProcessInputMapParam(input_map)
return_elements = _ProcessReturnElementsParam(return_elements)
if producer_op_list is not None:
# TODO(skyewm): make a copy of graph_def so we're not mutating the argument?
_RemoveDefaultAttrs(op_dict, producer_op_list, graph_def)
graph = ops.get_default_graph()
with ops.name_scope(name, 'import', input_map.values()) as scope:
# Save unique prefix generated by name_scope
if scope:
assert scope.endswith('/')
prefix = scope[:-1]
else:
prefix = ''
# Generate any input map tensors inside name scope
input_map = _ConvertInputMapValues(name, input_map)
scoped_options = c_api_util.ScopedTFImportGraphDefOptions()
options = scoped_options.options
_PopulateTFImportGraphDefOptions(options, prefix, input_map,
return_elements)
# _ProcessNewOps mutates the new operations. _mutation_lock ensures a
# Session.run call cannot occur between creating the TF_Operations in the
# TF_GraphImportGraphDefWithResults call and mutating the them in
# _ProcessNewOps.
with graph._mutation_lock(): # pylint: disable=protected-access
with c_api_util.tf_buffer(graph_def.SerializeToString()) as serialized:
try:
results = c_api.TF_GraphImportGraphDefWithResults(
graph._c_graph, serialized, options) # pylint: disable=protected-access
results = c_api_util.ScopedTFImportGraphDefResults(results)
except errors.InvalidArgumentError as e:
# Convert to ValueError for backwards compatibility.
raise ValueError(str(e))
# Create _DefinedFunctions for any imported functions.
#
# We do this by creating _DefinedFunctions directly from `graph_def`, and
# adding them to `graph`. Adding an existing function to a TF_Graph is a
# no-op, so this only has the effect of updating the Python state (usually
# _DefinedFunction.add_to_graph also adds the function to the TF_Graph).
#
# TODO(skyewm): fetch the TF_Functions directly from the TF_Graph
# TODO(skyewm): avoid sending serialized FunctionDefs back to the TF_Graph
_ProcessNewOps(graph)
if graph_def.library and graph_def.library.function:
functions = function.from_library(graph_def.library)
for f in functions:
f.add_to_graph(graph)
# Treat input mappings that don't appear in the graph as an error, because
# they are likely to be due to a typo.
missing_unused_input_keys = (
c_api.TF_ImportGraphDefResultsMissingUnusedInputMappings_wrapper(
results.results))
if missing_unused_input_keys:
missing_unused_input_keys = [
compat.as_str(s) for s in missing_unused_input_keys
]
raise ValueError(
'Attempted to map inputs that were not found in graph_def: [%s]' %
', '.join(missing_unused_input_keys))
if return_elements is None:
return None
else:
return _GatherReturnElements(return_elements, graph, results.results)
def _import_graph_def_internal( # pylint: disable=invalid-name
graph_def,
input_map=None,
return_elements=None,
validate_colocation_constraints=True,
name=None,
op_dict=None,
producer_op_list=None):
"""Imports the graph from `graph_def` into the current default `Graph`.
This function provides a way to import a serialized TensorFlow
[`GraphDef`](https://www.tensorflow.org/code/tensorflow/core/framework/graph.proto)
protocol buffer, and extract individual objects in the `GraphDef` as
`tf.Tensor` and `tf.Operation` objects. Once extracted,
these objects are placed into the current default `Graph`. See
`tf.Graph.as_graph_def` for a way to create a `GraphDef`
proto.
Args:
graph_def: A `GraphDef` proto containing operations to be imported into the
default graph.
input_map: A dictionary mapping input names (as strings) in `graph_def` to
`Tensor` objects. The values of the named input tensors in the imported
graph will be re-mapped to the respective `Tensor` values.
return_elements: A list of strings containing operation names in `graph_def`
that will be returned as `Operation` objects; and/or tensor names in
`graph_def` that will be returned as `Tensor` objects.
validate_colocation_constraints: Whether to validate colocation constraints.
name: (Optional.) A prefix that will be prepended to the names in
`graph_def`. Note that this does not apply to imported function names.
Defaults to `"import"`.
op_dict: (Optional.) Deprecated, do not use.
producer_op_list: (Optional.) An `OpList` proto with the (possibly stripped)
list of `OpDef`s used by the producer of the graph. If provided,
unrecognized attrs for ops in `graph_def` that have their default value
according to `producer_op_list` will be removed. This will allow some more
`GraphDef`s produced by later binaries to be accepted by earlier binaries.
Returns:
A list of `Operation` and/or `Tensor` objects from the imported graph,
corresponding to the names in `return_elements`,
and None if `returns_elements` is None.
Raises:
TypeError: If `graph_def` is not a `GraphDef` proto,
`input_map` is not a dictionary mapping strings to `Tensor` objects,
or `return_elements` is not a list of strings.
ValueError: If `input_map`, or `return_elements` contains names that
do not appear in `graph_def`, or `graph_def` is not well-formed (e.g.
it refers to an unknown tensor).
"""
op_dict = op_def_registry.get_registered_ops()
graph_def = _ProcessGraphDefParam(graph_def, op_dict)
input_map = _ProcessInputMapParam(input_map)
return_elements = _ProcessReturnElementsParam(return_elements)
if producer_op_list is not None:
# TODO(skyewm): make a copy of graph_def so we're not mutating the argument?
_RemoveDefaultAttrs(op_dict, producer_op_list, graph_def)
graph = ops.get_default_graph()
with ops.name_scope(name, 'import', input_map.values()) as scope:
# Save unique prefix generated by name_scope
if scope:
assert scope.endswith('/')
prefix = scope[:-1]
else:
prefix = ''
# Generate any input map tensors inside name scope
input_map = _ConvertInputMapValues(name, input_map)
scoped_options = c_api_util.ScopedTFImportGraphDefOptions()
options = scoped_options.options
_PopulateTFImportGraphDefOptions(options, prefix, input_map,
return_elements,
validate_colocation_constraints)
# _ProcessNewOps mutates the new operations. _mutation_lock ensures a
# Session.run call cannot occur between creating the TF_Operations in the
# TF_GraphImportGraphDefWithResults call and mutating the them in
# _ProcessNewOps.
with graph._mutation_lock(): # pylint: disable=protected-access
with c_api_util.tf_buffer(graph_def.SerializeToString()) as serialized:
try:
results = c_api.TF_GraphImportGraphDefWithResults(
graph._c_graph, serialized, options) # pylint: disable=protected-access
results = c_api_util.ScopedTFImportGraphDefResults(results)
except errors.InvalidArgumentError as e:
# Convert to ValueError for backwards compatibility.
raise ValueError(str(e))
# Create _DefinedFunctions for any imported functions.
#
# We do this by creating _DefinedFunctions directly from `graph_def`, and
# adding them to `graph`. Adding an existing function to a TF_Graph is a
# no-op, so this only has the effect of updating the Python state (usually
# _DefinedFunction.add_to_graph also adds the function to the TF_Graph).
#
# TODO(skyewm): fetch the TF_Functions directly from the TF_Graph
# TODO(skyewm): avoid sending serialized FunctionDefs back to the TF_Graph
_ProcessNewOps(graph)
if graph_def.library and graph_def.library.function:
functions = function.from_library(graph_def.library)
for f in functions:
f.add_to_graph(graph)
# Treat input mappings that don't appear in the graph as an error, because
# they are likely to be due to a typo.
missing_unused_input_keys = (
c_api.TF_ImportGraphDefResultsMissingUnusedInputMappings_wrapper(
results.results))
if missing_unused_input_keys:
missing_unused_input_keys = [
compat.as_str(s) for s in missing_unused_input_keys
]
raise ValueError(
'Attempted to map inputs that were not found in graph_def: [%s]' %
', '.join(missing_unused_input_keys))
if return_elements is None:
return None
else:
return _GatherReturnElements(return_elements, graph, results.results)
