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/*
Copyright 2017 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================
*/
#
include
"
tensorflow/c/python_api.h
"
#
include
"
tensorflow/c/c_api_internal.h
"
#
include
"
tensorflow/python/framework/cpp_shape_inference.pb.h
"
namespace
tensorflow
{
void
AddControlInput
(TF_Graph* graph, TF_Operation* op, TF_Operation* input) {
mutex_lock
l
(graph->
mu
);
graph->
graph
.
AddControlEdge
(&input->
node
, &op->
node
);
RecordMutation
(graph, *op,
"
adding control input
"
);
}
void
SetAttr
(TF_Graph* graph, TF_Operation* op,
const
char
* attr_name,
TF_Buffer* attr_value_proto, TF_Status* status) {
AttrValue attr_val;
if
(!attr_val.
ParseFromArray
(attr_value_proto->
data
,
attr_value_proto->
length
)) {
status->
status
=
tensorflow::errors::InvalidArgument
(
"
Invalid AttrValue proto
"
);
return
;
}
mutex_lock
l
(graph->
mu
);
op->
node
.
AddAttr
(attr_name, attr_val);
RecordMutation
(graph, *op,
"
setting attribute
"
);
}
void
SetRequestedDevice
(TF_Graph* graph, TF_Operation* op,
const
char
* device) {
mutex_lock
l
(graph->
mu
);
op->
node
.
set_requested_device
(device);
RecordMutation
(graph, *op,
"
setting device
"
);
}
void
UpdateEdge
(TF_Graph* graph, TF_Output new_src, TF_Input dst,
TF_Status* status) {
mutex_lock
l
(graph->
mu
);
tensorflow::shape_inference::InferenceContext* ic =
graph->
refiner
.
GetContext
(&new_src.
oper
->
node
);
if
(ic->
num_outputs
() <= new_src.
index
) {
status->
status
=
tensorflow::errors::OutOfRange
(
"
Cannot update edge. Output index [
"
, new_src.
index
,
"
] is greater than the number of total outputs [
"
, ic->
num_outputs
(),
"
].
"
);
return
;
}
tensorflow::shape_inference::ShapeHandle shape = ic->
output
(new_src.
index
);
tensorflow::shape_inference::InferenceContext* ic_dst =
graph->
refiner
.
GetContext
(&dst.
oper
->
node
);
if
(ic_dst->
num_inputs
() <= dst.
index
) {
status->
status
=
tensorflow::errors::OutOfRange
(
"
Cannot update edge. Input index [
"
, dst.
index
,
"
] is greater than the number of total inputs [
"
, ic_dst->
num_inputs
(),
"
].
"
);
return
;
}
if
(!ic_dst->
MergeInput
(dst.
index
, shape)) {
status->
status
=
tensorflow::errors::InvalidArgument
(
"
Cannot update edge, incompatible shapes:
"
, ic_dst->
DebugString
(shape),
"
and
"
, ic_dst->
DebugString
(ic_dst->
input
(dst.
index
)),
"
.
"
);
return
;
}
status->
status
= graph->
graph
.
UpdateEdge
(&new_src.
oper
->
node
, new_src.
index
,
&dst.
oper
->
node
, dst.
index
);
if
(status->
status
.
ok
()) {
//
This modification only updates the destination node for
//
the purposes of running this graph in a session. Thus, we don't
//
record the source node as being modified.
RecordMutation
(graph, *dst.
oper
,
"
updating input tensor
"
);
}
}
void
RemoveAllControlInputs
(TF_Graph* graph, TF_Operation* op) {
mutex_lock
l
(graph->
mu
);
std::vector<
const
Edge*> control_edges;
for
(
const
Edge* edge : op->
node
.
in_edges
()) {
if
(!edge->
IsControlEdge
())
continue
;
control_edges.
push_back
(edge);
}
for
(
const
Edge* edge : control_edges) {
graph->
graph
.
RemoveControlEdge
(edge);
}
}
void
SetRequireShapeInferenceFns
(TF_Graph* graph,
bool
require) {
mutex_lock
l
(graph->
mu
);
graph->
refiner
.
set_require_shape_inference_fns
(require);
}
void
ExtendSession
(TF_Session* session, TF_Status* status) {
ExtendSessionGraphHelper
(session, status);
session->
extend_before_run
=
false
;
}
std::string
GetResourceHandleShapeAndType
(TF_Graph* graph, TF_Output output) {
Node* node = &output.
oper
->
node
;
CppShapeInferenceResult::HandleData handle_data;
handle_data.
set_is_set
(
true
);
{
mutex_lock
l
(graph->
mu
);
tensorflow::shape_inference::InferenceContext* ic =
graph->
refiner
.
GetContext
(node);
CHECK
(ic !=
nullptr
);
CHECK_LT
(output.
index
, ic->
num_outputs
());
const
auto
* shapes_and_types =
ic->
output_handle_shapes_and_types
(output.
index
);
if
(shapes_and_types ==
nullptr
)
return
"
"
;
for
(
const
auto
& p : *shapes_and_types) {
auto
* out_shape_and_type = handle_data.
add_shape_and_type
();
ic->
ShapeHandleToProto
(p.
shape
, out_shape_and_type->
mutable_shape
());
out_shape_and_type->
set_dtype
(p.
dtype
);
}
}
string result;
handle_data.
SerializeToString
(&result);
return
result;
}
void
SetResourceHandleShapeAndType
(TF_Graph* graph, TF_Output output,
const
void
* proto,
size_t
proto_len,
TF_Status* status) {
tensorflow::CppShapeInferenceResult::HandleData handle_data;
if
(!handle_data.
ParseFromArray
(proto, proto_len)) {
status->
status
=
tensorflow::errors::InvalidArgument
(
"
Couldn't deserialize HandleData proto
"
);
return
;
}
DCHECK
(handle_data.
is_set
());
tensorflow::mutex_lock
l
(graph->
mu
);
tensorflow::shape_inference::InferenceContext* ic =
graph->
refiner
.
GetContext
(&output.
oper
->
node
);
std::vector<tensorflow::shape_inference::ShapeAndType> shapes_and_types;
for
(
const
auto
& shape_and_type_proto : handle_data.
shape_and_type
()) {
tensorflow::shape_inference::ShapeHandle shape;
status->
status
=
ic->
MakeShapeFromShapeProto
(shape_and_type_proto.
shape
(), &shape);
if
(status->
status
.
ok
())
return
;
shapes_and_types.
emplace_back
(shape, shape_and_type_proto.
dtype
());
}
ic->
set_output_handle_shapes_and_types
(output.
index
, shapes_and_types);
}
}
//
namespace tensorflow
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