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59b2a17c22
STT-tensorflow/tensorflow/python/client/tf_session.i
James Ring 59b2a17c22 Split TF_AttrType into its own header 
This will allow us to remove the c_api_headers target, which is necessary to be
able to continue pruning dependencies on the c_api target.

PiperOrigin-RevId: 248621032
2019-05-16 16:25:05 -07:00

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/* Copyright 2015 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/python/platform/base.i"
%{
#include "tensorflow/c/python_api.h"
#include "tensorflow/core/framework/session_state.h"
#include "tensorflow/core/lib/core/errors.h"
#include "tensorflow/core/lib/strings/stringprintf.h"
#include "tensorflow/core/public/version.h"
#include "tensorflow/python/client/tf_session_helper.h"
// Helper function to convert a Python list of Tensors to a C++ vector of
// TF_Outputs.
//
// Returns true if successful. Otherwise, returns false and sets error_msg.
bool PyTensorListToVector(PyObject* py_tensor_list,
std::vector<TF_Output>* vec,
string* error_msg) {
if (!PyList_Check(py_tensor_list)) {
*error_msg = "expected Python list.";
return false;
}
size_t size = PyList_Size(py_tensor_list);
for (int i = 0; i < size; ++i) {
PyObject* item = PyList_GetItem(py_tensor_list, i);
TF_Output* input_ptr;
if (!SWIG_IsOK(SWIG_ConvertPtr(item, reinterpret_cast<void**>(&input_ptr),
SWIGTYPE_p_TF_Output, 0))) {
*error_msg = "expected Python list of wrapped TF_Output objects. "
"Found python list of something else.";
return false;
}
vec->push_back(*input_ptr);
}
return true;
}
// Helper function to convert a TF_Output to a wrapped TF_Output Python object.
PyObject* CreateWrappedTFOutput(TF_Output tf_output) {
// We used heap-allocated pointers in the Python runtime (this is what SWIG
// generates by default for functions returning TF_Output).
TF_Output* tf_output_ptr = new TF_Output(tf_output);
// Use SWIG_POINTER_OWN so the TF_Output* is deleted by Python.
return SWIG_NewPointerObj(tf_output_ptr, SWIGTYPE_p_TF_Output,
SWIG_POINTER_OWN);
}
// Helper function to convert a TF_Operation to a wrapped TF_Operation Python
// object.
PyObject* CreateWrappedTFOperation(TF_Operation* tf_operation) {
// No flags since operation is owned by TF_Graph.
return SWIG_NewPointerObj(tf_operation, SWIGTYPE_p_TF_Operation, 0);
}
// Helper function to convert a Python list of ints to a C++ vector of int64s
void PyInt64ListToVector(PyObject* py_int_seq, std::vector<int64_t>* vec) {
int size = PySequence_Fast_GET_SIZE(py_int_seq);
for (int i = 0; i < size; ++i) {
PyObject* item = PySequence_Fast_GET_ITEM(py_int_seq, i);
vec->push_back(PyLong_AsLongLong(item));
}
}
%}
%include "tensorflow/python/client/tf_sessionrun_wrapper.i"
// Required to use PyArray_* functions.
%init %{
tensorflow::ImportNumpy();
%}
// TensorFlow version and GraphDef versions
%constant const char* __version__ = TF_VERSION_STRING;
%constant int GRAPH_DEF_VERSION = TF_GRAPH_DEF_VERSION;
%constant int GRAPH_DEF_VERSION_MIN_CONSUMER = TF_GRAPH_DEF_VERSION_MIN_CONSUMER;
%constant int GRAPH_DEF_VERSION_MIN_PRODUCER = TF_GRAPH_DEF_VERSION_MIN_PRODUCER;
// Git version information
%constant const char* __git_version__ = tf_git_version();
// Compiler
%constant const char* __compiler_version__ = tf_compiler_version();
// _GLIBCXX_USE_CXX11_ABI flag value
%constant const int __cxx11_abi_flag__ = tf_cxx11_abi_flag();
// Flag indicating whether the build is monolithic
%constant const int __monolithic_build__ = tf_monolithic_build();
// Release the Python GIL for the duration of most methods.
%exception {
Py_BEGIN_ALLOW_THREADS;
$action
Py_END_ALLOW_THREADS;
}
// The target input to TF_SetTarget() is passed as a null-terminated
// const char*.
%typemap(in) (const char* target) {
$1 = PyBytes_AsString($input);
if (!$1) {
// Python has raised an error.
SWIG_fail;
}
}
// Constants used by TensorHandle (get_session_handle).
%constant const char* TENSOR_HANDLE_KEY = tensorflow::SessionState::kTensorHandleResourceTypeName;
// Convert TF_OperationName output to unicode python string
%typemap(out) const char* TF_OperationName {
$result = PyUnicode_FromString($1);
}
// Convert TF_OperationOpType output to unicode python string
%typemap(out) const char* TF_OperationOpType {
$result = PyUnicode_FromString($1);
}
// Convert TF_DeviceListMemoryBytes and TF_Dim int64_t output to Python integers
%typemap(out) int64_t {
$result = PyLong_FromLongLong($1);
}
// Convert TF_DeviceListIncarnation uint64_t output to Python integer
%typemap(out) uint64_t {
$result = PyLong_FromUnsignedLongLong($1);
}
// We use TF_OperationGetControlInputs_wrapper instead of
// TF_OperationGetControlInputs
%ignore TF_OperationGetControlInputs;
%unignore TF_OperationGetControlInputs_wrapper;
// See comment for "%noexception TF_SessionRun_wrapper;"
%noexception TF_OperationGetControlInputs_wrapper;
// Build a Python list of TF_Operation* and return it.
%typemap(out) std::vector<TF_Operation*> tensorflow::TF_OperationGetControlInputs_wrapper {
$result = PyList_New($1.size());
if (!$result) {
SWIG_exception_fail(SWIG_MemoryError, "$symname: couldn't create list");
}
for (size_t i = 0; i < $1.size(); ++i) {
PyList_SET_ITEM($result, i, CreateWrappedTFOperation($1[i]));
}
}
// We use TF_OperationGetControlOutputs_wrapper instead of
// TF_OperationGetControlOutputs
%ignore TF_OperationGetControlOutputs;
%unignore TF_OperationGetControlOutputs_wrapper;
// See comment for "%noexception TF_SessionRun_wrapper;"
%noexception TF_OperationGetControlOutputs_wrapper;
// Build a Python list of TF_Operation* and return it.
%typemap(out) std::vector<TF_Operation*> tensorflow::TF_OperationGetControlOutputs_wrapper {
$result = PyList_New($1.size());
if (!$result) {
SWIG_exception_fail(SWIG_MemoryError, "$symname: couldn't create list");
}
for (size_t i = 0; i < $1.size(); ++i) {
PyList_SET_ITEM($result, i, CreateWrappedTFOperation($1[i]));
}
}
%ignore TF_OperationOutputConsumers;
%unignore TF_OperationOutputConsumers_wrapper;
// See comment for "%noexception TF_SessionRun_wrapper;"
%noexception TF_OperationGetOutputConsumers_wrapper;
// Build a Python list of unicode strings and return it. (Operation names are
// always represented as unicode.)
%typemap(out) std::vector<const char*>
tensorflow::TF_OperationOutputConsumers_wrapper {
$result = PyList_New($1.size());
if (!$result) {
SWIG_exception_fail(SWIG_MemoryError, "$symname: couldn't create list");
}
for (size_t i = 0; i < $1.size(); ++i) {
PyList_SET_ITEM($result, i, PyUnicode_FromString($1[i]));
}
}
%unignore GetOperationInputs;
// See comment for "%noexception TF_SessionRun_wrapper;"
%noexception GetOperationInputs;
// Build a Python list of TF_Outputs and return it.
// TODO(skyewm): is there some way to generalize this pattern? Maybe a macro?
%typemap(out) std::vector<TF_Output> tensorflow::GetOperationInputs {
$result = PyList_New($1.size());
if (!$result) {
SWIG_exception_fail(SWIG_MemoryError, "$symname: couldn't create list");
}
// Unwrap the generated SwigValueWrapper<std::vector<TF_Output>>
const std::vector<TF_Output>& tf_outputs = $1;
for (size_t i = 0; i < tf_outputs.size(); ++i) {
PyList_SET_ITEM($result, i, CreateWrappedTFOutput(tf_outputs[i]));
}
}
%ignore TF_ImportGraphDefResultsMissingUnusedInputMappings;
%unignore TF_ImportGraphDefResultsMissingUnusedInputMappings_wrapper;
// See comment for "%noexception TF_SessionRun_wrapper;"
%noexception TF_ImportGraphDefResultsMissingUnusedInputMappings_wrapper;
%typemap(out) std::vector<string>
TF_ImportGraphDefResultsMissingUnusedInputMappings_wrapper{
$result = PyList_New($1.size());
if (!$result) {
SWIG_exception_fail(SWIG_MemoryError, "$symname: couldn't create list");
}
for (size_t i = 0; i < $1.size(); ++i) {
const string& input_str = $1[i];
PyList_SET_ITEM($result, i, PyBytes_FromStringAndSize(input_str.data(),
input_str.size()));
}
}
////////////////////////////////////////////////////////////////////////////////
// BEGIN TYPEMAPS FOR tensorflow::TF_Run_wrapper()
////////////////////////////////////////////////////////////////////////////////
// Converts a python list of strings to NameVector.
// Has multiple users including feeds/fetches names and function output names
%typemap(in) const tensorflow::NameVector& (
tensorflow::NameVector temp,
tensorflow::Safe_PyObjectPtr temp_string_list(
tensorflow::make_safe(static_cast<PyObject*>(nullptr)))) {
if (!PyList_Check($input)) {
SWIG_exception_fail(
SWIG_TypeError,
tensorflow::strings::Printf(
"Expected a python list for conversion "
"to tensorflow::NameVector but got %s",
Py_TYPE($input)->tp_name).c_str());
}
Py_ssize_t len = PyList_Size($input);
temp_string_list = tensorflow::make_safe(PyList_New(len));
if (!temp_string_list) {
SWIG_exception_fail(
SWIG_MemoryError,
tensorflow::strings::Printf("Failed to create a list of size %zd",
len).c_str());
}
for (Py_ssize_t i = 0; i < len; ++i) {
PyObject* elem = PyList_GetItem($input, i);
if (!elem) {
SWIG_fail;
}
// Keep a reference to the string in case the incoming list is modified.
PyList_SET_ITEM(temp_string_list.get(), i, elem);
Py_INCREF(elem);
char* string_elem = PyBytes_AsString(elem);
if (!string_elem) {
SWIG_exception_fail(
SWIG_TypeError,
tensorflow::strings::Printf(
"Element %zd was of type %s instead of a string",
i, Py_TYPE(elem)->tp_name).c_str());
}
// TODO(mrry): Avoid copying the fetch name in, if this impacts performance.
temp.push_back(string_elem);
}
$1 = &temp;
}
// Define temporaries for the argout outputs.
%typemap(in, numinputs=0) tensorflow::PyObjectVector* out_values (
tensorflow::PyObjectVector temp) {
$1 = &temp;
}
// TODO(iga): move this and the corresponding typemap(argout) to
// tf_sessionrun_wrapper.i once we get rid of this code for DeprecatedSession.
%typemap(in, numinputs=0) char** out_handle (
char* temp) {
$1 = &temp;
}
// Build a Python list of outputs and return it.
%typemap(argout) tensorflow::PyObjectVector* out_values {
std::vector<tensorflow::Safe_PyObjectPtr> out_values_safe;
for (size_t i = 0; i < $1->size(); ++i) {
out_values_safe.emplace_back(tensorflow::make_safe($1->at(i)));
}
$result = PyList_New($1->size());
if (!$result) {
SWIG_exception_fail(
SWIG_MemoryError,
tensorflow::strings::Printf("Failed to create a list of size %zd",
$1->size()).c_str());
}
for (size_t i = 0; i < $1->size(); ++i) {
PyList_SET_ITEM($result, i, $1->at(i));
out_values_safe[i].release();
}
}
// Return the handle as a python string object.
%typemap(argout) char** out_handle {
%#if PY_MAJOR_VERSION < 3
$result = PyString_FromStringAndSize(
%#else
$result = PyUnicode_FromStringAndSize(
%#endif
*$1, *$1 == nullptr ? 0 : strlen(*$1));
delete[] *$1;
}
////////////////////////////////////////////////////////////////////////////////
// END TYPEMAPS FOR tensorflow::TF_Run_wrapper()
////////////////////////////////////////////////////////////////////////////////
// Typemap for TF_Status* inputs that automatically unwraps a ScopedTFStatus.
// This can also handle a wrapped TF_Status* input.
%typemap(in) (TF_Status*) {
PyObject* wrapped_tf_status;
if (strcmp(Py_TYPE($input)->tp_name, "ScopedTFStatus") == 0) {
DCHECK(PyObject_HasAttrString($input, "status"))
<< "ScopedTFStatus.status not found! Do you need to modify "
"tf_session.i?";
wrapped_tf_status = PyObject_GetAttrString($input, "status");
} else {
// Assume wrapped TF_Status*
wrapped_tf_status = $input;
}
DCHECK_EQ(strcmp(Py_TYPE(wrapped_tf_status)->tp_name, "SwigPyObject"), 0)
<< Py_TYPE(wrapped_tf_status)->tp_name;
// The following is the default SWIG code generated for TF_Status*
void* tf_status = nullptr;
int r = SWIG_ConvertPtr(wrapped_tf_status, &tf_status,
$descriptor(TF_Status*), 0 | 0);
if (!SWIG_IsOK(r)) {
SWIG_exception_fail(
SWIG_ArgError(r),
"in method '_TF_DeleteStatus', argument 1 of type 'TF_Status *'");
}
$1 = reinterpret_cast<TF_Status*>(tf_status);
}
// Typemap for functions that return a TF_Buffer struct. This typemap creates a
// Python string from the TF_Buffer and returns it. The TF_Buffer.data string
// is not expected to be NULL-terminated, and TF_Buffer.length does not count
// the terminator.
%typemap(out) TF_Buffer (TF_GetOpList,TF_GetBuffer) {
$result = PyBytes_FromStringAndSize(
reinterpret_cast<const char*>($1.data), $1.length);
}
// Converts input Python list of wrapped TF_Outputs into a single array
%typemap(in) (const TF_Output* inputs, int num_inputs)
(std::vector<TF_Output> inputs) {
string error_msg;
if (!PyTensorListToVector($input, &inputs, &error_msg)) {
SWIG_exception_fail(SWIG_TypeError, ("$symname: " + error_msg).c_str());
}
$1 = inputs.data();
$2 = inputs.size();
}
// Typemaps for TF_ImportGraphDefResultsReturnOutputs
%typemap(in, numinputs=0) (int* num_outputs, TF_Output** outputs)
(int num_outputs, TF_Output* outputs) {
$1 = &num_outputs;
$2 = &outputs;
}
%typemap(argout) (int* num_outputs, TF_Output** outputs) {
$result = PyList_New(*$1);
if (!$result) {
SWIG_exception_fail(SWIG_MemoryError, "$symname: couldn't create list");
}
int num_outputs = *$1;
TF_Output* outputs = *$2;
for (int i = 0; i < num_outputs; ++i) {
PyList_SET_ITEM($result, i, CreateWrappedTFOutput(outputs[i]));
}
}
// Typemaps for TF_ImportGraphDefResultsReturnOperations
%typemap(in, numinputs=0) (int* num_opers, TF_Operation*** opers)
(int num_opers, TF_Operation** opers) {
$1 = &num_opers;
$2 = &opers;
}
%typemap(argout) (int* num_opers, TF_Operation*** opers) {
$result = PyList_New(*$1);
if (!$result) {
SWIG_exception_fail(SWIG_MemoryError, "$symname: couldn't create list");
}
int num_opers = *$1;
TF_Operation** opers = *$2;
for (int i = 0; i < num_opers; ++i) {
PyList_SET_ITEM($result, i, CreateWrappedTFOperation(opers[i]));
}
}
// Typemaps for TF_GraphNextOperation().
%typemap(in) size_t* pos (size_t pos) {
pos = PyLong_AsUnsignedLong($input);
$1 = &pos;
}
// Returns a (TF_Operation*, int pos) tuple.
%typemap(argout) size_t* pos {
PyObject* new_result = PyTuple_New(2);
if (!new_result) {
SWIG_exception_fail(SWIG_MemoryError, "$symname: couldn't create tuple");
}
// Steals $result reference
PyTuple_SET_ITEM(new_result, 0, $result);
PyTuple_SET_ITEM(new_result, 1, PyLong_FromSize_t(*$1));
$result = new_result;
}
%typemap(in, numinputs=0) int64_t* out_handle (int64_t out_handle) {
$1 = &out_handle;
}
%typemap(argout) int64_t* out_handle {
$result = PyLong_FromLongLong(*$1);
}
%typemap(in) int64_t handle {
if (!PyLong_Check($input)) {
SWIG_exception_fail(
SWIG_TypeError,
tensorflow::strings::Printf(
"Expected a python long for conversion to callable handle but got %s",
Py_TYPE($input)->tp_name).c_str());
}
$1 = PyLong_AsLongLong($input);
}
// Override default py3 behavior of attempting to encode into Unicode.
%typemap(out) std::string tensorflow::GetHandleShapeAndType {
$result = PyBytes_FromStringAndSize($1.data(), $1.size());
}
// TODO(skyewm): SWIG emits a warning for the const char* in TF_WhileParams,
// skip for now
%ignore TF_WhileParams;
%ignore TF_NewWhile;
%ignore TF_FinishWhile;
%ignore TF_AbortWhile;
// These are defined below, avoid duplicate definitions
%ignore TF_Run;
%ignore TF_PRun;
%ignore TF_PRunSetup;
// We use TF_SessionRun_wrapper instead of TF_SessionRun
%ignore TF_SessionRun;
%unignore TF_SessionRun_wrapper;
// The %exception block above releases the Python GIL for the length of each
// wrapped method. We disable this behavior for TF_SessionRun_wrapper because it
// uses Python method(s) that expect the GIL to be held (at least
// PyArray_Return, maybe others).
%noexception TF_SessionRun_wrapper;
// We use TF_SessionPRunSetup_wrapper instead of TF_SessionPRunSetup
%ignore TF_SessionPRunSetup;
%unignore TF_SessionPRunSetup_wrapper;
// See comment for "%noexception TF_SessionRun_wrapper;"
%noexception TF_SessionPRunSetup_wrapper;
// We use TF_SessionPRun_wrapper instead of TF_SessionPRun
%ignore TF_SessionPRun;
%unignore TF_SessionPRun_wrapper;
// See comment for "%noexception TF_SessionRun_wrapper;"
%noexception TF_SessionPRun_wrapper;
%unignore TF_DeprecatedSessionMakeCallable;
%unignore TF_SessionMakeCallable;
%unignore TF_DeprecatedSessionRunCallable;
%unignore TF_SessionRunCallable;
%unignore TF_DeprecatedSessionReleaseCallable;
%unignore TF_SessionReleaseCallable;
// See comment for "%noexception TF_SessionRun_wrapper;"
%noexception TF_DeprecatedSessionRunCallable;
%noexception TF_SessionRunCallable;
%rename("_TF_SetTarget") TF_SetTarget;
%rename("_TF_SetConfig") TF_SetConfig;
%rename("_TF_NewSessionOptions") TF_NewSessionOptions;
%include "tensorflow/c/c_api.h"
%include "tensorflow/c/tf_attrtype.h"
%include "tensorflow/c/python_api.h"
%ignoreall
%insert("python") %{
def TF_NewSessionOptions(target=None, config=None):
# NOTE: target and config are validated in the session constructor.
opts = _TF_NewSessionOptions()
if target is not None:
_TF_SetTarget(opts, target)
if config is not None:
from tensorflow.python.framework import errors
config_str = config.SerializeToString()
_TF_SetConfig(opts, config_str)
return opts
%}
// Include the wrapper for TF_Run from tf_session_helper.h.
// The %exception block above releases the Python GIL for the length
// of each wrapped method. We disable this behavior for TF_Run
// because it uses the Python allocator.
%noexception tensorflow::TF_Run_wrapper;
%rename(TF_Run) tensorflow::TF_Run_wrapper;
%unignore tensorflow;
%unignore TF_Run;
%unignore EqualGraphDefWrapper;
%unignore EqualAttrValueWrapper;
// Include the wrapper for TF_PRunSetup from tf_session_helper.h.
// The %exception block above releases the Python GIL for the length
// of each wrapped method. We disable this behavior for TF_PRunSetup
// because it uses the Python allocator.
%noexception tensorflow::TF_PRunSetup_wrapper;
%rename(TF_PRunSetup) tensorflow::TF_PRunSetup_wrapper;
%unignore tensorflow;
%unignore TF_PRunSetup;
// Include the wrapper for TF_PRun from tf_session_helper.h.
// The %exception block above releases the Python GIL for the length
// of each wrapped method. We disable this behavior for TF_PRun
// because it uses the Python allocator.
%noexception tensorflow::TF_PRun_wrapper;
%rename(TF_PRun) tensorflow::TF_PRun_wrapper;
%unignore tensorflow;
%unignore TF_PRun;
%unignore tensorflow::TF_Reset_wrapper;
%insert("python") %{
def TF_Reset(target, containers=None, config=None):
from tensorflow.python.framework import errors
opts = TF_NewSessionOptions(target=target, config=config)
try:
TF_Reset_wrapper(opts, containers)
finally:
TF_DeleteSessionOptions(opts)
%}
// We use TF_GraphToFunction_wrapper instead of TF_GraphToFunction
%ignore TF_GraphToFunction;
// TF_GraphToFunction_wrapper does not use any Python methods and
// does not require GIL to be held.
%unignore TF_GraphToFunction_wrapper;
// $input is a Python list of wrapped TF_Operations
%typemap(in) (const std::vector<TF_Operation*>* opers)
(std::vector<TF_Operation*> opers) {
if ($input != Py_None) {
if (!PyList_Check($input)) {
SWIG_exception_fail(SWIG_TypeError, "$symname: expected list");
}
size_t size = PyList_Size($input);
for (int i = 0; i < size; ++i) {
PyObject* item = PyList_GetItem($input, i);
TF_Operation* oper_ptr;
SWIG_ConvertPtr(item, reinterpret_cast<void**>(&oper_ptr),
$descriptor(TF_Operation*), 0);
opers.push_back(oper_ptr);
}
$1 = &opers;
} else {
$1 = nullptr;
}
}
// $input is a Python list of wrapped TF_Operations
%typemap(in) (const std::vector<TF_Operation*>* control_outputs)
(std::vector<TF_Operation*> control_outputs) {
if ($input != Py_None) {
if (!PyList_Check($input)) {
SWIG_exception_fail(SWIG_TypeError, "$symname: expected list");
}
size_t size = PyList_Size($input);
for (int i = 0; i < size; ++i) {
PyObject* item = PyList_GetItem($input, i);
TF_Operation* oper_ptr;
SWIG_ConvertPtr(item, reinterpret_cast<void**>(&oper_ptr),
$descriptor(TF_Operation*), 0);
control_outputs.push_back(oper_ptr);
}
$1 = &control_outputs;
} else {
$1 = nullptr;
}
}
// Typemaps for TF_GraphGetTensorShapeHelper.
// Convert from C++ integer vector to Python list of ints.
%typemap(out) tensorflow::gtl::InlinedVector<int64_t, 6>
tensorflow::TF_GraphGetTensorShapeHelper {
$result = PyList_New($1.size());
if (!$result) {
SWIG_exception_fail(SWIG_MemoryError, "$symname: couldn't create list");
}
for (size_t i = 0; i < $1.size(); ++i) {
PyList_SET_ITEM($result, i, PyLong_FromLongLong($1[i]));
}
}
%typemap(in, numinputs=0) bool* unknown_shape (bool temp) {
$1=&temp;
}
// Returns a (list(int), bool) tuple.
%typemap(argout) bool* unknown_shape {
PyObject* new_result = PyTuple_New(2);
if (!new_result) {
SWIG_exception_fail(SWIG_MemoryError, "$symname: couldn't create tuple");
}
// Steals $result reference
PyTuple_SET_ITEM(new_result, 0, $result);
PyTuple_SET_ITEM(new_result, 1, PyBool_FromLong(*$1));
$result = new_result;
}
%unignore tensorflow;
%unignore TF_GraphGetTensorShapeHelper;
%ignore TF_GraphGetTensorShape;
// We use TF_GraphSetTensorShape_wrapper instead of
// TF_GraphSetTensorShape
%ignore TF_GraphSetTensorShape;
%unignore tensorflow;
%unignore TF_GraphSetTensorShape_wrapper;
// $input is a Python list of ints to a vector<int> for TF_GraphSetTensorShape_wrapper
%typemap(in) (const std::vector<int64_t>& dims)
(std::vector<int64_t> dims_local){
if ($input != Py_None) {
PyObject* py_int_seq = PySequence_Fast($input, tensorflow::strings::Printf(
"$symname: expected list but got %s ",
Py_TYPE($input)->tp_name).c_str());
if (py_int_seq == nullptr) {
SWIG_exception_fail(SWIG_RuntimeError, tensorflow::strings::Printf(
"$symname: PySequence_Fast returned NULL.").c_str());
}
PyInt64ListToVector(py_int_seq, &dims_local);
Py_DECREF(py_int_seq);
$1 = &dims_local;
} else {
$1 = nullptr;
}
}
// We use TF_GraphGetTensorShape_wrapper instead of
// TF_GraphGetTensorShape
%ignore TF_GraphGetTensorShape;
%unignore tensorflow;
%unignore TF_GraphGetTensorShape_wrapper;
// Build a Python list of ints and return it.
%typemap(out) std::vector<int64_t> tensorflow::TF_GraphGetTensorShape_wrapper {
$result = PyList_New($1.size());
if (!$result) {
SWIG_exception_fail(SWIG_MemoryError, "$symname: couldn't create list");
}
for (size_t i = 0; i < $1.size(); ++i) {
PyList_SET_ITEM($result, i, PyLong_FromLongLong($1[i]));
}
}
// We use TF_GraphSetOutputHandleShapesAndTypes_wrapper instead of
// TF_GraphSetOutputHandleShapesAndTypes
%ignore TF_GraphSetOutputHandleShapesAndTypes;
%unignore tensorflow;
%unignore TF_GraphSetOutputHandleShapesAndTypes_wrapper;
// The space between the double angle brackets below looks extraneous, but
// our version of SWIG cannot parse ">>".
%typemap(in) (const std::vector<std::vector<int64_t> >& shapes)
(std::vector<std::vector<int64_t> > shapes_local){
PyObject* seq = PySequence_Fast($input, tensorflow::strings::Printf(
"$symname: expected list but got %s ",
Py_TYPE($input)->tp_name).c_str());
if (seq == nullptr) {
SWIG_exception_fail(SWIG_RuntimeError, tensorflow::strings::Printf(
"$symname: PySequence_Fast returned NULL.").c_str());
}
int size = PySequence_Fast_GET_SIZE(seq);
if (size == 0) {
SWIG_exception_fail(SWIG_ValueError, tensorflow::strings::Printf(
"$symname: shapes list must be non-empty").c_str());
}
for (int i = 0; i < size; ++i) {
PyObject* item = PySequence_Fast_GET_ITEM(seq, i);
std::vector<int64_t> dims;
if (item != Py_None) {
PyObject* py_int_seq = PySequence_Fast(item, tensorflow::strings::Printf(
"$symname: expected list but got %s ",
Py_TYPE($input)->tp_name).c_str());
if (py_int_seq == nullptr) {
SWIG_exception_fail(SWIG_RuntimeError, tensorflow::strings::Printf(
"$symname: PySequence_Fast returned NULL.").c_str());
}
PyInt64ListToVector(py_int_seq, &dims);
Py_DECREF(py_int_seq);
}
shapes_local.push_back(dims);
}
Py_DECREF(seq);
$1 = &shapes_local;
}
%typemap(in) (const std::vector<int>& ranks)
(std::vector<int> ranks_local){
PyObject* seq = PySequence_Fast($input, tensorflow::strings::Printf(
"$symname: expected list but got %s ",
Py_TYPE($input)->tp_name).c_str());
if (seq == nullptr) {
SWIG_exception_fail(SWIG_RuntimeError, tensorflow::strings::Printf(
"$symname: PySequence_Fast returned NULL.").c_str());
}
int size = PySequence_Fast_GET_SIZE(seq);
if (size == 0) {
SWIG_exception_fail(SWIG_ValueError, tensorflow::strings::Printf(
"$symname: shapes list must be non-empty").c_str());
}
for (int i = 0; i < size; ++i) {
PyObject* item = PySequence_Fast_GET_ITEM(seq, i);
ranks_local.push_back((int) PyInt_AsLong(item));
}
Py_DECREF(seq);
$1 = &ranks_local;
}
%typemap(in) (const std::vector<TF_DataType>& types)
(std::vector<TF_DataType> types_local){
PyObject* seq = PySequence_Fast($input, tensorflow::strings::Printf(
"$symname: expected list but got %s ",
Py_TYPE($input)->tp_name).c_str());
if (seq == nullptr) {
SWIG_exception_fail(SWIG_RuntimeError, tensorflow::strings::Printf(
"$symname: PySequence_Fast returned NULL.").c_str());
}
int size = PySequence_Fast_GET_SIZE(seq);
if (size == 0) {
SWIG_exception_fail(SWIG_ValueError, tensorflow::strings::Printf(
"$symname: shapes list must be non-empty").c_str());
}
for (int i = 0; i < size; ++i) {
PyObject* item = PySequence_Fast_GET_ITEM(seq, i);
types_local.push_back((TF_DataType) PyInt_AsLong(item));
}
Py_DECREF(seq);
$1 = &types_local;
}
%unignore TF_NewSessionRef;
%unignore SetRequireShapeInferenceFns;
%unignore TF_TryEvaluateConstant_wrapper;
%noexception TF_TryEvaluateConstant_wrapper;
%unignore ExtendSession;
%unignore HandleShapeAndType;
%include "tensorflow/python/client/tf_session_helper.h"
%unignoreall
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