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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.
==============================================================================*/
#ifndef TENSORFLOW_C_C_TEST_UTIL_H_
#define TENSORFLOW_C_C_TEST_UTIL_H_
#include "tensorflow/c/c_api.h"
#include <vector>
#include "tensorflow/core/framework/attr_value.pb.h"
#include "tensorflow/core/framework/function.pb.h"
#include "tensorflow/core/framework/graph.pb.h"
#include "tensorflow/core/framework/node_def.pb.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/platform/test.h"
using ::tensorflow::string;
typedef std::unique_ptr<TF_Tensor, decltype(&TF_DeleteTensor)>
unique_tensor_ptr;
TF_Tensor* BoolTensor(int32_t v);
// Create a tensor with values of type TF_INT8 provided by `values`.
TF_Tensor* Int8Tensor(const int64_t* dims, int num_dims, const char* values);
// Create a tensor with values of type TF_INT32 provided by `values`.
TF_Tensor* Int32Tensor(const int64_t* dims, int num_dims,
const int32_t* values);
// Create 1 dimensional tensor with values from `values`
TF_Tensor* Int32Tensor(const std::vector<int32_t>& values);
TF_Tensor* Int32Tensor(int32_t v);
TF_Tensor* DoubleTensor(double v);
TF_Tensor* FloatTensor(float v);
TF_Operation* Placeholder(TF_Graph* graph, TF_Status* s,
const char* name = "feed",
TF_DataType dtype = TF_INT32,
const std::vector<int64_t>& dims = {});
TF_Operation* Const(TF_Tensor* t, TF_Graph* graph, TF_Status* s,
const char* name = "const");
TF_Operation* ScalarConst(bool v, TF_Graph* graph, TF_Status* s,
const char* name = "scalar");
TF_Operation* ScalarConst(int32_t v, TF_Graph* graph, TF_Status* s,
const char* name = "scalar");
TF_Operation* ScalarConst(double v, TF_Graph* graph, TF_Status* s,
const char* name = "scalar");
TF_Operation* ScalarConst(float v, TF_Graph* graph, TF_Status* s,
const char* name = "scalar");
TF_Operation* Add(TF_Operation* l, TF_Operation* r, TF_Graph* graph,
TF_Status* s, const char* name = "add");
TF_Operation* AddNoCheck(TF_Operation* l, TF_Operation* r, TF_Graph* graph,
TF_Status* s, const char* name = "add");
TF_Operation* AddWithCtrlDependency(TF_Operation* l, TF_Operation* r,
TF_Graph* graph, TF_Operation* ctrl_op,
TF_Status* s, const char* name = "add");
TF_Operation* Add(TF_Output l, TF_Output r, TF_Graph* graph, TF_Status* s,
const char* name = "add");
TF_Operation* Min(TF_Operation* l, TF_Operation* r, TF_Graph* graph,
TF_Status* s, const char* name = "min");
TF_Operation* Mul(TF_Operation* l, TF_Operation* r, TF_Graph* graph,
TF_Status* s, const char* name = "mul");
// If `op_device` is non-empty, set the created op on that device.
TF_Operation* MinWithDevice(TF_Operation* l, TF_Operation* r, TF_Graph* graph,
const string& op_device, TF_Status* s,
const char* name = "min");
TF_Operation* Neg(TF_Operation* n, TF_Graph* graph, TF_Status* s,
const char* name = "neg");
TF_Operation* LessThan(TF_Output l, TF_Output r, TF_Graph* graph, TF_Status* s);
TF_Operation* RandomUniform(TF_Operation* shape, TF_DataType dtype,
TF_Graph* graph, TF_Status* s);
// Split `input` along the first dimension into 3 tensors
TF_Operation* Split3(TF_Operation* input, TF_Graph* graph, TF_Status* s,
const char* name = "split3");
bool IsPlaceholder(const tensorflow::NodeDef& node_def);
bool IsScalarConst(const tensorflow::NodeDef& node_def, int v);
bool IsAddN(const tensorflow::NodeDef& node_def, int n);
bool IsNeg(const tensorflow::NodeDef& node_def, const string& input);
bool GetGraphDef(TF_Graph* graph, tensorflow::GraphDef* graph_def);
bool GetNodeDef(TF_Operation* oper, tensorflow::NodeDef* node_def);
bool GetFunctionDef(TF_Function* func, tensorflow::FunctionDef* func_def);
bool GetAttrValue(TF_Operation* oper, const char* attr_name,
tensorflow::AttrValue* attr_value, TF_Status* s);
// Returns a sorted vector of std::pair<function_name, gradient_func> from
// graph_def.library().gradient()
std::vector<std::pair<string, string>> GetGradDefs(
const tensorflow::GraphDef& graph_def);
// Returns a sorted vector of names contained in `grad_def`
std::vector<string> GetFuncNames(const tensorflow::GraphDef& graph_def);
class CSession {
public:
CSession(TF_Graph* graph, TF_Status* s, bool use_XLA = false);
explicit CSession(TF_Session* session);
~CSession();
void SetInputs(std::vector<std::pair<TF_Operation*, TF_Tensor*>> inputs);
void SetOutputs(std::initializer_list<TF_Operation*> outputs);
void SetOutputs(const std::vector<TF_Output>& outputs);
void SetTargets(std::initializer_list<TF_Operation*> targets);
void Run(TF_Status* s);
void CloseAndDelete(TF_Status* s);
TF_Tensor* output_tensor(int i) { return output_values_[i]; }
TF_Session* mutable_session() { return session_; }
private:
void DeleteInputValues();
void ResetOutputValues();
TF_Session* session_;
std::vector<TF_Output> inputs_;
std::vector<TF_Tensor*> input_values_;
std::vector<TF_Output> outputs_;
std::vector<TF_Tensor*> output_values_;
std::vector<TF_Operation*> targets_;
};
#endif // TENSORFLOW_C_C_TEST_UTIL_H_
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