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/**
* Copyright (c) 2019 Samsung Electronics Co., Ltd 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 __INFERENCE_ENGINE_IMPL_TFLite_H__
#define __INFERENCE_ENGINE_IMPL_TFLite_H__
#include <inference_engine_common.h>
#include <inference_engine_vision.h>
#include "tensorflow/contrib/lite/string.h"
#include "tensorflow/contrib/lite/kernels/register.h"
#include "tensorflow/contrib/lite/model.h"
#include "tensorflow/contrib/lite/context.h"
#include <opencv2/core.hpp>
#include <opencv2/imgproc.hpp>
#include <memory>
#include <dlog.h>
/**
* @file inference_engine_tflite_private.h
* @brief This file contains the InferenceTFLite class which
* provide Tensorflow-lite based inference functionality
*/
#ifdef LOG_TAG
#undef LOG_TAG
#endif
#define LOG_TAG "INFERENCE_ENGINE_TFLITE"
using namespace InferenceEngineInterface::Vision;
using namespace InferenceEngineInterface::Common;
namespace InferenceEngineImpl {
namespace TFLiteImpl {
class InferenceTFLite : public IInferenceEngineVision, public IInferenceEngineCommon {
public:
InferenceTFLite(std::string protoFile,
std::string weightFile,
std::string userFile);
~InferenceTFLite();
// InputTensor
int SetInputTensorParam() override;
int SetInputTensorParamNode(std::string node = "input") override;
int SetInputTensorParamInput(int width, int height, int dim, int ch) override;
int SetInputTensorParamNorm(double deviation = 1.0, double mean = 0.0) override;
// Output Tensor Params
int SetOutputTensorParam() override;
int SetOutputTensorParamThresHold(double threshold) override;
int SetOutputTensorParamNumbers(int number) override;
int SetOutputTensorParamType(int type) override;
int SetOutputTensorParamNodes(std::vector<std::string> nodes) override;
int SetTargetDevice(inference_target_type_e type) override;
// Load and Run
int Load() override;
int CreateInputLayerPassage() override;
int PrepareInputLayerPassage() override;
int PrepareInputLayerPassage(inference_input_type_e type) override;
int Run(cv::Mat tensor) override;
int Run(std::vector<float> tensor) override;
int GetInferenceResult(ImageClassificationResults& results);
int GetInferenceResult(ObjectDetectionResults& results);
int GetInferenceResult(FaceDetectionResults& results);
int GetInferenceResult(FacialLandMarkDetectionResults& results);
int GetInferenceResult(std::vector<std::vector<int>>& dimInfo, std::vector<float*>& results);
int GetNumberOfOutputs() override;
void SetUserListName(std::string userList) override;
public:
int SetUserFile();
int setInput(cv::Mat cvImg);
private:
std::unique_ptr<tflite::Interpreter> mInterpreter;
std::unique_ptr<tflite::FlatBufferModel> mFlatBuffModel;
std::string mInputLayer;
std::vector<std::string> mOutputLayer; /**< Output layer name */
int mInputLayerId;
std::vector<int> mOutputLayerId;
int mMatType;
TfLiteType mInputAttrType;
void *mInputData;
cv::Mat mInputBuffer;
int mCh;
int mDim;
cv::Size mInputSize;
double mDeviation;
double mMean;
double mThreshold;
int mOutputNumbers;
cv::Size mSourceSize;
std::string mConfigFile;
std::string mWeightFile;
std::string mUserFile;
std::vector<std::string> mUserListName;
};
} /* InferenceEngineImpl */
} /* TFLiteImpl */
#endif /* __INFERENCE_ENGINE_IMPL_TFLite_H__ */
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