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/*
* Copyright (c) 2018 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.
*/
#include <iostream>
#include "tflite_nativewrapper.h"
#include "tflite_log.h"
#include <string.h>
#include <unistd.h>
#include <errno.h>
#include <assert.h>
int getNumBytes(TFLiteNativeType dataType)
{
switch (dataType)
{
case INT32:
return 4;
case FLOAT32:
return 4;
case UINT8:
return 1;
case INT64:
return 8;
default:
return 1;
}
}
/// <summary>
/// Set the number of threads available to the interpreter.
/// </summary>
/// <param name="interpreterHandle">Handle of the interpreter instance.</param>
/// <param name="numThreads">Number of threads.</param>
void tflite_interpreter_setNumThreads(long *interpreterHandle, int numThreads)
{
assert(interpreterHandle != nullptr);
tflite::Interpreter *interpreter = reinterpret_cast<tflite::Interpreter *>(*interpreterHandle);
interpreter->SetNumThreads(numThreads);
TFLITE_NATIVE_LOG(DEBUG, "Number of threads: %d", numThreads);
return;
}
/// <summary>
/// Creates a Flat Buffer Model from the given .tflite model.
/// </summary>
/// <param name="modelPath">Path of the model.</param>
long long tflite_flatbuffermodel_BuildFromFile(char *modelPath)
{
if (modelPath == nullptr)
{
TFLITE_NATIVE_LOG(ERROR, "Invalid parameter");
return 0;
}
TFLITE_NATIVE_LOG(ERROR, "Model Path: %s", modelPath);
if (access(modelPath, F_OK) == -1)
{
TFLITE_NATIVE_LOG(ERROR, "Failed to access model [%s]", strerror(errno));
return 0;
}
auto model = tflite::FlatBufferModel::BuildFromFile(modelPath);
TFLITE_NATIVE_LOG(DEBUG, "Successfully loaded model");
return reinterpret_cast<long>(model.release());
}
/// <summary>
/// Creates an interpreter instance taking the flatbuffer model as input.
/// </summary>
/// <param name="modelHandle">Address of the flatbuffer model.</param>
long long tflite_builder_interpreterBuilder(long *modelHandle)
{
assert(modelHandle != nullptr);
tflite::FlatBufferModel *model = reinterpret_cast<tflite::FlatBufferModel *>(*modelHandle);
tflite::ops::builtin::BuiltinOpResolver resolver;
std::unique_ptr<tflite::Interpreter> interpreter;
TfLiteStatus status = tflite::InterpreterBuilder(*model, resolver)(&interpreter);
if (status != kTfLiteOk)
{
TFLITE_NATIVE_LOG(DEBUG, "Cannot create interpreter");
return 0;
}
TFLITE_NATIVE_LOG(DEBUG, "CheckPoint interpreter");
return reinterpret_cast<long>(interpreter.release());
}
/// <summary>
/// Runs the inference given the inputs.
/// </summary>
/// <param name="interpreterHandle">Address of the interpreter instance.</param>
/// <param name="values">Input values for the model.</param>
/// <param name="inpLength">Length of the input.</param>
/// <param name="dataType">Data type key of the input.</param>
void *tflite_interpreter_run(long *interpreterHandle, void *values, int inputLength, int dataType)
{
assert(interpreterHandle != nullptr);
tflite::Interpreter *interpreter = reinterpret_cast<tflite::Interpreter *>(*interpreterHandle);
int inputTensorIndex = interpreter->inputs()[0];
// TODO:: input tensor size will be passed as a parameter. It is hardcoded for now.
interpreter->ResizeInputTensor(inputTensorIndex, {1, 224, 224, 3});
if (interpreter->AllocateTensors() != kTfLiteOk)
{
TFLITE_NATIVE_LOG(ERROR, "Failed to allocate tensors!");
return nullptr;
}
float *inputTensorPointer = interpreter->typed_tensor<float>(inputTensorIndex);
int numBytes = getNumBytes((TFLiteNativeType)dataType);
memcpy(inputTensorPointer, values, inputLength * numBytes);
if (interpreter->Invoke() != kTfLiteOk)
{
TFLITE_NATIVE_LOG(ERROR, "Failed to invoke");
}
float *output = interpreter->typed_output_tensor<float>(0);
return output;
}
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