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//
// Copyright © 2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include <armnn_delegate.hpp>
#include <tensorflow/lite/c/common.h>
#include <tensorflow/lite/core/model.h>
#include <tensorflow/lite/interpreter.h>
#include <tensorflow/lite/kernels/register.h>
int main()
{
std::unique_ptr<tflite::FlatBufferModel> model;
model = tflite::FlatBufferModel::BuildFromFile("./simple_conv2d_1_op.tflite");
if (!model)
{
std::cout << "Failed to load TfLite model from: ./simple_conv2d_1_op.tflite" << std::endl;
return -1;
}
std::unique_ptr<tflite::Interpreter> m_TfLiteInterpreter;
m_TfLiteInterpreter = std::make_unique<tflite::Interpreter>();
tflite::ops::builtin::BuiltinOpResolver resolver;
tflite::InterpreterBuilder builder(*model, resolver);
if (builder(&m_TfLiteInterpreter) != kTfLiteOk)
{
std::cout << "Error loading the model into the TfLiteInterpreter." << std::endl;
return -1;
}
// Use default settings until options have been enabled
flatbuffers::FlatBufferBuilder flatBufferBuilder;
tflite::TFLiteSettingsBuilder tfliteSettingsBuilder(flatBufferBuilder);
flatbuffers::Offset<tflite::TFLiteSettings> tfliteSettings = tfliteSettingsBuilder.Finish();
flatBufferBuilder.Finish(tfliteSettings);
const tflite::TFLiteSettings* settings =
flatbuffers::GetRoot<tflite::TFLiteSettings>(flatBufferBuilder.GetBufferPointer());
std::unique_ptr<tflite::delegates::DelegatePluginInterface> delegatePlugIn =
tflite::delegates::DelegatePluginRegistry::CreateByName("armnn_delegate", *settings);
// Create Armnn Opaque Delegate from Armnn Delegate Plugin
tflite::delegates::TfLiteDelegatePtr armnnDelegate = delegatePlugIn->Create();
// Add Delegate to the builder
builder.AddDelegate(armnnDelegate.get());
if (builder(&m_TfLiteInterpreter) != kTfLiteOk)
{
std::cout << "Unable to add the Arm NN delegate to the TfLite runtime." << std::endl;
return -1;
}
if (m_TfLiteInterpreter->AllocateTensors() != kTfLiteOk)
{
std::cout << "Failed to allocate tensors in the TfLiteInterpreter." << std::endl;
return -1;
}
// Really should populate the tensors here, but it'll work without it.
int status = m_TfLiteInterpreter->Invoke();
if (status != kTfLiteOk)
{
std::cout << "Inference failed." << std::endl;
return -1;
}
}
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