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//
// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include <armnn_delegate.hpp>
#include <flatbuffers/flatbuffers.h>
#include <tensorflow/lite/interpreter.h>
#include <tensorflow/lite/kernels/register.h>
#include <tensorflow/lite/model.h>
#include <tensorflow/lite/schema/schema_generated.h>
#include <tensorflow/lite/version.h>
#include <doctest/doctest.h>
namespace
{
std::vector<char> CreateElementwiseUnaryTfLiteModel(tflite::BuiltinOperator unaryOperatorCode,
tflite::TensorType tensorType,
const std::vector <int32_t>& tensorShape)
{
using namespace tflite;
flatbuffers::FlatBufferBuilder flatBufferBuilder;
std::array<flatbuffers::Offset<tflite::Buffer>, 1> buffers;
buffers[0] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}));
std::array<flatbuffers::Offset<Tensor>, 2> tensors;
tensors[0] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), tensorShape.size()),
tensorType);
tensors[1] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), tensorShape.size()),
tensorType);
// create operator
const std::vector<int> operatorInputs{{0}};
const std::vector<int> operatorOutputs{{1}};
flatbuffers::Offset <Operator> unaryOperator =
CreateOperator(flatBufferBuilder,
0,
flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()));
const std::vector<int> subgraphInputs{{0}};
const std::vector<int> subgraphOutputs{{1}};
flatbuffers::Offset <SubGraph> subgraph =
CreateSubGraph(flatBufferBuilder,
flatBufferBuilder.CreateVector(tensors.data(), tensors.size()),
flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()),
flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()),
flatBufferBuilder.CreateVector(&unaryOperator, 1));
flatbuffers::Offset <flatbuffers::String> modelDescription =
flatBufferBuilder.CreateString("ArmnnDelegate: Elementwise Unary Operator Model");
flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, unaryOperatorCode);
flatbuffers::Offset <Model> flatbufferModel =
CreateModel(flatBufferBuilder,
TFLITE_SCHEMA_VERSION,
flatBufferBuilder.CreateVector(&operatorCode, 1),
flatBufferBuilder.CreateVector(&subgraph, 1),
modelDescription,
flatBufferBuilder.CreateVector(buffers.data(), buffers.size()));
flatBufferBuilder.Finish(flatbufferModel);
return std::vector<char>(flatBufferBuilder.GetBufferPointer(),
flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize());
}
void ElementwiseUnaryFP32Test(tflite::BuiltinOperator unaryOperatorCode,
std::vector<armnn::BackendId>& backends,
std::vector<float>& inputValues,
std::vector<float>& expectedOutputValues)
{
using namespace tflite;
const std::vector<int32_t> inputShape { { 3, 1, 2} };
std::vector<char> modelBuffer = CreateElementwiseUnaryTfLiteModel(unaryOperatorCode,
::tflite::TensorType_FLOAT32,
inputShape);
const Model* tfLiteModel = GetModel(modelBuffer.data());
// Create TfLite Interpreters
std::unique_ptr<Interpreter> armnnDelegateInterpreter;
CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver())
(&armnnDelegateInterpreter) == kTfLiteOk);
CHECK(armnnDelegateInterpreter != nullptr);
CHECK(armnnDelegateInterpreter->AllocateTensors() == kTfLiteOk);
std::unique_ptr<Interpreter> tfLiteInterpreter;
CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver())
(&tfLiteInterpreter) == kTfLiteOk);
CHECK(tfLiteInterpreter != nullptr);
CHECK(tfLiteInterpreter->AllocateTensors() == kTfLiteOk);
// Create the ArmNN Delegate
armnnDelegate::DelegateOptions delegateOptions(backends);
std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)>
theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions),
armnnDelegate::TfLiteArmnnDelegateDelete);
CHECK(theArmnnDelegate != nullptr);
// Modify armnnDelegateInterpreter to use armnnDelegate
CHECK(armnnDelegateInterpreter->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk);
// Set input data
auto tfLiteDelegateInputId = tfLiteInterpreter->inputs()[0];
auto tfLiteDelageInputData = tfLiteInterpreter->typed_tensor<float>(tfLiteDelegateInputId);
for (unsigned int i = 0; i < inputValues.size(); ++i)
{
tfLiteDelageInputData[i] = inputValues[i];
}
auto armnnDelegateInputId = armnnDelegateInterpreter->inputs()[0];
auto armnnDelegateInputData = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateInputId);
for (unsigned int i = 0; i < inputValues.size(); ++i)
{
armnnDelegateInputData[i] = inputValues[i];
}
// Run EnqueWorkload
CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk);
CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk);
// Compare output data
auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0];
auto tfLiteDelageOutputData = tfLiteInterpreter->typed_tensor<float>(tfLiteDelegateOutputId);
auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0];
auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateOutputId);
for (size_t i = 0; i < inputValues.size(); i++)
{
CHECK(expectedOutputValues[i] == armnnDelegateOutputData[i]);
CHECK(tfLiteDelageOutputData[i] == armnnDelegateOutputData[i]);
}
}
} // anonymous namespace
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