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//
// Copyright © 2021, 2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include "TestUtils.hpp"
#include <armnn_delegate.hpp>
#include <DelegateTestInterpreter.hpp>
#include <flatbuffers/flatbuffers.h>
#include <tensorflow/lite/kernels/register.h>
#include <tensorflow/lite/version.h>
#include <schema_generated.h>
#include <doctest/doctest.h>
namespace
{
std::vector<char> CreatePreluTfLiteModel(tflite::BuiltinOperator preluOperatorCode,
tflite::TensorType tensorType,
const std::vector<int32_t>& inputShape,
const std::vector<int32_t>& alphaShape,
const std::vector<int32_t>& outputShape,
std::vector<float>& alphaData,
bool alphaIsConstant)
{
using namespace tflite;
flatbuffers::FlatBufferBuilder flatBufferBuilder;
std::vector<flatbuffers::Offset<tflite::Buffer>> buffers;
buffers.push_back(CreateBuffer(flatBufferBuilder));
buffers.push_back(CreateBuffer(flatBufferBuilder));
buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector(
reinterpret_cast<const uint8_t *>(alphaData.data()), sizeof(float) * alphaData.size())));
buffers.push_back(CreateBuffer(flatBufferBuilder));
auto quantizationParameters =
CreateQuantizationParameters(flatBufferBuilder,
0,
0,
flatBufferBuilder.CreateVector<float>({ 1.0f }),
flatBufferBuilder.CreateVector<int64_t>({ 0 }));
auto inputTensor = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(inputShape.data(),
inputShape.size()),
tensorType,
1,
flatBufferBuilder.CreateString("input"),
quantizationParameters);
auto alphaTensor = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(alphaShape.data(),
alphaShape.size()),
tensorType,
2,
flatBufferBuilder.CreateString("alpha"),
quantizationParameters);
auto outputTensor = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(outputShape.data(),
outputShape.size()),
tensorType,
3,
flatBufferBuilder.CreateString("output"),
quantizationParameters);
std::vector<flatbuffers::Offset<Tensor>> tensors = { inputTensor, alphaTensor, outputTensor };
const std::vector<int> operatorInputs{0, 1};
const std::vector<int> operatorOutputs{2};
flatbuffers::Offset <Operator> preluOperator =
CreateOperator(flatBufferBuilder,
0,
flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()));
std::vector<int> subgraphInputs{0};
if (!alphaIsConstant)
{
subgraphInputs.push_back(1);
}
const std::vector<int> subgraphOutputs{2};
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(&preluOperator, 1));
flatbuffers::Offset <flatbuffers::String> modelDescription =
flatBufferBuilder.CreateString("ArmnnDelegate: Prelu Operator Model");
flatbuffers::Offset <OperatorCode> opCode = CreateOperatorCode(flatBufferBuilder, preluOperatorCode);
flatbuffers::Offset <Model> flatbufferModel =
CreateModel(flatBufferBuilder,
TFLITE_SCHEMA_VERSION,
flatBufferBuilder.CreateVector(&opCode, 1),
flatBufferBuilder.CreateVector(&subgraph, 1),
modelDescription,
flatBufferBuilder.CreateVector(buffers.data(), buffers.size()));
flatBufferBuilder.Finish(flatbufferModel, armnnDelegate::FILE_IDENTIFIER);
return std::vector<char>(flatBufferBuilder.GetBufferPointer(),
flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize());
}
void PreluTest(tflite::BuiltinOperator preluOperatorCode,
tflite::TensorType tensorType,
const std::vector<armnn::BackendId>& backends,
const std::vector<int32_t>& inputShape,
const std::vector<int32_t>& alphaShape,
std::vector<int32_t>& outputShape,
std::vector<float>& inputData,
std::vector<float>& alphaData,
std::vector<float>& expectedOutput,
bool alphaIsConstant)
{
using namespace delegateTestInterpreter;
std::vector<char> modelBuffer = CreatePreluTfLiteModel(preluOperatorCode,
tensorType,
inputShape,
alphaShape,
outputShape,
alphaData,
alphaIsConstant);
// Setup interpreter with just TFLite Runtime.
auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer);
CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk);
// Setup interpreter with Arm NN Delegate applied.
auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, backends);
CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk);
CHECK(armnnInterpreter.FillInputTensor<float>(inputData, 0) == kTfLiteOk);
CHECK(tfLiteInterpreter.FillInputTensor<float>(inputData, 0) == kTfLiteOk);
// Set alpha data if not constant
if (!alphaIsConstant)
{
CHECK(tfLiteInterpreter.FillInputTensor<float>(alphaData, 1) == kTfLiteOk);
CHECK(armnnInterpreter.FillInputTensor<float>(alphaData, 1) == kTfLiteOk);
}
CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk);
std::vector<float> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<float>(0);
CHECK(armnnInterpreter.Invoke() == kTfLiteOk);
std::vector<float> armnnOutputValues = armnnInterpreter.GetOutputResult<float>(0);
armnnDelegate::CompareOutputData<float>(tfLiteOutputValues, armnnOutputValues, expectedOutput);
// Don't compare shapes on dynamic output tests, as output shape gets cleared.
if(!outputShape.empty())
{
std::vector<int32_t> tfLiteOutputShape = tfLiteInterpreter.GetOutputShape(0);
std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(0);
armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, outputShape);
}
tfLiteInterpreter.Cleanup();
armnnInterpreter.Cleanup();
}
} // anonymous namespace
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