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//
// Copyright © 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 <doctest/doctest.h>
namespace
{
std::vector<char> CreateBroadcastToTfLiteModel(tflite::BuiltinOperator operatorCode,
tflite::TensorType inputTensorType,
const std::vector<int32_t>& inputTensorShape,
const std::vector<int32_t>& shapeTensorShape,
const std::vector<int32_t>& shapeTensorData,
const std::vector<int32_t>& outputTensorShape)
{
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*>(shapeTensorData.data()),
sizeof(int32_t) * shapeTensorData.size())));
buffers.push_back(CreateBuffer(flatBufferBuilder));
float qScale = 1.0f;
int32_t qOffset = 0;
auto quantizationParameters =
CreateQuantizationParameters(flatBufferBuilder,
0,
0,
flatBufferBuilder.CreateVector<float>({ qScale }),
flatBufferBuilder.CreateVector<int64_t>({ qOffset }));
std::array<flatbuffers::Offset<Tensor>, 3> tensors;
tensors[0] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(),
inputTensorShape.size()),
inputTensorType,
1,
flatBufferBuilder.CreateString("input_tensor"),
quantizationParameters);
tensors[1] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(shapeTensorShape.data(),
shapeTensorShape.size()),
TensorType_INT32,
2,
flatBufferBuilder.CreateString("shape_input_tensor"),
quantizationParameters);
tensors[2] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
outputTensorShape.size()),
inputTensorType,
3,
flatBufferBuilder.CreateString("output_tensor"),
quantizationParameters);
// Create Operator
tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_BroadcastToOptions;
flatbuffers::Offset<void> operatorBuiltinOption = 0;
const std::vector<int> operatorInputs {0, 1};
const std::vector<int> operatorOutputs {2};
flatbuffers::Offset<Operator> broadcastOperator =
CreateOperator(flatBufferBuilder,
0,
flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
operatorBuiltinOptionsType,
operatorBuiltinOption);
const std::vector<int> subgraphInputs{0, 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(&broadcastOperator, 1));
flatbuffers::Offset <flatbuffers::String> modelDescription =
flatBufferBuilder.CreateString("ArmnnDelegate: BrodacastTo Operator Model");
flatbuffers::Offset <OperatorCode> opCode = CreateOperatorCode(flatBufferBuilder,0,
0, 2,
tflite::BuiltinOperator_BROADCAST_TO);
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());
}
template<typename T>
void BroadcastToTestImpl(tflite::TensorType inputTensorType,
tflite::BuiltinOperator operatorCode,
std::vector<T>& inputValues,
std::vector<int32_t> inputShape,
std::vector<int32_t> shapeShapes,
std::vector<int32_t> shapeData,
std::vector<T>& expectedOutputValues,
std::vector<int32_t> expectedOutputShape,
const std::vector<armnn::BackendId>& backends)
{
using namespace delegateTestInterpreter;
std::vector<char> modelBuffer = CreateBroadcastToTfLiteModel(operatorCode,
inputTensorType,
inputShape,
shapeShapes,
shapeData,
expectedOutputShape);
// Setup interpreter with just TFLite Runtime.
auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer);
CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk);
CHECK(tfLiteInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk);
CHECK(tfLiteInterpreter.FillInputTensor<int32_t>(shapeData, 1) == kTfLiteOk);
CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk);
std::vector<T> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<T>(0);
std::vector<int32_t> tfLiteOutputShape = tfLiteInterpreter.GetOutputShape(0);
// Setup interpreter with Arm NN Delegate applied.
auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, CaptureAvailableBackends(backends));
CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk);
CHECK(armnnInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk);
CHECK(armnnInterpreter.FillInputTensor<int32_t>(shapeData, 1) == kTfLiteOk);
CHECK(armnnInterpreter.Invoke() == kTfLiteOk);
std::vector<T> armnnOutputValues = armnnInterpreter.GetOutputResult<T>(0);
std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(0);
armnnDelegate::CompareOutputData<T>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues);
armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, expectedOutputShape);
tfLiteInterpreter.Cleanup();
armnnInterpreter.Cleanup();
}
} // anonymous namespace
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