delegate/test/SplitTestHelper.hpp - ml/armnn - Gitiles

 //
 // Copyright © 2020, 2023-2024 Arm Ltd and Contributors. All rights reserved.
 // SPDX-License-Identifier: MIT
 //

 #pragma once

 #include "TestUtils.hpp"

 #include <armnn_delegate.hpp>
 #include <DelegateTestInterpreter.hpp>

 #include <tensorflow/lite/version.h>

 namespace
 {

 std::vector<char> CreateSplitTfLiteModel(tflite::TensorType tensorType,
                                          std::vector<int32_t>& axisTensorShape,
                                          std::vector<int32_t>& inputTensorShape,
                                          const std::vector<std::vector<int32_t>>& outputTensorShapes,
                                          std::vector<int32_t>& axisData,
                                          const int32_t numSplits,
                                          float quantScale = 1.0f,
                                          int quantOffset  = 0)
 {
     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*>(axisData.data()),
                                                                   sizeof(int32_t) * axisData.size())));

     auto quantizationParameters =
             CreateQuantizationParameters(flatBufferBuilder,
                                          0,
                                          0,
                                          flatBufferBuilder.CreateVector<float>({ quantScale }),
                                          flatBufferBuilder.CreateVector<int64_t>({ quantOffset }));

     std::array<flatbuffers::Offset<Tensor>, 4> tensors;
     tensors[0] = CreateTensor(flatBufferBuilder,
                               flatBufferBuilder.CreateVector<int32_t>(axisTensorShape.data(),
                                                                       axisTensorShape.size()),
                               ::tflite::TensorType_INT32,
                               2,
                               flatBufferBuilder.CreateString("axis"),
                               quantizationParameters);
     tensors[1] = CreateTensor(flatBufferBuilder,
                               flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(),
                                                                       inputTensorShape.size()),
                               tensorType,
                               1,
                               flatBufferBuilder.CreateString("input"),
                               quantizationParameters);

     // Create output tensor
     for (unsigned int i = 0; i < outputTensorShapes.size(); ++i)
     {
         buffers.push_back(CreateBuffer(flatBufferBuilder));
         tensors[i + 2] = CreateTensor(flatBufferBuilder,
                                       flatBufferBuilder.CreateVector<int32_t>(outputTensorShapes[i].data(),
                                                                               outputTensorShapes[i].size()),
                                       tensorType,
                                       (i+3),
                                       flatBufferBuilder.CreateString("output"),
                                       quantizationParameters);
     }

     // create operator. Mean uses ReducerOptions.
     tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_SplitOptions;
     flatbuffers::Offset<void> operatorBuiltinOptions = CreateSplitOptions(flatBufferBuilder, numSplits).Union();

     const std::vector<int> operatorInputs{ {0, 1} };
     const std::vector<int> operatorOutputs{ {2, 3} };
     flatbuffers::Offset <Operator> controlOperator =
             CreateOperator(flatBufferBuilder,
                            0,
                            flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
                            flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
                            operatorBuiltinOptionsType,
                            operatorBuiltinOptions);

     const std::vector<int> subgraphInputs{ {0, 1} };
     const std::vector<int> subgraphOutputs{ {2, 3} };
     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(&controlOperator, 1));

     flatbuffers::Offset <flatbuffers::String> modelDescription =
             flatBufferBuilder.CreateString("ArmnnDelegate: SPLIT Operator Model");
     flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, BuiltinOperator_SPLIT);

     flatbuffers::Offset <Model> flatbufferModel =
             CreateModel(flatBufferBuilder,
                         TFLITE_SCHEMA_VERSION,
                         flatBufferBuilder.CreateVector(&operatorCode, 1),
                         flatBufferBuilder.CreateVector(&subgraph, 1),
                         modelDescription,
                         flatBufferBuilder.CreateVector(buffers));

     flatBufferBuilder.Finish(flatbufferModel, armnnDelegate::FILE_IDENTIFIER);

     return std::vector<char>(flatBufferBuilder.GetBufferPointer(),
                              flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize());
 }

 template <typename T>
 void SplitTest(tflite::TensorType tensorType,
                std::vector<int32_t>& axisTensorShape,
                std::vector<int32_t>& inputTensorShape,
                std::vector<std::vector<int32_t>>& outputTensorShapes,
                std::vector<int32_t>& axisData,
                std::vector<T>& inputValues,
                std::vector<std::vector<T>>& expectedOutputValues,
                const int32_t numSplits,
                const std::vector<armnn::BackendId>& backends = {},
                float quantScale = 1.0f,
                int quantOffset  = 0)
 {
     using namespace delegateTestInterpreter;
     std::vector<char> modelBuffer = CreateSplitTfLiteModel(tensorType,
                                                            axisTensorShape,
                                                            inputTensorShape,
                                                            outputTensorShapes,
                                                            axisData,
                                                            numSplits,
                                                            quantScale,
                                                            quantOffset);
     // Setup interpreter with just TFLite Runtime.
     auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer);
     CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk);
     CHECK(tfLiteInterpreter.FillInputTensor<T>(inputValues, 1) == kTfLiteOk);
     CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk);

     // Setup interpreter with Arm NN Delegate applied.
     auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, CaptureAvailableBackends(backends));
     CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk);
     CHECK(armnnInterpreter.FillInputTensor<T>(inputValues, 1) == kTfLiteOk);
     CHECK(armnnInterpreter.Invoke() == kTfLiteOk);

     // Compare output data
     for (unsigned int i = 0; i < expectedOutputValues.size(); ++i)
     {
         std::vector<T>       tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<T>(i);
         std::vector<int32_t> tfLiteOutputShape  = tfLiteInterpreter.GetOutputShape(i);

         std::vector<T>       armnnOutputValues = armnnInterpreter.GetOutputResult<T>(i);
         std::vector<int32_t> armnnOutputShape  = armnnInterpreter.GetOutputShape(i);

         armnnDelegate::CompareOutputData<T>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues[i]);
         armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, outputTensorShapes[i]);
     }

     tfLiteInterpreter.Cleanup();
     armnnInterpreter.Cleanup();

 } // End of SPLIT Test

 std::vector<char> CreateSplitVTfLiteModel(tflite::TensorType tensorType,
                                           std::vector<int32_t>& inputTensorShape,
                                           std::vector<int32_t>& splitsTensorShape,
                                           std::vector<int32_t>& axisTensorShape,
                                           const std::vector<std::vector<int32_t>>& outputTensorShapes,
                                           std::vector<int32_t>& splitsData,
                                           std::vector<int32_t>& axisData,
                                           const int32_t numSplits,
                                           float quantScale = 1.0f,
                                           int quantOffset  = 0)
 {
     using namespace tflite;
     flatbuffers::FlatBufferBuilder flatBufferBuilder;

     std::array<flatbuffers::Offset<tflite::Buffer>, 3> buffers;
     buffers[0] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}));
     buffers[1] = CreateBuffer(flatBufferBuilder,
                               flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(splitsData.data()),
                                                              sizeof(int32_t) * splitsData.size()));
     buffers[2] = CreateBuffer(flatBufferBuilder,
                               flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(axisData.data()),
                                                              sizeof(int32_t) * axisData.size()));

     auto quantizationParameters =
             CreateQuantizationParameters(flatBufferBuilder,
                                          0,
                                          0,
                                          flatBufferBuilder.CreateVector<float>({ quantScale }),
                                          flatBufferBuilder.CreateVector<int64_t>({ quantOffset }));

     std::array<flatbuffers::Offset<Tensor>, 5> tensors;
     tensors[0] = CreateTensor(flatBufferBuilder,
                               flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(),
                                                                       inputTensorShape.size()),
                               tensorType,
                               0,
                               flatBufferBuilder.CreateString("input"),
                               quantizationParameters);
     tensors[1] = CreateTensor(flatBufferBuilder,
                               flatBufferBuilder.CreateVector<int32_t>(splitsTensorShape.data(),
                                                                       splitsTensorShape.size()),
                               ::tflite::TensorType_INT32,
                               1,
                               flatBufferBuilder.CreateString("splits"),
                               quantizationParameters);
     tensors[2] = CreateTensor(flatBufferBuilder,
                               flatBufferBuilder.CreateVector<int32_t>(axisTensorShape.data(),
                                                                       axisTensorShape.size()),
                               ::tflite::TensorType_INT32,
                               2,
                               flatBufferBuilder.CreateString("axis"),
                               quantizationParameters);

     // Create output tensor
     for (unsigned int i = 0; i < outputTensorShapes.size(); ++i)
     {
         tensors[i + 3] = CreateTensor(flatBufferBuilder,
                                       flatBufferBuilder.CreateVector<int32_t>(outputTensorShapes[i].data(),
                                                                               outputTensorShapes[i].size()),
                                       tensorType,
                                       0,
                                       flatBufferBuilder.CreateString("output"),
                                       quantizationParameters);
     }

     // create operator. Mean uses ReducerOptions.
     tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_SplitVOptions;
     flatbuffers::Offset<void> operatorBuiltinOptions = CreateSplitVOptions(flatBufferBuilder, numSplits).Union();

     const std::vector<int> operatorInputs{ {0, 1, 2} };
     const std::vector<int> operatorOutputs{ {3, 4} };
     flatbuffers::Offset <Operator> controlOperator =
             CreateOperator(flatBufferBuilder,
                            0,
                            flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
                            flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
                            operatorBuiltinOptionsType,
                            operatorBuiltinOptions);

     const std::vector<int> subgraphInputs{ {0, 1, 2} };
     const std::vector<int> subgraphOutputs{ {3, 4} };
     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(&controlOperator, 1));

     flatbuffers::Offset <flatbuffers::String> modelDescription =
             flatBufferBuilder.CreateString("ArmnnDelegate: SPLIT_V Operator Model");
     flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, BuiltinOperator_SPLIT_V);

     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, armnnDelegate::FILE_IDENTIFIER);

     return std::vector<char>(flatBufferBuilder.GetBufferPointer(),
                              flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize());
 }

 template <typename T>
 void SplitVTest(tflite::TensorType tensorType,
                 std::vector<int32_t>& inputTensorShape,
                 std::vector<int32_t>& splitsTensorShape,
                 std::vector<int32_t>& axisTensorShape,
                 std::vector<std::vector<int32_t>>& outputTensorShapes,
                 std::vector<T>& inputValues,
                 std::vector<int32_t>& splitsData,
                 std::vector<int32_t>& axisData,
                 std::vector<std::vector<T>>& expectedOutputValues,
                 const int32_t numSplits,
                 const std::vector<armnn::BackendId>& backends = {},
                 float quantScale = 1.0f,
                 int quantOffset  = 0)
 {
     using namespace delegateTestInterpreter;
     std::vector<char> modelBuffer = CreateSplitVTfLiteModel(tensorType,
                                                             inputTensorShape,
                                                             splitsTensorShape,
                                                             axisTensorShape,
                                                             outputTensorShapes,
                                                             splitsData,
                                                             axisData,
                                                             numSplits,
                                                             quantScale,
                                                             quantOffset);

     // Setup interpreter with just TFLite Runtime.
     auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer);
     CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk);
     CHECK(tfLiteInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk);
     CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk);

     // 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.Invoke() == kTfLiteOk);

     // Compare output data
     for (unsigned int i = 0; i < expectedOutputValues.size(); ++i)
     {
         std::vector<T>       tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<T>(i);
         std::vector<int32_t> tfLiteOutputShape  = tfLiteInterpreter.GetOutputShape(i);

         std::vector<T>       armnnOutputValues = armnnInterpreter.GetOutputResult<T>(i);
         std::vector<int32_t> armnnOutputShape  = armnnInterpreter.GetOutputShape(i);

         armnnDelegate::CompareOutputData<T>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues[i]);
         armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, outputTensorShapes[i]);
     }

     tfLiteInterpreter.Cleanup();
     armnnInterpreter.Cleanup();
 } // End of SPLIT_V Test

 } // anonymous namespace
	//
	// Copyright © 2020, 2023-2024 Arm Ltd and Contributors. All rights reserved.
	// SPDX-License-Identifier: MIT
	//

	#pragma once

	#include "TestUtils.hpp"

	#include <armnn_delegate.hpp>
	#include <DelegateTestInterpreter.hpp>

	#include <tensorflow/lite/version.h>

	namespace
	{

	std::vector<char> CreateSplitTfLiteModel(tflite::TensorType tensorType,
	std::vector<int32_t>& axisTensorShape,
	std::vector<int32_t>& inputTensorShape,
	const std::vector<std::vector<int32_t>>& outputTensorShapes,
	std::vector<int32_t>& axisData,
	const int32_t numSplits,
	float quantScale = 1.0f,
	int quantOffset = 0)
	{
	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*>(axisData.data()),
	sizeof(int32_t) * axisData.size())));

	auto quantizationParameters =
	CreateQuantizationParameters(flatBufferBuilder,
	0,
	0,
	flatBufferBuilder.CreateVector<float>({ quantScale }),
	flatBufferBuilder.CreateVector<int64_t>({ quantOffset }));

	std::array<flatbuffers::Offset<Tensor>, 4> tensors;
	tensors[0] = CreateTensor(flatBufferBuilder,
	flatBufferBuilder.CreateVector<int32_t>(axisTensorShape.data(),
	axisTensorShape.size()),
	::tflite::TensorType_INT32,
	2,
	flatBufferBuilder.CreateString("axis"),
	quantizationParameters);
	tensors[1] = CreateTensor(flatBufferBuilder,
	flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(),
	inputTensorShape.size()),
	tensorType,
	1,
	flatBufferBuilder.CreateString("input"),
	quantizationParameters);

	// Create output tensor
	for (unsigned int i = 0; i < outputTensorShapes.size(); ++i)
	{
	buffers.push_back(CreateBuffer(flatBufferBuilder));
	tensors[i + 2] = CreateTensor(flatBufferBuilder,
	flatBufferBuilder.CreateVector<int32_t>(outputTensorShapes[i].data(),
	outputTensorShapes[i].size()),
	tensorType,
	(i+3),
	flatBufferBuilder.CreateString("output"),
	quantizationParameters);
	}

	// create operator. Mean uses ReducerOptions.
	tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_SplitOptions;
	flatbuffers::Offset<void> operatorBuiltinOptions = CreateSplitOptions(flatBufferBuilder, numSplits).Union();

	const std::vector<int> operatorInputs{ {0, 1} };
	const std::vector<int> operatorOutputs{ {2, 3} };
	flatbuffers::Offset <Operator> controlOperator =
	CreateOperator(flatBufferBuilder,
	0,
	flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
	flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
	operatorBuiltinOptionsType,
	operatorBuiltinOptions);

	const std::vector<int> subgraphInputs{ {0, 1} };
	const std::vector<int> subgraphOutputs{ {2, 3} };
	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(&controlOperator, 1));

	flatbuffers::Offset <flatbuffers::String> modelDescription =
	flatBufferBuilder.CreateString("ArmnnDelegate: SPLIT Operator Model");
	flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, BuiltinOperator_SPLIT);

	flatbuffers::Offset <Model> flatbufferModel =
	CreateModel(flatBufferBuilder,
	TFLITE_SCHEMA_VERSION,
	flatBufferBuilder.CreateVector(&operatorCode, 1),
	flatBufferBuilder.CreateVector(&subgraph, 1),
	modelDescription,
	flatBufferBuilder.CreateVector(buffers));

	flatBufferBuilder.Finish(flatbufferModel, armnnDelegate::FILE_IDENTIFIER);

	return std::vector<char>(flatBufferBuilder.GetBufferPointer(),
	flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize());
	}

	template <typename T>
	void SplitTest(tflite::TensorType tensorType,
	std::vector<int32_t>& axisTensorShape,
	std::vector<int32_t>& inputTensorShape,
	std::vector<std::vector<int32_t>>& outputTensorShapes,
	std::vector<int32_t>& axisData,
	std::vector<T>& inputValues,
	std::vector<std::vector<T>>& expectedOutputValues,
	const int32_t numSplits,
	const std::vector<armnn::BackendId>& backends = {},
	float quantScale = 1.0f,
	int quantOffset = 0)
	{
	using namespace delegateTestInterpreter;
	std::vector<char> modelBuffer = CreateSplitTfLiteModel(tensorType,
	axisTensorShape,
	inputTensorShape,
	outputTensorShapes,
	axisData,
	numSplits,
	quantScale,
	quantOffset);
	// Setup interpreter with just TFLite Runtime.
	auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer);
	CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk);
	CHECK(tfLiteInterpreter.FillInputTensor<T>(inputValues, 1) == kTfLiteOk);
	CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk);

	// Setup interpreter with Arm NN Delegate applied.
	auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, CaptureAvailableBackends(backends));
	CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk);
	CHECK(armnnInterpreter.FillInputTensor<T>(inputValues, 1) == kTfLiteOk);
	CHECK(armnnInterpreter.Invoke() == kTfLiteOk);

	// Compare output data
	for (unsigned int i = 0; i < expectedOutputValues.size(); ++i)
	{
	std::vector<T> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<T>(i);
	std::vector<int32_t> tfLiteOutputShape = tfLiteInterpreter.GetOutputShape(i);

	std::vector<T> armnnOutputValues = armnnInterpreter.GetOutputResult<T>(i);
	std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(i);

	armnnDelegate::CompareOutputData<T>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues[i]);
	armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, outputTensorShapes[i]);
	}

	tfLiteInterpreter.Cleanup();
	armnnInterpreter.Cleanup();

	} // End of SPLIT Test

	std::vector<char> CreateSplitVTfLiteModel(tflite::TensorType tensorType,
	std::vector<int32_t>& inputTensorShape,
	std::vector<int32_t>& splitsTensorShape,
	std::vector<int32_t>& axisTensorShape,
	const std::vector<std::vector<int32_t>>& outputTensorShapes,
	std::vector<int32_t>& splitsData,
	std::vector<int32_t>& axisData,
	const int32_t numSplits,
	float quantScale = 1.0f,
	int quantOffset = 0)
	{
	using namespace tflite;
	flatbuffers::FlatBufferBuilder flatBufferBuilder;

	std::array<flatbuffers::Offset<tflite::Buffer>, 3> buffers;
	buffers[0] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}));
	buffers[1] = CreateBuffer(flatBufferBuilder,
	flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(splitsData.data()),
	sizeof(int32_t) * splitsData.size()));
	buffers[2] = CreateBuffer(flatBufferBuilder,
	flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(axisData.data()),
	sizeof(int32_t) * axisData.size()));

	auto quantizationParameters =
	CreateQuantizationParameters(flatBufferBuilder,
	0,
	0,
	flatBufferBuilder.CreateVector<float>({ quantScale }),
	flatBufferBuilder.CreateVector<int64_t>({ quantOffset }));

	std::array<flatbuffers::Offset<Tensor>, 5> tensors;
	tensors[0] = CreateTensor(flatBufferBuilder,
	flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(),
	inputTensorShape.size()),
	tensorType,
	0,
	flatBufferBuilder.CreateString("input"),
	quantizationParameters);
	tensors[1] = CreateTensor(flatBufferBuilder,
	flatBufferBuilder.CreateVector<int32_t>(splitsTensorShape.data(),
	splitsTensorShape.size()),
	::tflite::TensorType_INT32,
	1,
	flatBufferBuilder.CreateString("splits"),
	quantizationParameters);
	tensors[2] = CreateTensor(flatBufferBuilder,
	flatBufferBuilder.CreateVector<int32_t>(axisTensorShape.data(),
	axisTensorShape.size()),
	::tflite::TensorType_INT32,
	2,
	flatBufferBuilder.CreateString("axis"),
	quantizationParameters);

	// Create output tensor
	for (unsigned int i = 0; i < outputTensorShapes.size(); ++i)
	{
	tensors[i + 3] = CreateTensor(flatBufferBuilder,
	flatBufferBuilder.CreateVector<int32_t>(outputTensorShapes[i].data(),
	outputTensorShapes[i].size()),
	tensorType,
	0,
	flatBufferBuilder.CreateString("output"),
	quantizationParameters);
	}

	// create operator. Mean uses ReducerOptions.
	tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_SplitVOptions;
	flatbuffers::Offset<void> operatorBuiltinOptions = CreateSplitVOptions(flatBufferBuilder, numSplits).Union();

	const std::vector<int> operatorInputs{ {0, 1, 2} };
	const std::vector<int> operatorOutputs{ {3, 4} };
	flatbuffers::Offset <Operator> controlOperator =
	CreateOperator(flatBufferBuilder,
	0,
	flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
	flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
	operatorBuiltinOptionsType,
	operatorBuiltinOptions);

	const std::vector<int> subgraphInputs{ {0, 1, 2} };
	const std::vector<int> subgraphOutputs{ {3, 4} };
	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(&controlOperator, 1));

	flatbuffers::Offset <flatbuffers::String> modelDescription =
	flatBufferBuilder.CreateString("ArmnnDelegate: SPLIT_V Operator Model");
	flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, BuiltinOperator_SPLIT_V);

	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, armnnDelegate::FILE_IDENTIFIER);

	return std::vector<char>(flatBufferBuilder.GetBufferPointer(),
	flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize());
	}

	template <typename T>
	void SplitVTest(tflite::TensorType tensorType,
	std::vector<int32_t>& inputTensorShape,
	std::vector<int32_t>& splitsTensorShape,
	std::vector<int32_t>& axisTensorShape,
	std::vector<std::vector<int32_t>>& outputTensorShapes,
	std::vector<T>& inputValues,
	std::vector<int32_t>& splitsData,
	std::vector<int32_t>& axisData,
	std::vector<std::vector<T>>& expectedOutputValues,
	const int32_t numSplits,
	const std::vector<armnn::BackendId>& backends = {},
	float quantScale = 1.0f,
	int quantOffset = 0)
	{
	using namespace delegateTestInterpreter;
	std::vector<char> modelBuffer = CreateSplitVTfLiteModel(tensorType,
	inputTensorShape,
	splitsTensorShape,
	axisTensorShape,
	outputTensorShapes,
	splitsData,
	axisData,
	numSplits,
	quantScale,
	quantOffset);

	// Setup interpreter with just TFLite Runtime.
	auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer);
	CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk);
	CHECK(tfLiteInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk);
	CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk);

	// 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.Invoke() == kTfLiteOk);

	// Compare output data
	for (unsigned int i = 0; i < expectedOutputValues.size(); ++i)
	{
	std::vector<T> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<T>(i);
	std::vector<int32_t> tfLiteOutputShape = tfLiteInterpreter.GetOutputShape(i);

	std::vector<T> armnnOutputValues = armnnInterpreter.GetOutputResult<T>(i);
	std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(i);

	armnnDelegate::CompareOutputData<T>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues[i]);
	armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, outputTensorShapes[i]);
	}

	tfLiteInterpreter.Cleanup();
	armnnInterpreter.Cleanup();
	} // End of SPLIT_V Test

	} // anonymous namespace