src/backends/backendsCommon/test/QuantizedLstmEndToEndTestImpl.cpp - platform/external/armnn - Git at Google

 //
 // Copyright © 2019 Arm Ltd. All rights reserved.
 // SPDX-License-Identifier: MIT
 //

 #include "QuantizedLstmEndToEndTestImpl.hpp"

 #include "CommonTestUtils.hpp"
 #include "EndToEndTestImpl.hpp"

 #include <ResolveType.hpp>

 #include <armnn/INetwork.hpp>
 #include <armnn/QuantizedLstmParams.hpp>

 #include <test/TensorHelpers.hpp>

 #include <boost/test/unit_test.hpp>

 #include <type_traits>

 namespace
 {

 using MultiArray = const boost::multi_array<uint8_t, 2>&;

 armnn::INetworkPtr CreateQuantizedLstmNetwork(MultiArray input,
                                               MultiArray expectedOutput)
 {
     auto batchSize = boost::numeric_cast<unsigned int>(input.shape()[0]);
     auto inputSize = boost::numeric_cast<unsigned int>(input.shape()[1]);
     auto outputSize = boost::numeric_cast<unsigned int>(expectedOutput.shape()[1]);

     float inputOutputScale = 0.0078125f;
     int32_t inputOutputOffset = 128;

     float weightsScale = 0.00408021f;
     int32_t weightsOffset = 100;

     float biasScale = 3.1876640625e-05f;
     int32_t biasOffset = 0;

     float cellStateScale = 0.00048828125f;
     int32_t cellStateOffset = 0;

     armnn::TensorInfo inputWeightsInfo({outputSize, inputSize},
                                        armnn::DataType::QAsymmU8,
                                        weightsScale,
                                        weightsOffset);

     armnn::TensorInfo recurrentWeightsInfo({outputSize, outputSize},
                                            armnn::DataType::QAsymmU8,
                                            weightsScale,
                                            weightsOffset);

     armnn::TensorInfo biasInfo({outputSize}, armnn::DataType::Signed32, biasScale, biasOffset);

     armnn::QuantizedLstmInputParams data;

     const std::vector<uint8_t> inputToInputWeightsVector = {146, 250, 235, 171, 10, 218, 171, 108};
     armnn::ConstTensor inputToInputWeightsTensor(inputWeightsInfo, inputToInputWeightsVector.data());

     const std::vector<uint8_t> inputToForgetWeightsVector = {24, 50, 132, 179, 158, 110, 3, 169};
     armnn::ConstTensor inputToForgetWeightsTensor(inputWeightsInfo, inputToForgetWeightsVector.data());

     const std::vector<uint8_t> inputToCellWeightsTensorVector = {133, 34, 29, 49, 206, 109, 54, 183};
     armnn::ConstTensor inputToCellWeightsTensor(inputWeightsInfo, inputToCellWeightsTensorVector.data());

     const std::vector<uint8_t> inputToOutputWeightsTensorVector = {195, 187, 11, 99, 109, 10, 218, 48};
     armnn::ConstTensor inputToOutputWeightsTensor(inputWeightsInfo, inputToOutputWeightsTensorVector.data());

     const std::vector<uint8_t> recurrentToInputWeightsTensorVector =
             {254, 206, 77, 168, 71, 20, 215, 6, 223, 7, 118, 225, 59, 130, 174, 26};
     armnn::ConstTensor recurrentToInputWeightsTensor(recurrentWeightsInfo, recurrentToInputWeightsTensorVector.data());

     const std::vector<uint8_t> recurrentToForgetWeightsTensorVector =
             {137, 240, 103, 52, 68, 51, 237, 112, 0, 220, 89, 23, 69, 4, 207, 253};
     armnn::ConstTensor recurrentToForgetWeightsTensor(recurrentWeightsInfo,
                                                       recurrentToForgetWeightsTensorVector.data());

     const std::vector<uint8_t> recurrentToCellWeightsTensorVector =
             {172, 60, 205, 65, 14, 0, 140, 168, 240, 223, 133, 56, 142, 64, 246, 216};
     armnn::ConstTensor recurrentToCellWeightsTensor(recurrentWeightsInfo, recurrentToCellWeightsTensorVector.data());

     const std::vector<uint8_t> recurrentToOutputWeightsTensorVector =
             {106, 214, 67, 23, 59, 158, 45, 3, 119, 132, 49, 205, 129, 218, 11, 98};
     armnn::ConstTensor recurrentToOutputWeightsTensor(recurrentWeightsInfo,
                                                       recurrentToOutputWeightsTensorVector.data());

     const std::vector<int32_t> inputGateBiasTensorVector = {-7876, 13488, -726, 32839};
     armnn::ConstTensor inputGateBiasTensor(biasInfo, inputGateBiasTensorVector.data());

     const std::vector<int32_t> forgetGateBiasTensorVector = {9206, -46884, -11693, -38724};
     armnn::ConstTensor forgetGateBiasTensor(biasInfo, forgetGateBiasTensorVector.data());

     const std::vector<int32_t> cellBiasTensorVector = {39481, 48624, 48976, -21419};
     armnn::ConstTensor cellBiasTensor(biasInfo, cellBiasTensorVector.data());

     const std::vector<int32_t> outputGateBiasTensorVector = {-58999, -17050, -41852, -40538};
     armnn::ConstTensor outputGateBiasTensor(biasInfo, outputGateBiasTensorVector.data());

     data.m_InputToInputWeights = &inputToInputWeightsTensor;
     data.m_InputToForgetWeights = &inputToForgetWeightsTensor;
     data.m_InputToCellWeights = &inputToCellWeightsTensor;
     data.m_InputToOutputWeights = &inputToOutputWeightsTensor;
     data.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor;
     data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor;
     data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor;
     data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor;
     data.m_InputGateBias = &inputGateBiasTensor;
     data.m_ForgetGateBias = &forgetGateBiasTensor;
     data.m_CellBias = &cellBiasTensor;
     data.m_OutputGateBias = &outputGateBiasTensor;

     armnn::INetworkPtr net(armnn::INetwork::Create());

     armnn::IConnectableLayer* const inputLayer   = net->AddInputLayer(0);
     armnn::IConnectableLayer* const cellStateIn = net->AddInputLayer(1);
     armnn::IConnectableLayer* const outputStateIn = net->AddInputLayer(2);
     armnn::IConnectableLayer* const quantizedLstmLayer = net->AddQuantizedLstmLayer(data, "quantizedLstm");
     armnn::IConnectableLayer* const cellStateOut  = net->AddOutputLayer(0);
     armnn::IConnectableLayer* const outputStateOut  = net->AddOutputLayer(1);

     armnn::TensorInfo inputTensorInfo({batchSize , inputSize},
                                       armnn::DataType::QAsymmU8,
                                       inputOutputScale,
                                       inputOutputOffset);

     armnn::TensorInfo cellStateInTensorInfo({batchSize , outputSize},
                                             armnn::DataType::QSymmS16,
                                             cellStateScale,
                                             cellStateOffset);

     armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize},
                                               armnn::DataType::QAsymmU8,
                                               inputOutputScale,
                                               inputOutputOffset);

     armnn::TensorInfo cellStateOutTensorInfo({batchSize, outputSize},
                                              armnn::DataType::QSymmS16,
                                              cellStateScale,
                                              cellStateOffset);

     armnn::TensorInfo outputTensorInfo({batchSize, outputSize},
                                        armnn::DataType::QAsymmU8,
                                        inputOutputScale,
                                        inputOutputOffset);

     // connect up
     // inputs
     Connect(inputLayer, quantizedLstmLayer, inputTensorInfo, 0, 0);
     Connect(cellStateIn, quantizedLstmLayer, cellStateInTensorInfo, 0, 1);
     Connect(outputStateIn, quantizedLstmLayer, outputStateInTensorInfo, 0, 2);

     // outputs
     Connect(quantizedLstmLayer, cellStateOut, cellStateOutTensorInfo, 0, 0);
     Connect(quantizedLstmLayer, outputStateOut, outputTensorInfo, 1, 0);

     return net;
 }

 // Checks if two values of an arithmetic type are close enough to each other
 // with regard to a given tolerance value.
 template<typename T>
 typename std::enable_if<std::is_arithmetic<T>::value, bool>::type
 IsCloseEnough(T value1, T value2, T tolerance)
 {
     if (tolerance < 0)
     {
         throw armnn::InvalidArgumentException("Tolerance cannot be < 0");
     }

     T diff = value1 >= value2 ? static_cast<T>(value1 - value2) : static_cast<T>(value2 - value1);
     return diff <= tolerance;
 }

 } // anonymous namespace

 void QuantizedLstmEndToEnd(const std::vector<armnn::BackendId>& backends)
 {
     std::vector<uint8_t> inputVector = {166, 179, 50, 150};
     armnn::TensorInfo inputDesc({2, 2}, armnn::DataType::QAsymmU8);
     boost::multi_array<uint8_t, 2> input = MakeTensor<uint8_t, 2>(inputDesc, inputVector);

     std::vector<int16_t> cellStateInVector = {876, 1034, 955, -909, 761, 1029, 796, -1036};
     armnn::TensorInfo cellStateInDesc({2, 4}, armnn::DataType::QSymmS16);
     boost::multi_array<int16_t, 2> cellStateIn = MakeTensor<int16_t, 2>(cellStateInDesc, cellStateInVector);

     std::vector<uint8_t> outputStateInVector = {136, 150, 140, 115, 135, 152, 138, 112};
     armnn::TensorInfo outputStateInDesc({2, 4}, armnn::DataType::QAsymmU8);
     boost::multi_array<uint8_t, 2> outputStateIn = MakeTensor<uint8_t, 2>(outputStateInDesc, outputStateInVector);

     std::vector<int16_t> cellStateOutVector = {1485, 1177, 1373, -1023, 1019, 1355, 1097, -1235};
     armnn::TensorInfo cellStateOutVectorDesc({2, 4}, armnn::DataType::QSymmS16);
     boost::multi_array<int16_t, 2> cellStateOut = MakeTensor<int16_t, 2>(cellStateOutVectorDesc, cellStateOutVector);

     std::vector<uint8_t> outputStateOutVector = {140, 151, 146, 112, 136, 156, 142, 112};
     armnn::TensorInfo outputDesc({2, 4}, armnn::DataType::QAsymmU8);
     boost::multi_array<uint8_t, 2> outputStateOut = MakeTensor<uint8_t, 2>(outputDesc, outputStateOutVector);

     // Builds up the structure of the network
     armnn::INetworkPtr net = CreateQuantizedLstmNetwork(input, outputStateOut);

     BOOST_TEST_CHECKPOINT("create a network");

     IRuntime::CreationOptions options;
     IRuntimePtr runtime(IRuntime::Create(options));

     // optimize the network
     IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec());

     // Loads it into the runtime.
     NetworkId netId;
     runtime->LoadNetwork(netId, std::move(optNet));

     InputTensors inputTensors;
     inputTensors.reserve(3);

     // input
     inputTensors.push_back({0, ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputVector.data())});
     inputTensors.push_back({1, ConstTensor(runtime->GetInputTensorInfo(netId, 1), cellStateInVector.data())});
     inputTensors.push_back({2, ConstTensor(runtime->GetInputTensorInfo(netId, 2), outputStateInVector.data())});

     OutputTensors outputTensors;
     outputTensors.reserve(2);

     //output
     std::vector<int16_t > cellStateOutResult(cellStateOutVector.size());
     std::vector<uint8_t > outputStateOutResult(outputStateOutVector.size());
     outputTensors.push_back({0, Tensor(runtime->GetOutputTensorInfo(netId, 0), cellStateOutResult.data())});
     outputTensors.push_back({1, Tensor(runtime->GetOutputTensorInfo(netId, 1), outputStateOutResult.data())});

     // Does the inference.
     runtime->EnqueueWorkload(netId, inputTensors, outputTensors);

     // Checks the results
     constexpr int16_t toleranceInt16 = 2;
     for (unsigned int i = 0u; i < cellStateOutResult.size(); ++i)
     {
         BOOST_CHECK(IsCloseEnough(cellStateOutVector[i], cellStateOutResult[i], toleranceInt16));
     }

     constexpr uint8_t toleranceUint8 = 1;
     for (unsigned int i = 0u; i < outputStateOutResult.size(); ++i)
     {
         BOOST_TEST(IsCloseEnough(outputStateOutVector[i], outputStateOutResult[i], toleranceUint8));
     }
 }
	//
	// Copyright © 2019 Arm Ltd. All rights reserved.
	// SPDX-License-Identifier: MIT
	//

	#include "QuantizedLstmEndToEndTestImpl.hpp"

	#include "CommonTestUtils.hpp"
	#include "EndToEndTestImpl.hpp"

	#include <ResolveType.hpp>

	#include <armnn/INetwork.hpp>
	#include <armnn/QuantizedLstmParams.hpp>

	#include <test/TensorHelpers.hpp>

	#include <boost/test/unit_test.hpp>

	#include <type_traits>

	namespace
	{

	using MultiArray = const boost::multi_array<uint8_t, 2>&;

	armnn::INetworkPtr CreateQuantizedLstmNetwork(MultiArray input,
	MultiArray expectedOutput)
	{
	auto batchSize = boost::numeric_cast<unsigned int>(input.shape()[0]);
	auto inputSize = boost::numeric_cast<unsigned int>(input.shape()[1]);
	auto outputSize = boost::numeric_cast<unsigned int>(expectedOutput.shape()[1]);

	float inputOutputScale = 0.0078125f;
	int32_t inputOutputOffset = 128;

	float weightsScale = 0.00408021f;
	int32_t weightsOffset = 100;

	float biasScale = 3.1876640625e-05f;
	int32_t biasOffset = 0;

	float cellStateScale = 0.00048828125f;
	int32_t cellStateOffset = 0;

	armnn::TensorInfo inputWeightsInfo({outputSize, inputSize},
	armnn::DataType::QAsymmU8,
	weightsScale,
	weightsOffset);

	armnn::TensorInfo recurrentWeightsInfo({outputSize, outputSize},
	armnn::DataType::QAsymmU8,
	weightsScale,
	weightsOffset);

	armnn::TensorInfo biasInfo({outputSize}, armnn::DataType::Signed32, biasScale, biasOffset);

	armnn::QuantizedLstmInputParams data;

	const std::vector<uint8_t> inputToInputWeightsVector = {146, 250, 235, 171, 10, 218, 171, 108};
	armnn::ConstTensor inputToInputWeightsTensor(inputWeightsInfo, inputToInputWeightsVector.data());

	const std::vector<uint8_t> inputToForgetWeightsVector = {24, 50, 132, 179, 158, 110, 3, 169};
	armnn::ConstTensor inputToForgetWeightsTensor(inputWeightsInfo, inputToForgetWeightsVector.data());

	const std::vector<uint8_t> inputToCellWeightsTensorVector = {133, 34, 29, 49, 206, 109, 54, 183};
	armnn::ConstTensor inputToCellWeightsTensor(inputWeightsInfo, inputToCellWeightsTensorVector.data());

	const std::vector<uint8_t> inputToOutputWeightsTensorVector = {195, 187, 11, 99, 109, 10, 218, 48};
	armnn::ConstTensor inputToOutputWeightsTensor(inputWeightsInfo, inputToOutputWeightsTensorVector.data());

	const std::vector<uint8_t> recurrentToInputWeightsTensorVector =
	{254, 206, 77, 168, 71, 20, 215, 6, 223, 7, 118, 225, 59, 130, 174, 26};
	armnn::ConstTensor recurrentToInputWeightsTensor(recurrentWeightsInfo, recurrentToInputWeightsTensorVector.data());

	const std::vector<uint8_t> recurrentToForgetWeightsTensorVector =
	{137, 240, 103, 52, 68, 51, 237, 112, 0, 220, 89, 23, 69, 4, 207, 253};
	armnn::ConstTensor recurrentToForgetWeightsTensor(recurrentWeightsInfo,
	recurrentToForgetWeightsTensorVector.data());

	const std::vector<uint8_t> recurrentToCellWeightsTensorVector =
	{172, 60, 205, 65, 14, 0, 140, 168, 240, 223, 133, 56, 142, 64, 246, 216};
	armnn::ConstTensor recurrentToCellWeightsTensor(recurrentWeightsInfo, recurrentToCellWeightsTensorVector.data());

	const std::vector<uint8_t> recurrentToOutputWeightsTensorVector =
	{106, 214, 67, 23, 59, 158, 45, 3, 119, 132, 49, 205, 129, 218, 11, 98};
	armnn::ConstTensor recurrentToOutputWeightsTensor(recurrentWeightsInfo,
	recurrentToOutputWeightsTensorVector.data());

	const std::vector<int32_t> inputGateBiasTensorVector = {-7876, 13488, -726, 32839};
	armnn::ConstTensor inputGateBiasTensor(biasInfo, inputGateBiasTensorVector.data());

	const std::vector<int32_t> forgetGateBiasTensorVector = {9206, -46884, -11693, -38724};
	armnn::ConstTensor forgetGateBiasTensor(biasInfo, forgetGateBiasTensorVector.data());

	const std::vector<int32_t> cellBiasTensorVector = {39481, 48624, 48976, -21419};
	armnn::ConstTensor cellBiasTensor(biasInfo, cellBiasTensorVector.data());

	const std::vector<int32_t> outputGateBiasTensorVector = {-58999, -17050, -41852, -40538};
	armnn::ConstTensor outputGateBiasTensor(biasInfo, outputGateBiasTensorVector.data());

	data.m_InputToInputWeights = &inputToInputWeightsTensor;
	data.m_InputToForgetWeights = &inputToForgetWeightsTensor;
	data.m_InputToCellWeights = &inputToCellWeightsTensor;
	data.m_InputToOutputWeights = &inputToOutputWeightsTensor;
	data.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor;
	data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor;
	data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor;
	data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor;
	data.m_InputGateBias = &inputGateBiasTensor;
	data.m_ForgetGateBias = &forgetGateBiasTensor;
	data.m_CellBias = &cellBiasTensor;
	data.m_OutputGateBias = &outputGateBiasTensor;

	armnn::INetworkPtr net(armnn::INetwork::Create());

	armnn::IConnectableLayer* const inputLayer = net->AddInputLayer(0);
	armnn::IConnectableLayer* const cellStateIn = net->AddInputLayer(1);
	armnn::IConnectableLayer* const outputStateIn = net->AddInputLayer(2);
	armnn::IConnectableLayer* const quantizedLstmLayer = net->AddQuantizedLstmLayer(data, "quantizedLstm");
	armnn::IConnectableLayer* const cellStateOut = net->AddOutputLayer(0);
	armnn::IConnectableLayer* const outputStateOut = net->AddOutputLayer(1);

	armnn::TensorInfo inputTensorInfo({batchSize , inputSize},
	armnn::DataType::QAsymmU8,
	inputOutputScale,
	inputOutputOffset);

	armnn::TensorInfo cellStateInTensorInfo({batchSize , outputSize},
	armnn::DataType::QSymmS16,
	cellStateScale,
	cellStateOffset);

	armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize},
	armnn::DataType::QAsymmU8,
	inputOutputScale,
	inputOutputOffset);

	armnn::TensorInfo cellStateOutTensorInfo({batchSize, outputSize},
	armnn::DataType::QSymmS16,
	cellStateScale,
	cellStateOffset);

	armnn::TensorInfo outputTensorInfo({batchSize, outputSize},
	armnn::DataType::QAsymmU8,
	inputOutputScale,
	inputOutputOffset);

	// connect up
	// inputs
	Connect(inputLayer, quantizedLstmLayer, inputTensorInfo, 0, 0);
	Connect(cellStateIn, quantizedLstmLayer, cellStateInTensorInfo, 0, 1);
	Connect(outputStateIn, quantizedLstmLayer, outputStateInTensorInfo, 0, 2);

	// outputs
	Connect(quantizedLstmLayer, cellStateOut, cellStateOutTensorInfo, 0, 0);
	Connect(quantizedLstmLayer, outputStateOut, outputTensorInfo, 1, 0);

	return net;
	}

	// Checks if two values of an arithmetic type are close enough to each other
	// with regard to a given tolerance value.
	template<typename T>
	typename std::enable_if<std::is_arithmetic<T>::value, bool>::type
	IsCloseEnough(T value1, T value2, T tolerance)
	{
	if (tolerance < 0)
	{
	throw armnn::InvalidArgumentException("Tolerance cannot be < 0");
	}

	T diff = value1 >= value2 ? static_cast<T>(value1 - value2) : static_cast<T>(value2 - value1);
	return diff <= tolerance;
	}

	} // anonymous namespace

	void QuantizedLstmEndToEnd(const std::vector<armnn::BackendId>& backends)
	{
	std::vector<uint8_t> inputVector = {166, 179, 50, 150};
	armnn::TensorInfo inputDesc({2, 2}, armnn::DataType::QAsymmU8);
	boost::multi_array<uint8_t, 2> input = MakeTensor<uint8_t, 2>(inputDesc, inputVector);

	std::vector<int16_t> cellStateInVector = {876, 1034, 955, -909, 761, 1029, 796, -1036};
	armnn::TensorInfo cellStateInDesc({2, 4}, armnn::DataType::QSymmS16);
	boost::multi_array<int16_t, 2> cellStateIn = MakeTensor<int16_t, 2>(cellStateInDesc, cellStateInVector);

	std::vector<uint8_t> outputStateInVector = {136, 150, 140, 115, 135, 152, 138, 112};
	armnn::TensorInfo outputStateInDesc({2, 4}, armnn::DataType::QAsymmU8);
	boost::multi_array<uint8_t, 2> outputStateIn = MakeTensor<uint8_t, 2>(outputStateInDesc, outputStateInVector);

	std::vector<int16_t> cellStateOutVector = {1485, 1177, 1373, -1023, 1019, 1355, 1097, -1235};
	armnn::TensorInfo cellStateOutVectorDesc({2, 4}, armnn::DataType::QSymmS16);
	boost::multi_array<int16_t, 2> cellStateOut = MakeTensor<int16_t, 2>(cellStateOutVectorDesc, cellStateOutVector);

	std::vector<uint8_t> outputStateOutVector = {140, 151, 146, 112, 136, 156, 142, 112};
	armnn::TensorInfo outputDesc({2, 4}, armnn::DataType::QAsymmU8);
	boost::multi_array<uint8_t, 2> outputStateOut = MakeTensor<uint8_t, 2>(outputDesc, outputStateOutVector);

	// Builds up the structure of the network
	armnn::INetworkPtr net = CreateQuantizedLstmNetwork(input, outputStateOut);

	BOOST_TEST_CHECKPOINT("create a network");

	IRuntime::CreationOptions options;
	IRuntimePtr runtime(IRuntime::Create(options));

	// optimize the network
	IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec());

	// Loads it into the runtime.
	NetworkId netId;
	runtime->LoadNetwork(netId, std::move(optNet));

	InputTensors inputTensors;
	inputTensors.reserve(3);

	// input
	inputTensors.push_back({0, ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputVector.data())});
	inputTensors.push_back({1, ConstTensor(runtime->GetInputTensorInfo(netId, 1), cellStateInVector.data())});
	inputTensors.push_back({2, ConstTensor(runtime->GetInputTensorInfo(netId, 2), outputStateInVector.data())});

	OutputTensors outputTensors;
	outputTensors.reserve(2);

	//output
	std::vector<int16_t > cellStateOutResult(cellStateOutVector.size());
	std::vector<uint8_t > outputStateOutResult(outputStateOutVector.size());
	outputTensors.push_back({0, Tensor(runtime->GetOutputTensorInfo(netId, 0), cellStateOutResult.data())});
	outputTensors.push_back({1, Tensor(runtime->GetOutputTensorInfo(netId, 1), outputStateOutResult.data())});

	// Does the inference.
	runtime->EnqueueWorkload(netId, inputTensors, outputTensors);

	// Checks the results
	constexpr int16_t toleranceInt16 = 2;
	for (unsigned int i = 0u; i < cellStateOutResult.size(); ++i)
	{
	BOOST_CHECK(IsCloseEnough(cellStateOutVector[i], cellStateOutResult[i], toleranceInt16));
	}

	constexpr uint8_t toleranceUint8 = 1;
	for (unsigned int i = 0u; i < outputStateOutResult.size(); ++i)
	{
	BOOST_TEST(IsCloseEnough(outputStateOutVector[i], outputStateOutResult[i], toleranceUint8));
	}
	}