src/armnnTfLiteParser/test/ParserFlatbuffersFixture.hpp - platform/external/armnn - Git at Google

 //
 // Copyright © 2017 Arm Ltd. All rights reserved.
 // SPDX-License-Identifier: MIT
 //

 #pragma once

 #include <armnn/Descriptors.hpp>
 #include <armnn/IRuntime.hpp>
 #include <armnn/TypesUtils.hpp>

 #include "Schema.hpp"
 #include <boost/filesystem.hpp>
 #include <boost/assert.hpp>
 #include <boost/format.hpp>

 #include "test/TensorHelpers.hpp"

 #include <ResolveType.hpp>
 #include "armnnTfLiteParser/ITfLiteParser.hpp"

 #include <backendsCommon/BackendRegistry.hpp>

 #include "flatbuffers/idl.h"
 #include "flatbuffers/util.h"
 #include "flatbuffers/flexbuffers.h"

 #include <schema_generated.h>
 #include <iostream>

 using armnnTfLiteParser::ITfLiteParser;
 using armnnTfLiteParser::ITfLiteParserPtr;

 using TensorRawPtr = const tflite::TensorT *;
 struct ParserFlatbuffersFixture
 {
     ParserFlatbuffersFixture() :
         m_Parser(nullptr, &ITfLiteParser::Destroy),
         m_Runtime(armnn::IRuntime::Create(armnn::IRuntime::CreationOptions())),
         m_NetworkIdentifier(-1)
     {
         ITfLiteParser::TfLiteParserOptions options;
         options.m_StandInLayerForUnsupported = true;

         m_Parser.reset(ITfLiteParser::CreateRaw(armnn::Optional<ITfLiteParser::TfLiteParserOptions>(options)));
     }

     std::vector<uint8_t> m_GraphBinary;
     std::string          m_JsonString;
     ITfLiteParserPtr     m_Parser;
     armnn::IRuntimePtr   m_Runtime;
     armnn::NetworkId     m_NetworkIdentifier;

     /// If the single-input-single-output overload of Setup() is called, these will store the input and output name
     /// so they don't need to be passed to the single-input-single-output overload of RunTest().
     std::string m_SingleInputName;
     std::string m_SingleOutputName;

     void Setup()
     {
         bool ok = ReadStringToBinary();
         if (!ok) {
             throw armnn::Exception("LoadNetwork failed while reading binary input");
         }

         armnn::INetworkPtr network =
                 m_Parser->CreateNetworkFromBinary(m_GraphBinary);

         if (!network) {
             throw armnn::Exception("The parser failed to create an ArmNN network");
         }

         auto optimized = Optimize(*network, { armnn::Compute::CpuRef },
                                   m_Runtime->GetDeviceSpec());
         std::string errorMessage;

         armnn::Status ret = m_Runtime->LoadNetwork(m_NetworkIdentifier, move(optimized), errorMessage);

         if (ret != armnn::Status::Success)
         {
             throw armnn::Exception(
                 boost::str(
                     boost::format("The runtime failed to load the network. "
                                     "Error was: %1%. in %2% [%3%:%4%]") %
                     errorMessage %
                     __func__ %
                     __FILE__ %
                     __LINE__));
         }
     }

     void SetupSingleInputSingleOutput(const std::string& inputName, const std::string& outputName)
     {
         // Store the input and output name so they don't need to be passed to the single-input-single-output RunTest().
         m_SingleInputName = inputName;
         m_SingleOutputName = outputName;
         Setup();
     }

     bool ReadStringToBinary()
     {
         std::string schemafile(&tflite_schema_start, &tflite_schema_end);

         // parse schema first, so we can use it to parse the data after
         flatbuffers::Parser parser;

         bool ok = parser.Parse(schemafile.c_str());
         BOOST_ASSERT_MSG(ok, "Failed to parse schema file");

         ok &= parser.Parse(m_JsonString.c_str());
         BOOST_ASSERT_MSG(ok, "Failed to parse json input");

         if (!ok)
         {
             return false;
         }

         {
             const uint8_t * bufferPtr = parser.builder_.GetBufferPointer();
             size_t size = static_cast<size_t>(parser.builder_.GetSize());
             m_GraphBinary.assign(bufferPtr, bufferPtr+size);
         }
         return ok;
     }

     /// Executes the network with the given input tensor and checks the result against the given output tensor.
     /// This assumes the network has a single input and a single output.
     template <std::size_t NumOutputDimensions,
               armnn::DataType ArmnnType,
               typename DataType = armnn::ResolveType<ArmnnType>>
     void RunTest(size_t subgraphId,
                  const std::vector<DataType>& inputData,
                  const std::vector<DataType>& expectedOutputData);

     /// Executes the network with the given input tensors and checks the results against the given output tensors.
     /// This overload supports multiple inputs and multiple outputs, identified by name.
     template <std::size_t NumOutputDimensions,
               armnn::DataType ArmnnType,
               typename DataType = armnn::ResolveType<ArmnnType>>
     void RunTest(size_t subgraphId,
                  const std::map<std::string, std::vector<DataType>>& inputData,
                  const std::map<std::string, std::vector<DataType>>& expectedOutputData);

     /// Multiple Inputs, Multiple Outputs w/ Variable Datatypes and different dimension sizes.
     /// Executes the network with the given input tensors and checks the results against the given output tensors.
     /// This overload supports multiple inputs and multiple outputs, identified by name along with the allowance for
     /// the input datatype to be different to the output
     template <std::size_t NumOutputDimensions,
               armnn::DataType ArmnnType1,
               armnn::DataType ArmnnType2,
               typename DataType1 = armnn::ResolveType<ArmnnType1>,
               typename DataType2 = armnn::ResolveType<ArmnnType2>>
     void RunTest(size_t subgraphId,
                  const std::map<std::string, std::vector<DataType1>>& inputData,
                  const std::map<std::string, std::vector<DataType2>>& expectedOutputData);


     /// Multiple Inputs, Multiple Outputs w/ Variable Datatypes and different dimension sizes.
     /// Executes the network with the given input tensors and checks the results against the given output tensors.
     /// This overload supports multiple inputs and multiple outputs, identified by name along with the allowance for
     /// the input datatype to be different to the output
     template<armnn::DataType ArmnnType1,
              armnn::DataType ArmnnType2,
              typename DataType1 = armnn::ResolveType<ArmnnType1>,
              typename DataType2 = armnn::ResolveType<ArmnnType2>>
     void RunTest(std::size_t subgraphId,
                  const std::map<std::string, std::vector<DataType1>>& inputData,
                  const std::map<std::string, std::vector<DataType2>>& expectedOutputData);

     static inline std::string GenerateDetectionPostProcessJsonString(
         const armnn::DetectionPostProcessDescriptor& descriptor)
     {
         flexbuffers::Builder detectPostProcess;
         detectPostProcess.Map([&]() {
             detectPostProcess.Bool("use_regular_nms", descriptor.m_UseRegularNms);
             detectPostProcess.Int("max_detections", descriptor.m_MaxDetections);
             detectPostProcess.Int("max_classes_per_detection", descriptor.m_MaxClassesPerDetection);
             detectPostProcess.Int("detections_per_class", descriptor.m_DetectionsPerClass);
             detectPostProcess.Int("num_classes", descriptor.m_NumClasses);
             detectPostProcess.Float("nms_score_threshold", descriptor.m_NmsScoreThreshold);
             detectPostProcess.Float("nms_iou_threshold", descriptor.m_NmsIouThreshold);
             detectPostProcess.Float("h_scale", descriptor.m_ScaleH);
             detectPostProcess.Float("w_scale", descriptor.m_ScaleW);
             detectPostProcess.Float("x_scale", descriptor.m_ScaleX);
             detectPostProcess.Float("y_scale", descriptor.m_ScaleY);
         });
         detectPostProcess.Finish();

         // Create JSON string
         std::stringstream strStream;
         std::vector<uint8_t> buffer = detectPostProcess.GetBuffer();
         std::copy(buffer.begin(), buffer.end(),std::ostream_iterator<int>(strStream,","));

         return strStream.str();
     }

     void CheckTensors(const TensorRawPtr& tensors, size_t shapeSize, const std::vector<int32_t>& shape,
                       tflite::TensorType tensorType, uint32_t buffer, const std::string& name,
                       const std::vector<float>& min, const std::vector<float>& max,
                       const std::vector<float>& scale, const std::vector<int64_t>& zeroPoint)
     {
         BOOST_CHECK(tensors);
         BOOST_CHECK_EQUAL(shapeSize, tensors->shape.size());
         BOOST_CHECK_EQUAL_COLLECTIONS(shape.begin(), shape.end(), tensors->shape.begin(), tensors->shape.end());
         BOOST_CHECK_EQUAL(tensorType, tensors->type);
         BOOST_CHECK_EQUAL(buffer, tensors->buffer);
         BOOST_CHECK_EQUAL(name, tensors->name);
         BOOST_CHECK(tensors->quantization);
         BOOST_CHECK_EQUAL_COLLECTIONS(min.begin(), min.end(), tensors->quantization.get()->min.begin(),
                                       tensors->quantization.get()->min.end());
         BOOST_CHECK_EQUAL_COLLECTIONS(max.begin(), max.end(), tensors->quantization.get()->max.begin(),
                                       tensors->quantization.get()->max.end());
         BOOST_CHECK_EQUAL_COLLECTIONS(scale.begin(), scale.end(), tensors->quantization.get()->scale.begin(),
                                       tensors->quantization.get()->scale.end());
         BOOST_CHECK_EQUAL_COLLECTIONS(zeroPoint.begin(), zeroPoint.end(),
                                       tensors->quantization.get()->zero_point.begin(),
                                       tensors->quantization.get()->zero_point.end());
     }
 };

 /// Single Input, Single Output
 /// Executes the network with the given input tensor and checks the result against the given output tensor.
 /// This overload assumes the network has a single input and a single output.
 template <std::size_t NumOutputDimensions,
           armnn::DataType armnnType,
           typename DataType>
 void ParserFlatbuffersFixture::RunTest(size_t subgraphId,
                                        const std::vector<DataType>& inputData,
                                        const std::vector<DataType>& expectedOutputData)
 {
     RunTest<NumOutputDimensions, armnnType>(subgraphId,
                                             { { m_SingleInputName, inputData } },
                                             { { m_SingleOutputName, expectedOutputData } });
 }

 /// Multiple Inputs, Multiple Outputs
 /// Executes the network with the given input tensors and checks the results against the given output tensors.
 /// This overload supports multiple inputs and multiple outputs, identified by name.
 template <std::size_t NumOutputDimensions,
           armnn::DataType armnnType,
           typename DataType>
 void ParserFlatbuffersFixture::RunTest(size_t subgraphId,
                                        const std::map<std::string, std::vector<DataType>>& inputData,
                                        const std::map<std::string, std::vector<DataType>>& expectedOutputData)
 {
     RunTest<NumOutputDimensions, armnnType, armnnType>(subgraphId, inputData, expectedOutputData);
 }

 /// Multiple Inputs, Multiple Outputs w/ Variable Datatypes
 /// Executes the network with the given input tensors and checks the results against the given output tensors.
 /// This overload supports multiple inputs and multiple outputs, identified by name along with the allowance for
 /// the input datatype to be different to the output
 template <std::size_t NumOutputDimensions,
           armnn::DataType armnnType1,
           armnn::DataType armnnType2,
           typename DataType1,
           typename DataType2>
 void ParserFlatbuffersFixture::RunTest(size_t subgraphId,
                                        const std::map<std::string, std::vector<DataType1>>& inputData,
                                        const std::map<std::string, std::vector<DataType2>>& expectedOutputData)
 {
     // Setup the armnn input tensors from the given vectors.
     armnn::InputTensors inputTensors;
     for (auto&& it : inputData)
     {
         armnn::BindingPointInfo bindingInfo = m_Parser->GetNetworkInputBindingInfo(subgraphId, it.first);
         armnn::VerifyTensorInfoDataType(bindingInfo.second, armnnType1);
         inputTensors.push_back({ bindingInfo.first, armnn::ConstTensor(bindingInfo.second, it.second.data()) });
     }

     // Allocate storage for the output tensors to be written to and setup the armnn output tensors.
     std::map<std::string, boost::multi_array<DataType2, NumOutputDimensions>> outputStorage;
     armnn::OutputTensors outputTensors;
     for (auto&& it : expectedOutputData)
     {
         armnn::LayerBindingId outputBindingId = m_Parser->GetNetworkOutputBindingInfo(subgraphId, it.first).first;
         armnn::TensorInfo outputTensorInfo = m_Runtime->GetOutputTensorInfo(m_NetworkIdentifier, outputBindingId);

         // Check that output tensors have correct number of dimensions (NumOutputDimensions specified in test)
         auto outputNumDimensions = outputTensorInfo.GetNumDimensions();
         BOOST_CHECK_MESSAGE((outputNumDimensions == NumOutputDimensions),
             boost::str(boost::format("Number of dimensions expected %1%, but got %2% for output layer %3%")
                                        % NumOutputDimensions
                                        % outputNumDimensions
                                        % it.first));

         armnn::VerifyTensorInfoDataType(outputTensorInfo, armnnType2);
         outputStorage.emplace(it.first, MakeTensor<DataType2, NumOutputDimensions>(outputTensorInfo));
         outputTensors.push_back(
                 { outputBindingId, armnn::Tensor(outputTensorInfo, outputStorage.at(it.first).data()) });
     }

     m_Runtime->EnqueueWorkload(m_NetworkIdentifier, inputTensors, outputTensors);

     // Compare each output tensor to the expected values
     for (auto&& it : expectedOutputData)
     {
         armnn::BindingPointInfo bindingInfo = m_Parser->GetNetworkOutputBindingInfo(subgraphId, it.first);
         auto outputExpected = MakeTensor<DataType2, NumOutputDimensions>(bindingInfo.second, it.second);
         BOOST_TEST(CompareTensors(outputExpected, outputStorage[it.first]));
     }
 }

 /// Multiple Inputs, Multiple Outputs w/ Variable Datatypes and different dimension sizes.
 /// Executes the network with the given input tensors and checks the results against the given output tensors.
 /// This overload supports multiple inputs and multiple outputs, identified by name along with the allowance for
 /// the input datatype to be different to the output.
 template <armnn::DataType armnnType1,
           armnn::DataType armnnType2,
           typename DataType1,
           typename DataType2>
 void ParserFlatbuffersFixture::RunTest(std::size_t subgraphId,
                                        const std::map<std::string, std::vector<DataType1>>& inputData,
                                        const std::map<std::string, std::vector<DataType2>>& expectedOutputData)
 {
     // Setup the armnn input tensors from the given vectors.
     armnn::InputTensors inputTensors;
     for (auto&& it : inputData)
     {
         armnn::BindingPointInfo bindingInfo = m_Parser->GetNetworkInputBindingInfo(subgraphId, it.first);
         armnn::VerifyTensorInfoDataType(bindingInfo.second, armnnType1);

         inputTensors.push_back({ bindingInfo.first, armnn::ConstTensor(bindingInfo.second, it.second.data()) });
     }

     armnn::OutputTensors outputTensors;
     outputTensors.reserve(expectedOutputData.size());
     std::map<std::string, std::vector<DataType2>> outputStorage;
     for (auto&& it : expectedOutputData)
     {
         armnn::BindingPointInfo bindingInfo = m_Parser->GetNetworkOutputBindingInfo(subgraphId, it.first);
         armnn::VerifyTensorInfoDataType(bindingInfo.second, armnnType2);

         std::vector<DataType2> out(it.second.size());
         outputStorage.emplace(it.first, out);
         outputTensors.push_back({ bindingInfo.first,
                                   armnn::Tensor(bindingInfo.second,
                                   outputStorage.at(it.first).data()) });
     }

     m_Runtime->EnqueueWorkload(m_NetworkIdentifier, inputTensors, outputTensors);

     // Checks the results.
     for (auto&& it : expectedOutputData)
     {
         std::vector<DataType2> out = outputStorage.at(it.first);
         {
             for (unsigned int i = 0; i < out.size(); ++i)
             {
                 BOOST_TEST(it.second[i] == out[i], boost::test_tools::tolerance(0.000001f));
             }
         }
     }
 }
	//
	// Copyright © 2017 Arm Ltd. All rights reserved.
	// SPDX-License-Identifier: MIT
	//

	#pragma once

	#include <armnn/Descriptors.hpp>
	#include <armnn/IRuntime.hpp>
	#include <armnn/TypesUtils.hpp>

	#include "Schema.hpp"
	#include <boost/filesystem.hpp>
	#include <boost/assert.hpp>
	#include <boost/format.hpp>

	#include "test/TensorHelpers.hpp"

	#include <ResolveType.hpp>
	#include "armnnTfLiteParser/ITfLiteParser.hpp"

	#include <backendsCommon/BackendRegistry.hpp>

	#include "flatbuffers/idl.h"
	#include "flatbuffers/util.h"
	#include "flatbuffers/flexbuffers.h"

	#include <schema_generated.h>
	#include <iostream>

	using armnnTfLiteParser::ITfLiteParser;
	using armnnTfLiteParser::ITfLiteParserPtr;

	using TensorRawPtr = const tflite::TensorT *;
	struct ParserFlatbuffersFixture
	{
	ParserFlatbuffersFixture() :
	m_Parser(nullptr, &ITfLiteParser::Destroy),
	m_Runtime(armnn::IRuntime::Create(armnn::IRuntime::CreationOptions())),
	m_NetworkIdentifier(-1)
	{
	ITfLiteParser::TfLiteParserOptions options;
	options.m_StandInLayerForUnsupported = true;

	m_Parser.reset(ITfLiteParser::CreateRaw(armnn::Optional<ITfLiteParser::TfLiteParserOptions>(options)));
	}

	std::vector<uint8_t> m_GraphBinary;
	std::string m_JsonString;
	ITfLiteParserPtr m_Parser;
	armnn::IRuntimePtr m_Runtime;
	armnn::NetworkId m_NetworkIdentifier;

	/// If the single-input-single-output overload of Setup() is called, these will store the input and output name
	/// so they don't need to be passed to the single-input-single-output overload of RunTest().
	std::string m_SingleInputName;
	std::string m_SingleOutputName;

	void Setup()
	{
	bool ok = ReadStringToBinary();
	if (!ok) {
	throw armnn::Exception("LoadNetwork failed while reading binary input");
	}

	armnn::INetworkPtr network =
	m_Parser->CreateNetworkFromBinary(m_GraphBinary);

	if (!network) {
	throw armnn::Exception("The parser failed to create an ArmNN network");
	}

	auto optimized = Optimize(*network, { armnn::Compute::CpuRef },
	m_Runtime->GetDeviceSpec());
	std::string errorMessage;

	armnn::Status ret = m_Runtime->LoadNetwork(m_NetworkIdentifier, move(optimized), errorMessage);

	if (ret != armnn::Status::Success)
	{
	throw armnn::Exception(
	boost::str(
	boost::format("The runtime failed to load the network. "
	"Error was: %1%. in %2% [%3%:%4%]") %
	errorMessage %
	__func__ %
	__FILE__ %
	__LINE__));
	}
	}

	void SetupSingleInputSingleOutput(const std::string& inputName, const std::string& outputName)
	{
	// Store the input and output name so they don't need to be passed to the single-input-single-output RunTest().
	m_SingleInputName = inputName;
	m_SingleOutputName = outputName;
	Setup();
	}

	bool ReadStringToBinary()
	{
	std::string schemafile(&tflite_schema_start, &tflite_schema_end);

	// parse schema first, so we can use it to parse the data after
	flatbuffers::Parser parser;

	bool ok = parser.Parse(schemafile.c_str());
	BOOST_ASSERT_MSG(ok, "Failed to parse schema file");

	ok &= parser.Parse(m_JsonString.c_str());
	BOOST_ASSERT_MSG(ok, "Failed to parse json input");

	if (!ok)
	{
	return false;
	}

	{
	const uint8_t * bufferPtr = parser.builder_.GetBufferPointer();
	size_t size = static_cast<size_t>(parser.builder_.GetSize());
	m_GraphBinary.assign(bufferPtr, bufferPtr+size);
	}
	return ok;
	}

	/// Executes the network with the given input tensor and checks the result against the given output tensor.
	/// This assumes the network has a single input and a single output.
	template <std::size_t NumOutputDimensions,
	armnn::DataType ArmnnType,
	typename DataType = armnn::ResolveType<ArmnnType>>
	void RunTest(size_t subgraphId,
	const std::vector<DataType>& inputData,
	const std::vector<DataType>& expectedOutputData);

	/// Executes the network with the given input tensors and checks the results against the given output tensors.
	/// This overload supports multiple inputs and multiple outputs, identified by name.
	template <std::size_t NumOutputDimensions,
	armnn::DataType ArmnnType,
	typename DataType = armnn::ResolveType<ArmnnType>>
	void RunTest(size_t subgraphId,
	const std::map<std::string, std::vector<DataType>>& inputData,
	const std::map<std::string, std::vector<DataType>>& expectedOutputData);

	/// Multiple Inputs, Multiple Outputs w/ Variable Datatypes and different dimension sizes.
	/// Executes the network with the given input tensors and checks the results against the given output tensors.
	/// This overload supports multiple inputs and multiple outputs, identified by name along with the allowance for
	/// the input datatype to be different to the output
	template <std::size_t NumOutputDimensions,
	armnn::DataType ArmnnType1,
	armnn::DataType ArmnnType2,
	typename DataType1 = armnn::ResolveType<ArmnnType1>,
	typename DataType2 = armnn::ResolveType<ArmnnType2>>
	void RunTest(size_t subgraphId,
	const std::map<std::string, std::vector<DataType1>>& inputData,
	const std::map<std::string, std::vector<DataType2>>& expectedOutputData);


	/// Multiple Inputs, Multiple Outputs w/ Variable Datatypes and different dimension sizes.
	/// Executes the network with the given input tensors and checks the results against the given output tensors.
	/// This overload supports multiple inputs and multiple outputs, identified by name along with the allowance for
	/// the input datatype to be different to the output
	template<armnn::DataType ArmnnType1,
	armnn::DataType ArmnnType2,
	typename DataType1 = armnn::ResolveType<ArmnnType1>,
	typename DataType2 = armnn::ResolveType<ArmnnType2>>
	void RunTest(std::size_t subgraphId,
	const std::map<std::string, std::vector<DataType1>>& inputData,
	const std::map<std::string, std::vector<DataType2>>& expectedOutputData);

	static inline std::string GenerateDetectionPostProcessJsonString(
	const armnn::DetectionPostProcessDescriptor& descriptor)
	{
	flexbuffers::Builder detectPostProcess;
	detectPostProcess.Map([&]() {
	detectPostProcess.Bool("use_regular_nms", descriptor.m_UseRegularNms);
	detectPostProcess.Int("max_detections", descriptor.m_MaxDetections);
	detectPostProcess.Int("max_classes_per_detection", descriptor.m_MaxClassesPerDetection);
	detectPostProcess.Int("detections_per_class", descriptor.m_DetectionsPerClass);
	detectPostProcess.Int("num_classes", descriptor.m_NumClasses);
	detectPostProcess.Float("nms_score_threshold", descriptor.m_NmsScoreThreshold);
	detectPostProcess.Float("nms_iou_threshold", descriptor.m_NmsIouThreshold);
	detectPostProcess.Float("h_scale", descriptor.m_ScaleH);
	detectPostProcess.Float("w_scale", descriptor.m_ScaleW);
	detectPostProcess.Float("x_scale", descriptor.m_ScaleX);
	detectPostProcess.Float("y_scale", descriptor.m_ScaleY);
	});
	detectPostProcess.Finish();

	// Create JSON string
	std::stringstream strStream;
	std::vector<uint8_t> buffer = detectPostProcess.GetBuffer();
	std::copy(buffer.begin(), buffer.end(),std::ostream_iterator<int>(strStream,","));

	return strStream.str();
	}

	void CheckTensors(const TensorRawPtr& tensors, size_t shapeSize, const std::vector<int32_t>& shape,
	tflite::TensorType tensorType, uint32_t buffer, const std::string& name,
	const std::vector<float>& min, const std::vector<float>& max,
	const std::vector<float>& scale, const std::vector<int64_t>& zeroPoint)
	{
	BOOST_CHECK(tensors);
	BOOST_CHECK_EQUAL(shapeSize, tensors->shape.size());
	BOOST_CHECK_EQUAL_COLLECTIONS(shape.begin(), shape.end(), tensors->shape.begin(), tensors->shape.end());
	BOOST_CHECK_EQUAL(tensorType, tensors->type);
	BOOST_CHECK_EQUAL(buffer, tensors->buffer);
	BOOST_CHECK_EQUAL(name, tensors->name);
	BOOST_CHECK(tensors->quantization);
	BOOST_CHECK_EQUAL_COLLECTIONS(min.begin(), min.end(), tensors->quantization.get()->min.begin(),
	tensors->quantization.get()->min.end());
	BOOST_CHECK_EQUAL_COLLECTIONS(max.begin(), max.end(), tensors->quantization.get()->max.begin(),
	tensors->quantization.get()->max.end());
	BOOST_CHECK_EQUAL_COLLECTIONS(scale.begin(), scale.end(), tensors->quantization.get()->scale.begin(),
	tensors->quantization.get()->scale.end());
	BOOST_CHECK_EQUAL_COLLECTIONS(zeroPoint.begin(), zeroPoint.end(),
	tensors->quantization.get()->zero_point.begin(),
	tensors->quantization.get()->zero_point.end());
	}
	};

	/// Single Input, Single Output
	/// Executes the network with the given input tensor and checks the result against the given output tensor.
	/// This overload assumes the network has a single input and a single output.
	template <std::size_t NumOutputDimensions,
	armnn::DataType armnnType,
	typename DataType>
	void ParserFlatbuffersFixture::RunTest(size_t subgraphId,
	const std::vector<DataType>& inputData,
	const std::vector<DataType>& expectedOutputData)
	{
	RunTest<NumOutputDimensions, armnnType>(subgraphId,
	{ { m_SingleInputName, inputData } },
	{ { m_SingleOutputName, expectedOutputData } });
	}

	/// Multiple Inputs, Multiple Outputs
	/// Executes the network with the given input tensors and checks the results against the given output tensors.
	/// This overload supports multiple inputs and multiple outputs, identified by name.
	template <std::size_t NumOutputDimensions,
	armnn::DataType armnnType,
	typename DataType>
	void ParserFlatbuffersFixture::RunTest(size_t subgraphId,
	const std::map<std::string, std::vector<DataType>>& inputData,
	const std::map<std::string, std::vector<DataType>>& expectedOutputData)
	{
	RunTest<NumOutputDimensions, armnnType, armnnType>(subgraphId, inputData, expectedOutputData);
	}

	/// Multiple Inputs, Multiple Outputs w/ Variable Datatypes
	/// Executes the network with the given input tensors and checks the results against the given output tensors.
	/// This overload supports multiple inputs and multiple outputs, identified by name along with the allowance for
	/// the input datatype to be different to the output
	template <std::size_t NumOutputDimensions,
	armnn::DataType armnnType1,
	armnn::DataType armnnType2,
	typename DataType1,
	typename DataType2>
	void ParserFlatbuffersFixture::RunTest(size_t subgraphId,
	const std::map<std::string, std::vector<DataType1>>& inputData,
	const std::map<std::string, std::vector<DataType2>>& expectedOutputData)
	{
	// Setup the armnn input tensors from the given vectors.
	armnn::InputTensors inputTensors;
	for (auto&& it : inputData)
	{
	armnn::BindingPointInfo bindingInfo = m_Parser->GetNetworkInputBindingInfo(subgraphId, it.first);
	armnn::VerifyTensorInfoDataType(bindingInfo.second, armnnType1);
	inputTensors.push_back({ bindingInfo.first, armnn::ConstTensor(bindingInfo.second, it.second.data()) });
	}

	// Allocate storage for the output tensors to be written to and setup the armnn output tensors.
	std::map<std::string, boost::multi_array<DataType2, NumOutputDimensions>> outputStorage;
	armnn::OutputTensors outputTensors;
	for (auto&& it : expectedOutputData)
	{
	armnn::LayerBindingId outputBindingId = m_Parser->GetNetworkOutputBindingInfo(subgraphId, it.first).first;
	armnn::TensorInfo outputTensorInfo = m_Runtime->GetOutputTensorInfo(m_NetworkIdentifier, outputBindingId);

	// Check that output tensors have correct number of dimensions (NumOutputDimensions specified in test)
	auto outputNumDimensions = outputTensorInfo.GetNumDimensions();
	BOOST_CHECK_MESSAGE((outputNumDimensions == NumOutputDimensions),
	boost::str(boost::format("Number of dimensions expected %1%, but got %2% for output layer %3%")
	% NumOutputDimensions
	% outputNumDimensions
	% it.first));

	armnn::VerifyTensorInfoDataType(outputTensorInfo, armnnType2);
	outputStorage.emplace(it.first, MakeTensor<DataType2, NumOutputDimensions>(outputTensorInfo));
	outputTensors.push_back(
	{ outputBindingId, armnn::Tensor(outputTensorInfo, outputStorage.at(it.first).data()) });
	}

	m_Runtime->EnqueueWorkload(m_NetworkIdentifier, inputTensors, outputTensors);

	// Compare each output tensor to the expected values
	for (auto&& it : expectedOutputData)
	{
	armnn::BindingPointInfo bindingInfo = m_Parser->GetNetworkOutputBindingInfo(subgraphId, it.first);
	auto outputExpected = MakeTensor<DataType2, NumOutputDimensions>(bindingInfo.second, it.second);
	BOOST_TEST(CompareTensors(outputExpected, outputStorage[it.first]));
	}
	}

	/// Multiple Inputs, Multiple Outputs w/ Variable Datatypes and different dimension sizes.
	/// Executes the network with the given input tensors and checks the results against the given output tensors.
	/// This overload supports multiple inputs and multiple outputs, identified by name along with the allowance for
	/// the input datatype to be different to the output.
	template <armnn::DataType armnnType1,
	armnn::DataType armnnType2,
	typename DataType1,
	typename DataType2>
	void ParserFlatbuffersFixture::RunTest(std::size_t subgraphId,
	const std::map<std::string, std::vector<DataType1>>& inputData,
	const std::map<std::string, std::vector<DataType2>>& expectedOutputData)
	{
	// Setup the armnn input tensors from the given vectors.
	armnn::InputTensors inputTensors;
	for (auto&& it : inputData)
	{
	armnn::BindingPointInfo bindingInfo = m_Parser->GetNetworkInputBindingInfo(subgraphId, it.first);
	armnn::VerifyTensorInfoDataType(bindingInfo.second, armnnType1);

	inputTensors.push_back({ bindingInfo.first, armnn::ConstTensor(bindingInfo.second, it.second.data()) });
	}

	armnn::OutputTensors outputTensors;
	outputTensors.reserve(expectedOutputData.size());
	std::map<std::string, std::vector<DataType2>> outputStorage;
	for (auto&& it : expectedOutputData)
	{
	armnn::BindingPointInfo bindingInfo = m_Parser->GetNetworkOutputBindingInfo(subgraphId, it.first);
	armnn::VerifyTensorInfoDataType(bindingInfo.second, armnnType2);

	std::vector<DataType2> out(it.second.size());
	outputStorage.emplace(it.first, out);
	outputTensors.push_back({ bindingInfo.first,
	armnn::Tensor(bindingInfo.second,
	outputStorage.at(it.first).data()) });
	}

	m_Runtime->EnqueueWorkload(m_NetworkIdentifier, inputTensors, outputTensors);

	// Checks the results.
	for (auto&& it : expectedOutputData)
	{
	std::vector<DataType2> out = outputStorage.at(it.first);
	{
	for (unsigned int i = 0; i < out.size(); ++i)
	{
	BOOST_TEST(it.second[i] == out[i], boost::test_tools::tolerance(0.000001f));
	}
	}
	}
	}