src/armnnTfLiteParser/test/ParserFlatbuffersFixture.hpp - ml/armnn - Gitiles

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

 #pragma once

 #include "Schema.hpp"

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

 #include "../TfLiteParser.hpp"

 #include <ResolveType.hpp>

 #include <armnnTestUtils/TensorHelpers.hpp>

 #include <fmt/format.h>
 #include <doctest/doctest.h>

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

 #include <schema_generated.h>


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

 using TensorRawPtr = const tflite::TensorT *;
 struct ParserFlatbuffersFixture
 {
     ParserFlatbuffersFixture() :
             m_Runtime(armnn::IRuntime::Create(armnn::IRuntime::CreationOptions())),
             m_NetworkIdentifier(0),
             m_DynamicNetworkIdentifier(1)
     {
         ITfLiteParser::TfLiteParserOptions options;
         options.m_StandInLayerForUnsupported = true;
         options.m_InferAndValidate = true;

         m_Parser = std::make_unique<armnnTfLiteParser::TfLiteParserImpl>(
                         armnn::Optional<ITfLiteParser::TfLiteParserOptions>(options));
     }

     std::vector<uint8_t> m_GraphBinary;
     std::string          m_JsonString;
     armnn::IRuntimePtr   m_Runtime;
     armnn::NetworkId     m_NetworkIdentifier;
     armnn::NetworkId     m_DynamicNetworkIdentifier;
     bool                 m_TestDynamic;
     std::unique_ptr<armnnTfLiteParser::TfLiteParserImpl> m_Parser;

     /// 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 testDynamic = true)
     {
         m_TestDynamic = testDynamic;
         loadNetwork(m_NetworkIdentifier, false);

         if (m_TestDynamic)
         {
             loadNetwork(m_DynamicNetworkIdentifier, true);
         }
     }

     std::unique_ptr<tflite::ModelT> MakeModelDynamic(std::vector<uint8_t> graphBinary)
     {
         const uint8_t* binaryContent = graphBinary.data();
         const size_t len = graphBinary.size();
         if (binaryContent == nullptr)
         {
             throw armnn::InvalidArgumentException(fmt::format("Invalid (null) binary content {}",
                                                                CHECK_LOCATION().AsString()));
         }
         flatbuffers::Verifier verifier(binaryContent, len);
         if (verifier.VerifyBuffer<tflite::Model>() == false)
         {
             throw armnn::ParseException(fmt::format("Buffer doesn't conform to the expected Tensorflow Lite "
                                                     "flatbuffers format. size:{} {}",
                                                     len,
                                                     CHECK_LOCATION().AsString()));
         }
         auto model =  tflite::UnPackModel(binaryContent);

         for (auto const& subgraph : model->subgraphs)
         {
             std::vector<int32_t> inputIds = subgraph->inputs;
             for (unsigned int tensorIndex = 0; tensorIndex < subgraph->tensors.size(); ++tensorIndex)
             {
                 if (std::find(inputIds.begin(), inputIds.end(), tensorIndex) != inputIds.end())
                 {
                     continue;
                 }
                 for (auto const& tensor : subgraph->tensors)
                 {
                     if (tensor->shape_signature.size() != 0)
                     {
                         continue;
                     }

                     for (unsigned int i = 0; i < tensor->shape.size(); ++i)
                     {
                         tensor->shape_signature.push_back(-1);
                     }
                 }
             }
         }

         return model;
     }

     void loadNetwork(armnn::NetworkId networkId, bool loadDynamic)
     {
         if (!ReadStringToBinary())
         {
             throw armnn::Exception("LoadNetwork failed while reading binary input");
         }

         armnn::INetworkPtr network = loadDynamic ? m_Parser->LoadModel(MakeModelDynamic(m_GraphBinary))
                                                  : 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(networkId, move(optimized), errorMessage);

         if (ret != armnn::Status::Success)
         {
             throw armnn::Exception(
                 fmt::format("The runtime failed to load the network. "
                             "Error was: {}. in {} [{}:{}]",
                             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(g_TfLiteSchemaText, g_TfLiteSchemaText + g_TfLiteSchemaText_len);

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

         bool ok = parser.Parse(schemafile.c_str());
         CHECK_MESSAGE(ok, std::string("Failed to parse schema file. Error was: " + parser.error_).c_str());

         ok = parser.Parse(m_JsonString.c_str());
         CHECK_MESSAGE(ok, std::string("Failed to parse json input. Error was: " + parser.error_).c_str());

         {
             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>
     void RunTest(size_t subgraphId,
                  const std::vector<armnn::ResolveType<ArmnnType>>& inputData,
                  const std::vector<armnn::ResolveType<ArmnnType>>& 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>
     void RunTest(size_t subgraphId,
                  const std::map<std::string, std::vector<armnn::ResolveType<ArmnnType>>>& inputData,
                  const std::map<std::string, std::vector<armnn::ResolveType<ArmnnType>>>& 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>
     void RunTest(size_t subgraphId,
                  const std::map<std::string, std::vector<armnn::ResolveType<ArmnnType1>>>& inputData,
                  const std::map<std::string, std::vector<armnn::ResolveType<ArmnnType2>>>& expectedOutputData,
                  bool isDynamic = false);

     /// Multiple Inputs with different DataTypes, 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 inputType1,
         armnn::DataType inputType2,
         armnn::DataType outputType>
     void RunTest(size_t subgraphId,
                  const std::map<std::string, std::vector<armnn::ResolveType<inputType1>>>& input1Data,
                  const std::map<std::string, std::vector<armnn::ResolveType<inputType2>>>& input2Data,
                  const std::map<std::string, std::vector<armnn::ResolveType<outputType>>>& 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>
     void RunTest(std::size_t subgraphId,
                  const std::map<std::string, std::vector<armnn::ResolveType<ArmnnType1>>>& inputData,
                  const std::map<std::string, std::vector<armnn::ResolveType<ArmnnType2>>>& 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)
     {
         CHECK(tensors);
         CHECK_EQ(shapeSize, tensors->shape.size());
         CHECK(std::equal(shape.begin(), shape.end(), tensors->shape.begin(), tensors->shape.end()));
         CHECK_EQ(tensorType, tensors->type);
         CHECK_EQ(buffer, tensors->buffer);
         CHECK_EQ(name, tensors->name);
         CHECK(tensors->quantization);
         CHECK(std::equal(min.begin(), min.end(), tensors->quantization.get()->min.begin(),
                                       tensors->quantization.get()->min.end()));
         CHECK(std::equal(max.begin(), max.end(), tensors->quantization.get()->max.begin(),
                                       tensors->quantization.get()->max.end()));
         CHECK(std::equal(scale.begin(), scale.end(), tensors->quantization.get()->scale.begin(),
                                       tensors->quantization.get()->scale.end()));
         CHECK(std::equal(zeroPoint.begin(), zeroPoint.end(),
                                       tensors->quantization.get()->zero_point.begin(),
                                       tensors->quantization.get()->zero_point.end()));
     }

 private:
     /// Fills the InputTensors with given input data
     template <armnn::DataType dataType>
     void FillInputTensors(armnn::InputTensors& inputTensors,
                           const std::map<std::string, std::vector<armnn::ResolveType<dataType>>>& inputData,
                           size_t subgraphId);
 };

 /// Fills the InputTensors with given input data
 template <armnn::DataType dataType>
 void ParserFlatbuffersFixture::FillInputTensors(
                   armnn::InputTensors& inputTensors,
                   const std::map<std::string, std::vector<armnn::ResolveType<dataType>>>& inputData,
                   size_t subgraphId)
 {
     for (auto&& it : inputData)
     {
         armnn::BindingPointInfo bindingInfo = m_Parser->GetNetworkInputBindingInfo(subgraphId, it.first);
         bindingInfo.second.SetConstant(true);
         armnn::VerifyTensorInfoDataType(bindingInfo.second, dataType);
         inputTensors.push_back({ bindingInfo.first, armnn::ConstTensor(bindingInfo.second, it.second.data()) });
     }
 }

 /// 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>
 void ParserFlatbuffersFixture::RunTest(size_t subgraphId,
                                        const std::vector<armnn::ResolveType<armnnType>>& inputData,
                                        const std::vector<armnn::ResolveType<armnnType>>& 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>
 void ParserFlatbuffersFixture::RunTest(size_t subgraphId,
     const std::map<std::string, std::vector<armnn::ResolveType<armnnType>>>& inputData,
     const std::map<std::string, std::vector<armnn::ResolveType<armnnType>>>& 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>
 void ParserFlatbuffersFixture::RunTest(size_t subgraphId,
     const std::map<std::string, std::vector<armnn::ResolveType<armnnType1>>>& inputData,
     const std::map<std::string, std::vector<armnn::ResolveType<armnnType2>>>& expectedOutputData,
     bool isDynamic)
 {
     using DataType2 = armnn::ResolveType<armnnType2>;

     // Setup the armnn input tensors from the given vectors.
     armnn::InputTensors inputTensors;
     FillInputTensors<armnnType1>(inputTensors, inputData, subgraphId);

     // Allocate storage for the output tensors to be written to and setup the armnn output tensors.
     std::map<std::string, std::vector<DataType2>> 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();
         CHECK_MESSAGE((outputNumDimensions == NumOutputDimensions),
             fmt::format("Number of dimensions expected {}, but got {} for output layer {}",
                         NumOutputDimensions,
                         outputNumDimensions,
                         it.first));

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

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

     // Set flag so that the correct comparison function is called if the output is boolean.
     bool isBoolean = armnnType2 == armnn::DataType::Boolean ? true : false;

     // Compare each output tensor to the expected values
     for (auto&& it : expectedOutputData)
     {
         armnn::BindingPointInfo bindingInfo = m_Parser->GetNetworkOutputBindingInfo(subgraphId, it.first);
         auto outputExpected = it.second;
         auto result = CompareTensors(outputExpected, outputStorage[it.first],
                                      bindingInfo.second.GetShape(), bindingInfo.second.GetShape(),
                                      isBoolean, isDynamic);
         CHECK_MESSAGE(result.m_Result, result.m_Message.str());
     }

     if (isDynamic)
     {
         m_Runtime->EnqueueWorkload(m_DynamicNetworkIdentifier, 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 = it.second;
             auto result = CompareTensors(outputExpected, outputStorage[it.first],
                                          bindingInfo.second.GetShape(), bindingInfo.second.GetShape(),
                                          false, isDynamic);
             CHECK_MESSAGE(result.m_Result, result.m_Message.str());
         }
     }
 }

 /// 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>
 void ParserFlatbuffersFixture::RunTest(std::size_t subgraphId,
     const std::map<std::string, std::vector<armnn::ResolveType<armnnType1>>>& inputData,
     const std::map<std::string, std::vector<armnn::ResolveType<armnnType2>>>& expectedOutputData)
 {
     using DataType2 = armnn::ResolveType<armnnType2>;

     // Setup the armnn input tensors from the given vectors.
     armnn::InputTensors inputTensors;
     FillInputTensors<armnnType1>(inputTensors, inputData, subgraphId);

     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<armnn::ResolveType<armnnType2>> out = outputStorage.at(it.first);
         {
             for (unsigned int i = 0; i < out.size(); ++i)
             {
                 CHECK(doctest::Approx(it.second[i]).epsilon(0.000001f) == out[i]);
             }
         }
     }
 }

 /// Multiple Inputs with different DataTypes, 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 inputType1,
           armnn::DataType inputType2,
           armnn::DataType outputType>
 void ParserFlatbuffersFixture::RunTest(size_t subgraphId,
     const std::map<std::string, std::vector<armnn::ResolveType<inputType1>>>& input1Data,
     const std::map<std::string, std::vector<armnn::ResolveType<inputType2>>>& input2Data,
     const std::map<std::string, std::vector<armnn::ResolveType<outputType>>>& expectedOutputData)
 {
     using DataType2 = armnn::ResolveType<outputType>;

     // Setup the armnn input tensors from the given vectors.
     armnn::InputTensors inputTensors;
     FillInputTensors<inputType1>(inputTensors, input1Data, subgraphId);
     FillInputTensors<inputType2>(inputTensors, input2Data, subgraphId);

     // Allocate storage for the output tensors to be written to and setup the armnn output tensors.
     std::map<std::string, std::vector<DataType2>> 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();
         CHECK_MESSAGE((outputNumDimensions == NumOutputDimensions),
             fmt::format("Number of dimensions expected {}, but got {} for output layer {}",
                         NumOutputDimensions,
                         outputNumDimensions,
                         it.first));

         armnn::VerifyTensorInfoDataType(outputTensorInfo, outputType);
         outputStorage.emplace(it.first, std::vector<DataType2>(outputTensorInfo.GetNumElements()));
         outputTensors.push_back(
                 { outputBindingId, armnn::Tensor(outputTensorInfo, outputStorage.at(it.first).data()) });
     }

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

     // Set flag so that the correct comparison function is called if the output is boolean.
     bool isBoolean = outputType == armnn::DataType::Boolean ? true : false;

     // Compare each output tensor to the expected values
     for (auto&& it : expectedOutputData)
     {
         armnn::BindingPointInfo bindingInfo = m_Parser->GetNetworkOutputBindingInfo(subgraphId, it.first);
         auto outputExpected = it.second;
         auto result = CompareTensors(outputExpected, outputStorage[it.first],
                                      bindingInfo.second.GetShape(), bindingInfo.second.GetShape(),
                                      isBoolean);
         CHECK_MESSAGE(result.m_Result, result.m_Message.str());
     }
 }
	//
	// Copyright © 2017 Arm Ltd and Contributors. All rights reserved.
	// SPDX-License-Identifier: MIT
	//

	#pragma once

	#include "Schema.hpp"

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

	#include "../TfLiteParser.hpp"

	#include <ResolveType.hpp>

	#include <armnnTestUtils/TensorHelpers.hpp>

	#include <fmt/format.h>
	#include <doctest/doctest.h>

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

	#include <schema_generated.h>


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

	using TensorRawPtr = const tflite::TensorT *;
	struct ParserFlatbuffersFixture
	{
	ParserFlatbuffersFixture() :
	m_Runtime(armnn::IRuntime::Create(armnn::IRuntime::CreationOptions())),
	m_NetworkIdentifier(0),
	m_DynamicNetworkIdentifier(1)
	{
	ITfLiteParser::TfLiteParserOptions options;
	options.m_StandInLayerForUnsupported = true;
	options.m_InferAndValidate = true;

	m_Parser = std::make_unique<armnnTfLiteParser::TfLiteParserImpl>(
	armnn::Optional<ITfLiteParser::TfLiteParserOptions>(options));
	}

	std::vector<uint8_t> m_GraphBinary;
	std::string m_JsonString;
	armnn::IRuntimePtr m_Runtime;
	armnn::NetworkId m_NetworkIdentifier;
	armnn::NetworkId m_DynamicNetworkIdentifier;
	bool m_TestDynamic;
	std::unique_ptr<armnnTfLiteParser::TfLiteParserImpl> m_Parser;

	/// 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 testDynamic = true)
	{
	m_TestDynamic = testDynamic;
	loadNetwork(m_NetworkIdentifier, false);

	if (m_TestDynamic)
	{
	loadNetwork(m_DynamicNetworkIdentifier, true);
	}
	}

	std::unique_ptr<tflite::ModelT> MakeModelDynamic(std::vector<uint8_t> graphBinary)
	{
	const uint8_t* binaryContent = graphBinary.data();
	const size_t len = graphBinary.size();
	if (binaryContent == nullptr)
	{
	throw armnn::InvalidArgumentException(fmt::format("Invalid (null) binary content {}",
	CHECK_LOCATION().AsString()));
	}
	flatbuffers::Verifier verifier(binaryContent, len);
	if (verifier.VerifyBuffer<tflite::Model>() == false)
	{
	throw armnn::ParseException(fmt::format("Buffer doesn't conform to the expected Tensorflow Lite "
	"flatbuffers format. size:{} {}",
	len,
	CHECK_LOCATION().AsString()));
	}
	auto model = tflite::UnPackModel(binaryContent);

	for (auto const& subgraph : model->subgraphs)
	{
	std::vector<int32_t> inputIds = subgraph->inputs;
	for (unsigned int tensorIndex = 0; tensorIndex < subgraph->tensors.size(); ++tensorIndex)
	{
	if (std::find(inputIds.begin(), inputIds.end(), tensorIndex) != inputIds.end())
	{
	continue;
	}
	for (auto const& tensor : subgraph->tensors)
	{
	if (tensor->shape_signature.size() != 0)
	{
	continue;
	}

	for (unsigned int i = 0; i < tensor->shape.size(); ++i)
	{
	tensor->shape_signature.push_back(-1);
	}
	}
	}
	}

	return model;
	}

	void loadNetwork(armnn::NetworkId networkId, bool loadDynamic)
	{
	if (!ReadStringToBinary())
	{
	throw armnn::Exception("LoadNetwork failed while reading binary input");
	}

	armnn::INetworkPtr network = loadDynamic ? m_Parser->LoadModel(MakeModelDynamic(m_GraphBinary))
	: 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(networkId, move(optimized), errorMessage);

	if (ret != armnn::Status::Success)
	{
	throw armnn::Exception(
	fmt::format("The runtime failed to load the network. "
	"Error was: {}. in {} [{}:{}]",
	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(g_TfLiteSchemaText, g_TfLiteSchemaText + g_TfLiteSchemaText_len);

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

	bool ok = parser.Parse(schemafile.c_str());
	CHECK_MESSAGE(ok, std::string("Failed to parse schema file. Error was: " + parser.error_).c_str());

	ok = parser.Parse(m_JsonString.c_str());
	CHECK_MESSAGE(ok, std::string("Failed to parse json input. Error was: " + parser.error_).c_str());

	{
	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>
	void RunTest(size_t subgraphId,
	const std::vector<armnn::ResolveType<ArmnnType>>& inputData,
	const std::vector<armnn::ResolveType<ArmnnType>>& 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>
	void RunTest(size_t subgraphId,
	const std::map<std::string, std::vector<armnn::ResolveType<ArmnnType>>>& inputData,
	const std::map<std::string, std::vector<armnn::ResolveType<ArmnnType>>>& 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>
	void RunTest(size_t subgraphId,
	const std::map<std::string, std::vector<armnn::ResolveType<ArmnnType1>>>& inputData,
	const std::map<std::string, std::vector<armnn::ResolveType<ArmnnType2>>>& expectedOutputData,
	bool isDynamic = false);

	/// Multiple Inputs with different DataTypes, 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 inputType1,
	armnn::DataType inputType2,
	armnn::DataType outputType>
	void RunTest(size_t subgraphId,
	const std::map<std::string, std::vector<armnn::ResolveType<inputType1>>>& input1Data,
	const std::map<std::string, std::vector<armnn::ResolveType<inputType2>>>& input2Data,
	const std::map<std::string, std::vector<armnn::ResolveType<outputType>>>& 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>
	void RunTest(std::size_t subgraphId,
	const std::map<std::string, std::vector<armnn::ResolveType<ArmnnType1>>>& inputData,
	const std::map<std::string, std::vector<armnn::ResolveType<ArmnnType2>>>& 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)
	{
	CHECK(tensors);
	CHECK_EQ(shapeSize, tensors->shape.size());
	CHECK(std::equal(shape.begin(), shape.end(), tensors->shape.begin(), tensors->shape.end()));
	CHECK_EQ(tensorType, tensors->type);
	CHECK_EQ(buffer, tensors->buffer);
	CHECK_EQ(name, tensors->name);
	CHECK(tensors->quantization);
	CHECK(std::equal(min.begin(), min.end(), tensors->quantization.get()->min.begin(),
	tensors->quantization.get()->min.end()));
	CHECK(std::equal(max.begin(), max.end(), tensors->quantization.get()->max.begin(),
	tensors->quantization.get()->max.end()));
	CHECK(std::equal(scale.begin(), scale.end(), tensors->quantization.get()->scale.begin(),
	tensors->quantization.get()->scale.end()));
	CHECK(std::equal(zeroPoint.begin(), zeroPoint.end(),
	tensors->quantization.get()->zero_point.begin(),
	tensors->quantization.get()->zero_point.end()));
	}

	private:
	/// Fills the InputTensors with given input data
	template <armnn::DataType dataType>
	void FillInputTensors(armnn::InputTensors& inputTensors,
	const std::map<std::string, std::vector<armnn::ResolveType<dataType>>>& inputData,
	size_t subgraphId);
	};

	/// Fills the InputTensors with given input data
	template <armnn::DataType dataType>
	void ParserFlatbuffersFixture::FillInputTensors(
	armnn::InputTensors& inputTensors,
	const std::map<std::string, std::vector<armnn::ResolveType<dataType>>>& inputData,
	size_t subgraphId)
	{
	for (auto&& it : inputData)
	{
	armnn::BindingPointInfo bindingInfo = m_Parser->GetNetworkInputBindingInfo(subgraphId, it.first);
	bindingInfo.second.SetConstant(true);
	armnn::VerifyTensorInfoDataType(bindingInfo.second, dataType);
	inputTensors.push_back({ bindingInfo.first, armnn::ConstTensor(bindingInfo.second, it.second.data()) });
	}
	}

	/// 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>
	void ParserFlatbuffersFixture::RunTest(size_t subgraphId,
	const std::vector<armnn::ResolveType<armnnType>>& inputData,
	const std::vector<armnn::ResolveType<armnnType>>& 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>
	void ParserFlatbuffersFixture::RunTest(size_t subgraphId,
	const std::map<std::string, std::vector<armnn::ResolveType<armnnType>>>& inputData,
	const std::map<std::string, std::vector<armnn::ResolveType<armnnType>>>& 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>
	void ParserFlatbuffersFixture::RunTest(size_t subgraphId,
	const std::map<std::string, std::vector<armnn::ResolveType<armnnType1>>>& inputData,
	const std::map<std::string, std::vector<armnn::ResolveType<armnnType2>>>& expectedOutputData,
	bool isDynamic)
	{
	using DataType2 = armnn::ResolveType<armnnType2>;

	// Setup the armnn input tensors from the given vectors.
	armnn::InputTensors inputTensors;
	FillInputTensors<armnnType1>(inputTensors, inputData, subgraphId);

	// Allocate storage for the output tensors to be written to and setup the armnn output tensors.
	std::map<std::string, std::vector<DataType2>> 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();
	CHECK_MESSAGE((outputNumDimensions == NumOutputDimensions),
	fmt::format("Number of dimensions expected {}, but got {} for output layer {}",
	NumOutputDimensions,
	outputNumDimensions,
	it.first));

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

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

	// Set flag so that the correct comparison function is called if the output is boolean.
	bool isBoolean = armnnType2 == armnn::DataType::Boolean ? true : false;

	// Compare each output tensor to the expected values
	for (auto&& it : expectedOutputData)
	{
	armnn::BindingPointInfo bindingInfo = m_Parser->GetNetworkOutputBindingInfo(subgraphId, it.first);
	auto outputExpected = it.second;
	auto result = CompareTensors(outputExpected, outputStorage[it.first],
	bindingInfo.second.GetShape(), bindingInfo.second.GetShape(),
	isBoolean, isDynamic);
	CHECK_MESSAGE(result.m_Result, result.m_Message.str());
	}

	if (isDynamic)
	{
	m_Runtime->EnqueueWorkload(m_DynamicNetworkIdentifier, 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 = it.second;
	auto result = CompareTensors(outputExpected, outputStorage[it.first],
	bindingInfo.second.GetShape(), bindingInfo.second.GetShape(),
	false, isDynamic);
	CHECK_MESSAGE(result.m_Result, result.m_Message.str());
	}
	}
	}

	/// 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>
	void ParserFlatbuffersFixture::RunTest(std::size_t subgraphId,
	const std::map<std::string, std::vector<armnn::ResolveType<armnnType1>>>& inputData,
	const std::map<std::string, std::vector<armnn::ResolveType<armnnType2>>>& expectedOutputData)
	{
	using DataType2 = armnn::ResolveType<armnnType2>;

	// Setup the armnn input tensors from the given vectors.
	armnn::InputTensors inputTensors;
	FillInputTensors<armnnType1>(inputTensors, inputData, subgraphId);

	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<armnn::ResolveType<armnnType2>> out = outputStorage.at(it.first);
	{
	for (unsigned int i = 0; i < out.size(); ++i)
	{
	CHECK(doctest::Approx(it.second[i]).epsilon(0.000001f) == out[i]);
	}
	}
	}
	}

	/// Multiple Inputs with different DataTypes, 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 inputType1,
	armnn::DataType inputType2,
	armnn::DataType outputType>
	void ParserFlatbuffersFixture::RunTest(size_t subgraphId,
	const std::map<std::string, std::vector<armnn::ResolveType<inputType1>>>& input1Data,
	const std::map<std::string, std::vector<armnn::ResolveType<inputType2>>>& input2Data,
	const std::map<std::string, std::vector<armnn::ResolveType<outputType>>>& expectedOutputData)
	{
	using DataType2 = armnn::ResolveType<outputType>;

	// Setup the armnn input tensors from the given vectors.
	armnn::InputTensors inputTensors;
	FillInputTensors<inputType1>(inputTensors, input1Data, subgraphId);
	FillInputTensors<inputType2>(inputTensors, input2Data, subgraphId);

	// Allocate storage for the output tensors to be written to and setup the armnn output tensors.
	std::map<std::string, std::vector<DataType2>> 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();
	CHECK_MESSAGE((outputNumDimensions == NumOutputDimensions),
	fmt::format("Number of dimensions expected {}, but got {} for output layer {}",
	NumOutputDimensions,
	outputNumDimensions,
	it.first));

	armnn::VerifyTensorInfoDataType(outputTensorInfo, outputType);
	outputStorage.emplace(it.first, std::vector<DataType2>(outputTensorInfo.GetNumElements()));
	outputTensors.push_back(
	{ outputBindingId, armnn::Tensor(outputTensorInfo, outputStorage.at(it.first).data()) });
	}

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

	// Set flag so that the correct comparison function is called if the output is boolean.
	bool isBoolean = outputType == armnn::DataType::Boolean ? true : false;

	// Compare each output tensor to the expected values
	for (auto&& it : expectedOutputData)
	{
	armnn::BindingPointInfo bindingInfo = m_Parser->GetNetworkOutputBindingInfo(subgraphId, it.first);
	auto outputExpected = it.second;
	auto result = CompareTensors(outputExpected, outputStorage[it.first],
	bindingInfo.second.GetShape(), bindingInfo.second.GetShape(),
	isBoolean);
	CHECK_MESSAGE(result.m_Result, result.m_Message.str());
	}
	}