src/armnn/Network.cpp

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

#include "Network.hpp"
#include "Graph.hpp"
#include "Layer.hpp"
#include "DeviceSpec.hpp"
#include "Optimizer.hpp"
#include "SubGraphSelector.hpp"
#include "BackendSettings.hpp"
#include "optimizations/All.hpp"

#include <backendsCommon/CpuTensorHandle.hpp>
#include <backendsCommon/WorkloadFactory.hpp>
#include <backendsCommon/BackendRegistry.hpp>
#include <backendsCommon/IBackendInternal.hpp>

#include <armnn/Exceptions.hpp>
#include <armnn/Utils.hpp>
#include <armnn/TypesUtils.hpp>

#include <fcntl.h>
#include <algorithm>
#include <fstream>
#include <memory>
#include <vector>
#include <algorithm>

#include <boost/assert.hpp>
#include <boost/format.hpp>
#include <boost/log/trivial.hpp>
#include <boost/numeric/conversion/converter_policies.hpp>
#include <boost/cast.hpp>

namespace armnn
{

armnn::INetwork* INetwork::CreateRaw()
{
    return new Network();
}

armnn::INetworkPtr INetwork::Create()
{
    return INetworkPtr(CreateRaw(), &INetwork::Destroy);
}

void INetwork::Destroy(INetwork* network)
{
    delete boost::polymorphic_downcast<Network*>(network);
}

Status Network::PrintGraph()
{
    m_Graph->Print();
    return Status::Success;
}

void IOptimizedNetwork::Destroy(IOptimizedNetwork* network)
{
    delete boost::polymorphic_downcast<OptimizedNetwork*>(network);
}

Status OptimizedNetwork::PrintGraph()
{
    m_Graph->Print();
    return Status::Success;
}

Status OptimizedNetwork::SerializeToDot(std::ostream& stream) const
{
    return m_Graph->SerializeToDot(stream);
}

struct OptimizationResult
{
    bool m_Warning;
    bool m_Error;

    OptimizationResult()
        : m_Warning(false)
        , m_Error(false)
    {}
};

void ReportError(const std::string& errorMessage,
                 Optional<std::vector<std::string>&> errorMessages)
{
    std::stringstream fullErrorMessage;
    fullErrorMessage << "ERROR: " << errorMessage;
    BOOST_LOG_TRIVIAL(warning) << fullErrorMessage.str();
    if (errorMessages)
    {
        errorMessages.value().push_back(fullErrorMessage.str());
    }
}

void ReportWarning(const std::string& warningMessage,
                   Optional<std::vector<std::string>&> warningMessages)
{
    std::stringstream fullWarningMessage;
    fullWarningMessage << "WARNING: " << warningMessage;
    BOOST_LOG_TRIVIAL(warning) << fullWarningMessage.str();
    if (warningMessages)
    {
        warningMessages.value().push_back(fullWarningMessage.str());
    }
}

bool CheckScaleSetOnQuantizedType(Layer* layer, Optional<std::vector<std::string>&> errMessages)
{
    bool noErrors = true;
    unsigned int numOutputs = layer->GetNumOutputSlots();
    for (unsigned int i = 0; i < numOutputs; i++) {
        const OutputSlot &outputSlot = layer->GetOutputSlot(i);
        const TensorInfo &info = outputSlot.GetTensorInfo();
        if (DataType::QuantisedAsymm8 == info.GetDataType()) {
            if (0.f == info.GetQuantizationScale()) {
                noErrors = false;
                std::stringstream ss;
                ss << "output " << i << " of layer " << GetLayerTypeAsCString(layer->GetType())
                   << " (" << layer->GetNameStr() << ") is of type"
                   << " Quantized 8 bit but its scale parameter has not been set";
                ReportError(ss.str(), errMessages);
            }
        }
    }
    return noErrors;
}

OptimizationResult AssignBackends(OptimizedNetwork* optNetObjPtr,
                                  BackendSettings& backendSettings,
                                  Graph::Iterator& firstLayer,
                                  Graph::Iterator& lastLayer,
                                  Optional<std::vector<std::string>&> errMessages)
{
    OptimizationResult result;

    // Helper lambda to compose meaningful error message before returning with error
    auto ReturnWithError = [&](const Layer* layer)
    {
        std::stringstream failureMsg;
        failureMsg << "Layer of type " << GetLayerTypeAsCString(layer->GetType())
                   << " is not supported on any preferred backend " << backendSettings.m_PreferredBackends;
        ReportError(failureMsg.str(), errMessages);

        result.m_Error = true;
        return result;
    };

    auto availablePreferredBackends = backendSettings.GetAvailablePreferredBackends();
    if (availablePreferredBackends.empty())
    {
        std::stringstream failureMsg;
        failureMsg << "No preferred backends are available";
        ReportError(failureMsg.str(), errMessages);

        result.m_Error = true;
        return result;
    }

    for (auto it = firstLayer; it != lastLayer; ++it)
    {
        auto layer = *it;
        DataType dataType = layer->GetDataType();
        std::string reasonIfUnsupported;
        bool found = false;
        if (!CheckScaleSetOnQuantizedType(layer, errMessages))
        {
            // don't bomb immediately, find all the quantized outputs
            // which haven't had a scale set and report them all back.
            result.m_Error = true;
        }

        for (const auto& backend : availablePreferredBackends)
        {
            // need to set the compute device on the layer
            // before we can check if it is supported
            layer->SetBackendId(backend);
            if (!IWorkloadFactory::IsLayerSupported(*layer, dataType, reasonIfUnsupported))
            {
                if (dataType == DataType::Float16)
                {
                    if (IWorkloadFactory::IsLayerSupported(*layer, DataType::Float32, reasonIfUnsupported)
                        && layer->GetType() != LayerType::ConvertFp32ToFp16
                        && layer->GetType() != LayerType::ConvertFp16ToFp32)
                    {
                        // Insert FP16 -> FP32 conversion layer before current layer
                        std::vector<ConvertFp16ToFp32Layer*> convertFp16ToFp32Layers =
                            InsertConvertFp16ToFp32LayersBefore(optNetObjPtr->GetGraph(), *layer);

                        // Insert FP32 -> FP16 conversion layer after current layer
                        std::vector<ConvertFp32ToFp16Layer*> convertFp32ToFp16Layers =
                            InsertConvertFp32ToFp16LayersAfter(optNetObjPtr->GetGraph(), *layer);

                        // Assign a supported backend to the newly introduced conversion layers
                        auto AssignFirstSupportedBackend = [&](Layer* layer, BackendId preferredBackend)
                        {
                            bool supportedBackendFound = false;
                            std::string reasonIfUnsupported;

                            // Try preferred backend first
                            layer->SetBackendId(preferredBackend);
                            if (IWorkloadFactory::IsLayerSupported(*layer,
                                                                   EmptyOptional(),
                                                                   reasonIfUnsupported))
                            {
                                supportedBackendFound = true;
                            }
                            else
                            {
                                for (const auto& backend : availablePreferredBackends)
                                {
                                    // Skip preferred backend (we already determined that it is not supported)
                                    if (backend == preferredBackend)
                                    {
                                        continue;
                                    }

                                    layer->SetBackendId(backend);
                                    if (IWorkloadFactory::IsLayerSupported(*layer,
                                                                           EmptyOptional(),
                                                                           reasonIfUnsupported))
                                    {
                                        supportedBackendFound = true;
                                        break;
                                    }
                                }
                            }

                            return supportedBackendFound;
                        };

                        for (ConvertFp16ToFp32Layer* convertLayer : convertFp16ToFp32Layers)
                        {
                            if (!AssignFirstSupportedBackend(convertLayer, backend))
                            {
                                return ReturnWithError(convertLayer);
                            }
                        }

                        for (ConvertFp32ToFp16Layer* convertLayer : convertFp32ToFp16Layers)
                        {
                            if (!AssignFirstSupportedBackend(convertLayer, backend))
                            {
                                return ReturnWithError(convertLayer);
                            }
                        }

                        found = true;
                        break;
                    }
                }
                std::stringstream warningMsg;
                warningMsg << "Layer of type " << GetLayerTypeAsCString(layer->GetType())
                           << " is not supported on requested backend " << layer->GetBackendId().Get()
                           << " for data type " << GetDataTypeName(dataType)
                           << " (reason: " << reasonIfUnsupported
                           << "), falling back to the next backend.";
                ReportWarning(warningMsg.str(), errMessages);
            }
            else
            {
                found = true;
                backendSettings.m_SelectedBackends.insert(backend);
                break;
            }
        }

        // If the layer is unsupported by any devices, log and return a null network.
        if (!found)
        {
            // NOTE: if the layer is not an operation queue type AND we have not got CpuRef as a
            //       fallback we should set the compute device on the layer to CpuRef (these are not
            //       available as accelerated operations, or are only available under certain
            //       conditions, currently they comprise MemCopy, Constant, Permute)
            armnn::LayerType layerType = layer->GetType();
            if (!backendSettings.IsCpuRefUsed() && (layerType == armnn::LayerType::MemCopy ||
                                                    layerType == armnn::LayerType::Constant ||
                                                    layerType == armnn::LayerType::Permute))
            {
                BackendId cpuBackendId(armnn::Compute::CpuRef);
                layer->SetBackendId(cpuBackendId);
                backendSettings.m_SelectedBackends.insert(cpuBackendId);
            }
            else
            {
                return ReturnWithError(layer);
            }
        }
    }

    return result;
}

OptimizationResult InsertPreCompiledLayers(OptimizedNetwork* optNetObjPtr,
                                           const IBackendInternalUniquePtr& backendObjPtr,
                                           BackendSettings& backendSettings,
                                           Optional<std::vector<std::string>&> errMessages)
{
    BOOST_ASSERT(backendObjPtr);

    OptimizationResult result;

    // Select sub-graphs based on backend
    SubGraphSelector::SubGraphs subGraphs =
            SubGraphSelector::SelectSubGraphs(optNetObjPtr->GetGraph(),
                                              // select layers assigned to requested backend
                                              [&](const Layer& layer)
                                              {
                                                  return layer.GetType() != LayerType::Input &&
                                                         layer.GetType() != LayerType::Output &&
                                                         layer.GetBackendId() == backendObjPtr->GetId();
                                              });

    if (subGraphs.empty())
    {
        // No sub-graphs found -> return with no error
        return result;
    }

    // Convert sub-graphs and substitute them with pre-compiled layers
    unsigned int index = 0u;
    for (auto& subGraph : subGraphs)
    {
        // Create a pre-compiled layer
        PreCompiledLayer* preCompiledLayer = CreatePreCompiledLayer(optNetObjPtr->GetGraph(),
                                                                    *subGraph,
                                                                    index++,
                                                                    backendObjPtr);
        if (preCompiledLayer)
        {
            // Substitute sub-graph with pre-compiled layer in graph
            optNetObjPtr->GetGraph().SubstituteSubGraph(std::move(subGraph), preCompiledLayer);
        }
        else
        {
            // Failed to create pre-compiled layer from sub-graph ->
            // re-assign sub-graph layers to other available backends
            std::stringstream warningMsg;
            warningMsg << "Sub-graph #" << index << " failed to compile on "
                       << backendObjPtr->GetId() << ". Re-assigning backends to "
                       << subGraph->GetLayers().size() << " layers inside sub-graph";
            ReportWarning(warningMsg.str(), errMessages);

            backendSettings.m_IgnoredBackends = { backendObjPtr->GetId() };

            Graph::Iterator firstLayer = subGraph->begin();
            Graph::Iterator lastLayer  = subGraph->end();
            OptimizationResult reassignmentResult = AssignBackends(optNetObjPtr,
                                                                   backendSettings,
                                                                   firstLayer,
                                                                   lastLayer,
                                                                   errMessages);

            if (reassignmentResult.m_Error)
            {
                result.m_Error = true;
                return result;
            }
        }
    }

    return result;
}

IOptimizedNetworkPtr Optimize(const INetwork& inNetwork,
                              const std::vector<BackendId>& backendPreferences,
                              const IDeviceSpec& deviceSpec,
                              const OptimizerOptions& options,
                              Optional<std::vector<std::string>&> errMessages)
{
    if (backendPreferences.empty())
    {
        throw armnn::InvalidArgumentException("Invoked Optimize with no backends specified");
    }

    const Network& network = *boost::polymorphic_downcast<const Network*>(&inNetwork);
    std::unique_ptr<Graph> graph = std::make_unique<Graph>(network.GetGraph());

    auto optNet = IOptimizedNetworkPtr(new OptimizedNetwork(std::move(graph)), &IOptimizedNetwork::Destroy);

    OptimizedNetwork* optNetObjPtr = boost::polymorphic_downcast<OptimizedNetwork*>(optNet.get());

    // Perform optimisation passes
    using namespace optimizations;
    Optimizer::Pass(optNetObjPtr->GetGraph(), MakeOptimizations(SquashEqualPermuteSiblings(),
                                                                SquashEqualReshapeSiblings(),
                                                                OptimizeInversePermutes(),
                                                                MovePermuteUp(),
                                                                PermuteAsReshape(),
                                                                OptimizeConsecutiveReshapes()));

    // Infer the tensor infos for all output slots. Throws an exception on failure.
    optNetObjPtr->GetGraph().InferTensorInfos();

    // If Fp32 to Fp16 optimization is set convert Fp32 network to Fp16
    if (options.m_ReduceFp32ToFp16)
    {
        Optimizer::Pass(optNetObjPtr->GetGraph(), MakeOptimizations(Fp32NetworkToFp16Converter()));
    }

    // Initialize backend settings
    BackendSettings backendSettings(backendPreferences, deviceSpec);
    if (backendSettings.GetAvailablePreferredBackends().empty())
    {
        std::stringstream failureMsg;
        failureMsg << "None of the preferred backends " << backendPreferences
                   << " are supported. Current platform provides " << backendSettings.m_SupportedBackends;
        ReportError(failureMsg.str(), errMessages);
        return IOptimizedNetworkPtr(nullptr, &IOptimizedNetwork::Destroy);
    }

    // Assign an available backend to each layer
    Graph::Iterator firstLayer = optNetObjPtr->GetGraph().begin();
    Graph::Iterator lastLayer  = optNetObjPtr->GetGraph().end();
    OptimizationResult assigBackendsResult = AssignBackends(optNetObjPtr,
                                                            backendSettings,
                                                            firstLayer,
                                                            lastLayer,
                                                            errMessages);
    if (assigBackendsResult.m_Error)
    {
        // Failed to assign a backend to each layer
        return IOptimizedNetworkPtr(nullptr, &IOptimizedNetwork::Destroy);
    }

    Optimizer::Pass(optNetObjPtr->GetGraph(), MakeOptimizations(OptimizeInverseConversionsFp16(),
                                                                OptimizeInverseConversionsFp32()));

    // Insert pre-compiled layers where required by the backend
    // TODO: This is a dummy/default backend id used for making the code build until
    //       we've properly refactored the optimizer
    const BackendId backendId(Compute::Undefined);
    auto const& backendRegistry = BackendRegistryInstance();
    if (backendRegistry.IsBackendRegistered(backendId))
    {
        // Obtain a backend object using the registered factory
        auto backendFactory = backendRegistry.GetFactory(backendId);
        auto backendObjPtr  = backendFactory();

        OptimizationResult insertPreCompiledLayersResult = InsertPreCompiledLayers(optNetObjPtr,
                                                                                   backendObjPtr,
                                                                                   backendSettings,
                                                                                   errMessages);
        if (insertPreCompiledLayersResult.m_Error)
        {
            // Failed to insert pre-compiled layers
            return IOptimizedNetworkPtr(nullptr, &IOptimizedNetwork::Destroy);
        }
    }

    // If the debug flag is set, then insert a DebugLayer after each layer.
    // NOTE: This optimization can only happen strictly after the PreCompiled layers have
    //       already been inserted
    if (options.m_Debug)
    {
        Optimizer::Pass(optNetObjPtr->GetGraph(), MakeOptimizations(InsertDebugLayer()));
    }

    optNetObjPtr->GetGraph().AddCopyLayers();

    // Convert constants
    Optimizer::Pass(optNetObjPtr->GetGraph(), MakeOptimizations(ConvertConstantsFloatToHalf()));
    Optimizer::Pass(optNetObjPtr->GetGraph(), MakeOptimizations(ConvertConstantsHalfToFloat()));

    // Run backend specific optimizations
    for (auto&& chosenBackend : backendSettings.m_SelectedBackends)
    {
        auto factoryFun = BackendRegistryInstance().GetFactory(chosenBackend);
        auto backendPtr = factoryFun();
        BOOST_ASSERT(backendPtr.get() != nullptr);

        auto backendSpecificOptimizations = backendPtr->GetOptimizations();
        if (!backendSpecificOptimizations.empty())
        {
            Optimizer::Pass(optNetObjPtr->GetGraph(), backendSpecificOptimizations);
        }
    }

    return optNet;
}


Network::Network()
: m_Graph(std::make_unique<Graph>())
{
}

Network::~Network()
{
}

IConnectableLayer* Network::AddInputLayer(LayerBindingId id, const char* name)
{
    return m_Graph->AddLayer<InputLayer>(id, name);
}

IConnectableLayer* Network::AddBatchToSpaceNdLayer(const BatchToSpaceNdDescriptor& batchToSpaceNdDescriptor,
                                            const char* name)
{
    return m_Graph->AddLayer<BatchToSpaceNdLayer>(batchToSpaceNdDescriptor, name);
}

IConnectableLayer* Network::AddFullyConnectedLayerImpl(const FullyConnectedDescriptor& fullyConnectedDescriptor,
                                                       const ConstTensor& weights,
                                                       const ConstTensor* biases,
                                                       const char* name)
{
    if (fullyConnectedDescriptor.m_BiasEnabled && (biases == nullptr))
    {
        throw InvalidArgumentException("AddFullyConnectedLayer: biases cannot be NULL");
    }

    const auto layer = m_Graph->AddLayer<FullyConnectedLayer>(fullyConnectedDescriptor, name);

    layer->m_Weight = std::make_unique<ScopedCpuTensorHandle>(weights);

    if (fullyConnectedDescriptor.m_BiasEnabled)
    {
        layer->m_Bias = std::make_unique<ScopedCpuTensorHandle>(*biases);
    }

    return layer;
}

IConnectableLayer* Network::AddFullyConnectedLayer(const FullyConnectedDescriptor& fullyConnectedDescriptor,
                                                   const ConstTensor& weights,
                                                   const char* name)
{
    return AddFullyConnectedLayerImpl(fullyConnectedDescriptor, weights, nullptr, name);
}

IConnectableLayer* Network::AddFullyConnectedLayer(const FullyConnectedDescriptor& fullyConnectedDescriptor,
                                                   const ConstTensor& weights,
                                                   const ConstTensor& biases,
                                                   const char* name)
{
    return AddFullyConnectedLayerImpl(fullyConnectedDescriptor, weights, &biases, name);
}

IConnectableLayer* Network::AddConvolution2dLayerImpl(const Convolution2dDescriptor& convolution2dDescriptor,
                                                      const ConstTensor& weights,
                                                      const ConstTensor* biases,
                                                      const char* name)
{
    if (convolution2dDescriptor.m_BiasEnabled && (biases == nullptr))
    {
        throw InvalidArgumentException("AddConvolution2dLayer: biases cannot be NULL");
    }

    const auto layer = m_Graph->AddLayer<Convolution2dLayer>(convolution2dDescriptor, name);

    layer->m_Weight = std::make_unique<ScopedCpuTensorHandle>(weights);

    if (convolution2dDescriptor.m_BiasEnabled)
    {
        layer->m_Bias = std::make_unique<ScopedCpuTensorHandle>(*biases);
    }

    return layer;
}

IConnectableLayer* Network::AddConvolution2dLayer(const Convolution2dDescriptor& convolution2dDescriptor,
                                                  const ConstTensor& weights,
                                                  const char* name)
{
    return AddConvolution2dLayerImpl(convolution2dDescriptor, weights, nullptr, name);
}
IConnectableLayer* Network::AddConvolution2dLayer(const Convolution2dDescriptor& convolution2dDescriptor,
                                                  const ConstTensor& weights,
                                                  const ConstTensor& biases,
                                                  const char* name)
{
    return AddConvolution2dLayerImpl(convolution2dDescriptor, weights, &biases, name);
}

IConnectableLayer* Network::AddDepthwiseConvolution2dLayerImpl(
    const DepthwiseConvolution2dDescriptor& convolution2dDescriptor,
    const ConstTensor& weights,
    const ConstTensor* biases,
    const char* name)
{
    if (convolution2dDescriptor.m_BiasEnabled && (biases == nullptr))
    {
        throw InvalidArgumentException("AddDepthwiseConvolution2dLayer: biases cannot be NULL");
    }

    const auto layer = m_Graph->AddLayer<DepthwiseConvolution2dLayer>(convolution2dDescriptor,
            name);

    layer->m_Weight = std::make_unique<ScopedCpuTensorHandle>(weights);

    if (convolution2dDescriptor.m_BiasEnabled)
    {
        layer->m_Bias = std::make_unique<ScopedCpuTensorHandle>(*biases);
    }

    return layer;
}

IConnectableLayer* Network::AddDepthwiseConvolution2dLayer(
    const DepthwiseConvolution2dDescriptor& convolution2dDescriptor,
    const ConstTensor& weights,
    const char* name)
{
    return AddDepthwiseConvolution2dLayerImpl(convolution2dDescriptor, weights, nullptr, name);
}
IConnectableLayer* Network::AddDepthwiseConvolution2dLayer(
    const DepthwiseConvolution2dDescriptor& convolution2dDescriptor,
    const ConstTensor& weights,
    const ConstTensor& biases,
    const char* name)
{
    return AddDepthwiseConvolution2dLayerImpl(convolution2dDescriptor, weights, &biases, name);
}

IConnectableLayer* Network::AddPermuteLayer(const PermuteDescriptor& permuteDescriptor,
                                            const char* name)
{
    return m_Graph->AddLayer<PermuteLayer>(permuteDescriptor, name);
}

IConnectableLayer* Network::AddPooling2dLayer(const Pooling2dDescriptor& pooling2dDescriptor,
    const char* name)
{
    return m_Graph->AddLayer<Pooling2dLayer>(pooling2dDescriptor, name);
}

IConnectableLayer* Network::AddActivationLayer(const ActivationDescriptor& activationDescriptor,
    const char* name)
{
    return m_Graph->AddLayer<ActivationLayer>(activationDescriptor, name);
}

IConnectableLayer* Network::AddNormalizationLayer(const NormalizationDescriptor&
normalizationDescriptor,
    const char* name)
{
    return m_Graph->AddLayer<NormalizationLayer>(normalizationDescriptor, name);
}

IConnectableLayer* Network::AddSoftmaxLayer(const SoftmaxDescriptor& softmaxDescriptor,
    const char* name)
{
    return m_Graph->AddLayer<SoftmaxLayer>(softmaxDescriptor, name);
}

IConnectableLayer* Network::AddSplitterLayer(const ViewsDescriptor& splitterDescriptor,
    const char* name)
{
    return m_Graph->AddLayer<SplitterLayer>(splitterDescriptor, name);
}

IConnectableLayer* Network::AddMaximumLayer(const char* name)
{
    return m_Graph->AddLayer<MaximumLayer>(name);
}

IConnectableLayer* Network::AddMinimumLayer(const char* name)
{
    return m_Graph->AddLayer<MinimumLayer>(name);
}

IConnectableLayer* Network::AddMergerLayer(const OriginsDescriptor& mergerDescriptor,
    const char* name)
{
    return m_Graph->AddLayer<MergerLayer>(mergerDescriptor, name);
}

IConnectableLayer* Network::AddAdditionLayer(const char* name)
{
    return m_Graph->AddLayer<AdditionLayer>(name);
}

IConnectableLayer* Network::AddMultiplicationLayer(const char* name)
{
    return m_Graph->AddLayer<MultiplicationLayer>(name);
}

IConnectableLayer* Network::AddOutputLayer(LayerBindingId id, const char* name)
{
    return m_Graph->AddLayer<OutputLayer>(id, name);
}

IConnectableLayer* Network::AddBatchNormalizationLayer(const BatchNormalizationDescriptor& desc,
                                                       const ConstTensor&                  mean,
                                                       const ConstTensor&                  variance,
                                                       const ConstTensor&                  beta,
                                                       const ConstTensor&                  gamma,
                                                       const char*                         name)
{
    const auto layer = m_Graph->AddLayer<BatchNormalizationLayer>(desc, name);

    layer->m_Mean = std::make_unique<ScopedCpuTensorHandle>(mean);
    layer->m_Variance = std::make_unique<ScopedCpuTensorHandle>(variance);
    layer->m_Beta = std::make_unique<ScopedCpuTensorHandle>(beta);
    layer->m_Gamma = std::make_unique<ScopedCpuTensorHandle>(gamma);

    return layer;
}

IConnectableLayer* Network::AddResizeBilinearLayer(const ResizeBilinearDescriptor&
resizeDescriptor, const char* name)
{
    return m_Graph->AddLayer<ResizeBilinearLayer>(resizeDescriptor,name);
}

IConnectableLayer* Network::AddL2NormalizationLayer(const L2NormalizationDescriptor& desc,
                                                    const char* name)
{
    return m_Graph->AddLayer<L2NormalizationLayer>(desc, name);
}

IConnectableLayer* Network::AddConstantLayer(const ConstTensor& input, const char* name)
{
    auto layer = m_Graph->AddLayer<ConstantLayer>(name);

    layer->m_LayerOutput = std::make_unique<ScopedCpuTensorHandle>(input);

    return layer;
}

IConnectableLayer* Network::AddReshapeLayer(const ReshapeDescriptor& reshapeDescriptor,
                                            const char* name)
{
    return m_Graph->AddLayer<ReshapeLayer>(reshapeDescriptor, name);
}

IConnectableLayer* Network::AddSpaceToBatchNdLayer(const SpaceToBatchNdDescriptor& spaceToBatchNdDescriptor,
                                                   const char* name)
{
    return m_Graph->AddLayer<SpaceToBatchNdLayer>(spaceToBatchNdDescriptor, name);
}

IConnectableLayer* Network::AddFloorLayer(const char* name)
{
    return m_Graph->AddLayer<FloorLayer>(name);
}

IConnectableLayer* Network::AddLstmLayer(const LstmDescriptor&  descriptor,
                                         const LstmInputParams& params,
                                         const char* name)
{
    const auto layer = m_Graph->AddLayer<LstmLayer>(descriptor, name);

    //Lstm Basic Parameters
    layer->m_BasicParameters.m_InputToForgetWeights =
        std::make_unique<ScopedCpuTensorHandle>(*(params.m_InputToForgetWeights));
    layer->m_BasicParameters.m_InputToCellWeights =
        std::make_unique<ScopedCpuTensorHandle>(*(params.m_InputToCellWeights));
    layer->m_BasicParameters.m_InputToOutputWeights =
        std::make_unique<ScopedCpuTensorHandle>(*(params.m_InputToOutputWeights));
    layer->m_BasicParameters.m_RecurrentToForgetWeights =
        std::make_unique<ScopedCpuTensorHandle>(*(params.m_RecurrentToForgetWeights));
    layer->m_BasicParameters.m_RecurrentToCellWeights =
        std::make_unique<ScopedCpuTensorHandle>(*(params.m_RecurrentToCellWeights));
    layer->m_BasicParameters.m_RecurrentToOutputWeights =
        std::make_unique<ScopedCpuTensorHandle>(*(params.m_RecurrentToOutputWeights));
    layer->m_BasicParameters.m_ForgetGateBias =
            std::make_unique<ScopedCpuTensorHandle>(*(params.m_ForgetGateBias));
    layer->m_BasicParameters.m_CellBias =
            std::make_unique<ScopedCpuTensorHandle>(*(params.m_CellBias));
    layer->m_BasicParameters.m_OutputGateBias =
            std::make_unique<ScopedCpuTensorHandle>(*(params.m_OutputGateBias));

    //Lstm Cifg parameters
    if(!descriptor.m_CifgEnabled)
    {
        if(params.m_InputToInputWeights == nullptr)
        {
            throw InvalidArgumentException("AddLstmLayer: Input To Input Weights cannot be NULL");
        }
        if(params.m_RecurrentToInputWeights == nullptr)
        {
            throw InvalidArgumentException(
                    "AddLstmLayer: Recurrent To Input Weights cannot be NULL");
        }
        if(params.m_InputGateBias == nullptr)
        {
            throw InvalidArgumentException("AddLstmLayer: Input Gate Bias cannot be NULL");
        }
        layer->m_CifgParameters.m_InputToInputWeights =
            std::make_unique<ScopedCpuTensorHandle>(*(params.m_InputToInputWeights));
        layer->m_CifgParameters.m_RecurrentToInputWeights =
            std::make_unique<ScopedCpuTensorHandle>(*(params.m_RecurrentToInputWeights));
        // In the VTS tests, cell-to-input weights may be null, even if the other CIFG params are not.
        if(params.m_CellToInputWeights != nullptr)
        {
            layer->m_CifgParameters.m_CellToInputWeights =
                    std::make_unique<ScopedCpuTensorHandle>(*(params.m_CellToInputWeights));
        }
        layer->m_CifgParameters.m_InputGateBias =
            std::make_unique<ScopedCpuTensorHandle>(*(params.m_InputGateBias));
    }

    //Lstm projection parameters
    if(descriptor.m_ProjectionEnabled)
    {
        if(params.m_ProjectionWeights == nullptr)
        {
            throw InvalidArgumentException("AddLstmLayer: Projection Weights cannot be NULL");
        }
        layer->m_ProjectionParameters.m_ProjectionWeights =
            std::make_unique<ScopedCpuTensorHandle>(*(params.m_ProjectionWeights));
        if(params.m_ProjectionBias != nullptr)
        {
            layer->m_ProjectionParameters.m_ProjectionBias =
                std::make_unique<ScopedCpuTensorHandle>(*(params.m_ProjectionBias));
        }
    }

    //Lstm Peephole params
    if(descriptor.m_PeepholeEnabled)
    {
        if(params.m_CellToForgetWeights == nullptr)
        {
            throw InvalidArgumentException("AddLstmLayer: Cell To Forget Weights cannot be NULL");
        }
        if(params.m_CellToOutputWeights == nullptr)
        {
            throw InvalidArgumentException("AddLstmLayer: Cell To Output Weights cannot be NULL");
        }
        layer->m_PeepholeParameters.m_CellToForgetWeights =
            std::make_unique<ScopedCpuTensorHandle>(*(params.m_CellToForgetWeights));
        layer->m_PeepholeParameters.m_CellToOutputWeights =
            std::make_unique<ScopedCpuTensorHandle>(*(params.m_CellToOutputWeights));
    }
    return layer;
}

IConnectableLayer* Network::AddDivisionLayer(const char* name)
{
    return m_Graph->AddLayer<DivisionLayer>(name);
}

IConnectableLayer* Network::AddSubtractionLayer(const char* name)
{
    return m_Graph->AddLayer<SubtractionLayer>(name);
}

IConnectableLayer* Network::AddMeanLayer(const MeanDescriptor& meanDescriptor, const char* name)
{
    return m_Graph->AddLayer<MeanLayer>(meanDescriptor,name);
}

IConnectableLayer* Network::AddPadLayer(const PadDescriptor& padDescriptor, const char* name)
{
    return m_Graph->AddLayer<PadLayer>(padDescriptor,name);
}

IConnectableLayer* Network::AddStridedSliceLayer(const StridedSliceDescriptor& stridedSliceDescriptor,
                                                 const char* name)
{
    return m_Graph->AddLayer<StridedSliceLayer>(stridedSliceDescriptor, name);
}

IConnectableLayer* Network::AddGreaterLayer(const char* name)
{
    return m_Graph->AddLayer<GreaterLayer>(name);
}

IConnectableLayer* Network::AddEqualLayer(const char* name)
{
    return m_Graph->AddLayer<EqualLayer>(name);
}

IConnectableLayer* Network::AddRsqrtLayer(const char * name)
{
    return m_Graph->AddLayer<RsqrtLayer>(name);
}

IConnectableLayer* Network::AddGatherLayer(const char* name)
{
    return m_Graph->AddLayer<GatherLayer>(name);
}

OptimizedNetwork::OptimizedNetwork(std::unique_ptr<Graph> graph)
    : m_Graph(std::move(graph))
{
}

OptimizedNetwork::~OptimizedNetwork()
{
}

} // namespace armnn