Release 18.02

Change-Id: I41a89c149534a7c354a58e2c66a32cba572fc0c1

1 files changed, 1848 insertions, 0 deletions

diff --git a/ModelToINetworkConverter.cpp b/ModelToINetworkConverter.cpp
new file mode 100644
index 00000000..68ebef00
--- /dev/null
+++ b/ModelToINetworkConverter.cpp
@@ -0,0 +1,1848 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// See LICENSE file in the project root for full license information.
+//
+
+#define LOG_TAG "ArmnnDriver"
+
+#include "ModelToINetworkConverter.hpp"
+#include "OperationsUtils.h"
+
+#include <armnn/LayerSupport.hpp>
+#include <Permute.hpp>
+
+#include <log/log.h>
+#include <cassert>
+
+#include <boost/format.hpp>
+#include <boost/core/ignore_unused.hpp>
+#include <boost/test/tools/floating_point_comparison.hpp>
+#include <boost/cast.hpp>
+
+namespace
+{
+using namespace armnn_driver;
+using namespace android::nn;
+
+// Convenience function to log the reason for failing to convert a model.
+// @return Always returns false (so that it can be used by callers as a quick way to signal an error and return)
+template<class... Args>
+static bool Fail(const char* formatStr, Args&&... args)
+{
+    ALOGD(formatStr, std::forward<Args>(args)...);
+    return false;
+}
+
+// Convenience function to call an Is*Supported function and log caller name together with reason for lack of support.
+// Called as: IsLayerSupported(__func__, Is*Supported, a, b, c, d, e)
+template<typename IsLayerSupportedFunc, typename ... Args>
+bool IsLayerSupported(const char* funcName, IsLayerSupportedFunc f, Args&&... args)
+{
+    std::vector<char> unsupportedReason(1024+1);
+    bool isSupported = f(std::forward<Args>(args)..., unsupportedReason.data(), unsupportedReason.size()-1);
+    if(isSupported)
+    {
+        return true;
+    }
+    else
+    {
+        std::string sUnsupportedReason(unsupportedReason.data());
+        if (sUnsupportedReason.size() > 0)
+        {
+            ALOGD("%s: not supported by armnn: %s", funcName, sUnsupportedReason.c_str());
+        } else
+        {
+            ALOGD("%s: not supported by armnn", funcName);
+        }
+        return false;
+    }
+}
+
+armnn::TensorShape GetTensorShapeForOperand(const Operand& operand)
+{
+    return armnn::TensorShape(operand.dimensions.size(), operand.dimensions.data());
+}
+
+inline bool IsOperandTypeSupportedForTensors(OperandType type)
+{
+    return type == OperandType::TENSOR_FLOAT32      ||
+           type == OperandType::TENSOR_QUANT8_ASYMM ||
+           type == OperandType::TENSOR_INT32;
+}
+
+void CalcPadding(uint32_t input, uint32_t kernel, uint32_t stride, uint32_t& outPadHead, uint32_t& outPadTail,
+                 android::nn::PaddingScheme scheme)
+{
+    int32_t padHead;
+    int32_t padTail;
+    calculateExplicitPadding(input, stride, kernel, scheme, &padHead, &padTail);
+    outPadHead = boost::numeric_cast<uint32_t>(padHead);
+    outPadTail = boost::numeric_cast<uint32_t>(padTail);
+}
+
+bool ValidateBroadcast(const Model& model, const Operation& operation, uint32_t numInputs)
+{
+    assert(operation.inputs.size() > 0); // This should have been validated by the caller
+    // validateModel() has been called already so we know the operation.inputs indexes are valid within model.operands.
+    const Operand& firstInput = model.operands[operation.inputs[0]];
+
+    // We don't support broadcasting yet - we require all input operands to have the same shape
+    for (uint32_t i = 1; i < numInputs; ++i)
+    {
+        const Operand& otherInput = model.operands[operation.inputs[i]];
+
+        if (firstInput.dimensions.size() != otherInput.dimensions.size())
+        {
+            return Fail("%s: Broadcasting not supported (Input 0 dims: %i Input %i dims: %i)",
+                __func__, firstInput.dimensions.size(), i, otherInput.dimensions.size());
+        }
+
+        for (unsigned int d = 0; d < firstInput.dimensions.size(); ++d)
+        {
+            if (firstInput.dimensions[d] != otherInput.dimensions[d])
+            {
+                return Fail("%s: Broadcasting not supported (Dimension %i size mismatch. "
+                    "Input 0: %i Input %i: %i)",
+                    __func__, d, firstInput.dimensions[d], i, otherInput.dimensions[d]);
+            }
+        }
+    }
+
+    return true;
+}
+
+Shape GetOperandShape(const Operand& operand)
+{
+    Shape shape;
+    shape.type = operand.type;
+    shape.dimensions = operand.dimensions;
+    shape.scale = operand.scale;
+    shape.offset = operand.zeroPoint;
+    return shape;
+}
+
+// ArmNN requires the bias scale to be equal to the product of the weight and input scales, which is also
+// what AndroidNN requires. However for some of the AndroidNN tests the values don't exactly match so
+// we accept some tolerance. We don't want to ArmNN itself to accept these inconsistencies as it is up to the user
+// (us, in this case) to ensure they match.
+void SanitizeBiasQuantizationScale(armnn::TensorInfo& biasInfo,
+    const armnn::TensorInfo& weightInfo, const armnn::TensorInfo& inputInfo)
+{
+    const float expectedBiasScale = weightInfo.GetQuantizationScale() * inputInfo.GetQuantizationScale();
+    if (biasInfo.GetQuantizationScale() != expectedBiasScale)
+    {
+        boost::math::fpc::close_at_tolerance<float> comparer(boost::math::fpc::percent_tolerance(1.0f));
+        if (comparer(biasInfo.GetQuantizationScale(), expectedBiasScale))
+        {
+            ALOGW("Bias quantization scale has been modified to match input*weights");
+            biasInfo.SetQuantizationScale(expectedBiasScale);
+        }
+    }
+}
+
+const armnn::PermutationVector NHWCToArmNN({ 0U, 2U, 3U, 1U });
+
+template <typename OSlot>
+armnn::IConnectableLayer& AddPermuteLayer(armnn::INetwork& network, OSlot& input,
+                                          const armnn::PermutationVector& mappings)
+{
+    // Add swizzle layer
+    armnn::IConnectableLayer* const layer = network.AddPermuteLayer(mappings);
+
+    assert(layer != nullptr);
+
+    // Connect intput to swizzle layer
+    input.Connect(layer->GetInputSlot(0));
+
+    // Setup swizzled output
+    const armnn::TensorInfo outInfo = armnnUtils::Permuted(input.GetTensorInfo(), mappings);
+    layer->GetOutputSlot(0).SetTensorInfo(outInfo);
+
+    return *layer;
+}
+
+armnn::IConnectableLayer& SwizzleInDeswizzleOut(armnn::INetwork& network, LayerInputHandle& input,
+                                                armnn::IConnectableLayer& firstLayer,
+                                                armnn::IConnectableLayer& lastLayer)
+{
+    static const armnn::PermutationVector ArmNNToNHWC({ 0U, 3U, 1U, 2U });
+
+    // Add swizzle layer
+    armnn::IConnectableLayer& swizzleLayer = AddPermuteLayer(network, input, NHWCToArmNN);
+
+    // Connect swizzled input to layer
+    swizzleLayer.GetOutputSlot(0).Connect(firstLayer.GetInputSlot(0));
+
+    // Add deswizzle layer
+    armnn::IConnectableLayer& deswizzleLayer = AddPermuteLayer(network, lastLayer.GetOutputSlot(0), ArmNNToNHWC);
+
+    return deswizzleLayer;
+}
+
+armnn::IConnectableLayer& SwizzleInDeswizzleOut(armnn::INetwork& network, LayerInputHandle& input,
+                                                armnn::IConnectableLayer& layer)
+{
+    return SwizzleInDeswizzleOut(network, input, layer, layer);
+}
+} // namespace
+
+namespace armnn_driver
+{
+
+class ConstTensorPin
+{
+public:
+    // Creates an invalid tensor pin (can be used to signal errors)
+    ConstTensorPin() {}
+
+    // @param tensorInfo TensorInfo associated with the tensor.
+    // @param valueStart Start address of tensor data. Belongs to one of the memory pools associated with
+    // the model being converted.
+    // @param numBytes Number of bytes for the tensor data.
+    ConstTensorPin(const armnn::TensorInfo& tensorInfo, const void* valueStart, uint32_t numBytes,
+                   const armnn::PermutationVector& mappings)
+    {
+        boost::ignore_unused(numBytes);
+        assert(tensorInfo.GetNumBytes() == numBytes);
+
+        const bool needsSwizzling = (mappings.GetSize() > 0);
+        if (needsSwizzling)
+        {
+            m_SwizzledTensorData.resize(tensorInfo.GetNumBytes());
+            SwizzleAndroidNn4dTensorToArmNn(tensorInfo, valueStart, m_SwizzledTensorData.data(), mappings);
+
+            m_ConstTensor = armnn::ConstTensor(armnnUtils::Permuted(tensorInfo, mappings), m_SwizzledTensorData.data());
+        }
+        else
+        {
+            m_ConstTensor = armnn::ConstTensor(tensorInfo, valueStart);
+        }
+    }
+
+    ConstTensorPin(const ConstTensorPin& other) = delete;
+    ConstTensorPin(ConstTensorPin&& other) = default;
+
+    bool IsValid() const { return m_ConstTensor.GetMemoryArea() != nullptr; }
+    const armnn::ConstTensor& GetConstTensor() const { return m_ConstTensor; }
+
+private:
+    armnn::ConstTensor m_ConstTensor;
+    // Owned memory for swizzled tensor data, only required if the tensor needed
+    // swizzling. Otherwise, @ref m_ConstTensor will reference memory from one of
+    // the pools associated with the model being converted.
+    std::vector<uint8_t> m_SwizzledTensorData;
+};
+
+ModelToINetworkConverter::ModelToINetworkConverter(armnn::Compute compute, const Model& model,
+    const std::set<unsigned int>& forcedUnsupportedOperations)
+    : m_Compute(compute)
+    , m_Model(model)
+    , m_ForcedUnsupportedOperations(forcedUnsupportedOperations)
+    , m_Network(nullptr, nullptr)
+    , m_ConversionResult(ConversionResult::Success)
+{
+    try
+    {
+        Convert();
+    }
+    catch (armnn::Exception& e)
+    {
+        m_ConversionResult = ConversionResult::UnsupportedFeature;
+        ALOGE("%s: Unexpected exception: %s", __func__, e.what());
+        assert(false);
+    }
+}
+
+void ModelToINetworkConverter::Convert()
+{
+    ALOGV("ModelToINetworkConverter::Convert(): %s", GetModelSummary(m_Model).c_str());
+
+    // map the memory pool into shared pointers
+    m_MemPools.clear();
+    if (!setRunTimePoolInfosFromHidlMemories(&m_MemPools, m_Model.pools))
+    {
+        Fail("%s: Setting of run time pool infos from Hidl Memories has failed.", __func__);
+        m_ConversionResult = ConversionResult::ErrorMappingPools;
+        return;
+    }
+
+    uint32_t totalPoolSize = 0;
+    for (auto&& pool : m_Model.pools)
+    {
+        totalPoolSize += pool.size();
+    }
+
+    // Create armnn::INetwork
+    m_Network = armnn::INetwork::Create();
+
+    // add operations to it
+    // track which layer outputs each operand
+    m_OutputSlotForOperand = std::vector<armnn::IOutputSlot*>(m_Model.operands.size(), nullptr);
+
+    try
+    {
+        for (uint32_t i = 0; i < m_Model.inputIndexes.size(); i++)
+        {
+            // inputs in android nn are represented by operands
+            uint32_t inputIndex = m_Model.inputIndexes[i];
+            const Operand& operand = m_Model.operands[inputIndex];
+            const armnn::TensorInfo& tensor = GetTensorInfoForOperand(operand);
+            armnn::IConnectableLayer* layer = m_Network->AddInputLayer(i);
+
+            armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0);
+            outputSlot.SetTensorInfo(GetTensorInfoForOperand(operand));
+
+            // store for later layers
+            m_OutputSlotForOperand[inputIndex] = &outputSlot;
+        }
+    }
+    catch (UnsupportedOperand& e)
+    {
+        Fail("%s: Operand type %s not supported in ArmnnDriver", __func__, toString(e.m_type).c_str());
+        m_ConversionResult = ConversionResult::UnsupportedFeature;
+    }
+    catch (const armnn::InvalidArgumentException& e)
+    {
+        Fail("%s: Failed to convert input operand to TensorShape: %s", __func__, e.what());
+        m_ConversionResult = ConversionResult::UnsupportedFeature;
+    }
+
+    for (uint32_t operationIdx = 0; operationIdx < m_Model.operations.size(); operationIdx++)
+    {
+        const auto& operation = m_Model.operations[operationIdx];
+
+        bool ok = true;
+        if (m_ForcedUnsupportedOperations.find(operationIdx) != m_ForcedUnsupportedOperations.end())
+        {
+            Fail("%s: Operation at index %i has been forced to be unsupported.", __func__, operationIdx);
+            ok = false;
+        }
+
+        if (ok)
+        {
+            try
+            {
+                ok = ConvertOperation(operation);
+            }
+            catch (UnsupportedOperand& e)
+            {
+                Fail("%s: Operand type %s not supported in ArmnnDriver", __func__, toString(e.m_type).c_str());
+                ok = false;
+            }
+            catch (const armnn::InvalidArgumentException& e)
+            {
+                Fail("%s: Failed to convert operation in %s", __func__, e.what());
+                ok = false;
+            }
+        }
+
+        // Store whether this operation was successfully converted.
+        m_OperationSupported.emplace(operationIdx, ok);
+
+        // Any single operation failing will fail the entire conversion.
+        // We still need to continue and check the other ones.
+        if (!ok)
+        {
+            m_ConversionResult = ConversionResult::UnsupportedFeature;
+        }
+    }
+    try
+    {
+        if (m_ConversionResult == ConversionResult::Success)
+        {
+            for (uint32_t i = 0; i < m_Model.outputIndexes.size(); i++)
+            {
+                // outputs in android nn are represented by operands
+                uint32_t outputIndex = m_Model.outputIndexes[i];
+                const Operand& operand = m_Model.operands[outputIndex];
+                const armnn::TensorInfo& tensor = GetTensorInfoForOperand(operand);
+                armnn::IConnectableLayer* layer = m_Network->AddOutputLayer(i);
+
+                assert(m_OutputSlotForOperand[outputIndex]);
+                m_OutputSlotForOperand[outputIndex]->Connect(layer->GetInputSlot(0));
+            }
+        }
+    }
+    catch (const armnn::InvalidArgumentException& e)
+    {
+        Fail("%s: Failed to convert output operand to TensorShape: %s", __func__, e.what());
+        m_ConversionResult = ConversionResult::UnsupportedFeature;
+    }
+}
+
+bool ModelToINetworkConverter::ConvertOperation(const Operation& operation)
+{
+    switch (operation.type)
+    {
+        case OperationType::ADD: return ConvertAdd(operation);
+        case OperationType::AVERAGE_POOL_2D: return ConvertAveragePool2d(operation);
+        case OperationType::CONCATENATION: return ConvertConcatenation(operation);
+        case OperationType::CONV_2D: return ConvertConv2d(operation);
+        case OperationType::DEPTHWISE_CONV_2D: return ConvertDepthwiseConv2d(operation);
+        case OperationType::FLOOR: return ConvertFloor(operation);
+        case OperationType::FULLY_CONNECTED: return ConvertFullyConnected(operation);
+        case OperationType::LOCAL_RESPONSE_NORMALIZATION: return ConvertLocalResponseNormalization(operation);
+        case OperationType::LOGISTIC: return ConvertLogistic(operation);
+        case OperationType::L2_NORMALIZATION: return ConvertL2Normalization(operation);
+        case OperationType::L2_POOL_2D: return ConvertL2Pool2d(operation);
+        case OperationType::MAX_POOL_2D: return ConvertMaxPool2d(operation);
+        case OperationType::MUL: return ConvertMul(operation);
+        case OperationType::RELU: return ConvertReLu(operation);
+        case OperationType::RELU1: return ConvertReLu1(operation);
+        case OperationType::RELU6: return ConvertReLu6(operation);
+        case OperationType::SOFTMAX: return ConvertSoftmax(operation);
+        case OperationType::TANH: return ConvertTanH(operation);
+        case OperationType::RESHAPE: return ConvertReshape(operation);
+        case OperationType::RESIZE_BILINEAR: return ConvertResizeBilinear(operation);
+        default: return Fail("%s: Operation type %s not supported in ArmnnDriver",
+            __func__, toString(operation.type).c_str());
+    }
+}
+
+class LayerInputHandle
+{
+public:
+    LayerInputHandle()
+        : m_OutputSlot(nullptr)
+        , m_Valid(false)
+    {}
+
+    LayerInputHandle(bool valid, armnn::IOutputSlot* outputSlot, armnn::TensorInfo tensorInfo)
+        : m_OutputSlot(outputSlot)
+        , m_Valid(valid)
+        , m_TensorInfo(tensorInfo)
+    {}
+
+    bool IsValid() const { return m_Valid; }
+    void Connect(armnn::IInputSlot& inputSlot)
+    {
+        assert(IsValid());
+
+        if (m_OutputSlot)
+        {
+            m_OutputSlot->Connect(inputSlot);
+        }
+    }
+    const armnn::TensorInfo& GetTensorInfo() const { return m_TensorInfo; }
+
+private:
+    armnn::IOutputSlot* m_OutputSlot;
+    bool m_Valid;
+    armnn::TensorInfo m_TensorInfo;
+};
+
+bool ModelToINetworkConverter::ConvertAdd(const Operation& operation)
+{
+    LayerInputHandle input0 = ConvertToLayerInputHandle(operation, 0);
+    LayerInputHandle input1 = ConvertToLayerInputHandle(operation, 1);
+
+    if (!input0.IsValid() || !input1.IsValid())
+    {
+        return Fail("%s: Operation has invalid inputs", __func__);
+    }
+
+    ActivationFn activationFunction;
+    if (!GetInputActivationFunction(operation, 2, activationFunction))
+    {
+        return Fail("%s: Operation has invalid inputs", __func__);
+    }
+
+    const Operand* outputOperand = GetOutputOperand(operation, 0);
+    if (!outputOperand)
+    {
+        return false;
+    }
+
+    const armnn::TensorInfo outInfo = GetTensorInfoForOperand(*outputOperand);
+
+    if (!IsLayerSupported(__func__,
+                          armnn::IsAdditionSupported,
+                          m_Compute,
+                          input0.GetTensorInfo(),
+                          input1.GetTensorInfo(),
+                          outInfo))
+    {
+        return false;
+    }
+
+    armnn::IConnectableLayer* const startLayer = m_Network->AddAdditionLayer();
+    armnn::IConnectableLayer* const endLayer = ProcessActivation(outInfo, activationFunction, startLayer);
+
+    const armnn::TensorInfo& inputTensorInfo0 = input0.GetTensorInfo();
+    const armnn::TensorInfo& inputTensorInfo1 = input1.GetTensorInfo();
+
+    if (endLayer != nullptr)
+    {
+        // If the number of dimensions do not match then we need to add degenerate dimensions
+        // to the "smaller" tensor using a reshape:
+        //   Small  Big
+        //     |     |
+        //  Reshape  |
+        //      \   /
+        //       Add
+        if (inputTensorInfo0.GetNumDimensions() != inputTensorInfo1.GetNumDimensions())
+        {
+            bool input0IsBigger = inputTensorInfo0.GetNumDimensions() > inputTensorInfo1.GetNumDimensions();
+
+            LayerInputHandle& smallTensorHandle = input0IsBigger ? input1 : input0;
+            const armnn::TensorInfo& smallTensorDims = smallTensorHandle.GetTensorInfo();
+
+            LayerInputHandle& bigTensorHandle =  input0IsBigger ? input0 : input1;
+            const armnn::TensorInfo& bigTensorDims = bigTensorHandle.GetTensorInfo();
+
+            std::vector<unsigned int> reshapedDims(bigTensorDims.GetNumDimensions(), 1);
+            unsigned int sizeDifference = bigTensorDims.GetNumDimensions() - smallTensorDims.GetNumDimensions();
+            for (unsigned i = sizeDifference; i < bigTensorDims.GetNumDimensions(); ++i)
+            {
+                reshapedDims[i] = smallTensorDims.GetShape()[i-sizeDifference];
+            }
+            armnn::TensorInfo reshapedInfo = smallTensorDims;
+            reshapedInfo.SetShape(armnn::TensorShape{ static_cast<unsigned int>(reshapedDims.size()),
+                                                      reshapedDims.data() });
+
+            armnn::ReshapeDescriptor reshapeDesc;
+            reshapeDesc.m_TargetShape = reshapedInfo.GetShape();
+            armnn::IConnectableLayer* const reshapeLayer = m_Network->AddReshapeLayer(reshapeDesc);
+            smallTensorHandle.Connect(reshapeLayer->GetInputSlot(0));
+            reshapeLayer->GetOutputSlot(0).SetTensorInfo(reshapedInfo);
+
+            // Connect the outputs from new reshape and original input layer
+            reshapeLayer->GetOutputSlot(0).Connect(startLayer->GetInputSlot(0));
+            bigTensorHandle.Connect(startLayer->GetInputSlot(1));
+        }
+        else
+        {
+            input0.Connect(startLayer->GetInputSlot(0));
+            input1.Connect(startLayer->GetInputSlot(1));
+        }
+
+        return SetupAndTrackLayerOutputSlot(operation, 0, *endLayer);
+    }
+    else
+    {
+        return Fail("%s: ProcessActivation failed", __func__);
+    }
+}
+
+bool ModelToINetworkConverter::ConvertAveragePool2d(const Operation& operation)
+{
+    return ConvertPooling2d(operation, __func__, armnn::PoolingAlgorithm::Average);
+}
+
+bool ModelToINetworkConverter::ConvertConcatenation(const Operation& operation)
+{
+    // The first N (0..N-1) inputs are tensors. The Nth input is the concatenation axis.
+    if (operation.inputs.size() <= 1)
+    {
+        return Fail("%s: Operation has insufficient arguments", __func__);
+    }
+
+    // Get inputs and outputs
+    const std::size_t numInputTensors = operation.inputs.size() - 1;
+
+    std::vector<LayerInputHandle> inputHandles;
+    std::vector<armnn::TensorShape> inputShapes;
+
+    inputHandles.reserve(numInputTensors);
+    inputShapes.reserve(numInputTensors);
+
+    for (uint32_t i = 0; i < numInputTensors; ++i)
+    {
+        const Operand* const operand = GetInputOperand(operation, i);
+        if (!operand)
+        {
+            return Fail("%s: Operation has invalid inputs", __func__);
+        }
+
+        inputShapes.emplace_back(GetTensorShapeForOperand(*operand));
+        inputHandles.emplace_back(ConvertToLayerInputHandle(operation, i));
+        if (!inputHandles.back().IsValid())
+        {
+            return Fail("%s: Operation has invalid inputs", __func__);
+        }
+    }
+
+    assert(inputShapes.size() == inputHandles.size());
+
+    uint32_t concatDim;
+    if (!GetInputScalar(operation, numInputTensors, OperandType::INT32, concatDim))
+    {
+        return Fail("%s: Operation has invalid inputs", __func__);
+    }
+
+    const Operand* const outputOperand = GetOutputOperand(operation, 0);
+    if (!outputOperand)
+    {
+        return Fail("%s: Operation has no outputs", __func__);
+    }
+    const armnn::TensorShape outputShape = GetTensorShapeForOperand(*outputOperand);
+
+    // Create an armnn merger layer descriptor - this will also perform validation on the input shapes
+    armnn::OriginsDescriptor mergerDescriptor;
+    try
+    {
+        mergerDescriptor = armnn::CreateMergerDescriptorForConcatenation(inputShapes.begin(), inputShapes.end(),
+            concatDim);
+    }
+    catch (const armnn::Exception& error)
+    {
+        return Fail("%s: Error preparing merger descriptor. %s", __func__, error.what());
+    }
+
+    // Validate the output shape is correct given the input shapes (which have just been validated)
+    unsigned int numDimensions = inputShapes[0].GetNumDimensions();
+    if (outputShape.GetNumDimensions() != numDimensions)
+    {
+        return Fail("%s: Output shape has wrong number of dimensions", __func__);
+    }
+
+    unsigned int outputSizeAlongConcatenatedDimension = 0;
+    for (unsigned int i = 0; i < inputShapes.size(); i++)
+    {
+        outputSizeAlongConcatenatedDimension += inputShapes[i][concatDim];
+    }
+
+    for (unsigned int i = 0; i < numDimensions; ++i)
+    {
+        if (i == concatDim)
+        {
+            if (outputShape[i] != outputSizeAlongConcatenatedDimension)
+            {
+                return Fail("%s: Invalid output shape", __func__);
+            }
+        }
+        else
+        {
+            if (outputShape[i] != inputShapes[0][i])
+            {
+                return Fail("%s: Invalid output shape", __func__);
+            }
+        }
+    }
+
+    std::vector<const armnn::TensorInfo*> inputTensorInfos;
+    std::transform(inputHandles.begin(), inputHandles.end(), std::back_inserter(inputTensorInfos),
+        [](const LayerInputHandle& h) -> const armnn::TensorInfo*{ return &h.GetTensorInfo(); });
+    if (!IsLayerSupported(__func__,
+                          armnn::IsMergerSupported,
+                          m_Compute,
+                          inputTensorInfos,
+                          mergerDescriptor))
+    {
+        return false;
+    }
+
+    armnn::IConnectableLayer* layer = m_Network->AddMergerLayer(mergerDescriptor);
+    assert(layer != nullptr);
+
+    // Connect inputs to the layer
+    const int numInputSlots = layer->GetNumInputSlots();
+    assert(static_cast<std::size_t>(numInputSlots) == inputHandles.size());
+    for (int i = 0; i < numInputSlots; ++i)
+    {
+        inputHandles[static_cast<unsigned int>(i)].Connect(layer->GetInputSlot(i));
+    }
+
+    return SetupAndTrackLayerOutputSlot(operation, 0, *layer);
+}
+
+bool ModelToINetworkConverter::ConvertConv2d(const Operation& operation)
+{
+    LayerInputHandle input = ConvertToLayerInputHandle(operation, 0);
+    if (!input.IsValid())
+    {
+        return Fail("%s: Operation has invalid inputs", __func__);
+    }
+
+    const Operand* output = GetOutputOperand(operation, 0);
+    if (!output)
+    {
+        return Fail("%s: Could not read output 0", __func__);
+    }
+
+    const armnn::TensorInfo& inputInfo = input.GetTensorInfo();
+    const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
+
+    const armnn::TensorInfo swizzledInputInfo = armnnUtils::Permuted(inputInfo, NHWCToArmNN);
+    const armnn::TensorInfo swizzledOutputInfo = armnnUtils::Permuted(outputInfo, NHWCToArmNN);
+
+    // ArmNN does not currently support non-fixed weights or bias
+    const ConstTensorPin weightsPin = ConvertOperationInputToConstTensorPin(operation, 1, NHWCToArmNN);
+    const ConstTensorPin biasPin = ConvertOperationInputToConstTensorPin(operation, 2);
+
+    if (!weightsPin.IsValid() || !biasPin.IsValid())
+    {
+        return Fail("%s: Operation has invalid inputs", __func__);
+    }
+
+    armnn::ConstTensor weights = weightsPin.GetConstTensor();
+    armnn::ConstTensor bias = biasPin.GetConstTensor();
+    SanitizeBiasQuantizationScale(bias.GetInfo(), weights.GetInfo(), swizzledInputInfo);
+
+    armnn::Convolution2dDescriptor desc;
+    ActivationFn activation;
+
+    if (operation.inputs.size() == 10)
+    {
+        if (!GetInputScalar(operation, 3, OperandType::INT32, desc.m_PadLeft)   ||
+            !GetInputScalar(operation, 4, OperandType::INT32, desc.m_PadRight)  ||
+            !GetInputScalar(operation, 5, OperandType::INT32, desc.m_PadTop)    ||
+            !GetInputScalar(operation, 6, OperandType::INT32, desc.m_PadBottom) ||
+            !GetInputScalar(operation, 7, OperandType::INT32, desc.m_StrideX)   ||
+            !GetInputScalar(operation, 8, OperandType::INT32, desc.m_StrideY)   ||
+            !GetInputActivationFunction(operation, 9, activation))
+        {
+            return Fail("%s: Operation has invalid inputs", __func__);
+        }
+    }
+    else if (operation.inputs.size() == 7)
+    {
+        android::nn::PaddingScheme paddingScheme;
+
+        if (!GetInputPaddingScheme(operation, 3, paddingScheme)               ||
+            !GetInputScalar(operation, 4, OperandType::INT32, desc.m_StrideX) ||
+            !GetInputScalar(operation, 5, OperandType::INT32, desc.m_StrideY) ||
+            !GetInputActivationFunction(operation, 6, activation))
+        {
+            return Fail("%s: Operation has invalid inputs", __func__);
+        }
+
+        const uint32_t kernelX = weights.GetShape()[3];
+        const uint32_t kernelY = weights.GetShape()[2];
+        const uint32_t inputX  = swizzledInputInfo.GetShape()[3];
+        const uint32_t inputY  = swizzledInputInfo.GetShape()[2];
+
+        CalcPadding(inputX, kernelX, desc.m_StrideX, desc.m_PadLeft, desc.m_PadRight, paddingScheme);
+        CalcPadding(inputY, kernelY, desc.m_StrideY, desc.m_PadTop, desc.m_PadBottom, paddingScheme);
+    }
+    else
+    {
+        return Fail("%s: Unsupported number of operation inputs", __func__);
+    }
+
+    desc.m_BiasEnabled    = true;
+
+    if (!IsLayerSupported(__func__,
+                          armnn::IsConvolution2dSupported,
+                          m_Compute,
+                          swizzledInputInfo,
+                          desc,
+                          weights.GetInfo()))
+    {
+        return false;
+    }
+
+    armnn::IConnectableLayer* startLayer = m_Network->AddConvolution2dLayer(desc, weights, bias);
+    armnn::IConnectableLayer* endLayer = ProcessActivation(swizzledOutputInfo, activation, startLayer);
+
+    if (endLayer != nullptr)
+    {
+        armnn::IConnectableLayer& outSwizzleLayer = SwizzleInDeswizzleOut(*m_Network, input, *startLayer, *endLayer);
+        return SetupAndTrackLayerOutputSlot(operation, 0, outSwizzleLayer);
+    }
+    else
+    {
+        return Fail("%s: ProcessActivation failed", __func__);
+    }
+}
+
+bool ModelToINetworkConverter::ConvertDepthwiseConv2d(const Operation& operation)
+{
+    LayerInputHandle input = ConvertToLayerInputHandle(operation, 0);
+    if (!input.IsValid())
+    {
+        return Fail("%s: Operation has invalid inputs", __func__);
+    }
+
+    const Operand* output = GetOutputOperand(operation, 0);
+    if (!output)
+    {
+        return Fail("%s: Could not read output 0", __func__);
+    }
+
+    const armnn::TensorInfo& inputInfo = input.GetTensorInfo();
+    const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
+
+    const armnn::TensorInfo swizzledInputInfo = armnnUtils::Permuted(inputInfo, NHWCToArmNN);
+    const armnn::TensorInfo swizzledOutputInfo = armnnUtils::Permuted(outputInfo, NHWCToArmNN);
+
+    // ArmNN does not currently support non-fixed weights or bias
+
+    // Find the shape of the weights tensor. In AndroidNN this will be [ 1, H, W, I * M ]
+    // but in ArmNN it needs to be [ M, I, H, W ]
+    const Operand* weightsOperand = GetInputOperand(operation, 1);
+
+    if (weightsOperand == nullptr)
+    {
+        return Fail("%s: Operand is invalid", __func__);
+    }
+
+    // Reinterpret weight data as [ H, W, I, M ]
+    armnn::TensorShape weightsShape({ weightsOperand->dimensions[1], weightsOperand->dimensions[2],
+                                      inputInfo.GetShape()[3],
+                                      weightsOperand->dimensions[3] / inputInfo.GetShape()[3] });
+
+    // Swizzle weight data [ H, W, I, M ] -> [ M, I, H, W ]
+    const armnn::PermutationVector HWIMToMIHW = { 2U, 3U, 1U, 0U };
+    ConstTensorPin weightsPin = ConvertOperationInputToConstTensorPin(operation, 1, HWIMToMIHW, &weightsShape);
+
+    // Bias is a 1D tensor
+    ConstTensorPin biasPin = ConvertOperationInputToConstTensorPin(operation, 2);
+
+    if (!weightsPin.IsValid() || !biasPin.IsValid())
+    {
+        return Fail("%s: Operation has invalid inputs", __func__);
+    }
+
+    armnn::ConstTensor weights = weightsPin.GetConstTensor();
+    armnn::ConstTensor bias = biasPin.GetConstTensor();
+    SanitizeBiasQuantizationScale(bias.GetInfo(), weights.GetInfo(), swizzledInputInfo);
+
+    armnn::DepthwiseConvolution2dDescriptor desc;
+    ActivationFn activation;
+
+    if (operation.inputs.size() == 11)
+    {
+        if (!GetInputScalar(operation, 3, OperandType::INT32, desc.m_PadLeft)         ||
+            !GetInputScalar(operation, 4, OperandType::INT32, desc.m_PadRight)        ||
+            !GetInputScalar(operation, 5, OperandType::INT32, desc.m_PadTop)          ||
+            !GetInputScalar(operation, 6, OperandType::INT32, desc.m_PadBottom)       ||
+            !GetInputScalar(operation, 7, OperandType::INT32, desc.m_StrideX)         ||
+            !GetInputScalar(operation, 8, OperandType::INT32, desc.m_StrideY)         ||
+            !GetInputActivationFunction(operation,  10, activation))
+        {
+            return Fail("%s: Operation has invalid inputs", __func__);
+        }
+    }
+    else if (operation.inputs.size() == 8)
+    {
+        android::nn::PaddingScheme paddingScheme;
+
+        if (!GetInputPaddingScheme(operation, 3, paddingScheme)                       ||
+            !GetInputScalar(operation, 4, OperandType::INT32, desc.m_StrideX)         ||
+            !GetInputScalar(operation, 5, OperandType::INT32, desc.m_StrideY)         ||
+            !GetInputActivationFunction(operation, 7, activation))
+        {
+            return Fail("%s: Operation has invalid inputs", __func__);
+        }
+
+        const uint32_t kernelX = weights.GetShape()[3];
+        const uint32_t kernelY = weights.GetShape()[2];
+        const uint32_t inputX  = swizzledInputInfo.GetShape()[3];
+        const uint32_t inputY  = swizzledInputInfo.GetShape()[2];
+
+        CalcPadding(inputX, kernelX, desc.m_StrideX, desc.m_PadLeft, desc.m_PadRight, paddingScheme);
+        CalcPadding(inputY, kernelY, desc.m_StrideY, desc.m_PadTop, desc.m_PadBottom, paddingScheme);
+    }
+    else
+    {
+        return Fail("%s: Unsupported number of operation inputs", __func__);
+    }
+
+    desc.m_BiasEnabled = true;
+
+    if (!IsLayerSupported(__func__,
+                          armnn::IsDepthwiseConvolutionSupported,
+                          m_Compute,
+                          swizzledInputInfo,
+                          desc,
+                          weights.GetInfo()))
+    {
+        return false;
+    }
+
+    armnn::IConnectableLayer* startLayer = m_Network->AddDepthwiseConvolution2dLayer(desc, weights, bias);
+    armnn::IConnectableLayer* endLayer = ProcessActivation(swizzledOutputInfo, activation, startLayer);
+
+    if (endLayer != nullptr)
+    {
+        armnn::IConnectableLayer& outSwizzleLayer = SwizzleInDeswizzleOut(*m_Network, input, *startLayer, *endLayer);
+        return SetupAndTrackLayerOutputSlot(operation, 0, outSwizzleLayer);
+    }
+    else
+    {
+        return Fail("%s: ProcessActivation failed", __func__);
+    }
+}
+
+bool ModelToINetworkConverter::ConvertFloor(const Operation& operation)
+{
+    LayerInputHandle input = ConvertToLayerInputHandle(operation, 0);
+    if (!input.IsValid())
+    {
+        return Fail("%s: Operation has invalid inputs", __func__);
+    }
+
+    const Operand* const outputOperand = GetOutputOperand(operation, 0);
+    if (!outputOperand)
+    {
+        return Fail("%s: Operation has invalid outputs", __func__);
+    }
+
+    if (!IsLayerSupported(__func__,
+                          armnn::IsFloorSupported,
+                          m_Compute,
+                          input.GetTensorInfo(),
+                          GetTensorInfoForOperand(*outputOperand)))
+    {
+        return false;
+    }
+
+    armnn::IConnectableLayer* layer = m_Network->AddFloorLayer();
+    assert(layer != nullptr);
+    input.Connect(layer->GetInputSlot(0));
+
+    return SetupAndTrackLayerOutputSlot(operation, 0, *layer);
+}
+
+bool ModelToINetworkConverter::ConvertFullyConnected(const Operation& operation)
+{
+    LayerInputHandle input = ConvertToLayerInputHandle(operation, 0);
+    if (!input.IsValid())
+    {
+        return Fail("%s: Operation has invalid inputs", __func__);
+    }
+
+    const Operand* output = GetOutputOperand(operation, 0);
+    if (!output)
+    {
+        return Fail("%s: Could not read output 0", __func__);
+    }
+
+    const armnn::TensorInfo& inputInfo = input.GetTensorInfo();
+    const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
+
+    armnn::TensorInfo reshapedInfo = inputInfo;
+
+    if (inputInfo.GetNumDimensions() > 2U)
+    {
+        unsigned int dim1 = inputInfo.GetShape()[1];
+        for (unsigned int i = 2U; i < inputInfo.GetNumDimensions(); ++i)
+        {
+            dim1 *= inputInfo.GetShape()[i];
+        }
+        reshapedInfo.SetShape(armnn::TensorShape({inputInfo.GetShape()[0], dim1}));
+    }
+
+    // ArmNN does not currently support non-fixed weights or bias
+    ConstTensorPin weightsPin = ConvertOperationInputToConstTensorPin(operation, 1); // 2D
+    ConstTensorPin biasPin = ConvertOperationInputToConstTensorPin(operation, 2);    // 1D
+
+    if (!weightsPin.IsValid() || !biasPin.IsValid())
+    {
+        return Fail("%s: Operation has invalid inputs", __func__);
+    }
+
+    // ensuring that the bias value is within 1% of the weights input (small float differences can exist)
+    armnn::ConstTensor weights = weightsPin.GetConstTensor();
+    armnn::ConstTensor bias = biasPin.GetConstTensor();
+    SanitizeBiasQuantizationScale(bias.GetInfo(), weights.GetInfo(), reshapedInfo);
+
+    ActivationFn activationFunction;
+    if (!GetInputActivationFunction(operation, 3, activationFunction))
+    {
+        return Fail("%s: Operation has invalid inputs", __func__);
+    }
+
+    armnn::FullyConnectedDescriptor desc;
+    desc.m_TransposeWeightMatrix = true;
+    desc.m_BiasEnabled           = true;
+
+    if (!IsLayerSupported(__func__,
+                          armnn::IsFullyConnectedSupported,
+                          m_Compute,
+                          reshapedInfo,
+                          desc))
+    {
+        return false;
+    }
+
+    armnn::IConnectableLayer* startLayer = m_Network->AddFullyConnectedLayer(desc, weights, bias);
+    armnn::IConnectableLayer* endLayer = ProcessActivation(outputInfo, activationFunction, startLayer);
+
+    if (endLayer != nullptr)
+    {
+        if (inputInfo.GetNumDimensions() > 2U)
+        {
+            armnn::ReshapeDescriptor reshapeDescriptor;
+            reshapeDescriptor.m_TargetShape = reshapedInfo.GetShape();
+
+            armnn::IConnectableLayer* reshapeLayer = m_Network->AddReshapeLayer(reshapeDescriptor);
+            assert(reshapeLayer != nullptr);
+            input.Connect(reshapeLayer->GetInputSlot(0));
+            reshapeLayer->GetOutputSlot(0).SetTensorInfo(reshapedInfo);
+            reshapeLayer->GetOutputSlot(0).Connect(startLayer->GetInputSlot(0));
+        }
+        else
+        {
+            input.Connect(startLayer->GetInputSlot(0));
+        }
+
+        return SetupAndTrackLayerOutputSlot(operation, 0, *endLayer);
+    }
+    else
+    {
+        return Fail("%s: ProcessActivation failed", __func__);
+    }
+}
+
+bool ModelToINetworkConverter::ConvertLocalResponseNormalization(const Operation& operation)
+{
+    LayerInputHandle input = ConvertToLayerInputHandle(operation, 0);
+    if (!input.IsValid())
+    {
+        return Fail("%s: Operation has invalid inputs", __func__);
+    }
+
+    const Operand* output = GetOutputOperand(operation, 0);
+    if (!output)
+    {
+        return Fail("%s: Could not read output 0", __func__);
+    }
+
+    const armnn::TensorInfo& inputInfo = input.GetTensorInfo();
+    const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
+
+    const armnn::TensorInfo swizzledInputInfo = armnnUtils::Permuted(inputInfo, NHWCToArmNN);
+    const armnn::TensorInfo swizzledOutputInfo = armnnUtils::Permuted(outputInfo, NHWCToArmNN);
+
+    armnn::NormalizationDescriptor descriptor;
+
+    descriptor.m_NormChannelType = armnn::NormalizationAlgorithmChannel::Across;
+    descriptor.m_NormMethodType = armnn::NormalizationAlgorithmMethod::LocalBrightness;
+
+    if (!input.IsValid() ||
+        !GetInputScalar(operation, 1, OperandType::INT32, descriptor.m_NormSize) ||
+        !GetInputFloat32(operation, 2, descriptor.m_K) ||
+        !GetInputFloat32(operation, 3, descriptor.m_Alpha) ||
+        !GetInputFloat32(operation, 4, descriptor.m_Beta))
+    {
+        return Fail("%s: Operation has invalid inputs", __func__);
+    }
+
+    // ArmNN expects normSize to be the full size of the normalization
+    // window rather than the radius as in AndroidNN.
+    descriptor.m_NormSize = 1 + (2 * descriptor.m_NormSize);
+
+    if (!IsLayerSupported(__func__,
+                        armnn::IsNormalizationSupported,
+                        m_Compute,
+                        swizzledInputInfo,
+                        swizzledOutputInfo,
+                        descriptor))
+    {
+        return false;
+    }
+
+
+    armnn::IConnectableLayer* layer = m_Network->AddNormalizationLayer(descriptor);
+    assert(layer != nullptr);
+    layer->GetOutputSlot(0).SetTensorInfo(swizzledOutputInfo);
+
+    armnn::IConnectableLayer& outSwizzleLayer = SwizzleInDeswizzleOut(*m_Network, input, *layer);
+
+    return SetupAndTrackLayerOutputSlot(operation, 0, outSwizzleLayer);
+}
+
+bool ModelToINetworkConverter::ConvertLogistic(const Operation& operation)
+{
+    armnn::ActivationDescriptor desc;
+    desc.m_Function == armnn::ActivationFunction::Sigmoid;
+
+    return ConvertToActivation(operation, __func__, desc);
+}
+
+bool ModelToINetworkConverter::ConvertL2Normalization(const Operation& operation)
+{
+    LayerInputHandle input = ConvertToLayerInputHandle(operation, 0);
+    if (!input.IsValid())
+    {
+        return Fail("%s: Operation has invalid inputs", __func__);
+    }
+
+    const Operand* output = GetOutputOperand(operation, 0);
+    if (!output)
+    {
+        return Fail("%s: Could not read output 0", __func__);
+    }
+
+    const armnn::TensorInfo& inputInfo = input.GetTensorInfo();
+    const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
+
+    const armnn::TensorInfo swizzledInputInfo = armnnUtils::Permuted(inputInfo, NHWCToArmNN);
+    const armnn::TensorInfo swizzledOutputInfo = armnnUtils::Permuted(outputInfo, NHWCToArmNN);
+
+    if (!IsLayerSupported(__func__,
+                          armnn::IsL2NormalizationSupported,
+                          m_Compute,
+                          swizzledInputInfo))
+    {
+        return false;
+    }
+
+    armnn::IConnectableLayer* layer = m_Network->AddL2NormalizationLayer();
+    assert(layer != nullptr);
+    layer->GetOutputSlot(0).SetTensorInfo(swizzledOutputInfo);
+
+    armnn::IConnectableLayer& outSwizzleLayer = SwizzleInDeswizzleOut(*m_Network, input, *layer);
+
+    return SetupAndTrackLayerOutputSlot(operation, 0, outSwizzleLayer);
+}
+
+bool ModelToINetworkConverter::ConvertL2Pool2d(const Operation& operation)
+{
+    return ConvertPooling2d(operation, __func__, armnn::PoolingAlgorithm::L2);
+}
+
+bool ModelToINetworkConverter::ConvertMaxPool2d(const Operation& operation)
+{
+    return ConvertPooling2d(operation, __func__, armnn::PoolingAlgorithm::Max);
+}
+
+bool ModelToINetworkConverter::ConvertMul(const Operation& operation)
+{
+    LayerInputHandle input0 = ConvertToLayerInputHandle(operation, 0);
+    LayerInputHandle input1 = ConvertToLayerInputHandle(operation, 1);
+
+    if (!input0.IsValid() || !input1.IsValid())
+    {
+        return Fail("%s: Operation has invalid inputs", __func__);
+    }
+
+    ActivationFn activationFunction;
+    if (!GetInputActivationFunction(operation, 2, activationFunction))
+    {
+        return Fail("%s: Operation has invalid inputs", __func__);
+    }
+
+    if (!ValidateBroadcast(m_Model, operation, 2u))
+    {
+        return Fail("%s is invalid due to broadcasting", __func__);
+    }
+
+    if (!IsLayerSupported(__func__,
+                          armnn::IsMultiplicationSupported,
+                          m_Compute,
+                          input0.GetTensorInfo(),
+                          input1.GetTensorInfo()))
+    {
+        return false;
+    }
+
+    const Operand* outputOperand = GetOutputOperand(operation, 0);
+
+    if (outputOperand == nullptr)
+    {
+        return false;
+    }
+
+    const armnn::TensorInfo& outInfo = GetTensorInfoForOperand(*outputOperand);
+
+    armnn::IConnectableLayer* const startLayer = m_Network->AddMultiplicationLayer();
+    armnn::IConnectableLayer* const endLayer = ProcessActivation(outInfo, activationFunction, startLayer);
+
+    if (endLayer != nullptr)
+    {
+        input0.Connect(startLayer->GetInputSlot(0));
+        input1.Connect(startLayer->GetInputSlot(1));
+
+        return SetupAndTrackLayerOutputSlot(operation, 0, *endLayer);
+    }
+    else
+    {
+        return Fail("%s: ProcessActivation failed", __func__);
+    }
+}
+
+bool ModelToINetworkConverter::ConvertReLu(const Operation& operation)
+{
+    armnn::ActivationDescriptor desc;
+    desc.m_Function = armnn::ActivationFunction::ReLu;
+
+    return ConvertToActivation(operation, __func__, desc);
+}
+
+bool ModelToINetworkConverter::ConvertReLu1(const Operation& operation)
+{
+    armnn::ActivationDescriptor desc;
+    desc.m_Function = armnn::ActivationFunction::BoundedReLu;
+    desc.m_A        = 1.0f;
+    desc.m_B        = -1.0f;
+
+    return ConvertToActivation(operation, __func__, desc);
+}
+
+bool ModelToINetworkConverter::ConvertReLu6(const Operation& operation)
+{
+    armnn::ActivationDescriptor desc;
+    desc.m_Function = armnn::ActivationFunction::BoundedReLu;
+    desc.m_A        = 6.0f;
+
+    return ConvertToActivation(operation, __func__, desc);
+}
+
+bool ModelToINetworkConverter::ConvertSoftmax(const Operation& operation)
+{
+    LayerInputHandle input = ConvertToLayerInputHandle(operation, 0);
+    if (!input.IsValid())
+    {
+        return Fail("%s: Operation has invalid inputs", __func__);
+    }
+
+    armnn::SoftmaxDescriptor desc;
+    if (!GetInputFloat32(operation, 1, desc.m_Beta))
+    {
+        return Fail("%s: Operation has invalid inputs", __func__);
+    }
+
+    if (!IsLayerSupported(__func__,
+                          armnn::IsSoftmaxSupported,
+                          m_Compute,
+                          input.GetTensorInfo(),
+                          desc))
+    {
+        return false;
+    }
+
+    armnn::IConnectableLayer* layer = m_Network->AddSoftmaxLayer(desc);
+    assert(layer != nullptr);
+    input.Connect(layer->GetInputSlot(0));
+
+    return SetupAndTrackLayerOutputSlot(operation, 0, *layer);
+}
+
+bool ModelToINetworkConverter::ConvertTanH(const Operation& operation)
+{
+    armnn::ActivationDescriptor desc;
+    desc.m_Function = armnn::ActivationFunction::TanH;
+    desc.m_A = 1.0f; // android nn does not support tanH parameters
+    desc.m_B = 1.0f; // set to 1.0f for unity scaling
+
+    return ConvertToActivation(operation, __func__, desc);
+}
+
+bool ModelToINetworkConverter::ConvertReshape(const Operation& operation)
+{
+    const Operand* inputOperand = GetInputOperand(operation, 0);
+    const Operand* requestedShapeOperand = GetInputOperand(operation, 1);
+    const Operand* outputOperand = GetOutputOperand(operation, 0);
+
+    if (inputOperand == nullptr
+        || requestedShapeOperand == nullptr
+        || outputOperand == nullptr)
+    {
+        return Fail("%s: Operation has invalid inputs", __func__);
+    }
+
+
+    if (requestedShapeOperand->dimensions.size() != 1)
+    {
+        return Fail("%s: Input 1 expected to be one-dimensional (found %i dimensions)",
+            __func__, requestedShapeOperand->dimensions.size());
+    }
+
+    std::vector<int32_t> targetDimensions;
+    if (!GetTensorInt32Values(*requestedShapeOperand, targetDimensions))
+    {
+        return Fail("%s: Could not read values of input 1", __func__);
+    }
+
+    const Shape inputOperandShape = GetOperandShape(*inputOperand);
+
+    Shape requestedShape;
+    // targetDimensions may contain special values (e.g. -1). reshapePrepare() is an AndroidNN provided utility
+    // function that resolves these values into a fully specified tensor shape.
+    if (!reshapePrepare(inputOperandShape, targetDimensions.data(), targetDimensions.size(), &requestedShape))
+    {
+        return Fail("%s: Failed to resolve the requested shape", __func__);
+    }
+
+    const Shape outputOperandShape = GetOperandShape(*outputOperand);
+    if (!SameShape(requestedShape, outputOperandShape))
+    {
+        return Fail("%s: Shape of output operand does not match resolved requested shape", __func__);
+    }
+
+    LayerInputHandle input = ConvertToLayerInputHandle(operation, 0);
+    if (!input.IsValid())
+    {
+        return Fail("%s: Could not read input 0", __func__);
+    }
+
+    if (!IsLayerSupported(__func__,
+                          armnn::IsReshapeSupported,
+                          m_Compute,
+                          input.GetTensorInfo()))
+    {
+        return false;
+    }
+
+
+    armnn::ReshapeDescriptor reshapeDescriptor;
+    reshapeDescriptor.m_TargetShape = armnn::TensorShape(requestedShape.dimensions.size(),
+                                                         requestedShape.dimensions.data());
+
+    armnn::IConnectableLayer* layer = m_Network->AddReshapeLayer(reshapeDescriptor);
+    assert(layer != nullptr);
+    input.Connect(layer->GetInputSlot(0));
+
+    return SetupAndTrackLayerOutputSlot(operation, 0, *layer);
+}
+
+bool ModelToINetworkConverter::ConvertResizeBilinear(const Operation& operation)
+{
+    LayerInputHandle input = ConvertToLayerInputHandle(operation, 0);
+    if (!input.IsValid())
+    {
+        return Fail("%s: Could not read input 0", __func__);
+    }
+
+    const Operand* output = GetOutputOperand(operation, 0);
+    if (!output)
+    {
+        return Fail("%s: Could not read output 0", __func__);
+    }
+
+    const armnn::TensorInfo& inputInfo = input.GetTensorInfo();
+    const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
+
+    const armnn::TensorInfo swizzledInputInfo = armnnUtils::Permuted(inputInfo, NHWCToArmNN);
+    const armnn::TensorInfo swizzledOutputInfo = armnnUtils::Permuted(outputInfo, NHWCToArmNN);
+
+    if (!IsLayerSupported(__func__,
+                          armnn::IsResizeBilinearSupported,
+                          m_Compute,
+                          swizzledInputInfo))
+    {
+        return false;
+    }
+
+    armnn::ResizeBilinearDescriptor desc;
+
+    if (   !GetInputScalar(operation, 1, OperandType::INT32, desc.m_TargetHeight)
+        || !GetInputScalar(operation, 2, OperandType::INT32, desc.m_TargetWidth))
+    {
+        return Fail("%s: Operation has invalid inputs", __func__);
+    }
+
+    armnn::IConnectableLayer* layer = m_Network->AddResizeBilinearLayer(desc);
+    assert(layer != nullptr);
+    layer->GetOutputSlot(0).SetTensorInfo(swizzledOutputInfo);
+
+    armnn::IConnectableLayer& outSwizzleLayer = SwizzleInDeswizzleOut(*m_Network, input, *layer);
+
+    return SetupAndTrackLayerOutputSlot(operation, 0, outSwizzleLayer);
+
+}
+
+bool ModelToINetworkConverter::ConvertToActivation(const Operation& operation,
+    const char* operationName,
+    const armnn::ActivationDescriptor& activationDesc)
+{
+    LayerInputHandle input = ConvertToLayerInputHandle(operation, 0);
+    if (!input.IsValid())
+    {
+        return Fail("%s: Input 0 is invalid", operationName);
+    }
+
+    if (!IsLayerSupported(__func__,
+                          armnn::IsActivationSupported,
+                          m_Compute,
+                          input.GetTensorInfo(),
+                          activationDesc))
+    {
+        return false;
+    }
+
+    armnn::IConnectableLayer* layer = m_Network->AddActivationLayer(activationDesc);
+    assert(layer != nullptr);
+    input.Connect(layer->GetInputSlot(0));
+
+    return SetupAndTrackLayerOutputSlot(operation, 0, *layer);
+}
+
+bool ModelToINetworkConverter::ConvertPooling2d(const Operation& operation,
+    const char* operationName,
+    armnn::PoolingAlgorithm poolType)
+{
+    LayerInputHandle input = ConvertToLayerInputHandle(operation, 0);
+    if (!input.IsValid())
+    {
+        return Fail("%s: Could not read input 0", operationName);
+    }
+
+    const Operand* output = GetOutputOperand(operation, 0);
+    if (!output)
+    {
+        return Fail("%s: Could not read output 0", __func__);
+    }
+
+    const armnn::TensorInfo& inputInfo = input.GetTensorInfo();
+    const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
+
+    const armnn::TensorInfo swizzledInputInfo = armnnUtils::Permuted(inputInfo, NHWCToArmNN);
+    const armnn::TensorInfo swizzledOutputInfo = armnnUtils::Permuted(outputInfo, NHWCToArmNN);
+
+    armnn::Pooling2dDescriptor desc;
+    desc.m_PoolType = poolType;
+    desc.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor;
+
+    ActivationFn activation;
+
+    if (operation.inputs.size() == 7)
+    {
+        // one input, 6 parameters (padding, stridex, stridey, width, height, activation type)
+        android::nn::PaddingScheme scheme;
+
+        if (   !GetInputPaddingScheme(operation, 1, scheme)
+            || !GetInputScalar(operation, 2, OperandType::INT32, desc.m_StrideX)
+            || !GetInputScalar(operation, 3, OperandType::INT32, desc.m_StrideY)
+            || !GetInputScalar(operation, 4, OperandType::INT32, desc.m_PoolWidth)
+            || !GetInputScalar(operation, 5, OperandType::INT32, desc.m_PoolHeight)
+            || !GetInputActivationFunction(operation, 6, activation))
+        {
+            return Fail("%s: Operation has invalid inputs", operationName);
+        }
+
+        const unsigned int inputWidth = swizzledInputInfo.GetShape()[3];
+        const unsigned int inputHeight = swizzledInputInfo.GetShape()[2];
+
+        CalcPadding(inputWidth, desc.m_PoolWidth, desc.m_StrideX, desc.m_PadLeft, desc.m_PadRight, scheme);
+        CalcPadding(inputHeight, desc.m_PoolHeight, desc.m_StrideY, desc.m_PadTop, desc.m_PadBottom, scheme);
+    }
+    else
+    {
+        // one input, 9 parameters (padding l r t b, stridex, stridey, width, height, activation type)
+        if (   !GetInputScalar(operation, 1, OperandType::INT32, desc.m_PadLeft)
+            || !GetInputScalar(operation, 2, OperandType::INT32, desc.m_PadRight)
+            || !GetInputScalar(operation, 3, OperandType::INT32, desc.m_PadTop)
+            || !GetInputScalar(operation, 4, OperandType::INT32, desc.m_PadBottom)
+            || !GetInputScalar(operation, 5, OperandType::INT32, desc.m_StrideX)
+            || !GetInputScalar(operation, 6, OperandType::INT32, desc.m_StrideY)
+            || !GetInputScalar(operation, 7, OperandType::INT32, desc.m_PoolWidth)
+            || !GetInputScalar(operation, 8, OperandType::INT32, desc.m_PoolHeight)
+            || !GetInputActivationFunction(operation, 9, activation))
+        {
+            return Fail("%s: Operation has invalid inputs", operationName);
+        }
+    }
+
+    // ArmNN does not accept a pool size of 1, but the ArmNN driver is expected to cope.
+    // This is mapped to a trivial splitter instead.
+    armnn::IConnectableLayer* startLayer = nullptr;
+    if (desc.m_PoolWidth != 1 || desc.m_PoolHeight != 1)
+    {
+        if (!IsLayerSupported(__func__,
+                              armnn::IsPooling2dSupported,
+                              m_Compute,
+                              swizzledInputInfo,
+                              swizzledOutputInfo,
+                              desc))
+        {
+            return false;
+        }
+
+        startLayer = m_Network->AddPooling2dLayer(desc);
+    }
+    else
+    {
+        const unsigned int numDims = swizzledOutputInfo.GetNumDimensions();
+
+        armnn::ViewsDescriptor viewsDesc(1, numDims);
+
+        for (unsigned int i = 0; i < numDims; ++i)
+        {
+            viewsDesc.SetViewOriginCoord(0, i, 0);
+            viewsDesc.SetViewSize(0, i, swizzledOutputInfo.GetShape()[i]);
+        }
+
+        if (!IsLayerSupported(__func__,
+                              armnn::IsSplitterSupported,
+                              m_Compute,
+                              swizzledInputInfo,
+                              viewsDesc))
+        {
+            return false;
+        }
+
+        startLayer = m_Network->AddSplitterLayer(viewsDesc);
+    }
+
+    armnn::IConnectableLayer* endLayer = ProcessActivation(swizzledOutputInfo, activation, startLayer);
+
+    if (endLayer != nullptr)
+    {
+        armnn::IConnectableLayer& outSwizzleLayer = SwizzleInDeswizzleOut(*m_Network, input, *startLayer, *endLayer);
+        return SetupAndTrackLayerOutputSlot(operation, 0, outSwizzleLayer);
+    }
+    else
+    {
+        return Fail("%s: ProcessActivation failed", operationName);
+    }
+}
+
+const void* ModelToINetworkConverter::GetOperandValueReadOnlyAddress(const Operand& operand) const
+{
+    const void* valueStart = nullptr;
+
+    switch (operand.lifetime)
+    {
+        case OperandLifeTime::CONSTANT_COPY:
+        {
+            // Constant found in model.operandValues
+            valueStart = &m_Model.operandValues[operand.location.offset];
+            break;
+        }
+        case OperandLifeTime::CONSTANT_REFERENCE:
+        {
+            // Constant specified via a Memory object
+            valueStart = GetMemoryFromPool(operand.location, m_MemPools);
+            break;
+        }
+        default:
+        {
+            // Unsupported/invalid (e.g. can't get value of an input to the model)
+            Fail("%s: unsupported/invalid operand lifetime: %s",
+                __func__, toString(operand.lifetime).c_str());
+            valueStart = nullptr;
+        }
+    }
+
+    return valueStart;
+}
+
+const Operand* ModelToINetworkConverter::GetInputOperand(const Operation& operation, uint32_t inputIndex) const
+{
+    if (inputIndex >= operation.inputs.size())
+    {
+        Fail("%s: invalid input index: %i out of %i", __func__, inputIndex, operation.inputs.size());
+        return nullptr;
+    }
+
+    assert(operation.inputs[inputIndex] < m_Model.operands.size()); // Model should have been validated beforehand
+    return &m_Model.operands[operation.inputs[inputIndex]];
+}
+
+const Operand* ModelToINetworkConverter::GetOutputOperand(const Operation& operation, uint32_t outputIndex) const
+{
+    if (outputIndex >= operation.outputs.size())
+    {
+        Fail("%s: invalid output index: %i out of %i", __func__, outputIndex, operation.outputs.size());
+        return nullptr;
+    }
+
+    assert(operation.outputs[outputIndex] < m_Model.operands.size()); // Model should have been validated beforehand
+    return &m_Model.operands[operation.outputs[outputIndex]];
+}
+
+template<typename T>
+bool ModelToINetworkConverter::GetInputScalar(const Operation& operation, uint32_t inputIndex,
+    OperandType type, T& outValue) const
+{
+    const Operand* operand = GetInputOperand(operation, inputIndex);
+    if (!operand)
+    {
+        return Fail("%s: invalid input operand at index %i", __func__, inputIndex);
+    }
+
+    if (operand->type != type)
+    {
+        return Fail("%s: unexpected operand type: %s (should be %s)",
+            __func__, toString(operand->type).c_str(), toString(type).c_str());
+    }
+
+    if (operand->location.length != sizeof(T))
+    {
+        return Fail("%s: incorrect operand location length: %i (should be %i)",
+            __func__, operand->location.length, sizeof(T));
+    }
+
+    const void* valueAddress = GetOperandValueReadOnlyAddress(*operand);
+    if (!valueAddress)
+    {
+        return Fail("%s: failed to get address for operand", __func__);
+    }
+
+    outValue = *(static_cast<const T*>(valueAddress));
+    return true;
+}
+
+bool ModelToINetworkConverter::GetInputInt32(const Operation& operation, uint32_t inputIndex, int32_t& outValue) const
+{
+    return GetInputScalar(operation, inputIndex, OperandType::INT32, outValue);
+}
+
+bool ModelToINetworkConverter::GetInputFloat32(const Operation& operation, uint32_t inputIndex, float& outValue) const
+{
+    return GetInputScalar(operation, inputIndex, OperandType::FLOAT32, outValue);
+}
+
+bool ModelToINetworkConverter::GetInputActivationFunction(const Operation& operation,
+    uint32_t inputIndex,
+    ActivationFn& outActivationFunction) const
+{
+    int32_t activationFunctionAsInt;
+    if (!GetInputInt32(operation, inputIndex, activationFunctionAsInt))
+    {
+        return Fail("%s: failed to get activation input value", __func__);
+    }
+
+    outActivationFunction = static_cast<ActivationFn>(activationFunctionAsInt);
+    return true;
+}
+
+bool ModelToINetworkConverter::GetInputPaddingScheme(const Operation& operation,
+    uint32_t inputIndex,
+    android::nn::PaddingScheme& outPaddingScheme) const
+{
+    int32_t paddingSchemeAsInt;
+    if (!GetInputInt32(operation, inputIndex, paddingSchemeAsInt))
+    {
+        return Fail("%s: failed to get padding scheme input value", __func__);
+    }
+
+    outPaddingScheme = static_cast<android::nn::PaddingScheme>(paddingSchemeAsInt);
+    return true;
+}
+
+LayerInputHandle ModelToINetworkConverter::ConvertToLayerInputHandle(
+    const Operation& operation,
+    uint32_t inputIndex)
+{
+    const Operand* operand = GetInputOperand(operation, inputIndex);
+    if (!operand)
+    {
+        Fail("%s: failed to get input operand %i", __func__, inputIndex);
+        return LayerInputHandle();
+    }
+
+    if (!IsOperandTypeSupportedForTensors(operand->type))
+    {
+        Fail("%s: unsupported operand type for tensor %s", __func__, toString(operand->type).c_str());
+        return LayerInputHandle();
+    }
+
+    armnn::TensorInfo operandTensorInfo = GetTensorInfoForOperand(*operand);
+
+    switch (operand->lifetime)
+    {
+        case OperandLifeTime::TEMPORARY_VARIABLE: // intentional fallthrough
+        case OperandLifeTime::MODEL_INPUT:
+        {
+            // The tensor is either an operand internal to the model, or a model input.
+            // It can be associated with an ArmNN output slot for an existing layer.
+
+            // m_OutputSlotForOperand[...] can be nullptr if the previous layer could not be converted
+            const uint32_t operandIndex = operation.inputs[inputIndex];
+            return LayerInputHandle(true, m_OutputSlotForOperand[operandIndex], operandTensorInfo);
+            break;
+        }
+        case OperandLifeTime::CONSTANT_COPY:
+        case OperandLifeTime::CONSTANT_REFERENCE:
+        {
+            // The tensor has an already known constant value, and can be converted into an ArmNN Constant layer.
+            ConstTensorPin tensorPin = ConvertOperandToConstTensorPin(*operand);
+            if (tensorPin.IsValid())
+            {
+                if (!IsLayerSupported(__func__,
+                                      armnn::IsConstantSupported,
+                                      m_Compute,
+                                      tensorPin.GetConstTensor().GetInfo()))
+                {
+                    return LayerInputHandle();
+                }
+
+                armnn::IConnectableLayer* constantLayer = m_Network->AddConstantLayer(tensorPin.GetConstTensor());
+                armnn::IOutputSlot& outputSlot = constantLayer->GetOutputSlot(0);
+                outputSlot.SetTensorInfo(tensorPin.GetConstTensor().GetInfo());
+
+                return LayerInputHandle(true, &outputSlot, operandTensorInfo);
+            }
+            else
+            {
+                Fail("%s: invalid operand tensor", __func__);
+                return LayerInputHandle();
+            }
+            break;
+        }
+        default:
+        {
+            // Unsupported lifetime for an input tensor
+            Fail("%s: unsupported lifetime for input tensor: %s",
+                __func__, toString(operand->lifetime).c_str());
+            return LayerInputHandle();
+        }
+    }
+}
+
+ConstTensorPin ModelToINetworkConverter::ConvertOperationInputToConstTensorPin(const Operation& operation,
+    uint32_t inputIndex, const armnn::PermutationVector& dimensionMappings,
+    const armnn::TensorShape* overrideTensorShape)
+{
+    const Operand* operand = GetInputOperand(operation, inputIndex);
+    if (!operand)
+    {
+        Fail("%s: failed to get input operand", __func__);
+        return ConstTensorPin();
+    }
+
+    return ConvertOperandToConstTensorPin(*operand, dimensionMappings, overrideTensorShape);
+}
+
+ConstTensorPin ModelToINetworkConverter::ConvertOperandToConstTensorPin(const Operand& operand,
+    const armnn::PermutationVector& dimensionMappings, const armnn::TensorShape* overrideTensorShape)
+{
+    if (!IsOperandTypeSupportedForTensors(operand.type))
+    {
+        Fail("%s: unsupported operand type for tensor %s", __func__, toString(operand.type).c_str());
+        return ConstTensorPin();
+    }
+
+    if (operand.lifetime != OperandLifeTime::CONSTANT_COPY && operand.lifetime != OperandLifeTime::CONSTANT_REFERENCE)
+    {
+        Fail("%s: invalid operand lifetime: %s", __func__, toString(operand.lifetime).c_str());
+        return ConstTensorPin();
+    }
+
+    const void* const valueStart = GetOperandValueReadOnlyAddress(operand);
+    if (!valueStart)
+    {
+        Fail("%s: failed to get operand address", __func__);
+        return ConstTensorPin();
+    }
+
+    armnn::TensorInfo tensorInfo = GetTensorInfoForOperand(operand);
+    if (overrideTensorShape != nullptr)
+    {
+        tensorInfo.SetShape(*overrideTensorShape);
+    }
+    return ConstTensorPin(tensorInfo, valueStart, operand.location.length, dimensionMappings);
+}
+
+bool ModelToINetworkConverter::GetTensorInt32Values(const Operand& operand, std::vector<int32_t>& outValues) const
+{
+    if (operand.type != OperandType::TENSOR_INT32)
+    {
+        return Fail("%s: invalid operand type: %s", __func__, toString(operand.type).c_str());
+    }
+
+    const void* startAddress = GetOperandValueReadOnlyAddress(operand);
+    if (!startAddress)
+    {
+        return Fail("%s: failed to get operand address", __func__, operand.type);
+    }
+
+    // Check number of bytes is sensible
+    const uint32_t numBytes = operand.location.length;
+    if (numBytes % sizeof(int32_t) != 0)
+    {
+        return Fail("%s: invalid number of bytes: %i, expected to be a multiple of %i",
+            __func__, numBytes, sizeof(int32_t));
+    }
+
+    outValues.resize(numBytes / sizeof(int32_t));
+    memcpy(outValues.data(), startAddress, numBytes);
+    return true;
+}
+
+// Creates an ArmNN activation layer and connects it to the given layer, if the
+// passed in AndroidNN activation function requires so.
+// @return The end layer of the sequence of layers built for the given AndroidNN
+// activation function or nullptr if an error occurred (e.g. unsupported activation).
+// Note that the end layer matches the input layer if no activation is required
+// (the sequence of layers has length 1).
+armnn::IConnectableLayer* ModelToINetworkConverter::ProcessActivation(const armnn::TensorInfo& tensorInfo,
+    ActivationFn activation, armnn::IConnectableLayer* prevLayer)
+{
+    assert(prevLayer->GetNumOutputSlots() == 1);
+
+    prevLayer->GetOutputSlot(0).SetTensorInfo(tensorInfo);
+
+    armnn::IConnectableLayer* activationLayer = prevLayer;
+
+    if (activation != ActivationFn::kActivationNone)
+    {
+        armnn::ActivationDescriptor activationDesc;
+        switch (activation)
+        {
+            case ActivationFn::kActivationRelu:
+            {
+                activationDesc.m_Function = armnn::ActivationFunction::ReLu;
+                break;
+            }
+            case ActivationFn::kActivationRelu1:
+            {
+                activationDesc.m_Function = armnn::ActivationFunction::BoundedReLu;
+                activationDesc.m_A = 1.0f;
+                activationDesc.m_B = -1.0f;
+                break;
+            }
+            case ActivationFn::kActivationRelu6:
+            {
+                activationDesc.m_Function = armnn::ActivationFunction::BoundedReLu;
+                activationDesc.m_A = 6.0f;
+                break;
+            }
+            case ActivationFn::kActivationSigmoid:
+            {
+                activationDesc.m_Function = armnn::ActivationFunction::Sigmoid;
+                break;
+            }
+            case ActivationFn::kActivationTanh:
+            {
+                activationDesc.m_Function = armnn::ActivationFunction::TanH;
+                activationDesc.m_A = 1.0f;
+                activationDesc.m_B = 1.0f;
+                break;
+            }
+            default:
+            {
+                Fail("%s: Invalid activation enum value %i", __func__, activation);
+                return nullptr;
+            }
+        }
+
+        if (!IsLayerSupported(__func__, armnn::IsActivationSupported, m_Compute,
+                              prevLayer->GetOutputSlot(0).GetTensorInfo(), activationDesc))
+        {
+            return nullptr;
+        }
+
+        activationLayer = m_Network->AddActivationLayer(activationDesc);
+
+        prevLayer->GetOutputSlot(0).Connect(activationLayer->GetInputSlot(0));
+        activationLayer->GetOutputSlot(0).SetTensorInfo(tensorInfo);
+    }
+
+    return activationLayer;
+}
+
+bool ModelToINetworkConverter::SetupAndTrackLayerOutputSlot(const Operation& operation, uint32_t outputIndex,
+                                                            armnn::IConnectableLayer& layer)
+{
+    const Operand* outputOperand = GetOutputOperand(operation, outputIndex);
+
+    if ((outputOperand == nullptr) || (outputIndex >= layer.GetNumOutputSlots()))
+    {
+        return false;
+    }
+
+    armnn::IOutputSlot& outputSlot = layer.GetOutputSlot(outputIndex);
+
+    const uint32_t operandIndex = operation.outputs[outputIndex];
+    m_OutputSlotForOperand[operandIndex] = &outputSlot;
+
+    outputSlot.SetTensorInfo(GetTensorInfoForOperand(*outputOperand));
+
+    return true;
+}
+
+bool ModelToINetworkConverter::IsOperationSupported(uint32_t operationIndex) const
+{
+    std::map<uint32_t, bool>::const_iterator it = m_OperationSupported.find(operationIndex);
+    assert(it != m_OperationSupported.end());
+    return it->second;
+}
+
+
+} // armnn_driver
\ No newline at end of file