Release 18.08

1 files changed, 1676 insertions, 0 deletions

diff --git a/src/armnnOnnxParser/OnnxParser.cpp b/src/armnnOnnxParser/OnnxParser.cpp
new file mode 100644
index 0000000000..fdf43076ef
--- /dev/null
+++ b/src/armnnOnnxParser/OnnxParser.cpp
@@ -0,0 +1,1676 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// See LICENSE file in the project root for full license information.
+//
+#include "OnnxParser.hpp"
+
+#include <armnn/ArmNN.hpp>
+#include <armnn/Utils.hpp>
+#include <VerificationHelpers.hpp>
+
+#include <google/protobuf/text_format.h>
+#include <google/protobuf/io/zero_copy_stream_impl.h>
+
+#include <boost/format.hpp>
+
+#include <numeric>
+
+using namespace armnn;
+
+namespace armnnOnnxParser
+{
+namespace
+{
+void CheckValidDataType(std::initializer_list<onnx::TensorProto::DataType> validInputTypes,
+                        const onnx::TensorProto::DataType actualValue,
+                        const char* validExpr,
+                        std::string nodeName,
+                        std::string tensorName,
+                        const armnn::CheckLocation& location)
+{
+    bool isValid = std::any_of(validInputTypes.begin(),
+                               validInputTypes.end(),
+                               [&actualValue](onnx::TensorProto::DataType x) { return x == actualValue; } );
+    if (!isValid)
+    {
+        throw ParseException(
+            boost::str(
+                boost::format("Datatype %1% is not valid for tensor '%2%' of node '%3%', not in {%4%}. %5%") %
+                              onnx::TensorProto::DataType_Name(actualValue) %
+                              tensorName %
+                              nodeName %
+                              validExpr %
+                              location.AsString()));
+    }
+}
+
+#define CHECK_VALID_DATATYPE(NODE, TENSOR, ACTUAL, ...) \
+CheckValidDataType({__VA_ARGS__}, ACTUAL, #__VA_ARGS__, NODE, TENSOR, CHECK_LOCATION())
+
+using StrTypeListPair = std::pair<const char*, std::initializer_list<onnx::TensorProto::DataType>>;
+#define STR_LIST(...) StrTypeListPair(#__VA_ARGS__, {__VA_ARGS__})
+
+template <typename Callable>
+void ReadMandatoryNodeAttributeImpl(const onnx::NodeProto& node,
+                                    const std::string& attribName,
+                                    onnx::AttributeProto::AttributeType expectedType,
+                                    Callable callable)
+{
+  auto attribs = node.attribute();
+  int attriNum = 0;
+  while (attriNum < node.attribute_size())
+  {
+      if (attribs.Get(attriNum).name() == attribName)
+      {
+          if (attribs.Get(attriNum).type() == expectedType)
+          {
+              callable(attribs.Get(attriNum));
+          }
+          else
+          {
+              throw ParseException(boost::str(boost::format(
+                  "Attribute %1% of node %2% expected to have %3% as onnx::AttributeProto::AttributeType, "
+                  "but found %4% instead %5%")
+                  % attribName
+                  % node.name()
+                  % onnx::AttributeProto::AttributeType_Name(expectedType)
+                  % onnx::AttributeProto::AttributeType_Name(attribs.Get(attriNum).type())
+                  % CHECK_LOCATION().AsString()));
+          }
+          break;
+      }
+      ++attriNum;
+  }
+  if (attriNum == node.attribute_size())
+  {
+      throw ParseException(boost::str(boost::format("Could not find required attribute %1% in node %2% %3%")
+          % attribName % node.name() % CHECK_LOCATION().AsString()));
+  }
+}
+
+template <typename Callable>
+void ReadOptionalNodeAttributeImpl(const onnx::NodeProto& node,
+                                   const std::string& attribName,
+                                   onnx::AttributeProto::AttributeType expectedType,
+                                   Callable callable)
+{
+    auto attribs = node.attribute();
+    for (int attriNum = 0; attriNum < node.attribute_size(); ++attriNum)
+    {
+        if (attribs.Get(attriNum).name() == attribName)
+        {
+            if (attribs.Get(attriNum).type() == expectedType)
+            {
+                callable(attribs.Get(attriNum));
+            }
+            else
+            {
+                throw ParseException(boost::str(boost::format(
+                    "Attribute %1% of node %2% expected to have %3% as onnx::AttributeProto::AttributeType, "
+                    "but found %4% instead %5%")
+                    % attribName
+                    % node.name()
+                    % onnx::AttributeProto::AttributeType_Name(expectedType)
+                    % onnx::AttributeProto::AttributeType_Name(attribs.Get(attriNum).type())
+                    % CHECK_LOCATION().AsString()));
+            }
+        }
+    }
+}
+
+std::vector<uint32_t> ReadMandatoryNodeUint32ListAttribute(const onnx::NodeProto& node,
+                                                           const std::string& name)
+{
+    std::vector<uint32_t> attriList;
+    ReadMandatoryNodeAttributeImpl(node, name, onnx::AttributeProto::INTS,
+        [&attriList](const onnx::AttributeProto& attrValue)
+    {
+        for (int attriNum = 0; attriNum < attrValue.ints_size(); ++attriNum)
+        {
+            attriList.push_back(CHECKED_NON_NEGATIVE(CHECKED_INT32(attrValue.ints().Get(attriNum))));
+        }
+    });
+    return attriList;
+}
+
+uint32_t ReadOptionalNodeUint32Attribute(const onnx::NodeProto& node,
+                                         const std::string& name,
+                                         const uint32_t defaultVal = 0u)
+{
+    uint32_t attribValue = defaultVal;
+    ReadOptionalNodeAttributeImpl(node, name, onnx::AttributeProto::INT,
+        [&attribValue](const onnx::AttributeProto& attrValue)
+    {
+        attribValue = CHECKED_NON_NEGATIVE(CHECKED_INT32((attrValue.i())));
+    });
+    return attribValue;
+}
+
+std::vector<uint32_t> ReadOptionalNodeUint32ListAttribute(const onnx::NodeProto& node,
+                                                          const std::string& name)
+{
+    std::vector<uint32_t> attriList;
+    ReadOptionalNodeAttributeImpl(node, name, onnx::AttributeProto::INTS,
+        [&attriList](const onnx::AttributeProto& attrValue)
+    {
+        for (int attriNum = 0; attriNum < attrValue.ints_size(); ++attriNum)
+        {
+            attriList.push_back(CHECKED_NON_NEGATIVE(CHECKED_INT32(attrValue.ints().Get(attriNum))));
+        }
+    });
+
+    return attriList;
+}
+
+float ReadOptionalNodeFloatAttribute(const onnx::NodeProto& node,
+                                     const std::string& name,
+                                     const float defaultValue = 0.0f)
+{
+    float attribValue = defaultValue;
+    ReadOptionalNodeAttributeImpl(node, name, onnx::AttributeProto::FLOAT,
+        [&attribValue](const onnx::AttributeProto& attrValue)
+    {
+        attribValue = attrValue.f();
+    });
+    return attribValue;
+}
+
+std::string ReadOptionalNodeStringAttribute(const onnx::NodeProto& node, const std::string& name)
+{
+    std::string attribValue = "";
+    ReadOptionalNodeAttributeImpl(node, name, onnx::AttributeProto::STRING,
+        [&attribValue](const onnx::AttributeProto& attrValue)
+    {
+        attribValue = attrValue.s();
+    });
+    return attribValue;
+}
+
+armnn::TensorInfo ToTensorInfo(const onnx::ValueInfoProto& info)
+{
+  const onnx::TensorShapeProto onnxShape = info.type().tensor_type().shape();
+  std::vector<unsigned int> shapeDims;
+  for (int i = 0; i < onnxShape.dim_size(); ++i)
+  {
+      shapeDims.push_back(CHECKED_NON_NEGATIVE(CHECKED_INT32(onnxShape.dim(i).dim_value())));
+  }
+  DataType type;
+  switch(info.type().tensor_type().elem_type())
+  {
+      case onnx::TensorProto::FLOAT:
+      {
+        type = DataType::Float32;
+        break;
+      }
+      case onnx::TensorProto::INT32:
+      case onnx::TensorProto::INT64:
+      {
+          type = DataType::Signed32;
+          break;
+      }
+      default:
+      {
+          throw ParseException(
+              boost::str(
+                  boost::format("'%1%' is not a currently supported datatype for tensor %2%."
+                                " Supported dataTypes are FLOAT, INT32 and INT64.  %3%") %
+                                onnx::TensorProto::DataType_Name(info.type().tensor_type().elem_type()) %
+                                info.name() %
+                                CHECK_LOCATION().AsString() ));
+      }
+
+  }
+  return TensorInfo(TensorShape(static_cast<unsigned int>(shapeDims.size()), shapeDims.data()), type);
+}
+
+std::string TensorInfoAsString(const TensorInfo& info,
+                               const std::string& name,
+                               const onnx::TensorProto::DataType& type)
+{
+    const TensorShape shape = info.GetShape();
+    std::stringstream ss;
+    ss << "tensor '" << name << "' contains "
+       << onnx::TensorProto::DataType_Name(type)
+       << " and has shape [";
+
+    for (uint32_t i = 0; i < shape.GetNumDimensions() - 1; ++i)
+    {
+        ss << shape[i] << ", ";
+    }
+    ss << shape[shape.GetNumDimensions() - 1] << "]";
+    return ss.str();
+}
+
+void CalcPadding(uint32_t inputSize, uint32_t filterSize, uint32_t stride, uint32_t* paddingFront,
+                 uint32_t* paddingBack, bool isUpper)
+{
+    uint32_t outputSize = (inputSize + stride - 1) / stride;
+    uint32_t temp = (outputSize - 1) * stride + filterSize;
+    *paddingFront = (temp - inputSize) / 2;
+    *paddingBack = *paddingFront;
+    if((temp - inputSize) % 2 == 1)
+    {
+        if (isUpper)
+        {
+          *paddingBack += 1;
+        }
+        else
+        {
+          *paddingFront += 1;
+        }
+    }
+}
+
+TensorInfo ComputeReshapeInfo(const onnx::TensorProto& targetShapeTensor,
+                              const TensorShape& inShape,
+                              const std::string& outName)
+{
+    std::vector<int> targetDims;
+    for(int i = 0; i < targetShapeTensor.int64_data_size(); ++i)
+    {
+        int val = CHECKED_INT32(targetShapeTensor.int64_data(i));
+        if(val == 0)
+        {
+            targetDims.push_back(static_cast<int>(inShape[static_cast<uint>(i)]));
+        }
+        else
+        {
+            targetDims.push_back(val);
+        }
+    }
+
+    std::vector<unsigned int> outDims(targetDims.begin(), targetDims.end());
+    const auto stretchDim = std::find(targetDims.begin(), targetDims.end(), -1);
+    if (stretchDim != targetDims.end())
+    {
+        if (std::find(std::next(stretchDim), targetDims.end(), -1) != targetDims.end())
+        {
+            std::stringstream ss;
+            ss << "[ ";
+            for(uint i = 0; i < targetDims.size() - 1; ++i)
+            {
+                ss << targetDims[i] << ", ";
+            }
+            ss << targetDims[targetDims.size() - 1] << " ]";
+
+            throw ParseException(boost::str(
+                boost::format("Error during creation of reshaped tensor '%1%'. At most one component of shape can be "
+                              " -1 and here, shape is %2% %3%")
+                              % outName
+                              % ss.str()
+                              % CHECK_LOCATION().AsString()));
+        }
+
+        auto targetNumElements = boost::numeric_cast<unsigned int>(std::accumulate(targetDims.begin(), targetDims.end(),
+            -1, std::multiplies<int32_t>()));
+        auto stretchIndex = static_cast<size_t>(std::distance(targetDims.begin(), stretchDim));
+        outDims[stretchIndex] = inShape.GetNumElements() / targetNumElements;
+    }
+    TensorShape outShape = TensorShape{static_cast<unsigned int>(outDims.size()), outDims.data()};
+    return TensorInfo(outShape, DataType::Float32);
+}
+
+} //namespace
+
+const std::map<std::string, OnnxParser::OperationParsingFunction> OnnxParser::m_ParserFunctions = {
+    { "BatchNormalization",    &OnnxParser::ParseBatchNormalization},
+    { "GlobalAveragePool",     &OnnxParser::ParseGlobalAveragePool},
+    { "AveragePool",           &OnnxParser::ParseAveragePool },
+    { "Constant",              &OnnxParser::ParseConstant },
+    { "MaxPool",               &OnnxParser::ParseMaxPool },
+    { "Reshape",               &OnnxParser::ParseReshape },
+    { "Relu",                  &OnnxParser::ParseRelu },
+    { "Conv",                  &OnnxParser::ParseConv },
+    { "Add",                   &OnnxParser::ParseAdd },
+};
+
+template<typename TypePair, typename Location>
+void OnnxParser::ValidateInputs(const onnx::NodeProto& node,
+                                TypePair validInputs,
+                                const Location& location)
+{
+    for(auto input : node.input())
+    {
+        CheckValidDataType(validInputs.second,
+                           m_TensorsInfo[input].m_dtype,
+                           validInputs.first,
+                           node.name(),
+                           input,
+                           location);
+    }
+}
+
+#define VALID_INPUTS(NODE, VALID_INPUTS) \
+    OnnxParser::ValidateInputs(NODE, \
+                               VALID_INPUTS, \
+                               CHECK_LOCATION())
+
+std::vector<TensorInfo> OnnxParser::ComputeOutputInfo(std::vector<std::string> outNames,
+                                                       const IConnectableLayer* layer,
+                                                       std::vector<TensorShape> inputShapes)
+{
+    BOOST_ASSERT(! outNames.empty());
+    bool needCompute = std::any_of(outNames.begin(),
+                                   outNames.end(),
+                                   [this](std::string name)
+                                   {
+                                       return (m_TensorsInfo.count(name) == 0 || m_TensorsInfo[name].m_info == nullptr);
+                                   });
+     std::vector<TensorInfo> outInfo;
+     //if the output info(s) are not here, we need to compute them
+     std::vector<TensorShape> inferredShapes;
+     if(needCompute)
+     {
+         inferredShapes = layer->InferOutputShapes(inputShapes);
+         BOOST_ASSERT(inferredShapes.size() == outNames.size());
+     }
+     for (uint i = 0; i < outNames.size(); ++i)
+     {
+         if(needCompute)
+         {
+             m_TensorsInfo[outNames[i]] = OnnxTensor();
+             m_TensorsInfo[outNames[i]].m_info = std::make_unique<TensorInfo>(
+                TensorInfo(inferredShapes[i], DataType::Float32));
+         }
+        outInfo.push_back(*m_TensorsInfo[outNames[i]].m_info);
+     }
+     return outInfo;
+}
+
+IOnnxParser* IOnnxParser::CreateRaw()
+{
+    return new OnnxParser();
+}
+
+IOnnxParserPtr IOnnxParser::Create()
+{
+    return IOnnxParserPtr(CreateRaw(), &IOnnxParser::Destroy);
+}
+
+void IOnnxParser::Destroy(IOnnxParser* parser)
+{
+    delete parser;
+}
+
+OnnxParser::OnnxParser()
+    : m_Network(nullptr, nullptr)
+{
+}
+
+void OnnxParser::ResetParser()
+{
+    m_Network = armnn::INetworkPtr(nullptr, nullptr);
+    m_Graph = nullptr;
+}
+
+void OnnxParser::Cleanup()
+{
+    m_TensorConnections.clear();
+    m_TensorsInfo.clear();
+    m_OutputsMap.clear();
+    m_OutputsFusedAndUsed.clear();
+}
+
+std::pair<ConstTensor, std::unique_ptr<float[]>> OnnxParser::CreateConstTensor(const std::string name)
+{
+    const TensorInfo tensorInfo = *m_TensorsInfo[name].m_info;
+    onnx::TensorProto onnxTensor = *m_TensorsInfo[name].m_tensor;
+
+    auto srcData = onnxTensor.float_data().data();
+    if(tensorInfo.GetNumElements() != static_cast<uint>(onnxTensor.float_data_size()))
+    {
+        throw ParseException(boost::str(
+            boost::format("The number of data provided (%1%) does not match the tensor '%2%' number of elements"
+                          " (%3%) %4%")
+                          % onnxTensor.float_data_size()
+                          % name
+                          % tensorInfo.GetNumElements()
+                          % CHECK_LOCATION().AsString()));
+    }
+    std::unique_ptr<float[]> tensorData(new float[tensorInfo.GetNumElements()]);
+
+    // Copy the value list entries into the destination
+    ::memcpy(tensorData.get(),srcData, tensorInfo.GetNumBytes());
+
+    // Const tensors requires at least a list of values
+    if (tensorInfo.GetNumElements() == 0)
+    {
+        throw ParseException(boost::str(
+            boost::format("No tensor data found for Const tensor '%1%' %2%")
+                          % name
+                          % CHECK_LOCATION().AsString()));
+    }
+    return std::make_pair(ConstTensor(tensorInfo, tensorData.get()), std::move(tensorData));
+}
+
+ModelPtr OnnxParser::LoadModelFromTextFile(const char* graphFile)
+{
+    FILE* fd = fopen(graphFile, "r");
+
+    if (fd == nullptr)
+    {
+        throw FileNotFoundException(boost::str(
+            boost::format("Invalid (null) filename %1%") % CHECK_LOCATION().AsString()));
+    }
+
+    // Parse the file into a message
+    ModelPtr     modelProto = std::make_unique<onnx::ModelProto>();
+    using google::protobuf::io::FileInputStream;
+    std::unique_ptr<FileInputStream> input = std::make_unique<FileInputStream>(fileno(fd));
+    bool                 success = google::protobuf::TextFormat::Parse(input.get(), modelProto.get());
+    fclose(fd);
+
+    if (!success)
+    {
+        std::stringstream error;
+        error << "Failed to parse graph file";
+        throw ParseException(boost::str(
+            boost::format("%1% %2%") % error.str() % CHECK_LOCATION().AsString()));
+    }
+    return modelProto;
+}
+
+INetworkPtr OnnxParser::CreateNetworkFromTextFile(const char* graphFile)
+{
+    ResetParser();
+    ModelPtr modelProto = LoadModelFromTextFile(graphFile);
+    return CreateNetworkFromModel(*modelProto);
+}
+
+
+ModelPtr OnnxParser::LoadModelFromBinaryFile(const char* graphFile)
+{
+    FILE* fd = fopen(graphFile, "rb");
+
+    if (fd == nullptr)
+    {
+        throw FileNotFoundException(boost::str(
+            boost::format("Invalid (null) filename %1%") % CHECK_LOCATION().AsString()));
+    }
+
+    // Parse the file into a message
+    ModelPtr modelProto = std::make_unique<onnx::ModelProto>();
+
+    google::protobuf::io::FileInputStream  inStream(fileno(fd));
+    google::protobuf::io::CodedInputStream codedStream(&inStream);
+    codedStream.SetTotalBytesLimit(INT_MAX, INT_MAX);
+    bool success = modelProto.get()->ParseFromCodedStream(&codedStream);
+    fclose(fd);
+
+    if (!success)
+    {
+        std::stringstream error;
+        error << "Failed to parse graph file";
+        throw ParseException(boost::str(
+            boost::format("%1% %2%") % error.str() % CHECK_LOCATION().AsString()));
+    }
+    return modelProto;
+
+}
+
+INetworkPtr OnnxParser::CreateNetworkFromBinaryFile(const char* graphFile)
+{
+    ResetParser();
+    ModelPtr modelProto = LoadModelFromBinaryFile(graphFile);
+    return CreateNetworkFromModel(*modelProto);
+}
+
+ModelPtr OnnxParser::LoadModelFromString(const std::string& protoText)
+{
+    if (protoText == "")
+    {
+        throw InvalidArgumentException(boost::str(
+                boost::format("Invalid (empty) string for model parameter %1%") % CHECK_LOCATION().AsString()));
+    }
+    // Parse the string into a message
+    ModelPtr modelProto = std::make_unique<onnx::ModelProto>();
+    bool success = google::protobuf::TextFormat::ParseFromString(protoText, modelProto.get());
+    if (!success)
+    {
+        std::stringstream error;
+        error << "Failed to parse graph file";
+        throw ParseException(boost::str(
+                boost::format("%1% %2%") % error.str() % CHECK_LOCATION().AsString()));
+    }
+    return modelProto;
+}
+
+INetworkPtr OnnxParser::CreateNetworkFromString(const std::string& protoText)
+{
+    ResetParser();
+    ModelPtr modelProto = LoadModelFromString(protoText);
+    return CreateNetworkFromModel(*modelProto);
+}
+
+INetworkPtr OnnxParser::CreateNetworkFromModel(onnx::ModelProto& model)
+{
+    m_Network = INetwork::Create();
+    try
+    {
+        m_Graph = std::make_unique<onnx::GraphProto>(*model.mutable_graph());
+        LoadGraph();
+    }
+    catch (const ParseException& e)
+    {
+        Cleanup();
+        throw e;
+    }
+    Cleanup();
+    return std::move(m_Network);
+}
+
+void OnnxParser::LoadGraph()
+{
+    BOOST_ASSERT(m_Graph.get() != nullptr);
+
+    //Fill m_TensorsInfo with the shapes and value of every tensor
+    SetupInfo(m_Graph->mutable_output());
+    SetupInfo(m_Graph->mutable_input());
+    SetupInfo(m_Graph->mutable_value_info());
+
+    for (auto tensor : m_Graph->initializer())
+    {
+        m_TensorsInfo[tensor.name()].m_tensor = std::make_unique<const onnx::TensorProto>(tensor);
+    }
+
+    SetupInputLayers();
+    SetupOutputLayers();
+
+    //Detect FullyConnected layers with bias and update the FusedAndUsed map acccordingly
+    DetectFullyConnected();
+
+    //Parsing the graph
+    for(size_t nodeIndex = 0; nodeIndex < static_cast<size_t>(m_Graph->node_size()); nodeIndex++)
+    {
+        auto node = m_Graph->node(static_cast<int>(nodeIndex));
+        const std::string& operation = node.op_type();
+
+        // check which layers we handled already (add and matmul fused as FC)
+        if(operation == "MatMul" )
+        {
+            if(m_OutputsFusedAndUsed[nodeIndex].inputForNodes != m_OutputsFusedAndUsed[nodeIndex].fusedWithNodes.size())
+            {
+                //Node which can not be fused as a FullyConnected layer (used in layers as a simple matmul output)
+                AddFullyConnected(node);
+            }
+        }
+        else if (!(m_OutputsFusedAndUsed[nodeIndex].fusedWithNodes.empty()) && operation == "Add")
+        {
+            int matmulIndex = static_cast<int> (m_OutputsFusedAndUsed[nodeIndex].fusedWithNodes[0]);
+            AddFullyConnected(m_Graph->node(matmulIndex), &node);
+        }
+        else if (m_OutputsFusedAndUsed[nodeIndex].fusedWithNodes.empty()) //node is not part of a fused layer
+        {
+            auto it = m_ParserFunctions.find(operation);
+            if (it != m_ParserFunctions.end())
+            {
+                auto func = it->second;
+                (this->*func)(node);
+            }
+            else
+            {
+                throw ParseException(boost::str(
+                    boost::format("Unsupported operation %1% for node '%2%' %3%")
+                    % operation
+                    % node.name()
+                    % CHECK_LOCATION().AsString()));
+            }
+        }
+    }
+
+    //Making the connections between outputs and inputs of each layers
+    for (const auto& tensorCon : m_TensorConnections)
+    {
+        if (tensorCon.second.outputSlot != nullptr)
+        {
+            for (size_t inputSlotIdx = 0; inputSlotIdx < tensorCon.second.inputSlots.size(); ++inputSlotIdx)
+            {
+                tensorCon.second.outputSlot->Connect(*(tensorCon.second.inputSlots[inputSlotIdx]));
+            }
+        }
+    }
+}
+
+void OnnxParser::SetupInfo(const google::protobuf::RepeatedPtrField<onnx::ValueInfoProto >* list)
+{
+    for (auto tensor : *list)
+    {
+        m_TensorsInfo[tensor.name()] = OnnxTensor();
+        m_TensorsInfo[tensor.name()].m_info = std::make_unique<TensorInfo>(ToTensorInfo(tensor));
+        m_TensorsInfo[tensor.name()].m_dtype = tensor.type().tensor_type().elem_type();
+    }
+}
+
+void OnnxParser::DetectFullyConnected()
+{
+    m_OutputsFusedAndUsed = std::vector<UsageSummary> (static_cast<size_t>(m_Graph->node_size()), UsageSummary());
+    auto matmulAndConstant = [&](const std::string& constInput,
+                                 const std::string& matmulInput,
+                                 int& nodeIndex)
+    {
+        auto matmulIt = m_OutputsMap.find(matmulInput);
+        if(matmulIt != m_OutputsMap.end()  && matmulIt->second.first->op_type() == "MatMul"
+            && m_TensorsInfo[constInput].isConstant())
+        {
+            nodeIndex = matmulIt->second.second;
+            return true;
+        }
+        return false;
+    };
+
+    for(int nodeIndex = 0; nodeIndex < m_Graph->node_size(); nodeIndex++)
+    {
+        const onnx::NodeProto* node = &m_Graph->node(nodeIndex);
+        for (const std::string& output : node->output())
+        {
+            m_OutputsMap[output] = std::make_pair(node, nodeIndex);
+        }
+
+        for (const std::string& input : node->input()) //count how many time a node is used as input
+        {
+            auto matmulIt = m_OutputsMap.find(input);
+            if(matmulIt != m_OutputsMap.end()){
+                ++m_OutputsFusedAndUsed[static_cast<size_t>(matmulIt->second.second)].inputForNodes; //node used
+            }
+        }
+
+        if (node->op_type() == "Add")
+        {
+            int matmulIndex = 0;
+            if (matmulAndConstant(node->input(0), node->input(1), matmulIndex) ||
+                matmulAndConstant(node->input(1), node->input(0), matmulIndex))
+            {
+                //matmul and add were fused
+                m_OutputsFusedAndUsed[static_cast<size_t>(matmulIndex)].fusedWithNodes
+                                                                       .push_back(static_cast<size_t>(nodeIndex));
+
+                m_OutputsFusedAndUsed[static_cast<size_t>(nodeIndex)].fusedWithNodes
+                                                                     .push_back(static_cast<size_t>(matmulIndex));
+            }
+        }
+    }
+
+    for (auto output: m_Graph->output()) { //Add usages as output of the graph in count of usages
+        auto matmulIt = m_OutputsMap.find(output.name());
+        if(matmulIt != m_OutputsMap.end()){
+            ++m_OutputsFusedAndUsed[static_cast<size_t>(matmulIt->second.second)].inputForNodes;
+        }
+    }
+}
+
+template<typename Location>
+void OnnxParser::GetInputAndParam(const onnx::NodeProto& node,
+                                  std::string* inputName,
+                                  std::string* constName,
+                                  const Location& location)
+{
+    int cstIndex;
+    if (m_TensorsInfo[node.input(0)].isConstant())
+    {
+        cstIndex = 0;
+    }
+    else if (m_TensorsInfo[node.input(1)].isConstant())
+    {
+        cstIndex = 1;
+    }
+    else
+    {
+        throw ParseException(boost::str(
+            boost::format("One of the input tensors ('%1%' or '%2%') should be constant in node '%3%' %4%")
+                          % node.input(0)
+                          % node.input(1)
+                          % node.name()
+                          % location.AsString()));
+    }
+    if(constName)
+    {
+        *constName = node.input(cstIndex);
+    }
+    if(inputName)
+    {
+        *inputName = node.input(!cstIndex);
+    }
+}
+
+template<typename Location>
+void OnnxParser::To1DTensor(const std::string& name, const Location& location)
+{
+    TensorShape shape = m_TensorsInfo[name].m_info->GetShape();
+    std::vector<uint32_t> newShape;
+    for(uint i = 0; i < shape.GetNumDimensions() - 1; ++i)
+    {
+        if(shape[i] != 1)
+        {
+            throw ParseException(boost::str(
+                boost::format("Only tensors with shape [1, ..., 1, X] can be converted to 1D and %1% %2%")
+                             % TensorInfoAsString(*m_TensorsInfo[name].m_info, name, m_TensorsInfo[name].m_dtype)
+                             % location.AsString()));
+        }
+    }
+    newShape.push_back(shape[shape.GetNumDimensions() - 1]);
+
+    m_TensorsInfo[name].m_info->SetShape(TensorShape(static_cast<unsigned int>(newShape.size()), newShape.data()));
+}
+
+void OnnxParser::AddFullyConnected(const onnx::NodeProto& matmulNode, const onnx::NodeProto* addNode)
+{
+
+    // find matmul inputs
+    std::string weightName;
+    std::string inputName;
+    CHECK_VALID_SIZE(static_cast<size_t>(matmulNode.input_size()), 2);
+    CHECK_VALID_SIZE(static_cast<size_t>(matmulNode.output_size()), 1);
+    VALID_INPUTS(matmulNode, STR_LIST(onnx::TensorProto::FLOAT));
+
+    GetInputAndParam(matmulNode, &inputName, &weightName, CHECK_LOCATION());
+
+    FullyConnectedDescriptor desc;
+    desc.m_BiasEnabled = addNode != nullptr;
+
+    IConnectableLayer* layer = nullptr;
+    if(desc.m_BiasEnabled)
+    {
+        // find bias const
+        std::string biasName;
+        CHECK_VALID_SIZE(static_cast<size_t>(addNode->input_size()), 2);
+        CHECK_VALID_SIZE(static_cast<size_t>(addNode->output_size()), 1);
+        VALID_INPUTS(*addNode, STR_LIST(onnx::TensorProto::FLOAT));
+
+        GetInputAndParam(*addNode, nullptr, &biasName, CHECK_LOCATION());
+
+        //Output shape is [1, weights[1]] and 1d vec in ONNX can be [1,X] so we convert biases to "armnn" 1D
+        To1DTensor(biasName, CHECK_LOCATION());
+        TensorInfo weightInfo = *m_TensorsInfo[weightName].m_info;
+        TensorInfo biasInfo = *m_TensorsInfo[biasName].m_info;
+
+        if (weightInfo.GetShape()[1] != biasInfo.GetShape()[0])
+        {
+            throw ParseException(boost::str(
+                boost::format("Shape of weights '%1%' and bias of following Add node '%2%' do not match : %3%"
+                              " and %4% ( /!\\ bias should be a 1D tensor) %5%")
+                              % weightName
+                              % addNode->name()
+                              % TensorInfoAsString(*m_TensorsInfo[weightName].m_info,
+                                                   weightName,
+                                                   m_TensorsInfo[weightName].m_dtype)
+                              % TensorInfoAsString(*m_TensorsInfo[biasName].m_info, biasName,
+                                                   m_TensorsInfo[biasName].m_dtype )
+                              % CHECK_LOCATION().AsString()));
+        }
+        layer = m_Network->AddFullyConnectedLayer(desc,
+                                                  CreateConstTensor(weightName).first,
+                                                  CreateConstTensor(biasName).first,
+                                                  matmulNode.name().c_str());
+        BOOST_ASSERT(layer != nullptr);
+
+        auto outputInfo = ComputeOutputInfo({addNode->output(0)}, layer,
+                                            {m_TensorsInfo[inputName].m_info->GetShape(),
+                                             m_TensorsInfo[weightName].m_info->GetShape()});
+
+        layer->GetOutputSlot(0).SetTensorInfo(outputInfo[0]);
+
+        RegisterInputSlots(layer, {inputName});
+        RegisterOutputSlots(layer, {addNode->output(0)});
+    }
+    else
+    {
+        layer = m_Network->AddFullyConnectedLayer(desc, CreateConstTensor(weightName).first, matmulNode.name().c_str());
+        BOOST_ASSERT(layer != nullptr);
+
+        auto outputInfo = ComputeOutputInfo({matmulNode.output(0)}, layer,
+                                            {m_TensorsInfo[inputName].m_info->GetShape(),
+                                             m_TensorsInfo[weightName].m_info->GetShape()});
+        layer->GetOutputSlot(0).SetTensorInfo(outputInfo[0]);
+
+        RegisterInputSlots(layer, {inputName});
+        RegisterOutputSlots(layer, {matmulNode.output(0)});
+    }
+}
+
+void OnnxParser::CreateConstantLayer(const std::string& tensorName, const std::string& layerName)
+{
+    auto armnnTensor = CreateConstTensor(tensorName);
+
+    IConnectableLayer* layer = m_Network->AddConstantLayer(armnnTensor.first, layerName.c_str());
+    layer->GetOutputSlot(0).SetTensorInfo(armnnTensor.first.GetInfo());
+    RegisterOutputSlots(layer, {tensorName});
+}
+
+void OnnxParser::ParseConstant(const onnx::NodeProto& node)
+{
+    CHECK_VALID_SIZE(static_cast<size_t>(node.attribute_size()), 1);
+
+    if (!node.attribute(0).has_t())
+    {
+        throw ParseException(boost::str(
+              boost::format("Value not found for Constant node '%1%' %2%")
+              % node.name()
+              % CHECK_LOCATION().AsString()));
+    }
+    const onnx::TensorProto& onnxTensor = node.attribute(0).t();
+
+    //ONNX can have Float16 and double constant nodes but ArmNN only supports float32
+    CHECK_VALID_DATATYPE(node.name(), onnxTensor.name(), onnxTensor.data_type(), onnx::TensorProto::FLOAT);
+
+    //Register this as a m_ConstParam so we know we can use it as a constant param in future layers.
+    m_TensorsInfo[node.output(0)].m_tensor = std::make_unique<const onnx::TensorProto>(onnxTensor);
+
+    CreateConstantLayer(node.output(0), node.name());
+
+}
+
+void OnnxParser::ParseMaxPool(const onnx::NodeProto& node)
+{
+    Pooling2dDescriptor desc;
+    desc.m_PoolType = PoolingAlgorithm::Max;
+    desc.m_PaddingMethod = PaddingMethod::Exclude;
+    AddPoolingLayer(node, desc);
+}
+
+void OnnxParser::ParseGlobalAveragePool(const onnx::NodeProto& node)
+{
+    Pooling2dDescriptor desc = Pooling2dDescriptor();
+    desc.m_PoolType = PoolingAlgorithm::Average;
+
+    //kernel size is the same as input
+    TensorShape inputShape = m_TensorsInfo[node.input(0)].m_info->GetShape();
+    desc.m_PoolWidth  = inputShape[3];
+    desc.m_PoolHeight = inputShape[2];
+
+    IConnectableLayer* layer = m_Network->AddPooling2dLayer(desc, node.name().c_str());
+    BOOST_ASSERT(layer != nullptr);
+
+    auto outputInfo = ComputeOutputInfo({node.output(0)}, layer, {inputShape});
+    layer->GetOutputSlot(0).SetTensorInfo(outputInfo[0]);
+
+    // register the input connection slots for the layer, connections are made after all layers have been created
+    // only the tensors for the inputs are relevant, exclude the const tensors
+    RegisterInputSlots(layer, {node.input(0)});
+
+    // register the output connection slots for the layer, connections are made after all layers have been created
+    RegisterOutputSlots(layer, {node.output(0)});
+}
+
+void OnnxParser::ParseAveragePool(const onnx::NodeProto& node)
+{
+    Pooling2dDescriptor desc;
+    desc.m_PoolType = PoolingAlgorithm::Average;
+
+    uint32_t count_include_pad = 0;
+    count_include_pad = ReadOptionalNodeUint32Attribute(node, "count_include_pad");
+    if(count_include_pad) {
+        desc.m_PaddingMethod = PaddingMethod::IgnoreValue;
+    }
+    AddPoolingLayer(node, desc);
+}
+
+void OnnxParser::AddPoolingLayer(const onnx::NodeProto& node, Pooling2dDescriptor& desc)
+{
+
+    CHECK_VALID_SIZE(static_cast<size_t>(node.input_size()), 1);
+    CHECK_VALID_SIZE(static_cast<size_t>(node.output_size()), 1);
+
+    VALID_INPUTS(node, STR_LIST(onnx::TensorProto::FLOAT));
+
+    std::vector<uint32_t> kernel_shape = ReadMandatoryNodeUint32ListAttribute(node, "kernel_shape"); //size of pool win
+    std::vector<uint32_t> strides = ReadOptionalNodeUint32ListAttribute(node, "strides");
+    std::vector<uint32_t> pads = ReadOptionalNodeUint32ListAttribute(node, "pads");
+
+    desc.m_OutputShapeRounding = OutputShapeRounding::Floor;
+    desc.m_PoolWidth  = kernel_shape[1];
+    desc.m_PoolHeight = kernel_shape[0];
+
+    if(strides.empty())
+    {
+        desc.m_StrideX    = 1;
+        desc.m_StrideY    = 1;
+    }
+    else
+    {
+        desc.m_StrideX    = strides[1];
+        desc.m_StrideY    = strides[0];
+    }
+
+    //Check new padding version first
+    if(pads.empty())
+    {
+        //Check deprecated version
+        std::string paddingString = ReadOptionalNodeStringAttribute(node, "auto_pad");
+        if(paddingString != "VALID" && paddingString != "" && paddingString != "NOTSET")
+        {
+            bool isUpper;
+            if( paddingString == "SAME_LOWER")
+            {
+                isUpper = false;
+            }
+            else if (paddingString == "SAME_UPPER")
+            {
+                isUpper = true;
+            }
+            else
+            {
+                throw ParseException(boost::str(
+                    boost::format("Invalid auto_pad attribute for node %1%. "
+                    "Only SAME_UPPER, SAME_LOWER or VALID supported and found %2% %3%")
+                    % node.name()
+                    % paddingString
+                    % CHECK_LOCATION().AsString()));
+            }
+            auto inputInfo = *m_TensorsInfo[node.input(0)].m_info;
+            uint32_t inputHeight = inputInfo.GetShape()[2];
+            uint32_t inputWidth  = inputInfo.GetShape()[3];
+            CalcPadding(inputHeight, desc.m_PoolHeight, desc.m_StrideY, &desc.m_PadTop, &desc.m_PadBottom, isUpper);
+            CalcPadding(inputWidth, desc.m_PoolWidth, desc.m_StrideX, &desc.m_PadLeft, &desc.m_PadRight, isUpper);
+        }
+    }
+    else
+    {
+        desc.m_PadTop     = pads[0];
+        desc.m_PadLeft    = pads[1];
+        desc.m_PadBottom  = pads[2];
+        desc.m_PadRight   = pads[3];
+    }
+
+    IConnectableLayer* layer = m_Network->AddPooling2dLayer(desc, node.name().c_str());
+    BOOST_ASSERT(layer != nullptr);
+
+    auto outputInfo = ComputeOutputInfo({node.output(0)}, layer, {m_TensorsInfo[node.input(0)].m_info->GetShape()});
+    layer->GetOutputSlot(0).SetTensorInfo(outputInfo[0]);
+
+    // register the input connection slots for the layer, connections are made after all layers have been created
+    // only the tensors for the inputs are relevant, exclude the const tensors
+    RegisterInputSlots(layer, {node.input(0)});
+
+    // register the output connection slots for the layer, connections are made after all layers have been created
+    RegisterOutputSlots(layer, {node.output(0)});
+}
+
+void OnnxParser::CreateReshapeLayer(const std::string& inputName,
+                                    const std::string& outputName,
+                                    const std::string& layerName)
+{
+    const TensorInfo outputTensorInfo = *m_TensorsInfo[outputName].m_info;
+    ReshapeDescriptor reshapeDesc;
+    reshapeDesc.m_TargetShape = outputTensorInfo.GetShape();
+
+    IConnectableLayer* layer = m_Network->AddReshapeLayer(reshapeDesc, layerName.c_str());
+    BOOST_ASSERT(layer != nullptr);
+    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
+
+    // register the input connection slots for the layer, connections are made after all layers have been created
+    // only the tensors for the inputs are relevant, exclude the const tensors
+    RegisterInputSlots(layer, {inputName});
+
+    // register the output connection slots for the layer, connections are made after all layers have been created
+    RegisterOutputSlots(layer, {outputName});
+}
+
+void OnnxParser::ParseReshape(const onnx::NodeProto& node)
+{
+    CHECK_VALID_SIZE(static_cast<size_t>(node.input_size()), 2);
+    CHECK_VALID_SIZE(static_cast<size_t>(node.output_size()), 1);
+
+    CHECK_VALID_DATATYPE(node.name(), node.input(0),
+                         m_TensorsInfo[node.input(0)].m_dtype,
+                         onnx::TensorProto::FLOAT); //input
+    CHECK_VALID_DATATYPE(node.name(), node.input(1),
+                         m_TensorsInfo[node.input(1)].m_dtype,
+                         onnx::TensorProto::INT64); //shape
+
+    if(!m_TensorsInfo[node.input(1)].isConstant())
+    {
+        throw ParseException(boost::str(
+            boost::format("Shape '%1%' should be constant in Reshape layer '%2%' %3%")
+                          % node.input(1)
+                          % node.name()
+                          % CHECK_LOCATION().AsString()));
+    }
+
+    if(m_TensorsInfo[node.input(0)].isConstant())
+    {
+        //make a new cst tensor -> move the data to the output tensor (the shape is already good in the output tensor)
+        if(m_TensorsInfo.count(node.output(0)) == 0)
+        {
+            m_TensorsInfo[node.output(0)] = OnnxTensor();
+        }
+        m_TensorsInfo[node.output(0)].m_tensor =
+            std::make_unique<onnx::TensorProto>(*m_TensorsInfo[node.input(0)].m_tensor);
+    }
+    else
+    {
+        TensorShape inputShape = m_TensorsInfo[node.input(0)].m_info->GetShape();
+
+        if(m_TensorsInfo.count(node.output(0)) == 0 || m_TensorsInfo[node.output(0)].m_info == nullptr)
+        {
+            auto outInfo = ComputeReshapeInfo(*m_TensorsInfo[node.input(1)].m_tensor, inputShape, node.output(0));
+            m_TensorsInfo[node.output(0)].m_info = std::make_unique<TensorInfo>(outInfo);
+        }
+
+        CreateReshapeLayer(node.input(0), node.output(0), node.name());
+    }
+}
+
+void OnnxParser::ParseRelu(const onnx::NodeProto& node)
+{
+    CHECK_VALID_SIZE(static_cast<size_t>(node.input_size()), 1);
+    CHECK_VALID_SIZE(static_cast<size_t>(node.output_size()), 1);
+
+    VALID_INPUTS(node, STR_LIST(onnx::TensorProto::FLOAT));
+
+    ActivationDescriptor desc;
+    desc.m_Function = ActivationFunction::ReLu;
+
+    IConnectableLayer* const layer = m_Network->AddActivationLayer(desc, node.name().c_str());
+    BOOST_ASSERT(layer != nullptr);
+
+    auto outputInfo = ComputeOutputInfo({ node.output(0)}, layer, {m_TensorsInfo[node.input(0)].m_info->GetShape()});
+    layer->GetOutputSlot(0).SetTensorInfo(outputInfo[0]);
+
+    // register the input connection slots for the layer, connections are made after all layers have been created
+    // only the tensors for the inputs are relevant, exclude the const tensors
+    RegisterInputSlots(layer, {node.input(0)});
+
+    // register the output connection slots for the layer, connections are made after all layers have been created
+    RegisterOutputSlots(layer, {node.output(0)});
+}
+
+
+void OnnxParser::AddConvLayerWithDepthwiseConv(const onnx::NodeProto& node, const Convolution2dDescriptor& convDesc)
+{
+    BOOST_ASSERT(node.op_type() == "Conv");
+
+    DepthwiseConvolution2dDescriptor desc;
+    desc.m_PadLeft      = convDesc.m_PadLeft;
+    desc.m_PadRight     = convDesc.m_PadRight;
+    desc.m_PadTop       = convDesc.m_PadTop;
+    desc.m_PadBottom    = convDesc.m_PadBottom;
+    desc.m_StrideX      = convDesc.m_StrideX;
+    desc.m_StrideY      = convDesc.m_StrideY;
+    desc.m_BiasEnabled  = convDesc.m_BiasEnabled;
+
+    armnn::IConnectableLayer* layer;
+    auto weightTensor = CreateConstTensor(node.input(1));
+    TensorShape& weightShape = weightTensor.first.GetShape();
+    weightShape[1] = weightShape[0];
+    weightShape[0] = 1;
+    m_TensorsInfo[node.input(1)].m_info->SetShape(weightShape);
+
+    if (node.input_size() == 3)
+    {
+        if(!m_TensorsInfo[node.input(2)].isConstant())
+        {
+            throw ParseException(boost::str(
+                boost::format("Bias '%1%' should be constant in Conv layer '%2%' %3%")
+                              % node.input(2)
+                              % node.name()
+                              % CHECK_LOCATION().AsString()));
+        }
+        desc.m_BiasEnabled = true;
+        auto biasTensor = CreateConstTensor(node.input(2));
+        layer = m_Network->AddDepthwiseConvolution2dLayer(desc,
+                                                          weightTensor.first,
+                                                          biasTensor.first,
+                                                          node.name().c_str());
+    }
+    else
+    {
+        layer = m_Network->AddDepthwiseConvolution2dLayer(desc,
+                                                          weightTensor.first,
+                                                          node.name().c_str());
+    }
+    BOOST_ASSERT(layer != nullptr);
+
+    auto outputInfo = ComputeOutputInfo({ node.output(0) }, layer,
+                                        { m_TensorsInfo[node.input(0)].m_info->GetShape(),
+                                          m_TensorsInfo[node.input(1)].m_info->GetShape() });
+
+    layer->GetOutputSlot(0).SetTensorInfo(outputInfo[0]);
+
+    // register the input connection slots for the layer, connections are made after all layers have been created
+    // only the tensors for the inputs are relevant, exclude the const tensors
+    RegisterInputSlots(layer, {node.input(0)});
+
+    // register the output connection slots for the layer, connections are made after all layers have been created
+    RegisterOutputSlots(layer, {node.output(0)});
+}
+
+void OnnxParser::ParseConv(const onnx::NodeProto& node)
+{
+    CHECK_VALID_SIZE(static_cast<size_t>(node.input_size()), 2, 3); //input, weight, (bias)
+    CHECK_VALID_SIZE(static_cast<size_t>(node.output_size()), 1);
+
+    VALID_INPUTS(node, STR_LIST(onnx::TensorProto::FLOAT));
+
+    if(m_TensorsInfo[node.input(0)].m_info->GetNumDimensions() != 4)
+    {
+        throw ParseException(boost::str(
+            boost::format("ArmNN only supports 2D convolution and Conv layer '%1%' input %2% %3%")
+                          % node.name()
+                          % TensorInfoAsString(*m_TensorsInfo[node.input(0)].m_info, node.input(0),
+                                               m_TensorsInfo[node.input(0)].m_dtype)
+                          % CHECK_LOCATION().AsString()));
+    }
+
+    if(!m_TensorsInfo[node.input(1)].isConstant())
+    {
+        throw ParseException(boost::str(
+            boost::format("Weights '%1%' should be constant in Conv layer '%2%' %3%")
+            % node.input(1)
+            % node.name()
+            % CHECK_LOCATION().AsString()));
+    }
+
+    auto inputInfo = *m_TensorsInfo[node.input(0)].m_info;
+
+    std::vector<uint32_t> dilations = ReadOptionalNodeUint32ListAttribute(node, "dilations");
+    if (!dilations.empty())
+    {
+        std::stringstream ss;
+        ss << "[ ";
+        for (auto dilation : dilations)
+        {
+            ss << dilation << ", ";
+            if (dilation != 1u)
+            {
+                ss << "... ]";
+                throw ParseException(boost::str(
+                    boost::format("ArmNN only supports Convolution layers with dilations [1,1], and node '%1%' "
+                                  "has dilatation %2% %3%")
+                                   % node.name()
+                                   % ss.str()
+                                   % CHECK_LOCATION().AsString()));
+            }
+        }
+    }
+
+    Convolution2dDescriptor desc;
+    desc.m_BiasEnabled = false;
+
+    std::vector<uint32_t> strides = ReadOptionalNodeUint32ListAttribute(node, "strides");
+    if(strides.empty())
+    {
+        desc.m_StrideX    = 1;
+        desc.m_StrideY    = 1;
+    }
+    else
+    {
+        desc.m_StrideX    = strides[1];
+        desc.m_StrideY    = strides[0];
+    }
+
+    std::vector<uint32_t> pads = ReadOptionalNodeUint32ListAttribute(node, "pads");
+    //Check new padding version first
+    if(pads.empty())
+    {
+        //Check deprecated version
+        std::string paddingString = ReadOptionalNodeStringAttribute(node, "auto_pad");
+        if(paddingString != "VALID" && paddingString != "" && paddingString != "NOTSET")
+        {
+            bool isUpper;
+            if( paddingString == "SAME_LOWER")
+            {
+                isUpper = false;
+            }
+            else if (paddingString == "SAME_UPPER")
+            {
+                isUpper = true;
+            }
+            else
+            {
+                throw ParseException(boost::str(
+                    boost::format("Invalid auto_pad attribute for node %1%. "
+                    "Only SAME_UPPER, SAME_LOWER or VALID supported and found %2% %3%")
+                    % node.name()
+                    % paddingString
+                    % CHECK_LOCATION().AsString()));
+            }
+            uint32_t inputHeight = inputInfo.GetShape()[2];
+            uint32_t inputWidth  = inputInfo.GetShape()[3];
+
+            uint32_t weightHeight;
+            uint32_t weightWidth;
+            std::vector<uint32_t> kernel_shape = ReadOptionalNodeUint32ListAttribute(node, "kernel_shape");
+            if (kernel_shape.empty())
+            {
+                const TensorInfo weightTensorInfo = *m_TensorsInfo[node.input(1)].m_info;
+                weightHeight = weightTensorInfo.GetShape()[2];
+                weightWidth = weightTensorInfo.GetShape()[3];
+            }
+            else
+            {
+                weightHeight = kernel_shape[0];
+                weightWidth = kernel_shape[1];
+            }
+            CalcPadding(inputHeight, weightHeight, desc.m_StrideY, &desc.m_PadTop, &desc.m_PadBottom, isUpper);
+            CalcPadding(inputWidth, weightWidth, desc.m_StrideX, &desc.m_PadLeft, &desc.m_PadRight, isUpper);
+        }
+    }
+    else
+    {
+        desc.m_PadTop     = pads[0];
+        desc.m_PadLeft    = pads[1];
+        desc.m_PadBottom  = pads[2];
+        desc.m_PadRight   = pads[3];
+    }
+
+    uint32_t group = ReadOptionalNodeUint32Attribute(node, "group", 1);
+    if(group > 1)
+    {
+        if (group > inputInfo.GetShape()[1])
+        {
+            throw ParseException(
+                boost::str(
+                    boost::format(
+                        "Error parsing Convolution node: %1%. "
+                        "The 'group'=%2% parameter cannot be larger than the "
+                        "channel of the input shape=%3% (in NCHW format). %4%") %
+                        node.name() %
+                        group %
+                        inputInfo.GetShape()[1] %
+                        CHECK_LOCATION().AsString()));
+        }
+        else if (group == inputInfo.GetShape()[1])
+        {
+            // we use a depthwise convolution here, because the number of groups equals to the
+            // input channels
+            AddConvLayerWithDepthwiseConv(node, desc);
+            return;
+        }
+        else
+        {
+            // TODO: split the input by channels into channels/groups separate convolutions
+            //  and merger the results afterwards
+            throw ParseException(boost::str(
+                boost::format("Error parsing Convolution node: %1%. "
+                "The 'group'=%2% parameter should be 1 or be equal to the "
+                "channel of the input shape=%3% (in NCHW format). %4%") %
+                node.name() %
+                group %
+                inputInfo.GetShape()[1] %
+                CHECK_LOCATION().AsString()));
+        }
+    }
+
+    armnn::IConnectableLayer* layer;
+    auto weightTensor = CreateConstTensor(node.input(1));
+
+    if (node.input_size() == 3)
+    {
+        if(!m_TensorsInfo[node.input(2)].isConstant())
+        {
+            throw ParseException(boost::str(
+                boost::format("Bias '%1%' should be constant in Conv layer '%2%' %3%")
+                              % node.input(2)
+                              % node.name()
+                              % CHECK_LOCATION().AsString()));
+        }
+        desc.m_BiasEnabled = true;
+        auto biasTensor = CreateConstTensor(node.input(2));
+        layer = m_Network->AddConvolution2dLayer(desc,
+                                                 weightTensor.first,
+                                                 biasTensor.first,
+                                                 node.name().c_str());
+    }
+    else
+    {
+        layer = m_Network->AddConvolution2dLayer(desc,
+                                                 weightTensor.first,
+                                                 node.name().c_str());
+    }
+    BOOST_ASSERT(layer != nullptr);
+
+    auto outputInfo = ComputeOutputInfo({ node.output(0) }, layer,
+                                        { m_TensorsInfo[node.input(0)].m_info->GetShape(),
+                                          m_TensorsInfo[node.input(1)].m_info->GetShape() });
+    layer->GetOutputSlot(0).SetTensorInfo(outputInfo[0]);
+
+    // register the input connection slots for the layer, connections are made after all layers have been created
+    // only the tensors for the inputs are relevant, exclude the const tensors
+    RegisterInputSlots(layer, {node.input(0)});
+
+    // register the output connection slots for the layer, connections are made after all layers have been created
+    RegisterOutputSlots(layer, {node.output(0)});
+}
+
+void OnnxParser::PrependForBroadcast(const std::string& outputName,
+                                     const std::string& input0,
+                                     const std::string& input1)
+{
+    //input0 should be reshaped to have same number of dim as input1
+    TensorInfo outputTensorInfo = TensorInfo(*m_TensorsInfo[input0].m_info);
+
+    TensorShape input0Shape = m_TensorsInfo[input0].m_info->GetShape();
+    TensorShape input1Shape = m_TensorsInfo[input1].m_info->GetShape();
+
+    uint32_t diff = input1Shape.GetNumDimensions() - input0Shape.GetNumDimensions();
+    std::vector<uint32_t> newShape;
+    while(diff > 0)
+    {
+        newShape.push_back(1);
+        diff--;
+    }
+    for (uint dim = 0; dim < input0Shape.GetNumDimensions(); ++dim)
+    {
+        newShape.push_back(input0Shape[dim]);
+    }
+    outputTensorInfo.SetShape(TensorShape(static_cast<unsigned int>(newShape.size()), newShape.data()));
+
+    //add the new tensor to m_TensorsInfo
+    m_TensorsInfo[outputName] = OnnxTensor();
+    m_TensorsInfo[outputName].m_info = std::make_unique<TensorInfo>(outputTensorInfo);
+
+    //add reshape layer if the parent was not constant...
+    if( ! m_TensorsInfo[input0].isConstant())
+    {
+        CreateReshapeLayer(input0, outputName, boost::str(boost::format("Add:reshapeOf%1%") % input0));
+    }
+    else //make it constant and it will be create in Add
+    {
+        m_TensorsInfo[outputName].m_tensor = std::make_unique<onnx::TensorProto>(*m_TensorsInfo[input0].m_tensor);
+
+    }
+}
+
+std::pair<std::string, std::string> OnnxParser::AddPrepareBroadcast(const std::string& input0,
+                                                                    const std::string& input1)
+{
+    std::pair<std::string, std::string> inputs = std::make_pair(input0, input1);
+
+    TensorShape input0Shape = m_TensorsInfo[input0].m_info->GetShape();
+    TensorShape input1Shape = m_TensorsInfo[input1].m_info->GetShape();
+
+    if(input1Shape.GetNumDimensions() < input0Shape.GetNumDimensions())
+    {
+        auto outputName = boost::str(boost::format("reshape_output_%1%") % input1);
+        PrependForBroadcast(outputName, input1, input0);
+        inputs.second = outputName;
+    }
+    else if(input0Shape.GetNumDimensions() < input1Shape.GetNumDimensions())
+    {
+        auto outputName = boost::str(boost::format("reshape_output_%1%") % input0);
+        PrependForBroadcast(outputName, input0, input1);
+        inputs.first = outputName;
+    }
+    return inputs;
+}
+
+void OnnxParser::ParseAdd(const onnx::NodeProto& node)
+{
+    CHECK_VALID_SIZE(static_cast<size_t>(node.input_size()), 2);
+    CHECK_VALID_SIZE(static_cast<size_t>(node.output_size()), 1);
+
+    VALID_INPUTS(node, STR_LIST(onnx::TensorProto::FLOAT));
+
+     // TODO: unify broadcast validation code across layers
+     // tracked by: IVGCVSW-1576
+
+     // Checking broadcast compatibility : only scalar or 1D tensors
+     auto inputs = AddPrepareBroadcast(node.input(0), node.input(1));
+     auto input0 = *m_TensorsInfo[inputs.first].m_info;
+     auto input1 = *m_TensorsInfo[inputs.second].m_info;
+     BOOST_ASSERT(input0.GetNumDimensions() == input1.GetNumDimensions());
+
+     unsigned int numDims = input0.GetNumDimensions();
+     for (unsigned int i = 0; i < numDims; i++)
+     {
+         unsigned int dim0 = input0.GetShape()[i];
+         unsigned int dim1 = input1.GetShape()[i];
+         if (dim0 != dim1 && dim0 != 1 && dim1 != 1)
+         {
+             throw ParseException(boost::str(
+                 boost::format("Broadcast is only supported for scalar or 1D tensors in Add node '%1%'. "
+                               "Input dimensions should either match or one should be of size 1 and here, "
+                               "%2% and %3% %4%")
+                               % node.name()
+                               % TensorInfoAsString(*m_TensorsInfo[inputs.first].m_info, inputs.first,
+                                                    m_TensorsInfo[inputs.first].m_dtype)
+                               % TensorInfoAsString(*m_TensorsInfo[inputs.second].m_info, inputs.second,
+                                                    m_TensorsInfo[inputs.second].m_dtype)
+                               % CHECK_LOCATION().AsString()));
+         }
+     }
+
+
+     IConnectableLayer* layer = m_Network->AddAdditionLayer(node.name().c_str());
+     BOOST_ASSERT(layer != nullptr);
+
+     auto outputInfo = ComputeOutputInfo({ node.output(0) }, layer,
+                                         { m_TensorsInfo[inputs.first].m_info->GetShape(),
+                                           m_TensorsInfo[inputs.second].m_info->GetShape() });
+     layer->GetOutputSlot(0).SetTensorInfo(outputInfo[0]);
+
+     // register the input connection -> for constant inputs, we need to make a newDim constant layer
+     if(m_TensorsInfo[inputs.first].isConstant()) {
+
+         CreateConstantLayer(inputs.first, boost::str(boost::format("Add:constant_of_%1%") % node.input(0)));
+     }
+     if(m_TensorsInfo[inputs.second].isConstant()) {
+
+         CreateConstantLayer(inputs.second, boost::str(boost::format("Add:constant_of_%1%") % node.input(1)));
+     }
+     RegisterInputSlots(layer, {inputs.first, inputs.second});
+
+     // register the output connection
+     RegisterOutputSlots(layer, {node.output(0)});
+}
+
+void OnnxParser::ParseBatchNormalization(const onnx::NodeProto& node)
+{
+    //IGNORE momentum parameter and spatial parameters
+
+    CHECK_VALID_SIZE(static_cast<size_t>(node.input_size()), 5);
+    CHECK_VALID_SIZE(static_cast<size_t>(node.output_size()), 1);
+
+    VALID_INPUTS(node, STR_LIST(onnx::TensorProto::FLOAT));
+    for(int ind = 1; ind < node.input_size(); ++ind)
+    {
+        auto tensor = node.input(ind);
+        if(! m_TensorsInfo[tensor].isConstant())
+        {
+            throw ParseException(boost::str(
+                boost::format("Input tensor '%1%' should be constant in BatchNormalization node '%2%' %3%")
+                              % tensor
+                              % node.name()
+                              % CHECK_LOCATION().AsString()));
+        }
+    }
+
+    float epsilon = ReadOptionalNodeFloatAttribute(node, "epsilon", 1e-5f);
+    BatchNormalizationDescriptor desc;
+    desc.m_Eps = epsilon;
+
+    auto scaleTensor = CreateConstTensor(node.input(1));
+    auto biasTensor = CreateConstTensor(node.input(2));
+    auto meanTensor = CreateConstTensor(node.input(3));
+    auto varTensor = CreateConstTensor(node.input(4));
+
+    IConnectableLayer* layer = m_Network->AddBatchNormalizationLayer(desc,
+                                                                     meanTensor.first,
+                                                                     varTensor.first,
+                                                                     biasTensor.first,
+                                                                     scaleTensor.first,
+                                                                     node.name().c_str());
+    BOOST_ASSERT(layer != nullptr);
+
+    auto outputInfo = ComputeOutputInfo({node.output(0)}, layer, {m_TensorsInfo[node.input(0)].m_info->GetShape()});
+    layer->GetOutputSlot(0).SetTensorInfo(outputInfo[0]);
+
+    RegisterInputSlots(layer, {node.input(0)}); //don't register constant inputs
+
+    // register the output connection
+    RegisterOutputSlots(layer, {node.output(0)});
+}
+
+void OnnxParser::SetupInputLayers()
+{
+    //Find user input and add their layers
+    for(int inputIndex = 0; inputIndex < m_Graph->input_size(); ++inputIndex)
+    {
+        auto input = m_Graph->input(inputIndex);
+        if (! m_TensorsInfo[input.name()].isConstant())
+        {
+            IConnectableLayer* layer =
+              m_Network->AddInputLayer(static_cast<armnn::LayerBindingId>(inputIndex), input.name().c_str());
+            auto tensorInfo = ToTensorInfo(input);
+            layer->GetOutputSlot(0).SetTensorInfo(tensorInfo);
+
+            RegisterOutputSlots(layer,{ input.name() });
+        }
+    }
+}
+
+void OnnxParser::SetupOutputLayers()
+{
+    if(m_Graph->output_size() == 0)
+    {
+        throw ParseException(boost::str(boost::format("The given model does not have any outputs %1%")
+                                                      % CHECK_LOCATION().AsString()));
+    }
+
+    for(int outputIndex = 0; outputIndex < m_Graph->output_size(); ++outputIndex)
+    {
+        IConnectableLayer* layer =
+            m_Network->AddOutputLayer(static_cast<armnn::LayerBindingId>(outputIndex),
+                m_Graph->output(outputIndex).name().c_str());
+
+        RegisterInputSlots(layer, { m_Graph->output(outputIndex).name() });
+    }
+}
+
+void OnnxParser::RegisterInputSlots(IConnectableLayer* layer, const std::vector<std::string>& tensorIds)
+{
+    BOOST_ASSERT(layer != nullptr);
+    if (tensorIds.size() != layer->GetNumInputSlots())
+    {
+        throw ParseException(
+            boost::str(boost::format("The number of tensor inputs (%1%) does not match the number expected (%2%) %3%") %
+                       tensorIds.size() %
+                       layer->GetNumInputSlots() %
+                       CHECK_LOCATION().AsString()));
+    }
+    for (unsigned int slotIndex = 0; slotIndex < layer->GetNumInputSlots(); ++slotIndex)
+    {
+        std::string tensorId = tensorIds[slotIndex];
+        armnn::IInputSlot* slot = &(layer->GetInputSlot(slotIndex));
+
+        auto it = m_TensorConnections.find(tensorId);
+
+        if (it == m_TensorConnections.end())
+        {
+            //First time seing this tensor, we need to map it
+            m_TensorConnections[tensorId] = TensorSlots();
+        }
+        m_TensorConnections[tensorId].inputSlots.push_back(slot);
+    }
+}
+
+void OnnxParser::RegisterOutputSlots(IConnectableLayer* layer, const std::vector<std::string>& tensorIds)
+{
+    BOOST_ASSERT(layer != nullptr);
+    if (tensorIds.size() != layer->GetNumOutputSlots())
+    {
+        throw ParseException(
+            boost::str(boost::format("The number of tensor outputs (%1%) does not match the number expected (%2%) %3% ")
+                       % tensorIds.size()
+                       % layer->GetNumOutputSlots()
+                       % CHECK_LOCATION().AsString()));
+    }
+
+    for (unsigned int slotIndex = 0; slotIndex < layer->GetNumOutputSlots(); ++slotIndex)
+    {
+        std::string tensorId = tensorIds[slotIndex];
+        armnn::IOutputSlot* slot = &(layer->GetOutputSlot(slotIndex));
+
+        auto it = m_TensorConnections.find(tensorId);
+
+        if (it == m_TensorConnections.end())
+        {
+            //First time seing this tensor, we need to map it
+            m_TensorConnections[tensorId] = TensorSlots();
+        }
+
+        TensorSlots & tensorSlots = m_TensorConnections[tensorId];
+
+        // assuming there is only one producer for that tensor
+        if (tensorSlots.outputSlot != nullptr)
+        {
+            throw ParseException(boost::str(
+                    boost::format("Another layer has already registered itself as the producer of "
+                                  "tensor:%2% %3%") %
+                                   tensorId %
+                                   CHECK_LOCATION().AsString()));
+        }
+        tensorSlots.outputSlot = slot;
+    }
+}
+
+BindingPointInfo OnnxParser::GetNetworkInputBindingInfo(const std::string& name) const
+{
+    for(int i = 0; i < m_Graph->input_size(); ++i)
+    {
+        auto input = m_Graph->input(i);
+        if(input.name() == name)
+        {
+            return std::make_pair(static_cast<armnn::LayerBindingId>(i), ToTensorInfo(input));
+        }
+    }
+    throw InvalidArgumentException(boost::str(boost::format("The input layer '%1%' does not exist %2%")
+                                                            % name % CHECK_LOCATION().AsString()));
+}
+
+BindingPointInfo OnnxParser::GetNetworkOutputBindingInfo(const std::string& name) const
+{
+    for(int i = 0; i < m_Graph->output_size(); ++i)
+    {
+        auto output = m_Graph->output(i);
+        if(output.name() == name)
+        {
+            return std::make_pair(static_cast<armnn::LayerBindingId>(i), ToTensorInfo(output));
+        }
+    }
+    throw InvalidArgumentException(boost::str(boost::format("The output layer '%1%' does not exist %2%")
+                                                            % name % CHECK_LOCATION().AsString()));
+}
+
+std::vector<std::string> OnnxParser::GetInputs(ModelPtr& model)
+{
+    if(model == nullptr) {
+        throw InvalidArgumentException(boost::str(
+            boost::format("The given model cannot be null %1%")
+            % CHECK_LOCATION().AsString()));
+    }
+
+    std::vector<std::string> inputNames;
+    std::map<std::string, bool> isConstant;
+    for(auto tensor : model->graph().initializer())
+    {
+        isConstant[tensor.name()] = true;
+    }
+    for(auto input : model->graph().input())
+    {
+        auto it = isConstant.find(input.name());
+        if(it == isConstant.end())
+        {
+            inputNames.push_back(input.name());
+        }
+    }
+    return inputNames;
+}
+
+std::vector<std::string> OnnxParser::GetOutputs(ModelPtr& model)
+{
+    if(model == nullptr) {
+        throw InvalidArgumentException(boost::str(
+            boost::format("The given model cannot be null %1%")
+            % CHECK_LOCATION().AsString()));
+    }
+
+    std::vector<std::string> outputNames;
+    for(auto output : model->graph().output())
+    {
+        outputNames.push_back(output.name());
+    }
+    return outputNames;
+}
+
+} // namespace armnnOnnxParser