Release 18.08

1 files changed, 732 insertions, 0 deletions

diff --git a/src/armnnCaffeParser/RecordByRecordCaffeParser.cpp b/src/armnnCaffeParser/RecordByRecordCaffeParser.cpp
new file mode 100644
index 0000000000..60747f3bce
--- /dev/null
+++ b/src/armnnCaffeParser/RecordByRecordCaffeParser.cpp
@@ -0,0 +1,732 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// See LICENSE file in the project root for full license information.
+//
+
+#include "RecordByRecordCaffeParser.hpp"
+
+#include "armnn/Exceptions.hpp"
+#include "armnn/Utils.hpp"
+
+
+#include "GraphTopologicalSort.hpp"
+
+#include <boost/numeric/conversion/cast.hpp>
+
+// Caffe
+#include <google/protobuf/wire_format.h>
+
+
+//#include <stdio.h>
+#include <limits.h>
+#include <sstream>
+//#include <iostream>
+#include <fstream>
+
+namespace armnnCaffeParser
+{
+// class which holds information on the absolute position in the stream
+// of the data and the length of the data record.
+class VarLenDataInfo
+{
+public:
+    VarLenDataInfo(std::streamoff positionOfData, size_t sizeOfData) :
+        m_PositionOfData(positionOfData), m_SizeOfData(sizeOfData) {}
+
+    VarLenDataInfo(const VarLenDataInfo& x) :
+        m_PositionOfData(x.PositionOfData()), m_SizeOfData (x.SizeOfData()) {}
+
+    VarLenDataInfo& operator=(const VarLenDataInfo& x)
+    {
+        // handle self assignment
+        if (this == &x) {
+            return *this;
+        }
+        m_PositionOfData = x.PositionOfData(); m_SizeOfData = x.SizeOfData(); return *this;
+    }
+
+    std::streamoff PositionOfData() const {return m_PositionOfData;}
+    size_t SizeOfData() const {return m_SizeOfData;}
+
+private:
+    std::streamoff m_PositionOfData;
+    size_t m_SizeOfData;
+
+};
+
+// class which holds enough information on a LayerParameter in the Caffe protobuf
+// format to allow it to be resolved for in place layering and sorted topologically
+// prior to the entire record being parsed into memory.
+//
+// NOTE: function naming follows that of the protobuf classes these proxies are standing in for
+class LayerParameterInfo : public VarLenDataInfo
+{
+public:
+    static const std::string INPUT;
+    LayerParameterInfo(const VarLenDataInfo& varLenDataInfo) :
+        VarLenDataInfo(varLenDataInfo.PositionOfData(), varLenDataInfo.SizeOfData()),
+        m_newTops(false), m_newBottoms(false) {}
+
+    LayerParameterInfo(std::streamoff positionOfData, size_t sizeOfData) :
+        VarLenDataInfo(positionOfData, sizeOfData), m_newTops(false), m_newBottoms(false) {}
+
+    LayerParameterInfo(const LayerParameterInfo& x) :
+        VarLenDataInfo(x.PositionOfData(), x.SizeOfData()),
+        m_name(x.m_name),
+        m_type(x.m_type),
+        m_tops(x.m_tops),
+        m_bottoms(x.m_bottoms),
+        m_newTops(x.m_newTops),
+        m_newBottoms(x.m_newBottoms) {}
+
+    LayerParameterInfo& operator=(const LayerParameterInfo& x)
+    {
+        if (this == &x) {
+            return *this;
+        }
+        VarLenDataInfo::operator=(x);
+        m_name = x.m_name;
+        m_type = x.m_type;
+        m_tops = x.m_tops;
+        m_bottoms = x.m_bottoms;
+        m_newTops = x.m_newTops;
+        m_newBottoms = x.m_newBottoms;
+        return *this;
+    }
+
+    const std::string name() const {return m_name;}
+    void set_name(const std::unique_ptr<char[]>& theName, size_t length)
+    {
+        m_name = std::string(theName.get(), length);
+    }
+    void set_name(const std::string& theName) {m_name = theName;}
+
+    const std::string type() const {return m_type;}
+    void set_type(const std::unique_ptr<char[]>& theType, size_t length)
+    {
+        m_type = std::string(theType.get(), length);
+    }
+    void set_type(const std::string& theType) {m_type = theType;}
+
+    void add_top(const std::unique_ptr<char[]>& top, size_t length)
+    {
+        std::string topName(top.get(), length);
+        m_tops.push_back(topName);
+    }
+    void add_top(const std::string& topName)
+    {
+        m_tops.push_back(topName);
+    }
+    const std::string top(unsigned long i) const {return m_tops[i];}
+    unsigned long top_size() const {return m_tops.size();}
+    void set_top(unsigned long i, const std::string& newName) {m_tops[i] = newName; m_newTops = true;}
+    bool new_tops() const {return m_newTops;}
+
+    void add_bottom(const std::unique_ptr<char[]>& bottom, size_t length)
+    {
+        std::string bottomName(bottom.get(), length);
+        m_bottoms.push_back(bottomName);
+    }
+    unsigned long bottom_size() const {return m_bottoms.size();}
+    const std::string bottom(unsigned long i) const {return m_bottoms[i];}
+    void set_bottom(unsigned long i, const std::string& newName) {m_bottoms[i] = newName; m_newBottoms = true;}
+    bool new_bottoms() const {return m_newBottoms;}
+
+    // if the position and size of the data is zero and the type is "Input" then this is an 'Implicit Input Layer'
+    // and needs to be handled differently from ordinary layers.
+    bool isImplicitInputLayer() const
+    {
+        if ((PositionOfData() == 0) && (SizeOfData() == 0) && INPUT.compare(type()) == 0)
+        {return true;} else {return false;}
+    }
+
+private:
+    std::string m_name;
+    std::string m_type;
+    std::vector<std::string> m_tops;
+    std::vector<std::string> m_bottoms;
+    // mark the layers whose topology was changed
+    // by the ResolveInPlaceLayers method.
+    bool m_newTops;
+    bool m_newBottoms;
+};
+
+// class which holds the field type (wire type) and field id (id from the .proto schema)
+// read from the protobuf messages as per the binary encoding described in
+// https://developers.google.com/protocol-buffers/docs/encoding
+//
+// NOTE: function naming follows that of the protobuf classes these proxies are standing in for
+class ProtobufFieldInfo
+{
+public:
+    ProtobufFieldInfo(int field_type, int field_id) :
+        m_eof(false), m_field_type(field_type), m_field_id(field_id) {}
+    ProtobufFieldInfo() : m_eof(true), m_field_type(0), m_field_id(0) {}
+
+    bool eof() {return m_eof;}
+    int field_type() {return m_field_type;}
+    int field_id() {return m_field_id;}
+
+private:
+    bool m_eof;
+    int m_field_type;
+    int m_field_id;
+};
+
+
+// There are some NetParameter level data which are required
+// to correctly processes some Caffe models. Specifically those which
+// have 'implicit' input layers. Also it is nice to have the name of the model.
+//
+// NOTE: function naming follows that of the protobuf classes these proxies are standing in for
+class NetParameterInfo
+{
+public:
+    const std::string name() const {return m_name;}
+    void set_name(const std::unique_ptr<char[]>&  theName, size_t length)
+    {
+        m_name = std::string(theName.get(), length);
+    }
+
+    void add_input(const std::unique_ptr<char[]>&  input, size_t length)
+    {
+        std::string inputName(input.get(), length);
+        m_inputs.push_back(inputName);
+    }
+    const std::string input(unsigned long i) const {return m_inputs[i];}
+    unsigned long input_size() const {return m_inputs.size();}
+
+    void add_input_dimension(int input_dimension) {
+        m_input_dimensions.push_back(input_dimension);
+    }
+    int input_dimension(unsigned long i) const {return m_input_dimensions[i];}
+    unsigned long input_dimensions_size() const {return m_input_dimensions.size();}
+
+    void add_blob_shape(caffe::BlobShape shape) {
+        m_blob_shapes.push_back(shape);
+    }
+    const caffe::BlobShape blob_shape(unsigned long i) const {return m_blob_shapes[i];}
+    unsigned long blob_shapes_size() const {return m_blob_shapes.size();}
+
+private:
+    std::string m_name;
+    std::vector<std::string> m_inputs;
+    std::vector<int> m_input_dimensions;
+    std::vector<caffe::BlobShape> m_blob_shapes;
+
+};
+
+}; // namespace armnnCaffeParser
+
+using namespace armnnCaffeParser;
+
+// Initialise the class const
+const std::string LayerParameterInfo::INPUT = "Input";
+
+namespace
+{
+
+ProtobufFieldInfo readFieldInfo(std::ifstream& ifs)
+{
+    unsigned char first_byte = static_cast<unsigned char>(ifs.get());
+    if (!ifs.good())
+    {
+        ProtobufFieldInfo eof;
+        return eof;
+    }
+    int field_type = first_byte&7;
+    int field_id = first_byte>>3;
+    if ((field_id & 16) == 16)
+    {
+        unsigned char second_byte = static_cast<unsigned char>(ifs.get());
+        if (!ifs.good())
+        {
+            ProtobufFieldInfo eof;
+            return eof;
+        }
+        field_id = (field_id-16) + ((second_byte&127)<<4);
+    }
+    ProtobufFieldInfo fieldInfo(field_type, field_id);
+    return fieldInfo;
+}
+
+const static int MAX_NUM_BYTES = 5;
+
+int ReadBase128(std::ifstream& ifs)
+{
+    int result = 0;
+    unsigned int shift_by = 0;
+    int bytesRead = 0;
+    while (true)
+    {
+        unsigned char a_byte = static_cast<unsigned char>(ifs.get());
+        ++bytesRead;
+        if (bytesRead > MAX_NUM_BYTES)
+        {
+            throw armnn::ParseException(
+                "ReadBase128 exceeded the maximum number of bytes expected for an integer representation");
+        }
+        result += (a_byte & 127) << shift_by;
+        shift_by += 7;
+        if ((a_byte & 128) != 128)
+        {
+            break;
+        }
+    }
+    return result;
+}
+
+
+std::unique_ptr<char[]> AllocateBuffer(std::ifstream& ifs, VarLenDataInfo& dataInfo)
+{
+    std::unique_ptr<char[]> ptr(new char[dataInfo.SizeOfData()]);
+    ifs.clear();
+    ifs.seekg(dataInfo.PositionOfData(), std::ios_base::beg);
+    ifs.read(ptr.get(), boost::numeric_cast<std::streamsize>(dataInfo.SizeOfData()));
+    return ptr;
+}
+
+VarLenDataInfo CreateVarLenDataInfo(std::streamoff bufferStart, std::streamoff endOfLayer) {
+    std::streamoff sizeOfLayer = endOfLayer - bufferStart;
+    if (sizeOfLayer < 0)
+    {
+        std::stringstream ss;
+        ss << "error when determining buffer size, negative value [" << sizeOfLayer << "]";
+        throw armnn::ParseException(ss.str());
+    }
+    // NOTE: as some of the data being read in will be translated into strings (names of layers etc)
+    //       the maximum size we can deal with is the upper size limit of a string i.e. size_t
+    // on the platform in which I am currently compiling std::streamoff is signed long int and
+    // size_t is unsigned long int so there is no way this error condition can fire but this stuff
+    // is supposed to be portable so the check remains in place
+    if (boost::numeric_cast<size_t>(sizeOfLayer) > SIZE_MAX) {
+        std::stringstream ss;
+        ss << "layer is greater than " << SIZE_MAX << " in size cannot process. layer size = [" << sizeOfLayer << "]";
+        throw armnn::ParseException(ss.str());
+    }
+    LayerParameterInfo info(bufferStart, boost::numeric_cast<size_t>(sizeOfLayer));
+    return info;
+}
+
+void ReadTopologicalInfoForLayerParameter(LayerParameterInfo& layerInfo, std::ifstream& ifs)
+{
+    // position the file pointer to the start of the layer data
+    ifs.clear();
+    ifs.seekg(layerInfo.PositionOfData(), std::ios_base::beg);
+    std::streamoff endOfLayer = layerInfo.PositionOfData() +
+        boost::numeric_cast<std::streamoff>(layerInfo.SizeOfData());
+    while(true)
+    {
+        // check to see if we have reached the end of the record
+        std::streamoff currentPosition = ifs.tellg();
+        if (currentPosition >= endOfLayer) {
+            return;
+        }
+        // read the information for the next field.
+        ProtobufFieldInfo fieldInfo = readFieldInfo(ifs);
+        if (fieldInfo.eof())
+        {
+            return;
+            // TODO: figure out whether this is an error condition or not...
+            //throw armnn::ParseException("failed to read field from LayerParameter data");
+        }
+        // process the field
+        switch (fieldInfo.field_type())
+        {
+            case 0:
+            {
+                ReadBase128(ifs);
+                break;
+            }
+            case 2:
+            {
+                int size = ReadBase128(ifs);
+                std::streamoff posStartOfData = ifs.tellg();
+                VarLenDataInfo dataInfo(posStartOfData, boost::numeric_cast<size_t>(size));
+                //optional string name = 1; // the layer name
+                //optional string type = 2; // the layer type
+                //repeated string bottom = 3; // the name of each bottom blob
+                //repeated string top = 4; // the name of each top blob
+                if (fieldInfo.field_id() == 1)
+                {
+                    // read and set the name of the layer
+                    auto layerName = AllocateBuffer(ifs, dataInfo);
+                    layerInfo.set_name(layerName, dataInfo.SizeOfData());
+                }
+                else if (fieldInfo.field_id() == 2)
+                {
+                    // read and set the type of the layer
+                    auto layerType = AllocateBuffer(ifs, dataInfo);
+                    layerInfo.set_type(layerType, dataInfo.SizeOfData());
+                }
+                else if (fieldInfo.field_id() == 3)
+                {
+                    // read and add a bottom to the layer
+                    auto bottom = AllocateBuffer(ifs, dataInfo);
+                    layerInfo.add_bottom(bottom, dataInfo.SizeOfData());
+                }
+                else if (fieldInfo.field_id() == 4)
+                {
+                    // read and add a top to the layer
+                    auto top = AllocateBuffer(ifs, dataInfo);
+                    layerInfo.add_top(top, dataInfo.SizeOfData());
+                }
+                else
+                {
+                    ifs.seekg(size, std::ios_base::cur);
+                    if (!ifs.good())
+                    {
+                        // TODO: error out?
+                        return;
+                    }
+                }
+                break;
+            }
+            case 1:
+            {
+                // 64 bit
+                // advance by eight bytes
+                ifs.seekg(8, std::ios_base::cur);
+                if (!ifs.good())
+                {
+                    // TODO: error out?
+                    return;
+                }
+                break;
+            }
+            case 5:
+            {
+                // 32 bit
+                // advance by four bytes
+                ifs.seekg(4, std::ios_base::cur);
+                if (!ifs.good())
+                {
+                    // TODO: error out?
+                    return;
+                }
+                break;
+            }
+            default:
+            {
+                throw armnn::ParseException("Encounted an unknown field type");
+                break;
+            }
+        }
+    }
+}
+
+void ResolveInPlaceLayers(std::vector<LayerParameterInfo>& layerInfo)
+{
+    std::map<std::string, std::vector<LayerParameterInfo*>> layersByTop;
+    for (auto& info : layerInfo)
+    {
+        for (unsigned long i = 0; i < info.top_size(); ++i)
+        {
+            layersByTop[info.top(i)].push_back(&info);
+        }
+    }
+    // For each set of layers with the same top, resolve them to a linear chain rather than in-place layers.
+    // Note that for 'regular' layers, there will be a single layer in each group and so this will be a no-op.
+    for (auto& layersWithSameTopIterator : layersByTop)
+    {
+        const std::string& top = layersWithSameTopIterator.first;
+        const std::vector<LayerParameterInfo*> layersWithSameTop = layersWithSameTopIterator.second;
+
+        // Chain the layers together in the order that they are listed in the prototxt (hopefully this is correct).
+        // Note that the last layer will not have its top modified so that other layers will continue to reference it.
+        for (unsigned int layerIdx = 0; layerIdx < layersWithSameTop.size() - 1; ++layerIdx)
+        {
+            LayerParameterInfo* layer1 = layersWithSameTop[layerIdx];
+            LayerParameterInfo* layer2 = layersWithSameTop[layerIdx + 1];
+            if (layer1->top_size() != 1)
+            {
+                throw armnn::ParseException("Node '" + layer1->name() + "' is an in-place layer but "
+                                                                        "doesn't have exactly one top.");
+            }
+            std::string newTop = layer1->name() + "_top";
+            layer1->set_top(0, newTop);
+            if (layer2->bottom_size() != 1 || layer2->bottom(0) != top)
+            {
+                throw armnn::ParseException("Node '" + layer2->name() + "' is an in-place layer but "
+                    " doesn't have exactly one bottom, or it doesn't match its top.");
+            }
+            layer2->set_bottom(0, newTop);
+
+        }
+    }
+}
+
+} // anonymous namespace, can't be seen outside this source file
+
+RecordByRecordCaffeParser::RecordByRecordCaffeParser() : CaffeParserBase()
+{}
+
+armnn::INetworkPtr RecordByRecordCaffeParser::CreateNetworkFromBinaryFile(
+    const char* graphFile,
+    const std::map<std::string, armnn::TensorShape>& inputShapes,
+    const std::vector<std::string>& requestedOutputs)
+{
+
+    m_InputShapes = inputShapes;
+    if (requestedOutputs.size() == 0)
+    {
+        throw armnn::ParseException("requestedOutputs must have at least one entry");
+    }
+    m_RequestedOutputs = requestedOutputs;
+
+    //FILE * fp = fopen(graphFile, "rb");
+    std::ifstream ifs(graphFile, std::ifstream::in|std::ifstream::binary);
+    std::vector<LayerParameterInfo> layerInfo;
+    NetParameterInfo netParameterInfo;
+    while(true)
+    {
+        ProtobufFieldInfo fieldInfo = readFieldInfo(ifs);
+        if (fieldInfo.eof())
+        {
+            break;
+        }
+        switch(fieldInfo.field_type())
+        {
+            case 0:
+            {
+                ReadBase128(ifs);
+                break;
+            }
+            case 2:
+            {
+                // The values of interest from the caffe.proto schema are:
+                // optional string name = 1; // consider giving the network a name
+                // DEPRECATED. See InputParameter. The input blobs to the network.
+                // repeated string input = 3;
+                // DEPRECATED. See InputParameter. The shape of the input blobs.
+                // repeated BlobShape input_shape = 8;
+
+                // 4D input dimensions -- deprecated.  Use "input_shape" instead.
+                // If specified, for each input blob there should be four
+                // values specifying the num, channels, height and width of the input blob.
+                // Thus, there should be a total of (4 * #input) numbers.
+                // repeated int32 input_dim = 4;
+
+                // The layers that make up the net.  Each of their configurations, including
+                // connectivity and behavior, is specified as a LayerParameter.
+                // repeated LayerParameter layer = 100;  // ID 100 so layers are printed last.
+
+                // The first four will (if present) be read into the NetParameterInfo
+                // the LayerParameters will be read into the LayerParameterInfo vector.
+
+                int size = ReadBase128(ifs);
+                std::streamoff posStartOfData = ifs.tellg();
+                ifs.seekg(size, std::ios_base::cur);
+                if(!ifs.good())
+                {
+                    throw armnn::ParseException("failed to seek ahead in binary caffe file");
+                }
+                std::streamoff endOfLayer = ifs.tellg();
+                if (fieldInfo.field_id() == 1)
+                {
+                    VarLenDataInfo dataInfo = CreateVarLenDataInfo(posStartOfData, endOfLayer);
+                    auto graphName = AllocateBuffer(ifs, dataInfo);
+                    netParameterInfo.set_name(graphName, dataInfo.SizeOfData());
+                }
+                if (fieldInfo.field_id() == 3)
+                {
+                    VarLenDataInfo dataInfo = CreateVarLenDataInfo(posStartOfData, endOfLayer);
+                    auto inputName = AllocateBuffer(ifs, dataInfo);
+                    netParameterInfo.add_input(inputName, dataInfo.SizeOfData());
+                }
+                if (fieldInfo.field_id() == 8)
+                {
+                    VarLenDataInfo dataInfo = CreateVarLenDataInfo(posStartOfData, endOfLayer);
+                    auto inputShape = AllocateBuffer(ifs, dataInfo);
+                    caffe::BlobShape blobShape;
+                    bool bRet = blobShape.ParseFromArray(inputShape.get(), static_cast<int>(dataInfo.SizeOfData()));
+                    if (!bRet)
+                    {
+                        throw armnn::ParseException("Failed to parse input shape");
+                    }
+                    netParameterInfo.add_blob_shape(blobShape);
+                }
+                if (fieldInfo.field_id() == 4)
+                {
+                    int input_dim = ReadBase128(ifs);
+                    netParameterInfo.add_input_dimension(input_dim);
+                }
+                if (fieldInfo.field_id() == 100)
+                {
+                    LayerParameterInfo info(CreateVarLenDataInfo(posStartOfData, endOfLayer));
+                    ReadTopologicalInfoForLayerParameter(info, ifs);
+                    layerInfo.push_back(info);
+                }
+                break;
+            }
+            default:
+            {
+                break;
+            }
+        }
+    }
+    std::vector<const LayerParameterInfo*> sortedNodes;
+    ProcessLayers(netParameterInfo, layerInfo, m_RequestedOutputs, sortedNodes);
+    armnn::INetworkPtr networkPtr = LoadLayers(ifs, sortedNodes, netParameterInfo);
+    return networkPtr;
+
+}
+
+void RecordByRecordCaffeParser::ProcessLayers(
+    const NetParameterInfo& netParameterInfo,
+    std::vector<LayerParameterInfo>& layerInfo,
+    const std::vector<std::string>& m_RequestedOutputs,
+    std::vector<const LayerParameterInfo*>& sortedNodes)
+{
+    // if there is an implicit input layer add it to the layerInfo list
+    if (netParameterInfo.input_size() > 0)
+    {
+        LayerParameterInfo implicitInputLayer(0, 0);
+        implicitInputLayer.set_type(LayerParameterInfo::INPUT);
+        implicitInputLayer.set_name(netParameterInfo.input(0));
+        implicitInputLayer.add_top(netParameterInfo.input(0));
+        layerInfo.push_back(implicitInputLayer);
+    }
+    ::ResolveInPlaceLayers(layerInfo);
+
+    for (LayerParameterInfo& info : layerInfo)
+    {
+        for (unsigned long i = 0; i < info.top_size(); ++i)
+        {
+            m_CaffeLayersByTopName[info.top(i)] = &info;
+        }
+    }
+
+    // Find the output layers the user requested
+    std::vector<const LayerParameterInfo*> targetLayers;
+    for (const std::string& requestedOutputName : m_RequestedOutputs)
+    {
+        auto nodeIt = m_CaffeLayersByTopName.find(requestedOutputName);
+        if (nodeIt == m_CaffeLayersByTopName.end())
+        {
+            throw armnn::ParseException(
+                "Couldn't find requested output layer '" + requestedOutputName + "' in graph");
+        }
+        targetLayers.push_back(nodeIt->second);
+    }
+
+    // Sort them into a linear ordering such that all inputs of a node are before the node itself
+    if (!armnnUtils::GraphTopologicalSort<const LayerParameterInfo*>(
+        targetLayers,
+        [this](const LayerParameterInfo* node)
+            {
+                return GetInputs(*node);
+            },
+        sortedNodes))
+    {
+        throw armnn::ParseException("Cycle detected in graph");
+    }
+}
+
+
+std::vector<const LayerParameterInfo*> RecordByRecordCaffeParser::GetInputs(
+    const LayerParameterInfo& layerParam)
+{
+    std::vector<const LayerParameterInfo*> ret;
+    ret.reserve(layerParam.bottom_size());
+    for (unsigned long j = 0; j < layerParam.bottom_size(); ++j)
+    {
+        std::string inputName = layerParam.bottom(j);
+        auto inputIt = m_CaffeLayersByTopName.find(inputName);
+        if (inputIt == m_CaffeLayersByTopName.end())
+        {
+            throw armnn::ParseException(
+                "Can't find Caffe layer with top called '" + inputName + "', which is listed as an input of '" +
+                layerParam.name() + "'");
+        }
+        ret.push_back(inputIt->second);
+    }
+
+    return ret;
+}
+
+armnn::INetworkPtr RecordByRecordCaffeParser::LoadLayers(std::ifstream& ifs,
+                                                         std::vector<const LayerParameterInfo *>& sortedNodes,
+                                                         const NetParameterInfo& netParameterInfo)
+{
+
+    m_NetworkInputsBindingInfo.clear();
+    m_NetworkOutputsBindingInfo.clear();
+
+    m_Network = armnn::INetwork::Create();
+
+    for (auto info : sortedNodes)
+    {
+        caffe::LayerParameter layer;
+        if (info->isImplicitInputLayer())
+        {
+            // create the matching Layer Parameter programatically from the data in the
+            // net parameter info which has been passed in...
+            layer.set_type(LayerParameterInfo::INPUT);
+            layer.set_name(netParameterInfo.input(0));
+            layer.add_top(netParameterInfo.input(0));
+
+            caffe::InputParameter* inputParam = layer.mutable_input_param();
+            caffe::BlobShape* shape = inputParam->add_shape();
+
+            long unsigned int dim_size = netParameterInfo.input_dimensions_size();
+            for (long unsigned int i = 0; i < dim_size; ++i)
+            {
+                shape->add_dim(netParameterInfo.input_dimension(i));
+            }
+        }
+        else
+        {
+            char *buffer = new char[info->SizeOfData()];
+            ifs.clear();
+            ifs.seekg(info->PositionOfData(), std::ios_base::beg);
+            ifs.read(buffer, boost::numeric_cast<std::streamsize>(info->SizeOfData()));
+            bool bRet = layer.ParseFromArray(buffer, static_cast<int>(info->SizeOfData()));
+            delete[] buffer;
+            if (!bRet)
+            {
+                throw armnn::ParseException("Failed to parse layer [" + info->name() + "]");
+            }
+        }
+
+        if (info->new_tops())
+        {
+            //update the tops
+            layer.set_top(0, info->top(0));
+        }
+        if (info->new_bottoms())
+        {
+            //update the bottoms
+            layer.set_bottom(0, info->bottom(0));
+        }
+
+        auto it = ms_CaffeLayerNameToParsingFunctions.find(layer.type());
+        if (it == ms_CaffeLayerNameToParsingFunctions.end())
+        {
+            throw armnn::ParseException("Unsupported layer type '" + layer.type() + "'");
+        }
+        auto func = it->second;
+        (this->*func)(layer);
+    }
+    ifs.close();
+
+    // Add ArmNN output layers connected to each requested output
+    for (const std::string& requestedOutput : m_RequestedOutputs)
+    {
+        armnn::IOutputSlot& outputSlot = GetArmnnOutputSlotForCaffeTop(requestedOutput);
+
+        const armnn::LayerBindingId outputId = boost::numeric_cast<armnn::LayerBindingId>(
+            m_NetworkOutputsBindingInfo.size());
+        armnn::IConnectableLayer* const outputLayer = m_Network->AddOutputLayer(outputId, requestedOutput.c_str());
+        outputSlot.Connect(outputLayer->GetInputSlot(0));
+
+        TrackOutputBinding(outputLayer, outputId, outputLayer->GetInputSlot(0).GetConnection()->GetTensorInfo());
+    }
+
+    Cleanup();
+
+    return move(m_Network);
+}
+
+
+