| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #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); |
| } |
| |
| |
| |
