| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "Deserializer.hpp" |
| |
| #include <armnn/ArmNN.hpp> |
| #include <armnn/Exceptions.hpp> |
| |
| #include <ParserHelper.hpp> |
| #include <Permute.hpp> |
| #include <VerificationHelpers.hpp> |
| |
| #include <boost/filesystem.hpp> |
| #include <boost/format.hpp> |
| #include <boost/core/ignore_unused.hpp> |
| #include <boost/assert.hpp> |
| #include <boost/format.hpp> |
| #include <boost/log/trivial.hpp> |
| |
| // The generated code based on the Serialize schema: |
| #include <ArmnnSchema_generated.h> |
| |
| #include <fstream> |
| #include <algorithm> |
| #include <limits> |
| #include <numeric> |
| |
| using armnn::ParseException; |
| using namespace armnn; |
| using namespace armnnSerializer; |
| |
| namespace armnnDeserializer |
| { |
| |
| namespace |
| { |
| |
| const uint32_t VIRTUAL_LAYER_ID = std::numeric_limits<uint32_t>::max(); |
| |
| void CheckGraph(const Deserializer::GraphPtr& graph, |
| unsigned int layersIndex, |
| const CheckLocation& location) |
| { |
| if (graph->layers() == nullptr) |
| { |
| throw ParseException( |
| boost::str( |
| boost::format("%1% was called with invalid (null) graph. " |
| "Possible reason is that the graph is not yet loaded and Unpack(ed). " |
| "layers:%2% at %3%") % |
| location.m_Function % |
| layersIndex % |
| location.FileLine())); |
| } |
| else if (layersIndex >= graph->layers()->size()) |
| { |
| throw ParseException( |
| boost::str( |
| boost::format("%1% was called with an invalid layers index. " |
| "layers:%2% at %3%") % |
| location.m_Function % |
| layersIndex % |
| location.FileLine())); |
| } |
| } |
| |
| void CheckLayers(const Deserializer::GraphPtr& graph, |
| unsigned int layersIndex, |
| unsigned int layerIndex, |
| const CheckLocation& location) |
| { |
| if (graph->layers() == nullptr) |
| { |
| throw ParseException( |
| boost::str( |
| boost::format("%1% was called with invalid (null) graph. " |
| "Possible reason is that the graph is not yet loaded and Unpack(ed). " |
| "layers:%2% at %3%") % |
| location.m_Function % |
| layersIndex % |
| location.FileLine())); |
| } |
| else if (layersIndex >= graph->layers()->size()) |
| { |
| throw ParseException( |
| boost::str( |
| boost::format("%1% was called with an invalid layers index. " |
| "layers:%2% at %3%") % |
| location.m_Function % |
| layersIndex % |
| location.FileLine())); |
| } |
| else if (layerIndex >= graph->layers()[layersIndex].size() |
| && layerIndex != VIRTUAL_LAYER_ID) |
| { |
| throw ParseException( |
| boost::str( |
| boost::format("%1% was called with an invalid layer index. " |
| "layers:%2% layer:%3% at %4%") % |
| location.m_Function % |
| layersIndex % |
| layerIndex % |
| location.FileLine())); |
| } |
| } |
| |
| void CheckTensorPtr(Deserializer::TensorRawPtr rawPtr, |
| const CheckLocation& location) |
| { |
| if (rawPtr == nullptr) |
| { |
| throw ParseException( |
| boost::str( |
| boost::format("%1% was called with a null tensor pointer. " |
| "at %2%") % |
| location.m_Function % |
| location.FileLine())); |
| |
| } |
| } |
| |
| void CheckConstTensorPtr(Deserializer::ConstTensorRawPtr rawPtr, |
| const CheckLocation& location) |
| { |
| if (rawPtr == nullptr) |
| { |
| throw ParseException(boost::str(boost::format("%1% was called with a null const tensor pointer. at %2%") % |
| location.m_Function % |
| location.FileLine())); |
| } |
| } |
| |
| void CheckConstTensorSize(const unsigned int constTensorSize, |
| const unsigned int tensorSize, |
| const CheckLocation& location) |
| { |
| if (constTensorSize != tensorSize) |
| { |
| throw ParseException(boost::str(boost::format("%1% wrong number of components supplied to tensor. at:%2%") % |
| location.m_Function % |
| location.FileLine())); |
| } |
| } |
| |
| #define CHECK_TENSOR_PTR(TENSOR_PTR) \ |
| CheckTensorPtr(TENSOR_PTR, CHECK_LOCATION()) |
| |
| #define CHECK_CONST_TENSOR_SIZE(CONST_TENSOR_SIZE, TENSOR_SIZE) \ |
| CheckConstTensorSize(CONST_TENSOR_SIZE, TENSOR_SIZE, CHECK_LOCATION()) |
| |
| #define CHECK_CONST_TENSOR_PTR(TENSOR_PTR) \ |
| CheckConstTensorPtr(TENSOR_PTR, CHECK_LOCATION()) |
| |
| #define CHECK_LAYERS(GRAPH, LAYERS_INDEX, LAYER_INDEX) \ |
| CheckLayers(GRAPH, LAYERS_INDEX, LAYER_INDEX, CHECK_LOCATION()) |
| |
| #define CHECK_GRAPH(GRAPH, LAYERS_INDEX) \ |
| CheckGraph(GRAPH, LAYERS_INDEX, CHECK_LOCATION()) |
| } |
| |
| bool CheckShape(const armnn::TensorShape& actual, const std::vector<uint32_t>& expected) |
| { |
| const unsigned int actualSize = actual.GetNumDimensions(); |
| if (actualSize != expected.size()) |
| { |
| return false; |
| } |
| |
| for (unsigned int i = 0u; i < actualSize; i++) |
| { |
| if (actual[i] != static_cast<unsigned int>(expected[i])) |
| { |
| return false; |
| } |
| } |
| |
| return true; |
| } |
| |
| Deserializer::Deserializer() |
| : m_Network(nullptr, nullptr), |
| //May require LayerType_Max to be included |
| m_ParserFunctions(Layer_MAX+1, &Deserializer::ParseUnsupportedLayer) |
| { |
| // register supported layers |
| m_ParserFunctions[Layer_ActivationLayer] = &Deserializer::ParseActivation; |
| m_ParserFunctions[Layer_AdditionLayer] = &Deserializer::ParseAdd; |
| m_ParserFunctions[Layer_BatchToSpaceNdLayer] = &Deserializer::ParseBatchToSpaceNd; |
| m_ParserFunctions[Layer_BatchNormalizationLayer] = &Deserializer::ParseBatchNormalization; |
| m_ParserFunctions[Layer_ConstantLayer] = &Deserializer::ParseConstant; |
| m_ParserFunctions[Layer_Convolution2dLayer] = &Deserializer::ParseConvolution2d; |
| m_ParserFunctions[Layer_DepthwiseConvolution2dLayer] = &Deserializer::ParseDepthwiseConvolution2d; |
| m_ParserFunctions[Layer_DivisionLayer] = &Deserializer::ParseDivision; |
| m_ParserFunctions[Layer_EqualLayer] = &Deserializer::ParseEqual; |
| m_ParserFunctions[Layer_FullyConnectedLayer] = &Deserializer::ParseFullyConnected; |
| m_ParserFunctions[Layer_FloorLayer] = &Deserializer::ParseFloor; |
| m_ParserFunctions[Layer_GreaterLayer] = &Deserializer::ParseGreater; |
| m_ParserFunctions[Layer_MinimumLayer] = &Deserializer::ParseMinimum; |
| m_ParserFunctions[Layer_MaximumLayer] = &Deserializer::ParseMaximum; |
| m_ParserFunctions[Layer_MultiplicationLayer] = &Deserializer::ParseMultiplication; |
| m_ParserFunctions[Layer_NormalizationLayer] = &Deserializer::ParseNormalization; |
| m_ParserFunctions[Layer_PadLayer] = &Deserializer::ParsePad; |
| m_ParserFunctions[Layer_PermuteLayer] = &Deserializer::ParsePermute; |
| m_ParserFunctions[Layer_Pooling2dLayer] = &Deserializer::ParsePooling2d; |
| m_ParserFunctions[Layer_ReshapeLayer] = &Deserializer::ParseReshape; |
| m_ParserFunctions[Layer_ResizeBilinearLayer] = &Deserializer::ParseResizeBilinear; |
| m_ParserFunctions[Layer_RsqrtLayer] = &Deserializer::ParseRsqrt; |
| m_ParserFunctions[Layer_SoftmaxLayer] = &Deserializer::ParseSoftmax; |
| m_ParserFunctions[Layer_SpaceToBatchNdLayer] = &Deserializer::ParseSpaceToBatchNd; |
| } |
| |
| Deserializer::LayerBaseRawPtr Deserializer::GetBaseLayer(const GraphPtr& graphPtr, unsigned int layerIndex) |
| { |
| auto layerType = graphPtr->layers()->Get(layerIndex)->layer_type(); |
| |
| switch(layerType) |
| { |
| case Layer::Layer_ActivationLayer: |
| return graphPtr->layers()->Get(layerIndex)->layer_as_ActivationLayer()->base(); |
| case Layer::Layer_AdditionLayer: |
| return graphPtr->layers()->Get(layerIndex)->layer_as_AdditionLayer()->base(); |
| case Layer::Layer_BatchToSpaceNdLayer: |
| return graphPtr->layers()->Get(layerIndex)->layer_as_BatchToSpaceNdLayer()->base(); |
| case Layer::Layer_BatchNormalizationLayer: |
| return graphPtr->layers()->Get(layerIndex)->layer_as_BatchNormalizationLayer()->base(); |
| case Layer::Layer_ConstantLayer: |
| return graphPtr->layers()->Get(layerIndex)->layer_as_ConstantLayer()->base(); |
| case Layer::Layer_Convolution2dLayer: |
| return graphPtr->layers()->Get(layerIndex)->layer_as_Convolution2dLayer()->base(); |
| case Layer::Layer_DepthwiseConvolution2dLayer: |
| return graphPtr->layers()->Get(layerIndex)->layer_as_DepthwiseConvolution2dLayer()->base(); |
| case Layer::Layer_DivisionLayer: |
| return graphPtr->layers()->Get(layerIndex)->layer_as_DivisionLayer()->base(); |
| case Layer::Layer_EqualLayer: |
| return graphPtr->layers()->Get(layerIndex)->layer_as_EqualLayer()->base(); |
| case Layer::Layer_FullyConnectedLayer: |
| return graphPtr->layers()->Get(layerIndex)->layer_as_FullyConnectedLayer()->base(); |
| case Layer::Layer_FloorLayer: |
| return graphPtr->layers()->Get(layerIndex)->layer_as_FloorLayer()->base(); |
| case Layer::Layer_GreaterLayer: |
| return graphPtr->layers()->Get(layerIndex)->layer_as_GreaterLayer()->base(); |
| case Layer::Layer_InputLayer: |
| return graphPtr->layers()->Get(layerIndex)->layer_as_InputLayer()->base()->base(); |
| case Layer::Layer_MinimumLayer: |
| return graphPtr->layers()->Get(layerIndex)->layer_as_MinimumLayer()->base(); |
| case Layer::Layer_MaximumLayer: |
| return graphPtr->layers()->Get(layerIndex)->layer_as_MaximumLayer()->base(); |
| case Layer::Layer_MultiplicationLayer: |
| return graphPtr->layers()->Get(layerIndex)->layer_as_MultiplicationLayer()->base(); |
| case Layer::Layer_NormalizationLayer: |
| return graphPtr->layers()->Get(layerIndex)->layer_as_NormalizationLayer()->base(); |
| case Layer::Layer_OutputLayer: |
| return graphPtr->layers()->Get(layerIndex)->layer_as_OutputLayer()->base()->base(); |
| case Layer::Layer_PadLayer: |
| return graphPtr->layers()->Get(layerIndex)->layer_as_PadLayer()->base(); |
| case Layer::Layer_PermuteLayer: |
| return graphPtr->layers()->Get(layerIndex)->layer_as_PermuteLayer()->base(); |
| case Layer::Layer_Pooling2dLayer: |
| return graphPtr->layers()->Get(layerIndex)->layer_as_Pooling2dLayer()->base(); |
| case Layer::Layer_ReshapeLayer: |
| return graphPtr->layers()->Get(layerIndex)->layer_as_ReshapeLayer()->base(); |
| case Layer::Layer_ResizeBilinearLayer: |
| return graphPtr->layers()->Get(layerIndex)->layer_as_ResizeBilinearLayer()->base(); |
| case Layer::Layer_RsqrtLayer: |
| return graphPtr->layers()->Get(layerIndex)->layer_as_RsqrtLayer()->base(); |
| case Layer::Layer_SoftmaxLayer: |
| return graphPtr->layers()->Get(layerIndex)->layer_as_SoftmaxLayer()->base(); |
| case Layer::Layer_SpaceToBatchNdLayer: |
| return graphPtr->layers()->Get(layerIndex)->layer_as_SpaceToBatchNdLayer()->base(); |
| case Layer::Layer_NONE: |
| default: |
| throw ParseException(boost::str( |
| boost::format("Layer must have a type %1%") % |
| Layer::Layer_NONE)); |
| } |
| } |
| |
| std::string Deserializer::GetLayerName(const GraphPtr& graph, unsigned int index) |
| { |
| auto layer = GetBaseLayer(graph, index); |
| assert(layer); |
| return layer->layerName()->str(); |
| } |
| |
| int32_t Deserializer::GetBindingLayerInfo(const GraphPtr& graphPtr, unsigned int layerIndex) |
| { |
| auto layerType = graphPtr->layers()->Get(layerIndex)->layer_type(); |
| |
| if (layerType == Layer::Layer_InputLayer) |
| { |
| return graphPtr->layers()->Get(layerIndex)->layer_as_InputLayer()->base()->layerBindingId(); |
| } |
| else if ( layerType == Layer::Layer_OutputLayer ) |
| { |
| return graphPtr->layers()->Get(layerIndex)->layer_as_OutputLayer()->base()->layerBindingId(); |
| } |
| return 0; |
| } |
| |
| armnn::DataLayout ToDataLayout(armnnSerializer::DataLayout dataLayout) |
| { |
| switch (dataLayout) |
| { |
| case armnnSerializer::DataLayout::DataLayout_NHWC: |
| return armnn::DataLayout::NHWC; |
| case armnnSerializer::DataLayout::DataLayout_NCHW: |
| default: |
| return armnn::DataLayout::NCHW; |
| } |
| } |
| |
| armnn::ActivationFunction ToActivationFunction(armnnSerializer::ActivationFunction function) |
| { |
| switch (function) |
| { |
| case armnnSerializer::ActivationFunction_Sigmoid: |
| return armnn::ActivationFunction::Sigmoid; |
| case armnnSerializer::ActivationFunction_TanH: |
| return armnn::ActivationFunction::TanH; |
| case armnnSerializer::ActivationFunction_Linear: |
| return armnn::ActivationFunction::Linear; |
| case armnnSerializer::ActivationFunction_ReLu: |
| return armnn::ActivationFunction::ReLu; |
| case armnnSerializer::ActivationFunction_BoundedReLu: |
| return armnn::ActivationFunction::BoundedReLu; |
| case armnnSerializer::ActivationFunction_LeakyReLu: |
| return armnn::ActivationFunction::LeakyReLu; |
| case armnnSerializer::ActivationFunction_Abs: |
| return armnn::ActivationFunction::Abs; |
| case armnnSerializer::ActivationFunction_Sqrt: |
| return armnn::ActivationFunction::Sqrt; |
| case armnnSerializer::ActivationFunction_Square: |
| return armnn::ActivationFunction::Square; |
| default: |
| return armnn::ActivationFunction::Sigmoid; |
| } |
| } |
| |
| armnn::TensorInfo ToTensorInfo(Deserializer::TensorRawPtr tensorPtr) |
| { |
| armnn::DataType type; |
| CHECK_TENSOR_PTR(tensorPtr); |
| |
| switch (tensorPtr->dataType()) |
| { |
| case DataType_QuantisedAsymm8: |
| type = armnn::DataType::QuantisedAsymm8; |
| break; |
| case DataType_Signed32: |
| type = armnn::DataType::Signed32; |
| break; |
| case DataType_Float32: |
| type = armnn::DataType::Float32; |
| break; |
| case DataType_Float16: |
| type = armnn::DataType::Float16; |
| break; |
| case DataType_Boolean: |
| type = armnn::DataType::Boolean; |
| break; |
| default: |
| { |
| CheckLocation location = CHECK_LOCATION(); |
| throw ParseException( |
| boost::str( |
| boost::format("Unsupported data type %1% = %2%. %3%") % |
| tensorPtr->dataType() % |
| EnumNameDataType(tensorPtr->dataType()) % |
| location.AsString())); |
| } |
| } |
| float quantizationScale = tensorPtr->quantizationScale(); |
| int32_t quantizationOffset = tensorPtr->quantizationOffset(); |
| |
| auto dimensions = tensorPtr->dimensions(); |
| unsigned int size = dimensions->size(); |
| std::vector<unsigned int> outputDims(dimensions->begin(), dimensions->begin() + size); |
| |
| // two statements (on purpose) for easier debugging: |
| armnn::TensorInfo result(size, |
| outputDims.data(), |
| type, |
| quantizationScale, |
| quantizationOffset); |
| return result; |
| } |
| |
| armnn::ConstTensor ToConstTensor(Deserializer::ConstTensorRawPtr constTensorPtr) |
| { |
| CHECK_CONST_TENSOR_PTR(constTensorPtr); |
| armnn::TensorInfo tensorInfo = ToTensorInfo(constTensorPtr->info()); |
| |
| switch (constTensorPtr->data_type()) |
| { |
| case ConstTensorData_ByteData: |
| { |
| auto byteData = constTensorPtr->data_as_ByteData()->data(); |
| CHECK_CONST_TENSOR_SIZE(byteData->size(), tensorInfo.GetNumElements()); |
| return armnn::ConstTensor(tensorInfo, byteData->data()); |
| } |
| case ConstTensorData_ShortData: |
| { |
| auto shortData = constTensorPtr->data_as_ShortData()->data(); |
| CHECK_CONST_TENSOR_SIZE(shortData->size(), tensorInfo.GetNumElements()); |
| return armnn::ConstTensor(tensorInfo, shortData->data()); |
| } |
| case ConstTensorData_IntData: |
| { |
| auto intData = constTensorPtr->data_as_IntData()->data(); |
| CHECK_CONST_TENSOR_SIZE(intData->size(), tensorInfo.GetNumElements()); |
| return armnn::ConstTensor(tensorInfo, intData->data()); |
| } |
| case ConstTensorData_LongData: |
| { |
| auto longData = constTensorPtr->data_as_LongData()->data(); |
| CHECK_CONST_TENSOR_SIZE(longData->size(), tensorInfo.GetNumElements()); |
| return armnn::ConstTensor(tensorInfo, longData->data()); |
| } |
| default: |
| { |
| CheckLocation location = CHECK_LOCATION(); |
| throw ParseException( |
| boost::str(boost::format("Unsupported data type %1% = %2%. %3%") % |
| constTensorPtr->data_type() % |
| EnumNameConstTensorData(constTensorPtr->data_type()) % |
| location.AsString())); |
| } |
| } |
| } |
| |
| Deserializer::LayerBaseRawPtrVector Deserializer::GetGraphInputs(const GraphPtr& graphPtr) |
| { |
| |
| CHECK_GRAPH(graphPtr, 0); |
| const auto& numInputs = graphPtr->inputIds()->size(); |
| |
| LayerBaseRawPtrVector result(numInputs); |
| |
| for (unsigned int i=0; i<numInputs; ++i) |
| { |
| uint32_t inputId = graphPtr->inputIds()->Get(i); |
| result[i] = GetBaseLayer(graphPtr, static_cast<uint32_t>(inputId)); |
| } |
| return result; |
| } |
| |
| Deserializer::LayerBaseRawPtrVector Deserializer::GetGraphOutputs(const GraphPtr& graphPtr) |
| { |
| CHECK_GRAPH(graphPtr, 0); |
| const auto& numOutputs = graphPtr->outputIds()->size(); |
| LayerBaseRawPtrVector result(numOutputs); |
| |
| for (unsigned int i=0; i<numOutputs; ++i) |
| { |
| uint32_t outputId = graphPtr->outputIds()->Get(i); |
| |
| result[i] = GetBaseLayer(graphPtr, static_cast<uint32_t>(outputId)); |
| } |
| return result; |
| } |
| |
| Deserializer::TensorRawPtrVector Deserializer::GetInputs(const GraphPtr& graphPtr, |
| unsigned int layerIndex) |
| { |
| CHECK_LAYERS(graphPtr, 0, layerIndex); |
| auto layer = GetBaseLayer(graphPtr, layerIndex); |
| const auto& numInputs = layer->inputSlots()->size(); |
| |
| TensorRawPtrVector result(numInputs); |
| |
| for (unsigned int i=0; i<numInputs; ++i) |
| { |
| auto inputId = CHECKED_NON_NEGATIVE(static_cast<int32_t> |
| (layer->inputSlots()->Get(i)->connection()->sourceLayerIndex())); |
| result[i] = GetBaseLayer(graphPtr, inputId)->outputSlots()->Get(0)->tensorInfo(); |
| } |
| return result; |
| } |
| |
| Deserializer::TensorRawPtrVector Deserializer::GetOutputs(const GraphPtr& graphPtr, |
| unsigned int layerIndex) |
| { |
| CHECK_LAYERS(graphPtr, 0, layerIndex); |
| auto layer = GetBaseLayer(graphPtr, layerIndex); |
| const auto& numOutputs = layer->outputSlots()->size(); |
| |
| TensorRawPtrVector result(numOutputs); |
| |
| for (unsigned int i=0; i<numOutputs; ++i) |
| { |
| result[i] = layer->outputSlots()->Get(i)->tensorInfo(); |
| } |
| return result; |
| } |
| |
| void Deserializer::ParseUnsupportedLayer(GraphPtr graph, unsigned int layerIndex) |
| { |
| CHECK_LAYERS(graph, 0, layerIndex); |
| const auto layerName = GetBaseLayer(graph, layerIndex)->layerName()->c_str(); |
| throw ParseException( |
| boost::str( |
| boost::format("Layer not supported. " |
| "layerIndex: %1% " |
| "layerName: %2% / %3%") % |
| layerIndex % |
| layerName % |
| CHECK_LOCATION().AsString())); |
| } |
| |
| void Deserializer::ResetParser() |
| { |
| m_Network = armnn::INetworkPtr(nullptr, nullptr); |
| m_InputBindings.clear(); |
| m_OutputBindings.clear(); |
| } |
| |
| IDeserializer* IDeserializer::CreateRaw() |
| { |
| return new Deserializer(); |
| } |
| |
| IDeserializerPtr IDeserializer::Create() |
| { |
| return IDeserializerPtr(CreateRaw(), &IDeserializer::Destroy); |
| } |
| |
| void IDeserializer::Destroy(IDeserializer* parser) |
| { |
| delete parser; |
| } |
| |
| INetworkPtr Deserializer::CreateNetworkFromBinary(const std::vector<uint8_t>& binaryContent) |
| { |
| ResetParser(); |
| GraphPtr graph = LoadGraphFromBinary(binaryContent.data(), binaryContent.size()); |
| return CreateNetworkFromGraph(graph); |
| } |
| |
| armnn::INetworkPtr Deserializer::CreateNetworkFromBinary(std::istream& binaryContent) |
| { |
| ResetParser(); |
| std::vector<uint8_t> content((std::istreambuf_iterator<char>(binaryContent)), std::istreambuf_iterator<char>()); |
| GraphPtr graph = LoadGraphFromBinary(content.data(), content.size()); |
| return CreateNetworkFromGraph(graph); |
| } |
| |
| Deserializer::GraphPtr Deserializer::LoadGraphFromBinary(const uint8_t* binaryContent, size_t len) |
| { |
| if (binaryContent == nullptr) |
| { |
| throw InvalidArgumentException(boost::str(boost::format("Invalid (null) binary content %1%") % |
| CHECK_LOCATION().AsString())); |
| } |
| flatbuffers::Verifier verifier(binaryContent, len); |
| if (verifier.VerifyBuffer<SerializedGraph>() == false) |
| { |
| throw ParseException( |
| boost::str(boost::format("Buffer doesn't conform to the expected Armnn " |
| "flatbuffers format. size:%1% %2%") % |
| len % |
| CHECK_LOCATION().AsString())); |
| } |
| return GetSerializedGraph(binaryContent); |
| } |
| |
| INetworkPtr Deserializer::CreateNetworkFromGraph(GraphPtr graph) |
| { |
| m_Network = INetwork::Create(); |
| BOOST_ASSERT(graph != nullptr); |
| unsigned int layerIndex = 0; |
| m_GraphConnections.emplace_back(graph->layers()->size()); |
| for (AnyLayer const* layer : *graph->layers()) |
| { |
| if (layer->layer_type() != Layer_InputLayer && |
| layer->layer_type() != Layer_OutputLayer) |
| { |
| // lookup and call the parser function |
| auto& parserFunction = m_ParserFunctions[layer->layer_type()]; |
| (this->*parserFunction)(graph, layerIndex); |
| } |
| ++layerIndex; |
| } |
| |
| SetupInputLayers(graph); |
| SetupOutputLayers(graph); |
| |
| // establish the connections from the layer outputs to the inputs of the subsequent layers |
| for (size_t connectionIndex = 0; connectionIndex < m_GraphConnections[0].size(); ++connectionIndex) |
| { |
| if (m_GraphConnections[0][connectionIndex].outputSlot != nullptr) |
| { |
| for (size_t inputSlotIdx = 0; |
| inputSlotIdx < m_GraphConnections[0][connectionIndex].inputSlots.size(); |
| ++inputSlotIdx) |
| { |
| m_GraphConnections[0][connectionIndex].outputSlot->Connect( |
| *(m_GraphConnections[0][connectionIndex].inputSlots[inputSlotIdx])); |
| } |
| } |
| } |
| |
| return std::move(m_Network); |
| } |
| |
| BindingPointInfo Deserializer::GetNetworkInputBindingInfo(unsigned int layerIndex, |
| const std::string& name) const |
| { |
| for (auto inputBinding : m_InputBindings) |
| { |
| if (inputBinding.first == name) |
| { |
| return inputBinding.second; |
| } |
| } |
| throw ParseException( |
| boost::str( |
| boost::format("No input binding found for layer:%1% / %2%") % |
| name % |
| CHECK_LOCATION().AsString())); |
| } |
| |
| BindingPointInfo Deserializer::GetNetworkOutputBindingInfo(unsigned int layerIndex, |
| const std::string& name) const |
| { |
| for (auto outputBinding : m_OutputBindings) |
| { |
| if (outputBinding.first == name) |
| { |
| return outputBinding.second; |
| } |
| } |
| throw ParseException( |
| boost::str( |
| boost::format("No output binding found for layer:%1% / %2%") % |
| name % |
| CHECK_LOCATION().AsString())); |
| } |
| |
| void Deserializer::SetupInputLayers(GraphPtr graph) |
| { |
| CHECK_GRAPH(graph, 0); |
| auto inputs = GetGraphInputs(graph); |
| m_InputBindings.clear(); |
| m_InputBindings.reserve(inputs.size()); |
| for (auto const& input : inputs) |
| { |
| LayerBindingId bindingId = GetBindingLayerInfo(graph, input->index()); |
| IConnectableLayer* layer = |
| m_Network->AddInputLayer(bindingId, input->layerName()->c_str()); |
| |
| auto tensorInfo = ToTensorInfo(input->outputSlots()->Get(0)->tensorInfo()); |
| layer->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| |
| RegisterOutputSlots(graph, input->index(), layer); |
| |
| BOOST_ASSERT_MSG(input->layerName()->c_str(), "Input has no name."); |
| BindingPointInfo bindingInfo = {bindingId, tensorInfo}; |
| m_InputBindings.push_back(std::make_pair(input->layerName()->c_str(), bindingInfo)); |
| } |
| } |
| |
| void Deserializer::SetupOutputLayers(GraphPtr graph) |
| { |
| CHECK_GRAPH(graph, 0); |
| auto outputs = GetGraphOutputs(graph); |
| m_OutputBindings.clear(); |
| m_OutputBindings.reserve(outputs.size()); |
| for (auto const& output : outputs) |
| { |
| LayerBindingId bindingId = GetBindingLayerInfo(graph, output->index()); |
| IConnectableLayer* layer = |
| m_Network->AddOutputLayer(bindingId, output->layerName()->c_str()); |
| |
| RegisterInputSlots(graph, output->index(), layer); |
| |
| auto baseLayer = GetBaseLayer(graph, output->index()); |
| auto sourceLayerIndex = baseLayer->inputSlots()->Get(0)->connection()->sourceLayerIndex(); |
| auto sourceLayer = GetBaseLayer(graph, sourceLayerIndex); |
| auto tensorInfo = ToTensorInfo(sourceLayer->outputSlots()->Get(0)->tensorInfo()); |
| |
| BOOST_ASSERT_MSG(output->layerName()->c_str(), "Output has no name."); |
| BindingPointInfo bindingInfo = {bindingId, tensorInfo}; |
| m_OutputBindings.push_back(std::make_pair(output->layerName()->c_str(), bindingInfo)); |
| } |
| } |
| |
| void Deserializer::RegisterOutputSlots(GraphPtr graph, |
| uint32_t layerIndex, |
| IConnectableLayer* layer) |
| { |
| CHECK_LAYERS(graph, 0, layerIndex); |
| BOOST_ASSERT(layer != nullptr); |
| auto parsedLayer = GetBaseLayer(graph, layerIndex); |
| if (parsedLayer->outputSlots()->size() != layer->GetNumOutputSlots()) |
| { |
| throw ParseException( |
| boost::str(boost::format("The number of outputslots (%1%) does not match the number expected (%2%)" |
| " for layer index: %3% %4%") % |
| parsedLayer->outputSlots()->size() % |
| layer->GetNumOutputSlots() % |
| layerIndex % |
| CHECK_LOCATION().AsString())); |
| } |
| |
| for (unsigned int slotIndex = 0; slotIndex < layer->GetNumOutputSlots(); ++slotIndex) |
| { |
| armnn::IOutputSlot* slot = &(layer->GetOutputSlot(slotIndex)); |
| RegisterOutputSlotOfConnection(layerIndex, slot); |
| } |
| } |
| |
| void Deserializer::RegisterInputSlots(GraphPtr graph, |
| uint32_t layerIndex, |
| armnn::IConnectableLayer* layer) |
| { |
| CHECK_LAYERS(graph, 0, layerIndex); |
| BOOST_ASSERT(layer != nullptr); |
| auto parsedLayer = GetBaseLayer(graph, layerIndex); |
| if (parsedLayer->inputSlots()->size() != layer->GetNumInputSlots()) |
| { |
| throw ParseException( |
| boost::str(boost::format("The number of inputslots (%1%) does not match the number expected (%2%)" |
| " for layer index:%3% %4%") % |
| parsedLayer->inputSlots()->size() % |
| layer->GetNumInputSlots() % |
| layerIndex % |
| CHECK_LOCATION().AsString())); |
| } |
| |
| for (unsigned int slotIndex = 0; slotIndex < layer->GetNumInputSlots(); ++slotIndex) |
| { |
| armnn::IInputSlot* slot = &(layer->GetInputSlot(slotIndex)); |
| uint32_t sourceLayerIndex = parsedLayer->inputSlots()->Get(slotIndex)->connection()->sourceLayerIndex(); |
| RegisterInputSlotOfConnection(sourceLayerIndex, slot); |
| } |
| } |
| |
| void Deserializer::RegisterInputSlotOfConnection(uint32_t connectionIndex, |
| armnn::IInputSlot* slot) |
| { |
| BOOST_ASSERT(m_GraphConnections[0].size() > connectionIndex); |
| |
| Slots& slots = m_GraphConnections[0][connectionIndex]; |
| slots.inputSlots.push_back(slot); |
| } |
| |
| void Deserializer::RegisterOutputSlotOfConnection(uint32_t connectionIndex, |
| armnn::IOutputSlot* slot) |
| { |
| BOOST_ASSERT(m_GraphConnections[0].size() > connectionIndex); |
| |
| Slots& slots = m_GraphConnections[0][connectionIndex]; |
| |
| // assuming there is only one producer for that tensor |
| if (slots.outputSlot != nullptr) |
| { |
| throw ParseException(boost::str( |
| boost::format("Another layer has already registered itself as the producer of " |
| "connection:%1% / %2%") % |
| connectionIndex % |
| CHECK_LOCATION().AsString())); |
| } |
| |
| slots.outputSlot = slot; |
| } |
| |
| void Deserializer::ParseActivation(GraphPtr graph, unsigned int layerIndex) |
| { |
| CHECK_LAYERS(graph, 0, layerIndex); |
| auto inputs = GetInputs(graph, layerIndex); |
| CHECK_LOCATION(); |
| CHECK_VALID_SIZE(inputs.size(), 1); |
| |
| auto outputs = GetOutputs(graph, layerIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| auto serializerLayer = graph->layers()->Get(layerIndex)->layer_as_ActivationLayer(); |
| auto layerName = GetLayerName(graph, layerIndex); |
| auto serializerDescriptor = serializerLayer->descriptor(); |
| |
| armnn::ActivationDescriptor descriptor; |
| descriptor.m_Function = ToActivationFunction(serializerDescriptor->function()); |
| descriptor.m_A = serializerDescriptor->a(); |
| descriptor.m_B = serializerDescriptor->b(); |
| |
| IConnectableLayer* layer = m_Network->AddActivationLayer(descriptor, |
| layerName.c_str()); |
| armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| RegisterInputSlots(graph, layerIndex, layer); |
| RegisterOutputSlots(graph, layerIndex, layer); |
| } |
| |
| void Deserializer::ParseAdd(GraphPtr graph, unsigned int layerIndex) |
| { |
| CHECK_LAYERS(graph, 0, layerIndex); |
| auto inputs = GetInputs(graph, layerIndex); |
| CHECK_LOCATION(); |
| CHECK_VALID_SIZE(inputs.size(), 2); |
| |
| auto outputs = GetOutputs(graph, layerIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| auto layerName = GetLayerName(graph, layerIndex); |
| IConnectableLayer* layer = m_Network->AddAdditionLayer(layerName.c_str()); |
| |
| armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| RegisterInputSlots(graph, layerIndex, layer); |
| RegisterOutputSlots(graph, layerIndex, layer); |
| } |
| |
| void Deserializer::ParseBatchToSpaceNd(GraphPtr graph, unsigned int layerIndex) |
| { |
| CHECK_LAYERS(graph, 0, layerIndex); |
| |
| Deserializer::TensorRawPtrVector inputs = GetInputs(graph, layerIndex); |
| CHECK_VALID_SIZE(inputs.size(), 1); |
| |
| Deserializer::TensorRawPtrVector outputs = GetOutputs(graph, layerIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| auto flatBufferDescriptor = graph->layers()->Get(layerIndex)->layer_as_BatchToSpaceNdLayer()->descriptor(); |
| auto flatBufferCrops = flatBufferDescriptor->crops(); |
| auto flatBufferBlockShape = flatBufferDescriptor->blockShape(); |
| |
| if (flatBufferCrops->Length() % 2 != 0) |
| { |
| throw ParseException(boost::str( |
| boost::format("The size of crops must be divisible by 2 %1%") % CHECK_LOCATION().AsString())); |
| } |
| |
| std::vector<std::pair<unsigned int, unsigned int>> crops; |
| crops.reserve(flatBufferCrops->Length() / 2); |
| for (unsigned int i = 0; i < flatBufferCrops->Length() - 1; i += 2) |
| { |
| crops.emplace_back(flatBufferCrops->Get(i), flatBufferCrops->Get(i+1)); |
| } |
| |
| armnn::BatchToSpaceNdDescriptor descriptor; |
| descriptor.m_DataLayout = ToDataLayout(flatBufferDescriptor->dataLayout()); |
| descriptor.m_BlockShape = |
| std::vector<unsigned int>(flatBufferBlockShape->begin(), flatBufferBlockShape->end()); |
| descriptor.m_Crops = crops; |
| |
| auto layerName = GetLayerName(graph, layerIndex); |
| IConnectableLayer* layer = m_Network->AddBatchToSpaceNdLayer(descriptor, layerName.c_str()); |
| |
| armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| RegisterInputSlots(graph, layerIndex, layer); |
| RegisterOutputSlots(graph, layerIndex, layer); |
| } |
| |
| void Deserializer::ParseBatchNormalization(GraphPtr graph, unsigned int layerIndex) |
| { |
| CHECK_LAYERS(graph, 0, layerIndex); |
| |
| auto inputs = GetInputs(graph, layerIndex); |
| CHECK_VALID_SIZE(inputs.size(), 1); |
| |
| auto outputs = GetOutputs(graph, layerIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| auto outputInfo = ToTensorInfo(outputs[0]); |
| |
| auto layerName = boost::str(boost::format("BatchNormalization:%1%") % layerIndex); |
| |
| auto serializerLayer = graph->layers()->Get(layerIndex)->layer_as_BatchNormalizationLayer(); |
| auto serializerDescriptor = serializerLayer->descriptor(); |
| |
| armnn::BatchNormalizationDescriptor descriptor; |
| descriptor.m_Eps = serializerDescriptor->eps(); |
| descriptor.m_DataLayout = ToDataLayout(serializerDescriptor->dataLayout()); |
| |
| armnn::ConstTensor mean = ToConstTensor(serializerLayer->mean()); |
| armnn::ConstTensor variance = ToConstTensor(serializerLayer->variance()); |
| armnn::ConstTensor beta = ToConstTensor(serializerLayer->beta()); |
| armnn::ConstTensor gamma = ToConstTensor(serializerLayer->gamma()); |
| |
| IConnectableLayer* layer = m_Network->AddBatchNormalizationLayer(descriptor, |
| mean, |
| variance, |
| beta, |
| gamma, |
| layerName.c_str()); |
| layer->GetOutputSlot(0).SetTensorInfo(outputInfo); |
| |
| RegisterInputSlots(graph, layerIndex, layer); |
| RegisterOutputSlots(graph, layerIndex, layer); |
| } |
| |
| void Deserializer::ParseConstant(GraphPtr graph, unsigned int layerIndex) |
| { |
| CHECK_LAYERS(graph, 0, layerIndex); |
| CHECK_LOCATION(); |
| |
| auto outputs = GetOutputs(graph, layerIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| auto layerName = GetLayerName(graph, layerIndex); |
| |
| auto serializerLayer = graph->layers()->Get(layerIndex)->layer_as_ConstantLayer(); |
| auto serializerInput = serializerLayer->input(); |
| |
| armnn::ConstTensor input = ToConstTensor(serializerInput); |
| |
| IConnectableLayer* layer = m_Network->AddConstantLayer(input, layerName.c_str()); |
| |
| armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| RegisterOutputSlots(graph, layerIndex, layer); |
| } |
| |
| void Deserializer::ParseConvolution2d(GraphPtr graph, unsigned int layerIndex) |
| { |
| CHECK_LAYERS(graph, 0, layerIndex); |
| auto inputs = GetInputs(graph, layerIndex); |
| CHECK_LOCATION(); |
| CHECK_VALID_SIZE(inputs.size(), 1); |
| |
| auto outputs = GetOutputs(graph, layerIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| auto serializerLayer = graph->layers()->Get(layerIndex)->layer_as_Convolution2dLayer(); |
| auto layerName = GetLayerName(graph, layerIndex); |
| auto serializerDescriptor = serializerLayer->descriptor(); |
| |
| armnn::Convolution2dDescriptor descriptor; |
| descriptor.m_PadLeft = serializerDescriptor->padLeft(); |
| descriptor.m_PadRight = serializerDescriptor->padRight(); |
| descriptor.m_PadTop = serializerDescriptor->padTop(); |
| descriptor.m_PadBottom = serializerDescriptor->padBottom(); |
| descriptor.m_StrideX = serializerDescriptor->strideX(); |
| descriptor.m_StrideY = serializerDescriptor->strideY();; |
| descriptor.m_BiasEnabled = serializerDescriptor->biasEnabled();; |
| descriptor.m_DataLayout = ToDataLayout(serializerDescriptor->dataLayout()); |
| |
| armnn::ConstTensor weights = ToConstTensor(serializerLayer->weights()); |
| armnn::ConstTensor biases; |
| |
| if (descriptor.m_BiasEnabled) |
| { |
| biases = ToConstTensor(serializerLayer->biases()); |
| } |
| IConnectableLayer* layer = m_Network->AddConvolution2dLayer(descriptor, |
| weights, |
| biases, |
| layerName.c_str()); |
| armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| RegisterInputSlots(graph, layerIndex, layer); |
| RegisterOutputSlots(graph, layerIndex, layer); |
| } |
| |
| void Deserializer::ParseDepthwiseConvolution2d(GraphPtr graph, unsigned int layerIndex) |
| { |
| CHECK_LAYERS(graph, 0, layerIndex); |
| auto inputs = GetInputs(graph, layerIndex); |
| CHECK_LOCATION(); |
| CHECK_VALID_SIZE(inputs.size(), 1); |
| |
| auto outputs = GetOutputs(graph, layerIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| auto serializerLayer = graph->layers()->Get(layerIndex)->layer_as_DepthwiseConvolution2dLayer(); |
| auto layerName = GetLayerName(graph, layerIndex); |
| auto serializerDescriptor = serializerLayer->descriptor(); |
| |
| armnn::DepthwiseConvolution2dDescriptor descriptor; |
| descriptor.m_PadLeft = serializerDescriptor->padLeft(); |
| descriptor.m_PadRight = serializerDescriptor->padRight(); |
| descriptor.m_PadTop = serializerDescriptor->padTop(); |
| descriptor.m_PadBottom = serializerDescriptor->padBottom(); |
| descriptor.m_StrideX = serializerDescriptor->strideX(); |
| descriptor.m_StrideY = serializerDescriptor->strideY();; |
| descriptor.m_BiasEnabled = serializerDescriptor->biasEnabled();; |
| descriptor.m_DataLayout = ToDataLayout(serializerDescriptor->dataLayout()); |
| |
| armnn::ConstTensor weights = ToConstTensor(serializerLayer->weights()); |
| armnn::ConstTensor biases; |
| |
| if (descriptor.m_BiasEnabled) |
| { |
| biases = ToConstTensor(serializerLayer->biases()); |
| } |
| IConnectableLayer* layer = m_Network->AddDepthwiseConvolution2dLayer(descriptor, |
| weights, |
| biases, |
| layerName.c_str()); |
| |
| armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| RegisterInputSlots(graph, layerIndex, layer); |
| RegisterOutputSlots(graph, layerIndex, layer); |
| } |
| |
| void Deserializer::ParseDivision(GraphPtr graph, unsigned int layerIndex) |
| { |
| CHECK_LAYERS(graph, 0, layerIndex); |
| auto inputs = GetInputs(graph, layerIndex); |
| CHECK_LOCATION(); |
| CHECK_VALID_SIZE(inputs.size(), 2); |
| |
| auto outputs = GetOutputs(graph, layerIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| auto layerName = GetLayerName(graph, layerIndex); |
| IConnectableLayer* layer = m_Network->AddDivisionLayer(layerName.c_str()); |
| |
| armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| RegisterInputSlots(graph, layerIndex, layer); |
| RegisterOutputSlots(graph, layerIndex, layer); |
| } |
| |
| void Deserializer::ParseEqual(GraphPtr graph, unsigned int layerIndex) |
| { |
| CHECK_LAYERS(graph, 0, layerIndex); |
| auto inputs = GetInputs(graph, layerIndex); |
| CHECK_LOCATION(); |
| CHECK_VALID_SIZE(inputs.size(), 2); |
| |
| auto outputs = GetOutputs(graph, layerIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| auto layerName = GetLayerName(graph, layerIndex); |
| IConnectableLayer* layer = m_Network->AddEqualLayer(layerName.c_str()); |
| |
| armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| RegisterInputSlots(graph, layerIndex, layer); |
| RegisterOutputSlots(graph, layerIndex, layer); |
| } |
| |
| void Deserializer::ParseGreater(GraphPtr graph, unsigned int layerIndex) |
| { |
| CHECK_LAYERS(graph, 0, layerIndex); |
| auto inputs = GetInputs(graph, layerIndex); |
| CHECK_LOCATION(); |
| CHECK_VALID_SIZE(inputs.size(), 2); |
| |
| auto outputs = GetOutputs(graph, layerIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| auto layerName = GetLayerName(graph, layerIndex); |
| IConnectableLayer* layer = m_Network->AddGreaterLayer(layerName.c_str()); |
| |
| armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| RegisterInputSlots(graph, layerIndex, layer); |
| RegisterOutputSlots(graph, layerIndex, layer); |
| } |
| |
| void Deserializer::ParseMinimum(GraphPtr graph, unsigned int layerIndex) |
| { |
| CHECK_LAYERS(graph, 0, layerIndex); |
| auto inputs = GetInputs(graph, layerIndex); |
| CHECK_LOCATION(); |
| CHECK_VALID_SIZE(inputs.size(), 2); |
| |
| auto outputs = GetOutputs(graph, layerIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| auto layerName = GetLayerName(graph, layerIndex); |
| IConnectableLayer* layer = m_Network->AddMinimumLayer(layerName.c_str()); |
| |
| armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| RegisterInputSlots(graph, layerIndex, layer); |
| RegisterOutputSlots(graph, layerIndex, layer); |
| } |
| |
| void Deserializer::ParseMaximum(GraphPtr graph, unsigned int layerIndex) |
| { |
| CHECK_LAYERS(graph, 0, layerIndex); |
| auto inputs = GetInputs(graph, layerIndex); |
| CHECK_LOCATION(); |
| CHECK_VALID_SIZE(inputs.size(), 2); |
| |
| auto outputs = GetOutputs(graph, layerIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| auto layerName = GetLayerName(graph, layerIndex); |
| IConnectableLayer* layer = m_Network->AddMaximumLayer(layerName.c_str()); |
| |
| armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| RegisterInputSlots(graph, layerIndex, layer); |
| RegisterOutputSlots(graph, layerIndex, layer); |
| } |
| |
| void Deserializer::ParseMultiplication(GraphPtr graph, unsigned int layerIndex) |
| { |
| CHECK_LAYERS(graph, 0, layerIndex); |
| auto inputs = GetInputs(graph, layerIndex); |
| CHECK_LOCATION(); |
| CHECK_VALID_SIZE(inputs.size(), 2); |
| |
| auto outputs = GetOutputs(graph, layerIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| auto layerName = GetLayerName(graph, layerIndex); |
| IConnectableLayer* layer = m_Network->AddMultiplicationLayer(layerName.c_str()); |
| |
| armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| RegisterInputSlots(graph, layerIndex, layer); |
| RegisterOutputSlots(graph, layerIndex, layer); |
| } |
| |
| void Deserializer::ParseFloor(GraphPtr graph, unsigned int layerIndex) |
| { |
| CHECK_LAYERS(graph, 0, layerIndex); |
| CHECK_LOCATION(); |
| |
| auto inputs = GetInputs(graph, layerIndex); |
| CHECK_VALID_SIZE(inputs.size(), 1); |
| |
| auto outputs = GetOutputs(graph, layerIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| auto layerName = GetLayerName(graph, layerIndex); |
| |
| armnn::IConnectableLayer* layer; |
| |
| layer = m_Network->AddFloorLayer(); |
| |
| armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| RegisterInputSlots(graph, layerIndex, layer); |
| RegisterOutputSlots(graph, layerIndex, layer); |
| } |
| |
| void Deserializer::ParseFullyConnected(GraphPtr graph, unsigned int layerIndex) |
| { |
| CHECK_LAYERS(graph, 0, layerIndex); |
| auto inputs = GetInputs(graph, layerIndex); |
| CHECK_LOCATION(); |
| CHECK_VALID_SIZE(inputs.size(), 1); |
| |
| auto outputs = GetOutputs(graph, layerIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| auto flatBufferLayer = graph->layers()->Get(layerIndex)->layer_as_FullyConnectedLayer(); |
| auto layerName = GetLayerName(graph, layerIndex); |
| auto flatBufferDescriptor = flatBufferLayer->descriptor(); |
| |
| armnn::FullyConnectedDescriptor fullyConnectedDescriptor; |
| fullyConnectedDescriptor.m_BiasEnabled = flatBufferDescriptor->biasEnabled(); |
| fullyConnectedDescriptor.m_TransposeWeightMatrix = flatBufferDescriptor->transposeWeightsMatrix(); |
| |
| armnn::ConstTensor weightsTensor = ToConstTensor(flatBufferLayer->weights()); |
| |
| armnn::IConnectableLayer* layer; |
| if (flatBufferDescriptor->biasEnabled()) |
| { |
| armnn::ConstTensor biasTensorData = ToConstTensor(flatBufferLayer->biases()); |
| layer = m_Network->AddFullyConnectedLayer(fullyConnectedDescriptor, |
| weightsTensor, |
| biasTensorData, |
| layerName.c_str()); |
| } |
| else |
| { |
| layer = m_Network->AddFullyConnectedLayer(fullyConnectedDescriptor, |
| weightsTensor, |
| layerName.c_str()); |
| } |
| |
| armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| RegisterInputSlots(graph, layerIndex, layer); |
| RegisterOutputSlots(graph, layerIndex, layer); |
| } |
| |
| void Deserializer::ParsePad(GraphPtr graph, unsigned int layerIndex) |
| { |
| CHECK_LAYERS(graph, 0, layerIndex); |
| |
| Deserializer::TensorRawPtrVector inputs = GetInputs(graph, layerIndex); |
| CHECK_VALID_SIZE(inputs.size(), 1); |
| |
| Deserializer::TensorRawPtrVector outputs = GetOutputs(graph, layerIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| auto flatBufferDescriptor = graph->layers()->Get(layerIndex)->layer_as_PadLayer()->descriptor(); |
| auto flatBufferPadList = flatBufferDescriptor->padList(); |
| |
| if (flatBufferPadList->Length() % 2 != 0) |
| { |
| throw ParseException(boost::str( |
| boost::format("The size of the pad list must be divisible by 2 %1%") % CHECK_LOCATION().AsString())); |
| } |
| |
| std::vector<std::pair<unsigned int, unsigned int>> padList; |
| padList.reserve(flatBufferPadList->Length() / 2); |
| for (unsigned int i = 0; i < flatBufferPadList->Length() - 1; i += 2) |
| { |
| padList.emplace_back(flatBufferPadList->Get(i), flatBufferPadList->Get(i+1)); |
| } |
| |
| armnn::PadDescriptor descriptor(padList); |
| |
| auto layerName = GetLayerName(graph, layerIndex); |
| IConnectableLayer* layer = m_Network->AddPadLayer(descriptor, layerName.c_str()); |
| |
| armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| RegisterInputSlots(graph, layerIndex, layer); |
| RegisterOutputSlots(graph, layerIndex, layer); |
| } |
| |
| void Deserializer::ParsePermute(GraphPtr graph, unsigned int layerIndex) |
| { |
| CHECK_LAYERS(graph, 0, layerIndex); |
| |
| auto dimsMapping = |
| graph->layers()->Get(layerIndex)->layer_as_PermuteLayer()->descriptor()->dimMappings(); |
| |
| auto inputs = GetInputs(graph, layerIndex); |
| CHECK_VALID_SIZE(inputs.size(), 1); |
| |
| auto outputs = GetOutputs(graph, layerIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| auto outputInfo = ToTensorInfo(outputs[0]); |
| |
| auto layerName = GetLayerName(graph, layerIndex); |
| const armnn::PermuteDescriptor descriptor(armnn::PermutationVector(dimsMapping->data(), dimsMapping->Length())); |
| |
| IConnectableLayer* layer = m_Network->AddPermuteLayer(descriptor, layerName.c_str()); |
| layer->GetOutputSlot(0).SetTensorInfo(outputInfo); |
| |
| RegisterInputSlots(graph, layerIndex, layer); |
| RegisterOutputSlots(graph, layerIndex, layer); |
| } |
| |
| armnn::Pooling2dDescriptor Deserializer::GetPoolingDescriptor(Deserializer::PoolingDescriptor pooling2dDesc, |
| unsigned int layerIndex) |
| { |
| armnn::Pooling2dDescriptor desc; |
| |
| switch (pooling2dDesc->poolType()) |
| { |
| case PoolingAlgorithm_Average: |
| { |
| desc.m_PoolType = armnn::PoolingAlgorithm::Average; |
| break; |
| } |
| case PoolingAlgorithm_Max: |
| { |
| desc.m_PoolType = armnn::PoolingAlgorithm::Max; |
| break; |
| } |
| default: |
| { |
| BOOST_ASSERT_MSG(false, "Unsupported pooling algorithm"); |
| } |
| } |
| |
| switch (pooling2dDesc->outputShapeRounding()) |
| { |
| case OutputShapeRounding_Floor: |
| { |
| desc.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; |
| break; |
| } |
| case OutputShapeRounding_Ceiling: |
| { |
| desc.m_OutputShapeRounding = armnn::OutputShapeRounding::Ceiling; |
| break; |
| } |
| default: |
| { |
| BOOST_ASSERT_MSG(false, "Unsupported output shape rounding"); |
| } |
| } |
| |
| switch (pooling2dDesc->paddingMethod()) |
| { |
| case PaddingMethod_Exclude: |
| { |
| desc.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| break; |
| } |
| case PaddingMethod_IgnoreValue: |
| { |
| desc.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; |
| break; |
| } |
| default: |
| { |
| BOOST_ASSERT_MSG(false, "Unsupported padding method"); |
| } |
| } |
| |
| switch (pooling2dDesc->dataLayout()) |
| { |
| case DataLayout_NCHW: |
| { |
| desc.m_DataLayout = armnn::DataLayout::NCHW; |
| break; |
| } |
| case DataLayout_NHWC: |
| { |
| desc.m_DataLayout = armnn::DataLayout::NHWC; |
| break; |
| } |
| default: |
| { |
| BOOST_ASSERT_MSG(false, "Unsupported data layout"); |
| } |
| } |
| |
| desc.m_PadRight = pooling2dDesc->padRight(); |
| desc.m_PadLeft = pooling2dDesc->padLeft(); |
| desc.m_PadBottom = pooling2dDesc->padBottom(); |
| desc.m_PadTop = pooling2dDesc->padTop(); |
| desc.m_StrideX = pooling2dDesc->strideX(); |
| desc.m_StrideY = pooling2dDesc->strideY(); |
| desc.m_PoolWidth = pooling2dDesc->poolWidth(); |
| desc.m_PoolHeight = pooling2dDesc->poolHeight(); |
| |
| return desc; |
| } |
| |
| void Deserializer::ParsePooling2d(GraphPtr graph, unsigned int layerIndex) |
| { |
| CHECK_LAYERS(graph, 0, layerIndex); |
| |
| auto pooling2dDes = graph->layers()->Get(layerIndex)->layer_as_Pooling2dLayer()->descriptor(); |
| auto inputs = GetInputs(graph, layerIndex); |
| CHECK_VALID_SIZE(inputs.size(), 1); |
| |
| auto outputs = GetOutputs(graph, layerIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| auto outputInfo = ToTensorInfo(outputs[0]); |
| |
| auto pooling2dDescriptor = GetPoolingDescriptor(pooling2dDes, layerIndex); |
| auto layerName = GetLayerName(graph, layerIndex); |
| IConnectableLayer* layer = m_Network->AddPooling2dLayer(pooling2dDescriptor, layerName.c_str()); |
| layer->GetOutputSlot(0).SetTensorInfo(outputInfo); |
| |
| RegisterInputSlots(graph, layerIndex, layer); |
| RegisterOutputSlots(graph, layerIndex, layer); |
| } |
| |
| armnn::TensorInfo Deserializer::OutputShapeOfReshape(const armnn::TensorInfo& inputTensorInfo, |
| const std::vector<uint32_t>& targetDimsIn) |
| { |
| std::vector<unsigned int> outputDims(targetDimsIn.begin(), targetDimsIn.end()); |
| const auto stretchDim = std::find(targetDimsIn.begin(), targetDimsIn.end(), -1); |
| |
| if (stretchDim != targetDimsIn.end()) |
| { |
| if (std::find(std::next(stretchDim), targetDimsIn.end(), -1) != targetDimsIn.end()) |
| { |
| throw ParseException(boost::str( |
| boost::format("At most one component of shape can be -1 %1%") % CHECK_LOCATION().AsString())); |
| } |
| |
| auto targetNumElements = |
| boost::numeric_cast<unsigned int>( |
| std::accumulate(targetDimsIn.begin(), targetDimsIn.end(), -1, std::multiplies<int32_t>())); |
| |
| auto stretchIndex = static_cast<size_t>(std::distance(targetDimsIn.begin(), stretchDim)); |
| outputDims[stretchIndex] = inputTensorInfo.GetNumElements() / targetNumElements; |
| } |
| |
| TensorShape outputShape = TensorShape(static_cast<unsigned int>(outputDims.size()), outputDims.data()); |
| |
| armnn::TensorInfo reshapeInfo = inputTensorInfo; |
| reshapeInfo.SetShape(outputShape); |
| |
| return reshapeInfo; |
| } |
| |
| void Deserializer::ParseReshape(GraphPtr graph, unsigned int layerIndex) |
| { |
| CHECK_LAYERS(graph, 0, layerIndex); |
| auto inputs = GetInputs(graph, layerIndex); |
| |
| auto outputs = GetOutputs(graph, layerIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| armnn::TensorInfo inputTensorInfo = ToTensorInfo(inputs[0]); |
| armnn::TensorInfo actualOutputTensorInfo = ToTensorInfo(outputs[0]); |
| |
| const auto targetDims = graph->layers()->Get(layerIndex)->layer_as_ReshapeLayer()->descriptor()->targetShape(); |
| std::vector<uint32_t> outputDims(targetDims->begin(), targetDims->begin() + targetDims->size()); |
| |
| armnn::TensorInfo reshapeOutputTensorInfo = Deserializer::OutputShapeOfReshape(inputTensorInfo, outputDims); |
| const armnn::TensorShape& reshapeOutputTensorShape = reshapeOutputTensorInfo.GetShape(); |
| |
| const std::vector<uint32_t> expectedDims(outputs[0]->dimensions()->begin(), |
| outputs[0]->dimensions()->begin() + outputs[0]->dimensions()->size()); |
| |
| if (inputs.size() > 1 && !CheckShape(reshapeOutputTensorShape, expectedDims)) |
| { |
| std::stringstream ss; |
| ss << "New shape defined in reshape parameters " |
| << reshapeOutputTensorShape |
| << " does not equal output shape " |
| << actualOutputTensorInfo.GetShape() |
| << ": " |
| << CHECK_LOCATION().AsString(); |
| throw ParseException(ss.str()); |
| } |
| |
| armnn::ReshapeDescriptor reshapeDesc; |
| reshapeDesc.m_TargetShape = reshapeOutputTensorShape; |
| |
| auto layerName = GetLayerName(graph, layerIndex); |
| IConnectableLayer* layer = m_Network->AddReshapeLayer(reshapeDesc, layerName.c_str()); |
| layer->GetOutputSlot(0).SetTensorInfo(reshapeOutputTensorInfo); |
| |
| RegisterInputSlots(graph, layerIndex, layer); |
| RegisterOutputSlots(graph, layerIndex, layer); |
| } |
| |
| void Deserializer::ParseResizeBilinear(GraphPtr graph, unsigned int layerIndex) |
| { |
| CHECK_LAYERS(graph, 0, layerIndex); |
| |
| Deserializer::TensorRawPtrVector inputs = GetInputs(graph, layerIndex); |
| CHECK_VALID_SIZE(inputs.size(), 1); |
| |
| Deserializer::TensorRawPtrVector outputs = GetOutputs(graph, layerIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| auto flatBufferDescriptor = graph->layers()->Get(layerIndex)->layer_as_ResizeBilinearLayer()->descriptor(); |
| |
| armnn::ResizeBilinearDescriptor descriptor; |
| descriptor.m_TargetWidth = flatBufferDescriptor->targetWidth(); |
| descriptor.m_TargetHeight = flatBufferDescriptor->targetHeight(); |
| descriptor.m_DataLayout = ToDataLayout(flatBufferDescriptor->dataLayout()); |
| |
| auto layerName = GetLayerName(graph, layerIndex); |
| IConnectableLayer* layer = m_Network->AddResizeBilinearLayer(descriptor, layerName.c_str()); |
| |
| armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| RegisterInputSlots(graph, layerIndex, layer); |
| RegisterOutputSlots(graph, layerIndex, layer); |
| } |
| |
| void Deserializer::ParseSoftmax(GraphPtr graph, unsigned int layerIndex) |
| { |
| CHECK_LAYERS(graph, 0, layerIndex); |
| |
| Deserializer::TensorRawPtrVector inputs = GetInputs(graph, layerIndex); |
| CHECK_VALID_SIZE(inputs.size(), 1); |
| |
| Deserializer::TensorRawPtrVector outputs = GetOutputs(graph, layerIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| armnn::SoftmaxDescriptor descriptor; |
| descriptor.m_Beta = graph->layers()->Get(layerIndex)->layer_as_SoftmaxLayer()->descriptor()->beta(); |
| auto layerName = GetLayerName(graph, layerIndex); |
| |
| IConnectableLayer* layer = m_Network->AddSoftmaxLayer(descriptor, layerName.c_str()); |
| |
| armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| RegisterInputSlots(graph, layerIndex, layer); |
| RegisterOutputSlots(graph, layerIndex, layer); |
| } |
| |
| void Deserializer::ParseSpaceToBatchNd(GraphPtr graph, unsigned int layerIndex) |
| { |
| CHECK_LAYERS(graph, 0, layerIndex); |
| |
| Deserializer::TensorRawPtrVector inputs = GetInputs(graph, layerIndex); |
| CHECK_VALID_SIZE(inputs.size(), 1); |
| |
| Deserializer::TensorRawPtrVector outputs = GetOutputs(graph, layerIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| auto flatBufferDescriptor = graph->layers()->Get(layerIndex)->layer_as_SpaceToBatchNdLayer()->descriptor(); |
| auto flatBufferPadList = flatBufferDescriptor->padList(); |
| auto flatBufferBlockShape = flatBufferDescriptor->blockShape(); |
| |
| if (flatBufferPadList->Length() % 2 != 0) |
| { |
| throw ParseException(boost::str( |
| boost::format("The size of the pad list must be divisible by 2 %1%") % CHECK_LOCATION().AsString())); |
| } |
| |
| std::vector<std::pair<unsigned int, unsigned int>> padList; |
| padList.reserve(flatBufferPadList->Length() / 2); |
| for (unsigned int i = 0; i < flatBufferPadList->Length() - 1; i += 2) |
| { |
| padList.emplace_back(flatBufferPadList->Get(i), flatBufferPadList->Get(i+1)); |
| } |
| |
| armnn::SpaceToBatchNdDescriptor descriptor; |
| descriptor.m_DataLayout = ToDataLayout(flatBufferDescriptor->dataLayout()); |
| descriptor.m_BlockShape = |
| std::vector<unsigned int>(flatBufferBlockShape->begin(), flatBufferBlockShape->end()); |
| descriptor.m_PadList = padList; |
| |
| auto layerName = GetLayerName(graph, layerIndex); |
| IConnectableLayer* layer = m_Network->AddSpaceToBatchNdLayer(descriptor, layerName.c_str()); |
| |
| armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| RegisterInputSlots(graph, layerIndex, layer); |
| RegisterOutputSlots(graph, layerIndex, layer); |
| } |
| |
| armnn::NormalizationDescriptor Deserializer::GetNormalizationDescriptor( |
| Deserializer::NormalizationDescriptorPtr normalizationDescriptor, |
| unsigned int layerIndex) |
| { |
| armnn::NormalizationDescriptor desc; |
| |
| switch (normalizationDescriptor->normChannelType()) |
| { |
| case NormalizationAlgorithmChannel_Across: |
| { |
| desc.m_NormChannelType = armnn::NormalizationAlgorithmChannel::Across; |
| break; |
| } |
| case NormalizationAlgorithmChannel_Within: |
| { |
| desc.m_NormChannelType = armnn::NormalizationAlgorithmChannel::Within; |
| break; |
| } |
| default: |
| { |
| BOOST_ASSERT_MSG(false, "Unsupported normalization channel type"); |
| } |
| } |
| |
| switch (normalizationDescriptor->normMethodType()) |
| { |
| case NormalizationAlgorithmMethod_LocalBrightness: |
| { |
| desc.m_NormMethodType = armnn::NormalizationAlgorithmMethod::LocalBrightness; |
| break; |
| } |
| case NormalizationAlgorithmMethod_LocalContrast: |
| { |
| desc.m_NormMethodType = armnn::NormalizationAlgorithmMethod::LocalContrast; |
| break; |
| } |
| default: |
| { |
| BOOST_ASSERT_MSG(false, "Unsupported normalization method type"); |
| } |
| } |
| |
| switch (normalizationDescriptor->dataLayout()) |
| { |
| case DataLayout_NCHW: |
| { |
| desc.m_DataLayout = armnn::DataLayout::NCHW; |
| break; |
| } |
| case DataLayout_NHWC: |
| { |
| desc.m_DataLayout = armnn::DataLayout::NHWC; |
| break; |
| } |
| default: |
| { |
| BOOST_ASSERT_MSG(false, "Unsupported data layout"); |
| } |
| } |
| |
| desc.m_Alpha = normalizationDescriptor->alpha(); |
| desc.m_Beta = normalizationDescriptor->beta(); |
| desc.m_K = normalizationDescriptor->k(); |
| desc.m_NormSize = normalizationDescriptor->normSize(); |
| |
| return desc; |
| } |
| |
| void Deserializer::ParseNormalization(GraphPtr graph, unsigned int layerIndex) |
| { |
| CHECK_LAYERS(graph, 0, layerIndex); |
| |
| auto normalizationDes = graph->layers()->Get(layerIndex)->layer_as_NormalizationLayer()->descriptor(); |
| |
| Deserializer::TensorRawPtrVector inputs = GetInputs(graph, layerIndex); |
| CHECK_VALID_SIZE(inputs.size(), 1); |
| |
| Deserializer::TensorRawPtrVector outputs = GetOutputs(graph, layerIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| auto outputInfo = ToTensorInfo(outputs[0]); |
| |
| auto normalizationDescriptor = GetNormalizationDescriptor(normalizationDes, layerIndex); |
| auto layerName = GetLayerName(graph, layerIndex); |
| |
| IConnectableLayer* layer = m_Network->AddNormalizationLayer(normalizationDescriptor, layerName.c_str()); |
| layer->GetOutputSlot(0).SetTensorInfo(outputInfo); |
| |
| RegisterInputSlots(graph, layerIndex, layer); |
| RegisterOutputSlots(graph, layerIndex, layer); |
| } |
| |
| void Deserializer::ParseRsqrt(GraphPtr graph, unsigned int layerIndex) |
| { |
| CHECK_LAYERS(graph, 0, layerIndex); |
| auto inputs = GetInputs(graph, layerIndex); |
| CHECK_LOCATION(); |
| CHECK_VALID_SIZE(inputs.size(), 1); |
| |
| auto outputs = GetOutputs(graph, layerIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| auto layerName = GetLayerName(graph, layerIndex); |
| IConnectableLayer* layer = m_Network->AddRsqrtLayer(layerName.c_str()); |
| |
| armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| RegisterInputSlots(graph, layerIndex, layer); |
| RegisterOutputSlots(graph, layerIndex, layer); |
| } |
| |
| } // namespace armnnDeserializer |
