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Diffstat (limited to 'src/armnnDeserializer/Deserializer.cpp')
| -rw-r--r-- | src/armnnDeserializer/Deserializer.cpp | 984 |
1 files changed, 984 insertions, 0 deletions
diff --git a/src/armnnDeserializer/Deserializer.cpp b/src/armnnDeserializer/Deserializer.cpp new file mode 100644 index 0000000000..56a6570eee --- /dev/null +++ b/src/armnnDeserializer/Deserializer.cpp @@ -0,0 +1,984 @@ +// +// 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 <Schema_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())); + } +} + +#define CHECK_TENSOR_PTR(TENSOR_PTR) \ + CheckTensorPtr(TENSOR_PTR, 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_AdditionLayer] = &Deserializer::ParseAdd; + m_ParserFunctions[Layer_Convolution2dLayer] = &Deserializer::ParseConvolution2d; + m_ParserFunctions[Layer_DepthwiseConvolution2dLayer] = &Deserializer::ParseDepthwiseConvolution2d; + m_ParserFunctions[Layer_MultiplicationLayer] = &Deserializer::ParseMultiplication; + m_ParserFunctions[Layer_Pooling2dLayer] = &Deserializer::ParsePooling2d; + m_ParserFunctions[Layer_ReshapeLayer] = &Deserializer::ParseReshape; + m_ParserFunctions[Layer_SoftmaxLayer] = &Deserializer::ParseSoftmax; +} + +Deserializer::LayerBaseRawPtr Deserializer::GetBaseLayer(const GraphPtr& graphPtr, unsigned int layerIndex) +{ + auto layerType = graphPtr->layers()->Get(layerIndex)->layer_type(); + + switch(layerType) + { + case Layer::Layer_AdditionLayer: + return graphPtr->layers()->Get(layerIndex)->layer_as_AdditionLayer()->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_InputLayer: + return graphPtr->layers()->Get(layerIndex)->layer_as_InputLayer()->base()->base(); + case Layer::Layer_MultiplicationLayer: + return graphPtr->layers()->Get(layerIndex)->layer_as_MultiplicationLayer()->base(); + case Layer::Layer_OutputLayer: + return graphPtr->layers()->Get(layerIndex)->layer_as_OutputLayer()->base()->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_SoftmaxLayer: + return graphPtr->layers()->Get(layerIndex)->layer_as_SoftmaxLayer()->base(); + case Layer::Layer_NONE: + default: + throw ParseException(boost::str( + boost::format("Layer must have a type %1%") % + Layer::Layer_NONE)); + } +} + +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::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: + return armnn::ConstTensor(tensorInfo, constTensorPtr->data_as_ByteData()->data()->data()); + case ConstTensorData_ShortData: + return armnn::ConstTensor(tensorInfo, constTensorPtr->data_as_ShortData()->data()->data()); + case ConstTensorData_IntData: + return armnn::ConstTensor(tensorInfo, constTensorPtr->data_as_IntData()->data()->data()); + case ConstTensorData_LongData: + return armnn::ConstTensor(tensorInfo, constTensorPtr->data_as_LongData()->data()->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(unsigned int layerIndex) +{ + CHECK_LAYERS(m_Graph, 0, layerIndex); + const auto layerName = GetBaseLayer(m_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_Graph = nullptr; +} + +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(); + m_Graph = LoadGraphFromBinary(binaryContent.data(), binaryContent.size()); + return CreateNetworkFromGraph(); +} + +armnn::INetworkPtr Deserializer::CreateNetworkFromBinary(std::istream& binaryContent) +{ + ResetParser(); + m_Graph = LoadGraphFromBinary(binaryContent); + return CreateNetworkFromGraph(); +} + +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); +} + +Deserializer::GraphPtr Deserializer::LoadGraphFromBinary(std::istream& binaryContent) +{ + std::string content((std::istreambuf_iterator<char>(binaryContent)), std::istreambuf_iterator<char>()); + return GetSerializedGraph(content.data()); +} + +INetworkPtr Deserializer::CreateNetworkFromGraph() +{ + m_Network = INetwork::Create(); + BOOST_ASSERT(m_Graph != nullptr); + unsigned int layerIndex = 0; + m_GraphConnections.emplace_back(m_Graph->layers()->size()); + for (AnyLayer const* layer : *m_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)(layerIndex); + } + ++layerIndex; + } + + SetupInputLayers(); + SetupOutputLayers(); + + // 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 +{ + CHECK_LAYERS(m_Graph, 0, layerIndex); + auto inputs = GetGraphInputs(m_Graph); + + for (auto const& input : inputs) + { + if (input->layerName()->c_str() == name) + { + int bindingId = reinterpret_cast<armnn::LayerBindingId>(GetBindingLayerInfo(m_Graph, input->index())); + auto layerBase = GetBaseLayer(m_Graph,input->index())->outputSlots()->Get(layerIndex); + return std::make_pair(bindingId, ToTensorInfo(layerBase->tensorInfo())); + } + } + 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 +{ + CHECK_LAYERS(m_Graph, 0, layerIndex); + auto outputs = GetGraphOutputs(m_Graph); + + for (auto const& output : outputs) + { + if (output->layerName()->c_str() == name) + { + int bindingId = reinterpret_cast<armnn::LayerBindingId>(GetBindingLayerInfo(m_Graph, output->index())); + auto layer = GetBaseLayer(m_Graph, output->index()); + auto sourceLayerIndex = layer->inputSlots()->Get(0)->connection()->sourceLayerIndex(); + auto sourceLayer = GetBaseLayer(m_Graph, sourceLayerIndex); + return std::make_pair(bindingId, ToTensorInfo(sourceLayer->outputSlots()->Get(0)->tensorInfo())); + } + } + throw ParseException( + boost::str( + boost::format("No output binding found for layer:%1% / %2%") % + name % + CHECK_LOCATION().AsString())); +} + +void Deserializer::SetupInputLayers() +{ + CHECK_GRAPH(m_Graph, 0); + auto inputs = GetGraphInputs(m_Graph); + for (auto const& input : inputs) + { + IConnectableLayer* layer = + m_Network->AddInputLayer(GetBindingLayerInfo(m_Graph, input->index()), input->layerName()->c_str()); + + auto tensorInfo = ToTensorInfo(input->outputSlots()->Get(0)->tensorInfo()); + layer->GetOutputSlot(0).SetTensorInfo(tensorInfo); + + RegisterOutputSlots(input->index(), layer); + } +} + +void Deserializer::SetupOutputLayers() +{ + CHECK_GRAPH(m_Graph, 0); + auto outputs = GetGraphOutputs(m_Graph); + for (auto const& output : outputs) + { + IConnectableLayer* layer = + m_Network->AddOutputLayer(GetBindingLayerInfo(m_Graph, output->index()), output->layerName()->c_str()); + + RegisterInputSlots(output->index(), layer); + } +} + +void Deserializer::RegisterOutputSlots(uint32_t layerIndex, + IConnectableLayer* layer) +{ + CHECK_LAYERS(m_Graph, 0, layerIndex); + BOOST_ASSERT(layer != nullptr); + auto parsedLayer = GetBaseLayer(m_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(uint32_t layerIndex, + armnn::IConnectableLayer* layer) +{ + CHECK_LAYERS(m_Graph, 0, layerIndex); + BOOST_ASSERT(layer != nullptr); + auto parsedLayer = GetBaseLayer(m_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::ParseAdd(unsigned int layerIndex) +{ + CHECK_LAYERS(m_Graph, 0, layerIndex); + auto inputs = GetInputs(m_Graph, layerIndex); + CHECK_LOCATION(); + CHECK_VALID_SIZE(inputs.size(), 2); + + auto outputs = GetOutputs(m_Graph, layerIndex); + CHECK_VALID_SIZE(outputs.size(), 1); + + m_layerName = boost::str(boost::format("Addition:%1%") % layerIndex); + IConnectableLayer* layer = m_Network->AddAdditionLayer(m_layerName.c_str()); + + armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); + layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); + + RegisterInputSlots(layerIndex, layer); + RegisterOutputSlots(layerIndex, layer); +} + +void Deserializer::ParseConvolution2d(unsigned int layerIndex) +{ + CHECK_LAYERS(m_Graph, 0, layerIndex); + auto inputs = GetInputs(m_Graph, layerIndex); + CHECK_LOCATION(); + CHECK_VALID_SIZE(inputs.size(), 1); + + auto outputs = GetOutputs(m_Graph, layerIndex); + CHECK_VALID_SIZE(outputs.size(), 1); + + auto layerName = boost::str(boost::format("Convolution2d:%1%") % layerIndex); + + auto serializerLayer = m_Graph->layers()->Get(layerIndex)->layer_as_Convolution2dLayer(); + 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(layerIndex, layer); + RegisterOutputSlots(layerIndex, layer); +} + +void Deserializer::ParseDepthwiseConvolution2d(unsigned int layerIndex) +{ + CHECK_LAYERS(m_Graph, 0, layerIndex); + auto inputs = GetInputs(m_Graph, layerIndex); + CHECK_LOCATION(); + CHECK_VALID_SIZE(inputs.size(), 1); + + auto outputs = GetOutputs(m_Graph, layerIndex); + CHECK_VALID_SIZE(outputs.size(), 1); + + auto layerName = boost::str(boost::format("DepthwiseConvolution2d:%1%") % layerIndex); + + auto serializerLayer = m_Graph->layers()->Get(layerIndex)->layer_as_DepthwiseConvolution2dLayer(); + 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(layerIndex, layer); + RegisterOutputSlots(layerIndex, layer); +} + +void Deserializer::ParseMultiplication(unsigned int layerIndex) +{ + CHECK_LAYERS(m_Graph, 0, layerIndex); + auto inputs = GetInputs(m_Graph, layerIndex); + CHECK_LOCATION(); + CHECK_VALID_SIZE(inputs.size(), 2); + + auto outputs = GetOutputs(m_Graph, layerIndex); + CHECK_VALID_SIZE(outputs.size(), 1); + + m_layerName = boost::str(boost::format("Multiplication:%1%") % layerIndex); + IConnectableLayer* layer = m_Network->AddMultiplicationLayer(m_layerName.c_str()); + + armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); + layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); + + RegisterInputSlots(layerIndex, layer); + RegisterOutputSlots(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; + m_layerName = boost::str(boost::format("AveragePool2D:%1%") % layerIndex); + break; + } + case PoolingAlgorithm_Max: + { + desc.m_PoolType = armnn::PoolingAlgorithm::Max; + m_layerName = boost::str(boost::format("MaxPool2D:%1%") % layerIndex); + 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(unsigned int layerIndex) +{ + CHECK_LAYERS(m_Graph, 0, layerIndex); + + auto pooling2dDes = m_Graph->layers()->Get(layerIndex)->layer_as_Pooling2dLayer()->descriptor(); + + auto inputs = GetInputs(m_Graph, layerIndex); + CHECK_VALID_SIZE(inputs.size(), 1); + + auto outputs = GetOutputs(m_Graph, layerIndex); + CHECK_VALID_SIZE(outputs.size(), 1); + auto outputInfo = ToTensorInfo(outputs[0]); + + auto pooling2dDescriptor = GetPoolingDescriptor(pooling2dDes, layerIndex); + + IConnectableLayer* layer = m_Network->AddPooling2dLayer(pooling2dDescriptor, m_layerName.c_str()); + layer->GetOutputSlot(0).SetTensorInfo(outputInfo); + + RegisterInputSlots(layerIndex, layer); + RegisterOutputSlots(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(unsigned int layerIndex) +{ + CHECK_LAYERS(m_Graph, 0, layerIndex); + auto inputs = GetInputs(m_Graph, layerIndex); + + auto outputs = GetOutputs(m_Graph, layerIndex); + CHECK_VALID_SIZE(outputs.size(), 1); + + armnn::TensorInfo inputTensorInfo = ToTensorInfo(inputs[0]); + armnn::TensorInfo actualOutputTensorInfo = ToTensorInfo(outputs[0]); + + const auto targetDims = m_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 = boost::str(boost::format("Reshape:%1%") % layerIndex); + IConnectableLayer* layer = m_Network->AddReshapeLayer(reshapeDesc, layerName.c_str()); + layer->GetOutputSlot(0).SetTensorInfo(reshapeOutputTensorInfo); + + RegisterInputSlots(layerIndex, layer); + RegisterOutputSlots(layerIndex, layer); +} + +void Deserializer::ParseSoftmax(unsigned int layerIndex) +{ + CHECK_LAYERS(m_Graph, 0, layerIndex); + + Deserializer::TensorRawPtrVector inputs = GetInputs(m_Graph, layerIndex); + CHECK_VALID_SIZE(inputs.size(), 1); + + Deserializer::TensorRawPtrVector outputs = GetOutputs(m_Graph, layerIndex); + CHECK_VALID_SIZE(outputs.size(), 1); + + armnn::SoftmaxDescriptor descriptor; + descriptor.m_Beta = m_Graph->layers()->Get(layerIndex)->layer_as_SoftmaxLayer()->descriptor()->beta(); + + const std::string layerName = boost::str(boost::format("Softmax:%1%") % layerIndex); + IConnectableLayer* layer = m_Network->AddSoftmaxLayer(descriptor, layerName.c_str()); + + armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); + layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); + + RegisterInputSlots(layerIndex, layer); + RegisterOutputSlots(layerIndex, layer); +} + +} // namespace armnnDeserializer |