| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| #pragma once |
| |
| #include "armnnOnnxParser/IOnnxParser.hpp" |
| #include "google/protobuf/repeated_field.h" |
| #include <unordered_map> |
| |
| #include <onnx/onnx.pb.h> |
| |
| |
| namespace armnn |
| { |
| class TensorInfo; |
| enum class ActivationFunction; |
| } |
| |
| namespace armnnOnnxParser |
| { |
| |
| using ModelPtr = std::unique_ptr<onnx::ModelProto>; |
| |
| class OnnxParserImpl |
| { |
| |
| using OperationParsingFunction = void(OnnxParserImpl::*)(const onnx::NodeProto& NodeProto); |
| |
| public: |
| |
| using GraphPtr = std::unique_ptr<onnx::GraphProto>; |
| |
| /// Create the network from a protobuf binary file on disk |
| armnn::INetworkPtr CreateNetworkFromBinaryFile(const char* graphFile); |
| |
| /// Create the network from a protobuf binary file on disk, with inputShapes specified |
| armnn::INetworkPtr CreateNetworkFromBinaryFile(const char* graphFile, |
| const std::map<std::string, armnn::TensorShape>& inputShapes); |
| |
| /// Create the network from a protobuf text file on disk |
| armnn::INetworkPtr CreateNetworkFromTextFile(const char* graphFile); |
| |
| /// Create the network from a protobuf text file on disk, with inputShapes specified |
| armnn::INetworkPtr CreateNetworkFromTextFile(const char* graphFile, |
| const std::map<std::string, armnn::TensorShape>& inputShapes); |
| |
| /// Create the network directly from protobuf text in a string. Useful for debugging/testing |
| armnn::INetworkPtr CreateNetworkFromString(const std::string& protoText); |
| |
| /// Create the network directly from protobuf text in a string, with inputShapes specified. |
| /// Useful for debugging/testing |
| armnn::INetworkPtr CreateNetworkFromString(const std::string& protoText, |
| const std::map<std::string, armnn::TensorShape>& inputShapes); |
| |
| /// Retrieve binding info (layer id and tensor info) for the network input identified by the given layer name |
| BindingPointInfo GetNetworkInputBindingInfo(const std::string& name) const; |
| |
| /// Retrieve binding info (layer id and tensor info) for the network output identified by the given layer name |
| BindingPointInfo GetNetworkOutputBindingInfo(const std::string& name) const; |
| |
| public: |
| |
| OnnxParserImpl(); |
| ~OnnxParserImpl() = default; |
| |
| static ModelPtr LoadModelFromBinaryFile(const char * fileName); |
| static ModelPtr LoadModelFromTextFile(const char * fileName); |
| static ModelPtr LoadModelFromString(const std::string& inputString); |
| |
| /// Retrieve inputs names |
| static std::vector<std::string> GetInputs(ModelPtr& model); |
| |
| /// Retrieve outputs names |
| static std::vector<std::string> GetOutputs(ModelPtr& model); |
| |
| /// Retrieve version in X.Y.Z form |
| static const std::string GetVersion(); |
| |
| private: |
| |
| /// Parses a ModelProto loaded into memory from one of the other CreateNetwork* |
| armnn::INetworkPtr CreateNetworkFromModel(onnx::ModelProto& model); |
| |
| /// Parse every node and make the connection between the resulting tensors |
| void LoadGraph(); |
| |
| void SetupInfo(const google::protobuf::RepeatedPtrField<onnx::ValueInfoProto >* list); |
| |
| std::vector<armnn::TensorInfo> ComputeOutputInfo( |
| std::vector<std::string> outNames, |
| const armnn::IConnectableLayer* layer, |
| std::vector<armnn::TensorShape> inputShapes, |
| const onnx::TensorProto::DataType& type = onnx::TensorProto::FLOAT); |
| |
| void DetectFullyConnected(); |
| |
| template <typename Location> |
| void GetInputAndParam(const onnx::NodeProto& node, |
| std::string* inputName, |
| std::string* constName, |
| const Location& location); |
| |
| template <typename Location> |
| void To1DTensor(const std::string &name, const Location& location); |
| |
| //Broadcast Preparation functions |
| std::pair<std::string, std::string> AddPrepareBroadcast(const std::string& input0, const std::string& input1); |
| void PrependForBroadcast(const std::string& outputName, const std::string& input0, const std::string& input1); |
| |
| void AddConvLayerWithDepthwiseConv(const onnx::NodeProto& node, const armnn::Convolution2dDescriptor& convDesc); |
| void AddFullyConnected(const onnx::NodeProto& matmulNode, const onnx::NodeProto* addNode = nullptr); |
| void AddPoolingLayer(const onnx::NodeProto& nodeProto, armnn::Pooling2dDescriptor& desc); |
| |
| void CreateConstantLayer(const std::string& tensorName, const std::string& layerName); |
| void CreateInt64ConstantLayer(const std::string& tensorName, const std::string& layerName); |
| void CreateReshapeLayer(const std::string& inputName, |
| const std::string& outputName, |
| const std::string& layerName); |
| |
| void ParseActivation(const onnx::NodeProto& nodeProto, const armnn::ActivationFunction func); |
| void ParseClip(const onnx::NodeProto& nodeProto); |
| void ParseSigmoid(const onnx::NodeProto& nodeProto); |
| void ParseTanh(const onnx::NodeProto& nodeProto); |
| void ParseRelu(const onnx::NodeProto& nodeProto); |
| void ParseLeakyRelu(const onnx::NodeProto& nodeProto); |
| |
| void ParseAdd(const onnx::NodeProto& nodeProto); |
| void ParseAveragePool(const onnx::NodeProto& nodeProto); |
| void ParseBatchNormalization(const onnx::NodeProto& node); |
| void ParseConcat(const onnx::NodeProto& nodeProto); |
| void ParseConstant(const onnx::NodeProto& nodeProto); |
| void ParseConv(const onnx::NodeProto& nodeProto); |
| void ParseFlatten(const onnx::NodeProto& node); |
| void ParseGather(const onnx::NodeProto& node); |
| void ParseGemm(const onnx::NodeProto& node); |
| void ParseGlobalAveragePool(const onnx::NodeProto& node); |
| void ParseMaxPool(const onnx::NodeProto& nodeProto); |
| void ParseShape(const onnx::NodeProto& node); |
| void ParseReshape(const onnx::NodeProto& nodeProto); |
| void ParseUnsqueeze(const onnx::NodeProto& nodeProto); |
| |
| void RegisterInputSlot(armnn::IConnectableLayer* layer, |
| const std::string& tensorId, |
| unsigned int slotIndex); |
| void RegisterInputSlots(armnn::IConnectableLayer* layer, const std::vector<std::string>& tensorIndexes); |
| void RegisterOutputSlots(armnn::IConnectableLayer* layer, const std::vector<std::string>& tensorIndexes); |
| |
| void SetupInputLayers(); |
| void SetupOutputLayers(); |
| |
| void ResetParser(); |
| void Cleanup(); |
| |
| std::pair<armnn::ConstTensor, std::unique_ptr<float[]>> |
| CreateConstTensor(const std::string name, |
| armnn::Optional<armnn::PermutationVector&> permutationVector = armnn::EmptyOptional()); |
| |
| std::pair<armnn::ConstTensor, std::unique_ptr<int32_t[]>> |
| CreateInt64ConstTensor(const std::string name, |
| armnn::Optional<armnn::PermutationVector&> permutationVector = armnn::EmptyOptional()); |
| |
| template <typename TypeList, typename Location> |
| void ValidateInputs(const onnx::NodeProto& node, |
| TypeList validInputs, |
| const Location& location); |
| |
| /// The network we're building. Gets cleared after it is passed to the user |
| armnn::INetworkPtr m_Network; |
| |
| /// Ptr to the graph we're building the network from |
| GraphPtr m_Graph; |
| |
| /// Map of the information for every tensor |
| struct OnnxTensor |
| { |
| std::unique_ptr<armnn::TensorInfo> m_info; |
| std::unique_ptr<const onnx::TensorProto> m_tensor; |
| onnx::TensorProto::DataType m_dtype; |
| |
| OnnxTensor() : m_info(nullptr), m_tensor(nullptr), m_dtype(onnx::TensorProto::FLOAT) { } |
| bool isConstant() { return m_tensor != nullptr; } |
| }; |
| |
| std::unordered_map<std::string, OnnxTensor> m_TensorsInfo; |
| |
| /// map of onnx operation names to parsing member functions |
| static const std::map<std::string, OperationParsingFunction> m_ParserFunctions; |
| |
| /// A mapping of an output slot to each of the input slots it should be connected to |
| /// The outputSlot is from the layer that creates this tensor as one of its ouputs |
| /// The inputSlots are from the layers that use this tensor as one of their inputs |
| struct TensorSlots |
| { |
| armnn::IOutputSlot* outputSlot; |
| std::vector<armnn::IInputSlot*> inputSlots; |
| |
| TensorSlots() : outputSlot(nullptr) { } |
| }; |
| /// Map of the tensor names to their connections for the connections of the layers of the graph |
| std::unordered_map<std::string, TensorSlots> m_TensorConnections; |
| |
| /// Map of the tensor names to their node and index in graph.node() |
| std::unordered_map<std::string, std::pair<const onnx::NodeProto*, int>> m_OutputsMap; |
| |
| /// Number of times a specific node (identified by his index number) was used as input |
| /// and list of the nodes it was fused with |
| struct UsageSummary |
| { |
| std::vector<size_t> fusedWithNodes; |
| size_t inputForNodes; |
| |
| UsageSummary() : fusedWithNodes({}), inputForNodes(0) { } |
| |
| }; |
| |
| std::vector<UsageSummary> m_OutputsFusedAndUsed; |
| |
| std::map<std::string, armnn::TensorShape> m_InputShapes; |
| |
| std::unordered_map<std::string, armnn::TensorInfo> m_InputInfos; |
| |
| std::unordered_map<std::string, armnn::TensorInfo> m_OutputInfos; |
| |
| }; |
| } |