OnnxParser.hpp

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//
// Copyright © 2017,2022 Arm Ltd and Contributors. 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 binary
    armnn::INetworkPtr CreateNetworkFromBinary(const std::vector<uint8_t>& binaryContent);
 
    /// Create the network from a protobuf binary, with inputShapes specified
    armnn::INetworkPtr CreateNetworkFromBinary(const std::vector<uint8_t>& binaryContent,
                                               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 LoadModelFromBinary(const std::vector<uint8_t>& binaryContent);
    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 its 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;
 
};
}