ArmNN
 24.08
OnnxParser.hpp
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1 //
2 // Copyright © 2017,2022-2023 Arm Ltd and Contributors. All rights reserved.
3 // SPDX-License-Identifier: MIT
4 //
5 #pragma once
6 
8 #include "google/protobuf/repeated_field.h"
9 #include <unordered_map>
10 
11 #include <onnx/onnx.pb.h>
12 
13 
14 namespace armnn
15 {
16 class TensorInfo;
17 enum class ActivationFunction;
18 }
19 
20 namespace armnnOnnxParser
21 {
22 
23 using ModelPtr = std::unique_ptr<onnx::ModelProto>;
24 
26 {
27 
28 using OperationParsingFunction = void(OnnxParserImpl::*)(const onnx::NodeProto& NodeProto);
29 
30 public:
31 
32  using GraphPtr = std::unique_ptr<onnx::GraphProto>;
33 
34  /// Create the network from a protobuf binary file on disk
35  armnn::INetworkPtr CreateNetworkFromBinaryFile(const char* graphFile);
36 
37  /// Create the network from a protobuf binary file on disk, with inputShapes specified
38  armnn::INetworkPtr CreateNetworkFromBinaryFile(const char* graphFile,
39  const std::map<std::string, armnn::TensorShape>& inputShapes);
40 
41  /// Create the network from a protobuf binary
42  armnn::INetworkPtr CreateNetworkFromBinary(const std::vector<uint8_t>& binaryContent);
43 
44  /// Create the network from a protobuf binary, with inputShapes specified
45  armnn::INetworkPtr CreateNetworkFromBinary(const std::vector<uint8_t>& binaryContent,
46  const std::map<std::string, armnn::TensorShape>& inputShapes);
47 
48  /// Create the network from a protobuf text file on disk
49  armnn::INetworkPtr CreateNetworkFromTextFile(const char* graphFile);
50 
51  /// Create the network from a protobuf text file on disk, with inputShapes specified
52  armnn::INetworkPtr CreateNetworkFromTextFile(const char* graphFile,
53  const std::map<std::string, armnn::TensorShape>& inputShapes);
54 
55  /// Create the network directly from protobuf text in a string. Useful for debugging/testing
56  armnn::INetworkPtr CreateNetworkFromString(const std::string& protoText);
57 
58  /// Create the network directly from protobuf text in a string, with inputShapes specified.
59  /// Useful for debugging/testing
60  armnn::INetworkPtr CreateNetworkFromString(const std::string& protoText,
61  const std::map<std::string, armnn::TensorShape>& inputShapes);
62 
63  /// Retrieve binding info (layer id and tensor info) for the network input identified by the given layer name
64  BindingPointInfo GetNetworkInputBindingInfo(const std::string& name) const;
65 
66  /// Retrieve binding info (layer id and tensor info) for the network output identified by the given layer name
67  BindingPointInfo GetNetworkOutputBindingInfo(const std::string& name) const;
68 
69 public:
70 
72  ~OnnxParserImpl() = default;
73 
74  static ModelPtr LoadModelFromBinary(const std::vector<uint8_t>& binaryContent);
75  static ModelPtr LoadModelFromBinaryFile(const char * fileName);
76  static ModelPtr LoadModelFromTextFile(const char * fileName);
77  static ModelPtr LoadModelFromString(const std::string& inputString);
78 
79  /// Retrieve inputs names
80  static std::vector<std::string> GetInputs(ModelPtr& model);
81 
82  /// Retrieve outputs names
83  static std::vector<std::string> GetOutputs(ModelPtr& model);
84 
85  /// Retrieve version in X.Y.Z form
86  static const std::string GetVersion();
87 
88 private:
89 
90  /// Parses a ModelProto loaded into memory from one of the other CreateNetwork*
91  armnn::INetworkPtr CreateNetworkFromModel(onnx::ModelProto& model);
92 
93  /// Parse every node and make the connection between the resulting tensors
94  void LoadGraph();
95 
96  void SetupInfo(const google::protobuf::RepeatedPtrField<onnx::ValueInfoProto >* list);
97 
98  std::vector<armnn::TensorInfo> ComputeOutputInfo(
99  std::vector<std::string> outNames,
100  const armnn::IConnectableLayer* layer,
101  std::vector<armnn::TensorShape> inputShapes,
102  const onnx::TensorProto::DataType& type = onnx::TensorProto::FLOAT);
103 
104  void DetectFullyConnected();
105 
106  template <typename Location>
107  void GetInputAndParam(const onnx::NodeProto& node,
108  std::string* inputName,
109  std::string* constName,
110  const Location& location);
111 
112  template <typename Location>
113  void To1DTensor(const std::string &name, const Location& location);
114 
115  //Broadcast Preparation functions
116  std::pair<std::string, std::string> AddPrepareBroadcast(const std::string& input0, const std::string& input1);
117  void PrependForBroadcast(const std::string& outputName, const std::string& input0, const std::string& input1);
118 
119  void AddConvLayerWithDepthwiseConv(const onnx::NodeProto& node, const armnn::Convolution2dDescriptor& convDesc);
120  void AddFullyConnected(const onnx::NodeProto& matmulNode, const onnx::NodeProto* addNode = nullptr);
121  void AddPoolingLayer(const onnx::NodeProto& nodeProto, armnn::Pooling2dDescriptor& desc);
122 
123  void CreateConstantLayer(const std::string& tensorName, const std::string& layerName);
124  void CreateInt64ConstantLayer(const std::string& tensorName, const std::string& layerName);
125  void CreateReshapeLayer(const std::string& inputName,
126  const std::string& outputName,
127  const std::string& layerName);
128 
129  void ParseActivation(const onnx::NodeProto& nodeProto, const armnn::ActivationFunction func);
130  void ParseClip(const onnx::NodeProto& nodeProto);
131  void ParseSigmoid(const onnx::NodeProto& nodeProto);
132  void ParseTanh(const onnx::NodeProto& nodeProto);
133  void ParseRelu(const onnx::NodeProto& nodeProto);
134  void ParseLeakyRelu(const onnx::NodeProto& nodeProto);
135 
136  void ParseAdd(const onnx::NodeProto& nodeProto);
137  void ParseAveragePool(const onnx::NodeProto& nodeProto);
138  void ParseBatchNormalization(const onnx::NodeProto& node);
139  void ParseConcat(const onnx::NodeProto& nodeProto);
140  void ParseConstant(const onnx::NodeProto& nodeProto);
141  void ParseConv(const onnx::NodeProto& nodeProto);
142  void ParseFlatten(const onnx::NodeProto& node);
143  void ParseGather(const onnx::NodeProto& node);
144  void ParseGemm(const onnx::NodeProto& node);
145  void ParseGlobalAveragePool(const onnx::NodeProto& node);
146  void ParseMaxPool(const onnx::NodeProto& nodeProto);
147  void ParseShape(const onnx::NodeProto& node);
148  void ParseReshape(const onnx::NodeProto& nodeProto);
149  void ParseUnsqueeze(const onnx::NodeProto& nodeProto);
150 
151  void RegisterInputSlot(armnn::IConnectableLayer* layer,
152  const std::string& tensorId,
153  unsigned int slotIndex);
154  void RegisterInputSlots(armnn::IConnectableLayer* layer, const std::vector<std::string>& tensorIndexes);
155  void RegisterOutputSlots(armnn::IConnectableLayer* layer, const std::vector<std::string>& tensorIndexes);
156 
157  void SetupInputLayers();
158  void SetupOutputLayers();
159 
160  void ResetParser();
161  void Cleanup();
162 
163  std::pair<armnn::ConstTensor, std::unique_ptr<float[]>>
164  CreateConstTensor(const std::string name,
166 
167  std::pair<armnn::ConstTensor, std::unique_ptr<int32_t[]>>
168  CreateInt64ConstTensor(const std::string name,
170 
171  template <typename TypeList, typename Location>
172  void ValidateInputs(const onnx::NodeProto& node,
173  TypeList validInputs,
174  const Location& location);
175 
176  /// The network we're building. Gets cleared after it is passed to the user
177  armnn::INetworkPtr m_Network;
178 
179  /// Ptr to the graph we're building the network from
180  GraphPtr m_Graph;
181 
182  /// Map of the information for every tensor
183  struct OnnxTensor
184  {
185  std::unique_ptr<armnn::TensorInfo> m_info;
186  std::unique_ptr<const onnx::TensorProto> m_tensor;
188 
189  OnnxTensor() : m_info(nullptr), m_tensor(nullptr), m_dtype(onnx::TensorProto::FLOAT) { }
190  bool isConstant() { return m_tensor != nullptr; }
191  };
192 
193  std::unordered_map<std::string, OnnxTensor> m_TensorsInfo;
194 
195  /// map of onnx operation names to parsing member functions
196  static const std::map<std::string, OperationParsingFunction> m_ParserFunctions;
197 
198  /// A mapping of an output slot to each of the input slots it should be connected to
199  /// The outputSlot is from the layer that creates this tensor as one of its outputs
200  /// The inputSlots are from the layers that use this tensor as one of their inputs
201  struct TensorSlots
202  {
203  armnn::IOutputSlot* outputSlot;
204  std::vector<armnn::IInputSlot*> inputSlots;
205 
206  TensorSlots() : outputSlot(nullptr) { }
207  };
208  /// Map of the tensor names to their connections for the connections of the layers of the graph
209  std::unordered_map<std::string, TensorSlots> m_TensorConnections;
210 
211  /// Map of the tensor names to their node and index in graph.node()
212  std::unordered_map<std::string, std::pair<const onnx::NodeProto*, int>> m_OutputsMap;
213 
214  /// Number of times a specific node (identified by its index number) was used as input
215  /// and list of the nodes it was fused with
216  struct UsageSummary
217  {
218  std::vector<size_t> fusedWithNodes;
219  size_t inputForNodes;
220 
221  UsageSummary() : fusedWithNodes({}), inputForNodes(0) { }
222 
223  };
224 
225  std::vector<UsageSummary> m_OutputsFusedAndUsed;
226 
227  std::map<std::string, armnn::TensorShape> m_InputShapes;
228 
229  std::unordered_map<std::string, armnn::TensorInfo> m_InputInfos;
230 
231  std::unordered_map<std::string, armnn::TensorInfo> m_OutputInfos;
232 
233 };
234 }
armnn::INetworkPtr
std::unique_ptr< INetwork, void(*)(INetwork *network)> INetworkPtr
Definition: INetwork.hpp:339
armnnOnnxParser::OnnxParserImpl::GetOutputs
static std::vector< std::string > GetOutputs(ModelPtr &model)
Retrieve outputs names.
Definition: OnnxParser.cpp:2687
armnn::Optional
Definition: Optional.hpp:270
armnnOnnxParser
Definition: IOnnxParser.hpp:14
armnnOnnxParser::OnnxParserImpl::GetNetworkInputBindingInfo
BindingPointInfo GetNetworkInputBindingInfo(const std::string &name) const
Retrieve binding info (layer id and tensor info) for the network input identified by the given layer ...
Definition: OnnxParser.cpp:2625
armnnOnnxParser::OnnxParserImpl::LoadModelFromBinaryFile
static ModelPtr LoadModelFromBinaryFile(const char *fileName)
Definition: OnnxParser.cpp:813
armnnOnnxParser::OnnxParserImpl::GetNetworkOutputBindingInfo
BindingPointInfo GetNetworkOutputBindingInfo(const std::string &name) const
Retrieve binding info (layer id and tensor info) for the network output identified by the given layer...
Definition: OnnxParser.cpp:2644
armnnOnnxParser::OnnxParserImpl::CreateNetworkFromBinary
armnn::INetworkPtr CreateNetworkFromBinary(const std::vector< uint8_t > &binaryContent)
Create the network from a protobuf binary.
Definition: OnnxParser.cpp:775
armnnOnnxParser::OnnxParserImpl
Definition: OnnxParser.hpp:25
armnnOnnxParser::OnnxParserImpl::CreateNetworkFromString
armnn::INetworkPtr CreateNetworkFromString(const std::string &protoText)
Create the network directly from protobuf text in a string. Useful for debugging/testing.
Definition: OnnxParser.cpp:876
armnnOnnxParser::ModelPtr
std::unique_ptr< onnx::ModelProto > ModelPtr
Definition: OnnxParser.hpp:23
armnnOnnxParser::OnnxParserImpl::OnnxParserImpl
OnnxParserImpl()
Definition: OnnxParser.cpp:580
armnn::IOutputSlot
An output connection slot for a layer.
Definition: INetwork.hpp:53
armnnOnnxParser::OnnxParserImpl::~OnnxParserImpl
~OnnxParserImpl()=default
IOnnxParser.hpp
armnnOnnxParser::OnnxParserImpl::CreateNetworkFromBinaryFile
armnn::INetworkPtr CreateNetworkFromBinaryFile(const char *graphFile)
Create the network from a protobuf binary file on disk.
Definition: OnnxParser.cpp:841
armnn::EmptyOptional
EmptyOptional is used to initialize the Optional class in case we want to have default value for an O...
Definition: Optional.hpp:32
armnn::DataType
DataType
Definition: Types.hpp:48
armnnOnnxParser::OnnxParserImpl::LoadModelFromBinary
static ModelPtr LoadModelFromBinary(const std::vector< uint8_t > &binaryContent)
Definition: OnnxParser.cpp:791
armnnOnnxParser::OnnxParserImpl::LoadModelFromString
static ModelPtr LoadModelFromString(const std::string &inputString)
Definition: OnnxParser.cpp:857
armnn::ActivationFunction
ActivationFunction
Definition: Types.hpp:86
armnnOnnxParser::OnnxParserImpl::GetVersion
static const std::string GetVersion()
Retrieve version in X.Y.Z form.
Definition: OnnxParser.cpp:2702
armnn::Convolution2dDescriptor
A Convolution2dDescriptor for the Convolution2dLayer.
Definition: Descriptors.hpp:534
armnnOnnxParser::OnnxParserImpl::GraphPtr
std::unique_ptr< onnx::GraphProto > GraphPtr
Definition: OnnxParser.hpp:32
armnnOnnxParser::BindingPointInfo
armnn::BindingPointInfo BindingPointInfo
Definition: IOnnxParser.hpp:17
armnnOnnxParser::OnnxParserImpl::CreateNetworkFromTextFile
armnn::INetworkPtr CreateNetworkFromTextFile(const char *graphFile)
Create the network from a protobuf text file on disk.
Definition: OnnxParser.cpp:759
armnn
Copyright (c) 2021 ARM Limited and Contributors.
Definition: 01_00_quick_start.dox:6
armnn::IConnectableLayer
Interface for a layer that is connectable to other layers via InputSlots and OutputSlots.
Definition: INetwork.hpp:80
armnnOnnxParser::OnnxParserImpl::GetInputs
static std::vector< std::string > GetInputs(ModelPtr &model)
Retrieve inputs names.
Definition: OnnxParser.cpp:2663
armnn::Pooling2dDescriptor
A Pooling2dDescriptor for the Pooling2dLayer.
Definition: Descriptors.hpp:371
armnnOnnxParser::OnnxParserImpl::LoadModelFromTextFile
static ModelPtr LoadModelFromTextFile(const char *fileName)
Definition: OnnxParser.cpp:734