使用C++解析ONNX模型。使用C++从ONNX模型中提取层、输入和输出形状

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我想从一个onnx模型中提取输入层、输出层和它们的形状等数据。我知道有python接口可以做到这一点。我想做的是类似于这样的事情 code 但在C++中。我也粘贴了链接中的代码。我已经在Python中试过了，它对我来说是有效的。我想知道是否有C++的API来做同样的事情。

import onnx
model = onnx.load(r"model.onnx")
# The model is represented as a protobuf structure and it can be accessed
# using the standard python-for-protobuf methods
# iterate through inputs of the graph
for input in model.graph.input:
    print (input.name, end=": ")
    # get type of input tensor
    tensor_type = input.type.tensor_type
    # check if it has a shape:
    if (tensor_type.HasField("shape")):
        # iterate through dimensions of the shape:
        for d in tensor_type.shape.dim:
            # the dimension may have a definite (integer) value or a symbolic identifier or neither:
            if (d.HasField("dim_value")):
                print (d.dim_value, end=", ")  # known dimension
            elif (d.HasField("dim_param")):
                print (d.dim_param, end=", ")  # unknown dimension with symbolic name
            else:
                print ("?", end=", ")  # unknown dimension with no name
    else:
        print ("unknown rank", end="")
    print()
另外，我是C++的新手，请帮助我解决这个问题。


         python

c++


         onnx


         onnxruntime


        
         
         
          Hitesh Kumar
         
        
        
         发布于
         
         2021-04-28


        1
        
        个回答


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           ONNX格式本质上是一种
           
            protobuf
           
           因此，它可以用protoc编译器支持的任何语言打开。
          
          
           在C++的情况下
          
          
           Take onnx proto file (
           
            onnx repo
           
           )
          
          
           Compile it with
           
            protoc --cpp_out=. onnx.proto3
           
           command. It will generate
           
            onnx.proto3.pb.cc
           
           and
           
            onnx.proto3.pb.h
           
           files
          
          
           Link protobuf library (maybe protobuf-lite), generated cpp file and following code:
          
          #include <fstream>
#include <cassert>
#include "onnx.proto3.pb.h"
void print_dim(const ::onnx::TensorShapeProto_Dimension &dim)
  switch (dim.value_case())
  case onnx::TensorShapeProto_Dimension::ValueCase::kDimParam:
    std::cout << dim.dim_param();
    break;
  case onnx::TensorShapeProto_Dimension::ValueCase::kDimValue:
    std::cout << dim.dim_value();
    break;
  default:
    assert(false && "should never happen");
void print_io_info(const ::google::protobuf::RepeatedPtrField< ::onnx::ValueInfoProto > &info)
  for (auto input_data: info)
    auto shape = input_data.type().tensor_type().shape();
    std::cout << "  " << input_data.name() << ":";
    std::cout << "[";
    if (shape.dim_size() != 0)
      int size = shape.dim_size();
      for (int i = 0; i < size - 1; ++i)
        print_dim(shape.dim(i));
        std::cout << ", ";
      print_dim(shape.dim(size - 1));
    std::cout << "]\n";
int main(int argc, char **argv)
  std::ifstream input("mobilenet.onnx", std::ios::ate | std::ios::binary); // open file and move current position in file to the end
  std::streamsize size = input.tellg(); // get current position in file
  input.seekg(0, std::ios::beg); // move to start of file
  std::vector<char> buffer(size);
  input.read(buffer.data(), size); // read raw data
  onnx::ModelProto model;
  model.ParseFromArray(buffer.data(), size); // parse protobuf
  auto graph = model.graph();
  std::cout << "graph inputs:\n";
  print_io_info(graph.input());