python - 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

Question

我正在尝试从 onnx 模型中提取输入层、输出层及其形状等数据。我知道有 python 接口(interface)可以做到这一点。我想做类似的事情 code但在 C++ 中。我还粘贴了链接中的代码。我已经在 python 中尝试过了，它对我有用。我想知道是否有 C++ API 可以做同样的事情。

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()

我也是 c++ 的新手，请帮助我。

Answer 1

ONNX 格式本质上是一个 protobuf , 所以它可以在协议(protocol)编译器支持的任何语言中打开。

如果是 C++

1. 获取onnx原型(prototype)文件(onnx repo)
2. 用 protoc --cpp_out= 编译它。 onnx.proto3 命令。它将生成onnx.proto3.pb.cc 和onnx.proto3.pb.h 文件
3. 链接 protobuf 库(可能是 protobuf-lite)，生成的 cpp 文件和以下代码:

#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());

  std::cout << "graph outputs:\n";
  print_io_info(graph.output());
  return 0;
}