inference_demo.cc 文件参考

#include <gflags/gflags.h>
#include "torch/script.h"
#include "torch/torch.h"

inference_demo.cc 的引用(Include)关系图:

浏览源代码.

函数
	DEFINE_string (model_file, "/apollo/modules/planning/planning_base/tools/planning_demo_model.pt", "pytorch model file.")
int	main (int argc, char **argv)

函数说明

DEFINE_string()

DEFINE_string	(	model_file	,
		"/apollo/modules/planning/planning_base/tools/planning_demo_model.pt"	,
		"pytorch model file."
	)

main()

int main	(	int	argc,
		char **	argv
	)

在文件 inference_demo.cc 第 27 行定义.

                                {
  google::ParseCommandLineFlags(&argc, &argv, true);
 
  torch::jit::script::Module model;
  std::cout << "is_optimized:" << model.is_optimized() << std::endl;
  std::cout << "parameter size:" << model.parameters().size() << std::endl;
 
  torch::Device device(torch::kCPU);
 
  torch::set_num_threads(1);
  try {
    // Deserialize the ScriptModule from a file using torch::jit::load().
    model = torch::jit::load(FLAGS_model_file, device);
  } catch (const c10::Error& e) {
    std::cerr << "error loading the model\n";
    return -1;
  }
  std::cout << "is_optimized:" << model.is_optimized() << std::endl;
  std::cout << "after loading parameter size:" << model.parameters().size()
            << std::endl;
 
  std::vector<torch::jit::IValue> torch_inputs;
 
  int input_dim = 2 * 3 * 224 * 224 + 2 * 14;
  std::vector<double> feature_values(input_dim, 0.5);
 
  std::vector<torch::jit::IValue> inputs;
  std::vector<torch::jit::IValue> tuple;
  tuple.push_back(torch::zeros({2, 3, 224, 224}));
  tuple.push_back(torch::zeros({2, 14}));
  inputs.push_back(torch::ivalue::Tuple::create(tuple));
 
  auto torch_output = model.forward(inputs);
  std::cout << torch_output << std::endl;
  std::cout << "isDoubleList:" << torch_output.isDoubleList() << std::endl;
  std::cout << "isTensorList:" << torch_output.isTensorList() << std::endl;
  std::cout << "isTensor:" << torch_output.isTensor() << std::endl;
  auto torch_output_tensor = torch_output.toTensor();
  std::cout << "tensor dim:" << torch_output_tensor.dim() << std::endl;
  std::cout << "tensor sizes:" << torch_output_tensor.sizes() << std::endl;
  std::cout << "tensor toString:" << torch_output_tensor.toString()
            << std::endl;
  std::cout << "tensor [0,0,0] element:" << torch_output_tensor[0][0][0]
            << std::endl;
  std::cout << "tensor [0,0,1] element:" << torch_output_tensor[0][0][1]
            << std::endl;
  std::cout << "tensor [0,0,0] element:"
            << double(torch_output_tensor.accessor<float, 3>()[0][0][0])
            << std::endl;
  std::cout << "tensor [0,0,1] element:"
            << double(torch_output_tensor.accessor<float, 3>()[0][0][1])
            << std::endl;
 
  return 0;
}