pytorch to c++

CMakeLists.txt

cmake_minimum_required(VERSION 3.0 FATAL_ERROR)
project(custom_ops)

find_package(Torch REQUIRED)

add_executable(example-app example-app.cpp)
target_link_libraries(example-app "${TORCH_LIBRARIES}")
set_property(TARGET example-app PROPERTY CXX_STANDARD 14)

example-app.cpp

#include <torch/script.h> // One-stop header.

#include <iostream>
#include <memory>

int main(int argc, const char* argv[]) {
  if (argc != 2) {
    std::cerr << "usage: example-app <path-to-exported-script-module>\n";
    return -1;
  }


  torch::jit::script::Module module;
  try {
    // Deserialize the ScriptModule from a file using torch::jit::load().
    module = torch::jit::load(argv[1]);
  }
  catch (const c10::Error& e) {
    std::cerr << "error loading the model\n";
    return -1;
  }

  std::cout << "ok\n";

  //Create a vector of inputs.
  std::vector<torch::jit::IValue> inputs;
  //inputs.push_back(torch::ones({1, 3, 224, 224}));
  inputs.push_back(torch::tensor({-3., 0., 0.}));

  // Execute the model and turn its output into a tensor.
  at::Tensor output = module.forward(inputs).toTensor();
  std::cout << "###" << std::endl;
  std::cout << output << std::endl;
  std::cout << "###" << std::endl;
}

example.py

import torch
class MyModule(torch.nn.Module):
    def __init__(self, N, M):
        super(MyModule, self).__init__()
        self.weight = torch.nn.Parameter(torch.zeros(N, M))

    def forward(self, input):
        print(input.sum())
        if input[0] > 0:
          output = self.weight - input
        else:
          output = self.weight @ input
        return output

my_module = MyModule(2,3)
sm = torch.jit.script(my_module)

#my_module(torch.tensor([-1.,0.,0.]))
sm.save("traced_resnet_model.pt")

run.sh

mkdir build
cd build
cmake -DCMAKE_PREFIX_PATH=/path/to/libtorch ..
cmake --build . --config Release

Converting to Torch Script via Annotation
https://pytorch.org/tutorials/advanced/cpp_export.html#step-1-converting-your-pytorch-model-to-torch-script