text: // Copyright (c) Facebook, Inc. and its affiliates.
  
  #include <opencv2/opencv.hpp>
  #include <iostream>
  #include <string>
  
  #include <c10/cuda/CUDAStream.h>
  #include <torch/csrc/autograd/grad_mode.h>
  #include <torch/script.h>
  
  using namespace std;
  
  // experimental.
  int main(int argc, const char* argv[]) {
    // arguments:
    //   model.ts input.jpg
    if (argc != 3) {
      return 1;
    }
    std::string image_file = argv[2];
  
    torch::autograd::AutoGradMode guard(false);
    auto module = torch::jit::load(argv[1]);
  
    assert(module.buffers().size() > 0);
    // Assume that the entire model is on the same device.
    // We just put input to this device.
    auto device = (*begin(module.buffers())).device();
  
    cv::Mat input_img = cv::imread(image_file, cv::IMREAD_COLOR);
    const int height = input_img.rows;
    const int width = input_img.cols;
    // FPN models require divisibility of 32
    assert(height % 32 == 0 && width % 32 == 0);
    const int channels = 3;
  
    auto input = torch::from_blob(
        input_img.data, {1, height, width, channels}, torch::kUInt8);
    // NHWC to NCHW
    input = input.to(device, torch::kFloat).permute({0, 3, 1, 2}).contiguous();
  
    std::array<float, 3> im_info_data{height * 1.0f, width * 1.0f, 1.0f};
    auto im_info = torch::from_blob(im_info_data.data(), {1, 3}).to(device);
  
    // run the network
    auto output = module.forward({std::make_tuple(input, im_info)});
    if (device.is_cuda())
      c10::cuda::getCurrentCUDAStream().synchronize();
  
    // run 3 more times to benchmark
    int N_benchmark = 3;
    auto start_time = chrono::high_resolution_clock::now();
    for (int i = 0; i < N_benchmark; ++i) {
      output = module.forward({std::make_tuple(input, im_info)});
      if (device.is_cuda())
        c10::cuda::getCurrentCUDAStream().synchronize();
    }
    auto end_time = chrono::high_resolution_clock::now();
    auto ms = chrono::duration_cast<chrono::microseconds>(end_time - start_time)
                  .count();
    cout << "Latency (should vary with different inputs): "
         << ms * 1.0 / 1e6 / N_benchmark << " seconds" << endl;
  
    auto outputs = output.toTuple()->elements();
    // parse Mask R-CNN outputs
    auto bbox = outputs[0].toTensor(), scores = outputs[1].toTensor(),
         labels = outputs[2].toTensor(), mask_probs = outputs[3].toTensor();
  
    cout << "bbox: " << bbox.toString() << " " << bbox.sizes() << endl;
    cout << "scores: " << scores.toString() << " " << scores.sizes() << endl;
    cout << "labels: " << labels.toString() << " " << labels.sizes() << endl;
    cout << "mask_probs: " << mask_probs.toString() << " " << mask_probs.sizes()
         << endl;
  
    int num_instances = bbox.sizes()[0];
    cout << bbox << endl;
    return 0;
  }
  