git clone --recursive https://github.com/pytorch/pytorch
cd pytorch
mkdir build && cd build
sudo cmake .. -DPYTHON_INCLUDE_DIR=/usr/include/python3.6 -DUSE_MPI=OFF
make install
export PYTHONPATH=$PYTHONPATH:/opt/pytorch/build

# 输入模型
example = torch.randn(batch_size, 1, 224, 224, requires_grad=True)

# 导出模型
torch_out = torch_out = torch.onnx.export(model, # model being run
    example, # model input (or a tuple for multiple inputs)
    "peleeNet.onnx",
 verbose=False, # store the trained parameter weights inside the model file
 training=False,
 do_constant_folding=True,
 input_names=['input'],
 output_names=['output']) 

# convert pytorch model to torch script
# An example input you would normally provide to your model's forward() method.
example = torch.rand(1, 3, 12, 12).to(device)
# Use torch.jit.trace to generate a torch.jit.ScriptModule via tracing.
traced_script_module = torch.jit.trace(pnet, example)
# Save traced model
traced_script_module.save("pnet_model_final.pth")

#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;
 }

 // Deserialize the ScriptModule from a file using torch::jit::load().
 std::shared_ptr<torch::jit::script::Module> module = torch::jit::load(argv[1]);

 assert(module != nullptr);
 std::cout << "ok\n";
}