Python端生成pt模型文件
net.load(model_path)
net.eval()
net.to("cuda")example_input = torch.rand(1, 3, 240, 320).to("cuda")
traced_model = torch.jit.trace(net, example_input)
traced_model.save("model.pt")output = traced_model(example_input)
# 输出查看是否与c++输出一致。
print(len(output))
C++ 端进行调用
c++环境配置
libtorch常用API
#include <torch/script.h>
#include <torch/torch.h>#include <iostream>int main() {std::cout <<"cuda::is_available():" << torch::cuda::is_available() << std::endl;torch::Tensor tensor = torch::rand({2, 3}).to(at::kCUDA);std::cout << tensor << std::endl;torch::jit::script::Module module;module = torch::jit::load("/home/yang/Documents/demo/opencv/model.pt");// 创建一个示例输入std::vector<torch::jit::IValue> inputs;inputs.push_back(torch::rand({1, 3, 240, 320}).to(at::kCUDA));// 运行模型// at::Tensor output = module.forward(inputs).toTensor();//auto output = module.forward(inputs).toTensorList();auto out = module.forward(inputs);auto tpl = out.toTuple();auto out_ct_hm = tpl->elements()[0].toTensor();out_ct_hm.print();auto out_wh = tpl->elements()[1].toTensor();out_wh.print();// 打印输出//std::cout << output << "\n";}可能出错的问题
- terminate called after throwing an instance of ‘c10::Error’
what(): open file failed, file path: model.pt (FileAdapter at …/…/caffe2/serialize/file_adapter.cc:11)。 模型路径有问题，使用绝对路径解决。 - ‘c10::Error’ what(): isTensor() INTERNAL ASSERT FAILED。
很明显，模型的输出应该不是一个 Tensor，可能是一个列表或者元组什么的
