module = torch::jit::load(argv[1]); } catch (const c10::Error& e) { std::cerr << "error loading the model\n"; return -1; } std::cout << "Load model ok\n"; // test the model std::vector<torch::jit::IValue> inputs; inputs.push_back(torch::ones({1, 1, 30, 30}))...
module = torch::jit::load(argv[1]); } catch (const c10::Error& e) { std::cerr << "error loading the model\n"; return -1; } std::cout << "ok\n"; } 在这个最简单的C++程序中,torch::jit::load()函数用来加载模形,参数为模型文件名,返回torch::jit::script::Module类,<torch/sc...
36 module = torch::jit::load(argv[1]); 37 } 38 catch (const c10::Error& e) { 39 std::cerr << "error loading the model\n"; 40 return -1; 41 } 可以看到头文件里,有很多libtorch里面没有的库,抬头是torchvision。一想,torchvision,不是安装pytorch的时候就一起安装了吗,跑到conda对应目录下...
constchar*argv[]){if(argc!=2){std::cerr<<"usage: example-app <path-to-exported-script-module>\n";return-1;}torch::jit::script::Modulemodule;try{module=torch::jit::load(argv[1]);}catch(constc10::Error&e){std::cerr<<"error loading the module\n";return-1;}std::cout<<"ok\n"...
{module=torch::jit::load(module_path);}catch(constc10::Error&e){std::cout<<"error loading the model\n";return-1;}std::vector<torch::jit::IValue>x;x.push_back(torch::ones({1,1,28,28}));at::Tensor output=module.forward(x).toTensor();std::cout<<output.sum()<<std::endl;...
torch::jit::script::Module module; try { // Deserialize the ScriptModule from a file using torch::jit::load(). module = torch::jit::load("traced_model.pt"); } catch (const c10::Error &e) { std::cerr << "error loading the model\n"; } module.to(device); 1. 2. 3. 4....
} catch (const c10::Error& e) { std::cerr << "Error loading the model: " << e.what() << std::endl; return -1; } 5. 测试加载后的模型以确保其功能正常 加载模型后,你应该使用一些测试数据来验证模型的功能是否正常。这可以通过比较模型的输出与预期结果来实现。
(constc10::Error&e){std::cerr<<"error loading the model\n";return-1;}module->to(at::kCUDA);assert(module!=nullptr);std::cout<<"ok\n";// 建立一个输入,维度为(1,3,224,224),并移动至cudastd::vector<torch::jit::IValue>inputs;inputs.push_back(torch::ones({1,3,224,224}).to...
std::cerr<<"Error loading the model\n"; return-1; } // 输入数据 torch::Tensor input=torch::rand({1,3,224,224}); // 运行推理 torch::Tensor output=module.forward({input}).toTensor(); 模型推理 在加载模型后,可以使用libtorch进行模型推理。以下是模型推理的示例代码: // 加载模型 torch::...
35 // Deserialize the ScriptModule from a file using torch::jit::load(). 36 module = torch::jit::load(argv[1]); 37 } 38 catch (const c10::Error& e) { 39 std::cerr << "error loading the model\n"; 40 return -1; 41 } ...