int main()
{
int img_size = 224;
torch::DeviceType device_type = at::kCUDA;
cout << "use cuda" << endl;
// Deserialize the ScriptModule from a file using torch::jit::load().
Module module = torch::jit::load("G:/pytorch/model.pt");
std::cout << "ok\n";
module.to(device_type);
assert(module != nullptr);
std::cout << "ok\n";
Mat src, image, float_image;
src = imread("G:/pytorch/predict-app/data/1.jpg"); //读图
resize(src, image, Size(img_size, img_size));
cvtColor(image, image, CV_BGR2RGB);//bgr->rgb
image.convertTo(float_image, CV_32F, 1.0 / 255);//归一化到[0,1]区间
auto img_tensor = torch::from_blob(float_image.data, { 1, 224, 224, 3 });
img_tensor = img_tensor.permute({ 0, 3, 1, 2 });
// Create a vector of inputs.
std::vector<torch::jit::IValue> inputs;
//inputs.push_back(torch::ones({ 1, 3, 224, 224 }).to(at::kCUDA));
inputs.emplace_back((img_tensor).to(at::kCUDA));
// Execute the mo