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#include <torch/script.h>
#include <torch/torch.h>
#include <cstring>
#include <iostream>
#ifdef _WIN32
#include <torchvision/vision.h>
#endif // _WIN32
int main(int argc, const char* argv[]) {
if (argc != 2) {
std::cout << "Usage: run_model <path_to_scripted_model>\n";
return -1;
}
torch::DeviceType device_type;
device_type = torch::kCPU;
torch::jit::script::Module model;
try {
std::cout << "Loading model\n";
// Deserialize the ScriptModule from a file using torch::jit::load().
model = torch::jit::load(argv[1]);
std::cout << "Model loaded\n";
} catch (const torch::Error& e) {
std::cout << "error loading the model.\n";
return -1;
} catch (const std::exception& e) {
std::cout << "Other error: " << e.what() << "\n";
return -1;
}
// TorchScript models require a List[IValue] as input
std::vector<torch::jit::IValue> inputs;
if (std::strstr(argv[1], "fasterrcnn") != NULL) {
// Faster RCNN accepts a List[Tensor] as main input
std::vector<torch::Tensor> images;
images.push_back(torch::rand({3, 256, 275}));
images.push_back(torch::rand({3, 256, 275}));
inputs.push_back(images);
} else {
inputs.push_back(torch::rand({1, 3, 10, 10}));
}
auto out = model.forward(inputs);
std::cout << out << "\n";
if (torch::cuda::is_available()) {
// Move model and inputs to GPU
model.to(torch::kCUDA);
// Add GPU inputs
inputs.clear();
torch::TensorOptions options = torch::TensorOptions{torch::kCUDA};
if (std::strstr(argv[1], "fasterrcnn") != NULL) {
// Faster RCNN accepts a List[Tensor] as main input
std::vector<torch::Tensor> images;
images.push_back(torch::rand({3, 256, 275}, options));
images.push_back(torch::rand({3, 256, 275}, options));
inputs.push_back(images);
} else {
inputs.push_back(torch::rand({1, 3, 10, 10}, options));
}
auto gpu_out = model.forward(inputs);
std::cout << gpu_out << "\n";
}
}
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