Gabriele Cosmo
163 lines
7.4 KiB
C++
163 lines
7.4 KiB
C++
//
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// ********************************************************************
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// * License and Disclaimer *
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// * *
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// * The Geant4 software is copyright of the Copyright Holders of *
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// * the Geant4 Collaboration. It is provided under the terms and *
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// * conditions of the Geant4 Software License, included in the file *
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// * LICENSE and available at http://cern.ch/geant4/license . These *
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// * include a list of copyright holders. *
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// * *
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// * Neither the authors of this software system, nor their employing *
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// * institutes,nor the agencies providing financial support for this *
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// * work make any representation or warranty, express or implied, *
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// * regarding this software system or assume any liability for its *
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// * use. Please see the license in the file LICENSE and URL above *
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// * for the full disclaimer and the limitation of liability. *
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// * *
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// * This code implementation is the result of the scientific and *
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// * technical work of the GEANT4 collaboration. *
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// * By using, copying, modifying or distributing the software (or *
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// * any work based on the software) you agree to acknowledge its *
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// * use in resulting scientific publications, and indicate your *
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// * acceptance of all terms of the Geant4 Software license. *
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// ********************************************************************
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//
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#ifdef USE_INFERENCE_ONNX
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# include "Par04OnnxInference.hh"
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# include "Par04InferenceInterface.hh" // for Par04InferenceInterface
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# include <algorithm> // for copy, max
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# include <cassert> // for assert
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# include <cstddef> // for size_t
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# include <cstdint> // for int64_t
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# include <utility> // for move
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# include <core/session/onnxruntime_cxx_api.h> // for Value, Session, Env
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# ifdef USE_CUDA
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# include "cuda_runtime_api.h"
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# endif
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//....oooOO0OOooo........oooOO0OOooo........oooOO0OOooo........oooOO0OOooo......
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Par04OnnxInference::Par04OnnxInference(G4String modelPath, G4int profileFlag, G4int optimizeFlag,
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G4int intraOpNumThreads, G4int cudaFlag,
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std::vector<const char*>& cuda_keys,
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std::vector<const char*>& cuda_values,
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G4String ModelSavePath, G4String profilingOutputSavePath)
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: Par04InferenceInterface()
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{
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// initialization of the enviroment and inference session
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auto envLocal = std::make_unique<Ort::Env>(ORT_LOGGING_LEVEL_WARNING, "ENV");
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fEnv = std::move(envLocal);
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// Creating a OrtApi Class variable for getting access to C api, necessary for
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// CUDA
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const auto& ortApi = Ort::GetApi();
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fSessionOptions.SetIntraOpNumThreads(intraOpNumThreads);
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// graph optimizations of the model
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// if the flag is not set to true none of the optimizations will be applied
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// if it is set to true all the optimizations will be applied
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if (optimizeFlag) {
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fSessionOptions.SetOptimizedModelFilePath("opt-graph");
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fSessionOptions.SetGraphOptimizationLevel(ORT_ENABLE_ALL);
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// ORT_ENABLE_BASIC #### ORT_ENABLE_EXTENDED
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}
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else
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fSessionOptions.SetGraphOptimizationLevel(ORT_DISABLE_ALL);
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# ifdef USE_CUDA
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if (cudaFlag) {
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OrtCUDAProviderOptionsV2* fCudaOptions = nullptr;
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// Initialize the CUDA provider options, fCudaOptions should now point to a
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// valid CUDA configuration.
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(void)ortApi.CreateCUDAProviderOptions(&fCudaOptions);
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// Update the CUDA provider options
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(void)ortApi.UpdateCUDAProviderOptions(fCudaOptions, cuda_keys.data(), cuda_values.data(),
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cuda_keys.size());
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// Append the CUDA execution provider to the session options, indicating to
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// use CUDA for execution
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(void)ortApi.SessionOptionsAppendExecutionProvider_CUDA_V2(fSessionOptions, fCudaOptions);
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}
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# endif
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// save json file for model execution profiling
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if (profileFlag) fSessionOptions.EnableProfiling("opt.json");
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auto sessionLocal = std::make_unique<Ort::Session>(*fEnv, modelPath, fSessionOptions);
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fSession = std::move(sessionLocal);
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fInfo = Ort::MemoryInfo::CreateCpu(OrtAllocatorType::OrtArenaAllocator, OrtMemTypeDefault);
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}
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//....oooOO0OOooo........oooOO0OOooo........oooOO0OOooo........oooOO0OOooo......
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void Par04OnnxInference::RunInference(std::vector<float> aGenVector,
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std::vector<G4double>& aEnergies, int aSize)
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{
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// input nodes
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Ort::AllocatorWithDefaultOptions allocator;
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# if ORT_API_VERSION < 13
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// Before 1.13 we have to roll our own unique_ptr wrapper here
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auto allocDeleter = [&allocator](char* p) {
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allocator.Free(p);
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};
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using AllocatedStringPtr = std::unique_ptr<char, decltype(allocDeleter)>;
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# endif
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std::vector<int64_t> input_node_dims;
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size_t num_input_nodes = fSession->GetInputCount();
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std::vector<const char*> input_node_names(num_input_nodes);
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for (std::size_t i = 0; i < num_input_nodes; i++) {
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# if ORT_API_VERSION < 13
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const auto input_name =
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AllocatedStringPtr(fSession->GetInputName(i, allocator), allocDeleter).release();
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# else
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const auto input_name = fSession->GetInputNameAllocated(i, allocator).release();
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# endif
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fInames = {input_name};
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input_node_names[i] = input_name;
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Ort::TypeInfo type_info = fSession->GetInputTypeInfo(i);
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auto tensor_info = type_info.GetTensorTypeAndShapeInfo();
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input_node_dims = tensor_info.GetShape();
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for (std::size_t j = 0; j < input_node_dims.size(); j++) {
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if (input_node_dims[j] < 0) input_node_dims[j] = 1;
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}
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}
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// output nodes
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std::vector<int64_t> output_node_dims;
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size_t num_output_nodes = fSession->GetOutputCount();
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std::vector<const char*> output_node_names(num_output_nodes);
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for (std::size_t i = 0; i < num_output_nodes; i++) {
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# if ORT_API_VERSION < 13
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const auto output_name =
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AllocatedStringPtr(fSession->GetOutputName(i, allocator), allocDeleter).release();
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# else
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const auto output_name = fSession->GetOutputNameAllocated(i, allocator).release();
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# endif
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output_node_names[i] = output_name;
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Ort::TypeInfo type_info = fSession->GetOutputTypeInfo(i);
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auto tensor_info = type_info.GetTensorTypeAndShapeInfo();
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output_node_dims = tensor_info.GetShape();
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for (std::size_t j = 0; j < output_node_dims.size(); j++) {
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if (output_node_dims[j] < 0) output_node_dims[j] = 1;
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}
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}
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// create input tensor object from data values
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std::vector<int64_t> dims = {1, (unsigned)(aGenVector.size())};
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Ort::Value Input_noise_tensor = Ort::Value::CreateTensor<float>(
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fInfo, aGenVector.data(), aGenVector.size(), dims.data(), dims.size());
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assert(Input_noise_tensor.IsTensor());
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std::vector<Ort::Value> ort_inputs;
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ort_inputs.push_back(std::move(Input_noise_tensor));
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// run the inference session
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std::vector<Ort::Value> ort_outputs =
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fSession->Run(Ort::RunOptions{nullptr}, fInames.data(), ort_inputs.data(), ort_inputs.size(),
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output_node_names.data(), output_node_names.size());
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// get pointer to output tensor float values
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float* floatarr = ort_outputs.front().GetTensorMutableData<float>();
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aEnergies.assign(aSize, 0);
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for (int i = 0; i < aSize; ++i)
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aEnergies[i] = floatarr[i];
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}
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#endif
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