mirror of
https://github.com/saymrwulf/onnxruntime.git
synced 2026-05-16 21:00:14 +00:00
87cb6fd495
* model building * fix build * winml adapter model building api * model building * make build * make build again * add model building with audio op * inplace and inorder fft * add ifft * works! * cleanup * add comments * switch to iterative rather than recursive and use parallelization * batched parallelization * fft->dft * cleanup * window functions * add melweightmatrix op * updates to make spectrogram test work * push latest * add onesided * cleanup * Clean up building apis and fix mel * cleanup * cleanup * naive stft * fix test output * middle c complete * 3 tones * cleanup * signal def new line * Add save functionality * Perf improvements, 10x improvement * cleanup * use bitreverse lookup table for performance * implement constant initializers for tensors * small changes * add matmul tests * merge issues * support add attribute * add tests for double data type windowfunctions and minor cleanup * stft onesided/and not tests * cleanup * cleanup * clean up * cleanup * remove threading attribute * forward declare orttypeinfo * warnings * fwd declare * fix warnings * 1 more warning * remove saving to e drive... * cleanup and fix stft test * add opset picker * small additions * add onnxruntime tests * add signed/unsigned * fix warning * fix warning * finish onnxruntime tests * make windows namespace build succeed * add experimental flag * add experimental api into nuget package * add experimental api build flag and add to windows ai nuget package * turn experimental for tests * add minimum opset version to new experimental domain * api cleanup * disable ms experimental ops test when --ms_experimental is not enabled * add macro behind flag * remove unused x * pr feedback Co-authored-by: Sheil Kumar <sheilk@microsoft.com>
79 lines
3 KiB
C++
79 lines
3 KiB
C++
#include "pch.h"
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#include "LearningModelBuilder.h"
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#include "LearningModel.h"
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#include "TensorFeatureDescriptor.h"
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#include "LearningModelSession.h"
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#include "LearningModelInputs.h"
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#include "LearningModelOutputs.h"
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#include "LearningModelOperatorSet.h"
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#include "OnnxruntimeProvider.h"
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namespace WINML_EXPERIMENTALP {
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LearningModelBuilder::LearningModelBuilder(int64_t opset) : inputs_(nullptr), outputs_(nullptr), operators_(nullptr), inert_session_(nullptr) {
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WINML_THROW_IF_FAILED(CreateOnnxruntimeEngineFactory(engine_factory_.put()));
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WINML_THROW_IF_FAILED(engine_factory_->CreateEmptyModel(opset, model_.put()));
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inputs_ = winrt::make<winml_experimentalp::LearningModelInputs>(*this);
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outputs_ = winrt::make<winml_experimentalp::LearningModelOutputs>(*this);
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operators_ = winrt::make<winml_experimentalp::LearningModelOperatorSet>(*this);
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winrt::com_ptr<_winml::IEngineBuilder> builder;
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WINML_THROW_IF_FAILED(engine_factory_->CreateEngineBuilder(builder.put()));
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winrt::com_ptr<_winml::IEngine> engine;
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WINML_THROW_IF_FAILED(builder->CreateEngine(engine.put()));
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inert_session_ = winmlp::LearningModelSession::CreateInertSession(engine.get());
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}
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LearningModelBuilder::LearningModelBuilder(LearningModelBuilder& builder) : inputs_(builder.inputs_),
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outputs_(builder.outputs_),
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operators_(builder.operators_),
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inert_session_(nullptr)
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{
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}
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winml_experimental::LearningModelInputs LearningModelBuilder::Inputs() {
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return inputs_;
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}
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winml_experimental::LearningModelOutputs LearningModelBuilder::Outputs() {
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return outputs_;
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}
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winml_experimental::LearningModelOperatorSet LearningModelBuilder::Operators() {
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return operators_;
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}
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winml::LearningModel LearningModelBuilder::CreateModel() {
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com_ptr<_winml::IModel> model_clone;
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model_->CloneModel(model_clone.put());
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return winrt::make<winmlp::LearningModel>(engine_factory_.get(), model_clone.get(), nullptr);
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}
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void LearningModelBuilder::Save(const winrt::hstring& file_name) {
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model_->SaveModel(file_name.c_str(), file_name.size());
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}
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winml_experimental::LearningModelBuilder LearningModelBuilder::Create(int32_t opset) {
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return winrt::make<LearningModelBuilder>(static_cast<int64_t>(opset));
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}
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winml::TensorFeatureDescriptor LearningModelBuilder::CreateTensorFeatureDescriptor(
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hstring const& name,
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winml::TensorKind const& kind,
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array_view<int64_t const> shape) {
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return winrt::make<winmlp::TensorFeatureDescriptor>(name, L"", kind, shape);
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}
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winml::TensorFeatureDescriptor LearningModelBuilder::CreateTensorFeatureDescriptor(
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hstring const& name,
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hstring const& description,
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winml::TensorKind const& kind,
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array_view<int64_t const> shape) {
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return winrt::make<winmlp::TensorFeatureDescriptor>(name, description, kind, shape);
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}
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_winml::IModel* LearningModelBuilder::UseModel() {
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return model_.get();
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}
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} // namespace WINML_EXPERIMENTALP
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