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https://github.com/saymrwulf/onnxruntime.git
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eeef157888
winml/ was previously excluded from lintrunner config. This change includes the directory and adds the clang-format config file specific to winml/ that fits existing style. --------- Signed-off-by: Justin Chu <justinchu@microsoft.com>
86 lines
3 KiB
C++
86 lines
3 KiB
C++
#include "lib/Api.Experimental/pch/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)
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: inert_session_(nullptr),
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inputs_(nullptr),
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outputs_(nullptr),
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operators_(nullptr) {
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telemetry_helper.LogApiUsage("LearningModelBuilder::LearningModelBuilder");
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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)
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: inert_session_(nullptr),
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inputs_(builder.inputs_),
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outputs_(builder.outputs_),
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operators_(builder.operators_) {
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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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telemetry_helper.LogApiUsage("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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telemetry_helper.LogApiUsage("LearningModelBuilder::Save");
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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, winml::TensorKind const& kind, array_view<int64_t const> shape
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) {
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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, hstring const& description, winml::TensorKind const& kind, array_view<int64_t const> shape
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) {
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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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