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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>
81 lines
3.3 KiB
C++
81 lines
3.3 KiB
C++
#include "lib/Api.Experimental/pch/pch.h"
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#include "LearningModelInputs.h"
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#include "LearningModelOperator.h"
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#include "LearningModelSession.h"
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#include "LearningModelBuilder.h"
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#include "TensorFeatureDescriptor.h"
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#include "..\Api\inc\ILotusValueProviderPrivate.h"
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namespace WINML_EXPERIMENTALP {
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LearningModelInputs::LearningModelInputs(winml_experimental::LearningModelBuilder builder)
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: input_descriptors_(winrt::single_threaded_vector<winml::ILearningModelFeatureDescriptor>()),
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input_default_values_(winrt::single_threaded_vector<wf::IInspectable>()),
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constant_descriptors_(winrt::single_threaded_vector<winml::ILearningModelFeatureDescriptor>()),
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constant_values_(winrt::single_threaded_vector<wf::IInspectable>()),
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builder_(builder) {
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}
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winml_experimental::LearningModelBuilder LearningModelInputs::AddInput(
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winml::ILearningModelFeatureDescriptor const& input,
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Windows::Foundation::IInspectable const& default_value,
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bool is_constant
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) {
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// Perform model update inside the builder
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auto model = builder_.as<winml_experimentalp::LearningModelBuilder>()->UseModel();
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auto descriptor_provider = input.as<_winml::IDescriptorInfoProvider>();
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auto input_name = _winml::Strings::UTF8FromHString(input.Name());
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winrt::com_ptr<_winml::IValue> default_value_ivalue;
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if (default_value) {
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auto default_value_value_provider = default_value.as<_winml::ILotusValueProviderPrivate>();
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// Create the Binding Context to pass to the feature value
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_winml::BindingContext context{
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_winml::BindingType::kInput,
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builder_.as<winml_experimentalp::LearningModelBuilder>()->InertSession(),
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nullptr,
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nullptr,
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{} // SubresourceId is set by callee
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};
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default_value_value_provider->GetValue(context, default_value_ivalue.put());
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}
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model->AddModelInput(input_name.c_str(), descriptor_provider.get(), is_constant, default_value_ivalue.get());
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return builder_;
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}
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winml_experimental::LearningModelBuilder LearningModelInputs::Add(winml::ILearningModelFeatureDescriptor const& input) {
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return AddInput(input, nullptr, false);
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}
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winml_experimental::LearningModelBuilder LearningModelInputs::Add(
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hstring const& input_name, hstring const& input_description, Windows::Foundation::IInspectable const& default_value
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) {
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if (auto tensor = default_value.try_as<winml::ITensor>()) {
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auto shape = tensor.Shape();
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std::vector<int64_t> shape_vector(begin(shape), end(shape));
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auto descriptor =
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winrt::make<winmlp::TensorFeatureDescriptor>(input_name, input_description, tensor.TensorKind(), shape_vector);
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return AddInput(descriptor, default_value, false);
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}
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WINML_THROW_HR(E_UNEXPECTED);
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}
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winml_experimental::LearningModelBuilder LearningModelInputs::AddConstant(
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hstring const& input_name, Windows::Foundation::IInspectable const& value
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) {
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if (auto tensor = value.try_as<winml::ITensor>()) {
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winrt::hstring no_description_for_constants = L"";
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auto shape = tensor.Shape();
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std::vector<int64_t> shape_vector(begin(shape), end(shape));
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auto descriptor = winrt::make<winmlp::TensorFeatureDescriptor>(
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input_name, no_description_for_constants, tensor.TensorKind(), shape_vector
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);
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return AddInput(descriptor, value, true);
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}
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WINML_THROW_HR(E_UNEXPECTED);
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}
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} // namespace WINML_EXPERIMENTALP
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