mirror of
https://github.com/saymrwulf/onnxruntime.git
synced 2026-05-17 21:10:43 +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>
94 lines
3.9 KiB
C++
94 lines
3.9 KiB
C++
#include "pch.h"
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#include "LearningModelOperatorSet.h"
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#include "LearningModelOperator.h"
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#include "..\Api\inc\ILotusValueProviderPrivate.h"
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namespace WINML_EXPERIMENTALP {
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LearningModelOperatorSet::LearningModelOperatorSet(winml_experimental::LearningModelBuilder builder) :
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builder_(builder),
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operators_(winrt::single_threaded_vector<winml_experimental::LearningModelOperator>())
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{
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}
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winml_experimental::LearningModelBuilder LearningModelOperatorSet::Add(winml_experimental::LearningModelOperator const& op)
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{
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auto operator_private = op.as<winml_experimentalp::LearningModelOperator>();
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auto constant_input_map = operator_private->ConstantInputMapping();
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auto input_map = operator_private->InputMapping();
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auto output_map = operator_private->OutputMapping();
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auto attribute_map = operator_private->AttributeMap();
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auto operator_name = _winml::Strings::UTF8FromHString(operator_private->Name());
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auto operator_type = _winml::Strings::UTF8FromHString(operator_private->Type());
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auto operator_domain = _winml::Strings::UTF8FromHString(operator_private->Domain());
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std::vector<std::string> operator_input_names(input_map.Size());
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std::vector<std::string> actual_input_names(input_map.Size());
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std::vector<const char*> raw_operator_input_names(input_map.Size());
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std::vector<const char*> raw_actual_input_names(input_map.Size());
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int i = 0;
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for (auto kvp : input_map) {
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operator_input_names[i] = _winml::Strings::UTF8FromHString(kvp.Key());
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actual_input_names[i] = _winml::Strings::UTF8FromHString(kvp.Value());
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raw_operator_input_names[i] = operator_input_names[i].c_str();
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raw_actual_input_names[i] = actual_input_names[i].c_str();
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i++;
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}
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std::vector<std::string> operator_output_names(output_map.Size());
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std::vector<std::string> actual_output_names(output_map.Size());
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std::vector<const char*> raw_operator_output_names(output_map.Size());
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std::vector<const char*> raw_actual_output_names(output_map.Size());
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i = 0;
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for (auto kvp : output_map) {
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operator_output_names[i] = _winml::Strings::UTF8FromHString(kvp.Key());
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actual_output_names[i] = _winml::Strings::UTF8FromHString(kvp.Value());
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raw_operator_output_names[i] = operator_output_names[i].c_str();
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raw_actual_output_names[i] = actual_output_names[i].c_str();
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i++;
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}
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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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std::vector<std::string> attribute_names(attribute_map.Size());
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std::vector<const char*> raw_attribute_names(attribute_map.Size());
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std::vector<winrt::com_ptr<_winml::IValue>> attribute_values(attribute_map.Size());
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std::vector<_winml::IValue*> raw_attribute_values(attribute_map.Size());
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i = 0;
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for (auto kvp : attribute_map) {
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attribute_names[i] = _winml::Strings::UTF8FromHString(kvp.Key());
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auto default_value_value_provider = kvp.Value().as<_winml::ILotusValueProviderPrivate>();
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default_value_value_provider->GetValue(context, attribute_values[i].put());
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raw_attribute_names[i] = attribute_names[i].c_str();
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raw_attribute_values[i] = attribute_values[i].get();
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i++;
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}
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auto builder = builder_.as<winml_experimentalp::LearningModelBuilder>();
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WINML_THROW_IF_FAILED(builder->UseModel()->AddOperator(
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operator_type.c_str(),
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operator_name.c_str(),
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operator_domain.c_str(),
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raw_operator_input_names.data(), raw_actual_input_names.data(), input_map.Size(),
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raw_operator_output_names.data(), raw_actual_output_names.data(), output_map.Size(),
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raw_attribute_names.data(), raw_attribute_values.data(), attribute_map.Size()));
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// Add constants
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for (auto kvp : constant_input_map) {
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builder_.Inputs().AddConstant(kvp.Key(), kvp.Value());
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}
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return builder_;
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}
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}
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