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>
106 lines
No EOL
3 KiB
C++
106 lines
No EOL
3 KiB
C++
// Copyright (c) Microsoft Corporation. All rights reserved.
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// Licensed under the MIT License.
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#include "pch.h"
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#include "LearningModel.h"
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#include "TensorFeatureDescriptor.h"
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namespace WINMLP {
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TensorFeatureDescriptor::TensorFeatureDescriptor(
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const char* name,
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const char* description,
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winml::TensorKind tensor_kind,
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const std::vector<int64_t>& shape,
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bool is_required,
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bool has_unsupported_image_metadata) : name_(_winml::Strings::HStringFromUTF8(name)),
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description_(_winml::Strings::HStringFromUTF8(description)),
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tensor_kind_(tensor_kind),
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shape_(shape),
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is_required_(is_required),
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has_unsupported_image_metadata_(has_unsupported_image_metadata) {
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}
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TensorFeatureDescriptor::TensorFeatureDescriptor(
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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) : name_(name),
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description_(description),
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tensor_kind_(kind),
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shape_(shape.begin(), shape.end()),
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is_required_(true),
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has_unsupported_image_metadata_(false) {
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}
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winml::TensorKind
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TensorFeatureDescriptor::TensorKind() try {
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return tensor_kind_;
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}
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WINML_CATCH_ALL
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wfc::IVectorView<int64_t>
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TensorFeatureDescriptor::Shape() try {
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return winrt::single_threaded_vector<int64_t>(
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std::vector<int64_t>(
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std::begin(shape_),
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std::end(shape_)))
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.GetView();
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}
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WINML_CATCH_ALL
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winrt::hstring
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TensorFeatureDescriptor::Name() try {
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return name_;
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}
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WINML_CATCH_ALL
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winrt::hstring
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TensorFeatureDescriptor::Description() try {
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return description_;
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}
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WINML_CATCH_ALL
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winml::LearningModelFeatureKind
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TensorFeatureDescriptor::Kind() try {
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return LearningModelFeatureKind::Tensor;
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}
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WINML_CATCH_ALL
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bool TensorFeatureDescriptor::IsRequired() try {
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return is_required_;
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}
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WINML_CATCH_ALL
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bool TensorFeatureDescriptor::IsUnsupportedMetaData() try {
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return has_unsupported_image_metadata_;
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}
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WINML_CATCH_ALL
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HRESULT
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TensorFeatureDescriptor::GetName(
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const wchar_t** name,
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uint32_t* cchName) {
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*name = name_.data();
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*cchName = static_cast<uint32_t>(name_.size());
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return S_OK;
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}
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HRESULT
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TensorFeatureDescriptor::GetDescription(
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const wchar_t** description,
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uint32_t* cchDescription) {
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*description = description_.data();
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*cchDescription = static_cast<uint32_t>(description_.size());
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return S_OK;
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}
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HRESULT TensorFeatureDescriptor::GetDescriptorInfo(
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_winml::IEngineFactory* engine_factory,
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_winml::IDescriptorInfo** info){
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engine_factory->CreateTensorDescriptorInfo(tensor_kind_, shape_.data(), shape_.size(), info);
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return S_OK;
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};
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} // namespace WINMLP
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