mirror of
https://github.com/saymrwulf/onnxruntime.git
synced 2026-05-25 22:26:24 +00:00
5350abe19d
this is a big PR. we are going to move it up to layer_dev , which is still a L3 so we are still safe to do work there agile. we are going to move this into the L3 so that ryan can start doing intergration testing. we will pause for a full code review and integration test result prior to going into the L2. >>>> raw comments from previous commits >>> * LearningModelSession is cleaned up to use the adapter, and parts of binding are. * moved everything in the winmladapter made it all nano-com using, WRL to construct objects in the ORT side. base interfaces for everythign for winml to call cleaned up a bunch of winml to use the base interfaces. * more pieces * GetData across the abi. * renamed some namepsace cleaned up OrtValue cleaned up Tensor cleaned up custom ops. everything *but* learnignmodel should be clean * make sure it's building. winml.dll is still a monolith.
66 lines
2.8 KiB
C++
66 lines
2.8 KiB
C++
// Copyright (c) Microsoft Corporation.
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// Licensed under the MIT License.
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#pragma once
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#include "ImageFeatureValue.g.h"
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#include "inc/ILotusValueProviderPrivate.h"
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namespace winrt::Windows::AI::MachineLearning::implementation {
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struct ImageFeatureValue : ImageFeatureValueT<ImageFeatureValue, WinML::ILotusValueProviderPrivate> {
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// Metadata about the resource which helps in finding
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// compatible cached resources
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struct ImageResourceMetadata;
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ImageFeatureValue() = delete;
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ImageFeatureValue(Windows::Media::VideoFrame const& image);
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ImageFeatureValue(winrt::Windows::Foundation::Collections::IVector<Windows::Media::VideoFrame> const& images);
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ImageFeatureValue(winrt::Windows::Foundation::Collections::IVectorView<Windows::Media::VideoFrame> const& images);
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Windows::Media::VideoFrame VideoFrame();
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winrt::Windows::Foundation::Collections::IIterable<Windows::Media::VideoFrame> VideoFrames();
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Windows::AI::MachineLearning::LearningModelFeatureKind Kind();
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static Windows::AI::MachineLearning::ImageFeatureValue ImageFeatureValue::Create(
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uint32_t batchSize,
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Windows::Graphics::Imaging::BitmapPixelFormat format,
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uint32_t width,
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uint32_t height);
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static Windows::AI::MachineLearning::ImageFeatureValue CreateFromVideoFrame(Windows::Media::VideoFrame const& image);
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std::optional<ImageResourceMetadata> GetInputMetadata(const WinML::BindingContext& context);
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// ILotusValueProviderPrivate implementation
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STDMETHOD(GetOrtValue)
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(WinML::BindingContext& context, _winmla::IOrtValue** mlValue);
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STDMETHOD(IsPlaceholder)
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(bool* pIsPlaceHolder);
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STDMETHOD(UpdateSourceResourceData)
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(WinML::BindingContext& context, _winmla::IOrtValue* mlValue);
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STDMETHOD(AbiRepresentation)
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(winrt::Windows::Foundation::IInspectable& abiRepresentation);
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std::vector<uint32_t> Widths() { return m_widths; }
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std::vector<uint32_t> Heights() { return m_heights; }
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bool IsBatch() { return m_batchSize > 1; }
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private:
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winrt::Windows::Foundation::Collections::IVector<Windows::Media::VideoFrame> m_videoFrames;
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std::vector<uint32_t> m_widths = {};
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std::vector<uint32_t> m_heights = {};
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uint32_t m_batchSize = 1;
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// Crop the image with desired aspect ratio.
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// This function does not crop image to desried width and height, but crops to center for desired ratio
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Windows::Graphics::Imaging::BitmapBounds CenterAndCropBounds(
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uint32_t idx,
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uint32_t desiredWidth,
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uint32_t desiredHeight);
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void Initialize();
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};
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} // namespace winrt::Windows::AI::MachineLearning::implementation
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namespace winrt::Windows::AI::MachineLearning::factory_implementation {
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struct ImageFeatureValue : ImageFeatureValueT<ImageFeatureValue, implementation::ImageFeatureValue> {
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};
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} // namespace winrt::Windows::AI::MachineLearning::factory_implementation
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