onnxruntime/winml/api/Windows.AI.MachineLearning.idl
mindest 5b9369e93c
Fix typos according to reviewdog report. (#21335)
### Description
Fix typos based on reviewdog report but with some
exceptions/corrections.
2024-07-22 13:37:32 -07:00

768 lines
41 KiB
Text
Raw Blame History

This file contains invisible Unicode characters

This file contains invisible Unicode characters that are indistinguishable to humans but may be processed differently by a computer. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.

This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.

// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.
//! \file Windows.AI.MachineLearning.idl
import "Windows.Foundation.idl";
import "windows.graphics.idl";
import "windows.media.idl";
#ifndef WINDOWSAI_RAZZLE_BUILD
// Pull in definition for DualApiPartitionAttribute, because the WinML IDL
// does not build in the OS Repo, and needs to access internal definitions for
// various custom attribute definitions.
import "dualapipartitionattribute.idl";
import "windows.graphics.directx.direct3d11.idl";
import "windows.graphics.imaging.idl";
import "windows.storage.idl";
#endif
#include <sdkddkver.h>
#ifdef BUILD_INBOX
#define ROOT_NS Windows
#define INBOX_ONLY(x) x
#define OTB_ONLY(x)
#else
#define INBOX_ONLY(x)
#define OTB_ONLY(x) x
#endif
#ifndef ROOT_NS
#define ROOT_NS Microsoft
#endif
namespace ROOT_NS.AI.MachineLearning
{
[contractversion(5)]
apicontract MachineLearningContract{};
//! Forward declarations
runtimeclass LearningModelBinding;
//! \enum LearningModelFeatureKind
//! \brief Defines the list of input and output feature types for a machine learning model.
//! Each of these maps to a corresponding FeatureDescriptor that you can use to learn more
//! about how to pass the feature into and out of the the model.
[contract(MachineLearningContract, 1)]
enum LearningModelFeatureKind
{
//! The feature is a tensor, use TensorFeatureDescriptor
Tensor = 0,
//! The feature is a sequence, use SequenceFeatureDescriptor
Sequence,
//! The feature is a map, use MapFeatureDescriptor
Map,
//! The feature is an image, use ImageFeatureDescriptor
Image
};
//! \brief Describes the common properties that all features have.
INBOX_ONLY([uuid(bc08cf7c-6ed0-4004-97ba-b9a2eecd2b4f)])
[contract(MachineLearningContract, 1)]
interface ILearningModelFeatureDescriptor
{
//! \brief The name you use to bind values to this feature.
//! This property is required and will always be there. All features have a name as
//! primary key for the model. Usually as a single word. You use this name when
//! enumerating the features of the model and then later when binding a value to one
//! those feature using a LearningModelBinding. It will be unique across all features.
String Name{ get; };
//! \brief A description of what this feature is used for in the model
//! This property is optional. If provided by an author model it will be a description
//! of what the feature is for the model.
String Description{ get; };
//! \brief The kind of feature - use this to know which derived class to use.
LearningModelFeatureKind Kind{ get; };
//! \brief If true, you must bind a value to this feature before calling Evalaute().
Boolean IsRequired{ get; };
}
INBOX_ONLY([uuid(2a222e5d-afb1-47ed-bfad-b5b3a459ec04)])
OTB_ONLY([uuid(ae066239-6b19-4509-be3e-502ba40203b3)])
[contract(MachineLearningContract, 1)]
interface ILearningModelOperatorProvider : IInspectable
{
}
//! \interface LearningModel
//! \brief Represents a trained machine learning model.
//! \details This is the main object you use to interact with Windows Machine Learning. You use
//! it to load, bind, and evaluate trained ONNX models. To load the model you use
//! one of the Load constructors. You can then enumerate the InputFeatures and
//! OutputFeatures. To bind and evaluate you create a LearningModelSession.
[contract(MachineLearningContract, 1)]
INBOX_ONLY([static_name("Windows.AI.MachineLearning.ILearningModelStatics", e3b977e8-6952-4e47-8ef4-1f7f07897c6d)])
INBOX_ONLY([interface_name("Windows.AI.MachineLearning.ILearningModel", 5b8e4920-489f-4e86-9128-265a327b78fa)])
[threading(both)]
[marshaling_behavior(agile)]
[dualapipartition(1)]
runtimeclass LearningModel : Windows.Foundation.IClosable
{
//! Loads an ONNX model from a StorageFile asynchronously.
[remote_async]
static Windows.Foundation.IAsyncOperation<LearningModel> LoadFromStorageFileAsync(Windows.Storage.IStorageFile modelFile);
//! Loads an ONNX model from a stream asynchronously.
[remote_async]
static Windows.Foundation.IAsyncOperation<LearningModel> LoadFromStreamAsync(Windows.Storage.Streams.IRandomAccessStreamReference modelStream);
//! Loads an ONNX model from a file on disk.
static LearningModel LoadFromFilePath(String filePath);
//! Loads an ONNX model from a stream.
static LearningModel LoadFromStream(Windows.Storage.Streams.IRandomAccessStreamReference modelStream);
//! Loads an ONNX model from a StorageFile asynchronously.
[remote_async]
[method_name("LoadFromStorageFileWithOperatorProviderAsync")] static Windows.Foundation.IAsyncOperation<LearningModel> LoadFromStorageFileAsync(Windows.Storage.IStorageFile modelFile, ILearningModelOperatorProvider operatorProvider);
//! Loads an ONNX model from a stream asynchronously.
[remote_async]
[method_name("LoadFromStreamWithOperatorProviderAsync")] static Windows.Foundation.IAsyncOperation<LearningModel> LoadFromStreamAsync(Windows.Storage.Streams.IRandomAccessStreamReference modelStream, ILearningModelOperatorProvider operatorProvider);
//! Loads an ONNX model from a file on disk.
[method_name("LoadFromFilePathWithOperatorProvider")] static LearningModel LoadFromFilePath(String filePath, ILearningModelOperatorProvider operatorProvider);
//! Loads an ONNX model from a stream.
[method_name("LoadFromStreamWithOperatorProvider")] static LearningModel LoadFromStream(Windows.Storage.Streams.IRandomAccessStreamReference modelStream, ILearningModelOperatorProvider operatorProvider);
//! The name of the model author.
String Author{ get; };
//! The name of the model.
String Name{ get; };
//! The namespace of the imported model operator set. All models implicitly import the default ONNX operator set.
String Domain{ get; };
//! A description of the model.
String Description{ get; };
//! The ONNX version assumed by the model.
Int64 Version{ get; };
//! The raw ONNX model provided <string,string> metadata.
Windows.Foundation.Collections.IMapView <String, String> Metadata{ get; };
//! All of the input features.
Windows.Foundation.Collections.IVectorView<ILearningModelFeatureDescriptor > InputFeatures{ get; };
//! All of the output features.
Windows.Foundation.Collections.IVectorView<ILearningModelFeatureDescriptor > OutputFeatures{ get; };
}
//! \enum LearningModelDeviceKind
//! \brief Defines the list of devices that can evaluate a machine learning model.
[contract(MachineLearningContract, 1)]
enum LearningModelDeviceKind
{
//! Let the system decide which device to use.
Default = 0,
//! Use the CPU to evaluate the model.
Cpu,
//! Use a GPU or other DirectX device to evaluate the model.
DirectX,
//! Use the system policy defined device for high performance.
DirectXHighPerformance,
//! Use the system policy defined device for minimum power.
DirectXMinPower
};
//! \class LearningModelDevice
//! \brief Create an instance specific to which device you want to evaluate the machine learning model on.
//! \namespace Windows.AI.MachineLearning
[contract(MachineLearningContract, 1)]
INBOX_ONLY([constructor_name("Windows.AI.MachineLearning.ILearningModelDeviceFactory", 9cffd74d-b1e5-4f20-80ad-0a56690db06b)])
INBOX_ONLY([static_name("Windows.AI.MachineLearning.ILearningModelDeviceStatics", 49f32107-a8bf-42bb-92c7-10b12dc5d21f)])
INBOX_ONLY([interface_name("Windows.AI.MachineLearning.ILearningModelDevice", f5c2c8fe-3f56-4a8c-ac5f-fdb92d8b8252)])
[threading(both)]
[marshaling_behavior(agile)]
[dualapipartition(1)]
runtimeclass LearningModelDevice
{
//! Create a LearningModelDevice from the specified IDirect3DDevice.
//! During evaluation, the specified IDirect3DDevice will be used to create resources and queue work during execution.
static LearningModelDevice CreateFromDirect3D11Device(Windows.Graphics.DirectX.Direct3D11.IDirect3DDevice device);
//! Create a LearningModelDevice from the specified list of devices enumerated in LearningModelDeviceKind.
[method_name("Create")] LearningModelDevice(LearningModelDeviceKind deviceKind);
// BUGBUG: this needs to be Windows.Graphics.DisplayAdapterId which is only there in the RS4 winmd
//! Returns the unique identifier for the chosen adapter for model
Windows.Graphics.DisplayAdapterId AdapterId{ get; };
//! Returns the chosen IDirect3DDevice for model evaluation.
Windows.Graphics.DirectX.Direct3D11.IDirect3DDevice Direct3D11Device{ get; };
}
[contract(MachineLearningContract, 1)]
INBOX_ONLY([interface_name("Windows.AI.MachineLearning.ILearningModelEvaluationResult", b2f9bfcd-960e-49c0-8593-eb190ae3eee2)])
[marshaling_behavior(agile)]
[dualapipartition(1)]
runtimeclass LearningModelEvaluationResult
{
//! The optional user supplied string that was attached to the Evaluate() call to connect the output results.
String CorrelationId{ get; };
//! If the evaluation failed, returns an error code for what caused the failure.
Int32 ErrorStatus{ get; };
//! True if the evaluation completed successfully.
//! If False, use ErrorStatus to find out what caused the failure.
Boolean Succeeded{ get; };
//! A set of features representing the output prediction along with probabilities.
Windows.Foundation.Collections.IMapView<String, IInspectable> Outputs{ get; };
}
//! \class LearningModelSessionOptions
//! \brief TODO:Docs
[contract(MachineLearningContract, 2)]
[dualapipartition(1)]
runtimeclass LearningModelSessionOptions
{
// default constructor
LearningModelSessionOptions();
//! The BatchSizeOverride option will allow the model compiler to use constant batch size performance optimizations when setting up the LearningModelSession.
//! The default value for the BatchSizeOverride will be 1 indicating a static batch size of 1.
//! BatchSizeOverride = 0 indicates that the batch size present in the model should be honored.
//! BatchSizeOverride > 0 indicates the size of batch that will be used to override the model batch size and optimize evaluations.
UInt32 BatchSizeOverride { get; set; };
[contract(MachineLearningContract, 4)]
{
//! The OverrideNamedDimension method will allow the model compiler to use constant batch size performance optimizations when setting up the LearningModelSession.
//! The caller can specify the size of the dimension for a given named dimension.
//! dimension = 0 indicates that the dimension present in the model should be honored.
//! dimension > 0 indicates the size of the dimension that will be used to override the model "name" dimension and optimize evaluations.
void OverrideNamedDimension(String name, UInt32 dimension);
}
[contract(MachineLearningContract, 3)]
{
//! The CloseModelOnSessionCreation option will allow the LearningModelSession to take ownership of the LearningModel's
//! internal model representation. This will defunct the LearningModel session, but decreases the necessary peak working set.
//! CloseModelOnSessionCreation = True indicates that the model's internal data will be moved into the session during construction.
//! CloseModelOnSessionCreation = False indicates that the model's internal data will be copied into the session during construction.
Boolean CloseModelOnSessionCreation { get; set; };
}
}
//! \class LearningModelSession
//! \brief TODO:Docs
[contract(MachineLearningContract, 1)]
INBOX_ONLY([constructor_name("Windows.AI.MachineLearning.ILearningModelSessionFactory", 0f6b881d-1c9b-47b6-bfe0-f1cf62a67579)])
INBOX_ONLY([interface_name("Windows.AI.MachineLearning.ILearningModelSession", 8e58f8f6-b787-4c11-90f0-7129aeca74a9)])
[threading(both)]
[marshaling_behavior(agile)]
[dualapipartition(1)]
runtimeclass LearningModelSession : Windows.Foundation.IClosable
{
//! Create a session, on the system default device, to evaluate the specified model on.
[method_name("CreateFromModel")] LearningModelSession(LearningModel model);
//! Create a session, on the provided device, to evaluate the specified model on.
[method_name("CreateFromModelOnDevice")] LearningModelSession(LearningModel model, LearningModelDevice deviceToRunOn);
//! Returns the machine learning model attached to the session.
LearningModel Model{ get; };
//! Returns the evaluation device that the session was created on.
LearningModelDevice Device{ get; };
//! Returns the list of properties set for model evaluation.
Windows.Foundation.Collections.IPropertySet EvaluationProperties{ get; };
//! Evaluate the machine learning model using the feature values already bound in 'bindings'. (asynchronous)
[remote_async]
Windows.Foundation.IAsyncOperation<LearningModelEvaluationResult> EvaluateAsync(LearningModelBinding bindings, String correlationId);
//! Evaluate the machine learning model using the feature values in the map 'features'. (asynchronous)
//! This method is an alternative to the separated bind then eval form that takes a LearningModelBinding.
//! It will take the passed in features, create a LearningModelBinding for you, bind the features, and then evaluate the model.
[remote_async]
Windows.Foundation.IAsyncOperation<LearningModelEvaluationResult> EvaluateFeaturesAsync(Windows.Foundation.Collections.IMap<String, IInspectable> features, String correlationId);
//! Evaluate the machine learning model using the feature values bound in 'bindings'.
LearningModelEvaluationResult Evaluate(LearningModelBinding bindings, String correlationId);
//! Evaluate the machine learning model using the feature values in the map 'features'.
//! This method is an alternative to the separated bind then eval form that takes a LearningModelBinding.
//! It will take the passed in features, create a LearningModelBinding for you, bind the features, and then evaluate the model.
LearningModelEvaluationResult EvaluateFeatures(Windows.Foundation.Collections.IMap<String, IInspectable> features, String correlationId);
[contract(MachineLearningContract, 2)]
{
//! Create a session, on the provided device, with the desired model compilation options, to evaluate the specified model on.
[method_name("CreateFromModelOnDeviceWithSessionOptions")] LearningModelSession(LearningModel model, LearningModelDevice deviceToRunOn, LearningModelSessionOptions learningModelSessionOptions);
}
}
//! \interface ILearningModelFeatureValue
//! \brief The instantiated value for a feature.
[contract(MachineLearningContract, 1)]
INBOX_ONLY([uuid(f51005db-4085-4dfe-9fed-95eb0c0cf75c)])
interface ILearningModelFeatureValue
{
//! The data type of the feature.
LearningModelFeatureKind Kind{ get; };
};
//! \class LearningModelBinding
//! \brief Holderforassociationsbetweenmodelinputs/outputsandvariableinstances.
[contract(MachineLearningContract, 1)]
INBOX_ONLY([constructor_name("Windows.AI.MachineLearning.ILearningModelBindingFactory", c95f7a7a-e788-475e-8917-23aa381faf0b)])
INBOX_ONLY([interface_name("Windows.AI.MachineLearning.ILearningModelBinding", ea312f20-168f-4f8c-94fe-2e7ac31b4aa8)])
[threading(both)]
[marshaling_behavior(agile)]
[dualapipartition(1)]
runtimeclass LearningModelBinding : Windows.Foundation.Collections.IMapView <String, IInspectable>
{
//! Create a LearningModelBinding from the specified LearningModelSession.
//! During evaluation, the specified adapter will be used to create resources and queue work during execution.
[method_name("CreateFromSession")] LearningModelBinding(LearningModelSession session);
//! Bind a value to the specified feature.
void Bind(String name, IInspectable value);
//! TODO:Docs
[method_name("BindWithProperties")] void Bind(String name, IInspectable value, Windows.Foundation.Collections.IPropertySet props);
//! Remove all bindings.
void Clear();
}
//! \enum TensorKind
//! \brief Defines the list of supported tensor data types.
[contract(MachineLearningContract, 1)]
enum TensorKind
{
//! Supported by ONNX, but should never happen and is invalid for Windows ML.
Undefined = 0,
//! The tensor type is 32bit float.
Float,
//! The tensor type is 8bit unsigned int.
UInt8,
//! The tensor type is 8bit signed int.
Int8,
//! The tensor type is 16bit unsigned int.
UInt16,
//! The tensor type is 16bit signed int.
Int16,
//! The tensor type is 32bit signed int.
Int32,
//! The tensor type is 64bit signed int.
Int64,
//! The tensor type is String.
String,
//! The tensor type is Boolean.
Boolean,
//! The tensor type is 16bit float.
Float16,
//! The tensor type is 64bit float.
Double,
//! The tensor type is 32bit unsigned int.
UInt32,
//! The tensor type is 64bit unsigned int.
UInt64,
//! Supported by ONNX, but is not supported by Windows ML.
Complex64,
//! Supported by ONNX, but is not supported by Windows ML.
Complex128
};
//! \class MapFeatureDescriptor
//! \brief TODO:Docs
[contract(MachineLearningContract, 1)]
INBOX_ONLY([interface_name("Windows.AI.MachineLearning.IMapFeatureDescriptor", 530424bd-a257-436d-9e60-c2981f7cc5c4)])
[marshaling_behavior(agile)]
[dualapipartition(1)]
runtimeclass MapFeatureDescriptor : ILearningModelFeatureDescriptor
{
//! Returns the data type of the map's key.
TensorKind KeyKind{ get; };
//! Returns the properties of the map's value.
ILearningModelFeatureDescriptor ValueDescriptor{ get; };
}
//! \class SequenceFeatureDescriptor
//! \brief TODO:Docs
[contract(MachineLearningContract, 1)]
INBOX_ONLY([interface_name("Windows.AI.MachineLearning.ISequenceFeatureDescriptor", 84f6945a-562b-4d62-a851-739aced96668)])
[marshaling_behavior(agile)]
[dualapipartition(1)]
runtimeclass SequenceFeatureDescriptor : ILearningModelFeatureDescriptor
{
//! Gets the properties of the specified feature.
ILearningModelFeatureDescriptor ElementDescriptor{ get; };
}
//! \class TensorFeatureDescriptor
//! \brief TODO:Docs
[contract(MachineLearningContract, 1)]
INBOX_ONLY([interface_name("Windows.AI.MachineLearning.ITensorFeatureDescriptor", 74455c80-946a-4310-a19c-ee0af028fce4)])
[marshaling_behavior(agile)]
[dualapipartition(1)]
runtimeclass TensorFeatureDescriptor : ILearningModelFeatureDescriptor
{
//! Returns the data type of the tensor.
TensorKind TensorKind{ get; };
//! Returns the count and size of each dimension.
Windows.Foundation.Collections.IVectorView<Int64> Shape{ get; };
}
//! \enum LearningModelPixelRange
//! \brief Defines the list of image nominal pixel range specified in model metadata.
[contract(MachineLearningContract, 5)]
enum LearningModelPixelRange
{
//! [0...255] for 8bpp(8 Bits per Pixel) samples
ZeroTo255= 0,
//! [0...1] pixel data is stored normalized
ZeroToOne,
//! [-1...1] pixel data is stored normalized
MinusOneToOne
};
//! \class ImageFeatureDescriptor
//! \brief TODO:Docs
[contract(MachineLearningContract, 1)]
INBOX_ONLY([interface_name("Windows.AI.MachineLearning.IImageFeatureDescriptor", 365585a5-171a-4a2a-985f-265159d3895a)])
[marshaling_behavior(agile)]
[dualapipartition(1)]
runtimeclass ImageFeatureDescriptor : ILearningModelFeatureDescriptor
{
//! Specifies the pixel format (channel ordering, bit depth, and data type) of the pixel data.
Windows.Graphics.Imaging.BitmapPixelFormat BitmapPixelFormat{ get; };
//! Specifies the alpha mode of the pixel data.
Windows.Graphics.Imaging.BitmapAlphaMode BitmapAlphaMode{ get; };
[contract(MachineLearningContract, 5)]
{
//Specifies the nominal pixel range of pixel data.
LearningModelPixelRange PixelRange{ get; };
}
//! The width of the image.
UInt32 Width{ get; };
//! The height of the image.
UInt32 Height{ get; };
}
//! \interface ITensor
//! \brief TODO:Docs
[contract(MachineLearningContract, 1)]
INBOX_ONLY([uuid(05489593-a305-4a25-ad09-440119b4b7f6)])
interface ITensor : IInspectable requires ILearningModelFeatureValue
{
//! Returns the data type of the tensor.
TensorKind TensorKind{ get; };
//! TODO:Docs
Windows.Foundation.Collections.IVectorView<Int64> Shape{ get; };
}
//! \class TensorFloat
//! \brief A 32bit float tensor object.
[contract(MachineLearningContract, 1)]
INBOX_ONLY([static_name("Windows.AI.MachineLearning.ITensorFloatStatics", dbcd395b-3ba3-452f-b10d-3c135e573fa9)])
INBOX_ONLY([interface_name("Windows.AI.MachineLearning.ITensorFloat", f2282d82-aa02-42c8-a0c8-df1efc9676e1)])
[threading(both)]
[marshaling_behavior(agile)]
[dualapipartition(1)]
runtimeclass TensorFloat : ITensor, [contract(MachineLearningContract, 2)]Windows.Foundation.IMemoryBuffer
{
//! Creates a tensor object without allocating a buffer.
static TensorFloat Create();
//! Creates a tensor object and allocates a buffer of size 'shape'.
static TensorFloat Create(Windows.Foundation.Collections.IIterable<Int64> shape);
//! Creates a tensor object, allocates a buffer of size 'shape', and copies all of 'data' into it.
static TensorFloat CreateFromArray(Windows.Foundation.Collections.IIterable<Int64> shape, Single[] data);
//! Creates a tensor object, allocates a buffer of size 'shape', and copies all of 'data' into it.
static TensorFloat CreateFromIterable(Windows.Foundation.Collections.IIterable<Int64> shape, Windows.Foundation.Collections.IIterable<Single> data);
//! Returns a read only view of the data.
Windows.Foundation.Collections.IVectorView <Single> GetAsVectorView();
[contract(MachineLearningContract, 2)]
{
//! Creates a tensor object, allocates a buffer of size 'shape', and copies all of 'data' into it.
static TensorFloat CreateFromShapeArrayAndDataArray(Int64[] shape, Single[] data);
//! Creates a tensor object of size 'shape', and uses the data in 'buffer'.
static TensorFloat CreateFromBuffer(Int64[] shape, Windows.Storage.Streams.IBuffer buffer);
}
}
[contract(MachineLearningContract, 1)]
INBOX_ONLY([static_name("Windows.AI.MachineLearning.ITensorFloat16BitStatics", a52db6f5-318a-44d4-820b-0cdc7054a84a)])
INBOX_ONLY([interface_name("Windows.AI.MachineLearning.ITensorFloat16Bit", 0ab994fc-5b89-4c3c-b5e4-5282a5316c0a)])
[threading(both)]
[marshaling_behavior(agile)]
[dualapipartition(1)]
runtimeclass TensorFloat16Bit : ITensor, [contract(MachineLearningContract, 2)]Windows.Foundation.IMemoryBuffer
{
static TensorFloat16Bit Create();
static TensorFloat16Bit Create(Windows.Foundation.Collections.IIterable<Int64> shape);
static TensorFloat16Bit CreateFromArray(Windows.Foundation.Collections.IIterable<Int64> shape, Single[] data);
static TensorFloat16Bit CreateFromIterable(Windows.Foundation.Collections.IIterable<Int64> shape, Windows.Foundation.Collections.IIterable<Single> data);
Windows.Foundation.Collections.IVectorView <Single> GetAsVectorView();
[contract(MachineLearningContract, 2)]
{
//! Creates a tensor object, allocates a buffer of size 'shape', and copies all of 'data' into it.
static TensorFloat16Bit CreateFromShapeArrayAndDataArray(Int64[] shape, Single[] data);
//! Creates a tensor object, creates a tensor of size 'shape', and uses the data in 'buffer'.
//! 'buffer' contains a packed array of 16bit floating point values.
static TensorFloat16Bit CreateFromBuffer(Int64[] shape, Windows.Storage.Streams.IBuffer buffer);
}
}
[contract(MachineLearningContract, 1)]
INBOX_ONLY([static_name("Windows.AI.MachineLearning.ITensorUInt8BitStatics", 05f67583-bc24-4220-8a41-2dcd8c5ed33c)])
INBOX_ONLY([interface_name("Windows.AI.MachineLearning.ITensorUInt8Bit", 58e1ae27-622b-48e3-be22-d867aed1daac)])
[threading(both)]
[marshaling_behavior(agile)]
[dualapipartition(1)]
runtimeclass TensorUInt8Bit : ITensor, [contract(MachineLearningContract, 2)]Windows.Foundation.IMemoryBuffer
{
static TensorUInt8Bit Create();
static TensorUInt8Bit Create(Windows.Foundation.Collections.IIterable<Int64> shape);
static TensorUInt8Bit CreateFromArray(Windows.Foundation.Collections.IIterable<Int64> shape, UInt8[] data);
static TensorUInt8Bit CreateFromIterable(Windows.Foundation.Collections.IIterable<Int64> shape, Windows.Foundation.Collections.IIterable<UInt8> data);
Windows.Foundation.Collections.IVectorView <UInt8> GetAsVectorView();
[contract(MachineLearningContract, 2)]
{
//! Creates a tensor object, allocates a buffer of size 'shape', and copies all of 'data' into it.
static TensorUInt8Bit CreateFromShapeArrayAndDataArray(Int64[] shape, UInt8[] data);
//! Creates a tensor object, creates a tensor of size 'shape', and uses the data in 'buffer'.
//! 'buffer' contains a packed array of 8bit uint8 values.
static TensorUInt8Bit CreateFromBuffer(Int64[] shape, Windows.Storage.Streams.IBuffer buffer);
}
}
[contract(MachineLearningContract, 1)]
INBOX_ONLY([static_name("Windows.AI.MachineLearning.ITensorInt8BitStatics", b1a12284-095c-4c76-a661-ac4cee1f3e8b)])
INBOX_ONLY([interface_name("Windows.AI.MachineLearning.ITensorInt8Bit", cddd97c5-ffd8-4fef-aefb-30e1a485b2ee)])
[threading(both)]
[marshaling_behavior(agile)]
[dualapipartition(1)]
runtimeclass TensorInt8Bit : ITensor, [contract(MachineLearningContract, 2)]Windows.Foundation.IMemoryBuffer
{
static TensorInt8Bit Create();
static TensorInt8Bit Create(Windows.Foundation.Collections.IIterable<Int64> shape);
static TensorInt8Bit CreateFromArray(Windows.Foundation.Collections.IIterable<Int64> shape, BYTE[] data);
static TensorInt8Bit CreateFromIterable(Windows.Foundation.Collections.IIterable<Int64> shape, Windows.Foundation.Collections.IIterable<BYTE> data);
Windows.Foundation.Collections.IVectorView <BYTE> GetAsVectorView();
[contract(MachineLearningContract, 2)]
{
//! Creates a tensor object, allocates a buffer of size 'shape', and copies all of 'data' into it.
static TensorInt8Bit CreateFromShapeArrayAndDataArray(Int64[] shape, BYTE[] data);
//! Creates a tensor object, creates a tensor of size 'shape', and uses the data in 'buffer'.
static TensorInt8Bit CreateFromBuffer(Int64[] shape, Windows.Storage.Streams.IBuffer buffer);
}
}
[contract(MachineLearningContract, 1)]
INBOX_ONLY([static_name("Windows.AI.MachineLearning.ITensorUInt16BitStatics", 5df745dd-028a-481a-a27c-c7e6435e52dd)])
INBOX_ONLY([interface_name("Windows.AI.MachineLearning.ITensorUInt16Bit", 68140f4b-23c0-42f3-81f6-a891c011bc3f)])
[threading(both)]
[marshaling_behavior(agile)]
[dualapipartition(1)]
runtimeclass TensorUInt16Bit : ITensor, [contract(MachineLearningContract, 2)]Windows.Foundation.IMemoryBuffer
{
static TensorUInt16Bit Create();
static TensorUInt16Bit Create(Windows.Foundation.Collections.IIterable<Int64> shape);
static TensorUInt16Bit CreateFromArray(Windows.Foundation.Collections.IIterable<Int64> shape, UInt16[] data);
static TensorUInt16Bit CreateFromIterable(Windows.Foundation.Collections.IIterable<Int64> shape, Windows.Foundation.Collections.IIterable<UInt16> data);
Windows.Foundation.Collections.IVectorView <UInt16> GetAsVectorView();
[contract(MachineLearningContract, 2)]
{
//! Creates a tensor object, allocates a buffer of size 'shape', and copies all of 'data' into it.
static TensorUInt16Bit CreateFromShapeArrayAndDataArray(Int64[] shape, UInt16[] data);
//! Creates a tensor object, creates a tensor of size 'shape', and uses the data in 'buffer'.
static TensorUInt16Bit CreateFromBuffer(Int64[] shape, Windows.Storage.Streams.IBuffer buffer);
}
}
[contract(MachineLearningContract, 1)]
INBOX_ONLY([static_name("Windows.AI.MachineLearning.ITensorInt16BitStatics", 98646293-266e-4b1a-821f-e60d70898b91)])
INBOX_ONLY([interface_name("Windows.AI.MachineLearning.ITensorInt16Bit", 98a32d39-e6d6-44af-8afa-baebc44dc020)])
[threading(both)]
[marshaling_behavior(agile)]
[dualapipartition(1)]
runtimeclass TensorInt16Bit : ITensor, [contract(MachineLearningContract, 2)]Windows.Foundation.IMemoryBuffer
{
static TensorInt16Bit Create();
static TensorInt16Bit Create(Windows.Foundation.Collections.IIterable<Int64> shape);
static TensorInt16Bit CreateFromArray(Windows.Foundation.Collections.IIterable<Int64> shape, Int16[] data);
static TensorInt16Bit CreateFromIterable(Windows.Foundation.Collections.IIterable<Int64> shape, Windows.Foundation.Collections.IIterable<Int16> data);
Windows.Foundation.Collections.IVectorView <Int16> GetAsVectorView();
[contract(MachineLearningContract, 2)]
{
//! Creates a tensor object, allocates a buffer of size 'shape', and copies all of 'data' into it.
static TensorInt16Bit CreateFromShapeArrayAndDataArray(Int64[] shape, Int16[] data);
//! Creates a tensor object, creates a tensor of size 'shape', and uses the data in 'buffer'.
static TensorInt16Bit CreateFromBuffer(Int64[] shape, Windows.Storage.Streams.IBuffer buffer);
}
}
[contract(MachineLearningContract, 1)]
INBOX_ONLY([static_name("Windows.AI.MachineLearning.ITensorUInt32BitStatics", 417c3837-e773-4378-8e7f-0cc33dbea697)])
INBOX_ONLY([interface_name("Windows.AI.MachineLearning.ITensorUInt32Bit", d8c9c2ff-7511-45a3-bfac-c38f370d2237)])
[threading(both)]
[marshaling_behavior(agile)]
[dualapipartition(1)]
runtimeclass TensorUInt32Bit : ITensor, [contract(MachineLearningContract, 2)]Windows.Foundation.IMemoryBuffer
{
static TensorUInt32Bit Create();
static TensorUInt32Bit Create(Windows.Foundation.Collections.IIterable<Int64> shape);
static TensorUInt32Bit CreateFromArray(Windows.Foundation.Collections.IIterable<Int64> shape, UInt32[] data);
static TensorUInt32Bit CreateFromIterable(Windows.Foundation.Collections.IIterable<Int64> shape, Windows.Foundation.Collections.IIterable<UInt32> data);
Windows.Foundation.Collections.IVectorView <UInt32> GetAsVectorView();
[contract(MachineLearningContract, 2)]
{
//! Creates a tensor object, allocates a buffer of size 'shape', and copies all of 'data' into it.
static TensorUInt32Bit CreateFromShapeArrayAndDataArray(Int64[] shape, UInt32[] data);
//! Creates a tensor object, creates a tensor of size 'shape', and uses the data in 'buffer'.
static TensorUInt32Bit CreateFromBuffer(Int64[] shape, Windows.Storage.Streams.IBuffer buffer);
}
}
[contract(MachineLearningContract, 1)]
INBOX_ONLY([static_name("Windows.AI.MachineLearning.ITensorInt32BitStatics", 6539864b-52fa-4e35-907c-834cac417b50)])
INBOX_ONLY([interface_name("Windows.AI.MachineLearning.ITensorInt32Bit", 2c0c28d3-207c-4486-a7d2-884522c5e589)])
[threading(both)]
[marshaling_behavior(agile)]
[dualapipartition(1)]
runtimeclass TensorInt32Bit : ITensor, [contract(MachineLearningContract, 2)]Windows.Foundation.IMemoryBuffer
{
static TensorInt32Bit Create();
static TensorInt32Bit Create(Windows.Foundation.Collections.IIterable<Int64> shape);
static TensorInt32Bit CreateFromArray(Windows.Foundation.Collections.IIterable<Int64> shape, Int32[] data);
static TensorInt32Bit CreateFromIterable(Windows.Foundation.Collections.IIterable<Int64> shape, Windows.Foundation.Collections.IIterable<Int32> data);
Windows.Foundation.Collections.IVectorView <Int32> GetAsVectorView();
[contract(MachineLearningContract, 2)]
{
//! Creates a tensor object, allocates a buffer of size 'shape', and copies all of 'data' into it.
static TensorInt32Bit CreateFromShapeArrayAndDataArray(Int64[] shape, Int32[] data);
//! Creates a tensor object, creates a tensor of size 'shape', and uses the data in 'buffer'.
static TensorInt32Bit CreateFromBuffer(Int64[] shape, Windows.Storage.Streams.IBuffer buffer);
}
}
[contract(MachineLearningContract, 1)]
INBOX_ONLY([static_name("Windows.AI.MachineLearning.ITensorUInt64BitStatics", 7a7e20eb-242f-47cb-a9c6-f602ecfbfee4)])
INBOX_ONLY([interface_name("Windows.AI.MachineLearning.ITensorUInt64Bit", 2e70ffad-04bf-4825-839a-82baef8c7886)])
[threading(both)]
[marshaling_behavior(agile)]
[dualapipartition(1)]
runtimeclass TensorUInt64Bit : ITensor, [contract(MachineLearningContract, 2)]Windows.Foundation.IMemoryBuffer
{
static TensorUInt64Bit Create();
static TensorUInt64Bit Create(Windows.Foundation.Collections.IIterable<Int64> shape);
static TensorUInt64Bit CreateFromArray(Windows.Foundation.Collections.IIterable<Int64> shape, UInt64[] data);
static TensorUInt64Bit CreateFromIterable(Windows.Foundation.Collections.IIterable<Int64> shape, Windows.Foundation.Collections.IIterable<UInt64> data);
Windows.Foundation.Collections.IVectorView <UInt64> GetAsVectorView();
[contract(MachineLearningContract, 2)]
{
//! Creates a tensor object, allocates a buffer of size 'shape', and copies all of 'data' into it.
static TensorUInt64Bit CreateFromShapeArrayAndDataArray(Int64[] shape, UInt64[] data);
//! Creates a tensor object, creates a tensor of size 'shape', and uses the data in 'buffer'.
static TensorUInt64Bit CreateFromBuffer(Int64[] shape, Windows.Storage.Streams.IBuffer buffer);
}
}
[contract(MachineLearningContract, 1)]
INBOX_ONLY([static_name("Windows.AI.MachineLearning.ITensorInt64BitStatics", 9648ad9d-1198-4d74-9517-783ab62b9cc2)])
INBOX_ONLY([interface_name("Windows.AI.MachineLearning.ITensorInt64Bit", 499665ba-1fa2-45ad-af25-a0bd9bda4c87)])
[threading(both)]
[marshaling_behavior(agile)]
[dualapipartition(1)]
runtimeclass TensorInt64Bit : ITensor, [contract(MachineLearningContract, 2)]Windows.Foundation.IMemoryBuffer
{
static TensorInt64Bit Create();
static TensorInt64Bit Create(Windows.Foundation.Collections.IIterable<Int64> shape);
static TensorInt64Bit CreateFromArray(Windows.Foundation.Collections.IIterable<Int64> shape, Int64[] data);
static TensorInt64Bit CreateFromIterable(Windows.Foundation.Collections.IIterable<Int64> shape, Windows.Foundation.Collections.IIterable<Int64> data);
Windows.Foundation.Collections.IVectorView <Int64> GetAsVectorView();
[contract(MachineLearningContract, 2)]
{
//! Creates a tensor object, allocates a buffer of size 'shape', and copies all of 'data' into it.
static TensorInt64Bit CreateFromShapeArrayAndDataArray(Int64[] shape, Int64[] data);
//! Creates a tensor object, creates a tensor of size 'shape', and uses the data in 'buffer'.
static TensorInt64Bit CreateFromBuffer(Int64[] shape, Windows.Storage.Streams.IBuffer buffer);
}
}
[contract(MachineLearningContract, 1)]
INBOX_ONLY([static_name("Windows.AI.MachineLearning.ITensorBooleanStatics", 2796862c-2357-49a7-b476-d0aa3dfe6866)])
INBOX_ONLY([interface_name("Windows.AI.MachineLearning.ITensorBoolean", 50f311ed-29e9-4a5c-a44d-8fc512584eed)])
[threading(both)]
[marshaling_behavior(agile)]
[dualapipartition(1)]
runtimeclass TensorBoolean : ITensor, [contract(MachineLearningContract, 2)]Windows.Foundation.IMemoryBuffer
{
static TensorBoolean Create();
static TensorBoolean Create(Windows.Foundation.Collections.IIterable<Int64> shape);
static TensorBoolean CreateFromArray(Windows.Foundation.Collections.IIterable<Int64> shape, Boolean[] data);
static TensorBoolean CreateFromIterable(Windows.Foundation.Collections.IIterable<Int64> shape, Windows.Foundation.Collections.IIterable<Boolean> data);
Windows.Foundation.Collections.IVectorView <Boolean> GetAsVectorView();
[contract(MachineLearningContract, 2)]
{
//! Creates a tensor object, allocates a buffer of size 'shape', and copies all of 'data' into it.
static TensorBoolean CreateFromShapeArrayAndDataArray(Int64[] shape, Boolean[] data);
//! Creates a tensor object, creates a tensor of size 'shape', and uses the data in 'buffer'.
//! 'buffer' represents a byte packed array of boolean values.
static TensorBoolean CreateFromBuffer(Int64[] shape, Windows.Storage.Streams.IBuffer buffer);
}
}
[contract(MachineLearningContract, 1)]
INBOX_ONLY([static_name("Windows.AI.MachineLearning.ITensorDoubleStatics", a86693c5-9538-44e7-a3ca-5df374a5a70c)])
INBOX_ONLY([interface_name("Windows.AI.MachineLearning.ITensorDouble", 91e41252-7a8f-4f0e-a28f-9637ffc8a3d0)])
[threading(both)]
[marshaling_behavior(agile)]
[dualapipartition(1)]
runtimeclass TensorDouble : ITensor, [contract(MachineLearningContract, 2)]Windows.Foundation.IMemoryBuffer
{
static TensorDouble Create();
static TensorDouble Create(Windows.Foundation.Collections.IIterable<Int64> shape);
static TensorDouble CreateFromArray(Windows.Foundation.Collections.IIterable<Int64> shape, Double[] data);
static TensorDouble CreateFromIterable(Windows.Foundation.Collections.IIterable<Int64> shape, Windows.Foundation.Collections.IIterable<Double> data);
Windows.Foundation.Collections.IVectorView <Double> GetAsVectorView();
[contract(MachineLearningContract, 2)]
{
//! Creates a tensor object, allocates a buffer of size 'shape', and copies all of 'data' into it.
static TensorDouble CreateFromShapeArrayAndDataArray(Int64[] shape, Double[] data);
//! Creates a tensor object, creates a tensor of size 'shape', and uses the data in 'buffer'.
static TensorDouble CreateFromBuffer(Int64[] shape, Windows.Storage.Streams.IBuffer buffer);
}
}
[contract(MachineLearningContract, 1)]
INBOX_ONLY([static_name("Windows.AI.MachineLearning.ITensorStringStatics", 83623324-cf26-4f17-a2d4-20ef8d097d53)])
INBOX_ONLY([interface_name("Windows.AI.MachineLearning.ITensorString", 582335c8-bdb1-4610-bc75-35e9cbf009b7)])
[threading(both)]
[marshaling_behavior(agile)]
[dualapipartition(1)]
runtimeclass TensorString : ITensor, [contract(MachineLearningContract, 2)]Windows.Foundation.IMemoryBuffer
{
static TensorString Create();
static TensorString Create(Windows.Foundation.Collections.IIterable<Int64> shape);
static TensorString CreateFromArray(Windows.Foundation.Collections.IIterable<Int64> shape, String[] data);
static TensorString CreateFromIterable(Windows.Foundation.Collections.IIterable<Int64> shape, Windows.Foundation.Collections.IIterable<String> data);
Windows.Foundation.Collections.IVectorView <String> GetAsVectorView();
[contract(MachineLearningContract, 2)]
{
//! Creates a tensor object, allocates a buffer of size 'shape', and copies all of 'data' into it.
static TensorString CreateFromShapeArrayAndDataArray(Int64[] shape, String[] data);
}
}
[contract(MachineLearningContract, 1)]
INBOX_ONLY([static_name("Windows.AI.MachineLearning.IImageFeatureValueStatics", 1bc317fd-23cb-4610-b085-c8e1c87ebaa0)])
INBOX_ONLY([interface_name("Windows.AI.MachineLearning.IImageFeatureValue", f0414fd9-c9aa-4405-b7fb-94f87c8a3037)])
[threading(both)]
[marshaling_behavior(agile)]
[dualapipartition(1)]
runtimeclass ImageFeatureValue : ILearningModelFeatureValue
{
static ImageFeatureValue CreateFromVideoFrame(Windows.Media.VideoFrame image);
Windows.Media.VideoFrame VideoFrame{ get; };
}
}