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// Copyright (c) Microsoft Corporation.
// 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 attirbute definitions.
import "dualapipartitionattribute.idl";
import "windows.graphics.directx.direct3d11.idl";
import "windows.graphics.imaging.idl";
import "windows.storage.idl";
#endif
#include <sdkddkver.h>
namespace Windows.AI.MachineLearning
{
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[contractversion(4)]
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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.
[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; };
}
[uuid(2a222e5d-afb1-47ed-bfad-b5b3a459ec04)]
[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)]
[static_name("Windows.AI.MachineLearning.ILearningModelStatics", e3b977e8-6952-4e47-8ef4-1f7f07897c6d)]
[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)]
[constructor_name("Windows.AI.MachineLearning.ILearningModelDeviceFactory", 9cffd74d-b1e5-4f20-80ad-0a56690db06b)]
[static_name("Windows.AI.MachineLearning.ILearningModelDeviceStatics", 49f32107-a8bf-42bb-92c7-10b12dc5d21f)]
[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)]
[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, 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)]
[constructor_name("Windows.AI.MachineLearning.ILearningModelSessionFactory", 0f6b881d-1c9b-47b6-bfe0-f1cf62a67579)]
[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)]
[uuid(f51005db-4085-4dfe-9fed-95eb0c0cf75c)]
interface ILearningModelFeatureValue
{
//! The data type of the feature.
LearningModelFeatureKind Kind{ get; };
};
//! \class LearningModelBinding
//! \brief Holder
[contract(MachineLearningContract, 1)]
[constructor_name("Windows.AI.MachineLearning.ILearningModelBindingFactory", c95f7a7a-e788-475e-8917-23aa381faf0b)]
[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)]
[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; };
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[contract(MachineLearningContract, 4)]
{
//! allows creation of feature descriptors outside the runtime (immutable)
[method_name("Create")] MapFeatureDescriptor(
String Name,
String Description,
Boolean IsRequired,
TensorKind KeyKind,
ILearningModelFeatureDescriptor ValueDescriptor
);
}
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}
//! \class SequenceFeatureDescriptor
//! \brief TODO:Docs
[contract(MachineLearningContract, 1)]
[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; };
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[contract(MachineLearningContract, 4)]
{
//! allows creation of feature descriptors outside the runtime (immutable)
[method_name("Create")] SequenceFeatureDescriptor(
String Name,
String Description,
Boolean IsRequired,
ILearningModelFeatureDescriptor ElementDescriptor
);
}
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}
//! \class TensorFeatureDescriptor
//! \brief TODO:Docs
[contract(MachineLearningContract, 1)]
[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; };
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[contract(MachineLearningContract, 4)]
{
//! allows creation of feature descriptors outside the runtime (immutable)
[method_name("Create")] TensorFeatureDescriptor(
String Name,
String Description,
Boolean IsRequired,
TensorKind TensorKind,
Int64[] Shape,
Boolean HasUnsupportedImageMetadata
);
//! if this feature is an image but has unsupport image metadata (like a BitmapPixelFormat)
//! you can still use the runtime but without image conversion support.
//! this setting will be 'true'
Boolean HasUnsupportedImageMetadata{ get; } ;
}
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}
//! \class ImageFeatureDescriptor
//! \brief TODO:Docs
[contract(MachineLearningContract, 1)]
[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; };
//! The width of the image.
UInt32 Width{ get; };
//! The height of the image.
UInt32 Height{ get; };
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[contract(MachineLearningContract, 4)]
{
//! allows creation of feature descriptors outside the runtime (immutable)
[method_name("Create")] ImageFeatureDescriptor(
String Name,
String Description,
Boolean IsRequired,
TensorKind TensorKind,
Int64[] Shape,
Windows.Graphics.Imaging.BitmapPixelFormat BitmapPixelFormat,
Windows.Graphics.Imaging.BitmapAlphaMode BitmapAlphaMode,
UInt32 Width,
UInt32 Height
);
//! Returns the data type of the tensor. This is useful if you want to know
//! if it's fp16 or fp32
TensorKind TensorKind{ get; };
}
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}
//! \interface ITensor
//! \brief TODO:Docs
[contract(MachineLearningContract, 1)]
[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)]
[static_name("Windows.AI.MachineLearning.ITensorFloatStatics", dbcd395b-3ba3-452f-b10d-3c135e573fa9)]
[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)]
[static_name("Windows.AI.MachineLearning.ITensorFloat16BitStatics", a52db6f5-318a-44d4-820b-0cdc7054a84a)]
[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)]
[static_name("Windows.AI.MachineLearning.ITensorUInt8BitStatics", 05f67583-bc24-4220-8a41-2dcd8c5ed33c)]
[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)]
[static_name("Windows.AI.MachineLearning.ITensorInt8BitStatics", b1a12284-095c-4c76-a661-ac4cee1f3e8b)]
[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)]
[static_name("Windows.AI.MachineLearning.ITensorUInt16BitStatics", 5df745dd-028a-481a-a27c-c7e6435e52dd)]
[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)]
[static_name("Windows.AI.MachineLearning.ITensorInt16BitStatics", 98646293-266e-4b1a-821f-e60d70898b91)]
[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)]
[static_name("Windows.AI.MachineLearning.ITensorUInt32BitStatics", 417c3837-e773-4378-8e7f-0cc33dbea697)]
[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)]
[static_name("Windows.AI.MachineLearning.ITensorInt32BitStatics", 6539864b-52fa-4e35-907c-834cac417b50)]
[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)]
[static_name("Windows.AI.MachineLearning.ITensorUInt64BitStatics", 7a7e20eb-242f-47cb-a9c6-f602ecfbfee4)]
[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)]
[static_name("Windows.AI.MachineLearning.ITensorInt64BitStatics", 9648ad9d-1198-4d74-9517-783ab62b9cc2)]
[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)]
[static_name("Windows.AI.MachineLearning.ITensorBooleanStatics", 2796862c-2357-49a7-b476-d0aa3dfe6866)]
[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)]
[static_name("Windows.AI.MachineLearning.ITensorDoubleStatics", a86693c5-9538-44e7-a3ca-5df374a5a70c)]
[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)]
[static_name("Windows.AI.MachineLearning.ITensorStringStatics", 83623324-cf26-4f17-a2d4-20ef8d097d53)]
[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)]
[static_name("Windows.AI.MachineLearning.IImageFeatureValueStatics", 1bc317fd-23cb-4610-b085-c8e1c87ebaa0)]
[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; };
}
}
