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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>
104 lines
3.8 KiB
Text
104 lines
3.8 KiB
Text
// Copyright (c) Microsoft Corporation. All rights reserved.
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// Licensed under the MIT License.
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import "Windows.Foundation.idl";
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import "dualapipartitionattribute.idl";
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import "Windows.AI.MachineLearning.idl";
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#include <sdkddkver.h>
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#ifdef BUILD_INBOX
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#define ROOT_NS Windows
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#define INBOX_ONLY(x) x
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#define OTB_ONLY(x)
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#else
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#define INBOX_ONLY(x)
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#define OTB_ONLY(x) x
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#endif
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#ifndef ROOT_NS
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#define ROOT_NS Microsoft
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#endif
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namespace ROOT_NS.AI.MachineLearning.Experimental {
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runtimeclass LearningModelBuilder;
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[marshaling_behavior(agile)]
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[dualapipartition(1)]
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runtimeclass LearningModelSessionOptionsExperimental {
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Windows.Foundation.Collections.IMapView<String, UINT32> GetNamedDimensionOverrides();
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}
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[threading(both)]
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[marshaling_behavior(agile)]
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[dualapipartition(1)]
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runtimeclass LearningModelSessionExperimental {
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LearningModelSessionExperimental(ROOT_NS.AI.MachineLearning.LearningModelSession session);
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LearningModelSessionOptionsExperimental Options { get; };
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}
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[threading(both)]
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[marshaling_behavior(agile)]
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[dualapipartition(1)]
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runtimeclass LearningModelOperator {
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LearningModelOperator(String type);
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LearningModelOperator(String type, String domain);
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LearningModelOperator SetName(String name);
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LearningModelOperator SetInput(String operator_input_name, String input_name);
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LearningModelOperator SetConstant(String operator_input_name, IInspectable default_value);
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LearningModelOperator SetOutput(String operator_output_name, String output_name);
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LearningModelOperator SetAttribute(String name, IInspectable value);
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String Name { get; };
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String Type { get; };
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String Domain { get; };
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}
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[marshaling_behavior(agile)]
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[dualapipartition(1)]
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runtimeclass LearningModelOperatorSet {
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LearningModelBuilder Add(LearningModelOperator op);
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}
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[marshaling_behavior(agile)]
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[dualapipartition(1)]
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runtimeclass LearningModelInputs {
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LearningModelBuilder Add(ROOT_NS.AI.MachineLearning.ILearningModelFeatureDescriptor input);
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LearningModelBuilder Add(String input_name, String input_description, IInspectable default_value);
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LearningModelBuilder AddConstant(String input_name, IInspectable value);
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}
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[marshaling_behavior(agile)]
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[dualapipartition(1)]
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runtimeclass LearningModelOutputs {
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LearningModelBuilder Add(ROOT_NS.AI.MachineLearning.ILearningModelFeatureDescriptor output);
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}
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//! \interface LearningModelBuilder
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//! \brief Represents a trained machine learning model.
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//! \details This is the main object you use to interact with Windows Machine Learning. You use
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//! it to load, bind, and evaluate trained ONNX models. To load the model you use
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//! one of the Load constructors. You can then enumerate the InputFeatures and
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//! OutputFeatures. To bind and evaluate you create a LearningModelSession.
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[threading(both)]
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[marshaling_behavior(agile)]
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[dualapipartition(1)]
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runtimeclass LearningModelBuilder {
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LearningModelInputs Inputs { get; };
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LearningModelOutputs Outputs { get; };
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LearningModelOperatorSet Operators { get; };
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//! Create a builder.
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static LearningModelBuilder Create(Int32 opset);
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//! Creates a TensorFeatureDescriptor.. this should be a constructor on the TFD
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//TensorFeatureDescriptor(String name, String description, TensorKind kind, Int64[] shape);
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static ROOT_NS.AI.MachineLearning.TensorFeatureDescriptor CreateTensorFeatureDescriptor(String name, String description, ROOT_NS.AI.MachineLearning.TensorKind kind, Int64[] shape);
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static ROOT_NS.AI.MachineLearning.TensorFeatureDescriptor CreateTensorFeatureDescriptor(String name, ROOT_NS.AI.MachineLearning.TensorKind kind, Int64[] shape);
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ROOT_NS.AI.MachineLearning.LearningModel CreateModel();
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void Save(String file_name);
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}
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} // namespace Microsoft.AI.MachineLearning.Experimental
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