mirror of
https://github.com/saymrwulf/onnxruntime.git
synced 2026-05-26 22:35:43 +00:00
3787a071c5
* learning model doesn't need lotusEnvironment and CPU shouldn't include dmlEP headers * User/xianz/win ml telemetry (#2410) * add option to enable winml telemetry * add option to enable winml telemetry * clean logs while developping * clean the log of GUID * compile onnxruntime_common with winml telemetry * use option for use_telemetry * rename option winml_use_telemetry to onnxruntime_use_telemetry * little change * Add opset and IR check when loading model (#2413) * Add opset and IR check. * Add test case for future opsets. https://github.com/microsoft/onnxruntime/issues/2371 * WinML CI (#2412) * Pass flags to build/test WinML in CI * Add initial CMake config for unit tests in WinML * Set winml_unittests standard to C++17 * Add WinML API tests and port them to googletest * Install WinML test collateral * Add LearningModelSessionAPITests ported to googletest * Fix WinML test files encoding * Add GPU tests * Add parameterized test, skip GPU tests * Enable precompiled header * Remove unused code and collateral * Remove brand images * Add dllload.cpp * Remove images not used in API tests * Add LICENSE.md to image collaterals * Add models with licenses * Remove FNS Candy tests * Add API test models * Add ModelInSubdirectory * Install collaterals post-build with copy_if_different, split common lib * fix warnings * Link to gtest_main * fix bad merge
30 lines
964 B
C++
30 lines
964 B
C++
//-----------------------------------------------------------------------------
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//
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// Copyright (c) Microsoft Corporation. All rights reserved.
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//
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//-----------------------------------------------------------------------------
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#pragma once
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#include <std.h>
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enum OutputBindingStrategy { Bound, Unbound, Empty };
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namespace WinML::Engine::Test::ModelValidator
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{
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void FnsCandy16(
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std::string instance,
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winrt::Windows::AI::MachineLearning::LearningModelDeviceKind deviceKind,
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OutputBindingStrategy outputBindingStrategy,
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bool bindInputsAsIInspectable,
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float dataTolerance = false);
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void SqueezeNet(
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std::string instance,
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winrt::Windows::AI::MachineLearning::LearningModelDeviceKind deviceKind,
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float dataTolerance,
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bool bindAsImage = false,
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OutputBindingStrategy outputBindingStrategy = OutputBindingStrategy::Bound,
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bool bindInputsAsIInspectable = false
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);
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}
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