8fdfd20191
### Description
<!-- Describe your changes. -->
Split up OpTester to separate out operator vs model testing. This led to
a lot of other cleanups/refactoring.
- create BaseTester class and derived OpTester/ModelTester classes to
limit APIs to what is applicable for each test type
- e.g. adding an attribute isn't relevant to a model test
- cleanup structure
- don't expose member variables either directly or via public methods
returning them
- split out checkers so they can be easily re-used
- refactor so there's one public Check method for comparing two OrtValue
instances containing any data type
- refactor the GradientOpTester usage
- it required a lot of OpTester internals to be exposed and no other
tests needed this
- it also returned Status through various parts which prevented the
usage of the google test macros which provide better output. change to
return void and use the macros.
- fix some other minor issues
- update some cmake files so all the source files are included
- remove some low value helpers (FetchTensor and GetShapeVector)
- remove some outdated code to allow unreleased opset versions from when
onnx opset 15 wasn't released
- move files from test/util/include/test to test/util/include
- doesn't seem to be any reason for the additional subdirectory given
they're not files use to test the code in test/util
- files were moved with no changes
### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
Cleanup test infrastructure.
---------
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
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| onnxruntime | ||
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| CONTRIBUTING.md | ||
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| LICENSE | ||
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| ORT_icon_for_light_bg.png | ||
| Package.swift | ||
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| README.md | ||
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| requirements-doc.txt | ||
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| requirements-training.txt | ||
| requirements.txt.in | ||
| SECURITY.md | ||
| setup.py | ||
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ONNX Runtime is a cross-platform inference and training machine-learning accelerator.
ONNX Runtime inference can enable faster customer experiences and lower costs, supporting models from deep learning frameworks such as PyTorch and TensorFlow/Keras as well as classical machine learning libraries such as scikit-learn, LightGBM, XGBoost, etc. ONNX Runtime is compatible with different hardware, drivers, and operating systems, and provides optimal performance by leveraging hardware accelerators where applicable alongside graph optimizations and transforms. Learn more →
ONNX Runtime training can accelerate the model training time on multi-node NVIDIA GPUs for transformer models with a one-line addition for existing PyTorch training scripts. Learn more →
Get Started & Resources
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General Information: onnxruntime.ai
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Usage documention and tutorials: onnxruntime.ai/docs
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YouTube video tutorials: youtube.com/@ONNXRuntime
-
Companion sample repositories:
- ONNX Runtime Inferencing: microsoft/onnxruntime-inference-examples
- ONNX Runtime Training: microsoft/onnxruntime-training-examples
Builtin Pipeline Status
| System | Inference | Training |
|---|---|---|
| Windows | ||
| Linux | ||
| Mac | ||
| Android | ||
| iOS | ||
| Web | ||
| Other |
Third-party Pipeline Status
| System | Inference | Training |
|---|---|---|
| Linux |  |
Data/Telemetry
Windows distributions of this project may collect usage data and send it to Microsoft to help improve our products and services. See the privacy statement for more details.
Contributions and Feedback
We welcome contributions! Please see the contribution guidelines.
For feature requests or bug reports, please file a GitHub Issue.
For general discussion or questions, please use GitHub Discussions.
Code of Conduct
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
License
This project is licensed under the MIT License.