0d672e9112
### Description
Currently, C# test only load models with the directory structure as
`{modelroot}->{opsetXX}->{modelname}->{.onnx}`
In this PR, C# test can load models from
`{modelroot}->{model-source}->{opsetXX}->{modelname}->{.onnx}`
### Motivation and Context
There're multiple sources of testing models.
1. model zoo (Not in official image)
2. 1st party models
3. models with contrib-ops
4. others.
It'd better to insert a mid-directory for new sources.
**This PR is compatible with current models.**
From
https://dev.azure.com/onnxruntime/onnxruntime/_build/results?buildId=776643&view=logs&j=6df8fe70-7b8f-505a-8ef0-8bf93da2bac7&t=e7d9f128-b630-5ee6-a99e-2fca70d04619&l=79
the test result is same as master build `Passed: 583, Skipped: 14,
Total: 597`
**model zoo models (mounted in ..\models\zoo) could be loaded**
And from this test workflow, it can load both existing models and models
from model zoo.
https://dev.azure.com/onnxruntime/onnxruntime/_build/results?buildId=777018&view=logs&j=6df8fe70-7b8f-505a-8ef0-8bf93da2bac7&t=e7d9f128-b630-5ee6-a99e-2fca70d04619
Skipping failed models will be in other PRs
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| .config | ||
| .devcontainer | ||
| .gdn | ||
| .github | ||
| .pipelines | ||
| .vscode | ||
| cgmanifests | ||
| cmake | ||
| csharp | ||
| dockerfiles | ||
| docs | ||
| include/onnxruntime/core | ||
| java | ||
| js | ||
| objectivec | ||
| onnxruntime | ||
| orttraining | ||
| package/rpm | ||
| samples | ||
| tools | ||
| winml | ||
| .clang-format | ||
| .clang-tidy | ||
| .dockerignore | ||
| .flake8 | ||
| .gitattributes | ||
| .gitignore | ||
| .gitmodules | ||
| build.amd64.1411.bat | ||
| build.bat | ||
| build.sh | ||
| CITATION.cff | ||
| CODEOWNERS | ||
| CONTRIBUTING.md | ||
| lgtm.yml | ||
| LICENSE | ||
| NuGet.config | ||
| ort.wprp | ||
| ORT_icon_for_light_bg.png | ||
| packages.config | ||
| pyproject.toml | ||
| README.md | ||
| requirements-dev.txt | ||
| requirements-doc.txt | ||
| requirements-training.txt | ||
| requirements.txt.in | ||
| SECURITY.md | ||
| setup.py | ||
| ThirdPartyNotices.txt | ||
| VERSION_NUMBER | ||
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
General Information: onnxruntime.ai
Usage documention and tutorials: onnxruntime.ai/docs
Companion sample repositories:
- ONNX Runtime Inferencing: microsoft/onnxruntime-inference-examples
- ONNX Runtime Training: microsoft/onnxruntime-training-examples
Build Pipeline Status
| System | CPU | GPU | EPs |
|---|---|---|---|
| Windows | |||
| Linux | |||
| Mac | |||
| Android | |||
| iOS | |||
| WebAssembly |
Data/Telemetry
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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.