96ee72d7f8
### Description
- Adds support for Resize with the `asymmetric` coordinate
transformation mode on the QNN HTP backend.
- Adds unit test that shows this is only correct if the `nearest_mode`
is `"floor"`.
### Motivation and Context
This is needed to enable more models to run on the QNN HTP backend.
Note:
QNN's ONNX converter tool translates an ONNX Resize op with `{mode:
"nearest", coordinate_transformation_mode: "asymmetric", "nearest_mode":
<ANYTHING>}` to QNN's ResizeNearestNeighbor with `{align_corners: 0,
half_pixel: 0}`.
Unit tests show that this is only accurate if the ONNX attribute
nearest_mode is "floor". Need to investigate how to handle other
rounding modes. Ideally, we would use QNN's own Resize operator (instead
of ResizeNearestNeighbor), but that doesn't support the "asymmetric"
coordinate transformation mode on the HTP backend.
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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
Build Pipeline Status
| System | Inference | Training |
|---|---|---|
| Windows | ||
| Linux | ||
| Mac | ||
| Android | ||
| iOS | ||
| Web | ||
| Other |
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.