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### Description <!-- Describe your changes. --> As title. Special cases for ReduceMean: [UPDATE] The following cases are supported now by converting to providing an input with all axes for NNAPI. Behaviors when axes is not provided or axes provided as an empty vector: For ReduceMean Opset version 18: - Support case `axes` is provided as empty with `noop_with_empty_axes` set to true. - Support case `axes` is not provided with `noop_with_empty_axes` set to true. All treat as identity op. - Does not support the case when `axes` is not provided/provided as empty but `noop_with_empty_axes` is set to false. For ReduceMean OpSet Version 13-: - Does not support when `axes` attribute is not provided. (as onnx treats it as default behavior to reduce all dimensions, and the case is not implemented by NNAPI.) https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0a047fe95a35b27f45c05432b6ca18eb6c > 1: A 1-D Tensor of [ANEURALNETWORKS_TENSOR_INT32](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaf06d1affd33f3bc698d0c04eceb23298ac34965d8e76ac5acfddf5acd9e40f896). The dimensions to reduce. Must be in the range [-rank(input_tensor), rank(input_tensor)).NOTE: When the operation was introduced, the documentation incorrectly stated that if dimensions were empty, the operation would reduce across all dimensions. This behavior was never implemented. ### 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. --> Fixes issue #16194 --------- Co-authored-by: rachguo <rachguo@rachguos-Mini.attlocal.net> |
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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
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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.