388d3cf847
Previously OnnxSequence would flatten out a list of tensors into a single output array assuming they were all scalar values. This doesn't accurately represent the semantics of an ONNX sequence, but was what the semantics appeared to be years ago when I first wrote that class. This PR changes it so that the `getValue` method on `OnnxSequence` unwraps the sequence and returns `List<? extends OnnxValue>` allowing the user to process the individual ONNX values separately. It's done this way rather than returning a multidimensional array for a tensor and a Java map for a map as multidimensional arrays are very inefficient in Java and best practice when operating with a OnnxTensor in Java is to use a `java.nio.ByteBuffer`. So allowing users to access each `OnnxTensor`s individually allows them to control how the data is materialised on the Java heap. |
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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
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
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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.