6af02ae06a
This function call is confusing, since it is a function call without definition of the function. It was correctly repalced from compute_data to compute_range, but function call was reintroudced in a later PR. ### Description Problem as described in [this issue](https://github.com/microsoft/onnxruntime/issues/18893 ) In the examples, different calls of compute_range() from calibrate.py can be found, also in the calibrate.py itself. The problem is that it was [replaced here] (https://github.com/microsoft/onnxruntime/pull/16550/files#diff-75e84436a983e17527f8b5bc585087e7ad75b3b515c2101c2a82dcaecca490de ) from `compute_range()` to `cpmute_data() -> TensorsData` and then falsely [added as call here](https://github.com/microsoft/onnxruntime/pull/17029/files#diff-75e84436a983e17527f8b5bc585087e7ad75b3b515c2101c2a82dcaecca490de ). ### Motivation and Context I suggest in this PR to remove this confusing call `self.calibrate_range()` in calibrate.py. Once it is removed and packaged, somehow the examples from the onnx-runtime-examples repository must be adapted, since they are already not working. Examples of `compute_range()` in the examples are linked in [this issue](https://github.com/microsoft/onnxruntime/issues/18893 ). |
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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 documentation 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
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Third-party Pipeline Status
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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.