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tiruka 9b9b03c959
Add ONNX op Random Uniform Like (#2448) 
* add random normal like python code to generate onnx model

* add random normal like node

* modify onnx burn to add new op

* add test on test onnx

* revert commentouts

* fix review points to respond to dynamically shape

* add onnx op of random uniform like

* fix formats
 		2024-11-07 09:44:32 -05:00
..
onnx-tests Add ONNX op Random Uniform Like (#2448) 2024-11-07 09:44:32 -05:00
pytorch-tests Refactor xtask to use tracel-xtask and refactor CI workflow (#2063) 2024-08-28 15:57:13 -04:00
src Add ONNX op Random Uniform Like (#2448) 2024-11-07 09:44:32 -05:00
Cargo.toml Bump next version of Burn to 0.16.0 (#2434) 2024-10-28 16:41:58 -04:00
DEVELOPMENT.md Add subtract tensor from scalar for ONNX sub op (#1964) 2024-07-05 13:52:02 -05:00
LICENSE-APACHE Update licenses symlinks (#1613) 2024-04-12 14:43:58 -04:00
LICENSE-MIT Update licenses symlinks (#1613) 2024-04-12 14:43:58 -04:00
README.md modify url link to book (#2327) 2024-10-02 08:32:27 -04:00
SUPPORTED-ONNX-OPS.md Add ONNX op Random Uniform Like (#2448) 2024-11-07 09:44:32 -05:00

README.md

Importing Models

The Burn project supports the import of models from various frameworks, emphasizing efficiency and compatibility. Currently, it handles two primary model formats:

  1. ONNX: Facilitates direct import, ensuring the model's performance and structure are maintained.

  2. PyTorch: Enables the loading of PyTorch model weights into Burn’s native model architecture, ensuring seamless integration.

Contribution

Interested in contributing to burn-import? Check out our development guide for more information.