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Guillaume Lagrange cdd1fa1672
Refactor tensor data (#1916) 
* Move distribution to module

* Add new TensorData with serialization support

* Implement display and from for TensorData

* Add missing Cargo.lock

* Add missing bytemuck feature

* Add zeros, ones, full and random TensorData methods

* Refactor Data -> TensorData usage

* Fix tests

Since TensorData is not generic over the element type anymore no type inference can be done by the compiler. We must explicitly cast the expected results to the expected backend type.

* Remove commented line

* Fix import

* Add record-backward-compat

* Remove dim const generic from TensorData

* Support NestedValue de/serialization with TensorData

* Fix burn-jit tests

* Remove eprinln

* Refactor onnx import to use TensorData

* Fix tch from_data

* Fix nested value serialization for u8

* Fix missing import

* Fix reduce min onnx test

* Fix deprecated attribute

* Remove shape getter

* Remove strict assert in tests

* Add tensor data as_bytes

* Add tensor check for rank mismatch

* Fix typo (dimensions plural)

* Fix error message

* Update book examples with from_data and fix Display impl for TensorData

* Add deprecation note
 		2024-06-26 20:22:19 -04:00
..
onnx-tests Refactor tensor data (#1916) 2024-06-26 20:22:19 -04:00
pytorch-tests Refactor tensor data (#1916) 2024-06-26 20:22:19 -04:00
src Refactor tensor data (#1916) 2024-06-26 20:22:19 -04:00
Cargo.toml Fix `DataSerialize` conversion for elements of the same type (#1832) 2024-05-28 18:12:44 -04:00
DEVELOPMENT.md [refactor] Move burn crates to their own crates directory (#1336) 2024-02-20 13:57:55 -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 [refactor] Move burn crates to their own crates directory (#1336) 2024-02-20 13:57:55 -05:00
SUPPORTED-ONNX-OPS.md feat: added reduce min onnx import (#1894) 2024-06-18 09:04:24 -04:00
build.rs [refactor] Move burn crates to their own crates directory (#1336) 2024-02-20 13:57:55 -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.