Add keras.ops.rot90 for tf.image.rot90
#20723
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type:feature
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innat
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keras.ops.rot90* for tf.image.rot90keras.ops.rot90 for tf.image.rot90
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It is a numpy op: https://numpy.org/doc/stable/reference/generated/numpy.rot90.html Any op that is in numpy is in-scope for inclusion in Keras. Since there are already built-in functions for it in all backends, adding it should be easy. Are you able to open a PR? |
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I'm working on a related topic and can submit a PR if that helps. |
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Please do. |
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This is my first time contributing to this repo, and I’ve just raised a pull request for the issue. Let me know if there’s anything I need to fix or improve, I’d be happy to make changes! |
fchollet
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Jan 13, 2025
* feat(ops): Add keras.ops.image.rot90 operation Adds a new operation to rotate tensors by 90 degrees in the specified plane: - Implements rot90 operation in keras.ops.image module - Adds support for multiple rotations (k parameter) and custom axes - Matches numpy.rot90 behavior and API for consistency - Adds comprehensive test coverage including batch images support - Handles input validation for tensor dimensions and axes - Supports symbolic tensor execution The operation follows the same interface as numpy.rot90 and tf.image.rot90: rot90(array, k=1, axes=(0, 1)) * feat: add JAX, NumPy and PyTorch backends for rot90 Add implementations of rot90() for multiple backend frameworks: - JAX backend implementation - NumPy backend implementation - PyTorch backend implementation * Move rot90 from image to numpy ops Move rot90 operation to numpy.py files in backend implementations since it's a numpy op (https://numpy.org/doc/stable/reference/generated/numpy.rot90.html). Now exported as both keras.ops.rot90 and keras.ops.numpy.rot90. * Fix dtype conflict in PyTorch backend's rot90 function Resolved the 'Invalid dtype: object' error by explicitly using to avoid naming conflicts with the custom function. * Replace experimental NumPy rot90 with core TF ops Replace tf.experimental.numpy.rot90 with core TF ops for XLA compatibility. Use convert_to_tensor for input handling.
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tf api: https://www.tensorflow.org/api_docs/python/tf/image/rot90
torch api: https://pytorch.org/docs/stable/generated/torch.rot90.html
jax api: https://jax.readthedocs.io/en/latest/_autosummary/jax.numpy.rot90.html
In keras, it provides RandomRotation and probably not replacable with tf.image.90.
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