Comments (2)
We can leave it as it is with a "#TODO: Investigate how to make it faster". I am sure negative striding is a feature that people demand for pytorch, so eventually, it will be there.
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For sure, it seems like they might have done some things that make it fundamentally quite difficult though - the issues go back a long way (e.g. pytorch/pytorch#604)
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Related Issues (19)
- github issue filesystem
- FSC implementation is no longer correct HOT 1
- zyx handling in `central_slice_coordinates` doesn't work correctly HOT 1
- calculate_ctf support Tensors as input HOT 4
- backproject_fourier.py backproject_fourier(...) do_gridding_correction argument is not used HOT 1
- Add resolution threshold to correlate_dft_2d HOT 1
- use vmap instead of list comprehension in real space projection code
- add soft edge code breaks if device is cuda
- Disagrement between relion_project and from libtilt.projection.project_fourier HOT 14
- add 3d rescaling
- extract_central_slices_rfft performance HOT 4
- 2D +3D unified affine transforms API HOT 7
- 2D to 1D projection for common lines
- alignment API HOT 5
- sample dft 2d
- Defining generic wrapper decorators HOT 3
- unittesting device agnostic code HOT 3
- add 2D/3D rotations
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