Buckets:
| """Lightweight lossless packing for grouped 0/1 BSQ tensors.""" | |
| from __future__ import annotations | |
| import math | |
| import numpy as np | |
| import torch | |
| def pack_visual_codes(codes: torch.Tensor) -> tuple[torch.Tensor, tuple[int, ...]]: | |
| values = codes.detach().to(torch.uint8).cpu().numpy() | |
| shape = tuple(values.shape) | |
| packed = np.packbits(values.reshape(shape[0], -1), axis=1, bitorder="little") | |
| return torch.from_numpy(packed), shape | |
| def unpack_visual_codes( | |
| packed: torch.Tensor, shape: tuple[int, ...] | list[int] | |
| ) -> torch.Tensor: | |
| shape = tuple(int(value) for value in shape) | |
| bits_per_row = math.prod(shape[1:]) | |
| values = np.unpackbits( | |
| packed.cpu().numpy(), axis=1, count=bits_per_row, bitorder="little" | |
| ) | |
| return torch.from_numpy(values).reshape(shape).to(torch.uint8) | |
| __all__ = ["pack_visual_codes", "unpack_visual_codes"] | |
Xet Storage Details
- Size:
- 877 Bytes
- Xet hash:
- 5dc3c380c6ae99c3ed5514c89f19cc1a7471483fc341457276125f0b406b401e
·
Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.