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| # MIT License | |
| # Copyright (c) Microsoft | |
| # Permission is hereby granted, free of charge, to any person obtaining a copy | |
| # of this software and associated documentation files (the "Software"), to deal | |
| # in the Software without restriction, including without limitation the rights | |
| # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | |
| # copies of the Software, and to permit persons to whom the Software is | |
| # furnished to do so, subject to the following conditions: | |
| # The above copyright notice and this permission notice shall be included in all | |
| # copies or substantial portions of the Software. | |
| # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | |
| # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | |
| # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | |
| # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | |
| # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | |
| # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | |
| # SOFTWARE. | |
| # Copyright (c) [2025] [Microsoft] | |
| # SPDX-License-Identifier: MIT | |
| import torch | |
| def pixel_shuffle_3d(x: torch.Tensor, scale_factor: int) -> torch.Tensor: | |
| """ | |
| 3D pixel shuffle. | |
| """ | |
| B, C, H, W, D = x.shape | |
| C_ = C // scale_factor**3 | |
| x = x.reshape(B, C_, scale_factor, scale_factor, scale_factor, H, W, D) | |
| x = x.permute(0, 1, 5, 2, 6, 3, 7, 4) | |
| x = x.reshape(B, C_, H*scale_factor, W*scale_factor, D*scale_factor) | |
| return x | |
| def patchify(x: torch.Tensor, patch_size: int): | |
| """ | |
| Patchify a tensor. | |
| Args: | |
| x (torch.Tensor): (N, C, *spatial) tensor | |
| patch_size (int): Patch size | |
| """ | |
| DIM = x.dim() - 2 | |
| for d in range(2, DIM + 2): | |
| assert x.shape[d] % patch_size == 0, f"Dimension {d} of input tensor must be divisible by patch size, got {x.shape[d]} and {patch_size}" | |
| x = x.reshape(*x.shape[:2], *sum([[x.shape[d] // patch_size, patch_size] for d in range(2, DIM + 2)], [])) | |
| x = x.permute(0, 1, *([2 * i + 3 for i in range(DIM)] + [2 * i + 2 for i in range(DIM)])) | |
| x = x.reshape(x.shape[0], x.shape[1] * (patch_size ** DIM), *(x.shape[-DIM:])) | |
| return x | |
| def unpatchify(x: torch.Tensor, patch_size: int): | |
| """ | |
| Unpatchify a tensor. | |
| Args: | |
| x (torch.Tensor): (N, C, *spatial) tensor | |
| patch_size (int): Patch size | |
| """ | |
| DIM = x.dim() - 2 | |
| assert x.shape[1] % (patch_size ** DIM) == 0, f"Second dimension of input tensor must be divisible by patch size to unpatchify, got {x.shape[1]} and {patch_size ** DIM}" | |
| x = x.reshape(x.shape[0], x.shape[1] // (patch_size ** DIM), *([patch_size] * DIM), *(x.shape[-DIM:])) | |
| x = x.permute(0, 1, *(sum([[2 + DIM + i, 2 + i] for i in range(DIM)], []))) | |
| x = x.reshape(x.shape[0], x.shape[1], *[x.shape[2 + 2 * i] * patch_size for i in range(DIM)]) | |
| return x | |