Laurent Erignoux
144fb65246
git-subtree-dir: Stable3DGen git-subtree-mainline:846d6cfad8git-subtree-split:c29f668ece
73 lines
2.9 KiB
Python
Executable File
73 lines
2.9 KiB
Python
Executable File
# MIT License
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# Copyright (c) Microsoft
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# Permission is hereby granted, free of charge, to any person obtaining a copy
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# of this software and associated documentation files (the "Software"), to deal
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# in the Software without restriction, including without limitation the rights
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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# copies of the Software, and to permit persons to whom the Software is
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# furnished to do so, subject to the following conditions:
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# The above copyright notice and this permission notice shall be included in all
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# copies or substantial portions of the Software.
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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# SOFTWARE.
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# Copyright (c) [2025] [Microsoft]
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# SPDX-License-Identifier: MIT
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import torch
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def pixel_shuffle_3d(x: torch.Tensor, scale_factor: int) -> torch.Tensor:
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"""
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3D pixel shuffle.
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"""
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B, C, H, W, D = x.shape
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C_ = C // scale_factor**3
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x = x.reshape(B, C_, scale_factor, scale_factor, scale_factor, H, W, D)
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x = x.permute(0, 1, 5, 2, 6, 3, 7, 4)
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x = x.reshape(B, C_, H*scale_factor, W*scale_factor, D*scale_factor)
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return x
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def patchify(x: torch.Tensor, patch_size: int):
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"""
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Patchify a tensor.
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Args:
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x (torch.Tensor): (N, C, *spatial) tensor
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patch_size (int): Patch size
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"""
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DIM = x.dim() - 2
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for d in range(2, DIM + 2):
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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}"
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x = x.reshape(*x.shape[:2], *sum([[x.shape[d] // patch_size, patch_size] for d in range(2, DIM + 2)], []))
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x = x.permute(0, 1, *([2 * i + 3 for i in range(DIM)] + [2 * i + 2 for i in range(DIM)]))
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x = x.reshape(x.shape[0], x.shape[1] * (patch_size ** DIM), *(x.shape[-DIM:]))
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return x
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def unpatchify(x: torch.Tensor, patch_size: int):
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"""
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Unpatchify a tensor.
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Args:
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x (torch.Tensor): (N, C, *spatial) tensor
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patch_size (int): Patch size
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"""
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DIM = x.dim() - 2
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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}"
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x = x.reshape(x.shape[0], x.shape[1] // (patch_size ** DIM), *([patch_size] * DIM), *(x.shape[-DIM:]))
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x = x.permute(0, 1, *(sum([[2 + DIM + i, 2 + i] for i in range(DIM)], [])))
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x = x.reshape(x.shape[0], x.shape[1], *[x.shape[2 + 2 * i] * patch_size for i in range(DIM)])
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return x
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