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| # ------------------------------------------------------------------------------------------ | |
| # Copyright (c) 2024 Baifeng Shi. | |
| # All rights reserved. | |
| # | |
| # Licensed under the MIT License (MIT). See LICENSE in the repo root for license information. | |
| # ------------------------------------------------------------------------------------------ | |
| import torch | |
| def split_chessboard(x, num_split): | |
| """ | |
| x: b * c * h * w | |
| Deividing x into num_split**2 sub-squares, and concatenate all the sub-squares on the batch dimension | |
| """ | |
| B, C, H, W = x.shape | |
| assert H % num_split == 0 and W % num_split == 0 | |
| h, w = H // num_split, W // num_split | |
| x_split = torch.cat([x[:, :, i*h:(i+1)*h, j*w:(j+1)*w] for i in range(num_split) for j in range(num_split)], dim=0) | |
| return x_split | |
| def merge_chessboard(x, num_split): | |
| """ | |
| x: b * c * h * w | |
| Assuming x contains num_split**2 sub-squares concatenated along batch dimension, merge the sub-squares back to the original whole square. | |
| (inverse of split_chessboard) | |
| """ | |
| B, C, H, W = x.shape | |
| assert B % (num_split**2) == 0 | |
| b = B // (num_split**2) | |
| x_merge = torch.cat([torch.cat([x[(i*num_split + j)*b:(i*num_split + j + 1)*b] for j in range(num_split)], dim=-1) | |
| for i in range(num_split)], dim=-2) | |
| return x_merge |