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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