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Running
on
Zero
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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
def batched_forward(model, x, batch_size=-1):
if batch_size == -1:
return model(x)
else:
x_batched = x.split(batch_size)
outs = [model(x) for x in x_batched]
return torch.cat(outs, dim=0)
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