Fix torch.uint8 Indexing Deprecation in PyTorch

When working with PyTorch, you may run into a warning stating that indexing with torch.uint8 is deprecated. This happens when a mask made of 0 and 1 is used to select elements from a tensor. PyTorch expects boolean masks instead, using torch.bool.

Earlier versions of PyTorch allowed integer masks (0 / 1). However, this approach is ambiguous and inconsistent with Python's native boolean logic. PyTorch now requires explicit boolean masks (True / False) for indexing, which are clearer, safer, and easier to understand.

Here's a simple example that produces the deprecation warning:

import torch

x = torch.tensor([1.5, 2.5, 3.5, 4.5, 5.5])
mask = torch.ByteTensor([1, 0, 1, 0, 0])

y = x[mask]
print(y) # tensor([1.5000, 3.5000])

Output:

UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.

The recommended approach is to use a boolean mask from the start:

import torch

x = torch.tensor([1.5, 2.5, 3.5, 4.5, 5.5])
mask = torch.tensor([True, False, True, False, False])

y = x[mask]
print(y) # tensor([1.5000, 3.5000])

This approach avoids warnings and aligns with PyTorch's current and future indexing rules.

In some cases, you may not be able to change how the mask is created - for example:

• The mask comes from legacy code.
• It's loaded from a file.
• It's generated by another library and contains integers (0 / 1).

In such situations, you can safely convert the ByteTensor to a boolean tensor using .bool():

import torch

x = torch.tensor([1.5, 2.5, 3.5, 4.5, 5.5])
mask = torch.ByteTensor([1, 0, 1, 0, 0]).bool()

y = x[mask]
print(y) # tensor([1.5000, 3.5000])

This simple conversion removes the warning and ensures compatibility with newer PyTorch versions.