Hi, I want to shrink a continuous mask tensor to half size. Is there any one-step or non-loop method to do that?
e.g, for a 1D(N*L) mask tensor:
before = tensor([[1, 1, 0, 0], [1, 1, 1, 1]], dtype=torch.uint8)
after = tensor([[1, 0], [1, 1]], dtype=torch.uint8)
Currently, I do this like below:
after = F.max_pool1d(before.unsqueeze(1).to(dtype=torch.float), kernel_size=2, stride=2, padding=1).squeeze(1).to(dtype=torch.uint8)

I still want to know if there is a better way.
Thanks for your help.