I would like to be able to apply a function which is designed for a 3D tensor to each 3D tensor in a 4D tensor, namely image.translate()
. For example, I can apply the function individually to two images of dimension (3,50,50) but it would be great if I could feed their 4D concatenation of (2,3,50,50).
This could probably be done in a for loop but I was wondering if there was any built in function for this. Thanks.
I haven't managed to find such a function in Torch
. You can, of course, define one yourself to make your life a little bit happier:
function apply_to_slices(tensor, dimension, func, ...)
for i, slice in ipairs(tensor:split(1, dimension)) do
func(slice, i, ...)
end
return tensor
end
Example:
function power_fill(tensor, i, power)
power = power or 1
tensor:fill(i ^ power)
end
A = torch.Tensor(5, 6)
apply_to_slices(A, 1, power_fill)
1 1 1 1 1 1
2 2 2 2 2 2
3 3 3 3 3 3
4 4 4 4 4 4
5 5 5 5 5 5
[torch.DoubleTensor of size 5x6]
apply_to_slices(A, 2, power_fill, 3)
1 8 27 64 125 216
1 8 27 64 125 216
1 8 27 64 125 216
1 8 27 64 125 216
1 8 27 64 125 216
[torch.DoubleTensor of size 5x6]