Keeping the dimensions when using torch.diff on a tensor in pytorch
Question:
Suppose the following code:
a=torch.rand(size=(3,3,3), dtype=torch.float32)
a_diff=torch.diff(a, n=1, dim= 1, prepend=None, append=None).shape
print(a_diff)
torch.Size([3, 2, 3])
I would like to keep the dimensions like the original a with (3,3,3). How can I
append 0 to the beginning of the sequence so that the dimensions remain the same?
Answers:
You can simply use the "prepend" parameter.
a = torch.rand(size = (3,3,3), dtype = torch.float32)
a_diff = torch.diff(a, n=1, dim= 1, prepend=torch.zeros((3,1,3)), append=None).shape
print(a_diff)
The result is torch.Size([3, 3, 3]).
Suppose the following code:
a=torch.rand(size=(3,3,3), dtype=torch.float32)
a_diff=torch.diff(a, n=1, dim= 1, prepend=None, append=None).shape
print(a_diff)
torch.Size([3, 2, 3])
I would like to keep the dimensions like the original a with (3,3,3). How can I
append 0 to the beginning of the sequence so that the dimensions remain the same?
You can simply use the "prepend" parameter.
a = torch.rand(size = (3,3,3), dtype = torch.float32)
a_diff = torch.diff(a, n=1, dim= 1, prepend=torch.zeros((3,1,3)), append=None).shape
print(a_diff)
The result is torch.Size([3, 3, 3]).