Let's say that I have tensor
t = torch.tensor([1,2,3,4,5])
I want to split it using a same-sized tensor of indices that tells me for each element, in which split it should go.
indices = torch.tensor([0,1,1,0,2])
So that the final result is
splits
[tensor([1,4]), tensor([2,3]), tensor([5])]
Is there a neat way to do this in Pytorch?
EDIT : In general there will be more than 2 or 3 splits.
One could do it using argsort for general case:
def mask_split(tensor, indices):
sorter = torch.argsort(indices)
_, counts = torch.unique(indices, return_counts=True)
return torch.split(t[sorter], counts.tolist())
mask_split(t, indices)
Though it might be better to use @flawr answer if this is your real use case (also list comprehension might also be faster as it does not require sorting), something like this:
def mask_split(tensor, indices):
unique = torch.unique(indices)
return [tensor[indices == i] for i in unique]
That is indeed possible using logical indexing, you just have to make sure that the index "mask" is made from boolean vales, so in your case
splits = t[indices > 0] , t[indices < 1]
or alternatively you can first cast your tensor indices to have boolean dtype.
On top of other answers, for indexing in pytorch you can directly use index locations to access those elements:
t = torch.tensor([1,2,3,4])
print(t[[0,1,3]])
So you don't really need to store a tensor for indices. You can still store a numpy array with ones and zeros if you want, and then find access indices from that array:
a = np.array([0, 1, 1, 0])
ind_ones = np.argwhere(a == 1).squeeze()
ind_zers = np.argwhere(a == 0).squeeze()
print(t[ind_ones]) # tensor([2, 3])
print(t[ind_zers]) # tensor([1, 4])
