Finding Non-intersection Of Two Pytorch Tensors

Thanks everyone in advance for your help! What I'm trying to do in PyTorch is something like numpy's setdiff1d. For example given the below two tensors: t1 = torch.tensor([1, 9, 12

Solution 1:

I came across the same problem but the proposed solutions were far too slow when using larger arrays. The following simple solution works on CPU and GPU and is significantly faster than the other proposed solutions:

combined = torch.cat((t1, t2))
uniques, counts = combined.unique(return_counts=True)
difference = uniques[counts == 1]
intersection = uniques[counts > 1]

Solution 2:

if you don't want to leave cuda, a workaround could be:

t1 = torch.tensor([1, 9, 12, 5, 24], device = 'cuda')
t2 = torch.tensor([1, 24], device = 'cuda')
indices = torch.ones_like(t1, dtype = torch.uint8, device = 'cuda')
for elem in t2:
    indices = indices & (t1 != elem)  
intersection = t1[indices]  

Solution 3:

If you don't want a for loop this can compare all values in one go.

Also you can get the non intersection easily too

t1 = torch.tensor([1, 9, 12, 5, 24])
t2 = torch.tensor([1, 24])

# Create a tensor to compare all values at once
compareview = t2.repeat(t1.shape[0],1).T

# Intersectionprint(t1[(compareview == t1).T.sum(1)==1])
# Non Intersectionprint(t1[(compareview != t1).T.prod(1)==1])

tensor([ 1, 24])
tensor([ 9, 12,  5])