# Apply operation on tensor with masked value

Let’s say you have a tensor like this:
`mytensor = torch.tensor([[[-11,0,101,0],[0,5,50,0],[-1,-2,0,1.]]])`
And you define your mask as being 0: `mask = mytensor.eq(0.)`
Now you want to apply in dim 1 several metrics such has max, min, mean (and potentially custom one).
Doing something like this:

``````mytensor.mean(1)
tensor([[-4.0000,  1.0000, 50.3333,  0.3333]])
``````

Is not going to work for example for the first value it should be `(-11-1)/2=-6`
You can hack getting the mean like this: `mytensor.sum(1).div((~mask).sum(1).float())` but this is not going to work for `min` and `max` or any custom operation.
`nn.CrossEntropyLoss` has a parameter `ignore_index` to resolve that but I can’t see any way to do it for generic operation. Is there a clean non hacky way to do it?

1 Like

custom metrics may be complex.
when min, you can change 0 element in mytensor to inf;
when max, you can change 0 element in mytensor to -inf;

1 Like

Yes, using Inf’s is theoretically possible, but I am getting NaN’s in gradients afterwards… That’s why I wonder if that’s a safe solution…

sorry, I do not consider the gradients.

``````>>> import torch
>>> mytensor = torch.tensor([[-11, 0, 101, 0], [0, 5, 50, 0], [-1, -2, 0, 1]])
>>> mytensor_sum = mytensor.sum(1)
>>> mytensor_sum = mytensor_sum.unsqueeze(1)
>>> other = mytensor_sum.repeat(1, 4)
>>> other
tensor([[ 90,  90,  90,  90],
[ 55,  55,  55,  55],
[ -2,  -2,  -2,  -2]])
>>> final = torch.where(mask, other, mytensor)
>>> final
tensor([[ -11,   90,  101,   90],
[  55,    5,   50,   55],
[  -1,   -2,   -2,    1]])
>>> fianl.max(1)
(tensor([ 101,   55,    1]), tensor([ 2,  0,  3]))
>>> final.min(1)
(tensor([-11,   5,  -2]), tensor([ 0,  1,  1]))
``````

I believe that others will have a simpler approach.