Does anyone know how to quickly apply a mask to a torch tensor? Say I have a tensor t_in with some values in it and a mask t_mask with values 0 and 1. In numpy I could do the following:
t_result = np.ma.array(t_in, mask=t_mask)
That would mask all the values of t_in where the value of the equivalent index of t_mask is 0. Then I can do operations such as
t_result.sum() and it will only give me the sum of the non-masked values. I’m primarily looking to do just that, but cannot get my head around how it can be done efficiently with torch tensors.