I have a sequences collection in the following form:
sequences = torch.tensor([[2,1],[5,6],[3,0])
indexes = torch.tensor([1,0,1])
that is, the sequence 0
is made of just [5,6]
, and the sequence 1
is made of [2,1] , [3,0]
. Mathematically sequence[i] = { sequences[j] such that i = indexes[j] }
I need to feed these sequences into an LSTM. Since these are variable-length sequences, pytorch documentation states to use something like torch.nn.utils.rnn.pack_sequence
.
Sadly, this method and its like want, as input, a list of tensors where each of them is a L x *
, with L being the length of the single sequence.
How can build something that can be fed into a pytorch LSTM?
P.s. throughout the code I work with these tensors using scatter
and gather
functionalities but I can’t find a way to use them to achieve this goal.
p.p.s I posted this question also on stackoverflow