How does one padd a tensor of 3 dimensions?

1

I was trying to use the built in padding function but it wasn’t padding things for me for some reason. This is my reproducible code:

import torch

def padding_batched_embedding_seq():
    ## 3 sequences with embedding of size 300
    a = torch.ones(1, 4, 5) # 25 seq len (so 25 tokens)
    b = torch.ones(1, 3, 5) # 22 seq len (so 22 tokens)
    c = torch.ones(1, 2, 5) # 15 seq len (so 15 tokens)
    ##
    sequences = [a, b, c]
    batch = torch.nn.utils.rnn.pad_sequence(sequences)

if __name__ == '__main__':
    padding_batched_embedding_seq()

error message:

Traceback (most recent call last):
  File "padding.py", line 51, in <module>
    padding_batched_embedding_seq()
  File "padding.py", line 40, in padding_batched_embedding_seq
    batch = torch.nn.utils.rnn.pad_sequence(sequences)
  File "/Users/rene/miniconda3/envs/automl/lib/python3.7/site-packages/torch/nn/utils/rnn.py", line 376, in pad_sequence
    out_tensor[:length, i, ...] = tensor
RuntimeError: The expanded size of the tensor (4) must match the existing size (3) at non-singleton dimension 1.  Target sizes: [1, 4, 5].  Tensor sizes: [3, 5]

any idea?

2

do I really have to unsequeeze it first for it to work?