torch.nn.utils.rnn.pad_packed_sequence(sequence, batch_first=False, padding_value=0.0, total_length=None)[source]

Pads a packed batch of variable length sequences.

It is an inverse operation to pack_padded_sequence().

The returned Tensor’s data will be of size T x B x *, where T is the length of the longest sequence and B is the batch size. If batch_first is True, the data will be transposed into B x T x * format.


>>> from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
>>> seq = torch.tensor([[1, 2, 0], [3, 0, 0], [4, 5, 6]])
>>> lens = [2, 1, 3]
>>> packed = pack_padded_sequence(seq, lens, batch_first=True, enforce_sorted=False)
>>> packed
PackedSequence(data=tensor([4, 1, 3, 5, 2, 6]), batch_sizes=tensor([3, 2, 1]),
               sorted_indices=tensor([2, 0, 1]), unsorted_indices=tensor([1, 2, 0]))
>>> seq_unpacked, lens_unpacked = pad_packed_sequence(packed, batch_first=True)
>>> seq_unpacked
tensor([[1, 2, 0],
        [3, 0, 0],
        [4, 5, 6]])
>>> lens_unpacked
tensor([2, 1, 3])


total_length is useful to implement the pack sequence -> recurrent network -> unpack sequence pattern in a Module wrapped in DataParallel. See this FAQ section for details.

  • sequence (PackedSequence) – batch to pad

  • batch_first (bool, optional) – if True, the output will be in B x T x * format.

  • padding_value (float, optional) – values for padded elements.

  • total_length (int, optional) – if not None, the output will be padded to have length total_length. This method will throw ValueError if total_length is less than the max sequence length in sequence.


Tuple of Tensor containing the padded sequence, and a Tensor containing the list of lengths of each sequence in the batch. Batch elements will be re-ordered as they were ordered originally when the batch was passed to pack_padded_sequence or pack_sequence.

Return type

Tuple[Tensor, Tensor]


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