torch.nn.utils.rnn.pad_packed_sequence — PyTorch 2.7 documentation (original) (raw)
torch.nn.utils.rnn.pad_packed_sequence(sequence, batch_first=False, padding_value=0.0, total_length=None)[source][source]¶
Pad 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 * (if batch_first is False) or B x T x * (if batch_first is True) , where T is the length of the longest sequence and B is the batch size.
Example
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])
Note
total_length is useful to implement thepack sequence -> recurrent network -> unpack sequence pattern in aModule wrapped in DataParallel. See this FAQ section for details.
Parameters
- sequence (PackedSequence) – batch to pad
- batch_first (bool, optional) – if
True, the output will be inB x T x *format,T x B x *otherwise. - padding_value (float, optional) – values for padded elements.
- total_length (int, optional) – if not
None, the output will be padded to have lengthtotal_length. This method will throw ValueErroriftotal_lengthis less than the max sequence length insequence.
Returns
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