# tensorplay.nn.utils.rnn.pad_packed_sequence

Source: https://www.tensorplay.cn/docs/generated/tensorplay.nn.utils.rnn.pad_packed_sequence.html

# tensorplay.nn.utils.rnn.pad_packed_sequence

tensorplay.nn.utils.rnn.pad_packed_sequence(sequence: [PackedSequence](tensorplay.nn.utils.rnn.PackedSequence.html#tensorplay.nn.utils.rnn.PackedSequence), batch_first: [bool](https://docs.python.org/3/library/functions.html#bool) = False, padding_value: [float](https://docs.python.org/3/library/functions.html#float) = 0.0, total_length: [int](https://docs.python.org/3/library/functions.html#int) | [None](https://docs.python.org/3/library/constants.html#None) = None)[[source]](../_modules/tensorplay/nn/utils/rnn.html#pad_packed_sequence)

Pad a packed batch of variable length sequences.

It is an inverse operation to [pack_padded_sequence()](tensorplay.nn.utils.rnn.pack_padded_sequence.html#tensorplay.nn.utils.rnn.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 tensorplay.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
>>> seq = tp.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 the
pack sequence -> recurrent network -> unpack sequence pattern in a
model wrapped in DataParallel.

Parameters:

- sequence ([PackedSequence](tensorplay.nn.utils.rnn.PackedSequence.html#tensorplay.nn.utils.rnn.PackedSequence)) – batch to pad

- batch_first ([bool](https://docs.python.org/3/library/functions.html#bool) , optional ) – if True , the output will be in B x T x * format, T x B x * otherwise.

- padding_value ([float](https://docs.python.org/3/library/functions.html#float) , optional ) – values for padded elements.

- total_length ([int](https://docs.python.org/3/library/functions.html#int) , optional ) – if not None , the output will be padded to have length total_length . This method will throw [ValueError](https://docs.python.org/3/library/exceptions.html#ValueError) if total_length is less than the max sequence length in sequence .

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()](tensorplay.nn.utils.rnn.pack_padded_sequence.html#tensorplay.nn.utils.rnn.pack_padded_sequence) or [pack_sequence()](tensorplay.nn.utils.rnn.pack_sequence.html#tensorplay.nn.utils.rnn.pack_sequence).