torch.nn.utils.rnn.pack_sequence

torch.nn.utils.rnn.pack_sequence(sequences, enforce_sorted=True) [source]

Packs a list of variable length Tensors

Consecutive call of the next functions: pad_sequence, pack_padded_sequence.

sequences should be a list of Tensors of size L x *, where L is the length of a sequence and * is any number of trailing dimensions, including zero.

For unsorted sequences, use enforce_sorted = False. If enforce_sorted is True, the sequences should be sorted in the order of decreasing length. enforce_sorted = True is only necessary for ONNX export.

Example

>>> from torch.nn.utils.rnn import pack_sequence
>>> a = torch.tensor([1, 2, 3])
>>> b = torch.tensor([4, 5])
>>> c = torch.tensor([6])
>>> pack_sequence([a, b, c])
PackedSequence(data=tensor([1, 4, 6, 2, 5, 3]), batch_sizes=tensor([3, 2, 1]), sorted_indices=None, unsorted_indices=None)

Parameters

• sequences (list[Tensor]) – A list of sequences of decreasing length.
• enforce_sorted (bool, optional) – if True, checks that the input contains sequences sorted by length in a decreasing order. If False, this condition is not checked. Default: True.

Returns

a PackedSequence object

Return type

PackedSequence