pad_sequence(sequences: Union[oneflow.Tensor, List[oneflow.Tensor]], batch_first: bool = False, padding_value: float = 0.0) → oneflow.Tensor¶
The interface is consistent with PyTorch. The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.utils.rnn.pad_sequence.html.
Pad a list of variable length Tensors with
pad_sequencestacks a list of Tensors along a new dimension, and pads them to equal length. For example, if the input is list of sequences with size
L x *and if batch_first is False, and
T x B x *otherwise.
B is batch size. It is equal to the number of elements in
sequences. T is length of the longest sequence. L is length of the sequence. * is any number of trailing dimensions, including none.
This function returns a Tensor of size
T x B x *or
B x T x *where T is the length of the longest sequence. This function assumes trailing dimensions and type of all the Tensors in sequences are same.
sequences (list[Tensor]) – list of variable length sequences.
batch_first (bool, optional) – output will be in
B x T x *if True, or in
T x B x *otherwise. Default: False.
padding_value (float, optional) – value for padded elements. Default: 0.
Tensor of size
T x B x *if
False. Tensor of size
B x T x *otherwise
>>> from oneflow.nn.utils.rnn import pad_sequence >>> import oneflow as flow >>> a = flow.ones(25, 300) >>> b = flow.ones(22, 300) >>> c = flow.ones(15, 300) >>> out = pad_sequence([a, b, c]) >>> out.size() oneflow.Size([25, 3, 300])