nn_utils_rnn_pad_packed_sequence: Pads a packed batch of variable length sequences.

In torch: Tensors and Neural Networks with 'GPU' Acceleration

Pads a packed batch of variable length sequences.

Description

It is an inverse operation to nn_utils_rnn_pack_padded_sequence().

Usage

nn_utils_rnn_pad_packed_sequence(
  sequence,
  batch_first = FALSE,
  padding_value = 0,
  total_length = NULL
)

Arguments

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 NULL, 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.

Details

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.

Value

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 nn_utils_rnn_pack_padded_sequence() or nn_utils_rnn_pack_sequence().

Note

total_length is useful to implement the ⁠pack sequence -> recurrent network -> unpack sequence⁠ pattern in a nn_module wrapped in ~torch.nn.DataParallel.

Examples

if (torch_is_installed()) {
seq <- torch_tensor(rbind(c(1, 2, 0), c(3, 0, 0), c(4, 5, 6)))
lens <- c(2, 1, 3)
packed <- nn_utils_rnn_pack_padded_sequence(seq, lens,
  batch_first = TRUE,
  enforce_sorted = FALSE
)
packed
nn_utils_rnn_pad_packed_sequence(packed, batch_first = TRUE)
}

torch documentation built on May 29, 2024, 9:54 a.m.