nn_utils_rnn_pack_padded_sequence: Packs a Tensor containing padded sequences of variable...

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

Packs a Tensor containing padded sequences of variable length.

Description

input can be of size ⁠T x B x *⁠ where T is the length of the longest sequence (equal to lengths[1]), B is the batch size, and * is any number of dimensions (including 0). If batch_first is TRUE, ⁠B x T x *⁠ input is expected.

Usage

nn_utils_rnn_pack_padded_sequence(
  input,
  lengths,
  batch_first = FALSE,
  enforce_sorted = TRUE
)

Arguments

input	(Tensor): padded batch of variable length sequences.
lengths	(Tensor): list of sequences lengths of each batch element.
batch_first	(bool, optional): if TRUE, the input is expected in ⁠B x T x *⁠ format.
enforce_sorted	(bool, optional): if TRUE, the input is expected to contain sequences sorted by length in a decreasing order. If FALSE, the input will get sorted unconditionally. Default: TRUE.

Details

For unsorted sequences, use enforce_sorted = FALSE. If enforce_sorted is TRUE, the sequences should be sorted by length in a decreasing order, i.e. input[,1] should be the longest sequence, and input[,B] the shortest one. enforce_sorted = TRUE is only necessary for ONNX export.

Value

a PackedSequence object

Note

This function accepts any input that has at least two dimensions. You can apply it to pack the labels, and use the output of the RNN with them to compute the loss directly. A Tensor can be retrieved from a PackedSequence object by accessing its .data attribute.

torch documentation built on June 7, 2023, 6:19 p.m.