nn_utils_rnn_pad_sequence: Pad a list of variable length Tensors with 'padding_value'

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

Pad a list of variable length Tensors with padding_value

Description

pad_sequence stacks 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.

Usage

nn_utils_rnn_pad_sequence(sequences, batch_first = FALSE, padding_value = 0)

Arguments

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
padding_value	(float, optional): value for padded elements. Default: 0.

Details

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.

Value

Tensor of size ⁠T x B x *⁠ if batch_first is FALSE. Tensor of size ⁠B x T x *⁠ otherwise

Note

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.

Examples

if (torch_is_installed()) {
a <- torch_ones(25, 300)
b <- torch_ones(22, 300)
c <- torch_ones(15, 300)
nn_utils_rnn_pad_sequence(list(a, b, c))$size()
}

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