torchoutil.nn.functional.padding module¶

cat_padded_batch( x1: Tensor, x1_lens: Tensor, x2: Tensor, x2_lens: Tensor, seq_dim: int = -1, batch_dim: int = 0, ) → Tuple[Tensor, Tensor][source]¶

Concatenate padded batched of sequences.

Args:

x1: First batch with D dims of shape (batch_size, …, N1, …) x1_lens: First lengths of each element in sequence dim of shape (batch_size,). x2: Second batch with D dims of shape (batch_size, …, N2, …)

The shape must be the same than x1 unless for the dimension N2.

x2_lens: Second lengths of each element in sequence dim of shape (batch_size,). seq_dim: Dimension index of sequence. defaults to -1. batch_dim: Batch dimension index. defaults to 0.

Recursive version of torch.nn.utils.rnn.pad_sequence, with padding of Tensors.

Args:: sequence: The sequence to pad. Must be convertable to tensor by having the correct number of dims in all sublists. pad_value: The pad value used. defaults to 0. align: Alignement used for padding. defaults to “left”. device: The device of the output Tensor. defaults to None. dtype: The dtype of the output Tensor. defaults to None.

Example 1::¶

>>> sequence = [[1, 2], [3], [], [4, 5]]
>>> output = pad_sequence_rec(sequence, 0)
tensor([[1, 2], [3, 0], [0, 0], [4, 5]])

Example 2::¶

>>> invalid_sequence = [[1, 2, 3], 3]
>>> output = pad_sequence_rec(invalid_sequence, 0)
ValueError : Cannot pad sequence of tensors of differents number of dims.

pad_dim( x: Tensor, target_length: int, *, dim: int = -1, align: Literal['left', 'right', 'center', 'random'] = 'left', pad_value: int | float | bool | Callable[[Tensor], int | float | bool] = 0.0, mode: Literal['constant', 'reflect', 'replicate', 'circular'] = 'constant', generator: Generator | None | Literal['default'] | int = None, ) → Tensor[source]¶: Generic function for pad a single dimension.

pad_dims( x: Tensor, target_lengths: Iterable[int], *, dims: Iterable[int] = (-1,), aligns: Literal['left', 'right', 'center', 'random'] | Iterable[Literal['left', 'right', 'center', 'random']] = ('left',), pad_value: int | float | bool | Callable[[Tensor], int | float | bool] = 0.0, mode: Literal['constant', 'reflect', 'replicate', 'circular'] = 'constant', generator: Generator | None | Literal['default'] | int = None, ) → Tensor[source]¶: Generic function to pad multiple dimensions.