nnf_pad: Pad

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

Pad

Description

Pads tensor.

Usage

nnf_pad(input, pad, mode = "constant", value = NULL)

Arguments

input	(Tensor) N-dimensional tensor
pad	(tuple) m-elements tuple, where \frac{m}{2} \leq input dimensions and m is even.
mode	'constant', 'reflect', 'replicate' or 'circular'. Default: 'constant'
value	fill value for 'constant' padding. Default: 0.

Padding size

The padding size by which to pad some dimensions of input are described starting from the last dimension and moving forward. \left\lfloor\frac{\mbox{len(pad)}}{2}\right\rfloor dimensions of input will be padded. For example, to pad only the last dimension of the input tensor, then pad has the form (\mbox{padding\_left}, \mbox{padding\_right}); to pad the last 2 dimensions of the input tensor, then use (\mbox{padding\_left}, \mbox{padding\_right}, \mbox{padding\_top}, \mbox{padding\_bottom}); to pad the last 3 dimensions, use (\mbox{padding\_left}, \mbox{padding\_right}, \mbox{padding\_top}, \mbox{padding\_bottom} \mbox{padding\_front}, \mbox{padding\_back}).

Padding mode

See nn_constant_pad_2d, nn_reflection_pad_2d, and nn_replication_pad_2d for concrete examples on how each of the padding modes works. Constant padding is implemented for arbitrary dimensions. tensor, or the last 2 dimensions of 4D input tensor, or the last dimension of 3D input tensor. Reflect padding is only implemented for padding the last 2 dimensions of 4D input tensor, or the last dimension of 3D input tensor.

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