nnf_interpolate {torch}

Interpolate

Description

Down/up samples the input to either the given size or the given scale_factor

Usage

nnf_interpolate(
  input,
  size = NULL,
  scale_factor = NULL,
  mode = "nearest",
  align_corners = FALSE,
  recompute_scale_factor = NULL
)

Arguments

input

(Tensor) the input tensor

size

(int or Tuple[int] or Tuple[int, int] or Tuple[int, int, int]) output spatial size.

scale_factor

(float or Tuple[float]) multiplier for spatial size. Has to match input size if it is a tuple.

mode

(str) algorithm used for upsampling: 'nearest' | 'linear' | 'bilinear' | 'bicubic' | 'trilinear' | 'area' Default: 'nearest'

align_corners

(bool, optional) Geometrically, we consider the pixels of the input and output as squares rather than points. If set to TRUE, the input and output tensors are aligned by the center points of their corner pixels, preserving the values at the corner pixels. If set to False, the input and output tensors are aligned by the corner points of their corner pixels, and the interpolation uses edge value padding for out-of-boundary values, making this operation independent of input size when scale_factor is kept the same. This only has an effect when mode is 'linear', 'bilinear', 'bicubic' or 'trilinear'. Default: False

recompute_scale_factor

(bool, optional) recompute the scale_factor for use in the interpolation calculation. When scale_factor is passed as a parameter, it is used to compute the output_size. If recompute_scale_factor is “'True“ or not specified, a new scale_factor will be computed based on the output and input sizes for use in the interpolation computation (i.e. the computation will be identical to if the computed 'output_size' were passed-in explicitly). Otherwise, the passed-in 'scale_factor' will be used in the interpolation computation. Note that when 'scale_factor' is floating-point, the recomputed scale_factor may differ from the one passed in due to rounding and precision issues.

Details

The algorithm used for interpolation is determined by mode.

Currently temporal, spatial and volumetric sampling are supported, i.e. expected inputs are 3-D, 4-D or 5-D in shape.

The input dimensions are interpreted in the form: ⁠mini-batch x channels x [optional depth] x [optional height] x width⁠.

The modes available for resizing are: nearest, linear (3D-only), bilinear, bicubic (4D-only), trilinear (5D-only), area