torch.nn.functional.interpolate

torch.nn.functional.interpolate(input, size=None, scale_factor=None, mode='nearest', align_corners=None, recompute_scale_factor=None, antialias=False) [source]

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

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, nearest-exact

Parameters

• 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. If scale_factor is a tuple, its length has to match the number of spatial dimensions; input.dim() - 2.
• mode (str) – algorithm used for upsampling: 'nearest' | 'linear' | 'bilinear' | 'bicubic' | 'trilinear' | 'area' | 'nearest-exact'. 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. If recompute_scale_factor is True, then scale_factor must be passed in and scale_factor is used to compute the output size. The computed output size will be used to infer new scales for the interpolation. Note that when scale_factor is floating-point, it may differ from the recomputed scale_factor due to rounding and precision issues. If recompute_scale_factor is False, then size or scale_factor will be used directly for interpolation. Default: None.
• antialias (bool, optional) – flag to apply anti-aliasing. Default: False. Using anti-alias option together with align_corners=False, interpolation result would match Pillow result for downsampling operation. Supported modes: 'bilinear', 'bicubic'.

Return type

Tensor

Note

With mode='bicubic', it’s possible to cause overshoot, in other words it can produce negative values or values greater than 255 for images. Explicitly call result.clamp(min=0, max=255) if you want to reduce the overshoot when displaying the image.

Note

Mode mode='nearest-exact' matches Scikit-Image and PIL nearest neighbours interpolation algorithms and fixes known issues with mode='nearest'. This mode is introduced to keep backward compatibility. Mode mode='nearest' matches buggy OpenCV’s INTER_NEAREST interpolation algorithm.

Note

The gradients for the dtype float16 on CUDA may be inaccurate in the upsample operation when using modes ['linear', 'bilinear', 'bicubic', 'trilinear', 'area']. For more details, please refer to the discussion in issue#104157.

Note

This operation may produce nondeterministic gradients when given tensors on a CUDA device. See Reproducibility for more information.