class, size=None, scale_factor=None, mode='nearest', align_corners=None)[source]

Upsamples the input to either the given size or the given scale_factor


This function is deprecated in favor of This is equivalent with nn.quantized.functional.interpolate(...).

See torch.nn.functional.interpolate() for implementation details.

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


The input quantization parameters propagate to the output.


Only 2D input is supported for quantized inputs


Only the following modes are supported for the quantized inputs:

  • bilinear

  • nearest

  • input (Tensor) – quantized 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 be an integer.

  • mode (str) – algorithm used for upsampling: 'nearest' | 'bilinear'

  • 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 'bilinear'. Default: False


With align_corners = True, the linearly interpolating modes (bilinear) don’t proportionally align the output and input pixels, and thus the output values can depend on the input size. This was the default behavior for these modes up to version 0.3.1. Since then, the default behavior is align_corners = False. See Upsample for concrete examples on how this affects the outputs.


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