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# interpolate

class torch.ao.nn.quantized.functional.interpolate(input, size=None, scale_factor=None, mode='nearest', align_corners=None)

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

  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.

  Note

  The input quantization parameters propagate to the output.

  Note

  Only 2D/3D input is supported for quantized inputs

  Note

  Only the following modes are supported for the quantized inputs:

  - bilinear

  - nearest

  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. Has to match input size if it is a tuple.

    - 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`

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