- class torch.ao.nn.quantized.functional.interpolate(input, size=None, scale_factor=None, mode='nearest', align_corners=None)[source]¶
Down/up samples the input to either the given
sizeor the given
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/3D input is supported for quantized inputs
Only the following modes are supported for the quantized inputs:
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:
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_factoris kept the same. This only has an effect when