interpolate¶
-
class
torch.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
size
or the givenscale_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 toFalse
, 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 whenscale_factor
is kept the same. This only has an effect whenmode
is'bilinear'
. Default:False