upsample¶
- class torch.ao.nn.quantized.functional.upsample(input, size=None, scale_factor=None, mode='nearest', align_corners=None)[source]¶
Upsamples the input to either the given
sizeor the givenscale_factorWarning
This function is deprecated in favor of
torch.ao.nn.quantized.functional.interpolate(). This is equivalent withnn.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.
Note
The input quantization parameters propagate to the output.
Note
Only 2D input is supported for quantized inputs
Note
Only the following modes are supported for the quantized inputs:
bilinear
nearest
- Parameters
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 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_factoris kept the same. This only has an effect whenmodeis'bilinear'. Default:False
Warning
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 isalign_corners = False. SeeUpsamplefor concrete examples on how this affects the outputs.
