class oneflow.nn.UpsamplingBilinear2d(size: Optional[Tuple[int, int]] = None, scale_factor: Optional[Tuple[float, float]] = None)

Applies a 2D bilinear upsampling to an input signal composed of several input channels.

To specify the scale, it takes either the size or the scale_factor as it’s constructor argument.

When size is given, it is the output size of the image (h, w).

  • size (int or Tuple[int, int], optional) – output spatial sizes

  • scale_factor (float or Tuple[float, float], optional) – multiplier for spatial size.


This class is deprecated in favor of interpolate(). It is equivalent to nn.functional.interpolate(..., mode='bilinear', align_corners=True).

  • Input: \((N, C, H_{in}, W_{in})\)

  • Output: \((N, C, H_{out}, W_{out})\) where

\[H_{out} = \left\lfloor H_{in} \times \text{scale_factor} \right\rfloor\]
\[W_{out} = \left\lfloor W_{in} \times \text{scale_factor} \right\rfloor\]

For example:

>>> import numpy as np
>>> import oneflow as flow

>>> input = flow.tensor(np.arange(1, 5).reshape((1, 1, 2, 2)), dtype=flow.float32)
>>> input ="cuda")
>>> m = flow.nn.UpsamplingBilinear2d(scale_factor=2.0)
>>> output = m(input)
>>> output 
tensor([[[[1.0000, 1.3333, 1.6667, 2.0000],
          [3.0000, 3.3333, 3.6667, 4.0000]]]], device='cuda:0',