UpsamplingBilinear2d

class torch.nn.UpsamplingBilinear2d(size=None, scale_factor=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).

Parameters

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

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

Warning

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

Shape:

• Input: $(N, C, H_{in}, W_{in})$

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

$$H_{out} = \left\lfloor H_{in} \times \text{scale\_factor} \right\rfloor$$

$$W_{out} = \left\lfloor W_{in} \times \text{scale\_factor} \right\rfloor$$

Examples:

>>> input = torch.arange(1, 5, dtype=torch.float32).view(1, 1, 2, 2)
>>> input
tensor([[[[1., 2.],
          [3., 4.]]]])

>>> m = nn.UpsamplingBilinear2d(scale_factor=2)
>>> m(input)
tensor([[[[1.0000, 1.3333, 1.6667, 2.0000],
          [1.6667, 2.0000, 2.3333, 2.6667],
          [2.3333, 2.6667, 3.0000, 3.3333],
          [3.0000, 3.3333, 3.6667, 4.0000]]]])