UpsamplingBilinear2d — PyTorch 2.7 documentation (original) (raw)

class torch.nn.UpsamplingBilinear2d(size=None, scale_factor=None)[source][source]¶

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_factoras 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,Hin,Win)(N, C, H_{in}, W_{in})
• Output: (N,C,Hout,Wout)(N, C, H_{out}, W_{out}) where

Hout=⌊Hin×scale_factor⌋H_{out} = \left\lfloor H_{in} \times \text{scale\_factor} \right\rfloor

Wout=⌊Win×scale_factor⌋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]]]])