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# UpsamplingBilinear2d¶

class torch.nn.UpsamplingBilinear2d(size: Optional[Union[T, Tuple[T, T]]] = None, scale_factor: Optional[Union[T, Tuple[T, T]]] = None)[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_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]]]])


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