ReflectionPad3d

class torch.nn.ReflectionPad3d(padding)

Pads the input tensor using the reflection of the input boundary.

For N-dimensional padding, use torch.nn.functional.pad().

Parameters

padding (int, tuple) – the size of the padding. If is int, uses the same padding in all boundaries. If a 6-tuple, uses ($\text{padding\_left}$, $\text{padding\_right}$, $\text{padding\_top}$, $\text{padding\_bottom}$, $\text{padding\_front}$, $\text{padding\_back}$) Note that padding size should be less than the corresponding input dimension.

Shape:

• Input: $(N,C,D_{in},H_{in},W_{in})$ or $(C,D_{in},H_{in},W_{in})$.

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

  $D_{out}=D_{in}+\text{padding\_front}+\text{padding\_back}$

  $H_{out}=H_{in}+\text{padding\_top}+\text{padding\_bottom}$

  $W_{out}=W_{in}+\text{padding\_left}+\text{padding\_right}$

Examples:

>>> m = nn.ReflectionPad3d(1)
>>> input = torch.arange(8, dtype=torch.float).reshape(1, 1, 2, 2, 2)
>>> m(input)
tensor([[[[[7., 6., 7., 6.],
           [5., 4., 5., 4.],
           [7., 6., 7., 6.],
           [5., 4., 5., 4.]],
          [[3., 2., 3., 2.],
           [1., 0., 1., 0.],
           [3., 2., 3., 2.],
           [1., 0., 1., 0.]],
          [[7., 6., 7., 6.],
           [5., 4., 5., 4.],
           [7., 6., 7., 6.],
           [5., 4., 5., 4.]],
          [[3., 2., 3., 2.],
           [1., 0., 1., 0.],
           [3., 2., 3., 2.],
           [1., 0., 1., 0.]]]]])