ReflectionPad3d#

classtorch.nn.ReflectionPad3d(padding)[source]#

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

ForN-dimensional padding, usetorch.nn.functional.pad().

Parameters

padding (int,tuple) – the size of the padding. If isint, uses the samepadding in all boundaries. If a 6-tuple, uses($\text{padding\_left}$padding_left,$\text{padding\_right}$padding_right,$\text{padding\_top}$padding_top,$\text{padding\_bottom}$padding_bottom,$\text{padding\_front}$padding_front,$\text{padding\_back}$padding_back)Note that padding size should be less than the corresponding input dimension.

Shape:

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

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

  $D_{out}=D_{in}+\text{padding\_front}+\text{padding\_back}$Dout​=Din​+padding_front+padding_back

  $H_{out}=H_{in}+\text{padding\_top}+\text{padding\_bottom}$Hout​=Hin​+padding_top+padding_bottom

  $W_{out}=W_{in}+\text{padding\_left}+\text{padding\_right}$Wout​=Win​+padding_left+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.]]]]])