ConstantPad3d#

classtorch.nn.ConstantPad3d(padding,value)[source]#

Pads the input tensor boundaries with a constant value.

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)

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.ConstantPad3d(3,3.5)>>>input=torch.randn(16,3,10,20,30)>>>output=m(input)>>># using different paddings for different sides>>>m=nn.ConstantPad3d((3,3,6,6,0,1),3.5)>>>output=m(input)