ConstantPad2d#

classtorch.nn.ConstantPad2d(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 4-tuple, uses ($\text{padding\_left}$padding_left,$\text{padding\_right}$padding_right,$\text{padding\_top}$padding_top,$\text{padding\_bottom}$padding_bottom)

Shape:

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

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

  $H_{out}=H_{in}+\text{padding\_top}+\text{padding\_bottom}$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.ConstantPad2d(2,3.5)>>>input=torch.randn(1,2,2)>>>inputtensor([[[ 1.6585,  0.4320],         [-0.8701, -0.4649]]])>>>m(input)tensor([[[ 3.5000,  3.5000,  3.5000,  3.5000,  3.5000,  3.5000],         [ 3.5000,  3.5000,  3.5000,  3.5000,  3.5000,  3.5000],         [ 3.5000,  3.5000,  1.6585,  0.4320,  3.5000,  3.5000],         [ 3.5000,  3.5000, -0.8701, -0.4649,  3.5000,  3.5000],         [ 3.5000,  3.5000,  3.5000,  3.5000,  3.5000,  3.5000],         [ 3.5000,  3.5000,  3.5000,  3.5000,  3.5000,  3.5000]]])>>># using different paddings for different sides>>>m=nn.ConstantPad2d((3,0,2,1),3.5)>>>m(input)tensor([[[ 3.5000,  3.5000,  3.5000,  3.5000,  3.5000],         [ 3.5000,  3.5000,  3.5000,  3.5000,  3.5000],         [ 3.5000,  3.5000,  3.5000,  1.6585,  0.4320],         [ 3.5000,  3.5000,  3.5000, -0.8701, -0.4649],         [ 3.5000,  3.5000,  3.5000,  3.5000,  3.5000]]])