ConstantPad1d#

classtorch.nn.ConstantPad1d(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 both boundaries. If a 2-tuple, uses($\text{padding\_left}$padding_left,$\text{padding\_right}$padding_right)

Shape:

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

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

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

Examples:

>>>m=nn.ConstantPad1d(2,3.5)>>>input=torch.randn(1,2,4)>>>inputtensor([[[-1.0491, -0.7152, -0.0749,  0.8530],         [-1.3287,  1.8966,  0.1466, -0.2771]]])>>>m(input)tensor([[[ 3.5000,  3.5000, -1.0491, -0.7152, -0.0749,  0.8530,  3.5000,           3.5000],         [ 3.5000,  3.5000, -1.3287,  1.8966,  0.1466, -0.2771,  3.5000,           3.5000]]])>>>m=nn.ConstantPad1d(2,3.5)>>>input=torch.randn(1,2,3)>>>inputtensor([[[ 1.6616,  1.4523, -1.1255],         [-3.6372,  0.1182, -1.8652]]])>>>m(input)tensor([[[ 3.5000,  3.5000,  1.6616,  1.4523, -1.1255,  3.5000,  3.5000],         [ 3.5000,  3.5000, -3.6372,  0.1182, -1.8652,  3.5000,  3.5000]]])>>># using different paddings for different sides>>>m=nn.ConstantPad1d((3,1),3.5)>>>m(input)tensor([[[ 3.5000,  3.5000,  3.5000,  1.6616,  1.4523, -1.1255,  3.5000],         [ 3.5000,  3.5000,  3.5000, -3.6372,  0.1182, -1.8652,  3.5000]]])