Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

In code, as usual, PyTorch gives us two options: functional (F.pad()) and module
(nn.ConstantPad2d). Let’s start with the module version this time:
constant_padder = nn.ConstantPad2d(padding=1, value=0)
constant_padder(image)
Output
tensor([[[[0., 0., 0., 0., 0., 0., 0., 0.],
[0., 5., 0., 8., 7., 8., 1., 0.],
[0., 1., 9., 5., 0., 7., 7., 0.],
[0., 6., 0., 2., 4., 6., 6., 0.],
[0., 9., 7., 6., 6., 8., 4., 0.],
[0., 8., 3., 8., 5., 1., 3., 0.],
[0., 7., 2., 7., 0., 1., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0.]]]])
There are two arguments: padding, for the number of columns and rows to be
stuffed in the image; and value, for the value that is filling these new columns and
rows. One can also do asymmetric padding by specifying a tuple in the padding
argument representing (left, right, top, bottom). So, if we were to stuff our
image on the left and right sides only, the argument would go like this: (1, 1, 0,
0).
We can achieve the same result using the functional padding:
padded = F.pad(image, pad=(1, 1, 1, 1), mode='constant', value=0)
In the functional version, one must specify the padding as a tuple. The value
argument is straightforward, and there is yet another argument, mode, which was
set to constant to match the module version above.
Convolutions | 359