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

Output
tensor([[[[5., 5., 0., 8., 7., 8., 1., 1.],
[5., 5., 0., 8., 7., 8., 1., 1.],
[1., 1., 9., 5., 0., 7., 7., 7.],
[6., 6., 0., 2., 4., 6., 6., 6.],
[9., 9., 7., 6., 6., 8., 4., 4.],
[8., 8., 3., 8., 5., 1., 3., 3.],
[7., 7., 2., 7., 0., 1., 0., 0.],
[7., 7., 2., 7., 0., 1., 0., 0.]]]])
In reflection padding, it gets a bit trickier. It is like the outer columns and rows are
used as axes for the reflection. So, the left padded column (forget about the
corners for now) reflects the second column (since the first column is the axis of
reflection). The same reasoning goes for the right padded column. Similarly, the top
padded row reflects the second row (since the first row is the axis of reflection),
and the same reasoning goes for the bottom padded row. The values used in the
reflection are in a darker shade of orange. The corners have the same values as the
intersection of the reflected rows and columns of the original image. Hopefully,
the image can convey the idea better than my words.
In PyTorch, you can use the functional form F.pad() with mode="reflect", or use
the module version nn.ReflectionPad2d:
reflection_padder = nn.ReflectionPad2d(padding=1)
reflection_padder(image)
Output
tensor([[[[9., 1., 9., 5., 0., 7., 7., 7.],
[0., 5., 0., 8., 7., 8., 1., 8.],
[9., 1., 9., 5., 0., 7., 7., 7.],
[0., 6., 0., 2., 4., 6., 6., 6.],
[7., 9., 7., 6., 6., 8., 4., 8.],
[3., 8., 3., 8., 5., 1., 3., 1.],
[2., 7., 2., 7., 0., 1., 0., 1.],
[3., 8., 3., 8., 5., 1., 3., 1.]]]])
In circular padding, the left-most (right-most) column gets copied as the right (left)
Convolutions | 361