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

In PyTorch’s documentation there is a note warning about
possible reproducibility issues while using padding:
"When using the CUDA backend, this operation may induce
nondeterministic behaviour in its backward pass that is not easily
switched off. Please see the notes on reproducibility for background."
It strikes me as a bit odd that such a straightforward operation, of
all things, would jeopardize reproducibility. Go figure!
"What are the other available modes?"
There are three other modes: replicate, reflect, and circular. Let’s take a look at
them, starting with the visualization.
Figure 5.12 - Paddings modes
In replication padding, the padded pixels have the same value as the closest real
pixel. The padded corners have the same value as the real corners. The other
columns (left and right) and rows (top and bottom) replicate the corresponding
values of the original image. The values used in the replication are in a darker shade
of orange.
In PyTorch, one can use the functional form F.pad() with mode="replicate", or use
the module version nn.ReplicationPad2d:
replication_padder = nn.ReplicationPad2d(padding=1)
replication_padder(image)
360 | Chapter 5: Convolutions