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

Let’s apply this filter to our image so we can use the resulting image in our next
operation:
padded = F.pad(image, (1, 1, 1, 1), mode='constant', value=0)
conv_padded = F.conv2d(padded, kernel_edge, stride=1)
Pooling
Now we’re back in the business of shrinking images. Pooling is different than the
former operations: It splits the image into tiny chunks, performs an operation on
each chunk (that yields a single value), and puts the chunks together as the
resulting image. Again, an image is worth a thousand words.
Figure 5.15 - Max pooling
In the image above, we’re performing a max pooling with a kernel size of two. Even
though this is not quite the same as the filters we’ve already seen, it is still called a
kernel.
In this example, the stride is assumed to be the same size as the
kernel.
Our input image is split into nine chunks, and we perform a simple max operation
(hence, max pooling) on each chunk (really, it is just taking the largest value in each
chunk). Then, these values are put together, in order, to produce a smaller
resulting image.
The larger the pooling kernel, the smaller the resulting image.
364 | Chapter 5: Convolutions