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

A pooling kernel of two-by-two results in an image whose dimensions are half of
the original. A pooling kernel of three-by-three makes the resulting image onethird
the size of the original, and so on. Moreover, only full chunks count: If we try a
kernel of four-by-four in our six-by-six image, only one chunk fits, and the resulting
image would have a single pixel.
In PyTorch, as usual, we have both forms: F.max_pool2d() and nn.MaxPool2d. Let’s
use the functional form to replicate the max pooling in the figure above:
pooled = F.max_pool2d(conv_padded, kernel_size=2)
pooled
Output
tensor([[[[22., 23., 11.],
[24., 7., 1.],
[13., 13., 13.]]]])
And then let’s use the module version to illustrate the large four-by-four pooling:
maxpool4 = nn.MaxPool2d(kernel_size=4)
pooled4 = maxpool4(conv_padded)
pooled4
Output
tensor([[[[24.]]]])
A single pixel, as promised!
"Can I perform some other operation?"
Sure, besides max pooling, average pooling is also fairly common. As the name
suggests, it will output the average pixel value for each chunk. In PyTorch, we have
F.avg_pool2d() and nn.AvgPool2d.
"Can I use a stride of a different size?" Pooling | 365