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

dropout. You’re already familiar with all of this, except for the element in the
middle.
Adaptive Pooling
The element in the middle, AdaptiveAvgPool2d (F.adaptive_avg_pool2d() in its
functional form), is a special kind of pooling: Instead of requiring the kernel size
(and stride), it requires the desired output size. In other words, whatever the
image size it gets as input, it will return a tensor with the desired size.
"What’s so special about this?"
It gives you the freedom to use images of different sizes as inputs! We’ve seen that
convolutions and traditional max pooling, in general, shrink the image’s dimensions.
But the classifier part of the model expects an input of a determined size. That
means that the input image has to be of a determined size such that, at the end of
the shrinkage process, it matches what the classifier part is expecting. The
adaptive pooling, however, guarantees the output size, so it can easily match the
classifier expectations regarding input size.
Let’s say we have two dummy tensors representing feature maps produced by the
featurizer part of the model. The feature maps have different dimensions (32x32
and 12x12). Applying adaptive pooling to both of them ensures that both outputs
have the same dimensions (6x6):
result1 = F.adaptive_avg_pool2d(
torch.randn(16, 32, 32), output_size=(6, 6)
)
result2 = F.adaptive_avg_pool2d(
torch.randn(16, 12, 12), output_size=(6, 6)
)
result1.shape, result2.shape
Output
(torch.Size([16, 6, 6]), torch.Size([16, 6, 6]))
Cool, right? We have the architecture already, but our model is untrained (it still
has random weights). Let’s fix that by…
Transfer Learning in Practice | 507