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

Output
tensor([[[ 0.6241, -0.0274, -0.6412]]], grad_fn=<SqueezeBackward1>)
The resulting shape is given by the formula below, where l is the length of the
sequence, f is the filter size, p is the padding, and s is the stride:
Equation 8.15 - Resulting shape
If any of the resulting dimensions are not an integer, they must be rounded down.
Dilation
There is yet another operation that can be used with convolutions in any number of
dimensions, but that we haven’t discussed yet: dilation. The general idea is quite
simple: Instead of a contiguous kernel, it uses a dilated kernel. A dilation of size
two, for instance, means that the kernel uses every other element (be it a pixel or a
feature value in a sequence).
"Why would I want to do that?"
In a nutshell, the idea is to capture long-term properties of a sequence (like
seasonality in a time series, for example) or to integrate information from different
scales in an image (local and global context). We’re not delving deeper than
explaining the mechanism itself, though.
In our example, a kernel of size two (so it goes over two values in the sequence)
with a dilation of two (so it skips every other value in the sequence) works like
this:
674 | Chapter 8: Sequences