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

The constructor method defines the seven elements used by four branches (you
can identify each of them in Figure 7.7). In this example, I’ve configured all 1x1
convolutions to produce two channels each, but it is not required that they all have
the same number of output channels. The same applies to both 3x3 and 5x5
convolution branches: Although I’ve configured them both to produce the same
number of channels (three) each, this is not a requirement.
It is required, though, that all branches produce outputs with
matching height and width. This means that the padding must be
adjusted according to the kernel size in order to output the
correct dimensions.
The forward method feeds the input x to each of the four branches, and then it uses
torch.cat() to concatenate the resulting channels along the corresponding
dimension (according to PyTorch’s NCHW shape convention). This concatenation
would fail if the height and width of the outputs did not match across the different
branches.
What if we run our example image (scissors, in the color version) through the
Inception module?
inception = Inception(in_channels=3)
output = inception(image)
output.shape
Output
torch.Size([1, 10, 300, 300])
There we go: The output has the expected ten channels.
As you can see, the idea behind the Inception module is actually quite simple. Later
versions had slightly different architectures (like switching the 5x5 convolution by
two 3x3 convolutions in a row), but the overall structure remains. There is one
more thing, though.
If you check Inception’s code in PyTorch, you’ll find that it does not use nn.Conv2d
directly, but rather something called BasicConv2d (reproduced below):
Inception Modules | 531