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

Everything is in place now! The only thing left to do is to actually call the model, so
a forward pass is triggered, the hooks are executed, and the outputs to all these
layers are stored in the visualization dictionary.
Let’s fetch one mini-batch from the validation loader and use the predict()
method of our StepByStep class (which will then call the trained model):
images_batch, labels_batch = iter(val_loader).next()
logits = sbs_cnn1.predict(images_batch)
Now, if everything went well, our visualization dictionary should contain one key
for each layer we hooked a function to:
visualization.keys()
Output
dict_keys(['conv1', 'relu1', 'maxp1', 'flatten', 'fc1', 'relu2',
'fc2'])
Bingo! They are all there! But, before checking what’s stored inside it, let’s remove
the hooks:
for handle in handles.values():
handle.remove()
handles = {}
Make sure to always remove the hooks after they have served
their purpose to avoid unnecessary operations that may slow
down your model.
Maybe I got you hooked (sorry, I really like puns!), maybe not. Anyway, to make it
easier for you to get some layers hooked so you can take a peek at what they are
producing, we will append some methods to our StepByStep class: attach_hooks()
and remove_hooks().
First, we create two dictionaries as attributes, visualization and handles, which
400 | Chapter 5: Convolutions