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

train_loader.sampler.generator.manual_seed(42)
random.seed(42)
Now we can check if our sampler is doing its job correctly. Let’s have it sample a full
run (240 data points in 15 mini-batches of 16 points each), and sum up the labels so
we know how many points are in the positive class:
torch.tensor([t[1].sum() for t in iter(train_loader)]).sum()
Output
tensor(123.)
Close enough! We have 160 images of the positive class, and now, thanks to the
weighted sampler, we’re sampling only 123 of them. It means we’re oversampling
the negative class (which has 80 images) to a total of 117 images, adding up to 240
images. Mission accomplished, our dataset is balanced now.
"Wait a minute! Why on Earth there was an extra seed
(random.seed(42)) in the code above? Don’t we have enough
already?"
I agree, too many seeds. Besides one specific seed for the generator, we also have
to set yet another seed for Python’s random module.
Honestly, this came to me as a surprise too when I found out
about it! As weird as it may sound, in Torchvision versions prior
to 0.8, there was still some code that depended upon Python’s
native random module, instead of PyTorch’s own random
generators. The problem happened when some of the random
transformations for data augmentation were used, like
RandomRotation(), RandomAffine(), and others.
It’s better to be safe than sorry, so we better set yet another seed to ensure the
reproducibility of our code.
And that’s exactly what we’re going to do! Remember the set_seed() method we
294 | Chapter 4: Classifying Images