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

• weights: A sequence of weights like the one we have just computed.
• num_samples: How many samples are going to be drawn from the dataset.
◦ A typical value is the length of the sequence of weights, as you’re likely
sampling from the whole training set.
• replacement: If True (the default value), it draws samples with replacement.
◦ If num_samples equals the length—that is, if the whole training set is used—it
makes sense to draw samples with replacement to effectively compensate
for the imbalance.
◦ It only makes sense to set it to False if num_samples < length of the dataset.
• generator: Optional, it takes a (pseudo) random number Generator that will be
used for drawing the samples.
◦ To ensure reproducibility, we need to create and assign a generator (which
has its own seed) to the sampler, since the manual seed we’ve already set is
not enough.
OK, we’ll sample from the whole training set, and we have our sequence of weights
ready. We are still missing a generator, though. Let’s create both the generator and
the sampler now:
generator = torch.Generator()
sampler = WeightedRandomSampler(
weights=sample_weights,
num_samples=len(sample_weights),
generator=generator,
replacement=True
)
"Didn’t you say we need to set a seed for the generator?! Where is it?"
Indeed, I said it. We’ll set it soon, after assigning the sampler to the data loader.
You’ll understand the reasoning behind this choice shortly, so please bear with me.
Now, let’s (re-)create the data loaders using the weighted sampler with the training
set:
292 | Chapter 4: Classifying Images