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

train_loader = DataLoader(
dataset=train_dataset, batch_size=16, sampler=sampler
)
val_loader = DataLoader(dataset=val_dataset, batch_size=16)
Once again, if we’re using a sampler, we cannot use the shuffle argument.
There is a lot of boilerplate code here, right? Let’s build yet another function,
Helper Function #5, to wrap it all up:
Helper Function #5
1 def make_balanced_sampler(y):
2 # Computes weights for compensating imbalanced classes
3 classes, counts = y.unique(return_counts=True)
4 weights = 1.0 / counts.float()
5 sample_weights = weights[y.squeeze().long()]
6 # Builds sampler with compute weights
7 generator = torch.Generator()
8 sampler = WeightedRandomSampler(
9 weights=sample_weights,
10 num_samples=len(sample_weights),
11 generator=generator,
12 replacement=True
13 )
14 return sampler
sampler = make_balanced_sampler(y_train_tensor)
Much better! Its only argument is the tensor containing the labels: The function
will compute the weights and build the corresponding weighted sampler on its own.
Seeds and more (seeds)
Time to set the seed for the generator used in the sampler assigned to the data
loader. It is a long sequence of objects, but we can work our way through it to
retrieve the generator and call its manual_seed() method:
Data Preparation | 293