That shouldn’t be the case and I’ve never seen this behavior before.
Could you post a code snippet, so that we can reproduce this issue?
Here is a dummy example which results in [batch_size, nb_features] for each batch:
# Create dummy data with class imbalance 99 to 1
numDataPoints = 1000
data_dim = 5
bs = 100
data = torch.randn(numDataPoints, data_dim)
target = torch.cat((torch.zeros(int(numDataPoints * 0.99), dtype=torch.long),
torch.ones(int(numDataPoints * 0.01), dtype=torch.long)))
print('target train 0/1: {}/{}'.format(
(target == 0).sum(), (target == 1).sum()))
# Compute samples weight (each sample should get its own weight)
class_sample_count = torch.tensor(
[(target == t).sum() for t in torch.unique(target, sorted=True)])
weight = 1. / class_sample_count.float()
samples_weight = torch.tensor([weight[t] for t in target])
# Create sampler, dataset, loader
sampler = WeightedRandomSampler(samples_weight, len(samples_weight))
train_dataset = torch.utils.data.TensorDataset(data, target)
train_loader = DataLoader(
train_dataset, batch_size=bs, num_workers=1, sampler=sampler)
# Iterate DataLoader and check class balance for each batch
for i, (x, y) in enumerate(train_loader):
print("batch index {}, 0/1: {}/{}".format(
i, (y == 0).sum(), (y == 1).sum()))
print("x.shape {}, y.shape {}".format(x.shape, y.shape))