Hi, I have implemented the following piece of code to do oversampling of my training dataset that is highly imbalanced.
if OVERSAMPLING:
class_weights = 1. / torch.Tensor([25810, 2443, 5292, 873, 708]) # 1/number of samples in each class
weighted_sampler = torch.utils.data.sampler.WeightedRandomSampler(weights = class_weights, num_samples=NUM_SAMPLES_TRN, replacement=True)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, sampler = weighted_sampler)
However, I am getting a lot of error messages and therefore have a couple of questions regarding things, that I could not clearly understand from the docs or other questions posted here:

what should the argument num_samples in the WeightedRandomSampler value be? batchsize or the size of the whole dataset?

what exactly means replacement=True and why does my code not run with replacement=False  getting:
RuntimeError: cannot sample n_sample > prob_dist.size(1) samples without replacement
 the above code snippet with replacement=True results in the following batches
tensor([0, 0, 0, 0, 0, 0, 0, 0])
tensor([0, 0, 0, 0, 0, 0, 0, 0])
tensor([0, 0, 0, 0, 0, 0, 0, 0])
tensor([0, 0, 0, 0, 0, 0, 0, 0])
tensor([0, 0, 0, 0, 0, 0, 0, 0])
tensor([0, 0, 0, 0, 0, 0, 0, 0])
tensor([0, 0, 0, 0, 0, 0, 0, 0])
tensor([0, 0, 0, 0, 0, 0, 0, 0])
tensor([0, 0, 0, 0, 0, 0, 0, 0])
tensor([0, 0, 0, 0, 0, 0, 0, 0])
tensor([0, 0, 0, 0, 0, 0, 0, 0])
Its sampling only from the class 0, which is the most populous. However, I wanted to support sampling from classes, that don’t have that many samples, not this.
Could you please give me hand?
Thanks a lot
Vojtooo