For a dataset coming from a torchvision ImageFolder dataset the code that I’ve finally used, based on the suggestion of @smth is the next:
First, define the function:
def make_weights_for_balanced_classes(images, nclasses):
count = [0] * nclasses
for item in images:
count[item[1]] += 1
weight_per_class = [0.] * nclasses
N = float(sum(count))
for i in range(nclasses):
weight_per_class[i] = N/float(count[i])
weight = [0] * len(images)
for idx, val in enumerate(images):
weight[idx] = weight_per_class[val[1]]
return weight
And after this, use it in the next way:
dataset_train = datasets.ImageFolder(traindir)
# For unbalanced dataset we create a weighted sampler
weights = make_weights_for_balanced_classes(dataset_train.imgs, len(dataset_train.classes))
weights = torch.DoubleTensor(weights)
sampler = torch.utils.data.sampler.WeightedRandomSampler(weights, len(weights))
train_loader = torch.utils.data.DataLoader(dataset_train, batch_size=args.batch_size, shuffle = True,
sampler = sampler, num_workers=args.workers, pin_memory=True)