PyTorch Sampler for Intensional Overfitting!

sampler.py

from torch.utils.data.sampler import Sampler

class MySampler(Sampler):
    def __init__(self, main_source, indices):
        self.main_source = main_source
        self.indices = indices

        main_source_len = len(self.main_source)

        how_many = int(round(main_source_len / len(self.indices)))
        self.to_iter_from = []
        for _ in range(how_many):
            self.to_iter_from.extend(self.indices)

    def __iter__(self):
        return iter(self.to_iter_from)

    def __len__(self):
        return len(self.main_source)