Method __iter__

datasets/DistributedProxySampler.py:30–45

(self)

        self.sampler = sampler

    def __iter__(self):
        # deterministically shuffle based on epoch
        torch.manual_seed(self.epoch)
        indices = list(self.sampler)

        # add extra samples to make it evenly divisible
        indices += indices[:(self.total_size - len(indices))]
        if len(indices) != self.total_size:
            raise RuntimeError("{} vs {}".format(len(indices), self.total_size))

        # subsample
        indices = indices[self.rank:self.total_size:self.num_replicas]
        if len(indices) != self.num_samples:
            raise RuntimeError("{} vs {}".format(len(indices), self.num_samples))

        return iter(indices)