class DistributedSampler(_DistributedSampler): def __init__(self, dataset, num_replicas=None, rank=None, shuffle=True): super().__init__(dataset, num_replicas=num_replicas, rank=rank) self.shuffle = shuffle def __iter__(self): if self.shuffle: g = torch.Generator() g.manual_seed(self.epoch) indices = torch.randperm(len(self.dataset), generator=g).tolist() else: indices = torch.arange(len(self.dataset)).tolist() indices += indices[:(self.total_size - len(indices))] assert len(indices) == self.total_size indices = indices[self.rank:self.total_size:self.num_replicas] assert len(indices) == self.num_samples return iter(indices) def build_dataloader(dataset_cfg, class_names, batch_size, dist, root_path=None, workers=4, seed=None, logger=None, training=True, merge_all_iters_to_one_epoch=False, total_epochs=0): dataset = __all__[dataset_cfg.DATASET]( dataset_cfg=dataset_cfg, class_names=class_names, root_path=root_path, training=training, logger=logger, ) if merge_all_iters_to_one_epoch: assert hasattr(dataset, 'merge_all_iters_to_one_epoch') dataset.merge_all_iters_to_one_epoch(merge=True, epochs=total_epochs) if dist: if training: sampler = torch.utils.data.distributed.DistributedSampler(dataset) else: rank, world_size = common_utils.get_dist_info() sampler = DistributedSampler(dataset, world_size, rank, shuffle=False) else: sampler = None dataloader = DataLoader( dataset, batch_size=batch_size, pin_memory=True, num_workers=workers, shuffle=(sampler is None) and training, collate_fn=dataset.collate_batch, drop_last=False, sampler=sampler, timeout=0, worker_init_fn=partial(common_utils.worker_init_fn, seed=seed) ) return dataset, dataloader, sampler