Sampler that restricts data loading to a subset of the dataset. It is especially useful in conjunction with :class:`torch.nn.parallel.DistributedDataParallel`. In such case, each process can pass a DistributedSampler instance as a DataLoader sampler, and load a subset of the original
| 5 | |
| 6 | |
| 7 | class OrderedDistributedSampler(Sampler): |
| 8 | """Sampler that restricts data loading to a subset of the dataset. |
| 9 | It is especially useful in conjunction with |
| 10 | :class:`torch.nn.parallel.DistributedDataParallel`. In such case, each |
| 11 | process can pass a DistributedSampler instance as a DataLoader sampler, |
| 12 | and load a subset of the original dataset that is exclusive to it. |
| 13 | .. note:: |
| 14 | Dataset is assumed to be of constant size. |
| 15 | Arguments: |
| 16 | dataset: Dataset used for sampling. |
| 17 | num_replicas (optional): Number of processes participating in |
| 18 | distributed training. |
| 19 | rank (optional): Rank of the current process within num_replicas. |
| 20 | """ |
| 21 | |
| 22 | def __init__(self, dataset, num_replicas=None, rank=None): |
| 23 | if num_replicas is None: |
| 24 | if not dist.is_available(): |
| 25 | raise RuntimeError("Requires distributed package to be available") |
| 26 | num_replicas = dist.get_world_size() |
| 27 | if rank is None: |
| 28 | if not dist.is_available(): |
| 29 | raise RuntimeError("Requires distributed package to be available") |
| 30 | rank = dist.get_rank() |
| 31 | self.dataset = dataset |
| 32 | self.num_replicas = num_replicas |
| 33 | self.rank = rank |
| 34 | self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.num_replicas)) |
| 35 | self.total_size = self.num_samples * self.num_replicas |
| 36 | |
| 37 | def __iter__(self): |
| 38 | indices = list(range(len(self.dataset))) |
| 39 | |
| 40 | # add extra samples to make it evenly divisible |
| 41 | indices += indices[:(self.total_size - len(indices))] |
| 42 | assert len(indices) == self.total_size |
| 43 | |
| 44 | # subsample |
| 45 | indices = indices[self.rank:self.total_size:self.num_replicas] |
| 46 | assert len(indices) == self.num_samples |
| 47 | |
| 48 | return iter(indices) |
| 49 | |
| 50 | def __len__(self): |
| 51 | return self.num_samples |
| 52 | |
| 53 | |
| 54 | class RepeatAugSampler(Sampler): |