(self, dataset, batch_size, num_replicas=None, rank=None, add_extra_examples=True, shuffle=True)
| 368 | """Copied from torch DistributedSampler""" |
| 369 | |
| 370 | def __init__(self, dataset, batch_size, num_replicas=None, rank=None, add_extra_examples=True, shuffle=True): |
| 371 | if num_replicas is None: |
| 372 | if not dist.is_available(): |
| 373 | raise RuntimeError("Requires distributed package to be available") |
| 374 | num_replicas = dist.get_world_size() |
| 375 | if rank is None: |
| 376 | if not dist.is_available(): |
| 377 | raise RuntimeError("Requires distributed package to be available") |
| 378 | rank = dist.get_rank() |
| 379 | self.dataset = dataset |
| 380 | self.num_replicas = num_replicas |
| 381 | self.rank = rank |
| 382 | self.epoch = 0 |
| 383 | if add_extra_examples: |
| 384 | self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.num_replicas)) |
| 385 | self.total_size = self.num_samples * self.num_replicas |
| 386 | else: |
| 387 | self.total_size = len(dataset) |
| 388 | self.num_samples = len(self.available_indices) |
| 389 | self.batch_size = batch_size |
| 390 | self.add_extra_examples = add_extra_examples |
| 391 | self.shuffle = shuffle |
| 392 | |
| 393 | def __iter__(self) -> Iterable: |
| 394 | g = torch.Generator() |
nothing calls this directly
no outgoing calls
no test coverage detected