Class MyDistributedSampler

TinyViT/data/sampler.py:17–157

r"""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 a case, each process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a :class:`~torch.util

class MyDistributedSampler(Sampler[T_co]):
    r"""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 a case, each
    process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a
    :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the
    original dataset that is exclusive to it.

    .. note::
        Dataset is assumed to be of constant size and that any instance of it always
        returns the same elements in the same order.

    Args:
        dataset: Dataset used for sampling.
        num_replicas (int, optional): Number of processes participating in
            distributed training. By default, :attr:`world_size` is retrieved from the
            current distributed group.
        rank (int, optional): Rank of the current process within :attr:`num_replicas`.
            By default, :attr:`rank` is retrieved from the current distributed
            group.
        shuffle (bool, optional): If ``True`` (default), sampler will shuffle the
            indices.
        seed (int, optional): random seed used to shuffle the sampler if
            :attr:`shuffle=True`. This number should be identical across all
            processes in the distributed group. Default: ``0``.
        drop_last (bool, optional): if ``True``, then the sampler will drop the
            tail of the data to make it evenly divisible across the number of
            replicas. If ``False``, the sampler will add extra indices to make
            the data evenly divisible across the replicas. Default: ``False``.
        padding: (bool, optional): Whether to pad the dataset. Default: ``True``.
        pair: (bool, optional): Pair output for Mixup. Default: ``False``.

    .. warning::
        In distributed mode, calling the :meth:`set_epoch` method at
        the beginning of each epoch **before** creating the :class:`DataLoader` iterator
        is necessary to make shuffling work properly across multiple epochs. Otherwise,
        the same ordering will be always used.

    Example::

        >>> sampler = DistributedSampler(dataset) if is_distributed else None
        >>> loader = DataLoader(dataset, shuffle=(sampler is None),
        ...                     sampler=sampler)
        >>> for epoch in range(start_epoch, n_epochs):
        ...     if is_distributed:
        ...         sampler.set_epoch(epoch)
        ...     train(loader)
    """

    def __init__(self, dataset: Dataset, num_replicas: Optional[int] = None,
                 rank: Optional[int] = None, shuffle: bool = True,
                 seed: int = 0, drop_last: bool = False,
                 padding: bool = True,
                 pair: bool = False) -> None:
        if num_replicas is None:
            if not dist.is_available():
                num_replicas = 1