def __init__(
self,
sampler,
num_replicas: Optional[int] = None,
rank: Optional[int] = None,
shuffle: bool = True,
):
"""
Args:
sampler: Sampler used for subsampling
num_replicas (int, optional): Number of processes participating in
distributed training
rank (int, optional): Rank of the current process
within ``num_replicas``
shuffle (bool, optional): If true (default),
sampler will shuffle the indices
"""
super(DistributedSamplerWrapper, self).__init__(
DatasetFromSampler(sampler),
num_replicas=num_replicas,
rank=rank,
shuffle=shuffle,
)
self.sampler = sampler
def __init__(self, sampler, num_replicas=None, rank=None, shuffle=True):
super(DistributedSamplerWrapper,
self).__init__(DatasetFromSampler(sampler),
num_replicas=num_replicas,
rank=rank,
shuffle=shuffle)
self.sampler = sampler
def __iter__(self):
self.dataset = DatasetFromSampler(self.sampler)
indexes_of_indexes = super().__iter__()
subsampler_indexes = self.dataset
return iter(itemgetter(*indexes_of_indexes)(subsampler_indexes))
def __iter__(self):
"""@TODO: Docs. Contribution is welcome."""
self.dataset = DatasetFromSampler(self.sampler)
indexes_of_indexes = super().__iter__()
subsampler_indexes = self.dataset
return iter(itemgetter(*indexes_of_indexes)(subsampler_indexes))