Merge branch 'batch_sampler_update' into 'master'

    implementation is at the batch sampler level, instead of just the

    sampler level. This allows wrapping of arbitrary data samplers

    (sequential, random, WeightedRandomSampler, etc.) with this batch

    sampler."""

    sampler.

    The `interleave` argument specifies how to distribute a batch. A value

    of True combined with the above random sampler is equivalent to pytorch's

    torch.utils.data.distributed.DistributedSampler.

    For the following batch [0,1,2,3,4,5,6,7] and data parallelism of 2 

    specifying True will result in the following samples for each gpu:

        GPU0: [0,2,4,6] GPU1: [1,3,5,7]

    specifying False will result in the following samples:

        GPU0: [0,1,2,3] GPU1: [4,5,6,7]"""

    def __init__(self, sampler, batch_size, drop_last, rank=-1,

                 world_size=2, wrap_last=False):

                 world_size=2, wrap_last=False, interleave=False):

        super(DistributedBatchSampler, self).__init__(sampler, batch_size,

                                                      drop_last)

        if rank == -1:

        self.wrap_around = 0

        self.wrap_last = wrap_last

        self.start_iter = 0

        self.interleave = interleave

    def __iter__(self):

        batch = []

    def _batch(self, batch):

        """extracts samples only pertaining to this worker's batch"""

        if self.interleave:

            return batch[self.rank:self.batch_size:self.world_size]

        start = self.rank * self.batch_size // self.world_size

        end = (self.rank + 1) * self.batch_size // self.world_size

        return batch[start:end]