r"""Applies Synchronized Batch Normalization over a 2d or 3d input that is seen as a mini-batch. .. math:: y = \frac{x - mean[x]}{ \

Data Parallel with a replication callback. An replication callback `__data_parallel_replicate__` of each module will be invoked after being

r"Wraps an exception plus traceback to communicate across threads

Wraps another sampler to yield a mini-batch of indices. Args: sampler (Sampler): Base sampler. batch_size (int): Size of mini-bat

Dataset to concatenate multiple datasets. Purpose: useful to assemble different existing datasets, possibly large-scale datasets as the c

Iterates once over the DataLoader's dataset, as specified by the sampler

A thread-safe future implementation. Used only as one-to-one pipe.

Samples elements randomly, without replacement. Arguments: data_source (Dataset): dataset to sample from

Samples elements sequentially, always in the same order. Arguments: data_source (Dataset): dataset to sample from

Pipe for master-slave communication.

An abstract `SyncMaster` object. - During the replication, as the data parallel will trigger an callback of each module, all slave devices should

r"""Applies Batch Normalization over a 4d input that is seen as a mini-batch of 3d inputs .. math:: y = \frac{x - mean[x]}{ \sqrt{Va

Computes and stores the average and current value

Data loader. Combines a dataset and a sampler, and provides single- or multi-process iterators over the dataset. Arguments: data

An abstract class representing a Dataset. All other datasets should subclass it. All subclasses should override ``__len__``, that provides th

Sampler that restricts data loading to a subset of the dataset. It is especially useful in conjunction with :class:`torch.nn.parallel.Distrib

Base class for all Samplers. Every Sampler subclass has to provide an __iter__ method, providing a way to iterate over indices of dataset ele

Samples elements randomly from a given list of indices, without replacement. Arguments: indices (list): a list of indices

r"""Applies Batch Normalization over a 5d input that is seen as a mini-batch of 4d inputs .. math:: y = \frac{x - mean[x]}{ \sqrt{Va

Dataset wrapping data and target tensors. Each sample will be retrieved by indexing both tensors along the first dimension. Arguments:

Samples elements from [0,..,len(weights)-1] with given probabilities (weights). Arguments: weights (list) : a list of weights, not nece