o h@szddlZddlmZddlmZmZddlZddlmZ ddl m Z ddl m Z dgZeddd ZGd dde eZdS) N)Iterator)OptionalTypeVar)Dataset)SamplerDistributedSampler_T_coT) covariantc@s|eZdZdZ     ddedeedeed ed ed ed dfd dZd e e fddZ d efddZ ded dfddZ dS)ra' 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``. .. 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:: >>> # xdoctest: +SKIP >>> 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) NTrFdataset num_replicasrankshuffleseed drop_lastreturncCs|durts tdt}|dur tstdt}||ks(|dkr5td|d|dd||_||_||_d|_ ||_ |j rat |j|jdkrat t |j|j|j|_n t t |j|j|_|j|j|_||_||_dS)Nz,Requires distributed package to be availablerz Invalid rank z%, rank should be in the interval [0, ])dist is_available RuntimeErrorget_world_sizeget_rank ValueErrorr r r epochrlenmathceil num_samples total_sizer r)selfr r r r rrr p/var/www/html/construction_image-detection-poc/venv/lib/python3.10/site-packages/torch/utils/data/distributed.py__init__Bs2  zDistributedSampler.__init__cCs|jrt}||j|jtjt|j|d }n t t t|j}|j sQ|j t|}|t|kr?||d|7}n||t|t|d|7}n|d|j }t||j ksaJ||j|j |j}t||jkstJt|S)N) generator)r torch Generator manual_seedrrrandpermrr tolistlistrangerrrrr r riter)rgindices padding_sizer r r!__iter__ks" zDistributedSampler.__iter__cCs|jS)N)r)rr r r!__len__szDistributedSampler.__len__rcCs ||_dS)a1 Set the epoch for this sampler. When :attr:`shuffle=True`, this ensures all replicas use a different random ordering for each epoch. Otherwise, the next iteration of this sampler will yield the same ordering. Args: epoch (int): Epoch number. N)r)rrr r r! set_epochs zDistributedSampler.set_epoch)NNTrF)__name__ __module__ __qualname____doc__rrintboolr"rrr/r0r1r r r r!rs23 ))rcollections.abcrtypingrrr$torch.distributed distributedrtorch.utils.data.datasetrtorch.utils.data.samplerr__all__rrr r r r!s