a h$2@sddlZddlmZmZmZmZddlmZmZm Z m Z ddl Z gdZ e dddZ Gdd d ee ZGd d d eeZGd d d eeZGdddeeZGdddeeZGdddeeeZdS)N)IterableIteratorSequenceSized)GenericOptionalTypeVarUnion) BatchSampler RandomSamplerSamplerSequentialSamplerSubsetRandomSamplerWeightedRandomSampler_T_coT) covariantc@s8eZdZdZd eeddddZeedddZ dS) r a;Base class for all Samplers. Every Sampler subclass has to provide an :meth:`__iter__` method, providing a way to iterate over indices or lists of indices (batches) of dataset elements, and may provide a :meth:`__len__` method that returns the length of the returned iterators. Args: data_source (Dataset): This argument is not used and will be removed in 2.2.0. You may still have custom implementation that utilizes it. Example: >>> # xdoctest: +SKIP >>> class AccedingSequenceLengthSampler(Sampler[int]): >>> def __init__(self, data: List[str]) -> None: >>> self.data = data >>> >>> def __len__(self) -> int: >>> return len(self.data) >>> >>> def __iter__(self) -> Iterator[int]: >>> sizes = torch.tensor([len(x) for x in self.data]) >>> yield from torch.argsort(sizes).tolist() >>> >>> class AccedingSequenceLengthBatchSampler(Sampler[List[int]]): >>> def __init__(self, data: List[str], batch_size: int) -> None: >>> self.data = data >>> self.batch_size = batch_size >>> >>> def __len__(self) -> int: >>> return (len(self.data) + self.batch_size - 1) // self.batch_size >>> >>> def __iter__(self) -> Iterator[List[int]]: >>> sizes = torch.tensor([len(x) for x in self.data]) >>> for batch in torch.chunk(torch.argsort(sizes), len(self)): >>> yield batch.tolist() .. note:: The :meth:`__len__` method isn't strictly required by :class:`~torch.utils.data.DataLoader`, but is expected in any calculation involving the length of a :class:`~torch.utils.data.DataLoader`. N data_sourcereturncCs|durddl}|ddS)Nrzz`data_source` argument is not used and will be removed in 2.2.0.You may still have custom implementation that utilizes it.)warningswarn)selfrrrF/var/www/auris/lib/python3.9/site-packages/torch/utils/data/sampler.py__init__@s zSampler.__init__rcCstdSN)NotImplementedErrorrrrr__iter__IszSampler.__iter__)N) __name__ __module__ __qualname____doc__rrrrrrrrrrr s) r c@sJeZdZUdZeed<eddddZeeddd Z edd d Z dS) r z~Samples elements sequentially, always in the same order. Args: data_source (Dataset): dataset to sample from rNrcCs ||_dSr)r)rrrrrrqszSequentialSampler.__init__rcCsttt|jSr)iterrangelenrrrrrrtszSequentialSampler.__iter__cCs t|jSr)r&rrrrr__len__wszSequentialSampler.__len__) r r!r"r#r__annotations__rrintrr'rrrrr hs r c@sneZdZUdZeed<eed<deeeeddddZ e ed d d Z e ed d d Z ed ddZdS)r aSamples elements randomly. If without replacement, then sample from a shuffled dataset. If with replacement, then user can specify :attr:`num_samples` to draw. Args: data_source (Dataset): dataset to sample from replacement (bool): samples are drawn on-demand with replacement if ``True``, default=``False`` num_samples (int): number of samples to draw, default=`len(dataset)`. generator (Generator): Generator used in sampling. r replacementFN)rr* num_samplesrcCs^||_||_||_||_t|jts4td|jt|jtrJ|jdkrZt d|jdS)N;replacement should be a boolean value, but got replacement=rDnum_samples should be a positive integer value, but got num_samples=) rr* _num_samples generator isinstancebool TypeErrorr+r) ValueError)rrr*r+r/rrrrs   zRandomSampler.__init__rcCs|jdurt|jS|jSr)r.r&rrrrrr+s  zRandomSampler.num_samplesccst|j}|jdurDttjdtjd}t }| |n|j}|j rt |j dD]"}tj|dtj|dEdHq^tj||j dftj|dEdHnRt |j |D]}tj||dEdHqtj||dd|j |EdHdS)Nrdtype )r6)highsizer5r/r/)r&rr/r)torchemptyint64Zrandom_item GeneratorZ manual_seedr*r%r+randinttolistrandperm)rnseedr/_rrrrs,      zRandomSampler.__iter__cCs|jSrr+rrrrr'szRandomSampler.__len__)FNN)r r!r"r#rr(r1rr)rpropertyr+rrr'rrrrr {s   r c@sTeZdZUdZeeed<d eeddddZeeddd Z edd d Z dS) rzSamples elements randomly from a given list of indices, without replacement. Args: indices (sequence): a sequence of indices generator (Generator): Generator used in sampling. indicesN)rGrcCs||_||_dSr)rGr/)rrGr/rrrrszSubsetRandomSampler.__init__rccs0tjt|j|jdD]}|j|VqdSNr9)r:rAr&rGr/r@)rirrrrszSubsetRandomSampler.__iter__cCs t|jSr)r&rGrrrrr'szSubsetRandomSampler.__len__)N) r r!r"r#rr)r(rrrr'rrrrrs  rc@sfeZdZUdZejed<eed<eed<de e eedddd Z e ed d d Z ed d dZdS)raNSamples elements from ``[0,..,len(weights)-1]`` with given probabilities (weights). Args: weights (sequence) : a sequence of weights, not necessary summing up to one num_samples (int): number of samples to draw replacement (bool): if ``True``, samples are drawn with replacement. If not, they are drawn without replacement, which means that when a sample index is drawn for a row, it cannot be drawn again for that row. generator (Generator): Generator used in sampling. Example: >>> # xdoctest: +IGNORE_WANT("non-deterministic") >>> list(WeightedRandomSampler([0.1, 0.9, 0.4, 0.7, 3.0, 0.6], 5, replacement=True)) [4, 4, 1, 4, 5] >>> list(WeightedRandomSampler([0.9, 0.4, 0.05, 0.2, 0.3, 0.1], 5, replacement=False)) [0, 1, 4, 3, 2] weightsr+r*TN)rJr+r*rcCst|trt|ts|dkr*td|t|tsBtd|tj|tjd}t|jdkrttdt |j||_ ||_ ||_ ||_ dS)Nrr-r,r4z=weights should be a 1d sequence but given weights have shape )r0r)r1r3r:Z as_tensordoubler&shapetuplerJr+r*r/)rrJr+r*r/Zweights_tensorrrrrs0 zWeightedRandomSampler.__init__rccs0tj|j|j|j|jd}t|EdHdSrH)r:Z multinomialrJr+r*r/r$r@)rZ rand_tensorrrrrszWeightedRandomSampler.__iter__cCs|jSrrErrrrr'szWeightedRandomSampler.__len__)TN)r r!r"r#r:ZTensorr(r)r1rfloatrrrr'rrrrrs   rc@sXeZdZdZeeeeefeeddddZ e e edddZ edd d Z dS) r aiWraps another sampler to yield a mini-batch of indices. Args: sampler (Sampler or Iterable): Base sampler. Can be any iterable object batch_size (int): Size of mini-batch. drop_last (bool): If ``True``, the sampler will drop the last batch if its size would be less than ``batch_size`` Example: >>> list(BatchSampler(SequentialSampler(range(10)), batch_size=3, drop_last=False)) [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] >>> list(BatchSampler(SequentialSampler(range(10)), batch_size=3, drop_last=True)) [[0, 1, 2], [3, 4, 5], [6, 7, 8]] N)sampler batch_size drop_lastrcCsXt|trt|ts|dkr*td|t|tsBtd|||_||_||_dS)NrzBbatch_size should be a positive integer value, but got batch_size=z7drop_last should be a boolean value, but got drop_last=)r0r)r1r3rPrQrR)rrPrQrRrrrr-s  zBatchSampler.__init__rccsjt|j}|jr6|g|j}t|D]}g|Vq$n0gt||j}|rf|Vgt||j}qHdSr)r$rPrRrQzip itertoolsislice)rZ sampler_iterargsZbatch_droplastbatchrrrrFs   zBatchSampler.__iter__cCs4|jrt|j|jSt|j|jd|jSdS)NrK)rRr&rPrQrrrrr'TszBatchSampler.__len__)r r!r"r#r r r)rr1rrlistrr'rrrrr s r )rTcollections.abcrrrrtypingrrrr r:__all__rr r)r r rrrXr rrrrs RKA