o i1@sddlZddlmZmZmZmZddlmZmZddl Z gdZ edddZ Gdd d ee Z Gd d d e e ZGd d d e e ZGddde e ZGddde e ZGddde ee ZdS)N)IterableIteratorSequenceSized)GenericTypeVar) BatchSampler RandomSamplerSamplerSequentialSamplerSubsetRandomSamplerWeightedRandomSampler_T_coT) covariantc@s"eZdZdZdeefddZdS)r aBase 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. 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`. returncCstN)NotImplementedErrorselfrZ/sda-disk/www/egybert/egybert_env/lib/python3.10/site-packages/torch/utils/data/sampler.py__iter__BszSampler.__iter__N)__name__ __module__ __qualname____doc__rrrrrrrr s%r c@sLeZdZUdZeed<deddfddZdeefddZ defd d Z dS) r zSamples elements sequentially, always in the same order. Args: data_source (Sized): data source to sample from. Must implement __len__. data_sourcerNcCs ||_dSr)r)rrrrr__init__j zSequentialSampler.__init__cCsttt|jSr)iterrangelenrrrrrrmszSequentialSampler.__iter__cC t|jSr)r!rrrrr__len__przSequentialSampler.__len__) rrrrr__annotations__rrintrr#rrrrr as r c @szeZdZUdZeed<eed<   ddedededBddfdd Ze defd d Z de efd d Z defddZ dS)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 (Sized): data source to sample from. Must implement __len__. 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 num_samplesrcCs^||_||_||_||_t|jtstd|jt|jtr%|jdkr-t 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__cCs|jdur t|jS|jSr)r*r!rrrrrr's  zRandomSampler.num_samplesccst|j}|jdur#ttjdtjd}t }| |n|j}|j rWt |j dD]}tj|dtj|dEdHq0tj||j dftj|dEdHdSt |j |D]}tj||dEdHq^tj||dd|j |EdHdS)Nrdtype )r2)highsizer1r+r+)r!rr+r%torchemptyint64random_item Generator manual_seedr&r r'randinttolistrandperm)rnseedr+_rrrrs6        zRandomSampler.__iter__cC|jSrr'rrrrr#zRandomSampler.__len__)FNN)rrrrrr$r-r%rpropertyr'rrr#rrrrr ts(   r c@sVeZdZUdZeeed<d deeddfddZdeefddZ defd d Z dS) r zSamples elements randomly from a given list of indices, without replacement. Args: indices (sequence): a sequence of indices generator (Generator): Generator used in sampling. indicesNrcCs||_||_dSr)rGr+)rrGr+rrrrs zSubsetRandomSampler.__init__ccs2tjt|j|jdD]}|j|VqdSNr5)r6r?r!rGr+r>)rirrrrszSubsetRandomSampler.__iter__cCr"r)r!rGrrrrr#rzSubsetRandomSampler.__len__r) rrrrrr%r$rrrr#rrrrr s  r c @speZdZUdZejed<eed<eed<  dde e dededdfdd Z de efd d Z defd d ZdS)r aSamples 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&TNrcCst|trt|ts|dkrtd|t|ts!td|tj|tjd}t|jdkr:tdt |j||_ ||_ ||_ ||_ dS)Nrr)r(r0z=weights should be a 1d sequence but given weights have shape )r,r%r-r/r6 as_tensordoubler!shapetuplerJr'r&r+)rrJr'r&r+weights_tensorrrrrs.  zWeightedRandomSampler.__init__ccs2tj|j|j|j|jd}t|EdHdSrH)r6 multinomialrJr'r&r+rr>)r rand_tensorrrrrs zWeightedRandomSampler.__iter__cCrCrrDrrrrr#rEzWeightedRandomSampler.__len__)TN)rrrrr6Tensorr$r%r-rfloatrrrr#rrrrr s$    r c@sZeZdZdZdeeeeBdededdfddZde e efd d Z defd d Z dS) raWraps 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]] sampler batch_size drop_lastrNcCsXt|trt|ts|dkrtd|t|ts!td|||_||_||_dS)NrzBbatch_size should be a positive integer value, but got batch_size=z7drop_last should be a boolean value, but got drop_last=)r,r%r-r/rUrVrW)rrUrVrWrrrr4s   zBatchSampler.__init__ccszt|j}|jr |g|j}t|ddiD]}g|VqdSgt||j}|r;|Vgt||j}|s+dSdS)NstrictF)rrUrWrVzip itertoolsislice)r sampler_iterargsbatch_droplastbatchrrrrMs    zBatchSampler.__iter__cCs0|jr t|j|jSt|j|jd|jS)NrK)rWr!rUrVrrrrr#ZszBatchSampler.__len__) rrrrr r%rr-rrlistrr#rrrrrs  r)rZcollections.abcrrrrtypingrrr6__all__rr r%r r r r r`rrrrrs EKI