a h L@s~ddlZddlZddlZddlZddlmZddlmZmZm Z m Z m Z m Z ddl mZddlmZmZmZmZgdZe dZe dd d ZeeefZeed fZe d eeZGd ddeeZGdddeee eZGdddeeed fZGdddeeZ GdddeeZ!GdddeZ"GdddeeZ#efeeee e$e%fe ee&e#edddZ'dS)N)Sequence)castGenericIterableOptionalTypeVarUnion) deprecated)default_generator GeneratorrandpermTensor)DatasetIterableDataset TensorDataset StackDataset ConcatDataset ChainDatasetSubset random_split_T_T_coT) covariant._T_stackc@s.eZdZdZedddZddddd Zd S) raAn abstract class representing a :class:`Dataset`. All datasets that represent a map from keys to data samples should subclass it. All subclasses should overwrite :meth:`__getitem__`, supporting fetching a data sample for a given key. Subclasses could also optionally overwrite :meth:`__len__`, which is expected to return the size of the dataset by many :class:`~torch.utils.data.Sampler` implementations and the default options of :class:`~torch.utils.data.DataLoader`. Subclasses could also optionally implement :meth:`__getitems__`, for speedup batched samples loading. This method accepts list of indices of samples of batch and returns list of samples. .. note:: :class:`~torch.utils.data.DataLoader` by default constructs an index sampler that yields integral indices. To make it work with a map-style dataset with non-integral indices/keys, a custom sampler must be provided. )returncCs tddS)Nz3Subclasses of Dataset should implement __getitem__.)NotImplementedErrorselfindexrF/var/www/auris/lib/python3.9/site-packages/torch/utils/data/dataset.py __getitem__:szDataset.__getitem__zDataset[_T_co]zConcatDataset[_T_co])otherrcCs t||gSN)rrr"rrr __add__AszDataset.__add__N)__name__ __module__ __qualname____doc__rr!r%rrrr r'src@s"eZdZdZeedddZdS)raIAn iterable Dataset. All datasets that represent an iterable of data samples should subclass it. Such form of datasets is particularly useful when data come from a stream. All subclasses should overwrite :meth:`__iter__`, which would return an iterator of samples in this dataset. When a subclass is used with :class:`~torch.utils.data.DataLoader`, each item in the dataset will be yielded from the :class:`~torch.utils.data.DataLoader` iterator. When :attr:`num_workers > 0`, each worker process will have a different copy of the dataset object, so it is often desired to configure each copy independently to avoid having duplicate data returned from the workers. :func:`~torch.utils.data.get_worker_info`, when called in a worker process, returns information about the worker. It can be used in either the dataset's :meth:`__iter__` method or the :class:`~torch.utils.data.DataLoader` 's :attr:`worker_init_fn` option to modify each copy's behavior. Example 1: splitting workload across all workers in :meth:`__iter__`:: >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_DATALOADER) >>> # xdoctest: +SKIP("Fails on MacOS12") >>> class MyIterableDataset(torch.utils.data.IterableDataset): ... def __init__(self, start, end): ... super(MyIterableDataset).__init__() ... assert end > start, "this example code only works with end >= start" ... self.start = start ... self.end = end ... ... def __iter__(self): ... worker_info = torch.utils.data.get_worker_info() ... if worker_info is None: # single-process data loading, return the full iterator ... iter_start = self.start ... iter_end = self.end ... else: # in a worker process ... # split workload ... per_worker = int(math.ceil((self.end - self.start) / float(worker_info.num_workers))) ... worker_id = worker_info.id ... iter_start = self.start + worker_id * per_worker ... iter_end = min(iter_start + per_worker, self.end) ... return iter(range(iter_start, iter_end)) ... >>> # should give same set of data as range(3, 7), i.e., [3, 4, 5, 6]. >>> ds = MyIterableDataset(start=3, end=7) >>> # Single-process loading >>> print(list(torch.utils.data.DataLoader(ds, num_workers=0))) [tensor([3]), tensor([4]), tensor([5]), tensor([6])] >>> # xdoctest: +REQUIRES(POSIX) >>> # Multi-process loading with two worker processes >>> # Worker 0 fetched [3, 4]. Worker 1 fetched [5, 6]. >>> # xdoctest: +IGNORE_WANT("non deterministic") >>> print(list(torch.utils.data.DataLoader(ds, num_workers=2))) [tensor([3]), tensor([5]), tensor([4]), tensor([6])] >>> # With even more workers >>> # xdoctest: +IGNORE_WANT("non deterministic") >>> print(list(torch.utils.data.DataLoader(ds, num_workers=12))) [tensor([3]), tensor([5]), tensor([4]), tensor([6])] Example 2: splitting workload across all workers using :attr:`worker_init_fn`:: >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_DATALOADER) >>> class MyIterableDataset(torch.utils.data.IterableDataset): ... def __init__(self, start, end): ... super(MyIterableDataset).__init__() ... assert end > start, "this example code only works with end >= start" ... self.start = start ... self.end = end ... ... def __iter__(self): ... return iter(range(self.start, self.end)) ... >>> # should give same set of data as range(3, 7), i.e., [3, 4, 5, 6]. >>> ds = MyIterableDataset(start=3, end=7) >>> # Single-process loading >>> print(list(torch.utils.data.DataLoader(ds, num_workers=0))) [3, 4, 5, 6] >>> >>> # Directly doing multi-process loading yields duplicate data >>> print(list(torch.utils.data.DataLoader(ds, num_workers=2))) [3, 3, 4, 4, 5, 5, 6, 6] >>> # Define a `worker_init_fn` that configures each dataset copy differently >>> def worker_init_fn(worker_id): ... worker_info = torch.utils.data.get_worker_info() ... dataset = worker_info.dataset # the dataset copy in this worker process ... overall_start = dataset.start ... overall_end = dataset.end ... # configure the dataset to only process the split workload ... per_worker = int(math.ceil((overall_end - overall_start) / float(worker_info.num_workers))) ... worker_id = worker_info.id ... dataset.start = overall_start + worker_id * per_worker ... dataset.end = min(dataset.start + per_worker, overall_end) ... >>> # Mult-process loading with the custom `worker_init_fn` >>> # Worker 0 fetched [3, 4]. Worker 1 fetched [5, 6]. >>> print(list(torch.utils.data.DataLoader(ds, num_workers=2, worker_init_fn=worker_init_fn))) [3, 5, 4, 6] >>> # With even more workers >>> print(list(torch.utils.data.DataLoader(ds, num_workers=12, worker_init_fn=worker_init_fn))) [3, 4, 5, 6] )r"cCs t||gSr#)rr$rrr r%szIterableDataset.__add__N)r&r'r(r)rrr%rrrr rIslrc@sBeZdZUdZeedfed<eddddZdd Zd d Z dS) rzDataset wrapping tensors. Each sample will be retrieved by indexing tensors along the first dimension. Args: *tensors (Tensor): tensors that have the same size of the first dimension. .tensorsN)r*rcs(tfddDsJd|_dS)Nc3s&|]}dd|dkVqdS)rN)size.0Ztensorr*rr sz)TensorDataset.__init__..zSize mismatch between tensors)allr*)rr*rr.r __init__s   zTensorDataset.__init__cstfdd|jDS)Nc3s|]}|VqdSr#rr,rrr r/z,TensorDataset.__getitem__..)tupler*rrr2r r!szTensorDataset.__getitem__cCs|jddSNr)r*r+rrrr __len__szTensorDataset.__len__) r&r'r(r)r4r __annotations__r1r!r7rrrr rs rc@sZeZdZUdZeeefed<ee ee ddddZ ddZ e d d d Z d d ZdS)raDataset as a stacking of multiple datasets. This class is useful to assemble different parts of complex input data, given as datasets. Example: >>> # xdoctest: +SKIP >>> images = ImageDataset() >>> texts = TextDataset() >>> tuple_stack = StackDataset(images, texts) >>> tuple_stack[0] == (images[0], texts[0]) >>> dict_stack = StackDataset(image=images, text=texts) >>> dict_stack[0] == {'image': images[0], 'text': texts[0]} Args: *args (Dataset): Datasets for stacking returned as tuple. **kwargs (Dataset): Datasets for stacking returned as dict. datasetsN)argskwargsrcs|rD|rtdt|d_tfdd|Dr.zSize mismatch between datasetsc3s|]}jt|kVqdSr#r<r?r6rr r/r3z%At least one dataset should be passed) ValueErrorr>r=anyr9listvalues)rr:r;tmprr6r r1s  zStackDataset.__init__cs<t|jtr$fdd|jDStfdd|jDS)Ncsi|]\}}||qSrr)r-kr@r2rr r3z,StackDataset.__getitem__..c3s|]}|VqdSr#rr?r2rr r/r3z+StackDataset.__getitem__..) isinstancer9dictitemsr4rrr2r r!s zStackDataset.__getitem__indicesc Csrt|jtrdd|D}|jD]\}}tt|ddr||}t|t|krrtdt|dt|t ||D]\}}|||<q|q$t ||D]\}}||||<qq$|Sdd|D} |jD]}tt|ddr:||}t|t|krtdt|dt|t || D]\}} | |q"qt || D]\}} | ||qDqdd| D} | S)NcSsg|]}iqSrrr-_rrr r3z-StackDataset.__getitems__.. __getitems__z0Nested dataset's output size mismatch. Expected z, got cSsg|]}gqSrrrMrrr rOr3cSsg|] }t|qSr)r4)r-samplerrr rO%r3) rHr9rIrJcallablegetattrrPr>rAzipappend) rrLZ dict_batchrFr@rJdataZd_sampleidxZ list_batchZt_sampleZ tuple_batchrrr rPsH     zStackDataset.__getitems__cCs|jSr#)r=r6rrr r7(szStackDataset.__len__)r&r'r(r)rr4rIr8rrr1r!rCrPr7rrrr rs %rcs~eZdZUdZeeeed<eeed<e ddZ e eddfdd Z d d Z d d ZeededddZZS)rzDataset as a concatenation of multiple datasets. This class is useful to assemble different existing datasets. 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This class is useful to assemble different existing dataset streams. The chaining operation is done on-the-fly, so concatenating large-scale datasets with this class will be efficient. Args: datasets (iterable of IterableDataset): datasets to be chained together Nr_cst||_dSr#)r`r1r9)rr9rbrr r1ps zChainDataset.__init__ccs,|jD] }t|tsJd|EdHqdS)N*ChainDataset only supports IterableDataset)r9rHr)rrarrr __iter__ts zChainDataset.__iter__cCs2d}|jD]"}t|ts Jd|t|7}q |S)Nrrp)r9rHrr>)rtotalrarrr r7{s zChainDataset.__len__) r&r'r(r)rrr1rqr7rorrrbr res rc@sleZdZUdZeeed<eeed<eeeeddddZ dd Z e ee ed d d Z d dZ dS)rz Subset of a dataset at specified indices. Args: dataset (Dataset): The whole Dataset indices (sequence): Indices in the whole set selected for subset r@rLN)r@rLrcCs||_||_dSr#r@rL)rr@rLrrr r1szSubset.__init__cs2t|tr"jfdd|DSjj|S)Ncsg|]}j|qSrrK)r-ir6rr rOr3z&Subset.__getitem__..)rHrCr@rL)rrWrr6r r!s zSubset.__getitem__)rLrcsBttjddr,jfdd|DSfdd|DSdS)NrPcsg|]}j|qSrrKr-rWr6rr rOr3z'Subset.__getitems__..csg|]}jj|qSrrsrur6rr rOr3)rRrSr@rP)rrLrr6r rPszSubset.__getitems__cCs t|jSr#)r>rLr6rrr r7szSubset.__len__)r&r'r(r)rrr8rrkr1r!rCrPr7rrrr rs   r)r@lengths generatorrc s6tt|drt|dkrg}t|D]H\}}|dks@|dkrPtd|dttt|}||q(tt|}t |D] }|t|}||d7<q|}t|D]"\}} | dkrt d|dqt|tkrtdt t||d ttt|}fd d tt||DS) a Randomly split a dataset into non-overlapping new datasets of given lengths. If a list of fractions that sum up to 1 is given, the lengths will be computed automatically as floor(frac * len(dataset)) for each fraction provided. After computing the lengths, if there are any remainders, 1 count will be distributed in round-robin fashion to the lengths until there are no remainders left. Optionally fix the generator for reproducible results, e.g.: Example: >>> # xdoctest: +SKIP >>> generator1 = torch.Generator().manual_seed(42) >>> generator2 = torch.Generator().manual_seed(42) >>> random_split(range(10), [3, 7], generator=generator1) >>> random_split(range(30), [0.3, 0.3, 0.4], generator=generator2) Args: dataset (Dataset): Dataset to be split lengths (sequence): lengths or fractions of splits to be produced generator (Generator): Generator used for the random permutation. rfrzFraction at index z is not between 0 and 1zLength of split at index z- is 0. 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