Ph0(ddlZddlmZddlmZmZmZmZmZm Z m Z m Z ddl Z ddlmZddlmZmZddlmZgdZe dd Zd efd Zed GddeeZedGddeZedGddeeZy)N) defaultdict)AnyCallable DefaultDictIteratorListOptionalSizedTypeVar)functional_datapipe) DataChunk IterDataPipe)_check_unpickable_fn)BatcherIterDataPipeGrouperIterDataPipeUnBatcherIterDataPipeT_coT) covariantnamec|dvretjd|d|dtdttj j jjj|Stdtd|) N)SHARDING_PRIORITIESShardingFilterIterDataPipe`zc` from `torch.utils.data.datapipes.iter.grouping` is going to be removed in PyTorch 2.1Please use `z5` from the `torch.utils.data.datapipes.iter.sharding`)category stacklevelzmodule z has no attribute ) warningswarn FutureWarninggetattrtorchutilsdata datapipesitershardingAttributeError__name__)rs sC:\Users\daisl\Desktop\realtime-object-detection\venv\Lib\site-packages\torch/utils/data/datapipes/iter/grouping.py __getattr__r*sy DD $ %%)F*_a, <u{{''1166??FF 78*,>tfE FFbatchc xeZdZUdZeed<eed<eed<defdedededdffd Z de efd Z defd Z xZ S) ra2 Creates mini-batches of data (functional name: ``batch``). An outer dimension will be added as ``batch_size`` if ``drop_last`` is set to ``True``, or ``length % batch_size`` for the last batch if ``drop_last`` is set to ``False``. Args: datapipe: Iterable DataPipe being batched batch_size: The size of each batch drop_last: Option to drop the last batch if it's not full wrapper_class: wrapper to apply onto each batch (type ``List``) before yielding, defaults to ``DataChunk`` Example: >>> # xdoctest: +SKIP >>> from torchdata.datapipes.iter import IterableWrapper >>> dp = IterableWrapper(range(10)) >>> dp = dp.batch(batch_size=3, drop_last=True) >>> list(dp) [[0, 1, 2], [3, 4, 5], [6, 7, 8]] datapipe batch_size drop_lastFreturnNcr|dkDsJdt|||_||_||_||_y)Nrz+Batch size is required to be larger than 0!)super__init__r.r/r0 wrapper_class)selfr.r/r0r5 __class__s r)r4zBatcherIterDataPipe.__init__9s? A~LLL~   $"*r+c#Kg}|jD]A}|j|t||jk(s-|j |g}Ct|dkDr!|j s|j |yyywNr)r.appendlenr/r5r0)r6r,xs r)__iter__zBatcherIterDataPipe.__iter__FsyA LLO5zT__,((//  u:>>>((//" s ;BABc8t|jtr`|jr"t |j|j zSt |j|j zdz |j zSt t|jd)Nz# instance doesn't have valid length) isinstancer.r r0r;r/ TypeErrortyper(r6s r)__len__zBatcherIterDataPipe.__len__Qss dmmU +~~4==)T__<<DMM*T__>> # xdoctest: +SKIP >>> from torchdata.datapipes.iter import IterableWrapper >>> source_dp = IterableWrapper([[[0, 1], [2]], [[3, 4], [5]], [[6]]]) >>> dp1 = source_dp.unbatch() >>> list(dp1) [[0, 1], [2], [3, 4], [5], [6]] >>> dp2 = source_dp.unbatch(unbatch_level=2) >>> list(dp2) [0, 1, 2, 3, 4, 5, 6] r. unbatch_levelc ||_||_yN)r.rN)r6r.rNs r)r4zUnBatcherIterDataPipe.__init__ss! *r+c#|K|jD]'}|j||jEd{)y7w)NrN)r._diverN)r6elements r)r=zUnBatcherIterDataPipe.__iter__ys3}}Gzz'9K9KzL L L% Ls 0<:<c#vK|dkr td|dk(r>t|ttfr#|D]}|j |dEd{y|y|dk(r|yt|ttfr&|D] }|j ||dz Ed{"yt d|j d7k7"w)Nz unbatch_level must be -1 or >= 0rRrr?zunbatch_level z" exceeds the depth of the DataPipe) ValueErrorr@listr rS IndexErrorrN)r6rTrNitems r)rSzUnBatcherIterDataPipe._dive}s 2 ?@ @ B 'D)#45#D#zz$bzAAA$ a M'D)#45#D#zz$ma>OzPPP$!>$2D2D1EEg!hiiBQs%AB9 B5 A B9B7!B97B9N)r?) r(rErFrGrrIr4r=rSr+r)rr[s-0'(+'+ #+ Mjr+rgroupbyceZdZdZdddddddeedeegefded e d e e d e e d efd Z dZ dZ ddZdZdZdZy)ra! Groups data from IterDataPipe by keys from ``group_key_fn``, yielding a ``DataChunk`` with batch size up to ``group_size``. (functional name: ``groupby``). The samples are read sequentially from the source ``datapipe``, and a batch of samples belonging to the same group will be yielded as soon as the size of the batch reaches ``group_size``. When the buffer is full, the DataPipe will yield the largest batch with the same key, provided that its size is larger than ``guaranteed_group_size``. If its size is smaller, it will be dropped if ``drop_remaining=True``. After iterating through the entirety of source ``datapipe``, everything not dropped due to the buffer capacity will be yielded from the buffer, even if the group sizes are smaller than ``guaranteed_group_size``. Args: datapipe: Iterable datapipe to be grouped group_key_fn: Function used to generate group key from the data of the source datapipe keep_key: Option to yield the matching key along with the items in a tuple, resulting in `(key, [items])` otherwise returning [items] buffer_size: The size of buffer for ungrouped data group_size: The max size of each group, a batch is yielded as soon as it reaches this size guaranteed_group_size: The guaranteed minimum group size to be yielded in case the buffer is full drop_remaining: Specifies if the group smaller than ``guaranteed_group_size`` will be dropped from buffer when the buffer is full Example: >>> import os >>> # xdoctest: +SKIP >>> from torchdata.datapipes.iter import IterableWrapper >>> def group_fn(file): ... return os.path.basename(file).split(".")[0] >>> source_dp = IterableWrapper(["a.png", "b.png", "a.json", "b.json", "a.jpg", "c.json"]) >>> dp0 = source_dp.groupby(group_key_fn=group_fn) >>> list(dp0) [['a.png', 'a.json', 'a.jpg'], ['b.png', 'b.json'], ['c.json']] >>> # A group is yielded as soon as its size equals to `group_size` >>> dp1 = source_dp.groupby(group_key_fn=group_fn, group_size=2) >>> list(dp1) [['a.png', 'a.json'], ['b.png', 'b.json'], ['a.jpg'], ['c.json']] >>> # Scenario where `buffer` is full, and group 'a' needs to be yielded since its size > `guaranteed_group_size` >>> dp2 = source_dp.groupby(group_key_fn=group_fn, buffer_size=3, group_size=3, guaranteed_group_size=2) >>> list(dp2) [['a.png', 'a.json'], ['b.png', 'b.json'], ['a.jpg'], ['c.json']] Fi'N)keep_key buffer_size group_sizeguaranteed_group_sizedrop_remainingr. group_key_fnr^r_r`rarbc4t|||_||_||_||_t t |_d|_||_ d|_ ||d|cxkr|ksJJ||_ ||d|cxkr|ksJJ||_ ||_ t|_ yr9)rr.rcr^max_buffer_sizerrXbuffer_elementscurr_buffer_sizer`rarbr r5)r6r.rcr^r_r`rarbs r)r4zGrouperIterDataPipe.__init__s \*  (  *7B47H !$%)"  !k&=z0[0 00 00)3D & ,)a2G.U:.U UU.U UU)>D &,&r+cd}d}d}|jjD]8}t|j||kDst|j|}|}:|j=||jkr.|js"t dt |j||j||jk\r|j|}|xj|zc_|j|=|S)NrzFailed to group items)rfkeysr;rarb RuntimeErrorstrrg)r6 biggest_key biggest_sizeresult_to_yieldfindkeys r)_remove_biggest_keyz'GrouperIterDataPipe._remove_biggest_keys  ++002G4''01L@"4#7#7#@A % 3  % % 1lTE_E_6_hlh{h{6Drs#WWW/EGH  v&GcGW:Y,y1:Y:YzY1jL1j 1jhY[%,y1[% [%r+