h8ARSSKJr SSKrSSKrSSKrSSKJr SSKJr SSK J r SSK J r SSK rSSKrSSKJr SSKJr SSKJrJr SS KJr SS KJrJrJr SS KJrJrJ r J!r!J"r"J#r#J$r$J%r% SS K&J'r'J(r( SS K)J*r*J+r+ SSK,J-r- SSK.J/r/ "SS\R`5r1"SS5r2"SS\RfRhRj5r6SSjr7SSSjjr8SS Sjjr9S!S"Sjjr:Sr;S#S$Sjjr>> dataset = YOLODataset(...) >>> dataloader = InfiniteDataLoader(dataset, batch_size=16, shuffle=True) >>> for batch in dataloader: # Infinite iteration >>> train_step(batch) c>[(dURSS5 [TU] "U0UD6 [R US[ UR55 [TU]!5Ul g)zHInitialize the InfiniteDataLoader with the same arguments as DataLoader.prefetch_factorN batch_sampler) rpopsuper__init__object __setattr___RepeatSamplerr"__iter__iterator)selfargskwargs __class__s P/home/james-whalen/.local/lib/python3.13/site-packages/ultralytics/data/build.pyr%InfiniteDataLoader.__init__>sUy JJ($ / $)&)4.ASAS2TU(* c@[URR5$)z1Return the length of the batch sampler's sampler.)lenr"samplerr+s r/__len__InfiniteDataLoader.__len__Fs4%%--..r1c#p# [[U55Hn[UR5v M g7f)zICreate an iterator that yields indefinitely from the underlying iterator.N)ranger3nextr*)r+_s r/r)InfiniteDataLoader.__iter__Js's4y!At}}% %"s46c[URS5(dgURRH*nUR5(dMUR 5 M, URR 5 g![ a gf=f)zKEnsure that workers are properly terminated when the dataloader is deleted._workersN)hasattrr*r>is_alive terminate_shutdown_workers Exception)r+ws r/__del__InfiniteDataLoader.__del__Osg 4==*55]]++::<<KKM, MM + + -   sA=,A=.A== B  B c.UR5Ulg)zIReset the iterator to allow modifications to the dataset during training.N) _get_iteratorr*r5s r/resetInfiniteDataLoader.reset[s**, r1)r*)r,rr-r)returnintrKr) __name__ __module__ __qualname____firstlineno____doc__r%r6r)rErI__static_attributes__ __classcell__)r.s@r/rr%s&0+/& --r1rc,\rSrSrSrSSjrSSjrSrg) r(`a Sampler that repeats forever for infinite iteration. This sampler wraps another sampler and yields its contents indefinitely, allowing for infinite iteration over a dataset without recreating the sampler. Attributes: sampler (Dataset.sampler): The sampler to repeat. cXlg)zDInitialize the _RepeatSampler with a sampler to repeat indefinitely.Nr4)r+r4s r/r%_RepeatSampler.__init__ks r1c#N# [UR5ShvN MN7f)z=Iterate over the sampler indefinitely, yielding its contents.N)iterr4r5s r/r)_RepeatSampler.__iter__os"DLL) ) ) )s %#%rXN)r4rrM)rNrOrPrQrRr%r)rSr1r/r(r(`s*r1r(c:\rSrSrSrS SjrSrSrSrSr S r g) ContiguousDistributedSamplerua Distributed sampler that assigns contiguous batch-aligned chunks of the dataset to each GPU. Unlike PyTorch's DistributedSampler which distributes samples in a round-robin fashion (GPU 0 gets indices [0,2,4,...], GPU 1 gets [1,3,5,...]), this sampler gives each GPU contiguous batches of the dataset (GPU 0 gets batches [0,1,2,...], GPU 1 gets batches [k,k+1,...], etc.). This preserves any ordering or grouping in the original dataset, which is critical when samples are organized by similarity (e.g., images sorted by size to enable efficient batching without padding when using rect=True). The sampler handles uneven batch counts by distributing remainder batches to the first few ranks, ensuring all samples are covered exactly once across all GPUs. Args: dataset (torch.utils.data.Dataset): Dataset to sample from. Must implement __len__. num_replicas (int, optional): Number of distributed processes. Defaults to world size. batch_size (int, optional): Batch size used by dataloader. Defaults to dataset batch size. rank (int, optional): Rank of current process. Defaults to current rank. shuffle (bool, optional): Whether to shuffle indices within each rank's chunk. Defaults to False. When True, shuffling is deterministic and controlled by set_epoch() for reproducibility. Example: >>> # For validation with size-grouped images >>> sampler = ContiguousDistributedSampler(val_dataset, batch_size=32, shuffle=False) >>> loader = DataLoader(val_dataset, batch_size=32, sampler=sampler) >>> # For training with shuffling >>> sampler = ContiguousDistributedSampler(train_dataset, batch_size=32, shuffle=True) >>> for epoch in range(num_epochs): ... sampler.set_epoch(epoch) ... for batch in loader: ... ... NcUc1[R"5(a[R"5OSnUc1[R"5(a[R"5OSnUc [ USS5nXlX lX0lX@lSUl XPl [U5Ul [R"URUR- 5Ulg)zHInitialize the sampler with dataset and distributed training parameters.Nr batch_size)distis_initializedget_world_sizeget_rankgetattrdataset num_replicasrcrankepochshuffler3 total_sizemathceil num_batches)r+rirjrcrkrms r/r%%ContiguousDistributedSampler.__init__s  484G4G4I4I4..0qL <&*&9&9&;&;4==?D   ,:J ($   g,99T__t%FGr1cLURUR-nURUR-nXRU:aSOS-nURU-[URU5-nXC-nX@R-n[XPR-UR 5nXg4$)z9Calculate the start and end sample indices for this rank.rbr)rqrjrkminrcrn)r+batches_per_rank_base remainderbatches_for_this_rank start_batch end_batch start_idxend_idxs r/_get_rank_indices.ContiguousDistributedSampler._get_rank_indicess!% 0 0D4E4E E$$t'8'88 !6ii)>SYZ [ii"77#dii:SS 7  //1 i//14??C!!r1clUR5up[[X55nUR(an[R "5nUR UR5 [R"[U5US9R5Vs/sHoSUPM nn[U5$s snf)zAGenerate indices for this rank's contiguous chunk of the dataset.) generator) r|listr9rmtorch Generator manual_seedrlrandpermr3tolistr[)r+rzr{indicesgis r/r)%ContiguousDistributedSampler.__iter__s!335 uY01 <<!A MM$** %+0>>#g,RS+T+[+[+]^+]aqz+]G^G}_sB1c.UR5upX!- $)z2Return the number of samples in this rank's chunk.)r|)r+rzr{s r/r6$ContiguousDistributedSampler.__len__s!335 ""r1cXlg)z Set the epoch for this sampler to ensure different shuffling patterns across epochs. Args: epoch (int): Epoch number to use as the random seed for shuffling. N)rl)r+rls r/ set_epoch&ContiguousDistributedSampler.set_epochs  r1)rcrirlrqrjrkrmrn)NNNF) rNrOrPrQrRr%r|r)r6rrSr]r1r/r_r_us"@H$"& # r1r_c[R"5S-n[RR U5 [R"U5 g)zGSet dataloader worker seed for reproducibility across worker processes.lN)r initial_seednprandomseed) worker_id worker_seeds r/ seed_workerrs1$$&.KIINN; KK r1cTU(a[O[nU"UURUUS:HUUR=(d UUR=(d SUR =(d SUUS:XaSOS[ US35URURUUS:XaURS9$SS9$) zBBuild and return a YOLO dataset based on configuration parameters.trainNF?: ?)img_pathimgszrcaugmenthyprectcache single_clsstridepadprefixtaskclassesdatafraction) rr rrrrrrrr) cfgrbatchrmoderr multi_modalris r/build_yolo_datasetrs(3# G ii  XX ii4>>*U7?C4& $ XX !% 7: r1c x[S0SU_SU_SU_SUR_SU_SUS:H_SU_S UR=(d U_S UR=(d S _S UR=(d S _SU_SUS:XaSOS_S[ US35_SUR _SUR_SUS:XaUR_6$S_6$)zFBuild and return a GroundingDataset based on configuration parameters.r json_file max_samplesrrcrrrrrNrFrrrrrrrrrrr]) r rrrrrrrr)rrrrrrrrs r/build_groundingrs      ii          XX  ii4 >>*U  7?C 4& $ XX    "&!  7:! r1c([U[U55n[RR 5n[[ R "5[US5-U5nUS:XaSO%U(a[R"XS9O [U5n [R"5n U RS[-5 [UUU=(a U SLUU US:aSOSUS:=(a U[USS5[ U U=(a [U5U-S:gS 9 $) a0 Create and return an InfiniteDataLoader or DataLoader for training or validation. Args: dataset (Dataset): Dataset to load data from. batch (int): Batch size for the dataloader. workers (int): Number of worker threads for loading data. shuffle (bool, optional): Whether to shuffle the dataset. rank (int, optional): Process rank in distributed training. -1 for single-GPU training. drop_last (bool, optional): Whether to drop the last incomplete batch. pin_memory (bool, optional): Whether to use pinned memory for dataloader. Returns: (InfiniteDataLoader): A dataloader that can be used for training or validation. Examples: Create a dataloader for training >>> dataset = YOLODataset(...) >>> dataloader = build_dataloader(dataset, batch=16, workers=4, shuffle=True) rbN)rmlUU*UU*r collate_fn) rircrm num_workersr4r! pin_memoryrworker_init_fnr drop_last)rtr3rcuda device_countos cpu_countmaxr DistributedSamplerr_rrrrrhr) rirworkersrmrkrrndnwr4rs r/build_dataloaderrs: s7| $E  "B R\\^s2qz )7 3B 2:   + +G E )' 2  !I -45 +GtO!V6(j7L$7"9G u 4 9  r1c(Supp4n[U[[[45(a[U5nUR 5nUR S5nU(a[ U5ROURS5S[[-;nUR5=(d( URS5=(d U=(a U(+nUS:HnU(aU(a [U5nO[U[5(aSnO[U[[ 45(a[#U5nSnO_[U[$R$[&R(45(aSnO-[U[*R,5(aSnO [/S5eXX#XE4$) aT Check the type of input source and return corresponding flag values. Args: source (str | int | Path | list | tuple | np.ndarray | PIL.Image | torch.Tensor): The input source to check. Returns: source (str | int | Path | list | tuple | np.ndarray | PIL.Image | torch.Tensor): The processed source. webcam (bool): Whether the source is a webcam. screenshot (bool): Whether the source is a screenshot. from_img (bool): Whether the source is an image or list of images. in_memory (bool): Whether the source is an in-memory object. tensor (bool): Whether the source is a torch.Tensor. Examples: Check a file path source >>> source, webcam, screenshot, from_img, in_memory, tensor = check_source("image.jpg") Check a webcam source >>> source, webcam, screenshot, from_img, in_memory, tensor = check_source(0) )FFFFF)zhttps://zhttp://zrtsp://zrtmp://ztcp://.rz.streamsscreenTzZUnsupported image type. For supported types see https://docs.ultralytics.com/modes/predict) isinstancestrrLrlower startswithrpath rpartitionrr isnumericendswithrrrtuplerrrndarrayrTensor TypeError) sourcewebcam screenshotfrom_img in_memorytensor source_loweris_urlis_files r/ check_sourcerSsC,7X3FV&3T*++V||~ (()`a288L)..lVVWZ[\^_ + % !!#^vz'B^vG]V]R]!X- g'F FG $ $ FT5M * *v& FU[["**5 6 6 FELL ) )tuu : BBr1c2[U5uppgpU(a URO [XVXy5n U (a [U5n OHU(aUn O>U(a [ XX4S9n O,U(a [ XS9n OU(a [ XS9n O [XX$S9n [U SU 5 U $)a# Load an inference source for object detection and apply necessary transformations. Args: source (str | Path | torch.Tensor | PIL.Image | np.ndarray, optional): The input source for inference. batch (int, optional): Batch size for dataloaders. vid_stride (int, optional): The frame interval for video sources. buffer (bool, optional): Whether stream frames will be buffered. channels (int, optional): The number of input channels for the model. Returns: (Dataset): A dataset object for the specified input source with attached source_type attribute. Examples: Load an image source for inference >>> dataset = load_inference_source("image.jpg", batch=1) Load a video stream source >>> dataset = load_inference_source("rtsp://example.com/stream", vid_stride=2) ) vid_stridebufferchannels)r)rrr source_type) rrrrrrrrsetattr) rrrrrstreamrrrrrris r/load_inference_sourcers*?K6>R;FJ)(1&$${6W_7hKV$  fF^ !&< !&<%fjd G]K0 Nr1)rrL)rF F)rr rrrrLrzdict[str, Any]rrrboolrrLrr)rFrP)rr rrrrrrLrrrrrrLrrL)TrFT) rrLrrLrmrrkrLrrrr)NrbrbF)rrLrrLrrrrL)= __future__rrorrcollections.abcrpathlibrtypingr urllib.parsernumpyrrtorch.distributedr rdPILrtorch.utils.datar ultralytics.cfgr ultralytics.data.datasetr r rultralytics.data.loadersrrrrrrrrultralytics.data.utilsrrultralytics.utilsrrultralytics.utils.checksrultralytics.utils.torch_utilsr DataLoaderrr(utilsrSamplerr_rrrrrrr]r1r/rs# $! 43YY   <,/38-..8-v***^5;;#3#3#;#;^B         H        F5 55 5  5  55p.Cb)r1