o V h%@sddlZddlZddlmZddlZddlZddlmZddl m Z m Z ddl m Z mZmZddlmZmZmZmZmZmZmZmZddlmZmZmZddlmZmZdd l m!Z!Gd d d e j"Z#Gd d d Z$ddZ%d ddZ&d!ddZ'd"ddZ(ddZ)d#ddZ*dS)$N)Path)Image) dataloader distributed)GroundingDataset YOLODatasetYOLOMultiModalDataset)LOADERSLoadImagesAndVideosLoadPilAndNumpyLoadScreenshots LoadStreams LoadTensor SourceTypes autocast_list) IMG_FORMATS PIN_MEMORY VID_FORMATS)RANKcolorstr) check_filecs@eZdZdZfddZddZddZdd Zd d ZZ S) InfiniteDataLoadera Dataloader that reuses workers. This dataloader extends the PyTorch DataLoader to provide infinite recycling of workers, which improves efficiency for training loops that need to iterate through the dataset multiple times. Attributes: batch_sampler (_RepeatSampler): A sampler that repeats indefinitely. iterator (Iterator): The iterator from the parent DataLoader. Methods: __len__: Returns the length of the batch sampler's sampler. __iter__: Creates a sampler that repeats indefinitely. __del__: Ensures workers are properly terminated. reset: Resets the iterator, useful when modifying dataset settings during training. cs6tj|i|t|dt|jt|_dS)zHInitialize the InfiniteDataLoader with the same arguments as DataLoader. batch_samplerN)super__init__object __setattr___RepeatSamplerr__iter__iterator)selfargskwargs __class__J/var/www/vscode/kcb/lib/python3.10/site-packages/ultralytics/data/build.pyr.szInfiniteDataLoader.__init__cCs t|jjS)z1Return the length of the batch sampler's sampler.)lenrsamplerr r%r%r&__len__4s zInfiniteDataLoader.__len__ccs$tt|D]}t|jVqdS)zICreate an iterator that yields indefinitely from the underlying iterator.N)ranger'nextr)r _r%r%r&r8szInfiniteDataLoader.__iter__cCsVz t|jds WdS|jjD] }|r|q|jWdSty*YdSw)zKEnsure that workers are properly terminated when the dataloader is deleted._workersN)hasattrrr.is_alive terminate_shutdown_workers Exception)r wr%r%r&__del__=s   zInfiniteDataLoader.__del__cCs||_dS)zIReset the iterator to allow modifications to the dataset during training.N) _get_iteratorrr)r%r%r&resetIszInfiniteDataLoader.reset) __name__ __module__ __qualname____doc__rr*rr5r7 __classcell__r%r%r#r&rs  rc@s eZdZdZddZddZdS)rz Sampler that repeats forever. 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Attributes: sampler (Dataset.sampler): The sampler to repeat. cCs ||_dS)zDInitialize the _RepeatSampler with a sampler to repeat indefinitely.N)r()r r(r%r%r&rYs z_RepeatSampler.__init__ccs t|jEdHq)z=Iterate over the sampler indefinitely, yielding its contents.TN)iterr(r)r%r%r&r]sz_RepeatSampler.__iter__N)r8r9r:r;rrr%r%r%r&rNs rcCs&td}tj|t|dS)zGSet dataloader worker seed for reproducibility across worker processes.lN)torch initial_seednprandomseed) worker_id worker_seedr%r%r& seed_workercs  rEtrainF c Csz|rtnt}|||j||dk||jp||jpd|jpdt||dkr$dndt|d|j|j ||dkr9|j dSddS) zBBuild and return a YOLO dataset based on configuration parameters.rFNF?: ?)img_pathimgsz batch_sizeaugmenthyprectcache single_clsstridepadprefixtaskclassesdatafraction) rrrMrQrRrSintrrWrXrZ) cfgrLbatchrYmoderQrT multi_modaldatasetr%r%r&build_yolo_datasetjs(   racCsnt|||j||dk||jp||jpd|jpdt||dkrdndt|d|j|j|dkr3|j dSddS) zFBuild and return a GroundingDataset based on configuration parameters.rFNFrHrIrJrK)rL json_filerMrNrOrPrQrRrSrTrUrVrWrXrZ) rrMrQrRrSr[rrWrXrZ)r\rLrbr]r^rQrTr%r%r&build_groundings&  rcTc Cst|t|}tj}ttt|d|}|dkrdntj ||d}t }| dt t |||o8|du||tt|ddt|d S)a 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): Whether to shuffle the dataset. rank (int): Process rank in distributed training. -1 for single-GPU training. Returns: (InfiniteDataLoader): A dataloader that can be used for training or validation. rdN)shufflelUU*UU* collate_fn) r`rNrf num_workersr( pin_memoryrgworker_init_fn generator)minr'r>cuda device_countos cpu_countmaxrDistributedSampler Generator manual_seedrrrgetattrrE) r`r]workersrfrankndnwr(rkr%r%r&build_dataloaders"   rzcCsd\}}}}}t|tttfrDt|}t|jddttBv}|d}| p4| dp4|o4| }|dk}|rC|rCt |}n/t|t rLd}n't|t tfrZt|}d}nt|tjtjfrfd}n t|tjrod}ntd||||||fS) a 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. Raises: TypeError: If the source type is unsupported. )FFFFFreN)zhttps://zhttp://zrtsp://zrtmp://ztcp://z.streamsscreenTzZUnsupported image type. For supported types see https://docs.ultralytics.com/modes/predict) isinstancestrr[rsuffixrrlower startswith isnumericendswithrr listtuplerrr@ndarrayr>Tensor TypeError)sourcewebcam screenshotfrom_img in_memorytensoris_fileis_urlr%r%r& check_sources*   rrec Cst|\}}}}}}|r|jnt||||} |rt|} n$|r"|} n|r,t|||d} n|r3t|} n|r:t|} nt|||d} t| d| | S)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. Returns: (Dataset): A dataset object for the specified input source with attached source_type attribute. ) vid_stridebuffer)r]r source_type) rrrrr r r r setattr) rr]rrstreamrrrrrr`r%r%r&load_inference_sources     r)rFFrGF)rFFrG)Trd)NrereF)+rorApathlibrnumpyr@r>PILrtorch.utils.datarrultralytics.data.datasetrrrultralytics.data.loadersr r r r r rrrultralytics.data.utilsrrrultralytics.utilsrrultralytics.utils.checksr DataLoaderrrrErarcrzrrr%r%r%r&s(  (  2   !*