FPhndZddlmZddlZddlZddlmZmZm Z ddl m Z d dZ ddejfd Zy) zlFunctions for estimating the best YOLO batch size to use a fraction of the available CUDA memory in PyTorch.)deepcopyN) DEFAULT_CFGLOGGERcolorstr)profilectjjj|5t t |j |cdddS#1swYyxYw)as Check YOLO training batch size using the autobatch() function. Args: model (torch.nn.Module): YOLO model to check batch size for. imgsz (int): Image size used for training. amp (bool): If True, use automatic mixed precision (AMP) for training. Returns: (int): Optimal batch size computed using the autobatch() function. N)torchcudaampautocast autobatchrtrain)modelimgszr s fC:\Users\daisl\Desktop\realtime-object-detection\venv\Lib\site-packages\ultralytics/utils/autobatch.pycheck_train_batch_sizer s=   %%..0%8 & % %s #AA g333333?ctd}tj|d|t|j j }|j dk(rtj|d||Stjjjrtj|d||Sd}t|j}tjj|}|j|z } tjj!||z } tjj#||z } | | | zz } tj||d|j$d| d d | d d | d d | d d gd} | Dcgc]}tj&|d||}}t)||d|}|Dcgc] }|s|d }}t+j,| dt/||d}t1| |z|dz |dz }d|vr+|j3d}|| |k\r| t5|dz d}|dks|dkDr|}tj|d|dt+j6||| z| z| z }tj|d|d|d| |zd d| d d|dzdd |Scc}wcc}w#t8$r*}tj:|d!|d"|d|cYd}~Sd}~wwxYw)#a  Automatically estimate the best YOLO batch size to use a fraction of the available CUDA memory. Args: model (torch.nn.module): YOLO model to compute batch size for. imgsz (int, optional): The image size used as input for the YOLO model. Defaults to 640. fraction (float, optional): The fraction of available CUDA memory to use. Defaults to 0.67. batch_size (int, optional): The default batch size to use if an error is detected. Defaults to 16. Returns: (int): The optimal batch size. z AutoBatch: z'Computing optimal batch size for imgsz=cpuz0CUDA not detected, using default CPU batch-size uP ⚠️ Requires torch.backends.cudnn.benchmark=False, using default batch-size i@z (z) z.2fz G total, z G reserved, z G allocated, zG free))ndevicerNr)degriu?WARNING ⚠️ CUDA anomaly detected, using default batch-size .zUsing batch-size z for  zG/zG (dz.0fu%) ✅uWARNING ⚠️ error detected: z, using default batch-size )rrinfonext parametersrtyper backendscudnn benchmarkstrupperr get_device_properties total_memorymemory_reservedmemory_allocatednameemptyrnppolyfitlenintindexmaxpolyval Exceptionwarning)rrfraction batch_sizeprefixrgbd propertiestraf batch_sizesbimgresultsxypies rrrsm $F KK6(A%IJ %""$ % , ,F {{e vhNzl[\ ~~%% vhnoynz{| B F A11&9J"$A ""6*R/A ##F+b0A QU A KK6(1#R 01S'1S'VWX[U\\ijkloippvwx#K8CD 1u{{1a. D#u&9# (7aaQqT7 ( JJ{7CF+QA 6 X!$!, - 7? d#AKN"AE1 . q5AHA KK6("ablammno pJJq!$q(1,1 vh/s%s!AL;MRPQRUwVYZbehZhilYmmstu#E ) &!@C_`j_kklmnsCJ= J3(J==J8J8 C&J=3 J== K0K+%K0+K0)rT)__doc__copyrnumpyr1r ultralytics.utilsrrrultralytics.utils.torch_utilsrrbatchrrrUs3r ;;19"+:K:K:rT