FPhtddlZddlmZddlmZddlZddlmZddlm Z m Z m Z m Z m Z mZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZm Z m!Z!m"Z"m#Z#m$Z$m%Z%m&Z&ddl'm(Z(m)Z)m*Z*m+Z+m,Z,m-Z-ddl.m/Z/m0Z0m1Z1ddl2m3Z3m4Z4m5Z5m6Z6ddl7m8Z8dd l9m:Z:m;Z;mZ>m?Z?m@Z@mAZA ddlBZBGd d ejZEGd d eEZFGddeFZGGddeFZHGddeEZIGddeFZJGddejZLejd dZNdZOd!dZPd!dZQd"dZRdZSdZTdZUy#eC$rdZBYwxYw)#N)deepcopy)Path)AIFIC1C2C3C3TRSPPSPPF Bottleneck BottleneckCSPC2fC3GhostC3xClassifyConcatConvConv2 ConvTransposeDetectDWConvDWConvTranspose2dFocusGhostBottleneck GhostConvHGBlockHGStemPoseRepC3RepConv RTDETRDecoderSegment)DEFAULT_CFG_DICTDEFAULT_CFG_KEYSLOGGERcolorstremojis yaml_load)check_requirements check_suffix check_yaml)v8ClassificationLossv8DetectionLoss v8PoseLossv8SegmentationLoss)feature_visualization)fuse_conv_and_bnfuse_deconv_and_bninitialize_weightsintersect_dictsmake_divisible model_info scale_img time_synccreZdZdZdZddZddZdZdZddZ ddZ dd Z fd Z dd Z dd Zd ZxZS) BaseModelz]The BaseModel class serves as a base class for all the models in the Ultralytics YOLO family.czt|tr|j|g|i|S|j|g|i|S)a& Forward pass of the model on a single scale. Wrapper for `_forward_once` method. Args: x (torch.Tensor | dict): The input image tensor or a dict including image tensor and gt labels. Returns: (torch.Tensor): The output of the network. ) isinstancedictlosspredict)selfxargskwargss _C:\Users\daisl\Desktop\realtime-object-detection\venv\Lib\site-packages\ultralytics/nn/tasks.pyforwardzBaseModel.forwardsD a 499Q000 0t||A////cN|r|j|S|j|||S)a Perform a forward pass through the network. Args: x (torch.Tensor): The input tensor to the model. profile (bool): Print the computation time of each layer if True, defaults to False. visualize (bool): Save the feature maps of the model if True, defaults to False. augment (bool): Augment image during prediction, defaults to False. Returns: (torch.Tensor): The last output of the model. )_predict_augment _predict_once)r@rAprofile visualizeaugments rDr?zBaseModel.predict,s- ((+ +!!!Wi88rFcgg}}|jD]}|jdk7rMt|jtr||jn#|jDcgc]}|dk(r|n||c}}|r|j |||||}|j |j |jvr|nd|st||j|j ||Scc}w)a Perform a forward pass through the network. Args: x (torch.Tensor): The input tensor to the model. profile (bool): Print the computation time of each layer if True, defaults to False. visualize (bool): Save the feature maps of the model if True, defaults to False. Returns: (torch.Tensor): The last output of the model. Nsave_dir modelfr<int_profile_one_layerappendisaver0type)r@rArJrKydtmjs rDrIzBaseModel._predict_once=sB2Assby(c2AaccFYZY\Y\8]Y\TUa2g1Q49OY\8]''1b1!A HH!##*Q 5%ayI9^sC"c~tjd|jjd|j |S)zFPerform augmentations on input image x and return augmented inference.WARNING ⚠️ zW does not support augmented inference yet. 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Returns: None rNF)inputsverbosergeA dz time (ms)z>10s GFLOPsparamsz modulez10.2f10.0f -z TotalN)rRr<listthoprJcopyr8rangerVr%infonprYsum)r@r\rAr[cflopst_s rDrUzBaseModel._profile_one_layer[s1 B  7Jq$$7^b QA1'=uMaPSVVYZZhi KrA !affh # 9;?c)*  1  KK;t,Aht_Aht_HU V r"venAeE]!ADDz%BaseModel.is_fused..sD!4A! 1!4s c36K|]}t|ywN)r<)rrr{s rDrz%BaseModel.is_fused..s=n:a$ns)tuplenn__dict__itemsrur})r@threshr{s @rDr|zBaseModel.is_fuseds>D!2!2!4D D=dlln==FFrFc t||||S)a^ Prints model information. Args: detailed (bool): if True, prints out detailed information about the model. Defaults to False verbose (bool): if True, prints out the model information. Defaults to False imgsz (int): the size of the image that the model will be trained on. Defaults to 640 )detailedreimgsz)r6)r@rrers rDrszBaseModel.infos$7%PPrFct||}|jd}t|tt frE||j |_||j|_||j|_|S)a Applies a function to all the tensors in the model that are not parameters or registered buffers. Args: fn (function): the function to apply to the model Returns: (BaseModel): An updated BaseModel object. rN) super_applyrRr<rr"strideanchorsstrides)r@fnr\ras rDrzBaseModel._applysaw~b! JJrN a&'* +!((|AH199 AI199 AI rFc Zt|tr|dn|}|jj}t ||j}|j |d|rGt jdt|dt|jjdyy)z Load the weights into the model. Args: weights (dict | torch.nn.Module): The pre-trained weights to be loaded. verbose (bool, optional): Whether to log the transfer progress. Defaults to True. rRF)strictz Transferred /z items from pretrained weightsN) r<r=float state_dictr4load_state_dictr%rslenrR)r@weightsrerRcsds rDloadzBaseModel.loads%/w$= 7kkm&&(c4??#45 S/  KK,s3xj#djj6K6K6M2N1OOmn o rFct|ds|j|_||j|dn|}|j||S)z Compute loss. Args: batch (dict): Batch to compute loss on preds (torch.Tensor | List[torch.Tensor]): Predictions. criterionimg)r~init_criterionrrE)r@batchpredss rDr>zBaseModel.losssGt[)!002DN.3m U5\*~~eU++rFctd)z0Initialize the loss criterion for the BaseModel.z4compute_loss() needs to be implemented by task heads)NotImplementedErrorr@s rDrzBaseModel.init_criterions!"XYYrFFFF)FFT)rg)FTir)rb __module__ __qualname____doc__rEr?rIrHrUrr|rsrrr>r __classcell__ras@rDr:r:sGg 09"0% I26 G Q$ p ,ZrFr:cLeZdZdZdfd ZfdZeddZdZdZ xZ S) DetectionModelzYOLOv8 detection model.c p t t|tr|n t |_j j d|x}j d<|rI|j dk7r7tjdj dd||j d<ttj ||\_ _ tj dDcic]}||c}_j j dd_jd t t t"t$frd }j _ fd }t'j(|t'j*d |||Dcgc]}||j,d z c} _ j._ j1nt'j2d g_t5|r&jtjdyycc}wcc}w)zKInitialize the YOLOv8 detection model with the given config and parameters.chncOverriding model.yaml nc= with nc=rreinplaceTrNcztttfrj|dSj|S)Nr)r<r"rrE)rAr\r@s rDz)DetectionModel.__init__..s4jWdO6T Q 2 iZ^ZfZfghZi irF N)r__init__r<r=yaml_model_loadyamlgetr%rs parse_modelrrRrXrrnamesrrr"rtorchtensorzerosshaper bias_initTensorr3) r@cfgrrrerWsrErAr\ras ` @rDrzDetectionModel.__init__s %c40Coc6J  $yy}}T266TYYt_ " $' KK3DIIdO3DIbTR S DIIdO +HTYY,?BPW X DI).tyy)?@)?AaA3i)?@ yy}}Y5  JJrN a&'40 1A AIiG||gekkRSUWYZ\]F^>_$`>_Q_>_$`aAH((DK KKM,,t,DK 4  IIK KKO #A%as 2 H.H3c |jdd}gd}gd}g}t||D]~\}}t|r|j|n||t |j j }t |!|d} |j| |||} |j| |j|}tj|ddfS)zXPerform augmentations on input image x and return augmented inference and train outputs.rN)rg(\?gq= ףp?)NN)gsrrN)rzipr7fliprTrmaxrr? _descale_predrV_clip_augmentedrcat) r@rAimg_sizerrSrZsifixiyiras rDrHzDetectionModel._predict_augments7723<   !QiFB166":B3t{{?P;QRB$Q'B##BB9B HHRL    #yyB%%rFc|ddddfxx|zcc<|jddd|j|dz f|\}}}}|dk(r |d|z }n |dk(r|d|z }tj||||f|S)zGDe-scale predictions following augmented inference (inverse operation).Nrrfrr)splitrrr) pflipsscalerdimrArZwhclss rDrzDetectionModel._descale_pred s !RaR%EAq!''#,*: ;SA 1b# A: aA aZ aAyy!QC#..rFcz|jdjtdtD}d}|djd|ztdt|Dz}|ddd| f|d<|djd|ztfdt|Dz}|dd|df|d<|S) z$Clip YOLO augmented inference tails.rNc3&K|] }d|z ywrNrrrAs rDrz1DetectionModel._clip_augmented..s* 1Q rrc3&K|] }d|z ywrrrs rDrz1DetectionModel._clip_augmented..s'A1Qr.Nc34K|]}ddz |z zyw)rrNr)rrAnls rDrz1DetectionModel._clip_augmented..s(MHqrAvz):Hs)rRrrurrr)r@rZgerWrs @rDrzDetectionModel._clip_augmenteds ZZ^   *b * *  qTZZ^q C'Aa'A$A AtC1"H~! rU[[_ !S(ME!H(M%M M"c12g"rFct|S)z5Initialize the loss criterion for the DetectionModel.)r-rs rDrzDetectionModel.init_criterion"s t$$rF)z yolov8n.yamlrNT)r) rbrrrrrH staticmethodrrrrrs@rDrrs/!@ &// %rFrc*eZdZdZdfd ZdZxZS)SegmentationModelzYOLOv8 segmentation model.c,t|||||y)zFInitialize YOLOv8 segmentation model with given config and parameters.rrrreNrrr@rrrreras rDrzSegmentationModel.__init__*s SRB@rFct|S)z8Initialize the loss criterion for the SegmentationModel.)r/rs rDrz SegmentationModel.init_criterion.s !$''rF)zyolov8n-seg.yamlrNTrbrrrrrrrs@rDrr's$A(rFrc*eZdZdZdfd ZdZxZS) PoseModelzYOLOv8 pose model.ct|ts t|}t|r=t |t |dk7r#t j d|dd|||d<t|!||||y)zInitialize YOLOv8 Pose model. kpt_shapez Overriding model.yaml kpt_shape=z with kpt_shape=rN) r<r=ranyror%rsrr)r@rrrdata_kpt_shapereras rDrzPoseModel.__init__6sx#t$!#&C ~ 4#74K@P;Q#Q KK:3{;K:LL\]k\lm n-C  SRB@rFct|S)z0Initialize the loss criterion for the PoseModel.)r.rs rDrzPoseModel.init_criterion?s $rF)zyolov8n-pose.yamlrNNNTrrs@rDrr3sA rFrc@eZdZdZdfd ZdZedZdZxZ S)ClassificationModelzYOLOv8 classification model.cJt||j||||y)zNInit ClassificationModel with YAML, channels, number of classes, verbose flag.N)rr _from_yamlrs rDrzClassificationModel.__init__Gs   RW-rFct|tr|n t||_|jj d|x}|jd<|rJ||jdk7r8t j d|jdd|||jd<n)|s'|jj dds tdtt|j||\|_ |_ tjdg|_t|jdDcic]}||c}|_|j ycc}w) zBSet YOLOv8 model configurations and define the model architecture.rrrrNzFnc not specified. Must specify nc in model.yaml or function arguments.rr)r<r=rrrr%rs ValueErrorrrrRrXrrrrrr)r@rrrrerWs rDr zClassificationModel._from_yamlLs %c40Coc6J  $yy}}T266TYYt_ " $' KK3DIIdO3DIbTR S DIIdODIIMM$5ef f +HTYY,?BPW X DIllA3' ).tyy)?@)?AaA3i)?@  As& E cJtt|dr |jn|jd\}}t |t rJ|j j|k7r0tj|j j||_yyt |tjr<|j|k7r,t||tj|j|yyt |tjr|Dcgc] }t|}}tj|vrY|jtj}||j|k7r'tj||j|||<yytj|vr|jtj}||j |k7rTtj||j"|||j$||j&||j(du||<yyyycc}w)zIUpdate a TorchVision classification model to class count 'n' if required.rRrNN)bias)ror~rRnamed_childrenr<rlinear out_featuresrLinear in_featuressetattr SequentialrYindexConv2d out_channels in_channels kernel_sizerr)rRrnamer\rAtypesrWs rDreshape_outputsz#ClassificationModel.reshape_outputs\swug'> EYY[\]_`a a "xx$$*99QXX%9%92>+ 299 %~~#tRYYq}}b%AB$ 2== )&'(aT!WaE(yyE!KK *Q4$$*99QqT%5%5r:AaD+e#KK *Q4$$*99QqT%5%5r1Q4;K;KQqT[[_`ab_c_h_hpt_tuAaD+$ *(sH ctS)z:Initialize the loss criterion for the ClassificationModel.)r,rs rDrz"ClassificationModel.init_criterionqs #%%rF)zyolov8n-cls.yamlrNT) rbrrrrr rrrrrs@rDr r Ds+&.  vv(&rFr c:eZdZdZdfd ZdZddZddZxZS) RTDETRDetectionModela RTDETR (Real-time DEtection and Tracking using Transformers) Detection Model class. This class is responsible for constructing the RTDETR architecture, defining loss functions, and facilitating both the training and inference processes. RTDETR is an object detection and tracking model that extends from the DetectionModel base class. Attributes: cfg (str): The configuration file path or preset string. Default is 'rtdetr-l.yaml'. ch (int): Number of input channels. Default is 3 (RGB). nc (int, optional): Number of classes for object detection. Default is None. verbose (bool): Specifies if summary statistics are shown during initialization. Default is True. Methods: init_criterion: Initializes the criterion used for loss calculation. loss: Computes and returns the loss during training. predict: Performs a forward pass through the network and returns the output. c,t|||||y)a^ Initialize the RTDETRDetectionModel. Args: cfg (str): Configuration file name or path. ch (int): Number of input channels. nc (int, optional): Number of classes. Defaults to None. verbose (bool, optional): Print additional information during initialization. Defaults to True. rNrrs rDrzRTDETRDetectionModel.__init__s SRB@rFc6ddlm}||jdS)z;Initialize the loss criterion for the RTDETRDetectionModel.r)RTDETRDetectionLossT)ruse_vfl)ultralytics.models.utils.lossr%r)r@r%s rDrz#RTDETRDetectionModel.init_criterionsE"dggt<rs rDr>zRTDETRDetectionModel.lossst[)!002DNEl X+& r?rrs@rDr"r"vs& A= +FZrFr"c*eZdZdZfdZddZxZS)EnsemblezEnsemble of models.c"t|y)z!Initialize an ensemble of models.Nr)r@ras rDrzEnsemble.__init__s rFc v|Dcgc]}|||||d}}tj|d}|dfScc}w)z2Function generates the YOLO network's final layer.rrfN)rr)r@rArLrJrKmodulerZs rDrEzEnsemble.forwardsFEI JT6VAw 3A 6T J IIaO$w Ks6r)rbrrrrrErrs@rDrGrGsrFrGc#0K|si}ddl}ddl} |jD]#\}}|j||j|<%d|D]}||jvs|j|= y#|D]}||jvs|j|= wxYww)a Context manager for temporarily adding or modifying modules in Python's module cache (`sys.modules`). This function can be used to change the module paths during runtime. It's useful when refactoring code, where you've moved a module from one location to another, but you still want to support the old import paths for backwards compatibility. Args: modules (dict, optional): A dictionary mapping old module paths to new module paths. Example: ```python with temporary_modules({'old.module.path': 'new.module.path'}): import old.module.path # this will now import new.module.path ``` Note: The changes are only in effect inside the context manager and are undone once the context manager exits. Be aware that directly manipulating `sys.modules` can lead to unpredictable results, especially in larger applications or libraries. Use this function with caution. rN) importlibsysr import_moduler})r}rLrMoldnews rDtemporary_modulesrQs.  % HC(66s;CKK ( Cckk!KK$7Cckk!KK$s( B:A. BB.BBBc ddlm}t|d||} tdddd5t j |d |fcd d d S#1swYy xYw#t $r}|jd k(rttd |d|tjd|d|jd|jdt|jt j |d |fcYd }~Sd }~wwxYw)a This function attempts to load a PyTorch model with the torch.load() function. If a ModuleNotFoundError is raised, it catches the error, logs a warning message, and attempts to install the missing module via the check_requirements() function. After installation, the function again attempts to load the model using torch.load(). Args: weight (str): The file path of the PyTorch model. Returns: (dict): The loaded PyTorch model. r)attempt_download_assetz.pt)filesuffixzultralytics.utilszultralytics.models.yolozultralytics.data)zultralytics.yolo.utilszultralytics.yolo.v8zultralytics.yolo.datacpu) map_locationNmodelsu ERROR ❌️ ar appears to be an Ultralytics YOLOv5 model originally trained with https://github.com/ultralytics/yolov5. This model is NOT forwards compatible with YOLOv8 at https://github.com/ultralytics/ultralytics. Recommend fixes are to train a new model using the latest 'ultralytics' package or to run a command with an official YOLOv8 model, i.e. 'yolo predict model=yolov8n.pt'r_z appears to require 'zK', which is not in ultralytics requirements. AutoInstall will run now for 'z' but this feature will be removed in the future. Recommend fixes are to train a new model using the latest 'ultralytics' package or to run a command with an official YOLOv8 model, i.e. 'yolo predict model=yolov8n.pt') ultralytics.utils.downloadsrSr*rQrrModuleNotFoundErrorr TypeErrorr'r%r`r))weightrSrTrs rDtorch_safe_loadr]"s CfU+ !& )D: *='@); =>::d7= >>> : 66X vh/klmn tu  u 0EaffXN::;&&Bkl m 166"zz$U3T99:s;AA AAAA C7$BC2,C72C7c b t t|tr|n|gD]}t|\}}d|vr it|dnd}|j dxs|dj |j}||_||_ t||_ t|dstjdg|_ j!|r*t|dr|j#j%n|j% j'D]}t)|} | t*j,t*j.t*j0t*j2t*j4t6t8fvr||_o| t*j<ust|drd|_tA d k(r d StCjDd |d d D]} tG | tI d|  tjJtj Dcgc]}|jjMc}jOj _tQ fd Ds"Jd Dcgc]}|jRc} Scc}wcc}w)zWLoads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a. train_argsNemarRr@@rrecompute_scale_factorrrNzEnsemble created with  )rrrrc3VK|] }dj|jk("yw)rN)r)rr\ensembles rDrz'attempt_load_weights..ns#8x!x{~~%xs&)zModels differ in class counts )*rGr<ror]r#rr6rrBpt_pathguess_model_tasktaskr~rrrrVrevalr}rYr Hardswish LeakyReLUReLUReLU6SiLUrr"rUpsamplerbrr%rsrgetattrargmaxrrTallr) rr,rrwckptrBrRr\rxrres @rDattempt_load_weightsruJs>zH"7D1Wy @!!$a=IT=Q9"9d<&89W[%1DM55f=CCE  %e, uh' <<.EL tv8N ))+TYT^T^T`aA     G r||RWWbhhQXY YAI "++ ga1I&J'+A $   8}| KK( 45 "!WXa[!45#u||ELLRZ9[RZQ!((,,.RZ9[,\]aacdkkHO 8x8 8ut@tBtDtFtHtJtLtNtPtRtjTtVtXtZfvry|d}}||k7rt]t_||| zd }||gddtFtHtJtLtNtPtRtXtZf vr0jad!|d}ntbur |gntdtffvr6|dd}}}|||gd!dtfurىjad"|d}nĉtjhur|gntjurtmfd#|D}ntntptrfvrJju|Dcgc]}| c}tpurQt]t_d!|| zd d!<n1tvur$jad|Dcgc]}| c}n|}|dkDr%tjxfd$t{|Dn}t%d d%j}d&d}tmd'|jD_@||c|_A|_B|_C|rDt jdt%|d|djd(d|dt%d| jfd)t#|tr|gn|D| ju|dk(rgju|tjx| t| fS#1swYxYwcc}wcc}w)*z8Parse a YOLO model.yaml dictionary into a PyTorch model.rNinfc3@K|]}j|ywrrrrAds rDrzparse_model..sH)GAquuQx)Gs)r activationscalesc3BK|]}j|dyw)g?Nr{r|s rDrzparse_model..sh6gquuQ}6gs)depth_multiplewidth_multiplerru6WARNING ⚠️ no model scale passed. Assuming scale='z'.z activation:rircrz>3fromz>20nrkz>10rmrJz<45 argumentsz<30rNbackbonerEznn.rrrfrc3(K|] }| ywrr)rrArs rDrzparse_model..s&AqRUAc3(K|] } ywrr)rryrBr\s rDrzparse_model..s8x!QXxrrz __main__.c3<K|]}|jywr)numelrs rDrzparse_model..s6o1779osrlc34K|]}|dk7s |zyw)rNNr)rrArWs rDrzparse_model..sQ$DabAE$Ds  )Gastrrrkeysr%r`rir default_actrsr& enumeraterprrglobalsr<str contextlibsuppressrlocals literal_evalrroundrrrr rr r rrr rrrrr rConvTranspose2drrrr5mininsertrrr BatchNorm2drrurr"rrVr!rrrreplace parametersrtrWrSrYextendrTsorted)r}rrer max_channelsractrdepthwidthrrlayersrXc2rSrr]an_c1cmrAm_rxrBrWr\s`` @@@rDrrsHS>R]^aQbcd B2r"v"DF'* & (AB?Aq!T(- GEHHae $ ! dODAq!S!((4-.&(]fhqk@P@PQR@SDG54$ ./UU1u9%q)9B 4 :X[]acikpBRw@R@RTegjlqs sUDGBRx#CL$9E$A1E&T!"X&D]BCT7COO Aq! $YqE>D>D 67# #AQaBBB*ab*DG| Aq! ".. qE7D &[&A&&B 67D) ) KK*1A* +G|(T!Wl)Ce)KQOQ -  KK1-1aBqE1- .AB=>UR]]8uQx8 94 F1RL b 16bmmo66AbdBG  KK1R&Q RGADD.s%Fgv!uQCs!_v_gsnsmlxz(\d+)([nslmx])6(.+)?$z \1\2-p6\3uGWARNING ⚠️ Ultralytics YOLO P6 models now use -p6 suffix. Renaming z to .z(\d+)([nslmx])(.+)?$z\1\3F)hardr yaml_file) rerstemsubr%r` with_namerUrr+r(guess_model_scale)pathrnew_stem unified_pathrr}s rDrrs :D yyFgFF662L$))L`aeajaj`kkopxoyyz{|~~h456617CIFL.cfg2task sL KOB  % % ' 7 7 = > ; rFN)z model.argszmodel.model.argszmodel.model.model.argsrh)z model.yamlzmodel.model.yamlzmodel.model.model.yamlrrrrz-segz-clsz-poseuWARNING ⚠️ Unable to automatically guess model task, assuming 'task=detect'. Explicitly define task for your model, i.e. 'task=detect', 'segment', 'classify', or 'pose'.)r<r=rr ExceptionrModulerir}rr"rrrrrpartsr%r`)rRrrAr\s rDrgrgs %   +E?, +%#KA$$Y/Awv0/LLA$$Y/Q(0/LA!V$Aw' Ax(!At$!%#t%U  UZZ 9 #; uzz !Z5;;%>  "f &;  $ NNrs K, + 0/0/s#G7G0G"GG "G, r)NTFr)Vrrqrpathlibrrtorch.nnrultralytics.nn.modulesrrrrr r r r r rrrrrrrrrrrrrrrrrrr r!r"ultralytics.utilsr#r$r%r&r'r(ultralytics.utils.checksr)r*r+ultralytics.utils.lossr,r-r.r/ultralytics.utils.plottingr0ultralytics.utils.torch_utilsr1r2r3r4r5r6r7r8rp ImportErrorrr:rrrr r" ModuleListrGcontextmanagerrQr]rurwrrrrgrrFrDrs2 <<<<<<<<feQQhh<]]] }Z }Z@I%YI%X ( (  "/&)/&dm>m` r}} & %%%%P%:P%P6K0\ $$AQ DsD--D76D7