FPhKddlZddlZddlmZddlmZddlmZmZm Z ddl m Z ddl m Z mZmZmZddlmZmZmZmZmZmZmZmZddlmZGd d ej8Zy) N)Path)Union) TASK2DATAget_cfg get_save_dir) HUB_WEB_ROOT)attempt_load_one_weightguess_model_tasknnyaml_model_load)ASSETSDEFAULT_CFG_DICTLOGGERRANK callbackschecksemojis yaml_load)GITHUB_ASSETS_STEMScPeZdZdZd$deeefddffd Zd%dZe dZ e dZ d&d efd Z d'd efd Z d ZdZd(dZd)dZdZd*dZd+dZd'dZdZdZd'dZd,dZfdZedZedZedZdefdZdefdZ d Z!e d!Z"d"Z#ed#Z$xZ%S)-Modelao A base class to unify APIs for all models. Args: model (str, Path): Path to the model file to load or create. task (Any, optional): Task type for the YOLO model. Defaults to None. Attributes: predictor (Any): The predictor object. model (Any): The model object. trainer (Any): The trainer object. task (str): The type of model task. ckpt (Any): The checkpoint object if the model loaded from *.pt file. cfg (str): The model configuration if loaded from *.yaml file. ckpt_path (str): The checkpoint file path. overrides (dict): Overrides for the trainer object. metrics (Any): The data for metrics. Methods: __call__(source=None, stream=False, **kwargs): Alias for the predict method. _new(cfg:str, verbose:bool=True) -> None: Initializes a new model and infers the task type from the model definitions. _load(weights:str, task:str='') -> None: Initializes a new model and infers the task type from the model head. _check_is_pytorch_model() -> None: Raises TypeError if the model is not a PyTorch model. reset() -> None: Resets the model modules. info(verbose:bool=False) -> None: Logs the model info. fuse() -> None: Fuses the model for faster inference. predict(source=None, stream=False, **kwargs) -> List[ultralytics.engine.results.Results]: Performs prediction using the YOLO model. Returns: list(ultralytics.engine.results.Results): The prediction results. Nmodelreturnct|tj|_d|_d|_d|_d|_d|_d|_ i|_ d|_ d|_ ||_ t|j}|j!|r*ddlm}|||_ |jj&}n |j)|r||_||_ yt+|j,}|s7t+|j.t0vrt+|j3dd}}|dvr|j5||y|j7||y)a Initializes the YOLO model. Args: model (Union[str, Path], optional): Path or name of the model to load or create. Defaults to 'yolov8n.pt'. task (Any, optional): Task type for the YOLO model. Defaults to None. Nr)HUBTrainingSession.pt)z.yamlz.yml)super__init__rget_default_callbacks predictorrtrainerckptcfg ckpt_path overridesmetricssessiontaskstrstrip is_hub_modelultralytics.hub.sessionr model_fileis_triton_modelrsuffixstemr with_suffix_new_load)selfrr(rr/ __class__s cC:\Users\daisl\Desktop\realtime-object-detection\venv\Lib\site-packages\ultralytics/engine/model.pyrzModel.__init__8s* "88:      E   "   U # B-e4DLLL++E ! !% (DJDI e##$u+**.AA K33E:E6E & & IIeT " JJud #c *|j||fi|S)zNCalls the 'predict' function with given arguments to perform object detection.)predict)r4sourcestreamkwargss r6__call__zModel.__call__cst||FF5f55r7crddlm}||}|jxr|jxr|jdvS)zTIs model a Triton Server URL string, i.e. :////r)urlsplit>grfchttp) urllib.parser?netlocpathscheme)rr?urls r6r.zModel.is_triton_modelgs2 *uozzIchhI3::9I+IIr7c (tjtdjdDcgc] }t |c}ddgk(t dk(xr0t j xrtfddDfScc}w)z+Check if the provided model is a HUB model.z/models/_*c3&K|]}|v ywN).0xrs r6 z%Model.is_hub_model..tsAaZ`UV!5.Z`sz./\)any startswithrsplitlenrexistsall)rrOs` r6r+zModel.is_hub_modelns    ~X6 7"[[- .-SV- .2r( : J"  aT%[%7%7%9!9 acAaZ`Aa>acd d .sB r#ct|}||_|xs t||_|xs|j d||xr t dk(|_|j|jd<|j|jd<it|j|j _ |j|j _y)a/ Initializes a new model and infers the task type from the model definitions. Args: cfg (str): model configuration file task (str | None): model task model (BaseModel): Customized model. verbose (bool): display model info on load r)verboser(N) r r#r r( _smart_loadrrr%rargs)r4r#r(rrYcfg_dicts r6r2z Model._newvs#3'6,X6 8e8t//8(GLbX\`bXbc "&((w!%vA-@@ )) r7weightsc t|j}|dk(rt|\|_|_|jj d|_|j|jj x|_|j_|jj|_ n?tj|}|dc|_|_|xs t||_||_ ||jd<|j |jd<y)z Initializes a new model and infers the task type from the model head. Args: weights (str): model checkpoint to be loaded task (str | None): model task rr(Nr)rr/r rr"r[r(_reset_ckpt_argsr%pt_pathr$r check_filer )r4r]r(r/s r6r3z Model._loadsg%% U?$;G$D !DJ  /DI/3/D/DTZZ__/U UDNTZZ_!ZZ//DN''0G$+T !DJ 9 0 9DI$DN")w!%vr7ct|jttfxr"t|jjdk(}t|jt j }|s|std|jdyy)z1Raises TypeError is model is not a PyTorch model.rzmodel='a' should be a *.pt PyTorch model to run this method, but is a different format. 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Args: detailed (bool): Show detailed information about model. verbose (bool): Controls verbosity. )detailedrY)rhrinfo)r4rurYs r6rvz Model.infos' $$&zz'BBr7cX|j|jjy)z+Fuse PyTorch Conv2d and BatchNorm2d layers.N)rhrfuser4s r6rxz Model.fuses $$& r7c |t}tjd|dtjdj dxs"tjdj dxrt ddD}d |d }i|j||d d i}|jd d}|jsT|xs|jd||j|_ |jj|j|net|jj||j_d|vsd|vr.t!|jj|j_|r1t%|jdr|jj'||r|jj)|S|j||S)a Perform prediction using the YOLO model. Args: source (str | int | PIL | np.ndarray): The source of the image to make predictions on. Accepts all source types accepted by the YOLO model. stream (bool): Whether to stream the predictions or not. Defaults to False. predictor (BasePredictor): Customized predictor. **kwargs : Additional keyword arguments passed to the predictor. Check the 'configuration' section in the documentation for all available options. Returns: (List[ultralytics.engine.results.Results]): The prediction results. Nu2WARNING ⚠️ 'source' is missing. Using 'source=z'.ryolo ultralyticsc3@K|]}|tjvywrL)sysargv)rNrOs r6rPz Model.predict..s_W#UaAM#Us)r9trackz mode=predictz mode=trackg?)confsavemoder9promptsr r% _callbacks)rrYprojectname set_prompts)r:r:r;)r rwarningr~rendswithrQr%popr rZr setup_modelrrr[rsave_dirrlr predict_cli) r4r:r;r r<is_clicustomr[rs r6r9z Model.predicts >F NNOPVxWYZ [((1+&&v.U#((1+2F2F}2UW[^_W#U_W\W/H$..HFHfHfiH((9d+~~HiH4+;+;K+HTXeiesestDN NN & &TZZ & H")$..*=*=t"DDNN D FdN*6t~~7J7J*K' wt~~}= NN & &w /JJy)+ +r7c |jddlm}|jjddddd}i|j ||ddi}|||j |j S) z Export model. Args: **kwargs : Any other args accepted by the Exporter. To see all args check 'configuration' section in docs. )ExporterrNF)rbatchrrYrexportrr)rhexporterrrr[r%r)r4r<rrr[s r6rz Model.export-sj $$&&::??73aZ_`G$..GFGfGfhGBx$4>>BTTr7c |j|jr6t|rtjd|jj }t j|jdr!tt j|dn |j}dt|ji}i|||ddi}|jdr|j|d<|xs|jd||j |_|jdss|j"j%|j&r |j(nd |j(j* |j"_|j"j(|_|j|j"_|j"j/t0d vr|j"j2j5r|j"j2n|j"j6}t9|\|_}|j(j:|_ t=|j"j>d d |_ |j@S) z Trains the model on a given dataset. 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