# Ultralytics YOLO 🚀, GPL-3.0 license import sys from pathlib import Path from typing import List from ultralytics import yolo # noqa from ultralytics.nn.tasks import (ClassificationModel, DetectionModel, SegmentationModel, attempt_load_one_weight, guess_model_task, nn) from ultralytics.yolo.cfg import get_cfg from ultralytics.yolo.engine.exporter import Exporter from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, RANK, callbacks, yaml_load from ultralytics.yolo.utils.checks import check_file, check_imgsz, check_yaml from ultralytics.yolo.utils.downloads import GITHUB_ASSET_STEMS from ultralytics.yolo.utils.torch_utils import smart_inference_mode # Map head to model, trainer, validator, and predictor classes MODEL_MAP = { "classify": [ ClassificationModel, 'yolo.TYPE.classify.ClassificationTrainer', 'yolo.TYPE.classify.ClassificationValidator', 'yolo.TYPE.classify.ClassificationPredictor'], "detect": [ DetectionModel, 'yolo.TYPE.detect.DetectionTrainer', 'yolo.TYPE.detect.DetectionValidator', 'yolo.TYPE.detect.DetectionPredictor'], "segment": [ SegmentationModel, 'yolo.TYPE.segment.SegmentationTrainer', 'yolo.TYPE.segment.SegmentationValidator', 'yolo.TYPE.segment.SegmentationPredictor']} class YOLO: """ YOLO A python interface which emulates a model-like behaviour by wrapping trainers. """ def __init__(self, model='yolov8n.pt', type="v8") -> None: """ Initializes the YOLO object. Args: model (str, Path): model to load or create type (str): Type/version of models to use. Defaults to "v8". """ self.type = type self.ModelClass = None # model class self.TrainerClass = None # trainer class self.ValidatorClass = None # validator class self.PredictorClass = None # predictor class self.predictor = None # reuse predictor self.model = None # model object self.trainer = None # trainer object self.task = None # task type self.ckpt = None # if loaded from *.pt self.cfg = None # if loaded from *.yaml self.ckpt_path = None self.overrides = {} # overrides for trainer object self.metrics_data = None # Load or create new YOLO model suffix = Path(model).suffix if not suffix and Path(model).stem in GITHUB_ASSET_STEMS: model, suffix = Path(model).with_suffix('.pt'), '.pt' # add suffix, i.e. yolov8n -> yolov8n.pt if suffix == '.yaml': self._new(model) else: self._load(model) def __call__(self, source=None, stream=False, **kwargs): return self.predict(source, stream, **kwargs) def _new(self, cfg: str, verbose=True): """ Initializes a new model and infers the task type from the model definitions. Args: cfg (str): model configuration file verbose (bool): display model info on load """ self.cfg = check_yaml(cfg) # check YAML cfg_dict = yaml_load(self.cfg, append_filename=True) # model dict self.task = guess_model_task(cfg_dict) self.ModelClass, self.TrainerClass, self.ValidatorClass, self.PredictorClass = self._assign_ops_from_task() self.model = self.ModelClass(cfg_dict, verbose=verbose) # initialize def _load(self, weights: str): """ Initializes a new model and infers the task type from the model head. Args: weights (str): model checkpoint to be loaded """ suffix = Path(weights).suffix if suffix == '.pt': self.model, self.ckpt = attempt_load_one_weight(weights) self.task = self.model.args["task"] self.overrides = self.model.args self._reset_ckpt_args(self.overrides) else: check_file(weights) self.model, self.ckpt = weights, None self.task = guess_model_task(weights) self.ckpt_path = weights self.ModelClass, self.TrainerClass, self.ValidatorClass, self.PredictorClass = self._assign_ops_from_task() def _check_is_pytorch_model(self): """ Raises TypeError is model is not a PyTorch model """ if not isinstance(self.model, nn.Module): raise TypeError(f"model='{self.model}' must be a PyTorch model, but is a different type. PyTorch models " f"can be used to train, val, predict and export, i.e. " f"'yolo export model=yolov8n.pt', but exported formats like ONNX, TensorRT etc. only " f"support 'predict' and 'val' modes, i.e. 'yolo predict model=yolov8n.onnx'.") def reset(self): """ Resets the model modules. """ self._check_is_pytorch_model() for m in self.model.modules(): if hasattr(m, 'reset_parameters'): m.reset_parameters() for p in self.model.parameters(): p.requires_grad = True def info(self, verbose=False): """ Logs model info. Args: verbose (bool): Controls verbosity. """ self._check_is_pytorch_model() self.model.info(verbose=verbose) def fuse(self): self._check_is_pytorch_model() self.model.fuse() def predict(self, source=None, stream=False, **kwargs): """ 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. **kwargs : Additional keyword arguments passed to the predictor. Check the 'configuration' section in the documentation for all available options. Returns: (List[ultralytics.yolo.engine.results.Results]): The prediction results. """ overrides = self.overrides.copy() overrides["conf"] = 0.25 overrides.update(kwargs) overrides["mode"] = "predict" overrides["save"] = kwargs.get("save", False) # not save files by default if not self.predictor: self.predictor = self.PredictorClass(overrides=overrides) self.predictor.setup_model(model=self.model) else: # only update args if predictor is already setup self.predictor.args = get_cfg(self.predictor.args, overrides) is_cli = sys.argv[0].endswith('yolo') or sys.argv[0].endswith('ultralytics') return self.predictor.predict_cli(source=source) if is_cli else self.predictor(source=source, stream=stream) @smart_inference_mode() def val(self, data=None, **kwargs): """ Validate a model on a given dataset . Args: data (str): The dataset to validate on. Accepts all formats accepted by yolo **kwargs : Any other args accepted by the validators. To see all args check 'configuration' section in docs """ overrides = self.overrides.copy() overrides["rect"] = True # rect batches as default overrides.update(kwargs) overrides["mode"] = "val" args = get_cfg(cfg=DEFAULT_CFG, overrides=overrides) args.data = data or args.data args.task = self.task if args.imgsz == DEFAULT_CFG.imgsz and not isinstance(self.model, (str, Path)): args.imgsz = self.model.args['imgsz'] # use trained imgsz unless custom value is passed args.imgsz = check_imgsz(args.imgsz, max_dim=1) validator = self.ValidatorClass(args=args) validator(model=self.model) self.metrics_data = validator.metrics return validator.metrics @smart_inference_mode() def export(self, **kwargs): """ Export model. Args: **kwargs : Any other args accepted by the predictors. To see all args check 'configuration' section in docs """ self._check_is_pytorch_model() overrides = self.overrides.copy() overrides.update(kwargs) args = get_cfg(cfg=DEFAULT_CFG, overrides=overrides) args.task = self.task if args.imgsz == DEFAULT_CFG.imgsz: args.imgsz = self.model.args['imgsz'] # use trained imgsz unless custom value is passed if args.batch == DEFAULT_CFG.batch: args.batch = 1 # default to 1 if not modified exporter = Exporter(overrides=args) return exporter(model=self.model) def train(self, **kwargs): """ Trains the model on a given dataset. Args: **kwargs (Any): Any number of arguments representing the training configuration. """ self._check_is_pytorch_model() overrides = self.overrides.copy() overrides.update(kwargs) if kwargs.get("cfg"): LOGGER.info(f"cfg file passed. Overriding default params with {kwargs['cfg']}.") overrides = yaml_load(check_yaml(kwargs["cfg"]), append_filename=True) overrides["task"] = self.task overrides["mode"] = "train" if not overrides.get("data"): raise AttributeError("Dataset required but missing, i.e. pass 'data=coco128.yaml'") if overrides.get("resume"): overrides["resume"] = self.ckpt_path self.trainer = self.TrainerClass(overrides=overrides) if not overrides.get("resume"): # manually set model only if not resuming self.trainer.model = self.trainer.get_model(weights=self.model if self.ckpt else None, cfg=self.model.yaml) self.model = self.trainer.model self.trainer.train() # update model and cfg after training if RANK in {0, -1}: self.model, _ = attempt_load_one_weight(str(self.trainer.best)) self.overrides = self.model.args self.metrics_data = self.trainer.validator.metrics def to(self, device): """ Sends the model to the given device. Args: device (str): device """ self._check_is_pytorch_model() self.model.to(device) def _assign_ops_from_task(self): model_class, train_lit, val_lit, pred_lit = MODEL_MAP[self.task] trainer_class = eval(train_lit.replace("TYPE", f"{self.type}")) validator_class = eval(val_lit.replace("TYPE", f"{self.type}")) predictor_class = eval(pred_lit.replace("TYPE", f"{self.type}")) return model_class, trainer_class, validator_class, predictor_class @property def names(self): """ Returns class names of the loaded model. """ return self.model.names if hasattr(self.model, 'names') else None @property def transforms(self): """ Returns transform of the loaded model. """ return self.model.transforms if hasattr(self.model, 'transforms') else None @property def metrics(self): """ Returns metrics if computed """ if not self.metrics_data: LOGGER.info("No metrics data found! Run training or validation operation first.") return self.metrics_data @staticmethod def add_callback(event: str, func): """ Add callback """ callbacks.default_callbacks[event].append(func) @staticmethod def _reset_ckpt_args(args): for arg in 'augment', 'verbose', 'project', 'name', 'exist_ok', 'resume', 'batch', 'epochs', 'cache', \ 'save_json', 'half', 'v5loader', 'device', 'cfg', 'save', 'rect', 'plots', 'opset': args.pop(arg, None)