import torch from pathlib import Path from ultralytics import yolo # noqa from ultralytics.nn.tasks import ClassificationModel, DetectionModel, SegmentationModel, attempt_load_weights from ultralytics.yolo.configs import get_config from ultralytics.yolo.engine.exporter import Exporter from ultralytics.yolo.utils import DEFAULT_CONFIG, HELP_MSG, LOGGER, yaml_load from ultralytics.yolo.utils.checks import check_imgsz, check_yaml from ultralytics.yolo.utils.torch_utils import guess_task_from_head, 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.yaml', 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.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.overrides = {} # overrides for trainer object self.init_disabled = False # disable model initialization # Load or create new YOLO model {'.pt': self._load, '.yaml': self._new}[Path(model).suffix](model) 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 """ cfg = check_yaml(cfg) # check YAML cfg_dict = yaml_load(cfg) # model dict self.task = guess_task_from_head(cfg_dict["head"][-1][-2]) self.ModelClass, self.TrainerClass, self.ValidatorClass, self.PredictorClass = \ self._guess_ops_from_task(self.task) self.model = self.ModelClass(cfg_dict, verbose=verbose) # initialize self.cfg = cfg 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 """ self.ckpt = torch.load(weights, map_location="cpu") self.task = self.ckpt["train_args"]["task"] self.overrides = dict(self.ckpt["train_args"]) self.overrides["device"] = '' # reset device self.ModelClass, self.TrainerClass, self.ValidatorClass, self.PredictorClass = \ self._guess_ops_from_task(self.task) self.model = attempt_load_weights(weights, fuse=False) def reset(self): """ Resets the model modules . """ 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. """ if not self.model: LOGGER.info("model not initialized!") self.model.info(verbose=verbose) def fuse(self): if not self.model: LOGGER.info("model not initialized!") self.model.fuse() @smart_inference_mode() def predict(self, source, **kwargs): """ Visualize prediction. Args: source (str): Accepts all source types accepted by yolo **kwargs : Any other args accepted by the predictors. To see all args check 'configuration' section in docs """ overrides = self.overrides.copy() overrides.update(kwargs) overrides["mode"] = "predict" predictor = self.PredictorClass(overrides=overrides) predictor.args.imgsz = check_imgsz(predictor.args.imgsz, min_dim=2) # check image size predictor.setup(model=self.model, source=source) predictor() @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 """ if not self.model: raise ModuleNotFoundError("model not initialized!") overrides = self.overrides.copy() overrides.update(kwargs) overrides["mode"] = "val" args = get_config(config=DEFAULT_CONFIG, overrides=overrides) args.data = data or args.data args.task = self.task validator = self.ValidatorClass(args=args) validator(model=self.model) @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 """ overrides = self.overrides.copy() overrides.update(kwargs) args = get_config(config=DEFAULT_CONFIG, overrides=overrides) args.task = self.task exporter = Exporter(overrides=args) exporter(model=self.model) def train(self, **kwargs): """ Trains the model on given dataset. Args: **kwargs (Any): Any number of arguments representing the training configuration. List of all args can be found in 'config' section. You can pass all arguments as a yaml file in `cfg`. Other args are ignored if `cfg` file is passed """ if not self.model: raise AttributeError("model not initialized. Use .new() or .load()") overrides = kwargs if kwargs.get("cfg"): LOGGER.info(f"cfg file passed. Overriding default params with {kwargs['cfg']}.") overrides = yaml_load(check_yaml(kwargs["cfg"])) overrides["task"] = self.task overrides["mode"] = "train" if not overrides.get("data"): raise AttributeError("dataset not provided! Please define `data` in config.yaml or pass as an argument.") self.trainer = self.TrainerClass(overrides=overrides) self.trainer.model = self.trainer.load_model(weights=self.ckpt, model_cfg=self.model.yaml if self.task != "classify" else None) self.model = self.trainer.model # override here to save memory self.trainer.train() def resume(self, task=None, model=None): """ Resume a training task. Requires either `task` or `model`. `model` takes the higher precedence. Args: task (str): The task type you want to resume. Automatically finds the last run to resume if `model` is not specified. model (str): The model checkpoint to resume from. If not found, the last run of the given task type is resumed. If `model` is specified """ if task: if task.lower() not in MODEL_MAP: raise SyntaxError(f"unrecognised task - {task}. Supported tasks are {MODEL_MAP.keys()}") else: ckpt = torch.load(model, map_location="cpu") task = ckpt["train_args"]["task"] del ckpt self.ModelClass, self.TrainerClass, self.ValidatorClass, self.PredictorClass = self._guess_ops_from_task( task=task.lower()) self.trainer = self.TrainerClass(overrides={"task": task.lower(), "resume": model or True}) self.trainer.train() def to(self, device): self.model.to(device) def _guess_ops_from_task(self, task): model_class, train_lit, val_lit, pred_lit = MODEL_MAP[task] # warning: eval is unsafe. Use with caution 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 @smart_inference_mode() def __call__(self, imgs): if not self.model: LOGGER.info("model not initialized!") return self.model(imgs) def forward(self, imgs): return self.__call__(imgs)