from pathlib import Path import torch from ultralytics import yolo # noqa required for python usage 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 from ultralytics.yolo.utils.checks import check_yaml from ultralytics.yolo.utils.files import yaml_load from ultralytics.yolo.utils.torch_utils import smart_inference_mode # map head: [model, trainer, validator, predictor] 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: """ Python interface which emulates a model-like behaviour by wrapping trainers. """ __init_key = object() def __init__(self, init_key=None, type="v8") -> None: """ Args: type (str): Type/version of models to use """ if init_key != YOLO.__init_key: raise SyntaxError(HELP_MSG) self.type = type self.ModelClass = None self.TrainerClass = None self.ValidatorClass = None self.PredictorClass = None self.model = None self.trainer = None self.task = None self.ckpt = None # if loaded from *.pt self.cfg = None # if loaded from *.yaml self.overrides = {} self.init_disabled = False @classmethod def new(cls, cfg: str): """ Initializes a new model and infers the task type from the model definitions Args: cfg (str): model configuration file """ cfg = check_yaml(cfg) # check YAML cfg_dict = yaml_load(cfg) # model dict obj = cls(init_key=cls.__init_key) obj.task = obj._guess_task_from_head(cfg_dict["head"][-1][-2]) obj.ModelClass, obj.TrainerClass, obj.ValidatorClass, obj.PredictorClass = obj._guess_ops_from_task(obj.task) obj.model = obj.ModelClass(cfg_dict) # initialize obj.cfg = cfg return obj @classmethod def load(cls, weights: str): """ Initializes a new model and infers the task type from the model head Args: weights (str): model checkpoint to be loaded """ obj = cls(init_key=cls.__init_key) obj.ckpt = torch.load(weights, map_location="cpu") obj.task = obj.ckpt["train_args"]["task"] obj.overrides = dict(obj.ckpt["train_args"]) obj.overrides["device"] = '' # reset device LOGGER.info("Device has been reset to ''") obj.ModelClass, obj.TrainerClass, obj.ValidatorClass, obj.PredictorClass = obj._guess_ops_from_task( task=obj.task) obj.model = attempt_load_weights(weights) return obj 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 the docs """ overrides = self.overrides.copy() overrides.update(kwargs) overrides["mode"] = "predict" predictor = self.PredictorClass(overrides=overrides) # check size type sz = predictor.args.imgsz if type(sz) != int: # received listConfig predictor.args.imgsz = [sz[0], sz[0]] if len(sz) == 1 else [sz[0], sz[1]] # expand else: predictor.args.imgsz = [sz, sz] 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 the 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: format (str): Export format **kwargs : Any other args accepted by the predictors. To see all args check 'configuration' section in the docs """ overrides = self.overrides.copy() overrides.update(kwargs) args = get_config(config=DEFAULT_CONFIG, overrides=overrides) args.task = self.task exporter = Exporter(overrides=overrides) 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() @staticmethod def _guess_task_from_head(head): task = None if head.lower() in ["classify", "classifier", "cls", "fc"]: task = "classify" if head.lower() in ["detect"]: task = "detect" if head.lower() in ["segment"]: task = "segment" if not task: raise SyntaxError("task or model not recognized! Please refer the docs at : ") # TODO: add docs links return task 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)