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316 lines
12 KiB
316 lines
12 KiB
# Ultralytics YOLO 🚀, GPL-3.0 license
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import sys
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from pathlib import Path
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from typing import List
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from ultralytics import yolo # noqa
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from ultralytics.nn.tasks import (ClassificationModel, DetectionModel, SegmentationModel, attempt_load_one_weight,
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guess_model_task, nn)
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from ultralytics.yolo.cfg import get_cfg
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from ultralytics.yolo.engine.exporter import Exporter
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from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, RANK, callbacks, yaml_load
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from ultralytics.yolo.utils.checks import check_file, check_imgsz, check_yaml
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from ultralytics.yolo.utils.downloads import GITHUB_ASSET_STEMS
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from ultralytics.yolo.utils.torch_utils import smart_inference_mode
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# Map head to model, trainer, validator, and predictor classes
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MODEL_MAP = {
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"classify": [
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ClassificationModel, 'yolo.TYPE.classify.ClassificationTrainer', 'yolo.TYPE.classify.ClassificationValidator',
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'yolo.TYPE.classify.ClassificationPredictor'],
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"detect": [
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DetectionModel, 'yolo.TYPE.detect.DetectionTrainer', 'yolo.TYPE.detect.DetectionValidator',
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'yolo.TYPE.detect.DetectionPredictor'],
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"segment": [
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SegmentationModel, 'yolo.TYPE.segment.SegmentationTrainer', 'yolo.TYPE.segment.SegmentationValidator',
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'yolo.TYPE.segment.SegmentationPredictor']}
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class YOLO:
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"""
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YOLO
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A python interface which emulates a model-like behaviour by wrapping trainers.
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"""
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def __init__(self, model='yolov8n.pt', type="v8") -> None:
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"""
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Initializes the YOLO object.
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Args:
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model (str, Path): model to load or create
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type (str): Type/version of models to use. Defaults to "v8".
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"""
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self._reset_callbacks()
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self.type = type
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self.ModelClass = None # model class
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self.TrainerClass = None # trainer class
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self.ValidatorClass = None # validator class
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self.PredictorClass = None # predictor class
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self.predictor = None # reuse predictor
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self.model = None # model object
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self.trainer = None # trainer object
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self.task = None # task type
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self.ckpt = None # if loaded from *.pt
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self.cfg = None # if loaded from *.yaml
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self.ckpt_path = None
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self.overrides = {} # overrides for trainer object
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self.metrics_data = None
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# Load or create new YOLO model
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suffix = Path(model).suffix
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if not suffix and Path(model).stem in GITHUB_ASSET_STEMS:
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model, suffix = Path(model).with_suffix('.pt'), '.pt' # add suffix, i.e. yolov8n -> yolov8n.pt
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if suffix == '.yaml':
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self._new(model)
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else:
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self._load(model)
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def __call__(self, source=None, stream=False, **kwargs):
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return self.predict(source, stream, **kwargs)
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def _new(self, cfg: str, verbose=True):
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"""
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Initializes a new model and infers the task type from the model definitions.
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Args:
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cfg (str): model configuration file
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verbose (bool): display model info on load
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"""
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self.cfg = check_yaml(cfg) # check YAML
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cfg_dict = yaml_load(self.cfg, append_filename=True) # model dict
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self.task = guess_model_task(cfg_dict)
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self.ModelClass, self.TrainerClass, self.ValidatorClass, self.PredictorClass = self._assign_ops_from_task()
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self.model = self.ModelClass(cfg_dict, verbose=verbose and RANK == -1) # initialize
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def _load(self, weights: str):
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"""
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Initializes a new model and infers the task type from the model head.
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Args:
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weights (str): model checkpoint to be loaded
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"""
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suffix = Path(weights).suffix
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if suffix == '.pt':
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self.model, self.ckpt = attempt_load_one_weight(weights)
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self.task = self.model.args["task"]
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self.overrides = self.model.args
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self._reset_ckpt_args(self.overrides)
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else:
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check_file(weights)
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self.model, self.ckpt = weights, None
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self.task = guess_model_task(weights)
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self.ckpt_path = weights
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self.overrides['model'] = weights
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self.ModelClass, self.TrainerClass, self.ValidatorClass, self.PredictorClass = self._assign_ops_from_task()
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def _check_is_pytorch_model(self):
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"""
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Raises TypeError is model is not a PyTorch model
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"""
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if not isinstance(self.model, nn.Module):
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raise TypeError(f"model='{self.model}' must be a *.pt PyTorch model, but is a different type. "
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f"PyTorch models can be used to train, val, predict and export, i.e. "
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f"'yolo export model=yolov8n.pt', but exported formats like ONNX, TensorRT etc. only "
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f"support 'predict' and 'val' modes, i.e. 'yolo predict model=yolov8n.onnx'.")
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def reset(self):
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"""
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Resets the model modules.
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"""
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self._check_is_pytorch_model()
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for m in self.model.modules():
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if hasattr(m, 'reset_parameters'):
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m.reset_parameters()
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for p in self.model.parameters():
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p.requires_grad = True
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def info(self, verbose=False):
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"""
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Logs model info.
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Args:
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verbose (bool): Controls verbosity.
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"""
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self._check_is_pytorch_model()
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self.model.info(verbose=verbose)
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def fuse(self):
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self._check_is_pytorch_model()
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self.model.fuse()
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def predict(self, source=None, stream=False, **kwargs):
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"""
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Perform prediction using the YOLO model.
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Args:
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source (str | int | PIL | np.ndarray): The source of the image to make predictions on.
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Accepts all source types accepted by the YOLO model.
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stream (bool): Whether to stream the predictions or not. Defaults to False.
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**kwargs : Additional keyword arguments passed to the predictor.
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Check the 'configuration' section in the documentation for all available options.
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Returns:
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(List[ultralytics.yolo.engine.results.Results]): The prediction results.
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"""
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overrides = self.overrides.copy()
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overrides["conf"] = 0.25
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overrides.update(kwargs)
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overrides["mode"] = kwargs.get("mode", "predict")
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assert overrides["mode"] in ['track', 'predict']
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overrides["save"] = kwargs.get("save", False) # not save files by default
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if not self.predictor:
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self.predictor = self.PredictorClass(overrides=overrides)
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self.predictor.setup_model(model=self.model)
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else: # only update args if predictor is already setup
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self.predictor.args = get_cfg(self.predictor.args, overrides)
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is_cli = sys.argv[0].endswith('yolo') or sys.argv[0].endswith('ultralytics')
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return self.predictor.predict_cli(source=source) if is_cli else self.predictor(source=source, stream=stream)
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@smart_inference_mode()
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def track(self, source=None, stream=False, **kwargs):
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from ultralytics.tracker.track import register_tracker
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register_tracker(self)
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# bytetrack-based method needs low confidence predictions as input
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conf = kwargs.get("conf") or 0.1
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kwargs['conf'] = conf
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kwargs['mode'] = 'track'
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return self.predict(source=source, stream=stream, **kwargs)
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@smart_inference_mode()
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def val(self, data=None, **kwargs):
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"""
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Validate a model on a given dataset .
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Args:
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data (str): The dataset to validate on. Accepts all formats accepted by yolo
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**kwargs : Any other args accepted by the validators. To see all args check 'configuration' section in docs
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"""
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overrides = self.overrides.copy()
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overrides["rect"] = True # rect batches as default
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overrides.update(kwargs)
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overrides["mode"] = "val"
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args = get_cfg(cfg=DEFAULT_CFG, overrides=overrides)
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args.data = data or args.data
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args.task = self.task
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if args.imgsz == DEFAULT_CFG.imgsz and not isinstance(self.model, (str, Path)):
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args.imgsz = self.model.args['imgsz'] # use trained imgsz unless custom value is passed
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args.imgsz = check_imgsz(args.imgsz, max_dim=1)
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validator = self.ValidatorClass(args=args)
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validator(model=self.model)
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self.metrics_data = validator.metrics
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return validator.metrics
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@smart_inference_mode()
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def export(self, **kwargs):
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"""
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Export model.
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Args:
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**kwargs : Any other args accepted by the predictors. To see all args check 'configuration' section in docs
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"""
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self._check_is_pytorch_model()
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overrides = self.overrides.copy()
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overrides.update(kwargs)
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args = get_cfg(cfg=DEFAULT_CFG, overrides=overrides)
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args.task = self.task
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if args.imgsz == DEFAULT_CFG.imgsz:
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args.imgsz = self.model.args['imgsz'] # use trained imgsz unless custom value is passed
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if args.batch == DEFAULT_CFG.batch:
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args.batch = 1 # default to 1 if not modified
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exporter = Exporter(overrides=args)
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return exporter(model=self.model)
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def train(self, **kwargs):
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"""
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Trains the model on a given dataset.
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Args:
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**kwargs (Any): Any number of arguments representing the training configuration.
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"""
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self._check_is_pytorch_model()
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overrides = self.overrides.copy()
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overrides.update(kwargs)
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if kwargs.get("cfg"):
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LOGGER.info(f"cfg file passed. Overriding default params with {kwargs['cfg']}.")
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overrides = yaml_load(check_yaml(kwargs["cfg"]), append_filename=True)
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overrides["task"] = self.task
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overrides["mode"] = "train"
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if not overrides.get("data"):
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raise AttributeError("Dataset required but missing, i.e. pass 'data=coco128.yaml'")
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if overrides.get("resume"):
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overrides["resume"] = self.ckpt_path
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self.trainer = self.TrainerClass(overrides=overrides)
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if not overrides.get("resume"): # manually set model only if not resuming
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self.trainer.model = self.trainer.get_model(weights=self.model if self.ckpt else None, cfg=self.model.yaml)
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self.model = self.trainer.model
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self.trainer.train()
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# update model and cfg after training
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if RANK in {0, -1}:
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self.model, _ = attempt_load_one_weight(str(self.trainer.best))
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self.overrides = self.model.args
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self.metrics_data = getattr(self.trainer.validator, 'metrics', None) # TODO: no metrics returned by DDP
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def to(self, device):
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"""
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Sends the model to the given device.
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Args:
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device (str): device
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"""
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self._check_is_pytorch_model()
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self.model.to(device)
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def _assign_ops_from_task(self):
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model_class, train_lit, val_lit, pred_lit = MODEL_MAP[self.task]
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trainer_class = eval(train_lit.replace("TYPE", f"{self.type}"))
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validator_class = eval(val_lit.replace("TYPE", f"{self.type}"))
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predictor_class = eval(pred_lit.replace("TYPE", f"{self.type}"))
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return model_class, trainer_class, validator_class, predictor_class
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@property
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def names(self):
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"""
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Returns class names of the loaded model.
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"""
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return self.model.names if hasattr(self.model, 'names') else None
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@property
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def transforms(self):
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"""
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Returns transform of the loaded model.
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"""
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return self.model.transforms if hasattr(self.model, 'transforms') else None
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@property
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def metrics(self):
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"""
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Returns metrics if computed
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"""
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if not self.metrics_data:
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LOGGER.info("No metrics data found! Run training or validation operation first.")
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return self.metrics_data
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@staticmethod
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def add_callback(event: str, func):
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"""
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Add callback
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"""
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callbacks.default_callbacks[event].append(func)
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@staticmethod
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def _reset_ckpt_args(args):
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for arg in 'augment', 'verbose', 'project', 'name', 'exist_ok', 'resume', 'batch', 'epochs', 'cache', \
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'save_json', 'half', 'v5loader', 'device', 'cfg', 'save', 'rect', 'plots', 'opset':
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args.pop(arg, None)
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@staticmethod
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def _reset_callbacks():
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for event in callbacks.default_callbacks.keys():
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callbacks.default_callbacks[event] = [callbacks.default_callbacks[event][0]]
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